A Joint Scheduling Strategy for Wind and Solar Photovoltaic Systems to Grasp Imbalance Cost in Competitive Market
The integration of renewable energy sources with active thermal power plants contributes to the green environment all over the globe. To achieve maximum reliability and sustainability of the renewable-thermal hybrid system, plentiful constraints need to be considered for minimizing the situation, wh...
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description | The integration of renewable energy sources with active thermal power plants contributes to the green environment all over the globe. To achieve maximum reliability and sustainability of the renewable-thermal hybrid system, plentiful constraints need to be considered for minimizing the situation, which creates due to the unpredictable nature of renewable energy. In wind integrated deregulated system, wind farms need to submit the power generation scenario for future days to Independent System Operator (ISO) before the date of operation. Based on their submitted bid, ISO scheduled the power generation from different generating stations, including thermal and renewable. Due to the uncertain nature of the wind flow, there is always a chance of not fulfilling the scheduling amount of power from the wind farm. This violation in the market can impose an economic burden (i.e., imbalance cost) on the generating companies. The solar photovoltaic cell can be used to decrease the adverse economic effects of unpredicted wind saturation in the deregulated system. This paper presents consistent, competent, and effective operating schemes for the hybrid operation of solar PV and wind farms to maximize the economic profit by minimizing the imbalance cost, which occurs due to the mismatch between the actual and predicted wind speed. Modified IEEE 14-bus and modified IEEE 30-bus test systems have been used to check the usefulness of the proposed approach. Three optimization techniques (i.e., Sequential Quadratic Programming (SQP), Smart Flower Optimization Algorithm (SFOA), Honey Badger Algorithm (HBA)) have been used in this work for the comparative study. Bus Loading Factor (BLF) has been proposed here to identify the most sensitive bus in the system, used to place wind farms. The SFOA and HBA optimization technique has been used first time in this type of economic assessment problem, which is the novelty of this paper. The Bus Loading Factor (BLF) has been introduced here to identify the most sensitive bus in the system. After implementing the work, it has been seen that the operation of the solar PV system has reduced the adverse effect of imbalance cost on the renewable integrated deregulated power system. |
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To achieve maximum reliability and sustainability of the renewable-thermal hybrid system, plentiful constraints need to be considered for minimizing the situation, which creates due to the unpredictable nature of renewable energy. In wind integrated deregulated system, wind farms need to submit the power generation scenario for future days to Independent System Operator (ISO) before the date of operation. Based on their submitted bid, ISO scheduled the power generation from different generating stations, including thermal and renewable. Due to the uncertain nature of the wind flow, there is always a chance of not fulfilling the scheduling amount of power from the wind farm. This violation in the market can impose an economic burden (i.e., imbalance cost) on the generating companies. The solar photovoltaic cell can be used to decrease the adverse economic effects of unpredicted wind saturation in the deregulated system. This paper presents consistent, competent, and effective operating schemes for the hybrid operation of solar PV and wind farms to maximize the economic profit by minimizing the imbalance cost, which occurs due to the mismatch between the actual and predicted wind speed. Modified IEEE 14-bus and modified IEEE 30-bus test systems have been used to check the usefulness of the proposed approach. Three optimization techniques (i.e., Sequential Quadratic Programming (SQP), Smart Flower Optimization Algorithm (SFOA), Honey Badger Algorithm (HBA)) have been used in this work for the comparative study. Bus Loading Factor (BLF) has been proposed here to identify the most sensitive bus in the system, used to place wind farms. The SFOA and HBA optimization technique has been used first time in this type of economic assessment problem, which is the novelty of this paper. The Bus Loading Factor (BLF) has been introduced here to identify the most sensitive bus in the system. After implementing the work, it has been seen that the operation of the solar PV system has reduced the adverse effect of imbalance cost on the renewable integrated deregulated power system.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su14095005</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Alternative energy sources ; Comparative studies ; Customers ; Deregulation ; Economic impact ; Economics ; Efficiency ; Electricity ; Energy management ; Energy resources ; Energy sources ; Energy storage ; Generators ; Optimization ; Optimization techniques ; Photovoltaics ; Power plants ; Renewable energy sources ; Renewable resources ; Solar cells ; Solar energy ; Sustainability ; Thermal power ; Thermal power plants ; Wind ; Wind effects ; Wind farms ; Wind power ; Wind speed</subject><ispartof>Sustainability, 2022-05, Vol.14 (9), p.5005</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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After implementing the work, it has been seen that the operation of the solar PV system has reduced the adverse effect of imbalance cost on the renewable integrated deregulated power system.</description><subject>Algorithms</subject><subject>Alternative energy sources</subject><subject>Comparative studies</subject><subject>Customers</subject><subject>Deregulation</subject><subject>Economic impact</subject><subject>Economics</subject><subject>Efficiency</subject><subject>Electricity</subject><subject>Energy management</subject><subject>Energy resources</subject><subject>Energy sources</subject><subject>Energy storage</subject><subject>Generators</subject><subject>Optimization</subject><subject>Optimization techniques</subject><subject>Photovoltaics</subject><subject>Power plants</subject><subject>Renewable energy sources</subject><subject>Renewable resources</subject><subject>Solar cells</subject><subject>Solar energy</subject><subject>Sustainability</subject><subject>Thermal power</subject><subject>Thermal power plants</subject><subject>Wind</subject><subject>Wind effects</subject><subject>Wind farms</subject><subject>Wind power</subject><subject>Wind speed</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpNUF9LwzAcDKLgmHvxEwR8E6r5s6Tt4xg6JxOFKj6WtP1ly2ybLkkH_fZWJujBcfdw3MEhdE3JHecpufc9nZNUECLO0ISRmEaUCHL-z1-imfd7MoJzmlI5QYcFframDTgrd1D1tWm3OAtOBdgOWFuHP01bYTUys7Vy-G1ngz3aOihT4mzwARqPg8Urp3yH102hatWWgJfWB2zaUZsOggnmCPhFuS8IV-hCq9rD7Fen6OPx4X35FG1eV-vlYhOVLBUhkgJUrIoCmKx0XAnQJQAvKIvZHHSqE0l4yYBKSiEh6ZxRpkRcaKaThKsE-BTdnHo7Zw89-JDvbe_acTJnUnJKEprKMXV7SpXOeu9A550zjXJDTkn-82r-9yr_BmVianI</recordid><startdate>20220501</startdate><enddate>20220501</enddate><creator>Das, Shreya Shree</creator><creator>Das, Arup</creator><creator>Dawn, Subhojit</creator><creator>Gope, Sadhan</creator><creator>Ustun, Taha Selim</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-2413-8421</orcidid><orcidid>https://orcid.org/0000-0002-3639-502X</orcidid><orcidid>https://orcid.org/0000-0003-2415-8913</orcidid></search><sort><creationdate>20220501</creationdate><title>A Joint Scheduling Strategy for Wind and Solar Photovoltaic Systems to Grasp Imbalance Cost in Competitive Market</title><author>Das, Shreya Shree ; 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To achieve maximum reliability and sustainability of the renewable-thermal hybrid system, plentiful constraints need to be considered for minimizing the situation, which creates due to the unpredictable nature of renewable energy. In wind integrated deregulated system, wind farms need to submit the power generation scenario for future days to Independent System Operator (ISO) before the date of operation. Based on their submitted bid, ISO scheduled the power generation from different generating stations, including thermal and renewable. Due to the uncertain nature of the wind flow, there is always a chance of not fulfilling the scheduling amount of power from the wind farm. This violation in the market can impose an economic burden (i.e., imbalance cost) on the generating companies. The solar photovoltaic cell can be used to decrease the adverse economic effects of unpredicted wind saturation in the deregulated system. This paper presents consistent, competent, and effective operating schemes for the hybrid operation of solar PV and wind farms to maximize the economic profit by minimizing the imbalance cost, which occurs due to the mismatch between the actual and predicted wind speed. Modified IEEE 14-bus and modified IEEE 30-bus test systems have been used to check the usefulness of the proposed approach. Three optimization techniques (i.e., Sequential Quadratic Programming (SQP), Smart Flower Optimization Algorithm (SFOA), Honey Badger Algorithm (HBA)) have been used in this work for the comparative study. Bus Loading Factor (BLF) has been proposed here to identify the most sensitive bus in the system, used to place wind farms. The SFOA and HBA optimization technique has been used first time in this type of economic assessment problem, which is the novelty of this paper. The Bus Loading Factor (BLF) has been introduced here to identify the most sensitive bus in the system. 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subjects | Algorithms Alternative energy sources Comparative studies Customers Deregulation Economic impact Economics Efficiency Electricity Energy management Energy resources Energy sources Energy storage Generators Optimization Optimization techniques Photovoltaics Power plants Renewable energy sources Renewable resources Solar cells Solar energy Sustainability Thermal power Thermal power plants Wind Wind effects Wind farms Wind power Wind speed |
title | A Joint Scheduling Strategy for Wind and Solar Photovoltaic Systems to Grasp Imbalance Cost in Competitive Market |
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