Currently, small islands are facing an energy supply shortage, which has led to considerable concern. Establishing an island microgrid is a relatively good solution to the problem. However, high investment costs restrict its application. In this paper, micro pumped storage (MPS) is used as an energy storage system (ESS) for islands with good geographical conditions, and deferrable appliance is treated as the virtual power source which can be used in the planning and operational processes. Household acceptance of demand response (DR) is indicated by the demand response participation degree (DRPD), and a sizing optimization model for considering the demand response of household appliances in an island microgrid is proposed. The particle swarm optimization (PSO) is used to obtain the optimal sizing of all major devices. In addition, the battery storage (BS) scheme is used as the control group. The results of case studies demonstrate that the proposed method is effective, and the DR of deferrable appliances and the application of MPS can significantly reduce island microgrid investment. Sensitivity analysis on the total load of the island and the water head of the MPS are conducted.

Abstract. Electric vehicles connected to the electricity grid will have a great impact on the power system, especially with the high penetration of electric vehicles, the safety and stability of the power system could be threatened. In order to accurately simulate the electric taxi operation behavior and load characteristic, this paper proposes a mothed based on multi-agent technique and reinforcement learning algorithm to study the operation behavior of electric taxis. Different charging strategies on the operation of electric taxi are studied. Based on the charging strategy of shortest path, a charging guidance strategy model is proposed, which considers the charging distance, the charging queue time and the charging equilibrium degree of the charging equipment. The simulation results show that the electric taxi adopting the charging guidance strategy will not only decrease the charging queue time, improve the operation time and income of the electric taxi, but also help improve the equilibrium degree and utilization rate of the charging equipment, reduce the power grid loss and voltage offset.

This paper studies the optimal decision-making problem for a plug-in electric taxi (PET) in a time-varying complex environment, i.e., a passenger environment, charging station environment, traffic environment, and taxi company management system, in order to maximize PET profit in a short-term operating cycle. First, this problem is formulated as a sequential decision-making problem composed of multiple decision slots. Then, to make the model more practical, the model is divided into two parts: an external environment and an electric taxi model for refinement. The uncertainty and time-varying characteristics of four environmental aspects, including passengers, charging stations, traffic, and taxi company management systems, are analysed and modelled. The transitions between adjacent processes and the environmental feedback of each process are modelled by further subdividing both the serving process and the charging process of the PET into multiple subprocesses, including cruising, carrying passengers, driving to the charging station, queueing before charging, and connecting to the power grid for charging. There are several uncertain factors in the sequential decision-making process for the PET, which leads to difficulty in solving the problem. To address this difficulty, the model-free algorithm SARSA is chosen. Finally, the effectiveness of the proposed method is verified by simulation results.INDEX TERMS Plug-in electric taxi, decision making, uncertainty, SARSA algorithm, load modelling.

With the continuous deepening of China‘s electricity market reform, the problem that units with different costs compete in the same plat has become increasingly prominent. In the transitional period of market reform without capacity market, it is urgent to design a reasonable and effective subsidy mechanism for high-cost units to ensure their survival. This paper first summarizes the common subsidy mechanisms in China, and then introduces the design of subsidy mechanism for high-cost unit participation in the market in Guangdong, Zhejiang and Yunnan provinces. Finally, comparing the advantages and disadvantages of the above three subsidy mechanisms, the design idea of participating in market competition for high-cost units is given.

Abstract. China's electricity market reform is undergoing an upsurge. Different market models and market rules are gradually released. As an essential tool to research complex bidding behavior of market player under different market mechanisms, agent-based simulation is widely adopted. In this paper, two market models that implemented in Guangdong province, China, during the past two years including price spread and uniform clearing are concluded and modeled. And Roth-Erev reinforcement learning algorithm is used to model players bidding behavior. Numerical case focuses on the influence of models on the market profit of players. Results show that market model and its parameter setting have considerable influence on profit of players in the short term. But the influence becomes slight in the long term.