diff --git a/.gitignore b/.gitignore index bf65263..c576766 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,10 @@ HAMLET/ +tools/citrineos-core-main/.git +tools/EVerest-main/.git +tools/shapeshifter-specification-main/.git +tools/shapeshifter-library-python-main/.git +tools/openocpp/.git +*.zip +tools/*/node_modules +tools/*/dist +tools/*/__pycache__ diff --git a/docs/hal_article.txt b/docs/hal_article.txt new file mode 100644 index 0000000..354eef0 --- /dev/null +++ b/docs/hal_article.txt @@ -0,0 +1,653 @@ + 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 + + + + + HAL is a multi-disciplinary open access archive L’archive ouverte pluridisciplinaire HAL, est des- +for the deposit and dissemination of scientific re- tinée au dépôt et à la diffusion de documents scien- +search documents, whether they are published or not. tifiques de niveau recherche, publiés ou non, émanant +The documents may come from teaching and research des établissements d’enseignement et de recherche +institutions in France or abroad, or from public or pri- français ou étrangers, des laboratoires publics ou +vate research centers. privés. + + + Distributed under a Creative Commons CC BY-NC 4.0 - Attribution - Non-commercial use - International + License + 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 +generators, such as ALPG, further extend these capabilities by 9. REFERENCES +embedding device-level flexibility constraints and leveraging [1] D. D’Agostino, P. M. Congedo, P. M. 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