An Assistant Decision-making Method of User Side Resource Interactive Transaction Based on Scipy Solver and Genetic Algorithm

Abstract

In the current electricity market environment, this paper proposes a user-side resource interactive transaction decision-making method based on scipy solver and genetic algorithm, which significantly improves the user-side resource transaction volume and reduces the risk loss of e-commerce. Compared with traditional methods, this study classifies controllable load resources more accurately by introducing fuzzy C-means clustering method, which provides more reliable data support for auxiliary decision making. At the same time, the construction and solution of the two-layer programming model not only considers the peak-valley price strategy of e-commerce, but also comprehensively coordinates the user demand response, and realizes the maximization of income expectations and the minimization of transaction risks. In addition, scipy solver is used to solve the power consumption model, which further optimizes the user-side resource transaction. After testing, the method in this paper not only significantly improved the user-side resource transaction volume, but also made the power user satisfaction as high as 0.99, which fully demonstrated the significant effect of this research method in improving user satisfaction, and provided a strong support for the intelligent and sustainable development of the power market.