         Open-Source Datasets, Models, and Tools for Renewable
                      Energy Community Design
        Mohammed Qasem, Sesil Koutra, Stephane Brisset, Arnaud Davigny, Benoit
                                                      Durillon



      To cite this version:
     Mohammed Qasem, Sesil Koutra, Stephane Brisset, Arnaud Davigny, Benoit Durillon. Open-Source Datasets,
     Models, and Tools for Renewable Energy Community Design. Symposium de Génie Électrique SGE 2025,
     cnrs, ups, Jul 2025, Toulouse, France. ⟨hal-05506759⟩




                                        HAL Id: hal-05506759
                              https://hal.science/hal-05506759v1
                                           Submitted on 12 Feb 2026




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                                                      SYMPOSIUM DE GENIE ELECTRIQUE (SGE 2025), 1 - 3 JUILLET 2025, TOULOUSE, FRANCE




             Open-Source Datasets, Models, and Tools for
               Renewable Energy Community Design
        Mohammed QASEM1,2, Arnaud DAVIGNY2, Benoit DURILLON2, Sesil KOUTRA1, Stephane BRISSET2
                 1
                  Faculty of Architecture and Urban Planning, University of Mons, St. Havré 88, 7000 Mons, Belgium.
   2
       Univ. Lille, Arts et Metiers Institute of Technology, Centrale Lille Institute, Junia, ULR 2697 L2EP, F-59000 Lille, France

    Abstract -Renewable Energy Communities (RECs) show                    simulation environments. As illustrated in Fig. 2, the primary
promising potential for achieving energy transition goals by fostering    components required for modeling REC-based distributed
collective ownership of energy systems and empowering citizens to         energy systems include datasets, models, and simulation
take control of energy processes (generating, managing, consuming,        frameworks. These tools enable researchers and professionals to
storing, or even distributing). To facilitate the study and               simulate and explore different configurations of RECs to test
implementation of RECs, a comprehensive analysis of their dynamics        their hypotheses. Particularly, these components are typically
through modeling is needed. Achieving this requires access to data,       classified according to their accessibility and licensing terms:
models, and tools with open-source options that present a viable and      commercial, partially open-source, and fully open-source
accessible alternative. However, literature lacks comprehensive
                                                                          solutions [4], [5]. This paper focuses only on fully open-source
reviews of state-of-the-art open-source datasets, models, and tools
specifically tailored for RECs modeling. To fill this gap, this paper
                                                                          toolkits for their accessibility, flexibility, and innovation by
conducts a systematic review to evaluate the available up-to-date open-   enabling community-driven development and inclusive design
source materials applicable to REC analysis, including electrical,        through collaborative problem-solving [6], [7]. Their
thermal, and multi-energy system models, and develop a                    transparency and adaptability align with research interests in
comprehensive list of open-source toolkits that effectively support       advancing equitable and decentralized energy systems, such as
their simulation and optimization.                                        RECs [8].

   Keywords: datasets, energy modeling tools, open-source,
renewable energy communities.

1. INTRODUCTION
    In the EU, the buildings sector accounted for approximately
40% of final energy consumption [1] underscoring its pivotal
role in realizing the ambitious objectives of achieving carbon
neutrality through the integration of clean energy by 2050 [2]. In
this context, RECs contribute significantly to the green energy
transition by putting citizens at the core of this shift and actively
involving them in the decision-making process and daily energy            Fig. 2. The main elements of a simplified REC modeling diagram.
system operations (see Fig. 1) [3] .
                                                                               In this paper, the publicly accessible datasets, models, and
                                                                          tools related to energy modeling across various building types
                                                                          are compiled and analyzed. For each resource, a brief overview
                                                                          of its structure, scope, and key features is provided, followed by
                                                                          a comparative analysis in terms of scale, purpose, and relevance
                                                                          to specific energy research domains. This analysis supports
                                                                          researchers in selecting appropriate resources for eco-feedback,
                                                                          demand-side management, and sustainable energy applications
                                                                          [9].
                                                                              Previous studies, such as [7], explored the available datasets,
                                                                          models, and tools tailored for RECs and provided a brief
                                                                          introduction for each. Additionally, the studies [10], [11]
Fig. 1. A general architecture of a REC.
                                                                          provided a comparative review of urban building energy
                                                                          modeling (UBEM) frameworks, focusing on their capabilities to
   Fig. 1 represents a simple structure of a REC, where the               simulate and optimize building energy performance. However,
members of this kind of energy systems share the energy                   despite the rapid progress of digital technologies in recent years,
produced locally.                                                         these studies were limited by the fact that their most recent
                                                                          updates were in 2022, and they only addressed one or two of the
    To fully understand the complexities of the RECs and
                                                                          essential modeling components (datasets, models, and tools). In
evaluate effective implementation strategies, robust modeling
                                                                          contrast, no study has yet comprehensively addressed all three
and analytical frameworks are needed; achieving this requires
                                                                          components together.
the integration of relevant datasets, computational models, and
     Therefore, to fill this gap, this review paper aims to conduct                                         Transparency and innovation: Open science enhances
a literature review focusing on evaluating, categorizing, and                                           transparency by enabling repeatable analyses, which are critical
listing the pros and cons of the available up-to-date open-source                                       for scientific credibility, especially in energy systems modeling
materials that support and enhance effective modeling and                                               that often involves complex datasets and methodologies [19].
design by optimization of RECs. This review will give insights                                          Furthermore, the implementation of open-source tools and data-
for researchers on energy systems modeling, providing a                                                 sharing methodologies enables rigorous peer review and the
complete package of open-source datasets, models, and                                                   independent validation of results [20]. As emphasized by [21],
frameworks that facilitate their research, with an open and                                             open science plays a critical role in enhancing the quality and
reproducible science approach.                                                                          transparency of scientific research.
    This review paper is structured as follows: the second section                                          Reproducibility and replicability: Open-source practices
explores the role of open-source science in advancing research                                          strengthen reproducibility by promoting transparency through
with a specific focus on its application to REC modeling. The                                           publicly available code, data, and modeling environments [22].
third section provides a comprehensive review of the state-of-                                          For example, open-source energy modeling tools, such as
the-art datasets available for various types of energy loads                                            OSeMOSYS provide researchers with accessible, standardized
(including electric vehicles (EVs) and heat pumps), energy                                              frameworks to inspect, validate, and adapt methodologies. This
generation, and energy storage systems. The fourth section                                              openness not only builds trust in results but also accelerates
explores energy models suitable for simulation and                                                      innovation by reducing redundant efforts and encouraging
optimization, as well as models designed for profile generation.                                        iterative improvements across the scientific community (see
While the fifth focuses on open-source tools relevant to these                                          section 4.2).
analyses, such as UBEM frameworks based on EnergyPlus [12]
(e.g. CityBES, UrbanOpt, …etc.), PyPSA [13] and oemof [14].                                                 Financial accessibility: Reduction of costs compared to
Finally, the discussion and concluding sections of this paper                                           proprietary software, enabling broader participation, especially
offer a summary and synthesis of the key findings and provide                                           in resource-limited RECs. Reducing barriers to data sharing
insights and suggestions for future studies in this area.                                               enables researchers to build upon each other’s work, accelerates
                                                                                                        discovery, and facilitates the translation of findings into
             • Load profiles
                                                                                                        effective policy measures [23].
             • Renewable energy profiles
 Datasets*
                                                                                                        2.2. Challenges
             • Models for generating profiles (load, renewable energy, and battery profiles)
                                                                                                            Despite the significant benefits of open science for
  Models
             • Models for energy systems modeling and design by optimization                            accelerating research collaboration, improving reproducibility,
                                                                                                        and enhancing transparency in energy systems modeling, these
             • Frameworks for energy systems modeling and design by optimization
                                                                                                        open-access materials face notable challenges, including:
             • Urban Building Energy Modeling (UBEM) frameworks
Frameworks                                                                                                  Technical complexity: A major barrier to the adoption of
                                                                                                        open-access materials is the technical complexity involved in
             * As mentioned in the Models section, several models are also used to generate datasets.
                                                                                                        their implementation and customization processes. Moreover,
Fig. 3. Categorization of open-source resources for energy modeling and                                 key technical challenges include inadequate institutional
simulation presented in this paper.                                                                     infrastructure, limited support, diverse data, and scalability
                                                                                                        issues [24], [25].
  Fig. 3 presents a categorization of open-source resources
employed in energy modeling research, as outlined in this paper.                                            Sustainability of initiatives: Open-source projects often
                                                                                                        face challenges in maintenance and updates due to limited
2. ROLE OF OPEN-SOURCE SCIENCE IN ENERGY MODELING                                                       resources and financial support. Additionally, concerns arise
    Open-source solutions play a pivotal role in enabling                                               over recognition and publication opportunities, particularly
researchers and engineers to design, implement, and optimize                                            when independently generated datasets fail to yield appropriate
sustainable energy systems by ensuring transparency,                                                    credit or return on investment for researchers [24].
replicability, encouraging collaboration, and reducing financial
                                                                                                            Interoperability issues: Challenges arise in integrating
barriers to entry, thereby supporting the advancement of RECs
                                                                                                        open-source solutions with existing proprietary systems and
on multiple fronts. [15], [16]. This section critically examines
                                                                                                        heterogeneous data formats. [26].
the role of open-source collaborative initiatives in advancing
REC research, highlighting both the opportunities they offer,                                               Data security and privacy: Protecting sensitive energy data
and the challenges associated with their adoption and                                                   in open-source environments is crucial for ensuring REC
implementation.                                                                                         compliance and maintaining stakeholder trust. While this
                                                                                                        presents a challenge, it also offers an opportunity, as open access
2.1. Opportunities                                                                                      to code facilitates collaborative debugging and enhances overall
    Open science is pivotal for accelerating advances in energy-                                        system reliability [26]. Privacy and confidentiality concerns
systems modeling, especially in cases where the availability of                                         underscore the importance of ownership structures and data
data or modeling tools is economically challenging. Open-                                               governance. The absence of standardization and data
source datasets and tools may bring several opportunities in                                            governance strategies poses a challenge [24].
energy modeling that can be summarized as follows:
                                                                                                        3. DATA FOR RECS
    Interoperability and collaboration: Interoperability refers
to the ability of diverse systems or technologies to collaborate,                                          Over the past decade, interest in open science has grown
communicate, and function cohesively [17]. Open-source tools                                            markedly, as reflected by both the proliferation of open‑source
play a critical role in advancing interoperability by prioritizing                                      energy datasets and a corresponding increase in related
collaboration and community-driven innovation [18].                                                     academic publications, as is seen in Fig. 4.
                          12                                                     The criteria for selecting the models in this section are
                          10                                                 twofold. First, models are considered based on their ability to
                                                                             generate relevant datasets, such as load profiles and renewable
 Number of publications




                          8                                                  energy production estimates, essential for simulating energy
                          6                                                  systems. Second, models are selected for their capacity to
                                                                             simulate energy generation and consumption dynamics, as well
                          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
                          0                                                  settings. Consequently, the selected models are classified into
                           2012   2014   2016   2018    2020   2022   2024
                                                 Year                        two primary groups: one group of models is employed for
                                                                             generating datasets, while the other includes models utilized for
Fig. 4. The number of publications that used public open data for modeling   modeling and simulating energy systems.
energy per year in the Scopus database.
                                                                             4.1. Models for data generation
    Fig. 4 shows that the number of publications that use open-
source materials for energy systems modeling has notably                         Besides benchmark datasets that are published publicly,
increased since 2017. The results are based on a research query              numerous models and software programs can be used to generate
using these keywords ("energy modeling" AND "open data") in                  load profiles, renewable energy profiles, or battery storage
Scopus database.                                                             profiles.

    The utilization of publicly accessible datasets is a critical            4.1.1. Load profiles data generating models
component of the testing, validation, and benchmarking of                        The publicly accessible models that can be used for
simulating models [27], including RECs models. The input                     generating load profiles are: demandlib, Load Profile Generator
datasets, considered the fuel for energy systems modeling, can               (LPG), Artificial Load Profile Generator (ALPG), Office Load
be classified according to their acquisition method into two                 MATLAB App, and CREST Demand Model.
principal categories: empirical (real‑world) datasets and
                                                                                Demandlib
synthetic (generated) datasets. In this section, the empirical
publicly available datasets (load and renewable energy profiles)                 Demandlib is a Python library used for generating time-
from diverse regions around the world are reviewed. These                    resolved electrical and thermal demand profiles at various
profiles serve as benchmarks for advancing energy‑modeling                   frequencies (e.g., 15-minute, hourly, or daily) from annual
methodologies and simulation tools, thereby supporting the                   energy values. This model is typically used in energy-system
transition to greener and more sustainable energy systems [7].               modeling and scenario analysis to provide realistic demand
                                                                             inputs for tools like oemof and PyPSA. It covers multiple end-
Load profiles: publicly available load profiles datasets can be
                                                                             use sectors, notably residential (single- and multi-family houses)
categorized by various criteria, including building type (e.g.,
                                                                             and various commercial and industrial categories. A key
residential:   GREEND,          UK-DALE-2017,         SustData;
                                                                             limitation of this tool is its reliance on fixed profile templates,
commercial/public: Building Data Genome, BLOND, BERDS),
                                                                             which are often based on standardized German energy norms
geographical location (e.g., Asia, Europe, North America), and
                                                                             and historical weather data. This approach may struggle to
data resolution (e.g., per second, 10 seconds, 1 minute, 15
                                                                             account for region-specific or culturally distinct energy
minutes, hourly, or daily). These classifications encompass
                                                                             consumption patterns outside of the original design context [30].
diverse usage types such as household or industrial
consumption, as well as base and flexible loads.                                LPG
Renewable energy profiles: these datasets can be categorized                     LPG is a tool designed for simulating residential energy
based on the type of energy source, such as solar (solar                     consumption through detailed behavioural modeling of
irradiation) or wind (wind speed). Additionally, many of these               household occupants. It generates load curves at various
datasets incorporate relevant climatic and meteorological                    resolutions, ranging from 1 minute and 15 minutes to 1 hour,
variables, including temperature and humidity, which are                     based on predefined German household profiles, and outputs the
essential for accurate modeling and forecasting.                             data in CSV format, ensuring compatibility with other
                                                                             simulation tools. However, its application is limited to
The publicly available datasets are shown in Appendix 1.
                                                                             residential settings; it does not support commercial or office
4. MODELS FOR RECS                                                           building simulations [31].
    Models transform data into actionable insights [28] and serve               ALPG
as critical tools for understanding, predicting, and optimizing the
                                                                                 ALPG is a Python tool designed to generate time-resolved
performance of distributed energy systems, including RECs,
                                                                             electrical and thermal load profiles with embedded flexibility
allowing for better decision-making and resource allocation.
                                                                             constraints for smart grid simulation and control algorithm
These models, based on modeling techniques, can be classified
                                                                             evaluation. Unlike traditional static profiles, ALPG simulates
into data-driven models and physical models. Also, they can be
                                                                             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
models focused on modeling RECs. Still, since RECs are
                                                                             standardized input data. The tool integrates thermal modeling
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.
                                                                             neighbourhood levels. Importantly, ALPG's output is tailored
                                                                             for use as input data in downstream optimization or simulation
tools, offering a robust foundation for evaluating demand-side        energy cost savings using local weather data and NREL’s PV
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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