Demand-Side Management by Regulating Charging and Discharging of the EV, ESS, and Utilizing Renewable Energy

被引:245
作者
Tushar, Mosaddek Hossain Kamal [1 ]
Zeineddine, Adel W. [2 ]
Assi, Chadi [1 ]
机构
[1] Concordia Univ, Concordia Inst Informat Syst Engn, Montreal, PQ H3G 2W1, Canada
[2] Concordia Univ, Montreal, PQ H3G 2W1, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Demand-side management (DSM); demand forecasting; electric vehicles (EVs); energy storage; game theory; home energy management system (HEMS); mixed strategy; microgrids; optimization; renewable energy sources (RESs); smart grids; SMART; STORAGE; GRIDS;
D O I
10.1109/TII.2017.2755465
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The evolution in microgrid technologies as well as the integration of electric vehicles (EVs), energy storage systems (ESSs), and renewable energy sources will all play a significant role in balancing the planned generation of electricity and its real-time use. We propose a real-time decentralized demand-side management (RDCDSM) to adjust the real-time residential load to follow a preplanned day-ahead energy generation by the microgrid, based on predicted customers' aggregate load. A deviation from the predicted demand at the time of consumption is assumed to result in additional cost or penalty inflicted on the deviated customers. To develop our system, we formulate a game with mixed strategy which in the first phase (i.e., prediction phase) allows each customer to process the day ahead raw predicted demand to reduce the anticipated electricity cost by generating a flattened curve for its forecasted future demand. Then, in the second stage (i.e., allocation phase), customers play another game with mixed strategy to mitigate the deviation between the instantaneous real-time consumption and the day-ahead predicted one. To achieve this, customers exploit renewable energy and ESSs and decide optimal strategies for their charging/discharging, taking into account their operational constraints. RDCDSM will help the microgrid operator to better deal with uncertainties in the system through better planning its day-ahead electricity generation and purchase, thus increasing the quality of power delivery to the customer. We evaluate the performance of our method against a centralized allocation and an existing decentralized EV charge control noncooperative game method both of which rely on a day ahead demand prediction without any refinement. We run simulations with various microgrid configurations, by varying the load and generated power, and compare the outcomes.
引用
收藏
页码:117 / 126
页数:10
相关论文
共 33 条
[1]  
[Anonymous], 2016, P IEEE INT SMART CIT
[2]  
Bajada Josef., 2013, Innovative Smart Grid Technologies Europe (ISGT EUROPE), 2013 4th IEEE/PES, P1, DOI DOI 10.1109/ISGTEUROPE.2013.6695250
[3]  
Caron S, 2010, INT CONF SMART GRID, P391, DOI 10.1109/SMARTGRID.2010.5622073
[4]  
EPA, 2016, TECH REP
[5]   Optimal operation of energy storage devices with RESs to improve efficiency of distribution grids; technical and economical assessment [J].
Farrokhifar, M. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2016, 74 :153-161
[6]  
Goswami D. Y., 2014, ENERGY EFFICIENCY RE
[7]  
Greer M., 2012, ELECT MARGINAL COST
[8]  
Grigoras G, 2014, 2014 12TH SYMPOSIUM ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING (NEUREL), P73, DOI 10.1109/NEUREL.2014.7011464
[9]   Adaptive Electricity Scheduling in Microgrids [J].
Huang, Yingsong ;
Mao, Shiwen ;
Nelms, R. M. .
IEEE TRANSACTIONS ON SMART GRID, 2014, 5 (01) :270-281
[10]   Energy Management and Operational Planning of a Microgrid With a PV-Based Active Generator for Smart Grid Applications [J].
Kanchev, Hristiyan ;
Lu, Di ;
Colas, Frederic ;
Lazarov, Vladimir ;
Francois, Bruno .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2011, 58 (10) :4583-4592