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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]

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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]

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.)

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]

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]

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]

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]

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]

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]

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.)

This paper investigates a long-term average total energy cost minimization problem via resource management, including admission control, power allocation, and Energy Sharing (ES) of renewable energy in Heterogeneous Cellular Networks powered by Grid-connected Smart Micro Grids (GSMG-HCNs). In GSMG-HCNs, both renewable and grid energy power the base stations. Unlike existing works, we consider the cost of both renewable and grid energy and formulate the power line loss process caused by ES into our model. To solve the proposed problem, we transform it into a real-time issue by the Lyapunov technique. The proposed Cost-Aware Online Resource Management (CAORM) algorithm decouples the real-time issue into two sub-problems, one of which is linear and the other is addressed based on the successive convex approximation approach. We theoretically prove the asymptotic optimality of the CAORM algorithm and a tradeoff between the average total energy cost and the average queue length. Simulation results reveal that the CAORM algorithm outperforms benchmarks in reducing total energy cost and can make appropriate decisions according to different unit costs of renewable energy. Besides, the designed distance-related ES loss rate can help obtain better solutions with lower ES losses. [ABSTRACT FROM AUTHOR]

Energy sharing has the potential to decrease operation costs by exploiting the complementarity of different entities. The further increase of microgrids’ generation capacity in the distribution network could trigger the emergence of MW-level microgrids aggregator (MGA) to participate in the regional energy sharing market (ESM) and sell excess energy. However, the uncertainty of renewable energy generation will cause the failure of energy sharing and an economic loss for the energy sharing market. Therefore, a novel two-sided market with the clearing price and risk management is proposed in this paper. In order to optimize the energy sharing strategy of MGAs, a network-constrained risk-averse two-stage stochastic game model based on DC optimal power flow (OPF) is established. The objective of the MGA is to maximize the revenue in the ESM while the subtransmission network operator (SNO) aims to achieve regional energy self-sufficiency. The sample average approximation (SAA) method is employed to approximate the stochastic Cournot-Nash equilibrium. A distributed market clearing algorithm based on the alternating direction method of multipliers (ADMM) and best response seeking is developed to solve the optimal bidding problem of MGAs and the optimal dispatch problem of the SNO. Furthermore, the existence of the SAA Nash equilibrium is also investigated. Numerical simulations prove that the proposed game model can effectively control the overbidding risk and promote regional energy self-sufficiency. [ABSTRACT FROM AUTHOR]

Radiation (RT) remains the most frequently used treatment against cancer. The main limitation of RT is its lack of specificity for cancer tissues and the limited maximum radiation dose that can be safely delivered without damaging the surrounding healthy tissues. A step forward in the development of better RT is achieved by coupling it with other treatments, such as photodynamic therapy (PDT). PDT is an anti-cancer therapy that relies on the light activation of non-toxic molecules—called photosensitizers—to generate ROS such as singlet oxygen. By conjugating photosensitizers to dense nanoscintillators in hybrid architectures, the PDT could be activated during RT, leading to cell death through an additional pathway with respect to the one activated by RT alone. Therefore, combining RT and PDT can lead to a synergistic enhancement of the overall efficacy of RT. However, the involvement of hybrids in combination with ionizing radiation is not trivial: the comprehension of the relationship among RT, scintillation emission of the nanoscintillator, and therapeutic effects of the locally excited photosensitizers is desirable to optimize the design of the hybrid nanoparticles for improved effects in radio-oncology. Here, we discuss the working principles of the PDT-activated RT methods, pointing out the guidelines for the development of effective coadjutants to be tested in clinics. [ABSTRACT FROM AUTHOR]

A virtual energy storage system (VESS) logically shares a physical energy storage system among multiple units. In resource sharing, the distribution of benefits is a critical problem. As a resolution, this study proposes a fair VESS operation method for smart energy communities that involve groups of energy consumption units. First, the cost and resource fairness indices are defined as the benefit and VESS usage proportional to the investment cost, respectively. The fair VESS operation problem is formulated considering the fairness indices that could be solved optimally by applying gradient methods without additional computational burden. The simulation results using the dataset in Korea demonstrate that the proposed operation allows the fair distribution of the benefit and resource usage among units with a marginal benefit reduction of approximately 5% in relation to the VESS operation to maximize the benefit. Moreover, it is shown that the resource fairness that controls the VESS usage limits the total benefit, and the cost fairness distributes the benefit among units according to the cost contribution. Furthermore, the proposed VESS operation can manage the VESS lifetime and improve the system performance of the utility grid. [ABSTRACT FROM AUTHOR]

The energy hub has gained a lot of importance as a way to optimize multicarrier energy infrastructure to enhance its flexibility and proficiency. In the presence of energy storage in energy hubs, it is possible to efficiently coordinate energy supply and demand while taking advantage of changing energy tariffs and local renewable energy supply sources. In this article, we propose a novel method based on gray wolf optimization (GWO), which breaks down the complex optimization problem into subproblems, including the storage scheduling and another part in the grid, and comes up with the respective solutions via GWO and interior point. As a result, the proposed method avoids the drawbacks of numerical ways and artificial intelligence techniques, which tend to converge only to a local minimum or require heavy computational times. Furthermore, the article proposes a multilayer cloud computing framework for optimally scheduling energy hubs. The present study applies the new technique to a two-hub and three-hub system during 24 h. The approach is also examined in the context of an 11-hub system to investigate the efficiency and potential applications. The outcomes prove that the approach can achieve a very near-global point, which is confirmed through an analytical method, as well as being rapid sufficient to permit for online implementations with receding time horizons. [ABSTRACT FROM AUTHOR]

The huge success of electric vehicles across the world is challenged by a lack of infrastructure and a major increase in battery material prices. This challenge positions internal combustion engine vehicles (ICEVs) to remain a vehicle of choice. The majority of these vehicles use a lead-acid battery (LAB) for starting, lighting, and ignition (SLI) functions. However, these LABs are faced with challenges of short lifespan and low storage capacity because of improved electronic systems in modern ICEVs. In this manuscript, we propose an extension application of a hybrid LAB and lithium-ion energy storage system (ESS) for a vehicle using a single source of 70 Ah and 90 Ah capacity. Whereas previously, a hybrid energy storage system (HESS) for use in a vehicle using a source of 50 Ah battery capacity was proposed. Hence, the unique contribution of the study is using an integrated fuzzy-logic and triple-loop-proportional-integral-based battery management strategy (BMS) to improve LAB performance in a wide range of vehicles with different battery capacities sizes. The results show that the proposed BMS can help increase LAB lifespan and improve the storage capacity of the system, thus ensuring reliability. Additionally, compared to a single use of LAB, the combined energy storage system shows superior performance. [ABSTRACT FROM AUTHOR]

Renewable energy communities (RECs) are non-profit organizations made up of members who join to produce and exchange clean energy for sustainable development. This work analyzes different REC scenarios, considering energetic, economic, and environmental perspectives. The case study is a typical condominium of eight apartments with a low energy class in Cagliari (Italy). This study considers the condominium with different energy efficiency levels before and after retrofit interventions together with solar technologies to produce energy. Future scenarios include both the share of energy between the eight apartments within the condominium and a REC composed of two neighboring condominiums. At condominium scale, results showed better outcomes in aggregating the energy share from the PV generation into a single point of sharing (PoS). In the REC scenario with a neighboring building, and after retrofit interventions, the self-sufficiency index was increased by 26% with a decrease of 23% in GHG emissions, which shows the importance of having retrofitted and smart buildings boosting the renewable energy sources in achieving a more sustainable built environment. The methodology of this work with a new software can be a useful decision-making tool to test the effectiveness of RECs and it can be applied to building, neighborhood, or district scales. [ABSTRACT FROM AUTHOR]

The transition towards more sustainable energy management can be supported by the diffusion of energy communities, i.e., coalitions of prosumers that are willing to exchange the energy produced locally. The optimization of energy management requires the solution of a prosumer problem that can become impractical when the number of users increases. This paper presents a parallel approach, based on an edge computing architecture, which is suitable for large communities. The users are partitioned into groups whose proportions, in terms of producers and consumers, mirror the composition of the whole community. The prosumer problems for the different groups are first solved separately and in parallel by local edge nodes. Then, the solutions are combined by a central entity to redistribute the energy among the groups and minimize the exchange of energy with the external grid. A set of experiments show that the parallel approach, when compared with an approach that solves the optimization problem in a single stage, leads to a notable reduction of computing resources, and becomes feasible in large communities for which the single-stage approach is impossible. Moreover, the achieved solution is close to the optimal solution in terms of energy costs. [ABSTRACT FROM AUTHOR]

The use of energy sharing models in smart grids has been widely addressed in the literature. However, feasible technical solutions that can deploy these models into reality, as well as the correct use of energy forecasts are not properly addressed. This paper proposes a simple, yet viable and feasible, solution to deploy energy management systems on the end-user-side in order to enable not only energy forecasting but also a distributed discriminatory-price auction peer-to-peer energy transaction market. This work also analyses the impact of four energy forecasting models on energy transactions: a mathematical model, a support-vector machine model, an eXtreme Gradient Boosting model, and a TabNet model. To test the proposed solution and models, the system was deployed in five small offices and three residential households, achieving a maximum of energy costs reduction of 10.89% within the community, ranging from 0.24% to 57.43% for each individual agent. The results demonstrated the potential of peer-to-peer energy transactions to promote energy cost reductions and enable the validation of auction-based energy transactions and the use of energy forecasting models in today's buildings and end-users. [ABSTRACT FROM AUTHOR]

The rapid growth of microgrids with various distributed energy resources (DERs) brings new opportunities for local energy sharing in the microgrids. However, the uncertainties of renewable distributed generation and loads pose a great technical challenge for a microgrid operator (MGO). Thus, this paper proposes a transactive energy sharing (TES) approach for the MGO and DER aggregators to minimize the total social cost, considering network operating constraints. Accordingly, a two-settlement transactive energy (TE) market with an incentive energy pricing scheme is developed to encourage the participation in the energy sharing. Besides, the real-time energy transactions of the aggregators are considered in a day-ahead optimization stage to address their negative impacts on microgrid operation. To solve the proposed TES problem, an alternating direction method of multipliers (ADMM) is applied. The uncertainties in each ADMM local problem are further addressed by an adaptive robust optimization (ARO) method solved by a column-and-constraint generation (C&CG) algorithm. The updated dual and coupling variables at each ADMM iteration could interact with the C&CG algorithm, impairing ADMM convergence. To solve this issue, an alternating uncertainty-update procedure is developed. The simulation results verify the high efficiency and solution robustness of the proposed TES method. [ABSTRACT FROM AUTHOR]

Energy efficiency in building section has gained extensive attention for modern cities. Nowadays, the research of achieving zero energy buildings (ZEBs) is popular, which, however, is usually costly. The reason is that the randomness and intermittence of the renewable energy resources require the renewable equipment with relatively large capacities. Fortunately, it is promising to promote the sustainability by motivating the energy sharing among the neighboring energy buildings. Therefore, we present a peer-to-peer energy sharing model for neighboring energy buildings. Specifically, the typical energy buildings with controllable loads and renewable equipment are investigated. The buildings decide their energy sharing profiles and the corresponding payments by their own under the constraints of community energy balance and payment balance. We formulate and solve the problem of minimizing the total energy costs, and obtain the optimal energy sharing profiles. Then we design a fair payment distribution model, which leverages the cost reduction ratios to decide the energy sharing payments. Both the problems are solved by distributed algorithms. The simulation cases have verified the energy efficiency, the economic benefits, and the scalability of the proposed energy sharing model. [ABSTRACT FROM AUTHOR]

The concept of a virtual energy storage system (VESS) is based on the sharing of a large energy storage system by multiple units; however, the capacity allocation for each unit limits the operation performance of the VESS. This study proposes an operation strategy of a dynamic VESS for smart energy communities. The proposed VESS operation strategy considers the usage-limited constraint rather than the capacity allocation constraint and it guarantees the usage of VESS resources of each participant for an operation period. Therefore, the degrees of freedom for VESS operation can be increased at each operation time. The dynamic VESS operation problem is formulated as a mixed-integer linear problem that could be solved optimally by applying gradient methods and dual decomposition. The dataset of a VESS in Korea is used for simulation. The simulation results demonstrate that, when the proposed operation strategy is used, the cost efficiency achieved is more than twice that achieved when the existing VESS operation strategy is used. Furthermore, the proposed strategy accurately reflects the characteristics of the participants; thus, more units can participate in the VESS operation service. The proposed VESS operation can improve the system performance of the utility grid and increase the net benefit of the participants. [ABSTRACT FROM AUTHOR]

Energy communities enable members to create jointly value by investing in and operating distributed energy resources (DERs), including generation and storage. In this paper, we develop value allocation and stabilization algorithms for energy communities based on cooperative game theory. The method considers realistic relationships between the consumers of energy within an energy community and potential outside investors, who can form coalitions, while accounting for investment and operation of DERs. We apply the model to a numerical example using data from the Austrian electricity market. The results show how the created value from DERs could be shared between the members of the energy community. Moreover, limited area for solar photovoltaic (PV) installations may destabilize the energy community by removing incentives to participate. Still, the introduction of capacity based payments or the expansion of the limited PV resources show an stabilization effect of the energy community. [ABSTRACT FROM AUTHOR]

With the proliferation of distributed generators and energy storage systems, traditional passive consumers in power systems have been gradually evolving into the so-called “prosumers,” i.e., proactive consumers, which can both produce and consume power. To encourage energy exchange among prosumers, energy sharing is increasingly adopted, which is usually formulated as a generalized Nash game (GNG). In this paper, a distributed approach is proposed to seek the Generalized Nash equilibrium (GNE) of the energy sharing game. To this end, we first prove the strong monotonicity of the game. Then, the GNG is converted into an equivalent optimization problem. An algorithm based on Nesterov's methods is thereby devised to solve the equivalent problem and consequently find the GNE in a distributed manner. The convergence of the proposed algorithm is proved rigorously based on the nonexpansive operator theory. The performance of the algorithm is validated by experiments with three prosumers, and the scalability is tested by simulations using 1888 prosumers. [ABSTRACT FROM AUTHOR]

Residential solar installations are becoming increasingly popular among homeowners. However, renters and homeowners living in shared buildings cannot go solar as they do not own the shared spaces. Community-owned solar arrays and energy storage have emerged as a solution, which enables ownership even when they do not own the property or roof. However, such community-owned systems do not allow individuals to control their share for optimizing a home's electricity bill. To overcome this limitation, inspired by the concept of virtualization in operating systems, we propose virtual community-owned solar and storage—a logical abstraction to allow individuals to independently control their share of the system. We argue that such individual control can benefit all owners and reduce their reliance on grid power. We present mechanisms and algorithms to provide a virtual solar and battery abstraction to users and understand their cost benefits. In doing so, our comparison with a traditional community-owned system shows that our AutoShare approach can achieve the same global savings of 43% while providing independent control of the virtual system. Further, we show that independent energy sharing through virtualization provides an additional 8% increase in savings to individual owners. [ABSTRACT FROM AUTHOR]

Energy storage (ES) plays a significant role in modern smart grids and energy systems. To facilitate and improve the utilization of ES, appropriate system design and operational strategies should be adopted. The traditional approach of utilizing ES is the individual distributed framework in which an individual ES is installed for each user separately. Due to the cost inefficiency of the individual framework and the difficulty of applying this framework to the grid-scale ES, many studies have suggested the sharing strategy for the utilization of ES to further exploit the potentials of ES. This paper provides a comprehensive review of the papers on shared ES that are published in the last decade. In this review, we characterize the design of the shared ES systems and explain their potential and challenges. We also provide a detailed comparison of the literature on shared ES based on multiple criteria. Finally, we discuss some promising directions for the future study on shared ES. [ABSTRACT FROM AUTHOR]

In this article, a novel machine learning based data-driven pricing method is proposed for sharing rooftop photovoltaic (PV) generation and energy storage in an electrically interconnected residential building cluster (RBC). In the studied problem, the energy sharing process is modeled by the leader–follower Stackelberg game where the owner of the rooftop PV system is responsible for pricing self-generated PV energy and operating ES devices. Meanwhile, local electricity consumers in the RBC choose their energy consumption with the given internal electricity prices. To track the stochastic rooftop PV panel outputs, the long short-term memory network based rolling-horizon prediction function is developed to dynamically predict future trends of PV generation. With system information, the predicted information is fed into a Q-learning based decision-making process to find near-optimal pricing strategies. The simulation results verify the effectiveness of the proposed approach in solving energy sharing problems with partial or uncertain information. [ABSTRACT FROM AUTHOR]

With the advent of prosumers, the traditional centralized operation may become impracticable due to computational burden, privacy concerns, and conflicting interests. In this article, an energy sharing mechanism is proposed to accommodate prosumers’ strategic decision-making on their self-production and demand in the presence of capacity constraints. Under this setting, prosumers play a generalized Nash game. We prove main properties of the game: an equilibrium exists and is partially unique; no prosumer is worse off by energy sharing and the price-of-anarchy is 1-O(1/I) where I is the number of prosumers. In particular, the PoA tends to 1 with a growing number of prosumers, meaning that the resulting total cost under the proposed energy sharing approaches social optimum. We prove that the corresponding prosumers’ strategies converge to the social optimal solution as well. Finally we propose a bidding process and prove that it converges to the energy sharing equilibrium under mild conditions. Illustrative examples are provided to validate the results. [ABSTRACT FROM AUTHOR]

This article proposes a flexible event-triggered online scheduling (ETOS) method for residential multi-microgrid (MMG) systems integrating internal scheduling and external energy sharing levels. Our novelties lie in the flexible ETOS framework embedding customized functions and the fair energy sharing mechanism. Unlike time-triggered methods with a holistic controller and complex mechanisms, the presented ETOS framework achieves systematic management of MMG by simulating its real-life dynamics from the perspective of event-triggering. In the internal scheduling level, universal working logics are designed through active “demand response” interactions, which has high scalability and openness in function and makes event customization possible for future use. In the energy sharing level, the concept of energy level is introduced to organize a localized energy sharing, in which the sharing strategies can be decided through stimulation and transition with the advantages of fairness and low complexity. Moreover, we specify detailed function and algorithm for ETOS entities, helping to realize flexible online operation of MMG in a decentralized and natural way. Numerical simulations show that proposed ETOS is economically beneficial, computationally efficient, and is promising to facilitate the online scheduling of a practical real world MMG project. [ABSTRACT FROM AUTHOR]

The high cost of an energy storage system (ESS) is a barrier to its use. This paper proposes a risk-based virtual ESS (VESS) service strategy for prosumers. The basic concept of the VESS service is to logically refer to a physical ESS by multiple users. The VESS service can install ESS with a larger capacity compared to the case of installing ESS individually. Therefore, the VESS reduces the cost barrier through economies of scale. Moreover, ESS is not always being utilized at its maximum in the VESS service. Considering the risk, a VESS can offer a greater capacity than an installed ESS capacity. In this paper, the VESS service model suggested considers not only the economic benefit of increasing the VESS installation capacity but also the value at risk arising from servicing a greater capacity. The VESS service problem is formulated as a convex problem according to the VESS installation capacity and service price by applying stochastic approximation and is optimally solved using the gradient descent method in an iterative manner. The simulation results demonstrate that, when the proposed service strategy is used, the service provider that considers the risk achieves a significantly greater economic benefit of around 30% for the 128-prosumer pair case as compared to the one that does not consider risk. The benefit of the prosumer is increased by approximately 3.5% for the 128-prosumer pair case because the mismatched quantity is reduced during the peer-to-peer energy transaction. In addition, it is discussed how the proposed VESS service strategy achieves benefit through unit ESS cost reduction by the economics of scale and achieves increased service capacity with the multi-user diversity gain of participants. [ABSTRACT FROM AUTHOR]

Given that the capital cost of energy storage systems is still high, the concept of energy sharing attracts more attention. In this article, an energy sharing model in the forms of hydrogen and electricity is proposed. In this integrated sharing system, besides the aggregators who own power-to-gas (P2G) devices, plug-in hybrid electric and hydrogen vehicles (PH2EVs) aggregators become the new coupling points of the electricity network and gas network, because they can either consume electricity or hydrogen. In the objective function, the total social welfare considering energy dispatch in different systems is maximized. The distributed optimization method is applied to solve the formulated problem. In this way, the privacy of each aggregator can be protected. When the aggregator solves its sub-problem, it is not necessary to obtain all the system information. Besides, the computational complexity is reduced through the distributed optimization. Simulations are conducted on a 35-bus electricity network coupled with a 15-bus gas network. The simulation results reveal that the sharing amount of different kinds of energy can be obtained and the unified market clearing prices of each kind of energy can be achieved. It can be concluded that with the integrated energy sharing of hydrogen and electricity, the total system cost is the lowest and the largest total social welfare can be reached. Besides, the distributed energy storage can be more effective to improve the system stability. [ABSTRACT FROM AUTHOR]

This article presents an efficient peer-to-peer energy-sharing framework for numerous community prosumers to reduce energy costs and to promote renewable energy utilization. Specifically, for day-ahead and real-time energy management of prosumers, an intercommunity energy-sharing strategy and an intracommunity energy-sharing strategy are proposed, respectively. In the former strategy, prosumers can share energy with any community peers, and community aggregators represent their own prosumers to coordinate energy sharing. A two-phase model is designed. In the first phase, the optimal energy-sharing profiles of prosumers are derived to minimize the global energy costs, and in the second phase, equilibrium-based energy-sharing prices are induced considering the individual interests of prosumers. In the latter strategy, prosumers share energy only with its community peers for time saving to handle real-time uncertainties collaboratively to reduce real-time costs. The framework efficiency is verified by the simulation cases on a typical distribution network. [ABSTRACT FROM AUTHOR]

With the Clean Energy Package, the European Union introduced the concept of Renewable Energy Communities: groups of citizens, small and medium enterprises and local authorities that decide to join forces to equip themselves with systems to produce and share energy from renewable energy sources. The Italian legislation recently started an experimental phase in which renewable energy communities receive an incentivising tariff for the energy produced and shared within the community. This paper faces the problem of creating a new renewable energy community in two steps. First, a mathematical model of the energy flows among the members of the community is characterised according to the Italian schema. This model is used to find the optimal portfolio for the energy community, given energy requests and local source availability. Secondly, the Shapley value, a particular solution of cooperative games known to be the most fair method to allocate costs and profits of shared infrastructures, is proposed to distribute benefits among community members. The methodology has been applied to a case study based on a real low voltage network, and the economics for consumers and producers in participating to the project have been evaluated. The proposed solution, simulated adopting real economic parameters defined in the Italian regulatory framework, results to be economically viable from the point of view of the investors with a profitability index of 1.36 and, at the same time, aligned with the social purposes of the energy communities. [ABSTRACT FROM AUTHOR]

This paper describes the design, development, and testing of both hardware and software for a visual servoing-based system that enables agents within a heterogeneous marine robotic swarm to share energy. The goal of this system is prolonging the active operational time of the swarm as a whole, allowing it to perform long-term environmental monitoring and data collection missions. The implementation presented in the paper features an over-actuated autonomous surface platform docking up to four floating sensor nodes at a time and replenishing their batteries using wireless inductive charging. Mechanical solutions for each robot segment related to the docking procedure are presented, along with pertinent high-level and low-level control structures. A node featuring an extended Kalman filter and additional heuristics is used to fuse measurements from a neural network trained for object detection and a hue thresholding image processing algorithm, in order to track the docking target and achieve visual servoing. Finally, experimental results in both a controlled environment and challenging conditions on-site are presented. Once deployed, the developed system successfully enables the approach and docking of the designated target, showing its feasibility in different real-life conditions. [ABSTRACT FROM AUTHOR]