The Best of both Worlds: Social and Technical Challenges of

Creating Energy Islands

Sonja Klingert

, Michael Niederkofler

, Hermann de Meer

, Mona Bielig

Stepan Gagin

, Celina Kacperski

and Matthias Strobbe

IPVS, University of Stuttgart, Stuttgart, Germany

Energiekompass GmbH, Stegersbach, Austria

University of Passau, Passau, Germany

Seeburg Castle University, Seekirchen, Austria

Ghent University - imec, Ghent, Belgium

Keywords: Energy Island, Self-Sufficiency, Renewable Energy, Inter-Disciplinarity, Energy Community.

Abstract: Creating so-called “energy islands” with a high level of energetic self-sufficiency is one strategy to fight

climate crisis. To become a realistic goal, such a concept needs trans-disciplinary research that defines

promising transformation paths towards reaching this vision. The presented paper introduces a conceptual

framework that provides approaches for technical optimization across all energy vectors, socio-technical

optimization of the usage of energy demand flexibility, socio-psychological interventions, and a replication

strategy that considers all these different aspects. The focus lies on the architecture of a management system

that answers requirements also from social sciences, on engagement strategies and on defining a cross-vector,

cross-disciplinary design for flexibility in terms of demand-response schemes.

1 INTRODUCTION

We are in the midst of a climate crisis. The impacts

will endanger the lives of millions of people around

the world, so a plethora of ideas to limit climate

change by reducing the emissions of CO2-equivalents

are currently being developed. One of the approaches

is to start from geographically delineated, inhabited

areas and develop strategies for net-zero GHG

emissions. The origin of this idea lies in the CO2

footprint concept: if people consume a lot more

energy than can be generated locally in a CO2 neutral

way, there will not be enough “space” for everybody.

If it can be shown further that such strategies are

socio-techno-economically viable, they can be

replicated elsewhere, finally creating a network of

sustainable districts, cities, or villages. This approach

https://orcid.org/0000-0003-0653-003X

https://orcid.org/0000-0002-0238-1455

https://orcid.org/0000-0002-3466-8135

https://orcid.org/0000-0001-7535-8961

https://orcid.org/0000-0001-5004-5600

https://orcid.org/0000-0002-8844-5164

https://orcid.org/0000-0003-1730-0862

results in the concept of (urban) “Energy Islands” (EI)

and has a high overlap with the concept of renewable

energy communities (REC), with REC potentially

being the organizational side of an EI. The authors of

this work define an urban EI in the following way: An

urban EI is a geographically delineated system that

is largely self-sufficient across all present energy

vectors. Given a pre-existing energy infra-structure,

this implies to maximize local generation and

optimize its distribution, and it means to optimize

demand across all energy vectors, adapting demand

profiles as necessary, both by shifting demand

temporarily and reducing it absolutely. From an

organizational and social point of view, the EI is

inhabited by people living or working there, who are

the end-users of energy. They are tied to the EI

through their energy usage patterns and directly and

indirectly by contracts. EI inhabitants can contribute

Klingert, S., Niederkoﬂer, M., de Meer, H., Bielig, M., Gagin, S., Kacperski, C. and Strobbe, M.

The Best of both Worlds: Social and Technical Challenges of Creating Energy Islands.

DOI: 10.5220/0011974600003491

In Proceedings of the 12th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2023), pages 129-136

ISBN: 978-989-758-651-4; ISSN: 2184-4968

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

129

individually, in the context of collective energy

actions, and in the context of energy communities

(EC) to EI objectives.

The differentiating factor between self-

sufficiency and autarchy is the role of the EI in the

context of energy grids: if necessary, the EI draws

power from or injects into the external grid, providing

ancillary grid services. So, it can be an active part in

a cell-based system of interdependent EI cells that

together form the mesh of the future energy grid,

characterized by a high share of renewable energy

sources (REN) and partially flexible energy demand.

The electricity vector of an EI might be technically

implemented by a micro-grid with a local energy

market

The clear goal of this definition avoids the pitfalls

of energy efficiency objectives where, quite

regularly, efficiency gains are compensated by

increasing demand, enabled by a seemingly increased

financial or resource-based budget. Thus, this

“rebound effect” cannot emerge. From a social

viewpoint, pursuing EI objectives can take different

organizational forms: Collective energy actions are

depending on “the collective involvement of energy

consumers or prosumers” (DECIDE Consortium,

2022), and ECs are a subset thereof involving

continuous group interactions (Bielig et al., 2022).

Also, individual behaviour changes are an option –

which concept to apply where and when is part of the

set of social challenges of creating and operating EIs.

To start such a transition this endeavour requires

a truly trans-disciplinary approach: energy flows

must be optimized cross-sectorally, based on data

science and ICT communication, the technical

approach must make economic sense and be legally

feasible, and most of all, it must not only be accepted

but truly supported by the inhabitants. Thus, every

system aimed at achieving such goals has to build

upon a fruitful interconnection of the different

disciplines: ICT, energy physics, law, psychology

and sociology as well as business economics. This

insight is the foundation of the EU H2020 project

RENergetic that considers all these issues, while

putting the island inhabitants into the centre of

activities. These basic requirements result in the

following actionable tasks:

 Technical optimization of supply across all

available energy carriers, here: heat, electricity,

and electric mobility

 Socio-technical optimization of the usage of

energy demand flexibility across all available

https://im.iism.kit.edu/english/1093_2058.php

energy carriers, to adapt demand to currently

available energy supply

 Socio-psychological interventions, including

incentives, for reduction of energy demand

when overall yearly demand exceeds overall

yearly supply

 To achieve a real-world impact, replication

needs to be integrated into modelling.

These tasks are reflected in the sections of the

presented paper: related work is discussed in section

2. Section 3 deals with the creation of an EI from a

trans-disciplinary viewpoint, i.e., engaging EI

inhabitants, optimizing supply, managing demand

temporarily, and finally reducing demand. Section 4

presents a replication framework, and section 5 draws

conclusions for future work.

2 RELATED WORK

The term EI has been mostly used in the context of

real islands that have a severe challenge of being off-

grid and aim to decrease their dependency from fossil

fuels (e.g (Droege, 2012; Riva Sanseverino et al.,

2014)). The idea of urban EIs has entered into the

discussion only recently, mainly in the context of a

case study of the University of Genua (Bracco et al.,

2018), however, without defining the term “urban

energy island”. The technical, and partially also

business, challenges have been mainly dealt with in

the context of “positive energy districts” (e.g. (Monti

et al., 2017)) or “multi energy districts” (review in

(Martinez Cesena et al., 2020)). An operating

perspective is given within discussions about energy

management systems. Energy management systems

in modern buildings control installed equipment and

are often used for energy optimization. Combining

such systems with IoT concepts makes it possible to

use data from the sensors for data analytics and

forecasting. Generation units can also exchange

information through ICT architecture, which enables

provision of ancillary services in the electricity grid

(Stocker et al., 2022). As a result, optimization

algorithms can be applied to balance both supply of

distributed renewable energy resources and energy

consumption of smart build-ings, taking into account

uncertainty of the sources (Saatloo et al., 2022).

Disjunct from this is the discussion about energy

communities, which is often characterized by the

analysis of drivers and barriers from a governmental,

legal, or behavioural points of view (e.g. (Bauwens &

SMARTGREENS 2023 - 12th International Conference on Smart Cities and Green ICT Systems

130

Devine-Wright, 2018; Walker, 2008)). There are

hardly any works that try to reconcile the challenges

of geographically defined EIs and socio-

economically defined EC, arriving at a holis-tic

framework for creating and operating an EI.

An exception is presented by Bukovszki et al. 2020

(Bukovszki et al., 2020) who identify so-called

progression-factors (i.e. desired characteristics) of EC

and match them with building energy modelling

decision support tools. However, also in this work, the

operation of an EI is not treated nor the required change

of energy related behaviour of the inhabitants, contrary

to the trans-disciplinary methodology in the work

presented here. This approach requires understanding

how to motivate people to both engage in the EI and

change their behaviour, not only once but by adopting

new habits. This is done best through a collective lens:

evidence shows that participatory and community-

based approaches in the diffusion of renewable energy

technologies promote broader acceptance and support

innovation (Berka & Creamer, 2018).

Intrinsic motivation to engage and change

behaviour is required. One way is to leverage the

social identity model for pro-environmental actions

(SIMPEA; (Fritsche et al., 2018)), which describes

the relevance of social identity related factors (e.g.

social identification, collective emotions, social

norms) for pro-environmental decision-making and

collective action. Meta-analyses (Schulte et al., 2020;

Udall et al., 2021) demonstrated strong links between

social identification both on individual and group

level and intentions for pro-environmental behavior.

Using inherent demand side flexibility can be one

reason for behaviour change. For decades, demand

response (DR), i.e. the planned activation of demand

side flexibility, has been discussed from strictly

disciplinary points of view: either technically, as e.g.

optimizing transformer load curves or minimizing the

usage of reserve energy, for various different use

cases be it electric vehicles (Klingert & Lee, 2022;

Sadeghianpourhamami et al., 2018), data centres

(Basmadjian et al., 2018), or any electrical load

(Subramanian et al., 2013). Or it has been viewed

from a behavioural viewpoint: While there are many

studies which investigate shifting electricity usage

(e.g. (Kacperski et al., 2022; Laura M. Andersen et

al., 2017)), DR acceptance in heating is less

researched. This is critical, as flexibility for shifting

behavior in heating seems less acceptable than for

electricity usage (Spence et al., 2015), although

heating accounts for the majority of energy usage in

Europe. Therefore, a unifying DR model, merging the

views of different disciplines, automation levels and

energy vectors, is missing.

3 CREATING ENERGY ISLANDS

As mentioned, creating EIs needs the technical

concertation and optimization, and socio-economic

support of the affected inhabitants.

3.1 Involving EI Inhabitants

The question is - how can the local population be

incentivized beyond simple acceptance to participate,

take responsibility, and actively contribute and invest

in community energy actions? In order to define an

involvement strategy for the RENergetic project, we

build on literature from psychology and sociology for

collective pro-environmental actions (SIMPEA,

(Fritsche et al., 2018)), technology acceptance (e.g.

UTAUT/UTAUT2, (Venkatesh et al., 2003)),

behaviour change (e.g. COM-B, (Michie et al.,

2011)), and trust (Mayer et al., 1995). Two additional

sources of information are a) to investigate

involvement strategies in different types of collective

actions, such as human rights movements or protests

and b) to analyse success stories in related areas as

positive energy districts or real islands (e.g. (Droege,

2012). For example, for the Samsø EI, “soft topics,

such as the political and socio-cultural context,

planning processes, communication and local

ownership" have been named to have been the keys

to its success (Sperling, 2017).

The goal is to develop and test a toolbox approach

(Figure 1) to integrate the social aspects of EIs within

the technical framework, building on well-established

theories from psychology and sociology (level 1). As

evidence shows that there is no “one-size-fits-all” tool

to bring about social change (Hewitt et al., 2019), on

level 2 a context analysis needs to be carried out,

analysing the “situational context”, i.e. environmental

or technical constraints, motivations and needs of

stakeholders, and defining the required level of

involvement. For the latter different methods and

tools are tested and evaluated through randomized

controlled trials whenever feasible.

Communication and collaboration instruments in

the RENergetic pilot activities are selected based on

three main guidelines:

 Consideration of the local social identity and, if

possible, build on it (Fritsche et al., 2018)

 Trustworthiness, i.e. transparency and

consistency, in order to show good-will and

assure the ability to implement communicated

plans (Mayer et al., 1995)

The Best of both Worlds: Social and Technical Challenges of Creating Energy Islands

131

 Usage of general and local social norms for

communication, to encourage connection with

the social environment (Perlaviciute, 2022).

Figure 1: The RENergetic Toolbox.

3.2 Matching Supply and Demand

The main challenge of an EI is to “make ends meet”

regarding energy at all points in time. There are a lot

of examples of districts claiming to be “climate

neutral” or “energy positive”, in terms of producing

more energy than is consumed locally during some

period of time. But, they are still dependent on the

national grid or on fuel deliveries as they consume

energy at different times than they produce it. This is

already a very big step forward – however, it is still

only half-way towards the overall goal of being self-

sustained and additionally delivering ancillary ser-

vices to an external grid. In order to achieve this

overall goal, energy supply needs to be optimized,

supply and demand need to match at all times, and

finally, if demand in general exceeds supply, beyond

mere efficiency, energy demand needs to be reduced.

In RENergetic, the technical support of EI activities

is provided by the RENergetic platform serving the

abovementioned functionalities for heating and

electricity (including EV) domains. To integrate these

functionalities, it is proposed to use the service-

oriented architecture shown in Figure 2. Each service

is a software element that performs a specific

functionality, for example forecasting, optimizing,

DR services for heating. A service might interact with

other services, the data storage and the interactive

platform. The API and Access Management service

is responsible for orchestrating the operation of all

other services. It also provides an API for com-

municating with external systems. Most services are

implemented using Java Spring Boot framework,

although each service could utilize a different

software stack. For example, forecasting and

optimization services utilize Kubeflow platform.

Services are managed by WSO2 software, and the

API follows OpenAPI specifications in order to

ensure compatibility with other systems. The user

management service relies on the Keycloack software

and is used to control access of users to the different

parts of the system. The service architecture allows

the system to be flexible. It is important because not

all EIs have necessary data or systems required for the

operation of all services. In case some functionality is

not needed, the corresponding service could be

excluded from installation. The service-oriented

architecture also simplifies future system extensions.

For instance, if optimization in an additional domain

is required, a new service for that can be developed.

The RENergetic Platform provides an API for

communicating with the EI systems using the Data

Acquisition service which is based on Apache NiFi

software to ingest data from EI devices supporting

different protocols. The data from EI sensors and

meters, as well as forecasts and other types of time-

dependent data are stored in time series database

inside RENergetic platform. Metadata, user related

data and connections between different assets are

stored in relational database. Utilization of two

different databases provides an efficient way of

storing and accessing different kinds of the data.

Figure 2: An Energy Island Architectural Framework.

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In RENergetic, the PostgreSQL and InfluxDB are

chosen as relational data storage and time series

storage, respectively. All services are defined in line

with the requirements of the social-science work

3.2.1 Energy Supply Optimization

With the transition of the energy system, the different

sectors are becoming ever increasingly coupled.

While this all being similarly true for heating,

electricity and mobility, the significance of electricity

as the connecting link between the different sectors

deserves particular attention, as evidenced by

phenomena such as e-mobility, heat-pumps based

heating systems or combined heat and power plants.

Therefore, an overarching global optimization across

sectors is key to overall sustainability. At the same

time, each sector requires specific optimization due to

its particularities. Consequently, results an iterative

multi-layer optimization architecture. As an example,

let us assume a shortage in natural gas both for

electricity and heat imposing restrictions both on

electricity and on heat consumption. At the same

time, electricity may be an energy source of heat via

heat pumps or pure resistance driven heat production,

resulting in a complex interaction pattern for global

optimization on the supply side within a “web of

energy”.

An optimization of supply-and-demand matching

in the context of EIs is particularly challenging for the

electricity domain, as matching has to be done

instantaneously with very limited storage or buffering

capacity and due to the dynamic nature of AC

electricity. It is performed in two forms, proactively

and reactively. By approaching 100 % renewable

supply, one of the key questions becomes to identify

and procure the sources of flexibility that can best be

exploited for the “matchmaking”. This key question

is arguably challenging in the context of EI with

limited expansion and resources in order to achieve a

maximum level of self-sustainability. Arguably the

most promising resources of flexibility are due to

demand-side management within the web of energy,

resulting in strategies for the adaptation of both heat

and electricity demand as a main focus of the

RENergetic project. In contrast, a procurement of

flexibility resources usable for reactive compensation

of imperfect predictions seems harder to be found on

the load side but rather on the supply side. Therefore,

it was decided to investigate these supply side

resources prototypically and selectively in a labora-

tory setting based on smart converters with power

electronic interfaces to the grid in order to provide

ancillary services to the EI and, possibly, to the

preceding grid as well. By means of droop curves, the

potential of grid supporting actions via power supply

adaptations are investigated and feasibility is

investigated in terms of technical, social and

regulatory conditions in various pilot studies. While

the main focus being here on primary reserves

provisioning, preliminary studies on grid forming for

voltage control of EIs are also performed. For the

sake of complexity and effort, other EI specific

ancillary services such as protection, inertia or

harmonic filtering, and are left for future studies.

3.2.2 Demand Response

Optimization is almost entirely a technical challenge

whereas sufficiency is almost entirely a behavioural

issue (e.g., buying more efficient products or

reducing the consumption of energy services).

Contrary to that, DR in many cases requires a

complex interconnection of data science, adaptation

algorithms, communication and behavioural

reactions of the end-users if it is supposed to be

tapped to its full potential. This implies a trans-

disciplinary approach. Traditional DR concepts as

e.g. the European Commission’s DR definition

(European Commission, 2013) have two major

drawbacks in the context of urban EIs: 1) They relate

only to electricity, which is derived from the history

of DR that originates in electricity grid quality issues.

For other energy vectors such as heat, this idea has

not yet been fully explored. 2) DR has until recently

only been discussed in the context of different, but

unconnected electricity use cases such as resident

DR, data center DR or EV DR, missing an

overarching conceptual approach.

Therefore, in this work we extend the concept in two

ways: comprising all available energy vectors,

targeting both energy end-users and managers as

intermediate users and interconnecting use cases.

This requires a conceptual model that positions and

connects these different issues. Due to the overall

guiding principle of replication this should be done in

a way that it can be instantiated into different use

cases that are configurable for different EI projects.

An overarching model for DR needs to contain the

following main design elements to allow for a full

exploitation of its potential (Figure 3):

 Automated vs. manual DR: This describes the

trade-off between automation that relieves

people from the burden of taking active

decisions but at the same time limits end-users’

autarky in decision making. Automated DR

implies communication and actuation of pre-

defined steering points for power in-/decrease.

The Best of both Worlds: Social and Technical Challenges of Creating Energy Islands

133

End-users are not actively involved in the

implementation of each adaptation process, but

to increase technology acceptance, they should

be invited to configure the system at the start of

the adaptation period (e.g. a contract period).

Automated DR is driven by technology (data

science, algorithms) and gives the control to a

central operator so that it comes with a high

level of certainty of the flexibility harvest.

Manual DR, on the other hand, requires end-

users as e.g. home owners, tenants or EI energy

managers to actively manipulate their power

consumption upon being requested to do so.

For the operator requesting flexibility, manual

DR implies a lower level of certainty of the

flexibility harvest.

 Trigger: Depending on the use case and energy

vector, a trigger needs to be defined that starts

a DR event, which implies the existence of a

trigger metric and a threshold. This metric

might be used to differentiate between

automated and manual DR.

 Use Case: This is the context in which DR is

applied, e.g. residential electricity con-

sumption, bulk charging of EV fleets or the

heating of a building complex. The use case

characteristics include the main flow of actions,

the corresponding stakeholders, business

model as well as the legal framework of the DR

solution.

 End-user rights and duties: Mirroring

automated vs. manual DR, end-users rights and

duties can be manifold, be it periodical

configurations or over-riding rights for au-

tomated DR vs. information or capacity rights

for the case of manual DR.

 Business and legal issues: The higher the level

of duties and responsibility for either side, the

higher the share of the system-wide benefit

they will want to have. Depending on the data

available and on the different options of end-

users to participate in a DR program, incentives

might be created. These might be financial or

non-financial, as e.g. CO2 reduction

information or a planned community event.

To our knowledge this is the first model of its

kind, that integrates behavioural, technical, business

and legal aspects into a concept of DR.

3.2.3 Reducing Energy Demand

One major problem when engaging people in energy

related pro-environmental behavior is the rebound

effect (Sorrell et al., 2009), which reflects a negative

behavioral spillover where one pro-environmental

behavior decreases the likelihood of other pro-

environmental behaviors (Truelove et al., 2014).

Figure 3: A Trans-disciplinary Framework for DR.

The RENergetic approach, integrating both

technical and social aspects, particularly targets to

counteract this effect, going beyond technical

optimization and efficiency measures to reach a

positive spillover, i.e. the activation of further pro-

environmental behaviors based on a first one. A meta-

analysis (Truelove et al., 2014) showed that the two

main aspects which account for positive spillover are

consistency and identity. Thereby, building on the

SIMPEA model and fostering a high level of

behavioral involvements, our approach aims to build

a foundation for the development of a social identity

related to the EI, which can then reinforce social

norms and positive behavioral spillover instead of

rebound effect. Collective pro-environmental

activities can foster a social identity which activates

social norms in further group situations.

4 REPLICATION

Replicable results are a key goal of innovation efforts

and one focus of the RENergetic approach. In a sense,

the RENergetic pilot actions are designed as the first

replications of the developed solutions, following the

core principle of the replication package of providing

general solutions to be applied in specific contexts. In

a first step towards developing a replication

methodology a definition of “replicability” in the

RENergetic context is needed. To this end,

reproducibility and replicability need to be

distinguished. Reproducibility means that results can

be reproduced by a different team using the original

team’s tools or software artifacts. Whereas

replicability means that results can be replicated by a

different team using their own tools or software

artifacts.In the RENergetic project a variety of

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134

solutions will be developed and provided via the

RENergetic platform. These are designed to be

reproducible, that is the software developed by

RENergetic is to be taken as is and utilized by other

teams to reproduce the intended results. However,

these software modules need a certain context in

order to be able to function as desired. This context,

which is the sum of all technical, infrastructural,

social, economic, and legal framework conditions is

highly specific and not easily (or even impossible to

be) reproduced in another site. Following from that

the framework conditions, the context in which the

RENergetic modules are operable, will need to be

replicated by any follower site that intends to utilize

the RENergetic solutions.

Figure 4: An Illustration of an EI Transformation Pathway.

For the replication methodology the concept of

the “Transformation Pathway” (Figure 4) has been

developed, which is the sum of all interventions that

carried out to achieve the sustainability goals of a

given EI. It is important to note that this does not only

include technical interventions, but also all social,

behavioural, and economic actions that are needed to

accompany the base technical solutions.

As all these provided solutions need the correct

technical, social, legal and economic context in order

to be meaningfully deployed, the RENergetic

replication package does provide a methodological

toolset to replicate this needed context -

infrastructure, social, legal and economic – in order

to successfully reproduce the results from the

RENergetic pilot sites. To build on already

established concepts, the framework provided with

the SGAM methodology was chosen as basis for the

replication methodology which makes use of all five

layers of the SGAM model. This approach allows for

a standardized comparison of different approaches,

paradigms, and viewpoints. The SGAM methodology

is not only applied to the electric but also to the heat

domain, resulting in multi-energy vector SGAM

reference models of the RENergetic solutions.

5 CONCLUSION

As a summary, it could be shown that the RENergetic

approach to defining a socio-technical framework for

an EI operation merges the expertise from the

respective disciplines in an over-arching way:

specifically with regards to DR, behaviour change,

incentives, communication guidelines, and

constructing RENergetic DR services in the platform

are tightly integrated. Next steps will be mainly the

refinement of the first drafts to global optimization,

as well as socio-technical DR designs for the main

RENergetic use cases, i.e. EV DR, heat DR and

electricity DR, based on both data availability and

results from first ongoing user experiments. This will

be done in-line with the requirements from the

RENergetic replication package, both for ICT and

social science components.

ACKNOWLEDGEMENTS

Supported by EU H2020 RENergetic (#957845)

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