The value of compressed air energy storage in energy and reserve markets

By E. Drury, Paul Denholm, Ramteen Sioshansi. 2011

265 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Finance

View Paper: https://www.semanticscholar.org/paper/9b93e1938313a05521d89724c60f9403e8038925

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Ramteen Sioshansi
2. Paul Denholm
3. T. Jenkin
4. Vincenzo Marano
5. Xiaomin Xi
6. D. McConnell
7. Mike Sandiford
8. Tim Forcey

In Finance

1. T. Jenkin
2. Paul Denholm
3. D. McConnell
4. Mike Sandiford
5. Tim Forcey
6. Ramteen Sioshansi
7. Xiaomin Xi
8. Vincenzo Marano

Citations in the corpus

• Effect of Battery Degradation on Multi-Service Portfolios of Energy Storage (2016, background): “Various studies have investigated the ability of energy storage plants to support integration of low carbon generation [1], [2], provide balancing services [3], support energy market operations [4], [5], and alleviate network congestion [6].”
• Estimating the value of electricity storage in an energy-only wholesale market (2015, methodology): “[20] also used reserve price data, separate from the energy market data, to optimally dispatch the storage devise.”
• A Bilevel Model for Participation of a Storage System in Energy and Reserve Markets (2018, methodology): “Over the past few years, a number of works studied the pool strategy of a merchant storage system in energy-only markets [4]–[7], the joint energy and reserve markets as day-ahead settlement [2], [3], and the joint energy and reserve markets (dayahead settlement) along with balancing settlement in r”
• Risk-Constrained Bidding and Offering Strategy for a Merchant Compressed Air Energy Storage Plant (2017, background): “Energy ratio indicates the amount of energy that the compressor of the plant consumes per unit of energy that the expander generates during the peak hours [9].”
• A stochastic dynamic programming model for co-optimization of distributed energy storage (2013, background): “[9] examine the value of arbitrage and AS from storage in a number of markets in the United States.”
• Economics of compressed air energy storage to integrate wind power: A case study in ERCOT (2011, background): “Denholm and Sioshansi (2009) use hourly electricity price data from Independent System Operators (ISOs), while the other two studies examine the cost-effectiveness of storage for wind integration and make no assumptions about electricity price.”
• Market and Policy Barriers to Deployment of Energy Storage (2012, methodology): “Sioshansi and Denholm (2010a,b); Drury et al. (2011) study the value of arbitrage and spinning reserves, however they explicitly neglect the effect of AS calls on the ability of storage to provide energy in subsequent hours. They justify this assumption by the fact that spinning reserves tend to be ”