Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Grid-aware Scheduling and Control of Electric Vehicle Charging Stations for Dispatching Active Distribution Networks. Part-II: Intra-day and Experimental Validation (2404.12870v1)

Published 19 Apr 2024 in eess.SY and cs.SY

Abstract: In Part-I, we presented an optimal day-ahead scheduling scheme for dispatching active distribution networks accounting for the flexibility provided by electric vehicle charging stations (EVCSs) and other controllable resources such as battery energy storage systems (BESSs). Part-II presents the intra-day control layer for tracking the dispatch plan computed from the day-ahead scheduling stage. The control problem is formulated as model predictive control (MPC) with an objective to track the dispatch plan setpoint every 5 minutes, while actuated every 30 seconds. MPC accounts for the uncertainty of the power injections from stochastic resources (such as demand and generation from photovoltaic - PV plants) by short-term forecasts. MPC also accounts for the grid's operational constraints (i.e., the limits on the nodal voltages and the line power-flows) by a linearized optimal power flow (LOPF) model based on the power-flow sensitivity coefficients, and for the operational constraints of the controllable resources (i.e., BESSs and EVCSs). The proposed framework is experimentally validated on a real-life ADN at the EPFL's Distributed Electrical Systems Laboratory and is composed of a medium voltage (MV) bus connected to three low voltage distribution networks. It hosts two controllable EVCSs (172 kWp and 32 F~kWp), multiple PV plants (aggregated generation of 42~kWp), uncontrollable demand from office buildings (20 kWp), and two controllable BESSs (150kW/300kWh and 25kW/25kWh).

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. J. Zhong, L. He, C. Li, Y. Cao, J. Wang, B. Fang, L. Zeng, and G. Xiao, “Coordinated control for large-scale ev charging facilities and energy storage devices participating in frequency regulation,” Applied Energy, vol. 123, pp. 253–262, 2014.
  2. A. Janjic, L. Velimirovic, M. Stankovic, and A. Petrusic, “Commercial electric vehicle fleet scheduling for secondary frequency control,” Electric Power Systems Research, vol. 147, pp. 31–41, 2017.
  3. S. Bansal, M. N. Zeilinger, and C. J. Tomlin, “Plug-and-play model predictive control for electric vehicle charging and voltage control in smart grids,” in 53rd IEEE Conference on Decision and Control.   IEEE, 2014, pp. 5894–5900.
  4. S. Weckx and J. Driesen, “Load balancing with ev chargers and pv inverters in unbalanced distribution grids,” IEEE Transactions on Sustainable Energy, vol. 6, no. 2, pp. 635–643, 2015.
  5. S. Fahmy, R. Gupta, and M. Paolone, “Grid-aware distributed control of electric vehicle charging stations in active distribution grids,” Electric Power Systems Research, vol. 189, p. 106697, 2020.
  6. H. Liu, Z. Hu, Y. Song, J. Wang, and X. Xie, “Vehicle-to-grid control for supplementary frequency regulation considering charging demands,” IEEE Transactions on Power Systems, vol. 30, no. 6, pp. 3110–3119, 2014.
  7. J. C. Hernández, F. Sanchez-Sutil, P. Vidal, and C. Rus-Casas, “Primary frequency control and dynamic grid support for vehicle-to-grid in transmission systems,” International Journal of Electrical Power & Energy Systems, vol. 100, pp. 152–166, 2018.
  8. A. Nottrott, J. Kleissl, and B. Washom, “Energy dispatch schedule optimization and cost benefit analysis for grid-connected, photovoltaic-battery storage systems,” Renewable Energy, vol. 55, pp. 230–240, 2013.
  9. E. Reihani, S. Sepasi, L. R. Roose, and M. Matsuura, “Energy management at the distribution grid using a battery energy storage system (bess),” IJEPES, vol. 77, pp. 337–344, 2016.
  10. M. Bozorg, F. Sossan, J.-Y. Le Boudec, and M. Paolone, “Influencing the bulk power system reserve by dispatching power distribution networks using local energy storage,” Electric Power Systems Research, vol. 163, pp. 270–279, 2018.
  11. J. Hu, H. Morais, M. Lind, and H. W. Bindner, “Multi-agent based modeling for electric vehicle integration in a distribution network operation,” Electric Power Systems Research, vol. 136, pp. 341–351, 2016.
  12. W. Qi, Z. Xu, Z.-J. M. Shen, Z. Hu, and Y. Song, “Hierarchical coordinated control of plug-in electric vehicles charging in multifamily dwellings,” IEEE Transactions on Smart Grid, vol. 5, no. 3, pp. 1465–1474, 2014.
  13. A. Imran, G. Hafeez, I. Khan, M. Usman, Z. Shafiq, A. B. Qazi, A. Khalid, and K.-D. Thoben, “Heuristic-based programable controller for efficient energy management under renewable energy sources and energy storage system in smart grid,” IEEE Access, vol. 8, pp. 139 587–139 608, 2020.
  14. A. Al-Obaidi, H. Khani, H. E. Farag, and M. Mohamed, “Bidirectional smart charging of electric vehicles considering user preferences, peer to peer energy trade, and provision of grid ancillary services,” International Journal of Electrical Power & Energy Systems, vol. 124, p. 106353, 2021.
  15. R. Wang and S. M. Lukic, “Dynamic programming technique in hybrid electric vehicle optimization,” in 2012 IEEE international electric vehicle conference.   IEEE, 2012, pp. 1–8.
  16. F. Tuchnitz, N. Ebell, J. Schlund, and M. Pruckner, “Development and evaluation of a smart charging strategy for an electric vehicle fleet based on reinforcement learning,” Applied Energy, vol. 285, p. 116382, 2021.
  17. M. Dabbaghjamanesh, A. Moeini, and A. Kavousi-Fard, “Reinforcement learning-based load forecasting of electric vehicle charging station using q-learning technique,” IEEE Transactions on Industrial Informatics, vol. 17, no. 6, pp. 4229–4237, 2020.
  18. A. Di Giorgio, F. Liberati, and S. Canale, “Electric vehicles charging control in a smart grid: A model predictive control approach,” Control Engineering Practice, vol. 22, pp. 147–162, 2014.
  19. W. Tang and Y. J. Zhang, “A model predictive control approach for low-complexity electric vehicle charging scheduling: Optimality and scalability,” IEEE transactions on power systems, vol. 32, no. 2, pp. 1050–1063, 2016.
  20. L. Wang, A. Dubey, A. H. Gebremedhin, A. K. Srivastava, and N. Schulz, “Mpc-based decentralized voltage control in power distribution systems with ev and pv coordination,” IEEE Transactions on Smart Grid, vol. 13, no. 4, pp. 2908–2919, 2022.
  21. F. Zhou, Y. Li, W. Wang, and C. Pan, “Integrated energy management of a smart community with electric vehicle charging using scenario based stochastic model predictive control,” Energy and Buildings, vol. 260, p. 111916, 2022.
  22. G. Van Kriekinge, C. De Cauwer, N. Sapountzoglou, T. Coosemans, and M. Messagie, “Peak shaving and cost minimization using model predictive control for uni-and bi-directional charging of electric vehicles,” Energy reports, vol. 7, pp. 8760–8771, 2021.
  23. Y. Li, M. Han, Z. Yang, and G. Li, “Coordinating flexible demand response and renewable uncertainties for scheduling of community integrated energy systems with an electric vehicle charging station: A bi-level approach,” IEEE Transactions on Sustainable Energy, vol. 12, no. 4, pp. 2321–2331, 2021.
  24. R. Gupta, F. Sossan, and M. Paolone, “Grid-aware distributed model predictive control of heterogeneous resources in a distribution network: Theory and experimental validation,” IEEE Transactions on Energy Conversion, vol. 36, no. 2, pp. 1392–1402, 2020.
  25. R. Gupta, A. Zecchino, J.-H. Yi, and M. Paolone, “Reliable dispatch of active distribution networks via a two-layer grid-aware model predictive control: Theory and experimental validation,” IEEE Open Access Journal of Power and Energy, 2022.
  26. F. Sossan, E. Scolari, R. Gupta, and M. Paolone, “Solar irradiance estimations for modeling the variability of photovoltaic generation and assessing violations of grid constraints: A comparison between satellite and pyranometers measurements with load flow simulations,” Journal of Renewable and Sustainable Energy, vol. 11, no. 5, p. 056103, 2019.
  27. EvTec, “espresso&charge - 6in1,” https://www.evtec.ch/download_file/view/356, accessed last: 2022-09-01.
  28. R. K. Gupta, “Methods for grid-aware operation and planning of active distribution networks,” p. 247, 2023. [Online]. Available: http://infoscience.epfl.ch/record/299705
  29. L. Yao, W. H. Lim, and T. S. Tsai, “A real-time charging scheme for demand response in electric vehicle parking station,” IEEE Transactions on Smart Grid, vol. 8, no. 1, pp. 52–62, 2016.
  30. M. Nick, R. Cherkaoui, and M. Paolone, “Optimal allocation of dispersed energy storage systems in active distribution networks for energy balance and grid support,” IEEE Transactions on Power Systems, vol. 29, no. 5, pp. 2300–2310, 2014.
  31. International Electrotechnical Commission, “Iec 61851-1: 2017-02 “electric vehicle conductive charging system—part 1: General requirements,” International Electrotechnical Commission:, Geneva, Switzerland, Tech. Rep., 2017.
  32. R. Rudnik, J.-Y. Le Boudec, S. Fahmy, and M. Paolone, “Experimental validation of the real-time control of an electric-vehicle charging station,” in 2021 IEEE Madrid PowerTech.   IEEE, 2021, pp. 1–6.
  33. S. A. S. Fahmy, “Efficient methods for the operation of active distribution networks in unsymmetric and uncertain states,” EPFL, Tech. Rep., 2022.
  34. “Guide for phasor data concentrator requirements for power system protection, control, and monitoring,” IEEE Std. C37.244-2013, pp. 1–65.
  35. A. M. e. a. Kettner, “Sequential discrete kalman filter for real-time state estimation in power distribution systems: Theory and implementation,” IEEE Trans. Inst. Meas., vol. 66, no. 9, pp. 2358–2370, 2017.
  36. L. Zanni et al., “Pmu-based linear state estimation of lausanne subtransmission network: Experimental validation,” EPSR, vol. 189, 2020.
  37. L. E. Reyes Chamorro, “Real-time control framework for active distribution networks theoretical definition and experimental validation,” EPFL, Tech. Rep., 2016.
  38. E. Scolari, “Modelling and forecasting of photovoltaic generation for microgrid applications: from theory to validation,” EPFL, Tech. Rep., 2019.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now