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Coordinated short-term scheduling and long-term expansion planning in microgrids incorporating renewable energy resources and energy storage systems

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  • Hemmati, Reza
  • Saboori, Hedayat
  • Siano, Pierluigi

Abstract

this paper presents a two-level stochastic microgrid planning tool. The proposed tool determines the optimal location and size of different technologies through a long-term plan as well as the optimal operation strategy for technologies through a short-term plan. The proposed planning tool considers distributed generation resources, energy storage systems, and lines as candidates for the expansion. One of the key characteristics of the introduced planning is its ability to tackle load and renewable energies uncertainties through stochastic planning. Both the long-term and short-term plans are mathematically expressed as mixed integer nonlinear programming problems and solved by using a strong Meta-heuristic optimization algorithm. Simulation results demonstrate that the proposed two-level planning method reduces the planning cost compared to the conventional method (i.e., only long-term planning). As well, it is indicated that considering line as an option for the expansion reduces the planning cost and increases the flexibility of the planning.

Suggested Citation

  • Hemmati, Reza & Saboori, Hedayat & Siano, Pierluigi, 2017. "Coordinated short-term scheduling and long-term expansion planning in microgrids incorporating renewable energy resources and energy storage systems," Energy, Elsevier, vol. 134(C), pages 699-708.
    • Handle: RePEc:eee:energy:v:134:y:2017:i:c:p:699-708
    DOI: 10.1016/j.energy.2017.06.081
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    6. Radhanon Diewvilai & Kulyos Audomvongseree, 2021. "Generation Expansion Planning with Energy Storage Systems Considering Renewable Energy Generation Profiles and Full-Year Hourly Power Balance Constraints," Energies, MDPI, vol. 14(18), pages 1-25, September.
    7. Firouzmakan, Pouya & Hooshmand, Rahmat-Allah & Bornapour, Mosayeb & Khodabakhshian, Amin, 2019. "A comprehensive stochastic energy management system of micro-CHP units, renewable energy sources and storage systems in microgrids considering demand response programs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 355-368.
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    9. Pang, Kexin & Zhou, Jian & Tsianikas, Stamatis & Coit, David W. & Ma, Yizhong, 2024. "Long-term microgrid expansion planning with resilience and environmental benefits using deep reinforcement learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    10. Pinto, Rafael S. & Unsihuay-Vila, Clodomiro & Tabarro, Fabricio H., 2021. "Coordinated operation and expansion planning for multiple microgrids and active distribution networks under uncertainties," Applied Energy, Elsevier, vol. 297(C).
    11. Nazar, Mehrdad Setayesh & Jafarpour, Pourya & Shafie-khah, Miadreza & Catalão, João P.S., 2024. "Optimal planning of self-healing multi-carriers energy systems considering integration of smart buildings and parking lots energy resources," Energy, Elsevier, vol. 286(C).
    12. Bakhtiari, Hamed & Zhong, Jin & Alvarez, Manuel, 2021. "Predicting the stochastic behavior of uncertainty sources in planning a stand-alone renewable energy-based microgrid using Metropolis–coupled Markov chain Monte Carlo simulation," Applied Energy, Elsevier, vol. 290(C).
    13. Dranka, Géremi Gilson & Ferreira, Paula & Vaz, A. Ismael F., 2021. "A review of co-optimization approaches for operational and planning problems in the energy sector," Applied Energy, Elsevier, vol. 304(C).
    14. Li, Bei & Roche, Robin & Paire, Damien & Miraoui, Abdellatif, 2018. "Optimal sizing of distributed generation in gas/electricity/heat supply networks," Energy, Elsevier, vol. 151(C), pages 675-688.
    15. Zhou, Huansheng & Zheng, J.H. & Li, Zhigang & Wu, Q.H. & Zhou, X.X., 2019. "Multi-stage contingency-constrained co-planning for electricity-gas systems interconnected with gas-fired units and power-to-gas plants using iterative Benders decomposition," Energy, Elsevier, vol. 180(C), pages 689-701.
    16. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    17. Wang, B. & Liu, L. & Huang, G.H. & Li, W. & Xie, Y.L., 2018. "Effects of carbon and environmental tax on power mix planning - A case study of Hebei Province, China," Energy, Elsevier, vol. 143(C), pages 645-657.
    18. Mandhir Kumar Verma & Vivekananda Mukherjee & Vinod Kumar Yadav & Santosh Ghosh, 2020. "Constraints for effective distribution network expansion planning: an ample review," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(3), pages 531-546, June.
    19. Mahdavi, Sajad & Hemmati, Reza & Jirdehi, Mehdi Ahmadi, 2018. "Two-level planning for coordination of energy storage systems and wind-solar-diesel units in active distribution networks," Energy, Elsevier, vol. 151(C), pages 954-965.
    20. Gazijahani, Farhad Samadi & Salehi, Javad, 2018. "Reliability constrained two-stage optimization of multiple renewable-based microgrids incorporating critical energy peak pricing demand response program using robust optimization approach," Energy, Elsevier, vol. 161(C), pages 999-1015.
    21. Mukhopadhyay, Bineeta & Das, Debapriya, 2021. "Optimal multi-objective expansion planning of a droop-regulated islanded microgrid," Energy, Elsevier, vol. 218(C).
    22. Verástegui, Felipe & Lorca, Álvaro & Olivares, Daniel & Negrete-Pincetic, Matias, 2021. "Optimization-based analysis of decarbonization pathways and flexibility requirements in highly renewable power systems," Energy, Elsevier, vol. 234(C).
    23. Bakhtiari, Hamed & Zhong, Jin & Alvarez, Manuel, 2022. "Uncertainty modeling methods for risk-averse planning and operation of stand-alone renewable energy-based microgrids," Renewable Energy, Elsevier, vol. 199(C), pages 866-880.

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