Optimizing Wind and Solar Energy Systems

Optimizing variable renewable resources, such as wind and solar power, is an important step toward decarbonizing energy systems. Doing so requires effectively planning where wind and solar generation should be sited, but knowing where these sites should be to support power systems while minimizing costs remains challenging. To tackle this challenge, a team of MIT researchers, led by Professor Michael Howland, has found that high-resolution weather data combined with high-resolution energy system modeling significantly enhances cost-effective decision-making. In their new study, recently published in Cell Reports Sustainability and supported by the MCSC, the team used kilometer-scale resolution data in three diverse regions across the United States — California (CAISO), Texas (ERCOT), and New England (ISO-NE) — and discovered ideal ranges for measuring wind and solar data. The authors – Liying Qiu and Rahman Khorramfar, Postdoctoral Associates, and Professors Saurabh Amin and Howland – found that optimal resolution data utilizes the complementarity of natural resources, allowing different renewable sources to mutually compensate for fluctuations in production. This enhanced complementarity is necessary for designing efficient and resilient decarbonized energy systems. The insights from this study can guide stakeholders in selecting their optimal resolutions tailored to different regions and specific objectives.

Read this MIT News article to hear more from the authors.