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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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    1. Shahbazitabar, Maryam & Abdi, Hamdi, 2018. "A novel priority-based stochastic unit commitment considering renewable energy sources and parking lot cooperation," Energy, Elsevier, vol. 161(C), pages 308-324.
    2. Madzharov, D. & Delarue, E. & D'haeseleer, W., 2014. "Integrating electric vehicles as flexible load in unit commitment modeling," Energy, Elsevier, vol. 65(C), pages 285-294.
    3. Warut Pannakkong & Van-Hai Pham & Van-Nam Huynh, 2017. "A Novel Hybridization of ARIMA, ANN, and K-Means for Time Series Forecasting," International Journal of Knowledge and Systems Science (IJKSS), IGI Global Scientific Publishing, vol. 8(4), pages 30-53, October.
    4. Schachter, Jonathan A. & Mancarella, Pierluigi & Moriarty, John & Shaw, Rita, 2016. "Flexible investment under uncertainty in smart distribution networks with demand side response: Assessment framework and practical implementation," Energy Policy, Elsevier, vol. 97(C), pages 439-449.
    5. Deetjen, Thomas A. & Rhodes, Joshua D. & Webber, Michael E., 2017. "The impacts of wind and solar on grid flexibility requirements in the Electric Reliability Council of Texas," Energy, Elsevier, vol. 123(C), pages 637-654.
    6. Djørup, Søren & Thellufsen, Jakob Zinck & Sorknæs, Peter, 2018. "The electricity market in a renewable energy system," Energy, Elsevier, vol. 162(C), pages 148-157.
    7. Rakipour, Davood & Barati, Hassan, 2019. "Probabilistic optimization in operation of energy hub with participation of renewable energy resources and demand response," Energy, Elsevier, vol. 173(C), pages 384-399.
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    Cited by:

    1. 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).
    2. 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).
    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. Alirezazadeh, Atefeh & Disfani, Vahid & Kleissl, Jan, 2026. "A new model of energy management for maximum social welfare and minimum carbon dioxide emissions considering parking sharing," Applied Energy, Elsevier, vol. 404(C).
    6. Hua, Lyu-Guang & Hafeez, Ghulam & Alghamdi, Baheej & Alghamdi, Hisham & Khan, Farrukh Aslam & Ullah, Safeer, 2025. "Demand response with pricing schemes and consumers mode constraints for energy balancing in smart grids," Applied Energy, Elsevier, vol. 377(PB).
    7. 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).
    8. 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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