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This paper presents a methodological framework to evaluate the resilience, with the primary focus on performance degradation, of levee-protected electric power networks to flooding in a changing climate. To this end, a multi-disciplinary framework is established by integrating climate science, hydrology, and electric power network analysis. The framework quantifies the effect of climate change on flood hazard levels in a levee-protected area and the subsequent changes in the resilience of the electric power network. In the first step, the changes in the exposure of levee-protected regions to flooding hazard in a warming climate are calculated. Then, the probabilities of failure of power network components due to flooding under current and projected future climate are determined. Finally, the power system resilience index is used to assess the system resiliency for pre-flooding (baseline), historic flooding, and two projected future flooding scenarios. For demonstration, the proposed framework is applied to a levee-protected area in Northern California. The IEEE 118-bus standard test system is employed to represent the power network in the study area. Results reveal that climate change can considerably decrease the system resilience of the levee-protected electric power network. The findings of this study can contribute towards more resilient power network systems under a changing climate. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic model is proposed for a joint statistical description of solar photovoltaic (PV) power and outdoor temperature. The underlying correlation emerges from solar irradiance that is responsible in part for both the variability in solar PV power and temperature. The proposed model can be used to capture the uncertainty in solar PV power and its correlation with the electric power consumption of thermostatically controlled loads. First, a model for solar PV power that explicitly incorporates the stochasticity due to clouds via a regime-switching process between the three classes of sunny, overcast and partly cloudy is proposed. Then, the relationship between temperature and solar power is postulated using a second-order Volterra model. This joint modeling is leveraged to develop a joint probabilistic forecasting method for solar PV power and temperature. Real-world datasets that include solar PV power and temperature measurements in California are analyzed and the effectiveness of the joint model in providing probabilistic forecasts is verified. The proposed forecasting methodology outperforms several reference methods thus portraying that the underlying correlation between temperature and solar PV power is well defined and only requires a simple lower-complexity sampling space. [ABSTRACT FROM AUTHOR]

When critical infrastructure system services are disrupted, households typically respond by reducing, delaying, or relocating their demand (e.g., delaying laundry), or augmenting supply (e.g., using a generator). While this phenomenon is well known, there has been little systematic empirical investigation of it. Focusing on electric power and water service interruptions and using revealed and stated preference survey data from Los Angeles County, California, we develop 24 mixed logit models, one each to predict the probability an individual undertakes a specified adaptation as a function of outage duration and characteristics of the individual. The analysis aims to determine: (1) how common different household adaptations are; (2) how adaptation implementation varies with infrastructure type, outage duration, and uses of the service; (3) what household characteristics are associated with implementation of different adaptations; and (4) how adaptations tend to occur together. The percentage of individuals who report doing an adaptation varies greatly across adaptations and outage durations, from 2% to 88%. In general, adaptations that require moving out of the home are the least common of those investigated. For electric power outages, adaptations that could be done at home are less likely as the outage duration increases, while those that require going somewhere are more likely as the duration increases. For water outages, all adaptations (except delaying consumption) are more likely as an outage lasts longer. Using electric power or water for medical devices and/or work and business has a large effect on the likelihood of implementing many adaptations. Preevent conservation habits are also associated with an increased likelihood of implementing adaptations. The influence of household characteristics varies greatly across adaptations. There is evidence that some adaptations tend to occur together (e.g., using water from lakes and the government) and others tend not to (e.g., delaying electricity use and going to a hotel). Knowing what kinds of adaptations are found among different segments of the population, how common they are, and when they are likely to be implemented can help policymakers know what sorts of crisis-coping behaviors to expect in their locality, and can help them to know the timeframe in which those adaptations would be implemented. This in turn can provide needed knowledge for how to support those adaptations. Such support could take the form of enhanced contacts and outreach in specific populations (e.g., medically fragile), advance planning (having materials on hand, or including adaptations in drills or exercises), or timely improvising as an event unfolds. [ABSTRACT FROM AUTHOR]

The California Independent System Operator (CAISO) has implemented Convergence Bidding (CB) on February 1, 2011 under Federal Energy Regulatory Commission's September 21, 2006 Market Redesign and Technology Upgrade Order. CB is a financial mechanism that allows market participants, including electricity suppliers, consumers and virtual traders, to arbitrage price differences between the day-ahead (DA) market and the real-time (RT) market without physically consuming or producing energy. In this paper, market efficiency is defined in terms of trading profitability, where a zero-profit competitive equilibrium implies market efficiency (Jensen in, J Financial Econ 6(2):95-101, ). We analyze market data in the CAISO electric power markets, and empirically test for market efficiency by assessing the performance of trading strategies from the perspective of virtual traders. By viewing DA-RT spreads as payoffs from a basket of correlated assets, we can formulate a chance constrained portfolio selection problem, where the chance constraint takes two different forms as a value-at-risk constraint and a conditional value-at-risk constraint, to find the optimal trading strategy. A hidden Markov model (HMM) is further proposed to capture the presence of the time-varying forward premium. This is meant to be a contribution to the modeling of regime shifts in the electricity forward premium with unobservable states. Our backtesting results cast doubt on the efficiency of the CAISO electric power markets, as the trading strategy generates consistent profits after the introduction of CB, even in the presence of transaction costs. Nevertheless, by comparing with the performance before the introduction of CB, we find that the profitability decreases significantly, which enables us to identify the efficiency gain brought about by CB. Convincing evidence for the improvement of market efficiency in the presence of CB is further provided by the test for the Bessembinder and Lemmon (J Finance 57(3):1347-1382, ) model. [ABSTRACT FROM AUTHOR]

The paper analyzes the impact of integrating wind generation on the regulation and load following requirements of the California Independent System Operator (CAISO). These requirements are simulated and compared for the study cases with and without wind generation impacts included into the study for the years 2006 and 2010. Regulation and load following models were built based on hour-ahead and five-minute ahead load and wind generation forecasts. In 2006, the CAISO system peaked at 50270 MW. Wind generation (at the installed capacity of 2600 MW) had limited impact on the requirement of load following and regulation in the CAISO Balancing Authority. However, in 2010 (with an expected installed capacity of approximately 6700 MW), this impact will significantly increase. The results provide very useful information for the CAISO to adjust its scheduling and real-time dispatch systems to reliably accommodate future wind generation additions within the CAISO Balancing Authority. [ABSTRACT FROM AUTHOR]

The article comments on papers by Oren, Smith, and Wilson (1985) and Wilson (1989) which consider means of mitigating the social consequences of rationing by matching the high value users to the power provision. The papers are analyzed in light of the rolling blackout used by California as a means of rationing electricity. The goal is to use ex ante pricing, or priority pricing, to insure that high value users have a higher probability of receiving the good. The mathematical fact proved in the article is that if the distributions satisfy standard hazard rate hypotheses, the social value of using two priorities is at least half of the gains of using infinitely many priority classes.

ABSTRACT The California generation fleet manages the existing variability and uncertainty in the demand for electric power (load). When wind power is added, the dispatchable generators manage the variability and uncertainty of the net load (load minus wind power). The variability and uncertainty of the load and the net load are compared when 8790 MW of wind power are added to the California power system, a level expected when California achieves its 33% renewable portfolio standard, using a data set of 26,296 h of synchronous historic load and modeled historic wind power output. Variability was calculated as the rate of change in power generated by wind farms or consumed by the load from 1 h to the next (MW/h). Uncertainty was calculated as the 1 h ahead forecast error [MW] of the wind power or of the load. The data show that wind power adds no additional variability than is already present in the load variability. However, wind power adds additional uncertainty through increased forecast errors in the net load compared with the load. Forecast errors in the net load increase 18.7% for negative forecast errors (actual less than forecast) and 5.4% for positive forecast errors (actual greater than forecast). The increase in negative forecast errors occurs only during the afternoon hours when negative load forecasts and positive wind forecasts are strongly correlated. Managing the integration of wind power in the California power system should focus on reducing wind power forecast uncertainty for wind ramp ups during the afternoon hours. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article presents a study on an intelligent simulator to examine why a price hike occurred in the California electricity crisis. A multiagent intelligent simulator (MAIS) is compared with other methods, such as neural network (NN) and genetic algorithm (GA), using a real data set on power trading related to the crisis. The comparison showed that MAIS and the other methods performed in predicting the price fluctuation of wholesale electricity. Findings indicate that the price hike was due to an increase in fuel prices and real demand.

Theory provides limited understanding of auctions in complex, multi-unit settings. This absence became apparent when performance concerns were raised over wholesale electric power auctions in England and California. This research used experiments involving 234 subjects to partially bridge the gap in knowledge with regard to bidding behaviour. Using a complex procurement setting motivated by power markets, bidding in the last accepted and first rejected uniform price auctions and the multi-unit Vickrey were examined across group sizes of 2, 4, and 6. To allow opportunity for tacit collusion, constant cost and demand conditions were maintained for 75 periods. Group size and experience were found to be most significant in explaining the differences between bids and costs. For groups of 2, this difference was significantly higher than with the other sizes, suggesting market power. Across sizes, after experience there was no significant difference in bidding behaviour between the uniform price auctions, with subjects in both bidding well above costs. Differences in the multi-unit Vickrey decreased after experience but, unexpectedly, bids were often below costs. Under no combinations did bids equal costs as would be desired by market designers. Results should give concern to policy analysts, and research suggestions to theorists. [ABSTRACT FROM AUTHOR]

Examines the impact of electricity deregulation to electric utility industry and the quality of people's lives. Benefits of the deregulation to the industry; Role of the federal government in encouraging deregulation; Legislation on electricity deregulation in California; Reasons for the failure of deregulation; Criticism against measures to remove retail price controls in electricity.

Presents information on the restructuring efforts of the California electric market caused by changes in electric wholesale and retail market in the area. Independent system operator (ISO); Power exchange; Impact on system reliability; Challenges in the implementation of ISO.

This case study discusses post-event reconnaissance research of the September 8, 2011, power outage that left San Diego County, California, without electricity for up to 12 h. The objective of this case study is to synthesize and analyze the impacts of the outage and responses to the event. Understanding the outage's impacts and responses helps to reveal restoration practices and contexts that promote meeting both technical and nontechnical goals. This study reveals several issues related to restoration decision-making and communication related to critical customers, particularly those responsible for health care, wastewater and potable water management, fuel provision, and food service. Restoration did not occur and was not communicated in such a way to avoid impacts to dependent critical infrastructure, reflect state restoration criteria, or meet expectations of a variety of power customers. Insight from this case study suggests three themes to guide research and development of best practices for power restoration: (1) emergency preparedness and public information, (2) power continuity, and (3) protocols and planning. Assessment of these themes are put in context with other studies of contemporary power outages and restoration to argue that power restoration protocols, policy, and planning are an important area for interdisciplinary research. [ABSTRACT FROM AUTHOR]