{"title":"Optimizing the flexible design of hybrid renewable energy systems","authors":"Ramin Giahi, C. MacKenzie, Chang-Fen Hu","doi":"10.1080/0013791X.2022.2028047","DOIUrl":null,"url":null,"abstract":"Abstract Engineering systems often operate for a long period of time under varying conditions. The system should be designed based on the best available information at the time of the decision. Designers should also account for future uncertainties in the initial design of the system. The initial design may or may not change as the future evolves and conditions change. The goal of this study is to optimize the design of a hybrid renewable energy system (HRES) to deliver electricity under highly uncertain demand. This research explores designing the hybrid system while taking into account uncertainties over a long period of time (i.e., 20 years in this study). The objective is to minimize the expected discounted cost of the HRES during the next 20 years. A design solution may also be flexible, which means that the design can be adapted or modified in the future to meet new scenarios. This article incorporates flexibility or capacity expansion into engineering design under long-range uncertainty when the objective function is evaluated via a Monte Carlo simulation. The value of expanding capacity is measured by comparing the cost without capacity expansion and cost with capacity expansion.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":"67 1","pages":"25 - 51"},"PeriodicalIF":1.0000,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Economist","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1080/0013791X.2022.2028047","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract Engineering systems often operate for a long period of time under varying conditions. The system should be designed based on the best available information at the time of the decision. Designers should also account for future uncertainties in the initial design of the system. The initial design may or may not change as the future evolves and conditions change. The goal of this study is to optimize the design of a hybrid renewable energy system (HRES) to deliver electricity under highly uncertain demand. This research explores designing the hybrid system while taking into account uncertainties over a long period of time (i.e., 20 years in this study). The objective is to minimize the expected discounted cost of the HRES during the next 20 years. A design solution may also be flexible, which means that the design can be adapted or modified in the future to meet new scenarios. This article incorporates flexibility or capacity expansion into engineering design under long-range uncertainty when the objective function is evaluated via a Monte Carlo simulation. The value of expanding capacity is measured by comparing the cost without capacity expansion and cost with capacity expansion.
Engineering EconomistENGINEERING, INDUSTRIAL-OPERATIONS RESEARCH & MANAGEMENT SCIENCE
CiteScore
2.00
自引率
0.00%
发文量
14
审稿时长
>12 weeks
期刊介绍:
The Engineering Economist is a refereed journal published jointly by the Engineering Economy Division of the American Society of Engineering Education (ASEE) and the Institute of Industrial and Systems Engineers (IISE). The journal publishes articles, case studies, surveys, and book and software reviews that represent original research, current practice, and teaching involving problems of capital investment.
The journal seeks submissions in a number of areas, including, but not limited to: capital investment analysis, financial risk management, cost estimation and accounting, cost of capital, design economics, economic decision analysis, engineering economy education, research and development, and the analysis of public policy when it is relevant to the economic investment decisions made by engineers and technology managers.