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Profit Maximization with Imbalance Cost Improvement by Solar PV-Battery Hybrid System in Deregulated Power Market

Author

Listed:
  • Ganesh Sampatrao Patil

    (Department of Technology, Shivaji University, Kolhapur 416004, India)

  • Anwar Mulla

    (Dr. Daulatrao Aher College of Engineering, Shivaji University, Kolhapur 416004, India)

  • Subhojit Dawn

    (Department of Electrical & Electronics Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada 520007, India)

  • Taha Selim Ustun

    (Fukushima Renewable Energy Institute, AIST (FREA), Koriyama 963-0298, Japan)

Abstract

The changeable nature of renewable sources creates difficulties in system security and stability. Therefore, it is necessary to study system risk in several power system scenarios. In a wind-integrated deregulated power network, the wind farm needs to submit the bid for its power-generating quantities a minimum of one day ahead of the operation. The wind farm submits the data based on the expected wind speed (EWS). If any mismatch occurs between real wind speed (RWS) and expected wind speed, ISO enforces the penalty/rewards to the wind farm. In a single word, this is called the power market imbalance cost, which directly distresses the system profit. Here, solar PV and battery energy storage systems are used along by the wind farm to exploit system profit by grasping the negative outcome of imbalance cost. Along with system profit, the focus has also been on system risk. The system risk has been calculated using the risk assessment factors, i.e., Value-at-Risk (VaR) and Cumulative Value-at-risk (CVaR). The work is performed on a modified IEEE 14 and modified IEEE 30 bus test system. The solar PV-battery storage system can supply the demand locally first, and then the remaining power is given to the electrical grid. By using this concept, the system risk can be minimized by the incorporation of solar PV and battery storage systems, which have been studied in this work. A comparative study has been performed using three dissimilar optimization methods, i.e., Artificial Gorilla Troops Optimizer Algorithm (AGTO), Artificial Bee Colony Algorithm (ABC), and Sequential Quadratic Programming (SQP) to examine the consequence of the presented technique. The AGTO has been used for the first time in the risk assessment and alleviation problem, which is the distinctiveness of this work.

Suggested Citation

  • Ganesh Sampatrao Patil & Anwar Mulla & Subhojit Dawn & Taha Selim Ustun, 2022. "Profit Maximization with Imbalance Cost Improvement by Solar PV-Battery Hybrid System in Deregulated Power Market," Energies, MDPI, vol. 15(14), pages 1-21, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5290-:d:868153
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    References listed on IDEAS

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    Cited by:

    1. Mitul Ranjan Chakraborty & Subhojit Dawn & Pradip Kumar Saha & Jayanta Bhusan Basu & Taha Selim Ustun, 2022. "System Profit Improvement of a Thermal–Wind–CAES Hybrid System Considering Imbalance Cost in the Electricity Market," Energies, MDPI, vol. 15(24), pages 1-25, December.
    2. Julian David Hunt & Behnam Zakeri & Andreas Nascimento & Diego Augusto de Jesus Pacheco & Epari Ritesh Patro & Bojan Đurin & Márcio Giannini Pereira & Walter Leal Filho & Yoshihide Wada, 2023. "Isothermal Deep Ocean Compressed Air Energy Storage: An Affordable Solution for Seasonal Energy Storage," Energies, MDPI, vol. 16(7), pages 1-18, March.
    3. Taha Selim Ustun, 2022. "Power Systems Imitate Nature for Improved Performance Use of Nature-Inspired Optimization Techniques," Energies, MDPI, vol. 15(17), pages 1-2, August.
    4. Hasan Huseyin Coban, 2023. "Hydropower Planning in Combination with Batteries and Solar Energy," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    5. Yan Xiong & Jiakun Fang, 2022. "Co-Operative Optimization Framework for Energy Management Considering CVaR Assessment and Game Theory," Energies, MDPI, vol. 15(24), pages 1-17, December.

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