In order to facilitate energy sharing and improve system flexibility, a hybrid energy sharing framework of multiple microgrids (MGs) is proposed for a heat-electricity integrated energy system (HE-IES) with combined heat and power (CHP) and demand response (DR). First, considering multi-time scale characteristics, an electrical and thermal energy sharing model of interconnected MGs with CHP and PV systems is built, in which CHP can operate in a hybrid mode by selecting the operating point flexibly. Moreover, the local subproblem of each MG is formulated and solved considering a comprehensive set of factors including the generating heat and power cost, trading cost with utility grid, trading electrical and thermal energy cost with other MGs, load characteristic, power consumption utility, thermal discomfort cost. In addition, a distributed optimization algorithm is used to solve the hybrid energy sharing problem, where the electrical and thermal energy prices can be obtained. Finally, the effectiveness of the proposed energy sharing method is demonstrated by a case study simulation.

From the perspective of transactive energy, the energy trading among interconnected microgrids (MGs) is promising to improve the economy and reliability of system operations. In this paper, a distributed energy management method for interconnected operations of combined heat and power (CHP)-based MGs with demand response (DR) is proposed. First, the system model of operational cost including CHP, DR, renewable distributed sources, and diesel generation is introduced, where the DR is modeled as a virtual generation unit. Second, the optimal scheduling model is decentralized as several distributed scheduling models in accordance with the number of associated MGs. Moreover, a distributed iterative algorithm based on subgradient with dynamic search direction is proposed. During the iterative process, the information exchange between neighboring MGs is limited to Lagrange multipliers and expected purchasing energy. Finally, numerical results are given for an interconnected MGs system consisting of three MGs, and the effectiveness of the proposed method is verified.

In order to facilitate the local sharing of renewable energy, an energy sharing management method of multiple microgrids (MGs) with a battery energy storage system (BESS) and renewable energy sources (RESs) is developed. First, a virtual entity named the energy sharing provider (ESP), which acts as an agent for MGs, is introduced to minimize the power loss cost. Second, a distributed optimal model and a two-level iterative algorithm for the MGs and ESP are proposed, which minimize the total operation cost including purchasing electricity cost, energy storage cost and power loss cost. Based on the energy sharing framework, considering the local objectives of MGs and the objective of ESP, the optimal scheduling can be achieved through the bidirectional interaction between MGs and ESP. During the optimization, the shared information between MGs and ESP is limited to expected exchange power, which protects the privacy of MGs and ESP. Finally, the effectiveness of the proposed model and algorithm in different scenarios is verified through a case study.

With the development of microgrids (MGs), interconnected operation of multiple MGs is becoming a promising strategy for the smart grid. In this paper, a privacy-preserving distributed optimal scheduling method is proposed for the interconnected microgrids (IMG) with a battery energy storage system (BESS) and renewable energy resources (RESs). The optimal scheduling problem is modeled to minimize the coalitional operation cost of the IMG, including the fuel cost of conventional distributed generators and the life loss cost of BESSs. By using the framework of the alternating direction method of multipliers (ADMM), a distributed optimal scheduling model and an iteration solution algorithm for the IMG is introduced; only the expected exchanging power (EEP) of each MG is required during the iterations. Furthermore, a privacy-preserving strategy for the sharing of the EEP among MGs is designed to work with the mechanism of the distributed algorithm. According to the security analysis, the EEP can be delivered in a cooperative and privacy-preserving way. A case study and numerical results are given in terms of the convergence of the algorithm, the comparison of the costs and the implementation efficiency.