Wind-solar-storage hybrid power plants represent a significant and growing share of new proposed projects in the United States. Their uptake is supported by increasing renewable energy market share, enhanced technical abilities for dispatch and control, and decreasing costs for wind energy, solar energy, and battery storage. Simultaneously, there is also increased use of generation and storage resources in distributed power systems. The diversification of energy resources through hybridization or spatial distribution provides an opportunity to enhance power system resilience (compared to single-source generation), addressing growing concerns about the reliability of the aging, transforming U.S. electric grid. The question of where to build hybrid plants for resilience value-rather than for bulk power supply-has not been fully explored in previous studies. Therefore, in this study we complete a national complementarity analysis to identify areas in the United States that are particularly suited for wind-solar hybrid power plant development. The authors show the importance of seasonal and diurnal patterns in assessing complementarity and identify that regions in the Great Plains, Midwest, and Southeast are particularly suited for hybrid power plants. We demonstrate the resilience value of hybridization for a reference system based near Memphis, Tennessee, and show optimal sizing of wind, solar, and storage assets given 1.0 and 0.9 critical load factors. Our results indicate that the pairing of wind and solar assets better meets constant load demand and reduces storage requirements compared to using only solar assets. These results will enable future work to integrate complementarity metrics with resilience frameworks. The results also indicate a need for more finely resolved data for local resources, demand, and hazards. v
Existing methods for optimizing wind array layouts typically use power or cost objectives and rarely consider reliability-based objectives. Component and system failure rates, however, are dependent on location-specific wind conditions, are influenced by array layout and wake interactions, and have a direct and significant impact on capital costs, operational costs, and power production. Although wind power plant models exist that calculate wind loads with sufficient resolution to capture component loading dynamics from wind conditions, they are computationally expensive and thus not suitable for research applications requiring many evaluations, particularly optimization. This study describes the development of computationally efficient, reliability-based layout optimization methods, enabling us to explore the relationship between component reliability and layout optimization. These methods include the surrogate modeling of the planet bearing life based on varying wind conditions simulated in FAST.Farm and the formulation of reliability-based objectives based on failure cost and power production models. Through demonstration of this method, we explore how wind conditions, objective functions, and capacity density influence reliability-based layout optimization. Results indicate that considering reliability alongside power production can reduce failure costs associated with replacement costs and downtime whilemaintaining or improving power production. Our conclusions highlight the opportunity for wind power plant developers to integrate reliability and operational expenditures alongside performance and capital expenditure objectives in plant design and development to improve plant performance and costs.
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