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One of the challenges of renewable energy is its uncertain nature. Community shared energy storage (CSES) is a solution to alleviate the uncertainty of renewable resources by aggregating excess energy during appropriate periods and discharging it when renewable generation is low. CSES involves multiple consumers or producers sharing an energy storage system. This work presents an optimal strategy for CSES operators and community members to determine their optimal energy trading strategy based on social welfare maximization. Energy communities contain diverse resources, demand patterns, and constraints. The Alternating Direction Method of Multipliers (ADMM) technique is an iterative nature that decomposes the original problem into smaller sub-problems and enables parallel processing of these sub-problems. The final decomposed subproblems enable community agents to determine their optimal strategies independently decreasing the solving time, speeding up convergence to the optimal solution, and improving the scalability. The performance of the proposed model is evaluated by a case study. The presented model demonstrates that by leasing the capacity of CSES, community members could decrease their energy supply costs. Moreover, the CSES can supply the required charging and discharging power of community members from the local grid. [ABSTRACT FROM AUTHOR]

In the past decade, the global distribution of energy resources has expanded significantly. The increasing number of prosumers creates the prospect for a more decentralized and accessible energy market, where the peer-to-peer energy trading paradigm emerges. This paper proposes a methodology to optimize the participation in peer-to-peer markets based on the double-auction trading mechanism. This novel methodology is based on two reinforcement learning algorithms, used separately, to optimize the amount of energy to be transacted and the price to pay/charge for the purchase/sale of energy. The proposed methodology uses a competitive approach, and that is why all agents seek the best result for themselves, which in this case means reducing as much as possible the costs related to the purchase of energy, or if we are talking about sellers, maximizing profits. The proposed methodology was integrated into an agent-based ecosystem where there is a direct connection with agents, thus allowing application to real contexts in a more efficient way. To test the methodology, a case study was carried out in an energy community of 50 players, where each of the proposed models were used in 20 different players, and 10 were left without training. The players with training managed, over the course of a week, to save 44.65 EUR when compared to a week of peer-to-peer without training, a positive result, while the players who were left without training increased costs by 17.07 EUR. [ABSTRACT FROM AUTHOR]

Consumer-centric energy management approaches are emerging as a major solution for future power systems. In this context, intelligent home management systems should control different kinds of devices existing in the houses assuring convenient comfort levels and understanding the users' behaviour. At the same time, the home management systems should be able to interact with other actors such as energy communities, aggregators, and system operators. The main contribution of this work is a new methodology allowing intelligent management, in near real-time (1 minute), of different types of energy resources existing in a smart home. The energy resources include appliances and other loads, micro-generation, and electric vehicles. The proposed system includes a permanent evaluation of the operation state of each energy resource considering their functional model and the behaviour and comfort level defined by the users. Participation in demand response programs reducing the power consumption limits is also considered showing the advantage of the proposed approach. The case study contains two scenarios considering a demand response program of power limitation with 120 minutes duration. To guarantee the participation in these demand response events, the system should evaluate the priority of each device according to its model. A domestic consumer with 45 energy resources (appliances, generation, and electric vehicles) is used for demonstration purposes. [ABSTRACT FROM AUTHOR]

Nowadays, internet of things devices are becoming more prominent since they can integrate energy management systems. However, data sharing between end-users and other external entities is a concern that must be addressed to ensure data privacy. Given the above, this paper proposes a trustworthy energy management system for energy communities to enable direct internet of things devices' participation in demand response programs at the community level. This solution is based on a building energy management system framework that considers signed tokens to maintain end-user data privacy and manage data access. Likewise, this solution can plan future demand response events to balance the consumption and generation, using the internet of things device's flexibility. The results demonstrated that the planned demand response event for one hour efficiently balanced the energy community energy resources, enabling a reduction of approximately 2.8 kWh. Thus, the proposed solution allows safeguarding data ownership rights while efficiently managing energy resources. [ABSTRACT FROM AUTHOR]

Given the current state of the society in which we live, in terms of energy pollution, several objectives have been set to try to reduce environmental problems. Some of these goals include an exponential increase in production through renewable energy, and Electric Vehicles (EVs) circulating on roads. Due to this high penetration of distributed energy resources in the electricity grid, several problems may exist: grid congestion, causing severe energy systems damage. Innovative coordination strategies must be developed to mitigate these situations. This paper proposes a methodology to minimize this problem in a smart grid with high penetration of Distributed Generation (DG) and EVs, taking into account multiple aggregators. Initially, the proposed model calculates each aggregator's profit through some business models and analyzes the network without any congestion strategy. This analysis is then done in the presence of Distribution Locational Marginal Pricing (DLMPs), which the aggregator receives from the Distributed System Operator (DSO). The DSO gets these prices after running the Optimal Power Flow (OPF), where these prices involve the market price, the cost of losses, and the cost of congestion at a given point in the network. Here the aggregators react according to these costs, such as trying to buy flexibility from their customers. In this study, the results prove that dynamic prices are more viable for the power grid by reducing congestion by analyzing each aggregator's profit. [ABSTRACT FROM AUTHOR]

• Energy sharing in the frame of energy communities. • Energy sharing of community-owned renewable-based distributed energy resources. • Free and fair energy sharing among the community members. • Fair distribution of generation using shapley value to account for the individual members' contributions. Energy communities enable close cooperation and collaboration among members (i.e., consumers, producers, and prosumers), where buildings, energy resources, and loads can be managed at the members' level while cooperating with the community. To influence and promote the community's members' interactions, models of energy sharing can be applied. This paper addresses the distribution of community-owned renewable energy among the community members' buildings. The proposed costless renewable energy distribution model considers the individual participation of each member in demand response events launched to promote the balance between consumption and generation inside the energy community. The distribution model can distribute renewable energy in a fair and costless approach, meaning that the energy receivers will not pay for the energy they receive. The proposed model uses the Shapley value to distribute renewable energy according to its members' individual contributions to the community. The proposed model is tested using a case study of an energy community, using a dataset for one summer month. The results show an energy cost reduction of 12.86 % for the community and an average energy cost reduction of 31.03 % for the community's members. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed methodology can be used in the electric vehicles charging/discharging scheduling considering different multi-objective functions. The introduction of electric vehicles (EVs) will have an important impact on global power systems, in particular on distribution networks. Several approaches can be used to schedule the charge and discharge of EVs in coordination with the other distributed energy resources connected on the network operated by the distribution system operator (DSO). The aggregators, as virtual power plants (VPPs), can help the system operator in the management of these distributed resources taking into account the network characteristics. In the present work, an innovative hybrid methodology using deterministic and the elitist nondominated sorting genetic algorithm (NSGA-II) for the EV scheduling problem is proposed. The main goal is to test this method with two conflicting functions (cost and greenhouse gas (GHG) emissions minimization) and performing a comparison with a deterministic approach. The proposed method shows clear advantages in relation to the deterministic method, namely concerning the execution time (takes only 2% of the time) without impacting substantially the obtained results in both objectives (less than 5%). [ABSTRACT FROM AUTHOR]

Triggered by the increased fluctuations of renewable energy sources, the European Commission stated the need for integrated short-term energy markets (e.g., intraday), and recognized the facilitating role that local energy communities could play. In particular, microgrids and energy communities are expected to play a crucial part in guaranteeing the balance between generation and consumption on a local level. Local energy markets empower small players and provide a stepping stone toward fully transactive energy systems. In this paper, we evaluate such a fully integrated transactive system by, first, modeling the energy resource management problem of a microgrid under uncertainty considering flexible loads and market participation (solved via two-stage stochastic programming), second, modeling a wholesale market and a local market, and, third, coupling these elements into an integrated transactive energy simulation. Results under a realistic case study (varying prices and competitiveness of local markets) show the effectiveness of the transactive system resulting in a reduction of up to 75% of the expected costs when local markets and flexibility are considered. This illustrates that how local markets can facilitate the trade of energy, thereby increasing the tolerable penetration of renewable resources and facilitating the energy transition. [ABSTRACT FROM AUTHOR]

Distributed energy resource integration in power systems has advantages and challenges in both the economic and the technical operation of the system. An aggregator, as in the case of a Virtual Power Player, is essential in order to support the operation of these small size resources. Innovative approaches capable of supporting the decisions made in terms of resource scheduling, aggregation and remuneration are needed. The present paper addresses a methodology capable of managing resources through the activities of an aggregator, providing different choices of aggregation and remuneration strategies. The methodology is validated in a case study regarding a 21-bus network, composed of 20 consumers and 26 producers. [ABSTRACT FROM AUTHOR]

The need for developing new methodologies in order to improve power system stability has increased due to the recent growth of distributed energy resources. In this paper, the inclusion of a voltage stability index in distributed energy resources scheduling is proposed. Two techniques were used to evaluate the resulting multiobjective optimization problem: the sum-weighted Pareto front and an adapted goal programming methodology. With this new methodology, the system operators can consider both the costs and voltage stability. Priority can be assigned to one objective function according to the operating scenario. Additionally, it is possible to evaluate the impact of the distributed generation and the electric vehicles in the management of voltage stability in the future electric networks. One detailed case study considering a distribution network with high penetration of distributed energy resources is presented to analyse the proposed methodology. Additionally, the methodology is tested in a real distribution network. [ABSTRACT FROM AUTHOR]

Evolutionary computation is attracting attention in the energy domain as an alternative to tackle inherent mathematical complexity of some problems related to high-dimensionality, non-linearity, non-convexity, multimodality, or discontinuity of the search space. In this context, the research community launched the 2020 "Competition on Evolutionary Computation in the Energy Domain: Smart Grid Applications" and an associated simulation framework to evaluate the performance of state-of-the-art evolutionary algorithms solving real-world problems. The competition includes two testbeds: (1) Day-ahead energy resource management problem in smart grids under uncertain environments; and (2) Bi-level optimization of end-users' bidding strategies in local energy markets. This paper describes the general framework of the competition, the two testbeds, and the evolutionary algorithms that participated. A special section is dedicated to the winner approach, CUMDANCauchy++ , a cellular Estimation Distribution Algorithm (EDA). A thorough analysis of the results reveals that, led by CUMDANCauchy++ , the top three algorithms form a block of approaches all based on cellular EDAs. Moreover, for testbed 2, in which CUMDANCauchy++ did not achieve the best performance, the winner approach is also based on EDAs. The outcomes of the competition show that CUMDANCauchy++ is an effective algorithm solving both testbeds, and EDAs emerge as an algorithm class with promising performance for solving smart grid applications. [ABSTRACT FROM AUTHOR]

Demand response concept has been gaining increasing importance while the success of several recent implementations makes this resource benefits unquestionable. This happens in a power systems operation environment that also considers an intensive use of distributed generation. However, more adequate approaches and models are needed in order to address the small size consumers and producers aggregation, while taking into account these resources goals. The present paper focuses on the demand response programs and distributed generation resources management by a Virtual Power Player that optimally aims to minimize its operation costs taking the consumption shifting constraints into account. The impact of the consumption shifting in the distributed generation resources schedule is also considered. The methodology is applied to three scenarios based on 218 consumers and 4 types of distributed generation, in a time frame of 96 periods. [ABSTRACT FROM AUTHOR]

Abstract: The massification of electric vehicles (EVs) can have a significant impact on the power system, requiring a new approach for the energy resource management. The energy resource management has the objective to obtain the optimal scheduling of the available resources considering distributed generators, storage units, demand response and EVs. The large number of resources causes more complexity in the energy resource management, taking several hours to reach the optimal solution which requires a quick solution for the next day. Therefore, it is necessary to use adequate optimization techniques to determine the best solution in a reasonable amount of time. This paper presents a hybrid artificial intelligence technique to solve a complex energy resource management problem with a large number of resources, including EVs, connected to the electric network. The hybrid approach combines simulated annealing (SA) and ant colony optimization (ACO) techniques. The case study concerns different EVs penetration levels. Comparisons with a previous SA approach and a deterministic technique are also presented. For 2000 EVs scenario, the proposed hybrid approach found a solution better than the previous SA version, resulting in a cost reduction of 1.94%. For this scenario, the proposed approach is approximately 94 times faster than the deterministic approach. [Copyright &y& Elsevier]

This paper presents an electricity consumer characterization framework based on a knowledge discovery in databases (KDD) procedure, supported by data mining (DM) techniques, applied on the different stages of the process. The core of this framework is a data mining model based on a combination of unsupervised and supervised learning techniques. Two main modules compose this framework: the load profiling module and the classification module. The load profiling module creates a set of consumer classes using a clustering operation and the representative load profiles for each class. The classification module uses this knowledge to build a classification model able to assign different consumers to the existing classes. The quality of this framework is illustrated with a case study concerning a real database of LV consumers from the Portuguese distribution company. [ABSTRACT FROM AUTHOR]

This Special Issue, "Sustainable Energy Consumption", addresses the different perspectives of energy demand and use, commonly referred as energy consumption, for ensuring energy sustainability, increased energy efficiency, and reasonable energy costs. Cao, MO Esangbedo, Bai, and CO Esangbedo propose a multi-criteria decision-making approach for contractor selection for installing solar panel energy systems [[3]], presenting a case study of a floating solar panel energy system installation. Energy use in homes and buildings represents an important part of the load of electrical systems. [Extracted from the article]

Operation scheduling in electric power grids is one of the most practical optimization problems as it sets a target for the efficient management of the electric power supply and demand. Advancement of a method to solve this issue is crucially required, especially in microgrids. This is because the operational capability of microgrids is generally lower than that of conventional bulk power grids, and therefore, it is extremely important to develop an appropriate, coordinated operation schedule of the microgrid components. Although various techniques have been developed to solve the problem, there is no established solution. The authors propose a problem framework and a solution method that finds the optimal operation schedule of the microgrid components considering the uncertainty in the available data. In the authors' proposal, the objective function of the target problem is formulated as the expected cost of the microgrid's operations. Since the risk of imbalance in the power supply and demand is evaluated as a part of the objective function, the necessary operational reserve power is automatically calculated. The usefulness of the proposed problem framework and its solution method was verified through numerical simulations and the results are discussed. [ABSTRACT FROM AUTHOR]

This paper presents MARTINE (Multi-Agent based Real-Time INfrastruture for Energy), a simulation, emulation and energy management platform for the study of problems related to buildings and smart grids. Relevant advances related to buildings and smart grid management and operation have been proposed, focusing either on software models for decision support or on physical infrastructure and control approaches. These two perspectives are, however, complementary, and no practical assessment can be achieved without a suitable interaction and analysis of the impact that decision-making models have on physical resources, and vice-versa. MARTINE overcomes this limitation by integrating, in a single platform: real buildings with the associated devices and resources; emulated components that complement the ones present in the buildings; simulated resources, players and buildings using multi-agent systems, real-time simulation with hardware in the loop capabilities, which enables integrating virtual and physical components; and a knowledge layer that incorporates all the required decision support and energy management models. MARTINE thus provides a comprehensive platform for the study and management of energy resources. The advantages of this platform are demonstrated in this paper through three use cases, related to agriculture irrigation, practical implementation of demand response and load modeling using various network configurations. [ABSTRACT FROM AUTHOR]

Due to the increasing penetration of distributed energy resources (DERs) required for the sustainable distribution system, new voltage control strategy is needed by utilities. Traditional voltage control strategy can not support the increasing number of DERs in a coordinated and scalable manner to meet the operational voltage regulation requirement. Supported by the power electronics converter, the energy storage system can provide fast, smooth, and flexible voltage control services. In this paper, an effective and easy to implement sensitivity-based voltage control strategy is developed for the energy storage system. The developed control strategy is validated using an industrial feeder data in Northwest Washington. The proposed strategy can mitigate the voltage unbalance issue, improve the voltage profile, and correct power factors while supporting sustainable distribution system operation. [ABSTRACT FROM AUTHOR]

Households equipped with distributed energy resources, such as storage units and renewables, open the possibility of self-consumption of on-site generation, sell energy to the grid, or do both according to the context of operation. In this paper, a model for optimizing the energy resources of households by an energy service provider is developed. We consider houses equipped with technologies that support the actual reduction of energy bills and therefore perform demand response actions. A mathematical formulation is developed to obtain the optimal scheduling of household devices that minimizes energy bill and demand response curtailment actions. In addition to the scheduling model, the innovative approach in this paper includes evolutionary algorithms used to solve the problem under two optimization approaches: (a) the non-parallel approach combine the variables of all households at once; (b) the parallel-based approach takes advantage of the independence of variables between households using a multi-population mechanism and independent optimizations. Results show that the parallel-based approach can improve the performance of the tested evolutionary algorithms for larger instances of the problem. Thus, while increasing the size of the problem, namely increasing the number of households, the proposed methodology will be more advantageous. Overall, vortex search overcomes all other tested algorithms (including the well-known differential evolution and particle swarm optimization) achieving around 30% better fitness value in all the cases, demonstrating its effectiveness in solving the proposed problem. [ABSTRACT FROM AUTHOR]

Agriculture is the very backbone of every country. Unfortunately, agricultural sustainability is threatened by the lack of energy-efficient solutions. The threat becomes more evident with the constantly growing world population. The research community must, therefore, focus on resolving the problem of high energy consumption. This paper proposes a model of energy scheduling in agricultural contexts. Greater energy efficiency is achieved by means of PV (photovoltaics) and hydropower, as demonstrated in the conducted case study. The developed model is intended for contexts where the farm is located near a river, so the farmer can use the flowing water to produce energy. Moreover, the model has been emulated using a variety of state-of-the-art laboratory devices. Optimal energy scheduling is performed via a decision tree approach, optimizing the use of energy resources and reducing electricity costs. Finally, a realistic scenario is presented to show the technical features and the practical behaviors of each emulator when adapting the results of the decision tree. The research outcomes demonstrate the importance of the technical validation of each model. In addition, the results of the emulation reveal practical issues that had not been discovered during the theoretical study or during the simulation. [ABSTRACT FROM AUTHOR]

Demand response as a distributed resource has proved its significant potential for power systems. It is capable of providing flexibility that, in some cases, can be an advantage to suppress the unpredictability of distributed generation. The ability for participating in demand response programs for small or medium facilities has been limited; with the new policy regulations this limitation might be overstated. The prosumers are a new entity that is considered both as producers and consumers of electricity, which can provide excess production to the grid. Moreover, the decision-making in facilities with different generation resources, energy storage systems, and demand flexibility becomes more complex according to the number of considered variables. This paper proposes a demand response optimization methodology for application in a generic residential house. In this model, the users are able to perform actions of demand response in their facilities without any contracts with demand response service providers. The model considers the facilities that have the required devices to carry out the demand response actions. The photovoltaic generation, the available storage capacity, and the flexibility of the loads are used as the resources to find the optimal scheduling of minimal operating costs. The presented results are obtained using a particle swarm optimization and compared with a deterministic resolution in order to prove the performance of the model. The results show that the use of demand response can reduce the operational daily cost. [ABSTRACT FROM AUTHOR]

Distributed energy resources can contribute to an improved operation of power systems, improving economic and technical efficiency. However, aggregation of resources is needed to make these resources profitable. The present paper proposes a methodology for distributed resources management by a Virtual Power Player (VPP), addressing the resources scheduling, aggregation and remuneration based on the aggregation made. The aggregation is made using K-means algorithm. The innovative aspect motivating the present paper relies on the remuneration definition considering multiple scenarios of operation, by performing a multi-observation clustering. Resources aggregation and remuneration profiles are obtained for 2592 operation scenarios, considering 548 distributed generators, 20,310 consumers, and 10 suppliers. [ABSTRACT FROM AUTHOR]

Demand response aggregators have been developed and implemented all through the world with more seen in Europe and the United States. The participation of aggregators in energy markets improves the access of small-size resources to these, which enables successful business cases for demand-side flexibility. The present paper proposes aggregator’s assessment of the integration of distributed energy resources in energy markets, which provides an optimized reschedule. An aggregation and remuneration model is proposed by using the k-means and group tariff, respectively. The main objective is to identify the available options for the aggregator to define tariff groups for the implementation of demand response. After the first schedule, the distributed energy resources are aggregated into a given number of groups. For each of the new groups, a new tariff is computed and the resources are again scheduled according to the new group tariff. In this way, the impact of implementing the new tariffs is analyzed in order to support a more sustained decision to be taken by the aggregator. A 180-bus network in the case study accommodates 90 consumers, 116 distributed generators, and one supplier. [ABSTRACT FROM AUTHOR]

In this paper, a multi-objective framework is proposed for the daily operation of a Smart Grid (SG) with high penetration of sensitive loads. The Virtual Power Player (VPP) manages the day-ahead energy resource scheduling in the smart grid, considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G), while maintaining a highly reliable power for the sensitive loads. This work considers high penetration of sensitive loads, i.e. loads such as some industrial processes that require high power quality, high reliability and few interruptions. The weighted-sum approach is used with the distributed and parallel computing techniques to efficiently solve the multi-objective problem. A two-stage optimization method is proposed using a Particle Swarm Optimization (PSO) and a deterministic technique based on Mixed-Integer Linear Programming (MILP). A realistic mathematical formulation considering the electric network constraints for the day-ahead scheduling model is described. The execution time of the large-scale problem can be reduced by using a parallel and distributed computing platform. A Pareto front algorithm is applied to determine the set of non-dominated solutions. The maximization of the minimum available reserve is incorporated in the mathematical formulation in addition to the cost minimization, to take into account the reliability requirements of sensitive and vulnerable loads. A case study with a 180-bus distribution network and a fleet of 1000 gridable Electric Vehicles (EVs) is used to illustrate the performance of the proposed method. The execution time to solve the optimization problem is reduced by using distributed computing. [ABSTRACT FROM AUTHOR]

Highlights: [•] Demand Response design based in nodal locational marginal prices. [•] Inclusion of storage systems in Demand Response programs definition. [•] Demand Response impact in distributed energy resources scheduling. [Copyright &y& Elsevier]

• Contextual learning model for energy forecasting. • Clustering model that identifies different observed contexts. • Artificial intelligence-based forecasting methods for energy resources. • Case study based on real data from an office building. • Results show improvement of forecasts of energy consumption, generation and other relevant factors. Energy consumers are becoming active players in the power and energy system. However, their informed and real-time responsiveness to the variations of renewable-based generation and, consequently, energy prices, is not possible without decision support solutions. This paper proposes a novel contextual learning approach for energy forecasting, which supports the decisions of Building Energy Management Systems (BEMS). The proposed forecasting approach includes a contextual dimension that identifies different observed contexts and clusters them according to their similarity. The identification of such contexts is used by the learning process of state-of-the-art artificial intelligence-based forecasting methods to select and adapt the most relevant data that is used in the training phase in each context. Forecasts for energy consumption, generation, temperature, brightness and occupancy are used by the BEMS to provide recommendations to the consumers and to support automated control of building devices. Real consumption, generation and contextual data gathered from several sensors in a building are used to validate the results, which show that the proposed contextual learning model improves forecasts of energy consumption, generation and other relevant factors for energy management in buildings. [ABSTRACT FROM AUTHOR]

The high reliability and flexibility of Battery Energy Storage (BES) resources in comparison with other renewable technologies promote the development of this technology in smart grids. The fast response of BES to load variations could help the power system operators to maintain the balance of generation and consumption in real-time, and improve the flexibility of the smart grid, effectively. In this work, a new model is presented that determines the aggregated scheduling of BES and Wind Power Resource (WPR) in the joint energy and reserve markets. To evaluate the performance of BES in different markets, the proposed model is divided into day-ahead and real-time planning horizons. According to market prices, ramp rates, marginal costs, and technical constraints of units, the optimal participation levels in different markets are determined. The deployed power in real-time and wind power are considered as the uncertain parameters and the Robust Optimization (RO) framework is proposed to manage the related financial risk based on the worst-case realizations of uncertain parameters. The robust strategy is formulated based on the Mixed Integer Linear Programming (MILP) technique, which can be solved via the branch-and-bound method. Finally, the performance and effectiveness of the model are analyzed via different case studies. Simulation results show that the day-ahead and real-time markets are the best options for buying and selling the energy of BESs, and participation in the reserve market and regulation service increases their profit, significantly. Furthermore, the expected profit greatly depends on the risk preferences of decision-makers, and reducing the variation interval of wind generation by 40 % leads to an increase of 74.65 % in revenues. • Participation of BES in the multiple markets increases the profit. • Aggregated bidding strategy of BES and WPR hedges the risk of uncertain parameters. • Increasing the ramp rate of BES leads to a higher profit for the owners. • Uncertainty of deployed power reduces participation level of BES in reserve market. [ABSTRACT FROM AUTHOR]

• Implementation of several metaheuristics for energy resource management. • Consideration of extreme event occurrence that cause a high-cost impact. • Implementation of conditional value-at-risk to measure the risk of extreme events. • Risk-aversion in the day-ahead reduces hour-ahead extreme scenario cost. Demand response programs, energy storage systems, electric vehicles, and local electricity markets are appropriate solutions to offset the uncertainty associated with the high penetration of distributed energy resources. It aims to enhance efficiency by adding such technologies to the energy resource management problem while also addressing current concerns using smart grid technologies and optimization methodologies. This paper presents an efficient intraday energy resource management starting from the day-ahead time horizon, which considers the uncertainty associated with load consumption, renewable generation, electric vehicles, electricity market prices, and the existence of extreme events in a 13-bus distribution network with high integration of renewables and electric vehicles. A risk analysis is implemented through conditional value-at-risk to address these extreme events. In the intraday model, we assume that an extreme event will occur to analyze the outcome of the developed solution. We analyze the solution's impact departing from the day-ahead, considering different risk aversion levels. Multiple metaheuristics optimize the day-ahead problem, and the best-performing algorithm is used for the intraday problem. Results show that HyDE gives the best day-ahead solution compared to the other algorithms, achieving a reduction of around 37% in the cost of the worst scenarios. For the intraday model, considering risk aversion also reduces the impact of the extreme scenarios. [ABSTRACT FROM AUTHOR]

• An adaptive robust model is proposed for aggregating the flexible resources. • The characteristics of flexible resources are considered in the proposed model. • The objective function is formulated as min–max-min problem. • The strong duality theorem and decomposition techniques are used to solve the problem. Aggregators, as intermediaries between consumers, prosumers, and local market operators, manage their clients' resources for participation in the multi-markets. Various resources of consumers and prosumers have different technical characteristics, which shall be considered in the scheduling strategy. The fast response and controllability of some resources may increase the profit of aggregators and improve the flexibility of distribution grids. Therefore, aggregators need a scheduling strategy to determine their participation level in different markets. However, the expected profit of aggregators can be affected by uncertainties in some resources such as PV generation or consumption of prosumers. This work provides a decomposed bi-level strategy approach for aggregators to deploy the resources of prosumers in the local energy and flexibility markets. The upper and lower sub-problems determine the optimal commitment status of the aggregator's resources and the participation level in the local energy and flexibility markets, respectively. In the proposed model, the adaptive robust optimization (ARO) is addressed to model uncertainties of consumption and generation of renewable resources based on the worst-case realizations of uncertain parameters. In addition, impacts of consumption control programs such as load-shifting and load reduction are considered in the proposed model. Through a case study and different scenarios, the performance and efficiency of the proposed model are validated. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic two-stage model for energy aggregators (EAs) in the energy and balancing markets to supply electricity and natural gas to end-users equipped with combined heat and power (CHP) units. The suggested model takes into account the battery energy storage (BES) as a self-generating unit of EA. The upper and lower subproblems determine the optimal energy supply strategy of EA and consumption of consumers, respectively. In the lower subproblem, the McCormick relaxation is used to linearize the cost function of the CHP unit. To solve the proposed model, the two-stage problem is transformed into a linear single-stage problem using the KKT conditions of the lower subproblem, the Big M method, and the strong duality theory. The performance and efficiency of the proposed model are evaluated using a case study and three scenarios. According to the simulation results, adding CHP units to the energy-scheduling problem of BES-owned aggregators increases the profit of EA by 5.96% and decreases the cost of consumers by 1.57%. • Optimal strategy of EA to supply the electricity and natural gas of consumers is presented. • The proposed bi-level model optimizes the objectives of EA and consumers, simultaneously. • The McCormick relaxation approach is used to linearize the cost function of CHP units. [ABSTRACT FROM AUTHOR]

Typically, residential buildings neither allow flexibility in the individual contract power capacity nor considers buildings as unique electricity consumers. In this work, a smart building is designed that each electricity customer has flexible contract power and the whole collective residential building has a single contract power. A management entity is considered to manage all energy resources of the building such as the photovoltaic generation, electric vehicles, and battery energy storage system, taking into consideration the consumption from apartments and common services, to minimize the electricity bill. Hence, the best/optimal contract power capacity will contribute to minimizing electricity costs. Therefore, finding the optimal decision of the contract power value has received a significant role from the energy management in smart buildings. In this paper, a mixed binary optimization problem is formulated in which not only the optimal value of contract power is yield but the optimal schedule of the electric vehicle/battery storage charge and discharge are found, taking into consideration the photovoltaic generation and load consumption profiles. The proposed model is implemented for three scenarios, and the obtained results show that the model efficiency has a high performance with a significant electricity cost reduction, around 47%. The results pointed that using an optimal value of single contract power and intelligent management system, the building electricity costs decrease remarkably. • Building energy resources management with intensive use of electrical vehicles. • Flexible contract power capacity. • Optimal single contract power for multi-unit residential buildings. • Mathematical modeling towards energy efficiency and electricity reduction costs. • Energy storage energy in residential building context. [ABSTRACT FROM AUTHOR]

Abstract: Energy resource scheduling becomes increasingly important, as the use of distributed resources is intensified and massive gridable vehicle use is envisaged. The present paper proposes a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and of gridable vehicles, usually referred as Vehicle-to-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with V2G owners. It takes into account these contracts, the users’ requirements subjected to the VPP, and several discharge price steps. Full AC power flow calculation included in the model allows taking into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33 bus distribution network and V2G is used to illustrate the good performance of the proposed method. [Copyright &y& Elsevier]