An electric-heat integrated energy system coordination optimization method, system and equipment and a storage medium

Abstract

The invention provides an electric-heat integrated energy system coordination optimization method, system and equipment and a storage medium. The method comprises the steps of obtaining real-time electric-heat integrated energy system parameters; based on the parameters of the electric-thermal comprehensive energy system, respectively calculating the generated power of an electric power system, a thermodynamic system and a coupling device of the electric-thermal comprehensive energy system; and inputting the generated power into a pre-trained SAC framework-based optimization scheduling model, and outputting a scheduling action to form an electric-thermal integrated energy system coordination strategy. According to the invention, the scheduling action can be directly given through the trained strategy network, traditional nonlinear overall iterative solution does not need to be carried out, the calculation speed is remarkably improved, and the calculation efficiency is higher.

Description

Technical field [0001] The present invention relates to an integrated energy system optimization scheduling, and more particularly relates to an electrically - thermally integrated energy system coordination and optimization method, system, apparatus and a storage medium. Background technique [0002] In the context of the energy of the Internet, in order to further improve energy efficiency, ease the energy crisis, breaking the traditional energy architecture and trade barriers, an integrated electric energy systems research coordination optimization has become an effective way. [0003] - The integrated thermal energy systems through the use of the complementary nature of heat and electricity, and promoting the elimination on renewable energy, improve energy efficiency. Today on the power - optimization of the heat integrated energy system to solve the problem, including traditional methods for solving nonlinear such as particle swarm optimization, intelligent algorithms such a...

AI Technical Summary

Problems solved by technology

[0009] Its disadvantages are: (1) The particle swarm optimization algorithm has insufficient convergence, and it may even be difficult to converge

And it is easy to fall into the local optimal solution, and it is impossible to obtain the optimal solution for the economic dispatch of the electric-thermal integrated energy system, and the calculation accuracy is lacking, thus losing the meaning of multi-energy collaborative optimization

(2) The electrothermal integrated energy system with increasingly close coupling presents highly nonlinear characteristics. The calculation speed of the particle swarm algorithm is significantly reduced when solving the problem, and the calculation efficiency cannot meet the economic dispatch of the electrothermal integrated energy system.

Calculation based on the discrete action space and state space will lead to a significant drop in calculation accuracy;

[0017] (2) The Q-learning algorithm is not suitable for solving large-scale electric-thermal integrated energy system optimization problems

[0024] Its disadvantages are: (1) Compared with the Q learning method, it is more suitable for continuous control action scenes, but the continuous action space exploration is more difficult, and the exploration is difficult. It is difficult for DQN to use a specific strategy algorithm to ensure effective exploration of the state space. There may be a local optimal solution problem

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