Open Access

ARTICLE

Bio-Inspired Optimal Dispatching of Wind Power Consumption Considering Multi-Time Scale Demand Response and High-Energy Load Participation

by Peng Zhao¹, Yongxin Zhang¹, Qiaozhi Hua^2,*, Haipeng Li³, Zheng Wen⁴

1 School of Information Technology, Luoyang Normal University, Luoyang, 471022, China
2 Computer School, Hubei University of Arts and Science, Xiangyang, 441000, China
3 Capinfo Company, Ltd., Beijing, 100010, China
4 School of Fundamental Science and Engineering, Waseda University, Tokyo, 169-8050, Japan

* Corresponding Author: Qiaozhi Hua. Email:

(This article belongs to the Special Issue: Bio-inspired Computer Modelling: Theories and Applications in Engineering and Sciences)

Computer Modeling in Engineering & Sciences 2023, 134(2), 957-979. https://doi.org/10.32604/cmes.2022.021783

Received 04 February 2022; Accepted 24 March 2022; Issue published 31 August 2022

Abstract

Bio-inspired computer modelling brings solutions from the living phenomena or biological systems to engineering domains. To overcome the obstruction problem of large-scale wind power consumption in Northwest China, this paper constructs a bio-inspired computer model. It is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy load. First, the principle of wind power obstruction with the involvement of a high-energy load is examined in this work. In this step, highenergy load model with different regulation characteristics is established. Then, considering the multi-time scale characteristics of high-energy load and other demand-side resources response speed, a multi-time scale model of coordination optimization is built. An improved bio-inspired model incorporating particle swarm optimization is applied to minimize system operation and wind curtailment costs, as well as to find the most optimal energy configuration within the system. Lastly, we take an example of regional power grid in Gansu Province for simulation analysis. Results demonstrate that the suggested scheduling strategy can significantly enhance the wind power consumption level and minimize the system’s operational cost.

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Keywords

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