With the large amount of distributed generation in use, the structure of the distribution system is increasingly complex. Therefore, it is necessary to establish a method to improve load reliability. Based on the reliability model of distributed generation, this paper investigates the time sequential simulation of a wind/solar/storage combined power supply system under off-grid operation. After classifying the load by power supply region, the load weight coefficient is established, which modifies the reliability index of the load point and system. The modified expected energy not supplied (EENS) is adopted as the objective function, and the particle swarm optimization algorithm is used to solving the optimal energy scheduling for improving the load reliability. Finally, the load reliability is calculated with a hybrid method. Using the IEEE-RBTS Bus 6 system as an example, the correctness and validity of the proposed method are verified as an effective way to improve load reliability.
Time‐of‐use (TOU) electricity prices are implemented based on the response characteristics of users. TOU prices guide the electricity consumption behaviour of users and make them actively respond to peak shaving and valley filling. In this paper, the conventional psychology response model of consumers is improved based on the history data of customer response to the TOU price. The paper defines four types of response load curves and proposes the response reliability indices. Additionally, it establishes the probability models of customer response. According to the characteristics of customer response, the sensitivity analysis of influencing factors and the risk analysis of customers are carried out according to the probability model of response. For instance, risk and characteristics analyses of the customer response were performed using the load data of residential areas in Nanjing city. The results indicate that the probability distribution of the customer response is approximate to the trapezoid distribution. The sequence of sensitivity coefficients of customer response factors is as follows: customer income, type of customer, and electrical equipment. The proposed method provides a scientific basis for electricity price implementers to accurately grasp the response behaviour of the user and establish a reasonable demand response strategy.
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