Solution of constrained mixed‐integer multi‐objective optimal power flow problem considering the hybrid multi‐objective evolutionary algorithm
An Optimal power flow (OPF) is non‐linear and constrained multi‐objective problem. OPF problems are expensive and evolutionary algorithms (EAs) are computationally complex to obtain uniformly distributed and global Pareto front (PF). Therefore, here, hybrid two‐phase algorithm integrated with parame...
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| Vydané v: | IET generation, transmission & distribution Ročník 17; číslo 1; s. 66 - 90 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Wiley
01.01.2023
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| ISSN: | 1751-8687, 1751-8695 |
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| Abstract | An Optimal power flow (OPF) is non‐linear and constrained multi‐objective problem. OPF problems are expensive and evolutionary algorithms (EAs) are computationally complex to obtain uniformly distributed and global Pareto front (PF). Therefore, here, hybrid two‐phase algorithm integrated with parameter less constraint technique is applied to solve OPF problem. Proposed technique combines single and multi‐objective EAs to find better convergence and evenly distributed PF. For the validation and effectiveness of proposed algorithm, various conflicting objective functions are formulated and implemented on IEEE 30 and 300‐bus network. Each case is independently run twenty times. Hyper volume indicator technique is employed to find the best PF, and the best‐compromised solution is obtained by using fuzzy decision‐making technique. Recently, maximum integration of wind and solar power is highly encouraged. Complexity of OPF is increased with the integration of uncertain renewable energy resources. Hence, 30‐bus test system is modified by replacing some conventional generators with the wind and solar generation. Uncertainties in wind, solar and load demand are modelled by appropriate probability distribution functions. Simulation results show that the proposed method can find the near global PF of highly complex problems subject to satisfying all the operational constraints. |
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| AbstractList | An Optimal power flow (OPF) is non‐linear and constrained multi‐objective problem. OPF problems are expensive and evolutionary algorithms (EAs) are computationally complex to obtain uniformly distributed and global Pareto front (PF). Therefore, here, hybrid two‐phase algorithm integrated with parameter less constraint technique is applied to solve OPF problem. Proposed technique combines single and multi‐objective EAs to find better convergence and evenly distributed PF. For the validation and effectiveness of proposed algorithm, various conflicting objective functions are formulated and implemented on IEEE 30 and 300‐bus network. Each case is independently run twenty times. Hyper volume indicator technique is employed to find the best PF, and the best‐compromised solution is obtained by using fuzzy decision‐making technique. Recently, maximum integration of wind and solar power is highly encouraged. Complexity of OPF is increased with the integration of uncertain renewable energy resources. Hence, 30‐bus test system is modified by replacing some conventional generators with the wind and solar generation. Uncertainties in wind, solar and load demand are modelled by appropriate probability distribution functions. Simulation results show that the proposed method can find the near global PF of highly complex problems subject to satisfying all the operational constraints. Abstract An Optimal power flow (OPF) is non‐linear and constrained multi‐objective problem. OPF problems are expensive and evolutionary algorithms (EAs) are computationally complex to obtain uniformly distributed and global Pareto front (PF). Therefore, here, hybrid two‐phase algorithm integrated with parameter less constraint technique is applied to solve OPF problem. Proposed technique combines single and multi‐objective EAs to find better convergence and evenly distributed PF. For the validation and effectiveness of proposed algorithm, various conflicting objective functions are formulated and implemented on IEEE 30 and 300‐bus network. Each case is independently run twenty times. Hyper volume indicator technique is employed to find the best PF, and the best‐compromised solution is obtained by using fuzzy decision‐making technique. Recently, maximum integration of wind and solar power is highly encouraged. Complexity of OPF is increased with the integration of uncertain renewable energy resources. Hence, 30‐bus test system is modified by replacing some conventional generators with the wind and solar generation. Uncertainties in wind, solar and load demand are modelled by appropriate probability distribution functions. Simulation results show that the proposed method can find the near global PF of highly complex problems subject to satisfying all the operational constraints. |
| Author | Koondhar, Mohsin Ali Ali, Aamir Abbas, Ghulam Chandni, Kiran Keerio, Muhammad Usman Mirsaeidi, Sohrab |
| Author_xml | – sequence: 1 givenname: Aamir surname: Ali fullname: Ali, Aamir organization: Quaid‐e‐Awam University of Engineering, Science and Technology – sequence: 2 givenname: Ghulam orcidid: 0000-0001-7383-6798 surname: Abbas fullname: Abbas, Ghulam email: lashariabbas@gmail.com organization: Southeast University – sequence: 3 givenname: Muhammad Usman surname: Keerio fullname: Keerio, Muhammad Usman organization: Quaid‐e‐Awam University of Engineering, Science and Technology – sequence: 4 givenname: Mohsin Ali surname: Koondhar fullname: Koondhar, Mohsin Ali organization: Quaid‐e‐Awam University of Engineering, Science and Technology – sequence: 5 givenname: Kiran surname: Chandni fullname: Chandni, Kiran organization: Southeast University – sequence: 6 givenname: Sohrab orcidid: 0000-0002-9564-0101 surname: Mirsaeidi fullname: Mirsaeidi, Sohrab email: msohrab@bjtu.edu.cn organization: Beijing Jiaotong University |
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| Title | Solution of constrained mixed‐integer multi‐objective optimal power flow problem considering the hybrid multi‐objective evolutionary algorithm |
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