Optimal, Reliable and Cost-Effective Framework of Photovoltaic-Wind-Battery Energy System Design Considering Outage Concept Using Grey Wolf Optimizer Algorithm-Case Study for Iran
In this paper, an optimal, reliable, and cost-effective framework for designing a renewable hybrid photovoltaic-wind-battery system is presented to minimize the total net present cost (TNPC) and to consider reliability constraint as loss of load probability (LPP) for the city of Ahvaz, Iran, conside...
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| Veröffentlicht in: | IEEE access 2019, Vol.7, p.182611-182623 |
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| creator | Naderipour, Amirreza Abdul-Malek, Zulkurnain Zahedi Vahid, Masoud Mirzaei Seifabad, Zahra Hajivand, Mohammad Arabi-Nowdeh, Saber |
| description | In this paper, an optimal, reliable, and cost-effective framework for designing a renewable hybrid photovoltaic-wind-battery system is presented to minimize the total net present cost (TNPC) and to consider reliability constraint as loss of load probability (LPP) for the city of Ahvaz, Iran, considering the components outage rate (COR). The decision variables include the number of photovoltaic panels, wind turbines, batteries, and the angle of the photovoltaic panel optimized by the grey wolf optimizer (GWO) algorithm. The performance of the proposed method is compared with the particle swarm optimization (PSO) method. The results of a system designed in different combinations with and without considering COR are evaluated. The simulation results confirm that the GWO algorithm is superior to the PSO method by yielding lower TNPC (1.199 M for GWO and 1.201 M for PSO) and better LPP (0.653% for GWO and 0.655% for PSO) for optimal combination (photovoltaic-battery system). The results also showed that a photovoltaic-wind combination is not the most cost-effective and reliable for the Ahvaz region, and the implementation of hybrid systems based on wind power is not cost-effective in this region. In addition, the results showed that considering COR gives the designers of these systems a more accurate view of the cost and reliability. Moreover, considering COR increases the cost of load supply and undermines the load reliability. |
| doi_str_mv | 10.1109/ACCESS.2019.2958964 |
| format | Article |
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The decision variables include the number of photovoltaic panels, wind turbines, batteries, and the angle of the photovoltaic panel optimized by the grey wolf optimizer (GWO) algorithm. The performance of the proposed method is compared with the particle swarm optimization (PSO) method. The results of a system designed in different combinations with and without considering COR are evaluated. The simulation results confirm that the GWO algorithm is superior to the PSO method by yielding lower TNPC (1.199 M for GWO and 1.201 M for PSO) and better LPP (0.653% for GWO and 0.655% for PSO) for optimal combination (photovoltaic-battery system). The results also showed that a photovoltaic-wind combination is not the most cost-effective and reliable for the Ahvaz region, and the implementation of hybrid systems based on wind power is not cost-effective in this region. In addition, the results showed that considering COR gives the designers of these systems a more accurate view of the cost and reliability. Moreover, considering COR increases the cost of load supply and undermines the load reliability.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2019.2958964</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Batteries ; cost/reliability assessment ; grey Wolf optimizer ; Hybrid photovoltaic-wind-battery systems ; Hybrid systems ; loss of load probability ; Optimization ; outage rate ; Particle swarm optimization ; Photovoltaic systems ; Reliability ; Reliability engineering ; System analysis and design ; System effectiveness ; Systems design ; Wind power ; Wind turbines</subject><ispartof>IEEE access, 2019, Vol.7, p.182611-182623</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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The decision variables include the number of photovoltaic panels, wind turbines, batteries, and the angle of the photovoltaic panel optimized by the grey wolf optimizer (GWO) algorithm. The performance of the proposed method is compared with the particle swarm optimization (PSO) method. The results of a system designed in different combinations with and without considering COR are evaluated. The simulation results confirm that the GWO algorithm is superior to the PSO method by yielding lower TNPC (1.199 M for GWO and 1.201 M for PSO) and better LPP (0.653% for GWO and 0.655% for PSO) for optimal combination (photovoltaic-battery system). The results also showed that a photovoltaic-wind combination is not the most cost-effective and reliable for the Ahvaz region, and the implementation of hybrid systems based on wind power is not cost-effective in this region. In addition, the results showed that considering COR gives the designers of these systems a more accurate view of the cost and reliability. Moreover, considering COR increases the cost of load supply and undermines the load reliability.</description><subject>Algorithms</subject><subject>Batteries</subject><subject>cost/reliability assessment</subject><subject>grey Wolf optimizer</subject><subject>Hybrid photovoltaic-wind-battery systems</subject><subject>Hybrid systems</subject><subject>loss of load probability</subject><subject>Optimization</subject><subject>outage rate</subject><subject>Particle swarm optimization</subject><subject>Photovoltaic systems</subject><subject>Reliability</subject><subject>Reliability engineering</subject><subject>System analysis and design</subject><subject>System effectiveness</subject><subject>Systems design</subject><subject>Wind power</subject><subject>Wind turbines</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUU1v2zAMNYYNWNH1F_QiYNc5k6wPW8fMS9sABTIsK3oUJJtylTlWJiktvL_VPzi7KYrxQuKR75Hgy7JLgheEYPl1Wder7XZRYCIXheSVFOxddlYQIXPKqXj_X_0xu4hxh6eoJoiXZ9nz5pDcXvdf0E_onTY9ID20qPYx5StroUnuEdBV0Ht48uE38hb9ePDJP_o-adfk925o8286JQgjWg0QuhFtx5hgj75DdN0wSQ3RtRDc0KHNMekOZqiBQ0J3cQavA4zo3vcWvdzi_kJAy77zwaWHfV7rCGibju2IrA9oHfTwKftgdR_h4jWfZ3dXq1_1TX67uV7Xy9u8YbhKudWiNIy0BcUSi6YyjFcWDJNAC9qYknNsSkF4KdnUI9RYwLYkhSBaGks4Pc_WJ93W6506hOlPYVReO_UC-NApHZJrelAcgAnJLKtwySrgFbbUct5iaqRlppy0Pp-0DsH_OUJMauePYZjOVwXjXJACi3kjPU01wccYwL5tJVjNZquT2Wo2W72aPbEuTywHAG-MStKClYT-Az7xpzs</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Naderipour, Amirreza</creator><creator>Abdul-Malek, Zulkurnain</creator><creator>Zahedi Vahid, Masoud</creator><creator>Mirzaei Seifabad, Zahra</creator><creator>Hajivand, Mohammad</creator><creator>Arabi-Nowdeh, Saber</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The decision variables include the number of photovoltaic panels, wind turbines, batteries, and the angle of the photovoltaic panel optimized by the grey wolf optimizer (GWO) algorithm. The performance of the proposed method is compared with the particle swarm optimization (PSO) method. The results of a system designed in different combinations with and without considering COR are evaluated. The simulation results confirm that the GWO algorithm is superior to the PSO method by yielding lower TNPC (1.199 M for GWO and 1.201 M for PSO) and better LPP (0.653% for GWO and 0.655% for PSO) for optimal combination (photovoltaic-battery system). The results also showed that a photovoltaic-wind combination is not the most cost-effective and reliable for the Ahvaz region, and the implementation of hybrid systems based on wind power is not cost-effective in this region. In addition, the results showed that considering COR gives the designers of these systems a more accurate view of the cost and reliability. Moreover, considering COR increases the cost of load supply and undermines the load reliability.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2019.2958964</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-7114-7005</orcidid><orcidid>https://orcid.org/0000-0003-2638-2484</orcidid><orcidid>https://orcid.org/0000-0002-7603-7737</orcidid><orcidid>https://orcid.org/0000-0002-5250-7108</orcidid><orcidid>https://orcid.org/0000-0002-5599-057X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Batteries cost/reliability assessment grey Wolf optimizer Hybrid photovoltaic-wind-battery systems Hybrid systems loss of load probability Optimization outage rate Particle swarm optimization Photovoltaic systems Reliability Reliability engineering System analysis and design System effectiveness Systems design Wind power Wind turbines |
| title | Optimal, Reliable and Cost-Effective Framework of Photovoltaic-Wind-Battery Energy System Design Considering Outage Concept Using Grey Wolf Optimizer Algorithm-Case Study for Iran |
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