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Combining wind, solar, storage and transmission in a highly renewable power grid for the USA

Activity: Talk or presentation typesLecture and oral contribution

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Gorm Bruun Andresen - Lecturer

In a highly or fully renewable energy system, wind and solar are likely to provide the largest fraction of the total energy. The main challenge for these weather-driven variable sources is that their production does not generally match demand well. This problem can be addressed by: (A) Optimizing the generator portfolio to provide a better match between renewable energy production and demand, (B) Interconnecting geographically dispersed wind or solar sources to reduce their combined variability, and (C) Moving either supply (storage) or demand (load shifting) in time to increase the integration of variable sources. Here, a top down approach is used to show how wind, solar, power transmission and storage can be combined in the US power system to meet an average of 50, 80 and 95% of the hourly electricity demand with wind and solar energy. Results are presented for a technical optimization, where over installation is minimized; and for cost optimal combinations of the technologies. We find that wind power is the most cost effective technology in the lower penetration scenario. Solar power becomes increasingly competitive as the wind-solar penetration increase beyond 50%, and it reaches a cost optimal energy share of up to 40% in the high penetration scenario. Large-scale investments in storage and transmission technologies are able to significantly reduce the total cost of energy in both the 80 and 95% scenarios. But not in the 50% scenario. The analysis is based on a new high-resolution dataset of hourly wind and solar power for the entire USA and hourly load data for each of the 10 Federal Energy Regulatory Commission (FERC) Regions.
24 Jun 2014

Event (Conference)

TitleEnergy Systems Conference
Abbreviated titleENER2014
LocationQueen Elizabeth II Conference Centre, Westminster, London, UK
Country/TerritoryUnited Kingdom

ID: 81611711