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We estimate market power in California's surface water market. Market power may distort the potential welfare gains from water marketing. We use a Nash‐Cournot model and derive a closed‐form solution for the extent of market power in a water market setting. We then use this solution to estimate market power in a newly assembled dataset on California's water economy. We show that, under the assumptions of the Nash‐Cournot model, market power in this thin market is limited. [ABSTRACT FROM AUTHOR]

Decentralization of the electricity sector has mainly been studied in relation to its infrastructural aspect, particularly location and size of the generation units, and only recently more attention has been paid to the governance aspects. This article examines power sector (de)centralization operationalized along three functional dimensions: political, administrative and economic. We apply this framework to empirically assess the changes in California's electricity market, which saw the emergence of institutional innovation in the form of community choice aggregation (CCA). Unpacking the Californian case illustrates how decision-making has moved from central state government and regulators to the municipal level in uneven ways and without decentralized generation keeping pace. We also explore the impacts this multidimensional and diversified decentralization has on the ultimate goals of energy transition: decarbonization and energy security. Our framework and empirical findings challenge the conventional view on decentralization and problematize the widespread assumptions of its positive influence on climate mitigation and grid stability. [ABSTRACT FROM AUTHOR]

Market designers work as social engineers. They create institutional structures to enforce the assumptions of theoretical market mechanisms. Despite much work on performative effects of economics, sociology has not analyzed the feasibility of designers' theoretical blueprints. This article suggests that this may be done by identifying assumptions that derail the mechanism if they are not enforced. Sociologists then need to trace obstacles to their realization in practices of implementation. Blueprints are infeasible if the assumptions cannot be enforced either by themselves or jointly. To illustrate the approach, the article identifies one such assumption and traces its impact on a historical experiment of market design: the creation of markets for transmission capacity in California's electricity markets. To realize the design's explicit assumptions, designers built institutions that violated an implicit uniformity assumption and created opportunities for manipulations. Since the design's assumptions could not be realized simultaneously, it was infeasible. [ABSTRACT FROM AUTHOR]

To explain cognitive capture, economic sociologists often examine the structure of relationships between regulators and market participants. This paper argues that the nature of regulators' misperception should be subject to analysis as well. Different types of misperceptions develop over timelines of varying lengths. Depending on the misperception, different sets of relationships and parties may therefore be the cause of regulators' capture. The paper illustrates this point with a case study of regulators' failure to detect pervasive market power in California's electricity markets between 1998 and 2001. Existing explanations focus on sellers' short‐term attempts to distract regulators from widespread evidence of market power. Using data from three archives and in‐depth interviews, I show that the regulators did not fall prey to such "information problems." Instead, their misperception resulted from a more foundational "worldview problem." This error affects regulators' basic conception of the marketplace and can be traced to earlier and more gradual forms of influence exerted by utilities that, ironically, would become the victims of market power. [ABSTRACT FROM AUTHOR]

Convergence bidding, a.k.a., virtual bidding, has been widely adopted in wholesale electricity markets in recent years. It provides opportunities for market participants to arbitrage on the difference between the day-ahead market locational marginal prices and the real-time market locational marginal prices. Given the fact that convergence bids (CBs) have a significant impact on the operation of electricity markets, it is important to understand how market participants strategically select their CBs in real-world electricity markets. We address this open problem with focus on the electricity market that is operated by the California Independent System Operator (ISO). In this regard, we use the publicly available electricity market data to learn, characterize, and evaluate different types of convergence bidding strategies that are currently used by market participants. Our analysis includes developing a data-driven reverse engineering method that we apply to three years of real-world California ISO market data. Our analysis involves feature selection and density-based data clustering. It results in identifying three main clusters of CB strategies in the California ISO market. Different characteristics and the performance of each cluster of strategies are analyzed. Interestingly, we unmask a common real-world strategy that does not match any of the existing strategic convergence bidding methods in the literature. Next, we build upon the lessons learned from the advantages and disadvantages of the existing real-world strategies in order to propose a new CB strategy that can significantly outperform them. Our analysis includes developing a new strategy for convergence bidding. The new strategy has three steps: net profit maximization by capturing price spikes, dynamic node labeling, and strategy selection algorithm. We show through case studies that the annual net profit for the most lucrative market participants can increase by over 40% if the proposed convergence bidding strategy is used. [ABSTRACT FROM AUTHOR]

Economic sociologists have rarely studied organizational reasons why market design processes fail. Drawing on the organizational literature on mistakes and accidents, the paper identifies such reasons for a fatal design decision during the creation of California’s first electricity markets. Designers proposed weak oversight structures even though their models called for active and permanent regulatory control. Sellers like Enron could therefore manipulate the market without fear of detection, prolonging the western energy crisis. A process of “structural abstraction” explains this mistake. Designers were split into three groups that worked in different divisions and relied on local frames to understand the oversight requirements. Each group missed information the others were aware of and arrived at the conclusion that minimal oversight would suffice. Higher levels of the hierarchy should have discovered and resolved these discrepancies. However, these levels considered the issue at a higher level of abstraction. Such structural abstraction made room for ambiguities that obscured the local disagreements. [ABSTRACT FROM AUTHOR]

The United States (U.S.) West Coast power system is strongly influenced by variability and extremes in air temperatures (which drive electricity demand) and streamflows (which control hydropower availability). As hydroclimate changes across the West Coast, a combination of forces may work in tandem to make its bulk power system more vulnerable to physical reliability issues and market price shocks. In particular, a warmer climate is expected to increase summer cooling (electricity) demands and shift the average timing of peak streamflow (hydropower production) away from summer to the spring and winter, depriving power systems of hydropower when it is needed the most. Here, we investigate how climate change could alter interregional electricity market dynamics on the West Coast, including the potential for hydroclimatic changes in one region (e.g., Pacific Northwest (PNW)) to "spill over" and cause price and reliability risks in another (e.g., California). We find that the most salient hydroclimatic risks for the PNW power system are changes in streamflow, while risks for the California system are driven primarily by changes in summer air temperatures, especially extreme heat events that increase peak system demand. Altered timing and amounts of hydropower production in the PNW do alter summer power deliveries into California but show relatively modest potential to impact prices and reliability there. Instead, our results suggest future extreme heat in California could exert a stronger influence on prices and reliability in the PNW, especially if California continues to rely on its northern neighbor for imported power to meet higher summer demands. Plain Language Summary: Electric power systems are already strongly influenced by extreme weather, and climate change could make this problem worse. In this study, we explore how long‐term shifts in water availability and higher air temperatures could impact supply and demand for electricity in the West Coast region of the United States, and how interactions between two interconnected parts of the West Coast grid (the Pacific Northwest and California) could change as well. We focus on measuring impacts to the grid in terms of reliability (frequency of electricity shortfalls, or blackouts) and wholesale electricity prices. We find that shifts in water availability are likely to be the strongest influence on the Pacific Northwest, while increases in air temperatures are likely to be the strongest influence in California. We hypothesized that altered water availability in the Pacific Northwest could impact California too, through reduced summer deliveries of hydropower. However, our evidence points to future excessive heat in California as the major regional influence, which "pulls" more electricity from the Pacific Northwest, causing price and reliability impacts. Key Points: The U.S. West Coast power system is vulnerable to altered streamflow patterns and air temperature increases caused by climate changeRegional power systems are most sensitive to altered streamflows in the Pacific Northwest and air temperature changes in California, respectivelyResults show that climate‐related impacts in one regional power system can spill over to others in complex ways [ABSTRACT FROM AUTHOR]

Electricity end-users have been increasingly generating their own electricity via rooftop solar panels. We study the impact of such distributed renewable energy (DRE) on utility profits and social welfare under net metering, which is a widespread policy in the United States. Utilities have been lobbying against net-metered distributed solar based on the common belief that it harms utility profits. We find that when wholesale market dynamics are considered, net-metered DRE may be a positive for utilities. That is, net-metered DRE strictly improves the expected utility profit when the utility's self-supply is below a threshold and the wholesale electricity price is sufficiently responsive to wholesale demand fluctuations. Our paper distinctively considers both downstream and upstream impacts of net-metered DRE on utilities and analyzes the tradeoff between these impacts. Net-metered DRE can increase utilities' expenses because of their required buyback from generating customers, and reduces their retail sales revenues. In addition, it can either reduce utilities' wholesale procurement costs or affect their wholesale market revenues. Our results suggest that utilities might benefit from emerging business strategies that motivate their customers to install solar panels. Our numerical study uses data on the distributed solar in California and the wholesale electricity market operated by the California Independent System Operator, and demonstrates that our findings hold under realistic parameters. This paper was accepted by David Simchi-Levi, operations management. [ABSTRACT FROM AUTHOR]

We analyse how the introduction of the same renewable energy technology at different parts of the electricity supply chain has different price formation effects on wholesale power markets. We develop a multi-stage competitive equilibrium model to evaluate the effects on short-term price formation of a technology shift from conventional to both large-scale renewable energy production (e.g. wind and solar farms) and distributed renewable energy sources (e.g. rooftop solar). We find that wind and solar technologies oppositely affect the forward risk premium, and this is related to technology-varying, risk-related hedging pressures of producers and retailers. We form a multi-factor propositional framework and empirically validate the model by analyzing data from California and Britain; two markets which recently experienced significant increases of renewable power, in terms of utility scale and distributed sources. The work is innovative in showing theoretically and empirically how different types of renewable technologies influence market price formation differently. This has implications for market participants facing wholesale price risks, as well as regulators and policy-makers. [ABSTRACT FROM AUTHOR]

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Graphical abstract Highlights • Technology cost and utility rate structure are key drivers of economic viability of solar and storage systems. • Solar-plus-storage systems are more often economical under time of use and demand charge rates. • Savings from storage-only projects come mainly from demand charge reductions; solar combined with storage also provides energy charge savings. • Savings from solar with storage is largely independent of building load variability, likely due to the energy cost reductions from the solar. Abstract This paper explores the economics of solar-plus-storage projects for commercial-scale, behind-the-meter applications. It provides insight into the near-term and future solar-plus-storage market opportunities across the U.S. We explore the impacts of location, building load profile, technology cost, utility rate structure, and policies on solar-plus-storage economic viability, and identify which factors are most significant to project economics. While savings from storage-only projects are largely derived from demand charge reductions, solar combined with storage also provides significant energy charge savings. A common assumption is that load profiles with peaks are likely candidates for savings from storage, due to the opportunity for demand charge reduction. Our results indicate that potential for savings from combining solar with storage is independent of building load variability, likely due to the energy cost reductions from the solar. Systems are more often economical under time of use and demand charge rates, particularly when demand charges are >$10 per kilowatt. Where systems were found to be economical, expected lifetime savings averaged between 7%–10%, with savings of 30% in numerous cases. Near term markets exist for solar-plus-storage in locations such as California and New York. As technology prices drop, the number of building types that can benefit increase, and additional markets appear in Colorado, New Mexico, and Alaska. All data from the study and interactive modeling results are available at: https://openei.org/wiki/Solar+Storage. [ABSTRACT FROM AUTHOR]

• Developing a framework for probabilistic electricity price forecasting. • A deep Gabor filter-oriented convolutional network is designed as the strong deep structure. • A deep mixture network is developed to predict a set of probability density functions in the next hours. This paper establishes a framework for probabilistic electricity price forecasting in the electrical markets using an end-to-end and direct approach. In the proposed method, firstly, a deep Gabor filter-oriented convolutional network is designed as the strong deep structure. Then, the designed deep Gabor network is developed as a deep mixture network to predict a set of probability density functions (PDFs) in the next hours. To do so, a probabilistic loss function is formulated. The results are validated by the actual electricity market data in California independent system operator (CAISO) and show high level of accuracy and reliability in comparison with other state-of-the-arts methods [ABSTRACT FROM AUTHOR]

In this paper, we develop a new method called asymmetric multiscale detrended fluctuation analysis, which is an extension of asymmetric detrended fluctuation analysis (A-DFA) and can assess the asymmetry correlation properties of series with a variable scale range. We investigate the asymmetric correlations in California 1999–2000 power market after filtering some periodic trends by empirical mode decomposition (EMD). Our findings show the coexistence of symmetric and asymmetric correlations in the price series of 1999 and strong asymmetric correlations in 2000. What is more, we detect subtle correlation properties of the upward and downward price series for most larger scale intervals in 2000. Meanwhile, the fluctuations of Δ α ( s ) (asymmetry) and | Δ α ( s ) | (absolute asymmetry) are more significant in 2000 than that in 1999 for larger scale intervals, and they have similar characteristics for smaller scale intervals. We conclude that the strong asymmetry property and different correlation properties of upward and downward price series for larger scale intervals in 2000 have important implications on the collapse of California power market, and our findings shed a new light on the underlying mechanisms of power price. [ABSTRACT FROM AUTHOR]

This paper hypothesizes that if day-ahead markets for electricity are efficient, then the day-ahead prices will reflect the processed information and expectations of all market participants regarding the next day's electricity load and thus the prices may be useful in actually predicting the next day's load. We test this hypothesis using data for the PG&E aggregation area in the California ISO. The results provide evidence of a positive and significant relationship between the hourly day-ahead electricity price (relative to the natural gas price) and the subsequent actual hourly load. The reported relationship is sufficiently robust to produce a forecast based on the day-ahead hourly price relative to the price of natural gas, some binary variables, and a number of estimated ARMA disturbances that is considerably more accurate than the ISO's day-ahead forecast. [ABSTRACT FROM AUTHOR]

A pseudo-tie resource model is a means by which a resource (i.e., generation unit or load) is operationally transferred from one balancing authority (BA) area in which the resource is physically located (i.e., native BA) to another (i.e., attaining BA area). The attaining BA has responsibility for operational control of the pseudo-tie resource for both system reliability and electricity market purposes. By implementing a pseudo-tie, the California Independent System Operator Corporation (ISO) BA is able to attain control of resources physically located external to its jurisdiction for its BA area services. [ABSTRACT FROM PUBLISHER]

This paper presents the results from an auction experiment using industry professionals and student participants who compete in a simulated wholesale market for electricity. Motivated by the intervention by FERC in response to the “meltdown” of the California spot market, we investigate the effect of including a “soft” price cap in a uniform price auction as a means of mitigating high prices. When prices are driven above the soft cap, offer curves become flat, in contrast to the hockey stick shape observed in a typical uniform price auction for electricity. This flat offer curve leads to market prices that are relatively insensitive to both generation costs and demand reductions. [ABSTRACT FROM AUTHOR]

This paper discusses the electricity reforms in California and in England and Wales. In both cases, a centralised spot market played a major role, and both markets have now been abolished. This paper argues that their disappearance is not evidence that future electricity restructuring should avoid the use of spot markets. Instead, the problems in England and Wales were largely due to market power. In California, problems arising from market power and a tightening demand-supply balance were turned into a disaster because the spot market had not been backed up by hedging contracts. [Copyright &y& Elsevier]

[Display omitted] • Automates extraction of key features of sub-daily hydropeaking flow. • Four distinct hydropeaking flow regimes exist across California. • Duration and frequency distinguish hydropeaking flow regimess more than rate of change. • Hydropeaking is stronger during drought years and the dry season of wetter years. • Improve understand of flow alteration induced by hydropeaking operation and the flow-ecology relationship. Hydropeaking, a hydroelectricity generation strategy involving rapid changes to flow releases from dams in response to fluctuations in hourly-adjusted electricity markets, has been widely applied due to its economic efficiency. However, these operational practices produce sub-daily flow fluctuations that pose substantial hazards to riverine ecosystems and human activities. To ascertain the downstream impacts of hydropeaking, features of hydropeaking have been analyzed with respect to ecologically relevant hydrologic variables. However, since studies aiming to characterize hydropeaking regime often require manual feature extraction, they are limited to small temporal and spatial scales. Additionally, riverine ecologists have commonly treated hydropeaking as a broadly similar flow-alteration pattern regardless of the complexities of the electricity market and differences in the natural settings where it is applied. Therefore, this study sought to determine whether significantly different hydropeaking patterns exist on a regional scale, as revealed by the variation in hydropeaking over a long temporal scale (>five years). To fulfill this goal, a new algorithm, the Hydropeaking Event Detection Algorithm (HEDA), was developed in R to automate the characterization of hydropeaking flow regimes. Clustering analyses were conducted to explore the similarities and differences of hydropeaking regimes among 33 sites in numerous hydrologic regions of California. Four distinct classes of hydropeaking flow regimes were identified and distinguished by the duration and frequency of hydropeaking. Meanwhile, rate of change, amplitude and timing of hdyropeaking played less important roles in the classification. [ABSTRACT FROM AUTHOR]

Hydrogen has the potential to be a key contributor toward a low-carbon economy. Generating hydrogen by electrolysis using renewable energy is one way to support a decarbonized economy; however, its cost is not typically competitive with the carbon-emitting incumbent technology, steam methane reforming. The ability of electrolysis to integrate with electricity markets presents a unique cost reduction opportunity due to the perceived future availability of low and zero-marginal cost renewable energy sources. Additionally, as renewables, and particularly, photovoltaics are installed on the grid, they have a value deflation effect. This work evaluates solar-electrolysis configurations using a mathematical programming framework to maximize system net present value. The framework has been tested with specific weather conditions and financial mechanisms in California. Our findings indicate that a spectrum of potential cost competitive solutions is available for systems that (i) have market configurations resembling hybrid retail/wholesale, resulting in a hydrogen production cost range of US$6.2 kg−1–US$6.6 kg−1, or full wholesale market participation, reducing production cost to US$2.6 kg−1–US$3.1 kg−1, and (ii) achieve projected future cost reductions. • An optimization framework is proposed for the design of renewable-electrolysis system. • A case study on California investigates the most affordable systems to produce H 2. • H 2 production and storage cost is mostly affected by capital costs and tax credits. • The model can provide insights of economic H 2 deployment in other geographic areas. [ABSTRACT FROM AUTHOR]

• Hydrometeorological variables drive prices and emissions on California's grid. • Time scale controls which combination of variables exert the greatest influence. • Market prices appear most sensitive to periods of extreme abundance (over supply) • Historical data severely underrepresents the occurrence of these low price events. Hydrometeorological conditions influence the operations of bulk electric power systems and wholesale markets for electricity. Streamflow is the "fuel" for hydropower generation, wind speeds and solar irradiance dictate the availability of wind and solar power production, and air temperatures strongly affect heating and cooling demands. Despite growing concern about the vulnerability of power systems to hydrometeorological uncertainty, including "compound" extremes (multiple extremes occurring simultaneously), quantifying baseline probabilistic risks remains difficult even without factoring in climate change. Here, we use newly developed power system simulation software to show how uncertainties in spatially and temporally correlated hydrometeorological processes affect market prices and greenhouse gas emissions in California's wholesale electricity market. Results highlight the need for large synthetic datasets to access rare, yet plausible system states that have not occurred in the recent historical record. We find that time scale strongly controls which combinations of hydrometeorological variables cause extreme outcomes. Although scarcity caused by low streamflows and high air temperatures has long been considered a primary concern in Western power markets, market prices are more profoundly impacted by weather and streamflow conditions that lead to an overabundance of energy on the grid. [ABSTRACT FROM AUTHOR]

• PV output can be temporally shaped through curtailment, tracking, and orientation. • We quantify the impact of output-shaping on day-ahead and real-time market revenues. • The 2017 California real-time market demonstrates the largest benefit from shaping. • Optimal azimuths for real-time revenue are ∼55° west of south in California in 2017. • The revenue benefit from temporal output-shaping increases with PV penetration. Large-scale deployment of solar photovoltaics (PV) contributes to the occurrence of depressed—and sometimes negative—electricity prices during daylight hours as PV displaces higher-cost generators in the merit-order dispatch stack. These changes in electricity price provide an opportunity to increase the wholesale energy revenue of PV generators through temporal shaping of PV output. Here, we explore the impact of three output-shaping strategies on PV wholesale energy revenue and capacity factor: utilization of 1-axis tracking, curtailment during negative-price hours, and modification of fixed-tilt array orientation. Utility-scale PV arrays are modeled at more than 10,000 pricing nodes across six United States electricity markets over the 2010–2017 time period. Large changes in revenue-optimized output profiles are observed for the California system, where solar capacity penetration has increased from ∼2% of peak load in 2010 to ∼28% of peak load in 2017, and the wholesale revenue benefits of temporal output shaping are increasing with time. On the California real-time market in 2017, compared to capacity-factor-optimized fixed-tilt arrays with must-run operation, curtailment increases revenues by 9%, curtailment in conjunction with fixed-tilt orientation optimization increases revenues by 20%, 1-axis tracking without curtailment increases revenues by 32%, and 1-axis tracking with curtailment increases revenues by 42% for the median node. Median optimal fixed-tilt azimuths for PV on the real-time market in California have increased from 192° in 2010 to 235° (i.e. 55° west of south) in 2017. Among the markets and years studied, the California market in 2017 demonstrates the largest potential benefit from temporal output shaping. These results highlight mechanisms for mitigating some of the decline in PV wholesale value at high solar penetrations, and illustrate the importance of adapting PV installation and dispatch strategies to changing power system conditions. [ABSTRACT FROM AUTHOR]