Impact of risk measures and degradation cost on the optimal arbitrage schedule for battery energy storage systems

The deployment of energy storage systems to the grid is expected to mitigate the effects of load imbalances caused by the variability of renewable energy sources. To motivate the investments in grid-connected energy storage systems, these projects must be economically feasible. This can be approached by optimizing the energy arbitrage scheduling process. A common approach is to consider this process as a stochastic optimization problem. Recent works tend to implement risk measures such as Conditional Value-at-Risk (CVaR) in the optimization process. In this work, the use of risk measures in the bidding strategy formulation is evaluated with a detailed simulation of a Battery Energy Storage System (BESS) performing arbitrage in the Iberian electricity market using historical data. A degradation model based on the Rainflow counting method is implemented to avoid neglecting battery ageing with usage. Results demonstrate that the addition of risk measures to a stochastic optimization approach yields poorer results than using a deterministic strategy such as a point forecast.

Impact of risk measures and degradation cost on the optimal arbitrage schedule for battery energy storage systems Articles