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

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

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

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

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

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

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

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

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

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

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

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]

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.

The proliferation of distributed renewable energy and the extensive use of household energy storage have gradually transformed the users of active distribution network (ADN) from traditional consumers to prosumers. The flexible resources of prosumers on the demand side need a suitable trading mechanism to realize the optimal allocation of resources. Unlike the traditional electricity market with high entry threshold, this paper proposed an energy sharing mechanism based on prosumers with household energy storage devices. Firstly, taking into account the charge and discharge utility of household energy storage equipment as well as the production and consumption utility of prosumers, a transaction decision model of prosumers is established. Then, based on the centralized social welfare maximization energy sharing problem, a distributed energy sharing mechanism of ADN is constructed through alternating direction method of multipliers (ADMM) algorithm. Finally, an illustrative example of two prosumers and a prosumer network of the ADN with 10 nodes are calculated and analyzed respectively. The results show that the proposed energy sharing mechanism can maximize social welfare and improve the local accommodation degree of renewable energy from 84.85% to 85.27%. The social welfare increases with the growth of the number of prosumers with household energy storage.

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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]

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.

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]

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]

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 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]

Abstract 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.

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]

Multiple 5G base stations (BSs) equipped with distributed photovoltaic (PV) generation devices and energy storage (ES) units participate in active distribution network (ADN) demand response (DR), which is expected to be the best way to reduce the energy cost of 5G BSs and provide flexibility resources for the ADN. However, the standalone PV-integrated 5G BS has the characteristics of wide distribution, small volume, and large load fluctuations, which will bring strong uncertainty to the ADN by directly participating in the DR. Therefore, a system architecture for multiple PV-integrated 5G BSs to participate in the DR is proposed, where an energy aggregator is introduced to effectively aggregate the PV energy and ES resources of 5G BSs. Then, a two-stage optimal dispatch method is proposed. Specifically, in the large-timescale DR planning stage, an incentive mechanism for multiple PV-integrated 5G BSs to participate in the DR is constructed based on the contract theory, which ensures that multiple 5G BSs respond to and satisfy the peak-shaving demand of the ADN. In the small-timescale online energy optimization stage, based on the energy sharing mode among 5G BSs, a Lyapunov-based online energy optimization algorithm is proposed to optimize the shared energy flow between the internal layer and the interactive layer of 5G BSs, which further improves PV absorption and ensures operation stability of ES in the 5G BS. Simulation results show that the proposed two-stage optimal dispatch method can effectively encourage multiple 5G BSs to participate in DR and achieve the win–win effect of assisting the ADN peak-shaving and low-carbon economic operation of 5G BSs.

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]

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.

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]

Due to the technological revolution and higher user data demand, the telecommunication industry is expanding at an exponential rate. Fulfilling the increasing demand of energy for the rising cellular networks has become a great challenge to the network operators because of the limited reservation of fuel energy sources and the growing concern about global warming. Energy harvesting (EH) from renewable energy sources (RES) has become an overwhelming initiative to minimize energy deficiency and carbon footprints. This paper investigates the feasibility of solar photovoltaic (PV) and biomass resources based hybrid supply systems for powering the off-grid Long Term Evolution (LTE) cellular macrocell base stations (BSs) in Bangladesh focusing the technical, economic and environmental issues. In addition, the green energy sharing technique has been incorporated via a low resistive path for optimal use of RES. The proposed system has enough potential to achieve long term sustainability and reduction of pollution rates by fulfilling the future energy demand of BS. In this work, Hybrid Optimization Model for Electric Renewables (HOMER) simulation-based feasibility analysis is used to assess the optimal system, energy production, total net present cost (NPC), cost of electricity (COE) and greenhouse gas (GHG) emission depending on different system parameters. Furthermore, the performance of the network has been evaluated in terms of throughput and energy efficiency using Matlab-based Monte Carlo simulations. Results demonstrate that the proposed hybrid renewable energy powered BSs would be a reliable and longer-lasting green solution for the telecom sector while maintaining the quality of service (QoS). Finally, an extensive comparison with other systems has also been done to justify network validity.

Heterogeneous network, which is a key technology of fifth generation(5G) mobile communication system, can effectively solve the spectrum resource shortage in the communication system. However, with densified deployment of base stations (BSs) and growth of user quantity and communication data size, the system energy consumption of BSs has imposed enormous economic pressure on network operators. As a result, energy consumption decreases and cost of communication system becomes a problem that requires urgent solutions. Renewable energy power generation devices of BSs have provided an opportunity for solving this problem with the development of smart power grids. However, the production rate of renewable energy sources presents intense volatility because of the influence of weather factors. This condition brings new challenges to the energy allocation of communication system. Therefore, an energy sharing link between BSs was established in a heterogeneous wireless network, and the distance between macro-BS and micro-BS was taken as an index. The energy consumption-based energy and user joint allocation method and energy cost-based energy and user joint allocation method were proposed. The two multi-objective optimization problems were converted into two convex optimization problems, and the optimal allocation strategies were solved using convex optimization toolbox. Simulation results indicate that the energy consumption-based allocation method takes energy consumption as the optimization objective and makes the shared link transmit energy sources as few as possible. This way reduces energy consumptions of the link and the system. Starting from the economic angle, the energy cost-based allocation method considers the influence of energy price, coordinates energy allocation between BSs based on energy cost using the shared link, and optimizes energy allocation among BSs at each time slot. Therefore, the method can reduce the energy cost of the system by a large margin.

With the rapid growth in clean distributed energy resources involving micro-generation and flexible loads, users can actively manage their own energy and have the capability to enter in a market of energy services as prosumers while reducing their carbon footprint. The coordination between these distributed energy resources is essential in order to ensure fair trading and equality in resource sharing among a community of prosumers. Peer-to-Peer (P2P) networks can provide the underlying mechanisms for supporting such coordination and offer incentives to prosumers to participate in the energy market. In particular, the federation of energy clusters with P2P networks has the potential to unlock access to energy resources and lead to the development of new energy services in a fast-growing sharing energy economy. In this paper, we present the formation and federation of smart energy clusters using P2P networks with a view to decentralise energy markets and enable access and use of clean energy resources. We implement a P2P framework to support the federation of energy clusters and study the interaction of consumers and producers in a market of energy resources and services. We demonstrate how energy exchanges and energy costs in a federation are influenced by the energy demand, the size of energy clusters and energy types. We conduct our modelling and analysis based on a real fish industry case study in Milford Haven, South Wales, as part of the EU H2020 INTERREG piSCES project.

Energy is one of the key enablers for robotic swarms. The feasibility of heterogeneous swarms to perform complex missions depends upon the energy stored in the robot batteries. Allowing multiple access energy transfer (MAET) in robot swarms provides more flexibility to accomodate uncertain missions. In this paper, we present a MAET solution for heterogeneous swarms using code division multiple access wireless power transfer (CDMA-WPT). CDMA-WPT allows robots in heterogeneous swarms to exchange energy while in motion, allows faster charging rates by enabling multiple robots to exchange energy, is easily scalable, offers power flow selectivity to enable energy encryption, and does not require a centralized controller. The encoding scheme, power flow model, and code construction algorithm for a particular implementation of CDMA-WPT is presented. The proof of concept is verified using a hardware implementation for a heterogeneous swarm of four robots. Finally, the application of CDMA-WPT to a heterogeneous swarm with 30 robots is presented.