Utilizing plug-in electric vehicles for peak shaving and valley filling in non-residential buildings with solar photovoltaic systems

This paper examines the concept of utilizing plug-in electric vehicles (PEVs) and solar photovoltaic (PV) systems in large non-residential buildings for peak shaving and valley filling the power consumption profile, given that the energy cost of commercial electricity customers typically depends on both actual consumption and peak power demand within the billing period. Specifically, it describes a hybrid approach that combines an artificial neural network (ANN) for solar irradiance forecasting with a MATLAB/Simulink model to simulate the power output of solar PV systems, as well as the development of a mathematical model to control the charging/discharging process of the PEVs. The results obtained from simulating the case of the power consumption of a university building, along with experimental parking occupancy data from a university parking lot, demonstrate the applicability and effectiveness of the proposed approach.

[1]  William G. Temple,et al.  Intelligent electric vehicle charging: Rethinking the valley-fill , 2011 .

[2]  Li Zhang,et al.  Coordinating plug-in electric vehicle charging with electric grid: Valley filling and target load following , 2014 .

[3]  Xiang Cheng,et al.  Electrified Vehicles and the Smart Grid: The ITS Perspective , 2014, IEEE Transactions on Intelligent Transportation Systems.

[4]  Ranganath Muthu,et al.  Mathematical modeling of photovoltaic module with Simulink , 2011, 2011 1st International Conference on Electrical Energy Systems.

[5]  Christos S. Ioakimidis,et al.  Wind Power Forecasting in a Residential Location as Part of the Energy Box Management Decision Tool , 2014, IEEE Transactions on Industrial Informatics.

[6]  Alec Brooks,et al.  Demand Dispatch , 2010, IEEE Power and Energy Magazine.

[7]  Vittorio Maniezzo,et al.  A distributed geographic information system for the daily car pooling problem , 2004, Comput. Oper. Res..

[8]  Chee Wei Tan,et al.  A review of energy sources and energy management system in electric vehicles , 2013 .

[9]  Dragan Simic,et al.  Solar production forecasting based on irradiance forecasting using artificial neural networks , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[10]  Zhenpo Wang,et al.  Grid Power Peak Shaving and Valley Filling Using Vehicle-to-Grid Systems , 2013, IEEE Transactions on Power Delivery.

[11]  Christos S. Ioakimidis,et al.  Design, architecture and implementation of a residential energy box management tool in a SmartGrid , 2014 .

[12]  Dragan Simic,et al.  A non-myopic approach for a domotic battery management system , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[13]  Christos S. Ioakimidis,et al.  Simulation and design of a fast charging battery station in a parking lot of an e-carsharing system , 2015, 2015 International Conference on Renewable Energy Research and Applications (ICRERA).

[14]  Santiago Grijalva,et al.  Prosumer-based smart grid architecture enables a flat, sustainable electricity industry , 2011, ISGT 2011.

[15]  Ling Guan,et al.  Optimal Scheduling for Charging and Discharging of Electric Vehicles , 2012, IEEE Transactions on Smart Grid.

[16]  Taskin Koçak,et al.  Smart Grid Technologies: Communication Technologies and Standards , 2011, IEEE Transactions on Industrial Informatics.

[17]  Jin-Woo Jung,et al.  Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration , 2014 .

[18]  Richard Katzev,et al.  Car Sharing: A New Approach to Urban Transportation Problems , 2003 .

[19]  Willett Kempton,et al.  Vehicle-to-grid power fundamentals: Calculating capacity and net revenue , 2005 .

[20]  Christos S. Ioakimidis,et al.  Short-term wind speed forecasting model based on ANN with statistical feature parameters , 2015, IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society.