Coordinated Scheduling of Fuel Cell-Electric Vehicles and Solar Power Generation Considering Vehicle to Grid Bidirectional Energy Transfer Mode
Abstract
Home-to-Vehicle (H2V) appears as an interesting research area due to its public services that incorporates new technologies and new devices for better life quality. The objective is to study and analyze house energy needs to optimize more efficiently the energy production for an optimal economy. In this context, hydrogen-based hybrid electric stand-alone systems are considered as a promising option to ensure efficient power generation without interruption and to meet fuel vehicles requirements. To perform this, a specific H2V simulation system is developed incorporating electrolyzer technology, solar energy and a Supercapacitor. Thus, to maintain the energy balance between demand and production, the excess electrical energy will be stored under different forms (electrical or chemical (H2 gas)) according to system constrains. Therefore, the produced hydrogen through the excess will fueled the vehicle after the analysis of its state need. In fact, the flows exchange will be performed between the home and the PEMFC hybrid electric vehicle while supplying the appropriate amount H2. Therefore, it is necessary to develop an intelligent energy management (IEM) for the H2V system. The proposed IEM processes user preferences and manages the energy production and storage. The results obtained are discussed and tested using MATLAB/Simulink software.
Keywords
Hydrogen Supercapacitor Vehicle Control Storage Production H2VNomenclature
- IGEN
PV generated current (A)
- IDEM
Load consumption current (A)
- IAP
Appliance consumption current (A)
- QP
H2 produced amount (mol)
- SOCSC
Supercapacitor state of charge
- ISC
Supercapacitor current (A)
- ISCmax
Supercapacitor maximum current (A)
- Pst
Tank pressure (bar)
- Tst
Tank temperature (°C)
- Vst
Tank volume (l)
- SOCH2
H2 tank state of charge
- QS
H2 tank Stored amount (mol)
- QmaxS
H2 tank maximum stored amount (mol)
- NEL
Electrolyze cell numbers
- SOCEST
Estimated H2 tank state of charge
- SOCCH
Estimated Supercapacitor state of charge
- IST
Estimated excess generated current (A)
- ISCCH
Supercapacitor charging current (A)
- QH2V
Vehicle fuel delivery (mol)
- Qn
H2 needed amount (mol)
- QVEH
Vehicle fuel reserve (mol)
- F
Faraday constant
- R
Ideal gas constant
References
- 1.Gupta, E.: Global warming and electricity demand in the rapidly growing city of Delhi: A semi-parametric variable coefficient approach. Energy Econ. 34(5), 1407–1421 (2012)CrossRefGoogle Scholar
- 2.Celik, B., Roche, R., Bouquain, D., Miraoui, A.: Decentralized neighborhood energy management with coordinated smart home energy sharing. IEEE Trans. Smart Grid 9, 6387–6397 (2017)CrossRefGoogle Scholar
- 3.Wang, L., Kusiak, A., Dounis, A.: Guest Editorial special section on intelligent buildings and home energy management in a smart grid environment. IEEE Trans. Smart Grid 3(4), 2119–2120 (2012)CrossRefGoogle Scholar
- 4.Zhao, W., Ding, L., Cooper, P., Perez, P.: Smart home electricity management in the context of local power resources and smart grid. J. Clean Energy Technol. 73–79 (2014)Google Scholar
- 5.Zhang, Q., Zhang, S.: Smart home energy management with electric vehicles considering battery degradation. Adv. Mater. Res. 860–863, 1085–1091 (2013)CrossRefGoogle Scholar
- 6.Nezamoddini, N., Wang, Y.: Risk management and participation planning of electric vehicles in smart grids for demand response. Energy 116, 836–850 (2016)CrossRefGoogle Scholar
- 7.Hajizadeh, A., Kikhavani, M.: Coordination of bidirectional charging for plug-in electric vehicles in smart distribution systems. Electr. Eng. 100, 1085–1096 (2017)CrossRefGoogle Scholar
- 8.Kiat, L., Barsoum, N.: Smart home meter measurement and appliance control. Int. J. Innovative Res. Dev. 6(7) (2017)Google Scholar
- 9.Shirazi, E., Zakariazadeh, A., Jadid, S.: Optimal joint scheduling of electrical and thermal appliances in a smart home environment. Energy Convers. Manag. 106, 181–193 (2015)CrossRefGoogle Scholar
- 10.Kim, J.: HEMS (home energy management system) base on the IoT smart home. Contemp. Eng. Sci. 9, 21–28 (2016)CrossRefGoogle Scholar
- 11.Dinh, D.L., Kim, J.T., Kim, T.S.: Hand gesture recognition and interface via a depth imaging sensor for smart home appliances. Energy Procedia 62, 576–582 (2014)CrossRefGoogle Scholar
- 12.Tushar, M.H.K., Assi, C., Maier, M., Uddin, M.: Smart microgrids: Optimal joint scheduling for electric vehicles and home appliances. IEEE Trans. Smart Grid 5(1), 239–250 (2014)CrossRefGoogle Scholar
- 13.Setlhaolo, D., Xia, X.: Optimal scheduling of household appliances with a battery storage system and coordination. Energy Build. 94, 61–70 (2015)CrossRefGoogle Scholar
- 14.Yang, Y., Zhang, W., Jiang, J., Huang, M., Niu, L.: Optimal scheduling of a battery energy storage system with electric vehicles’ auxiliary for a distribution network with renewable energy integration. Energies 8(10), 10718–10735 (2015)CrossRefGoogle Scholar
- 15.Chen, Q., Ma, Y.: The research on cloud platform considered privacy household load data processing. Adv. Mater. Res. 1049–1050, 1929–1933 (2014)CrossRefGoogle Scholar
- 16.Wu, X., Hu, X., Teng, Y., Qian, S., Cheng, R.: Optimal integration of a hybrid solar-battery power source into smart home nanogrid with plug-in electric vehicle. J. Power Sources 363, 277–283 (2017)CrossRefGoogle Scholar
- 17.Wu, X., Hu, X., Teng, Y., Qian, S., Cheng, R.: Optimal integration of a hybrid solar-battery power source into smart home nanogrid with plug-in electric vehicle. J. Power Sources 363, 277–283 (2017)CrossRefGoogle Scholar
- 18.Jian, L., Zheng, Y., Xiao, X., Chan, C.: Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid. Appl. Energy 146, 150–161 (2015)CrossRefGoogle Scholar
- 19.Li, C.H., Zhu, X.J., Cao, G.Y., Sui, S., Hu, M.R.: Dynamic modeling and sizing optimization of stand-alone photovoltaic power systems using hybrid energy storage technology. Renew. Energy J. 34, 815–826 (2009)CrossRefGoogle Scholar
- 20.Hadartz, M., Julander, M.: Battery-Supercapacitor Energy Storage. Master of Science thesis in Electrical Engineering, Department of Energy and Environment, Division of Electric Power Engineering Chalmers University Of Technology, Göteborg, Sweden (2008)Google Scholar
- 21.Lajnef, T., Abid, S., Ammous, A.: Modeling, control, and simulation of a solar hydrogen/fuel cell hybrid energy system for grid-connected applications. Adv. Power Electron. 2013, 9 (2013). Hindawi Publishing CorporationCrossRefGoogle Scholar