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Multi-Input Nonlinear Programming Based Deterministic Optimization Framework for Evaluating Microgrids with Optimal Renewable-Storage Energy Mix

Author

Listed:
  • Yousef Alhumaid

    (Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Khalid Khan

    (Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Fahad Alismail

    (Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
    K.A. CARE Energy Research & Innovation Center, Dhahran 31261, Saudi Arabia
    Center for Environmental & Water, Research Institute, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Muhammad Khalid

    (Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
    K.A. CARE Energy Research & Innovation Center, Dhahran 31261, Saudi Arabia)

Abstract

Integration of renewable energy sources (RES) in a distribution network facilities the establishment of sustainable power systems. Concurrently, the incorporation of energy storage system (ESS) plays a pivotal role to maintain the economical significance as well as mitigates the technical liabilities associated with uncontrollable and fluctuating renewable output power. Nevertheless, ESS technologies require additional investments that imposes a techno-economic challenge of selection, allocation and sizing to ensure a reliable power system that is operationally optimized with reduced cost. In this paper, a deterministic cost-optimization framework is presented based on a multi-input nonlinear programming to optimally solve the sizing and allocation problem. The optimization is performed to obviate the demand-generation mismatch, that is violated with the introduction of variable renewable energy sources. The proposed optimization method is tested and validated on an IEEE 24-bus network integrated with solar and wind energy sources. The deterministic approach is solved using GAMS optimization software considering the system data of one year. Based on the optimization framework, the study also presents various different scenarios of renewable energy mix in combination with advanced ESS technologies to outline an technical as well as economical framework for ESS sizing, allocation, and selection. Based on the optimal results obtained, the optimal sizing and allocation were obtained for lead-acid, lithium-ion, nickel-cadmium and sodium-sulfur (NaS) battery energy storage system. While all these storage technologies mitigated the demand-generation mismatch with optimal size and location. However, the NaS storage technology was found to be the best among the given storage technologies for the given system minimum possible cost. Furthermore, it was observed that the cost of hybrid wind-solar mix system results in the lowest overall cost.

Suggested Citation

  • Yousef Alhumaid & Khalid Khan & Fahad Alismail & Muhammad Khalid, 2021. "Multi-Input Nonlinear Programming Based Deterministic Optimization Framework for Evaluating Microgrids with Optimal Renewable-Storage Energy Mix," Sustainability, MDPI, vol. 13(11), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:5878-:d:560792
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    References listed on IDEAS

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    1. Ali Ahmad & Syed Abdul Rahman Kashif & Arslan Ashraf & Muhammad Majid Gulzar & Mohammed Alqahtani & Muhammad Khalid, 2023. "Coordinated Economic Operation of Hydrothermal Units with HVDC Link Based on Lagrange Multipliers," Mathematics, MDPI, vol. 11(7), pages 1-19, March.
    2. Bogdan Popa & Otilia Nedelcu & Florica Popa & Khalid Ahmad-Rashid & Eliza-Isabela Tică, 2021. "Small Hydropower Plant for Sustainable Electricity from RES Mix," Sustainability, MDPI, vol. 13(22), pages 1-14, November.
    3. Muhammad Riaz & Aamir Hanif & Haris Masood & Muhammad Attique Khan & Kamran Afaq & Byeong-Gwon Kang & Yunyoung Nam, 2021. "An Optimal Power Flow Solution of a System Integrated with Renewable Sources Using a Hybrid Optimizer," Sustainability, MDPI, vol. 13(23), pages 1-12, December.
    4. Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
    5. Luay Elkhidir & Khalid Khan & Mohammad Al-Muhaini & Muhammad Khalid, 2022. "Enhancing Transient Response and Voltage Stability of Renewable Integrated Microgrids," Sustainability, MDPI, vol. 14(7), pages 1-21, March.

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