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The increasing sales of electric vehicles (EVs) in the market poses critical questions for the public on how to deal with millions of retired batteries (RBs). A number of studies have shown that second-life batteries (SLBs) from EVs usually have a considerable remaining capacity and hence still have additional utilization value. This article proposes a novel fuzzy inference system (FIS)-based planning framework for EV charging stations with wind, PV power, battery energy storage system and the utilization of SLBs (EVCS-WPE-SLBs system). In this framework, a narrowed and the most probable candidate node set for EVCS-WPE-SLBs system which makes the trade-off of several off-site factors from different fields that jointly determine the optimal planning results is obtained, including the installation and utilization requirements of SLBs, the transportation network features and power system properties. Numerical case studies are conducted on a coupled IEEE 33-bus distribution system and 25-node transportation system. According to the simulation results, the reasonable utilization of SLBs in the EVCS-WPE-SLBs system can reduce the total system cost while maintaining the normal operation of the system. Besides, a narrowed and most probable candidate node set not only reduces the search space and simplifies the optimization problem, but the results obtained are more practical according to the actual system conditions in real life.

Increasing interdependencies between power and gas systems and integrating large-scale intermittent renewable energy increase the complexity of energy management problems. This article proposes a model-free safe deep reinforcement learning (DRL) approach to find fast optimal energy flow (OEF), guaranteeing its feasibility in real-time operation with high computational efficiency. A constrained Markov decision process model is standardized for the optimization problem of OEF with a limited number of state and control actions and developing a robust integrated environment. Because state-of-the-art DRL algorithms lack safety guarantees, this article develops a soft-constraint enforcement method to adaptively encourage the control policy in the safety direction with non-conservative control actions. The overall procedure, namely the constrained soft actor-critic (C-SAC) algorithm, is off-policy, entropy maximization-based, sample-efficient, and scalable with low hyper-parameter sensitivity. The proposed C-SAC algorithm validates its superiority over the existing learning-based safety ones and OEF solution methods by finding fast OEF decisions with near-zero degrees of constraint violations. The proposed approach indicates its practicability for real-time energy system operation and extensions for other potential applications.

Distributed energy resource (DER) owners have many concerns when participating in virtual power plants (VPPs), including low returns and a limited variety of plans. Therefore, it lacks incentives for them to participate in VPPs. This paper proposes a flexible VPP assembly approach to recruit DERs by energy service providers (ESPs). The contributions of each ESP and DER to VPPs are evaluated separately as the basis for a fair payoff allocation. Rebates are adopted to incentivize DER owners to participate in the VPPs. Based on the above fundamentals, a multi-ESP VPP assembly and competition method are proposed and simulated, considering diverse preference behaviors and electricity price fluctuations. The existence of the solution to the proposed multi-ESP VPP rebate competition problem is discussed. Case studies are conducted to simulate a 35-VPP assembly and disassembly process and a 6-ESP rebate competition process. The results indicate that DER owners are motivated to participate in VPPs with high payoffs, and the payoffs of ESPs increase by attracting more customers.

This paper proposes a data-driven coordinated volt/var control (VVC) strategy for active distribution networks (ADN) with multi-microgrids, which can achieve online economic and secure operations under false data injection attacks (FDIA). Based on voltage and power measurements, the microgrid central controller (MGCC) obtains optimal reactive power supports at the point of common coupling (PCC) and sends them to the distribution system operator (DSO). The MGCC is formulated with a convolution neural network (CNN) to emulate the optimal behaviors in microgrids (MGs), which can reduce computational burdens and facilitate its online application. The DSO then performs centralized optimization to dispatch VVC devices and update voltages at PCC. A voltage sensitivity-based reactive power adjustment method is also developed to simplify the iterative optimization process between ADN and MGs without deteriorating the VVC performance in each MG. Finally, data integrity and privacy are protected through an encrypted communication process against FDIA. The GGH (Goldreich-Goldwasser-Halevi) encryption algorithm directly prevents attackers from accessing the original transmitted data, while the RSA (Rivest-Shamir-Adleman) digital signature algorithm helps detect malicious tampering with the ciphertext during communication. Numerical simulations on a modified IEEE 33-bus ADN with three EU 16-bus MGs verify the effectiveness of the proposed method in mitigating voltage violations, reducing voltage regulation costs and protecting data security.

An aggregation scheme is an effective transactive manner of Distributed Energy Resources (DER) spreading across distribution networks. Distributed approach locally achieves cost minimization of an aggregator and customers. The uncertainties of wholesale market price and rooftop PV output will impact on aggregator's scheduling decision and each customer's cost, while solar energy fluctuation can cause an overvoltage problem in distribution networks. However, the probability distributions of these uncertainties always have errors, even in emerging data-based methods. There is no stochastic method using real data with an out-of-sample guarantee suitable for this distributed approach so far to help an aggregator avoid price risk and manage customers’ energy against solar energy fluctuation. To address these unsolved issues, we propose a data-driven Wasserstein distributionally robust formulation of the aggregator's agent and customer's agent respectively. The Wasserstein metric is employed to construct the Wasserstein ambiguity set. The mathematical models are then reformulated equivalently to convex programming respectively so that the operating model can be solved by the off-the-shelf solver. To improve the efficiency of the distributed solving framework, an alternating optimization procedure (AOP) process is proposed to overcome the issue caused by binary variables in the alternating direction method of multipliers (ADMM). The proposed operation framework is verified on the modified IEEE 33-bus distribution network and realistic single-feeder LV network.

A modern electricity market is essentially a complex network, characterized by complicated interactions among cyber communications, physical systems, and social agents. Trading behavior modeling has always been complicated in the physical system-based market. In this paper, trading behavior modeling in the electricity market is solved by a data-driven method combining experimental economics and machine learning, called Hybrid Experimental Learning (HEL). Based on the historical and experiment simulated data, HEL models the trading behavior by a machine learning generative model which will be interpreted by a post hoc interpretation approach. Taking a simulated electricity market based on the trial spot market rule in Guangdong, China as an example, a generative adversarial network (GAN) is employed to generate the offering strategies of a gas generator. Local interpretable model-agnostic explanation (LIME) as a post hoc interpretation approach is applied to explain the relationship between the output of GAN and some of the inputs of HEL, which can be described as offering mechanisms for the gas generator.