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Reserve constrained dynamic optimal power flow subject to valve-point effects, prohibited zones and multi-fuel constraints

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  • Niknam, Taher
  • Azizipanah-Abarghooee, Rasoul
  • Narimani, Mohammad Rasoul

Abstract

This paper proposes a new method called the enhanced charged system search algorithm in order to solve the reserve constrained dynamic optimal power flow. The optimal power flow (OPF) is applied to develop the corrective strategies and to make the least cost dispatches. The static OPF solutions will usually result in unattractive generation cost in a dynamic environment, so to cope with this problem it is necessary to solve the dynamic optimal power flow which considers time-related constraints along with time-separated limits. Also, some restrictions such as prohibited operating zones and valve-point effects beside the multi-fuel type of generation units should be taken into account in order to get closer to the real condition of the power systems. Furthermore, to ensure secure real-time power system operations, the system operator must schedule sufficient resources to meet energy demand and operating reserve requirements, simultaneously. This problem is a complex optimization problem spontaneously which its complexity is increased with considering all above constraints. This paper utilizes an enhanced charged system search algorithm, which is a newly developed optimization algorithm, to solve the proposed problem. This algorithm is equipped with a novel mutation strategy in order to increase the population diversity and to amend the convergence criteria. For validating the ability of the proposed algorithm, it is applied to two small- and large-scale case studies including 30 and 118 buses test systems.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:47:y:2012:i:1:p:451-464
    DOI: 10.1016/j.energy.2012.07.053
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    4. 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.
    5. Yu, L. & Li, Y.P. & Huang, G.H., 2016. "A fuzzy-stochastic simulation-optimization model for planning electric power systems with considering peak-electricity demand: A case study of Qingdao, China," Energy, Elsevier, vol. 98(C), pages 190-203.
    6. Ghasemi, Mojtaba & Ghavidel, Sahand & Akbari, Ebrahim & Vahed, Ali Azizi, 2014. "Solving non-linear, non-smooth and non-convex optimal power flow problems using chaotic invasive weed optimization algorithms based on chaos," Energy, Elsevier, vol. 73(C), pages 340-353.
    7. Lin, Shin-Yeu & Chen, Jyun-Fu, 2013. "Distributed optimal power flow for smart grid transmission system with renewable energy sources," Energy, Elsevier, vol. 56(C), pages 184-192.
    8. Zare, Mohsen & Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Amiri, Babak, 2014. "Multi-objective probabilistic reactive power and voltage control with wind site correlations," Energy, Elsevier, vol. 66(C), pages 810-822.
    9. Adhvaryyu, P.K. & Chattopadhyay, P.K. & Bhattacharya, A., 2017. "Dynamic optimal power flow of combined heat and power system with Valve-point effect using Krill Herd algorithm," Energy, Elsevier, vol. 127(C), pages 756-767.
    10. Ghasemi, Mojtaba & Ghavidel, Sahand & Ghanbarian, Mohammad Mehdi & Gharibzadeh, Masihallah & Azizi Vahed, Ali, 2014. "Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm," Energy, Elsevier, vol. 78(C), pages 276-289.
    11. Ismail Marouani & Tawfik Guesmi & Hsan Hadj Abdallah & Badr M. Alshammari & Khalid Alqunun & Ahmed S. Alshammari & Salem Rahmani, 2022. "Combined Economic Emission Dispatch with and without Consideration of PV and Wind Energy by Using Various Optimization Techniques: A Review," Energies, MDPI, vol. 15(12), pages 1-35, June.
    12. Bahmani-Firouzi, Bahman & Farjah, Ebrahim & Azizipanah-Abarghooee, Rasoul, 2013. "An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimization approach for dynamic economic emission dispatch considering load and wind power uncertainties," Energy, Elsevier, vol. 50(C), pages 232-244.
    13. Kamankesh, Hamidreza & Agelidis, Vassilios G. & Kavousi-Fard, Abdollah, 2016. "Optimal scheduling of renewable micro-grids considering plug-in hybrid electric vehicle charging demand," Energy, Elsevier, vol. 100(C), pages 285-297.
    14. 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.
    15. Bahmani-Firouzi, Bahman & Farjah, Ebrahim & Seifi, Alireza, 2013. "A new algorithm for combined heat and power dynamic economic dispatch considering valve-point effects," Energy, Elsevier, vol. 52(C), pages 320-332.
    16. 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.

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