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Energy storage effects on day-ahead operation of power systems with high wind penetration
Authors: R.D. Christie; Cesar A. Silva Monroy;
Abstract
This paper simulates the optimal day-ahead operation of energy storage in a power system with high wind penetration. Mixed integer programming is employed to formulate the optimal scheduling problem of a thermal based power system with energy storage as a centralized optimization. Simulation of the operation of a 10-generator power system with near-ideal storage is employed to identify the effects of energy storage on generation cost and conventional power plants. Simulation results show that implementation of energy storage reduces total energy generation costs for wind energy penetration levels up to 30%, while also reducing the variability in the output power of conventional generation units and wind energy spillage.
Related Organizations
- University of Mary United States
- Washington State University United States
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).9 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average

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citations
Citations provided by BIP!
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
popularity
Popularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
9
Average
Top 10%
Average