Assessing the economic value of co-optimized grid-scale energy storage investments in supporting high renewable portfolio standards

Highlights: • We present a MILP to co-optimize generation, transmission, and storage investments. • We find significant value in co-optimized storage via investment deferrals. • Operational savings from bulk services are small relative to investment deferrals. • Co-optimized energy storage significantly reduces prices associated with RPS. - Abstract: Worldwide, environmental regulations such as Renewable Portfolio Standards (RPSs) are being broadly adopted to promote renewable energy investments. With corresponding increases in renewable energy deployments, there is growing interest in grid-scale energy storage systems (ESS) to provide the flexibility needed to efficiently deliver renewable power to consumers. Our contribution in this paper is to introduce a unified generation, transmission, and bulk ESS expansion planning model subject to an RPS constraint, formulated as a two-stage stochastic mixed-integer linear program (MILP) optimization model, which we then use to study the impact of co-optimization and evaluate the economic interaction between investments in these three asset classes in achieving high renewable penetrations. We present numerical case studies using the 24-bus IEEE RTS-96 test system considering wind and solar as available renewable energy resources, and demonstrate that up to $180 million/yr in total cost savings can result from the co-optimization of all three assets, relative to a situation in which no ESS investment options are available. Surprisingly, we find that co-optimized bulk ESS investments provide significant economic value through investment deferrals in transmission and generation capacity, but very little savings in operational cost. Finally, we observe that planning transmission and generation infrastructure first and later optimizing ESS investments—as is common in industry—captures at most 1.7% ($3 million/yr) of the savings that result from co-optimizing all assets simultaneously.