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Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty

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  • Nikmehr, Nima
  • Najafi-Ravadanegh, Sajad
  • Khodaei, Amin

Abstract

Networked microgrids (NMGs) are beneficial and economical for both microgrids’ owners and consumers as this structure could potentially play a significant role in energy efficiency, power system reliability and sustainability. Renewable energy sources (RESs) and sharp fluctuations in load consumption impose new challenges in solving operational problems in smart distribution grids. As a result, deterministic methods are not able to provide a precise analysis of microgrids operation and planning. Therefore, stochastic algorithms are used as powerful tools in ensuring reliable solutions especially in operation problems. In this paper, daily optimal scheduling problem of NMGs considering intermittent behavior in generation and load is investigated in a proposed energy management system (EMS). Two demand response programs (DRPs) based on time of use (TOU) and real time pricing (RTP) are integrated into the optimal scheduling model and the developed model is solved using a metaheuristic algorithm under uncertainties of RESs and loads. The numerical simulations show the effectiveness of the proposed model through comparison with solution from stochastic optimization.

Suggested Citation

  • Nikmehr, Nima & Najafi-Ravadanegh, Sajad & Khodaei, Amin, 2017. "Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty," Applied Energy, Elsevier, vol. 198(C), pages 267-279.
    • Handle: RePEc:eee:appene:v:198:y:2017:i:c:p:267-279
    DOI: 10.1016/j.apenergy.2017.04.071
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    References listed on IDEAS

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    1. Fang, Xinli & Yang, Qiang & Wang, Jianhui & Yan, Wenjun, 2016. "Coordinated dispatch in multiple cooperative autonomous islanded microgrids," Applied Energy, Elsevier, vol. 162(C), pages 40-48.
    2. Haddadian, Hossein & Noroozian, Reza, 2017. "Multi-microgrids approach for design and operation of future distribution networks based on novel technical indices," Applied Energy, Elsevier, vol. 185(P1), pages 650-663.
    3. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems)," Energy, Elsevier, vol. 55(C), pages 1044-1054.
    4. Dimitris Fouskakis & David Draper, 2002. "Stochastic Optimization: a Review," International Statistical Review, International Statistical Institute, vol. 70(3), pages 315-349, December.
    5. Kopanos, Georgios M. & Georgiadis, Michael C. & Pistikopoulos, Efstratios N., 2013. "Energy production planning of a network of micro combined heat and power generators," Applied Energy, Elsevier, vol. 102(C), pages 1522-1534.
    6. Kou, Peng & Liang, Deliang & Gao, Lin, 2017. "Distributed EMPC of multiple microgrids for coordinated stochastic energy management," Applied Energy, Elsevier, vol. 185(P1), pages 939-952.
    7. Zhang, Jingrui & Wu, Yihong & Guo, Yiran & Wang, Bo & Wang, Hengyue & Liu, Houde, 2016. "A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints," Applied Energy, Elsevier, vol. 183(C), pages 791-804.
    8. Radhakrishnan, Bharat Menon & Srinivasan, Dipti, 2016. "A multi-agent based distributed energy management scheme for smart grid applications," Energy, Elsevier, vol. 103(C), pages 192-204.
    9. Mazidi, Mohammadreza & Monsef, Hassan & Siano, Pierluigi, 2016. "Robust day-ahead scheduling of smart distribution networks considering demand response programs," Applied Energy, Elsevier, vol. 178(C), pages 929-942.
    10. Peter C. Reiss & Matthew W. White, 2005. "Household Electricity Demand, Revisited," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 853-883.
    11. 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.
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