Risk-Aware Control Approach for Decision-Making System of a Shared EVs Aggregator

Semaria Ruiz-Alvarez, Danny Arlen de Jesus Gomez-Ramirez, Manuel Ospina-Alarcón

Abstract


This paper proposes a risk-aware control approach intended to generate the most profitable decisions for the manager of a public fleet of electric vehicles that can interact bidirectionally with the electrical network, providing different energy services to it. Specifically, the proposed control approach is intended to generate the best charging/discharging decisions for the fleet, including car-sharing uncertainties and the desired confidence level at which the fleet operator wants to cover these uncertainties. It considers a hierarchical control structure at whose first level an economic dynamic optimization is executed, and, at whose second level, a risk-aware reference tracking of the first-level references is performed. Using this a stochastic MPC controller at the second level, whose mathematical approach has as a novelty that it extends the current methodologies in the state of the art, allowing the inclusion of the linear time-varying behavior of the dynamic system, whose constraints are also time-varying, and whose uncertainties are additive-multiplicative with non-zero mean and non-unitary variance. Finally, the approach established is tested in a hypothetical car-sharing system located in Colombia.

 


Keywords


Aggregator; Car-sharing; Decision-Making System; Electric Vehicles; Robust Control; Robust Optimization

Full Text:

PDF

References


E. Sortomme and M. A. El-Sharkawi, “Optimal scheduling of vehicle-to-grid energy and ancillary services,” IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 351–359, 2012.

J. V. Kadam and W. Marquardt, “Integration of Economical Optimization and Control for Intentionally Transient Process Operation,” in Findeisen R., Allgöwer F., Biegler L.T. (eds) Assessment and Future Directions of Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences, Berlin, Heidelberg: vol 358. Springer, 2007, pp. 419–434.

E. Sortomme and M. A. El-Sharkawi, “Optimal combined bidding of vehicle-to-grid ancillary services,” IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 70–79, 2012.

H. Turker and M. Ponzio, “New Predictive Control Method for Optimal Minimization of Plug-in Electric Vehicle (PEV) Charging Cost with Vehicle-to-Home (V2H) capability,” 11th IEEE Int. Conf. SMART GRID - ICRERA, 2023.

H. Turker, A. Radu, S. Bacha, D. Frey, J. Richer, and P. Lebrusq, “Optimal charge control of electric vehicles in parking stations for cost minimization in V2G concept,” 3rd Int. Conf. Renew. Energy Res. Appl. ICRERA 2014, pp. 945–951, 2014.

E. A. Buehler, J. A. Paulson, and A. Mesbah, “Lyapunov-based stochastic nonlinear model predictive control: Shaping the state probability distribution functions,” Proc. Am. Control Conf., vol. 2016-July, no. Section IV, pp. 5389–5394, 2016.

S. Liu and J. Liu, “Economic Model Predictive Control with Zone Tracking,” IFAC-PapersOnLine, vol. 51, no. 20, pp. 16–21, 2018.

M. Gonzalez Vaya and G. Andersson, “Optimal Bidding Strategy of a Plug-In Electric Vehicle Aggregator in Day-Ahead Electricity Markets under Uncertainty,” IEEE Trans. Power Syst., vol. 30, no. 5, pp. 2375–2385, 2015.

H. Liu, Z. Hu, Y. Song, J. Wang, and X. Xie, “Vehicle-to-Grid Control for Supplementary Frequency Regulation Considering Charging Demands,” IEEE Trans. Power Syst., vol. 30, no. 6, pp. 3110–3119, 2015.

S. R.- Álvarez, J. Patiño, A. Márquez, and J. Espinosa, “Optimal Design for an Electrical Hybrid Micro Grid in Colombia Under Fuel Price Variation,” Int. J. Renew. Energy Res., vol. 7, no. 4, 2017.

S. M. Sotoudeh and B. Homchaudhuri, “A Robust MPC-based Hierarchical Control Strategy for Energy Management of Hybrid Electric Vehicles in Presence of Uncertainty,” Proc. Am. Control Conf., vol. 2020-July, pp. 3065–3070, 2020.

C. Hou, M. Ouyang, L. Xu, and H. Wang, “Approximate Pontryagin’s minimum principle applied to the energy management of plug-in hybrid electric vehicles,” Appl. Energy, vol. 115, pp. 174–189, 2014.

J. Löfberg, “Minimax approaches to Robust Model Predictive Control,” Linköping University, 2014.

B. Kouvaritakis and M. Cannon, “Stochastic Model Predictive Control,” Encycl. Syst. Control, pp. 1–9, 2014.

S. Abderrahim, M. Allouche, and M. Chaabane, “A New Robust Control Strategy for a Wind Energy Conversion System Based on a T-S Fuzzy Model,” Int. J. Smart grid, vol. 4, no. 2, 2020.

S. Zymler, D. Kuhn, and B. Rustem, “Distributionally robust joint chance constraints with second-order moment information,” Math. Program., vol. 137, no. 1–2, pp. 167–198, 2013.

R. Bellman, “Dynamic programming,” Science (80-. )., vol. 153, no. 3731, pp. 34–37, 1966.

C. Hou, L. Xu, H. Wang, M. Ouyang, and H. Peng, “Energy management of plug-in hybrid electric vehicles with unknown trip length,” J. Franklin Inst., vol. 352, no. 2, pp. 500–518, 2015.

M. Farina and R. Scattolini, “Model predictive control of linear systems with multiplicative unbounded uncertainty and chance constraints,” Automatica, vol. 70, pp. 258–265, 2016.

C. Goebel and H. A. Jacobsen, “Aggregator-Controlled EV Charging in Pay-as-Bid Reserve Markets with Strict Delivery Constraints,” IEEE Trans. Power Syst., vol. 31, no. 6, pp. 4447–4461, 2016.

M. Akil, E. Dokur, and R. Bayindir, “A coordinated EV Charging Scheduling Containing PV System,” Int. J. Smart Grid, vol. 6, no. 3, pp. 65–71, 2022.

E. Kilic, M. Akil, R. Bayindir, A. Sebati, and R. Malek, “Electric Vehicles Charging Management with Monte Carlo Simulation,” 2021 10th International Conference on Renewable Energy Research and Application (ICRERA), 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9598807. [Accessed: 15-Jul-2023].

S. Ruiz-Alvarez, “Methodology to design an optimal decision making system for the operator of a shared electric vehicle fleet providing electrical ancillary services,” Universidad Nacional de Colombia, 2021.

B. Kouvaritakis and M. Cannon, Model Predictive Control Classical, Robust and Stochastic. Advanced Textbooks in Control and Signal Processing Series- Springer, 2008.

S. Ruiz-Álvarez and D. Gómez-Ramírez, “Optimal Management Strategy for a Shared EVs Aggregator Participating in Electricity and Frequency Regulation Reserves Markets,” Technol. Econ. Smart Grids Sustain. Energy, vol. 7, 2021.

J. A. Paulson, E. A. Buehler, R. D. Braatz, and A. Mesbah, “Stochastic model predictive control with joint chance constraints,” Int. J. Control, vol. 93, no. 1, pp. 126–139, 2020.




DOI (PDF): https://doi.org/10.20508/ijrer.v13i4.14238.g8840

Refbacks

  • There are currently no refbacks.


Online ISSN: 1309-0127

Publisher: Gazi University

IJRER is cited in SCOPUS, EBSCO, WEB of SCIENCE (Clarivate Analytics);

IJRER has been cited in Emerging Sources Citation Index from 2016 in web of science.

WEB of SCIENCE between 2020-2022; 

h=30,

Average citation per item=5.73

Impact Factor=(1638+1731+1808)/(189+170+221)=9.24

Category Quartile:Q4