Coordinated optimization method, system, equipment and storage medium for electric-thermal integrated energy system

Abstract

The present invention provides a coordinated optimization method, system, equipment, and storage medium for an electric-thermal integrated energy system. The method includes: obtaining real-time electric-thermal integrated energy system parameters; The power generation power of the power system, thermal system and coupling device of the thermal integrated energy system; input the generated power into the pre-trained optimal scheduling model based on the SAC framework, output scheduling actions, and form an electric-thermal integrated energy system coordination strategy. Scheduling actions can be given directly through the trained policy network, without the need for traditional nonlinear overall iterative solutions, the calculation speed has been significantly improved, and the calculation efficiency is higher.

Description

technical field [0001] The invention relates to the field of optimal dispatching of comprehensive energy systems, in particular to a coordination and optimization method, system, equipment and storage medium for electric-thermal comprehensive energy systems. Background technique [0002] In the context of Energy Internet, in order to further improve energy utilization, alleviate energy crisis, and break through the traditional energy system structure and industry barriers, it has become an effective way to study the coordination and optimization of electric-thermal integrated energy systems. [0003] The electric-thermal integrated energy system is conducive to promoting the consumption of renewable energy and improving energy utilization by utilizing the complementary characteristics of heat and electricity. Nowadays, the solution to the optimization problem of the electric-thermal integrated energy system mainly includes the traditional nonlinear method such as the particl...

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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