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<ArticleSet>
<Article>
<Journal>
				<PublisherName>University of Tehran Press</PublisherName>
				<JournalTitle>Advances in Industrial Engineering</JournalTitle>
				<Issn>2783-1744</Issn>
				<Volume>56</Volume>
				<Issue>1</Issue>
				<PubDate PubStatus="epublish">
					<Year>2022</Year>
					<Month>01</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Proposing a New Mathematical Model for Optimizing the Purchase of Electricity Required by Large Consumers Based on Modern Portfolio Theory: A Case Study of the Iranian Electricity Market</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>87</FirstPage>
			<LastPage>113</LastPage>
			<ELocationID EIdType="pii">87337</ELocationID>
			
<ELocationID EIdType="doi">10.22059/aie.2022.339757.1829</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>Saeed</FirstName>
					<LastName>Khalili</LastName>
<Affiliation>Department of Industrial Management, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran.</Affiliation>

</Author>
<Author>
					<FirstName>Ebrahim</FirstName>
					<LastName>Abbasi</LastName>
<Affiliation>Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran.</Affiliation>

</Author>
<Author>
					<FirstName>Bardia</FirstName>
					<LastName>Behnia</LastName>
<Affiliation>Department of Industrial Engineering and Management, Rouzbahan Educational Institute, Sari, Iran.</Affiliation>

</Author>
<Author>
					<FirstName>Mohammad</FirstName>
					<LastName>Amirkhan</LastName>
<Affiliation>Department of Industrial Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2022</Year>
					<Month>02</Month>
					<Day>27</Day>
				</PubDate>
			</History>
		<Abstract>In deregulated electricity markets, the electricity consumer should optimally divide the necessary electrical energy into different markets such as cash markets with spot prices and bilateral contract markets. This study aims to design a model to optimally select the electrical energy portfolio to minimize purchase costs by considering a risk level. For this purpose, an optimization model is proposed through the modern portfolio theory (MPT), mean-variance analysis, and conditional value-at-risk (CVaR) for cost minimization and risk reduction in the electricity supply problem. The mean-variance and CVaR were used as appropriate criteria for reducing unfavorable states in decision-making under uncertainty. Moreover, an artificial neural network was employed to predict the spot prices of the energy pool and the Iran Energy Exchange (IRENEX). The simulation was based on the actual data of Iran for 2018 and 2019. The entire statistical population was analyzed due to the small number of industrial subscribers, and the proposed model was implemented and executed in MATLAB software. Different sensitivity analyses proved the efficiency of the proposed models. According to the results, if an energy purchaser evades more risks, — i.e., the risk evasion coefficient increases a lower ratio of the electrical energy portfolio is allocated to cash markets, especially the IRENEX. In addition, the CVaR provided electricity markets with a more stable energy allocation than the mean-variance model</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Portfolio optimization</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Electrical Energy Market</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Uncertainty</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Modern Portfolio Theory (MPT)</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Conditional Value-at-Risk (CVAR)</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://aie.ut.ac.ir/article_87337_a0e8f33fa4cbef45c9967350c1713e2a.pdf</ArchiveCopySource>
</Article>
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