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Long-term chronological load modeling in power system studies with energy storage systems

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  • Marini, Abbas
  • Latify, Mohammad Amin
  • Ghazizadeh, Mohammad Sadegh
  • Salemnia, Ahmad

Abstract

Smartening and restructuring of power industry lead to introduction of new energy resources in both supply and demand sides of energy sectors. In this regard, energy storage systems (ESSs) are appropriate alternatives for reducing the utilization of current declining non-renewable energy resources. Consequently, it is essential to consider various aspects of ESS application and face its related implementation challenges. This paper investigates the simulation of ESSs in long-term power system studies and proposes two long-term chronological load modeling methods. At first, a review of current load modeling methods in long-term studies including ESSs is provided and then two new load modeling methods are proposed. The proposed models are implemented in a typical unit commitment problem and solved for IEEE reliability test system (RTS) and IEEE 118-bus test system. Finally, a comparative study among examined load modeling methods is presented. The key feature of the proposed load models is that they are able to provide a tradeoff between computational burden and model accuracy in terms of calculating the desired requirements of the system planner.

Suggested Citation

  • Marini, Abbas & Latify, Mohammad Amin & Ghazizadeh, Mohammad Sadegh & Salemnia, Ahmad, 2015. "Long-term chronological load modeling in power system studies with energy storage systems," Applied Energy, Elsevier, vol. 156(C), pages 436-448.
    • Handle: RePEc:eee:appene:v:156:y:2015:i:c:p:436-448
    DOI: 10.1016/j.apenergy.2015.07.047
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    Cited by:

    1. Cebulla, F. & Fichter, T., 2017. "Merit order or unit-commitment: How does thermal power plant modeling affect storage demand in energy system models?," Renewable Energy, Elsevier, vol. 105(C), pages 117-132.
    2. Abbas Marini & Luigi Piegari & S-Saeedallah Mortazavi & Mohammad-S Ghazizadeh, 2020. "Coordinated Operation of Energy Storage Systems for Distributed Harmonic Compensation in Microgrids," Energies, MDPI, vol. 13(3), pages 1-22, February.
    3. Wang, Ziyi & Wennersten, Ronald & Sun, Qie, 2017. "Outline of principles for building scenarios – Transition toward more sustainable energy systems," Applied Energy, Elsevier, vol. 185(P2), pages 1890-1898.
    4. Das, Choton K. & Bass, Octavian & Kothapalli, Ganesh & Mahmoud, Thair S. & Habibi, Daryoush, 2018. "Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm," Applied Energy, Elsevier, vol. 232(C), pages 212-228.
    5. Das, Choton K. & Bass, Octavian & Mahmoud, Thair S. & Kothapalli, Ganesh & Mousavi, Navid & Habibi, Daryoush & Masoum, Mohammad A.S., 2019. "Optimal allocation of distributed energy storage systems to improve performance and power quality of distribution networks," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    6. Adefarati, T. & Bansal, R.C., 2019. "Reliability, economic and environmental analysis of a microgrid system in the presence of renewable energy resources," Applied Energy, Elsevier, vol. 236(C), pages 1089-1114.

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