Stochastic valuation of energy storage in wholesale power markets

Abstract

Energy storage systems are well poised to mitigate uncertainties of renewable generation outputs. Gridscale energy storage projects are major investments which call for rigorous valuation and risk analysis. This paper provides a stochastic energy storage valuation framework in wholesale power markets which considers all key revenue streams simultaneously. As part of this framework, an operational optimization model is developed to determine the energy storage system's optimal dispatch sequences. A future curve model is built to capture the volatilities of electricity prices. In addition, a frequency regulation service price forecasting model is developed. Simulation results with a realistic battery storage system reveal that the majority of the market revenues comes from frequency regulation services. Simulation results also show that both round-trip efficiency and power-to-energy ratio are crucial to the cost effectiveness of energy storage systems.

Degradation-aware Valuation and Sizing of Behind-the-Meter Battery Energy Storage Systems for Commercial Customers

“…The demand charge for commercial customers is 18.34$/kW. The power-based and energy based capital costs of the battery are 551$/kW and 614$/kWh [11]. The battery death line is assumed to be 70% of its initial energy rating.…”

Improved Battery Storage Valuation Through Degradation Reduction

“…Reference [6] introduced a linear program (LP) for valuating energy storage systems. The LP partitions the battery's available capacity at each hour t = 1, 2, ..., T into a set of profitable actions.…”

“…The second term is the change in energy from the battery's actions, and the third term represents resistive losses that scale with total output power. The resistive losses, ρ, are derived from the battery's roundtrip efficiency κ. ρ = 1 − √ κ Constraints (5), (6), and (7) capture the fact that the battery's capacity must be partitioned. E max is the battery's maximum state of charge.…”

“…In this optimization scheme and all optimization schemes to come, we considered a realization of prices that combines the methods of future price curve modeling in [6] with expert opinions and industry price models. The two data sets used (one for locational marginal prices and one for ancillary service prices) can be found at [19].…”

“…While much research has been done on the value of BESS systems directly linked to renewable sources [2] [3], for energy arbitrage [4], and for primary frequency regulation [5], a large amount of the existing literature has underestimated potential profits by failing to optimize these actions simultaneously. By correcting these issues, [6] and [7] obtained a significantly more optimal valuation of BESS. However, this result was obtained by considering only the effect of time on the battery's deterioration.…”