Industrial Flexible Load Scheduling Optimization Method Based on Probabilistic Demand Response Potential Evaluation

With the increasing penetration rate of new energy, the reliability and flexible operation ability of new power system are limited, and the application of demand response technology can effectively solve the contradiction of insufficient peak regulation of power grid. In order to solve the problem of how industrial flexible loads participate in system scheduling, this paper proposes an optimization method of industrial flexible coincidence scheduling based on probabilistic demand response potential assessment, and analyzes the typical industrial loads. Firstly, the basic idea of iterative adaptive clustering algorithm is introduced, and an improved fuzzy C-means clustering algorithm based on adaptive clustering center selection is proposed to generate typical daily load scenarios. Then, the load production characteristics of typical continuous and non-continuous production industries are studied, and their adjustable potential is analyzed. Secondly, the demand response potential evaluation method is established for two types of demand response types: peak cutting and valley filling. Finally, with the aim of minimizing the comprehensive operation cost of power system, the optimal scheduling model of industrial flexible load based on probabilistic demand response potential evaluation is established. Numerical results show that the proposed method is effective and reasonable.