{"title":"Application of a multi-reservoir dynamic collaborative optimization strategy based on extrema marginal benefits for optimizing reservoir operation","authors":"Lingxi Li, Yonggang Wu, Xiaohui Shen","doi":"10.1016/j.energy.2025.135867","DOIUrl":null,"url":null,"abstract":"<div><div>Despite the numerous methods proposed for optimizing reservoir operation, few strategies effectively guarantee both solution optimality and efficiency. Therefore, this study proposes an extrema marginal benefits optimization method that rapidly maximizes benefits by adjusting water usage during periods of maximum and minimum marginal benefits. Given that the single-reservoir optimization model can result in a pseudo-optimal solution, this study builds on a rotational optimization model for individual reservoirs to introduce a multi-reservoir dynamic collaborative optimization strategy. This strategy targets various reservoir-boundary challenges, clearly identifying and synchronously optimizing collaborating reservoirs, thereby significantly improving the operational efficiency and optimization quality of multi-reservoir systems. The operation results of the hydropower station indicate that extrema marginal benefits optimization guarantees optimal solutions with minimal time consumption, even under high-precision conditions, where the solution time is less than one-thousandth of that required by dynamic programming. In the operation scenarios for systems with four and ten reservoirs, the multi-reservoir dynamic collaborative optimization strategy based on extrema marginal benefits reached the theoretical optimum, with the solving time never exceeding 1 s, thereby proving its efficiency and practicality.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"324 ","pages":"Article 135867"},"PeriodicalIF":9.0000,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0360544225015099","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Application of a multi-reservoir dynamic collaborative optimization strategy based on extrema marginal benefits for optimizing reservoir operation
Despite the numerous methods proposed for optimizing reservoir operation, few strategies effectively guarantee both solution optimality and efficiency. Therefore, this study proposes an extrema marginal benefits optimization method that rapidly maximizes benefits by adjusting water usage during periods of maximum and minimum marginal benefits. Given that the single-reservoir optimization model can result in a pseudo-optimal solution, this study builds on a rotational optimization model for individual reservoirs to introduce a multi-reservoir dynamic collaborative optimization strategy. This strategy targets various reservoir-boundary challenges, clearly identifying and synchronously optimizing collaborating reservoirs, thereby significantly improving the operational efficiency and optimization quality of multi-reservoir systems. The operation results of the hydropower station indicate that extrema marginal benefits optimization guarantees optimal solutions with minimal time consumption, even under high-precision conditions, where the solution time is less than one-thousandth of that required by dynamic programming. In the operation scenarios for systems with four and ten reservoirs, the multi-reservoir dynamic collaborative optimization strategy based on extrema marginal benefits reached the theoretical optimum, with the solving time never exceeding 1 s, thereby proving its efficiency and practicality.
期刊介绍:
Energy is a multidisciplinary, international journal that publishes research and analysis in the field of energy engineering. Our aim is to become a leading peer-reviewed platform and a trusted source of information for energy-related topics.
The journal covers a range of areas including mechanical engineering, thermal sciences, and energy analysis. We are particularly interested in research on energy modelling, prediction, integrated energy systems, planning, and management.
Additionally, we welcome papers on energy conservation, efficiency, biomass and bioenergy, renewable energy, electricity supply and demand, energy storage, buildings, and economic and policy issues. These topics should align with our broader multidisciplinary focus.