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• A decentralized approach is developed for the local community market. • Peer-to-peer energy trading in a low-voltage network is considered. • The intraday congestion management problem is solved using two-stage procedure. • The demand-side flexibility market is developed and analyzed in a P2P market structure. • DSO acts as an independent entity to manage the congestion at the distribution level. The communication network development and rising penetration of prosumers in distribution networks pave the way to emerge a fully decentralized energy transactions framework. Prosumers can share their excess energy production in a new market called the peer-to-peer (P2P) market. This paper proposes a decentralized P2P energy trading market that facilitates electrical energy trading between customers. In this new market, customers act as individual players and decide on their own willingness to maximize their social welfare by selling the extra energy production in the P2P market. Other participants can compensate for their energy deficiency in the P2P market at lower prices. In the decentralized framework, the participant's privacy is protected by sharing a limited amount of data between peers. The P2P market paved the way for another technology named the DSF market. The DSF market is activated by the distribution system operator (DSO) in the local P2P market framework. When congestion is detected in the following day, the DSO requests flexibility from the demand-side flexibility (DSF) and publishes a flexibility requirement table (FRT). Such market structure can incentivize customers to participate in DSF programs and help the main grid by mitigating congestion. This paper employed the alternating direction method of multipliers (ADMM) algorithm for solving the decentralized market. Numerical studies are carried out for several peers in a local community. Simulation results demonstrate that the customers participating in the P2P market supply 15.9% of their demand within the P2P market. Additionally, the findings indicate that customers exhibit a willingness to engage in the DSF program. Moreover, it is observed that 99% of the demand changes requested from the DSO in the FRT are adhered to by the peers. [ABSTRACT FROM AUTHOR]

• This paper investigates multi-energy transactive microgrid (METMG) systems. • The X2X technologies are used for converting different forms of energy to each other. • The energy hub is connected to all the METMGs and both receive and sends converted forms of energy to METMGs. • Both the economic and environmental concerns are considered. • The fuzzy algorithm is used to ensure fairness in optimizing dual cooperative objectives. The environmental concerns, most notably the release of greenhouse gases due to global warming effects, are significant to energy production processes. This paper investigates multi-energy transactive microgrid (METMG) systems considering environmental and economic concerns using energy conversion technologies (X2X). Utilizing the conversion of a different form of energy by the X2X technologies is a new concept that this paper tries to investigate its usefulness in economic and environmental aspects. The X2X technologies are used in an energy-hub unit that has the duty of converting different forms of energy to each other. The energy hub is connected to all the METMGs and both receive and sends converted forms of energy to METMGs. Both the economic and environmental concerns are considered by optimizing two separate objective functions for each one in a cooperative manner. The goal of the economic objective function is to decrease operational costs, and the goal of the environmental objective function is to reduce the release of emission gases. The fuzzy algorithm is used to ensure fairness in optimizing dual cooperative objectives. The simulation model consists of three METMG and an energy hub where the conversion of different forms of energy is conducted. The METMGs have different forms of energy sources and energy carrier loads such as power, heat, and hydrogen. It is concluded that by applying X2X technologies in the energy hub operation, the costs and the released emissions are reduced by 56 % and 48 %, respectively. This shows the effectiveness of the proposed structure in the reduction of costs and emissions which answers the economic and environmental concerns. [ABSTRACT FROM AUTHOR]

Highlights • A developed concept of PV-wind-CCHP based microgrid is studied. • Optimal multi-objective model for solving economic and environmental problems. • Employ weighted sum technique to solve proposed multi-objective function. • Implementation of max-min fuzzy technique to obtain compromise solution. • Implementation of real time DRP to reduce operation cost and carbon emission. Abstract In this article, an improved system in micro energy grid is investigated composing of energy hub system as combined cooling, heating and power (CCHP) based microgrid integrated with renewable energies like photovoltaic (PV) and wind turbine (WT). The main goal is to solve a multi-objective model that includes reducing carbon emission and operation cost in the presence of real time demand response program (DRP). The flexibility of suggested energy hub system is the result of utilizing three hubs, storage devices and optimal energy flow. The multi-objective model has been solved using weighted sum approach and max-min fuzzy to make the optimal Pareto solutions and select the trade-off solution, respectively. This paper implemented the priced based real time DRP to flatten the load curve by shifting some amounts of loads from peak periods to off-peak periods. Two cases studied in the article that indicates the effects of real time DRP on optimization of energy hub systems. Finally, the energy hub system model is formulated and solved using CPLEX solver in GAMS optimization software. The results show that the operation cost and carbon emission are reduced 3.97% and 2.26%, respectively due to implementation of real time DRP. [ABSTRACT FROM AUTHOR]

Load forecasting in power microgrids and load management systems is still a challenge and needs an accurate method. Although in recent years, short-term load forecasting is done by statistical or learning algorithms. There are still two unsolvable challenges in the conventional data-driven-based prediction methods. The first challenge is that extracting the correlation between metrological and load data still cannot be taken full advantage of, and the other is that extracting patterns independently of the fixed pattern length is not supported. To address these challenges, a fixed SPM-LSTM approach is proposed. SPM for discrete data and LSTM for continuous data. The proposed model uses past consumption, temperature, humidity, wind speed, and weather data. A sequential pattern mining algorithm is used to extract sequential patterns that are independent of the fixed pattern length of correlated data between load and metrological data and can predict only the range of future load. Then, an LSTM network is used for exact load forecasting. It was tested using real data from the consumption and generation data retrieved from the ENTSOE project and its performance was compared with other forecasting methods. Results have shown that the proposed approach with the correlation squared (R 2) of 0.951 in the best situation outperformed other methods like LSTM, LSTM-ANN, and CNN-GA. Also, the proposed method reduced training time by one-fifth against others. • A combined SPM-LSTM is proposed for short-term load forecasting. • The past consumption, temperature, humidity, wind speed, and weather data are used. • SPM is used for sequential pattern mining independent of the fixed pattern length. • LSTM determines the accurate future load once the relevant pattern is specified. • An accurate method with acceptable training time using load and meteorological data. [ABSTRACT FROM AUTHOR]

In this paper, optimal energy management of interconnected multi-smart apartment buildings considering energy flow among them is presented. It is considered that each smart apartment building includes various equipment such as combined heat and power (CHP) generator, battery storage system, thermal storage system and smart appliances. To assess the effects of power exchange capability among the multiple-smart apartment buildings, two case studies are evaluated with and without considering mentioned capability. It can be found that the operation cost of the interconnected multi-smart apartment buildings by considering the effects of power exchange capability among smart apartment buildings under control and management of a home energy management system is decreased. The proposed model is formulated as mixed-integer programming (MIP) which guarantees the global optimum solution, and it is solved by General Algebraic Modeling System (GAMS) optimization software. [ABSTRACT FROM AUTHOR]

• This paper addresses the transactive multiple microgrids (TMMGs). • A cooperative game is proposed for optimal day-ahead scheduling of the TMMGs. • The possibility of main grid disconnection for several hours is considered. • The interactions is considered in cooperative game and best strategies are determined. • Both the normal and resiliency operation modes of the TMMG is modeled. The growth of distributed energy resources and microgrids (MGs) attracts attention to the transactive multiple MG (TMMG) concept. This paper proposes a novel framework that belongs to the competitive-based resilient scheduling of TMMGs. A cooperative game approach is investigated for optimal day-ahead scheduling of TMMGs, which concerns the possibility of long-duration disconnection from the main grid. The TMMG comprises multiple MGs that can have energy transactions through a local market. This local energy market lets the TMMG operator receive the MGs' 24-hour bid-offer amounts and set the hourly transaction prices. The proposed game fulfills the minimum operation cost for MGs in the normal operation mode (NOM) and ensures the serving of TMMG's load during the resiliency operation mode (ROM). Thus, the paper seeks to find an equilibrium point so that all the MGs reach acceptable strategies by meeting the constraints in the NOM and ROM. It is shown that the market tends to provide the NOM transaction at clearing prices lower than the wholesale price. Also, it tends to disperse a higher price in the ROM condition. Notwithstanding the profitable opportunity for renewable-penetrated MGs, they are exposed to high costs due to the inherent uncertainty and risk of generation deficiency. [ABSTRACT FROM AUTHOR]

• The multi-objective optimal despatch model of the water-energy nexus is proposed. • The impact of multiple storages on the integrated system operation is investigated. • Minimizing the energy and emission costs, extraction of wells' freshwater as aims. • Improving the water system flexibility by simultaneous considering of technologies. This paper proposes an integrated scheduling model for optimal despatch of cooling, heating, power, gas and water sources in an energy-water microgrid, where the microgrid operator participates in the power, heat, and gas markets and utilizes energy conversion facilities to meet various demands. Further, the role of water and energy storage systems (WESSs) and demand response program (DRP) is investigated on optimal scheduling of the combined cooling, heating, power, gas, and water-based microgrid. In addition, a multi-objective two-stage stochastic optimization model is adopted to minimize the total cost, including operating and emission costs and the amount of potable water extracted from water wells due to the uncertainties of electrical demand, wind power, and electricity market price. Moreover, the epsilon-constraint method and fuzzy satisfying approach are applied to obtain the optimal solution in the multi-objective problem. Ultimately, the simulation results confirm the advantages of simultaneous consideration of WESSs and DRP on the total cost of the proposed energy-water microgrid. [ABSTRACT FROM AUTHOR]

• A way to run the optimal economic DLC program for charging stations is provided. • Optimal allocation of PHEV loads for priority/non-priority stations simultaneously. • The lowest total incentive costs in three proposed managerial modes are achieved. • Pay attention to the new capacity of charging stations and their prioritization. • The lowest aggregate consumed fuel, taken time, and traveled distance are achieved. Optimal participation of charging stations is considered to increase the performance of smart grids. During the implementation of the direct load control (DLC) demand response program (DRP), the optimal distribution of plug-in hybrid electric vehicles (PHEVs) from primary charging stations (PCSs) to alternative or secondary charging stations (SCSs) could alleviate congestion of charging stations, power lines, and traffic of paths. To this end, the optimal economic dispatch of charging stations during the self-healing period in three proposed managerial modes, including 1) equal-economic optimization (EEO), 2) economic optimization (EO), and 3) priority-economic optimization (PEO) has been performed. The results show the maximum reduction in incentive costs for the operating company and taken times for customers. Execution of operations in urban areas, the ability to be combined with electricity grid constraints, and attention to the responsiveness of SCSs are other advantages. The project optimizations and calculations with numerical experiments based on Gams solution approach are performed. [ABSTRACT FROM AUTHOR]

There is little room for doubt that distributed generation systems including renewable energy, microgrids (MGs), combined heat and power (CHP) units and storage systems have been of particular importance in sustaining low-carbon and cost-effective operations due to the tremendous increase in greenhouse gas emissions in recent years. Additionally, hydrogen-based power technologies have earned a great deal of publicity that hydrogen can serve as a zero-emission fuel for electrical power and thermal energy production. In this regard, the current paper proposes an optimal energy management strategy for a combined hydrogen, heat, and power MG (CHHP-MG) with hydrogen fueling stations (HFSs) for hydrogen vehicles (HVs), electric vehicle parking lots (EVPLs) and fuel cell micro-CHP (FC-MCHP) units to meet power and heat requirements. In order to reduce the regular operating expense, the presented CHHP-MG could also communicate with both electricity and hydrogen markets. In addition, to compensate for the associated heat and hydrogen requirements, power-to-X technologies such as the power to heat (P2HT) and power to hydrogen (P2H) are integrated. In order to improve flexibility and build a low carbon MG, multi-energy storage (MES) system along with heat and power demand response (HPDR) programs will be taken into consideration. As the uncertainties associated with the predicted wind and photovoltaic power have a major impact on the energy management of the CHHP-MG, a multi-objective information gap decision theory (IGDT)-based robust approach is applied as an effective non-probabilistic modeling technique for handling such uncertainties. The empirical results show that the proposed model can efficiently handle the uncertainties and reduce the overall operation cost by 76.35%. • A novel low-carbon CHHP-MG is presented by considering FC-MCHP units, HFs and EVPLs. • The P2H, H2P and P2HT technologies are incorporated to minimize the operation cost. • A multi-objective IGDT is proposed to model wind and PV power uncertainty simultaneously. • The augmented ε-constraint approach is utilized to solve the multi-objective problem. [ABSTRACT FROM AUTHOR]

• A multiple energy distribution company is proposed to supply various energy demands. • The limitations of power, gas and heat distribution network are considered simultaneously. • Variable mass flow and variable temperature control strategy are considered to model the HDN • The effect of integrated demand response and multi-energy storages is investigated. • A hybrid robust-stochastic approach is adopted to manage uncertainties. Multi-energy systems have been developed to supply the multi-energy users economically by considering the physical limitations of different energy networks. This paper proposes a new entity called multiple energy distribution company (MEDC) to meet the electricity, gas, and heat demands of consumers in the presence of renewable energy resources (RESs) and multi-energy conversion technologies with the lowest operating cost. To achieve a more accurate scheduling model, a multi-energy flow model is used that involves practical constraints of the power distribution network, Heating distribution network (HDN) and natural gas distribution network simultaneously. A variable mass flow and temperature control strategy is applied in the HDN to make a high-performance energy supply scheme. Multi-energy storage systems (MESSs) and integrated demand response (IDR) are also considered to increase the flexibility of the MEDC for serving multi-type energy demands. Moreover, a hybrid robust-stochastic optimization technique is adopted to handle the system uncertainties, where the uncertainties related to RESs and energy prices are addressed under a scenario-based stochastic programming and a robust optimization technique, respectively. The simulation results demonstrate that the efficient use of MESSs and IDR improves the performance of multi-energy generation units in the presence of multi-energy distribution network constraints and reduces the total operation cost by 15%. [ABSTRACT FROM AUTHOR]

• Solving an optimal scheduling problem for CHP-based MG with reconfigurable capability. • Analyzing economic and environmental operation of CHP-based RMG based on multi-objective problem. • Consider incentive-based DR and tri-state CAES as emerging sources to provide more flexibility. • Proposing a two-stage scenario-based stochastic approach to handle the fluctuation. Microgrids (MGs) are solutions to integrate high shares of variable renewable energy which can contribute to more economical and environmental benefits, as well as improving the energy supply efficiency. One significant potential of MGs is an expanded opportunity to use the waste heating energy from the conversion of the primary fuel (such as natural gas) to generate electricity. The use of waste heat in combined heat and power (CHP)-based MG is more efficient to meet local load and decrease the emission pollution. Hence, this paper elaborates on optimal multi-objective scheduling of CHP-based MG coupled with compressed air energy storage (CAES), renewable energy, thermal energy storage (TES), and demand response programs through shiftable loads, which considers a reconfiguration capability. The embedded CAES, in addition to the charging/discharging scheme, can operate in a simple cycling mode and serve as a generation resource to supply local load in an emergency condition. The daily reconfiguration of MG will introduce a new generation of MG named reconfigurable microgrid (RMG) that offers more flexibility and enhances system reliability. The RMG is coupled with TES to facilitate the integration of the CHP unit that enables the operator to participate in the thermal market, in addition to the power market. The main intents of the proposed multi-objective problem are to minimize the operation cost along with a reduction in carbon emission. The epsilon-constraint technique is used to solve the multi-objective problem while fuzzy decision making is implemented to select an optimal solution among all the Pareto solutions. The electricity prices and wind power generation variation are captured as random variables in the model and the scenario-based stochastic approach is used to handle them. Simulation results prove that the simultaneous integration of multiple technologies in CHP-based RMG decreases the operation cost and emission up to 3 % and 10.28 %, respectively. [ABSTRACT FROM AUTHOR]

• Proposing a decentralized mechanism for coordinated local-regional energy systems. • Using an iteration based two-step framework to solve the introduced model. • Including energy storage resources in the integrated local-regional energy systems. • Applying a stochastic approach to handle the uncertainties of the integrated system. • Presenting a linearized gas network model considering linepack. A multi-energy system (MES) provides greater flexibility for the operation of different energy carriers. It increases the reliability and efficiency of the networks in the presence of renewable energy sources (RESs). Various energy carriers such as power, gas, and heat can be interconnected by energy storage systems (ESSs) and combined heat and power units at different levels (e.g., within a region or a local). Non-coordinated optimization of energy systems at local and regional levels does not verify the whole optimal operation of systems since the systems are operated without considering their interactions with each other. One of the most famous sources of flexibility is ESSs. Hence, this paper presents a stochastic decentralized approach to evaluate the impact of ESSs on regional-local MES market-clearing within a bi-level framework. On the regional level, the economic interaction between the electricity and natural gas (NG) systems is carried out by a centralized system operator (CSO). In addition, coordination between various energy carriers is implemented by the energy hub operator at the local level. To ameliorate the flexibility of the NG system in the regional MES, the linepack model of gas pipelines has been considered. Local MES modeling is performed through multiple input/output ports using a linear energy hub model. The proposed model is a mixed-integer linear programming (MILP), which is solved by CPLEX solver in GAMS software. [ABSTRACT FROM AUTHOR]

• Proposing an integrated energy infrastructure based on power to hydrogen technology. • Considering thermal and electrical energy storages and integrated demand response programs. • Applying a hybrid robust-stochastic optimization approach to manage uncertainties. Global consensus on energy economic and environmental issues, as well as increasing penetration of renewable resources, has created new challenges for researchers to explore more financial-effective, environment-friendly, reliable, and secure energy systems. To this end, this paper focuses on combined cooling, heating, and power microgrid to alleviate these issues. In this regard, a multi-carrier energy storage system composed of thermal storage system, ice storage system, and hydrogen storage system are integrated into the proposed system to benefit from their techno-economic advantages. The proposed combined cooling, heating, and power microgrid not only participates in power, gas, and thermal market to supply power, thermal, and cooling demands but also can participate in the hydrogen market using a novel power-to-hydrogen technology utilized in the hydrogen storage system to increase the entire system efficiency. Moreover, a multi-energy demand response model is applied as a new concept of demand response on both electrical and thermal loads, which provides more options for multi-energy end-users in energy management policies. Additionally, a novel hybrid robust-stochastic approach is proposed to deal with the uncertainty of wind speed and electricity price fluctuations, which enables the operator to use the advantages of both scenario-based stochastic and robust optimization methods simultaneously to handle such uncertainties. The scenario-based stochastic technique is used to handle the uncertainty of wind speed while the robust optimization method is also considered to manage the uncertainty associated with electricity price in a conservative environment. The main objective of the proposed model is to minimize the operation cost of multi-energy microgrid, which is formulated as a mixed-integer linear programming problem. Finally, to verify the effectiveness of the proposed methodology, a case study and numerical results are provided. Obtained results shows that utilizing hydrogen storage and multi-energy demand response can reduce the system operation cost up to 5% for the studied test system. [ABSTRACT FROM AUTHOR]

• Optimal operational scheduling of Smart Distribution Networks in the presence of independent financial entities is addressed. • An interactive relationship between players of Demand Response Programs, Distribution System Operator, Aggregator and Customer, is provided. • An approach based on the Game Theory, the Multi Follower Bi-Level Programming, is proposed to obtain optimal decisions. • Forward Bilateral Financial Contracts are made between autonomous financial organizations of the studied Smart Distribution System. With utilizing the advanced automation services and communication technologies, distribution systems have been transferred from passive to smart networks. In this regard, owing to widespread and complicated interactions between varied entities, distribution system operators (DSOs) encounter various challenges in terms of energy management. Hence, in this paper, a novel Multi Follower Bi-Level framework is presented for operational scheduling of smart distribution networks (SDNs) in the presence of two players, the Demand Response Aggregator (DRA) and Microgrid Owner (MGO). In the proposed method, the upper-level problem minimizes the DSO's operating costs, while, the lower-levels maximize the MGO and DRA's profits from exchanging power. In this procedure, a non-profit agent, the Distribution-Independent System Operator is introduced to coordinate operational conflicts and interests of the network. The considered model is a non-linear Bi-Level problem which is converted into a linear Single-Level problem through KKT conditions and the Big-M method. The presented scheme is implemented on two modified SDNs. The results demonstrate that the Bi-level technique is appropriate for each entity to promote its benefit without any negative influence on the other players. Finally, to evaluate effectiveness of the proposed model, sensitivity analysis is conducted by transferring the DRA's interest to the upper-level. [ABSTRACT FROM AUTHOR]

• Optimal probabilistic planning of MT and EV parking lot under demand response is proposed. • Demand response is performed based on offering an optimal 24 h selling energy price. • Due to the interaction between the strategies of DisCo and customers, game approach is applied. • Non-cooperative Stackelberg game is implemented to cope with the existing interaction. • PCPM method is employed to decompose the problem and solve it in a distributed iterative manner. With respect to the penetration of electric vehicles (EVs) due to pursuing environmental targets, EV parking lots establishment for giving charge and discharge services is necessary. Nevertheless, distribution companies (DisCos) seek to reduce the planning cost as much as possible and in this regard, demand response can be one of their effective options. However the customers participated in this program reduce or shift their consumption for the sake of increasing their benefit and accordingly DisCo must provide an incentive like the decrease in energy price which causes a competition between the DisCo and customers decisions. Because selling energy with higher price is preferable for DisCo. Hence what is proposed in this paper is the optimal probabilistic and long term planning of DGs and EV parking lots in the presence of demand response in which the mentioned competition is controlled via implementing Stackelberg game theory. In this game, DisCo plays as the leader and the customers interested in demand response play as the followers and their interaction is conducted though a decentralized distributed algorithm which finally determines the consumption profiles and the related energy selling price for all scenarios through the panning period. [ABSTRACT FROM AUTHOR]

• Energy storage arbitrage in day-ahead market (DAM) and real-time market (RTM) is studied. • A hybrid stochastic-robust optimization approach is proposed to model uncertainties. • The price uncertainty in DAM is modeled using scenario-based stochastic programming. • The price uncertainty in RTM is handled via proposed robust-optimization method. • Optimal price-quantity bidding and offering strategy is obtained with proposed method. In recent years energy storage systems (ESSs) have been used widely in most European and American countries because of their economic and technical benefits. One of their most exciting capabilities is the ability to participate in deregulated power markets. This paper proposes a hybrid stochastic-robust optimization approach, in which an ESS owner can engage in energy arbitrage in the day-ahead market (DAM) and provide extra bids and offers in the real-time market (RTM) to increase his profit. Stochastic programming is used to model DAM price uncertainties, while the robust optimization approach is proposed for more conservative decision making in RTM considering its high volatile prices. The results are optimal price-quantity pairs, which should be submitted to the DAM and RTM by the ESS owner. [ABSTRACT FROM AUTHOR]

• Robust scheduling of a smart apartment building in the presence of market price uncertainty is considered. • The lower and upper limits of market price are considered instead of deterministic market price. • The proposed robust mixed integer linear programming scheduling guarantees global optimal solution. • Robust performance of equipment in the smart apartment building is obtained. Nowadays market price uncertainty is one of the important challenging issues in the optimal scheduling of smart apartment building (SAB). So, in this paper, a robust optimization approach (ROA) is proposed for robust scheduling of SAB in the presence of price uncertainty. For modeling the market price uncertainty, upper and lower limits of the market price are considered instead of the forecasted market prices. The proposed sample system includes a SAB that contains ten smart homes (SHs) with different living habits and equipped with different equipment, i.e. combined heat and power (CHP), boiler, battery storage system (BSS), thermal storage system (TSS) and smart appliances. To assess the effectiveness of a home energy management system (HEMS) on the performance of the proposed problem, two different controlling scenarios are studied, namely normal and smart scenarios. It should be mentioned that the proposed model is formulated as mixed-integer linear programming (MILP) which guarantees global optimal solution and carried out with general algebraic modeling system (GAMS) software. [ABSTRACT FROM AUTHOR]

Highlights • Bi-objective model for optimal economic-environmental operation of HSS. • Weighted sum technique to solve the proposed model. • Max-min fuzzy satisfying approach to select the trade-off solution. • Reduction of cost of microgrid due to implementation of DRP. Abstract Due to arisen concerns over emission of different pollutant gases, generation of clean energy is an important issue in energy systems. Although, economic goals have been always prior to such environmental issues, there has been great efforts to consider environmental issues in the scheduling of power systems. Hence, in this paper, a bi-objective scheduling framework is proposed for hydrogen storage system (HSS) to optimize performance of HSS from both economic and environmental viewpoints. The proposed bi-objective model is solved via weighted sum approach and max-min fuzzy satisfying technique is implemented to determine the trade-off solution between the conflicting objective functions. It is noteworthy that energy consumers can participate in demand response program (DRP) to enhance the performance of system. The proposed model is studied regarding a sample microgrid energy system under a mixed-integer linear programming (MILP) and general algebraic modeling system (GAMS) is used to solve it. According to the obtained results in typical days in summer and winter, total operating cost of system under DRP is reduced which represents that economic goals of microgrid are prior to environmental ones under DRP in different seasonal conditions. [ABSTRACT FROM AUTHOR]