From ESS News
Traditionally, storage planning has relied on production cost models or capacity expansion models. The former optimize dispatch based on levelized costs, while the latter assess investment decisions in a marginal pricing framework, typically assuming that thermal generation sets market prices. In both approaches, however, storage is imperfectly represented, particularly in terms of its real 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 proxy for demand elasticity. The objective is to estimate expected net revenues from new battery energy storage system (BESS) installations and to assess how incremental capacity additions affect wholesale prices and storage profitability.
To continue reading, please visit our ESS News website.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com.
By submitting this form you agree to pv magazine using your data for the purposes of publishing your comment.
Your personal data will only be disclosed or otherwise transmitted to third parties for the purposes of spam filtering or if this is necessary for technical maintenance of the website. Any other transfer to third parties will not take place unless this is justified on the basis of applicable data protection regulations or if pv magazine is legally obliged to do so.
You may revoke this consent at any time with effect for the future, in which case your personal data will be deleted immediately. Otherwise, your data will be deleted if pv magazine has processed your request or the purpose of data storage is fulfilled.
Further information on data privacy can be found in our Data Protection Policy.