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Dissertation--Yin Guo.pdf (959.44 KB)

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Predictive On-Line Operational Management of V2G Participating in the Frequency Regulation for an Office Garage

Guo, Yin
http://rave.ohiolink.edu/etdc/view?acc_num=case1551708169877708

Abstract Details

2019, Doctor of Philosophy, Case Western Reserve University, EECS - Electrical Engineering.
The development of vehicle-to-grid (V2G) technology offers potential benefits to both electric vehicle owners and grid operators by providing frequency regulation service. Such benefits could be significant, because the vehicles are usually idle for most of the time in a day. However, it is difficult to maximize such benefits via cost-optimized on-line scheduling based on predictive real-time prices. First and foremost, this is due to the uncertainty of actual prices and the variability of prediction obtained at different time points. In addition, the energy needed for providing regulation service is unknown when making the schedule. Second, in order to participate in the frequency regulation market, an aggregator is usually needed as an interface between vehicles and grid operators. However, the optimal bidding capacity for the aggregator may conflict with the optimal schedule of individual vehicles. Last, the vehicle’s ability to gain maximum profit is limited when the battery’s state of charge (SOC) is close to its extreme values. To overcome those limitations, this thesis proposes a new tool for the operational management of V2G frequency regulation. The proposed tool integrates: 1) a cost-optimized predictive on-line scheduling model for individual vehicles, 2) a cost-optimized frequency regulation capacity bidding model for an aggregator, and 3) a real-time synergetic dispatch model. The scheduling model for individual vehicles is formulated as a three-stage stochastic linear program which considers the uncertainties in 1) energy and frequency regulation prices, and 2) hourly average frequency regulation signal. The advantages of the proposed tool are: 1) the proposed tool properly integrates scheduling, capacity bidding and real-time dispatch; 2) the proposed scheduling models for individual EVs and the aggregator are on-line scheduling models which take the advantage of the most recent predicted electricity prices; 3) the proposed scheduling models for individual EVs and the aggregator forms a semi-centralized scheduling schema, which overcomes the disadvantages of centralized and decentralized models; 4) the consideration of the stochastic property of the parameters helps reduce the risk associate with the uncertainty, and also increase the potential profit; 5) the proposed three-stage stochastic linear programming model for the scheduling of individual vehicles greatly reduces the computational effort required by a conventional multi-stage model; 6) the proposed heuristic solution method with the utilization of GPU for parallel computation can significantly further increase the computational efficiency; 7) the proposed real-time synergetic dispatch can properly maintain the battery’s SOC within a designated range, which insures that the battery energy constraint can be eliminated from the scheduling model, and helps reduce the complexity of the scheduling model. The proposed tool is evaluated via a simulation of an arbitrary aggregator for an office garage which consists of 100 EVs. The simulation result shows that the proposed integrated tool is suitable for practical deployment.
Marija Prica (Committee Chair)
Kenneth Loparo (Committee Member)
Vira Chankong (Committee Member)
Cenk Cavusoglu (Committee Member)
Electrical Engineering; Energy

Recommended Citations

Citations

  • Guo, Y. (2019). Predictive On-Line Operational Management of V2G Participating in the Frequency Regulation for an Office Garage [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1551708169877708

    APA Style (7th edition)

  • Guo, Yin. Predictive On-Line Operational Management of V2G Participating in the Frequency Regulation for an Office Garage . 2019. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1551708169877708.

    MLA Style (8th edition)

  • Guo, Yin. "Predictive On-Line Operational Management of V2G Participating in the Frequency Regulation for an Office Garage ." Doctoral dissertation, Case Western Reserve University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1551708169877708

    Chicago Manual of Style (17th edition)

case1551708169877708
299
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This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.