Some Properties for Adjustable Entropy Function Method and Its Application to Power Systems Dynamic Economic Dispatch with Wind Integrated

• Published in: Journal of electrical and computer engineering 2021, Vol.2021, p.1-12
• Main Author: Ren, Yaming
• Format: Article
• Language: English
• Subjects: Alternative energy sources; Approximation; Economic models; Electricity distribution; Energy storage; Entropy; Genetic algorithms; Global economy; Infinity; Mathematical models; Mathematical programming; Optimization; Parameters; Power dispatch; Power plants; Renewable resources; Scheduling; Solar energy; Volatility; Wind power
• ISSN: 2090-0147; 2090-0155
• DOI: 10.1155/2021/2497209

With the continuous development of the world economy, the development and utilization of environmentally friendly and renewable energy have become the trend in many countries. In this paper, we study the dynamic economic dispatch with wind integrated. Firstly, we take advantage of the positive and negative spinning reserve to deal with wind power output prediction errors in order to establish a dynamic economic dispatch model of wind integrated. The existence of a min function makes the dynamic economic dispatch model nondifferentiable, which results in the inability to directly use the traditional mathematical methods based on gradient information to solve the model. Inspired by the aggregate function, we can easily transform the nondifferentiable model into a smooth model when parameter p tends to infinity. However, the aggregate function will cause data overflow when p tends to infinity. Then, for solving this problem, we take advantage of the adjustable entropy function method to replace of aggregate function method. In addition, we further discuss the adjustable entropy function method and point out that the solution generated by the adjustable entropy function method can effectively approximate the solution of the original problem without parameter p tending to infinity. Finally, simulation experiments are given, and the simulation results prove the effectiveness and correctness of the adjustable entropy function method.