Risk-based optimal operation of hybrid power system using multiobjective optimization

References

• Aboli, R., M. Ramezani, and H. Falaghi. 2019. Joint optimization of day-ahead and uncertain near real-time operation of microgrids. International Journal of Electrical Power & Energy Systems 107:34–46. doi:10.1016/j.ijepes.2018.10.032.
   Web of Science ®Google Scholar
• Ahamdi, S. P., S. N. Lordejani, A. R. Kian, A. M. Milasi, and P. M. Vahdati. 2013. Uncertainty based configuration design and optimal operation of a grid-connected Micro-Grid. Smart Grid Conference, Tehran: 102–07. doi: 10.1109/SGC.2013.6733814.
   Google Scholar
• Aluisio, B., A. Conserva, M. Dicorato, G. Forte, and M. Trovato. 2017. Optimal operation planning of V2G-equipped Microgrid in the presence of EV aggregator. Electric Power Systems Research 152:295–305. doi:10.1016/j.epsr.2017.07.015.
   Web of Science ®Google Scholar
• Basu, M. 2014. Fuel constrained economic emission dispatch using nondominated sorting genetic algorithm-II. Energy 78:649–64. doi:10.1016/j.energy.2014.10.052.
   Web of Science ®Google Scholar
• Chamandoust, H. 2018. Economic scheduling of microgrid based on energy management and demand response. Electrical, Control and Communication Engineering 14 (2):100–07. doi:10.2478/ecce-2018-0012.
   Web of Science ®Google Scholar
• Fenglei, G., X. Tao, X. Hao, W. Chong, and N. Jingmeng. 2016. An uncertainty reduction strategy to schedule and operate microgrids with renewable energy sources. IEEE PES Asia-Pacific Power and Energy Engineering Conference, Xi’an. 1191–99. doi: 10.1109/APPEEC.2016.7779681.
   Google Scholar
• Gazijahani, F. S., and J. Salehi. 2017. Stochastic multi-objective framework for optimal dynamic planning of interconnected microgrids. IET Renewable Power Generation 11 (14):1749–59. doi:10.1049/iet-rpg.2017.0278.
   Web of Science ®Google Scholar
• Hemmati, M., B. M. Ivatloo, S. Ghasemzadeh, and E. Reihani. 2018. Risk-based optimal scheduling of reconfigurable smart renewable energy based microgrids. International Journal of Electrical Power & Energy Systems 101:415–28. doi:10.1016/j.ijepes.2018.04.005.
   Web of Science ®Google Scholar
• Hu, Y., Z. Bie, T. Ding, and Y. Lin. 2016. An NSGA-II based multi-objective optimization for combined gas and electricity network expansion planning. Applied Energy 167:280–93. doi:10.1016/j.apenergy.2015.10.148.
   Web of Science ®Google Scholar
• Jafari, E. 2019. Determining optimal strategy of a micro-grid through hybrid method of nash equilibrium –genetic algorithm. International Journal of Emerging Electric Power Systems 20 (3). doi: 10.1515/ijeeps-2017-0148.
   Web of Science ®Google Scholar
• Khalid, M. 2019. Wind power economic dispatch – impact of radial basis functional networks and battery energy storage. IEEE Access 7:36819–32. doi:10.1109/ACCESS.2019.2905620.
   Web of Science ®Google Scholar
• Kumari, M. S., and S. Maheswarapu. 2010. Enhanced genetic algorithm based computation technique for multi-objective optimal power flow solution. International Journal of Electrical Power & Energy Systems 32 (6):736–42. doi:10.1016/j.ijepes.2010.01.010.
   Web of Science ®Google Scholar
• Li, X., and C. Jiang. 2011. Short-term operation model and risk management for wind power penetrated system in electricity market. IEEE Transactions on Power Systems 26 (2):932–39. doi:10.1109/TPWRS.2010.2070882.
   Web of Science ®Google Scholar
• Mao, M., P. Jin, L. Chang, and H. Xu. 2014. Economic analysis and optimal design on microgrids with SS-PVs for industries. IEEE Transactions on Sustainable Energy 5 (4):1328–36. doi:10.1109/TSTE.2014.2327067.
   Web of Science ®Google Scholar
• Maulik, A., and D. Das. 2017. Optimal operation of microgrid using four different optimization techniques. Sustainable Energy Technologies and Assessments 21:100–20. doi:10.1016/j.seta.2017.04.005.
   Web of Science ®Google Scholar
• Meiqin, M., S. Shujuan, and L. Chang. 2011. Economic analysis of the microgrid with multi-energy and electric vehicles. 8th International Conference on Power Electronics - ECCE Asia, Jeju: 2067–72. doi: 10.1109/ICPE.2011.5944505.
   Google Scholar
• Parag, Y., and M. Ainspan. 2019. Sustainable microgrids: Economic, environmental and social costs and benefits of microgrid deployment. Energy for Sustainable Development 52:72–81. doi:10.1016/j.esd.2019.07.003.
   Web of Science ®Google Scholar
• Parvizimosaed, M., F. Farmani, A. Rahimi-Kian, and H. Monsef. 2014. A multi-objective optimization for energy management in a renewable micro-grid system: A data mining approach. Journal of Renewable and Sustainable Energy 6 (2):023127. doi:10.1063/1.4873997.
   Web of Science ®Google Scholar
• Rabiee, A., S. M. M. Bonab, T. Soltani, and L. Bayat. 2017. A risk-based two-stage stochastic optimal power flow considering the impact of multiple operational uncertainties. Journal of Energy Management and Technology 30–42. doi:10.22109/JEMT.2017.46746.
   Google Scholar
• Reddy, S. S. 2017. Optimal scheduling of thermal-wind-solar power system with storage. Renewable Energy 101:1357–68. doi:10.1016/j.renene.2016.10.022.
   Web of Science ®Google Scholar
• Reddy, S. S., P. R. Bijwe, and A. R. Abhyankar. 2015. Optimal posturing in day-ahead market clearing for uncertainties considering anticipated real-time adjustment costs. IEEE Systems Journal 9 (1):177–90. doi:10.1109/JSYST.2013.2265664.
   Web of Science ®Google Scholar
• Reddy, S. S., J. Y. Park, and C. M. Jung. 2016. Optimal operation of microgrid using hybrid differential evolution and harmony search algorithm. Frontiers in Energy 10 (3):355–62. doi:10.1007/s11708-016-0414-x.
   Web of Science ®Google Scholar
• Reddy, S. S., and C. S. Rathnam. 2016. Optimal power flow using glowworm swarm optimization. International Journal of Electrical Power & Energy Systems 80:128–39. doi:10.1016/j.ijepes.2016.01.036.
   Web of Science ®Google Scholar
• Salkuti, S. R. 2019. Optimal operation management of grid-connected microgrids under uncertainty. Indonesian Journal of Electrical Engineering and Computer Science 16 (3):1163–70. doi:10.11591/ijeecs.v16.i3.pp1163-1170.
   Google Scholar
• Savari, G. F., V. Krishnasamy, and J. M. Guerrero. 2018. Optimal scheduling and economic analysis of hybrid electric vehicles in a microgrid. International Journal of Emerging Electric Power Systems 19 (6). doi: 10.1515/ijeeps-2018-0104.
   Web of Science ®Google Scholar
• Shams, M. H., M. Shahabi, M. Kia, A. Heidari, M. Lotfi, M. S. Khah, and J. P. S. Catalão. 2019. Optimal operation of electrical and thermal resources in microgrids with energy hubs considering uncertainties. Energy 187:1–12. doi:10.1016/j.energy.2019.115949.
   Web of Science ®Google Scholar
• Silva, E. G., M. Heleno, S. Mashayekh, G. Cardoso, R. Girard, and G. Kariniotakis. 2018. A stochastic optimal power flow for scheduling flexible resources in microgrids operation. Applied Energy 229:201–08. doi:10.1016/j.apenergy.2018.07.114.
   Web of Science ®Google Scholar
• Tavakoli, M., F. Shokridehaki, M. F. Akorede, M. Marzband, I. Vechiu, and E. Pouresmaeil. 2018. CVaR-based energy management scheme for optimal resilience and operational cost in commercial building microgrids. International Journal of Electrical Power & Energy Systems 100:1–9. doi:10.1016/j.ijepes.2018.02.022.
   Web of Science ®Google Scholar
• Wang, L., Q. Li, B. Zhang, R. Ding, and M. Sun. 2019a. Robust multi-objective optimization for energy production scheduling in microgrids. Engineering Optimization 51 (2):332–51. doi:10.1080/0305215X.2018.1457655.
   Web of Science ®Google Scholar
• Wang, Y., F. Li, H. Yu, Y. Wang, C. Qi, J. Yang, and F. Song. 2019b. Optimal operation of microgrid with multi-energy complementary based on moth flame optimization algorithm. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. doi:10.1080/15567036.2019.1587067.
   Web of Science ®Google Scholar
• Wu, J., Z. Wu, F. Wu, and X. Mao. 2017. A power balancing method of distributed generation and electric vehicle charging for minimizing operation cost of distribution systems with uncertainties. Energy Science and Engineering 5 (3):167–79. doi:10.1002/ese3.157.
   Web of Science ®Google Scholar
• Xiong, X., W. Wu, N. Li, L. Yang, J. Zhang, and Z. Wei. 2019. Risk-based multi-objective optimization of distributed generation based on GPSO-BFA algorithm. IEEE Access 7:30563–72. doi:10.1109/ACCESS.2019.2902886.
   Web of Science ®Google Scholar
• Yang, D., Q. Chong, B. Hu, and M. Ma. 2018. Optimal operation of microgrid based on user power unsatisfactory. Energy Procedia 145:76–81. doi:10.1016/j.egypro.2018.04.013.
   Google Scholar
• Zadsar, M., S. S. Sebtahmadi, M. Kazemi, S. M. M. Larimi, and M. R. Haghifam. 2020. Two stage risk based decision making for operation of smart grid by optimal dynamic multi-microgrid. International Journal of Electrical Power & Energy Systems 118. doi:10.1016/j.ijepes.2019.105791.
   Web of Science ®Google Scholar