Search

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]

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]

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]

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]

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]

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]

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]

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]

Local Energy Markets (LEMs) are virtual market places that allow for energy sharing of prosumers in proximity to each other. In LEMs, participants' offers and bids are coordinated by a pricing mechanism, which sets price signals for generation and loads. This impacts distribution grid utilization. Dynamic grid fees could be used by the distribution system operator to add price signals for market participants that are beneficial to the distribution grid operation. To develop such a grid-serving LEM design, we propose a framework for the design of alternative grid tariffs as a trade-off between cost-reflective and cost-recovering grid tariffs. We analyse three alternative grid tariff components in the context of LEMs: time-varying energy fees, critical peak pricing (CPP) and capacity fees for power not traded on the LEM. A case study on a synthetically generated grid region shows that while the alternative tariff components can reduce the aggregated peak load of the region by up to 31% with the CPP, the fees can also lead to increased line utilization. Also, while total local added value can be increased with all alternative grid tariffs, especially capacity-based grid tariffs are connected to a strong redistribution of grid costs from flexible to inflexible prosumers. • Proposal of design for cost-reflexive, cost-adequate and grid-serving grid tariffs. • Simulative case study of grid tariffs in local energy market with 3031 participants. • Up to 31% reduction of peak aggregated grid load with grid-serving grid tariffs. • Increased line utilization in scenarios with homogeneous grid fees among consumers. • High cost redistribution from flexible to inflexible consumers with capacity fees. [ABSTRACT FROM AUTHOR]

Interconnected distributed energy systems (DESs) can facilitate multi-energy consumption, improve energy efficiency, and advance decarbonization goals. In this context, this study proposes an energy sharing framework that considers multiple uncertainties to optimize the low-carbon robust economic operation of interconnected DESs. First, a low-carbon dispatch model for DESs that includes electricity and heat sharing, integrated demand response (IDR), and low-carbon policies is constructed. Then, a two-stage robust optimization model is developed considering the source-load uncertainty, and the Karush-Kuhn-Tucker (KKT) condition is introduced to transform the max-min problem in the second stage into a single-layer issue. In addition, an approach combining the alternating direction multiplier method (ADMM) with the column-and-constraint generation algorithm (CCG) is proposed for a distributed and hierarchical solving of the two-stage energy sharing problem. Finally, to address the issue of transactional payments for energy sharing, a profit allocation model based on multi-factor contributions is developed to ensure that the benefits generated by the sharing system are fairly distributed. Based on actual data simulation, the effectiveness of the two-stage robust sharing scheme presented in this study is demonstrated for economy and carbon reduction. • Proposing a low-carbon robust economic dispatch framework for interconnected DESs. • Handling source-load uncertainty in DESs based on two-stage robust optimization. • Solving the two-stage robust energy sharing problem for DESs via ADMM-CCG algorithm. • Realizing fair profit distribution in DESs based on Nash-Harsanyi bargaining game. [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]

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]

In a fifth-generation district heating and cooling (5GDHC) network, energy demands are satisfied without using fossil fuel-based sources, rather by sharing participants' energy loads through decentralized consumption and production. However, finding such locations where the complementary energy demands of participants are matched is difficult. The problem becomes more complicated due to uncertain energy markets (equipment investment and operational costs). These uncertain market trends make it difficult to decide whether to build the network. Finding the feasible network temperatures for the 5GDHC network is challenging due to the conflicting participants, as at one-time instance some need cooling and others need heating. The sensitivity analysis using Sobol' indices is performed to find the most influencing input parameters. Then, a techno-economical assessment of the considered location is performed against influencing inputs. Two different sites Diest and Alpha Cloud (Flanders, Belgium) are evaluated. The systematic techno-economic analysis under the influence of an uncertain energy market and network operating temperatures reveals that building a network might be not feasible due to the high payback period. The methodology offers greater flexibility in identifying key uncertain input parameters and evaluating the robustness of critical performance indicators, overcoming the limitations of current GIS-based approaches. This improved capability assists decision-makers in more accurately determining feasibility. [Display omitted] • A generalized thermodynamic framework for modeling 5GDHC systems. • Uncertainty investigation in the network design. • Probabilistic descriptions of the energy market-related inputs. • Finding the feasible network temperatures in the presence of prosumers. • Energy cost and emission analysis for network scenario and competing technologies. [ABSTRACT FROM AUTHOR]

The increase in the share of renewable energy sources in the integrated energy system (IES) has led to the bidirectional flow of electricity and carbon in the transmission and distribution network, which has increased dispatch complexity. To address these issues, this paper proposes an optimal scheduling method for IES considering the seasonal carbon trading mechanism and electricity-carbon quota sharing method. First, an energy hub model of the integrated electric-heat-carbon energy system was established, with the introduction of carbon flow. Then, a seasonal carbon trading mechanism that considers the characteristics of carbon emissions in each period of the system is proposed, and the ladder-type carbon price model and carbon quota allocation method are optimized. Moreover, to fully utilize the interaction between electricity and carbon to achieve low-carbon economic dispatch, a dual time-scale electricity-carbon quota sharing method is proposed. Finally, the effectiveness of the proposed method is verified by the case studies. The results show that the scheduling method considering seasonal carbon trading mechanism and electricity-carbon quota energy sharing can optimize the allocation of resources such as electricity and carbon quota among the systems, and reduce the cost of the system by 6.04 % and carbon emission by 4.27 %. • Dispatch method considers the long-term energy storage properties of hydrogen. • A seasonal carbon trading mechanism is proposed. • An electricity-carbon quota dual time scale energy sharing mechanism is proposed. • The low carbon and economy of the model are proved. [ABSTRACT FROM AUTHOR]

Solar thermal energy usage in the Baltic countries is currently low, so there is a need to find technically and economically feasible solutions to increase the appeal of this technology. Integration of solar energy into existing district heating systems is a way to increase the share of renewable energy sources while utilizing the existing infrastructure, thereby reducing capital investments, as well as mitigating the risk of solar collector overheating and eliminating the need for local storage tanks in buildings. This study presents a parametric study for evaluating the effectiveness of using a district heating system to store heat produced by decentralized solar thermal collectors and comparing the district solution to the traditional local solution. The TRNSYS 18 model was developed for this purpose. The model investigates the impact of different hot tap water demand profiles as well as pipeline diameters and lengths on system energy efficiency, showing a statistically significant difference (p < 0.05) between installing solar collectors on residential and restaurant or office roofs, and no significant difference (p = 0.24) between restaurant and office. The model shows that solar energy can be shared within a 500 m distance between neighborhoods and that a larger main pipeline diameter can increase overall system efficiency. • Evaluating of district heating systems as storage for solar energy with TRNSYS 18. • Increasing solar thermal energy production with district heating integration. • Impact of thermal storage size and heating load profile on solar energy production. • Efficient solar collector distribution for district heating networks. • Influence of pipeline length and diameter for efficient prosumer energy share. [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 combined computing workload and energy coordination of data centers is studied. • A prediction-free online coordination algorithm is built with proven properties. • An accelerated ADMM algorithm is developed for distributed implementation. • The proposed coordination method greatly reduces the total cost of data centers. • The computation time is largely reduced compared to the traditional ADMM. [ABSTRACT FROM AUTHOR]

• A novel bi-level optimization framework is applied for system day-ahead dispatching. • The hydraulic stability and the economy are taken as the optimization objectives. • A dynamic-correction quasi-dynamic model is proposed for the heating networks. • The dynamic impacts of heat energy sharing on heating networks are considered. • The proposed framework reduces the costs by 22.19% and carbon emissions by 42.35%. The multi-community integrated energy system holds significant potential in facilitating the decarbonization of energy distribution in the context of carbon neutrality. However, the literature review reveals the gaps in the comprehensive examination of hybrid energy sharing and hydraulic-thermal dynamic processes within the system, which can compromise the security and stability of the energy supply and lead to insufficient dispatch. To address these challenges, this research proposes a bi-level optimization framework for the day-ahead dispatch of the multi-community integrated energy system, novelly taking hydraulic stability and economic efficiency as optimization objectives. Hybrid energy sharing is considered as one of the processes of system operation rather than merely a constraint. To accurately capture the hydraulic and thermal dynamic characteristics, a dynamic-correction quasi-dynamic model is proposed for hydraulic-thermal coupling calculation in the heating networks. Additionally, Latin hypercube sampling and backward reduction are applied to address the uncertainty of renewable energy. The results show that the proposed optimization framework can achieve a 22.19 % reduction in operation costs and a 42.35 % reduction in carbon dioxide emissions. Furthermore, the maximum hydraulic steady time is significantly reduced from 360.02 s to 136.39 s, indicating effective suppression of extreme hydraulic instability conditions. In conclusion, the proposed optimization model offers a viable solution to enhance the economic efficiency of the multi-community integrated energy system and mitigate hydraulic instability in heat transmission. [ABSTRACT FROM AUTHOR]

The potential of sharing energy from production plants is characterized as a new paradigm for the production and consumption of energy from renewable sources. The emergence of Renewable Energy Communities (REC) and Collective Self Consumption (CSC) in the European context has supported the regulation of the concept of shared energy and provided economic saving to its members. Many countries have adopted a virtual scheme for local energy sharing without a physical basis for calculating intra-community energy exchanges and national legislation often provides economic incentives for shared energy within the community. However, many of the management aspects regarding the distribution of shared energy and therefore economic gain are managed internally by members, allowing for various configurations that depending on the type of generation systems, users, and purposes of the community. Since a unique method is not established, it is crucial to define fair criteria for energy allocation among the community members rewarding virtuous behaviour. This work proposes four algorithms for dynamic sharing keys based on participants' contributions to the community: a consumption-proportional key, a Pearson correlation coefficient-based key to evaluate synchronism between electricity drawn from the grid and the surplus fed into the grid, a trend-based key that accounts for the difference between purchased and injected energy, and a combination of the previous two keys. A Renewable Energy Community (REC), under Italian regulation, consisting of eight representative users was simulated using real hourly energy consumption and production profiles. The aim was to perform an annual comparative analysis between the developed methods and identify the different amount of shared energy assigned to each user based on their contribution, highlighting their strengths and limitations. The results show how some of the algorithms assign to users with the highest consumption an amount of shared energy higher than their real sharing potential, while users with greater sharing potential are penalised. [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]

This paper aims to broaden the concept of Net Zero Energy Building (NZEB), assessing its suitability to the emerging grid-based energy sharing models. The energy exchange between two buildings with shifted load schedules, namely, a new residential NZEB (prosumer) and an existing school building (consumer), is analysed to investigate energy sharing dynamics. The buildings are in Southern Italy, a region with high solar radiation. The model of the existing school building is calibrated on measured data and the study is performed by dynamic energy simulation. Three energy sharing scenarios based on the design of a photovoltaic system installed on the roof of the NZEB have been investigated, i.e., PV system peak power designed: to satisfy the NZEB target on annual basis; to satisfy the NZEB target on monthly basis; to fully exploit the available roof area of the NZEB. Considering annual and monthly net energy balances, oversizing the NZEB PV system leads to the compensation of a high share of school building non-renewable energy consumption. However, self-consumption analysis performed on hourly basis shows that increasing the PV system size negatively affects the self-consumption rate of the renewable energy community, and the presence of the school building only mitigates this effect. [ABSTRACT FROM AUTHOR]

Citizen Energy Communities (CECs) have emerged as a promising solution with numerous benefits for society. CECs have contributed to a higher adoption rate of Distributed Energy Resources (DERs). However, the variable power generation of DERs is a critical challenge, resulting in an increase in the ramp rate of the system's net load. To address this challenge, this paper introduces a unique robust optimization model that utilizes the flexibility constraint to limit the ramp rate of power trading CECs with the upstream grid and assesses the impact of flexibility limits on energy costs. Moreover, two primary goals of CECs are to ensure the prosumer's comfort, which includes thermal comfort, and to achieve self-sufficiency in their energy supply. In this context, this paper uses the Predicted Mean Vote (PMV) index to calculate the thermal comfort of prosumers. Additionally, a sensitivity analysis has been conducted on CECs self-sufficiency. Moreover, a robust optimization approach is utilized to consider the uncertainty in upstream energy prices. The results indicate that the Community Energy Storage (CES) leads to a 34.6% reduction in operation cost. Additionally, the inclusion of CES in CECs allows the Energy Community Manager (ECM) to reduce curtailment power to prosumers by 602.98 kW during their engagement in the self-sufficiency program. [Display omitted] • An optimization model considering community energy storage for the CEC is proposed. • The flexibility-constraint impact on the CEC is investigated. • The prosumer's thermal comfort is regulated through the PMV index. • The robust optimization is utilized to model the upstream price fluctuations. • The sensitivity analysis of self-sufficiency impact on CEC is evaluated. [ABSTRACT FROM AUTHOR]

A transactive energy coordination mechanism is proposed in this study where community microgrids are supplying power to multi‐dwelling residential apartments. The proposed transactive energy coordination mechanism coordinates the energy sharing among apartments based on the energy profile of the community microgrid where the excess energy is traded with non‐contributing apartments. The proposed coordination mechanism is embedded within an energy management controller which uses the energy profile and determines the valuation of energy. A choice factor along with the bound on electricity prices is also incorporated to calculate the bidding price and a double‐sided auction mechanism is considered for the bidding purpose. The utility maximisation approach is used to clear the market. Different scenarios based on the flexibility in the pricing strategy are considered to evaluate the performance of the proposed transactive energy coordination mechanism. The potential economic benefits of the proposed scheme are also analysed which clearly demonstrate that it is beneficial for all participants. [ABSTRACT FROM AUTHOR]

The recently approved regulation about self-consumption in Spain will dramatically change the level of penetration of photovoltaic distributed generation combined with storage devices at residential level in short term. In this paper, we will study the impact of the massive installation of such technologies under the constraints imposed by the new regulation, over the terminal distribution network. As it will be demonstrated, this impact will be drastic in terms of energy demand, voltage profile, and load curves. [ABSTRACT FROM AUTHOR]

[Display omitted] • Developed a dual-layer optimization design method for PV systems in building clusters. • Employed LP in under-layer design to optimize the energy storage capacity for each CBG. • Utilized NSGA-II and TOPSIS in the upper-layer design to establish the optimal CBG scheme. • Enhanced PV self-consumption by 15.4% and reduced electricity costs by 4.6% within the cluster. Renewable energy sharing effectively enhances the collaborative benefits of building clusters. Distributed photovoltaic (PV) generation is the most widely applied renewable energy method for building clusters due to its high space utilization and broad applicability. However, the majority of building cluster planning and energy storage system designs tend to neglect the impact of energy sharing potential, thereby leading to limited improvements in cluster-level benefits and oversized storage capacity. Therefore, we propose a dual-layer optimization design method to maximize the energy sharing potential, enhance collaborative benefits, and reduce the storage capacity of building clusters. Firstly, the building cluster is randomly divided into Clustered Building Groups (CBGs). Secondly, linear programming (LP) is applied in the under-layer to optimize the shared energy storage capacity for each CBG. Finally, the upper-layer utilizes NSGA-II and TOPSIS to identify the optimal grouping scheme. Case studies on a building cluster showed that compared with two existing design methods, the proposed design could maximize the energy sharing potential, significantly improve the building cluster's performance, and reduce the energy storage capacity. Specifically, it achieved the highest TOPSIS score of 0.673, reduced the electricity purchase cost by 4.6 %, increased PV self-consumption by 15.4 %, reduced the storage capacity by 17.4 %, and shortened the payback period from 9.31 years to 4.33 years. These results suggest that employing similar procedures can be advantageous in achieving energy autonomy for building clusters. [ABSTRACT FROM AUTHOR]

• Proposed an energy sharing model of MMGs considering the multiple uncertainties and the cheating behaviors. • Using robust optimization to deal with uncertainties brought by grid tariffs and renewable energy. • Cheating behaviors in energy transaction are analyzed and a cheating equilibrium mechanism based on ITM is proposed. Multi-microgrid system (MMGs) has drawn extensive attention recently because of its high energy efficiency. However, MMGs' operational efficiency can be affected by market price fluctuations and intermittent renewable energy. This paper proposes an energy-sharing model based on the Nash bargaining game between multi-microgrids. The proposed model provides a robust energy trading schedule to deal with uncertainties brought by grid tariffs and renewable energy. To ensure the model is tractable, the original game problem is equivalently converted into a system benefit maximization subproblem and an additional profit distribution subproblem to get optimal energy sharing power and prices. In addition, microgrid has the motivation to cheat for maximizing its benefits which may lead to the breakdown of cooperation. Furthermore, cheating behaviors in energy transaction are analyzed; the energy sharing scheme based on cheating equilibrium is derived by proposing an intermediary transaction mode. Finally, the alternating direction method of multipliers (ADMM) is used to protect the players' privacies in a distributed way. Simulation results show that the proposed model can realize stable cooperation, effectively reduce operating costs and immunize against multiple uncertainties and cheating behaviors. [ABSTRACT FROM AUTHOR]

The EU electricity market reform has created new opportunities for citizens to be involved into market dynamics as active customers. A new set of rules entitles people to collectively become self-consumer of renewable energy, which means to consume the electricity produced by production units located on different sites from where they operate or live, conferring them rights and duties which can be exercised either individually or collectively. Whenever two or more grid users are committed to produce and consume renewable energy within a limited area of the public grid corresponding to the same electrical substation a collective self-consumption scheme (or CSC scheme) is formed, regardless of any formal structure and qualification they have. According to the EU Law, electricity exchanges carried out within a CSC scheme can be classified in terms of sharing or trading, even though both reflects the same physical phenomenon on the grid. However, the EU directives does not provide strong elements to draw the line between such concepts. In addition, literature reviewing highlighted that there is not a clear understanding of what exactly distinguishes trading from sharing of electricity. Such distinction has relevant consequences in Law, including for what concerns a value-oriented analysis between individualism-based and pluralism-based models. Moving from these premises, this study aims to examine whether the private law might be able to provide instruments to address such distinction. • Sharing and trading are two different ways to manage renewable electricity within collective self-consumption schemes. • According to the EU Law and the academic literature it is unclear what distinguishes trading from sharing of electricity. • Private law provides a useful approach to draw the line differences between trading and sharing of renewable energy. • Private law approach allows also for a value-oriented analysis between individualism-based and pluralism-based models. [ABSTRACT FROM AUTHOR]

The unpredictability of renewable energy systems can affect the stability of the electricity grid, causing voltage and frequency imbalances. In this work, a suitable methodology based on the peer-to-peer scheme applied to energy communities is developed and implemented in a simulation tool useful for investigating energy management strategies for decision-making aims. The developed model discretizes the energy community and its users into multiple control volumes, taking into account various technologies. It incorporates energy balances for individual users as well as the entire energy community, considering prosumers, consumers, energy storage systems, and electric vehicles. Moreover, the model enables the exploration of different solutions for grid frequency regulation and optimization of distributed energy resources. Additionally, the tool can predict electricity demand one day ahead, facilitating the organization of renewable energy availability and storage systems to minimize grid interactions and flatten electricity demand. The model incorporates different objective functions, including self-consumption, self-sufficiency, and grid-balancing factors, to evaluate the performance of energy communities. To show the capability of the developed model, it will be adopted to optimize the performance of an investigated community. As a result, an increase in renewable energy self-consumption from 59.4 to 83.9 MW h/year is achieved. Furthermore, the objective of grid balancing was achieved by guaranteeing a non-fluctuating load and providing 1.46 and 7.71 MW h/year for upward and downward grid frequency regulation. These findings illustrate the positive impact of energy dispatching management on the integration of renewable energy sources and the importance of further studying this topic to ensure grid stability. [Display omitted] • Development of a flexible mathematical model for energy management in smart grids; • Characterize the role of energy communities in electricity grid balancing; • Implementation of energy-sharing strategies: Peer to Peer and Vehicle to cluster; • Comparison of different optimization problems to ensure electricity grid stability; • Energy management assessment of a smart grid consisting of prosumers, consumers; [ABSTRACT FROM AUTHOR]

This paper describes the development of community energy management systems (CEMS). A CEMS allows optimal energy sharing within energy communities, as it is a central system that makes the global management of the entire community. The proposed CEMS is based on mixed-integer linear programming (MILP), operating under the receding horizon concept of Model Predictive Control (MPC). A systematic classification of electric appliances, the use of external information such as weather information and energy prices, as well as the use of intelligent forecasting techniques enables the proposed approach to achieve an excellent efficiency. It also allows for an easy installation of as well as a smooth scaling with an increasing number of houses. The system is tested in a real community in Algarve, Portugal. Different simulations are compared to experimental operation and include cases with and without sharing of energy, different resources allocated to the houses considered, and the use of different tariffs. CEMS formulations include sharing of energy without restriction, as well as employing different allocation coefficients strategies. The results show that for the community under study when managed by CEMS such as the one presented in this paper, it would result in significant cost reductions when compared to the case where there is no energy community. [ABSTRACT FROM AUTHOR]

Shared energy storage is widely recognized as an energy hub for the coordinated operation of regional integrated energy systems (RIESs). Multi-energy systems (MESs) share centralized energy storage to store excess renewable energy sources (RESs). This paper proposes a novel energy-sharing framework considering hydrogen trading, which is suitable for residential, industrial, and commercial RIES structures. Shared energy storage operator (SESO) promotes hydrogen energy transactions by formulating time-of-use (TOU) hydrogen prices. The proposed hydrogen energy trading method can be regarded as a master-client structure, and a hierarchical optimal scheduling method based on the Stackelberg game relationship is designed. Finally, a case study is performed based on an optimal scheduling algorithm combining particle swarm optimization (PSO) and mixed integer linear programming (MILP). The cases show the electricity‑hydrogen shared energy storage mechanism in RIESs can improve the RESs utilization rate and effectively reduce the operating costs of each system. Moreover, compared with RIESs with a single centralized electric energy storage, the TOU hydrogen price mechanism can further lower the energy prices and improve the economy of the RIES. The cases show that hydrogen energy sharing reduces the operating cost of RIES by 9.96% and increases the regional energy utilization rate by 2.97%. And the shared energy storage business model can satisfy the economy of both SESO and RIES. It confirms the rationality of the electric hydrogen shared energy storage project. • A sharing energy storage mechanism in multi-energy systems is presented in this paper. • The hydrogen trading methods based on time-of-use hydrogen prices are considered in this paper. • A hierarchical optimal scheduling method based on the Stackelberg game is designed. [ABSTRACT FROM AUTHOR]

• Novel strategy to maximize overall cost saving and utilization of renewable energy. • Proposed Energy Sharing Coordinator lets optimize and flexibility surplus electricity. • Buildings like thermal batteries are implemented on a district by DSM approaches. • Self-consumption ratio can be increased up to 42% and Self-sufficiency ratio to 64%. • Precooling strategies were chosen on around 66% of the summer days. Many studies have demonstrated the potential of large scale energy analyses when it comes to achieving highly energy-efficient districts. Much emphasis has been placed on giving insights into how to obtain accurate energy demand and solar potential estimations for whole regions, as a means of enhancing the integration of renewable energies, achieving higher self-sufficiencies or decreasing the energy costs. However, it is necessary to figure out whether it would be possible to enhance the economic savings and integration of renewable energies even more. This could be done by considering on a district scale the synergies between buildings, DSM strategies and electricity storage at building level. Using as a case study a microgrid in a district located in La Graciosa Island (Spain), this work proposes a novel strategy that encourages sharing surplus electricity between different prosumers, in order to maximize the overall cost savings and the utilization of renewable energy in the district. In this case, the prosumers are equipped with thermostatically controlled loads (heat pumps) and renewable generation with electricity storage (PV modules). In addition, a comparison between different preconditioning strategies is carried out, determining the optimal strategies during a whole year and reaching important conclusions. The influence of considering different time horizons for the optimization of these preconditioning strategies is also examined. Last of all, a sensitivity analysis regarding the main design parameters is performed. The results prove that, by promoting energy sharing, the proposed concept is capable of raising the self-consumption and self-sufficiency ratios of a district (reaching 42% and 64% respectively) as well as the cost savings. The results are very dependent on the considered feed-in-tariff: the lower it is, the more profitable it is to implement the proposed concept. The present study also shows the benefits that can be obtained from DSM approaches on a district scale: different precooling strategies, which may be implemented without additional investments, were chosen instead of the normal schedule on around 66% of the summer days. Altogether, the proposed approaches may effectively contribute to resource conservation at large scales. [ABSTRACT FROM AUTHOR]

With a view to the expected increased data traffic volume and energy consumption of the fifth generation networks, the use of renewable energy (RE) sources and infrastructure sharing have been embraced as energy and cost-saving technologies. Aiming at reducing cost and grid energy consumption, in the present paper, we study RE exchange (REE) possibilities in late-trend network deployments of energy harvesting (EH) macrocell and small cell base stations (EH-MBSs, EH-SBSs) that use an EH system, an energy storage system, and the smart grid as energy procurement sources. On this basis, we study a two-tier network composed of EH-MBSs that are passively shared among a set of mobile network operators (MNOs), and EH-SBSs that are provided to MNOs by an infrastructure provider (InP). Taking into consideration the infrastructure location and the variety of stakeholders involved in the network deployment, we propose as REE approaches 1) a cooperative RE sharing, based on bankruptcy theory, for the shared EH-MBSs and 2) a non-cooperative, aggregator-assisted RE trading, which uses double auctions to describe the REE acts among the InP provided EH-SBSs managed by different MNOs, after an initial internal REE among the ones managed by a single MNO. Our results display that our proposals outperform baseline approaches, providing a considerable reduction in SG energy utilization and costs, with satisfaction of the participant parties. [ABSTRACT FROM AUTHOR]

Abstract Due to their significant internal heat gain resulting from computer server banks, data centres require cooling year-round, creating an opportunity to transport the waste heat to heat-deficient neighbouring buildings. This paper evaluates the quantity of multi-unit residential buildings (MURBs) that should be connected to a given data centre in order to maximize the portion of shared energy which provides the MURBs' heating energy and the data centre's cooling energy simultaneously. The paper then evaluates the financial viability and greenhouse gas (GHG) emissions of three different methods with which energy can be shared from a data centre to surrounding MURBs in a community energy network (CEN). The first method, called the Energy Sharing System involves using a heat pump to produce heating and cooling at the same time for the MURBs and the data centre. The second, called the One-Borefield System, has the same energy sharing aspect as the first, with additional heating and cooling coming from geo-exchange. The third method, called the Two-Borefield System, is an innovative approach to geo-exchange, which uses two separate borefields to achieve free cooling, while also incorporating the energy sharing base. The investigation finds that the optimal MURB area that should be connected to a 4000 kW cooling load data centre is 110,000 m2 for the Toronto (Canada) climate. The financial analysis shows that the Energy Sharing System was the most profitable, with a 11.9% 30-year unlevered after-tax internal rate of return (IRR). [ABSTRACT FROM AUTHOR]

Regional integrated energy systems (RIES) have garnered increased attention because they highly utilize renewable energy, comprehensive efficiency, and flexible operation. However, the current research on RIESs is mainly based on burning natural gas and supplemented by renewable energy, which will consumes much primary energy and generates corresponding carbon emissions. Therefore, a RIES contained various renewable energy sources and station energy sharing was constructed in this paper. Both the collaborative optimization model, which considered the operating parameters and system configuration, and the hierarchical optimization model, which optimizes the equipment configuration at the upper layer and the operational parameters at the underlayer, were constructed, respectively. Taking a multi-function low-carbon park as an example, the isolated integrated energy system (IIES) and RIES performances were explored. Finally, the operation cost, carbon emission, and interactive power in the separated production system, IIES and RIES were analyzed and discussed from a long-short time scale perspective. The results showed that the carbon emission, interactive power, and energy supply cost of the RIES's unit area are 7.62 kg/m2, 10.07 kWh/m2, and 40.55 CNY/m2, respectively. Long time scale perspective (Years or months), RIESs could significantly reduce carbon emissions and interactive power. However, on a short time scale viewpoint (Hours), the system's instantaneous energy conservation and carbon reduction effect was general; meanwhile, the maximum grid-connected power in IIES and RIES was 2617.7 and 2712.0 kW, respectively. • A RIES is constructed integrating various renewable energy sources and energy sharing between stations. • Collaborative and hierarchical optimization is used to solve the complex RIES optimization problem. • Carbon emission and interactive power of the unit area for the RIES are 7.62 kg/m2 and 10.07 kWh/m2. • The maximum grid-connected power of IIES and RIES is 2617.7 and 2712.0 kW, respectively. [ABSTRACT FROM AUTHOR]

This paper presents an energy-sharing market coupled with carbon emission trading to quantify peer-to-peer energy-sharing markets' economic and environmental influences. We establish a bi-level peer-to-peer energy sharing market considering the interaction between energy prosumers and the system operator. An interaction algorithm and generalized Nash game are thus applied in this work. With the simulation results, we find that the daily disutility of provincial systems is reduced by about 7 ¥ when time-of-use electricity rates are applied. This pricing strategy can encourage prosumers to participate in energy-sharing transactions. The subsidies of electricity rates, carbon prices, and renewable energy investments can improve the social welfare of the whole system. Considering the development of power generation technology and emission reduction targets, the change of optimization parameters in the next 30 years is analyzed in the example. It can be concluded that the total CO2 emission in 2050 will be reduced to almost half of that in 2020 in the energy-sharing markets. The subsidy of renewable energy generation and stricter CO2 emission constraints may accelerate the reduction of carbon emissions. [Display omitted] • It is the first time to assess the environmental effect of peer-to-peer energy trading with actual data. • We analyze the peer-to-peer energy trading impacts on the carbon emission amount in China. • We compare the market participants' revenue and CO2 emission considering the different scenarios. • We develop a bi-level coupled emission right trading market to verify whether total emission is reduced. • The development of technologies and various energy structures are considered quantitatively. [ABSTRACT FROM AUTHOR]

The challenge of energy conservation and emission reduction makes the energy management research for zero-carbon communities (ZCCs) quite important. In this context, a Stackelberg game based energy sharing model for ZCC considering the reward and punishment of carbon emission is proposed. Firstly, a framework of ZCC including a ZCC operator (ZCCO) and multiple building prosumers (BPs) is established. To improve the energy conservation and emission reduction of ZCC, two reward and punishment mechanisms respectively from short-term and long-term perspectives are introduced. In the day-ahead scheduling stage, the scheduling model of energy storage systems (ESSs) considering the economy and environmental protection is established. Especially, the ESS is scheduled by ZCCO to further reduce the overall carbon emission. In the real-time optimization stage, considering the reward and punishment mechanisms for carbon emission, benefit functions of ZCCO and BPs are constructed and modified accordingly. Then, the Stackelberg game model of the ZCC considering BPs and ZCCO is constructed based on the internal prices from ZCCO. The energy sharing among BPs is also realized. Finally, the proposed model is solved by the particle swarm optimization algorithm and CPLEX. Simulation results show the proposed model and algorithm are reasonable and effective in the energy sharing. • A framework of ZCC is established, including a ZCC operator and multiple building prosumers. • Carbon emission targets are introduced into the optimal scheduling model of the ESS. • The Stackelberg method is adopted and modified to realize the energy sharing of ZCC. • Two kinds of reward and punishment mechanism are introduced into the energy sharing model. • The impact of some factors on the reward and punishment mechanisms are analyzed. [ABSTRACT FROM AUTHOR]

The Energy Hub has become an important concept for formally optimizing multi-carrier energy infrastructure to increase system flexibility and efficiency. The existence of energy storage within energy hubs enables the dynamic coordination of energy supply and demand against varying energy tariffs and local renewable generation to save energy cost. The battery lifetime cost may be included in the optimization objective function to better utilize battery for long term use. However, the operational optimization of an interconnected energy hub system with battery lifetime considered presents a highly constrained, multi-period, non-convex problem. This paper proposes Particle Swarm Optimization (PSO) hybridised with a numerical method, referred to collectively as the decomposition technique. It decouples the complicated optimization problem into sub-problems, namely the scheduling of storage and other elements in the energy hub system, and separately solves these by PSO and the numerical method ‘interior-point’. This approach thus overcomes the disadvantages of numerical methods and artificial intelligence algorithms that suffer from convergence only to a local minimum or prohibitive computation times, respectively. The new approach is applied to an example two-hub system and a three-hub system over a time horizon of 24 h. It is also applied to a large eleven-hub system to test the performance of the approach and discuss the potential applications. The results demonstrate that the method is capable of achieving very near the global minimum, verified by an analytical approach, and is fast enough to allow an online, receding time horizon implementation. [ABSTRACT FROM AUTHOR]

This paper presents an analytical framework to develop a hierarchical energy management system (EMS) for energy sharing among neighbouring households in residential microgrids. The houses in residential microgrids are categorized into three different types, traditional, proactive and enthusiastic, based on the inclusion of solar photovoltaic (PV) systems and battery energy storage systems (BESSs). Each of these three houses has an individual EMS, which is defined as the primary EMS. Two other EMSs (secondary and tertiary) are also considered in the proposed hierarchical energy management framework for the purpose of effective energy sharing. The intelligences of each EMS are presented in this paper for the purpose of energy sharing in a residential microgrid along with the priorities. The effectiveness of the proposed hierarchical framework is evaluated on a residential microgrid in Australia. The analytical results clearly reflect that the proposed scheme effectively and efficiently shares the energy among neighbouring houses in a residential microgrid. [ABSTRACT FROM AUTHOR]

• Innovative approach to achieve high-energy performance in retail and greenhouses. • Design optimization shows 49% improvement over the code compliant base design. • Heat sharing between individual buildings achieves further 21% site energy reduction. • Net-zero energy design has been achieved by on-site solar PV arrays. • Prototypical template for carbon neutral urban centric infrastructure is explored. Devising methods to recover waste heat and to share it within a neighborhood can lower the energy footprint of this neighborhood and reduce its dependence on the utility grids. This paper presents a novel methodology for capturing waste energy from individual buildings and sharing the recovered energy with an entire neighborhood. A survey of the current state of research on energy sharing is presented. A mixed-use neighborhood consisting of retail, residential, and greenhouse buildings is modeled to develop a holistic design of energy sharing strategy. Neighborhood-level waste heat recovery and distribution network is modeled in the EnergyPlus-DesignBuilder co-simulation platform. The energy sharing methodology is applied to different neighborhood configurations and neighborhood densities. The results show that with the application of energy sharing methodology integrated with energy efficient design and on-site solar energy, a net reduction of 74% energy in the mixed-use neighborhood is achieved compared to a design complying with the minimum requirement of the applicable energy codes. Similarly, a net 90.46% reduction in greenhouse gas (GHG) emissions is realized by energy sharing and on-site carbon capturing using neighborhood greenhouse. [ABSTRACT FROM AUTHOR]

According to the feed-in tariff for encouraging local consumption of photovoltaic (PV) energy, the energy sharing among neighboring PV prosumers in the microgrid could be more economical than the independent operation of prosumers. For microgrids of peer-to-peer PV prosumers, an energy-sharing model with price-based demand response is proposed. First, a dynamical internal pricing model is formulated for the operation of energy-sharing zone, which is defined based on the supply and demand ratio (SDR) of shared PV energy. Moreover, considering the energy consumption flexibility of prosumers, an equivalent cost model is designed in terms of economic cost and users’ willingness. As the internal prices are coupled with SDR in the microgrid, the algorithm and implementation method for solving the model is designed on a distributed iterative way. Finally, through a practical case study, the effectiveness of the method is verified in terms of saving PV prosumers’ costs and improving the sharing of the PV energy. [ABSTRACT FROM PUBLISHER]

Due to the decreasing revenues from the surplus renewable energy injected into the grid, mechanisms promoting self-consumption of this energy are becoming increasingly important. Demand response (DR) and local storage are among the widely used mechanisms for reaching higher self-consumption levels. Deploying a shared storage unit in a residential microgrid is an alternative scenario that allows households to store their surplus renewable energy for a later use. However, this creates some challenges in managing the battery and the available energy resource in a fair way. In this paper, a reputation-based centralized energy management system (EMS) is proposed to deal with these issues by considering households’ reputations in the reallocation of available energy in the shared storage unit. This framework is used in an optimization problem, in which the EMS jointly schedules households’ appliances power consumption and the energy that each household can receive from the storage unit. The scheduling problem is formulated as a Mixed Integer Linear Programming (MILP) with the objective of minimizing the amount and price of energy absorbed from the main grid. The MILP problem is coded in GAMS and solved using CPLEX. Numerical analysis is conducted using real data of renewable energy production and appliances’ demand profiles for different classes of households and different annual periods in Spain. Simulation results of the different scenarios show that by using the proposed framework higher cost savings can be achieved, in comparison with the classical scheduling scenario. The saving can reach up to 68% when different classes of households exist in the microgrid. The results also show that the fairness in energy allocation is guaranteed by the reputation-based policy, and that the total power absorbed from the main grid by the whole microgrid is significantly decreased. [ABSTRACT FROM AUTHOR]

Cellular operators are increasingly turning toward renewable energy (RE) as an alternative to using traditional electricity in order to reduce operational expenditure and carbon footprint. Due to the randomness in both RE generation and mobile traffic at each base station (BS), a surplus or shortfall of energy may occur at any given time. To increase energy self-reliance and minimize the network’s energy cost, the operator needs to efficiently exploit the RE generated across all BSs. In this paper, a hybrid energy sharing framework for cellular network is proposed, where a combination of physical power lines and energy trading with other BSs using smart grid is used. Algorithms for physical power lines deployment between BSs, based on average and complete statistics of the net RE available, are developed. Afterward, an energy management framework is formulated to optimally determine the quantities of electricity and RE to be procured and exchanged among BSs, respectively, while considering battery capacities and real-time energy pricing. Three cases are investigated, where RE generation is unknown, perfectly known, and partially known ahead of time. Results investigate the time varying energy management of BSs and demonstrate considerable reduction in average energy cost thanks to the hybrid energy sharing scheme. [ABSTRACT FROM PUBLISHER]