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An Economic Risk Analysis in Wind and Pumped Hydro Energy Storage Integrated Power System Using Meta-Heuristic Algorithm

Author

Listed:
  • Nitesh Kumar Singh

    (Department of Electronics and Communication Engineering, National Institute of Technology Mizoram, Aizawl 796012, India)

  • Chaitali Koley

    (Department of Electronics and Communication Engineering, National Institute of Technology Mizoram, Aizawl 796012, India)

  • Sadhan Gope

    (Department of Electrical Engineering, Mizoram University, Aizawl 796004, India)

  • Subhojit Dawn

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

  • Taha Selim Ustun

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

Abstract

Due to the restructuring of the power system, customers always try to obtain low-cost power efficiently and reliably. As a result, there is a chance to violate the system security limit, or the system may run in risk conditions. In this paper, an economic risk analysis of a power system considering wind and pumped hydroelectric storage (WPHS) hybrid system is presented with the help of meta-heuristic algorithms. The value-at-risk (VaR) and conditional value-at-risk (CVaR) are used as the economic risk analysis tool with two different confidence levels (i.e., 95% and 99%). The VaR and CVaR with higher negative values represent the system in a higher-risk condition. The value of VaR and CVaR on the lower negative side or towards a positive value side indicates a less risky system. The main objective of this work is to minimize the system risk as well as minimize the system generation cost by optimal placement of wind farm and pumped hydro storage systems in the power system. Sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth flame optimization algorithms (MFO) are used to solve optimal power flow problems. The novelty of this paper is that the MFO algorithm is used for the first time in this type of power risk curtailment problem. The IEEE 30 bus system is considered to analyze the system risk with the different confidence levels. The MVA flow of all transmission lines is considered here to calculate the value of VaR and CVaR. The hourly VaR and CVaR values of the hybrid system considering the WPHS system are reported here and the numerical case studies of the hybrid WPHS system demonstrate the effectiveness of the proposed approach. To validate the presented approach, the results obtained by using the MFO algorithm are compared with the SQP and ABC algorithms’ results.

Suggested Citation

  • Nitesh Kumar Singh & Chaitali Koley & Sadhan Gope & Subhojit Dawn & Taha Selim Ustun, 2021. "An Economic Risk Analysis in Wind and Pumped Hydro Energy Storage Integrated Power System Using Meta-Heuristic Algorithm," Sustainability, MDPI, vol. 13(24), pages 1-19, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13542-:d:697018
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    References listed on IDEAS

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    1. Minhui Qian & Ning Chen & Yuge Chen & Changming Chen & Weiqiang Qiu & Dawei Zhao & Zhenzhi Lin, 2021. "Optimal Coordinated Dispatching Strategy of Multi-Sources Power System with Wind, Hydro and Thermal Power Based on CVaR in Typhoon Environment," Energies, MDPI, vol. 14(13), pages 1-35, June.
    2. Li, Xiaozhu & Wang, Weiqing & Wang, Haiyun, 2021. "A novel bi-level robust game model to optimize a regionally integrated energy system with large-scale centralized renewable-energy sources in Western China," Energy, Elsevier, vol. 228(C).
    3. Shin, Hunyoung & Baldick, Ross, 2018. "Mitigating market risk for wind power providers via financial risk exchange," Energy Economics, Elsevier, vol. 71(C), pages 344-358.
    4. Xuan, Ang & Shen, Xinwei & Guo, Qinglai & Sun, Hongbin, 2021. "A conditional value-at-risk based planning model for integrated energy system with energy storage and renewables," Applied Energy, Elsevier, vol. 294(C).
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    Citations

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    Cited by:

    1. Yanis Hamoudi & Hocine Amimeur & Djamal Aouzellag & Maher G. M. Abdolrasol & Taha Selim Ustun, 2023. "Hyperparameter Bayesian Optimization of Gaussian Process Regression Applied in Speed-Sensorless Predictive Torque Control of an Autonomous Wind Energy Conversion System," Energies, MDPI, vol. 16(12), pages 1-19, June.
    2. Jianfeng Dai & Cangbi Ding & Xia Zhou & Yi Tang, 2022. "Adaptive Frequency Control Strategy for PMSG-Based Wind Power Plant Considering Releasable Reserve Power," Sustainability, MDPI, vol. 14(3), pages 1-17, January.
    3. 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.
    4. Preeti Ranjan Sahu & Rajesh Kumar Lenka & Rajendra Kumar Khadanga & Prakash Kumar Hota & Sidhartha Panda & Taha Selim Ustun, 2022. "Power System Stability Improvement of FACTS Controller and PSS Design: A Time-Delay Approach," Sustainability, MDPI, vol. 14(21), pages 1-22, November.
    5. Arup Das & Subhojit Dawn & Sadhan Gope & Taha Selim Ustun, 2022. "A Strategy for System Risk Mitigation Using FACTS Devices in a Wind Incorporated Competitive Power System," Sustainability, MDPI, vol. 14(13), pages 1-21, July.
    6. 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.
    7. Mitul Ranjan Chakraborty & Subhojit Dawn & Pradip Kumar Saha & Jayanta Bhusan Basu & Taha Selim Ustun, 2023. "System Economy Improvement and Risk Shortening by Fuel Cell-UPFC Placement in a Wind-Combined System," Energies, MDPI, vol. 16(4), pages 1-30, February.
    8. Sheikh Safiullah & Asadur Rahman & Shameem Ahmad Lone & S. M. Suhail Hussain & Taha Selim Ustun, 2022. "Novel COVID-19 Based Optimization Algorithm (C-19BOA) for Performance Improvement of Power Systems," Sustainability, MDPI, vol. 14(21), pages 1-27, November.

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