Stochastic Multi-Objective Optimal Reactive Power Dispatch with the Integration of Wind and Solar Generation
The exponential growth of unpredictable renewable power production sources in the power grid results in difficult-to-regulate reactive power. The ultimate goal of optimal reactive power dispatch (ORPD) is to find the optimal voltage level of all the generators, the transformer tap ratio, and the MVA...
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| Online Access: | https://www.mdpi.com/1996-1073/16/13/4896 |
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| author | Faraz Bhurt Aamir Ali Muhammad U. Keerio Ghulam Abbas Zahoor Ahmed Noor H. Mugheri Yun-Su Kim |
| author_facet | Faraz Bhurt Aamir Ali Muhammad U. Keerio Ghulam Abbas Zahoor Ahmed Noor H. Mugheri Yun-Su Kim |
| author_sort | Faraz Bhurt |
| collection | DOAJ |
| description | The exponential growth of unpredictable renewable power production sources in the power grid results in difficult-to-regulate reactive power. The ultimate goal of optimal reactive power dispatch (ORPD) is to find the optimal voltage level of all the generators, the transformer tap ratio, and the MVAR injection of shunt VAR compensators (SVC). More realistically, the ORPD problem is a nonlinear multi-objective optimization problem. Therefore, in this paper, the multi-objective ORPD problem is formulated and solved considering the simultaneous minimization of the active power loss, voltage deviation, emission, and the operating cost of renewable and thermal generators. Usually, renewable power generators such as wind and solar are uncertain; therefore, Weibull and lognormal probability distribution functions are considered to model wind and solar power, respectively. Due to the unavailability and uncertainty of wind and solar power, appropriate PDFs have been used to generate 1000 scenarios with the help of Monte Carlo simulation techniques. Practically, it is not possible to solve the problem considering all the scenarios. Therefore, the scenario reduction technique based on the distance metric is applied to select the 24 representative scenarios to reduce the size of the problem. Moreover, the efficient non-dominated sorting genetic algorithm II-based bidirectional co-evolutionary algorithm (BiCo), along with the constraint domination principle, is adopted to solve the multi-objective ORPD problem. Furthermore, a modified IEEE standard 30-bus system is employed to show the performance and superiority of the proposed algorithm. Simulation results indicate that the proposed algorithm finds uniformly distributed and near-global final non-dominated solutions compared to the recently available state-of-the-art multi-objective algorithms in the literature. |
| first_indexed | 2024-03-11T01:42:49Z |
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| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T01:42:49Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-3e57a29aabd14fb3b2e5ba4133bf51892023-11-18T16:27:27ZengMDPI AGEnergies1996-10732023-06-011613489610.3390/en16134896Stochastic Multi-Objective Optimal Reactive Power Dispatch with the Integration of Wind and Solar GenerationFaraz Bhurt0Aamir Ali1Muhammad U. Keerio2Ghulam Abbas3Zahoor Ahmed4Noor H. Mugheri5Yun-Su Kim6Department of Electrical Engineering, Quaid-e-Awam University of Engineering Science and Technology, Nawabshah 67450, Sindh, PakistanDepartment of Electrical Engineering, Quaid-e-Awam University of Engineering Science and Technology, Nawabshah 67450, Sindh, PakistanDepartment of Electrical Engineering, Quaid-e-Awam University of Engineering Science and Technology, Nawabshah 67450, Sindh, PakistanSchool of Electrical Engineering, Southeast University, Nanjing 210096, ChinaDepartment of Electrical Engineering, Balochistan University of Engineering and Technology, Khuzdar 89100, Balochistan, PakistanDepartment of Electrical Engineering, Quaid-e-Awam University of Engineering Science and Technology, Nawabshah 67450, Sindh, PakistanGraduate School of Energy Convergence, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of KoreaThe exponential growth of unpredictable renewable power production sources in the power grid results in difficult-to-regulate reactive power. The ultimate goal of optimal reactive power dispatch (ORPD) is to find the optimal voltage level of all the generators, the transformer tap ratio, and the MVAR injection of shunt VAR compensators (SVC). More realistically, the ORPD problem is a nonlinear multi-objective optimization problem. Therefore, in this paper, the multi-objective ORPD problem is formulated and solved considering the simultaneous minimization of the active power loss, voltage deviation, emission, and the operating cost of renewable and thermal generators. Usually, renewable power generators such as wind and solar are uncertain; therefore, Weibull and lognormal probability distribution functions are considered to model wind and solar power, respectively. Due to the unavailability and uncertainty of wind and solar power, appropriate PDFs have been used to generate 1000 scenarios with the help of Monte Carlo simulation techniques. Practically, it is not possible to solve the problem considering all the scenarios. Therefore, the scenario reduction technique based on the distance metric is applied to select the 24 representative scenarios to reduce the size of the problem. Moreover, the efficient non-dominated sorting genetic algorithm II-based bidirectional co-evolutionary algorithm (BiCo), along with the constraint domination principle, is adopted to solve the multi-objective ORPD problem. Furthermore, a modified IEEE standard 30-bus system is employed to show the performance and superiority of the proposed algorithm. Simulation results indicate that the proposed algorithm finds uniformly distributed and near-global final non-dominated solutions compared to the recently available state-of-the-art multi-objective algorithms in the literature.https://www.mdpi.com/1996-1073/16/13/4896non-dominated sorting genetic algorithmrenewable power sourcesoptimal reactive power dispatchprobability distribution function |
| spellingShingle | Faraz Bhurt Aamir Ali Muhammad U. Keerio Ghulam Abbas Zahoor Ahmed Noor H. Mugheri Yun-Su Kim Stochastic Multi-Objective Optimal Reactive Power Dispatch with the Integration of Wind and Solar Generation Energies non-dominated sorting genetic algorithm renewable power sources optimal reactive power dispatch probability distribution function |
| title | Stochastic Multi-Objective Optimal Reactive Power Dispatch with the Integration of Wind and Solar Generation |
| title_full | Stochastic Multi-Objective Optimal Reactive Power Dispatch with the Integration of Wind and Solar Generation |
| title_fullStr | Stochastic Multi-Objective Optimal Reactive Power Dispatch with the Integration of Wind and Solar Generation |
| title_full_unstemmed | Stochastic Multi-Objective Optimal Reactive Power Dispatch with the Integration of Wind and Solar Generation |
| title_short | Stochastic Multi-Objective Optimal Reactive Power Dispatch with the Integration of Wind and Solar Generation |
| title_sort | stochastic multi objective optimal reactive power dispatch with the integration of wind and solar generation |
| topic | non-dominated sorting genetic algorithm renewable power sources optimal reactive power dispatch probability distribution function |
| url | https://www.mdpi.com/1996-1073/16/13/4896 |
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