Multi-virtual power plant dynamic game transaction behavior analysis method based on finite rationality

Abstract

The invention discloses a multi-virtual-power-plant dynamic game transaction behavior analysis method based on finite rationality, and the method comprises: firstly enabling each virtual power plant bidding main body to fully consider the target demands of the virtual power plant bidding main body at the development stage, and researching the dynamic pricing behaviors of an upper-layer operator; secondly, performing transaction behavior analysis on the multi-virtual power plant by adopting different transaction target modeling, performing evolutionary learning on finite rational transaction behavior information by adopting a particle swarm algorithm, and further improving a self target by learning a competitor strategy so as to gradually optimize the self target; and finally, researching amulti-virtual power plant dynamic game calculation process, and proposing a dynamic game particle swarm optimization algorithm to be combined with an optimization toolbox to solve the game model. Thesolution of the proposed dynamic game model has good convergence, and provides new ideas and references for the virtual power plant to formulate different transaction targets to participate in markettransactions.

Description

technical field [0001] The invention belongs to the technical field of transaction behavior analysis of multiple virtual power plants, and in particular relates to a dynamic game transaction behavior analysis method of multiple virtual power plants based on bounded rationality. Background technique [0002] Under the power development strategy of renewable energy and electric energy substitution, distributed power, energy storage, and responsive loads are vigorously promoted and applied at the distribution network level, making traditional large-scale power production and long-distance transmission gradually develop into distributed energy. However, its large-scale network access poses a huge challenge to the safe operation of the power grid. At present, scholars at home and abroad have done a lot of research on the generation scheduling and bidding models of virtual power plants from the perspective of a single stakeholder. However, with the changes in the development stra...

AI Technical Summary

Problems solved by technology

However, with the changes in the development strategy of the State Grid in recent years, more diversified social capital has poured into the power market, and each VPP may be operated by different stakeholders.

Driven by the autonomy and rationality of various stakeholders, each subject pursues the maximization of its own interests, and the traditional single-subject optimal scheduling method will be difficult to apply

However, most of the existing research focuses on the game relationship between VPPs under the premise of a given transaction price, but there are still the following problems: First, the virtual power plant does not consider the impact of the market transaction price, and only participates in the market operation as a price taker , it is still internal optimization of VPP in essence; the second is that the decision-making environment is too ideal, and most of them are game studies under the condition of complete rationality, which not only requires the subject to have perfect rationality, but also requires each subject to trust each other's rationality. hard to reach

At the same time, most of the existing literature studies are based on the traditional optimal bidding scheme with the goal of maximizing revenue or minimizing operating costs. However, in the future diversified market, various stakeholders have different development needs at different stages of development. Trading behaviors that only aim to maximize revenue or minimize operating costs cannot gain a bidding advantage in a perfectly competitive market

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