Process and Policy Insights from Intercomparing Electricity System Capacity Expansion Models
Abstract: This study undertakes a detailed intercomparison of four open-source electricity system capacity expansion models--Temoa, Switch, GenX, and USENSYS--to examine their suitability for guiding U.S. power sector decarbonization policies. We isolate the effects of model-specific differences on policy outcomes and investment decisions by harmonizing empirical inputs via PowerGenome and systematically defining "scenarios" (policy conditions) and "configurations" (model setup choices). Our framework allows each model to be tested on identical assumptions for policy, technology costs, and operational constraints, thus distinguishing results that arise from data inputs or configuration versus inherent model structure. Key findings highlight that, when harmonized, models produce very similar capacity portfolios under each current policies and net-zero configuration, with less than 1 percent difference in system costs for most configurations. This agreement across models allows us to examine the impact of configuration choices. For example, configurations that assume unit commitment constraints or economic retirement of generators reveal the difference in investment decisions and system costs that arise from these modeling choices, underscoring the need for clear scenario and configuration definitions in policy guidance. Through this study, we identify critical structural assumptions that influence model outcomes and demonstrate the advantages of a standardized approach when using capacity expansion models. This work offers a valuable benchmark and identifies a few key modeling choices for policymakers, which ultimately will enhance transparency and reliability in modeling efforts to inform the clean energy transition for clean energy planning.
Optimal Transmission Expansion Modestly Reduces Decarbonization Costs of U.S. Electricity
Abstract: Solar and wind power are cost-competitive with fossil fuels, yet their intermittent nature presents challenges. Significant temporal and geographic differences in land, wind, and solar resources suggest that long-distance transmission could be particularly beneficial. Using a detailed, open- source model, we analyze optimal transmission expansion jointly with storage, generation, and hourly operations across the three primary interconnects in the United States. Transmission expansion offers far more benefits in a high-renewable system than in a system with mostly conventional generation. Yet while an optimal nationwide plan would have more than triple current interregional transmission, transmission decreases the cost of a 100% clean system by only 7% compared to a plan that relies solely on current transmission. Expanding capacity only within existing interconnects can achieve most of these savings. Adjustments to energy storage and generation mix can leverage the current interregional transmission infrastructure to build a clean power system at a reasonable cost.
Abstract: Interstate natural gas transmission and storage infrastructure is facilitated using regulated, private transactions. Pipeline companies obtain long-term contracts from producers and wholesale purchasers, typically local distribution companies (LDCs). Historically, the Federal Energy Regulatory Commission (FERC) accepted these counterparty contracts as sufficient justification of need. Typically the LDCs are themselves regulated firms, which sometimes possess affiliations with pipeline companies. But with contracted costs largely passed through to retail customers via regulated prices, it is unclear whether contracting parties face sufficient competition or otherwise possess an incentive to find least-cost alternatives. To aid evaluation of past and future investments, we develop a national-level optimization model that can assess the need for new interstate pipeline and storage facilities. The model takes production and demand pathways as fixed and minimizes the infrastructure and operation costs of transport and storage in order to balance supply and demand on each day in each state. Transport of gas can be achieved using pipeline transmission of dry gas, or using truck or ship transport of liquefied natural gas (LNG), and optimal placement of liquefaction and gasification facilities. The model also accounts for international imports and exports of both dry gas and LNG. Three underground drygas storage facilities are considered, as well as LNG storage. We compare the model’s optimized plan with observed outcomes as the sector grew rapidly with hydraulic fracturing. We find that the U.S. has built 38 percent more pipeline and 27 percent more underground storage than necessary, amounting to roughly $179 billion in excess investment. It would have been more economic to expand pipeline far less than observed and instead satisfy critical-peak demands for gas using LNG, plus necessary liquefaction and gasification facilities. Differences between optimized and observed investments vary across the interstate network, while flows between states and into and out of storage bear a close resemblance to observed outcomes.
Real Time Pricing and the Cost of Clean Power
New Update! (August 2022) replaces old paper here
Newer Update! (Nov 2024) published in AEJ Policy here
Abstract: Solar and wind power are now cheaper than fosil fuels but are intermittent. The extra supply-side variability implies growing benefits of using real-time retail pricing (RTP). We evaluate the potential gains of RTP using a model that jointly solves investment, supply, storage, and demand to obtain a chronologically detailed dynamic equilibrium for the island of Oahu, Hawai'i. Across a wide range of cost and demand assumptions, we find the gains from RTP in high-renewable systems to exceed those in a conventional fossil system by roughly 6 times to 12 times, markedly lowering the cost of renewable energy integration.
Interactive webiste of results can be found here: https://www2.hawaii.edu/~mjrobert/power_production/. Looks best using Chrome.
Commodity Price Adjustment in a Competitive Storage Model with an Application to US Biofuel Policies
Abstract: When demand for a commodity shifts permanently outward, the effect of the shift on the level and volatility of prices will differ over the short run and long run depending on: (1) how early the market anticipates the demand shock; (2) the elasticities of supply and demand in the new environment as compared to the old environment; and (3) inventories available when news of the impending demand shock arrives. We analyze the relative importance of these three features on food prices using a modified version of the storage model that allows demand to change over time. Model parameters are found using a grid search that minimizes the difference between simulated and observed data. We then use the model to evaluate the effect of the US ethanol mandate on world food prices. In line with earlier studies, we find that the US ethanol mandate, by shifting the world demand out by more than 5 percent in 2015, can account for 11 to 30 percent of the food price increase between 2005 and 2011 depending on demand and supply elasticities before and after the demand shift. Unlike earlier studies, the analysis shows how the ethanol mandate may have exacerbated the initial price spike or increased volatility during the transition to the new demand environment.
Commodity Prices and Volatility in Response to Anticipated Climate Change
Abstract: Mounting evidence indicates climate change will adversely influence agricultural crop yields and cause greater year-to-year variability. This paper considers how a rational, forward-looking and competitive commodity market would account for these anticipated changes and thereby influence time path of storage, prices, price volatility, and social welfare. We forecast 1600 hypothetical yield paths from 2000 to 2080 using estimates from a recent global statistical analysis of weather and crop yields combined with projections from 16 climate models. We then extend the dynamic competitive storage model to account for land response to price and anticipated yield shift. We simulate 1600 stochastic-equilibrium price paths under climate change relative to a baseline of stable prices using our hypothetical yield paths together with estimated demand and supply elasticities and storage cost from the literature. Our results indicate that, under the impact of climate change, world crop price level will increase twofold and world crop price volatility will increase fivefold between 2000 and 2080. Welfare analysis suggests that by 2020, the world would have welfare loss equivalent to food for 180 to 200 million people annually.
Abstract: We use data from the administrative files of the U.S. Department of Agriculture's Risk Management Agency to examine how the distribution of crop yields changed as individual farmers shifted into and out of the federal crop insurance program. The large panel facilitates use of fixed effects that span each combination of farmer and production practice to account for unobserved differences in farmer abilities, risk preferences and soils, in addition to fixed effects for interactions between all years and all counties to account for geographically-specific technological change, local prices, and weather. We also account for farm-specific yield variances. Conditional on this large set of fixed effects, we estimate the mean shift in yield and non-parametrically estimate the shift in the distribution around the conditional mean associated with enrollment incrop insurance. Because differences between farmer and land types have been accounted for (i.e., controlling for adverse selection), the estimated shifts in yield distributions likely reflect moral hazard. For most crops in most states we find insurance is associated with statistically signi cant but small downward shifts in average yield. The largest shifts occur for cotton and rice, the highest-value of ve crops considered. By integrating the estimated shift in yield distributions over actual indemnities paid, we provide estimates of the total indemnities paid due to moral hazard. Our results indicate moral hazard accounted for an estimated $53.7 million in indemnities between 1992 and 2001, which amounts to 0.9% of indemnities paid to the insured crops and states considered.
Abstract: We study multiple-unit asymmetric procurement auctions wherein sellers from two classes draw costs from different distributions. When sellers are asymmetric, a cost-minimizing buyer discriminates among classes of sellers to enhance competition. Establishing a quota—a limit on the number of offers that can be accepted from any one class—discriminates simply and effectively. The quota increases demand scarcity from the perspective of low- cost sellers, which causes them to lower their offers. To solve for approximate equilibrium strategies of asymmetric auctions with and without a quota, we develop a new method that is similar but distinctly different from the constrained strategic equilibrium (CSE) approach. The new method finds the constrained strategies that minimize the expected gain from a randomly chosen seller unilaterally deviating from the constrained strategy. We find quota can enhance competition and lower total procurement cost. We subject the same auctions to laboratory testing and find savings from quota in excess of that predicted by the approximate equilibrium strategies. This study is first to combine theory and experimental evidence of auctions with quotas, though similar mechanisms are widely used in practice. Because the mechanism is widely used to promote social goals and can also lead to better outcomes for the buyer, our findings have both positive and normative implications. One potentially interesting application of quota auctions would be for large-scale procurement of ecosystem services like carbon sequestration.