Comprehensive energy system optimization method and system in multiple uncertain environments

Through the TD3 and MPC collaborative optimization framework and the hybrid priority experience replay mechanism, the problems of conservative operation strategies and decision reliability caused by multiple uncertainties in the integrated energy system are solved, and a more efficient and stable optimization effect is achieved.

CN120822667AActive Publication Date: 2025-10-21SHANDONG UNIV
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Patent Information

Application Number
CN202511315858.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-16
Publication Date
2025-10-21
Estimated Expiration
2045-09-16

AI Technical Summary

Technical Problem

Traditional integrated energy system optimization methods are unable to effectively deal with multiple uncertainties, resulting in overly conservative operating strategies, waste of resources, and decision reliability relying on the accuracy of the prediction model. Deep reinforcement learning has problems in the scheduling field, such as difficult parameter adjustment, sparse rewards, and insufficient stability.

Method used

The TD3 and MPC collaborative optimization framework is adopted, combined with a hybrid priority experience replay mechanism. Through dynamic adjustment of double-buffer sampling weights, the demonstration experience generated by MPC actors is learned first, multiple uncertainty association rules are independently explored, and the adaptive ability of the intelligent agent is improved.

Benefits of technology

It improves the operating efficiency and decision-making stability of the integrated energy system, simplifies the difficulty of parameter adjustment, enhances the adaptability to multiple uncertainties, and achieves faster convergence speed and better optimization results.

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Abstract

The invention relates to the technical field of integrated energy systems, and provides an integrated energy system optimization method and system in multiple uncertain environments, and the method comprises the steps: obtaining the state of an integrated energy system, and obtaining a multi-energy-flow equipment action through an actor network; wherein MPC actors are embedded into a double-delay depth deterministic strategy gradient architecture, an actor network and transfer tuples of the MPC actors are respectively stored in an agent experience playback pool and an expert experience playback pool, a priority is given to each sample according to a time sequence difference error of the samples in the two experience playback pools, and a mixing ratio is determined according to a training time step; in combination with the mixing ratio and the priority, the sampling probability of samples is calculated, and then the degree of dependence of updating of the comment network on an expert experience playback pool in the initial training stage is controlled to be high, and the degree of dependence on an agent experience playback pool in the later training stage is controlled to be high. And the performance is better in a multi-uncertainty coupled IES environment.
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