=Paper=
{{Paper
|id=Vol-3855/m4s1_invited
|storemode=property
|title=Optimizing Energy Communities: a Model-Based Approach (invited paper)
|pdfUrl=https://ceur-ws.org/Vol-3855/m4s1_invited.pdf
|volume=Vol-3855
|authors=Alexandra Jäger,Philipp Zech,Ruth Breu
|dblpUrl=https://dblp.org/rec/conf/ifip8-1/JagerZB24
}}
==Optimizing Energy Communities: a Model-Based Approach (invited paper)==
https://ceur-ws.org/Vol-3855/m4s1_invited.pdf
Optimizing Energy Communities: a Model-Based
Approach
Alexandra Jäger1 , Philipp Zech1 and Ruth Breu1
1
Department of Computer Science, University of Innsbruck (UIBK), Technikerstraße 21a, 6020 Innsbruck, Austria
Keywords
Model-based simulation, Multi-layer simulation, Digital twins, Energy communities
1. Extended Abstract
Renewable Energy Communities (RECs) are a relatively new concept introduced in the EU (EU Directive
2018/2001, REDII) that empowers communities, municipalities and cities to generate, share and use
sustainable energy to reduce environmental impacts and drive the energy transition. However, an
ecosystem of services is needed to establish the concept of RECs in the long term. The establishment,
development and operation of RECs are still associated with considerable organizational hurdles, high
time expenditure and cost risks [1]. Many issues that have not yet been sufficiently investigated include
considering the whole REC lifecycle, encompassing simulation and impact forecasting when setting
up or extending an REC, advanced modeling techniques for simulation of RECs, as well as efficient
dynamic load management during operation [2][3][4]. Additionally, RECs may have adverse effects
on grid stability due to their use of renewable energy sources like solar and wind, whose intermittent
and unpredictable nature can make grid management more complex, as balancing supply and demand
becomes less predictable [5].
To address the challenges associated with RECs and their impact on the grid, several research
questions arise:
1. How can RECs be modeled in a comprehensive, scalable and extensible way?
2. How can RECs be simulated and optimized to effectively manage their dynamic interactions over
their whole lifecycle?
3. What strategies can be implemented to mitigate the adverse effects of RECs on grid stability?
In order to address these research questions, our objective is to implement a generic digital twin,
adaptable to specific requirements of instance RECs (e.g. different participants, objectives, regulatory
contexts), which supports all phases of an REC from conception to operation to expansion. Model-based
simulation of RECs is central to our approach, beginning with the development of an REC model that
incorporates parametric representations of specific characteristics like the electrical capacity of energy
production, consumption, and storage devices.
In the conception phase, simulations will assess the economic, ecological, and social viability of
proposed RECs by modeling prospective member behaviors using load profiles to predict consumption
patterns and resource allocation. As RECs grow, this simulation will evolve to evaluate the impact of
new model elements and optimize the community’s configuration. During the operational phase, we
will apply a combination of simulation and multi-objective optimization to enhance REC performance,
allowing for the prioritization of goals such as minimizing energy consumption and maximizing local
energy use while adhering to operational constraints. The optimization process will be grounded in
REC simulations that predict energy production and consumption, refined through consideration of
external models like weather forecasts and expected market prices. A predictive control approach will
manage devices within the REC, adapting to real-time data and unforeseen events. By employing a
Companion Proceedings of the 17th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modeling Forum, M4S, FACETE,
AEM, Tools and Demos co-located with PoEM 2024, Stockholm, Sweden, December 3-5, 2024
$ alexandra.jaeger@uibk.ac.at (A. Jäger); philipp.zech@uibk.ac.at (P. Zech); ruth.breu@uibk.ac.at (R. Breu)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
multi-layer modeling strategy, we can appropriately represent RECs at different lifecycle stages. For
example, a high-level model focusing on aggregate behavior is sufficient in the conception phase, but a
more fine-grained model is needed for optimization in the operation phase.
By modeling arbitrary RECs, simulating various scenarios to identify optimal control strategies,
and subsequently executing these controls, a comprehensive digital twin of the REC is established.
Grid stabilization will be addressed through two mechanisms: first, optimization will increase internal
energy consumption within the REC, reducing the amount of energy fed back into the grid. Second, our
simulations will enable more accurate predictions of the energy that will still reach the grid, enabling
more informed planning for grid operators.
We will conduct our research within the Design Science Methodology framework [6], which builds
on the following activities:
1. Problem identification and motivation
2. Definition of objectives for a solution
3. Design and Implementation
4. Demonstration
5. Evaluation
The motivation and research questions at the start of this paper will guide our research. In order to
define objectives for a solution, we will specify functional and non-functional requirements of an REC
digital twin by reviewing existing literature, defining relevant user groups by stakeholder mapping,
as well as conducting user surveys. Based on the extracted requirements, we will design a digital
twin system capable of supporting RECs through their entire lifecycle. This system will encompass a
flexible simulation framework, a multi-objective optimization module, an IoT-enabled control module,
as well as a visualization module to promote inclusivity and engagement among stakeholders. The
defined system will be implemented in iterations, and its usefulness will be demonstrated by continuous
testing and integration. To evaluate our proposed system, it will be deployed in two demo RECs. Its
effectiveness will be assessed through a comparative analysis, contrasting the outcomes of the digital
twin simulations with real-world operational data, as well as a conclusive user survey.
In conclusion, this work contributes a novel digital twin system designed to support RECs through-
out their entire lifecycle, addressing critical gaps in existing research, particularly in REC modeling,
lifecycle simulation, optimization, and dynamic load management. We recognize potential challenges
in modeling, simulating and optimizing RECs, as well as in scalability and real-time accuracy, especially
in unpredictable environments with intermittent renewable energy, and will work to mitigate them
throughout our research.
References
[1] S. Ahmed, A. Ali, A. D’Angola, A Review of Renewable Energy Communities: Concepts, Scope,
Progress, Challenges, and Recommendations, Sustainability 16 (2024) 1749.
[2] E. Cruz-De-Jesús, J. L. Martínez-Ramos, A. Marano-Marcolini, Optimal Scheduling of Controllable
Resources in Energy Communities: An Overview of the Optimization Approaches, Energies 16
(2023) 101.
[3] W. Yu, P. Patros, B. Young, E. Klinac, T. G. Walmsley, Energy digital twin technology for industrial
energy management: Classification, challenges and future, Renewable and Sustainable Energy
Reviews 161 (2022) 112407.
[4] I. Nica, I. Georgescu, N. Chirit, ă, Simulation and Modelling as Catalysts for Renewable Energy: A
Bibliometric Analysis of Global Research Trends, Energies 17 (2024) 3090.
[5] K. Schmietendorf, J. Peinke, O. Kamps, The impact of turbulent renewable energy production on
power grid stability and quality, The European Physical Journal B 90 (2017) 222.
[6] K. Peffers, T. Tuunanen, M. A. Rothenberger, S. Chatterjee, A Design Science Research Methodology
for Information Systems Research, Journal of Management Information Systems 24 (2007) 45–77.