The Optimal Dispatching of Micro Grid Based on Improved Limit Learning Machine Under Source-Load Interactive Electric Market

EasyChair Preprint 8369

Abstract

In order to improve the absorption rate of photovoltaic output power and realize the optimal configuration of renewable energy under source-load interactive electric market. Based on the SAE-ELM algorithm, the output of photovoltaic units and load demand of the micro-grid were predicted by the historical data of the source and load in this paper. On this basis, taking the minimum generation cost as the objective function, and considering the constraints of the micro-grid system, the source-load interactive optimal scheduling model of the micro-grid was established, and the ant lion optimizer (ALO) was used to solve the scheduling model. The preliminary scheme of unit commitment and output plan is obtained, then the scheduling scheme is fine-tuned according to the data of real-time scheduling stage. The simulation results show that the proposed optimal scheduling strategy can improve the photoelectric consumption rate, reduce the amount of abandoned solar power by about 41%, improve the economic benefit of micro grid, the cost can be saved about 33% compared with the conventional scheduling method. The micro grid optimal dispatching method proposed in this paper has certain reference value for guiding the power sales of micro grid in the open power market.

Keyphrases: Micro-grid, SAE-ELM algorithm, optimal scheduling, source- load interactive, the ant lion optimizer -ALO

@booklet{EasyChair:8369,
  author    = {Wenzhe Zhang and Liang Qiao and Zhicheng Yu and Zhenyu Han and Tao Wang},
  title     = {The Optimal Dispatching of Micro Grid Based on Improved Limit Learning Machine Under Source-Load Interactive Electric Market},
  howpublished = {EasyChair Preprint 8369},
  year      = {EasyChair, 2022}}