Aggregation of V2H Systems to Participate in Regulation Market

Ancillary services are becoming an indispensable tool for maintaining power grid stability due to the increasing adoption of renewable energy resources, many of which (e.g., wind and solar power) are inherently variable. Some energy resources, such as electric vehicles (EVs), have a significant potential for providing their own ancillary services and creating ancillary service markets in smart electric grids. The installation convenience of EVs and plug-in hybrid vehicles (PHVs) has made them the target of many studies. In previous works, the grid-integrated-vehicle (GIV) mechanisms are recognized as a suitable approach to exploit EVs and PHVs for ancillary service markets, particularly regulation markets, which require fast responses. It is important to consider individual consumption behavior (e.g., vehicle usage and energy consumption) in selecting optimal operational points of EV and PHV for maximizing resource effectiveness and user profit. There is, however, currently no mechanism that takes the individual consumption behavior of market participants into account. In this article, a new vehicle-to-home (V2H) aggregator is proposed, which allows individuals to participate in a regulation market using the in-vehicle batteries of their EVs or PHVs. The results show that the proposed V2H aggregator can successfully supply predictable power to the power grid and maximize the profits of individual market participants. Note to Practitioners—This article proposes an architecture of home energy management systems (HEMSs) with electric vehicles (EVs) and plug-in hybrid vehicles (PHVs) to participate in a regulation market using the in-vehicle batteries. Ancillary services are the mechanism for the power grid to ensure the quality of electricity. The proposed architecture is composed of two stages: 1) calculation of the charge and discharge profiles considering minimizing the electricity charge at home and maximizing the capacity to provide for ancillary services and 2) real-time control of charging and discharging the in-vehicle batteries to follow the regulation signal provided from the manager of ancillary services. The simulation result shows the estimated benefit of the aggregator obtained by the trade in the market and the precision of HEMSs’ charging and discharging to follow the request signal.

[1]  Shinkichi Inagaki,et al.  Real-Time Prediction for Future Profile of Car Travel Based on Statistical Data and Greedy Algorithm , 2015 .

[2]  Shinkichi Inagaki,et al.  Apartment Building Energy Management System in Group Optimization with Electricity Interchange Using In-Vehicle Batteries , 2015 .

[3]  Zhi Zhou,et al.  Survey of U.S. Ancillary Services Markets , 2016 .

[4]  Tatsuya Suzuki,et al.  Energy management systems based on real data and devices for apartment buildings , 2015, IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society.

[5]  P. McSharry,et al.  Short-Term Load Forecasting Methods: An Evaluation Based on European Data , 2007, IEEE Transactions on Power Systems.

[6]  Chi Zhou,et al.  Optimal electric vehicle scheduling in smart home with V2H/V2G regulation , 2015, 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA).

[7]  Michael T. Manry,et al.  Comparison of very short-term load forecasting techniques , 1996 .

[8]  Jianxiao Zou,et al.  An optimal dispatching strategy for V2G aggregator participating in supplementary frequency regulation considering EV driving demand and aggregator’s benefits , 2017 .

[9]  Tom Holvoet,et al.  A comparison of two GIV mechanisms for providing ancillary services at the University of Delaware , 2013, 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[10]  Johanna L. Mathieu,et al.  Ancillary services to the grid from commercial buildings through demand scheduling and control , 2015, 2015 American Control Conference (ACC).

[11]  D. Kirschen,et al.  A Survey of Frequency and Voltage Control Ancillary Services—Part II: Economic Features , 2007, IEEE Transactions on Power Systems.

[12]  Duncan S. Callaway,et al.  Demand Response Providing Ancillary Services A Comparison of Opportunities and Challenges in the US Wholesale Markets , 2012 .

[13]  Willett Kempton,et al.  Cost-minimized combinations of wind power, solar power and electrochemical storage, powering the grid up to 99.9% of the time , 2013 .

[14]  Tatsuya Suzuki,et al.  Maximum likelihood estimation of Departure and Travel Time of Individual Vehicle using statistics and dynamic programming , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[15]  Jie Li,et al.  Multi-market bidding strategy considering probabilistic real time ancillary service deployment , 2016, 2016 IEEE Electrical Power and Energy Conference (EPEC).

[16]  Joao P. S. Catalao,et al.  Impacts of different renewable energy resources on optimal behavior of Plug-in Electric Vehicle parking lots in energy and ancillary services markets , 2015, 2015 IEEE Eindhoven PowerTech.

[17]  Abdulsalam Yassine,et al.  Enhancing frequency regulation coverage for electric vehicles in a smart grid environment , 2016, 2016 3rd International Conference on Renewable Energies for Developing Countries (REDEC).

[18]  Hajir Pourbabak,et al.  Operating cost optimization of interconnected nanogrids considering bidirectional effect of V2G and V2H , 2017, 2017 North American Power Symposium (NAPS).

[19]  Willett Kempton,et al.  Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy , 2005 .

[20]  Colin N. Jones,et al.  Economic Advantages of Office Buildings Providing Ancillary Services With Intraday Participation , 2018, IEEE Transactions on Smart Grid.

[21]  Volker Pickert,et al.  Stochastic control of smart home energy management with plug-in electric vehicle battery energy storage and photovoltaic array , 2016 .

[22]  Na Li,et al.  Solar generation prediction using the ARMA model in a laboratory-level micro-grid , 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).

[23]  D. Kirschen,et al.  A Survey of Frequency and Voltage Control Ancillary Services—Part I: Technical Features , 2007, IEEE Transactions on Power Systems.

[24]  Ping Wang,et al.  Two-Stage Mechanism for Massive Electric Vehicle Charging Involving Renewable Energy , 2016, IEEE Transactions on Vehicular Technology.

[25]  Yifan Li,et al.  Design of a V2G aggregator to optimize PHEV charging and frequency regulation control , 2013, 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[26]  Alberto Bemporad,et al.  Control of systems integrating logic, dynamics, and constraints , 1999, Autom..

[27]  Rajit Gadh,et al.  Integration of V2H/V2G hybrid system for demand response in distribution network , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[28]  Xiaofeng Yin,et al.  Stochastic Optimal Energy Management of Smart Home With PEV Energy Storage , 2018, IEEE Transactions on Smart Grid.

[29]  Kenji Hirata,et al.  Real-time pricing leading to optimal operation under distributed decision makings , 2014, 2014 American Control Conference.

[30]  Sean P. Meyn,et al.  Ancillary service for the grid via control of commercial building HVAC systems , 2013, 2013 American Control Conference.

[31]  Tatsuya Suzuki,et al.  Model Predictive Charging Control of In-Vehicle Batteries for Home Energy Management Based on Vehicle State Prediction , 2018, IEEE Transactions on Control Systems Technology.

[32]  Ali T. Al-Awami,et al.  Coordinated bidding of ancillary services for vehicle-to-grid using fuzzy optimization , 2015, IEEE Transactions on Smart Grid.

[33]  Ali M. Pirbazari.  Ancillary services definitions, markets and practices in the world , 2010, 2010 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America (T&D-LA).

[34]  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.

[35]  Akira Ito,et al.  Optimal energy storage management in DC power networks , 2013, 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[36]  Zechun Hu,et al.  Vehicle-to-Grid Control for Supplementary Frequency Regulation Considering Charging Demands , 2015, IEEE Transactions on Power Systems.

[37]  Anastasios G. Bakirtzis,et al.  Real-Time Charging Management Framework for Electric Vehicle Aggregators in a Market Environment , 2016, IEEE Transactions on Smart Grid.

[38]  Luigi Vanfretti,et al.  Voltage control-based ancillary service using thermostatically controlled loads , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[39]  H. T. Mouftah,et al.  Flexible charging and discharging algorithm for electric vehicles in smart grid environment , 2016, 2016 IEEE Wireless Communications and Networking Conference.