Add extracted tools and HAL article text
- CitrineOS core extracted (docker-compose ready) - OpenOCPP extracted - ShapeShifter specification extracted - EVerest extracted - HAL article extracted (Renewable Energy Community Design) - .gitignore updated for large files
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Open-Source Datasets, Models, and Tools for Renewable
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Energy Community Design
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Mohammed Qasem, Sesil Koutra, Stephane Brisset, Arnaud Davigny, Benoit
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Durillon
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To cite this version:
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Mohammed Qasem, Sesil Koutra, Stephane Brisset, Arnaud Davigny, Benoit Durillon. Open-Source Datasets,
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Models, and Tools for Renewable Energy Community Design. Symposium de Génie Électrique SGE 2025,
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cnrs, ups, Jul 2025, Toulouse, France. ⟨hal-05506759⟩
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HAL Id: hal-05506759
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https://hal.science/hal-05506759v1
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Submitted on 12 Feb 2026
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HAL is a multi-disciplinary open access archive L’archive ouverte pluridisciplinaire HAL, est des-
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for the deposit and dissemination of scientific re- tinée au dépôt et à la diffusion de documents scien-
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search documents, whether they are published or not. tifiques de niveau recherche, publiés ou non, émanant
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The documents may come from teaching and research des établissements d’enseignement et de recherche
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institutions in France or abroad, or from public or pri- français ou étrangers, des laboratoires publics ou
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vate research centers. privés.
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Distributed under a Creative Commons CC BY-NC 4.0 - Attribution - Non-commercial use - International
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License
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SYMPOSIUM DE GENIE ELECTRIQUE (SGE 2025), 1 - 3 JUILLET 2025, TOULOUSE, FRANCE
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Open-Source Datasets, Models, and Tools for
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Renewable Energy Community Design
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Mohammed QASEM1,2, Arnaud DAVIGNY2, Benoit DURILLON2, Sesil KOUTRA1, Stephane BRISSET2
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1
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Faculty of Architecture and Urban Planning, University of Mons, St. Havré 88, 7000 Mons, Belgium.
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2
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Univ. Lille, Arts et Metiers Institute of Technology, Centrale Lille Institute, Junia, ULR 2697 L2EP, F-59000 Lille, France
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Abstract -Renewable Energy Communities (RECs) show simulation environments. As illustrated in Fig. 2, the primary
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promising potential for achieving energy transition goals by fostering components required for modeling REC-based distributed
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collective ownership of energy systems and empowering citizens to energy systems include datasets, models, and simulation
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take control of energy processes (generating, managing, consuming, frameworks. These tools enable researchers and professionals to
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storing, or even distributing). To facilitate the study and simulate and explore different configurations of RECs to test
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implementation of RECs, a comprehensive analysis of their dynamics their hypotheses. Particularly, these components are typically
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through modeling is needed. Achieving this requires access to data, classified according to their accessibility and licensing terms:
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models, and tools with open-source options that present a viable and commercial, partially open-source, and fully open-source
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accessible alternative. However, literature lacks comprehensive
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solutions [4], [5]. This paper focuses only on fully open-source
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reviews of state-of-the-art open-source datasets, models, and tools
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specifically tailored for RECs modeling. To fill this gap, this paper
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toolkits for their accessibility, flexibility, and innovation by
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conducts a systematic review to evaluate the available up-to-date open- enabling community-driven development and inclusive design
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source materials applicable to REC analysis, including electrical, through collaborative problem-solving [6], [7]. Their
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thermal, and multi-energy system models, and develop a transparency and adaptability align with research interests in
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comprehensive list of open-source toolkits that effectively support advancing equitable and decentralized energy systems, such as
|
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their simulation and optimization. RECs [8].
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Keywords: datasets, energy modeling tools, open-source,
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renewable energy communities.
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1. INTRODUCTION
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In the EU, the buildings sector accounted for approximately
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40% of final energy consumption [1] underscoring its pivotal
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role in realizing the ambitious objectives of achieving carbon
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neutrality through the integration of clean energy by 2050 [2]. In
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this context, RECs contribute significantly to the green energy
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transition by putting citizens at the core of this shift and actively
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involving them in the decision-making process and daily energy Fig. 2. The main elements of a simplified REC modeling diagram.
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system operations (see Fig. 1) [3] .
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In this paper, the publicly accessible datasets, models, and
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tools related to energy modeling across various building types
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are compiled and analyzed. For each resource, a brief overview
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of its structure, scope, and key features is provided, followed by
|
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a comparative analysis in terms of scale, purpose, and relevance
|
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to specific energy research domains. This analysis supports
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researchers in selecting appropriate resources for eco-feedback,
|
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demand-side management, and sustainable energy applications
|
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[9].
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Previous studies, such as [7], explored the available datasets,
|
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models, and tools tailored for RECs and provided a brief
|
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introduction for each. Additionally, the studies [10], [11]
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Fig. 1. A general architecture of a REC.
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provided a comparative review of urban building energy
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modeling (UBEM) frameworks, focusing on their capabilities to
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Fig. 1 represents a simple structure of a REC, where the simulate and optimize building energy performance. However,
|
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members of this kind of energy systems share the energy despite the rapid progress of digital technologies in recent years,
|
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produced locally. these studies were limited by the fact that their most recent
|
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updates were in 2022, and they only addressed one or two of the
|
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To fully understand the complexities of the RECs and
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essential modeling components (datasets, models, and tools). In
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evaluate effective implementation strategies, robust modeling
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contrast, no study has yet comprehensively addressed all three
|
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and analytical frameworks are needed; achieving this requires
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components together.
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the integration of relevant datasets, computational models, and
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Therefore, to fill this gap, this review paper aims to conduct Transparency and innovation: Open science enhances
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a literature review focusing on evaluating, categorizing, and transparency by enabling repeatable analyses, which are critical
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listing the pros and cons of the available up-to-date open-source for scientific credibility, especially in energy systems modeling
|
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materials that support and enhance effective modeling and that often involves complex datasets and methodologies [19].
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design by optimization of RECs. This review will give insights Furthermore, the implementation of open-source tools and data-
|
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for researchers on energy systems modeling, providing a sharing methodologies enables rigorous peer review and the
|
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complete package of open-source datasets, models, and independent validation of results [20]. As emphasized by [21],
|
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frameworks that facilitate their research, with an open and open science plays a critical role in enhancing the quality and
|
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reproducible science approach. transparency of scientific research.
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This review paper is structured as follows: the second section Reproducibility and replicability: Open-source practices
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explores the role of open-source science in advancing research strengthen reproducibility by promoting transparency through
|
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with a specific focus on its application to REC modeling. The publicly available code, data, and modeling environments [22].
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third section provides a comprehensive review of the state-of- For example, open-source energy modeling tools, such as
|
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the-art datasets available for various types of energy loads OSeMOSYS provide researchers with accessible, standardized
|
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(including electric vehicles (EVs) and heat pumps), energy frameworks to inspect, validate, and adapt methodologies. This
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generation, and energy storage systems. The fourth section openness not only builds trust in results but also accelerates
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explores energy models suitable for simulation and innovation by reducing redundant efforts and encouraging
|
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optimization, as well as models designed for profile generation. iterative improvements across the scientific community (see
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While the fifth focuses on open-source tools relevant to these section 4.2).
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analyses, such as UBEM frameworks based on EnergyPlus [12]
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(e.g. CityBES, UrbanOpt, …etc.), PyPSA [13] and oemof [14]. Financial accessibility: Reduction of costs compared to
|
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Finally, the discussion and concluding sections of this paper proprietary software, enabling broader participation, especially
|
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offer a summary and synthesis of the key findings and provide in resource-limited RECs. Reducing barriers to data sharing
|
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insights and suggestions for future studies in this area. enables researchers to build upon each other’s work, accelerates
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discovery, and facilitates the translation of findings into
|
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• Load profiles
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effective policy measures [23].
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• Renewable energy profiles
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Datasets*
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2.2. Challenges
|
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• Models for generating profiles (load, renewable energy, and battery profiles)
|
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Despite the significant benefits of open science for
|
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Models
|
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• Models for energy systems modeling and design by optimization accelerating research collaboration, improving reproducibility,
|
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and enhancing transparency in energy systems modeling, these
|
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• Frameworks for energy systems modeling and design by optimization
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open-access materials face notable challenges, including:
|
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• Urban Building Energy Modeling (UBEM) frameworks
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Frameworks Technical complexity: A major barrier to the adoption of
|
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open-access materials is the technical complexity involved in
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* As mentioned in the Models section, several models are also used to generate datasets.
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their implementation and customization processes. Moreover,
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Fig. 3. Categorization of open-source resources for energy modeling and key technical challenges include inadequate institutional
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simulation presented in this paper. infrastructure, limited support, diverse data, and scalability
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issues [24], [25].
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Fig. 3 presents a categorization of open-source resources
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employed in energy modeling research, as outlined in this paper. Sustainability of initiatives: Open-source projects often
|
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face challenges in maintenance and updates due to limited
|
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2. ROLE OF OPEN-SOURCE SCIENCE IN ENERGY MODELING resources and financial support. Additionally, concerns arise
|
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Open-source solutions play a pivotal role in enabling over recognition and publication opportunities, particularly
|
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researchers and engineers to design, implement, and optimize when independently generated datasets fail to yield appropriate
|
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sustainable energy systems by ensuring transparency, credit or return on investment for researchers [24].
|
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replicability, encouraging collaboration, and reducing financial
|
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Interoperability issues: Challenges arise in integrating
|
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barriers to entry, thereby supporting the advancement of RECs
|
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open-source solutions with existing proprietary systems and
|
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on multiple fronts. [15], [16]. This section critically examines
|
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heterogeneous data formats. [26].
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the role of open-source collaborative initiatives in advancing
|
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REC research, highlighting both the opportunities they offer, Data security and privacy: Protecting sensitive energy data
|
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and the challenges associated with their adoption and in open-source environments is crucial for ensuring REC
|
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implementation. compliance and maintaining stakeholder trust. While this
|
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presents a challenge, it also offers an opportunity, as open access
|
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2.1. Opportunities to code facilitates collaborative debugging and enhances overall
|
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Open science is pivotal for accelerating advances in energy- system reliability [26]. Privacy and confidentiality concerns
|
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systems modeling, especially in cases where the availability of underscore the importance of ownership structures and data
|
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data or modeling tools is economically challenging. Open- governance. The absence of standardization and data
|
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source datasets and tools may bring several opportunities in governance strategies poses a challenge [24].
|
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energy modeling that can be summarized as follows:
|
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3. DATA FOR RECS
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Interoperability and collaboration: Interoperability refers
|
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to the ability of diverse systems or technologies to collaborate, Over the past decade, interest in open science has grown
|
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communicate, and function cohesively [17]. Open-source tools markedly, as reflected by both the proliferation of open‑source
|
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play a critical role in advancing interoperability by prioritizing energy datasets and a corresponding increase in related
|
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collaboration and community-driven innovation [18]. academic publications, as is seen in Fig. 4.
|
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12 The criteria for selecting the models in this section are
|
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10 twofold. First, models are considered based on their ability to
|
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generate relevant datasets, such as load profiles and renewable
|
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Number of publications
|
||||
|
||||
|
||||
|
||||
|
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8 energy production estimates, essential for simulating energy
|
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6 systems. Second, models are selected for their capacity to
|
||||
simulate energy generation and consumption dynamics, as well
|
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4 as their compatibility and interoperability with other modeling
|
||||
2 tools and platforms, which is crucial for integrated and scalable
|
||||
energy system analysis in decentralized and community-based
|
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0 settings. Consequently, the selected models are classified into
|
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2012 2014 2016 2018 2020 2022 2024
|
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Year two primary groups: one group of models is employed for
|
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generating datasets, while the other includes models utilized for
|
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Fig. 4. The number of publications that used public open data for modeling modeling and simulating energy systems.
|
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energy per year in the Scopus database.
|
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4.1. Models for data generation
|
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Fig. 4 shows that the number of publications that use open-
|
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source materials for energy systems modeling has notably Besides benchmark datasets that are published publicly,
|
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increased since 2017. The results are based on a research query numerous models and software programs can be used to generate
|
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using these keywords ("energy modeling" AND "open data") in load profiles, renewable energy profiles, or battery storage
|
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Scopus database. profiles.
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|
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The utilization of publicly accessible datasets is a critical 4.1.1. Load profiles data generating models
|
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component of the testing, validation, and benchmarking of The publicly accessible models that can be used for
|
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simulating models [27], including RECs models. The input generating load profiles are: demandlib, Load Profile Generator
|
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datasets, considered the fuel for energy systems modeling, can (LPG), Artificial Load Profile Generator (ALPG), Office Load
|
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be classified according to their acquisition method into two MATLAB App, and CREST Demand Model.
|
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principal categories: empirical (real‑world) datasets and
|
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Demandlib
|
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synthetic (generated) datasets. In this section, the empirical
|
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publicly available datasets (load and renewable energy profiles) Demandlib is a Python library used for generating time-
|
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from diverse regions around the world are reviewed. These resolved electrical and thermal demand profiles at various
|
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profiles serve as benchmarks for advancing energy‑modeling frequencies (e.g., 15-minute, hourly, or daily) from annual
|
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methodologies and simulation tools, thereby supporting the energy values. This model is typically used in energy-system
|
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transition to greener and more sustainable energy systems [7]. modeling and scenario analysis to provide realistic demand
|
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inputs for tools like oemof and PyPSA. It covers multiple end-
|
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Load profiles: publicly available load profiles datasets can be
|
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use sectors, notably residential (single- and multi-family houses)
|
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categorized by various criteria, including building type (e.g.,
|
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and various commercial and industrial categories. A key
|
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residential: GREEND, UK-DALE-2017, SustData;
|
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limitation of this tool is its reliance on fixed profile templates,
|
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commercial/public: Building Data Genome, BLOND, BERDS),
|
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which are often based on standardized German energy norms
|
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geographical location (e.g., Asia, Europe, North America), and
|
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and historical weather data. This approach may struggle to
|
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data resolution (e.g., per second, 10 seconds, 1 minute, 15
|
||||
account for region-specific or culturally distinct energy
|
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minutes, hourly, or daily). These classifications encompass
|
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consumption patterns outside of the original design context [30].
|
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diverse usage types such as household or industrial
|
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consumption, as well as base and flexible loads. LPG
|
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Renewable energy profiles: these datasets can be categorized LPG is a tool designed for simulating residential energy
|
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based on the type of energy source, such as solar (solar consumption through detailed behavioural modeling of
|
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irradiation) or wind (wind speed). Additionally, many of these household occupants. It generates load curves at various
|
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datasets incorporate relevant climatic and meteorological resolutions, ranging from 1 minute and 15 minutes to 1 hour,
|
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variables, including temperature and humidity, which are based on predefined German household profiles, and outputs the
|
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essential for accurate modeling and forecasting. data in CSV format, ensuring compatibility with other
|
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simulation tools. However, its application is limited to
|
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The publicly available datasets are shown in Appendix 1.
|
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residential settings; it does not support commercial or office
|
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4. MODELS FOR RECS building simulations [31].
|
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Models transform data into actionable insights [28] and serve ALPG
|
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as critical tools for understanding, predicting, and optimizing the
|
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ALPG is a Python tool designed to generate time-resolved
|
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performance of distributed energy systems, including RECs,
|
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electrical and thermal load profiles with embedded flexibility
|
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allowing for better decision-making and resource allocation.
|
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constraints for smart grid simulation and control algorithm
|
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These models, based on modeling techniques, can be classified
|
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evaluation. Unlike traditional static profiles, ALPG simulates
|
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into data-driven models and physical models. Also, they can be
|
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realistic, minute-resolution consumption behaviours of
|
||||
classified by the degree of physical interpretability into black-
|
||||
controllable domestic devices, enabling benchmarking and
|
||||
box, white-box, and grey-box models [29]. There are no specific
|
||||
comparative analysis of energy management solutions using
|
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models focused on modeling RECs. Still, since RECs are
|
||||
standardized input data. The tool integrates thermal modeling
|
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considered one of the innovative energy systems, this section
|
||||
techniques and reflects real-world consumption dynamics
|
||||
will focus on the open-source models that can be used for
|
||||
validated through field test measurements at both household and
|
||||
modeling and optimizing energy systems.
|
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neighbourhood levels. Importantly, ALPG's output is tailored
|
||||
for use as input data in downstream optimization or simulation
|
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tools, offering a robust foundation for evaluating demand-side energy cost savings using local weather data and NREL’s PV
|
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management strategies [32]. performance models. While less customizable than tools like
|
||||
pvlib, PVWatts is ideal for early-stage project assessment due to
|
||||
Office Load MATLAB App its ease of use, default assumptions, and reliable integration with
|
||||
The Office Load MATLAB App is a simulation tool NREL’s extensive solar resource database [37].
|
||||
designed to model electricity consumption in office buildings by GSEE
|
||||
integrating behavioural and physical approaches. It generates
|
||||
load profiles by considering occupancy patterns, appliance GSEE is a Python library designed for rapid and user-
|
||||
usage, and HVAC system operations, validated against real- friendly simulation of PV power output. It offers streamlined
|
||||
world data from Northern European office buildings. While the modeling of PV systems, supporting both fixed and tracking
|
||||
application offers flexibility in choosing time resolutions, the configurations, and is particularly suited for large-scale or long-
|
||||
default or commonly used time step is 10 minutes. The app is term energy assessments. GSEE integrates modules for
|
||||
useful for analysing demand response potential, optimizing estimating diffuse irradiance (via the BRL model), calculating
|
||||
energy use, and assessing the impact of different building irradiance on inclined planes, and interfacing with climate
|
||||
parameters on electricity consumption. However, this tool does datasets in formats like NetCDF. The BRL model is a reliable,
|
||||
not apply to large-scale simulations, which are computationally logistic regression-based approach that efficiently decomposes
|
||||
intensive [33]. global solar radiation into its diffuse and direct components
|
||||
using minimal input data, and its simple yet adaptable structure
|
||||
CREST Demand Model makes it particularly valuable in data-sparse environments such
|
||||
The CREST demand model is designed to simulate thermal as semiarid regions [38]. GSEE is utilized by platforms such as
|
||||
and electrical demand, with a default time step of 1 minute, Renewables.ninja for simulating solar energy generation across
|
||||
within residential settings, primarily for low-voltage network various geographical and temporal scales [39].
|
||||
analysis. It employs a bottom-up, activity-based approach Renewables.ninja
|
||||
integrated with stochastic programming techniques to capture
|
||||
occupant behaviour and dwelling diversity. By incorporating Renewables.ninja is a web-based platform that simulates
|
||||
reduced-order thermal-electrical networks, the model effectively hourly power output from wind and solar power systems at any
|
||||
represents thermal dynamics while generating high-resolution, global location. Developed at TU Delft and Imperial College
|
||||
validated outputs. Its design prioritizes the accurate timing of London, the tool aims to make scientific-grade energy and
|
||||
demand to reflect the inherently stochastic nature of household weather data accessible to a broad audience. It features validated
|
||||
energy use [34]. models for both solar and wind power and is widely used in
|
||||
energy research and planning [40].
|
||||
4.1.2. Renewable energy data generating models
|
||||
This group of models aims to generate renewable energy feedinlib (oemof)
|
||||
production profiles: Photovoltaic Geographical Information feedinlib is a model designed within the oemof tool that
|
||||
System (PVGIS), pvlib, PVWatts, Renewables.ninja, Global models feed-in time series from renewable energy sources,
|
||||
Solar Energy Estimator (GSEE), OEMOF’s feedinlib, and atlite. specifically wind and solar PV systems to simulate location-
|
||||
PVGIS specific power generation from renewables. feedinlib is
|
||||
designed to integrate seamlessly into energy system models built
|
||||
PVGIS is a web-based tool that is developed for providing with oemof, providing reproducible and transparent inputs for
|
||||
solar irradiation and PV performance data worldwide (except simulation and optimization. It supports different PV and wind
|
||||
polar regions). It includes modules for estimating grid- turbine models and allows for batch processing of multiple
|
||||
connected and off-grid (battery‑backed) PV yields, generating locations or assets [41].
|
||||
solar radiation time series (hourly/daily/monthly), and
|
||||
compiling typical meteorological year climate datasets [35]. atlite
|
||||
|
||||
pvlib atlite is a Python library developed for the conversion of
|
||||
meteorological data into energy-relevant time series, including
|
||||
pvlib is a Python and MATLAB toolbox designed for outputs for PV systems, wind power generation, solar thermal
|
||||
simulating the performance of PV energy systems. It provides a energy, heating demand, and hydropower potential. Optimized
|
||||
comprehensive collection of over 100 empirical and physics- for large-scale energy system simulations, it offers efficient
|
||||
based models from peer-reviewed literature, covering solar memory and CPU usage. The library supports integration with
|
||||
position algorithms, irradiance models, thermal models, and PV high-resolution climate datasets such as ERA5 and COSMO-
|
||||
electrical models. With contributions from a growing global user REA6 and is compatible with energy modeling frameworks like
|
||||
base, pvlib supports high-level workflows for complete PyPSA. Built upon the x-array data structure, atlite provides
|
||||
"weather-to-power" simulations and includes tools for fetching extensive spatial and temporal flexibility, making it a widely
|
||||
and standardizing weather data and has become a reliable adopted tool in power system modeling and energy research
|
||||
standard for PV performance analysis [36]. [42].
|
||||
PVWatts 4.1.3. Battery profile generating models
|
||||
PVWatts Calculator is a web-based tool designed to estimate A battery profile is defined as a set of performance
|
||||
the electricity production and energy value of grid-connected PV characteristics (e.g., state-of-charge, cycle depth, number of sign
|
||||
systems. It provides a simple interface for users, ranging from changes, length of resting periods, energy between sign changes)
|
||||
homeowners to project developers, to input key system of battery storage under various operating conditions [43]. These
|
||||
parameters such as location, system size, module tilt, orientation, profiles are essential for comprehending battery behavior,
|
||||
and system losses. Based on this input, PVWatts calculates optimizing performance, and ensuring safety in various
|
||||
monthly and annual AC energy output and the corresponding applications, such as EVs and grid storage systems [43]. The
|
||||
models that are tailored for battery profile generation, and Balmorel
|
||||
mentioned in [7], are: QUantum Electronic Structure (QuESt),
|
||||
Open-Source Energy Storage Model (OSESMO), and Balmorel is an energy system optimization model written in
|
||||
EnergyBoost. GAMS modeling language and built in a generic, extensible
|
||||
modular structure. This model is designed to simulate and
|
||||
QuESt 2.0 optimize the generation, transmission, storage, and consumption
|
||||
of electricity and heat. This is achieved by assuming the
|
||||
QuESt 2.0 is a Python-based platform developed for presence of perfectly competitive markets. The objective is to
|
||||
advanced energy storage analytics, aiming to simplify these maximize social welfare, while considering a wide range of
|
||||
analyses and democratize access to these tools. Evolving from technical, economic, and regulatory constraints [49].
|
||||
its original version, QuESt 2.0 offers a centralized suite of tools
|
||||
(e.g. App Hub, Workspace, and QuESt GPT), that enable NEMO
|
||||
streamlined workflows, AI-driven insights, and scalable data
|
||||
processing. Its key innovations include a unified interface, NEMO is a high-performance model developed in Julia and
|
||||
integration of generative AI for complex analytics, and modular used for modeling and optimizing electrical energy systems.
|
||||
extensibility. Unlike traditional tools focused on isolated tasks, This tool can be used in stand-alone mode or with the Low
|
||||
QuESt 2.0 supports holistic and collaborative energy storage Emissions Analysis Platform (LEAP) as a front-end. NEMO
|
||||
evaluation, aligned with U.S. Department of Energy objectives facilitates the incorporation of energy storage capacity into the
|
||||
by democratizing access to advanced energy storage analytics long-term simulation of power system expansion. This is
|
||||
tools [44]. particularly relevant when assessing the significance of
|
||||
balancing intermittent renewable energy, especially in scenarios
|
||||
OSESMO with a high penetration of renewable energy sources [50] [51].
|
||||
OSESMO is an energy storage dispatch optimization tool Other models have been built for a specific function to solve
|
||||
originally developed in MATLAB and now being translated into a challenge in the energy systems. Such as the URBS model,
|
||||
Python for broader accessibility and future web deployment. which is designed for linear programming tasks related to energy
|
||||
OSESMO evaluates the dispatch (charge/discharge) of energy dispatch and expansion planning that enables users to flexibly
|
||||
storage to evaluate the GHG emissions of an energy storage define multiple regions, associated energy conversion and
|
||||
project. It supports analysis of behind-the-meter and solar-plus- storage processes, and interregional transmission lines. Its
|
||||
storage systems with a focus on time-of-use arbitrage, demand modular structure allows for the integration of emerging
|
||||
charge management, solar self-consumption, and GHG technologies, such as Power-to-X, making it suitable for diverse
|
||||
emissions reduction [45]. applications [52]. The IDEAS model, a Modelica-based model,
|
||||
allows simultaneous transient simulation of thermal and
|
||||
EnergyBoost electrical systems at both building and feeder levels. The
|
||||
EnergyBoost is a Python-based control software for home AMIRIS model is designed for modeling electricity markets and
|
||||
battery systems, designed to run on a Raspberry Pi. It integrates focuses on the business-oriented decisions of actors, who are
|
||||
supervised learning models to forecast the next-day solar represented as agents, in the energy system [53]. Finally, the
|
||||
generation and household demand, alongside physical models Dispa-SET model is used for solving energy balancing and
|
||||
simulating inverter output and battery state-of-charge (SoC). flexibility problems and modeling the power system on different
|
||||
EnergyBoost formulates an optimal control problem to schedule scales (e.g., district, region, country) [54].
|
||||
battery charging/discharging over a finite horizon, using both
|
||||
model-based and model-free control methods, and aims at 5. FRAMEWORKS FOR MODELING RECS
|
||||
optimizing residential energy use [46]. Frameworks are essential tools that integrate data and
|
||||
models within software platforms to support the simulation,
|
||||
4.2. Models for energy systems modeling
|
||||
analysis, and optimization of building energy performance. In
|
||||
In this section, the models used for modeling energy systems this paper, based on their purpose and primary modeling
|
||||
(electrical, thermal, and multi-energy systems) as well as for approaches, these frameworks are classified into two main
|
||||
optimization are briefly summarized and explained. The models groups: energy system optimization frameworks and UBEM
|
||||
explored here are: Open-Source Energy Modeling System frameworks.
|
||||
(OSeMOSYS), Balmorel, and Next Energy Modeling System
|
||||
for Optimization (NEMO). 5.1. Energy system modeling frameworks
|
||||
These frameworks are primarily designed to model and
|
||||
OSeMOSYS
|
||||
optimize energy flows and interactions within multi-sectoral
|
||||
OSeMOSYS is a completely developed optimization model energy systems and are typically used to simulate energy
|
||||
that is utilized for long-run integrated assessment and energy systems (electrical, thermal and multi-energy systems) at
|
||||
planning. This tool is designed to develop energy systems various spatial and temporal scales.
|
||||
(electrical, thermal and transport) models for different scales
|
||||
oemof
|
||||
(districts, cities, states, countries, continents, or even the globe).
|
||||
The low learning curve and the availability of OSeMOSYS for oemof is a modeling framework used to construct
|
||||
free extend the availability of energy modeling to a wide group comprehensive energy system models and incorporate various
|
||||
of researchers and users [47]. Based on this model, the modeling approaches. It is particularly well suited to flexibly
|
||||
GENeSYS-MOD model, an optimization model that uses the model complex cross-sectoral systems, linking the heat, power,
|
||||
CPLEX solver, is developed [19]. Just like OSeMOSYS, and mobility sectors. The framework includes various packages
|
||||
GENeSYS-MOD consists of multiple blocks of functionality, such as oemof.solph for linear optimization of energy systems,
|
||||
which work as separate entities that can be changed or extended oemof.outputlib and oemof-visio for results processing, feedinlib
|
||||
[48]. and windpowerlib for renewable generation profiles, and
|
||||
demandlib for generating load profiles [55].
|
||||
PyPSA residential consumption patterns, respectively, yet their domain-
|
||||
specific design underscores a persistent need for more
|
||||
PyPSA is a framework designed to simulate and optimize generalized frameworks that can seamlessly accommodate
|
||||
energy power systems over multiple periods. It encompasses diverse building typologies and end-use sectors.
|
||||
conventional generators with unit commitment, variable
|
||||
renewable generation, storage units, integration with other Finaly, in regard to the frameworks, physics-based models
|
||||
energy sectors, and mixed alternating and direct current deliver high-fidelity simulations of building energy dynamics at
|
||||
networks. PyPSA is designed to facilitate seamless extensibility the expense of scalability and computational speed. EnergyPlus-
|
||||
and ensure optimal scaling, both within the context of extensive based tools (e.g., UrbanOpt, AutoBEM, ComStock, CityBES,
|
||||
networks and in the management of extensive time series [13]. and UMI) leverage detailed thermodynamic calculations to
|
||||
capture multi-zone interactions and temporal variations, making
|
||||
5.2. UBEM frameworks them ideal for district-level energy planning, but challenging for
|
||||
UBEM frameworks are primarily developed to simulate large-scale deployment. Conversely, reduced-order RC model–
|
||||
energy performance in buildings at the urban scale, with a focus based platforms (e.g., CitySim, SimStadt, OpenIDEAS, CEA,
|
||||
on building stock analysis and district-level energy planning. and TEASER) employ simplified data-driven methods to
|
||||
These frameworks, summarized in [4], [5], can be classified achieve rapid simulations while preserving key thermal
|
||||
based on the modeling methodology to which they are built upon behaviours, albeit with reduced spatial and temporal resolution.
|
||||
reduced-order resistance-capacitance (RC) and heat-balanced A balanced integration of these methodologies could enhance
|
||||
physics model-based frameworks. the development of adaptable, efficient frameworks capable of
|
||||
Heat-balanced physics model-based frameworks: These addressing diverse urban contexts and scales.
|
||||
tools are based on models that rely on fundamental 7. CONCLUSION
|
||||
thermodynamic principles to simulate the detailed energy
|
||||
behavior of buildings. These frameworks simulate multi- To conclude, this paper underscores the critical role of open-
|
||||
thermal-zone dynamics and allow for detailed temporal and source datasets and tools in supporting the modeling and
|
||||
spatial resolution of energy flows, and are typically used for optimizing the energy systems, including RECs. Through a
|
||||
high-resolution, dynamic simulations. Frameworks in this literature review, an updated and comprehensive inventory of
|
||||
category include: EnergyPlus-based tools such as UrbanOpt, open-source datasets, models, and frameworks has been
|
||||
AutoBEM, ComStock, CityBES, and UMI. developed. These resources address key aspects of energy
|
||||
systems modeling, including energy generation, consumption,
|
||||
Reduced-order RC model-based frameworks: These storage, and distribution. The primary findings of this research
|
||||
platforms simplify physics to improve computational efficiency include:
|
||||
while preserving key thermal behaviors. These models often use
|
||||
resistance-capacitance (RC) networks or data-driven approaches • A thorough evaluation of existing open-source
|
||||
to estimate energy demand. Tools in this category include: materials, highlighting their strengths and limitations.
|
||||
CitySim, SimStadt, OpenIDEAS, CEA, and TEASER. • A structured categorization of these tools based on their
|
||||
relevance and application to REC modeling.
|
||||
A detailed comparison of the characteristics of these
|
||||
frameworks is provided in Appendix 3, highlighting their core This review provides a robust foundation for advancing the
|
||||
modeling approaches, input, output and temporal resolution. adoption and optimization of RECs, contributing significantly to
|
||||
This comparative overview facilitates understanding of the the achievement of European climate and energy goals.
|
||||
trade-offs between model fidelity and computational efficiency. Finally, this work aims to lower barriers to entry for REC
|
||||
6. DISCUSSION development by improving data availability and modeling tools.
|
||||
The future work can address two interconnected challenges in
|
||||
Despite the critical role of publicly accessible data in RECs modeling: First, we propose developing open datasets, for
|
||||
informing research and policy, the vast majority of these datasets different geographical contexts, tailored to RECs, integrating
|
||||
are produced by institutions in Europe, US, and UK, resulting in synthetic data generation to overcome gaps in real-world data.
|
||||
a pronounced regional bias that complicates their applicability Second, we aim to adapt or build open-source energy models
|
||||
in diverse geographic contexts. based on the already developed models (e.g., PyPSA, oemof) to
|
||||
Regarding the models used for generating load profile explicitly address REC-specific dynamics like peer-to-peer
|
||||
datasets, since they are calibrated on historical data from a single trading, grid constraints, and policy frameworks. By bridging
|
||||
geographical region, their applicability, and thus their accuracy, these gaps, this work seeks to empower RECs with actionable
|
||||
is inherently constrained to contexts and climate regimes similar tools for optimizing decentralized systems and informing
|
||||
to those for which they were originally developed. Models, such policies that accelerate equitable energy transitions.
|
||||
as CREST demand model, adopt a bottom-up, stochastic,
|
||||
8. ACKNOWLEDGEMENTS
|
||||
activity-based methodology that enhances the
|
||||
representativeness of thermal–electrical demand timing; The authors express their gratitude to the Hauts-de-France
|
||||
however, this approach introduces considerable computational Region and the University of Mons for funding the project.
|
||||
complexity and data input requirements. Advanced load profile
|
||||
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