This letter proposes a novel energy sharing cloud (ESC) mechanism for a smart microgrid (MG) with renewable energy sources and energy storage. The proposed ESC can be seen as an energy sharing environment, which mainly consists of an energy service provider (ESP) and a lot of cloud users. The ESP will help cloud users build virtual prosumers in the form of energy services and realize energy sharing among multiple entities. The basic operation optimization model with energy services for cloud users and the secondary operation optimization model with generalized load for ESP are designed, respectively. The proposed ESC mechanism is tested on a simple smart MG with cloud users. The simulation results reveal the excellent performance of the proposed sharing mechanism for smart MG.

In the interaction between electric vehicle (EV) and grid, research on the coordinated optimization of different EV aggregators to reduce the adverse effect of EVs uncertainty is of great significance. This paper commits to formulating an imbalanced liability trading cooperation platform among multiple EV aggregators in real-time (RT) market. First, we propose a joint bidding model of multiple EV aggregators in RT market from the individual perspective. Each aggregator optimizes its internal bidding strategy and external imbalanced liability trading simultaneously. Next, considering the self-interest of each EV aggregator, the cooperative model is built based on general Nash bargaining (GNB) theory. To promote EV aggregators to trade but avoid over-dependence, each EV aggregator’s bargaining power is formulated by both individual trading quantity and trading dependence. Then, we decompose the model into two subproblems and introduce the neurodynamic-based algorithm for distributed solving. Finally, numerical results show that the proposed coordinated model can reduce the adverse effect of EVs’ uncertainties on grid power balance to some extent, and realize cost saving or even profit earning for aggregators.

With the energy transition, the cyber-physical system (CPS) is expanded to the cyber-physical-social system (CPSS), and connected by the communication network, which is introduced in the form of a highly reliable cellular star network in this paper. Due to the uncertainties of wind turbine/photovoltaic power outputs, an improved rolling ultra-short-term forecast method with high reliability is proposed, which is based on long short-term memory networks, and the superiority is proved by comparing with multi-layer perception neural networks, linear regression, support vector regression and random forest. Then, two stochastic optimization models are constructed, one is based on CPS, the other is based on CPSS. As for social factors, electric vehicle aggregator is stimulated by cost to actively participate in the dispatch operation. Moreover, in order to ensure the stable operation of the smart energy system, the frequency deviation of automatic generation control system is studied as one security constraint. The optimal results display that social factors can not only make the system more economic and flexible, but also further increase the consumption of RE. Moreover, accurate forecast results can improve the performance of optimization dispatch operations.