Value of energy storage coordination: managing renewables uncertainty and the maximization of profitability

Aggressive carbon emissions reduction targets in electricity industry lead to the large-scale penetration of intermittent renewables. Renewables’ uncertain and volatile nature, however, negatively impacts power systems operation by increasing the need for larger volumes of operating reserve and flexible, ideally carbon-free, generation technologies for ensuring a cost-effective operation. Energy storage systems are widely agreed to be the key for integrating renewables due to their flexibly and quick response. However, poor regulation and lack of long-term economic incentives have lead to a slow deployment of this technology. This research analyses and models how renewable generation and energy storage coordination create value by managing uncertainty and maximizing profitability. A novel large-scale two-stage stochastic mixed-integer linear programming investment and operational model is proposed. The model allows considering different levels of risk aversion for a renewable producer that looks to determine the optimal size of an energy storage system and bidding strategy to maximize profit. Profit maximization is achieved by managing renewable generation imbalances and by offering ancillary services. In order to minimize the renewable operator’s exposure to low profit outcomes, the Conditional Value-at-Risk of the expected profit is included in the problem’s formulation. Computational tractability and efficiency is achieved by using the Progressive Hedging Algorithm decomposition technique to decompose the large-scale investment problem into smaller, easier to solve and scenario-specific sub-problems. A series of case studies not only provide evidence of the significant benefits that energy storage system brings to a risk-averse renewables’ operator, but also shows how energy storage allows storing cheap energy that otherwise would need to be curtailed to improve ancillary services provision. Finally, the developed models can be of help for market participants to make more informed decisions, and also provides regulators and system operators with flexible solutions to mitigate the operational challenges of future low-carbon power systems.Open Acces