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Intelligent demand side management for optimal energy scheduling of grid connected microgrids

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  • Kumar, R. Seshu
  • Raghav, L. Phani
  • Raju, D. Koteswara
  • Singh, Arvind R.

Abstract

The incorporation of renewables and communication technologies to the utility paves a way for self-sustained microgrids (MG). The volatile nature of these resources, uncertainties associated with the time-varying load, and market prices impose the significance of an efficient energy management system (EMS). So far, the MG optimal operation has been referred to optimize the operating costs only. However, the prospects of incorporating demand-side management (DSM) with the EMS problem and its effect on total operating cost and peak reduction is needed to be evaluated. To fill this gap, the impact of utility induced flexible load shaping strategy on non-dispatchable energy sources is investigated in this paper. A three-stage stochastic EMS framework is proposed for solving optimal day-ahead scheduling and minimizing the operational cost of grid-connected MG. In the first stage, four possible scenarios for solar and wind power generation profiles are created to address the uncertainty problem by considering real-time meteorological data. The second stage deals with the MG system configuration, operational constraints, and assigning DSM load participation data to be incorporated with the objective function. In this regard, the Quantum Particle Swarm Optimization is devised at stage three to obtain the optimal power dispatch configuration for DG units, maximizing the power export to the utility and compare the results with and without incorporating DSM participation for all scenarios. The obtained simulation results show the competence of the proposed stochastic framework about cost reduction by 43.81% with the implementation of the load participation level of 20% DSM.

Suggested Citation

  • Kumar, R. Seshu & Raghav, L. Phani & Raju, D. Koteswara & Singh, Arvind R., 2021. "Intelligent demand side management for optimal energy scheduling of grid connected microgrids," Applied Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:appene:v:285:y:2021:i:c:s0306261921000040
    DOI: 10.1016/j.apenergy.2021.116435
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    1. Hossain, Md Alamgir & Pota, Hemanshu Roy & Squartini, Stefano & Zaman, Forhad & Guerrero, Josep M., 2019. "Energy scheduling of community microgrid with battery cost using particle swarm optimisation," Applied Energy, Elsevier, vol. 254(C).
    2. Amrollahi, Mohammad Hossein & Bathaee, Seyyed Mohammad Taghi, 2017. "Techno-economic optimization of hybrid photovoltaic/wind generation together with energy storage system in a stand-alone micro-grid subjected to demand response," Applied Energy, Elsevier, vol. 202(C), pages 66-77.
    3. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Kendall, Alissa & Træholt, Chresten, 2018. "Optimization of a biomass-integrated renewable energy microgrid with demand side management under uncertainty," Applied Energy, Elsevier, vol. 230(C), pages 836-844.
    4. Moghaddam, Amjad Anvari & Seifi, Alireza & Niknam, Taher & Alizadeh Pahlavani, Mohammad Reza, 2011. "Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source," Energy, Elsevier, vol. 36(11), pages 6490-6507.
    5. 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.
    6. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2015. "Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule," Applied Energy, Elsevier, vol. 149(C), pages 194-203.
    7. Wang, Dongxiao & Qiu, Jing & Reedman, Luke & Meng, Ke & Lai, Loi Lei, 2018. "Two-stage energy management for networked microgrids with high renewable penetration," Applied Energy, Elsevier, vol. 226(C), pages 39-48.
    8. Yan, Jie & Menghwar, Mohan & Asghar, Ehtisham & Kumar Panjwani, Manoj & Liu, Yongqian, 2019. "Real-time energy management for a smart-community microgrid with battery swapping and renewables," Applied Energy, Elsevier, vol. 238(C), pages 180-194.
    9. Mehdizadeh, Ali & Taghizadegan, Navid & Salehi, Javad, 2018. "Risk-based energy management of renewable-based microgrid using information gap decision theory in the presence of peak load management," Applied Energy, Elsevier, vol. 211(C), pages 617-630.
    10. Zhu, Junjie & Huang, Shengjun & Liu, Yajie & Lei, Hongtao & Sang, Bo, 2021. "Optimal energy management for grid-connected microgrids via expected-scenario-oriented robust optimization," Energy, Elsevier, vol. 216(C).
    11. Poolla, Chaitanya & Ishihara, Abraham K. & Milito, Rodolfo, 2019. "Designing near-optimal policies for energy management in a stochastic environment," Applied Energy, Elsevier, vol. 242(C), pages 1725-1737.
    12. Meng, Lexuan & Sanseverino, Eleonora Riva & Luna, Adriana & Dragicevic, Tomislav & Vasquez, Juan C. & Guerrero, Josep M., 2016. "Microgrid supervisory controllers and energy management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1263-1273.
    13. Moretti, Luca & Martelli, Emanuele & Manzolini, Giampaolo, 2020. "An efficient robust optimization model for the unit commitment and dispatch of multi-energy systems and microgrids," Applied Energy, Elsevier, vol. 261(C).
    14. Elattar, Ehab E. & ElSayed, Salah K., 2020. "Probabilistic energy management with emission of renewable micro-grids including storage devices based on efficient salp swarm algorithm," Renewable Energy, Elsevier, vol. 153(C), pages 23-35.
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    11. Khaizaran Abdulhussein Al Sumarmad & Nasri Sulaiman & Noor Izzri Abdul Wahab & Hashim Hizam, 2022. "Microgrid Energy Management System Based on Fuzzy Logic and Monitoring Platform for Data Analysis," Energies, MDPI, vol. 15(11), pages 1-19, June.
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    13. Romain Mannini & Julien Eynard & Stéphane Grieu, 2022. "A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-37, September.
    14. Ali M. Jasim & Basil H. Jasim & Habib Kraiem & Aymen Flah, 2022. "A Multi-Objective Demand/Generation Scheduling Model-Based Microgrid Energy Management System," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
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    17. Phani Raghav, L. & Seshu Kumar, R. & Koteswara Raju, D. & Singh, Arvind R., 2022. "Analytic Hierarchy Process (AHP) – Swarm intelligence based flexible demand response management of grid-connected microgrid," Applied Energy, Elsevier, vol. 306(PB).
    18. Zhou, Xu & Ma, Zhongjing & Zou, Suli & Zhang, Jinhui, 2022. "Consensus-based distributed economic dispatch for Multi Micro Energy Grid systems under coupled carbon emissions," Applied Energy, Elsevier, vol. 324(C).
    19. Seshu Kumar, R. & Phani Raghav, L. & Koteswara Raju, D. & Singh, Arvind R., 2021. "Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids," Applied Energy, Elsevier, vol. 301(C).

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