{"title":"基于自适应动态编程的数据驱动型实时家庭能源管理系统","authors":"","doi":"10.1016/j.epsr.2024.111055","DOIUrl":null,"url":null,"abstract":"<div><p>Real-time optimal control is crucial for the efficacy of Home Energy Management Systems (HEMS) in residential settings during actual operation. The time-varying and nonlinear nature of smart households — characterized by fluctuations in renewable energy generation, real-time electricity pricing, and load consumption — presents substantial challenges for both prediction and real-time control within HEMS. To tackle these issues, this paper introduces a real-time optimal control algorithm, augmented by predictive scheduling for HEMS. More specifically, the proposed real-time HEMS framework integrates an adaptive dynamic programming (ADP) algorithm, which is complemented by predictions of renewable energy generation and load consumption. Initially, data-driven methodologies generate accurate forecasts using available data collected and processed in real time. Gated Recurrent Unit (GRU) neural networks utilizing a range of data inputs such as electricity prices, battery charge/discharge rates, load consumption, and renewable energy generation, the system computes the optimal performance index function. Following this, we employ the ADP algorithm to reduce total electricity costs. This paper confirms the convergence properties of the value iteration ADP algorithm., demonstrating a monotonic approach of the iterative performance index function towards the optimal solution. The efficacy of the proposed algorithm is supported by numerical experiments, which verify its that solar energy efficiency has increased to 98% and electricity costs have been reduced by 64%.</p></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378779624009404/pdfft?md5=ec4c3a79e6720abbba529c036688eff2&pid=1-s2.0-S0378779624009404-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Data-driven real-time home energy management system based on adaptive dynamic programming\",\"authors\":\"\",\"doi\":\"10.1016/j.epsr.2024.111055\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Real-time optimal control is crucial for the efficacy of Home Energy Management Systems (HEMS) in residential settings during actual operation. The time-varying and nonlinear nature of smart households — characterized by fluctuations in renewable energy generation, real-time electricity pricing, and load consumption — presents substantial challenges for both prediction and real-time control within HEMS. To tackle these issues, this paper introduces a real-time optimal control algorithm, augmented by predictive scheduling for HEMS. More specifically, the proposed real-time HEMS framework integrates an adaptive dynamic programming (ADP) algorithm, which is complemented by predictions of renewable energy generation and load consumption. Initially, data-driven methodologies generate accurate forecasts using available data collected and processed in real time. Gated Recurrent Unit (GRU) neural networks utilizing a range of data inputs such as electricity prices, battery charge/discharge rates, load consumption, and renewable energy generation, the system computes the optimal performance index function. Following this, we employ the ADP algorithm to reduce total electricity costs. This paper confirms the convergence properties of the value iteration ADP algorithm., demonstrating a monotonic approach of the iterative performance index function towards the optimal solution. The efficacy of the proposed algorithm is supported by numerical experiments, which verify its that solar energy efficiency has increased to 98% and electricity costs have been reduced by 64%.</p></div>\",\"PeriodicalId\":50547,\"journal\":{\"name\":\"Electric Power Systems Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0378779624009404/pdfft?md5=ec4c3a79e6720abbba529c036688eff2&pid=1-s2.0-S0378779624009404-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electric Power Systems Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0378779624009404\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electric Power Systems Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378779624009404","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Data-driven real-time home energy management system based on adaptive dynamic programming
Real-time optimal control is crucial for the efficacy of Home Energy Management Systems (HEMS) in residential settings during actual operation. The time-varying and nonlinear nature of smart households — characterized by fluctuations in renewable energy generation, real-time electricity pricing, and load consumption — presents substantial challenges for both prediction and real-time control within HEMS. To tackle these issues, this paper introduces a real-time optimal control algorithm, augmented by predictive scheduling for HEMS. More specifically, the proposed real-time HEMS framework integrates an adaptive dynamic programming (ADP) algorithm, which is complemented by predictions of renewable energy generation and load consumption. Initially, data-driven methodologies generate accurate forecasts using available data collected and processed in real time. Gated Recurrent Unit (GRU) neural networks utilizing a range of data inputs such as electricity prices, battery charge/discharge rates, load consumption, and renewable energy generation, the system computes the optimal performance index function. Following this, we employ the ADP algorithm to reduce total electricity costs. This paper confirms the convergence properties of the value iteration ADP algorithm., demonstrating a monotonic approach of the iterative performance index function towards the optimal solution. The efficacy of the proposed algorithm is supported by numerical experiments, which verify its that solar energy efficiency has increased to 98% and electricity costs have been reduced by 64%.
期刊介绍:
Electric Power Systems Research is an international medium for the publication of original papers concerned with the generation, transmission, distribution and utilization of electrical energy. The journal aims at presenting important results of work in this field, whether in the form of applied research, development of new procedures or components, orginal application of existing knowledge or new designapproaches. The scope of Electric Power Systems Research is broad, encompassing all aspects of electric power systems. The following list of topics is not intended to be exhaustive, but rather to indicate topics that fall within the journal purview.
• Generation techniques ranging from advances in conventional electromechanical methods, through nuclear power generation, to renewable energy generation.
• Transmission, spanning the broad area from UHV (ac and dc) to network operation and protection, line routing and design.
• Substation work: equipment design, protection and control systems.
• Distribution techniques, equipment development, and smart grids.
• The utilization area from energy efficiency to distributed load levelling techniques.
• Systems studies including control techniques, planning, optimization methods, stability, security assessment and insulation coordination.