Millions of electric vehicles (EVs), especially plug-in hybrid EVs (PHEVs), will be integrated into the power grid in the near future. Due to their large quantity and complex charging behavior, the impact of substantial PHEVs charging on the power grid needs to be investigated. Since the charging behavior of PHEVs in a certain regional transmission network or a local distribution network is determined by different uncertain factors, their overall charging demand tends to be uncertain and in this situation probabilistic power flow (PPF) can be applied to analyze the impact of PHEVs charging on the power grid. However, currently there is no suitable model of the overall charging demand of PHEVs available for PPF calculations. In this paper, a methodology of modeling the overall charging demand of PHEVs is proposed. The proposed methodology establishes a single PHEV charging demand model, and then employs queuing theory to describe the behavior of multiple PHEVs. Moreover, two applications are given, i.e., modeling the overall charging demand of PHEVs at an EV charging station and in a local residential community, respectively. Comparison between PPF calculations and Monte Carlo simulation are made on a modified IEEE 30-bus system integrated with the two demand models proposed.

A novel method using particle swarm optimisation (PSO) is proposed for optimising parameters of controllers of a wind turbine (WT) with doubly fed induction generator (DFIG). The PSO algorithm is employed in the proposed parameter tuning method to search for the optimal parameters of controllers and achieve the optimal coordinated control of multiple controllers of WT system. The implementation of the algorithm for optimising the controllers' parameters is described in detail. In the analysis, the generic dynamic model of WT with DFIG and its associated controllers is presented, and the small signal stability model is derived; based on this, an eigenvalue-based objective function is utilised in the PSO-based optimisation algorithm to optimise the controllers' parameters. With the optimised controller parameters, the system stability is improved under both small and large disturbances. Furthermore, the fault ride-through capability of the WT with DFIG can be improved using the optimised controller. Simulations are performed to illustrate the control performance.

Two mathematical models for multiterminal voltage-sourced converter (VSC)-based HVDC are proposed in this paper. The first model assumes that all the converters are co-located in the same substation while the second model is a general one, in which DC network can be explicitly represented. For both models proposed, primary converters basically have the ability to independently control either active and reactive power or active power and voltage of the terminals while the secondary converter of the multiterminal VSC HVDC can be used to control terminal bus voltage and balance power exchange among the VSC converters. In addition, theoretic and numerical comparisons between the multiterminal VSC HVDC and the VSC FACTS controller-the Generalized Unified Power Flow Controller are also presented. Numerical examples are given on the IEEE 30-bus system, IEEE 118-bus system and IEEE 300-bus system.

Peer-to-peer (P2P) transactive energy trading has emerged as a promising paradigm towards maximizing the flexibility value of prosumers’ distributed energy resources (DERs). Despite reinforcement learning constitutes a well-suited model-free and data-driven methodological framework to optimize prosumers’ energy management decisions, its application to the large-scale coordinated management and P2P trading among multiple prosumers within an energy community is still challenging, due to the scalability, non-stationarity and privacy limitations of state-of-the-art multi-agent deep reinforcement learning (MADRL) approaches. This paper proposes a novel P2P transactive trading scheme based on the multi-actor-attention-critic (MAAC) algorithm, which addresses the above challenges individually. This method is complemented by a P2P trading platform that incentivizes prosumers to engage in local energy trading while also penalizes each prosumer’s addition to rebound peaks. Case studies involving a real-world, large-scale scenario with 300 residential prosumers demonstrate that the proposed method significantly outperforms the state-of-the-art MADRL methods in reducing the community’s cost and peak demand.

This paper investigates the real-time scheduling of residential appliances in the home energy management system (HEMS), including electric water heater (EWH), air conditioner (AC), clothes dryer (CD), electric vehicle (EV), photovoltaic cell (PV) and battery. A demand response (DR) mechanism is proposed to stimulate the household to participate in the DR services. The conditional value-at-risk (CVaR) is applied to make a trade-off between the expected costs and the risk of exposure to uncertainties of the electricity price and other factors. Fuzzy logic controller is utilized to determine the output power of the battery, and two sets of rules are designed to adjust the charging and discharging operations of the battery according to the DR request from the grid. Stochastic aspects of the electricity price, outdoor temperature, hot water usage, PV generation, and loads are considered. Simulation results show that the proposed DR mechanism can decrease the cost of the household considerably, and the proposed real-time scheduling algorithm can optimize the operation of appliances close to the ideal deterministic results, even when the gap between the forecast electricity price and the real price is large.