A Day-Ahead Optimal Scheduling Method of Virtual Power Plant Considering Demand Response

Abstract

The invention discloses a day-ahead optimization scheduling method of a virtual power plant considering demand response. It includes the following steps: 1) Estimating the probability density parameters of renewable distributed power output based on historical information; 2) Calculating the response cost of the two types of demand response based on the change in revenue before and after the virtual power plant performs demand response; 3) Using the opportunity in the form of probability Constraints describe the uncertainty of renewable distributed power and demand response. Aiming at maximizing the revenue of virtual power plant, an optimal scheduling model based on chance constraints is established and solved using a particle swarm optimization algorithm based on microbial behavior mechanism. Integrating distributed power sources into the power grid in the form of a virtual power plant can break through the geographical limitations of microgrids and realize coordinated dispatching of distributed power sources in different regions, types, and capacities; particle swarm optimization based on microbial symbiosis mechanism is adopted The algorithm solves the optimization model, which significantly improves the global convergence and convergence speed of the algorithm.

Description

technical field [0001] The invention belongs to the field of smart grids, and particularly relates to a day-ahead optimization scheduling method for virtual power plants considering demand response. Background technique [0002] Nowadays, distributed power generation has received extensive attention due to its good environmental protection, reliability, flexibility and other characteristics, and the amount of distributed power generation in the distribution network is increasing day by day. However, while distributed power has the above advantages, it also needs to face the shortcomings of small capacity, large quantity, scattered access locations, and uncertainty of power generation. new challenges. At this stage, distributed power sources are usually connected to the distribution network in the form of micro-grids to achieve effective management of distributed power output. However, micro-grids are limited by geographical location, and are effective for large-scale and wi...

AI Technical Summary

Problems solved by technology

On the one hand, because the user load on the demand side will be affected by many uncertain factors such as production and life, emergencies, etc., the demand side response will show uncertainty. On the other hand, the output of renewable distributed power in distributed power Affected by environmental factors, there will also be randomness and uncertainty in output

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