Master of Science Thesis

KTH School of Industrial Engineering and Management

Energy Technology: TRITA-ITM-EX 2021:77

Division of Heat & Power Technology

SE-100 44 STOCKHOLM

Towards Circular Economy:

Technoeconomic assessment of second-

life EV batteries for energy storage

applications in public buildings

Maria Gris Trillo

Master of Science Thesis in Energy Technology

TRITA-ITM-EX 2021:77

Towards Circular Economy:

Technoeconomic assessment of second-life

EV batteries for energy storage applications

in public buildings

Maria Gris Trillo

Approved

2021-03-26

Examiner

Supervisor

Francisco Díaz González, UPC Barcelona

Abstract

With the accelerated tendency of renewable energy penetration in the electricity grid, energy storage becomes a crucial asset for matching generation and demand. The growth of energy storage systems requires adequate new policies and regulatory frameworks. The battery value chain also requests for new ways of end-of-life management since battery recycling is not a viable single option yet. This is where circular economy offers different solutions and alternatives for prolonging the battery life and reducing the negative impact. This study analyses the technoeconomic feasibility of giving electric-vehicle (EV) batteries a second life as stationary energy storage systems in buildings with integrated on-site renewable energy production, such as for instance PV panels. Four different scenarios have been considered, including the refurbishment of the battery or its direct reuse, taking into account the degradation of capacity and thus, the amortisation price; against the possible load shifting benefit and the reduction of contracted grid power for the building. Results show that, effectively, the reuse of batteries for stationary energy storage is economically justified but may not be worth only in self-consumption applications, that is, for prosumers with some little renewable generation installed on site. The simulations reveal less than 2% relative energy cost savings on annual basis and up to 25% savings related to reduction of grid-contracted peak power, for the chosen case study of a mid-size office building. Second-life battery applications are still dependent on the development of tools for estimating and monitoring the battery’s state of health and potential performance in the new setting, for the technology to succeed. The increasing interest and necessity for circular economy together with the high volume of EV batteries expected to be released on the second-hand market, not suitable for automotive purposes anymore but reasonably applicable for stationary energy storage, will place this topic in the spotlight in the near future.

SAMMANFATTNING

Den fortsätta trenden för utvidgning av förnybar energi i elnätet gör att energilagring blir en ännu

viktigare tillgång för balansen mellan elproduktion och efterfrågan. Nya policyer och regelverk

krävs för att understödja en bredare tillämpning av småskaliga energilagringssystem.

Batteriets värdekedja kräver också nya sätt att hantera uttömda material eftersom batteriåtervinning

ännu inte hunnit utvecklas som ett genomförbart alternativ. En cirkulär ekonomi borde erbjuda

olika lösningar inte endast för materialåtervinning utan också gentemot förlängning av livslängden

och fördröjning av återvinningsprocessen tills nya metoder och verktyg finns på plats för effektiv

hantering med minimal miljöpåverkan.

Denna studie analyserar den teknoekonomiska genomförbarheten att ge begagnade batterier från

elektriska fordon (EV) en andra tillämpning, typ en utvidgad livslängd, som stationära

energilagringssystem för mellanstora kontorsbyggnader med integrerad lokal elproduktion såsom

t.ex. solpaneler på taket. Fyra olika scenarier har beaktats, inklusive delvis renovering av batteriet

eller dess direkta återanvändning, med hänsyn tagen till kapacitetsnedbrytningen och därmed

amorteringspriset, som vägs mot fördelarna i form av en uppnåelig tidsförskjutning av elbehovet

och minskning av kontrakterad nätkraft för byggnaden.

Resultaten visar att återanvändning av elfordonsbatterier för stationär energilagring är ekonomiskt

motiverad men troligen inte alltid värt i applikationer med låg förbrukning och låg egenproduktion

av förnyelsebar elkraft. Simuleringarna avslöjar mindre än 2% relativa energikostnadsbesparingar

på årsbasis och upp till 25% besparingar relaterade till minskning av nätavtagen toppeffekt för den

valda fallstudien av en medelstor kontorsbyggnad.

Praktiska tillämpningar av begagnade batterier är fortfarande beroende av utvecklingen av verktyg

för uppskattning och övervakning av batteriets hälsotillstånd och potentiella prestanda i den nya

installationen, för att konceptet skulle kunna bevisa sitt värde. Det ökande intresset och

nödvändigheten för cirkulär ekonomi tillsammans med den stora volymen EV-batterier som

förväntas släppas på den begagnade marknaden, inte längre lämpliga för fordonsändamål men

rimligt användbara för stationära energilagringssystem, kommer att föra detta ämnesområde in i

rampljuset inom en snar framtid.

i

Contents

1 INTRODUCTION ......................................................................................................... 1

1.1 Background .......................................................................................................................... 1 1.2 Motivation ............................................................................................................................ 4 1.3 Objectives ............................................................................................................................ 6 1.4 Methods and analytical framework/Research approaches ................................................. 7

2 CIRCULAR ECONOMY ............................................................................................... 8

2.1 Concept definition ............................................................................................................... 8 2.2 Forecast of the environmental impact of material use (predictions) ................................... 9 2.3 Global resource outlook (impacts) .................................................................................... 10 2.4 Action ................................................................................................................................. 11

3 BATTERY VALUE CHAIN ........................................................................................ 13

3.1 Working principle .............................................................................................................. 13 3.2 Main Lithium-ion battery types ......................................................................................... 14 3.3 Battery value chain for EV and Industry ........................................................................... 19

3.3.1 Raw materials ........................................................................................................................................19 3.3.2 Active materials synthesis ...................................................................................................................21 3.3.3 Cell manufacturing...............................................................................................................................21 3.3.4 Module and system assembling .........................................................................................................22 3.3.5 Application and integration ................................................................................................................22 3.3.6 Recycling and second life....................................................................................................................22

4 STUDY CASE ............................................................................................................... 26

4.1 Modelling concepts ............................................................................................................ 28 4.1.1 Modelling of the battery degradation ...............................................................................................28 4.1.2 Prices ......................................................................................................................................................35

4.2 Testing procedure .............................................................................................................. 37 4.3 Mathematical formulation ................................................................................................. 38

4.3.1 Overview of sets, parameters and variables ....................................................................................38 4.3.2 General constraints ..............................................................................................................................39 4.3.3 Prosumer model ...................................................................................................................................40

5 RESULTS AND DISCUSSION ................................................................................... 42

5.1 Simulation results .............................................................................................................. 42 5.2 Discussion .......................................................................................................................... 44

5.2.1 Peak load reduction target ..................................................................................................................44 5.2.2 Savings estimation ...............................................................................................................................46

6 CONCLUSIONS .......................................................................................................... 50

REFERENCES .................................................................................................................. 51

APPENDIX ....................................................................................................................... 54

Directly reused battery (0.053 amortisation) ..................................................................................................54 Refurbished battery (0.0905 amortisation) .....................................................................................................62 Energy-related costs: ..........................................................................................................................................70

ii

List of Figures

Figure.1: Total primary energy supply (TPES) by source, Worldwide 1990-2017………………….1

Figure.2: Energy-related CO2 emissions and reductions in the sustainable Development Scenario by source………………………………………………………………………………………..2

Figure 3: Cumulative global energy storage deployments.…………………………….………….3

Figure 4: Total final consumption (TFC) by sector, Europe 1990-2017.IEA…………………….5

Figure 5: Electric vehicle stock in the EV3030 scenario, 2018-2030.…………………………….6

Figure 6: The Circular Economy System diagram……………………………………………….9

Figure 7: Growth of materials use and GDP, 2011-2060……………………………………….10

Figure 8: Global resources outlook 2015-206…………………………………………………..10

Figure 9: Achieving resource decoupling as a result of policy packages………………………...12

Figure 10: Li-ion battery structure diagram…………………………………………………….13

Figure 11: Average Li-cobalt battery…………………………………………………………....18

Figure 12: Pure Li-manganese battery …………………………………………………………18

Figure 13: Typical NMC battery………………………………………………………………..18

Figure 14: Standard LFP battery……………………………………………………………….18

Figure 15: Snapshot of NCA…………………………………………………………………...18

Figure 16: Chart of Li-titanate………………………………………………………………….18

Figure 17: EV and Industry Batteries’ value chain……………………………………………...19

Figure 18: Capital investment cell manufacturing vs. module and system assembly…………… 22

Figure 19: schematic of the methods and processes involved in the consumed LIBs recycling…23

Figure 20: Closed loop for LIBs life……………………………………………………………25

Figure 21: Virtual map of UPC campus Terrassa………………………………………………26

Figure 22: Aerial picture of the building………………………………………………………..27

Figure 23: Picture of the building………………………………………………………………27

Figure 24: Daily total consumption, HVAC consumption and PV generation………………….27

Figure 25: Ri-SOC plot with different cycles…………………………………………………...28

Figure 26: Maximum charge storage capacity for each cycle number as a function of T………..29

Figure 27: Capacity degradation curves for different discharge C-rates………………………....30

Figure 28: Comparison of calendar aging and cyclic aging for three temperatures investigated…30

Figure 29: Alterations of the voltage vs. capacity at different cycles……………………………31

Figure 30: Cycle life at different DoD………………………………………………………….32

Figure 31: Winter season typical sypply/demand scenario……………………………………...41

Figure 32: Spring season typical sypply/demand scenario ……………………………………...41

Figure 33: Summer season typical sypply/demand scenario …………………………………....42

Figure 34: Autumn season typical sypply/demand scenario …………………………………....42

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Figure 35: Peak reduction……………………………………………………………………....43

Figure 36: Peak reduction……………………………………………...…………….…………44

List of Tables

Table 1: Energy consumption by sector…………………………………………………………4

Table 2: Characteristics of Lithium Cobalt Oxide……………………………………………....15

Table 3: Characteristics of Lithium Manganese Oxide……………………………………….…15

Table 4: Characteristics of Lithium Nickel Manganese Cobalt Oxide…………………………..16

Table 5: Characteristics of Lithium Iron Phosphate……………………………………………16

Table 6: Characteristics of Lithium Nickel Cobalt Aluminium Oxide…………………………..17

Table 7: Characteristics of Lithium Titanate……………………………………………………17

Table 8: Main characteristics of critical raw materials involved in a battery……………………..20

Table 9: Pretreatment methods comparison……………………………………………………23

Table 10: Comparison for metal-extraction processes………………………………………….24

Table 11: Gaia building electricity consumption and generation………………………………..28

Table 12: Battery components’ prices…………………………………………………………..33

Table 13: Invoice periods of the Spanish tariff 3.0A.…………………………………………...34

Table 14: Retailer power pries for each invoice period…………………………………………35

Table 15: Retailer consumption pries for each invoice period………………………………….35

Table 16: Sets used in the simulation…………………………………………………………...36

Table 17: Parameters used in the simulation.…………………………………………………...36

Table 18: Variables in the simulation…………………………………………………………...37

Table 19: Peak reduction cases..…………………….…………………………….……………43

Table 20: Costs breakdown........…………………….…………………………….……………44

Table 21: Annual energy costs and savings.…………………….………………………………45

Table 22: Power term invoice conditions………………………………………………………46

Table 23: Power savings………………………………………………………………………..46

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Abbreviations and Nomenclature

BESS: battery energy storage system

BMS: battery management system

CAES: compressed air energy storage

DoD: Depth of Discharge

EGD: European Green Deal

EGDIP: European Green Deal Investment Plan

EIB: European Investment Bank

EVs: electric vehicles

ICE: internal combustion engine

LIBs: Lithium ion batteries

OEM: original equipment manufacturer

SEIP: Sustainable Europe Investment Plan

SoC: State of Charge

SoH: State of Health

UPC: Universitat Politécnica de Catalunya (BarcelonaTech)

WEO: World Energy Outlook

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ACKNOWLEDGMENTS

I would like to acknowledge everyone who played a role in this Master Thesis. First of all, my

supervisor, Francisco Díaz González, who has provided valuable guidance and advice during this

research. Secondly, I would like to thank Pau Lloret for his help concerning the technical aspects.

Also I would like to express my gratitude to Miroslav Petrov, for making this project possible.

Additionally, many thanks to all the professors and PhD students in the electrochemistry

department at KTH for introducing me to this topic and for being sources of inspiration in the

classes and in the laboratory sessions they taught.

Finally, special thanks to my friends, family and Joel, who supported and encouraged me.

1

1 INTRODUCTION

1.1 Background

Energy is of crucial importance in our society and it has penetrated in almost all facets of the social

domain and is a pillar of the economy. In a world where the major primary energy supply is still

lead by coal and oil as it can be observed in Fig.1, a general awareness of their harmful effects has

been increasing the last few decades.

Fig.1: Total primary energy supply (TPES) by source, Worldwide 1990-2017. Source: IEA

One of the battles humanity is fighting nowadays is the climate crisis with the drawback of a

constant raise of energy demand. The objective of the Paris Agreement focuses on the need of

world CO2 emissions to be dropped drastically to reach a sustainable development scenario that

maintains the average global temperature increase below 2ºC above preindustrial levels and trying

to limit it to 1.5ºC (IEA, 2019). To achieve this goal both technology and policy need to take action

and work aligned, since less effective scenarios are drawn if one or another fail to meet their goals.

The International Energy Agency (IEA) published the World Energy Outlook (WEO) 2019, which

included The Sustainable Development Scenario. The WEO implies what should be done in order

to meet climate goals also aligned to what new policies are establishing. The following graph shows

different future scenarios depending on the amount of adopted measures:

2

Fig.2: Energy-related CO2 emissions and reductions in the sustainable Development Scenario by source.

Source: IEA

As it can be observed from the graph above, current trends of energy-related CO2 emissions will

lead to a scenario of 45 GT by 2040. However, integrating all the changes shown in the graph and

thus following the opposite tendency 0 emissions leads to an incredible drop of CO2 emissions

down to 10 GT in 2050. Although this is very promising it is not an easy task and even the current

target is not consistent with new stated policies. According to up-to-date policies, the future

emission scenario would flatter the rapid increasing curve tendency but it would not decrease the

current emissions as it can be noticed in the graph. These divergences between different scenarios

highlight the importance of the decisions made by governments in the few next months and years,

which will determine the new policies and the investment in technology development crucial for

accomplishing the lowest emission scenario.

Everything points to a huge investment in renewables replacing systems powered by fossil fuels, as

it can be observed in the trends of the past years. However, their stochastic and low predictable

nature dependant on weather and season together with their immediate consumption required

makes it difficult to maintain a high reliability and security in the supply of those renewable energy

sources.

Therefore, energy storage has grown drastically these past few years not only helping address the

intermittency of renewables but also responding rapidly to large fluctuations in demand, making

the grid more responsive and reducing the need to build backup power plants (Zablocki, 2019).

However, this growth of storage systems needs the adequate new policies and regulatory

frameworks in the electricity sector since there will be many different scenarios not contemplated

nor planned until now.

3

This future electricity market will need new rules to beneficiate all parts involved and prevent any

technological or legal right confusion (Bioenergy International, 2019).

In the upcoming years the energy storage market is going to experience a huge growth hitting the

741 GWh of global cumulative capacity in 2030 led by the U.S. and China, with 49 and 21 percent

respectively (McCarthy & Xu, 2020).

Fig.3: Cumulative global energy storage deployments. Source: Wood Mackenzie

All the research efforts that are being focused towards this technology will make reduce its price.

Still, a huge investment will be required to reach that capacity. However, since energy storage will

become a key grid asset, all market players will have to take part in this transition.

The success of an energy storage facility lies on the response capability in front of a demand

variation, the amount of energy lost in the storage system, the overall energy storage capacity and

the velocity in the recharging process rate (Zablocki, 2019).

Among some of the different ways to store electricity the most relevant ones that are currently

being investigated are hydrogen (fuel cells), supercapacitors, compressed air and batteries. In the

hydrogen storage, electricity is used to convert water and oxygen into hydrogen, which can be easily

stored and re-converted into the desired form (electricity, heat etc.). This technology presents many

advantages. Thanks to the large amounts of power and low cost for storing once transformed into

hydrogen, this technology is very suitable for industrial processes. Moreover, hydrogen storage is

a long-term storage system, which can last as long as needed. Secondly, supercapacitors are a very

high power-density storage system, being able to release high amounts of power in short periods

of time. They also have unlimited lifetime as their capacity is not affected by the amount of cycles

performed. However, supercapacitors are a short-term energy storage system only being able to

store energy up to some minutes. Thus, they are used for system disturbances providing short

electricity bursts when necessary. Furthermore, compressed-air energy storage (CAES) is a long-

term energy storage system that can store energy up to a week. And finally, batteries are an energy

storing system for comparatively short periods of time, from hours up to few days. They can be

employed in the frequency and voltage stabilisation of the power system, also helping in the

demand balance.

4

Energy storage is already an essential mechanism in the power and transportation sectors. While

there are several ways to store electricity nowadays, current tendency is pointing to Li-ion batteries

as the most viable solution in the short term. The key points to make a technology a success and

competitive are increasing their performance and reducing their cost. Li-ion batteries price has

dropped drastically from 1000 $/kWh to 200 $/kWh in the last 6 years and their energy density

has doubled. These aspects make them good enough for EVs industry and are already competitive

compared to internal combustion engines (ICE) because the lifecycle and cost can beat that of

fossil fuel vehicle. However, batteries haven’t hit in heavy vehicles and aviation yet. It is believed

this will occur by 2030-2040. Batteries will first be used on smaller planes, possibly many small

engines in one plane to provide more reliability and flexibility. In the aviation sector energy density

plays a crucial figure since there is the need to keep a low weight without loosing power range.

Battery energy density is rising by a significant 2 to 3 per cent each year. However, Tesla’s cars still

overcome these numbers with each iteration. “It’s not the same ballpark as Moore’s Law progress

because it’s chemistry, not electronics, but it’s still very good.” (Adams, 2017).

1.2 Motivation

For the last seven years I have been studying engineering and for the past three focusing on

renewable energy. My goal is to help society overcome the climate crisis providing the clean

alternatives to maintain our current life style as much as possible, in terms of energy use. Clearly,

there are some other aspects that need to be changed in order to successfully meet the goal of

preserving the environment, such as plastic use and waste management. Going back to energy use,

the current energy distribution in Europe is the following showed in Table 1 and the tendency

evolution of each sector is presented in Figure 4:

Table 1: Energy consumption by sector. Source: IEA, data and statistics.

Sector ktoe MWh %

Industry 333.947 3.883.803.610 23,79

Residential 346.149 4.025.834.259 24,66

Transport 391.169 4.549.295.470 27,87

Commercial and public services 179.653 2.089.364.390 12,80

Agriculture/Forestry 32.469 377.614.470 2,31

Fishing 2.030 23.608.900 0,14

Non-energy use1 118.264 1.375.410.320 8,43

1 Non-energy use: Non energy use includes energy products used as raw materials in the different sectors; that is not consumed as a fuel or transformed into another fuel.

5

Fig. 4: Total final energy consumption (TFC) by sector, Europe 1990-2017.

Source: IEA World Energy Balances 2019.

The industrial sector, for its economic resources and cover surface [m2] in the buildings, is one of

the main vectors to integrate renewables. In fact, it can generate more energy than its own building

consumption depending on the hour of the day and the economic activity carried out.

Thus, in order to maximize the local generation, renewable, and a neutral CO2 industrial sector,

batteries are fundamental tools.

The battery storage capacity of an EV is usually much larger than an industry storage system. The

requirements for a battery used in the transport sector are more demanding given their main

competitive characteristic. Once a little capacity is lost for the aging process, the car range is

reduced, compromising their good performance and eventually being replaced for a new EV.

However, the old batteries are still at a very high percentage of their initial capacity although not

being suitable for their original purpose. For instance, Tesla Model 3 has a battery degradation of

7% after 250.000 miles (Kane, 2017). Hence, a new employment needs to be found for these

batteries that are far from being at the end of their life.

As mentioned before, industry storage systems are not as size and capacity demanding as EV. Thus,

discarded EV batteries can be reused and given a second life in the industry sector, preventing the

over production of batteries risking the Earth resources of Lithium, taking also into account that

the scarcity of this material would eventually lead to a commercial fight and an important raise in

the prices.

6

Fig. 5: Electric vehicle stock in the EV3030 scenario, 2018-2030. Source: IEA Global EV Outlook 2019.

However, second life batteries will only be a considerable resource in 5-10 years time. Before being

a substantial source of second life batteries, EVs need to experiment a great increase in the vehicles

selling share. Current tendency points to a promising scenario, where there would be 250 million

vehicles by 2030 as revealed in Fig. 5. As this number increases, so do will the potential second life

batteries, with some years delay due to their improved first life performance.

Moreover, the batteries’ second life approach is particularly important due to the lack of recycling

capacity of the Li-ion batteries, which is totally insufficient in Europe. This is why the concept of

circular economy is interesting for the decarbonisation of the industrial sector and to reduce the

impact of the use of batteries in the natural environment.

1.3 Objectives

This project considers this current situation and proposes two major objectives. First, a definition

of the battery value chain from the cell manufacturer to the end user and secondly, a study of the

synergies between these two sectors: electro mobility and industry in order to quantify the impact

of giving a second life to these batteries, from an economic point of view. Such study will be done

by analysing two scenarios for the second life battery with the help of a simulation tool to check

the potential savings.

The concrete technical objectives are to significantly reduce the energy bill, both reducing the

electricity costs and power costs. In terms of power, since the battery is thought to be able to

reduce the demand peaks considerably, the target is to reduce 25% of the contracted power. Also,

the battery degradation is key to determine the viability of the project proposal.

The main research focus herein is therefore related to the feasibility and profitability of installing

second-life batteries from EVs to medium-scale energy storage applications in office buildings.

7

1.4 Methods, analytical framework and research

approaches

Research approaches and methods are procedures and strategies that define the way assumptions

are done and detailed data is collected and analysed to subsequently reach conclusions and results.

The choice of approach depends on the nature of the topic of study or the research problem.

This study will account with quantitative as well as qualitative methods. In order to give the most

accurate future scenario for batteries impact in their second life in the industrial sector, a

quantitative study will be executed with data provided by the energy resources and water

information system (SIRENA) from UPC campus and a simulation tool that accounts with a series

of equations describing the battery model, the different constraints, etc.

Afterwards, results will be analysed in order to reach conclusions about the feasibility, advantages

and drawbacks of the model proposed.

8

2 CIRCULAR ECONOMY

The key difference between the cradle-to-grave dynamic active throughout all modern production

history and the cradle-to-cradle system intended to accomplish, is the same as in between current

resources value chain and circular economy value cycles.

Two concepts arise from this difference: eco-efficiency and eco-effectiveness. The first one

assumes a linear flow of resources and materials with only one-way direction. This involves the

natural resources extraction, processing and transformation until the product desired, for finally

discarding it. In this value chain, the eco-efficient methods pursue only to diminish the quantity

and harmfulness of the material stream, but are unable to change the linear flow. Some of the

materials in these products are not recycled nor reused but undergo a downcycling process, which

downgrades material worth and thus, restricts its usage. This is only a transitional step since it does

not prevent the cradle-to-grave line.

Contrary to this methodology of resource reduction, eco-effectiveness aims to convert the products

and materials that have reached their end of life so they can be reused at the same value level for

any another purpose. This would establish an ecologically friendly as well as an economically

supportive system. It would be possible by creating a cradle-to-cradle cyclical strategic structure,

were materials maintain their worth and are used again as resources for a different aim (Webster,

Bleriot, & Johnson, 2012).

2.1 Concept definition

Current developed economies and societies are used to a fast use and throw away model that has

already compromised a wide range of natural resources on Earth. The material extraction not only

causes scarcity in natural resources, but also has an immense impact in the environment in terms

of land, water and air pollution. Moreover, it affects the ecosystems of millions of animals that

could be at risk of extinction with all its repercussions in the complex bionetworks.

As technology keeps evolving and our everyday life is more dependent on devices and material

assets, this devastating trend will not improve. Demand and waste are still correlated in the

resources equation governing our economy up to date. Following this model, the only way to

reduce the resources and the waste would be directly reducing the resource extraction. However,

this approach would not be considered an option from the consumer comfort point of view, as it

would cost the loss of facilities. Subsequently, this model does not meet our new need of urgently

turning eco-friendly maintaining our lifestyles. From a holistic perspective, modern lifestyle should

be reconsidered as an attempt to help reduce our footprint, but this is not a technical issue and

thus, is out of the scope of present work.

Hence, the new model needed is a circular economy, redefining growth decoupled from finite

resources consumption and redesigning the waste management systems. Together with renewable

energy sources transition, the circular model will bring benefits to the natural environment and

societies besides boosting economic activity.

This new system concept diagram flows as a ‘value circle’ instead of a ‘value chain’. The outline of

a circular economy could be sketch as follows:

9

Fig. 6: The Circular Economy System diagram. Source: Ellen MacArthur Foundation.

At the highest and more general level, the main objectives of a circular economy is to preserve and

regenerate natural capital by controlling finite stocks and balancing their use with renewable

resource flows. In the circular value flow, the goal is to optimise the resources yields by reusing

materials and components as much as possible and giving them different uses and shapes if needed

to get all their potential in the loop. Finally, raise system effectiveness by minimizing negative

externalities out of the value cycle.

2.2 Forecast of the environmental impact of material use

(predictions)

With current trend of growth, global population could reach 9.600 million by 2050 and therefore

more natural resources will be required to withstand living standards, up to the corresponding

resources of three planets Earth (United Nations, 2020).

Also, socioeconomic trends will determine the future material use. The three main drivers are

income convergence among countries, a structural change and technology developments (OECD,

2019).

All countries will face an improvement in living conditions and reach those of the wealthiest

countries. Emerging and developing countries will grow at higher rates than in the OECD region

(OECD, 2019). This may cause a boom in their construction demand and thus, a higher demand

of materials. It is believed that demand for services coming from any kind of customer (from

households, large companies or governments) will surpass that of agricultural or industrial supplies.

10

This structural change will lead to a less intense material use since agricultural and industrial sectors

have higher material intensity than the services ones.

Fig. 7: Growth of materials use and GDP, 2011-2060. Source: OECD.

As it can be seen in Fig. 7, GDP in developing countries will grow rapidly while Material Use will

not experiment such a rapid increase due to the mentioned less material intensive structural change.

2.3 Global resource outlook (impacts)

There are a wide range of environmental impacts related to material extraction, processing and use,

such as acidification, climate change, human toxicity, land use, photochemical oxidation, aquatic

and terrestrial ecotoxicity among others. More concretely, the resource provision involves

greenhouse gas (GHG) emissions from mining and treating raw materials, while the use (e.g. fossil

fuels) can cause air pollution produced by their combustion.

Products of the primary resources can also have serious environmental impacts at the end of their

useful life, if the waste management is not properly accomplished. The consequences of using iron,

aluminum, copper, zinc, lead, nickel and manganese are estimated to more than double by 2060

(OECD, 2019).

Resource2 extraction and processing cause half of the greenhouse emissions alongside 90% of

biodiversity loss and water stress (European Commission, 2020).

Past and current trends point to the following evolution from 2015 to 2060:

Fig. 8: Global resources outlook 2015-2060. Source: International resource panel, 2019.

2 Resource here encloses materials, fuels and food.

11

2.4 Action

Given the negative projections related to material use there is a need for urgent action.

Governments face a truly difficult challenge where a big transformation has to be shaped in several

new policies to address the dark resources forecast. All of the new political decisions and new

policies have to pursue the transition to a circular economy, where natural resources consumption

and environmental impacts disassociation from economic activity are key factors.

In a circular economy there exists a bidirectional correlation between mitigation of climate change

and resource efficiency. Shifting to a low-carbon emissions economy already involves taking action

in terms of resource-efficiency, and an enhancement of resource efficiency in policies will make a

repercussion on the climate crisis.

Following this purpose, the European Commission disclosed a roadmap concerning a sustainable

economy and with the goal of reaching a climate-neutral circular economy, where the economic

progress is not linked to resources use. This initiative known as The European Green Deal (EGD)

and presented on 11 December 2019 will be accompanied by a series of new policies, which are

fundamental to establish the bases for accomplishing this environmental challenge.

President of the European Commission Ursula von der Leyen claimed that “the European Green Deal

is our new growth strategy – for a growth that gives back more than it takes away”.

And as the First Vice-President of the European Commission Frans Timmermans added, “our plan

sets out how to cut emissions, restore the health of our natural environment, protect our wildlife, create new economic

opportunities, and improve the quality of life of our”.

Thus, the EGD main objectives are to cut to zero the GHG emissions by 2050, to make EU’s

economy sustainable by disjoining it from resource use and to make sure it is an inclusive transition

(European Comission, 2019). The actions postulated on its guideline comprised an enhancement

of the resource use efficiency through a clean, circular economy and a recovery of biodiversity.

The circular economy plan will highlight the reduction and reuse of materials before recycling them.

Also, the first sectors to be tackled are those with major resource intensity such as textiles,

construction, electronics and plastics. Another crucial strategy proposed by the Commission is the

support to business models based on renting services (e.g. cars, bycicles, scooters) that will enlarge

the use rate of those goods and lower their consumption from the levels they would have as if they

were an owned product (European Comission, 2019).

To set a long-term direction to meet the targets stated above and make this plan a reality, all sectors

of the economy will have to join the transition. It will require taking some actions from their side

involving investing in environmentally friendly technologies and transport, reinforcing industry

innovation, strengthening the decarbonisation of the energy sector, guaranteeing the buildings’

energy efficiency trend and collaborating internationally to expand sustainable standards. In order

to truly meet these commitments, the European Climate Law has been announced to make a legal

obligation and activate investment towards the European Green Deal (European Commission,

2019).

A fair transition fund will leverage private and public money including with the help of the

European Investment Bank (EIB), which will deliver a sustainable investment plan. The European

Green Deal Investment Plan (EGDIP), also known as Sustainable Europe Investment Plan (SEIP),

is the financing support of the EGD, which will employ at least 1 trillion euros in the following

12

decade. This plan includes the Just Transition Mechanism that accounts with minimum 100 billion

euros destined for funding the regions most impacted by the green transition throughout the period

2021-2027 (European Comission, 2019).

All in all, achieving the EU’s climate neutrality demands a new industrial policy based on circular

economy. The most important part of the EGD together with the financing is the new policy

framework, which will determine the actions required to accomplish by the different actors

involved in this huge transition.

Further, the OECD has raised a project for resource efficiency and circular economy as well. The

OECD’s RE-CIRCLE project goal is to predict the effects of unceasing natural resources use, and

forecast the outcome of applying new policies to recognize which ones would have the highest

impact to encourage circular economy transition (OECD, 2018).

Succeeding in the decoupling of resources and economy will bring significant improvement in

human well-being and environmental pressure, even restoring ecological impacts made in the past,

as well as boosting the economic growth. The most substantial change of trends could be glimpsed

in the increase of global GDP and area of forest and natural habitat, and decrease of global material

extraction, greenhouse gas emissions, area of agricultural land and global pastureland as shown in

Fig. 9.

Fig. 9: Achieving resource decoupling as a result of policy packages. Source: International resource panel, 2019.

13

3 BATTERY VALUE CHAIN

3.1 Working principle

The cell, which is the core element of a battery, can take different shapes and sizes but its working

principle remains the same. During the charge and discharge process lithium atoms split into ions

and electrons, which migrate between the two electrodes and circulate across the external circuit

respectively. In the discharge, the oxidation takes place in the anode and the lithium ions travel

from the anode to the cathode, where the reduction occurs, while the electrons circulate through

the external circuit providing energy. Contrary, the charge process absorbs energy, the lithium ions

travel form the cathode to the anode as well as the electrons in the external circuit. Lithium is a

critical component in a battery but it is not the limiting one. The greater volume of lithium, the

higher the capacity, and the larger the potential difference among anode and cathode, the higher

the voltage.

The cell comprises three main layers. At the anode, there is a current collector commonly made of

copper and covered with a film of active material, usually natural graphite with some mixture of

chemical additives used to increase the conductivity. At the cathode, there is also a metallic current

collector in this case made of aluminium and again coated with active material3, conductive

additives and binder, which acts as an adhesive between the active material and conductive additive.

And the third layer consists of a membrane between anode and cathode known as separator.

These three layers are wetted with the electrolyte, a high ionic conductive mixture of solvents, salts

and additives that increases efficiency in lithium ions movement through the active material and

separator.

Fig. 10: Li-ion battery structure diagram. Source: U.S. Department of Energy. Office of Basic Energy Sciences

3 Active material: depends on the type of Lithium-ion battery. Usually consists of a mixture of several transition metals such as cobalt, nickel, manganese, iron, and/or aluminium.

14

Before batteries are ready for commercialisation, an electrical insulation layer known as solid

electrolyte interface (SEI) has to be built for the good functioning of the battery. The creation of

this layer is carried out under specific conditions of battery cycling. It provides sufficient ionic

conductivity (lowering a bit the capacity) for an adequate performance but prevents the electrons

to circulate through the electrodes, which is crucial for the battery working principle.

SEI growth is a consequence of irrecoverable decomposition of the electrolyte forming a solid layer

on the surface of the negative electrode active material.

Given that the electrolyte is almost unable to penetrate the SEI, once the first layer has formed, it

will not reach the active material and thus, will not promote further SEI growth. However, if there

was an enduring SEI growth there would be an unceasing loss of lithium, which would lead to a

slow capacity decline (Pinson & Bazant, 2012).

Most of current research is focusing on battery performance during discharge and much little

attention is put in increasing battery lifespan, which is limited by the irreversibilities that occur in

the electrochemical reactions.

3.2 Main Lithium-ion battery types

In order to understand the batteries value chain properly, a deep study of the activities and agents

involved in each of the stages is carried out. Considering the amount of diverse types of batteries

and all their end purposes and end-users, the focus will specifically be put in Lithium ion batteries

(LIBs). This kind of batteries is widely adopted in several sectors such as phone devices and EVs

among the most important ones.

However, the demand on the industry sector as an energy storage device is gaining weight nowadays

thanks to the already mentioned raise of renewables mostly.

There are some requirements that need to be meet for a battery to be viable and reach basic

functioning. As an electric storage device the following eight characteristics are of major relevance:

- High specific energy [Ah/kg] - Long life - Low toxicity

- High specific power - Safety - Fast charging

- Affordable price - Wide operating range

In addition, it is very important for a battery to have low self-discharge and instant start-up when

required. However, all batteries have some self-discharge, which is aggravated and intensified with

age and temperature (Battery University, 2017).

There are many different types of Lithium-ion batteries. This kind of batteries is named after the

active materials of what they are composed of. The most common ones are Lithium Cobalt Oxide

(LiCoO2) — LCO, Lithium Manganese Oxide (LiMn2O4) — LMO, Lithium Nickel Manganese

Cobalt Oxide (LiNiMnCoO2) — NMC, Lithium Iron Phosphate (LiFePO4) — LFP, Lithium

Nickel Cobalt Aluminium Oxide (LiNiCoAlO2) — NCA and Lithium Titanate (Li2TiO3) — LTO.

Each of them has different characteristics such as voltages; specific energy; cycle life, which

depends on the cycling conditions; cost and applications. The following tables summarize the main

features of these types of batteries:

15

Lithium Cobalt Oxide (LiCoO2) – LCO

Table 2: Characteristics of lithium cobalt oxide. Source: Battery University.

This battery excels on high specific energy but has low specific power (load capability) and limited

life span.

Lithium-Manganese Oxide (LiMn2O4) – LMO

Table 3: Characteristics of Lithium Manganese Oxide. Source: Battery University.

LiMn2O4 cathode Graphite anode Since 1996

Voltages 3.70V, 3.80V nominal; typical operating range 3.0–4.2V/cell

Specific energy (capacity) 100–150Wh/kg

Cycle life Short

Applications Power tools, medical devices, electric powertrains

Lithium-manganese offers improvements in specific power and safety, but diminishes the capacity

decreasing the performance with respect to Lithium-cobalt.

Cathode (~60% Co) Graphite anode Since 1991

Voltages 3.60V nominal; usual operating range 3.0–4.2V/cell

Specific energy (capacity) 150–200Wh/kg.

Cycle life Limited

Applications Mobile phones, tablets, laptops, cameras

16

Lithium Nickel Manganese Cobalt Oxide (LiNiMnCoO2) – NMC

Table 4: Characteristics of lithium nickel manganese cobalt oxide (NMC). Source: Battery

University.

LiNiMnCoO2 cathode Graphite anode Since 2008

Voltages 3.60V, 3.70V nominal; typical operating range 3.0–4.2V/cell

Specific energy (capacity) 150–220Wh/kg

Cycle life Long

Applications E-bikes, medical devices, EVs, industrial

In this battery the addition of nickel and manganese play an important role. Nickel is recognised

for its great specific energy but reduced stability while manganese establishes a spinel structure,

which provides low internal resistance, but also provides low specific energy. However, the

combination of both metals boosts each other advantages.

Thus, this battery offers high capacity and high power, serving as both energy cell and power cell,

which is known as hybrid cell. Also, not using cobalt decreases the cost significantly and is making

this relatively new battery the dominant for cathode chemistry.

Lithium Iron Phosphate (LiFePO4) – LFP

Table 5: Characteristics of lithium iron phosphate. Source: Battery University.

LiFePO4 cathode Graphite anode Since 1996

Voltages 3.20, 3.30V nominal; typical operating range 2.5–3.65V/cell

Specific energy (capacity) 90–120Wh/kg

Cycle life Long

Applications E-bikes, medical devices, EVs, industrial

Lithium-phosphate battery is not stressed at sustained high voltage levels as it happens to other

lithium-ion systems, making it a safe battery with a very high thermal runaway (270ºC). Low specific

energy and high self-discharge but one of the fastest (high power) lithium-ion batteries.

17

Lithium Nickel Cobalt Aluminium Oxide (LiNiCoAlO2) – NCA

Table 6: Characteristics of Lithium Nickel Cobalt Aluminium Oxide. Source: Battery University.

LiNiCoAlO2 cathode (~9% Co) Graphite anode Since 1999

Voltages 3.60V nominal; typical operating range 3.0–4.2V/cell

Specific energy (capacity) 200-260Wh/kg

Cycle life Short

Applications Medical devices, industrial, electric powertrain (Tesla)

In this battery the addition of aluminium provides more stability than in nickel oxide. For its high

capacity it is used as an energy cell.

Lithium Titanate (Li2TiO3) – LTO

Table 7: Characteristics of lithium titanate. Source: Battery University.

LMO or NMC cathode Li2TiO3 anode Commercially available since 2008

Voltages 2.40V nominal; typical operating range 1.8–2.85V/cell

Specific energy (capacity) 50–80Wh/kg

Cycle life Very long

Applications

UPS, electric powertrain (Mitsubishi i-MiEV, Honda Fit

EV),

solar-powered street lighting

In this case, the graphite anode is replaced by a lithium-titanate, which arranges into a spinel

structure. With such configuration, zero-tension can be reached, SEI is not formed when fast

charging at low temperature and consequently there is not lithium loss. For all these reasons the

lifespan is the highest of all Li-ion types, it can be ultra-fast charged and discharged at a current of

10 times the rated capacity. However, the extremely high cost of this technology makes it only

available for very specific and special applications, far from massive usage.

As it can be observed from the tables above, the trend in newer systems is to incorporate materials

such as nickel, manganese and aluminium to benefit from their singular and distinctive

characteristics to enhance batteries performance. The following radar or spider charts plot the

18

values over a graded scale of the most important variables for each battery (Battery University,

2017)

LCO LMO

Fig. 11: Average Li-cobalt battery. Fig. 12: Pure Li-manganese battery.

Source: Cadex Source: Boston Consulting Group

NMC LFP

Fig. 13: Typical NMC battery. Fig. 14: Standard LFP battery.

Source: Boston Consulting Group Source: Cadex.

NCA LTO

Fig. 15: Snapshot of NCA. Fig. 16: Chart of Li-titanate.

Source: Cadex. Source: Boston Consulting Group.

19

3.3 Battery value chain for EV and Industry

Since the objective of the present work is to study the effect of second life batteries in the value

chain and explore the synergies between both first and end user in terms of bill and material

management savings afterwards, only one general value chain will be considered to analyse the

most important aspects relevant to the case study.

The value chain for lithium-ion batteries comprises several phases from the cell manufacturer to

the end user. Depending on who the end user is, this value chain will be split at the application and

integration point to reach each end user’s needs.

Regardless of the battery application the essential approach of the value chain is shared. The

following diagram shows the main stages.

Fig. 17: EV and Industry Batteries’ value chain.

The firsts and common segments in the value chain of batteries are raw material mining and

processing, cell manufacturing and system and module assembly. Although the energy storage

system end use determines the following stages of the manufacturing and integration process, the

final stage can also be common. When batteries reach the end of life for their first designed

purpose, they can be recycled or reused for another application instead.

Also, it is worth noting that certain companies deal with various segments of the value chain such

as some chemical industries covering the recycling and also the materials processing stages.

3.3.1 Raw materials

Natural resources are the departing point of the battery value chain journey. As previously

mentioned, there is a wide variety of elements used for Li-ion battery cells, including lithium (Li),

nickel (Ni), cobalt (Co), manganese (Mn), aluminium (Al), tin (Sn), titanium (Ti) and carbon (C)

mostly in natural graphite form. All these elements are obtained from raw material mining or earth

and water surface.

Some of these resources are of high importance to the EU economy and have a high supply-risk.

For both these features they are designated as “critical raw materials (CRMs)”. The European

Commission published the first list of 14 CRMs in 2011 (European Commission, 2011), which is

updated every 3 years to update production, technological progresses and market trends (European

Comission, 2020). The first reviewed list in 2014 contained 20 CRMs and the third one in 2017

comprised 27 CRMs. Further, in January 2018, the Commission issued a document featuring the

20

CRMs capacity for circular usage (Directorate-General for Internal Market, Industry,

Entrepreneurship; European Commission, 2018) and finally, in May 2019 the 'Recovery of critical and

other raw materials from mining waste and landfills - state of play on existing practices' was published presenting

the existing methods and processes for raw material recovery from mining waste and landfills

(Blengini, et al., 2019).

According to the last CRMs list published September the 5th 2020, which enclosed a larger number

of materials, among the 83 materials 63 where considered individually and the rest arranged in 3

groups: 10 in heavy rare earth (HREEs), 5 in light rare earth (LREEs) and 5 in platinum metals

(PGMs) (European Commission, 2020). To compare with previous lists, in 2011 41 elements were

assessed, 54 in 2014 and 61 in 2017. The final 2020 list identifies 30 Critical Raw Materials

(European Commission, 2020):

Among the materials used in Li-ion cells, cobalt, lithium, phosphate and natural graphite are

considered critical raw materials. In the following table the main global producer for each of them,

the stages assessed as critical, the economic importance and supply risk indexes and the recycling

rates are presented (European Commission, 2020):

Table 8: Main characteristics of critical raw materials involved in a battery. Source: European

Commission.

CRM Main global

producers

Stages

assessed as

critical

Substitution

indexes EI/SR4

EoL

recycling

input rate5

Cobalt

Congo, DR (59%)

China (7%)

Canada (5%)

Australia (4%)

mining/ extraction

0.92 / 0.92 22%

Lithium Chile (44%) processing/ refining

0.93/0.93 0%

Natural graphite

China (69%)

India (12%)

Brazil (8%)

mining/ extraction

0.95 / 0.97 3%

Phosphorus

China (74%)

Vietnam (9%)

Kazakhstan (9%)

United States (8%)

processing/ refining

0.99 / 0.99 0%

4 ‘Substitution index’ is a method to numerically determine the hardship in substituting the CRM. It is estimated for both Economic Importance (EI) and Supply Risk (SR) factors. The value ranges from 0 (substitutable) to 1 (irreplaceable). 5 ‘End-of-life recycling input rate’ calculates the ratio between the recycled scrap and the EU demand.

21

3.3.2 Active materials synthesis

Active materials are the core elements of a battery since are those participating in the

electrochemical reactions. These materials include anode, cathode (both electrodes) and the

electrolyte.

For the synthesis of these materials there are many different techniques. For example, for lithium

iron phosphate the process consists of continuous ball-milling6 at high temperature with shearing

capability, whereas for lithium nickel manganese cobalt oxide entails a batch wet synthesis on

organic or aqueous solvent.

Mechanochemichal (MC) methods have been widely used for preparing lithium-ion batteries

materials over the last years. MC methods shorten the synthesis process, display enhanced cycling

behaviour as well as diminish the energy used and material cost compared to previous procedures

including high temperature solid state reactions. However, current trends are pointing to

nanotechnology, as it will offer new options for the cathode synthesis materials (Uddin, Alaboina,

& Cho, 2017).

3.3.3 Cell manufacturing

The cell manufacturing process comprises four main steps: active material preparation, electrodes

manufacturing, cell assembly and cell formation.

First, for the active material arrangement, cathode material and graphite anode material are

separately placed into two tanks where they are mixed with binder, additives and solvents to

produce an ink.

Then, two metallic substrates, copper for the anode and aluminium for the cathode are covered

with the ink through a slot-die process. Once coated, these foils are put in an oven so that the

solvent evaporates and thus, a metallic bar coated with a solid substrate is the remaining product.

This foil then goes through a calendaring process where it reduces its thickness by roller

compression until a right porosity level is reached.

The next stage is to cut in smaller rolls the two large electrode rolls. If the cell assembly process

involves electrode piling, in order to obtain the electrode sheets it is required a roll-notching7 step.

In third place, the cell assembly involves the compilation of the separators and electrodes together.

The technique to do so is to coil together a separator (insulating sheet), the anode, another

separator and finally the cathode. Depending on the battery configuration these layers will be

enclosed in a cylindrical or prismatic casing, or will be assembled in single-sheet stacking or Z-

folding among other structures. Next, the battery is sealed and the metal contacts are adhered.

And finally, the cell formation or aging step consists of charging and discharging the new assembled

cell under very specific parameters depending on the chemistry composition, format and future

battery application. One of the objectives is to form the SEI, which is composed of lithium

carbonate and lithium oxide and grows in the anode active material surface. These initial charging

and discharging tests are also used to detect any faults errors and discard malfunctioning cells.

6 Ball-milling: grinding process into extremely fine dust. 7 Notching: metal cutting process.

22

3.3.4 Module and system assembling

Once the cells are ready, the modules can be built. The system assembly entails a few steps to

obtain the final module. The new cells are cabled and coupled together, and then inserted into a

casing made of plastic or metal. Additionally, to conclude with the final module, a control card is

connected, comprising a battery management system (BMS). Usually the modules account with 4

to 50 cells, reaching up to few kilowatt-hours.

Module and system assembly are way less capital intensive compared to cell manufacturing, around

5-7 times.

Fig. 18: Capital investment cell manufacturing vs. module and system assembly. Source: Saft

3.3.5 Application and integration

This stage is different for each battery end use. Depending on the battery first life purpose, the

battery pack will be integrated into the vehicle structure, comprising the battery-car interface

(connectors, plugs, mounts). This task is carried out by automotive OEMs (original equipment

manufacturer), such as Chrysler (Fiat), Ford, GM, Nissan and Tesla among the most important

ones (Lowe, Tokuoka, Trigg , & Gereffi , 2010).

In the other hand, stationary battery uses include off-grid applications, where storage is part of a

bigger energy solution; utility application, where energy storage can provide system reliability,

peaking capability, frequency response, regulation, power quality, forecast error mitigation and

renewable restriction mitigation among other values; and finally behind the meter, providing the

electricity consumer benefits determined by the tariff structure and in terms of power quality from

the grid.

3.3.6 Recycling and second life

Once the battery has reached the end of its first purpose life due to a capacity loss that no longer

satisfies the automotive industry needs, the objective is to repurpose the use of this battery instead

of discarding it right away in order to create a circular economy around them. The next steps are

conditioned by a series of economic and technical aspects that will be further discussed. However,

if the battery has already reached the end of its second life, meaning it has already been used for a

stationary purpose not as demanding as the first one, it will be recycled.

23

Fig. 19: Schematic of the methods and processes involved in the consumed LIBs recycling. Source: (Zheng, et al., A

Mini-Review on Metal Recycling from Spent Lithium Ion Batteries, 2018)

This process involves different stages, comprising pretreatments, metal-extraction and product

preparation. Initially, the battery is discharged for security reasons, the BMS, the battery cooling

system and packaging are disassembled, removed and handled separately. There are different

pretreatment methods: solvent dissolution; NaOH dissolution; ultrasonic assisted separation,

which allows stripping the cathode material thanks to cavitation effect; thermal treatment, to

decompose the binder and thus, reduce the bonding force between particles; and mechanical

methods including sieving, crushing and magnetic separation. Table 9 summarizes the advantages

and disadvantages of each process.

Table 9: Pretreatment methods comparison. Source: (Zhang, He, Wang, Ge, & Zhu, 2014).

Technology Advantages Disadvantages

Solvent dissolution

High separation efficiency Expensive solvent, environmental hazards

NaOH dissolution

High separation efficiency

Simple operation

Difficult aluminium recovery

Alkali wastewater emission

Ultrasonic-assisted

separation

Simple operation

Practically no exhaust emission

Noise pollution

High device investment

Thermal treatment

Simple operation

High output quantity

High energy consumption

High device investment

Toxic gas release

Mechanical methods

Simple and useful operation

Toxic gas release

Cannot separate all kind of components in spent LIBs completely

24

Some of these techniques have a more efficient separation process such as solvent dissolution and

NaOH dissolution whereas the others occur to be simpler operations.

Once opened, to inactivate the harmful substances, liquid nitrogen is used. Also, the cathode, anode

and separator are removed and put in an oven for 24h in order to dry them.

Next, each electrode is further separated for the metal extraction, which is done by pyrometallury,

hydrometallurgy, biometallurgy or a combination of these. This process consists on transforming

the solid metals into their liquid state to enable the later separation and retrieval of the metal

components. Again, the advantages and disadvantages are presented in a summary table:

Table 10: Comparison for metal-extraction processes. Source: (Georgi-Maschlera, Friedricha,

Weyheb, Heegnc, & Rutzc, 2012)

Technology Advantages Disadvantages

Pyrometallurgy Great capacity

Simple operation

High temperature and energy

consumption

Low metal recovery rate

Waste gas and dust

Hydrometallurgy

Low energy consumption

High metal recovery rate

High product purity

Long recovery process

High chemicals consumption

Waste water

Biometallurgy

Low energy consumption

Mild operating conditions

High metal recovery rate

Long reaction period

Bacteria are difficult to cultivate

This step of metal-extraction is critical to the whole process. The methods implemented are gaining

efficiency and capacity but still are very harmful for the environment because of the wastewater,

the management of chemicals involved and exhaust gas. Hence, further attention and research in

secondary pollution is needed to achieve a successful recycling process.

Subsequently, in the preparation step metal components present in the liquid mixture can be

recovered by a combination of solvent extraction, chemical precipitation and crystallization. For

the cathode material preparation, since the dissolved metal ions such as Ni, Mn and Co are difficult

to separate due to their nature similarity, a precursor material is used to ease the separation.

Then, the cathode material is regenerated through co-precipitation and sol-gel, both these are

synthesizing methods.

25

Further maturity in cathode recycling processes is key to produce elements that can already be used

in new batteries lowering or eradicating additional expensive reprocessing (Green Car Congress,

2019). In general terms, new batteries will be design to ease the recycling process and therefore,

reduce the whole battery life cost.

The final goal is to reach a close-loop, see Fig. 20, where spent batteries are recycled diminishing

processing steps and thus, reducing waste and energy consumption and positively affecting battery

production costs.

Figure 20: Closed loop for LIBs life. Source: Argonne National Laboratory

Currently Europe does not have the capacity to recycle all the batteries that are in use and thus,

even more emphasis needs to be placed in the repurposing and second-life of batteries to give time

to further develop the recycling industry.

The upcoming worldwide rise of EVs will certainly bring new strategies for the collection and waste

batteries management. Once the batteries are removed from the EVs their categorization and

testing are crucial for determining a suitable second-use.

26

4 STUDY CASE

From the battery collection until the second-use there are different strategies and approaches to

proceed. Depending on the state of the battery, the battery pack is tested without being

disassembled and, if it is apt and meets the market requirements for the second-use proposed,

directly reused; in the other hand, the battery pack can be dismantled at a module level involving

more technical procedures, materials and components to rebuild a new battery pack that will rise

the cost of the operation. However, this second repurposed battery would be more adaptable for

particular uses. The first strategy is known as “direct reuse” while the second one as “battery

repurposing/refurbishing” (Canals Casals & Amante Garcia, 2016).

The objective of this chapter is to introduce two different ways of proceeding with second life

batteries and the economic effects, pursuing the best scenario for the prosumer using the available

flexibility. To do so, an algorithm will be used to perform a consumption profile calculation using

a second life battery. This algorithm with the code for the calculation step will be taken from The

European project INVADE (Smart system of renewable energy storage based on INtegrated EVs and bAtteries

to empower mobile, Distributed and centralised Energy storage in the distribution grid) (INVADE, 2020).

INVADE is a 16 million euros budget project being one of the largest European research and

innovation in the field of SmartGrid & Storage. Both the present work and INVADE project

ultimately seek to increase renewable sources integration in the power system. However, while this

project only includes stationary storage, INVADE project incorporates both mobile (EVs) as well

as stationary storage and thus, those parts of the algorithm corresponding to EVs storage will have

to be omitted.

In the present work UPC public consumption and generation data will be used to perform the case

study. This data will be acquired from the UPC SIRENA (Sistema d’Informació de Recursos

Energètics i Aigua) tool (UPC, 2007), which was launched in 2007. SIRENA platform offers

publicly accessible data measured by the smart meters distributed throughout the installations of

the UPC for research and dissemination purposes (UPC, 2007). SIRENA’s main purpose is to lead

the new energy saving measures and get track of their implementation effect. UPC is a good model

to carry out such study since it has a plan for reducing energy consumption and implementing

renewable energy systems (solar PV) and thus, our second life batteries could benefit UPC both

technically and economically.

The targeted building is TR14 Gaia in UPC campus Terrassa. This building is intended to locate

university-company projects, technology-based companies, research centres and innovation units.

The solar power plant installed on the roof has 120 photovoltaic panels, with a power of 25kW.

Figure 21: Virtual map of UPC campus Terrassa. Source: UPC

27

Due to environmental conditions, aging in the PV panels and other technical aspects unknown,

the PV installation has been highly downgraded and performs far below the expectations. The

output peak power has been measured at steady levels lower than the 25 kW installed, reaching

down to about 12 kW.

Figure 22: Aerial picture of the building. Source: UPC Figure 23: Picture of the building. Source: UPC

A first description of the building and its main consumptions yield the following data:

About architecture, the total footprint of the building 7.247 m2, distributed in 3 floors and

a basement. It was designed and executed between 2006 and 2012 by the company CDB

Arquitectura (CDB Arquitectura, 2006). The total budget of the project was 10.6 M€. The

building is committed to isotropy in response to the demands of this type of buildings,

maximizing the continuity in the spaces and modularity to allow substitution and adaptation

according to requirements.

About its purpose, this building is embedded into the university campus of the UPC in

Terrassa, and it is usually used to host research centres, innovation hubs and start-ups.

About its electricity consumption and generation monitoring assets, according to SIRENA

platform, data is accessible for the smart meter at the point of connection to the grid; at the point

of connection of the PV installation with the rest of the building infrastructure; for heating,

ventilation and air conditioning (HVAC equipment).

All above-mentioned electrical loads are considered as inflexible for the purposes of the present

work. Only the data measured at the point of connection of the building with the electrical network

will be considered for optimization purposes. With the aim of completing the description of the

building, figure 24 depicts the daily total consumption, HVAC consumption and PV generation.

As noticeable below, the grid consumption of the building rises in summer due to HVAC

equipment. In terms of PV generation, the levels stay low compared to the consumption

throughout the year. Table 11 complements this overall description of the building electricity

consumption and generation.

28

Figure 24: Daily total consumption, HVAC consumption and PV generation. Source: SIRENA UPC.

Table 11: Gaia building electricity consumption and generation.

Grid consumption

(kWh) HVAC (kWh)

PV generation (kWh)

Total 275142 55473 20864

Average 753 151 57

Median 724 70 62

Max 1825 667 110

Min 176 0.3 0

4.1 Modelling concepts

4.1.1 Modelling of the battery degradation

Nowadays batteries have two major drawbacks: they are still expensive to manufacture and degrade

over time. As mentioned before (in chapter 3.1) lithium ions move between battery electrodes, but

as they go back and forth across the layers some of the ions get trapped diminishing it and being

an obstacle for the remaining cycleable lithium.

As a result battery cells slowly degrade after recurrent cycling and the cell capacity fades as its

resistance increases, reducing the battery safety and efficiency. As it can be observed in Figure 25,

the more cycles the battery carries, the higher the resistance leading to a loss of capacity in the

battery.

29

Figure 25: Ri-SOC plot with different cycles. Source: (Wang & Bao, 2017)

The battery cycle life refers to the amount of charging-discharging cycles a battery can undertake

before the battery capacity drops under a certain amount of the nominal value and needs to be

substituted. The main factors affecting to battery degradation are temperature,

charging/discharging current rate (C-rate), SoC and DoD.

4.1.1.1 Temperature

The first one affects in both extremes, low and high temperature. The low temperature mainly

affects the electrolyte increasing its viscosity, which reduces the ionic conductivity, increases the

impedance of the relocation of the chemical ions and thus, raises the resistance (Ma, et al., 2018).

On the contrary, the high temperature, although it improves Li-ion battery’s performance

temporarily by increasing its capacity, contributes to an advanced degradation rate mostly due to

the alteration of the electrode surface films (Leng, 2015).

In Figure 26 the temperature effect is detected in the different capacity drop evolution. As

mentioned above, high temperature can contribute to a transitory higher capacity, which would

make higher temperatures of 45 and 55 ºC the most suitable ones. However, it also increases

resistance and degradation as well as a low conductivity leading to a drop in capacity for high

temperatures. In this example 45 ºC would be the optimal temperature.

30

Figure 26: Maximum charge storage capacity for each cycle number as a function of temperature. Source: Battery

and Energy Technologies, MPower UK.

4.1.1.2 Current rate

Secondly, current rate (C-rate), which measures the speed at which a battery is charged/discharged

compared to its maximum capacity, also influences battery degradation. A 1C rate implies that the

charge/discharge current will charge/discharge the battery entirely in 1 hour (MIT Electric Vehicle

Team, 2008). Higher C-rates will shorten the time while lower C-rates will lengthen the

charging/discharging time.

At high C-rate, quick charge/discharge, chemical compounds in the battery will not have enough

time to react and move. Only part of the active material is transformed and thus, little energy is

obtained. Yet, at low C-rate, slow charge/discharge, more energy is released and the capacity is

higher (Honsberg & Bowden, 2019).

The C-rate "number" can be obtained from the current at which the battery is being

charged/discharged over the nominal battery capacity as follows:

𝐶 − 𝑟𝑎𝑡𝑒 =𝐼𝑐ℎ/𝑑𝑠𝑐ℎ

𝐶𝑛𝑜𝑚

31

Figure 27: Capacity degradation curves for different discharge C-rates. Source: (Perez, Montoya, & Quintero,

2018)

Figure 27 shows the faster degradation curve of a battery when discharged at higher C-rates. Lower

C-rates have lower capacity loss because at lower rates more active material has time to be

transformed and react.

4.1.1.3 State of Charge

The State of Charge (SoC) indicates the remaining energy in a battery expressed as a percentage of

the maximum capacity.

As shown in Figure 28, cycling at high states of charge of the battery shortens longevity in capacity,

while cycling at low states of charge prolongs capacity retention. This charging at lower states

combined with shallow cycling (as will be explained in the following point of DoD), are the clue

for diminishing capacity loss in battery cycling.

Figure 28: Comparison of calendar aging and cyclic aging for three temperatures investigated. Source: (Keil &

Jossen, 2015)

The voltage at which a battery is charged also contributes to the lifetime and capacity levels.

Lowering the peak charge voltage expands the cycle life.

32

However, lowering the voltage decreases the capacity stored. For the electric vehicle industry the

major concern is longevity and for this purpose the optimal charge voltage is 3.92V/cell (Battery

University, 2020).

Moreover, the State of Health of the battery is easily obtained by comparing how much electrical

charge is currently needed to move the battery from one point to another on the charge curve vs.

the amount it originally required when it was new. This calculation is carried out by the BMS.

Therefore, as the cell degrades less charge will be needed to move up the charging curve, since the

capacity will have diminished and it will reach the maximum earlier.

In Figure 29 this phenomenon is detected. The new battery with little cycles (blue line 5 cycles)

reaches both maximum voltage and capacity, while the old battery (purple line 800 cycles) reaches

sooner the maximum voltage only reaching at this point 0.7 times of the initial maximum capacity.

To exemplify an intermediate point, at 4.0 V at charge, the old battery already reaches 0.4 times the

initial maximum capacity whereas the new battery is at 0.8 times the maximum capacity.

Figure 29: Alterations of the voltage vs. capacity at different cycles. Source: (Huang & Tseng, 2017).

As it can be observed in Figure 28, same SoC level at lower capacities indicate a poorer SoH of the

battery.

4.1.1.4 Depth of discharge

The Depth of Discharge (DoD) is the percentage of the battery capacity discharged relative to the

maximum and thus, is complementary to SoC. Many studies have concluded that deeper DoD

shortens the lifetime of the battery whereas smaller DoD prolongs it. For example, a battery could

reach 15.000 cycles at a 10% DoD, but barely 3.000 at 80% DoD (Thoubboron, 2019).

33

Figure 30: Cycle life at different DoD. Source: (Wikner & Thiringer, 2018)

However, in the present work a specific DoD cannot be taken since the battery energy storage

system (BESS) will be driven by the cost of cycling the battery or energy prices in the energy market

tariffs and thus, will not be following a regular cycling (Wang, Zhou, Botterud, Zhang, & Ding,

2016).

After reviewing the main battery degrading causes, a numeric effect in terms of price is required.

Although Li-NMCs are one of the most long-lasting batteries nowadays, due to this degradation

and capacity fade, these batteries have a limited lifespan. Lifespan expected from manufacturers is

at least 2000 cycles but if used properly, can last up to 3000 cycles for the first life of the battery

(Lerma, 2019).

When quantifying a battery lifespan by a number cycles, 2000-3000 for example, it is referred to a

certain storage capacity, not zero. Battery capacity indicates the amount of charge battery cells can

deliver at the rated voltage and is directly correlated with the amount of electrode material

remaining. The first life ends when the capacity reaches 80% generally, and the second life up to

50% of the original new battery capacity.

For the current optimization, since a specific capacity value for the whole horizon of the problem

is needed, an average value between 80% and 50% is taken, for example 65%. Addressing the

typical size of second life battery from an EV type A, for instance Nissan Leaf or Volkswagen Golf,

it would correspond to 40-50kWh of capacity. Considering the average capacity for second life

mentioned above of 65%, the battery could offer 30kWh of storage capacity. Such capacity both

in terms of energy and power could fit with the requirements of a battery while associated to a PV

system rated at few tens of kW of installed power eg. PV system installed rated at 25kW as the one

in the building adopted for this work.

34

Concerning the study case, there is a cost associated to each charging and discharging cycle due to

degradation. This expense is the amortisation cost, which will be calculated according to the

lifespan of the second life battery. After the batteries are no longer suitable for transportation

purposes, they still have around 80% of their initial capacity as mentioned before.

According to the two study cases, refurbished and directly reused, different costs for the second

life battery will be considered. The directly reused battery will be cheaper since it has not been

improved and thus, its capacity will fade sooner, while the refurbished battery will be more

expensive because the BMS and other components will be changed in order to prolong the battery

life without reaching the cells recycling point.

Consistent with conversations with manufacturers and companies in second life batteries sector,

the battery prices considered for the study case are:

Table 12: Battery components’ prices. Source: Conversations with manufacturers.

Li-ion NMC refurbished Li-ion NMC not refurbished

Remaining

cycles 2000 cycles 1800 cycles

Cell 91 €/kWh 65 €/kWh

BMS 40 €/kWh 10 €/kWh

Packaging 50 €/kWh 20 €/kWh

Total 181 €/kWh 95 €/kWh

As specified in table 12 the cycles considered for the second life are 2000 for the refurbished ones,

since the technical improvements extend the battery life, and 1800 for the not refurbished battery.

However, the renewed one is more expensive than the directly reused for the investment required

in upgrading it. Therefore the amortisation costs for each case can be calculated as follows:

Li-ion NMC refurbished:

181€/𝑘𝑊ℎ

2000 𝑐𝑦𝑐𝑙𝑒𝑠= 0.0905 €/𝑘𝑊ℎ − 𝑐𝑦𝑐𝑙𝑒

Li-ion NMC not refurbished:

95€/𝑘𝑊ℎ

1800 𝑐𝑦𝑐𝑙𝑒𝑠= 0.053 €/𝑘𝑊ℎ − 𝑐𝑦𝑐𝑙𝑒

These prices are set per kWh since the algorithm works with any battery and thus, the capacity of

30kWh is also specified in the simulation process.

35

4.1.2 Prices

The electricity price will be determinant for the final decision to proceed with the flexibility request

from the storage system and thus, it is a crucial variable. The electricity price is determined by more

than one factor that affects the energy price. As for the electricity bill, there are 4 concepts that are

typically broken down:

- Power term: determines the fixed amount the consumer pays for being connected to the

grid at a certain load capacity.

- Equipment rental: It is charged to all those customers who do not own their electric meter.

Not applicable in this case.

- Consumption term: price for the energy consumed by a customer during a specific billing

period. Costs depend on the tariff contracted.

- Tax on electricity: Taxes the cost of manufacturing electricity and the term of consumption.

The overall objective is to follow a battery charging/discharging approach so that the total costs

are minimized. In order to achieve so, the model must take into account all of the listed points

when making the decisions and drawing a strategy:

- Lowering the purchase above the max contracted power.

- Shifting purchase from high price hours to low price hours.

- Taking into account the battery amortisation price when activating a flexibility request,

meaning a comparison between this cost and the grid electricity cost in that time period is

required.

Once the prices and the rate periods are set, the data is ready to be entered into the algorithm. The

main outputs of these simulations will be the optimal usage of the second life battery and the energy

costs and savings from using each battery (refurbished /directly reused) in terms of electricity bill

and battery amortisation.

4.1.2.1 Power term

In the present case, the tariff contracted is the Spanish rate 3.0A, which is the network access rate

determined by law for all low-voltage supply points with more than 15 kW of contracted power.

There are three invoice periods depending on the hour of the day. These invoice periods in the

region corresponding to the study case building (Iberian Peninsula) are listed in Table 13.

Moreover, the prices corresponding to each invoice period are determined by each retailer

contracted. Som Energia has been considered the electricity retailer for the present work. Their

power listed prices for the 3.0A rate and mentioned periods are presented in Table 14.

36

Table 13: Invoice periods of the Spanish tariff 3.0A. Source: Som Energia

Table 14: Retailer power pries for each invoice period. Source: Som Energia

For the simulation carried out in this paper, a single representative number needs to be taken. Then,

the value taken is the weighted average taking into account all invoicing periods and their

corresponding hours. This gives a value of 24.43 €/kW-year and will be used to calculate the power

expenses related to the year simulated.

4.1.2.2 Consumption term

As indicated in the power term section, the tariff contracted is the Spanish rate 3.0A. The listed

prices from Som Energia for the 3.0A rate and corresponding the three invoice periods are:

Table 15: Retailer consumption pries for each invoice period. Source: Som Energia.

Periods Jan - March

Oct - Dec April- Sept

P1 – tip period 18 - 22 h 11 - 15 h

P2 – flat period

8 - 18 h

22 - 24 h

8 - 11 h

15 - 24 h

P3 – valley period 0 - 8 h 0 - 8 h

P1 – tip period 40.718885 €/kW-year

P2 – flat period

24.437330 €/kW-year

P3 – valley period 16.291555 €/kW-year

P1 – tip period 0.13 €/kWh

P2 – flat period

0.093 €/kWh

P3 – valley period 0.066 €/kWh

37

4.1.2.3 Taxes

Taxes considered in the present work are:

- Electricity Tax: regulated by the government, applied to the sum of the terms energy and

power and equal to 5.11%.

- VAT: applied to the sum of the four concepts mentioned above (power term, equipment

rental, consumption term and tax on electricity) and equal to 21%.

4.2 Testing procedure

Fort he simulation process, 15 min have been set for the time resolution since it is the reference

number for all ancillary services markets (European Comission, Directorate-General for Energy,

2016). Given the high time resolution, the optimisation cannot run for a whole year at once due to

computational burden. Hence, one week per month is taken as a sample and the results will be

extrapolated to the other three remaining weeks to get an entire year result. Also, this

representative-week model can be done because the optimisation algorithm does not take into

account the battery installation cost and only considers the battery usage.

This study is a planning assessment and thus, the main goal is to find the technical and economic

viability for the second life battery installation in buildings. In this way, the present work relies on

historical data and evaluates a long operational period, one year, and applies the “rolling horizon”

technique, which means running the optimisation every hour to correct the photovoltaic resource

and demand forecast for the best battery operation adjustment.

In terms of contracted power, there have been three scenarios:

Initially, the power contracted was of 80 kW. In this situation, no battery was installed yet. This scenario has been used to check the load profile of the building and compute the current electricity bill. This is the base case without battery.

The evaluations of the base case without battery lead us to propose a second scenario with a lower contracted power, now including the battery for energy optimisation purposes. In particular, 5% reduction is proposed leading to a contracted power of 76 kW. This case was named as baseline optimisation scenario, where the system still had enough room for further power reduction.

This baseline scenario will be adjusted in terms of power until reaching the boundaries of optimisation feasibility. A presented later in chapter 5 and addressing the target of the project of reducing the contracted power of the building up to 25%, the minimum contracted power feasible for the optimization process is 60 kW. This means that the contracted power will be reduced until the mathematical optimisation reaches a point of no solution, according to the parameters characterizing the case study. This scenario will be identified as power reduction case.

The simulation procedure will be carried out four times, twice for a reconditioned battery and twice for a directly reused battery. The difference will stand in the amortization price and degradation (number of cycles the battery will be able to handle) in one hand, and in the power contracted in the other.

38

4.3 Mathematical formulation

4.3.1 Overview of sets, parameters and variables

4.3.1.1 Sets

Table 16: Sets used in the simulation. Source: INVADE project

4.3.1.2 Parameters

Table 17: Parameters used in the simulation. Source: INVADE project

T Set of periods/time slots in the planning horizon

Tc Subset of periods where curtailment is allowed

B Set of battery units

L Set of load units

Li Subset of inflexible load units

G Set of generation units

Gd Subset of curtailable disconnectable generation units

𝑷𝒕𝒓𝒆𝒕𝒂𝒊𝒍−𝒃𝒖𝒚

Price at energy part of retail contract for buying electricity in period

t [€/kWh]

𝑷𝒕𝒓𝒆𝒕𝒂𝒊𝒍−𝒔𝒆𝒍𝒍

Price at energy part of retail contract for selling electricity in period t

[€/kWh]

𝑷𝑽𝑨𝑻 Addition of VAT to the bought amount [fraction]

𝑿𝒊𝒎𝒑−𝒄𝒂𝒑 Maximum import capacity [average kW]

𝑿𝒆𝒙𝒑−𝒄𝒂𝒑 Maximum export capacity [average kW]

M Limitation of basis for peak fee [kW]

𝑶𝒃𝒎𝒊𝒏 Minimum state of charge for battery b [kWh]

𝑶𝒃𝒎𝒂𝒙 Maximum state of charge for battery b [kWh]

𝑶𝒃𝒊𝒏𝒊 Initial state of charge for battery b [kWh]

𝑨𝒃𝒄𝒉 Efficiency parameter for charging storage unit b [#]

𝑨𝒃𝒅𝒊𝒔 Efficiency parameter for discharging storage unit b [#]

𝑷𝒃,𝒕𝑩,𝒄𝒉 Price for charging battery unit b at period t [€/kWh]

𝑷𝒃,𝒕𝑩,𝒅𝒊𝒔 Price for discharging battery unit b at period t [€/kWh]

𝑾𝒍,𝒕𝒍𝒐𝒂𝒅 Baseline consumption at load unit l in period t [kWh]

𝑾𝒈,𝒕𝒑𝒓𝒐𝒅

Baseline production from generation unit g in period t [ kWh]

𝑵𝒉𝒐𝒖𝒓 Periods per hour [#]

39

4.3.1.3 Variables

Table 18: Variables in the simulation. Source: INVADE project

4.3.2 General constraints

4.3.2.1 Battery model

Each battery, referred as unit b, accounts with efficiency parameters both for charging and

discharging sequences (𝑨𝒃𝒄𝒉, 𝑨𝒃

𝒅𝒊𝒔). The battery current state of charge, 𝜎𝑏,𝑡𝑠𝑜𝑐 , depends on the SoC

in the previous period and on the charging/discharging (𝝈𝒃,𝒕𝒄𝒉 , 𝝈𝒃,𝒕

𝒅𝒊𝒔) in the current period.

𝜎𝑏,𝑡𝑠𝑜𝑐 = 𝜎𝑏,𝑡−1

𝑠𝑜𝑐 + 𝜎𝑏,𝑡𝑐ℎ ∗ 𝐴𝑏

𝑐ℎ −𝜎𝑏,𝑡

𝑑𝑖𝑠

𝐴𝑏𝑑𝑖𝑠 , ∀ 𝑏 𝜖 𝐵, 𝑡 𝜖 𝑇 (Eq. 1)

The SoC must be between the limits set:

𝑂𝑏𝑚𝑖𝑛 ≤ 𝜎𝑏,𝑡

𝑠𝑜𝑐 ≤ 𝑂𝑏𝑚𝑎𝑥 , ∀ 𝑏 𝜖 𝐵, 𝑡 𝜖 𝑇 (Eq. 2)

𝑿𝒕𝒃𝒖𝒚

Amount of electricity bought in period t [kWh]

𝑿𝒕𝒔𝒆𝒍𝒍 Amount of electricity sold in period t [kWh]

𝑿𝒕𝒔𝒆𝒍𝒍 Amount of electricity sold in period t [kWh]

𝑿𝒑𝒆𝒂𝒌 Basis for calculation of peak fee in cases where this is a part of the

grid contract [kW]

𝝍𝒈,𝒕 Amount of electricity produced from generating unit g in period t

[kWh]

𝜻𝒈𝒆𝒏 Total cost for utilizing generation flexibility [€] (curtailable

disconnectable).

𝝎𝒍,𝒕 Amount of electricity consumed from load unit l in period t [kWh]

𝝈𝒃,𝒕𝒄𝒉 Amount of electricity charged to battery unit b in period t [kWh]

𝝈𝒃,𝒕𝒅𝒊𝒔

Amount of electricity discharged from battery unit b in period t

[kWh]

𝝈𝒃,𝒕𝒔𝒐𝒄 Amount of energy in battery b in period t

𝜻𝒇𝒍𝒆𝒙𝒊𝒃𝒊𝒍𝒊𝒕𝒚 Total cost for utilizing internal flexibility [€]

𝜹𝒕𝒃𝒖𝒚

Binary variable = 1 if site is importing/buying electricity in period t,

else = 0

𝜹𝒕𝒔𝒆𝒍𝒍

Binary variable = 1 if site is exporting/selling electricity in period t,

else = 0

𝜹𝒈,𝒕𝒈𝒆𝒏 Binary variable = 0 if generating unit g is disconnected in period t,

else 1

40

Also, the initial SoC of the battery must be equal or lower to the SoC of the last time period. This

constrain ensures that the SoC at the end of the optimisation period is high enough so that the

battery can operate in subsequent periods.

𝑂𝑏𝑖𝑛𝑖𝑡 ≤ 𝜎𝑏,𝑇

𝑠𝑜𝑐 , ∀ 𝑏 𝜖 𝐵, 𝑡 𝜖 𝑇 (Eq. 3)

Charging and discharging must not exceed their maximum levels:

𝜎𝑏,𝑡𝑐ℎ ≤

𝑄𝑏𝑐ℎ

𝑁ℎ𝑜𝑢𝑟 , ∀ 𝑏 𝜖 𝐵, 𝑡 𝜖 𝑇 (Eq. 4)

𝜎𝑏,𝑡𝑑𝑖𝑠 ≤

𝑄𝑏𝑑𝑖𝑠

𝑁ℎ𝑜𝑢𝑟 , ∀ 𝑏 𝜖 𝐵, 𝑡 𝜖 𝑇 (Eq. 4)

4.3.2.2 Load model

For the present work the load studied includes the whole building and thus, it is considered as an

inflexible load unit. Other types of loads could be shiftable or curtailable. For the inflexible loads,

the planned load 𝜔𝑙,𝑡 needs to match the anticipated load 𝑊𝑙,𝑡𝑙𝑜𝑎𝑑 .

𝜔𝑙,𝑡 = 𝑊𝑙,𝑡𝑙𝑜𝑎𝑑 ∀ 𝑙 𝜖 𝐿𝑖 , 𝑡 𝜖 𝑇 (Eq. 5)

4.3.2.3 Generator model

In this study case the generation units are curtailable disconnectable. Solar photovoltaic panels

cannot regulate and reduce their power, but offer this slight flexibility by disconnecting them. For

curtailable disconnectable units, intended production must be either 0 or equivalent to expected

production.

𝜓𝑔,𝑡 = 𝛿𝑔,𝑡𝑔𝑒𝑛

𝑊𝑔,𝑡𝑝𝑟𝑜𝑑, ∀ 𝑔 𝜖 𝐺𝑑 , 𝑡 𝜖 𝑇 (Eq. 6)

4.3.3 Prosumer model

4.3.3.1 Prosumer services objective function

Since the main purpose of this study is helping reduce the energy costs for the prosumer by using

energy storage, the objective function seeks to minimize the electricity costs, electricity taxes and

costs for operating flexibility.

min 𝑧 = ∑[(𝑃𝑡𝑟𝑒𝑡𝑎𝑖𝑙−𝑏𝑢𝑦

+ 𝑃𝑡𝑡𝑎𝑥)𝒳𝑡

𝑏𝑢𝑦𝑃𝑉𝐴𝑇 − (𝑃𝑡

𝑟𝑒𝑡𝑎𝑖𝑙−𝑠𝑒𝑙𝑙)𝒳𝑡𝑠𝑒𝑙𝑙] + 𝜁𝑓𝑙𝑒𝑥𝑖𝑏𝑖𝑙𝑖𝑡𝑦

𝑡∈𝑇

(Eq. 7)

41

4.3.3.2 Prosumer services constraints

There must exist a balance between generation, consumption and battery charge/discharge, and

import/export from/to the grid in every time period.

∑ 𝜓𝑔,𝑡

𝑔∈𝐺

+ ∑ 𝜎𝑏,𝑡𝑑𝑖𝑠

𝑏∈𝐵

+ 𝜒𝑡𝑏𝑢𝑦

= 𝜒𝑡𝑠𝑒𝑙𝑙 + ∑ 𝜔𝑙,𝑡

𝑙∈𝐿

+ ∑ 𝜎𝑏,𝑡𝑐ℎ

𝑏∈𝐵

, ∀ 𝑡 𝜖 𝑇

(Eq. 8)

Additionally, two binary variables that take value 1 if the location is importing or exporting, which

corresponds to buying and selling respectively, else value 0. With this constraint, the site cannot be

buying and selling energy at the same time.

𝛿𝑡𝑏𝑢𝑦

+ 𝛿𝑡𝑠𝑒𝑙𝑙 ≤ 1, ∀ 𝑡 𝜖 𝑇 (Eq. 9)

Finally, last restriction consists on limiting the energy bought/sold to capacity limits.

𝜒𝑡𝑏𝑢𝑦

≤ 𝛿𝑡𝑏𝑢𝑦

Χ𝑖𝑚𝑝−𝑐𝑎𝑝, ∀ 𝑡 𝜖 𝑇 (Eq. 10)

𝜒𝑡𝑠𝑒𝑙𝑙 ≤ 𝛿𝑡

𝑠𝑒𝑙𝑙Χ𝑒𝑥𝑝−𝑐𝑎𝑝, ∀ 𝑡 𝜖 𝑇 (Eq. 11)

42

5 RESULTS AND DISCUSSION

5.1 Simulation results

The results for both refurbished and directly reused battery, which come from the analysed data of

SIRENA UPC, are plotted in twelve different graphs, forty-eight in total, which are presented in

the Appendix, one for each month and only taking one week as an average model to be extrapolated

as mentioned before. The simulation cases are two for 76kW power case, and 2 for the power

reduction scenario of 60kW. For each power scenario has been simulated a refurbished battery and

a directly reused battery differentiating the amortisation. Thus, the 4 simulated cases are:

Directly reused at 76kW,

Directly reused at 60kW,

Refurbished at 76kW and

Refurbished at 60kW.

The graphs are plotting the evolution lines corresponding to the load demand (building) presented

as “inflexible load”; PV generation pattern; the battery charge/discharge behaviour; the base net

load representing the PV generation minus the load demand without any battery implication; the

net energy exchange, which is the base net load plus/minus the battery use; the battery state of

charge; the momentary battery power; and the total electricity purchase price at which energy is

being bought from the retailer at each of the three invoice periods. The load profile of the building

represents the typical occupation of a public office building during working hours. The building

load is drastically reduced during weekend days and at nights.

As stated in section 4.1, the baseline scenario is run at 76 kW as contracted power. The second

simulation adjusting the power term thanks to the battery integration corresponds to a contracted

power of 60 kW, aligning with the 20% reduction objective. This value has been taken from the

results of the first simulation, where it was clear that it could be adjusted since only punctual peaks

were found, which can be attributed to measurement errors or punctual phenomenon that would

be more cost-effective to pay the eventual penalty rather than raising the power level only for this.

The simulation is run for a time period of a week, as a representative slot time per each month, the

results are extrapolated to a month and finally computing the annual results. The first week of each

month has been taken regardless of the weekly day it started. As a consequence the results shown

throughout the project do not reflect a Monday-Sunday week. The model months are of 28 days

to match with exactly 4 weeks for simplification purposes, so the remaining days have been

neglected. The week sequence simulation therefore operates with 672 input/output data points,

corresponding to the 15-minute frequency testing explained in chapter 4.2.

To exemplify the results, a set of graphs presenting typical seasons with representative range of

supply/demand conditions for one of the cases is presented below. The chosen case is the “directly

reused battery (0.053 amortisation cost) with the baseline power case scenario of 76 kW”. In all

graphs presented below the left hand axis of the graphs entails the energy in kWh since it plots the

electricity exchanged with the grid by the building (per each 15-minute time slot) including the load

demand variation, battery activity and local PV generation. The right hand axis refers to the battery

SoC and grid electricity price levels.

43

Figure 31: Winter Season typical supply/demand scenario (February).

In case of a winter month, the climatology does not provide many PV generation (negative curve

since it is an input energy into the system) compared to the inflexible load of the building. With

this, the storage system participation is minor, since most of the PV generation is immediately

consumed. This can be observed comparing the base net load and the net energy exchange.

During the weekend, which are the two days where the electricity consumption is nearly zero in

the building, all of the PV output is aimed at charging the battery with certain small exceptions

when local power generation is exported to the grid. Therefore, the energy storage system state of

charge is at its maximum at the beginning of each new week.

Figure 32: Spring Season typical supply/demand scenario (April).

During spring season there is more fluctuation given the higher amount of sun hours and thus, of

PV generation. Especially at midday, an increase of PV generation can be detected. Also, there is a

charging process of the battery during weekends as in the previous case.

44

Figure 33: Summer Season typical supply/demand scenario (July).

In summer, PV generation increases but also the consumption rises due to air conditioning load.

The simulation result shows the frequent peaks of building load are diminished thanks to the

battery intervention. Again, during the weekend the battery is able to recharge fully.

Figure 34: Autumn Season typical supply/demand scenario (October).

In autumn, the building demand is still high but also deeply fluctuating. This might happen because

the air conditioning load is still turned on intermittently.

5.2 Discussion

5.2.1 Peak load reduction target

The simulation graphs results, which there are 48 of them, correspond to one representative week

of each month and show the 4 different cases mentioned: refurbished battery, directly reused,

76kW baseline scenario and 60kW reduced power scenario.

Thanks to the battery installation, a peak demand reduction can be observed (see Appendix) in

some extreme cases, where the ‘base net load’ is mitigated to the final ‘net energy exchange’. As it

can be expected, ‘power storage’ in undertakes a negative peak meaning it is delivering energy to

the system. In this example can be clearly observed the value and effectiveness of a storage system

in terms of energy and power reduction when a peak consumption episode occurs.

45

The following excerpt from a graph observes the peak reduction event:

Figure 35: Peak reduction. Directly reused battery (0.053 amortisation).Baseline case 76kW August.

The results of the optimization, yields that for load peak shaving purposes, the stress of the battery

is usually important both in terms of depth of discharge and peak power exchanged. For instance,

note the instantaneous peak power developed by the battery highlighted in the figure 35 above

(around 32 kW) gives a SoC variation of 26% calculated from the SoC before the peak reduction.

This important instantaneous power exchange by the battery affects its lifespan. Thus, increasing

the battery ratings is identified as an option for extending battery lifespan in other scenarios.

Further, evaluating other peaks and as observable in table 19, no difference is found regarding

seasonality. In most cases reported in this table the instantaneous peaks developed by the battery

are between 28-30 kW. Considering the ratings of the battery, 30 kWh, the battery would work

near the nominal current for peak reduction purposes. In energy terms the SoC does not overcome

such a drastic reduction as in power terms.

See the Appendix for the source graphs corresponding to table 19.

Table 19: Peak reduction cases

Instantaneous peak power

developed by the battery

SoC variation

(%)

August 76kW- 0.053 32 kW 26%

December 60 kW 0.053 28 kW 23%

August 76 kW 0.0905 32 kW 26%

September 76 kW 0.0905 12 kW 10%

September 60 kW 0.0905 32 kW 26%

46

Figure 36: Peak reduction. Directly reused battery (0.053 amortisation).Baseline case 76kW September

Another insight about peak load reduction is addressing the output. As noted in Figure 36, the PV

generation is not coincident with the maximum peak of demand usually at the beginning of the

day. Thus, the battery storage helps reducing that peak, which could not be addressed efficiently in

any other way.

Even when the rate of local renewable generation in the building is increased, the existing challenge

with sharp load peaks would remain unresolved. In such case and for power reduction purposes

the battery becomes the main solution.

In addition, the battery appears to be stressed in some cases, almost reaching the 100% SoC and

also 0%. These extremes in the SoC and huge DoD affect the battery aging and degradation

process. Then, trying to use a bigger battery would involve a higher investment cost but it would

be worth it in the long term if the capacity fades slower than the smaller battery.

To generalise on the feasibility of extending battery capacity, extensive further research is needed.

There is a trade-off between technical performance and initial investment to be formally explored.

5.2.2 Savings estimation

Alongside the graphs of the battery use, demand load, etc. the simulation also provides information

about the energy savings, which already take into account the amortisation cost of having the

storage system, referred as flexibility cost. All the calculations for energy savings and all the data

from the simulation are again presented in the ”Energy-related costs” section of the Appendix.

Similar to the treatment of energy results, the cost estimation can be extrapolated from one week

to the whole month and thus calculate the total amount for one year, summing up the 12 months.

As an example, baseline case 76kW directly reused (0.053 amortisation) is presented in table 20.

Furthermore, it should be noted that the total weekly costs are equal to the total weekly electricity

costs plus the total weekly flexibility costs (A=B+C).

47

Table 20: Costs breakdown assessment strategy

Electricity consumption costs Baseline case 76 kW

Weekly cost

(A=B+C)

Weekly electricity cost

(B)

Weekly flexibility cost

(C)

Weekly cost base case

(D)

Monthly cost base case (E=Dx4)

Monthly total cost (F=Ax4)

January 183.2101783 182.4011301 0.809048244 185.4762937 741.9051748 732.8407132

February 148.4487534 147.0175018 1.431251634 152.0041695 608.016678 593.7950136

March 86.23954438 81.93143246 4.308111917 92.59085007 370.3634003 344.9581775

April 88.25154156 87.03161529 1.219926268 94.03290726 376.131629 353.0061662

May 73.74772933 69.44135253 4.306376798 81.03187522 324.1275009 294.9909173

June 275.1925852 273.7881696 1.404415599 278.780733 1115.122932 1100.770341

July 522.8372469 519.4592611 3.377985826 525.551889 2102.207556 2091.348988

August 443.8470397 437.9791134 5.867926271 445.4141185 1781.656474 1775.388159

September 420.7555532 418.311617 2.443936184 424.8965521 1699.586208 1683.022213

October 280.9540873 278.6547641 2.299323251 284.0274775 1136.10991 1123.816349

November 150.8715553 147.5662055 3.305349765 155.207416 620.829664 603.4862212

December 253.0762903 252.3850698 0.691220561 254.0222983 1016.089193 1012.305161

Total annual costs 11892.14632 11709.72842

Total annual savings 182.4179014

Percentage savings 1.53%

The summary of cost breakdown into months can be found in the Appendix. These savings are

presented in table 21 for the whole year, for both battery types and for each power limit:

Table 21: Annual energy costs and savings.

Directly reused

Costs Savings %Savings

76 kW power limit 11709.73 € 182.42 € 1.53%

60kW power limit 11718.60 € 173.54 € 1.46%

Refurbished

Cost Savings %Savings

76 kW power limit 11783.69 € 108.45€ 0.91%

60kW power limit 11798.36 € 93.78 € 0.79%

48

Percentage savings are based on the baseline cost, which is the cost associated to the energy needed

when only taking into account the demand and the PV generated (without any battery present in

the system).

Moreover, the savings for reducing the power term are calculated. Observing the simulation results

graphs, particularly the base net load, which draws the profile of the building inflexible demand

minus the instantaneous PV generation (cannot be stored and used when needed without a battery),

the power contracted before the battery installation can be considered 80kW.

Given the most demanding months are usually July and August for refrigeration reasons, these will

be tackled to check the power needs. In these months, the energy frequently surpasses 15 kWh

almost reaching the 20kWh each quarter of an hour according to simulation time resolution, which

corresponds to a power of 80 kW hourly. As Som Energia states in the power contract, the invoice

can be variable every month following the conditions presented in table 22:

Table 22: Power term invoice conditions. Souce: Som Energia, Nov. 2020.

For simplification purposes, neither the lower nor upper cases will be considered. The lower case,

less than 85%, if it is not taken into consideration in either case (80kW, 76kW, 60kW) it will not

be reflected in the final comparison. Same with the upper case, since there are no significant power

surpluses and only punctual episodes, which could even be considered meter reading errors, this

case is neglected too. And finally, the average power case, will contemplate the power contracted,

and not the power used, since the simulation does not provide this value and it is not possible to

calculate form the graphs due to its instantaneity. Therefore, the power term savings are calculated

as follows.

76kW power baseline case:

(80 𝑘𝑊 − 76𝑘𝑊) ∗ 24.43€

𝑘𝑊 − 𝑦𝑒𝑎𝑟= 97.72€/year

60kW power reduction case:

(80 𝑘𝑊 − 60𝑘𝑊) ∗ 24.43€

𝑘𝑊 − 𝑦𝑒𝑎𝑟= 488.6€/year

Power used Power invoiced

< 85% of the contracted power 85% of the contracted power will be charged

Between 85% and 105% of the contracted power The power used will be charged

>105% of the contracted power

The power used + a penalisation (double of the

difference between the registered value and the

value corresponding to 105% of the power

contracted) will be charged

49

Table 23: Power savings.

The percentage savings are calculated with respect to the base case of 80kW, not with respect to

the baseline case, which is already part of the study case.

As it can be observed from the results presented, the larger savings in absolute number are in the

energy bill, although they represent a very small percentage. On the contrary, the power savings

are a small amount compared to energy ones, but significantly higher in percentage, specially the

60kW case.

Costs [€/year] Savings

80kW without battery 1954.40 -

76kW baseline case 1856.68 5%

60kW reduction case 1465.80 25%

50

6 CONCLUSIONS

As to conclude with the present study and answering to the main research question, it is feasible

to install a second-life battery from EVs addressing the specifications of the evaluated case study

of a typical public building of medium size, such as a university office building. The integration of

the battery in the building electricity system permits a substantial reduction of the power contracted

from the grid and optimizes the load interaction with the grid. The energy bill has been reduced,

both the electricity costs and peak power costs are decreased. Particularly the peak load shaving

possibility has been highlighted, which corresponds to the 25% reduction target.

Besides the technical objectives of this study, the battery supply chain challenge had been discussed

and tackled. As expected, way more effort needs to be put into battery recycling, as it is still not a

feasible solution nowadays. Also, progress must be made in storage management and control tools

in order to get more information of the battery status in real time, as well as in the definition of

business models that consider the provision of more than one facility for the battery. A first and

important step in developing a circular economy around energy storage is thus, to give EV batteries

a second life for stationary services.

Results reveal very little relative savings in terms of electricity bill, in the order of 1% - 2% for the

particular case study. However, with the growing scale of energy storage applications, this relatively

inconspicuous benefit could expand to more sizeable levels in absolute numbers. On the other

hand, the reduction of contracted peak power for the building due to the battery peak mitigation

is considerably higher in percentage, up to 25%, but lower in absolute terms.

Furthermore, the battery amortisation costs are not significant in the computation yet meaningful

for the evaluation case. These results confirm how a directly reused battery has of course lower

amortisation costs because it has not been treated nor refurbished. Also, the poorer capacity of the

directly reused battery, compared to the refurbished one, does not affect given the energy demand

values in this project are not very high. Still, further research should be done in order to determine

if this would have a considerable effect in a larger scale.

As the main weak point is that the energy produced by the PV installed is almost immediately used

with very little battery intervention, an additional work on the topic could study the impact of

having larger renewable assets achieving higher renewable availability. With a large surplus of local

renewable energy production, other services could be provided apart from self-consumption. The

battery could provide congestion management services as well as voltage regulation, not only to

the prosumer, but also to third parties interested.

The outcomes of the project demonstrate that second-life battery application for stationary energy

storage is a feasible technology, but it is still necessary to develop tools for estimating the state of

health of batteries and monitoring their performance in the new environment, as the battery might

already be underperforming once rejected from the initial application. However, with the increasing

interest and necessity of circular economy, the high volume of EV batteries to be replaced as

unsuitable for automotive purposes anymore, the expanding deployment of renewable assets and

the insufficient end-of-life material recycling capacity, there will be a boost of research in this area,

which would produce a huge positive impulse for this technology.

51

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54

APPENDIX

Directly reused battery (0.053 amortisation)

Baseline power case: 76kW

January:

February:

March:

55

April:

May:

June:

56

July:

August:

September:

57

October:

November:

December:

58

Power reduction case 60 kW

January:

February:

March:

59

April:

May:

June:

60

July:

August:

September:

61

October:

November:

December:

62

Refurbished battery (0.0905 amortisation)

Baseline power case 76 kW

January:

February:

March:

63

April:

May:

June:

64

July:

August:

September:

65

October:

November:

December:

66

Power reduction case 60kW

January:

February:

March:

67

April:

May:

June:

68

July:

August:

September:

69

October:

November:

December:

70

Energy-related costs:

Please note, as stated in section 5.2.2., the costs have been extrapolated to a whole month from one week,

and then summed up to a whole year to have the final annual costs. Also notice that, the total weekly costs

are equal to the total weekly electricity costs plus the total weekly flexibility costs (A=B+C).

Directly reused:

Electricity consumption costs Baseline case 76 kW

Weekly cost

(A=B+C)

Weekly electricity cost

(B)

Weekly flexibility cost

(C)

Weekly cost base case

(D)

Monthly cost base case (E=Dx4)

Monthly total cost (F=Ax4)

January 183.2101783 182.4011301 0.809048244 185.4762937 741.9051748 732.8407132

February 148.4487534 147.0175018 1.431251634 152.0041695 608.016678 593.7950136

March 86.23954438 81.93143246 4.308111917 92.59085007 370.3634003 344.9581775

April 88.25154156 87.03161529 1.219926268 94.03290726 376.131629 353.0061662

May 73.74772933 69.44135253 4.306376798 81.03187522 324.1275009 294.9909173

June 275.1925852 273.7881696 1.404415599 278.780733 1115.122932 1100.770341

July 522.8372469 519.4592611 3.377985826 525.551889 2102.207556 2091.348988

August 443.8470397 437.9791134 5.867926271 445.4141185 1781.656474 1775.388159

September 420.7555532 418.311617 2.443936184 424.8965521 1699.586208 1683.022213

October 280.9540873 278.6547641 2.299323251 284.0274775 1136.10991 1123.816349

November 150.8715553 147.5662055 3.305349765 155.207416 620.829664 603.4862212

December 253.0762903 252.3850698 0.691220561 254.0222983 1016.089193 1012.305161

Total annual costs 11892.14632 11709.72842

Total annual savings 182.4179014

Percentage savings 1.53%

Electricity consumption costs Power reduction caseat 60 kW

Weekly cost

(A=B+C)

Weekly electricity cost

(B)

Weekly flexibility cost

(C)

Weekly cost base case

(D)

Monthly cost base case (E=Dx4)

Monthly total cost (F=Ax4)

January 183.2101783 182.4011301 0.809048247 185.4762937 741.9051748 732.8407132

February 148.4487534 147.0175018 1.431251633 152.0041695 608.016678 593.7950136

March 86.23954438 81.93143246 4.30811192 92.59085007 370.3634003 344.9581775

April 88.25153745 87.03161339 1.219924064 94.03290726 376.131629 353.0061498

May 73.74772933 69.44135252 4.306376802 81.03187522 324.1275009 294.9909173

June 275.1925852 273.7881696 1.404415601 278.780733 1115.122932 1100.770341

July 522.8372469 519.4592611 3.377985826 525.551889 2102.207556 2091.348988

August 443.8470397 437.9791134 5.867926271 445.4141185 1781.656474 1775.388159

September 422.963096 418.4896692 4.473426826 424.8965521 1699.586208 1691.852384

October 280.9540909 278.6547679 2.299322977 284.0274775 1136.10991 1123.816364

November 150.8827574 147.5761503 3.306607085 155.207416 620.829664 603.5310296

December 253.0762904 252.3850698 0.691220565 254.0222983 1016.089193 1012.305162

Total anual costs 11892.14632 11718.6034

Total anual savings 173.5429232

Percentage savings 1.46%

71

Refurbished:

Electricity consumption costs Baseline case 76 kW

Weekly cost

(A=B+C)

Weekly electricity cost

(B)

Weekly flexibility cost

(C)

Weekly cost base case

(D)

Monthly cost base case (E=Dx4)

Monthly total cost (F=Ax4)

January 183.6664538 182.7451678 0.921286002 185.4762937 741.9051748 734.6658152

February 149.349966 147.4370613 1.912904683 152.0041695 608.016678 597.399864

March 88.3551685 84.97312146 3.382047041 92.59085007 370.3634003 353.420674

April 89.72504921 88.55318668 1.171862521 94.03290726 376.131629 358.9001968

May 75.88916474 72.18382999 3.705334752 81.03187522 324.1275009 303.556659

June 276.0341635 274.412098 1.622065498 278.780733 1115.122932 1104.136654

July 525.1131169 520.9249733 4.188143552 525.551889 2102.207556 2100.452468

August 447.3130588 440.001245 7.311813765 445.4141185 1781.656474 1789.252235

September 422.6889896 420.257613 2.431376584 424.8965521 1699.586208 1690.755958

October 282.0132467 280.3347591 1.678487599 284.0274775 1136.10991 1128.052987

November 152.4007679 149.9873542 2.413413745 155.207416 620.829664 609.6030716

December 253.3743654 252.9253214 0.449044032 254.0222983 1016.089193 1013.497462

Total annual costs 11892.14632 11783.69404

Total annual savings 108.4522764

Percentage savings 0.91%

Electricity consumption costs Power reduction caseat 60 kW

Weekly cost

(A=B+C)

Weekly electricity cost

(B)

Weekly flexibility cost

(C)

Weekly cost base case

(D)

Monthly cost base case (E=Dx4)

Monthly total cost (F=Ax4)

January 183.6664538 182.7451678 0.921286019 185.4762937 741.9051748 734.6658152

February 149.349966 147.4370613 1.912904686 152.0041695 608.016678 597.399864

March 88.3551685 84.97312146 3.382047041 92.59085007 370.3634003 353.420674

April 89.7250492 88.55318585 1.171863356 94.03290726 376.131629 358.9001968

May 75.88916474 72.18382998 3.705334759 81.03187522 324.1275009 303.556659

June 276.0341635 274.412098 1.622065501 278.780733 1115.122932 1104.136654

July 525.1131169 520.9249733 4.188143552 525.551889 2102.207556 2100.452468

August 447.3130588 440.001245 7.311813765 445.4141185 1781.656474 1789.252235

September 426.3434182 420.4074464 5.935971855 424.8965521 1699.586208 1705.373673

October 282.0132467 280.3347591 1.678487598 284.0274775 1136.10991 1128.052987

November 152.4133475 149.995777 2.417570552 155.207416 620.829664 609.65339

December 253.3743653 252.9253214 0.449043934 254.0222983 1016.089193 1013.497461

Total annual costs 11892.14632 11798.36208

Total annual savings 93.78424404

Percentage savings 0.79%

72

Power-related costs:

Power term costs and savings

Power price considered: 24.43euros/kW-year Costs Savings

80kW without battery 1954.40 €/year -

76kW baseline case 1856.68 €/year 5%

60kW reduction case 1465.80 €/year 25%

Total costs:

Directly reused Refurbished

76 kW power limit 13,566.41 € 13,640.37 €

60kW power limit 13,184.40 € 13,264.16 €