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A key issue in the transition to decarbonized energy sources with constant power sources is inaccurate current sharing and voltage quality. The main reason which causes these issues is the inevitable system impedance mismatches. This study addresses this issue by proposing an enhanced power‐sharing strategy that regulates adjustable resistance and compensates for voltage drops across the line impedance at the output of distributed generation converters. The control approach involves analyzing the mathematical relationship between system impedance mismatches and voltage drops in DC residential systems. Utilizing low‐computation proportional‐integral controllers, the secondary current sharing and load voltage controllers regulate converter outputs to compensate for mismatched resistance and voltage, ensuring proper current sharing and load voltage quality across various load conditions. Through the implementation of the proposed scheme incorporating adjustable resistance and voltage shifting terms, both the current‐sharing error and load voltage deviation are gradually reduced to negligible values, while maintaining stable performance in steady‐state conditions, without the need for prior knowledge of system parameters. Simulations and experimental studies are both conducted to demonstrate the effectiveness of the proposed control scheme. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Renewable energy communities foster the users' engagement in the energy transition, paving the way to the integration of distributed renewable energy sources. So far, the scientific literature has focused on residential users in energy communities, thus overlooking the opportunities for industrial and commercial members. This paper seeks to bridge this gap by extending the analysis to the role of non-residential users. The proposed methodology develops an effective clustering approach targeted to actual non-residential consumption profiles. It is based on the k-means algorithm and statistical characterization based on relevant probability density function curves. The employed clusterization procedure allows for effectively reducing a sample of 49 real industrial load profiles up to 11 typical consumption curves, whilst capturing all the relevant characteristics of the initial population. Furthermore, a peer-to-peer sharing strategy is developed accounting for distributed and shared storage. Three scenarios are considered to validate the model with different shares of non-residential users, and the results are then evaluated by means of shared energy, self-consumption, and self-sufficiency indices. Moreover, the results show that the integration of a large non-residential prosumer in a REC may increase the self-sufficiency of residential members by 8.2%, self-consumption by 4.4%, and the overall shared energy by 37.3%. Therefore, the residential community consistently benefits from the presence of non-residential users, with larger users inducing more pronounced effects. [ABSTRACT FROM AUTHOR]

In the context of the dual-carbon goal, virtual power plants, microgrids and other forms of prosumers have emerged on a large scale. Energy sharing among multiple prosumers can improve the overall economic efficiency and new energy consumption. A collaborative operation mechanism for energy sharing among multiple prosumers under the weak centralization model is proposed. The mechanism considers the data privacy of each prosumer. Firstly, an internal scheduling model of prosumers with multiple distributed resources is constructed. The model considers the volatility and randomness of load demand and new energy output, quantifying the risk of uncertainty based on conditional value at risk (CVaR). Then, a weakly centralized tariff iteration mechanism for multiple prosumers is proposed, which uses the supply and demand relationship to guide the tariff update. Meanwhile, considering the privacy protection of prosumers, the power data aggregation method is designed based on Paillier homomorphic encryption algorithm and secret sharing principle. The method is able to obtain the system supply and demand information while ensuring the privacy of all parties. Finally, the effectiveness and rationality of the mechanism proposed in this paper is verified by an arithmetic example, and the overall cost of multiple prosumers is reduced by 12.6% after energy sharing.

The multi-microgrid structure is emerging as one of the most promising concept for future distribution systems to provide resilience and independence energy operation with the energy exchange of other entities. In the distribution system, all microgrid owners and other stakeholders are benefited by sharing the locally generated energy with the adjacent microgrid entities with the help of energy trading process. The peer-to-peer (P2P) energy trading is one of the best suitable energy-trading method for multi-microgrid systems because of the absence of third-party entity. This paper proposes a hierarchical P2P energy trading model with the incorporation of an energy management scheme for multi-microgrid systems to provide efficient and effective results in the energy trading market. The proposed model is applied to a typical multi-microgrid community system to show the economic benefits of various microgrid stakeholders. The different case studies have been performed in the result analysis to show the impact of energy management scheme on the P2P energy-trading model. From the simulation results, it is evident that the implementation of energy management scheme reduces the operation cost of all microgrids together in the community area by 28.38% in scenario 1, 38.86% in scenario 2, and 39.21% in scenario 3 respectively. The overall result analysis shows that the proposed hierarchical P2P model with the energy management integration can provide the better cost reduction when compared to other scenarios, and also the total load demand is reduced by 29.51%, the renewable energy utilization is improved by 22.39% within the multi-microgrid community system. [ABSTRACT FROM AUTHOR]

The energy hub provides a comprehensive solution uniting energy producers, consumers, and storage systems, thereby optimizing energy utilization efficiency. The single integrated energy system's limitations restrict renewable absorption and resource allocation, while uncoordinated demand responses create load peaks, and global warming challenges sustainable multi-energy system operations. Therefore, our work aims to enhance multi-energy flexibility by coordinating various energy hubs within a hydrogen-based integrated system. This study focuses on a cost-effective, ecologically sound, and flexible tertiary hub (producer, prosumer, and consumer) with integrated demand response programs, demonstrating a 17.30% reduction in operation costs and a 13.14% decrease in emissions. Power-to-gas technology enhances coupling efficiency among gas turbines, boilers, heat pumps, and chillers. A mixed-integer nonlinear programming model using a GAMS BARON solver will achieve the optimal results of this study. The proposed model's simulation results show reduced energy market costs, total emissions, and daily operation expenses. [ABSTRACT FROM AUTHOR]

European Union promoted the concept of collective self-consumption and the so-called energy communities, to involve citizens in energy management and to improve energy efficiency. In accordance, this work incorporates energy communities including public residential buildings in a city of the north of Spain to identify the possibilities of energy communities based on Photovoltaic energy systems, and to analyze their feasibility for self-production. In addition, the possibility of including a rooftop of a factory is studied to supply the complete electric demand. An economic analysis gives a 2 years payback, and an environmental analysis shows that this installation would save almost 1,600 tons/y of CO2. [ABSTRACT FROM AUTHOR]

Time‐of‐Flight Positron Emission Tomography (ToF‐PET) is a medical imaging technique, based on the detection of two back‐to‐back γ‐photons generated from radiotracers injected into the body. Its limit is the ability of employed scintillation detectors to discriminate in time the arrival of γ‐pairs, that is, the coincidence time resolution (CTR). A CTR < 50 ps will enable fast imaging with ultralow radiotracer dose. Monolithic materials do not have simultaneously the required high light output and fast emission characteristics, thus the concept of scintillating heterostructure is proposed, where the device is made of a dense scintillator coupled to a fast‐emitting light material. Here a composite polymeric scintillator loaded with hafnium oxide nanoparticles is presented. This enhanced by +300% its scintillation yield, by surpassing commercial plastic scintillators. The nanocomposite is coupled to bismuth germanate oxide (BGO) realizing a multilayer metascintillator. The energy sharing between its components is observed, which activates the nanocomposite's fast emission enabling a net CTR improvement of 25% with respect to monolithic BGO. These results demonstrate that a controlled loading with dense nanomaterials is an excellent strategy to enhance the performance of polymeric scintillators for their use in advanced radiation detection and imaging technologies. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Estudios Cooperativos is the property of Universidad Complutense de Madrid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract In recent years, energy sharing has attracted a lot of attention. However, the intermediate platforms in centralized energy‐sharing methods cause the rapid growth of communication complexity and the risk of privacy leakage. Unlike complex energy‐sharing market mechanisms, in this paper, a simple and efficient distributed energy sharing for microgrids (MGs) is proposed, where game theory is employed to form flexible prices for prosumers and only local information is used to improve the privacy protection of prosumers. First, prosumers located in different areas are characterized more precisely and a two‐tier carbon emission cost model is built. Next, a game‐theory‐based distributed energy‐sharing model is proposed, where a flexible pricing mechanism is developed to enable prosumers to independently reach price agreements and achieve supply–demand balance within MGs. In the process, optimization models for obtaining equilibrium are formed and only local information is needed. However, solving these optimization models is generally time‐consuming. So, a distributed optimization method based on weighted subgradients is proposed to accelerate the equilibrium‐finding process. Finally, four cases are designed, and simulation results demonstrate that the prosumers' costs of our method are reduced by 7.5%–22.5% compared to the costs obtained by feed‐in tariff. Moreover, in the case of solving the distributed trading model for an MG at a 24‐h time scale, the iteration numbers of our method are only 38.9% and 49.3% of the two traditional solving methods.

AbstractIn recent times, there has been a growing focus on multi-micro-grids (MMGs) system, owing to its well-suited structures for efficiently accommodating large-scale integration of distributed energy resources (DER). This attention is driven by the system’s cost-effectiveness, enhanced efficiency, stability, and reliability performance, achieved through the collaborative exchange of power flow among individual micro-grids (MGs) and the main grid. Fundamental strategies for attaining optimal energy flow and sharing involve the optimal sizing of MGs and the implementation of an Energy Management System (EMS). These strategies play a crucial role in addressing uncertainties associated with intermittent generation, load fluctuations and energy market dynamics. This paper offers a review of grid-connected MMG topologies, EMS structures, coordination methods and current optimization approaches designed to meet EMS objectives. To address the inherent volatilities, the paper introduces various uncertainty quantification techniques along with current challenges. Additionally, it suggests future directions, emphasizing intelligent and predictive modeling to handle uncertainties, as well as recommending the incorporation of energy storage systems (ESSs) to align with emerging trends.

In recent years, energy sharing has attracted a lot of attention. However, the intermediate platforms in centralized energy‐sharing methods cause the rapid growth of communication complexity and the risk of privacy leakage. Unlike complex energy‐sharing market mechanisms, in this paper, a simple and efficient distributed energy sharing for microgrids (MGs) is proposed, where game theory is employed to form flexible prices for prosumers and only local information is used to improve the privacy protection of prosumers. First, prosumers located in different areas are characterized more precisely and a two‐tier carbon emission cost model is built. Next, a game‐theory‐based distributed energy‐sharing model is proposed, where a flexible pricing mechanism is developed to enable prosumers to independently reach price agreements and achieve supply–demand balance within MGs. In the process, optimization models for obtaining equilibrium are formed and only local information is needed. However, solving these optimization models is generally time‐consuming. So, a distributed optimization method based on weighted subgradients is proposed to accelerate the equilibrium‐finding process. Finally, four cases are designed, and simulation results demonstrate that the prosumers' costs of our method are reduced by 7.5%–22.5% compared to the costs obtained by feed‐in tariff. Moreover, in the case of solving the distributed trading model for an MG at a 24‐h time scale, the iteration numbers of our method are only 38.9% and 49.3% of the two traditional solving methods. [ABSTRACT FROM AUTHOR]

Renewable Energy Communities (REC) can play a crucial role in enhancing citizen participation in the energy transition. Current European Union legislation enshrines energy communities and mandates Member States to encourage these organizations, promoting adequate conditions for their establishment. Nevertheless, uptake has been slow, and more research is needed to optimize the associated energy sharing. Using a Portuguese case study (REC Telheiras, Lisbon), this research aims to match local generation through four photovoltaic systems (totalizing 156.5 kWp of installed capacity) with household electricity consumption while cross evaluating the Portuguese legislation for energy sharing. The latter aim compares two scenarios: (a) current legislation (generated energy must be locally self-consumed before shared) and (b) equal share for members with a fixed coefficient. The evaluation is performed according to two indexes of self-consumption (SCI) and self-sufficiency (SSI), related to the simulation of four photovoltaic systems in public buildings, their associated consumption profiles, and an average household consumption profile of community members. The results show that, while maximizing self-consumption for the same values of generation and consumption, the number of participants is considerably lower for Scenario A (SCI = 100% is achieved with at least 491 residential members in Scenario A and 583 in Scenario B), implying that legislative changes enabling energy communities to better tailor sharing schemes may be necessary for them to become more attractive. The methods and results of this research can also be applied to other types of facilities, e.g., industrial and commercial consumers, if they are members of a REC and have smart meters in their installations. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The growing adoption of distributed energy production technologies and the potential for energy underutilisation when the energy is produced by non-connected groups has raised interest in developing 'sharing economy' concepts in the electricity sector. We suggest that mechanisms, such as peer-to-peer (P2P) energy trading, will allow users to exchange their surplus energy for mutual benefits, stimulate the adoption of renewable energy, encourage communities to 'democratically' control their own energy supplies for local development, improve energy efficiency, and create many other benefits This approach is receiving increasing attention across the world, particularly in Germany, the Netherlands and Australia. Nevertheless, the actual development and implementation of these platforms are slow and mostly limited to trial activities. This study investigates the challenges and barriers facing P2P energy trading developments based on previous academic and industry studies. We provide a comprehensive multidimensional barrier analysis through a PESTLE approach to assess the barriers from a variety of perspectives, including the political (P), economic (E), social (S), technological (T), legal (L), and environmental (E) aspects. This approach clarifies the many intersecting problem fields for P2P trading in renewable energy, and the paper identifies a list of such barriers and discusses the prospects for addressing these issues. We also elaborate on the importance of incentive-based P2P market design. [ABSTRACT FROM AUTHOR]

Regional data center clusters have flourished in recent years to serve customers in a major city with low latency. The optimal coordination of data centers in a regional cluster has become a pressing issue because of their rising energy consumptions. In this paper, an online algorithm based on Lyapunov optimization framework is developed for the combined computing workload and energy coordination of data centers in a regional cluster. The proposed online algorithm is prediction-free and easy to implement. We prove that the workload queues and battery energy level will be within their physical limits, though their related time-coupling constraints are not considered explicitly in the proposed algorithm. The previous online algorithms do not have such a guarantee. A theoretical upper bound on the optimality gap between the online and offline results is derived to provide a performance guarantee for the proposed algorithm. To enable distributed implementation, an accelerated distributed coordination algorithm is developed based on the alternating direction method of multipliers (ADMM) with iteration truncation and follow-up well-designed adjustments, whereby a nearly optimal solution is attained with much enhanced computational efficiency. Case studies show that the proposed algorithm reduces the operational costs and saves computation time compared to online benchmarks.

The rise of distributed energy resources (DERs) in the energy landscape underscores the pivotal role of prosumers in the ongoing energy transition. With the significant investment required for individual energy storage (IES), community energy storage (CES) emerges as a key facilitator, enabling the smooth incorporation of renewable energy sources and strengthening grid flexibility. This paper explores the dynamic interplay between CES owners, who serve as key economic actors in local energy communities, and prosumers within these communities through a Stackelberg game framework. A bi-level optimization framework aimed at empowering CES systems is introduced to address the proposed Stackelberg game framework. At the upper level of this framework, the goal is to maximize the profit of the CES system while the flexibility limits of the upstream electricity grid and other constraints are addressed. On the other hand, individual prosumers at the lower level optimize their actions to minimize billing costs and enhance comfort in alignment with their preferences. The bi-level optimization problem is reformulated into a linear single-level optimization problem using the Karush-Kuhn-Tucker (KKT) approach. The results indicate that implementing flexibility constraints can effectively smooth energy exchanges with the upstream grid. However, the distribution system operator must account for the potential reduction in the CES system’s operational profit to incentivize the CES owner to participate in the flexibility enhancement program.

The Paris Agreement, which is carried out in a bottom-up manner, is giving great vitality to the transition to low-carbon energy system. Accordingly, as the spread of renewable energy increases, securing flexibility resources is emerging as a keyword in the power system and power market, and many studies on demand response (DR) and energy storage system (ESS) classified as demand-side flexible resources are being conducted. However, most of the studies on DR are on voluntary DR programs that participate through bidding among incentive-based DR or price-based DR programs, and ESS faced difficulties in its distribution due to low economic feasibility. Therefore, in this work, we propose a feasible process for participating in a reliability DR program that guarantees high profitability but involves high uncertainty, and non-cooperative game model is suggested to mitigate the uncertainty through surplus electricity-trading model of ESS. In addition, a subjective decision-making based on prospect theory is proposed to reflect the impact of the uncertainty about the incentive-based program’s engagement. Case study results performed in an environment formed based on actual operation data of the Korean DR market prove that participants’ uncertainty is mitigated when participating in the DR market through power trading of ESS through the proposed model. In addition, a small-scale ESS with an average benefit–cost ratio of 0.78 improves by up to 1.08 (1.04 with a standard scenario).

Renewable energy communities foster the users’ engagement in the energy transition, paving the way to the integration of distributed renewable energy sources. So far, the scientific literature has focused on residential users in energy communities, thus overlooking the opportunities for industrial and commercial members. This paper seeks to bridge this gap by extending the analysis to the role of non-residential users. The proposed methodology develops an effective clustering approach targeted to actual non-residential consumption profiles. It is based on the k-means algorithm and statistical characterization based on relevant probability density function curves. The employed clusterization procedure allows for effectively reducing a sample of 49 real industrial load profiles up to 11 typical consumption curves, whilst capturing all the relevant characteristics of the initial population. Furthermore, a peer-to-peer sharing strategy is developed accounting for distributed and shared storage. Three scenarios are considered to validate the model with different shares of non-residential users, and the results are then evaluated by means of shared energy, self-consumption, and self-sufficiency indices. Moreover, the results show that the integration of a large non-residential prosumer in a REC may increase the self-sufficiency of residential members by 8.2%, self-consumption by 4.4%, and the overall shared energy by 37.3%. Therefore, the residential community consistently benefits from the presence of non-residential users, with larger users inducing more pronounced effects.

In recent times, there has been a growing focus on multi-micro-grids (MMGs) system, owing to its well-suited structures for efficiently accommodating large-scale integration of distributed energy resources (DER). This attention is driven by the system's cost-effectiveness, enhanced efficiency, stability, and reliability performance, achieved through the collaborative exchange of power flow among individual micro-grids (MGs) and the main grid. Fundamental strategies for attaining optimal energy flow and sharing involve the optimal sizing of MGs and the implementation of an Energy Management System (EMS). These strategies play a crucial role in addressing uncertainties associated with intermittent generation, load fluctuations and energy market dynamics. This paper offers a review of grid-connected MMG topologies, EMS structures, coordination methods and current optimization approaches designed to meet EMS objectives. To address the inherent volatilities, the paper introduces various uncertainty quantification techniques along with current challenges. Additionally, it suggests future directions, emphasizing intelligent and predictive modeling to handle uncertainties, as well as recommending the incorporation of energy storage systems (ESSs) to align with emerging trends. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The transition to a net-zero energy system is being promoted in the energy sector, which has led to the creation of energy prosumers. These produce, consume, and trade energy using renewable energy systems installed in buildings or complexes. Here, a community was set as the target to apply the concept of an energy prosumer at the individual building and regional levels. Energy-sharing systems were divided into three categories: energy production, energy storage, and energy management. Energy-sharing systems centered on electrical energy—photovoltaic, battery energy storage, and energy management systems—were installed in two communities located in South Korea, and the energy-sharing effects of the system operation were reported. Monthly power consumption in spring and fall exhibited significant savings of approximately three times that of winter consumption, owing to the energy-sharing systems. Daily hourly power-consumption patterns differed on weekdays and weekends because of the weekday working and building-use hours of the communities. Energy could be shared between communities and buildings because of surplus energy. More surplus power was available for energy sharing on weekends because power consumption was lower. Because energy trading and sharing are restricted, the related laws are being revised. Therefore, a low-carbon community can be realized through surplus energy trading and sharing technology between communities and buildings as renewable energy systems spread owing to low carbonization. [ABSTRACT FROM AUTHOR]

Environmental problems due to climate change, that have been affecting our planet for years, are the main issues which prompted European Union to establish the ambitious target of achieving carbon neutrality by 2050. This occurrence encouraged all Member States to undergo significant changes of their energy sectors, favouring the extensive use of renewable energy sources. In this scenario, the European Union introduced Renewable Energy Communities, innovative energy systems based on a new model of renewable energy production, consumption and sharing, guaranteeing environmental, economic, energy and social benefits. The objective of this paper is twofold: firstly, to examine the regulatory framework of Member States and, secondly, to present a standardized procedure for the implementation of a Renewable Energy Community, an aspect not yet covered in scientific literature. The roadmap includes four main phases: a feasibility study involving an energy analysis of end users’ consumption and a general assessment; the aggregation of members as producers, consumers or prosumers forming a legal entity, considering different funding opportunities; the operating phase, involving plant construction and project validation by national authorities; the technical and economic management phase. The dynamic structure of the roadmap allows for adjustments to accommodate different regulatory contexts, member typologies and project aim.

The widespread use of distributed energy sources provides exciting potential for demand-side energy sharing and collective self-consumption schemes. Demand-side energy sharing and collective self-consumption systems are committed to coordinating the operation of distributed generation, energy storage, and load demand. Recently, with the development of Internet technology, sharing economy is rapidly penetrating various fields. The application of sharing economy in the energy sector enables more and more end-users to participate in energy transactions. However, the deployment of energy sharing technologies poses many challenges. This paper comprehensively reviews recent developments in demand-side energy sharing and collective self-consumption schemes. The definition and classification of sharing economy are presented, with a focus on the applications in the energy sector: virtual power plants, peer-to-peer energy trading, shared energy storage, and microgrid energy sharing cloud. Challenges and future research directions are thoroughly discussed.

The building sector accounts for a significant proportion of global energy usage and carbon dioxide emissions. It is important to explore technological advances to curtail building energy usage to support the transition to a sustainable energy future. This study provides an overview of emerging and sustainable technologies and strategies that can assist in achieving building decarbonization. The main technologies reviewed include uncertainty-based design, renewable integration in buildings, thermal energy storage, heat pump technologies, thermal energy sharing, building retrofits, demand flexibility, data-driven modeling, improved control, and grid-buildings integrated control. The review results indicated that these emerging and sustainable technologies showed great potential in reducing building operating costs and carbon footprint. The synergy among these technologies is an important area that should be explored. An appropriate combination of these technologies can help achieve grid-responsive net-zero energy buildings, which is anticipated to be one of the best options to simultaneously reduce building emissions, energy consumption, and operating costs, as well as support dynamic supply conditions of the renewable energy-powered grids. However, to unlock the full potential of these technologies, collaborative efforts between different stakeholders are needed to facilitate their integration and deployment on a larger and wider scale. [ABSTRACT FROM AUTHOR]

In recent years, solar power technology and energy storage technology have advanced, leading to the increased use of solar power devices and energy storage systems in residential areas. Carbon management has become an important method to help the community manager guide energy consumption in a timely manner, effectively reduce the carbon emissions of the community, and reduce the substantial harm to the environment. This paper aims to study the issue of carbon management and resource allocation in an intelligent community with combined heat and power (CHP) systems and solar power. The presence of heterogeneous load demands in the power grid was considered. The main objective was to minimize the average system cost over time, which included the costs associated with the power grid and gas. The Lyapunov optimization theory was employed to solve the non-convex optimization problem of carbon management and resource allocation without energy sharing. To solve the energy-sharing problem, we designed an energy-sharing algorithm based on the Q-learning algorithm. Lastly, we conducted extensive simulations using actual trace data to validate the effectiveness of our proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a sustainable, intelligent energy management system for a microgrid based on a multi-agent system (MAS) is studied. The system is designed to address the challenges posed by the intermittence of renewable energy sources. Also, the system optimizes the use of available AC–DC renewable energy sources by utilizing load flexibility and the complementarity of renewable sources. To evaluate the effectiveness of this proposed multi-agent framework, a co-simulation using MATLAB and JADE platforms is conducted for a microgrid connected to the main grid. The results show that the proposed energy sharing system achieved over 82.34% of energy savings. This innovative solution has the potential to reduce the need for energy storage and improve energy efficiency while also reducing CO2 emissions. It offers a promising sustainable development solution for managing and controlling MG. The proposed system contributes to the development of sustainable energy systems based on artificial intelligence to meet the global goals. [ABSTRACT FROM AUTHOR]

Summary: A smart community grid is an electrical network, which connects several producers, consumers, and prosumers to share energy in an intelligent and secure way. The main challenges of smart community grid are demand response, demand bidding, dynamic electricity tariffs, demand‐side management, and prosumers handling. The current state‐of‐the‐art smart grid decision making is focused on consumers and producers behavior while the aim of this research is to achieve prosumer's different goals in an optimized and intelligent way. A genetic algorithm (GA)‐based solution is proposed to share energy in an optimized way without affecting the prosumers' preferences. Six prosumers smart community grids data sets are used to validate the performance of the proposed system. The results show that the proposed method significantly improves the loss of energy sharing without compromising the user's preferences. [ABSTRACT FROM AUTHOR]

The recent published European legal framework (Directives (EU) 2018/2001 and 2019/944) on renewable energy consumption, and its Portuguese transposition (the Decree-Law 15/2022), opens the possibility for buildings to operate as energy communities. One of the objectives is to increase the use of locally generated energy from renewable sources, by sharing available surplus among participants, using sharing coefficients defined by the entire community.Taking the actual legal framework into consideration, this paper presents an analysis of the energy sharing coefficients proposed by the newly published Portuguese legislation via the assessment of a renewable energy community, formed by public buildings, whose operation varies according to different sharing coefficient applied. Results show that time-variable energy sharing coefficients are the best option to the considered renewable energy community. Collected results also show that larger consumers can extract higher benefits from being integrated on a renewable energy community. These benefits decrease when buildings are allowed to self-consume local generated energy prior to the sharing process as demand inequalities become less important for the computation of the considered sharing coefficients. The entire community also presents better performance in this case.

Introduction: It is significant for energy sharing to study the complementary utilization of multiple energy sources, such as water, electricity and gas, and the interaction among multiple stakeholders.Methods: We propose a research on energy sharing between distribution network and multiple systems based on the mixed game strategy and water-electric-gas integrated energy complementation. Firstly, this paper describes the relationship and functions of all stakeholders under the research framework, and establishes the mathematical model of each unit in the water-electric-gas complementary IES. Secondly, the internal roles are layered based on the relationship between stakeholders in the system. Then a non-cooperative game model for the distribution network operator and multiple subsystems is established according to the theory of Stackelberg game, and a cooperative game model for multiple subsystems is further established based on the theory of Nash bargaining. In the next step, the complexity of the problem is analyzed, followed by the description of the specific algorithm and process of solving the model.Results: Finally, the results of example analysis show that the model proposed in this paper not only balances the interests of stakeholders at the upper and lower layers of the system, but also allocates the interests of multiple subsystems at the lower layer.Discussion: Thus effectively improving the energy utilization of the system.

Energy is the foundation for human survival and socio-economic development, and electricity is a key form of energy. Electricity prices are a key factor affecting the interests of various stakeholders in the electricity market, playing a significant role in the sustainable development of energy and the environment. As the number of distributed energy resources (DERs) increases, today's power systems no longer rely on a vertical market model and fixed electricity pricing scheme but instead depend on power dispatch and dynamic pricing to match supply and demand. This can help prevent significant fluctuations in supply–load imbalance and maintain system stability. Modern power grids have evolved by integrating information, communication, and intelligent control technologies with traditional power systems, giving rise to the concept of smart electric grids. Choosing an appropriate pricing scheme to manage large-scale DERs and controllable loads in today's power grid become very important. However, the existing literature lacks a comprehensive review of electricity pricing in power systems and its transformative impact on shaping the energy landscape. To fill this void, this paper provides a survey on the developments, methods, and frameworks related to electricity pricing and energy trading. The review mainly considers the development of pricing in a centralized power grid, peer-to-peer (P2P) and microgrid-to-microgrid (M2M) energy trading and sharing, and various pricing methods. The review will cover the pricing schemes in modern power systems, particularly with respect to renewable energy sources (RESs) and batteries, as well as controllable load applications, and the impact of pricing schemes based on demand-side ancillary services (DSAS) for grid frequency support. Lastly, this review article describes the current frameworks and limitations of electricity pricing in the current energy market, as well as future research directions. This review should offer a great overview and deep insights into today's electricity market and how pricing methods will drive and facilitate the future establishment of smart energy systems. [ABSTRACT FROM AUTHOR]

Energy sharing within building complexes depends greatly on the complex load adaptability and policy support. However, most building complex microgrid research concentrates on configuration and operation optimization, rather than energy contributions from different users. The objective of this study is to analyze energy sharing ability and load adaptability for a building complex comprising multiple building types, under transaction liberalization restrictions in different energy markets. The specific research for this study is a smart community in Kitakyushu, Japan, with 49 buildings of different types. The complex's microgrid system in this study comprises photovoltaic, battery, and vehicle to grid services. We obtained six representative typical clusters by reducing the 49 buildings' dimensionality using principal components and clustering, then Monte Carlo simulation to predict permissible discharge capacities for vehicle to grid (V2G) services. We propose three demand-side liberalization scenarios: self-use, photovoltaic feed-in tariffs (FITs), and free trade; optimizing system configuration under different scenarios using a genetic algorithm. Results confirm that shopping malls achieve best economic benefits with self-use scenario; whereas office building adaptability is more significant for the other scenarios. These results will contribute to user selection and system design for building complex microgrid systems. [ABSTRACT FROM AUTHOR]

With the extensive penetration of distributed renewable energy and self-interested prosumers, the emerging power market tends to enable user autonomy by bottom-up control and distributed coordination. This paper is devoted to solving the specific problems of distributed energy management and autonomous bidding and peer-to-peer (P2P) energy sharing among prosumers. A novel cloud-edge-based We-Market is presented, where the prosumers, as edge nodes with independent control, balance the electricity cost and thermal comfort by formulating a dynamic household energy management system (HEMS). Meanwhile, the autonomous bidding is initiated by prosumers via the modified Stone-Geary utility function. In the cloud center, a distributed convergence bidding (CB) algorithm based on consistency criterion is developed, which promotes faster and fairer bidding through the interactive iteration with the edge nodes. Besides, the proposed scheme is built on top of the commercial cloud platform with sufficiently secure and scalable computing capacity. Numerical results show the effectiveness and practicability of the proposed We-Market, which achieves 15% cost reduction with shorter running time. Comparative analysis indicates better scalability, which is more suitable for larger-scale We-Market implementation.

The sharing economy is a new economic model that can promote the optimal allocation of resources. Distributed energy sharing in the industrial cluster is of great significance for cluster enterprises to improve energy utilization efficiency and reduce carbon emissions. In this paper, we establish a differential game model for energy sharing in the industrial cluster under the cap-and-trade mechanism analyze the equilibrium strategies of core and supporting enterprises in the industrial cluster under three different decision scenarios. We then conduct a comparative analysis of the results, and the effect of the carbon cap and carbon trading prices on energy sharing in the industrial cluster is discussed in detail. Finally, the results of the theoretical analysis were verified through numerical simulations. The conclusions are as follows: 1) The energy sharing synergy, profit for both parties, and total system profit are the highest under the centralized decision, the Stackelberg game is better than the decentralized decision, and the cost-sharing contract can achieve the overall coordination of interests; 2) A higher carbon trading price can increase the low-carbon level of energy consumption, but interestingly, when the carbon cap is below a certain limit, the increase in carbon trading price within a certain time and interval will lead to a decrease in profits for both parties and total system profit; 3) In the Stackelberg game scenario where cost-sharing contracts are introduced, the cost-sharing ratio of core enterprises will increase as the proportion of benefits to core enterprises increases and government subsidies to supporting enterprises decrease.

With an increase in the electrification of end-use sectors, various resources on the demand side provide great flexibility potential for system operation, which also leads to problems such as the strong randomness of power consumption behavior, the low utilization rate of flexible resources, and difficulties in cost recovery. With the core idea of “access over ownership”, the concept of the sharing economy has gained substantial popularity in the local energy market in recent years. Thus, we provide an overview of the potential market design for the sharing economy in local energy markets (LEMs) and conduct a detailed review of research related to local energy sharing, enabling technologies, and potential practices. This paper can provide a useful reference and insights for the activation of demand-side flexibility potential. Hopefully, this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.

Many published studies debated electrical energy management. They mainly investigate the multi-source installation to develop energy efficiency during its different phases: production, distribution, and consumption. Although it is rarely discussed, energy sharing is a critical part of the energy management system. In this contribution, a demand-side management algorithm is developed, that incorporates energy consumption scheduler capacity. It provides optimal energy sharing, counting on suitable energy cost parameters and adequate multi-source installation. Using this proposal, the electrical bill decreases thanks to the optimal daily attribution of schedules to households formed by a multi-consumer microgrid. This application guarantees a maximal reduction of electrical cost for the set of energy partners as one prosumer used to consume and produce power. In addition, it maintains energy efficiency as it aids in avoiding breakdowns, and depressing the peak-to-average ratio. It admits that the utility company is, as usual, always reachable non-renewable source. At the same time, renewable energy was engendered by photovoltaic panels concomitant with wind turbines stations. The application is based on the JNET protocol stack. The proposed energy sharing algorithm is implemented by using Arduino board and JN5148 nodes as a star Wireless Sensors Network topology. It is installed as a prototype in the Digital Research Center of Sfax in Tunisia. This proposed incentive-based algorithm managed to reduce the smart microgrid annual cost by almost 55% without harming the public utility. It can even ensure a more significant diminution by selling the surplus of renewable power at the end of each day.

Peer-to-peer (P2P) energy sharing among neighboring households is a promising solution to mitigating the difficulties of renewable power (such as solar Photovoltaics (PV)) penetration on the power grid. Until now, there is still a lack of study on the impacts of future climate change on the P2P energy trading performances. The future climate change will cause variances in the renewable energy production and further lead to changes in the economic performances of households with various energy uses and affect the decision making in PV ownership and pricing strategies. Being unaware of these impacts could potentially hinder the P2P energy sharing application in practice. To bridge such knowledge gap, this paper conducts a systematic investigation of the climate change impacts on the energy sharing performance in solar PV power shared communities. The future weather data is generated using the Morphine method, and an agent-based modeling method is used for simulating the energy trading behaviors of households. Four comparative scenarios of different PV ownerships and pricing strategies are designed. The detailed energy trading performances (including the PV power self-sufficiency, cost saving, revenues, and compound annual growth rate) for the four comparative scenarios are analyzed under both the present and future climates and compared. The study results of a building community located in Sweden show that the future climate change is more beneficial to large energy use households while less beneficial to small households. High price of energy trading can improve the fairness of the economic performances in the community, especially when some of the households do not have any PV ownership. This study can help understand the future climate impacts on the energy sharing performances of building communities, which can in turn guide decision making in PV ownership and price setting for different households under the future climate change to facilitate real applications.

In the context of smart grid, the joint operation of smart buildings with distributed photovoltaic systems can promote the on-site consumption of photovoltaic power generation and improve its electricity economy. Therefore, this article builds an energy trading framework centered on smart building cluster operators (SBCO), and proposes an energy sharing method for smart buildings with different load types in the region. Considering the difference of time-of-use electricity prices set by the power grid for smart buildings with different load types and the comfort requirements of building users, this method establishes a benefit optimization model based on the master–slave game SBCO and smart buildings for power purchase transactions. Practical examples show that the method in this paper can improve the economic benefits of SBCO and smart buildings, and help to promote the on-site consumption of distributed photovoltaics and optimize the net load characteristics of smart building clusters.

In Europe, the recast of Directive 2018/2001 defined Renewable Energy Communities as innovative configurations for renewable energy sharing between different end user types. In this regard, this work aims to assess the benefits following the constitution of a Renewable Energy Community in the industrial area of Benevento (South of Italy), involving a mixed-use building and an industrial wastewater treatment plant. The alternative single end users' configuration has been also examined, and both solutions have been compared with the current state where the users' electric energy requests are fully met by the power grid. The users have been equipped with a 466 kWp photovoltaic plant, modelled in HOMER Pro®, providing in input experimental meteorological data (global solar radiation and air temperature) collected by one of the weather control units in Benevento. Real data about users' electric energy demand have been gathered from their electricity bills, and when unavailable their electric load profiles on an hourly basis have been reconstructed based on the aggregated monthly data. Energy sharing has been proven to increase energy self-consumption and the users' self-sufficiency. Annually, the primary energy demand is reduced by 577 MWh (1.2 MWh/kWp), carbon dioxide emissions by 84 tCO2 and operative costs by 101 kEUR. [ABSTRACT FROM AUTHOR]

The transition to a net-zero energy system is being promoted in the energy sector, which has led to the creation of energy prosumers. These produce, consume, and trade energy using renewable energy systems installed in buildings or complexes. Here, a community was set as the target to apply the concept of an energy prosumer at the individual building and regional levels. Energy-sharing systems were divided into three categories: energy production, energy storage, and energy management. Energy-sharing systems centered on electrical energy—photovoltaic, battery energy storage, and energy management systems—were installed in two communities located in South Korea, and the energy-sharing effects of the system operation were reported. Monthly power consumption in spring and fall exhibited significant savings of approximately three times that of winter consumption, owing to the energy-sharing systems. Daily hourly power-consumption patterns differed on weekdays and weekends because of the weekday working and building-use hours of the communities. Energy could be shared between communities and buildings because of surplus energy. More surplus power was available for energy sharing on weekends because power consumption was lower. Because energy trading and sharing are restricted, the related laws are being revised. Therefore, a low-carbon community can be realized through surplus energy trading and sharing technology between communities and buildings as renewable energy systems spread owing to low carbonization.

It is of great significance to introduce the conception of a sharing economy into the electricity industry, which can promote the dispatch of multiple integrated energy systems. On the one hand, it is difficult to reveal the behaviors of complex players with multi-energy coupling through the traditional centralized optimization method of single electric energy. On the other hand, the uncertain fluctuations of renewable energy, such as wind power and photovoltaic, have posed great challenges to market transactions. First, the relationship and the functions of all stakeholders in the system are described in this paper, followed by the establishment of flexible resource models such as demand response and energy storage devices. On this basis, a low-carbon dispatching framework of multiple regional gas–electric integrated energy systems is then constructed under the guidance of cooperative game theory. The contribution indexes are established to measure the degree of energy sharing among the subsystems, and the method of asymmetric Nash bargaining is used to settle the interests of each subsystem. Second, a robust optimization model of multiple regional systems is established in response to multiple uncertainties from renewable energy and load. Finally, the numerical example proves that the proposed mechanism can increase the benefits of each integrated energy system player. Moreover, it helps the system to yield optimal benefits in the face of uncertainties and provides a reference on how to realize energy sharing under uncertainties from source load. [ABSTRACT FROM AUTHOR]

Citizens are set to play an active role in the energy transition by transforming from 'passive' consumers to 'active' prosumers. Renewable Energy Communities (RECs) are envisioned as modes of collective prosumership by citizens under the Renewable Energy Directive of 2018 (RED II). A holistic understanding of RECs is essential to identify the benefits and challenges of collective prosumership. RECs have been the topic of several modelling studies, but a single model that simulates RECs from an integrated perspective—combining technical, economic and ecological analysis—is absent. Wide variability in the indicators discourages comparison of the results across studies. This article builds on the existing knowledge by proposing an integrated model to undertake a multi-disciplinary assessment of a potential REC. First, the proposed model analyses the technical possibilities of collective prosumership using energy flow analysis based on consumption and generation profiles. Second, the model evaluates the economic impacts of prosumership from two perspectives: from the consumers' perspective (in terms of the annual cost of energy consumption) and from an investor's perspective (in terms of the net present value of the investment). Thirdly, the model quantifies the annual greenhouse gas emissions of energy consumption (expressed in CO 2 equivalent) to evaluate the ecological impact of prosumership. Lastly, a set of key performance indicators (KPIs) are proposed that can be used to interpret and compare the results of simulations and are mapped to the actors in the REC in line with their objectives. The proposed approach offers a single, replicable model that can be used to simulate RECs in the different Member States of the European Union. The KPIs can be used to compare the impact of combinations of various prosumership activities within the same REC or to compare two different RECs on the benefits offered vis-a-vis the investments incurred. The KPIs also offer insights into the aligning and conflicting objectives of the stakeholders of the REC. [ABSTRACT FROM AUTHOR]

• Demonstrated the first real-world energy sharing in an energy community. • Fully electrified a community with a 107 MWh scale using high renewable energy. • Achieved 57.6% self-consumption, 38.4% self-sufficiency, 126.3% energy independence. • Used machine learning to uncover complex grid and building energy interactions. • Reduced annual electricity costs by 18.3% through demand side management. Achieving 'carbon neutrality' in the building sector involves high penetration of renewables, electrification of energy use, and improved flexible interactions with the grid to balance energy supply and demand. This study analyzed one year in an urban energy community powered entirely by electricity, using high levels of renewable sources. By facilitating energy sharing between residential and non-residential buildings, the community redirected surplus energy to meet demand, achieving 57.6% self-consumption, 38.4% self-sufficiency, and 126.3% energy independence rate. This achievement involved an annual electricity consumption of 107 MWh, sevenfold that of previous simulation-based studies, marking the first real-world demonstration. Using machine learning clustering with domain-based interpretation, the study unraveled complex interactions between building operations and the power grid, influenced by external factors (weather, occupant behavior), system design, and controls. Managing low-probability high-impact (LPHI) events of severe demand or production peaks, accounting for only 0.5% of the time and 1.8% of net energy annually, was crucial to grid stability and cost reduction. The study confirmed the limitations of increasing battery energy storage capacity in certain environments, highlighting the importance of demand-side management (DSM) with thermal systems. The DSM effectively handled LPHI events, reducing grid stress with an 18.3% decrease in annual electricity cost. [ABSTRACT FROM AUTHOR]

Hydrogen-based regional multi-energy system (RMES) formed by uniting multiple integrated energy systems (IESs) in the region and combining the hydrogen energy industry chain is conducive to promoting a clean and low-carbon energy transition. However, the new characteristics of hydrogen-based RMES, such as multiple energy flows, multiple time scales, and multiple spatial scales, increase the difficulty of low-cost planning and flexible operation. Therefore, a novel multi-time-space scale optimization framework is proposed for the hydrogen-based RMES. Firstly, a hydrogen-based RMES is constructed, with electricity and hydrogen shared between communities, and the cooling, heating, electricity and hydrogen supply and demand balance of the system is modeled. Then, a four-phase multi-time-space scale optimization framework is proposed. The first and second phases are co-optimized to determine system configuration and day-ahead hourly operation scheme for the region to minimize both total annual cost and daily operation cost. Intra-day operation scheme for each community with 15 min time interval is obtained through moving-horizon optimization method in the third phase. In the fourth phase, with the objective of minimizing the amount of adjustment, the operation scheme of the electricity-related equipment with 5 min time interval in each community is optimally obtained. Finally, a case study is conducted with a region consisting of industrial, residential, and commercial community. The results show that, compared to the isolated IES, the total annual cost, annual carbon emissions, and total grid interactions of the hydrogen-based RMES are reduced by 3.4 k$ (0.9%), 1309.9 t (9.4%), and 1365.1 MWh (9.2%), respectively. In addition, the operation scheme obtained can flexibly coordinate various types of equipment and promote the energy balance at multi-time and multi-space scales, the daily operation cost is reduced by 3.8% relative to the day-ahead scheme. [Display omitted] • A hydrogen-based regional multi-energy system considering energy sharing is proposed. • A multi-time-space scale optimization framework for the system is constructed. • The superiority of the planning and operation scheme is confirmed by the case study. [ABSTRACT FROM AUTHOR]

Fifth generation district heating and cooling systems are becoming increasingly popular due to their ability for working with low temperature of heat transfer fluids. Among the other benefits, this characteristic allows for a better exploitation of renewable energy sources. On the other hand, these networks require a fine design and precise management to exploit their full potential. Both these requirements can be met by using advanced simulation and optimisation tools. This research proposes a simulation tool purposely conceived for the design and the optimisation of fifth-generation district heating and cooling systems. This tool is capable of assessing the effects of each building-plant system on the whole district heating and cooling water loop, and to evaluate the effectiveness of diverse network morphology. These capabilities are due the level of detail of the mathematical modelling which takes into account the thermohydraulic characteristics of the network, each building thermo-physics properties, and the heat pump/chiller detailed operation. The described tool has been adopted to simulate an existing experimental network prototype (consisting of a central heat pump, behaving as thermal energy balancing station, and eight users), and the achieved results were compared to those experimentally obtained for validation aims. The capabilities of the validated tool have been demonstrated by investigating an innovative control logic (representing a further novelty of this research) for a "proof-of-concept" fifth-generation district heating and cooling network. In particular, by adopting a predictive control logic, the water loop temperature is dynamically optimised to minimise the entire network energy demand. The adopted control strategy has yielded significant primary energy savings, amounting to 10.3 MWh/year, with a rate of 6.5 % compared to the reference case characterised by a fixed network temperature. These results underscore the potential of the proposed method and demonstrate the effectiveness of the developed tool. • Thermodynamic model to design 5th Generation District Heating and Cooling. • Estimation of building thermal demands with an in-house Energy Simulation tool. • Modelling and calibration of control systems for systems operation at partial conditions. • Development of a network temperature control strategy to optimise energy performance. • Primary energy saving of 10.3 MWh/year reducing 6.5 % of traditional system consumption. [ABSTRACT FROM AUTHOR]

Renewable energy communities with members which own electric vehicles (EVs) can benefit financially from the flexibility that EVs provide to distributed energy resources, such as photovoltaic generators. However, in residential environments, this benefit is often lost because during working hours, most EVs are not plugged in at the premises of the community. In this paper, we hypothesize that such communities can regain some of this lost benefit by offering membership to outside EV owners which are interested in charging their vehicles using the community's charging stations during these periods. This paper introduces an optimization model for energy communities consisting of prosumers, electric vehicles, and EV chargers. The model aims to evaluate the potential advantages of integrating external EV owners into an energy community. A hypothetical community representing a multi-apartment building is analyzed, considering scenarios both with and without external members. The analysis focuses on assessing the technical and economic benefits. The findings indicate that the economic benefits for the examined community are relatively small. However, a notable increase in these benefits is observed when the community is incentivized to share energy among members in the form of a flat network charge reduction for shared energy. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal sizing of solar photovoltaic (PV) and battery energy storage (BES) for grid-connected houses based on mutually-agreed energy sharing prices by considering the flat and time-of-use (TOU) tariffs. The grid-tied house with PV-BES, referred to house 1 (H1) in the paper shares electricity with house 2 (H2) whenever needed with mutually-agreed electricity tariffs. The main objective function of the study is to minimize the cost of electricity (COE) for the H1 while decreasing COE for H2. Eight different schemes are investigated with the combination of flat and TOU tariffs for buying, selling, and mutually-agreed rates, respectively. For each scheme, optimal sizing of components and COE for both houses are evaluated. Realistic hourly-arranged annual data of temperature, solar irradiation, and load consumption of two houses are used as the input data. For each scheme, the results are compared with the situation when H1 does not have PV-BES and there is no electricity sharing. Sensitivity, operational, and uncertainty analyses are provided for the scheme with the lowest COE. [ABSTRACT FROM AUTHOR]

[Display omitted] • A methodology to assess carbon neutrality in district and communities is defined. • The proposed methodology is implemented in a ready-to-use and user-friendly tool. • The proposed approach aims at supporting the energy transition of cities. Positive Energy Districts are expected to play a major role in the energy transition of cities. Hence, this paper aims at introducing a novel methodology useful for district energy and environmental analysis and intended to support the accomplishment of the targets of Positive Energy Districts at the district or community level. The proposed approach relies on the basic concepts underpinning the "Baseline Emission Inventory" but encompasses the ambitious and challenging objectives characterizing Positive Energy Districts. For making the proposed evaluation framework accessible to as many user categories as possible (researchers, local institutions, urban planners, etc.), the calculation steps have been transposed in a user-friendly, ready-to-use tool called "En-to-EnD. Energy and Environmental analysis of Districts". The applicability and replicability of the outputs of this work have been proven through the energy and environmental assessment of a reference case study. In particular, the district being analysed is in the South of Italy and is equipped with photovoltaic plants and a wind turbine. The results obtained show that the proposed approach may serve not only to prove the achievement of the Positive Energy District status, but also to guide the design of more sustainable district-based energy systems, thus supporting the decarbonization of cities. [ABSTRACT FROM AUTHOR]