Interval Constrained Multi-Objective Optimization Scheduling Method for Island-Integrated Energy Systems Based on Meta-Learning and Enhanced Proximal Policy Optimization

Multiple uncertainties from source–load and energy conversion significantly impact the real-time dispatch of an island integrated energy system (IIES). This paper addresses the day-ahead scheduling problems of IIES under these conditions, aiming to minimize daily economic costs and maximize the outp...

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Vydané v:Electronics (Basel) Ročník 13; číslo 17; s. 3579
Hlavní autori: Jia, Dongbao, Cao, Ming, Sun, Jing, Wang, Feimeng, Xu, Wei, Wang, Yichen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.09.2024
Predmet:
Accuracy
Alternative energy sources
Constraints
Cooling
Costs
Decomposition
Economic impact
Efficiency
Electricity
Energy conversion
Energy resources
Evolutionary algorithms
Fuzzy sets
Genetic algorithms
Integrated energy systems
Learning
Multiple objective analysis
Optimization
Pareto optimization
Planning
Real time
Renewable resources
Scheduling
Systems stability
Uncertainty
Wave power
Wind power
ISSN:2079-9292, 2079-9292
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Shrnutí:Multiple uncertainties from source–load and energy conversion significantly impact the real-time dispatch of an island integrated energy system (IIES). This paper addresses the day-ahead scheduling problems of IIES under these conditions, aiming to minimize daily economic costs and maximize the output of renewable energies. We introduce an innovative algorithm for Interval Constrained Multi-objective Optimization Problems (ICMOPs), which incorporates meta-learning and an improved Proximal Policy Optimization with Clipped Objective (PPO-CLIP) approach. This algorithm fills a notable gap in the application of DRL to complex ICMOPs within the field. Initially, the multi-objective problem is decomposed into several single-objective problems using a uniform weight decomposition method. A meta-model trained via meta-learning enables fine-tuning to adapt solutions for subsidiary problems once the initial training is complete. Additionally, we enhance the PPO-CLIP framework with a novel strategy that integrates probability shifts and Generalized Advantage Estimation (GAE). In the final stage of scheduling plan selection, a technique for identifying interval turning points is employed to choose the optimal plan from the Pareto solution set. The results demonstrate that the method not only secures excellent scheduling solutions in complex environments through its robust generalization capabilities but also shows significant improvements over interval-constrained multi-objective evolutionary algorithms, such as IP-MOEA, ICMOABC, and IMOMA-II, across multiple multi-objective evaluation metrics including hypervolume (HV), runtime, and uncertainty.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13173579