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Stochastic Multi-Objective Optimal Reactive Power Dispatch with the Integration of Wind and Solar Generation

Author

Listed:
  • Faraz Bhurt

    (Department of Electrical Engineering, Quaid-e-Awam University of Engineering Science and Technology, Nawabshah 67450, Sindh, Pakistan)

  • Aamir Ali

    (Department of Electrical Engineering, Quaid-e-Awam University of Engineering Science and Technology, Nawabshah 67450, Sindh, Pakistan)

  • Muhammad U. Keerio

    (Department of Electrical Engineering, Quaid-e-Awam University of Engineering Science and Technology, Nawabshah 67450, Sindh, Pakistan)

  • Ghulam Abbas

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Zahoor Ahmed

    (Department of Electrical Engineering, Balochistan University of Engineering and Technology, Khuzdar 89100, Balochistan, Pakistan)

  • Noor H. Mugheri

    (Department of Electrical Engineering, Quaid-e-Awam University of Engineering Science and Technology, Nawabshah 67450, Sindh, Pakistan)

  • Yun-Su Kim

    (Graduate School of Energy Convergence, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea)

Abstract

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.

Suggested Citation

  • Faraz Bhurt & Aamir Ali & Muhammad U. Keerio & Ghulam Abbas & Zahoor Ahmed & Noor H. Mugheri & Yun-Su Kim, 2023. "Stochastic Multi-Objective Optimal Reactive Power Dispatch with the Integration of Wind and Solar Generation," Energies, MDPI, vol. 16(13), pages 1-22, June.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:4896-:d:1177526
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    References listed on IDEAS

    as
    1. Mohammed Hamouda Ali & Ahmed Mohammed Attiya Soliman & Mohamed Abdeen & Tarek Kandil & Almoataz Y. Abdelaziz & Adel El-Shahat, 2023. "A Novel Stochastic Optimizer Solving Optimal Reactive Power Dispatch Problem Considering Renewable Energy Resources," Energies, MDPI, vol. 16(4), pages 1-39, February.
    2. Tawfiq M. Aljohani & Ahmed F. Ebrahim & Osama Mohammed, 2019. "Single and Multiobjective Optimal Reactive Power Dispatch Based on Hybrid Artificial Physics–Particle Swarm Optimization," Energies, MDPI, vol. 12(12), pages 1-24, June.
    3. Zhi Wu & Yuxuan Zhuang & Suyang Zhou & Shuning Xu & Peng Yu & Jinqiao Du & Xiner Luo & Ghulam Abbas, 2020. "Bi-Level Planning of Multi-Functional Vehicle Charging Stations Considering Land Use Types," Energies, MDPI, vol. 13(5), pages 1-17, March.
    4. Mohseni-Bonab, Seyed Masoud & Rabiee, Abbas & Mohammadi-Ivatloo, Behnam, 2016. "Voltage stability constrained multi-objective optimal reactive power dispatch under load and wind power uncertainties: A stochastic approach," Renewable Energy, Elsevier, vol. 85(C), pages 598-609.
    5. Sushil Kumar Gupta & Lalit Kumar & Manoj Kumar Kar & Sanjay Kumar, 2022. "Optimal reactive power dispatch under coordinated active and reactive load variations using FACTS devices," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2672-2682, October.
    6. Biswas, Partha P. & Suganthan, P.N. & Qu, B.Y. & Amaratunga, Gehan A.J., 2018. "Multiobjective economic-environmental power dispatch with stochastic wind-solar-small hydro power," Energy, Elsevier, vol. 150(C), pages 1039-1057.
    7. Salman Habib & Ghulam Abbas & Touqeer A. Jumani & Aqeel Ahmed Bhutto & Sohrab Mirsaeidi & Emad M. Ahmed, 2022. "Improved Whale Optimization Algorithm for Transient Response, Robustness, and Stability Enhancement of an Automatic Voltage Regulator System," Energies, MDPI, vol. 15(14), pages 1-18, July.
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