As energy storage is integrated into grids through policies or market forces, it has an effect on the dispatch, economics, and retirement of other generators. While the complementary relationship between storage and renewables is well-known, the effect of storage additions is not necessarily limited to renewables. This work models the system effects of new storage on the generation, operating income, and retirement of power plants at three levels of inc. As energy storage is integrated into grids through policies or market forces, it has an effect on the dispatch, economics, and retirement of other generators. While the complementary relationship between storage and renewables is well-known, the effect of storage additions is not necessarily limited to renewables. This work models the system effects of new storage on the generation, operating income, and retirement of power plants at three levels of increasing complexity. First, we evaluate the marginal effects of storage on generation sources without any effects on market prices or dispatch. Second, we use a dispatch model to study bulk storage in New York Independent System Operator (NYISO), Midcontinent ISO (MISO), and California ISO (CAISO), allowing storage to shift dispatch patterns and affect the operation/income of existing generators. Third, we examine the mid- and long-term effects on the generation fleet by accounting for the retirement of power plants that lose sufficient annual revenue from new storage. Results suggest that marginal new storage increases coal generation and decreases natural gas generation in the West and Midwest, and does the opposite in New England and California. With bulk storage additions, the operating income of all other generating units is reduced unless retirement is included. With retirements considered, the least flexible generation—coal, nuclear, and solar—gain the most operating income with storage. In all cases, simple cycle gas turbines lose the most operating in. ••Quantifies the marginal effects of storage on generation sources across the U.S.••Examines the mid- and long-term effects of bulk storage on the generation fleet.••In long-term, least flexible baseload units gain the most with storage arbitrage.••Coal, nuclear, and solar can benefit from storage while gas turbines lose revenue.Energy storageDispatchRenewablesOperating incomeElectricity gridFuel mixEnergy policyElectricity pricesAs the costs of grid-scale electricity storage ('storage') decline, the technology is increasingly being used for power sector applications. Potential grid services in which storage might provide value include shifting energy generation surplus so as to better align it with demand, improving reliability by providing capacity to meet peak load, or increasing stability and flexibility through ancillary services, among others,. The flexibility provided by storage is also seen as a pathway for the adoption of higher levels of renewable electricity sources, thus facilitating decarbonization of the power sector,,. Despite these opportunities, in many instances the potential compensation for providing this value to the grid provides insufficient revenue to compensate storage owners or developers,. As a result, policymakers in the U.S. have sought to encourage the development and deployment of storage, with at least 15 states enacting procurement mandates or financial incentives for storage,,.Whether integrated by mandate or market forces, the system value that storage realizes—through the provision of grid services, investment deferment, emissions reductions, or other mechanisms—is highly dependent on the attributes of the storage, how it is operated, and the context of the system into which it is introduced,,,,. For example, although storage may help to enable long-term decarbonization strategies, studies have found that a. In the first part of this work, we estimate the impact of storage on net generation using actual electricity prices and the probability of a particular type (technology) of generator operating as the marginal generator ('marginal generator factors') at a given time from 22 different eGRID regions. A linear programming model is used to optimize the.
