Stochastic portfolio management of an electric vehicles aggregator under price uncertainty

This paper considers the portfolio management problem of a flexibility aggregator under uncertainty on real-time prices. Solving this stochastic optimal control problem in a reasonable time, considering overall scalability, comfort settings and grid constraints, is a challenging task. This paper tackles these problems by making use of a Three-Step Approach (TSA). Two control approaches are considered in the second step of the TSA: Model Predictive Control (MPC) and Approximate Dynamic Programming (ADP). The performance of both controllers for different temporal autocorrelated price profiles is illustrated for an aggregator with a fleet of 1000 electric vehicles. The simulations show that the TSA extended with a stochastic controller can reduce the cost of the aggregator compared to a certainty equivalent approach. The paper concludes by discussing the strength and weaknesses of MPC and ADP in a smart grid setting.

[1]  Goran Andersson,et al.  Investigating PHEV wind balancing capabilities using heuristics and model predictive control , 2010, IEEE PES General Meeting.

[2]  Jitka Dupacová,et al.  Scenario reduction in stochastic programming , 2003, Math. Program..

[3]  Gerard J. M. Smit,et al.  Integration of heat pumps in distribution grids: Economic motivation for grid control , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[4]  Tom Holvoet,et al.  Demand side management of electric vehicles with uncertainty on arrival and departure times , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[5]  Ronnie Belmans,et al.  Automated residential demand response based on dynamic pricing , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[6]  Zhi Chen,et al.  Real-Time Price-Based Demand Response Management for Residential Appliances via Stochastic Optimization and Robust Optimization , 2012, IEEE Transactions on Smart Grid.

[7]  Vincent W. S. Wong,et al.  An approximate dynamic programming approach for coordinated charging control at vehicle-to-grid aggregator , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[8]  Warren B. Powell,et al.  Approximate Dynamic Programming - Solving the Curses of Dimensionality , 2007 .

[9]  Juan M. Morales,et al.  Real-Time Demand Response Model , 2010, IEEE Transactions on Smart Grid.

[10]  Alberto Bemporad,et al.  Stochastic MPC for real-time market-based optimal power dispatch , 2011, IEEE Conference on Decision and Control and European Control Conference.

[11]  M.P.F. Hommelberg,et al.  Distributed Control Concepts using Multi-Agent technology and Automatic Markets: An indispensable feature of smart power grids , 2007, 2007 IEEE Power Engineering Society General Meeting.

[12]  Stephen P. Boyd,et al.  Design of Affine Controllers via Convex Optimization , 2010, IEEE Transactions on Automatic Control.

[13]  Mohammed H. Albadi,et al.  Demand Response in Electricity Markets: An Overview , 2007, 2007 IEEE Power Engineering Society General Meeting.

[14]  Daniel F. Salas,et al.  Benchmarking a Scalable Approximate Dynamic Programming Algorithm for Stochastic Control of Multidimensional Energy Storage Problems , 2013 .

[15]  Tom Holvoet,et al.  A Scalable Three-Step Approach for Demand Side Management of Plug-in Hybrid Vehicles , 2013, IEEE Transactions on Smart Grid.