• Energy Research

California has committed to ambitious decarbonization targets across multiple sectors, including decarbonizing the electrical grid by 2045. In addition, the medium- and heavy-duty truck fleets are expected to see rapid electrification over the next two decades. Considering these two pathways in tandem is critical for ensuring cost optimality and reliable power system operation. In particular, we examine the potential cost savings of electrical generation infrastructure by enabling flexible charging and bidirectional charging for these trucks. We also examine costs adjacent to enabling these services, such as charger upgrades and battery degradation. We deploy a large mixed-integer decarbonization planning model to quantify the costs associated with the electric generation decarbonization pathway. Example scenarios governing truck driving and charging behaviors are implemented to reveal the sensitivity of temporal driving patterns. Our experiments show that cost savings on the order of multiple billions of dollars are possible by enabling flexible and bidirectional charging in medium- and heavy-duty trucks in California.

With California's ambitious goal to achieve decarbonization of the electrical grid by the year 2045, significant challenges arise in power system investment planning. Existing modeling methods and software focus on computational efficiency, which is currently achieved by simplifying the associated unit commitment formulation. This may lead to unjustifiable inaccuracies in the cost and constraints of gas-fired generation operations, and may affect both the timing and the extent of investment in new resources, such as renewable energy and energy storage. To address this issue, this paper develops a more detailed and rigorous mixed-integer model, and more importantly, a solution methodology utilizing surrogate level-based Lagrangian relaxation to overcome the combinatorial complexity that results from the enhanced level of model detail. This allows us to optimize a model with approximately 12 million binary and 100 million total variables in under 48 hours. The investment plan is compared with those produced by E3's RESOLVE software, which is currently employed by the California Energy Commission and California Public Utilities Commission. Our model produces an investment plan that differs substantially from that of the existing method and saves California over 12 billion dollars over the investment horizon.

Abstract Variable renewable energy (VRE) droughts are periods of low renewable electricity production due to natural variability in the weather and climate. These compound renewable energy droughts occur when two or more (typically wind and solar) generation sources are in low availability conditions at the same time. Compound wind and solar droughts are most commonly studied at the hourly and daily timescale due to the short-term nature of energy markets and battery storage capacity. However the seasonal time scale allows for the examination of broader climate and hydrologic patterns that influence a broader renewable energy portfolio and inform the needs for long-duration energy storage. In this study, we use a newly developed dataset of coincident renewable generation to characterize seasonal compound VRE droughts which include wind, solar and hydropower at grid-relevant spatial scales across the contiguous United States (US). Along with the frequency, duration, magnitude, and spatial scale, we specifically examine these climate patterns with a composite climate analysis. Results for the historical period (1982–2019) indicate that seasonal compound VRE droughts can last up to 5 months and occur most frequently in the Fall. While not an established ‘climate stress’ to consider in reliability studies yet, we demonstrate the impact of seasonal energy droughts on a resource adequacy study over the Western US interconnection using a nodal bulk power grid model. We further discuss how seasonal compound VREs can inform the sizing of long-duration energy storage and market incentives to manage short-term extreme events like heat waves and cold snaps while considering seasonal conditions.

California has set ambitious decarbonization goals concerning both emissions and the portion of energy generated by renewable resources. However, climate change poses considerable uncertainties. Variation in load is driven both by the level of electrification as well as the impact of climate change on weather. Further, climate change stands to make weather more variable, impacting not just load but generation from renewable resources. In this paper, we approach the issue of the impacts of climate change on decarbonization planning from two perspectives. First, we look at the range of decarbonization pathways through 8 pathways that account for differences in socioeconomic development, global emissions, and warming. Second, we develop a more robust way of ensuring the reliability of energy resources planning than the commonly used planning reserve margin. We show that the proposed method can save between 6 and 14 billion dollars in investment and maintenance costs and outline critical policy implications concerning the reliance of power plants for satisfying planning reliability requirements, including the potential retirement of dozens of peaker power plants.