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THE ROLE OF BLOCKCHAIN TECHNOLOGY AND ENERGY COALITIONS IN REDUCING ENERGY

GRID VARIABILITY

MAXIMILIAN WIEDMAIER ROTTERDAM SCHOOL OF MANAGEMENT

ERASMUS UNIVERSITY ROTTERDAM

A THESIS SUBMITTED FOR THE DEGREE OF MASTER OF SCIENCE

BUSINESS INFORMATION MANAGEMENT AUGUST 11TH 2017

MAXIMILIAN WIEDMAIER

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STUDENT INFORMATION STUDENT NAME: Maximilian Wiedmaier STUDENT NUMBER: 366864 STUDY PROGRAMME: MSc Business Information Management DATE OF SUBMISSION: 11.08.2017

SUPPORTED BY UNIVERSITY COACH: Ir. Derck Koolen

PhD Candidate Department of Technology and Operations Management Rotterdam School of Management – RSM CO-READER: Iuliana Sandu Lecturer

Department of Accounting and Control Rotterdam School of Management – RSM EXTERNAL COACH: Baerte de Brey Elektrisch Vervoer Stedin

ACKNOWLEDGEMENTS

Work in this thesis was supported by the colleagues at Stedin. Their involvement is appreciated. Special thanks go to BAERTE DE BREY.

Special thanks go to DERCK KOOLEN, for without his involvement this study would not have been feasible.


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PREFACE The copyright of the master thesis rests with the author. The author is responsible for its

contents. RSM is only responsible for the educational coaching and cannot be held liable for the content.

The author declares that the text and work presented in this Master thesis is original and no sources other than those mentioned in the text and its references have been used in

creating this Master thesis.


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EXECUTIVE SUMMARY The goal of this study was to examine the effect of smart market solutions and blockchain technology on reducing local energy grid variability. Progressive liberalisation of energy markets contributes towards a decentralisation movement. Likewise, the advent of blockchain technology embodies similar characteristics. The cross-field of technology and energy is undergoing an interesting period of evolution. Technological advancements and better utilisation of gathered data are at the forefront. This raised the question of how we can innovatively combine these fields for the benefit of our future. Thus, came the interest in examining the effects of decentralisation on energy markets. The energy transition articulates the need for a sustainable future. As renewable energy resources are becoming more advanced and affordable, we observe increasing adoption rates from the residential sector. Prosumers play their part in the transition by installing solar panels that allow for greener energy sourcing. In turn, energy grids succumb to larger degrees of variability due to intermittent supply. This is expensive for operators and utilities, as these must, therefore, plan extra peak capacities and reserves to assure acceptable service levels for end-users. It is known that aggregating various types of renewables can help balance out national grids. Naturally, this spurred the interest to examine whether this is also applicable at smaller scales. It was thus decided to apply aggregation on household profiles to a local grid setting. This involved forming energy coalitions of various sizes and structures and evaluating their effectiveness in reducing grid variability. Research lead to examining (RQ1) the role of energy coalitions in light of increasing shares of renewable energy resource and (RQ2) the role of technology, and blockchain, on the effectiveness of energy coalitions. The data set was provided by the U.S. based ‘Pecan Street’ project, collecting data including household energy consumption and generation data. Three Monte Carlo simulations were performed to aggregate households, one for each coalition structure. Output data was further processed by moderated regression and cost analysis before obtaining final results. The findings show, firstly, that reductions in variability are related to an increase in the number of households in a coalition. This implies that reliability in grids increases by coalition size. We further find that the structure of a coalition moderates this relationship. Largest variability reductions are found for prosumer coalitions, whereas smallest for consumer coalitions. From a grid perspective, we conclude that heterogeneous coalitions achieve the best results as they allow for high and low variability profiles to be spread out mitigating risks. This confirms our theory concerning the applicability of aggregation across smaller scales. We also performed conceptual analysis of blockchain. This has concluded that a two-pronged approach is recommended. It involves the creation of a private and public blockchain that interact with one another. They are intended for households and grids respectively. We derive to this because of the expected large-scale proliferation of Internet-of-Things devices. Increased communication traffic and latency issues should not impede on the functionality of such information systems. Nevertheless, interpretation of such issues, as well as the novelty of blockchain, lead to believe that we must first emphasise focus towards semi-decentralised local market solutions prior to implementing blockchain. Not only does this allow for testing the waters of coalition formation across different populations, but it also provides some more time for unknown gaps in blockchain technology to be discovered. Smart market solutions should be seen as a stepping stone for later implementation of fully decentralised mechanisms in local energy grids.


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TABLE OF CONTENTS EXECUTIVE SUMMARY ....................................................................................... - 4 -TABLE OF CONTENTS ......................................................................................... - 5 -LIST OF FIGURES ................................................................................................. - 7 -LIST OF TABLES .................................................................................................... - 8 -1. INTRODUCTION ................................................................................................ - 9 -

1.1 MOTIVATION ......................................................................................................................... - 9 -1.2 RESEARCH GOAL ................................................................................................................. - 10 -1.3 RELEVANCE FOR ACADEMIA AND MANAGERS .................................................................. - 11 -1.4 STRUCTURE ......................................................................................................................... - 12 -

2. THEORY AND MODEL .................................................................................... - 13 -2.1 BLOCKCHAIN ....................................................................................................................... - 13 -

2.1.1 The Philosophy: Value Creation via Decentralisation .............................................. - 13 -2.1.2 General Architecture of the Blockchain .................................................................... - 14 -2.1.3 Hash Functions Solve Traditional Issues With Transactions .................................. - 16 -2.1.4 Smart Contracts Execute Themselves ....................................................................... - 17 -2.1.5 Smart Devices and the Internet of Things ................................................................ - 18 -

2.2 RENEWABLE ENERGY ......................................................................................................... - 19 -2.2.1 From Consumer to Prosumer ..................................................................................... - 19 -2.2.2 Distributed Generation .............................................................................................. - 20 -2.2.3 Local and Microgrids .................................................................................................. - 20 -2.2.4 Distributed Energy Resources ................................................................................... - 21 -2.2.5 Variability of Renewable Energy Resources ............................................................. - 21 -2.2.6 The Complexity of Electricity Markets Requires Careful Consideration ................ - 22 -

2.3 ELECTRICITY MARKETS ARE SUBJECT TO MARKET REDESIGN ..................................... - 23 -2.3.1 Energy Markets are in a Constant Transitional State ............................................. - 23 -2.3.2 Energy, Coalitions, and the Blockchain .................................................................... - 26 -2.3.3 Energy Cooperatives Reduce Grid Variability .......................................................... - 27 -2.3.4 Cooperative Structure Dictates the Magnitude of Variability Reduction ............... - 28 -2.3.5 Heterogeneous Coalitions Perform Best .................................................................... - 28 -2.3.6 Blockchain and Technology Will Enhance Coalition Effectiveness ......................... - 29 -

2.4 CONCEPTUAL MODEL ......................................................................................................... - 30 -


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3. METHODOLOGY .............................................................................................. - 31 -3.1 DATA AND DESCRIPTIVES .................................................................................................. - 31 -

3.1.1 Sample ......................................................................................................................... - 31 -3.1.2 Variables ..................................................................................................................... - 31 -3.1.3 Descriptives of Dataset ............................................................................................... - 32 -

3.2 METHODOLOGY ................................................................................................................... - 35 -3.2.1 Why Simulation .......................................................................................................... - 35 -3.2.2 Data Preparation ........................................................................................................ - 36 -3.2.3 Defining the Simulation Parameters ......................................................................... - 37 -3.2.4 Data Aggregation ........................................................................................................ - 39 -3.2.5 Research Design of Monte Carlo Simulation ............................................................ - 39 -

4. RESULTS AND DISCUSSION ........................................................................ - 41 -4.1 PRESENTATION OF MAIN RESULTS ................................................................................... - 41 -

4.1.1 Consumer Coalitions .................................................................................................. - 41 -4.1.2 Prosumer Coalitions ................................................................................................... - 42 -4.1.3 Mixed Coalitions ......................................................................................................... - 42 -

4.2 COALITION STRUCTURE DETERMINES MAGNITUDE OF PAR REDUCTION ................... - 43 -4.3 DETERMINING OPTIMAL COALITION SIZES ...................................................................... - 45 -

4.3.1 Consumers Are Best Managed Without Agent Representation ............................... - 46 -4.3.2 Prosumers Show Greatest Marginal Reduction In PAR .......................................... - 47 -4.3.3 Mixed Coalitions Provide Optimal Variability Reduction ........................................ - 48 -

4.4 THE ROLE OF BLOCKCHAIN IN ENERGY GRIDS ............................................................... - 49 -4.4.1 Blockchain Incentivises the Move Towards Prosumerism ....................................... - 49 -4.4.2 Automation .................................................................................................................. - 50 -4.4.3 Big Data and Separation of Objectives Requires Two Chains ................................. - 50 -

4.4.4 IMPLEMENTATION OF SMART MARKET BEFORE BLOCKCHAIN ................................... - 53 -

5. CONCLUSION ................................................................................................... - 55 -5.1 SUMMARY OF FINDINGS ..................................................................................................... - 55 -5.4 LIMITATIONS AND FUTURE RESEARCH ............................................................................ - 56 -5.2 ACADEMIC IMPLICATIONS .................................................................................................. - 57 -5.3 MANAGERIAL IMPLICATIONS ............................................................................................. - 57 -

6. BIBLIOGRAPHY ............................................................................................... - 59 -7. APPENDIX ......................................................................................................... - 65 -


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LIST OF FIGURES Figure 1: Illustration of Blockchain ........................................................................................ - 15 -Figure 2: Blockchain transaction verification ........................................................................ - 16 -Figure 3: Illustration of Hash Function ................................................................................. - 17 -Figure 4: New Investment in Clean Energy .......................................................................... - 26 -Figure 5: Conceptual Model .................................................................................................... - 30 -Figure 6: Monthly Loads of Consumer Households .............................................................. - 33 -Figure 7: Monthly Loads of Prosumer Households ............................................................... - 33 -Figure 8: Net Power Flow for Prosumer and Consumer ....................................................... - 34 -Figure 9: Monte Carlo Flow Diagram .................................................................................... - 39 -Figure 10: PAR of Consumer Coalitions ................................................................................ - 41 -Figure 11: PAR of Prosumer Coalitions ................................................................................. - 42 -Figure 12: PAR of Heterogeneous Coalitions ........................................................................ - 42 -Figure 13: Consumer Coalition Formation Cost ................................................................... - 46 -Figure 14: Prosumer Coalition Formation Cost .................................................................... - 47 -Figure 15: Heterogeneous Coalition Formation Cost ............................................................ - 48 -Figure 16: Local Network Grid ............................................................................................... - 51 -Figure 17: Household Circuit .................................................................................................. - 52 -Figure 18: Interaction between Local and Household Blockchains ..................................... - 53 -


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LIST OF TABLES Table 1: Definitions of Input Variables ........................................................................................... - 32 -Table 2: Descriptive Statistics of Prosumer and Consumer Electricity Consumption .............. - 34 -Table 3: Descriptive Statistics of PARs of Prosumers and Consumers ....................................... - 35 -Table 4: F-Test for Variance between Prosumer and Consumer ................................................. - 37 -Table 5: T-test for Equality of Means between Prosumer and Consumer .................................. - 38 -Table 6: Input Parameters for Monte Carlo Simulation ............................................................... - 39 -Table 7: Comparison of Regression Results for Interaction Effect .............................................. - 44 -Table 8: F-Test for Variance between Prosumer Weekend and Workday .................................. - 65 -Table 9: T-test for Equality of Means between Prosumer Weekend and Workday ................... - 65 -Table 10: F-Test for Variance between Consumer Weekend and Workday ............................... - 66 -Table 11: T-test for Equality of Means between Consumer Weekend and Workday ................ - 66 -Table 12: Pareto Optimal Coalition Size per Structure ................................................................ - 67 -


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1. INTRODUCTION

1.1 MOTIVATION The blockchain is larger than politics. It is coming to disrupt all markets and “alter the relationship between citizens and the State” (Tucker, 2017), decoupling us from traditional centralisation practices.

This technology is a tamper-proof, secure digital record of all transactions. It relies on encrypted, peer-to-peer communication to verify such transactions; all stored immutably in a digital ledger. The blockchain creates transparency without compromise in security and empowers by facilitating automation and smart contracts that may serve virtually any purpose that can be coded. Although still in primal stages, many use cases are emerging beyond financial sectors. Bitcoin, for example, is the first application of its kind leveraging the technology to create a decentralised cryptocurrency. Users anonymously process payments (transactions) over the blockchain negating the need for verification via trusted third-parties. It establishes simple, yet safe transactional environments.

The energy sector is one, amongst numerous others, that is also experimenting with proof-of-concepts and pilots. Take, for example, blockchain-based peer-to-peer energy trading platforms intended for brokers in wholesale electricity markets (Ponton, 2016). Likewise, there exist infrastructural platforms for machine-to-machine communication with which households are empowered to control smart homes (Mattila et al., 2016). An amplitude of differing solutions exists, each tailor-made to specific use cases. On one side, the widespread applicability of blockchain allows for a multitude of applications to surface. On the other, being at such primal stages, it leaves us with fewer questions solved than not. The energy sector, one characterised by constant innovation, is on the verge of experiencing radical change.

While the technological forefront seeks innovation, political and economic influences shape the parameters governing the energy transition. Germany’s transition policy, for example, puts decentralisation at the heart of its ‘Energiewende’ (Bundesregierung, 2016). More so, current political climate concerning the recent backtracks in policy for climate change, U.S. withdrawal out of the Paris Accord, have only magnified the need for decentralisation. Similarly, pioneers like Tesla continue to break the boundaries of energy storage, furthering such trend. Nevertheless, this transition requires infrastructural improvements from many facets. Although benefits are clearly outlined, the transition does come at a price too.

Inherent uncertainties presented hereby call for structural change in the energy sector. Likewise, the need for a sustainable future demands craftsmanship in engineering the energy transition. Conscious consumption increasingly characterises modern day consumers. Amongst others, solar panels are progressively installed in local grids to satisfy demands for larger dependency on renewable energy resources. This form of distributed self-generation may attain to achieve sustainable targets, yet also imposes greater pressure on grid operators as it encompasses certain uncertainties. Intermittency of renewables leads to variability on


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the supply-side. Nevertheless, energy consumers will not accept deteriorations in service levels. It is a double-edged sword that requires greater flexibility yet also greater control. Coupled with the consumer-driven demand for continuously expedited information delivery, energy markets must be reshaped and restructured.

It will no longer be efficient, nor effective, for humans to manually manage all activities currently performed by the likes of grid operators and utility providers. The decision boundaries are simply becoming too large (Bichler, Gupta, & Ketter, 2010). The increased proliferation of vast quantities of information, coupled with the velocity at which it must be processed, has many impositions on those expected to deliver. We are in need of new ways to share and utilise this information.

This notion holds especially true for the energy sector. We are experiencing a gradual, yet significant change in the value chain. The points of generation are proliferating and pointing towards self-generation, typically characterised by on-site consumption of electricity. Prosumers are also no longer interested in allowing providers and operators take control over energy management and pricing. They demand empowering consumer-centric solutions (Lacey, 2014).

This requires change. Energy markets must begin to utilise available data better. As households are increasingly equipped with smart devices, the platforms under which these communicate are in need of refurbishment. Firstly, such platforms must be equipped with better capabilities for handling the rapid increase of data. The Internet of Things, and its accompanying machine-to-machine communication, will drastically contribute to the amount of information available. This requires thoughtful governance to be effective. Likewise, it necessitates for a democratic utilisation of data that is beneficial to all involved stakeholders. We are in need of innovative solutions to accommodate such change.

The starting point here is the transition towards smart energy markets that gradually shift focus from national perspectives to those needs articulated at community levels. Similarly, we must place greater emphasis on the automation of futile ‘value-added’ processes currently performed by players like Transmission System Operators (TSOs) and Distribution System Operators (DSOs).

On the one side, extant literature exists that proposes smart market solutions and automation of market mechanisms. On the other, these are frequently highly context specific. Smart market solutions within the increasing role of decentralisation and moreover the blockchain domain remain at futile stages.

1.2 RESEARCH GOAL It is expected that the blockchain will be subject to much deeper exploration and become a major driver fuelling the energy transition. Similarly, the political environment will continue to push the boundaries of energy markets demanding compliance towards sharper targets. Combining both factors suggests a strong trend towards decentralisation. This study aims to


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bridge a fraction of the gap in smart market and blockchain-based solutions for local grid settings under increased exposure to renewable energy resources.

Decentralisation of energy markets inherently points towards disintermediation and self-generation. Firstly, this study aims to utilise electricity consumption data captured from said smart devices to reduce microgrid variability. A Monte Carlo simulation model will be evaluated in several settings corresponding to various household profiles; namely consumer, prosumer, and heterogeneous households. It is of interest to measure the impact of aggregating households on the gains in grid reliability. It is understood that renewable energy sources impose uncertainties due to intermittency, amongst others. Likewise, large-scale aggregation of a varied mix of sources over large areas can mitigate said uncertainties (American Physical Society, 2011). We thus emphasise the notion of heterogeneity, as evidence leads to believe that similar benefits can be achieved in local environments. The simulation model will thus measure reduction in variability in light of increasing the number of households included in energy coalitions.

Secondly, this study aims to bridge some of the information gaps for blockchain technology. It will be viewed as a platform, a tool that can efficiently facilitate and automate smart market mechanisms. A general blockchain architecture of this fully decentralised mechanism will be proposed as a conceptual use case. Whilst the act of digitising households and equipping them with smart technologies has its benefits, it also imposes certain costs to be considered. These will be considered in the evaluation and will provide means to an implementation guideline of the technology and its underlying infrastructural aspects.

Therefore, this study will address the following central research question:

[RQ]

How can decentralised mechanisms such as smart markets and blockchain markets be utilised to reduce variability inherent in local energy markets?

This will ensure several findings. Firstly, an optimal coalition size will be evaluated for several household profiles aiming to reduce (micro)grid variability. Secondly, an optimal coalition structure will be proposed that efficiently executes such variability reduction. Thirdly, blockchain-based implications will evaluate the effectiveness of automating smart markets and its role in energy cooperatives.

Subsequent results and analyses will ultimately formulate incentives for using smart market mechanisms and emerge with new business opportunities.

1.3 RELEVANCE FOR ACADEMIA AND MANAGERS Employing Monte Carlo simulation methodology will allow for findings to be generalised and attributed to populations beyond those considered in this study. This will ensure benefits to a diverse body of stakeholders in energy markets.


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Moreover, decentralisation of market structure will challenge the role of incumbent players in the energy market. Whether that role involves optimising peak loads via demand side management at the DSO level or wholesale market transmission at the TSO level, it is highly time-consuming and subject to inefficiencies. It is of interest to understand the role that smart technology plays, and with it, how aggregation of energy profiles impacts grid variability. Likewise, it is important to comprehend the implications smart market solutions and blockchain impose on energy markets. The possibility of a reduction in the need for intermediary involvement is apparent and could lead to reshaping of incumbent roles in current, highly saturated energy markets. Stakeholders will more clearly understand the efficiency gains that can be achieved. It is vital to acknowledge, thus, that decentralisation can lead to entirely different value propositions and offerings. For incumbents to stay relevant, firms will need to adjust their offerings in light of findings.

Naturally, this also leads to various contributions to academia. Current literature vaguely covers blockchain applications in the energy sector, let alone blockchain technology in itself. Although an early concept of timestamping and linking to previous data and the Bitcoin white paper have both existed for some years now (Haber & Stornetta, 1991; Nakamoto, 2008), the underlying concepts are novel. Ideas on what the term ‘blockchain’ refers to differ greatly across sources; consultants call it a “…distributed, decentralized transaction ledger…” (PWC, 2017) whereas a digital currency news site refers here to the chain of blocks mined since Bitcoin was released (CoinDesk, 2017). Thus, first and foremost, this paper aims to contribute to the understanding of what the blockchain is. A conceptual use case will further add to the limited body of blockchain applications in the energy sector, perhaps shedding light on yet more possible scenarios. Furthermore, this study aims to highlight underlying factors affecting microgrid variability in light of coalition formulation. Extant literature exists evaluating the aggregation of energy profiles. Nevertheless, it rarely evaluates optimal sizes of such coalitions. Similarly, this study will consider coalition structure in the analysis, another aspect not represented well in literature.

1.4 STRUCTURE Chapter two provides a theoretical background that deeply explores the main concepts relevant to this study. It allows for the formulation of central research question. Subsequently, the hypotheses are presented upon summarising the concepts, followed by the developed conceptual framework. Chapter three presents the methodology employed. This includes aspects such as sampling technique and use of simulation models to evaluate different sizes of aggregation for comparison. Chapter four presents the results as well as accompanying discussion analysis. The last chapter briefly highlights limitations, provides final summary of results and provides discussion of the main findings and implications of this study.


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2. THEORY AND MODEL The following section outlines existing literature concerning the topics blockchain and renewable energy. They will be introduced separately to familiarise with the concepts first. Subsequently, discussion of a summary of concepts will follow. This convocation is intended to address gaps in literature and the urgency for market reform. Hypotheses are formed and discussed throughout this section as they build up. Finally, a conceptual model is provided.

2.1 BLOCKCHAIN The Internet is arguably the most influential technology today. Its presence is apparent in nearly all day-to-day transactions. This enabling technology is supposed to foster (information) sharing as its foundation lies on the principles of openness in its “architecture, philosophy and technical protocols” (Cerf, 2013). Billions of people have access to, and rely on, this global warehouse of information. Still, we find many obscure examples breaching the integrity and true intent of the Internet. Take, for example, the reversal of net neutrality by U.S. Federal Communications Commission. It may be extrapolated that the Internet is closed. Furthermore, many value enhancing applications facilitated by the Internet are proprietary. Surely many systems are closed to protect the integrity of personal, sensitive information such as those in, for example, the financial and healthcare sectors. Nevertheless, no system is perfect, and data breaches are not uncommon events. Attackers predominantly exercise them for financial gains. It is observed that, thereby, the willingness to share confidential information online decreases (Internet Society, 2016). This order must be restored as privacy cannot truly exist without openness.

2.1.1 THE PHILOSOPHY: VALUE CREATION VIA DECENTRALISATION The emergence of blockchain and its underlying technology is a movement, or a philosophy, towards decentralisation. Whereas the Internet itself is a tool that is of value due to the information it holds, blockchain enables value-creation by automating information flows. No longer must these flows be carried out via manual processes. Instead, users of the blockchain are empowered by its accompanying computational processes and the dissemination of information.

The difference between a bank and Bitcoin, an intermediary and a value-added blockchain process, respectively, can be used to illustrate this. Traditional banks create value by managing the money (information) an individual owns. Not only are they considered havens for customers to store money and wealth. Banks also signal credibility to parties wishing to transact with another. Bitcoin conversely performs the same actions as banks, with one minute difference: Bitcoin creates value by automating the verification and processing of transactions. The OECD defines (internet) intermediaries as following: “bring[ing] together or facilitat[ing] transactions between third parties…” (OECD, 2010). By eliminating the need for an intermediary to perform managing and verification tasks, every participant of the system can focus on the value-creation potential.


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Centralisation creates a system of separation. It assumes responsibilities and duties in silos by appointed individuals or entities. The Byzantine General’s Problem carefully highlights this; it is challenging to coordinate silos without a trusted communication platform to achieve consensus (Lamport, Shostak, & Pease, 1982). Similarly, we observe the need for agents to separate management from ownership and its accompanying principal-agent issues. The need to verify faithful actions is redundant in a world where duties are fulfilled to the exact agreed upon codes of conduct. Unfortunately, we do not live in such a world. Therefore, traditional systems are designed with overarching authorities that govern and regulate transactions. Albeit efficient and scalable, it is often at the expense of trust, security, or both.

Systems store data in a (relatively) centralised manner to achieve scalability. Information access is limited to authorised parties. In turn, they are prone to attacks inflicted by external parties; hackers. They aim to steal sensitive, valuable information. Data loss and Denial of Service are amongst some of the forms attacks may come in. Blockchain tackles security issues by storing data in distributed file formats. This decentralised format stores information fragmented across every user within the network, making it less prone to corruption. Authorisation via digital keys can nevertheless be required in settings to preserve privileged access to sensitive information.

Efficiency is assumed to be created when authorised (groups of) individuals make decisions for the benefit of the greater. Hence the need for management within a bank, to further the illustration. Account holders confide in ‘experts’ to make faithful decisions on their behalves, yet this often does not occur. The blockchain ensures trust by creating an immutable digital ledger; an auditable paper trail. In the case of Bitcoin, every ten minutes a new block is created in which all transactions are stored, verified, and attached to the most recent block. Opportunistically behaving parties are immediately identified when suspected, and the incentive decreases as network adoption grows.

Decentralising information within the blockchain negates the need for a trusted intermediary to manage transactions. In turn, individuals are empowered to conduct meaningful, value-added transactions. Advantages include cost cutting and speedier processes. Whilst inter-bank transfers within the European Union typically require a day to process, time between block processing determines this for Bitcoin. Payments are automatically processed and sent to the right account, all without the need for intermediaries to charge unnecessary processing and transaction fees.

2.1.2 GENERAL ARCHITECTURE OF THE BLOCKCHAIN The blockchain is a concept that is crudely understood in its entirety. The following sections intend to familiarise the reader with blockchain terminology and lead a basic introduction to its functionality.

DISTRIBUTED LEDGER

The blockchain is nothing more than an open, digital book that documents and verifies transactions. It is a distributed ledger, defined as a “consensus of replicated, shared, and


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synchronised digital data” (BlockchainTechnologies, 2016). This ledger is shared across a multitude of locations and entities within the network; each participant is thus a witness. It is essentially a digital contract that allows for parties to transact directly with one another (PWC, 2016); making it a peer-to-peer network. Unlike traditional bookkeeping methods, it is immutable as data entries cannot be deleted once created. Furthermore, each transaction is traceable to its origin and cannot be manipulated.

IDENTITY AND PRIVACY

Every entity that partakes in the blockchain has an identity, characterised via a public key. This alphanumeric value traces back transactions performed by the accompanying entity while retaining anonymity by hiding its true name. Identities witness all transactions. Therefore, verification is inherent by nature and does not reveal sensitive information. Identities, also referred to as nodes (node and identity are used interchangeably), contribute towards the computations required to verify transactions.

MINING AND PROOF-OF-WORK

Because of its open source nature, the blockchain is not owned by a single entity. Subsequently, all decisions are formed via consensus (PWC, 2016). Achieving consensus requires a decentralised mechanism of verification called mining. This is defined as the “distributed computational review process” (BlockchainTechnologies, 2016) that takes place each time a block is added to the chain. This process involves feeding a complex, cryptographic mathematical problem to the system. Nodes use a significant amount of computational power to derive a solution. Figure 1 (below) illustrates the blocks and how they are linked.

Figure 1: Illustration of Blockchain

The most common method of finding said solution is called proof-of-work. Hereby, a node signifies that it has indeed spent resources to calculate the solution; namely computing power, time, and electricity. It involves adding a hash function to each block so that the smallest of alterations are immediately detected. This is useful when a node attempts to communicate a false entry of transaction value, for example. If a breach does occur, the returned hash value will not equate to the original. The blockchain will thus refuse to accept this entry since all nodes know the true value. As other nodes simultaneously seek to solve the problem, the correct block will be added at the end of the chain once verified. In the case of Bitcoin, solving the problem returns a reward of x amount Bitcoins. Although most common, proof-of-work is not environmentally friendly as it requires exhausting a significant amount of processing power (energy) to be spent.


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Alternatively, consensus can be achieved via proof-of-stake. Truthful transactions within the network also return a digital currency here. However, a voting mechanism is in place based on shares owned as opposed to the amount of energy invested. This incentivises users to join the network and can be structured to support sustainable initiatives, for example. SolarCoin is one of such that rewards network participants with one SolarCoin per generated MWh adhering to sustainable energy requirements (SolarCoin, 2016). Nevertheless, such coins currently symbolise more of a conceptual worth as their market value lies at around $0.07 per coin (CoinMarketCap, 2017).

TRANSACTIONS

Blockchain transactions require an electronic signature. This is done by digitally signing a hash of the previous transaction and identifying the public key of the next owner to whom the asset will be transferred to. The user must also provide his private key to initiate the transfer; similar to the two-man-rule when opening a vault in a bank. The receiving account will then use the chain to verify rightful ownership of the asset at hand by cross-referencing with the public key of the original owner. This notion is illustrated in Figure 2 (below).

Figure 2: Blockchain transaction verification

An issue that commonly arises here is double spending; when an owner uses an asset more than once to perform a transaction. Solving this will be explained in subsequent sections.

2.1.3 HASH FUNCTIONS SOLVE TRADITIONAL ISSUES WITH TRANSACTIONS Face-to-face transactions between parties infer a level of trust among participants. Contrarily, by nature digital transactions do not. Institutions take the role of trusted central authorities facilitating and verifying digital transactions, of course at a price. Whether that is time or money, it leads to inefficiencies. This section will address common issues associated with traditional transactions accompanied by blockchain solutions.

PROOF OF EXISTENCE AND OWNERSHIP

Parties wishing to transact must prove possession of assets to be exchanged. In brick-and-mortar stores, for example, this notion is trivial. However, when purchasing over the Internet, one cannot fully ensure faithfulness of merchants and buyers. This requires the


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need for trusted third-parties, like PayPal. In blockchain, a timestamp server serves this purpose by taking a hash of a block and broadcasting it to the network. Figure 3 (below) illustrates this.

Figure 3: Illustration of Hash Function

As all blocks on the blockchain refer to previous blocks, every broadcast inherently provides evidence as to the existence, or lack thereof, of the asset at question. Similarly, problems may arise with disputes over proof of ownership. The blockchain tackles this issue with the same mechanism, allowing for clear implementation and governance of alternative business models. Take, for example, shared ownership of assets such as solar panels. With blockchain, no disputes can arise as to rightful, legal ownership.

DOUBLE SPENDING

Although easily portable and liquid, digital assets pose a problem; electronic data can be copied and thus spent multiple times. Avoiding such fraudulent behaviour traditionally includes intermediary involvement by trusted central authorities, such as banks. These receive money from a sender and return it to the recipient; ensuring that no double spending can happen. Albeit redundant in a system established on trust, in today’s world this process remains a necessary mechanism that leads to inefficiencies. The blockchain uses no central mechanisms, as this would make its existence unnecessary. Instead, all transactions are publically broadcast on the digital ledger for the network to witness. Users attempting to double spend are immediately identified, as only one single truth of actions can exist. This is, once again, verified by the timestamp server.

TOLERANCE OF LOST INFORMATION

Amongst others, hardware errors can lead to data losses. In centralised systems, this is a huge issue as data recovery is not a guarantee. In the blockchain, however, transactions are broadcast to the network. Not all nodes must be reached as surely at least one will pick up the data. This is especially useful when latency issues arise. If, however, a working node does not receive a block while processing, it will notice eventually since the hash function will not return the desired number. The node will proceed to request the missing information from other nodes automatically.

2.1.4 SMART CONTRACTS EXECUTE THEMSELVES Use cases for the blockchain extend much further than merely sending and processing transactions across nodes. One of such is the ability to write smart contracts. These are digital contracts distributed over the blockchain that “facilitat[e], execut[e], and enforce[e] the


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negotiation or performance of an agreement” (BlockchainTechnologies, 2016). As smart contracts automatically allocate assets per pre-determined terms (Buterin, 2014), they facilitate contractual interactions between two parties without requiring intermediaries.

Currently, writing contracts imposes several issues. Not only do they consume a plethora of physical paper and time to write, proof, and distribute, but third-parties are necessary to enforce compliance. Terms are often so ambiguous that in the event of non-performance, arbitrage or legal help must be sought to settle disputes. Smart contracts do not only reduce paper waste as they are digital but they also clearly outline the benefits upon performance along with consequences in case of non-performance.

Smart contracts are written in logical statements taking the form of ‘if this…then that’. They outline specifics regarding the interacting parties as well as the exchange to take place and its amount. Execution of said contracts is straightforward as it follows the same procedure as transactions – via the distributed ledger. Once received, nodes (parties) execute per terms and subsequently update this in the ledger. Thus, they are also tamper-proof and witnessed by all.

2.1.5 SMART DEVICES AND THE INTERNET OF THINGS Estimates maintain that there will be over 50 billion connected devices by 2020 (Cisco, 2011). The Internet of Things (IoT) refers to this network in which physical devices mesh with technology allowing inter-device communication (Gartner, 2017). Moore’s Law dictates that such devices become cheaper by the day, favouring embedding computational capabilities into devices. In turn, it enables smart features for IoT-enabled devices that facilitate bi-directional communication. This machine-to-machine communication, in smart meters, for example, not only measures real-time electricity consumption but can also communicate data over the Internet. This allows for various insights to be drawn from a plethora of data, satisfying data-driven decision making. Furthermore, devices may be programmed to operate at specific times. For example, when the price of electricity is low. Similarly, smart electric vehicle charging stations can monitor and meter electricity flows. This allows for control over charging cycles to turn vehicles into batteries whilst maintaining user-specified minimum charge levels.

Such communication patterns (transactions) and, with it, the extensive customisability deems it suitable for devices to be installed in a blockchain environment. Firstly, this allows for automation of predefined programmes such as charge cycles of electric vehicles. Furthermore, the digital documentation of transactions in an immutable ledger reduces paper-burden and frees resources for value-added activities. The value proposition of IoT devices lies in its ability to interact with the environment and record data. As the collection of big data is subject to high volume, variety, velocity, and veracity (IBM, 2017), current centralised solutions are no longer suitable.

The increasing penetration of IoT and smart devices will be responsible for a surge in these four characteristics, as well as enhancing challenges inherent in IoT devices. Many


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perspectives exist. Some state complexity, connectivity, power, and security whilst others further include device management and increased traffic (Texas Instruments, 2016; Dickson, 2016). It is agreeable that the challenge lies in building an environment suitable for secure, trust-less, private transactions that maintains scalability (Loeb, 2015). The future IoT environment will consist of billions of players, many possibly holding malicious intents. This requires a protocol for validation and consensus. Cutting out expensive intermediaries and trusted third-parties is thus vital, as is the decentralisation and democratisation of devices. Success in IoT thus heavily relies on three success drivers attributed to decentralisation: namely creating a peer-to-peer, trustless, and robust and scalable environment (IBM, 2015).

2.2 RENEWABLE ENERGY We lead the introduction to renewable energy, a sector that is also in a transitional state and characterised by a similar philosophy as that of blockchain. Firstly, the political climate is introduced to lay the foundation for subsequent discussion to take place.

European Union Renewable Energy Directives require, on average, the EU to source at least 20% of total energy consumption from renewable sources, reduce CO2 emissions by 20%, and achieve 20% more energy efficiency by 2020. Reviewed directives even stipulate numbers up to 30% (European Commission, 2011; European Commission, 2016). Individual nations have set individual goals to reach such targets conjointly. Although the directives are binding, up to date only 11 of 28 participating countries have realised these. For example, the Netherlands and Germany respectively source roughly 6% and 15% of gross final energy consumption from renewable sources, whereas targets lie at 14% and 18%. (Eurostat, 2016; Eurostat, 2017). Achieving such goals calls for infrastructural change, one that is governed by transparency, accountability, and stimulation. The proceeding sections will introduce relevant literature and background vital to the understanding of the energy transition and its underlying components.

2.2.1 FROM CONSUMER TO PROSUMER Like for blockchain, the same principle of decentralisation characterises the energy transition. This means that technology and tools governing this transition must become more consumer-centric. Households play a pivotal role in the movement towards a future of renewables. This sector is responsible for 25% of final energy consumption (Eurostat, 2017). The European Commission regards energy consumers at the heart of its energy policy (Vansintjan, 2016). In turn, the concept of prosumer emerged. This refers to individuals that both consume and produce (electricity) typically directly at source of generation (BMWi, 2016). These are also becoming more aware of sustainable consumption, doing so by monitoring and optimising via the use of smart meters. Most of the EU-27 agreed to cover at least 80% of the market with such devices by 2020; estimated at nearly 200 million (European Commission, 2014). It is further projected that, by 2022, a typical home will bridge some 500 smart devices (Gartner, 2014). This requires a platform that can oversee the infrastructure of connected devices without compromising aforementioned security issues.


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The energy transition highly values prosumers, as they contribute towards decentralisation through of self-generation. Furthermore, they support microgrid effectiveness, enhance security of supply via local energy sourcing, and interact in ways allowing the formation of local energy markets. Nevertheless, the move towards prosumerism is also characterised by downsides which are introduced shortly. We present the notion that this mindset requires an enormous amount of flexibility, a vital factor for a successful transition as the energy market transformation increases supply-side uncertainties.

2.2.2 DISTRIBUTED GENERATION Prior to advancements facilitating the transportation of power across long distances through power grids, diesel generators supplied energy directly to manufacturing plants. This concept of distributed generation is not unfamiliar. It refers to the production of energy at the point of consumption. During those times, diesel generators were considered as a form of flexibility in energy sourcing (Bloom Energy, 2016). However, efficiencies gained by alternative currents outweighed distributed generation at the time and became the standard. Today, many issues attribute to this centralised method. Among others, technological advancements, security of energy supply, and climate change fuelled the recognition to revisit interest in distributed generation (IEA, 2002). Alternatively, better summarised by two central drivers: liberalisation of energy markets and concerns over the environment (Pepermans, Driesen, Haeseldonckx, Belmans, & D'haeseleer, 2005).

As addressed by the EU renewable energy directives, environmental concerns drive the energy transition. The support of sustainable, renewable sources further favours distributed generation. Crafting an efficient energy market is also at the forefront of the transition, distributed generation creates flexibility in times of changing market conditions. It reduces lead times in energy supply and hedges against price fluctuations inherent in spot markets (Pepermans et al., 2005). Furthermore, reliability of energy supply is the most important factor to consider (IEA, 2002). Some find that distributed generation can create positive effects in reliability (Dondi, Bayoumi, Haederli, Julian, & Suter, 2002). Conversely, practice finds that intermittency of electricity generation due to higher penetration of renewables can subdue said reliability effects (Planning Engineer, 2016). Distributed generation is also attributed to cost reductions. Transmission costs can potentially be reduced by 30%, but only upon correct placement of generators (IEA, 2002). This effect is however found to subdue as grid infrastructure becomes more complex, only completely independent microgrids will prove effective (Mohammadi, Hosseinian, & Gharehpetian, 2012).

2.2.3 LOCAL AND MICROGRIDS Connecting sources of distributed generation allows for the creation of microgrids. These are local communities with distinct control capabilities and the ability to operate islanded; or off-grid (U.S. Department of Energy, 2014; Perea, Oyarzabal, & Rodriguez, 2008). Autonomous operation requires appropriate design and control strategies. As previously mentioned, placement of distributed resources is crucial. Microgrids eliminate inefficiencies attributed


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to centralisation. They aggregate loads across the system allowing for flexibility especially during disruptions of the main grid. In turn, the grid should not disturb the operations of utility providers (Barnes, Kondoh, & Asano, 2007).

Other benefits are observed. For example, over a one-third reduction of power losses compared to current designs relying on main grid connection (Kamel, Chaouachi, & Nagasaka, 2010), supplementing the importance of reliability and quality of energy supply (IEA, 2002). The environmental driver Pepermans et al. (2005) mention is also satisfied with microgrids, as green energy production offsets CO2 emissions and gradually eliminates dependency on non-sustainable sources (Kamel et al., 2010). Lastly, as microgrid studies demonstrate the ability to aggregate loads efficiently, they can potentially create fully autonomous energy markets of self-generated electricity (Powerpeers, 2017).

2.2.4 DISTRIBUTED ENERGY RESOURCES Points of production in distributed energy systems are referred to as distributed energy resources. In ideal settings, these are coordinated and aggregated to feed the local grid to which they are connected. Several definitions are adopted in literature. Some incorporate diesel engines and other non-renewables, whereas others include only electrical energy sources as well as storage devices (Jiayi, Chuanwen, & Rong, 2007; Akorede, Hizam, & Pouresmaeil, 2010). As the role of environment and prosumer is central to this study, the technologies considered will only comprise of renewables. Specifically, small-scale devices suitable for installation in local, household settings will be of central focus; namely photovoltaic systems (PV).

Since around the 1930s, it has been possible to produce electricity by converting solar rays into electricity (Akorede et al., 2010). This technology is called PV. It comprises of an array of multiple panels that collect solar radiation. These may be installed in series or parallel on top of (residential) rooftops. The output energy is directly proportional to the surface area of the panels, albeit attaining a moderate operating efficiency of some 10-24% (Akorede et al., 2010). It is collected in the form of direct current, which requires inverters to be installed for residential use.

Although the past five years have seen an 80% price drop in PV panels, “complexity, durability, efficiency, and safety” are amongst the main barriers to residential adoption, as demonstrated by a low penetration rate of barely 3% (European Union, 2015; European Commission, 2016). Nevertheless, installation of residential PV panels is growing steadily and expected to continue (SEIA, 2017). In the context of this study, said barriers to adoption translate to concerns over the exposure to increased variability.

2.2.5 VARIABILITY OF RENEWABLE ENERGY RESOURCES The aforementioned notion of reliability can address an extensive array of topics. With regards to Dondi et al. (2002), it refers to the security of supply and service levels attributed to renewable, distributed energy resources. For this study, we investigate said reliability from a different perspective and define reliability as a measure of variability within a local


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grid. Due to inherent characteristics of renewable resources such as PV panels, the generated electricity is subject to intermittency. This factor may affect up to 70% of potential output capacity (American Physical Society, 2011). Just as we can predict the weather, it is possible, and successfully achieved in practice, to predict solar generation at acceptable accuracy levels. Nevertheless, it is found that this does not necessarily coincide with variations in electricity demand (European Parliament, 2010).

As households converge towards becoming prosumers and rely less on grids operated by DSOs, electricity supply available for consumption thus becomes less predictable. Variability measures this aspect and holds an inverse relationship to reliability. This is seen as one of the largest inhibitors of adopting renewable energy sources (European Commission, 2016). Inherently, this implies that local grids will be subject to greater volatility as penetration of renewables increases. It creates additional complexities in the scheduling and balancing of loads. As previously introduced, such variability effects can be offset by aggregation on a regional scale, especially amongst heterogeneous renewable sources. This notion could further apply to smaller, community environments, hence the focal point on local, neighbourhood settings in this study.

2.2.6 THE COMPLEXITY OF ELECTRICITY MARKETS REQUIRES CAREFUL CONSIDERATION Although abundant distributed energy resources exist, decentralised electricity allocation across households is not very common yet. Even deregulated markets are still subject to regulation and standards, and prohibit such exchange for many reasons One of such includes insider trading, leading cause of the California electricity crisis. For such purposes, legal frameworks such as REMIT exist to prohibit collusion and manipulation (Agency for the Cooperation of Energy Regulators, 2011). This section briefly discusses the core elements of current energy markets, allowing for issues and possible solutions to be highlighted later. Furthermore, this is pivotal to the understanding of why electricity markets are so complex by nature.

Similar to other commodities exchanges, wholesale electricity markets take on two forms. Brokers trade future deliveries in long-term forward markets, whereas short-term trades occur in spot markets. They attempt to match supply and demand to allocate over- and under-capacities in markets. The point at which the two meet determines the market price. As producers wish to set prices relative to operating costs, bids of different values can be found in the market. To minimise the cost of electricity for its consumers, bids are granted to plants with the lowest marginal costs of production first (arrhenius consult gmbh, 2015). This is known as the merit order. Pepermans et al. (2005) mention the liberalisation of electricity markets. In a fully renewable energy market, the costs of supplying energy would not allow for arbitrage as the marginal cost of production would be zero. This is, however, not the case. Fossil energy plants, subject to marginal costs of production, still partake in the market, requiring a market mechanism to balance this.


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Similarly, on a retail level, consumers are presented with a choice of electricity provider in deregulated markets. Such retailers are responsible for operations including the aggregation and sale of electricity. Furthermore, they balance said distribution grid with demand-side management and ensure acceptable service levels. Meeting electricity demand is difficult, factors like the environment and economics majorly influence supply. The transition towards renewable sources further shapes this as they are intermittent. Production occurs irregularly requiring non-renewables to bridge gaps. Subsequent sections of this study will explore the role that prosumers, smart markets, and blockchain play in influencing grid variability.

2.3 ELECTRICITY MARKETS ARE SUBJECT TO MARKET REDESIGN The following section introduces the changing environment of energy markets in light of the shifting technological landscape. Furthermore, it discusses concepts pivotal to this study and combines these considering the discussion lead in previous sections. Subsequently, a presentation of hypotheses will follow that form the basis of research.

2.3.1 ENERGY MARKETS ARE IN A CONSTANT TRANSITIONAL STATE

THE MOVE TOWARDS OPEN COMPETITION

Traditionally, energy markets succumbed to monopolistic market structures and government ownership and regulation (Pietrobon, 2015). Privatisation of the United Kingdom’s electricity supply marked a historical pinpoint under which others soon followed. Opening electricity markets was in the interest of fair competition and transparency. Thus, began the separation of duties carried out by natural monopolies, namely transmission and distribution, and Independent System Operators and Regional Transmission Operators, namely production and retail. Although the growing pool of demand could be met more easily and efficiently, market design was not executed carefully enough. The California electricity crisis is a classic example of underestimating the dynamics at stake in energy markets. Mass speculation and manipulation led to large-scale power supply shortages; allowed because of market deregulation without careful design.

The opening of electricity markets had subsequently lead to greater liberalisation and, with it, came the acceptance of renewable energy resources. Several severe market impacts followed this integration. Marginal costs of supplying the market experienced significant reductions as these renewables do not require fuels to generate electricity. Electricity prices decreased as a result. Nevertheless, numerous uncertainties fuelled the growing market volatility. Intermittent generation from solar panels and wind farms caused supply-side uncertainties. Similarly, the issue of non-storability lead to inefficiencies. In combination with a price-inelastic demand for electricity, the abovementioned factors caused substantial fluctuations in spot markets. Increased competition, lower prices, and greater price volatility completely changed operations in electricity markets.

Albeit simple introduction above, electricity markets are subject to high complexity. A multitude of players exist, each performing different duties. Often these tend to overlap


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causing markets to be saturated and intertwined. It leads to varying interests that may conflict, requiring coordination mechanisms to align the interests of an ever-growing body of relevant stakeholders. Take, for example, as introduced in previous section, the multitude of markets in which energy trade takes place. Such market dynamics further amplify uncertainties. From a macro perspective, this holds especially true for electricity supply as EU market policies favour increasing exposure to sustainable, intermittent resources. Utility functions to price electricity and schedule delivery on a national scale thus become even more multifaceted in the light of growing constraints and distributed energy resources. As more are added to grids, valuation and strategic complexity grow ever so causing decision sets to expand exponentially (Bichler et al., 2010).

Similarly, from a regional perspective, we experience an increase in the variability of microgrids. As households increasingly tend towards off-grid resources such as PV panels, energy grids become ever-so important in assuring a constant supply of electricity. Although policies such as EU Energy Directives favour steady disconnection from grids, these will gradually assume roles similar to the function of a backup generator. In essence, they will still be required to assure acceptable service levels. This only increases said uncertainties and leads to the revaluation of current energy market structure. In combination with the proliferation of IoT-enabled, smart devices and energy-related technological advancements, such as improving efficiency of PV panels, these trends favour the design of smart energy markets and technologies.

SMART MARKET REDESIGN

Various definitions are found in literature. Essentially, smart markets are optimisation-based mechanisms subject to constraints that require computational abilities to optimise (McCabe, Rassenti, & Smith, 1991; MacKie-Mason & Wellman, 2005; Bichler et al., 2010). The idea is that smart markets are automated, at least to some extent. This notion highlights thus that smart markets are considered as semi-decentralised markets. They help reduce decision-making time from days to minutes, thanks to ever so growing abilities of hardware and linear programming. As Bichler et al. (2010) state, the smart aspect of markets requires two forms of intelligence; namely instantaneous/real-time and collective. The former refers to the abovementioned market dynamics that lead to uncertainties and cause complexity, comparable to energy traders being exposed to an ever-growing pool of bid amounts. Modern technologies allow for such influx of information to be handled more efficiently. The latter refers to understanding of relationships in multi-echelon systems that encompass entire supply chains and structures. In the context of this study, this notion relates to the understanding that microgrid management is naturally subject to lots of complexity due to its underlying characteristics, as mentioned in previous sections.

We reiterate the regional perspective mentioned previously. As the generation of renewable energy is subject to decentralisation, the transition gradually shifts focus from national energy solutions towards more regional based ones (Debor, 2014). Such move presents a multitude of benefits for both electricity grids and energy consumers.


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Increasing proliferation of renewables implies more localised production, as well as more consumption directly at source. Local communities may greatly vary by characteristics like grid infrastructure and weather. In some areas, wind power may be dominant source of generation, whereas in others solar presence is stronger. This requires greater emphasis on demand flexibility and load balancing at local level than for nationally aggregated energy solutions (Energy21, 2017). The creation of local markets allows for community-specific, tailor-made solutions to tackle such issues.

From a grid perspective, this allows for reduction of complexity in energy scheduling and load balancing efforts. Furthermore, incumbents can retain roles in grid management with alternative servicing models to more accurately, and fairly, price and distribute flexibility via demand side management. Likewise, energy consumers can thus receive fairer prices for consumed and generated electricity. Additionally, such schemes provide greater incentives for switching to off-grid, renewable solutions. As can be inferred, the role of shared benefits plays a vital role in local market settings and outlines socioeconomic advantages.

Integration of ‘smart’ into local markets further allows for integration of data-driven decision making for optimisation purposes. As introduced by Bichler, Gupta, & Ketter (2010), the decision boundary is simply too large. The utilisation of smart local markets, still subject to some manual processes, thus becomes a foundational component in the move towards fully decentralised local markets.

Upon initial experimentation and clearer understanding of how underlying factors interact, a move towards blockchain energy markets can be made. Such markets are embodied by full automation of processes currently performed by intermediaries that lie between the initial and final stages of the energy chain; namely generation and consumption.

To explain benefits of such automation, we introduce the theory of disintermediation for electronic markets (Chaffey, 2002). As decentralisation cuts out the role of middleman, reductions in all three cost areas are experienced. Namely search costs, transaction costs, and allocation costs. This implies that TSOs are no longer required to operate in wholesale markets and national transmission, similarly, many regional services of DSOs become redundant. Instead, blockchain assumes responsibility for these roles and could lead to benefits including electricity price reduction and efficient load balancing.

Creating such decentralised, electronic energy markets further satisfies requirements necessary to become smart markets; as laid out by Bichler et al. (2010). Instantaneous/real-time intelligence achieves reduction of market complexity, whilst collective intelligence attributed to integration of the entire local grid achieves reduction of socioeconomic costs.

The discussion of smart markets naturally progresses to the concept of energy cooperatives. These will be introduced alongside abovementioned principles from a more granular perspective.


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2.3.2 ENERGY, COALITIONS, AND THE BLOCKCHAIN The concept of energy cooperatives is widely researched under several facets. Various definitions exist depending on the nature of research. One of such defines cooperatives broadly as an “…autonomous association of persons…meet[ing] their common economic, social, and cultural needs…” in a commonly owned enterprise (The International Co-operative Alliance, 2017). From a more granular perspective, we read decentralised energy systems jointly controlled by some small companies (Debor, 2014). A commonality in definitions found across literature is the aspect of aggregating any input of said coalitions. In this study, we define an energy cooperative as a decentralised aggregation of residential households. The term energy cooperative and coalition will be used interchangeably.

As built up in previous sections, it is of interest to research in the field of coalitions and renewable energy. Benefits of smart markets have been outlined in preceding section. To further the discussion, it is argued that energy cooperatives integrate said benefits of smart markets and aggregation, as will be introduced shortly. Such aggregation is to be firstly performed in a semi-decentralised manner, namely under supervision of governing bodies; namely agents. Thus, the first central question to be addressed in the study is as follows:

[RQ1]

RQ1: What is the role of energy coalitions in light of an increasing share of renewable energy sources?

The need for coalitions in energy markets is outlined. Not only do they contribute to increased local generation and prosumer behaviour, but they also address issues related to climate change (Debor, 2014). More broadly, they capture the aforementioned need for decentralisation and liberalisation of energy markets as discussed by Pepermans et al. (2005). More so, this contributes to said benefits of smart markets.

With this one naturally derives to the discussion of new technologies. The cross-field of energy and technology is of fickle nature as it is in a constant state of change. This notion is well illustrated in Figure 4 (below), showing the amount of new investment in clean energy.

Figure 4: New Investment in Clean Energy


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Although global total investment into clean has grown over the years, since the financial crisis of 2008 growth rate has been rather stagnant. Contrarily, new investments in solar have been highly volatile. More importantly, we can extrapolate that the growth of smart energy technology investments has shown prominent rise since then (Bloomberg New Energy Finance, 2017). In relation, as of late 2015 a total of $1 billion has been invested into blockchain-related projects (CNNMoney, 2015). This number surged to $1.4 billion alone in 2016 (cryptocoinsnews, 2016). Similarly, smart technologies experienced a surge in growth (15%) year-on-year in 2016. These figures provide evidence that energy and technology are on the rise, and thus lead to the next central question to be addressed in this study:

[RQ2]

RQ2: What is the role of technology, and blockchain, on the effectiveness of energy coalitions?

2.3.3 ENERGY COOPERATIVES REDUCE GRID VARIABILITY Keeping in mind uncertainties associated with increased reliance on renewable, distributed energy resources, the main goal of energy coalitions is to reduce grid variability through aggregation. Grids characterised by high variability are more prone to energy outages as peak load capacities cannot accurately be adjusted to peak consumption characteristics.

Cooperatives are viewed as mechanisms that mitigate risks of poor service levels by smoothening out household profiles. This allows for more accurate peak demand scheduling and increased predictability. We measure utility of a coalition by looking at said grid variability, a concept that is inversely related to grid reliability. More specifically, as will be introduced in subsequent sections, this measurement is taken in the form of a peak-to-average-ratio (PAR).

It is of interest to reduce said ratio by increasing the size of aggregation, as this type of study takes on novelty. By characteristic, energy cooperatives reduce grid variability. This aspect is of high importance to energy grids as it allows for smooth integration of renewables, reducing reliance on national grids, and improving service levels.

We are thus interested in evaluating the optimal coalition size to reduce said peak-to-average-ratio. Therefore, we derive at the following hypothesis:

[H1]

H1: Coalitions, supported by local smart markets, achieve reduction of peak-to-average-ratio as an increasing function of coalition size.

Furthermore, from a logical point of view, we expect said reduction to approach a minimum and experience no further marginal gains upon increasing size of cooperative. This leads to the following hypothesis:


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[H1b]

H1b: Coalitions, supported by local smart markets, are subject to diminishing marginal returns of peak-to-average-ratio reduction as coalition size increases.

2.3.4 COOPERATIVE STRUCTURE DICTATES THE MAGNITUDE OF VARIABILITY REDUCTION Numerous research evaluates cooperative effectiveness in the context of dynamic pricing or wind energy (Changder, Dutta, & Ghose, 2016; Baeyens, Bitar, Khargonekar, & Poolla, 2011), for example. More so, concerning grid variability, this is often managed by reserve up and down but on regional scales. Furthermore, it is evident that large-scale aggregation performs well at reducing risk of grid variability (American Physical Society, 2011). To the best of knowledge, local grid solutions are not well represented in literature. With said focus on local grids, these are still subject to volatility of distributed energy resources and thus more affected by the structure and proportion of such resources. We derive at the following hypothesis:

[H2]

H2: For coalitions supported by local smart markets, the magnitude of peak-to-average-ratio reduction is influenced by coalition structure.

Logically speaking, this further imposes that larger exposure to renewables leads to greater variability in grids. Consumer households are of predictable consumption behaviour profile relative to their prosumer counterparts. This leads to the following hypothesis:

[H2b]

H2b: Consumer coalitions, supported by local smart markets, achieve the lowest peak-to-average-ratios.

Contrarily, due to the presence of solar panels, prosumer households are subject to greater degrees of volatility. For example, intermittency of sunshine can affect up to 70% of potential solar panel output capacity (American Physical Society, 2011). Due to supply-side uncertainties of this household profile, we lead to the following hypothesis:

[H2c] H2c: Prosumer coalitions, supported by local smart markets, achieve larger

marginal reliability gains per unit of increase in coalition size.

2.3.5 HETEROGENEOUS COALITIONS PERFORM BEST We explored the hypothesis that coalition structure dictates PAR reduction and evaluate so by comparing differences between prosumer and consumer coalitions. Nevertheless, while electricity demand for both profiles can be somewhat accurately mapped and predicted, the generation output of prosumer households remains variable and intermittent. Thus, it is


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believed that the separation of two profiles would create one stable, consumer coalition and another variable, prosumer coalition. Aggregation of renewable resources of mixed types leads to lower exposure to variability of national grids (American Physical Society, 2011). It is expected to achieve the same outcome for local grids by aggregation of residential profiles of mixed types. This leads to the following hypothesis:

[H3] H3: Heterogeneous coalitions, supported by local smart markets, produce

most desirable peak-to-average-ratio reductions with respect to the grid.

Penetration of smart technologies is on the rise, 80% of EU households must be covered by 2020 (European Commission, 2014). Aside from benefits such as increased information awareness, this IoT-enabled technology also imposes several costs. Firstly, the installation of such devices must be performed at a certain scale to be economically feasible. The average meter to be installed in the EU requires additional financing for DSOs, who are expected to bear those costs (Meter On, 2014). Furthermore, the advent of increasing exposure to smart devices leads to more inter-device communication, in turn leading to higher energy consumption. Likewise, additional data centres must be invested in to accommodate for the increase in collected information. Lastly, as previously mentioned, representation of energy coalitions in smart markets must be agent-managed. These are responsible for forming and maintaining coalitions, as well as aggregation of households and subsequent energy resources.

The abovementioned factors all impose certain costs on the formation of coalitions and must also be considered. To simplify this consideration, when introducing an arbitrary cost function to the coalition formation process, we lead to the following hypothesis:

[H3b] H3b: For coalitions supported by local smart markets, optimal size of energy

coalitions increases when subject to an arbitrary coalition formation cost.

2.3.6 BLOCKCHAIN AND TECHNOLOGY WILL ENHANCE COALITION EFFECTIVENESS The discussion in the previous section introduced the hypothesised relationship between energy coalitions and grid variability in light of semi-decentralised, local smart markets. This notion is furthered by examining said relationship with respect to fully decentralised, local blockchain markets. As mentioned previously, difference between the two lies in degree of decentralisation.

Similarly to that of smart markets, blockchain markets play a pivotal role in the energy sector of the future. Due to differentiation primarily residing in said degree of decentralisation, we highlight that all aspects previously introduced for local smart markets apply to local blockchain markets. Thus, only significant differences will be mentioned.

Blockchain allows for full automation of processes previously performed manually. This implies that household aggregation processes are no longer performed by aggregators. Rather, fully negating the role of a middleman results in complete disintermediation. With


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regards to aforementioned (cost) implications, blockchain markets experience drastic reductions in search costs associated with intelligence gathering.

From a local grid perspective, this allows for more efficient coalition formation algorithms to be produced and implemented in real-time. Furthermore, the advent of data-driven approaches reduces cost of operations. More so, smart contracting here results in more accurate monitoring and reporting efforts. From the consumer-side, this attributes to yet higher savings in electricity bills, higher information awareness, and increased transparency. We reiterate the notion of socio-economic benefits and argue that local blockchain markets further increase these.

Preceding points thus lead to the following hypothesis:

[H4]

H4: Coalitions, supported by local blockchain markets, will receive same benefits as those supported by local smart markets but at higher magnitude.

Before proceeding to the following section, it must be mentioned that evaluation of local blockchain markets and underlying factors will be performed in an exploratory manner.

2.4 CONCEPTUAL MODEL Figure 5 (below) depicts the abovementioned concepts to be studied in this research. Ultimately, it is of interest to find an optimal coalition size that effectively minimises local grid variability.

Figure 5: Conceptual Model

The hypotheses allow for evaluation of three broad themes related to grid variability reduction. These include coalition size (H1), coalition structure (H2), coalition formation costs (H3), and impact of degree of decentralisation (H4).


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3. METHODOLOGY

3.1 DATA AND DESCRIPTIVES The data used for analysis comprises of granular household consumption of electricity in watt-hours [Wh] at 15-minute intervals. It is taken from the U.S. based project ‘Pecan Street’. Initiated in 2010, this smart grid project aims to derive real value for customers whilst maintaining the market roles of utility firms, and ultimately create an energy big data environment. Researchers and participants can analyse a variety of smart solutions including smart metering and home energy monitoring systems for a comprehensive set of residences. In the context of this study, it enables the monitoring of electricity consumption in real-time, for prosumers additionally generation. Participating residences vary from older, non-green to newer, green households as well as commercial properties and public schools.

3.1.1 SAMPLE The properties of interest for this study are residential buildings, including town houses, apartments, and lone standing family homes. Pecan Street’s directory encompasses a total population of some 500 households for a selected neighbourhood in the city of Austin, Texas. We drew a sample of 251 households based on various criteria.

Firstly, such size allows for inferences to be made with acceptable confidence and error margins (Krejave and Morgan, 1970). Furthermore, to attain generalisability of results probability sampling was implemented based on criteria such as information completeness and location of households. More specifically, as it is of interest to study prosumer and consumer households, we used random stratified sampling. Considering the move towards decentralisation of energy markets and self-generation, a disproportionate ratio was utilised as the role of prosumer will increase over time and, thus, its exposure in the study.

Within the sample 126 households are of profile consumers; they do not generate their own electricity. The remaining 49% are prosumers, equipped with PV panels. Households have also been equipped with smart technology that allows for display of real-time information and metering of all connected devices and appliances. Consumption and generation data used for analysis was gathered from a SQL server and comprises of the period September 2014.

3.1.2 VARIABLES This section introduces and explains the variables of interest for this study.

INDEPENDENT VARIABLE – COALITION SIZE

As introduced in previous sections, a coalition is the aggregation of households and their electricity profiles. Coalitions are managed virtually by smart market agents. Manipulation of coalition size will occur at a range from 2 to 50 households. Furthermore, three distinct coalition profiles are controlled for; namely consumer, prosumer, and heterogeneous. This allows for isolated comparison of simulation at different exposures of the variable. Both


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consumer and prosumer coalitions comprise of purely their accompanying household profiles, whereas mixed comprises of an even amount of both. This leads to an acceptable internal validity as the effects of confounding factors, such as intermittency of weather and consumption behaviour, are negated. Furthermore, it allows for differences between the coalition structures to be examined.

DEPENDENT VARIABLE – PEAK TO AVERAGE RATIO (PAR)

The variable of interest is the PAR. It is measured by dividing the peak of net power flow (NPF) of a given period by the average of net power flow of said period, thus making it of measurement level ratio.

[PAR(t)]

!"#(%) = (!)*+,-(%)(!),.+/,0+(%)

By exposing the households to varying coalitional sizes, it is of interest to decrease this ratio. Inherently, a change in PAR implies a change in grid reliability; a decline in variability thus leads to an increase in reliability and vice versa.

INPUT VARIABLES – NET POWER FLOW, USAGE, GENERATION, GRID

Table 1 (below) briefly defines the following terms necessary to understand the relationship between PAR and NPF.

use Electricity demand over time interval

gen Electricity generation (PV) over time interval

grid Electricity drawn from grid over time interval

NPF Net electricity flow from grid over time interval

Table 1: Definitions of Input Variables

To derive the PAR, first, the NPF must be calculated. For prosumer households, this is measured by subtracting the electricity generated (gen) from the electricity demand (use). In the case of consumer households, the NPF corresponds to the electricity drawn from the grid (grid). Net power flow is a bi-directional measurement taken from a grid perspective. Positive values thus attribute to electricity taken from the grid, whereas negative values imply the amount over-produced and returned to the grid.

3.1.3 DESCRIPTIVES OF DATASET This section comprises of the univariate analysis of variables selected for input in the experiment to be performed. It entails examining household electricity consumption and production data at various angles to provide the backbone of the remaining sections.


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Figure 6 below shows the monthly loads of consumer households, ranked in descending order. It is evident that one household consumes an abnormal amount of electricity in comparison to the remaining. Of all consumers, 42% of consumption is attributed to the top 20% of residences. Therefore, it can be stated that monthly consumption is not uniformly distributed with a sharp positive skew. Within the sample, an average consumer household consumes 1,088 kWh; slightly below the average for Texan households – 1,168 kWh (Electricity Local, 2017).

Figure 6: Monthly Loads of Consumer Households

Similarly, Figure 7 (below) shows the monthly loads of prosumer households. The levels are also ranked in descending order of consumption and further include the monthly generation loads. Note: For purposes of comparison the axes are scaled the same. Here it is evident that the household consumption is more evenly distributed; the top 20% of prosumers are responsible for some 30% of total monthly consumption. Nevertheless, it cannot be statistically inferred that as prosumers consume more they produce more.

Figure 7: Monthly Loads of Prosumer Households

Although this does not translate to a large difference, interestingly enough, as can be seen from Table 2 (below), the average monthly consumption of prosumers is higher than that of consumers by some 335Wh.

Similarly, median consumption of prosumers is also higher by some 868 Wh. Nevertheless, maximum consumption is trumped by consumers, as is the standard deviation – something that is rather expected as consumers are more bound to the constraints of the power grid and


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its pricing structure. These values above only highlight some basic aspects and differences between prosumers and consumers. Looking solely at the numbers above might give the impression that consumer households conform to a greater amount of variability, when in fact the NPF values are those that must be examined in greater detail.

Prosumers Consumers

Generation [Wh] Consumption[Wh] Consumption[Wh]

Mean 2,389 4,687 4,352

St. dev. 723 1,781 3,674

Min 199 1,816 661

Q1 1,952 3,456 2,429

Median 2,472 4,565 3,697

Q3 2,836 5,534 5,133

Max 4,689 12,614 33,226

Table 2: Descriptive Statistics of Prosumer and Consumer Monthly Electricity Consumption

Figure 8 (below) highlights the average of NPF over the month of September. It can be distinguished between prosumers and consumers, as well as weekday and weekend. From it, we can see that, as is expected, NPF during the night is stable and can be considered as uniform. We do see, however, that after sunrise, when PV panels begin to generate electricity, the NPF of prosumers experiences a sharp decline. So much, that between late morning and afternoon prosumer households supply the power grid with the overcapacity they are producing. For consumer households, on the other hand, NPF resembles true consumption. From this figure, we can infer that the profiles of prosumers are more variable by nature as they are fuelled by intermittency of renewable generation.

Figure 8: Net Power Flow of Average Day for Prosumer and Consumer


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Table 3 (below) further iterates this notion of variability providing the descriptives for monthly PAR values of prosumers and consumers.

As can be seen, prosumer PAR has a much higher standard deviation than that for consumers. Same applies to the minimum and maximum values. Evidently, prosumer profiles are subject to higher volatility in energy grids.

Prosumers Consumers

PARNPF PARNPF

Mean 20.098 6.267

St. dev. 78.134 2.922

Min -106.435 2.271

Q1 6.443 4.273

Median 9.178 5.340

Q3 15.532 7.469

Max 859.401 17.352

Table 3: Descriptive Statistics of Monthly PAR Values of Prosumers and Consumers

In subsequent sections, the parameters of the experiment will be defined as means of pre-testing on the NPF. However, first, the experimental setup and design must be introduced.

3.2 METHODOLOGY This section will present the experiment that is to be carried out, and the steps necessary to derive the design.

3.2.1 WHY SIMULATION The natural environment can be described as one of highly complex nature. More specifically, it is challenging to study an effect in isolation as there is a multitude of factors that contribute to said environment. Likewise, the specified problem does not take on a familiar nor examined form. This leads to the employment of a design science study in the field of information systems as discussed by Hevner, March, Park, & Ram (2004). Two domains characterise such studies. Firstly, the known body of knowledge from behavioural science is used. It derives from theories that have been articulated and tested to explain behaviour within, for example, organisations. Secondly, it aims to extend said knowledge with information to pertain from a new field of study. Design science studies involve the construction of artefacts to apply input data to meet a certain need specified by the environment. In the context of this study, this need translates to reducing local grid variability. Thus, instead of trying to provide a theory to predict behaviour, the methodology aims to extend by achieving measurable utility.


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Such studies do so via computational models to assess the robustness and quality of output (Hevner et al., 2004). The artefact employed in this case is an aggregation algorithm. Due to characteristics inherent to the problem, it cannot be solved efficiently. This classifies it as an intractable problem, for which a single algorithm does not exist (UMSL, 2017). Contrarily, we look to attain generalisability. Household variability is unique per input. Thus a large number of permutations for aggregation must be examined. In turn, the problem requires brute-force to be solved.

Simulations allow for such type of examination. These artificially represent systems found in the natural environment (Helleboogh, Vizzari, Uhrmacher, & Michel, 2007). Specifically, a Monte Carlo Simulation can address this required brute-force method. In this study, it involves the use of randomly generated numbers to derive a stochastic model for household aggregations. These are permutated by a large number of iterations to account for said intractability. This research design carries out 1,000 iterations of the aggregation process. This allows for normalisation of results as to derive an expected value to be observed across the general population. Several benefits are observed from this methodology, as discussed by (Berends & Romme, 1999; Helleboogh et al., 2007). Firstly, the number of iterations can be determined to achieve certain accuracy requirements. Additionally, input parameters and values can be altered to meet specific requirements or goals. Lastly, changes in output can be observed and compared.

This is of particular important for this study, as the PARs of individual households differ greatly; as mentioned previously and will be demonstrated with more evidence in subsequent sections. Therefore, single iteration of aggregation would not suffice to provide representative, generalisable, nor desirable outcomes. Monte Carlo is chosen because of its ability for the objective study of aggregated data with user defined parameters for the amount of differing simulations to be run. As opposed to fitting the optimal coalition sizes specifically to the Pecan Street dataset, this study aims to find generalisability across populations beyond the scope of the population of interest.

3.2.2 DATA PREPARATION The data used for analysis was gathered from two distinct sources within the SQL server. Firstly, this encompasses the energy consumption data, where all readings from various smart meters are stored at the interval level. The second source stores the metadata describing the profile of households. This includes aspects such as type of home, whether or not PV panels are installed. The two were subsequently merged by their unique household ID into one dataset, consisting of over 700,000 observations. As we already established data completeness as the parameter for choosing households in the study, there is no further need to clean data; for example, time-intervals during which no electricity consumption is recorded are plausible and should not be removed.


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3.2.3 DEFINING THE SIMULATION PARAMETERS To define the parameters of the simulation, the raw data set must be analysed. This encompasses running t-tests and F-tests for the energy profiles, namely NPF, of households to determine if there are significant differences in the mean and variance between them.

Firstly, as two distinct strata are identified, namely prosumers and consumers, an F-test will be conducted to determine equality of variance. These results will then be used for a two-samples t-test for equality of means. It is understood that renewable energy resources are variable by nature. This leads to the belief that presence of PV panels, and with it, the possibility to self-generate electricity, has a significant impact on a household’s energy profile. In turn, this should affect consumption and sourcing of electricity. The test allows to determine the true differences amongst the profiles and will conclude whether or not it is desirable to address the two disparately in simulation.

F-Test for Variance between Prosumer and Consumer

Average NPF: Prosumer Consumer

Mean 0.7980 1.5110

St. dev. 1.110 0.5184

Observations 96 96

df 95 95

F 4.5827

p-value 1.41e-12

P(F<=f) one-tail 0.7123

P(F>=f) one-tail 1.403841

Table 4: F-Test for Variance between Prosumer and Consumer

As is illustrated in Table 4 (above), significant differences in variance of average NPF between consumers and prosumers are observed. Thus, a two-sample t-test of unequal variances must be done. As illustrated in Table 5 (below), we can statistically infer that the means of NPF between prosumers and consumers are different, the one-tailed statistic implies that of consumers being higher.

From these tests, we can conclude that there are in fact differences between prosumer and consumer profiles, an aspect that must be incorporated into the simulation.

As it is of interest to see daily differences of coalitions, it is vital not to overcomplicate the model. Therefore, the tests will be constructed on workday and weekend differences in average electricity net power flow for both consumers and prosumers. It is expected that more members of a household are at home during the weekend, and therefore higher electricity


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consumption rates are expected in these periods. Subsequent results will determine whether or not a distinction needs to be made here in the simulation.

T-Test for Equality of Means between Prosumer and Consumer

Average NPF: Prosumer Consumer

Mean 0.7980 1.5110

St. dev. 1.110 0.5184

Observations 96 96

Hypothesised difference >0

df 134.58

T -5.7031

p-value 7.14e-08

P(T<=t) two-tail -1.6563

P(T>=t) two-tail 1.6563

Table 5: T-test for Equality of Means between Prosumer and Consumer

As these tests are of same nature as above, the accompanying tables may be found in Appendix. From Table 8 (Appendix) it can be inferred that the variances between prosumer weekend and workday profiles are unequal, and thus a two-sample t-test assuming unequal variances is conducted. Table 9 (Appendix) shows that equal means can be statistically inferred.

After conducting the same for consumer weekend and workday profiles, we can conclude equal variances for the two; Table 10 (Appendix). Interestingly, it is found that the means differ. Table 11 (Appendix) highlights that workday means are statistically higher. However, after examining more closely the difference in means, 0.16 Wh, and the standard deviations, 0.4662 and 0.5424 respectively – it is apparent that the difference is so minute that it would create unnecessary burden relative to the gain to separate the simulation work- and weekdays merely for consumers.

From these results, it becomes apparent that prosumers and consumers differ in consumption profiles significantly enough to separate them in a simulation. Additional to running two simulations for consumer and prosumer coalitions separately, the two will, nevertheless, be combined further in a third simulation. As discussed in previous sections, we hypothesise that the net benefit is greater for mixed coalitions. Before looking into the results, firstly, the steps comprising data aggregation will be explained. Secondly, more information regarding the experimental setup will be introduced to give perspective as to the functionality of the simulation itself.


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3.2.4 DATA AGGREGATION This section will briefly highlight the steps taken to derive the aggregated data set used for analysis. These steps are identical for preparing for each of the three simulations.

As the raw data set comprises of 15-minute interval data on a household level, it must be aggregated based on two factors. Namely: the identification number corresponding to the household, as well as the timestamp. This provides 30 distinct subsets, one for each day of the month September. Aggregation is performed based on the variable NPF, for which two values are measured over each time interval (day); peak NPF and average NPF. RStudio will be used for the aggregation, and subsequently for simulation.

Furthermore, results will be analysed using graphs, which are to be presented in subsequent sections.

3.2.5 RESEARCH DESIGN OF MONTE CARLO SIMULATION Once the subsets are created, the simulation can be initiated. Table 6 (below) illustrates the parameters used for input.

Parameter Input

Coalition Size 2 through 50

Days 1 through 30

Iterations 1,000

NPFpeak Daily values from each household

NPFaverage Daily values from each household

Table 6: Input Parameters for Monte Carlo Simulation

Furthermore, the Monte Carlo Simulation steps are illustrated by the sequence chart below (Error! Reference source not found.). As explained previously, the problem at hand is one characterised by intractability. The figure below is, therefore, a highly simplified illustration of the processes involved in generation of household aggregations. Each simulation begins with coalition size 2, day 1, and iteration 1.

Figure 9: Monte Carlo Flow Diagram


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Conducting these steps will yield in an output of one table per coalition size, composed of 1,000 maximum PAR values for each of the 30 days in September. This data is then further processed to generate an average over each iteration, to derive an expected PAR value of that aggregation amount.

Once these tables are summarised, they can be visualised to see the reliability gain as a function of increasing coalition size. The results of each of the three simulations will be portrayed in the proceeding sections.


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4. RESULTS AND DISCUSSION The following section will provide results and hypothesis support thereof. In total three simulations are performed. These are run on the daily averages of PAR per household, and the subsequent change as coalition size is increased. Namely for consumer, prosumer, and mixed. Discussion of insights will follow after presentation of main results.

4.1 PRESENTATION OF MAIN RESULTS

4.1.1 CONSUMER COALITIONS For the first simulation, the average daily PARs are used as input from the consumer subset. This coalition structure consists purely of households that consume electricity within the sample. As mentioned in the methodology section, these have been combined at 1,000 intervals of varying orders of aggregation. Furthermore, this aggregation is performed for every day of the month of September, 1 through 30, and each examined coalition size, 2 through 50.

The simulation results in daily PAR values for the respective days and coalition sizes. These values have further been averaged out to find an expected value resulting in 1,500 PARs. Figure 10 (below) highlights the average monthly PAR values for each coalition size. This is yet another aggregation of results for illustrative purposes. Note: the coalition size of 1 refers to values for non-coalition.

Figure 10: Mean PAR of Consumer Coalitions as Function of Increasing Coalition Size

As can be observed, the trend suggests that as we increase the independent variable, coalition size, inversely the dependent variable, PAR, decreases. This is in support of H1, stating that the reduction in PAR is an increasing function of increasing the coalition size. Furthermore, it supports H1b; we observe that this reduction is subject to diminishing marginal returns. Initially, a drastic reduction is experienced. At a certain point, this effect becomes asymptotic converging towards a minimum. The net effect of increasing aggregation sizes tends towards zero. Subsequent analysis of consumer coalitions will take place in following sections upon presentation of the remaining two.


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4.1.2 PROSUMER COALITIONS The same methodology is employed for simulation two on the prosumer subset. This coalition structure is composed purely of households that have the capability of self-generation. Figure 11 (below) highlights the average monthly PAR values for each coalition size. Note: Value for non-coalition has been removed for illustrative purposes as it is too large.

Figure 11: Mean PAR of Prosumer Coalitions as Function of Increasing Coalition Size

Once again, we observe similar trend as for the first simulation. This leads to support of H1 and H1b. However, as prosumer households are subject to higher volatility as discussed in previous sections, more noise is present at distinct aggregation sizes.

4.1.3 MIXED COALITIONS The final simulation is run for a heterogeneous coalition structure. It is composed of the entire sample and represents an even mix of both consumers and prosumers. Figure 12 (below) visualises main findings.

Likewise, to other simulations performed, we observe a trend in support of H1 and H1b. Aggregation of the complete data set provides evidence to a steady decrease in PAR as a function of increasing coalition size. Subsequent section will analyse the differences observed by changing parameters of the coalition structure; namely comparison of the three simulations.

Figure 12: Mean PAR of Heterogeneous Coalitions as Function of Increasing Coalition Size


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4.2 COALITION STRUCTURE DETERMINES MAGNITUDE OF PAR REDUCTION As mentioned, three distinct coalition structures have been studied, namely: consumer and prosumer, both homogeneous, and heterogeneous. Although similar trends characterise all three structures, we observe a variation in the magnitude of PAR reduction. Having kept all other factors constant, evidence suggests that this is due to different coalition structures. Each is characterised by household profiles subject to varying amounts of variability. This notion may be observed at first glance by comparing the scales of axes for three structures.

These observed differences will be evaluated via means of regression. This encompasses inspecting whether the coalition structure may be classified as an interaction term. To do so, we generate a multiple linear regression of the independent and dependent variable, namely coalition size and PAR. Furthermore, we add the term structure to account for the differences in PAR reduction witnessed across the coalition structures. This term can take on one value per examined structure. Subsequently, we compare with a moderated regression including the interaction between size and structure, which allows for differential effects of variable size to be considered. The summaries of both regressions are illustrated by Table 7 (below).

Results from multiple regression provide evidence as to an existing relationship between coalition size and PAR with the added term structure. The coefficient is -1.003 (0.1903) and significant at the 99% level. Grid variability is thus inversely related to coalition size. Similarly, the coefficients for prosumer, 26.8564 (6.5905), and heterogeneous, 9.2655 (6.5905), indicate that there are in fact differences in PAR reduction amongst the coalition types; only prosumer is significant at the 99% level. The coefficients are similar to that of trend experienced in Figures Figure 10, Figure 11, and Figure 12. Still, they do not accurately predict PARs.

The moderated regression furthers analysis by testing for interaction between coalition size and structure. Coalition size coefficient becomes -0.03665 (0.309665), not significant, and loses relative importance. The coalition structures prosumer, 78.87052 (12.96119), and heterogeneous, 32.65082 (12.96119), both gain relative weights, significant at the 99% and 90% respectively. The coefficients come closer to explaining the observed differences in PARs between consumer, prosumer, and heterogeneous coalitions. Likewise, evidence suggests the presence of an interaction effect between size and coalition structure. This effect furthers the notion that prosumer coalitions achieve greater PAR reductions than heterogeneous and consumer counterparts. The coefficients are -2.00054 (0.43792) for prosumer, and -0.89942 (0.43792) for heterogeneous, significant at the 99% and 90% level respectively.

Comparison of the two models suggests an increase in predictive power amongst the inclusion of a moderation effect. The increase in adjusted R-squared of 9.83% demonstrates a decrease in unexplained variance. Furthermore, evidence suggests that coalition size in itself is not a pure predictor of PAR. Instead, coalition structure seems to interact with the decrease in


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variability. The moderation model still does not allow for accurate calculation of the precise effects. Nevertheless, it can be extrapolated that the structure is a moderator of grid variability reduction upon increasing coalition size. This allows for the support of H2, indicating that coalition structure influences the magnitude of variability reduction.

Comparison of Regression Results

DV: Peak-to-Average-Ratio

Multiple Regression Moderated Regression

Constant 29.9671*** 4.83397

(6.7960) (9.16495)

Size -1.0033*** - 0.03665

(0.1903) (0.30965)

Heterogeneous 9.2655 32.65082*

(6.5905) (12.96119)

Prosumer 26.8564*** 78.87052***

(6.5905) (12.96119)

Size*Heterogeneous - 0.89943*

(0.43792)

Size*Prosumer - 2.00054***

(0.43792)

Observations 147 147

R-squared 0.2392 0.3375

Adjusted R-squared 0.2232 0.314

F-statistic 14.98*** (df = 3; 143) 14.37*** (df = 5; 141)

Note: *p<0.05; **p<0.01; ***p<0.001

Table 7: Comparison of Regression Results for Interaction Effect

Similarly, evidence from regression provides that consumer coalitions achieve the lowest PARs. This can be inferred by looking at the coefficient values of the term constant from Table 7. The moderated regression coefficients 4.83397 (9.16495), although not significant, explains that structures which are not prosumer nor heterogeneous, have lowest PARs to begin. The same analysis can be given to interaction term as this equates to 0 for consumer coalitions. This is in support of H2b. Likewise, as previously mentioned, coefficients of interaction term for prosumer coalitions provides evidence suggesting that marginal


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variability reductions are highest for said coalition type. This also allows for the support of H2c.

4.3 DETERMINING OPTIMAL COALITION SIZES Now that the relationship between coalition size and PAR is somewhat more understood, we continue with analysis to arrive at an optimal coalition size. We refer again to the results for average PARs as function of increasing coalition sizes (Figures Figure 10, Figure 11, Figure 12). These values determine absolute reductions in grid variability, but negate that there are other costs involved in the process of coalition formation. Firstly, these costs are introduced and explained to provide background for understanding of the derived cost function. Subsequently, said cost function is applied and discussed with regards to each coalition structure; namely consumer, prosumer, and heterogeneous.

Coalition formation includes gains by reduction of variability and costs as additional households are added to the cluster. We reiterate generalisability of Monte Carlo Simulation output and assume that the relationships established in Figures Figure 10, Figure 11, Figure 12 hold true for populations beyond the scope of this study. The cost function to be introduced will thus apply to finding an optimal coalition size regardless of local grid sample size. This means that the total cost for the grid of all coalitions formed will not be examined as such results would be too context specific. Instead, this will calculate the cost of forming one coalition. Local grids can apply further calculations to adjust total costs accordingly.

Implementing technology for energy grids imposes to certain fixed costs that are not generalisable across populations. Amongst others, these grid-specific costs include development and deployment of information systems and other infrastructural costs. Take, for example, the cost of deploying servers to store data. In absolute numbers, we will disregard these as they vary considerably across locations at question. On the contrary, variable costs play a vital role in determining optimal coalition sizes.

We reiterate the notion that grid variability is undesirable. It is an expensive characteristic as it requires operators and utilities to handle operating reserve capacities and peak loads for cases of abnormal consumption behaviour. PARs represent this aspect and provide figures to estimate the expected peak loads within a given timeframe. This further allows for putting a price to variability. Issues related to calculating outage costs for households, as well as the need to represent such on a risk index, makes the calculation of a price on variability difficult (Prada, 1999). To simplify calculations, we assume that there is a direct relationship between variability and its attributed costs. Therefore, in the context of this study, costs are directly proportional to the PARs. Similarly, as increasing variability is a decreasing function of utility, we define the cost function as a minimisation. It is of interest to find the localised minima.

We also identify other costs including the installation of smart meters, as well as service costs unrelated to management of peak load and operating reserves. We proceed to assume that


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such costs are attributable to every household within a grid at same amounts for simplification; thus, also disregarded in analysis.

As determined by graphs in results, PAR reduction is achieved at nearly every coalition size. To account for said marginal costs involved with adding another household to a coalition, we thus calculate these on a linear basis.

The goal is to find an optimal coalition size so to achieve Pareto optimality. This entails a move to a larger coalition size until no more gains are experienced; a move past this optimality sees reduction in socioeconomic benefits. The outcome of this analysis ultimately leads to a coalition formation cost for each aggregation size of each structure.

This analysis is performed by multiplying PAR values with accompanying coalition size. We further include an arbitrary cost value that represents the negated costs. Although said analysis results in abstraction of cost values, usefulness is retained by power of comparison amongst one another. Table 12 (Appendix) highlights the results. For each coalition size, the subsequent Pareto optimal coalition has been highlighted. The following section encompasses results for said analysis and will include visuals supplementary to Table 12.

4.3.1 CONSUMERS ARE BEST MANAGED WITHOUT AGENT REPRESENTATION Although consumer coalitions lead to a reduction in PAR, analysis concludes that this is offset by the introduction of a cost function. This provides evidence for the rejection of H2b; stating that consumer coalitions produce more desirable results. In other words: it is most desirable not to aggregate these profiles. Figure 13 (below) accompanies Table 12, it visualises results and indicates the localised minima.

Figure 13: Consumer Coalition Formation Cost as Function of Increasing Coalition Size

To explain this effect, it must be noted that consumer households are naturally subject to low volatility. As observed by the average daily net power flow (Figure 8) and its accompanying standard deviation of 2.922 Wh, such household profiles are predictable and stable in comparison to the other two profiles. Furthermore, the reduction in PAR is characterised by an asymptotic relationship. Combining said relationship with the minute scale of reduction


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imposes that the cost function is almost one of linear nature. This is because the marginal costs attributed to adding additional households outweigh the minuscule gains in PAR.

It would be useless to represent only consumer households with virtual agents as the cost associated with coalition formation does not outweigh the gains in grid reliability. It would impose a redundant hurdle as this entails creating additional points at which information is analysed. Thus, no net benefit is observed when aggregating consumer households.

4.3.2 PROSUMERS SHOW GREATEST MARGINAL REDUCTION IN PAR Different results are observed for prosumer households. As illustrated in Figure 14 (below), cost of coalition formation follows what can be considered as a U-shape. Note: scale has been altered for illustrative purposes as costs for size 1 and beyond 25 are too large to consider.

Figure 14: Prosumer Coalition Formation Cost as Function of Increasing Coalition Size

We observe an initial dip in formation cost. This is followed by a range within which two minima can be found. Ultimately, this is proceeded by a moderate amount of variation and cost increase. The latter can be explained by two phenomena. Firstly, as prosumer coalitions asymptotically converge to a minimum PAR value, the introduction of a cost function steadily increases formation costs beyond a certain point. This effect is same as for consumer coalitions, with difference that initially, benefits are observed for the prosumer set due to greater magnitude of PAR reduction. Here we see this past aggregation size 15. Secondly, as prosumer households are volatile by nature, the magnitude of impact decreases as coalition size increases. To determine optimal coalition size, we refer to the localised minima highlighted in Figure 14, also found in Table 12. We determine here an optimal coalition size of 15. This is chosen over the first minima as it adds more households within a network.

Evidence further supports that the marginal reductions in PAR achieved by prosumer coalitions are higher than those for consumer. This can be read by comparing the PAR reductions per increase in coalition size of the two (Table 12), a theory further supported by interaction effect of moderated regression (Table 7). We conclude thus, the support of H2c; prosumer coalitions marginally produce larger reliability gains per unit increase of coalition size.


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4.3.3 MIXED COALITIONS PROVIDE OPTIMAL VARIABILITY REDUCTION We continue discussion for mixed coalitions. Figure 15 (below) presents results. Note: scale has been altered for illustrative purposes.

Figure 15: Mixed Coalition Formation Cost as Function of Increasing Coalition Size

Similar to prosumer coalitions, we observe a sharp dip for initial aggregation sizes. However, upon reaching the minima, formation costs of mixed coalitions gradually increase. This follows similar trend to that of consumer coalitions. It is also explained by the fact that at a certain point the marginal costs outweigh the gains in grid reliability to be beneficial. For this set, we derive at an optimal size of 10 households. It can be inferred that the combination of stable, consumer households and its volatile, prosumer counterpart leads to an optimal synergy between PAR and cost minimisation. Furthermore, when evaluating optimal coalition sizes, it becomes evident that nearly 30% higher costs are attributed to forming prosumer coalitions. This evidently leads to the support of H3; mixed coalitions produce the most desirable results when it comes to PAR reduction.

As discussed in previous sections, the move towards decentralisation is attributed to an increase in uncertainty. Although households can become more independent from traditional grids, intermittency of solar generation creates uncertainty on the supply side of electricity. Invariably this leads to increases in grid variability. As demonstrated by the simulation, it is possible to reduce said variability by aggregating household profiles as it allows for more stable forecasting. Furthermore, it is also demonstrated that the coalition structure has a moderating effect on the reduction of PAR. Simulation for consumer leads to conclusion that this structure does not produce acceptable reductions, whereas prosumer simulation creates largest PAR reductions. Similarly, an even representation of both results in best reduction from a grid perspective. We conclude that smart market aggregation of households should not be discriminated by profile of said households. Instead, it should encompass an even distribution of the two.

In tangent with H3, we draw two conclusions that support mixed coalitions. Firstly, electricity grids are being increasingly exposed to larger shares of prosumer households. Such trend suggests not only an increased variability of future electricity networks but also the need to


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match both household profiles as a balancing act. Secondly, in support of H3b, the separation of said profiles leads to higher optimal coalition sizes upon introduction of a cost function. We find an evener balance between reliability gains and formation costs for smart market aggregation of mixed cooperatives. Similarly, evidence provides that as more prosumer profiles are added to a coalition, likewise optimal size will too increase.

4.4 THE ROLE OF BLOCKCHAIN IN ENERGY GRIDS In continuance with the discussion of energy coalitions comes the role of technology, and specifically the blockchain, in enhancing the effectiveness of said coalitions. The following section will provide a conceptual analysis and discussion comprising of recommendations and caveats to keep in mind when implementing smart technologies in energy grids. Firstly, this section highlights general infrastructural aspects and findings at which the blockchain excels. Subsequently, the limitations under which the technology is governed will be briefly explained before translating the results into managerial and academic implications in the proceeding section.

4.4.1 BLOCKCHAIN INCENTIVISES THE MOVE TOWARDS PROSUMERISM As introduced, the blockchain can be seen as a digital bookkeeping record of virtually any asset and transaction that can be digitised. It implies that said technology allows for a digital, paperless record of all transactions occurring within a local energy grid. This encompasses electricity consumption for all households and connected smart devices as well as power generation of solar panels. Furthermore, an asset recorded in the ledger is one that has been confirmed to exist by the participating nodes of the blockchain. Therefore, households are in fact truthfully informed of usage behaviour and need not be wary of misrepresentative or false information upon receiving electricity bills. The application of proof of existence extends beyond simple metering and billing activities.

As traceability characterises the distributed ledger, a blockchain-connected energy grid allows for households to follow each consumed watt-hour back to its source. This can provide a form of certification indicating whether or not the consumed energy is produced by in a green manner, for example. For households not willing or able to invest into solar panels this means they are still able to be part of a sustainable network. For households that generate electricity, or wish to do so in the future, there exist incentives too. The blockchain is subject to rewarding a set of actions with a cryptocurrency. As demonstrated by existing concepts such as SolarCoin, these can be tied to the generation of renewable electricity and spur the movement towards prosumerism.

Other elements of the blockchain further such incentives. As discussed in previous sections, prosumer households experience several benefits over consumer counterparts. Firstly, they achieve higher independence from utilities and energy providers. Prosumer households generate electricity at points of peak consumption, as demonstrated by the average net power flow (Figure 8), and therefore are not as affected by peak pricing events. Additionally, the notion of proof of ownership allows for an array of alternative models behind owning solar


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panels. Specifically, shared ownership can aide increasing penetration of renewables for households that cannot afford the long payback periods inherent in solar panels.

As demonstrated by the simulations, the move towards more prosumer households puts the electricity grid under more stress. These profiles, characterised by higher PARs, make grids more variable and thus harder to manage for operators. Although beneficial for the movement towards prosumerism, it becomes evident that the blockchain is a factor that may contribute towards higher grid variability. As demonstrated by the differences in optimal coalition sizes between simulations, conceptually speaking, evidence suggests that the blockchain will lead to the aggregation of larger cooperatives.

4.4.2 AUTOMATION As the blockchain records transactions and assets digitally, it enables the automation of several workflows; including billing and payment. Empowering applications extend beyond such simplistic measures.

On the consumer side, agreed-upon prices and tariffs negotiated with providers bind households. Load shifting behaviours are current practices that move activities of high consumption towards more price-friendly times of the day to reduce electricity bills. The move towards blockchain makes these practices easier as it is characterised by automation. More specifically, the use of smart contracts allows for any programmable execution to take place when a certain criterion is met. It enables the purchase of electricity at optimal prices, without manual operation of appliances during off-peak hours typically occurring at night.

4.4.3 BIG DATA AND SEPARATION OF OBJECTIVES REQUIRES TWO CHAINS Simulations performed in this study provide evidence that big data is pivotal to a closer understanding of households and consumption behaviour. We are presented with intractable problems that require large amounts of computational power and time. From a more technical point of view, incorporating blockchain to electricity grids implies a widespread adoption of smart devices and with it comes the role of IoT. Such devices continually communicate with one another reporting various data and providing deep insights into usage. This also implies that the number of connected devices within a blockchain environment drastically increases as more appliances become smart, and thus, IoT-enabled. Therefore, as more households join the network of coalitions, the number of resources necessary to record transactions, calculate aggregation sizes, and its benefits increase exponentially.

As the blockchain must verify all transactions within a period denominated by the time taken to create a new block, it is subject to a certain latency requirement. The increasing number of such devices would impose an unnecessary burden on verification of transactions and subsequent calculations, therefore a two-pronged solution is recommended to solve this issue. Namely: one blockchain to serve the local network itself, whilst another is responsible for a household and its interaction with the various devices within a household. As we are more interested in analysing from a grid perspective, the latter discussion will be elementary. The reader is asked to refer to literature review for technical background.


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LOCAL NETWORK BLOCKCHAIN

A local blockchain should be implemented to serve the need of local energy network. Within this infrastructure, any registered coalition of households can join freely to be part of the local community. Figure 16 (below) illustrates how such a grid can look. Within this blockchain, nodes are the central actors that communicate with one another. The dark nodes {Px, Cx} represent household cluster heads and include the bi-directionality of energy flow, represented by arrows, in the presence of solar panels. The interaction within such a network will be introduced with the second blockchain. Contrarily, the clear nodes {ECx} represent the cluster head of an energy cooperative, the node that will communicate with the overall local network chain.

To verify transactions within this chain, the proof-of-stake consensus method should be implemented. Each coalition should be given a stake representative of its relative size compared to the overall network for the voting mechanism. In turn, this provides a fair opportunity for all households to be rewarded for calculation and verification as each household is involved in the voting mechanism for the single truth.

Figure 16: Local Network Grid

This method is also highly energy efficient compared to proof-of-work. For reference: to verify such a transaction, in relation to electricity it costs roughly the equivalent of powering 1.57 households for one day (Vice, 2015). Lastly, proof-of-stake retains its security from malicious


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attacks by requiring nodes to deposit a refundable fee per transaction such that it is not economically rewarding to perform attacks.

PRIVATE HOUSEHOLD BLOCKCHAIN

Similar to the local network chain, households can also operate their own, private chains within which they retain control over connected appliances. This permissioned blockchain means that nobody can join without obtaining a key. Figure 17 (below) illustrates the infrastructure of the private chain in a typical household and its connection to the local blockchain. Smart meters (triangles) are connected to household appliances and communicate with the overall node (circle) responsible for the house itself. In turn, the readings from the node of the household and that of the dark node from the local chain coincide with one another as will be demonstrated shortly.

As mentioned previously, the abundance of IoT-enabled devices can induce strains on a network. As the objective of blockchain is to simplify the environment, a private chain allows for the individual management of household devices without impeding on the functionality of the local network. It also allows for differing latencies of individual appliances as the cluster head of the permissioned chain reads the net electricity passing through the household regardless of faulty operation of a household appliance.

Figure 17: Household Circuit


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INTERACTION BETWEEN THE CHAINS

Figure 18 (below) illustrates the way in which the local and household blockchains interact with one another. The top chain is the local blockchain, whereas bottom chain is private household chain.

Figure 18: Interaction between Local and Household Blockchains

Although both record different transactions, the cluster head referring to a household is the same appliance for each chain; namely the smart reader that connects the local grid with the household circuit. Therefore, both chains inherently read the same electricity net power flow for a given time interval. Moreover, the household blockchain can be recognised as an extension to the local one; it provides more granular perspective to consumption data.

Figure 18 indicates the chain of events taking place to transfer information from one blockchain to another. Firstly, node A uses its key to indicate that it has information to send and opens its portal. This is processed by the next block and subsequently recorded as a transaction that will take place between A and node B. The following block sends the request, along with the information, to B. The node then opens its portal with its key and receives said information within the next block. As can be inferred from this figure, the amount of time to transfer information from one blockchain to another is time-consuming. It is heavily dependent on the specified time between creation of new blocks. Coupled with the increased resources needed to, for example, calculate aggregation sizes, it is imperative that processing time between blocks be kept to a minimum to avoid latency issues.

4.4.4 IMPLEMENTATION OF SMART MARKET BEFORE BLOCKCHAIN We see, thus, a conceptual, comprehensive network comprised of different levels that allow households and microgrids to be managed by blockchain-enabled technology. Smooth integration of this fully decentralised market form must nevertheless be evaluated critically


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given above analyses. As discussed throughout previous sections, it becomes clear that a blockchain environment for local energy grids also has its downsides. It is more than imperative that the role and involvement of smart technology in energy grids must be evaluated from anything but a short-term perspective. We see great potential for implementation of blockchain, but its primal status means that it still must be researched further for local grids to achieve maximum utility.

Blockchain itself is a complementary technology, not a substitute. Its purposes are easily demonstrated for bookkeeping purposes and those of automation, and thus benefit in terms of reducing time spent performing redundant workflows. It also allows for the creation of alternative business models, which are to be introduced shortly. However, considering current practices and advancement of the blockchain, it is not seen as fit to reduce local grid variability. This does not invalidate its benefits. Rather, it implies that status quo is not entirely ready for a fully decentralised local grid solution.

We reiterate discussion of previous analyses regarding local smart market solutions as stepping stones. In turn, we declare that H4 cannot be fully supported. Many aspects of blockchain allow for more efficient management of energy grids. These are experienced from both operators of the grid and households. On the contrary, others lead to the conclusion that it will increase grid variability, an aspect unfavourable for both parties. We see its role in pushing the energy transition and, with it, an increased likeliness for consumers to become consumers.


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5. CONCLUSION The following section will briefly highlight once again a summary of main findings. Furthermore, it will provide the discussion of underlying academic and managerial implications of this study. Subsequently, the limitations of research will be provided and conclude with possible extensions and further research to be conducted.

5.1 SUMMARY OF FINDINGS The goal of this research was to evaluate the role of energy coalitions and blockchain in reducing local energy network variability. The increasing trend towards decentralised, liberal energy markets fuels the movement towards prosumerism. As residential areas are increasingly exposed to such profiles, in turn, evidence shows that grid variability increases. To mitigate such effects, it has been researched how the aggregation of households, via coalition formation, performs in reducing said variability. Furthermore, it was of interest to investigate the impact of emerging smart technology, such as the blockchain. This effect has been conceptually analysed to provide recommendations for further actions to keep in mind when implementing. With the ongoing transitional state in which energy markets find themselves in, it is important to understand these underlying factors and their implications.

Based on simulation results, we are presented with evidence to conclude with the preference over heterogeneous energy coalitions in local grid settings, as opposed to segregation of profiles. This allows for an optimal balance within the grid between both consumer and prosumer households and performs best at reducing PARs. Furthermore, it is found that the coalition structure itself plays a strong role in dictating the magnitude of variability reduction. Pure consumer coalitions do not achieve a substantial net benefit in reduction, whereas pure prosumer coalitions achieve largest marginal reductions per increase in coalition size. However, it is found that coalitions achieve the best net reductions for an overall grid in the case of heterogeneity. Such coalition structures enable the spreading of risk profiles, namely prosumers, to balance grid variability.

Upon introduction of an arbitrary cost function, an optimal coalition size of 10 households is found to balance the benefits gained by PAR reduction and costs attributed to coalition formation.

It is further concluded that as the penetration of technologies such as PV panels and smart meters induce higher rates self-generation, as well as optimal monitoring and consumption behaviour, evidently an increasing trend towards prosumerism is apparent. This, in turn, leads to a larger exposure to volatile household profiles. Evidence suggests that as variability exposure is increased, so should the coalition size to compensate for this effect.

From a conceptual analysis, we draw to the conclusion that smart technologies and blockchain will not attribute to PAR reduction. Rather, these should be viewed as means for incentivising the prosumer trend on the consumer-side. Similarly, on the utilities-side, as tools for automation of workflows that free up resources for value-added activities.


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Blockchain and technology are not a replacement to current practices, but rather a complementary tool that aids decision making via data-driven approaches.

5.4 LIMITATIONS AND FUTURE RESEARCH Although the reduction of grid variability has been demonstrated and is generalisable across populations as a whole, it may be the case that several specific findings are not attributable beyond the local network undertaken in this study.

The Pecan Street Project comprises of several characteristics that are not necessarily present in other areas of the world. This will specifically be compared to European households. Firstly, average electricity consumption of sample households is almost three-times higher. Similarly, the sample represents a larger proportion of prosumers than found in practice. This may lead to findings of optimal coalition sizes not being implementable in other grids. Having lower grid variability can likely, as demonstrated by consumer coalitions, prove not to be economically feasible as costs are involved with the formation of coalitions. Lastly, as there may be differences in subsidisation and penetration rates of PV panels, it may be the case that findings are likely to be confirmed in higher income areas.

The study itself also comprises of limitations. Firstly, as it is an artificial simulation based on Monte Carlo methodology, we find the expected values for results. This does not take into account specific settings, but rather offers a generalisation. Therefore, in practice results may substantially vary when using random modelling to aggregate households. Future research should investigate the optimisation aspects, especially when aiming to achieve neighbourhood specific results.

Secondly, the input values for grid variability are based on daily aggregated data. This leads to a static analysis of coalition formation, not considering that PARs are constantly changing; especially when simulating further rounds. Furthermore, this does not allow for insights to be made into effects of coalition formation on an as granular basis as consumption data allows for. Future research should incorporate these aspects into the formation algorithm.

Moreover, this study does not take into account the effect of adding storage capabilities such as electric vehicles. Similarly, we do not differentiate between output capacity of PV panels when in reality both can affect variability profiles of households. These aspects should be evaluated in future research, especially as the role of batteries is a vital aspect of decentralisation.

As we studied coalition structure, only having compared three structures with one another furthermore limits the conclusions that can be drawn with respect to the magnitude of variability reduction per structure. Future research should examine more precisely a larger amount of different structures to dictate such effects with higher accuracy. Likewise, the abstraction and simplification of costs involved in coalition formation should also be examined more closely, and environment specific, in future research.


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5.2 ACADEMIC IMPLICATIONS As well as adding to the understanding of energy coalitions and their role in grid variability, this study has furthered the pool of knowledge on blockchain technology. It is well known that electricity markets are becoming more and more decentralised and equipped with renewable energy resources. This study has shown that such trend also inherently leads to higher grid variability. As energy users increasingly become prosumers, it becomes harder to predict the loads necessary to support a local grid. Although electricity demand for consumer and prosumer households is predictable, the source from which it is drawn is not for the latter as intermittency characterises PV panel output.

This study has demonstrated that the implementation of energy coalitions performs well in mitigating the effect of increased variability. It has further shown that the structure of said coalitions plays a pivotal role as well. The aggregation of both household profiles leads to better net benefit for local grids. Firstly, due to easier prediction power for an aggregated set of households. Additionally, due to the heterogeneity of profiles. This study has demonstrated that homogeneous households are not attractive for aggregation across grids as these do not balance effects of higher variability profiles.

Furthermore, vital to the understanding of coalitions, it has demonstrated that the reduction of grid variability is a function of aggregation size. That is, up until a certain size. It has been demonstrated that, for a local grid of roughly 250 households and an even mix of both profiles, the optimal aggregation size is 10. To the best of knowledge, previous studies have not demonstrated optimal sizes, nor the relationship between grid reliability and energy coalition size.

This study has furthermore lead to a better understanding of a concept novel to the academic field: blockchain. Conceptual analysis of application in decentralised energy settings has provided a better understanding, especially of its limitations. The conclusion has been drawn that blockchain itself is a tool to derive value from. Although its ability to automate many activities, it must be seen as a means to enhancing coalition effectiveness as by itself it will not reduce grid variability. Instead, we find that blockchain will do the opposite. This technology will likely increase prosumer behaviour as it enables users to monitor consumption, displays real-time information feedback, and leads to more conscious households.

5.3 MANAGERIAL IMPLICATIONS The increasing trend of residential households self-generating electricity exhibits several difficulties for the likes of utility providers and grid operators. Although consumption behaviour has been adequately researched, the trend towards prosumerism still creates uncertainties in balancing grids. The increased penetration of technology such as smart meters allows for more data-driven approaches to such problems, as it presents the opportunity to analyse big data. Furthermore, the advent of blockchain can complement such approaches.


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On one side, the aggregation of households in coalitions allows for better prediction of grid variability greatly benefiting operators. Results demonstrate that heterogeneity of coalitions is a must when aggregating households. However, there are far more uses for such technology. Firstly, we find that smart technology, such as blockchain, allows for the automation of billing and monitoring of consumption to better understand behaviour. Secondly, looking at the larger picture, this technology enables for freed up resources to be spent on value-added activities. This encompasses a variety of alternative business models that would not be as easily implementable without smart technologies.

Both politics and economics understand the need for sustainability, yet this notion is limited to those cases in which it is economically feasible to implement changes for achieving such goals. Utility providers can use smart contracting features to drive down barriers to adoption of PV panels, for example. As discussed, elements inherent in blockchain allow for simple solutions to shared ownership of assets. This can enable lower income communities to take an active part in decentralisation. We are also presented with the possibility of creating decentralised, peer-to-peer energy markets. Although such initiatives occur in practice already, the impact on operations of utility providers and grid operators is too uncertain to present day. The creation of coalitions, and subsequently the optimisation of such, may lay the stepping stone for decentralised markets.


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7. APPENDIX F-Test for Variance between Prosumer Weekend and Workday

Average NPF: Weekend Workday

Mean 0.8239 0.7886

St. dev. 0.4923 1.2329

Observations 96 96

Df 95 95

F 0.4066

p-value 1.68e-05

P(F<=f) two-tail 0.6672

P(F>=f) two-tail 1.4987

Table 8: F-Test for Variance between Prosumer Weekend and Workday

T-Test for Equality of Means between Prosumer Weekend and Workday


Mean 0.8239 0.7886

St. dev. 0.4923 1.2329

Observations 96 96

Hypothesised difference 0

df 161.29

T 5.081

p-value 1.34e-06

P(T<=t) one-tail -1.6572

P(T>=t) one-tail 1.6572

Table 9: T-test for Equality of Means between Prosumer Weekend and Workday


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F-Test for Variance between Consumer Weekend and Workday


Mean 1.3898 1.5551

St. dev. 0.4662 0.5424

Observations 96 96

df 95 95

F 0.7389

p-value 0.1421

P(F<=f) one-tail 0.7123

P(F>=f) one-tail 1.1074

Table 10: F-Test for Variance between Consumer Weekend and Workday

T-Test for Equality of Means between Consumer Weekend and Workday


Mean 1.3898 1.5551

St. dev. 0.4662 0.5424

Observations 96 96

Hypothesised difference 0

df 190

T -2.265

p-value 0.02464

P(T<=t) two-tail -1.9725

P(T>=t) two-tail 1.9725

Table 11: T-test for Equality of Means between Consumer Weekend and Workday

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Consumer Coalition Prosumer Coalition Mixed Coalition

Coalition Size

Average PAR

Arbitrary Cost

Cost of Forming Coalition

Coalition Size

Average PAR

Arbitrary Cost


Coalition Size

Average PAR

Arbitrary Cost


1 11.64 x 11.64x 1 8794.06 x 8794.06x 1 8794.06 x 8794.06x

2 7.21 x 14.41x 2 304.29 x 608.58x 2 211.73 x 423.45x

3 6.14 x 18.43x 3 220.12 x 660.36x 3 102.54 x 307.63x

4 5.28 x 21.12x 4 214.86 x 859.45x 4 38.29 x 153.16x

5 4.88 x 24.40x 5 91.70 x 458.48x 5 26.41 x 132.05x

6 4.66 x 27.95x 6 59.51 x 357.06x 6 21.40 x 128.39x

7 4.49 x 31.40x 7 44.18 x 309.24x 7 12.26 x 85.83x

8 4.41 x 35.30x 8 56.64 x 453.11x 8 23.07 x 184.57x

9 4.24 x 38.16x 9 38.69 x 348.17x 9 9.52 x 85.66x

10 4.13 x 41.33x 10 28.30 x 283.00x 10 7.71 x 77.05x

11 4.07 x 44.73x 11 31.41 x 345.49x 11 7.15 x 78.62x

12 4.01 x 48.10x 12 21.92 x 263.09x 12 6.99 x 83.90x

13 3.99 x 51.91x 13 20.53 x 266.94x 13 6.59 x 85.64x

14 3.93 x 54.96x 14 21.75 x 304.56x 14 6.24 x 87.39x

15 3.91 x 58.62x 15 70.47 x 1057.12x 15 6.41 x 96.15x

Table 12: Pareto Optimal Coalition Size per Structure

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