Optimal energy consuming planning for a home-based microgrid with mobility constraint of electric vehicles and tractors

Control Theory and Technology - Tập 19 - Trang 465-483 - 2021
Shota Inuzuka1, Tielong Shen1
1Faculty of science and technology, Sophia University, Tokyo, Japan

Tóm tắt

This research deals with the energy management problem to minimize the cost of non-renewable energy for a small-scale microgrid with electric vehicles (EV) and electric tractors (ET). The EVs and ETs function as batteries in the power system, while they often have to leave it for their mobility and agricultural work. Each State of Charge (SoC), which is the charge rate of the battery from 0 to 1, and the operating time of ETs are optimized under the assumption that the required electrical energy, the arrival and departure time of EVs, and the working time of ETs are given by users, but they include uncertainties. In this paper, we deal with these uncertainties by constraints for robust energy planning and expected optimization based on scenarios, and show that the scheduling of the SoC assuming the worst case and the optimal home-based power consumption planning that considers the cost of each scenario corresponding to each variation can be obtained. Our proposed method is formulated as a mixed-integer linear programming (MILP), and numerical simulations show that the optimal cooperative operation among multiple houses can be obtained and its global optimal or sub-optimal solution can be quickly obtained by using CPLEX.

Tài liệu tham khảo

Hatziargyriou, N., Asano, H., Iravani, R., & Marnay, C. (2007). Microgrids. IEEE Power and Energy Magazine, 5(4), 78–94. https://doi.org/10.1109/MPAE.2007.376583 Parisio, A., Rikos, E., Kyntäjä, T., Elo, J., & HenrikJohansson, K. (2014). A model predictive control approach to microgrid operation optimization. IEEE Transactions on Control Systems Technology 22(5), 1813–1827. https://doi.org/10.1109/TCST.2013.2295737 Parisio, A., Wiezorek, C., & Glielmo, L. (2015). An MPC-based energy management system for multiple residential microgrids. In IEEE international conference on automation science and engineering (pp. 7–14). Gothenburg, Sweden. https://doi.org/10.1109/CoASE.2015.7294033 Parisio, A., Wiezorek, C., Kyntäjä, T., Elo, J., Strunz, K., & Henrik Johansson, K. (2017). Cooperative MPC-based energy management for networked microgrids. IEEE Transactions on Smart Grid., 8(6), 3066–3074. https://doi.org/10.1109/TSG.2017.2726941 Sou, K. C., Weimer, J., Sandberg, H. & Johansson, K. H. (2011). Scheduling smart home appliances using mixed integer linear programming. In 50th IEEE conference on decision and control and European control conference (pp. 5144–5149). Orlando, FL, USA. https://doi.org/10.1109/CDC.2011.6161081 Hou, X., Wang, J., Huang, T., Wang, T., & Wang, P. (2019). Smart home energy management optimization method considering energy storage and electric vehicle. IEEE Access, 7, 144010–144020. https://doi.org/10.1109/ACCESS.2019.2944878 Erdinc, O., Paterakis, N. G., Mendes, T. D. P., Bakirtzis, A. G., & Catalão, J. P. S. (2014). Smart household operation considering bi-directional EV and ESS utilization by real-time pricing-based DR. IEEE Transactions on Smart Grid, 6(3), 1281–1291. https://doi.org/10.1109/TSG.2014.2352650 Kou, P., Liang, D., Gao, L., & Gao, F. (2015). Stochastic coordination of plug-in electric vehicles and wind turbines in microgrid: A model predictive control approach. IEEE Transactions on Smart Grid, 7(3), 1537–1551. https://doi.org/10.1109/TSG.2015.2475316 Zou, Y., Dong, Y., Li, S., & Niu, Y. (2019). Multi-time hierarchical stochastic predictive control for energy management of an island microgrid with plug-in electric vehicles. IET Generation, Transmission & Distribution., 13(10), 1794–1801. https://doi.org/10.1049/iet-gtd.2018.5332 Wu, C., Gao, S., Liu, Y., Song, T. E., & Han, H. (2021). A model predictive control approach in microgrid considering multi-uncertainty of electric vehicles. Renewable Energy, 163, 1385–1396. https://doi.org/10.1016/j.renene.2020.08.137 Umetani, S., Fukushima, Y., & Morita, H. (2017). A linear programming based heuristic algorithm for charge and discharge scheduling of electric vehicles in a building energy management system. Omega, 67, 115–122. https://doi.org/10.1016/j.omega.2016.04.005 Corchero, C., Cruz-Zambrano, M., & Heredia, F. J. (2014). Optimal energy management for a residential microgrid including a vehicle-to-grid system. IEEE Transactions on Smart Grid., 5(4), 2163–2172. https://doi.org/10.1109/TSG.2014.2318836 Wang, T., O’Neill, D., & Kamath, H. (2015). Dynamic control and optimization of distributed energy resources in a microgrid. IEEE Transactions on Smart Grid, 6(6), 2884–2894. https://doi.org/10.1109/TSG.2015.2430286 Battistelli, C., Baringo, L., & Conejo, A. J. (2012). Optimal energy management of small electric energy systems including V2G facilities and renewable energy sources. Electric Power Systems Research, 92, 50–59. https://doi.org/10.1016/j.epsr.2012.06.002 Lasseter, R. H., & Paigi, P. (2004). Microgrid: A conceptual solution. In IEEE 35th annual power electronics specialists conference (pp. 4285–4290). Aachen, Germany. https://doi.org/10.1109/PESC.2004.1354758 Motjoadi, V., Bokoro, P. N., & Onibonoje, M. O. (2020). A review of microgrid-based approach to rural electrification in South Africa: Architecture and policy framework. Energies, 13(9), 2193. https://doi.org/10.3390/en13092193 Longe, O. M., Ouahada, K., Ferreira, H. C., & Chinnappen, S. (2014). Renewable energy sources microgrid design for rural area in South Africa. In Proceedings of the innovative smart grid technologies (ISGT). Washington, DC, USA. https://doi.org/10.1109/ISGT.2014.6816378 Yang, J., Liu, J., Fang, Z., & Liu, W. (2018). Electricity scheduling strategy for home energy management system with renewable energy and battery storage: A case study. IET Renewable Generation, 12(6), 639–648. https://doi.org/10.1049/iet-rpg.2017.0330 Zhao, Z., Lee, W. C., Shin, Y., & Song, K. B. (2013). An optimal power scheduling method for demand response in home energy management system. IEEE Transactions on Smart Grid, 4(3), 1391–1400. https://doi.org/10.1109/TSG.2013.2251018 Pedrasa, Michael Angelo, & A., Spooner, Ted D., & MacGill, Iain F. (2009). Scheduling of demand side resources using binary particle swarm optimization. IEEE Transactions on Power Systems, 24(3), 1173–1181. https://doi.org/10.1109/TPWRS.2009.2021219 Mahmood, D., Javaid, N., Alrajeh, N., Khan, Z. A., Qasim, U., Ahmed, I., & Ilahi, M. (2016). Realistic scheduling mechanism for smart homes. Energies, 9(3), 202. https://doi.org/10.3390/en9030202 Hooshmand, A. Poursaeidi, M. H., Mohammadpour, J., Malki, H. A. & Grigoriads, K. (2012). Stochastic model predictive control method for microgrid management. In Proceedings of the IEEE PES innovative smart grid technologies. Washington, DC, USA. https://doi.org/10.1109/ISGT.2012.6175660 UCI Machine Learning Repository. Individual household electric power consumption Data Set. https://archive.ics.uci.edu/ml/ datasets/individual+household+electric+power+consumption London Datastore. Photovoltaic (PV) Solar Panel Energy Generation data. https://data.london.gov.uk/dataset/photovoltaic-pv-solar-panel-energy-generation-data