Optimal control method of virtual power plant bidding based on igdt and demand response
A virtual power plant and demand response technology, applied in data processing applications, instruments, marketing, etc., can solve problems such as increasing problem complexity, difficulty in accurately describing the output probability of wind and solar, and declining economy.
Active Publication Date: 2022-03-04
SHANGHAI JIAOTONG UNIV +1
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However, probability-based chance-constrained programming and stochastic programming and other analysis methods are difficult to accurately describe the probability of wind and rain output, and need to generate a large number of scene samples to improve the reliability of the model, which will inevitably increase the complexity of the problem sharply; robust optimization Although the reliability of the system is improved, it leads to a decline in economy. Therefore, the results of robust optimization often have a certain degree of conservatism.
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[0069] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings to make the technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned herein.
[0070] The invention studies the bidding problem of the virtual power plant participating in the day-ahead market (DAM) and the real-time market (RM). First, with the goal of maximizing the revenue of the virtual power plant, a joint bidding model for the virtual power plant in the day-ahead market and the real-time market is established. After that, IGDT is used to deal with the uncertainty of wind power output and the day-ahead market clearing electricity price, so as to build a virtual power plant joint market bidding model based on information gap decision theory, and determine the virtual power plant's part...
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The invention discloses an optimal control method for virtual power plant bidding based on IGDT and demand response, including: establishing a virtual power plant deterministic bidding model and solving it; using the information gap decision theory to establish a system model; using the information gap decision theory to establish an uncertain According to different risk management strategies, select the corresponding decision-making strategy according to the required performance requirements in the information gap decision-making theoretical model; according to the selected decision-making strategy, solve the optimization result of the system model; determine the optimal virtual power plant Plan to contribute. It can integrate demand response resources, build an IGDT-based joint market bidding model for virtual power plants, and coordinate and dispatch internal resources to participate in the electricity market, thereby achieving economic optimization.
Description
technical field [0001] The invention relates to the technical field of virtual power plant bidding optimization control technology, in particular to an optimal control method of virtual power plant bidding based on IGDT and demand response. Background technique [0002] With the large-scale development and utilization of distributed energy and the gradual liberalization of the electricity market, the participation of distributed energy in the electricity market has become a major focus of future electricity market research. However, due to the strong uncertainty of distributed energy output and the small individual capacity, the unit cost of power generation is higher than that of traditional power generation resources. Therefore, distributed energy is less competitive when it participates in power market bidding alone. Virtual power plant (virtual power plant, VPP) is a new form of energy aggregation that can achieve reliable aggregation of distributed energy. It can not on...
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IPC IPC(8): G06Q10/04G06Q10/06G06Q30/02G06Q50/06
CPCG06Q10/04G06Q10/0635G06Q30/0206G06Q50/06Y02E40/70Y04S10/50Y04S50/14Y04S50/16
Inventor 李嘉媚艾芊孙东磊李雪亮李文升王男
Owner SHANGHAI JIAOTONG UNIV