Economic optimization of microgrid with demand response under source-load uncertainty

References

• Abdi, H., S. D. Beigvand, and M. La Scala. 2017. A review of optimal power flow studies applied to smart grids and microgrids. Renewable and Sustainable Energy Reviews 71:742–925. doi:10.1016/j.rser.2016.12.102.
   Web of Science ®Google Scholar
• Aghajani, G. R., H. A. Shayanfar, and H. Shayeghi. 2017. Demand side management in a smart microgrid in the presence of renewable generation and demand response. ENERGY 126:622–37. doi:10.1016/j.energy.2017.03.051.
   Web of Science ®Google Scholar
• Almadhor, A., H. T. Rauf, M. A. Khan, S. Kadry, and Y. Nam. 2021. A hybrid algorithm (BAPSO) for capacity configuration optimization in a distributed solar PV based microgrid. Energy Reports 7:7906–12. doi:10.1016/j.egyr.2021.01.034.
   Web of Science ®Google Scholar
• Askarzadeh, A. 2016. A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Computers & Structures 169:1–12. doi:10.1016/j.compstruc.2016.03.001.
   Web of Science ®Google Scholar
• Astriani, Y., G. M. Shafiullah, and F. Shahnia. 2021. Incentive determination of a demand response program for microgrids. Applied Energy 292:116624. doi:10.1016/j.apenergy.2021.116624.
   Web of Science ®Google Scholar
• Bahmani, R., H. Karimi, and S. Jadid. 2020. Stochastic electricity market model in networked microgrids considering demand response programs and renewable energy sources. International Journal of Electrical Power & Energy Systems 117:105606. doi:10.1016/j.ijepes.2019.105606.
   Web of Science ®Google Scholar
• Bakhshaei, P., A. Askarzadeh, and R. Arababadi. 2021. Operation optimization of a grid-connected photovoltaic/pumped hydro storage considering demand response program by an improved crow search algorithm. Journal of Energy Storage 44:103326. doi:10.1016/j.est.2021.103326.
   Web of Science ®Google Scholar
• Chaouachi, A., R. M. Kamel, R. Andoulsi, and K. Nagasaka. 2013. Multiobjective intelligent energy management for a microgrid. IEEE Transactions on Industrial Electronics 60 (4):1688–99. doi:10.1109/TIE.2012.2188873.
   Web of Science ®Google Scholar
• Chedid, R., A. Sawwas, and D. Fares. 2020. Optimal design of a university campus microgrid operating under unreliable grid considering PV and battery storage. ENERGY 200:117510. doi:10.1016/j.energy.2020.117510.
   Web of Science ®Google Scholar
• Chen, H., L. Gao, and Z. Zhang. 2021. Multi-objective optimal scheduling of a microgrid with uncertainties of renewable power generation considering user satisfaction. International Journal of Electrical Power & Energy Systems 131:107142. doi:10.1016/j.ijepes.2021.107142.
   Web of Science ®Google Scholar
• Chen, W., Z. Shao, K. Wakil, N. Aljojo, S. Samad, and A. Rezvani. 2020. An efficient day-ahead cost-based generation scheduling of a multi-supply microgrid using a modified krill herd algorithm. Journal of Cleaner Production 272:122364. doi:10.1016/j.jclepro.2020.122364.
   Web of Science ®Google Scholar
• Chen, Y., Y. Wang, D. Kirschen, and B. Zhang. 2018. Model-free renewable scenario generation using generative adversarial networks. IEEE Transactions on Power Systems 33 (3):3265–75. doi:10.1109/TPWRS.2018.2794541.
   Web of Science ®Google Scholar
• Dong, W., Q. Wang, and L. Yang. 2015. A coordinated dispatching model for a distribution utility and virtual power plants with wind/photovoltaic/hydro generators. Automation of Electric Power System 39:75–81.
   Google Scholar
• Ferruzzi, G., G. Cervone, L. Delle Monache, G. Graditi, and F. Jacobone. 2016. Optimal bidding in a Day-Ahead energy market for Micro Grid under uncertainty in renewable energy production. ENERGY 106:194–202. doi:10.1016/j.energy.2016.02.166.
   Web of Science ®Google Scholar
• Firouzmakan, P., R. Hooshmand, M. Bornapour, and A. Khodabakhshian. 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 108:355–68. doi:10.1016/j.rser.2019.04.001.
   Web of Science ®Google Scholar
• Gupta, R. A., and N. K. Gupta. 2015. A robust optimization based approach for microgrid operation in deregulated environment. Energy Conversion & Management 93:121–31. doi:10.1016/j.enconman.2015.01.008.
   Web of Science ®Google Scholar
• Hossain, M. A., H. R. Pota, S. Squartini, and A. F. Abdou. 2019. Modified PSO algorithm for real-time energy management in grid-connected microgrids. Renewable Energy 136:746–57. doi:10.1016/j.renene.2019.01.005.
   Web of Science ®Google Scholar
• Huang, S., and O. Abedinia. 2021. Investigation in economic analysis of microgrids based on renewable energy uncertainty and demand response in the electricity market. ENERGY 225:120247. doi:10.1016/j.energy.2021.120247.
   Web of Science ®Google Scholar
• Hwang Goh, H., S. Shi, X. Liang, D. Zhang, W. Dai, H. Liu, S. Yuong Wong, T. Agustiono Kurniawan, K. Chen Goh, and C. Leei Cham. 2022. Optimal energy scheduling of grid-connected microgrids with demand side response considering uncertainty. Applied Energy 327:120094. doi:10.1016/j.apenergy.2022.120094.
   Web of Science ®Google Scholar
• Jin, S., Z. Mao, and H. Li. 2018. Economic operation optimization of smart micro grid considering the uncertainty. Control Theory & Application 35:1357–70.
   Google Scholar
• Karimi, H., G. B. Gharehpetian, R. Ahmadiahangar, and A. Rosin. 2023. Optimal energy management of grid-connected multi-microgrid systems considering demand-side flexibility: A two-stage multi-objective approach. Electric Power Systems Research 214:108902. doi:10.1016/j.epsr.2022.108902.
   Web of Science ®Google Scholar
• Kriett, P. O., and M. Salani. 2012. Optimal control of a residential microgrid. ENERGY 42 (1):321–30. doi:10.1016/j.energy.2012.03.049.
   Web of Science ®Google Scholar
• Kumar, K. P., B. Saravanan, and K. S. Swarup. 2016. Optimization of renewable energy sources in a microgrid using artificial fish swarm algorithm. Energy Procedia 90:107–13. doi:10.1016/j.egypro.2016.11.175.
   Google Scholar
• Liu, Y., S. Yang, D. Li, and S. Zhang. 2023. Improved whale optimization algorithm for solving microgrid operations planning problems. Symmetry 15 (1):36. doi:10.3390/sym15010036.
   Google Scholar
• Luo, L., S. S. Abdulkareem, A. Rezvani, M. R. Miveh, S. Samad, N. Aljojo, and M. Pazhoohesh. 2020. Optimal scheduling of a renewable based microgrid considering photovoltaic system and battery energy storage under uncertainty. Journal of Energy Storage 28:101306. doi:10.1016/j.est.2020.101306.
   Web of Science ®Google Scholar
• Paliwal, N. K., A. K. Singh, and N. K. Singh. 2019. A day-ahead optimal energy scheduling in a remote microgrid along with battery storage system via global best guided ABC algorithm. Journal of Energy Storage 25:100877. doi:10.1016/j.est.2019.100877.
   Web of Science ®Google Scholar
• Rana, M. J., K. H. Rahi, T. Ray, and R. Sarker. 2021. An efficient optimization approach for flexibility provisioning in community microgrids with an incentive-based demand response scheme. Sustainable Cities and Society 74:103218. doi:10.1016/j.scs.2021.103218.
   Web of Science ®Google Scholar
• Raya-Armenta, J. M., N. Bazmohammadi, J. G. Avina-Cervantes, D. Sáez, J. C. Vasquez, and J. M. Guerrero. 2021. Energy management system optimization in islanded microgrids: An overview and future trends. Renewable and Sustainable Energy Reviews 149:111327. doi:10.1016/j.rser.2021.111327.
   Web of Science ®Google Scholar
• Rezvani, A., M. Gandomkar, M. Izadbakhsh, and A. Ahmadi. 2015. Environmental/Economic scheduling of a microgrid with renewable energy resources. Journal of Cleaner Production 87:216–26. doi:10.1016/j.jclepro.2014.09.088.
   Web of Science ®Google Scholar
• Saeed, M. H., W. Fangzong, S. Salem, Y. A. Khan, B. A. Kalwar, and A. Fars. 2021. Two-stage intelligent planning with improved artificial bee colony algorithm for a microgrid by considering the uncertainty of renewable sources. Energy Reports 7:8912–28. doi:10.1016/j.egyr.2021.10.123.
   Web of Science ®Google Scholar
• Samieiyan, B., P. MohammadiNasab, M. A. Mollaei, F. Hajizadeh, and M. Kangavari. 2022. Novel optimized crow search algorithm for feature selection. Expert Systems with Applications 204:117486. doi:10.1016/j.eswa.2022.117486.
   Web of Science ®Google Scholar
• Tang, S., M. Jiang, R. Abbassi, H. Jerbi, and M. Latifi. 2021. A cost-oriented resource scheduling of a solar-powered microgrid by using the hybrid crow and pattern search algorithm. Journal of Cleaner Production 313:127853. doi:10.1016/j.jclepro.2021.127853.
   Web of Science ®Google Scholar
• Wu, X., S. Qi, Z. Wang, C. Duan, X. Wang, and F. Li. 2019. Optimal scheduling for microgrids with hydrogen fueling stations considering uncertainty using data-driven approach. Applied Energy 253:113568. doi:10.1016/j.apenergy.2019.113568.
   Web of Science ®Google Scholar
• Xu, H., Z. Meng, and Y. Wang. 2020. Economic dispatching of microgrid considering renewable energy uncertainty and demand side response. Energy Reports 6:196–204. doi:10.1016/j.egyr.2020.11.261.
   Web of Science ®Google Scholar
• Yang, M., Y. Cui, and J. Wang. 2023. Multi-objective optimal scheduling of island microgrids considering the uncertainty of renewable energy output. International Journal of Electrical Power & Energy Systems 144:108619. doi:10.1016/j.ijepes.2022.108619.
   Web of Science ®Google Scholar
• Zhang, J., Y. Wu, Y. Guo, B. Wang, H. Wang, and H. Liu. 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 183:791–804. doi:10.1016/j.apenergy.2016.09.035.
   Web of Science ®Google Scholar