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Risk-Limiting Scheduling of Optimal Non-Renewable Power Generation for Systems with Uncertain Power Generation and Load Demand

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  • Shin-Yeu Lin

    (Department of Electrical Engineering, Chang Gung University, 259 Wen-Hwa 1st Road Kwei-Shan, Tao-Yuan 33302, Taiwan)

  • Ai-Chih Lin

    (Department of Electrical Engineering, Chang Gung University, 259 Wen-Hwa 1st Road Kwei-Shan, Tao-Yuan 33302, Taiwan)

Abstract

This study tackles a risk-limiting scheduling problem of non-renewable power generation for large power systems, and addresses potential violations of the security constraints owing to the volatility of renewable power generation and the uncertainty of load demand. To cope with the computational challenge that arises from the probabilistic constraints in the considered problem, a computationally efficient solution algorithm that involves a bisection method, an off-line constructed artificial neural network (ANN) and an on-line point estimation method is proposed and tested on the IEEE 118-bus system. The results of tests and comparisons reveal that the proposed solution algorithm is applicable to large power systems in real time, and the solution obtained herein is much better than the conventional optimal power flow (OPF) solution in obtaining a much higher probability of satisfying the security constraints.

Suggested Citation

  • Shin-Yeu Lin & Ai-Chih Lin, 2016. "Risk-Limiting Scheduling of Optimal Non-Renewable Power Generation for Systems with Uncertain Power Generation and Load Demand," Energies, MDPI, vol. 9(11), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:11:p:868-:d:81398
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    1. Annamaria Buonomano & Francesco Calise & Maria Vicidomini, 2016. "Design, Simulation and Experimental Investigation of a Solar System Based on PV Panels and PVT Collectors," Energies, MDPI, vol. 9(7), pages 1-17, June.
    2. Lin, Shin-Yeu & Lin, Ai-Chih, 2014. "RLOPF (risk-limiting optimal power flow) for systems with high penetration of wind power," Energy, Elsevier, vol. 71(C), pages 49-61.
    3. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "Reserve constrained dynamic optimal power flow subject to valve-point effects, prohibited zones and multi-fuel constraints," Energy, Elsevier, vol. 47(1), pages 451-464.
    4. Salgado, R.S. & Rangel, E.L., 2012. "Optimal power flow solutions through multi-objective programming," Energy, Elsevier, vol. 42(1), pages 35-45.
    5. Zhe Luo & Seung-Ho Hong & Jong-Beom Kim, 2016. "A Price-Based Demand Response Scheme for Discrete Manufacturing in Smart Grids," Energies, MDPI, vol. 9(8), pages 1-18, August.
    6. Niknam, Taher & Narimani, Mohammad rasoul & Jabbari, Masoud & Malekpour, Ahmad Reza, 2011. "A modified shuffle frog leaping algorithm for multi-objective optimal power flow," Energy, Elsevier, vol. 36(11), pages 6420-6432.
    7. Yun-Su Kim & Chul-Sang Hwang & Eung-Sang Kim & Changhee Cho, 2016. "State of Charge-Based Active Power Sharing Method in a Standalone Microgrid with High Penetration Level of Renewable Energy Sources," Energies, MDPI, vol. 9(7), pages 1-13, June.
    8. Amin Niayifar & Fernando Porté-Agel, 2016. "Analytical Modeling of Wind Farms: A New Approach for Power Prediction," Energies, MDPI, vol. 9(9), pages 1-13, September.
    9. Narimani, Mohammad Rasoul & Azizipanah-Abarghooee, Rasoul & Zoghdar-Moghadam-Shahrekohne, Behrouz & Gholami, Kayvan, 2013. "A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type," Energy, Elsevier, vol. 49(C), pages 119-136.
    10. Alessandra Curreli & Glòria Serra-Coch & Antonio Isalgue & Isabel Crespo & Helena Coch, 2016. "Solar Energy as a Form Giver for Future Cities," Energies, MDPI, vol. 9(7), pages 1-11, July.
    11. Youngho Cho & Choongman Lee & Kyeon Hur & Yong Cheol Kang & Eduard Muljadi & Sang-Ho Park & Young-Do Choy & Gi-Gab Yoon, 2016. "A Framework to Analyze the Stochastic Harmonics and Resonance of Wind Energy Grid Interconnection," Energies, MDPI, vol. 9(9), pages 1-16, August.
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