EERA EUROPEAN ENERGY RESEARCH ALLIANCE … 1 EERA EUROPEAN ENERGY RESEARCH ALLIANCE SUB-PROGRAMME 4:...

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EERA

EUROPEAN ENERGY RESEARCH ALLIANCE

SUB-PROGRAMME 4:

Urban City-related Supply Technologies

A sub-programme within the:

Joint Programme on Smart Cities

Description of Work

Version: draft, pending of approval by SC

Last modification date: 09.01.2015


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Summary Research Activity “Urban city-related supply technologies”

The overall aim of SP4 is to create an integrated analytic framework that identifies

tailored pathways to smart, sustainable cities from the perspective of energy supply technologies

and associated sub-systems.

SP4 framework is devoted to work out an improved short term performance of our urban

energy supply infrastructure via (a) enhanced control of existing supply sub-systems and (b)

optimised operation of appropriate new sub-systems in the new build or renovation real-world

contexts. More specifically, it is a means to achieve medium and long term forecasting of

possible scenario pathways to sustainable cities based on clear taxonomies, KPIs and

benchmarks. This does suggest modelling from sub-system to district scale, considering detail

level and careful selection of appropriate modelling approaches (empirical, stochastic,

probabilistic, deterministic…) is required, along with measured data integration.

The main objectives of this sub-programme are:

• Within the context of the ‘energy performance gap’, evaluate the fitness-for purpose

of current sub-system models, and where appropriate develop improved

approaches. Scaling up modelling techniques will be a key outcome of the

subprogramme.

• Given the needs of key end users, create an integrated adaptive ‘whole system’

approach that is capable of incorporating holistic factors (both technical and non-

technical) related to supply sub-systems, and is interoperable with approaches taken

in other SPs/JPs.

• Develop the ‘state-of-the-art’ in terms of system performance measurement, testing,

QA/risk management, benchmarking and control within the context of existing and

emerging EU standards.

• Test and validate the new framework by application to large-scale case studies in

conjunction with other SPs/JPs.

SP4 is structured in 6 working packages, namely:

• WP 1: Framework for development of multi-purpose component oriented models

• WP 2: Development of component oriented model libraries

• WP 3: System Integration

• WP 4: City-industry interaction

• WP 5: Technology Assessment

• WP 6: Scientific methods for quality assessment for urban related energy supply

technologies

At present, in the SP4 there are 29 participants from 12 different European countries. In

terms of human resources, SP4 will gather a minimum R&D effort summing up to 34.5 person

years/ year. Moreover, each participant will use its own infrastructures and facilities to

accomplish the proposed R&D activities and hence to meet the expected goals and results.


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1. Background

One of the principal ideas behind Smart Cities is the smart integration of a whole spectrum of

various technologies into an urban environment applying an integrated approach. In the field of

energy supply technologies research has to deal with the smart integration of on-site renewable

energy sources into buildings and networks, the cascade use of resources or polygeneration, the

development and integration of smart energy networks. Scientific tools for the optimal use of

hybrid supply systems will play a crucial role in this research field combined with large-scale

experimental testing and the development of new procedures and standards.

Urban city-related energy supply technologies like for instance heat pumps, solar thermal,

PV, energy storages, etc. are key elements in creating future smart cities. Currently these

technologies are primarily developed and optimised for “single operation”. That is supplying

heat, power and/or cold to single and multi-family houses or industrial/commercial buildings.

Not yet taken into account, are the challenges arising on system and component level, when

introducing these technologies on a large scale on district or city level e.g. an overload of the

electricity grid caused by a broad introduction of electrical heat pumps.

Both, new components and systems are needed, as well as a better understanding of how to

integrate distributed supply technologies in an efficient, effective and cost-effective manner on

district and city level. These integrative aspects are not covered yet by the respective European

Technology Platforms e.g. the Renewable Heating and Cooling (RHC) Platform and the PPP

EeB as they primarily focus on the component and system research needs of single operated

energy supply technology systems.

Therefore a methodology capable to deal with complex integration issues, encompassing both

thermal and electrical energy technologies, not only from a control point of view, but especially

from a design point of view needs to be developed. The development of such a tool involves as

well the development of a simulation framework, which is a crosscutting content within the

scope of this joint-programme. This framework shall be used to design complete sets of

distributed supply technologies.

2. General description of state-of-the-art

Around 100 projects (national and European) have been compiled among 12 different

institutions participating in SP4 from 10 different countries (Austria, Denmark, Sweden, Spain,

Norway, Finland, France, Germany, Poland and Italy). All these projects are related to SP4

contents and have been classified in accordance to its relevance to the different working

packages of the subprogramme, and most of them are running. Topics covered range from single

WP dedicated projects such as the development of a simplified heat pump model for high

temperature application, up to highly integrative projects assisting in the development of a

strong and sustainable market of large solar heating and cooling systems by focusing on cost

effectiveness, high performance and reliability of systems for large scale solar heating and

cooling systems. However, these projects are scarce and still do not cover the whole chain of

WP contents proposed in SP4.

Relevant for SP4 is the information and availability on energy components models and

energy systems models. A first wave of gathering all these background information has been

attempted at coarse level and a stock of existing models has been set. Compiled info has been

classified according to:

Component models: involved sector (i.e. heating, cooling, electricity, water, transport),

purpose of the model (design, forecasting, control, planning), temporal resolution, level

of development, identified gaps, availability for other EERA members use, software.


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Energy system models: involved sector, components included (i.e. solar thermal

systems, heat pumps, PV…), purpose of the model (forecasting, control, planning),

temporal resolution, spatial resolution and granularity (one node, multiple node, building

level, district level, city level), level of development, identified gaps, availability for

other EERA members use, software.

Current component models cover solar thermal systems and collectors, heat pumps, thermal

energy storage, CHP-plants, electrical energy storage, photovoltaics, wind turbines,

hydroelectricity, geothermal, waste to energy (incineration, combustion processes, pyrolisis,

gasification, hydrogen production...), wastewater treatment (sewage sludge treatment to produce

biogas, anaerobic digestion), fuel cells, refrigeration (industrial/HVAC), polygeneration

(including cold production, or even water), stochastic user models for building occupancy and

drive patterns. Part of these component models are the basis for current energy system models

such as SEEME (Socio-economic energy modelling environment) or PBEM (Probabilistic

Building Energy Model) including the heating, cooling, electricity and transport sectors, and

components such as solar thermal systems, heat pumps, thermal storage, CHP-plants, electrical

storage, PV and wind turbines.

3. Research objectives

The main objectives of this sub-programme are:

• Within the context of the ‘energy performance gap’, evaluate the fitness-for purpose of

current sub-system models, and where appropriate develop improved approaches.

• Given the needs of key end users, create an integrated adaptive ‘whole system’ approach that

is capable of incorporating holistic factors (both technical and non-technical) related to

supply sub-systems, and is interoperable with approaches taken in other SPs/JPs.

• Develop the ‘state-of-the-art’ in terms of system performance measurement, testing, QA/risk

management, benchmarking and control within the context of existing and emerging EU

standards.

• Test and validate the new framework by application to large-scale case studies in conjunction

with other SPs/JPs.

Specifically, the aims of each work package are as follows:

The main objective of WP1 is to establish methodologies to build frameworks for white as

well as grey box modelling of components in a Smart City context. Especially with focus on

model identification (simplification) and model validation based on monitored data:

• Development of methodologies for white and grey box modelling and mapping of existing

methodologies for applications in a smart city context.

• In a first stage, the focus will be on large-scale integrated solar energy (electricity and heat),

distributed heat pumps, poly-generation like (micro-)CHP including cold, thermal energy

storage on different scales and the integration of large fractions of wind and solar power.

• Development of methodologies for optimising models for different contexts: simulation,

forecasting and control.

• Development of model validation methods based on data as a part of the framework.

• Development of tools for optimisation and selection of suitable complexity of models based

on data.

• Development of methods for estimating uncertainty in forecasting

• Setting up of a modelling framework to be used in the SP in collaboration with the other WPs

and defining unified approaches for setting up and validating white and grey box models.

• Demonstration of how the proposed underlying modelling methods and tools can be used in a

wide range of applications.


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The main objective of WP2 is to set up at least two component libraries, one containing

white-box-models and the other one grey-box-models of the relevant components in a Smart

City’s context:

• In a first stage, the focus will be on large-scale integrated solar energy (electricity and heat),

distributed heat pumps, poly-generation like (micro-)CHP including cold, thermal energy

storage on different scales and the integration of large fractions of wind and solar power /

fluctuating renewable energy sources.

• The Input/output interfaces and parameter requirements of the utilised models should be in-

line with all simulation platforms used in the Joint Programme and in particular with the

crosscutting simulation framework according to the results of the simulation Task Force.

• The component-oriented models will be validated as far as possible, also with methodologies

developed in WP6 (e.g. lab testing, monitoring ...).

• For large-scale implementation, the models must be robust and reliable; therefore, studies on

the balance between complexity and robustness are included in this work package.

• For forecasting and control purposes, the models must be able to take advantage of online

monitoring data and other information on the boundaries of the considered system.

The aim of WP3 is to develop an integrated, flexible and adaptive multi-level decision support

framework for scenarios ranging from whole city/district energy generation/consumption to

single building, in order to reach as much as possible Nearly Zero Energy Cities (NZEC). In

terms of energy, the fulfilment of this objective requires large-scale utilization of renewable

energies as well as appropriate and innovative energy system integration. While in SP1 decision-

support tools are being developed from the point of view of a whole city to support long term

changes, in this working package are built on the basis of real time data from operation of supply

technologies.

• Define guidelines and procedures for the design of smart energy networks, i.e. smart

integration of heat-cold-electricity networks interacting with the electricity and energy

markets as well as with the national energy systems.

• Develop systematic methodologies for multi-criteria synthesis, design and operation of

multiproduct energy supply system (polygeneration), considering the importance of energy

storage of different nature –thermal and chemical.

• Produce standards-based framework for data integration, including standardised building

information models (BIMs) compliant with relevant WP outputs

• Develop a specification for an integrated cloud-based data repository, management and

modelling environment

• Evaluate the suitability of the framework against a number of case studies, in order to deliver

future optimised, risk analysed and cost optimised energy supply scenarios

Work Package 4 will look at following research topics:

• The use of waste heat from industrial processes as energy source for the energy infrastructure

of cities.

• District energy transfer systems (heating and cooling) for city-industry interaction.

• Integration of RES into industrial processes and the infrastructure of the cities.

• Modelling of industrial processes as part of the entire energy infrastructure.

• Energy storage concepts for industrial sites and their integration to the infrastructure of the

cities.

• Necessary regulations, standards and business models.

The aim of work package 5 is to assess new technologies and their integration into the

infrastructure:

• The identification and evaluation of sustainable materials/components/systems is performed

to gain insight and create new arguments for system solutions facing non-technical and

technical barriers.


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• Medium to large-scale energy supply technologies are the main focus areas, i.e. from city

blocks to entire parts of a town.

The idea behind WP6 is to identify what new quality assessment methods like test and

evaluation procedures, pre-normative standards and guidelines are needed to enable a

standardized assessment of urban-related energy supply technologies ranging from whole-

district to single building. Moreover, the developed performance assessment methods (e.g. lab

scale tests of components, monitoring data, short-term in-situ measurements…) can be used to

validate the simulation framework.

• The definition of generalized performance figures and key indicators appropriate to evaluate

entire systems (either on a household level, district level or city level) according to their

energy performance and to their environmental impact.

• The development of a test and evaluation procedure for the generalized performance figures

and key indicators applicable to entire energy systems.

4. Description of foreseen activities

The foreseen research activities are grouped into six work packages (see figure 1). The work

packages are strongly interconnected, and the interaction with the Taskforce is crucial.

Figure 1: Work Package structure

As shown in the figure, the contribution of this sub-programme to the development of a

simulation platform is mandatory. The component models developed in SP4 are specialized in

energy supply technologies. SP1 is focusing on a holistic view of a smart city where energy

supply technologies are a part of it. Therefore, it is obvious that SP4 can deliver appropriate

dynamic models on different levels of detail to determine the interaction effects of large-scale

implementation of renewable energy supply technologies in an overall smart city context.

Moreover, experimental validation of the component models from SP4 makes them useful for

reliable and accurate simulations. The component models can be validated in the available

experimental facilities. The effect of the integration effects can be partly validated with data

from field experiments already running. For large-scale integration and interaction effects, new

methods, facilities and standards need to be defined and developed.

Work packages distribution

The sub-programme is organized into six Work Packages that are further structured in Tasks as

described following.

WP 1: Framework for development of multi-purpose component oriented models

Component oriented models

WP1

Taskforce Simulation-Platform

Technology assessmentWP5

Integration

WP3

WP4

Quality assessmentWP6

Theo

ryPr

acti

ce

Single component Large scale

WP2


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WP leader: DTU Global partners: DTU, AIT, CRES, IHE WUT, I3A-UNIZAR Contact list: DTU: Henrik Madsen ([email protected]), Peder Bacher ([email protected])

AIT:Michael Hartl ([email protected])

CRES:Dimitrios Mendrinos ([email protected])

IHE-WUT:Dorota Chwieduk ([email protected])

I3A-UNIZAR: Pablo Dolado ([email protected])

LBORO:Paul Rowley ([email protected])

UPVLC: Carla Montagud ([email protected])

One of the aims of this WP is to establish frameworks for setting up component models

relevant in Smart Cities in order to create methodologies for design, operation, and control of

complex integrated urban-related supply and consumption systems like for energy or water.

Figure 2, taken from a Smart Cities research project in Denmark, illustrates a possible resulting

framework.

Figure 2: Scheme of a possible modelling framework for Smart Cities

The models within these frameworks have to suit several purposes, namely simulation,

forecasting and control. Within this context, different modelling approaches are seen as being

useful for the different purposes, reaching from deterministic physical models (white box

models) over stochastic models to an advanced stochastic modelling approach, combining prior

physical knowledge with data-driven statistical techniques (grey box modelling).

An important and omnipresent requirement for all models is the consideration and integration

of interdependencies between different technologies or sub-systems, to be able to cover systemic

effects. This requirement has to be kept in mind for the frameworks defined in this WP as well

as for the component models in WP2. Furthermore, the system is characterised by being

dynamic and at least in large parts stochastic.

All modelling tasks occur in different temporal and spatial dimensions, ranging from seconds

to decades and from household to city level. Depending on the dimension, the models have to

vary in complexity. Hence, methods that lead to robust, reliable and yet fast models have to be

developed where necessary. Furthermore, it is important to be able to validate the models with

measured data. For complex interdependent relations, where only data at practically possible

locations can be measured, the framework will provide modelling methodologies able to

represent the complexity of the underlying processes while enabling the use of statistical

methods to validate the models and to optimise the model complexity to measured data.

For forecasting and control applications, especially close to real time, it is crucial that the

models are able to use advanced methods for signal processing and statistical learning in real

time to take advantage of (online) monitored and other relevant data. Simplified stochastic grey

box models are known to fulfil these needs very well. Furthermore, their methodology is able to

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tailor the forecasts with a reliable estimation of the uncertainty, which again is crucial for

assessments of risks and for optimal decision-making. Consequently, the corresponding

component models, developed in WP2, have to be simplified and operational models of the

system due to the fact, that they must be able to provide real time, and complete solutions in a

smart city context.

Additionally the methodologies for model-based control, which to a large extend already

exist and have been used for optimised operational applications in many fields, have to be

adapted to match the needs for smart city applications.

Another task, the analysis of longer time periods up to decades, for example to identify a

possible or optimal future energy system or to answer the question of possible transition

processes of the whole energy system from one state to another, is a typical domain of white box

models.

Reliable descriptions of human influences and interactions are obviously important for smart

city applications. Models to predict user behaviour, like patterns in demand or flexibility of the

users, e.g. comfort levels or the use of transport infrastructure, are typically established using

stochastic models and statistical methodologies while IT based solutions are linking the models

with smart and intelligent applications.

Task 1.1 Definition and modelling of demand patterns.

Task 1.2 Methodologies related to formulating models on various temporal and spatial scales

and resolutions (FISE, CIEMAT, UPVLC, LBORO)

Task 1.3 Methodologies for setting up models for simulation in a Smart City’s context. (FISE,

UPVLC, LBORO)

Task 1.4 Methodologies for setting up models for forecasting and control in a Smart City’s

context

Task 1.5 Methodologies for model based control in a Smart City’s context

Task 1.6 Methodologies for setting up component oriented grey box modelling frameworks

(E3D-RWTH Aachen, UPVLC)

Task 1.7 Advanced poly-oriented models and integration aspects (FISE)

In Task 1.1 models for demand and user patterns for different levels in cities:

individual/household level, district level, city level will be developed. Learning from data, the

models will include stochastic parts, which are very useful for simulation, forecasting and

control, e.g. for load-demands.

In Task 1.2 the aspects of modelling on different levels, from household to district and up to

city level, for many applications will be carefully investigated. Methodologies that lead to the

most suitable type of model for each application, depending on its dynamical relations, the task,

and the temporal and spatial resolution, have to be identified. Corresponding statistical

evaluation techniques will be a part of the proposed modelling frameworks.

In Task 1.3 methodologies for setting up models for simulation in a Smart City’s context will

be developed. Simulation of complex systems cannot be carried out in all details and

furthermore the dependency structures are almost never fully known. Hence, simplified models

are necessary and it is very useful to be able to build and validate models based on measured

data with proper statistical techniques. It is furthermore important to be able to model time

dependent and stochastic properties of systems. The degradation of components over time and

user behaviour models might serve as examples here.

In Task 1.4 is focused on models for forecasting and control. Realising an efficient and

optimised integration of renewable energy production in the context of cities requires the use of

IT solutions, embedded or linked to a large number of smart and intelligent applications. This

relies heavily on having appropriate models for forecasting and control of many types of systems

and components. Therefore, guidelines for setting up these models will be developed in this task.

Optimal operation depends highly on forecast uncertainties and an important part of the task is to

provide methods for estimation of uncertainty and to include this into the forecasting tools.

In Task 1.5 the aspects of model based control will dealt with. Controls, either on the

household, district or city level, require a fair amount of knowledge about the system that is


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subject to control. Tasks regarding system controls are accomplished on the energy management

level in the first place, where set point and schedule settings for the subordinated local process

controllers play the predominant role. Nevertheless, these settings are mostly composed on a

heuristic basis, rarely considering the full scope of the controlled systems and therefore

neglecting their important aspects. This, in turn, leads to a rather poor energy-efficient control

operation. An elegant as well as a cost-effective way to circumvent this kind of issues is to

support the controls based upon a model of the controlled system. Model predictive control

(MPC) is a well-known technique for optimised operation, including the aspects of energy-

efficiency, where objectives such as energy optimality, power optimality, time optimality, etc.

will play an eminent role. MPC provides the means to act effectively on forecasts of demand and

climate dependent power production, by incorporating information from many sources and

modelled uncertainties. Targets of this task are, firstly, a dynamic set-point generation on the

energy management level, incorporating a model of the envisaged level (household, district,

city) and, secondly, measures to enable load shifting, matching energy production and demand,

and optimise the use of distribution systems.

In Task 1.6 the methodologies for setting up component oriented grey box models will be

developed. A set of guidelines for setting up grey box models will be developed. The guidelines

coupled with a set of tools will be the backbone of the proposed framework and will be

applicable in a wide context. They will be exemplified with studies in which a wide range of

components are modelled with grey box models based on data, such as the heat dynamics of

buildings, solar collectors, heat exchangers and sewer water flows. One focus in this task will be

on how to set up models for the use of online data, especially the model selection and robustness

of the modelling methods are issues to be dealt with.

In Task 1.7 advanced poly-oriented models and integration aspects will be studied. Modelling

of complex dynamic interdependent processes in cities, for example between electrical and

thermal systems, over to transport and water pumping systems, requires new modelling

methodologies in order to achieve a holistic approach to optimisation. It will be important to

consider models which are able to deal with very long time horizons on the one hand and which

adapt to real time measurements, recorded at practically possible locations, but still being able to

represent the complexity of the underlying processes, on the other hand. Intelligent choices of

the best modelling approach, reaching from white to grey box models, will have to be taken to

tackle this challenge. The resulting approaches will be applied to demonstrate their capabilities

and potential.

WP 2: Development of component oriented model libraries

WP leader: AIT& FISE Global partners: CRES

Contact list:


FISE:Jan-Bleicke Eggers ([email protected])


DTU: Henrik Madsen ([email protected]), Peder Bacher ([email protected])

EON-RWTH:Amir Javadi ([email protected])

E3D-RWTH: Mark Alexander Bruentjen ([email protected])



KTH:Hatef Madani ([email protected])


SINTEF: Ingrid Camila Claussen ([email protected])

UMONS:Marc Frere ([email protected])

UPVLC: Carla Montagud ([email protected])

VTT:Jussi Manninen ([email protected])

Different purposes require different modelling approaches, reaching from white to grey box

modelling. While WP 1 defines frameworks for these models, WP 2 complements them by

delivering model libraries of the components relevant in a Smart City’s context. The word




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‘component’ here is used to describe technical sub-systems of the whole energy system. A

component, in this sense, could be a solar thermal system or a heat pump, while design engineers

might refer to components rather as a single heat exchanger or a simple pump.

The purpose of such models is to deliver solutions regarding design and operation of complex

integrated urban-related supply technologies, including forecasting and control. Examples for

tasks to be tackled are the development of model based control frameworks, strategies for an

optimal operation of energy systems in Smart Cities with a large penetration of fluctuating

renewable energy sources, optimal start-up strategies for co-generation plants, or solutions to use

lower temperature levels on the client side, when using heat pumps or solar thermal systems.

This work package will benefit from the knowledge generated under WP 1.

Task 2.1 Mapping of existing component models (AIT, UMONS, FISE, KTH, EON-RWTH, UPVLC,

VTT, I3A-UNIZAR, DTU)

Task 2.2 Models to be developed within the SP (AIT, UMONS, FISE, KTH, DTU, E3D-RWTH,

SINTEF, EON-RWTH, UPVLC, VTT, I3A-UNIZAR, IHE WUT, LBORO)

Task 2.3 Setting up component libraries (AIT, UMONS, FISE, KTH, DTU, SINTEF, EON-RWTH,

UPVLC, VTT, I3A-UNIZAR)

Models for the relevant technologies will be identified and mapped according to their

properties and the requirements in SP 4. Model properties are availability, steady state or

transient formulation, accuracy, validity, including spatial-temporal validity, etc. The necessary

information on the models will be collected through a questionnaire circulated among the JP

members and through literature research.

As a result of Task 2.1 and according to existing knowledge, there is an existing lack of

appropriate models which fulfil all requirements needed for the simulation framework

established within this JP from a component level of view. In general the models have to be

highly dynamic (appropriate for controls), robust and reliable, simple (computational time

matters), validated and open to be coupled with other models, for instance on a co-simulation

platform. The models considered in this WP are generally white box models to be used in the

design and planning process, whereas also grey and stochastic models are considered especially

for demand and climate dependent modelling. Since the relevant topics in a Smart City are

spanning across different spatial and temporal levels, the respective type of model has to be

considered accordingly.

Even though it is obvious that there is an overlapping part with other SPs within the JP Smart

Cities, it is clear that for the purpose of supply technology planning and designing the according

models on the different levels in a Smart City have to cope with different requirements as

needed in the other SPs.

Different component libraries have to be set up according to the type and purpose of the

model with regard to Task 2.2.

WP3: System Integration

WP leader: LBORO & CNR Global partners: CRES

Contact list:


CNR:Vincenzo Antonucci ([email protected])



FISE:Ja- Bleicke Eggers ([email protected])



From the perspective of energy supply sub-systems operating within wider systems-of-

systems, the aim of this work package is to develop an integrated, flexible and adaptive multi-

level decision support framework for scenarios ranging from whole city/district energy

generation/consumption to single building, in order to reach as much as possible Nearly Zero





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Energy Cities (NZEC). In terms of energy, the fulfilment of this objective requires large-scale

utilization of renewable energies as well as appropriate and innovative energy system

integration.

Energy system integration – which presents a great potential of energy saving currently

clearly underutilized, allows the reduction of the consumption of energy and natural resources,

providing:

• Maximum usage of energy and natural resources as a consequence of increasing efficiency of

energy and materials;

• Reduction of unit cost of final products;

• Reduction of environmental burden.

The design and operation of sustainable energy system requires appropriate process

integration based on:

• Holistic approach;

• Modern information techniques, data managing, real demand profiles, etc.;

• Application of thermodynamics

Further energy process integration involves the consideration of different aspects/levels, i.e.:

• Integration of different energy networks of different nature electricity, thermal, chemical;

• Integration of different energy production/consumption technologies including energy

storage;

• Application of different integration methodologies for energy processes: thermodynamic

methods, numerical methods, heuristic methods;

• Utilization of building information system (BIS) and geographic information system (GIS)-

based tools;

• Moreover, the capability to assess complex interactions between social, technical,

environmental and economic factors is also a very important aspect that must be considered.

Developed in close cooperation with relevant parallel SPs and WPs across both JP4 and other

EERA JPs, the integrated framework will be designed to utilise a range of disparate datasets,

from wide spatial area to single building BIM-based data, both modelled and measured.

Furthermore, the integration and interfacing of best-in-class analytic applications within the

integrated environment (such as statistical, stochastic or deterministic modelling applications,

and validated historical empirical datasets) is a priority. With a focus on energy supply sub-

systems, the work will build upon existing tools and standards (such as BREEAM, SAP,

RETSCREEN, OPEN HOUSE, SuPerBuilding, Modelica, etc.) to realise a step-change in the

ability to evaluate urban energy supply scenarios at a level of accuracy and flexibility never

before possible..

Task 3.1 Definition of guidelines and procedures for the design of smart energy networks (I3A-

UNIZAR)

Task 3.2 Development of methodologies for multi-criteria synthesis, design and operation of

polygeneration urban energy supply systems datasets (I3A-UNIZAR)

Task 3.3 Create input data specifications, software requirements specification and system

architecture specification; identify candidate input datasets (DTU, FISE, LBORO)

Task 3.4 Develop functional prototype integrated environment using specific available datasets

Task 3.5 Integrate analytic component modules within integrated environment (KTH, LBORO)

Task 3.6 Real-life Application and testing (DTU, FISE, LBORO)

The overriding need of reducing the environmental impact of urban areas requires the

development of energy supply systems contributing to approach as much as possible the

challenging objective of transforming the urban areas in Nearly Zero Energy Districts/Cities.

This aim requires the development of smart energy networks in which the heating and cooling

networks are designed and operating in combination with smart electric grids and gas/fuels


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networks. In these smart urban energy networks, the integration of renewable energies at a large

scale is facilitated and feasible, with the help and combination of energy conversion devices, e.g.

heat pumps, connecting electric and heat networks. Further large-scale energy storage systems,

e.g. seasonal thermal energy storage, can play a fundamental role in reducing the size of the

energy generation equipment as well as in the integration of networks of different nature. The

valorisation and integration of urban/agriculture wastes, which represent chemical energy

storages, can also be significantly facilitated in these smart energy networks. These urban smart

energy networks can also be in connection with industrial sites, close to the cities, increasing

very significantly the energy efficiency of the whole energy system, e.g. higher utilization of the

industrial waste heat. Further urban smart energy networks can interact with the national electric

grid in a synergistic and stabilizing way, allowing the implementation at national level of large

scale highly dynamic and variable energy production systems, e.g. wind and solar.

Efficient urban energy supply systems are based on highly energy integrated

polygeration systems, in which is achieved the combined production of two or more energy

services and/or manufactured products, seeking to take advantage of the maximum

thermodynamic potential (maximum thermodynamic efficiency) of the consumed resources. The

synthesis and design of these systems encompass techniques based on the thermodynamic and

economic analysis of individual components as well as the system as a whole, oriented to design

and improve production systems, maximizing the efficiency of consumed resources. Their

fundamentals are found in exergy analysis, thermoeconomic analysis, pinch analysis and in the

mathematical optimization techniques applied to process synthesis.

Given the imperative to create an adaptive, interoperable and flexible integration

framework, benchmarking, metrics and data format approaches will be translated from external

sources for subsequent use in appropriate data structures. In parallel, case study energy supply

outcomes (derived from work carried out in other relevant WPs) will be transferred into

appropriate taxonomies and datasets for subsequent software integration. Additional datasets

(thermography, energy use data, Lidar, BIMs, met-data etc.) will be mapped onto case study

taxonomies and catalogued within the software repository.

The tool system architecture and specification will build upon relevant outputs of all

other SP4 WPs, along with those of other SPs of Smart Cities JP. Software architecture will be

defined using best practice approaches; tools such as Unified Modelling Language (UML)-based

descriptors will be evaluated in an object-oriented approach to further ensure effective usability

and interoperability for a broad range of end-users. A prototype umbrella GIS/BIM-based tool

framework will be constructed that interfaces with datasets and tools created via T2.1 and

parallel WPs.

The amount of data and its complexity need innovative forms of presentation to fulfil

stakeholders' needs and to integrate them in the process of transformation development.

Dynamics of supply scenarios and multivariates will be communicated using flexible technology

that is interoperable with modern ICT tools (tablets, web-apps). Building on parallel and

previous work, sample statistical and probabilistic models will be developed in specific

scenarios where significant uncertainty exists, such as in the performance of large-scale micro-

generation across whole urban districts. Limited testing and validation of new tools against

empirical data will then be carried out in conjunction with WP1.

In order to validate the integrated, flexible and adaptive multi-level decision support

framework (including the methodologies and procedures for the multi-criteria synthesis, design

and operation of poygeneration systems and smart energy networks, developed in this WP3) a

specific project of optimised energy systems for high performance-energy districts

encompassing different scenarios ranging from whole city/district energy

generation/consumption to single building, will be proposed. The main objective of this project

will be to reach as much as possible Nearly Zero Energy Cities (NZEC). In terms of energy, the

fulfilment of this objective will require large-scale utilization of renewable energies as well as

appropriate and innovative energy system integration. In other words, the proposed project will

define the transition process towards a low-carbon energy system - from system analysis,

through the vision development and pathway exploration to the development of the enhanced

decision support system developed in this WP3. Testing will be conducted to (a) verify


13

requirements have been attained; (b) provide stakeholders with information about the quality of

the product and satisfies the needs of stakeholders and (c) ensure the environment works as

expected and can be implemented subsequently with the same characteristics. To these ends,

real-life sub-system data outputs from specific existing tools used in case studies (such as SAP,

TAS, BREEAM, LEGEP, Global Mapper, MODELICA?? etc), as well as subsequent POE

measured datasets will be utilised within the EERA environment. The project proposal will be

presented at the appropriate EU calls and its development will be subject to the approval of the

proposed project and availability of the required funding.

WP 4: City-industry interaction

WP leader: VTT Global partners: CRES

Contact list:






EON-RWTH:Amir Javadi ([email protected])




CIRCE: Carlos Arsuaga ([email protected])

In many cities industries are substantial energy consumers and waste heat producers. This

waste heat is not technically or economically feasible to utilise as a heat source in the processes

and is discharged in the environment. The temperature level of waste heat varies depending on

the process, but a large portion of it exists at low temperature, which often makes it suitable to

be used for heating or cooling of residential or commercial buildings via district heating or

cooling network linking the heat source and the city.

It is important to define the role industry can play in the energy supply of the city. The supply

of waste heat from industries varies depending on production conditions and season, and often,

at least in colder climates, the supply of heat is at highest in summer when the demand is the

lowest, and vice versa in winter. Producing cooling from waste heat and energy storage solutions

can offset this temporal mismatch, but seldom can the industries take full responsibility of the

energy supply.

In order to take full advantage of city-industry energy integration, we need to understand the

dynamic interaction of industry, city and the district heating/cooling networks and define

business models and necessary regulations and standards to make it commercially viable.

Task 4.1 Integration of RES into industrial processes (VTT, AIT, UMONS, FISE, DTU, CNR,

SINTEF, EON-RWTH, CIRCE)

Task 4.2 Integration of industry in the city (VTT, AIT, CNR, SINTEF, EON-RWTH)

Task 4.3 Regulations and standards (VTT, AIT)

Task4.1will look at selection and integration of renewable energy systems into industrial

processes. The challenges relate to integration and connection between thermal and electrical

networks taking into account the dynamics of energy supply and demand, and the feasibility of

various RES options depending on size and location of industry. Energy storage solutions will be

investigated in order to respond to peak consumption of energy.

Task4.2 will look at integration between industrial processes and city via district heating

and/or cooling system. The technical challenges relate to designing the system to match the

temporal differences between energy supply and demand, and finding effective energy storage

solutions. Utilisation of low temperature heat sources through heat upgrading using heat pumps

or employing low-temperature district heating networks will be studied. Financial and business




14

models will be studied to find solutions that would make the energy integration financially

attractive to both parties.

In order to facilitate higher uptake of the industry-city connection of energy, standardised (or

guidelines for) interfaces and business models could serve as a means of shortening the

implementation process. A standardised boundary specification for the delivery point could

supply business models for setting proper prices on the recovered/upgraded heat when used as

DH source.

WP 5: Technology Assessment

WP leader: SINTEF & KTH Global partners: CRES

Contact list







ENEA: Nicolandrea Calabrese ([email protected])

E3D-RWTH: Mark Alexander Bruentjen ([email protected])






UPVLC: David Alfonso ([email protected])


CIRCE: Carlos Arsuaga ([email protected])

The identification and evaluation of sustainable materials/components/systems is performed

to gain insight and create new arguments for system solutions facing non-technical and technical

barriers. Medium to large-scale energy supply technologies are the main focus areas, i.e. from

city blocks to entire parts of a town.

Task 5.1 Review new technologies/material developments (SINTEF, AIT, UMONS, KTH, CNR,

E3D-RWTH, ENEA, VTT, I3A-UNIZAR, IHE WUT, UPVLC, CIRCE)

Task 5.2 Establish a set of systems requirements for the supply systems of the future, including

criteria for greenhouse gas emissions in base, medium and peak load operation, energy

efficiency criteria, energy security criteria, including energy system capacity and

stability requirements (SINTEF, AIT, FISE, KTH, DTU, ENEA, VTT, I3A-UNIZAR, IHE

WUT, UPVLC, LBORO, CIRCE)

Task 5.3 Evaluation of current technical and non-technical barriers and user-requirements for

large-scale integration of new technologies (SINTEF, KTH, DTU, VTT, I3A-UNIZAR)

The results of this work package are applied as input for new tasks within work packages 1,

2, 3 as well as for the simulation platform.

In parallel to the review of current installations an assessment will be performed on how to

integrate, utilize or upgrade surplus heat directly or indirect within the energy infrastructures.

Focus on energy efficiency technologies and their integration is another area which will be

covered by this work package. KPI's will be first of all the reduction potential for the use of

primary energy and the environmental impact of the infrastructure.

The integration of energy storage devices and advanced hybrid1 systems will play a key role

in sustainable infrastructures of the future. When distributed correctly inside the infrastructure,

1 An advanced hybrid system might be the combination of wind energy and storage in cold stores, another example is

the combination of PV and solar thermal.






15

they reduce peak loads on the installations and support for example heat-pumping systems to be

operated at high efficiencies most of the time.

The outcome of the assessments can be the starting point of new technology development

within other European Initiatives as EeB or Joint Programmes on PV and Energy Storage.

WP 6: Scientific methods for quality assessment for urban related energy supply

technologies

WP leader: AIT & I3A-UNIZAR Global partners: CRES

Contact list:



CIEMAT: Antonio Garrido ([email protected])





Urban-related energy supply technologies (e.g. conventional systems based on fossil fuels,

RES, polygeneration, industrial waste-heat integration and district heating) should be dealt from

the perspective of energy supply sub-systems operating within wider systems-of-systems

ranging from whole-district to single building energy generation.

The current test-procedures and standards of energy supply technologies were designed and

are used to evaluate the performance of single components. Methods (e.g. appropriate test

infrastructure, in-situ measurement techniques, monitoring, yield control,…) specially focusing

on large-scale integration of urban-related supply technologies are not yet available, but are

urgently needed. These methods may form the basis for the future development of corresponding

standards.

Developed in close cooperation with relevant parallel SPs across JP Smart Cities (e.g. SP2,

SP3) and other EERA JPs, the methods for quality assessment for urban-related energy supply

technologies will be designed, ranging from whole-district to single building energy generation,

to be utilised in the context of large-scale urban energy networks.

With a focus on energy supply sub-systems, the work will build upon existing tools and

standards (such as T44/A38, BREEAM, SAP, RETSCREEN, OPEN HOUSE, SuPerBuilding,

among others) to realise a step-change in the ability to evaluate urban energy supply scenarios at

a level of accuracy and flexibility never before possible. Thus, the integration and interfacing of

current test procedures and standards for the evaluation of single components within the

integrated environment of urban energy networks is a priority.

Task 6.1 Mapping ‘what’s addressed and done, and where’ (especially standards), ranging from

whole-district to single building energy generation (AIT, KTH)

Task 6.2 Review and benchmark of existing methodologies for the assessment of urban-related

energy supply technologies single components (AIT, KTH, DTU, LBORO)

Task 6.3 Review and benchmark of existing methodologies for the assessment of large scale

energy supply technologies integrated in urban energy networks (e.g. experimental

infrastructure, long term monitoring, short term in-situ measurements, standardized

yield control techniques…) (AIT, DTU, LBORO)

Task 6.4 Recommendations on new methodologies for the assessment and evaluation (e.g.

performance assessment, quality assurance, yield estimation and control…) for large-

scale integration of renewable energy supply systems, which are required for the

development of new standards (DTU, CIEMAT, LBORO)

Task 6.5 Recommendations on what kind of test and evaluation procedures and infrastructure

are needed for urban-related supply technologies (CIEMAT, LBORO)

Crosscutting content




16

Taskforce on “Simulation Platform Development”

From sub-programme 4 perspective, the foreseen result is a new design tool enabling both

assessment of detailed large-scale integration of (distributed) renewable energy sources and

technologies, and the design of such complex energy systems.

Input and output relations from the sub-programme to the simulation platform are reported along

the work packages details, but a closer work relation is mandatory between the Task Force and

WPs 1, 2 and 3 leaders.

Interactions

Synergy and Collaboration within SP4

The following table summarizes the foreseen R&D Tasks, sub-tasks and in particular the

synergy and collaboration level that will be achieved within SP4 activities in order to reach the

expected goals and results. The synergies arise from the different backgrounds of the

participants ranging from expertise in technology development, numerical modelling,

standardisation, testing, implementation and integration of the components and systems.

In order to identify synergies between WPs, the mechanism which this will occur is specified

following:

WP current operation and work streams

o WP leaders will report about how their corresponding WP is running; this report

will be based on management aspects regarding the communication level,

gathering information, engaging partners, etc.

o SP coordinator will compile and analyse that information, identifying positive

actions, and eventually giving feedback to WP leaders in order to apply them in

their WPs if appropriate.

Scientific work

There are clear connections between different WPs by themselves and with the

Taskforce (see Figure 1).

o In a very first stage, each WP leader will report on the detected specific needs

and contributions of their corresponding WP. Thus identifying required inputs

and feasible outputs (I/O).

o SP coordinator will compile all the information, identify connections between

WPs (and/or SPs) and going back to WP leaders to jointly check the opportunity

of common work. Furthermore, potential barriers and gaps will be identified.

o Each WP leader will update I/O of the corresponding WP.

Synergy and Collaboration outside SP4

There are clear necessities of interaction between the different sub-programmes, not for

overlapping but for complementarities and synergies. Although independent and self-standing in

terms of research results and organisation, the SP4 will strongly interact with the other SPs

within the JP Smart Cities. In particular, discussions and cross-fertilisation will be sought with

SP2 and SP3. With SP3 a combined work package is formulated on energy management and

building integration of renewable supply technologies.

The detailed models based on physical transport phenomena and fluid dynamics developed

within SP4 are input for the energy management models in SP2 (buildings level) and SP3

(district level) and the decision tools within SP1. WP1 and WP2 are closely interacting with

SP1. Task 3.1 from SP1 and even WP6 could be a valuable input for SP4 as well, highlighting

that tasks in SP4 are more focused on definition of the environment, constraints, boundary

conditions (and so on) that relates (integrates) the RES to the city. In general, SP4 models need

to know which inputs do require from some of the WPs of the different SPs (to be SP4 outputs).


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Interfaces at EERA level

SP level

In general, SP4 models need to know which inputs do require from some of the WPs of

the different SPs (to be SP4 outputs):

SP4 has links with SP1 (WP2, WP3, WP5, WP6);

SP4 has links with SP2 (WP1); SP4 has a clear interaction with the grid.

SP4 has links with SP3 (WP1, WP2, WP3). Exploit the synergy due to

simultaneous consideration of demand and supply. Therefore considering the

coupling between the building/district and the energy supply system.

JP level

Particularly with Energy Storage; dive in Smart Grids, Photovoltaic, Bioenergy,

AMPEA, Wind…

Interfaces “outdoors”

IEA (Annex 58, 60, 66, Joint Task-Annex 42-29, Task 45, Task 49...)

Roadmaps for supply technologies already existing: such as RHC, E2B, IEA…

Expertise needed and planning

The needed expertise for SP4 consists of applied physics, material sciences, chemistry, fluid

dynamics, mechanical engineering, electrical engineering and social sciences. Moreover,

knowledge about ICT is worth. The planning of the work packages and tasks are

elaborated in the Gantt chart (section 6). The work packages run in parallel throughout

the years 2012-2015.

5. SP-contribution to the simulation taskforce

The modelling activity in SP4 is highly relevant in all contexts of the JP and many of the

participants in SP4 are modelling experts, especially the first two WPs are concerned with the

generic modelling challenges that can be applied in the entire range of systems in the city. The

combination of prior knowledge and statistics is the key to handling the large amounts of data

that will be available in many of the projects throughout the JP and the modelling will provide

the basis for achieving the “smartness”, i.e. through optimization, better interaction and

organization of the processes in the city. The developed libraries of white and grey-box models

will form an important part of the foundation in the simulation taskforce and through the

interaction with other SPs the needs for new models will be covered.

6. Mobility/transport aspects covered by SP

As stated in the Action Plan on Urban Mobility, European towns and cities face ever-

growing challenges to reduce the negative impacts of transport activities on the climate, the

environment and citizens' health, to render urban mobility more sustainable, and to reduce

dependence on fossil fuels. A research question for SP4 is on how to consider mobility and

transportation issues: energy demand only (input) or even up to vehicle point of view? A key

aspect is the amount and type of energy to provide (energy audit). From a high level energy

supply technologies will have to deal with a transition from current situation (still need fossil

fuels) to a foreseeable future with increasingly other sources of energy as basis for vehicle

supply (electricity, biofuels, hydrogen). Alternative fuels vehicles with biofuels, synthetic fuels;

natural gas as well as electricity are part of the European alternative fuels strategy. The

performance of these fuels from the environmental and security of supply side will be improved

though production mainly from renewable energy. Therefore, a strong communication with SP2

will be maintained as well as with other JPs such as Bioenergy.

Modelling aspects of transport energy systems in the context of energy systems integration

are already covered in WP1 and WP2. Especially the stochastic properties are considered, for

example the modelling of driving patterns, which can be used for optimized charging of EVs and

use of EV batteries for energy storage. Data-driven transport pattern models in a city as function


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of the climate, calendar, etc. could be an addition, such could lead to better knowledge spatial

distribution of energy needs in the city.

New urban energy supply technologies and infrastructures, close to the end users and

citizens, need to be taken into account for services to the urban mobility and goods

transportation. In particular, renewable energy production in the city contest (in parking areas, in

public buildings, along the streets, etc) need to be developed to supply directly electricity to

charging stations and to cover the needs of the electrical vehicles. The last mile logistics should

be supported by new models of goods transportation considering the availability of the energy

resources in correlation with distances, vehicles autonomy, fleets management and parking

areas.

The resource of electrical energy existing in the vehicles batteries, during long time

parking, is itself a distributed energy source that can be used for energy management at

local/district level, for grid optimizing and security. This approach is strongly linked to the

activities of SP2.

On site hydrogen generation from renewables, through electrolysis, is another key element

to allow the introduction of low emissions mobility in the actual and future city contest and to

facilitate the spread of hydrogen-fuelled vehicles. Hydrogen and natural gas can be also

processed by fuel cells for energy production, heat and electricity, close to the end user, without

pollutants and CO2 emission.

These concepts should be applied at district level, in downtown contest, in industrial and

commercial city areas.

ICT infrastructures play a key role in the management of the urban mobility:

to supply info to the citizens about mobility: where the vehicles (buses, bicycles, cars,

for byke-sharing, car-sharing and pooling ) are, if the charging stations are available

and which parking slot is available;

to solve traffic issues;

to optimize routes and vehicles availability in function of the real needs.

Given the current focus on data-driven, stochastic and statistical modelling in the mobility

description in the DoW, it is proposed that an activity is commenced comprising:

A mechanism is developed that facilitates effective interfacing between other relevant

EERA JPs and other relevant SPs. In conjunction with the JP Simulation Taskforce, a candidate architecture and

specification for a mobility-related data repository is developed. Initial datasets related to mobility (especially lo low carbon vehicle usage and

infrastructure are collated ready for future use.


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7. GANTT Chart

The following table shows for the sub-programme SP4 the Gantt chart for the first 4-year period.

3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48

WP 1 2012-1

2012-2

2012-3

2012-4

2013-1

2013-2

2013-3

2013-4

2014-1

2014-2

2014-3

2014-4

2015-1

2015-2

2015-3

2015-4

WP leader: DTU Task Leader AR

M1.1

AR

M1.2

R4.1

1.1 Definition of demand patterns

1.2 Methodologies: temporal and spatial scales

1.3 Methodologies: setting up models for simulation

1.4 Methodologies: models for forecasting & control

1.5 Methodologies for model based control

1.6 Methodologies: component oriented grey box models

1.7 Advanced poly-oriented models and integration

WP 2 2012-1

2012-2

2012-3

2012-4

2013-1

2013-2

2013-3

2013-4

2014-1

2014-2

2014-3

2014-4

2015-1

2015-2

2015-3

2015-4

WP leader: AIT Task Leader W AR

AR

M2.1

AR

M2.2

R4.2

2.1 Mapping of existing component models

2.2 Models to be developed w ithin the SP

2.3 Setting up component libraries

WP 3 2012-1

2012-2

2012-3

2012-4

2013-1

2013-2

2013-3

2013-4

2014-1

2014-2

2014-3

2014-4

2015-1

2015-2

2015-3

2015-4

WP leader: LBORO Task Leader AR

M3.1

AR

M3.2

AR

M3.5

M3.3

M3.4

R4.3

M3.6

3.1 Specif ications; identify candidate input datasets

3.2 Guidelines for design of smart energy netw orks

3.3 Methodologies for polygeneration urban energy supply systems

3.4 Develop functional prototype integrated environment

3.5 Integrate analytic component modules

3.6 Real-life Application and testing

WP 4 2012-1

2012-2

2012-3

2012-4

2013-1

2013-2

2013-3

2013-4

2014-1

2014-2

2014-3

2014-4

2015-1

2015-2

2015-3

2015-4

WP leader: VTT Task Leader AR

AR

AR

M4.1

M4.3

R4.4

M4.2

4.1 Integration of RES into industrial processes

4.2 Integration of industry in the city

4.3 Regulation and standards

WP 5 2012-1

2012-2

2012-3

2012-4

2013-1

2013-2

2013-3

2013-4

2014-1

2014-2

2014-3

2014-4

2015-1

2015-2

2015-3

2015-4

WP leader: SINTEF Task Leader AR

AR

AR

M5.1

M5.2

M5.3

R4.5

5.1 Review new technologies/material developments

5.2 System requirements for supply systems of the future

5.4 Barriers & user requirements for L-scale integration

WP 6 2012-1

2012-2

2012-3

2012-4

2013-1

2013-2

2013-3

2013-4

2014-1

2014-2

2014-3

2014-4

2015-1

2015-2

2015-3

2015-4

WP leader: AIT / UZ-I3A Task Leader AR

AR

AR

M6.1

a

M6.1

b

M6.2

M6.3

R4.6

6.1 Mapping w hat’s addressed and done, and w here

6.2 Review & benchmark: single components

6.3 Review & benchmark: L-scale energy supply techs

6.4 Recoms on new methodologies for L-scale integration

6.5 Recoms on test & evaluation procedures

w workshop Mx milestone bi-annual meetings for each work package

Rx final report per work package AR Annual status Report

Scientific methods for Q-assess for URST

Framework for development of multi-purpose component

oriented models

Development of component oriented model libraries

System integration

City-industry interaction

Technology assessment


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8. Milestones

The milestones are summarized in the following table.

milestone Title measurable objective(s) project

month

M 1.1

Mapping of existing

methodologies for white and

grey box modelling, model

identification and validation

based on data

An overview is documented 24

M 1.2 White and grey box modelling

frameworks are developed

Guidelines for white and grey box modelling are

documented 45

M 2.1

Requirements of the

component models on the

different levels are defined

Definitions are documented 24

M 2.2 Component libraries are set up The Test cases are run and the libraries are

documented and available among the partners 45

M 3.1 Dissemination of system

concept

Presentation of paper at CISBAT 2013

conference 21

M 3.2 Data repository Specification of repository data structures. 30

M 3.3

Guidelines and procedures for

the design of smart energy

networks

Guidelines 39

M 3.4

Methodologies for multi-

criteria synthesis, design and

operation of polygeneration

urban energy supply systems

Report 42

M 3.5 Scenarios Delivery of case study scenarios. 45

M 3.6

Joint project proposal on

energy system integration (EU

call)

Project proposal submission 48

M 4.1 Models for waste heat Energy supply models specified for industrial

waste heat 39

M 4.2 Delivery of findings Key findings and recommendations are

documented and available to all partners 46

M 4.3 Boundaries A standardised boundary specification for

industry / city interface 39

M 5.1 RoadmapTech1 Technology roadmap urban city-related supply

technologies for smart cities 39

M 5.2 Sim-frame Integration Guidelines for simulation framework Model

Inputs and outputs 39

M 5.3 Roadmap Tech2 2-yearly update technology roadmap urban city-

related supply technologies for smart cities 42

M 6.1a Quality assessment review 1

Review and benchmark for the assessment of

urban-related energy supply technologies (both

single components and large-scale) ranging

from whole-district to single building energy

generation (on existing)

39

M 6.1b Quality assessment review 2

Review and benchmark for the assessment of

urban-related energy supply technologies (both

single components and large-scale) ranging

from whole-district to single building energy

generation

42

M 6.2 Large-scale validation

Guidelines on large scale validation activities

for urban-related energy supply technologies

(e.g. conventional systems based on fossil fuels,

45


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RES, polygeneration, industrial waste-heat

integration, district heating smart grids)

integrated in urban energy networks

M 6.3 Quality assessment and

validation

Recommendations for improved assessment of

large scale integration of urban-related energy

supply technologies (experimental

infrastructure, long term monitoring, etc.)

integrated in urban energy networks

46


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9. Participants and Human Resources

The following table lists all the participants to the sub-programme SP4 “Urban city-

related supply technologies” and reports for each institution its role and the committed human

resources (person years/year) for the foreseen research activity. At present, in SP4 there are 29

participants from 12 different European countries. In terms of human resources, SP4 will gather

a minimum R&D effort summing up to 34.5 person years/ year. Moreover, each participant will

use its own infrastructures and facilities to accomplish the proposed R&D activities and hence to

meet the expected goals and results.

participant Country Role Associated to (if associate)

Human Resources committed [py/y]

AIT Austria P WP SP JP - 4

CENAERO Belgium AP BERA 0.25

CIEMAT Spain P - 1

CIRCE Spain P - 0.5

CNR Italy P 1.5

DTU Denmark P WP 3

ENEA Italy P - 0.5

Fraunhofer ISE Germany P - 1

Fraunhofer IWES Germany P - 1.5

HFT Stuttgart Germany AP Fraunhofer ISE 0.3

IBBT Belgium AP BERA 0.5

KTH Sweden P - 2

KULeuven Belgium AP BERA 0.5

Laborelec Belgium AP BERA 0.5

Loughborough University - LBORO

UK AP WP UKERC EPSRC 2

Mons University - UMONS

Belgium AP BERA 1.5

NTNU Norway P - 1

RWTH Aachen University Germany AP AIT 0.5

SINTEF Norway P WP - 1

Strathclyde University UK AP UKERC EPSRC 0.3

Tecnalia Spain AP - 0.5

TNO Netherlands P - 2

University of Pisa Italy 0.25

UPVLC Spain P - 2

VITO Belgium P BERA 0.65

VTT Finland P WP - 1.5

WUT Poland P - 1.25

I3A-UNIZAR Spain P SP - 3

Total 34.5

P: participant WP: Work Package leader SP: Sub-Programme coordinator

AP: associated participant JP: Joint Programme coordinator


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10. Contact Point for the sub-programme

Pablo Dolado (sub-programme coordinator)

University of Zaragoza – I3A

María de Luna s/n

50018, Zaragoza

Spain

T: +34 876555584

M: +34 654988347

E: [email protected]

Michael Hartl (deputy sub-programme coordinator)

AIT Austrian Institute of Technology

Energy Department

Giefinggasse 2

1210 Vienna

Austria

T: +43(0) 505506040

M: +43(0) 664 88390018

E: [email protected]


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