Interval Constrained Multi-Objective Optimization Scheduling Method for Island-Integrated Energy Systems Based on Meta-Learning and Enhanced Proximal Policy Optimization
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Published:2024-09-09
Issue:17
Volume:13
Page:3579
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ISSN:2079-9292
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Container-title:Electronics
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language:en
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Short-container-title:Electronics
Author:
Jia Dongbao1ORCID, Cao Ming1, Sun Jing2, Wang Feimeng2, Xu Wei1, Wang Yichen1
Affiliation:
1. School of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, China 2. School of Data Science, Qingdao University of Science and Technology, Qingdao 266101, China
Abstract
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.
Funder
the National Natural Science Foundation of China Lianyungang City Science and Technology Plan Project Jiangsu Education Department ‘QingLan Project’
Reference51 articles.
1. Chen, M.F., Ning, G.T., Yu, Y., Miu, S.W., and Fang, B. (2017, January 26–28). Research on application of integrated energy planning for island. Proceedings of the IEEE Conference on Energy Internet and Energy System Integration, Beijing, China. 2. Lin, J.H., Wu, Y.K., and Lin, H.J. (2017, January 25–29). Successful experience of renewable energy development in several offshore islands. Proceedings of the International Conference on Power and Energy Systems Engineering, Berlin, Germany. 3. A model-based predictive dispatch strategy for unlocking and optimizing the building energy flexibilities of multiple resources in electricity markets of multiple services;Tang;Appl. Energy,2022 4. Robust optimal scheduling for integrated energy systems based on multi-objective confidence gap decision theory;Dong;Expert Syst. Appl.,2023 5. Bazmohammadi, N., Anvari-Moghaddam, A., Tahsiri, A., Madary, A., Vasquez, J.C., and Guerrero, J.M. (2020). Stochastic Predictive Energy Management of Multi-Microgrid Systems. Appl. Sci., 10.
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