A study of parameter aggregation algorithms for virtual power plant terminal decentralized resource scheduling characteristics in alpine regions

Abstract

To address the “secondary dispatch” problem in alpine virtual power plants caused by uncertainties in decentralized resource allocation, we develop an algorithm for aggregating dispatch parameters of distributed resources to achieve real-time load-demand matching. Based on alpine power generation resources, we design a specialized virtual power plant structure and analyze its market trading applications. For the actual operation of the decentralized resources in the alpine virtual power plant, we determined the power provided by the alpine virtual power plant to the electric power system as well as the adjustable power capacity and other scheduling parameters, and then designed the dispatch model objective with the decentralized resource power and scheduling parameters based on the imitator dynamic algorithm. The model incorporates constraints based on these parameters to enable effective aggregation of adjustable power ranges for both individual resources and the entire virtual power plant, while ensuring compliance with all power constraints. This approach enhances scheduling flexibility and resolves the grid-side secondary dispatch issue. An improved ant colony algorithm based on continuous optimization was used to solve the aggregation parameters. Experimental results demonstrate superior solution performance, with the aggregated parameters increasing wind farm planned output by over 12 MW across different periods. This significantly boosts power delivery to the main grid, provides more stable supply, and improves virtual power plant revenue.