Page 479 - Decision Making Applications in Modern Power Systems
P. 479
438 Decision Making Applications in Modern Power Systems
We briefly described the implementation platform for this project with sys-
tem components, communication networks, the control logics, etc. The real-
world EV usage datasets collected in AlcoPark, North California, are used to
the model the EV charging behaviors and quantify the benefits of EV
resources to achieve various objectives. V1G cases are extended with dis-
charging options and asynchronous ADMM algorithms to support the decen-
tralized and asynchronous V2G operations, which preserve the user privacy
and overcome the synchronization challenges in the real-world communica-
tion systems. The future work will focus on handling the shorter timescale
uncertainties in energy systems, including the EV driver behaviors, fluctuat-
ing solar generation profile, and building load profile. It’s also interesting to
investigate more complex modeling approaches with integer variables
involved, where the convergence performance and problem scalability will
be of major concern.
References
[1] J.A.P. Lopes, F.J. Soares, P.M.R. Almeida, Integration of electric vehicles in the electric
power system, Proc. IEEE 99 (1) (2011) 168 183.
[2] D.S. Callaway, I.A. Hiskens, Achieving controllability of electric loads, Proc. IEEE 99 (1)
(2011) 184 199.
[3] B. Wang, Smart EV Energy Management System to Support Grid Services, UCLA, 2016.
[4] N. DeForest, J.S. MacDonald, D.R. Black, Day ahead optimization of an electric vehicle
fleet providing ancillary services in the Los Angeles Air Force Base vehicle-to-grid dem-
onstration, Appl. Energy 210 (2018) 987 1001.
[5] C. Marnay et al., Los Angeles Air Force Base Vehicle to grid pilot project, ECEEE 2013
Summer Study on Energy Efficiency, 2013.
[6] C. Chen, S. Duan, Optimal integration of plug-in hybrid electric vehicles in microgrids,
IEEE Trans. Ind. Informat. 10 (3) (2014) 1917 1926.
[7] S. Mal, A. Chattopadhyay, A. Yang, R. Gadh, Electric vehicle smart charging and
vehicle-to-grid operation, Int. J. Parallel Emergent Distrib. Syst., 28 (3), 2013, 249 265.
[8] Y. Wang, B. Wang, C.-C. Chu, H. Pota, R. Gadh, Energy management for a commercial
building microgrid with stationary and mobile battery storage, Energy Build. 116 (2016)
141 150.
[9] B. Wang, Y. Wang, H. Nazaripouya, C. Qiu, C.C. Chu, R. Gadh, Predictive scheduling
framework for electric vehicles with uncertainties of user behaviors, IEEE Internet Things
J. 4 (1) (2017) 52 63.
[10] B. Wang et al., Predictive scheduling for electric vehicles considering uncertainty of load
and user behaviors, 2016 IEEE/PES Transmission and Distribution Conference and
Exposition (T&D), 2016, 1 5.
[11] M.D. Galus, M.G. Vay´ a, T. Krause, G. Andersson, The role of electric vehicles in smart
grids, WIREs Energy Environ 2 (4) (2013) 384 400.
[12] C. Guille, G. Gross, A conceptual framework for the vehicle-to-grid (V2G) implementa-
tion, Energy Policy 37 (11) (2009) 4379 4390.
[13] B. Wang, D. Wang, C. Chan, R. Yin, D. Black, Predictive Management of Electric
Vehicles in a Community Microgrid, arXiv:1802.01512 [eess, math], Feb. 2018.