Researchers from Imperial College London, including Gabriel D. Patrón, Di Zhang, and their colleagues, have explored a new approach to scheduling energy storage systems that accounts for the uncertainties in electricity markets. Their work, published in the journal Applied Energy, focuses on balancing the risks and rewards of operating energy storage assets in an environment where electricity prices can be unpredictable.
Energy storage systems, such as batteries and hydrogen systems, play a crucial role in integrating renewable energy sources into the grid. These systems can store excess energy generated from renewables when supply is high and demand is low, and then release it when demand is high and supply is low. However, the intermittent nature of renewable energy and the fluctuating prices in electricity markets make it challenging to schedule these storage systems efficiently.
The researchers developed a two-stage stochastic risk-constrained approach to address this challenge. This method allows for the observation of electricity price trajectories or specific power markets before making scheduling decisions. By using conditional value-at-risk, a measure of tail risk, the researchers could explicitly specify a probabilistic risk limit in their optimization problems. This means that operators can input their level of risk tolerance when making decisions about their energy storage assets.
The proposed approach was tested on two case studies: an integrated hydrogen system (IHS) and a battery energy storage system (BESS). In the IHS context, the risk constraint led to larger installed unit capacities, which increased capital costs but enabled more energy inventory to buffer price uncertainty. Both systems demonstrated an operational trade-off between risk and expected reward. As the level of risk aversion increased, so did the expected costs (or decreased expected profits). However, despite the decrease in expected reward, both systems exhibited substantial benefits from increasing risk aversion.
This research provides a general method for addressing uncertainties in energy storage scheduling. By allowing operators to specify their risk tolerance, the approach can help optimize the operation of energy storage systems in the face of unpredictable electricity prices. This can lead to more efficient integration of renewable energy sources and a more stable and resilient energy grid.
The study, titled “Risk-constrained stochastic scheduling of multi-market energy storage systems,” was published in the journal Applied Energy.
This article is based on research available at arXiv.