Title: Optimal Coordinated Bidding of a Profit Maximizing, Risk-Averse EV Aggregator in Three-Settlement Markets Under Uncertainty

Abstract:

Due to the growing penetration of distributed energy resources (DERs), including photovoltaic panels (PVs), electric vehicles (EVs), and thermostatically controlled loads (TCLs), power systems are benefiting an increasing control flexibility, not only from the supply but also from the demand side.

This work develops a two-stage stochastic and dynamically updated  multi-period mixed integer linear program (SD-MILP) for optimal coordinated bidding of an EV aggregator to maximize its profit from participating in competitive day-ahead, intra-day and real-time markets. The hourly conditional value at risk (T-CVaR) is applied to model the risk of trading in different markets. The objective of two-stage SD-MILP is modeled as a convex combination of the expected profit and the T-CVaR hourly risk measure. When day-ahead, intra-day and real-time market prices and fleet mobility are uncertain, the proposed two-stage SD-MILP model yields optimal EV charging/discharging plans for day-ahead, intra-day and real-time markets at per device level. The degradation costs of EV batteries are precisely modeled. To reflect the continuous clearing nature of the intra-day and real-time markets, rolling planning is applied, which allows re-forecasting and re-dispatching. The proposed two-stage SD-MILP is used to derive a bidding curve of an aggregator managing 1000 EVs. Furthermore, the model statistics and computation time are recorded while simulating the developed algorithm with 5000 EVs.

Biography:

Yelena Vardanyan is a Post Doctoral researcher at DTU Compute, DTU (Danish Technical University). She received her Master degree from the department of Industrial Engineering & Systems Management, at the American University of Armenia (2007). Yelena holds a PhD in Electric Power Systems division, KTH (Royal Institute of Technology), (2016). Her research interests include renewable energy planning, electricity markets and its economics, distributed energy resources and its integration of power systems, application of stochastic and bi-level optimization as mathematical tools to solve current and future smart grid challenges.