Estimating the value of electricity storage in PJM: Arbitrage and some welfare effects

By Ramteen Sioshansi, Paul Denholm, T. Jenkin, J. Weiss. 2009

608 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/e2db49a75f2e7da4eacdafd163c16059fc44bdf8

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In Electrical and Electronic Engineering

In Renewable Energy, Sustainability and the Environment

Citations in the corpus

A multi-scale optimization framework for electricity market participation (2017, background): “Several studies previously classify the impact of forecasting error on market revenues [56,66,52].”
A MILP model for optimising multi-service portfolios of distributed energy storage (2015, background): “(16) and (20) take account of potentially simultaneous exercise of reserve and response within a particular period t (with b e [1, 2)(3) e.”
The role of smart metering and decentralized electricity storage for smart grids: The importance of positive externalities (2012, background): “, 2006), their impact on power system stability (Divya and Østergaard, 2009), arbitrage value of storage devices in specific regions (Sioshansi et al., 2009) or specific applications such as wind farm repowering projects or island systems (Hennessy and Kuntz, 2005).”
Economic viability of energy storage systems based on price arbitrage potential in real-time U.S. electricity markets (2014, methodology): “Uncertainty also exists in capacity markets over what payment is appropriate for ESS capacity [26].”
Estimating the value of electricity storage in an energy-only wholesale market (2015, methodology): “[15] analysed the PJM interconnection, Walawalkar et al.”
The Value of Concentrating Solar Power and Thermal Energy Storage (2010, methodology): “Rather than trying to approximate the effectiveness of different forecasting techniques in optimizing the dispatch of a CSP plant, we use the “backcasting” technique described in [19].”
Modeling Strategic Electricity Storage: The Case of Pumped Hydro Storage in Germany (2011, unknown): “This result corresponds with the findings of Sioshansi et al. (2009).”
A stochastic dynamic programming model for co-optimization of distributed energy storage (2013, methodology): “[35,34] relax the perfect-foresight assumption by examining a ‘backcasting’ heuristic whereby storage is dispatched using historical price patterns that are assumed to repeat themselves.”
Practical operation strategies for pumped hydroelectric energy storage (PHES) utilising electricity price arbitrage (2011, background): “Sioshansi et al. (2009) investigated the arbitrage value of small-scale energy storage for the PJM market in the USA, while Walawalkar et al. (2007) analysed the potential of sodium sulphur batteries and flywheel energy storage systems in New York state’s electricity market.”
Economics of compressed air energy storage to integrate wind power: A case study in ERCOT (2011, background): “…more detailed analysis would be necessary to assess the change in economic surplus due to the wind/CAES systems of contract price scenarios, for example, their smaller size and operation in a smaller market both suggest that the benefit would be less than that calculated by Sioshansi et al. (2009).”
Risk-Constrained Bidding and Offering Strategy for a Merchant Compressed Air Energy Storage Plant (2017, background): “Furthermore, high electricity price volatility in some markets provides a business opportunity for energy arbitrage by these storage technologies [3].”
Modeling of financial incentives for investments in energy storage systems that promote the large-scale integration of wind energy (2013, background): “To this end, assignment of a value to social welfare attributes of energy storage technologies comprises a subject of major interest, recently designated by many authors [31,32], realizing that even if ancillary services were given a value, arbitrage would still comprise a high-risk source of revenu”
Energy Storage Arbitrage Under Day-Ahead and Real-Time Price Uncertainty (2018, unknown): “There is also a growing literature examining the use of battery storage for arbitrage maximization in distribution markets [18], [19].”
Maximising the value of electricity storage (2016, background): “investigated the impact large amounts of storage would have on the price spread and value of arbitrage by correlating historic prices to total system demand, and evaluating the extent to which storage would flatten peak demand and offpeak demand [55].”