A systematic review of the costs and impacts of integrating variable renewables into power grids

Solar and wind grid system value in the United States: The effect of transmission congestion, generation profiles, and curtailment

“…To do this, we decompose the observed value differences into three separate causes: output profile, transmission congestion, and curtailment. This decomposition loosely builds on concepts from modeling efforts 9,36 and complements evaluations of VRE integration costs [37][38][39][40] and efforts to identify value decline mitigation strategies. 8,[41][42][43][44][45] We also compare recent value decline trends with forward-looking modeling studies, providing context for how these trends might evolve in the future.…”

“…We are comfortable with this exclusion because balancing costs are generally found to be small compared with other VRE costs. 38,40 To summarize, our framework captures the major value streams for VRE that are internal to electricity markets (i.e., excluding environmental benefits). We are interested in how and why VRE value declines with penetration, and our scope can capture the major factors that influence that decline.…”

“…Although we explore VRE value decline, this topic is fundamentally similar to the question of VRE ''integration'' costs. [37][38][39][40] In our case, observed VRE value decline can be interpreted as a rough approximation of increased VRE integration costs, though our formulation of value decline does not allow for an assumption of exact equivalence. Note, Ueckerdt et al 37 provide more details on the equivalence between VRE integration costs and VRE value decline.…”

Renewable Energy Zones in Australia's National Electricity Market

“…Multiple jurisdictions (US, Great Britain, Europe, Australia) are experiencing sharp increases in utility-scale VRE. This is driving demand for costly transmission infrastructure, ancillary services, rising network congestion and greater Market Operator intervention (Neuhoff et al, 2013;Bird et al, 2016;Neuhoff et al, 2016;Bertsch et al, 2017;Joos and Staffell, 2018;Ambrosius et al, 2019;Wagner, 2019;Heptonstall and Gross, 2020;Simshauser and Gilmore, 2020;Pollitt and Anaya, 2021). The coordination of generation and transmission investment is therefore of rising interest (van der Weijde and Hobbs, 2012;Munoz et al, 2017;Pechan, 2017;AEMC, 2019;Ambrosius et al, 2019;Eicke et al, 2020).…”

Predicting Delinquency on Mortgage Loans: An Exhaustive Parametric Comparison of Machine Learning Techniques

“…The data has been extracted from the datasets provided by the University of Tennessee [37,38] for data mining and business intelligence. Few other studies have also used a similar data source for their data analysis [39,40]. From Table 1, our dataset has 11 variables in total: borrower's age, original loan amount, the ratio of loan to the home purchase price, borrower's credit score, first time home buyer, borrower's monthly debt expense, borrower's monthly income, the purchase price of a house, the appraised value of a home at origination and, finally, the outcome variable, that is, loan as delinquent or non-delinquent.…”