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A new flexible model for generation scheduling in a smart grid

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  • Alirezazadeh, Atefeh
  • Rashidinejad, Masoud
  • Abdollahi, Amir
  • Afzali, Peyman
  • Bakhshai, Alireza

Abstract

One of the main challenges and essentials of the power system is the flexibility of generation scheduling. The flexibility of a system can be enhanced by using a smart grid comprising demand response, hybrid/diesel generation units and energy storage system. In this paper, an improved flexibility index is defined with the concept of fast reserve supply. The uncertainties of wind/solar power plants and required reserve of thermal units are considered using Latin Hypercube Sampling (LHS). The smart grid supplies a part of load profile of commercial consumers and a part of charge profile of plug-in hybrid electric vehicles (PHEVs) through wind and solar virtual power plants (VPPs), responsive loads, distributed generators (DGs) and the energy storage system. Moreover, the PHEVs considered in this paper provide a system with more flexibility. This paper has solved the unit commitment problem in a single-node system that has no transmission constraints. The mixed integer linear programming (MILP) and the mixed integer non-linear programming (MINLP) methods have been used in order to solve the unit commitment problem and the smart grid scheduling, respectively. The results show that the presented model can optimize the costs of the system and causes the system to become more flexible.

Suggested Citation

  • Alirezazadeh, Atefeh & Rashidinejad, Masoud & Abdollahi, Amir & Afzali, Peyman & Bakhshai, Alireza, 2020. "A new flexible model for generation scheduling in a smart grid," Energy, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:energy:v:191:y:2020:i:c:s0360544219321334
    DOI: 10.1016/j.energy.2019.116438
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    References listed on IDEAS

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    Cited by:

    1. Azimi, Maryam & Salami, Abolfazl, 2021. "A new approach on quantification of flexibility index in multi-carrier energy systems towards optimally energy hub management," Energy, Elsevier, vol. 232(C).
    2. Levieux, Luis Ignacio & Ocampo-Martinez, Carlos & Inthamoussou, Fernando A. & De Battista, Hernán, 2021. "Predictive management approach for the coordination of wind and water-based power supplies," Energy, Elsevier, vol. 219(C).
    3. Fotopoulou, Maria & Rakopoulos, Dimitrios & Petridis, Stefanos & Drosatos, Panagiotis, 2024. "Assessment of smart grid operation under emergency situations," Energy, Elsevier, vol. 287(C).
    4. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    5. Qin, Zhijun & Mo, Yuhong & Liu, Hui & Zhang, Yihui, 2021. "Operational flexibility enhancements using mobile energy storage in day-ahead electricity market by game-theoretic approach," Energy, Elsevier, vol. 232(C).
    6. Yin, Linfei & Luo, Shikui & Ma, Chenxiao, 2021. "Expandable depth and width adaptive dynamic programming for economic smart generation control of smart grids," Energy, Elsevier, vol. 232(C).

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