Acting as a key to future environmentally friendly transportation systems, electric vehicles (EVs) have attached importance to develop fast charging technologies to accomplish the requirement of vehicle users. However, fast charging behaviors would cause degradations in EVs' batteries, as well as negative effects like new demand peak and feeder overloads to the connected distribution network, especially when plugging in large scale EVs. Decentralized coordination is encouraged and our goal is to achieve an optimal strategy profile for EVs in a decentralized way considering both the need of fast charging and reducing degradations in batteries and the distribution network. In this article, we innovatively model the EV fast charging problem as an optimization coordination problem subject to the coupled feeder capacity constraints in the distribution network. The need of fast charging is expressed by the total charging time, and the relative tendency to fully charge within the desired time period. We introduce a ℓ₁-norm of the charging strategy which is non-convex to represent the total charging time, and apply the ℓ₁-norm minimization to approximate the sparse solution of ℓ₁-norm minimization. The shorter the charging horizon is the stronger willing of fast charging the user has. The objective of the optimization problem tradeoffs the EVs' battery degradation cost, the load regulation in the distribution network, the satisfaction of charging and the total charging time, which is non-separable among individual charging behaviors. Even though alternating direction method of multipliers (ADMM) has been widely applied in distributed optimization with separable objective and coupled constraints, its decentralized scheme cannot be applied directly to the underlying non-separable EV charging coordination problem. Hence, a hierarchical algorithm based on ADMM is proposed such that the convergence to the optimal strategies is guaranteed under certain step-size parameter. Furthermore, a receding horizon based algorithm is proposed considering the forecast errors on the base demand and the EV arrival distribution. The results are demonstrated via some simulation results.

Carbon trading has emerged as an effective way to promote the renewable generation and sustainable energy development. Since carbon emissions are closely coupled to energy system, it is a challenge to design a market mechanism for joint energy and carbon trading to achieve better strategies. In this study, the energy management problem with a specific focus on joint trading in multi-microgrid system is investigated by utilizing a multi-agent deep reinforcement learning approach. Initially, a joint energy and carbon trading market is established and the dispatch optimization problem is formulated as a Markov decision process without modeling uncertainties accurately. This mechanism enables direct one-to-one energy transactions among all areas, avoiding the market clearing in traditional multi-party local energy trading markets. To enhance the learning efficiency and maintain agent privacy, an enhanced multi-agent proximal policy optimization (MAPPO) algorithm that incorporates a parameter sharing mechanism is introduced. Moreover, the recurrent neural networks (RNN) structure is leveraged to perform feature encoding for individual agents, which improves the overall feature extraction capability. Through comprehensive experiments involving various algorithms, the proposed approach reduce operating costs 14.86 $\%$ and carbon emissions 19.04 $\%$ compared with traditional MAPPO, which validates the effectiveness and performance benefits.

Network-induced packet losses and communication delays, as well as load fluctuations and uncertain wind power have a negative effect on the frequency regulation of power system. This paper is concerned with designing a robust load frequency control (LFC) scheme for a multi-area power system penetrated by wind power via sampled-data control. Firstly, considering the effects of the inertia reduction, packet losses, communication delays and measurement noise, a decentralized aperiodic sampled-data LFC model with time delay is constructed, where the maximum sampling interval is characterized by limiting the maximum count of successive packet losses (MCSPL). Secondly, by applying Lyapunov theory and linear matrix inequality technique, a new exponential stability condition for the modeled LFC system with given controller is proposed based on a combination method of Lyapunov-Krasovskii functional and loop function, which is dependent on delay and MCSPL. It can be used to find the delay margin and allowable MCSPL of the LFC system. Thirdly, based on the $\mathcal {L}_{2}$ -gain performance condition developed by the stability condition above, a design method of robust PI-type controller is proposed by maximizing an exponential decay rate (EDR). The larger EDR, the faster frequency response speed to cope with the effect of inertia reduction, but the worse robustness to load fluctuations, packet losses and delays, and vice versa. Finally, case studies are carried out by a one-area power system and a three-area power system with wind power. By comparing with the existing LFC schemes, simulation results show that the proposed LFC scheme for the tested system is more effective and has better robustness.