Understanding novel district concepts: A structured exploration of interdisciplinary clustering in urban energy systems

Contents lists available at ScienceDirect Energy Reports journal homepage: www.elsevier.com/locate/egyr Research paper Understanding novel district concepts: A structured exploration of interdisciplinary clustering in urban energy systems Johannes Galenzowski ∗, Simon Waczowicz , Hüseyin K. Çakmak , Erfan Tajalli-Ardekani , Sebastian Beichter , Ömer Ekin , Ralf Mikut , Veit Hagenmeyer Karlsruhe Institute of Technology, Kaiserstraße 12, Karlsruhe 76131, Germany A R T I C L E I N F O Keywords: Novel district concepts Device clustering Interdisciplinary research Clustering approaches Consistent terminology usage A B S T R A C T Recently, there has been a surge of interest in novel concepts for jointly operating devices in urban areas in clusters, motivated by their potential to support decarbonization, enhance power system flexibility, and promote energy justice. Such clusters encompass multiple devices or buildings but operate on a smaller scale than cities. Examples include Renewable Energy Communities and Positive Energy Districts. These Novel District Concepts (NDCs) integrate interdisciplinary urban planning and social sciences terminology into the energy domain. However, these concepts’ precise definitions and practical implementation lack consistency, leading to conceptual ambiguities in the literature. The present paper reviews clustering approaches from both the energy domain and the urban planning and social sciences disciplines to analyze rules for defining device clusters. The findings reveal that while numerous papers claim novelty using Novel District Concepts terminology, many rely on established energydomain methodologies, such as clustering techniques structured around electricity grid hierarchies. In contrast, clustering approaches from urban planning and social sciences, which employ spatial and social criteria, remain underutilized and lack systematic evaluation for energy system applications. The present paper’s key contribution lies in systematically identifying and differentiating clustering rules, establishing a robust foundation for subsequent cluster-based research, and ensuring methodological consistency. By integrating concepts from urban planning and social sciences with established energydomain approaches, this paper delineates clear boundaries and grounds them contextually. The present paper’s structured methodology provides a comprehensive workflow for distinguishing diverse clustering rules, mitigating the risk of misapplied terminology, and facilitating future evaluations of their applicability to specific energy-system tasks. 1. Introduction Terms such as Positive Energy Districts (PEDs) and Renewable Energy Communities (RECs) have recently attracted significant attention in the context of urban energy systems (Sassenou et al., 2024; Natanian et al., 2024; Haji Bashi et al., 2023). District-level concepts also play a central role in sustainable urban development initiatives worldwide, as illustrated by projects in Morocco (Echlouchi et al., 2022) and China (Xu et al., 2024). They are further relevant in the United States, Canada, and New Zealand, as reported by Barabino et al. (2023). These Novel District Concepts (NDCs) are predominantly applied in energyrelated applications that are inherently tied to devices that produce, consume, store, transmit, or convert energy (Galenzowski et al., 2023a; Haji Bashi et al., 2023). Examples of such devices include photovoltaic systems, battery energy storage systems, electric vehicles, combined ∗Corresponding author. E-mail address: [email protected] (J. Galenzowski). heat and power plants, wind turbines, heat pumps, chillers, and thermal energy storage systems (Haji Bashi et al., 2023). In addition to these controllable devices, the urban energy system includes non-controllable residual consumers, such as devices in residential units, offices, and workshops (Galenzowski et al., 2023b). NDCs are associated with a range of anticipated benefits (Caferra et al., 2024; Haji Bashi et al., 2023). Climate action benefits include decarbonization, the achievement of renewable energy targets, and an increase in public acceptance of the energy transition. Technical benefits encompass enhanced power system flexibility, self-sufficiency, and decreased dependence on national grids. Additionally, social benefits include promoting energy justice, fostering job creation, and facilitating investment and energy cost reduction (Haji Bashi et al., 2023). Due to their interdisciplinary nature, PEDs and RECs are applied across legal, economic, and governance contexts (European Parliament, Council of the European Union, 2019, 2018; Hinterberger et al., 2020). https://doi.org/10.1016/j.egyr.2025.10.021 Received 8 July 2025; Received in revised form 19 September 2025; Accepted 19 October 2025 Energy Reports 14 (2025) 3673–3689 Available online 7 November 2025 2352-4847/© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). J. Galenzowski et al. Abbreviations CEC Citizen Energy Community EMD Electricity Market Design GIS Geographic Information System IRIS Ilots Regroupés pour l’Information Statistique JPI Joint Programming Initiative LV Low-Voltage MV Medium-Voltage NDC Novel District Concept PCC Point of Common Coupling PED Positive Energy District PV Photovoltaic REC Renewable Energy Community RED Renewable Energy Directive ZCC Zero Carbon Community ZEC Zero Energy Community The foundational grounding in legislation is presented in Section 1.1. The present paper, however, adopts a technical energy system perspective, focusing on the operational role of NDCs in clustering devices and subsystems within urban environments to enable joint operation and control. These clusters represent an intermediate scale between entire cities and individual devices or buildings (Sassenou et al., 2024; Bauwens et al., 2022). The intermediate scale of the clusters addresses the limitations of single-building approaches by considering building interdependencies and enabling solutions involving multiple stakeholders, such as grid operators and energy producers (Sassenou et al., 2024). It balances the operational complexity of larger urban units while facilitating cross-sector integration, democratic energy planning, and the inclusion of local resources in broader energy strategies. To ensure operational coherence and to achieve the intended benefits, it is essential to define system boundaries consistently and systematically (Albert-Seifried et al., 2022). The legislative context is examined, focusing on this clusterand boundary-oriented technical perspective, in Section 1.1. As highlighted by Casamassima et al. (2022), an essential aspect of concepts, such as PEDs and RECs, is their interdisciplinary nature, which involves incorporating insights from social sciences to enhance community engagement and foster integrated energy solutions (Bielig et al., 2022) and urban planning (Natanian et al., 2024). In contrast to the extensively studied social impact of technology, the implications of interdisciplinary approaches, encompassing social sciences and urban planning, for the energy system as a technical system remain insufficiently explored. The implementation of NDCs continues to face challenges due to inconsistencies in definitions and the absence of systematic methodologies for establishing operational boundaries, as demonstrated by Sassenou et al. (2024) in the context of PEDs and RECs. To address these challenges, the present paper makes three main contributions. (1) It develops a methodological workflow for categorizing clustering approaches across energy, urban planning, and social sciences. (2) It identifies and analyzes inconsistencies in interdisciplinary terminology. (3) It also proposes a framework for consistent and scalable cluster definitions, supporting the integration of technical, social, and spatial perspectives in urban energy systems. A detailed discussion of these contributions is provided in Section 1.3. 1.1. Related work The challenges discussed in the introduction, particularly the lack of consistent definitions and systematic approaches to operational boundary-setting, are also evident in the literature discussed in the following: The terminology surrounding Renewable Energy Communities and Positive Energy Districts has been strongly shaped by the European Union’s key legislative and research frameworks. Relevant are the Electricity Market Design (EMD) Directive 2019/944 (European Parliament, Council of the European Union, 2019), which introduced Citizen Energy Communities (CECs), the Renewable Energy Directive (RED) 2018/2001 (European Parliament, Council of the European Union, 2018), which established Renewable Energy Communities, and the Joint Programming Initiative (JPI) Urban Europe (Hinterberger et al., 2020; Bossi et al., 2020; Gollner et al., 2020), which defined Positive Energy Districts. Directive 2019/944 outlines CECs as legal entities established through voluntary participation and collective control, capable of providing a broad range of energy services, but it does not specify proximity requirements or operational limits. In contrast, Directive 2018/2001 defines Renewable Energy Communities similarly, but it explicitly requires proximity to the renewable energy projects involved while refraining from providing a clear definition of system boundaries. The JPI Urban Europe introduces PEDs as spatially integrated, energy-efficient, and energy-flexible urban areas or groups of connected buildings, emphasizing the importance of well-defined boundaries. However, JPI Urban Europe acknowledges the complexity of boundary definitions and the necessity of further concretization through collaboration with local stakeholders, as guidelines remain under development (Hinterberger et al., 2020). Beyond the European context, similar community concepts have also been discussed in the United States. For example, Zero Energy Communities (ZEC) were introduced by the U.S. Department of Energy and the National Renewable Energy Laboratory as established concepts (Moghaddasi et al., 2021). Yet, the number of official government documents on these communities is limited. The initial definition dates back to 2015, describing a ZEC as an energy-efficient community where, on a source energy basis, the actual annual delivered energy is less than or equal to the on-site renewable exported energy (Peterson et al., 2015). Similarly, in China, the idea of Zero Carbon Communities (ZCC) appears in policy and research, with pilot initiatives such as the 2021 Shenzhen Implementation Programme and the 2022 national Guide to Building and Evaluating Zero-Carbon Community. More recent standards, including Shenzhen’s T/SZS4049-2022 and the 2023 draft Technical Standards for Zero-Carbon Buildings, signal a move toward stronger regulatory support, though a unified national framework is still missing (Zhou et al., 2025). Other countries, such as Nigeria, are pushing community renewable energy through local projects (Kaze et al., 2025). Recent studies highlight the potential of decentralized and citizen-led models to reduce energy poverty and support sustainability. However, legislation and government support structures are still under development (Kaze et al., 2025). Since the legislative context does not sufficiently clarify boundaryrelated aspects when viewed through a clusterand boundary-oriented technical system’s perspective, the following section examines relevant research that specifically addresses this dimension. Albert-Seifried et al. (2022) reviewed key challenges associated with PEDs, including defining boundaries to group devices into districts. They identified five boundary types: physical, political, economic, social, and legal. These boundaries should consider factors such as renewable energy potential, land use patterns, urban built forms, and infrastructure layout. While they described these general criteria, the authors acknowledge a lack of concrete guidance for deriving boundaries. As a result, systematic methods for clustering or partitioning cities into PEDs remain absent, particularly concerning the systematic scaling of such approaches across entire urban areas. European research projects, such as Cities4PEDs, presented by Schneider (2023), play a leading role in advancing the definition of PED concepts. The authors classified balance boundaries into three types: spatial, temporal, and functional. Spatial boundaries need to be defined such that they do not hinder neighboring districts from achieving the PED status in the future. However, they pointed out that, in practice, Energy Reports 14 (2025) 3673–3689 3674 J. Galenzowski et al. determining these boundaries becomes challenging and imprecise, particularly when a more nuanced distinction between different energy services is required. Additionally, the authors emphasize the lack of a uniform definition for system boundaries, complicating their practical implementation. Sassenou et al. (2024) systematically reviewed the challenges associated with the deployment of PEDs. Their paper highlighted ambiguities in definitions, the absence of holistic design methodologies, and the limited integration of social and environmental dimensions. Their review revealed a lack of interdisciplinary solutions and emphasized the need for clearer boundary definitions and flexible concepts to operationalize PEDs effectively across diverse urban contexts. Regarding RECs, Bauwens et al. (2022) reviewed the meaning of community in the context of energy systems. They identified that a community can refer to a group jointly investing in energy projects, such as wind turbines, while emphasizing economic and social perspectives. Additionally, the concept of community as a physical place facilitating peer-to-peer energy trading and community-based energy markets is becoming increasingly prominent in the literature. However, despite covering various aspects, the review lacks a systematic examination of the definition of boundaries for these communities. Haji Bashi et al. (2023) provided a comprehensive review of various RECs, explicitly addressing grid topology as a basis for defining physical boundaries within energy systems. They noted that energy communities can conflict with the natural monopoly of transmission and distribution system asset ownership, requiring regulatory interventions to address these challenges. However, the paper lacks a systematic analysis of the conceptual origins of RECs, particularly regarding the relative influence of energy-domain methodologies versus social sciences and urban planning concepts. Furthermore, a structured evaluation of the practical application of these boundaries is lacking in existing literature. Bielig et al. (2022) provided a systematic review of the social impacts associated with energy communities in Europe, focusing on constructs such as community empowerment, social capital, energy democracy, and energy justice. They identified a lack of rigorous quantitative evidence and emphasized the need for experimental and longitudinal studies to substantiate assumed social benefits. While they critically highlighted the lack of rigorous quantitative evidence for social benefits, the paper did not address the technical selection of devices for their joint operation within a district. The paper focuses on the social aspects of RECs but lacks an evaluation regarding the relevance of social sciences methodologies to technical energy systems. 1.2. Problem description and research gaps The present paper identifies several key aspects of the problem and corresponding research gaps. The current body of research lacks a clear, operational definition of spatial boundaries for NDCs, creating ambiguity in practical implementations. Moreover, the literature falls short in addressing standardization for scalability. Literature on optimization, control, and design in urban energy systems frequently references device clusters but lacks rigorous discussion on scaling these methods beyond single local areas to nationwide application. Standardization of device cluster selection is essential for embedding NDCs in legislation alongside existing well-defined legal rights, for example, in the legal form of RECs or CECs. Additionally, a comprehensive inventory of clustering rules used in current publications is missing. In particular, a systematic examination of existing literature to identify the diverse set of rules applied to form device clusters is currently absent, impeding further quantification and analytical efforts. Furthermore, a methodological workflow for analyzing interdisciplinary terminology alignment is missing. More specifically, there is insufficient discussion on whether the interdisciplinary terminology of NDCs aligns with the actual selection processes for device clusters. Specifically, it remains unexamined whether devices are grouped strictly by energy system boundaries or if boundaries from other disciplines, such as urban planning and social sciences, are considered. Finally, baseline definitions and benchmarking for urban planning and social sciences boundary selection for energy-related applications are absent. The absence of clear definitions for truly urban planning and social science-based clustering methods prevents rigorous analysis of their potential benefits for energy system operation. There is a need to establish baseline definitions and benchmarks by reviewing existing clustering methods and literature on urban planning and social science in various countries. 1.3. Contribution We respond to the shortcomings presented in Section 1.2 by clarifying key conceptual distinctions, establishing methodological coherence, and enhancing the practical applicability of NDCs in urban energy systems through the following core contributions: •Development of a systematic methodological workflow for analyzing literature that employs NDC terminology, specifically focusing on the clustering rules used to define spatial and technical boundaries. •Specification and distinction of the device-cluster-oriented perspective of NDCs, in contrast to perspectives that focus on legal structures, investment models, or community-oriented benefits. •Compilation and categorization of clustering approaches from both the energy domain and urban planning or social sciences, enabling a comparative assessment of their applicability and integration potential. •Identification and critical evaluation of inconsistencies in the use of interdisciplinary terminology, demonstrating the need for explicit, standardized criteria when applying clustering concepts in energy-related applications. In doing so, it lays the foundation for classifying and comparing existing work across disciplinary domains and proposes a coherent methodological workflow for analyzing and delineating NDC in future research and implementation. 1.4. Structure of the paper The paper is structured as follows: The methodology in Section 2 outlines the prerequisites for concepts to be included in the review and defines how NDCs are interpreted in the present paper. It also details the approach used to identify and analyze clustering methodologies. The results, presented in Section 3, classify the existing clustering approaches in the energy domain and review urban planning and social sciences methodologies, providing a foundation for examining interdisciplinary approaches. Section 4 critically evaluates the findings, addressing terminology inconsistencies and offering recommendations for future research. Section 5 summarizes the contributions and key insights. 2. Methodology This section outlines the methodology employed in the present paper, with Fig. 1 illustrating the key steps undertaken. The analytical workflow for evaluating existing papers on NDC is depicted as a topto-bottom process in Fig. 1. At the top, a filtering step (represented by a trapezoid) establishes the selection criteria for identifying relevant literature (see also Section 2.1 and Section 2.2). The primary division within this workflow arises from the categorization of the clustering concepts applied. Literature that utilizes energy-related clustering concepts flows through the green pathway on the right, whereas literature employing urban planning and social sciences clustering concepts is represented by the blue pathway on the left. Additionally, two side Energy Reports 14 (2025) 3673–3689 3675 J. Galenzowski et al. Fig. 1. Methodological approach of the present paper: A top-to-bottom workflow categorizes and evaluates existing NDC. Literature is filtered (inverted trapezoid) to include studies on joint device operation (Section 2.1) and using interdisciplinary NDC terminology (Section 2.2). Clustering concepts were defined outside the main workflow: energy-related concepts in Section 3.1 (methodology in Section 2.3a) and urban planning and social sciences concepts in Section 3.2 (methodology in Section 2.3b). Based on this categorization, energy-domain concepts are elaborated in Section 3.1 and evaluated regarding the alignment of terminology and clustering concepts in Section 4. Urban planning and social sciences concepts are presented in Section 3.2, and the question of whether NDCs are sufficiently supported by existing research is discussed in Section 4, before concluding in Section 5. tasks (indicated in the gray boxes) involve defining appropriate clustering rules for these concepts on a meta-level outside the main analytical workflow. The subsequent steps of the analysis are carried out in the results sections (Sections 3.1 to 3.3), followed by a discussion that reflects on the identified categories (Sections 4.1 and 4.2) and a comprehensive conclusion synthesizing all findings (Section 5). Certain aspects of the workflow depicted in Fig. 1 necessitate additional elaboration in dedicated Section 2.1, Section 2.2, and Section 2.3 of this methodology section. One such aspect is establishing clear selection criteria, as detailed in Section 2.1. Additionally, Section 2.2 provides a precise definition of NDCs, ensuring conceptual clarity and focus throughout the paper. Section 2.3 explains how clustering concepts are identified, classified, and analyzed across disciplines. Further methodological aspects, including proposing future research directions, refining terminology, and drawing conclusions, are straightforward and integrated into the main sections of the paper, aligning with the corresponding analytical findings. 2.1. Selection criteria for analyzed concepts The present paper considers concepts that are comparable to districtrelated energy systems (see also Section 1). Energy-related applications are inherently linked to devices that produce, consume, store, transmit, or convert energy (Galenzowski et al., 2023a; Haji Bashi et al., 2023). Examples of such devices include photovoltaic systems, battery storage, electric vehicles, combined heat and power plants, wind turbines, heat pumps, chillers, and thermal storage units (Haji Bashi et al., 2023). The key characteristic underlying the comparability of district-related energy systems, and thus the prerequisite for including literature in the present analysis, is the joint operation of multiple devices that form subgroups within a superordinate administrative entity such as a city, representing an intermediate scale of analysis (Sassenou et al., 2024; Bauwens et al., 2022). Building on this understanding of intermediate granularity, the present analysis focuses exclusively on the joint operation in energy devices. It deliberately excludes broader REC, CEC, or PED objectives, for example, related to investment, governance, or social impact. Accordingly, device clusters are a core concept of the analysis and are defined as follows: Device cluster: A device cluster is characterized as a collection of multiple energy-related devices (consumer, producer, or storage unit) that are collaboratively managed and constitute a subunit of a superordinate entity, such as a city.1 As illustrated in Fig. 2, relevant concepts must operate at an intermediate granularity, representing the scale between individual devices and an entire city. The focus lies on grouping these devices into 1To improve the readability, device clusters are hereafter called clusters. Fig. 2. To be considered in this paper, papers must focus on an intermediate granularity level between a single device and a superordinate administrative unit. Because energy-related applications link to devices rather than intermediate entities like buildings (see Section 2.1), only devices are considered. Energy Reports 14 (2025) 3673–3689 3676 J. Galenzowski et al. units for joint operation, enabling sustainable energy systems, localized renewable energy production, enhanced citizen engagement, reduced procurement costs, and improved energy reliability and quality (Ahmed et al., 2024). From a conceptual perspective, the real-world system may exhibit varying intermediate topographies. For instance, some buildings may utilize dedicated building management systems that aggregate data from individual devices and present an abstracted, aggregated interface to the cluster. From the cluster’s perspective, these systems are seen as devices with potentially different or limited boundary conditions. For simplification purposes, optional intermediate layers are not depicted in Fig. 2. In any approach to grouping devices, it is essential to determine the specific cluster to which each device belongs. This requires the establishment of clearly defined boundaries between the clusters based on a set of rules. These rules can vary depending on the domain or context, but are crucial for ensuring the coherence and functionality of the grouping. The present paper focuses on NDCs, whose definitions and specific characteristics are described in Section 2.2. Grouped and jointly operated devices inside cities are a fundamental prerequisite for defining district-related concepts in the energy context, making this criterion essential for inclusion in the review. 2.2. Definition of Novel District Concepts (NDCs) The term Novel District Concepts (NDCs) in the present paper refers to approaches that integrate at least one aspect of urban planning or social sciences into the energy domain. These concepts are characterized by the fact that they incorporate at least one term from urban planning or social sciences into their name. Natanian et al. (2024) and Haji Bashi et al. (2023) provided an overview of 22 such concepts, with the most prominent examples being Positive Energy Districts, community energy, and Renewable Energy Communities. We observe that all the discussed concepts merge energy-related terminology with at least one term originating from urban planning or social sciences. For instance, terms are drawn from the energy domain (e.g., energy, net-zero, renewable), urban planning (e.g., district, neighborhood, block), and social sciences (e.g., community, citizen, and consumer). A detailed overview illustrating the popularity and interdisciplinary characteristics of the concepts from Natanian et al. (2024) and Haji Bashi et al. (2023) is presented in Table A.4 in Appendix. The present paper does not aim to comprehensively quantify the prevalence of all NDCs. Rather, Table A.4 highlights exemplary works to illustrate the interdisciplinary nature of the adopted NDC definition. The interdisciplinary nature is summarized and visualized in Fig. 3. Moreover, we do not aim to evaluate the overlapping terminologies themselves but rather to investigate the fields from which these terms originate and their implications for energy systems. Terms such as districts, neighborhoods, and communities, commonly used in urban Fig. 3. The novelty in the definition of NDCs consists of applying terminology originating from urban planning or social sciences in the energy context. For instance, a PED combines positive energy (an energy-domain concept) with district (an urban planning concept). See also Table A.4 in Appendix. planning and social sciences, often refer to geographic regions, administrative divisions, or social groupings. For example, a district may denote an administrative division,2 whereas a neighborhood typically describes a city area inhabited by people with shared characteristics (for further definitions of the terms, see Appendix A.2). Consequently, we refine the literature selection to include only works that address concepts aligning with this interdisciplinary definition for further analysis. This initial selection is based solely on the terminology of the concepts, although the applied clustering approach is not necessarily from the same domain. 2.3. Identification of clustering concepts through literature review A key contribution of the present paper is the identification of clustering concepts derived from exemplary papers in the literature. The aim is to highlight the diversity of approaches and to present preliminary findings rather than conducting an exhaustive review of all available literature. The two primary groups of clustering approaches are those based on established energy concepts and those rooted in urban planning and social sciences. (a) Established energy concepts are well-known in the energy field and form the foundation of the present analysis. The aim is to create a comprehensive list of these concepts, categorize the reviewed papers, and highlight the variety of clustering rules applied in the energy domain. A significant issue in the current literature is the tendency to apply various clustering rules from the energy domain while claiming them to represent NDCs. To address this, the present paper emphasizes the importance of documenting all possible variations in the clustering rules in the energy domain to provide clarity and coherence. Various clusters and reviewed papers are presented in Section 3.1. (b) Urban planning and social sciences approaches are less explored in the energy context. Thus, the first step is to research and define these concepts and then discuss how they appear in the reviewed papers. These methods are straightforward to apply because many countries have well-defined administrative districts that can serve as boundaries for clustering. Moreover, large publicly available Geographic Information System (GIS) datasets established by governmental or administrative bodies provide reliable and standardized data. These datasets, which are available across numerous countries, enable a rapid and consistent definition of district boundaries. Additionally, these approaches offer significant advantages for defining clusters because they are based on publicly available or readily observable spatial and social data, ensuring accessibility and ease of application. By contrast, energy-focused methods often depend on inaccessible, fragmented, or outdated data, such as grid topology, which requires coordination among multiple stakeholders and lacks transparency for broader audiences. To leverage the advantages of urban planning and social sciences approaches, we review state-defined clustering methodologies and their associated rules, complemented by scientific definitions. These concepts provide a practical and scalable foundation for defining district boundaries. By integrating state-defined methodologies with scientific insights, the paper establishes a robust basis for interdisciplinary clustering, reviewed and described in Section 3.2. This paper establishes the groundwork for categorizing reviewed papers into distinct groups based on boundary origin from energy, urban 2In the context of urban planning and social sciences, as discussed in Sections 2.3 and 3.2, the term district is used to refer to a geographically delineated area defined by an interdisciplinary origin. Among the available interdisciplinary terminology, the geographical aspect is best reflected by the term district (see Section 3.1). All devices located within this area form a cluster according to interdisciplinary rules. The only exception to this use of district is in the review of established energy concepts (Section 3.1), where the term district is used exclusively as a citation of terminology from the presented literature. Energy Reports 14 (2025) 3673–3689 3677 J. Galenzowski et al. planning, and social sciences. By integrating these established concepts, the present paper provides a holistic and practical methodology for analyzing and defining NDC concepts, as outlined in Fig. 1. In this context, the idea of misapplication (see Fig. 1) needs clarification. We do not imply that terminology must be identical across domains or that general definitions are questioned. Instead, terminology should reflect the origin of the grouping logic: energy-domain terms are appropriate for clusters defined by energy system boundaries, whereas interdisciplinary terms are more suitable for clusters based on urban planning or social sciences. The key point is not whether a term is correct in itself, but whether it makes clear after which logic the devices were grouped. Based on the defined NDC terminology in Section 2.2 and the origin of clustering rules, the evaluation distinguishes: Misapplied Terminology: Use of NDC terminology (as defined in Section 2.2) for clusters derived solely from established energy system boundaries. Genuinely Novel Work: Use of NDC terminology (as defined in Section 2.2) for clusters derived from interdisciplinary urban planning or social science boundaries. Bias in the rating of misapplied terminology is not expected, since the definition of NDC terminology (Section 2.2) and boundary rules (Sections 3.1 and 3.2) are unambiguous. Potential uncertainty arises only in works where the rules for cluster creation are not explicitly described in the reviewed literature, which limits the available input. 3. Results This section presents the clustering approaches identified in the literature, categorized into three distinct sections. Section 3.1 focuses on established energy-domain concepts, directly integrating relevant literature to highlight the diversity of clustering rules within the field. Section 3.2 reviews and defines clustering approaches derived from urban planning and social sciences, providing a comprehensive foundation for interdisciplinary methodologies. Finally, Section 3.3 examines interdisciplinary research integrating urban planning and social sciences concepts into the energy domain. Together, these sections provide a systematic analysis of the clustering approaches across disciplines. 3.1. Review of established energy system concepts and literature adopting novel terminology In this section, we investigate the typical grouping of devices into clusters within the energy domain. Additionally, it analyzes existing papers that reference the terminology of NDCs (see Section 2.2), but rely on energy-domain clustering rules to select the included devices. To highlight which NDCs terminology is used by the authors of these papers, the terminology extracted from the papers is highlighted in the following in italic. The purpose of these terms is only to highlight the utilization of NDC terminology and not to explain them in detail; for details, the original publications should be consulted. The present analysis focuses on the actual processes underlying cluster formation, often requiring a detailed examination to reveal the criteria used for selecting specific devices. Many of these papers lack transparency and clarity regarding the selection criteria, necessitating a meticulous review to discern the rules actually applied. Table 1 provides an overview of the identified energy-domain clustering concepts detailed in the subsequent paragraph. To enhance clarity, these concepts are illustrated using examples in the Figures Figs. 4, 6, and 7. The displayed clusters are fictional, emphasizing the general applicability of concepts rather than specific locations. To demonstrate applicability within the same urban context, all figures depict the same city area (left part of the figures) with corresponding detailed clustering views (right part of the figures). The selected area measures 2.3 km in width and 1.3 km in height. The figures were generated using QGIS 3.30.1, with data from OpenStreetMap,3 GlobalMLBuildingFootprints,4 and Google Satellite imagery.5 The following sections present the nine clustering approaches identified in the present paper as commonly used in the energy domain. (a) Below the same MV/LV transformer substation An approach widely recognized within the energy community is to cluster devices below a single Medium-Voltage (MV) to Low-Voltage (LV) transformer (MV/LV transformer) as shown in Fig. 4a. It is described as having the MV/LV connection point as the point of common coupling, specifically mentioning the below-a-single-transformer aspect or referring to all devices sharing a single low-voltage grid as one cluster. For example, Terrier et al. (2024) focused on identifying different district types in Switzerland. They referred to local energy community, energy hubs, or district, even though they used the MV/LV transformer to cluster devices in their paper. Middelhauve et al. (2022) presented a novel algorithm for the optimal district design as renewable energy hubs. While referring to districts, they considered all devices below a single MV/LV transformer. Kharboutli et al. (2022) examined district energy system models, focusing on economics, ecology, system serviceability, and resilience. They highlighted the need for multi-criteria optimization to balance conflicting objectives of districts for a holistic assessment while, in fact, considering the cluster of all buildings at the connection point at the MV/LV transformer. Lang et al. (2023) examined power system architectures at the district level, using the term station area to describe the zone supplied by a single MV/LV transformer. Their case study focuses on devices connected to the low-voltage side of a 630 kVA transformer, aligning the district concept with the local distribution level. Dynge et al. (2021) examined a local electricity market in a neighborhood. Their results are based on devices connected to a single MV/LV transformer. Clustering at the MV/LV transformer level is not only performed in numerous scientific papers but also finds applications in laws such as the German EnWG, §14a (Bundesrepublik Deutschland, 2025) on grid relief measures that specify them for devices connected to the low-voltage grid. Moreover, the voltage control of distributed energy resources is realized at this low-voltage grid level (Demirel et al., 2025). (b) Private grid areas This approach considers all devices connected to a privately operated grid as one cluster. The boundary is defined by at least one connection point to the public grid. District concepts that apply this clustering focus on larger private grid areas such as college campuses, industrial sites, or large residential building complexes (see Fig. 4b). Unlike single-family buildings, campuses meet the scale and complexity expected of a district in urban planning and social sciences. However, they are a special case, considering the freedom from using a privately operated grid. Araújo et al. (2023) examined the increase of Photovoltaic (PV) and self-consumption for a local energy community, using the campus of the School of Technology and Management of the Polytechnic Institute of Viana do Castelo. Blumberga et al. (2024) conducted a simulation of production and consumption for a positive energy district, using buildings belonging to the Riga Technical University campus and, therefore, a closed campus private grid area. According to the private grid area approach, a larger large-scale industrial complex (see the purple area in Fig. 4b) would be considered as a cluster, but also each single-family home with its grid connection point. (c) Same cell manager If the grid is structured in a cellular way, the grid inside a cell is managed by a specific entity with a unique connection point to the distribution grid. Those entities act as subgrid-managers that are not private (see Fig. 4c). Community microgrids 3http://tile.openstreetmap.org/{z}/{x}/{y}.png (accessible as QGIS layer source, not via a web browser). 4https://github.com/microsoft/GlobalMLBuildingFootprints 5https://mt1.google.com/vt/lyrs=s&x=\{x\}&y=\{y\}&z=\{z\} (accessible as QGIS layer source, not via a web browser). Energy Reports 14 (2025) 3673–3689 3678 J. Galenzowski et al. Table 1 Key clustering approaches relevant in the energy domain, highlighting their primary boundary-defining criteria, scale of coverage (in terms of area or number of devices), and representative papers from the literature. A private grid area is a grid section owned and operated by a private actor with at least one connection to the public grid. A cell manager operates a defined grid cell that integrates multiple private areas below a single point of common coupling (PCC). A sub-balance group denotes a subgroup within an electricity market balance group collectively managed by a coordinator, such as a virtual power plant operator. The concepts are explained in depth in the corresponding paragraphs. Clustering concept Boundary definition criterion Scale References (a) Below the same MV/LV transformer substation Devices below a single MV/LV transformer within a shared low-voltage grid. Small Terrier et al. (2024), Middelhauve et al. (2022), Kharboutli et al. (2022), Lang et al. (2023), Dynge et al. (2021) (b) Private grid areas Privately operated grid areas, with defined connection points to the public grid. Small Araújo et al. (2023), Blumberga et al. (2024) (c) Same cell manager Managed by a single entity or community microgrid. Medium Cornélusse et al. (2019), Coelho et al. (2025), Ottenburger et al. (2024) (d) Belonging to a newly or commonly developed area Assets constructed during the same period or as part of a joint development project. Medium Cheng et al. (2025), De Rosa et al. (2024) (e) Shared medium-voltage line Devices sharing the same medium-voltage line. Medium to Large Kermani et al. (2022), De Barros et al. (2024), Liu and Ledwich (2021) (f) Sub-balance group Managed under a single energy market balance group. Medium to Large Reis et al. (2020), Sauerbrey et al. (2024), Van Summeren et al. (2020) (g) Common heating or cooling grid Devices sharing a local heating or cooling grid. Small to Medium Wakui et al. (2021) (h) Motivation to participate or data availability Chosen based on data availability or owner participation. Small to Medium Guarino et al. (2023) (i) No information on clustering No clear or systematic clustering criteria. Varies Chuat et al. (2024) are essential concepts of forming a cell with a single connection point - called the Point of Common Coupling (PCC) - to the superordinate grid (Salehi et al., 2022). Depending on their focus, they lie between a private grid area and a managed cell. According to Cornélusse et al. (2019), community microgrids consist of all devices connected to a local bus. The authors neither formulated the requirement that this bus is equivalent to an MV/LV transformer nor explicitly stated that the grid parts below this bus must be privately owned. They differentiate the grid below the bus from the public grid, but this does not mean that the grid is owned by a single building owner or a private company. Instead, it is a grid owned by a unique entity that belongs to the community, forming a cell that lies below the public grid and forms its grid area (Cornélusse et al., 2019). Coelho et al. (2025) simulated community microgrid operation. Their clusters are derived at a PCC, clearly delimiting the microgrid boundaries without aligning to a specific voltage level or ownership structure (Coelho et al., 2025). Ottenburger et al. (2024) examined the development of device clusters for microgrid design, employing a structured approach that incorporated both technical criteria and social dimensions, such as socioeconomic and housing conditions to address community vulnerabilities. Clusters were formed at two levels of aggregation: initially, subclusters were defined based on medium voltage circuit boundaries (devices sharing the same medium voltage line), and ultimately, final clusters were established as microgrids, representing physically distinct grid sections capable of operating independently. The paper avoids mislabeling microgrids by precisely defining their boundaries and clearly documenting the social rules and technical principles used to derive them, fostering transparency and scalability in cluster-based energy system design (Ottenburger et al., 2024). (d) Belonging to a newly or commonly developed area Assets within a newly or commonly developed area share a similar construction time frame, enabling the adoption of unified energy concepts and suggesting comparable energy efficiency and consumption profiles (see Fig. 4d). Unlike private grid areas, these regions encompass multiple grid connection points or low-voltage grids. For instance, Cheng et al. (2025) presented a co-simulation concept for district heating in a new residential area in Germany. De Rosa et al. (2024) presented an integrated planning methodology for new decarbonized urban districts that combines energy management in buildings, electric vehicle charging infrastructure, and electricity distribution network design, demonstrated through a large-scale case study in a commonly developed area of the new district in Madrid. (e) Shared medium-voltage line This approach clusters all devices connected to the same medium-voltage line by considering the cable originating from the major substations and all devices connected to one cable coming out of the substation (see Fig. 4e). This cable can originate from a bus in the substation, or from a single corresponding HV/MV transformer. Kermani et al. (2022) enhanced MV/LV transformer designs for energy communities, which in this publication are understood as aggregations of assets connected at a shared medium-voltage line. De Barros et al. (2024) targeted a regional improvement of the power quality in a distribution grid. Liu and Ledwich (2021) presented an algorithm for grid-friendly control of communities either referring to devices connected to the same voltage level or devices connected to the same medium-voltage network. Distribution system operators tend to know the power flows through a line at the substation but may not know what happens between assets further along the line. (f) Shared sub-balance group This clustering approach is defined by a single entity, such as a Virtual Power Plant (VPP) operator or a local small-scale energy supplier, responsible for managing energy market transactions and ensuring balance for the entire group (see Fig. 4f). While VPPs can encompass large geographical areas, this work focuses on smaller, localized VPPs that align with the definition of an intermediate scale, situated between individual devices and entire cities, as detailed in Section 2.1. Reis et al. (2020) proposed a multi-agent system to model an energy community, interconnected primarily through a common coordinator agent rather than strictly by grid boundaries. In their approach, clusters comprise residential and non-residential agents geographically co-located, whose demand flexibility is collectively optimized by the coordinator agent (Reis et al., 2020). Sauerbrey et al. (2024) defined a district as a group of spatially related buildings that pursue a joint energy supply and consumption. Their work introduced the role of a district energy manager, who centrally manages forecasting, optimization, and flexibility within this group, effectively coordinating energy supply and demand across sectors such as electricity, heat, and mobility through a modular district energy management system. Van Summeren et al. (2020) examined Energy Reports 14 (2025) 3673–3689 3679 J. Galenzowski et al. Fig. 4. Conceptual illustration of identified energy-domain clustering concepts (a) – (g) within a consistent urban context. Left panels show the same city area, right panels the corresponding detailed clustering views. The fictional examples demonstrate the general applicability of the clustering rules described in Table 1, independent of specific locations. Energy Reports 14 (2025) 3673–3689 3680 J. Galenzowski et al. Fig. 5. Examples of existing district definitions from publicly available GIS datasets. The maps showcase administrative boundaries for selected cities – IRIS in Strasbourg (France) on the left, Buurten in Amsterdam (Netherlands) in the middle, and Census Blocks in New York (United States) on the right – highlighting the accessibility and standardization of spatial data for district-level analysis. three VPPs in Ireland, Belgium, and the Netherlands, focusing on their goals, structures, and challenges. The so-called community-based VPPs (cVPPs) integrate diverse renewable energy resources, such as solar panels, batteries, and heat pumps. They promote local energy independence, democratize energy use, and help participants align with energy market rules. In doing so, they address the challenges of integrating community-driven models into established energy systems (Van Summeren et al., 2020). (g) Common heating or cooling grid The terminology for a district is well established when referring to local heating networks (see Fig. 4g). Wakui et al. (2021) presented a design method for distributed energy networks combining heat and power, providing an energy supply area. (h) Motivation to participate or data availability Guarino et al. (2023) modeled the consumption and generation of a positive energy district. All the selected buildings were used for public purposes. From the aerial image, it is evident that they are not contiguous, with no clearly visible outer boundaries. It is not explicitly stated by Guarino et al. (2023); however, based on these facts, it is reasonable to assume that the buildings were chosen based on the respective owners’ willingness and availability to participate in the research. (i) No information on clustering Chuat et al. (2024) discussed the solution space of possible configurations of devices for districts operated as energy hubs by performing a sensitivity analysis of the different configurations and price parameters. However, they do not discuss how the 15 buildings in their analysis were systematically selected and how a whole city could be clustered in a similar manner. Even laws such as the German GEIG (Bundesrepublik Deutschland, 2021) use the term district (Quartier in German). While the GEIG primarily regulates the installation of charging infrastructure in new buildings, it allows certain requirements, such as the mandated number of charging stations, to be fulfilled collectively at the district level. However, the law does not provide a precise and strict definition of what constitutes a district, leaving its interpretation open-ended despite its central role in fulfilling these obligations. Summary of existing energy system concepts In conclusion, this section underscores the diversity of clustering approaches within the energy domain, highlighting methods such as MV/LV transformer areas, shared medium-voltage lines, and private grid areas as well-defined boundaries. Examples of less-structured clustering are also discussed as approaches based on motivation and data availability or undefined criteria. These findings demonstrate the range and complexity of the methods used to cluster devices for energy applications, offering valuable insights into existing practices. 3.2. Review of established clustering approaches in urban planning and social sciences Urban planning and social sciences offer established approaches for subdividing cities into clusters, which are often developed for statistical evaluations or census data collection. These approaches focus on clustering populations because social aspects are inherently tied to people. In the context of energy-related applications, these methods can be adapted to group all devices associated with individuals in a cluster, or to focus on clustering buildings and their related devices. Many countries, including France, the Netherlands, the United States, Australia, and England, have well-defined concepts for city subdivisions, particularly for administrative or statistical purposes. Fig. 5 exemplarily shows the GIS boundaries of those concepts for the Ilots Regroupés pour l’Information Statistique (IRIS) in Strasbourg6 on the left, Buurten in Amsterdam7 in the middle, and Census Blocks in New York8 on the right, displayed on a base layer from OpenStreetMap.9 The following paragraphs and Table 2 provide an overview of these established concepts and their potential relevance to interdisciplinary applications. (a) IRIS Units in France The National Institute of Statistics and Economic Studies (Insee) in France uses the IRIS clusters for social sciences and urban planning purposes. IRIS stands for ‘‘Ilots Regroupés pour l’Information Statistique’’ or ‘‘grouped block for statistical information’’. IRISs are used for detailed spatial analysis and the statistical collection of population and social data at the local level to support targeted urban planning, public services, and policy decisions. They comprise building blocks of 1,800 to 5,000 inhabitants that are homogeneous in terms of settlement type. Major disruptions in the urban fabric, such as major roads, railways, and waterways, were defined to mark the borders of each IRIS. In total, approximately 16 thousand IRIS clusters existed throughout France in 2016 (Institut national de la statistique et des études économiques (Insee), 2016). (b) Buurten in the Netherlands The Centraal Bureau voor de Statistiek (CBS) in the Netherlands uses a similar concept with the Buurten for statistical purposes. Buurten, the Dutch word for neighborhoods, is defined as contiguous buildings or development areas (e.g., a similar year of construction or building type). Interruptions, such as roads, railroads, and waterways, are defined as delineating factors. They consist of 250 to 2,500 inhabitants. According to Statistiek, Centraal Bureau voor de (2024) the whole Netherlands, was clustered in around 18 thousand Buurten in 2024 (Centraal Bureau voor de Statistiek (CBS), 2023). (c) Census Blocks in the United States In the United States, the Census Block is the smallest unit used by the United States Census Bureau (USCB) to collect detailed demographic data. Census Blocks 6IRIS GIS source for Fig. 5: https://data.geopf.fr/wfs/ows?SERVICE=WFS& VERSION=2.0.0&REQUEST=GetCapabilities|layername=STATISTICALUNITS. IRISGE:iris_ge (accessible as QGIS layer source, not via a web browser). 7Buurten GIS source for Fig. 5:https://geodata.cbs.nl/files/ Wijkenbuurtkaart/WijkBuurtkaart_2020_v3.zip. 8Census Blocks GIS source for Fig. 5:https://hub.arcgis.com/datasets/ d795eaa6ee7a40bdb2efeb2d001bf823_0/about. 9http://tile.openstreetmap.org/{z}/{x}/{y}.png (accessible as QGIS layer source, not via a web browser). Energy Reports 14 (2025) 3673–3689 3681 J. Galenzowski et al. European Parliament, Council of the European Union, 2019. Directive (EU) 2019/944 of the European Parliament and of the Council of 5 June 2019 on common rules for the internal market for electricity and amending Directive 2012/27/EU. URL: https: //eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32019L0944. (Accessed 16 September 2025). Fan, M., Zhang, Z., Wang, C., 2019. Mathematical Models and Algorithms for Power System Optimization: Modeling Technology for Practical Engineering Problems. Academic Press. Fillaut, C., 2015. Definition of the Energy Territory. École Polytechnique de l’Université de Tours, Université François-Rabelais, Tours, France, URL: http://memoires.scd. univ-tours.fr/EPU_DA/LOCAL/2015PFE_Fillaut_Celine.pdf. (Accessed 16 September 2025). Galenzowski, J., Waczowicz, S., Hagenmeyer, V., 2023a. A new Data-Driven Approach for Comparative Assessment of Baseline Load Profiles Supporting the Planning of Future Charging Infrastructure. In: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems. E-Energy ’23, p. 8. http: //dx.doi.org/10.1145/3599733.3600245. Galenzowski, J., Waczowicz, S., Meisenbacher, S., Mikut, R., Hagenmeyer, V., 2023b. A Real-World District Community Platform as a Cyber-Physical-Social Infrastructure Systems in the Energy Domain. In: Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. ACM, Istanbul Turkey, pp. 434–441. http://dx.doi.org/10.1145/3600100.3626347. Geiger, A., Zhong, Y., Hempel, S., Häfele, K.-H., Hagenmeyer, V., 2024. CityGML Data Preparation for Thermal Building Simulation at District Level. In: Proceedings of BauSim Conference 2024: 10th Conference of IBPSA-Germany and Austria. In: BauSim Conference, vol. 10, IBPSA-Germany and Austria, Wien, pp. 104–111. http://dx.doi.org/10.26868/29761662.2024.14. Ghaffari, M., Aly, H.H., 2024. Voltage Deviation Improvement in Microgrid Operation through Demand Response Using Imperialist Competitive and Genetic Algorithms. Information 15 (10), 638. http://dx.doi.org/10.3390/info15100638. Gollner, C., Hinterberger, R., Bossi, S., Theierling, S., Noll, M., Meyer, S., Schwarz, H.-G., 2020. Europe towards positive energy districts. URL: https://jpi-urbaneurope.eu/wp-content/uploads/2020/06/PED-Booklet-UpdateFeb-2020_2.pdf. (Accessed 16 September 2025). Guarino, F., Rincione, R., Mateu, C., Teixidó, M., Cabeza, L.F., Cellura, M., 2023. Renovation Assessment of Building Districts: Case Studies and Implications to the Positive Energy Districts Definition. Energy Build. 296, 113414. http://dx.doi.org/ 10.1016/j.enbuild.2023.113414. Haji Bashi, M., De Tommasi, L., Le Cam, A., Relaño, L.S., Lyons, P., Mundó, J., Pandelieva-Dimova, I., Schapp, H., Loth-Babut, K., Egger, C., Camps, M., Cassidy, B., Angelov, G., Stancioff, C.E., 2023. A Review and Mapping Exercise of Energy Community Regulatory Challenges in European Member States Based on a Survey of Collective Energy Actors. Renew. Sustain. Energy Rev. 172, 113055. http://dx.doi.org/10.1016/j.rser.2022.113055. Hanseund Universitätsstadt Rostock – Kataster-, Vermessungsund Liegenschaftsamt, 2022. Statistische Blöcke - GovData. URL: https://www.govdata.de/suche/daten/ statistische-blocke. (Accessed 16 September 2025). Hinterberger, R., Gollner, C., Noll, M., Meyer, S., Schwarz, H.-G., 2020. White Paper on Reference Framework for Positive Energy Districts and Neighbourhoods. JPI Urban Europe / SET-Plan Action 3.2, Wien, URL: https://jpi-urbaneurope.eu/wp-content/ uploads/2020/04/White-Paper-PED-Framework-Definition-2020323-final.pdf. (Accessed 16 September 2025). Institut national de la statistique et des études économiques (Insee), 2016. Définition - IRIS | Insee. URL: https://www.insee.fr/fr/metadonnees/definition/c1523. (Accessed 16 September 2025). Kaze, K., Balta-Ozkan, N., Shrimpton, E., 2025. Connecting power to people: Integrating community renewable energy and multi-level governance towards low-carbon energy transition in Nigeria. Energy Res. Soc. Sci. 121, 103938. http://dx.doi.org/ 10.1016/j.erss.2025.103938. Kermani, M., Ferrari, G., Shirdare, E., Manganelli, M., Martirano, L., 2022. Compact and Smart Outdoor Medium/Low Voltage Substation for Energy Communities. IEEE Trans. Ind. Appl. 58 (3), 3123–3133. http://dx.doi.org/10.1109/TIA.2022.3148357. Kharboutli, S., Monsalve, C., Bretschneider, P., 2022. Investigation and Application of Systemic Assessment Variables and Concepts to Developed Reference Districts. In: 2022 57th International Universities Power Engineering Conference. UPEC, pp. 1–5. http://dx.doi.org/10.1109/UPEC55022.2022.9917667. Knight, I., Iorwerth, H., Lannon, D.S., 2016. Re-Energising Wales Project Work Package 1 – Half-Hourly Energy Demand Profiles for Wales for 2016. Welsh School of Architecture, Cardiff University, Cardiff, URL: https://orca.cardiff.ac. uk/id/eprint/120735/53/WP1%20Report%20-%20Half%20Hourly%20Energy% 20Demand%20Profiles%20for%20Wales%20for%202016%20FINAL.pdf. (Accessed 16 September 2025). Lang, R., Quan, K., Jun, Z., Tao, W., Gang, L., Bibo, T., Hu, L., 2023. A review of the different power system architecture at the district level and its analysis. Energy Rep. 9, 1456–1462. http://dx.doi.org/10.1016/j.egyr.2023.05.196. Liu, A., Ledwich, G., 2021. A Grid-Friendly Sustainable Neighborhood Energy Trading Mechanism for MV-LV Network. IEEE Trans. Smart Grid 12 (3), 2239–2248. http://dx.doi.org/10.1109/TSG.2020.3045976. Meinecke, S., Sarajlić, D., Drauz, S.R., Klettke, A., Lauven, L.-P., Rehtanz, C., Moser, A., Braun, M., 2020. SimBench—A Benchmark Dataset of Electric Power Systems to Compare Innovative Solutions Based on Power Flow Analysis. Energies 13 (12), 3290. http://dx.doi.org/10.3390/en13123290. Middelhauve, L., Terrier, C., Maréchal, F., 2022. Decomposition Strategy for Districts as Renewable Energy Hubs. IEEE Open Access J. Power Energy 9, 287–297. http://dx.doi.org/10.1109/OAJPE.2022.3194212. Moghaddasi, H., Culp, C., Vanegas, J., 2021. Net Zero Energy Communities: Integrated Power System, Building and Transport Sectors. Energies 14 (21), 7065. http: //dx.doi.org/10.3390/en14217065. Natanian, J., Guarino, F., Manapragada, N., Magyari, A., Naboni, E., De Luca, F., Cellura, S., Brunetti, A., Reith, A., 2024. Ten Questions on Tools and Methods for Positive Energy Districts. Build. Environ. 255, 111429. http://dx.doi.org/10. 1016/j.buildenv.2024.111429. Neppl, M., Becker, S., Burgbacher, M., 2016. Was ist ein Quartier? Quartiersforschung in Ettlingen. Karlsruher Institut für Technologie - Fakultät für Architektur Institut für Entwerfen von Stadt und Landschaft Fachgebiet Stadtquartiersplanung, Karlsruhe Germany, URL: https://stqp.iesl.kit.edu/downloads/Was_ist_ein_Quartier.pdf. (Accessed 16 September 2025). Office for National Statistics, 2022. Census geography - Office for National Statistics. URL: https://webarchive.nationalarchives.gov.uk/ukgwa/20220401215420/https: /www.ons.gov.uk/methodology/geography/ukgeographies/censusgeography. (Accessed 16 September 2025). Ottenburger, S.S., Cox, R., Chowdhury, B.H., Trybushnyi, D., Omar, E.A., Kaloti, S.A., Ufer, U., Poganietz, W.-R., Liu, W., Deines, E., Müller, T.O., Möhrle, S., Raskob, W., 2024. Sustainable urban transformations based on integrated microgrid designs. Nat. Sustain. 7 (8), 1067–1079. http://dx.doi.org/10.1038/s41893-024-01395-7. Patureau, R., Tran, C.T., Gavan, V., Stabat, P., 2021. The New Generation of District Heating & Cooling Networks and Their Potential Development in France. Energy 236, 121477. http://dx.doi.org/10.1016/j.energy.2021.121477. Peterson, K., Torcellini, P., Grant, R., 2015. A Common Definition for Zero Energy Buildings. URL: https://www.energy.gov/sites/prod/files/2015/09/f26/A%20Common% 20Definition%20for%20Zero%20Energy%20Buildings.pdf. (Accessed 16 September 2025). Psimopoulos, E., Johari, F., Bales, C., Widén, J., 2020. Impact of Boundary Conditions on the Performance Enhancement of Advanced Control Strategies for a Residential Building with a Heat Pump and PV System with Energy Storage. Energies 13 (6), 1413. http://dx.doi.org/10.3390/en13061413. Reis, I.F., Gonçalves, I., Lopes, M.A., Antunes, C.H., 2020. A multi-agent system approach to exploit demand-side flexibility in an energy community. Util. Policy 67, 101114. http://dx.doi.org/10.1016/j.jup.2020.101114. Reiter, S., Marique, A.-F., 2012. Toward Low Energy Cities: A Case Study of the Urban Area of Liége, Belgium. J. Ind. Ecol. 16 (6), 829–838. http://dx.doi.org/10.1111/ j.1530-9290.2012.00533.x. Salehi, N., Martinez-Garcia, H., Velasco-Quesada, G., Guerrero, J.M., 2022. A Comprehensive Review of Control Strategies and Optimization Methods for Individual and Community Microgrids. IEEE Access 10, 15935–15955. http://dx.doi.org/10.1109/ ACCESS.2022.3142810. Sassenou, L.-N., Olivieri, L., Olivieri, F., 2024. Challenges for Positive Energy Districts Deployment: A Systematic Review. Renew. Sustain. Energy Rev. 191, 114152. http://dx.doi.org/10.1016/j.rser.2023.114152. Sauerbrey, J., Bender, T., Flemming, S., Martin, A., Naumann, S., Warweg, O., 2024. Towards intelligent energy management in energy communities: Introducing the district energy manager and an IT reference architecture for district energy management systems. Energy Rep. 11, 2255–2265. http://dx.doi.org/10.1016/j. egyr.2024.01.068. Schavemaker, P., Sluis, L.v.d., 2025. Electrical Power System Essentials. John Wiley & Sons. Schneider, S., 2023. Definition of Positive Energy Districts Cities4PEDs - What is a Positive Energy District? University of Applied Sciences Vienna, Vienna, p. 79, URL: https://energy-cities.eu/wp-content/uploads/2023/07/Cities4PEDs-WP2Definition-.pdf. (Accessed 16 September 2025). Senatsverwaltung für Stadtentwicklung, Bauen und Wohnen Berlin, 2024. Statistische Blöcke des RBS. URL: https://gdi.berlin.de/geonetwork/srv/api/records/c61825652b04-3265-81fa-64f22eaac734. (Accessed 16 September 2025). Statistiek, Centraal Bureau voor de, 2024. Kerncijfers Wijken en Buurten 2024. Cent. Bur. Voor de Stat. URL: https://www.cbs.nl/nl-nl/cijfers/detail/85984NED. (Accessed 27 October 2024). Statistische Ämter des Bundes und der Länder, 2025. Zensus 2022. URL: https://www. zensus2022.de/. (Accessed 16 September 2025). Subramaniyan, V., Gomathi, V., 2023. A Soft Computing-Based Analysis of Congestion Management in Transmission Systems. Teh. Vjesn. - Tech. Gaz. 30 (1), http: //dx.doi.org/10.17559/TV-20220222072630. Terrier, C., Loustau, J.R.H., Lepour, D., Maréchal, F., 2024. From Local Energy Communities towards National Energy System: A Grid-Aware Techno-Economic Analysis. Energies 17 (4), 910. http://dx.doi.org/10.3390/en17040910. Urquizo, J., Calderón, C., James, P., 2017. Metrics of Urban Morphology and Their Impact on Energy Consumption: A Case Study in the United Kingdom. Energy Res. Soc. Sci. 32, 193–206. http://dx.doi.org/10.1016/j.erss.2017.03.011. Energy Reports 14 (2025) 3673–3689 3688 J. Galenzowski et al. US Census Bureau, 2022. 2020 Census Tallies. Census.Gov, URL: https://www.census. gov/geographies/reference-files/2020/geo/tallies.html. (Accessed 16 September 2025). U.S. Department of Commerce, Economics and Statistics Administration, Bureau of the Census, 1994. Geographic Areas Reference Manual. U.S. Department of Commerce, URL: https://en.wikisource.org/wiki/Geographic_Areas_Reference_Manual. (Accessed 16 September 2025). Van Summeren, L.F., Wieczorek, A.J., Bombaerts, G.J., Verbong, G.P., 2020. Community energy meets smart grids: Reviewing goals, structure, and roles in Virtual Power Plants in Ireland, Belgium and the Netherlands. Energy Res. Soc. Sci. 63, 101415. http://dx.doi.org/10.1016/j.erss.2019.101415. Wakui, T., Hashiguchi, M., Yokoyama, R., 2021. Structural Design of Distributed Energy Networks by a Hierarchical Combination of Variableand Constraint-Based Decomposition Methods. Energy 224, 120099. http://dx.doi.org/10.1016/j.energy. 2021.120099. Weber, M., Janecke, L., Çakmak, H.K., Hagenmeyer, V., 2024. Open Data-Driven Automation of Residential Distribution Grid Modeling With Minimal Data Requirements. IEEE Trans. Smart Grid 15 (6), 5721–5732. http://dx.doi.org/10.1109/TSG. 2024.3406765. Widagdo, R.S., Slamet, P., Ubaidillah, M., 2024. Prediction of Transformer Age Based on Temperature Due to Loading Using Linear Trend Method: Case Study of 60 MVA Transformer. J. Appl. Electr. Eng. 8 (1), 1–8. http://dx.doi.org/10.30871/jaee.v8i1. 6999. Xu, Y., Leonforte, F., Del Pero, C., 2024. District multi-energy systems: A comprehensive review of configurations, technologies, and performances. Build. Environ. 253, 111318. http://dx.doi.org/10.1016/j.buildenv.2024.111318. Zhang, Y., Vand, B., Baldi, S., 2022. A Review of Mathematical Models of Building Physics and Energy Technologies for Environmentally Friendly Integrated Energy Management Systems. Buildings 12 (2), 238. http://dx.doi.org/10.3390/ buildings12020238. Zhou, L., Li, S., Liu, Z., Zhou, Y., He, B.-J., Zhang, Z., Wang, H., Zhang, G., 2025. Advancing zero-carbon community in China: policy analysis, implementation challenges, and strategic recommendations. Energy Build. 328, 115106. http://dx. doi.org/10.1016/j.enbuild.2024.115106. Energy Reports 14 (2025) 3673–3689 3689