Modeling noise and lease soft costs improves wind farm design and cost-of-energy predictions. (November 2016)

• Record Type: Journal Article
• Authors: Chen, Le; Harding, Chris; Sharma, Anupam; MacDonald, Erin
• Is Part Of: Renewable energy. Volume 97(2016)
• Page Start: 849
• Page End: 859
• Publication Date: 2016-11
• Subjects: Wind farm layout optimization -- Cost-of-energy -- Soft costs -- Optimization under uncertainty -- Land lease cost -- Noise disturbance compensation; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2016.05.045
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: The Department of Energy uses the metric Cost-of-Energy to assess the financial viability of wind farms. Non-hardware costs, termed soft costs, make up approximately 21% of total cost for a land-based farm, yet are only represented with general assumptions in models of Cost-of-Energy. This work replaces these assumptions with a probabilistic model of the costs of land lease and noise disturbance compensation, which is incorporated into a wind-farm-layout-optimization-under-uncertainty model. These realistic representations are applied to an Iowa land area with real land boundaries and house locations to accentuate the challenges of accommodating landowners. The paper also investigates and removes a common but unnecessary term that overestimates cost-savings from installing multiple turbines. These three contributions combine to produce COE estimates in-line with industry data, replacing "soft" assumptions with specific parameters, identify noise and risk concerns prohibitive to the development of profitable wind farm. The model predicts COEs remarkably close to real-world costs. Wind energy policy-makers can use this model to promote new areas of soft-cost-focused research. Highlights: We model noise disturbance compensation and landowners' noise acceptances. This is housed within a system-wind-farm-layout-optimization-under-uncertainty model. Modeling performed on 22 real land plots with 12 residences, GIS data available. The model predicts COEs remarkably close to real-world costs. Accurate soft-costs modeling opens doors for new wind energy policy and research.