This article investigates the optimal planning of centralized charging and multipoint distribution network that includes centralized battery charging stations (CBCSs) and battery distribution stations (BDSs), which considers the battery logistics system. Based on the analysis of the characteristics of travel chains of swapping electric vehicles (SEVs), a time-space load forecasting model of SEVs is proposed. Then, based on function and characteristic analysis, operation models of CBCS, BDS, and the battery logistics system are built. Afterward, the sizing models of CBCSs and BDSs are established. Then, a double-level planning model of CBCSs considering battery logistics system is proposed. In the first level planning, the sizes and locations of CBCSs and BDSs are planned, and in the second-level planning, the economy of battery logistics company is optimized. Finally, the CPLEX toolbox and the Bellman–Ford algorithm are used to solve the planning model. Taking the actual situation of a certain region as the planning area, the feasibility and effectiveness of the method proposed in this article are verified.

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.

Load frequency control (LFC) system may be destroyed by false data injection attacks (FDIAs) and consequently the security of the power system will be impacted. High-efficiency FDIA detection can reduce the damage and power loss to the power system. This paper defines various typical and hybrid FDIAs, and the influence of several FDIAs with different characteristics on the multi-area LFC system is analyzed. To detect various attacks, we introduce an improved data-driven method, which consists of fuzzy logic and neural networks. Fuzzy logic has the features of high applicability, robustness, and agility, which can make full use of samples. Further, we construct the LFC system on MATLAB/Simulink platform, and systematically simulate the experiments that FDIAs affect the LFC system by tampering with measurement data. Among them, considering the large-scale penetration of renewable energy with intermittency and volatility, we generate three simulation scenarios with or without renewable energy generation. Then, the performance for detecting FDIAs of the improved method is verified by simulation data samples.

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.

As the cyber-physical system is developed into cyber-physical-social system, the importance of social factors is growing in the interaction between electric vehicle (EV) and power system. This paper aims to develop an incentive-compatible mechanism to coordinate multi-agent orderly charging. Firstly, the travel behaviors of EV cluster are simulated based on Monte Carlo sampling, and the load transfer model considering various social factors is constructed. Then, an orderly charging mechanism involving multiple agents based on Nash bargaining theory is proposed. In the first stage, the total profit of electric vehicle user, power grid company (PGC), and charge station operator is maximized. In the second stage, the revenue of each agent after participating in the cooperation is improved by transfer payment. Next, the carbon trading mechanism is applied in the incentive compatibility model, and the revenue of PGC participating in the carbon market under different scenarios and constraints are calculated. Finally, by comparing with the existing model, the simulation results show that the proposed multi-agent coordinated orderly charging model can reduce the pressure on the power grid caused by the randomness of EV travel, and through a fair profit distribution mechanism, it can maximize the social benefits of the coalition while increasing the revenue of each agent.

Distributed economic dispatch (ED) of microgrids (MGs) has been extensively investigated in recent years. However, most of these works focus either on the islanded or grid-connected mode of the MG, assume unified buying and selling prices, and suffer high communication and iteration burdens, which limit their practical application and real-time performance. To avoid the above limitations, a distributed learning-based ED (DisLED) algorithm is proposed for MGs considering the operational mode switch and differentiated buying and selling prices to achieve real-time optimization. To this end, analytical solutions to the MG ED problem under different operational modes categorized according to the grid-connection condition and MG power exchange direction with the main grid are first derived. Then, distributed deep neural networks and a dynamic mode-switching-oriented consensus algorithm are employed to obtain the optimal power generated by dispatchable generators (DGs) and exchanged with the main grid. In addition, a local feedback mechanism is proposed to meet the plug-and-play requirement of the MG. Case studies in 6-DG and 54-DG MG systems demonstrate the effectiveness and efficiency of the DisLED algorithm.