By ESS news
Traditionally, storage planning is based on production cost models or capacity expansion models. The former optimize dispatch on a levelized cost basis, while the latter assess investment decisions within a marginal price framework, typically assuming that thermal generation drives market prices. However, both approaches imperfectly capture storage, especially in terms of its actual interaction with market prices.
Researchers from the University of Seville propose an alternative methodology based on a Real-Time Optimization (RTO) model, which uses actual day-ahead market clearing curves as a measure of demand elasticity. The goal is to estimate the expected net revenues of new battery energy storage system (BESS) installations and assess how incremental capacity additions affect wholesale prices and storage profitability.
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