Optimization models for an EV aggregator selling secondary reserve in the electricity market

• A new day-ahead optimization problem for secondary reserve bids is described for an EV aggregator.
• The model takes as input forecasts for price and EV variables.
• A new operational optimization algorithm is described for minimizing situations with reserve shortage due to forecast errors.
• Two different penalization schemes for reserve shortage situations are tested in a realistic case-study.

Power system regulators and operators are creating conditions for encouraging the participation of the demand-side into reserve markets. The electric vehicle (EV), when aggregated by a market agent, holds sufficient flexibility for offering reserve bids. Nevertheless, due to the stochastic nature of the drivers’ behavior and market variables, forecasting and optimization algorithms are necessary for supporting an EV aggregator participating in the electricity market. This paper describes a new day-ahead optimization model between energy and secondary reserve bids and an operational management algorithm that coordinates EV charging in order to minimize differences between contracted and realized values. The use of forecasts for EV and market prices is included, as well as a market settlement scheme that includes a penalty term for reserve shortage. The optimization framework is evaluated in a test case constructed with synthetic time series for EV and market data from the Iberian electricity market.