Architectural and Algorithmic Solutions for Large-Scale PEV Integration into Power Grids



Project Information


This research is in part being supported by National Science Foundation (NSF) under Grant No. 1239408

Institutions: University of Washington

Investigators: Prof. Daniel S. Kirschen and Prof. Miguel A. Ortega-Vazquez

Researcher(s): Mushfiqur R. Sarker

Project Description

The need to decarbonize the transportation sector has brought the attention to electric vehicles. These devices are now a reality and it expected that in the near future their presence will be ubiquitous. This penetration of electric vehicles will be accompanied by the rollout of the smart grid, which will enable small scale loads to feature higher degrees of communication and control. This higher degrees of control will let the EVs to have an active participation when drawing the operating plans of the power system. Since large volumes of EVs will be integrated into the system, all this energy needs to be channeled and coordinated by an aggregating entity. This aggregator will act as a bridge and mediator between the large volumes of EVs and the power system players through market environments or in centrally coordinate systems.

Fig. 1 In this figure, the generators (upper left part of the figure) are scheduled to meet the time varying demand (lower left part of the figure) plus the EV load, through a market (middle left part of the figure). The energy bought by the aggregator at a market price is then channeled to its EV fleets; which respond to price-based signals. All this process takes place while the system constraints are observed.

If the EVs were allowed to charge in an uncontrolled manner, as soon as they are connected to the grid circuits, then the already high peak demands would increase, potentially creating over-loadings of forcing expensive generation to be synchronized. On the other hand, the EVs as controllable demand will allow more efficient scheduling of the system, by shifting the EV load to periods in which the system is lightly loaded, and the price of electricity is lowest. By being equipped with a battery, the EVs could even act as a power source, injecting power back to the grid in Vehicle-to-Grid (V2G) mode.

Fig. 2 EV demand in the uncontrolled case and in the optimized case

Furthermore the optimal management of EVs will also allow distribution system operators to have granular control on the flows through their wires, avoiding not only over-loadings, but also congestion and voltage depressions.

On the customer side, the control of the EVs could be direct or by means of signals with economic incentives. In the former, the customer should be rewarded by allowing their EVs load to be directly controlled; on the latter, the owner of the EV should schedule its EV load (through an energy management system) to strive to minimize the costs of consuming energy, or even injecting energy back to the system in V2G mode if this results into a benefit.

Fig. 3 EVs interactions with the electric power grid: (1) aggregated interactions at the wholesale level via energy and ancillary services markets, (2) optimal management at distribution level via incentives and (3) optimal scheduling at household level considering battery degradation.

This project proposes algorithmic solutions to optimally manage EVs in the power system at different levels. The particular research directions are:
  • Business models for aggregators scheduling the charging of large fleets of EVs
  • Business models for battery swapping stations to maximize profits and provide services
  • Optimal scheduling of electric vehicles at household level
  • Optimal integration of electric vehicles into distribution grids
  • Electric vehicles as a facilitator for the integration of renewable energy sources
  • Monetizing the cost of V2G services from electric vehicles


Book Sections
  1. M. A. Ortega-Vazquez and M. Kintner-Meyer, "Electric Vehicles and the Electric Grid", in Handbook of Clean Energy Systems, Vol. 4, John Wiley & Sons, 2014; [Online]. Available:
  1. M. R. Sarker, Y. Dvorkin and M. A. Ortega-Vazquez, "Optimal Participation of an Electric Vehicle Aggregator in Day-Ahead Energy and Reserve Markets", IEEE Transactions on Power Systems, Vol. PP, Issue 99, 2015, (early access).
  2. M. R. Sarker, M. A. Ortega-Vazquez and D. S. Kirschen, "Optimal Coordination and Scheduling of Demand Response via Monetary Incentives", IEEE Transactions on Smart Grid, Vol. 6, Issue 3, 2015.
  3. M. R. Sarker, H. Pandzic and M. A. Ortega-Vazquez, "Optimal Operation and Services Scheduling for an Electric Vehicle Battery Swapping Station", IEEE Transactions on Power Systems", Vol. 30, Issue 2, 2015.
  4. M. A. Ortega-Vazquez, "Optimal Scheduling of Electric Vehicle Charging and Vehicle-to-Grid Services at Household Level Including Battery Degradation and Price Uncertainty", IET Generation, Transmission & Distribution, Vol. 8, Issue 6, Jun. 2014.
  1. K. Sun, M. R. Sarker and M. A. Ortega-Vazquez,"Statistical Characterization of Electric Vehicle Charging in Different Locations of the Grid", IEEE PES 2015 General Meeting, Denver, CO, USA, 26-30 Jul. 2015.
  2. M. R. Sarker, H. Pandzic and M. A. Ortega-Vazquez, "Electric Vehicle Battery Swapping Station: Business Case and Optimization Model," 2013 International Conference on Connected Vehicles & Expo, Las Vegas, NV, USA, 2-6 Dec. 2013. (Best Paper Award Finalist).