Evaluating financial risk management strategies under climate change for hydropower producers on the Great Lakes

Abstract

Hydropower on the Great Lakes makes up a substantial fraction of regional electricity generation capacity. Hydropower producers on the Niagara River (flowing between lakes Erie and Ontario) operate as run‐of‐river, and changing lake levels alter interlake flows reducing both generation and revenues. Index‐based insurance contracts, wherein contract payouts are linked to lake levels, offer a tool for mitigating this risk. As a potentially useful tool, pricing of financial insurance is typically based on historical behavior of the index. However, uncertainty with respect to the impacts of climate change on lake level behavior and how this might translate to increased (or decreased) risk for those selling or buying the insurance remains unexplored. Portfolios of binary index‐insurance contracts are developed for hydropower producers on the Niagara River, and their performance is evaluated under a range of climate scenarios. Climate Informed Decision Analysis is used to inform the sensitivity of these portfolios to potential shifts in long‐term, climatological variations in water level behavior. Under historical conditions, hydropower producers can use portfolios costing 0.5% of mean revenues to increase their minimum revenue threshold by approximately 18%. However, a one standard deviation decrease in the 50 year mean water level potentially doubles the frequency with which these portfolios would underperform from the perspective of a potential insurer. Trade‐offs between portfolio cost and the frequency of underperformance are investigated over a range of climate futures.

Managing Financial Risk Trade‐Offs for Hydropower Generation Using Snowpack‐Based Index Contracts

“…However, many situations lack such a threshold and may be better served by a linear contract. Although threshold‐based index contracts have been suggested for hydropower producers (Denaro et al., 2018; Foster et al., 2015; Meyer et al., 2017), the approximately linear relationship between hydrology (e.g., the SWE index) and hydropower revenues (see Figure 2) suggests that a linear contract structure may be more appropriate. Such contracts have been less popular in the water resources literature, but are commonplace in other financial contexts.…”

“…However, index contracts that have been carefully tailored to the specific financial risks and needs of a user are more likely to be adopted in practice. For example, in recent years index contracts based on variables such as water level, streamflow, and the Palmer Hydrologic Drought Index have been demonstrated to effectively hedge the hydrologic financial risk faced by water utilities (Brown & Carriquiry, 2007;Baum et al, 2018;Zeff & Characklis, 2013), commercial shipping firms (Meyer et al, 2016), and hydropower producers (Denaro et al, 2018;Foster et al, 2015;Meyer et al, 2017) from adverse financial outcomes during drought. This work proposes an index contract based on snow water equivalent depth (SWE).…”

Integrating Physical and Financial Approaches to Manage Environmental Financial Risk on the Great Lakes

“…where Π = payout ($) l = mean monthly lake level (ft) Contract pricing, as described in Meyer et al (2017), is determined using the Wang Transform (Wang, 2002), an approach that attaches a higher relative loading to contracts that have small probability of large payouts with a higher loading than contracts with a greater probability of lower payouts. Portfolios of these binary contracts allow for coverage over a range of future lake levels, as well as continuous, monthly coverage.…”

“…Dredging depths considered range from 0 to 5 ft in 0.5 ft increments (0 to 1.524 m in 0.15 m increments), and index insurance strike levels range from 2 ft (0.61 m) below the initial level to the initial level 0.5 ft (0.15 m) increments, creating a total of 60 strategies considered. The index insurance strike level range reflects historical variance in water levels and a range of payout frequencies in line with previous analyses (Meyer et al, 2017). The dredging range was chosen based on USACE data on typical harbor dredging depths, including those used in USACE cost-benefit analysis of a dredging project Waxmonsky (1997).…”

“…Most of these financial measures act like insurance, with the goal of compensating the impacted parties for financial losses after some adverse event (i.e., drought). Various financial instruments have been proposed to mitigate drought-related financial losses for inland navigation (Meyer et al, 2016), water utilities (Brown & Carriquiry, 2007;Zeff & Characklis, 2013), and hydropower producers (Foster et al, 2015;Kern & Characklis, 2017;Meyer et al, 2017) and are used extensively in the agricultural sector (Chantarat et al, 2007;Fuchs & Wolff, 2011;Hellmuth et al, 2009;Kellner & Musshoff, 2011;Khalil et al, 2007;Lou et al, 2009). As interest in methods of managing environmental financial risk grows, understanding how financial instruments can be integrated with physical risk mitigation measures will become more important.…”