Day-ahead Management of Energy Sources and Storage in Hybrid Microgrid to reduce Uncertainty
Day-ahead Management of Energy Sources and Storage in Hybrid Microgrid to reduce Uncertainty
A day ahead management strategy is proposed in this article to schedule energy generators and storage in presence of Renewable Energy Sources under uncertainty conditions with an objective to optimize the cost of energy generation. Artificial Fish Swarm algorithm is used as optimization tool. The optimization problem is framed considering all the practical constraints of energy generators and storage units. The uncertainty of Renewable Energy Sources is treated with a proven uncertainty model and several scenarios are drawn for energy availability and demand. The proposed energy management algorithm is tested numerically on a grid connected microgrid hosting a group of hybrid energy sources and storage battery for day ahead scheduling under dynamic pricing and demand side management in one of the generated uncertainty scenarios. The obtained results show that the performance of Artificial Fish Swarm algorithm as an optimizing tool is validated and the proposed Energy Management System is found to optimize the cost of energy generation while matching the power generated with power required.
___
- [1] https://www.morganstanley.com/ideas/clean-energy-trump.html. (accessed on 25.12.2018). [2] http://economictimes.indiatimes.com/industry /energy/ power/ solar-power-tariff-drops-tohistoric-low-at-rs-2-44-per-nit/articleshow/58649942.cm,(accessed on 25.12.2018). [3] K. Prakash Kumar, B. Saravanan, “Recent techniques to model uncertainty in power generation from renewable energy sources in microgrids”, J. Renew Sustain Energy Rev, 71, 348-58, (2017). [4] Basu Ashoke Kumar, Chowdhury SP, Chowdhury S, Paul S. “Microgrids: energy management by strategic deployment of DERs-A comprehensive review”, Renew Sustain Energy Rev 15, 4348– 56, (2011). [5] El Bakari K, Kling WL., “Virtual power plant: Answer to increasing distributed generation”, IEEE Proc, PES conf Innov Smartgrid Technol (Eur), 11-13, 1-6, (2010). [6] K. Prakash Kumar, B. Saravanan and K.S.Swarup, “A two stage increase decrease algorithm to optimize distributed generation in a virtual power plant”, Energy Procedia, 90, 276 – 82, (2016). [7] GE Energy . Western wind and solar integration study [tech rep]. NREL (2010). [8] Hawkes AD, Leach MA. “Modelling high level system design and unit commitment for a microgrid”, Appl Energy, 86, 1253–65, (2009). [9] Van der Kam M, van Sark W. “Smart charging of electric vehicles with photovoltaic power and vehicle-to-grid technology in a microgrid; a case study”, Appl Energy, 152, 20–30, (2015). [10] Zhang Z, Wang J, Wang X. “An improved charging/discharging strategy of lithium batteries considering depreciation cost in day-ahead microgrid scheduling”, Energy Convers Manage, 105, 675–84, (2015). [11] Mallol-Poyato R, Salcedo-Sanz S, Jimenez-Fernandez S, Diaz-Villar P. “Optimal discharge scheduling of energy storage systems in MicroGrids based on hyperheuristics”, Renew Energy, 83, 13–24, (2015). [12] Thillainathan Logenthiran , Dipti Srinivasan, Tan Zong Shun, “Demand side management in smartgrid using heuristic optimization”, IEEE transaction on Smartgrid, 3, no.3, (2012). [13] Zakariazadeh Alireza, Jadid Shahram, Siano Pierluigi. “Smart microgrid energy and reserve scheduling with demand response using stochastic optimization”, Electr Power Energy Syst, 63, 523–33, (2014).
- [14] Montuori L, Alcazar-Ortega M, Alvarez-Bel C, Domijan A. “Integration of renewable energy in microgrids coordinated with demand response resources: economic evaluation of a biomass gasification plant by Homer Simulator”, Appl Energy, 132, 15–22, (2014). [15] Mazidi M, Zakariazadeh A, Jadid S, Siano P. “Integrated scheduling of renewable generation and demand response programs in a microgrid”, Energy Convers Manage, 86, 1118–27, (2014). [16] Mohammadreza Mazidi, Hassan Monsef,Pierluigi Siano, “Robust day-ahead scheduling of smart distribution networks considering demand response programs”, Applied Energy 178 (2016) 929– 942, (2016). [17] Cherukuri, S. Hari Charan, and B. Saravanan. "A novel energy management algorithm for reduction of main grid dependence in future smart grids using electric springs." Sustainable Energy Technologies and Assessments 21, 1-12, (2017). [18] Talari S, Yazdaninejad M, Haghifam M. “Stochastic-based scheduling of the microgrid operation including wind turbines, photovoltaic cells, energystorages and responsive loads”, IET Gener Transm Distrib, 9, 1498–509, (2015). [19] Najibi F, Niknam T. “Stochastic scheduling of renewable micro-grids considering photovoltaic source uncertainties”, Energy Convers Manage, 98, 484–99, (2015). [20] Najibi F, Niknam T. “Stochastic scheduling of renewable micro-grids considering photovoltaic source uncertainties”, Energy Convers Manage, 98, 484–99, (2015). [21] Zakariazadeh A, Jadid S, Siano P. “Smart microgrid energy and reserve scheduling with demand response using stochastic optimization”, Int J Electr Power Energy Syst, 63, 523–33, (2014). [22] Yu Zhang, NikolaosGatsis, and Georgios B. Giannakis, “Robust Energy Management for Microgrids With High-Penetration Renewables”, IEEE Transactions on Sustainable energy, 4, No.4, (2013). [23] Amin Khodaei, “Microgrid Optimal Scheduling With Multi-Period Islanding Constraints, IEEE Trasactions on power systems”, 29,No.3, (2014). [24] Emmanual Cristoni, Marco Raugi and Robert Shorten (2014), “Plug and Play distributed algorithm for Optimized power Generation in a Microgrid”, IEEE Transactions on Smart Grid, 4, 2145-54, (2014). [25] Yaowang Li, Shihong Miao, Xing Luo, Jihong Wang, “Optimization scheduling model based on source-load-energy storage coordination in power systems”, International Conference on Automation and Computing. IEEE, 120–125, (2016). [26] Zhigang Li, Wenchuan Wu, Boming Zhang, “Adjustable robust real-time power dispatch with large-scale wind power integration”, IEEE Trans. Sustain. Energy, 6 (2), 357–68, (2015). [27] Bo Xing, Wen Jing Gao, “Innovative computational intelligence: A rough guide to 134 clever algorithms”, Springer International Publishing, Switzerland, (2014). [28] K. Prakash Kumar, B. Saravanan, “Day ahead scheduling of generation and storage in a microgrid considering demand side management”, Journal of Energy Storage, 21, 78–86, (2019). [29] Sina Parhizi, Amin Khodaei, Mohammad Shahidehpour, “Market based verses Price based microgrid optimal scheduling”, IEEE Trans on Smartgrids, 9(2), 615-23, (2018).
- [30] Braun B, Philipp B, Swierczynski S, Jozef MM, Diosi D, Robert R, Stroe S, Loan D. Teodorescu T, Remus R. “Optimizing a hybrid energy storage system for a virtual power plant for improved wind power generation: A case study for Denmark”, Proceedings of the 6th International Renewable Energy Storage Conference and Exhibition, IRES, 1–9, (2011).