{"title":"Novel Metaheuristic Optimizers Based Load Shifting and Flexible Load Curve Techniques for Demand-side Load Management","authors":"Ashokkumar Parmar, P. Darji","doi":"10.1080/23080477.2023.2208398","DOIUrl":null,"url":null,"abstract":"ABSTRACT Supply-and-demand-side resource management and demand-side load management (DSLM) are important techniques for addressing power system uncertainties. Demand-side load management allows the load profile to be reshaped to reduce the peak demand and overall cost. Many demand-side load management problems have been solved using different demand response programmesprograms as well as conventional numeric and metaheuristic methods. However, it can be applied only toonly to a limited number of devices of certain types. Of the six direct load control demand response techniques for demand-side load management, the performance of the day-ahead load-shifting and flexible load curve DSLM techniques are compared in this study. These techniques can be used for a larger number of devices of more types. The demand-side load management problem is formulated as a minimization problem to achieve peak demand reduction and cost minimization. Novel metaheuristic optimizers are used to perform demand-side load management, and comparative analysis is conducted for the cost and peak load reduction. The simulation results are verified using the fmincon function of MATLAB. The simulation results indicate that the aforementioned algorithms can be applied to a larger number of devices of more types to achieve considerable savings by minimizing the cost and peak load demand. Moreover, the load-shifting demand-side load management technique is more beneficial from the system operator’s perspective than from the customer’s perspective. In contrast, the flexible load curve demand-side load management technique is more beneficial from the customer’s perspective. GRAPHICAL ABSTRACT","PeriodicalId":53436,"journal":{"name":"Smart Science","volume":"11 1","pages":"538 - 567"},"PeriodicalIF":2.4000,"publicationDate":"2023-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23080477.2023.2208398","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
ABSTRACT Supply-and-demand-side resource management and demand-side load management (DSLM) are important techniques for addressing power system uncertainties. Demand-side load management allows the load profile to be reshaped to reduce the peak demand and overall cost. Many demand-side load management problems have been solved using different demand response programmesprograms as well as conventional numeric and metaheuristic methods. However, it can be applied only toonly to a limited number of devices of certain types. Of the six direct load control demand response techniques for demand-side load management, the performance of the day-ahead load-shifting and flexible load curve DSLM techniques are compared in this study. These techniques can be used for a larger number of devices of more types. The demand-side load management problem is formulated as a minimization problem to achieve peak demand reduction and cost minimization. Novel metaheuristic optimizers are used to perform demand-side load management, and comparative analysis is conducted for the cost and peak load reduction. The simulation results are verified using the fmincon function of MATLAB. The simulation results indicate that the aforementioned algorithms can be applied to a larger number of devices of more types to achieve considerable savings by minimizing the cost and peak load demand. Moreover, the load-shifting demand-side load management technique is more beneficial from the system operator’s perspective than from the customer’s perspective. In contrast, the flexible load curve demand-side load management technique is more beneficial from the customer’s perspective. GRAPHICAL ABSTRACT
期刊介绍:
Smart Science (ISSN 2308-0477) is an international, peer-reviewed journal that publishes significant original scientific researches, and reviews and analyses of current research and science policy. We welcome submissions of high quality papers from all fields of science and from any source. Articles of an interdisciplinary nature are particularly welcomed. Smart Science aims to be among the top multidisciplinary journals covering a broad spectrum of smart topics in the fields of materials science, chemistry, physics, engineering, medicine, and biology. Smart Science is currently focusing on the topics of Smart Manufacturing (CPS, IoT and AI) for Industry 4.0, Smart Energy and Smart Chemistry and Materials. Other specific research areas covered by the journal include, but are not limited to: 1. Smart Science in the Future 2. Smart Manufacturing: -Cyber-Physical System (CPS) -Internet of Things (IoT) and Internet of Brain (IoB) -Artificial Intelligence -Smart Computing -Smart Design/Machine -Smart Sensing -Smart Information and Networks 3. Smart Energy and Thermal/Fluidic Science 4. Smart Chemistry and Materials