Since 2013, a series of air pollution prevention and control (APPC) measures have been promulgated in China for reducing the level of air pollution, which can affect regional short-term electricity power demand by changing the behavior of power users electricity consumption. This paper analyzes the policy system of the APPC measures and its impact on regional short-term electricity demand, and determines the regional short-term load impact factors considering the impact of APPC measures. On this basis, this paper proposes a similar day selection method based on the best and worst method and grey relational analysis (BWM-GRA) in order to construct the training sample set, which considers the difference in the influence degree of characteristic indicators on daily power load. Further, a short-term load forecasting method based on least squares support vector machine (LSSVM) optimized by salp swarm algorithm (SSA) is developed. By forecasting the load of a city affected by air pollution in Northern China, and comparing the results with several selected models, it reveals that the impact of APPC measures on regional short-term load is significant. Moreover, by considering the influence of APPC measures and avoiding the subjectivity of model parameter settings, the proposed load forecasting model can improve the accuracy of, and provide an effective tool for short-term load forecasting. Finally, some limitations of this paper are discussed.
Abstract. The increasing integration of high penetration of wind power into power systems have created great challenges for stable operation of the power system. It is important to design a reasonable demand response scheme (DRS) to make decision the optimal load relief, specially, for the wind power suppliers (WPS) and their customers (WPSC). Therefore, this paper proposes a demand response scheme for wind integrated power system based on mechanism design theory. Firstly, we derive the outage cost function and incentive function modeled by the two variables: the customers' type parameter and the location value. Secondly, incorporate customers' type parameter and locational flexibility into the DRS, and based on game theory, a demand response scheme with incentive compatible constraint and participation constraint is designed to maximize the benefit both the wind power suppliers and their customers. The implication of demand response scheme is to design an incentive structure that encourages customers to sign up for the right scheme and reveal their true value of load reduction. Finally, the eight-bus system example can demonstrate the effectiveness of the proposed method.
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