According to the energy policy, which encourages local consumption of photovoltaic (PV) energy, the energy sharing among neighboring PV prosumers is proved to be a more effective way compared with independent operations of each prosumer. In this paper, an energy storage (ES)-equipped energy-sharing provider (ESP) is proposed to facilitate the energy sharing of multiple PV prosumers. With the help of the ESP, the autonomous PV prosumers can be formed as an energy-sharing network, and the energy-sharing activities can be categorized as direct sharing and buffered sharing. First, with the assistance of the ES, a day-ahead scheduling model of the ESP is built to increase the operation profit and improve the net power profile of the energy-sharing network, which considers the uncertainty of PV energy, electricity prices, and prosumers' load via stochastic programming. Moreover, to further increase the energy sharing, a real-time demand response model based on a Stackelberg game is presented to coordinate the energy consumption behavior of prosumers by using internal prices. Finally, through a practical case study, the effectiveness of the method is verified in terms of improving the economic benefits and PV energy sharing.

The Photovoltaic–assisted Charging Station (PVCS) is regarded as one of the most promising charging facilities for future electric vehicle (EV) energy supplementation. In this paper, the operation mode and profitability of a commercialized PVCS are analyzed under the energy policy of China. In order to evaluate the long-term effectiveness of using the PVCS to provide guidance for the investors of the stations, a set of evaluation indexes is introduced, including the quality of service, the environmental and economic benefits, and the impacts on the grid. Furthermore, an easily-achieved charging strategy which considers the quality of service and the self-consumption of PV energy is proposed. Finally, an effectiveness evaluation for different operational scenarios of the PVCS is completed, based on the actual statistical data. The simulation and evaluation results indicate that the PVCS has the potential to produce satisfactory environmental/economic benefits and to reduce the impacts and dependence of an EV’s charging load on the grid.

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.

Motivated by the development of electric vehicles (EVs), this paper addresses the energy management problem for the PV-assisted charging station (PVCS) network. An hour-ahead optimization model for the operation of PVCS is proposed, considering the profit of the PVCS, the local consumption of the photovoltaic (PV) energy and the impacts on the grid. Moreover, a two-level feasible charging region (FCR) model is built to guarantee the service quality for EVs and learning-based decision-making is designed to assist the optimization of the PVCS in various scenarios. The multi-party energy management problem, including several kinds of energy flows of the PVCS network, is formulated as a non-cooperative game. Then, the strategies of the PVCSs are modeled as the demand response (DR) activities to achieve their own optimization goals and a two-level distributed heuristic algorithm is introduced to solve the problem. The simulation results show that the economic profit of the network is increased by 6.34% compared with the common time of use (TOU) prices approach. Besides, the percentage of the PV energy in total charging load (PPTCL) and load rate are promoted by 28.93% and 0.3125, respectively, which demonstrates the validity and practicability of the proposed method.