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Background: Injury prevention is an important goal for electric utility line workers who are among the top 10 U.S. occupational groups sustaining fatal injuries on the job., Methods: Using narrative text fields, we identified 10 high-risk tasks among electric utility line workers. We performed a case-control study of task-injury associations using data from the Electric Power Research Institute Occupational Health and Safety Database (1995-2013)., Results: Drawn from 12,323 line worker injuries, cases were individuals with a major injury (5 or more days lost work) matched to controls, individuals with a minor injury (less than 1 day lost work), on company and year of injury. Conditional logistic regression estimated adjusted odds ratios (OR) and 95% confidence intervals (95% CI). Some tasks associated with higher odds for major injuries among line workers included: climbing up/down ladder/stairs/elevator (OR = 4.3; 95% CI = 2.6, 7.4); climbing down poles and transmission towers (OR = 5.5; 95% CI = 3.6, 8.4); entering/stepping out/approaching utility trucks, bucket, or vaults (OR = 5.8; 95% CI = 4.0, 8.4); and performing repetitive work/overtime (OR = 5.5; 95% CI = 3.2, 9.5)., Conclusions: Knowledge gained can be used to focus efforts and plan efficient preventive measures that reduce injury rates, time lost from work, and costs within the electric power industry., (© 2020 Wiley Periodicals LLC.)

Nested Benders's decomposition is an efficient means to solve large-scale optimization problems with a natural time sequence of decisions. This paper examines the use of the technique to decompose and solve efficiently capacity-expansion problems for electricity systems with hydroelectric and renewable generators. To this end we develop an archetypal planning model that captures key features of hydroelectric and renewable generators and apply it to a case study that is based on the Columbia River system in the northwestern United States of America. We apply standard network and within-year temporal simplifications to reduce the problem's size. Nevertheless, the remaining problem is large-scale and we demonstrate the use of nested Benders's decomposition to solve it. We explore refinements of the decomposition method which yield further performance improvements. Overall, we show that nested Benders's decomposition yields good computational performance with minimal loss of model fidelity. [ABSTRACT FROM AUTHOR]

Disruptions to key lifelines, especially electrical power, can cause outsized impacts on human functioning. The state of the art on developed countries has focused on enhancing resilience to electrical grid infrastructure but has neglected to track changes regarding how the private market has developed electricity continuity measures over time. Backup generators are among the most accessible tools to maintain electricity continuity in case of power failure, but their role as a buffer remains understudied outside the technical domain, along with the humanitarian and emergency response sectors. This paper analyzes generator sales across the U.S. to understand some underlying trends that may have influenced changes in consumer preference for electricity resilience. Reports from major backup generator sellers and import data of backup generators reveal an increase in backup generators across the U.S. and find that private demand for energy resilience is likely increasing due to consumers' perceived risk and rising levels of intolerance to power disruptions. The discussion finds that an increase in private demand and use of backup generators may be impacting electricity resilience at a communal and societal level, which seems to be underexamined by studies focusing on private generator usage in the U.S.. [ABSTRACT FROM AUTHOR]

Summary: Many electricity consumers around the world are billed electric peak load charges. In the United States, these demand charges can contribute up to 70% of a consumer's electricity bill. Research has shown that the financial impact of these charges to the consumers can be reduced by acting on the intelligence provided by peak electric load day (PELD) forecasts. Unfortunately, published research detailing PELDs forecasting methodologies for facilities adopting behind the meter renewable electricity generation (BTMREG) is non‐existent. This paper contributes: (a) a PELD forecasting methodology applicable to consumers with BTMREG. A case study demonstrates the methodology's ability to achieve 93% of the potential savings in kW, translating to demand charge reductions of approximately US$ 142 129 per year. (b) The first side‐by‐side performance comparisons of electric load and PELD forecasting models for scenarios with and without BTMREG. (c) The first PELD forecasting model savings comparison for scenarios with and without BTMREG. Experimental results suggest that counterintuitively, BTMREG adoption can translate into higher peak load savings for electricity consumers. [ABSTRACT FROM AUTHOR]

During the last twenty years, the integration of Renewable Energy Resources (RESs) projects has become feasible in many electricity markets around the globe. The primary objective of these developments is to reduce carbon dioxide (CO2) emissions. In many countries, RESs integration projects could make a profit according to the subsidies policies of each electricity market. The United States (U.S.) electricity market is considered the most competitive one in the world. Hence, RES integration is subject to many financial and technical factors that eventually affect the integration process. Since the electricity production from RESs in the U.S. electricity market has become cost-effective, many projects have been implemented in a short period. In this paper, a discussion on the opportunities and challenges of renewable energy integration in the U.S. electricity market is demonstrated. The research starts with an overview of the electricity market in the U.S. with policies and bylaws governing the RESs integration. Some factors might empower the renewable energy deployment. Environmental aspects, government subsidies, regulations, and marginal cost price increase the renewable energy opportunities in the U.S. electricity market. By contrast, the paper discussed many challenges faced by renewable energy such as generation variability, intermittency, environmental issues, and capital and reserve cost. Such challenges might affect the electricity market prices. This paper provides an insight to the developed plans to implement RESs and control GHG emissions. In addition, corresponding policies to encourage RES integration and their technical impacts are highlighted. Restructuring the market and developing Renewable Energy Sources technologies are recommended to increase renewable energy market share in the future. [ABSTRACT FROM AUTHOR]

This paper presents a sensitivity analysis of similar days (SD) parameters to increase the accuracy of artificial neural network (ANN) and SD-based short-term price forecasting. Work presented in this paper is an extended version of previous works done by the authors to integrate ANN and SD method for predicting electricity price. The focus here is on sensitivity analysis of SD parameters while keeping the parameters same for ANN to forecast hourly electricity prices in the Pennsylvania--New Jersey--Maryland (PJM) (regional transmission organization in northeast America) electricity market. Several cases are simulated by choosing (a) two, (b) three, (c) four, and (d) five SD parameters to calculate the norm. In addition, sensitivity analysis has been carried out by changing the time framework of SD (d = 15,30, 45, 60) and the number of selected similar price days (N = 5, 10). From sensitivity analysis, it is identified that the optimized mean absolute percentage error (MAPE) is obtained using case-c with d = 30 and N = 10. MAPE of reasonably small value, along with forecast mean square error and mean absolute error of around 2$/MWh and 1$/MWh, is obtained for the PJM data, which has correlation coefficient of determination (R²) of 0.7758 between load and electricity price. Numerical results show that forecasts generated by the developed ANN model based on the optimized case are accurate and efficient. [ABSTRACT FROM AUTHOR]

Efficient electricity market operations and cost-effective electricity generations are fundamental to a low-carbon energy future. The Western Electricity Coordinating Council (WECC) and Northeast Power Coordinating Council (NPCC) systems were built to provide efficient electrical grid simulation solutions for their respective U.S. regions. Data reuse for electricity economic studies remains a challenge due to the lack of credible and realistic economic data. This paper delivers a comprehensive dataset containing generator aggregations, generator costs, transmission limits, load distributions, and electricity prices for the WECC and NPCC systems based on real-world grid operation data at year 2020, including power plant geographic locations, generation profiles, regional power flow interchanges, and load distributions in both regions. The electricity price from the developed dataset is simulated based on the other items in the dataset, and we show that the variation of the simulated electricity price reasonably aligns with the real-world electricity price in both the WECC and NPCC regions. Overall, the developed dataset is of interest for various electricity market and economic studies, such as the economic dispatch and locational marginal price (LMP) analysis. [ABSTRACT FROM AUTHOR]

The state-of-energy (SOE) and state-of-health (SOH) are two crucial quotas in the battery management systems, whose accurate estimation is facing challenges by electric vehicles' (EVs) complexity and changeable external environment. Although the machine learning algorithm can significantly improve the accuracy of battery estimation, it cannot be performed on the vehicle control unit as it requires a large amount of data and computing power. This paper proposes a joint SOE and SOH prediction algorithm, which combines long short-term memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural networks (CNNs) for EVs based on vehicle-cloud collaboration. Firstly, the indicator of battery performance degradation is extracted for SOH prediction according to the historical data; the Bayesian optimization approach is applied to the SOH prediction combined with Bi-LSTM. Then, the CNN-LSTM is implemented to provide direct and nonlinear mapping models for SOE. These direct mapping models avoid parameter identification and updating, which are applicable in cases with complex operating conditions. Finally, the SOH correction in SOE estimation achieves the joint estimation with different time scales. With the validation of the National Aeronautics and Space Administration battery data set, as well as the established battery platform, the error of the proposed method is kept within 3%. The proposed vehicle-cloud approach performs high-precision joint estimation of battery SOE and SOH. It can not only use the battery historical data of the cloud platform to predict the SOH but also correct the SOE according to the predicted value of the SOH. The feasibility of vehicle-cloud collaboration is promising in future battery management systems.

Several EU countries import wood pellets from the south-eastern United States. The imported wood pellets are (co-)fired in power plants with the aim of reducing overall greenhouse gas ( GHG) emissions from electricity and meeting EU renewable energy targets. To assess whether GHG emissions are reduced and on what timescale, we construct the GHG balance of wood-pellet electricity. This GHG balance consists of supply chain and combustion GHG emissions, carbon sequestration during biomass growth and avoided GHG emissions through replacing fossil electricity. We investigate wood pellets from four softwood feedstock types: small roundwood, commercial thinnings, harvest residues and mill residues. Per feedstock, the GHG balance of wood-pellet electricity is compared against those of alternative scenarios. Alternative scenarios are combinations of alternative fates of the feedstock materials, such as in-forest decomposition, or the production of paper or wood panels like oriented strand board ( OSB). Alternative scenario composition depends on feedstock type and local demand for this feedstock. Results indicate that the GHG balance of wood-pellet electricity equals that of alternative scenarios within 0-21 years (the GHG parity time), after which wood-pellet electricity has sustained climate benefits. Parity times increase by a maximum of 12 years when varying key variables (emissions associated with paper and panels, soil carbon increase via feedstock decomposition, wood-pellet electricity supply chain emissions) within maximum plausible ranges. Using commercial thinnings, harvest residues or mill residues as feedstock leads to the shortest GHG parity times (0-6 years) and fastest GHG benefits from wood-pellet electricity. We find shorter GHG parity times than previous studies, for we use a novel approach that differentiates feedstocks and considers alternative scenarios based on (combinations of) alternative feedstock fates, rather than on alternative land uses. This novel approach is relevant for bioenergy derived from low-value feedstocks. [ABSTRACT FROM AUTHOR]

This paper revisits the link between electrification and the rise in female labour force participation (LFP), and presents theoretical and empirical evidence showing that electrification triggered a rise in female LFP by increasing market opportunities for skilled women. I formalize my theory in an overlapping generations model and find that my mechanism explains one quarter of the rise in female LFP during the rollout of electricity in the U.S. (1880–1940), and matches the slow decline in female home production hours during this period. I then present micro-evidence supporting my theory using newly digitized data on the early electrification of the U.S. [ABSTRACT FROM AUTHOR]

The U.S. electricity sector has undergone a series of technological and political economic transformations between 1995 and 2020. Some, but not all, states have moved toward the provision of electricity through wholesale electricity markets, while others have remained traditionally regulated. In addition, electricity is increasingly generated from natural gas and renewable energy, and while total electricity consumption is stagnant in the United States, electric utility revenues have continued to grow. In combination with a growing emphasis on increasing shareholder value, the result of these changes has been the entry of a range of new electricity industry, finance, and regulatory actors, as well as a shift in the ways in which existing actors are seeking to realize surplus value. In light of these changes, in this paper, I outline the contemporary contours and contradictions of the U.S. electricity industry and detail five accumulation strategies being used in the industry. Building on scholarship in energy geography and geographies of finance, and based on the analysis of industry documents and financial analyst reports, I detail how the U.S. electricity has been reshaped by the introduction of new types of technologies, financial risk and investment opportunities, and changing modes of regulation. The resulting accumulation strategies are contested, partial, and incomplete, and are therefore a messy set of overlapping and at times contradictory strategies being deployed in an industry in flux. [ABSTRACT FROM AUTHOR]

Fossil fuels have been the major source of electricity supply in the United States of America (USA) for many decades. While a significant shift has occurred from coal-dominated supply to natural gas and renewable source based supplies in recent decades, there is still large potential to improve biomass supported electricity contribution in rural communities, which depend majorly on forest-related activities. Wood waste, an underutilized renewable resource, has the potential for electricity generation in rural areas to reduce the contribution of electricity generated from fossil fuels and assist in greenhouse gas savings. In this study, a life cycle assessment approach was used to estimate the emissions of electricity generated by wood residues in a rural community (Grenada County, Mississippi (MS), USA) and compared with those emissions from the recent electricity mix (2018), a previous electricity mix (2010) supply, along with natural gas (NG) and coal (Bituminous Coal, BC) based electricity generation options. A significant reduction (85.9-94.6%) in global warming impact was observed when compared with BC, NG and grid supplied electricity for years 2010 and 2018. When compared to the current electricity mix at the grid (2018), acidification, eutrophication, respiratory effects and smog formation showed higher emissions ranging from 30.8% to 72.4%. The sensitivity analysis showed an improvement in emissions savings with increased biomass to power conversion efficiency (40%), lower moisture content of the biomass (0%, bone dry biomass), and reduced transportation distance (35 km). This study showed an improvement in the electricity mix supplied to Grenada county, MS, which implies sustainable development opportunities for promoting energy security in rural communities with forest-based industries., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

This paper investigates the transmission of fossil fuel commodity spot market price changes to procurement costs of U.S. power producers. We measure and compare the speed and magnitude with which spot prices predict procurement costs using restricted access fuel price data. Natural gas spot prices are quickly reflected in procurement costs. Coal spot prices offer very little predictive power to coal procurement costs. Although not causal, the empirical results also show differences across regulatory status. These findings may have implications for the electricity market deregulation literature that creates marginal cost curves as a competitive benchmark. [ABSTRACT FROM AUTHOR]

The electrical power infrastructure of the modern world is advanced, efficient, and robust, yet power outages still occur. In addition to affecting millions of people around the world, these outage events cost billions of dollars to the global economy. In this paper, the revenue loss borne by electricity-supplying companies in the United States due to power outage events is estimated and predicted. Various factors responsible for power outages are considered in order to present an exploratory data analysis at the U.S. level, followed by the top ten affected states, which bear over 85% of the total revenue loss. The loss is computed using historic observational data of electricity usage patterns and the tariff offered by the energy suppliers. The study is supplemented with reliable and publicly available records, including electricity usage patterns, the consumer category distribution, climatological annotations, population density, socio-economic indicators and land area. Machine learning techniques are used to predict the revenue loss for future outage events, as well as to characterize the key parameters for efficient prediction and their partial dependence. The results show that the revenue loss is a function of several parameters, including residential sales, percentage of industrial customer, time-period of the year, and economic indicators. This study may help energy suppliers make risk-informed decisions, while developing revenue generation strategies as well as identifying safer investment avenues for long-term returns. [ABSTRACT FROM AUTHOR]

Electricity generation currently is the main industrial source of air emissions in the United States. Both researchers and practitioners are interested in conducting studies to evaluate the ecological performance of this industry, in order to propose solutions to curb emissions of air pollutants and to improve the efficiency of converting fossil resources into electric energy. In this paper, data envelopment analysis (DEA) is used to assess ecological efficiency where air emissions are used as undesirable outputs. Although conventional DEA does not require a priori information on the input and output weights, weight restrictions can be incorporated to reflect a user's preference over the performance metrics, or to refine the DEA results. Adding weight restrictions voids the fact that DEA scores are independent of the units of measurement. To incorporate weight constraints in ecological efficiency assessment, this paper develops a DEA model that is units-invariant when weight restrictions are imposed. Moreover, the proposed model is equivalent to the standard units-invariant DEA model when weight restrictions are not present. [ABSTRACT FROM AUTHOR]

This paper focuses on the history of a portable shock-producing electrotherapeutic device known as the medical battery (1870-1920), which provided both direct and alternating current and was thought to cure a wide variety of ailments. The product occupied a unique space at the nexus of medicine, consumerism and quackery: it was simultaneously considered a legitimate device by medical professionals who practiced electrotherapeutics, yet identical versions were sold directly to consumers, often via newspaper advertisements and with cure-all marketing language. Indeed, as I show in this paper, the line between what was considered a medical device and a consumer product was often blurred. Even though medical textbooks and journals never mentioned (much less promoted) the home use of electricity, every reputable electrotherapy instrument manufacturer sold a "family battery" for patients to use on themselves at home. While a handful of physicians spoke out against the use of electricity by the laity--as they felt it undermined the image of electrotherapy as a skilled medical procedure--existing evidence suggests that many physicians were likely recommending the home use of medical electricity to their patients. Taken together, this paper shows how the professional ideals of electrotherapeutics were not always aligned with physicians' actual practices. [ABSTRACT FROM AUTHOR]

In this paper, we present an analytical framework to establish a closed-form relationship between electricity generation expansion planning decisions and the resulting negative health externalities. Typical electricity generation expansion planning models determine the optimal technology-capacity-investment strategy that minimizes total investment costs as well as fixed and variable operation and maintenance costs. However, the relationship between these long-term planning decisions and the associated health externalities is highly stochastic and nonlinear, and it is computationally expensive to evaluate. Thus, we developed a closed-form metamodel by executing computer-based experiments of a generation expansion planning model, and we analyzed the resulting model outputs in a United States Environmental Protection Agency (EPA) screening tool that approximates the associated human health externalities. Procedural guidance to verify the accuracy and to select key metamodel parameters to enhance its prediction capability is presented. Specifically, the metamodel presented in this paper can predict the resulting health damages of long-term power grid expansion decisions, thus, enabling researchers and policy makers to quickly assess the health implications of power grid expansion decisions with a high degree of certainty.

This paper analyses the relationships existing between crude oil, natural gas and electricity prices in two deregulated markets: the USA and Europe. The analysis had to be performed separately on the US and the European markets, given the different level of deregulation reached in each market. In addition, the electricity and the gas markets had to be investigated at a regional level. The aim of the paper is to measure the level of integration existing within each energy commodity market using an error correction model (ECM) framework. The relationship between oil, gas and electricity is a crucial issue for risk management purposes. For instance, the wide-spread use of energy derivatives to hedge possible unexpected changes in the prices of fuel sources requires an accurate estimation of the existing correlation between these commodities. [ABSTRACT FROM AUTHOR]

Locational marginal prices (LMP)-based electricity markets are implemented in different countries around the world and are dominant in the United States. Even after ten years of the operation, some of the properties of the LMP are not well understood. There are many misconceptions about this clearing mechanism that led to some inefficient market designs. This study is an attempt to consistently present the current state of the LMP-based congestion management, including issues that market and system operators are facing, and analyse new directions of the research. The recommendations are made on which areas are of high priority and should be addressed first. Besides giving a systematic description on how the LMPs are produced, the paper describes both the modelling and implementation challenges and solutions. (This paper solely represents the view point of the author and not necessarily ISO New England's). [ABSTRACT FROM AUTHOR]

This paper mainly investigates a single-period spatial oligopolistic model of electricity generators under uncertain demands. We maximize the β -optimistic value of each generator's uncertain profit function in this game. Emphasis is first put on computing the β -optimistic value of the uncertain profit function through the inverse uncertainty distribution of the uncertain variable in the inverse demand function. We later focus on transforming the single-period spatial oligopolistic game, which is a generalized Nash equilibrium (short for GNE) problem, into a variational inequality problem. Then the model is applied to simulate the transmission price of the electricity system of America, which reveals that our model is effective. [ABSTRACT FROM AUTHOR]

This paper is the second of a two-part paper and describes the application of a multiperiod generalized network flow model that is used to analyze the economic efficiencies of the energy movements from the coal and natural gas suppliers to the electric load centers, providing a simulation tool that captures dynamics of and interdependencies between the coal, natural gas, and electricity networks. The modeling approach, mathematical formulation, and modeling assumptions are presented in part I. This sequel presents and discusses numerical results to illustrate the application of the model. The model enables public and private decision makers to carry out comprehensive analyses of a wide range of issues related to the energy sector, such as strategic planning, economic impact assessment, and different regulatory regimes. [ABSTRACT FROM AUTHOR]

This paper is the first of a two-part paper presenting a multiperiod generalized network flow model of the integrated energy system in the United States. Part I describes the modeling approach used to evaluate the economic efficiencies of the systemwide energy flows, from the coal and natural gas suppliers to the electric load centers. Under the proposed problem formulation, fuel supply and electricity demand nodes are connected via a transportation network, and the model is solved for the most efficient allocation of quantities and corresponding prices. The methodology includes physical, economic, and environmental aspects that characterize the different networks. Part II of this paper provides numerical results that demonstrate the application of the model. [ABSTRACT FROM AUTHOR]

In a competitive electricity market, a generation company (Genco) can manage its trading risk through trading electricity among multiple markets such as spot markets and contract markets. The question is how to decide the trading proportion of each market in order to maximize the Genco's profit and minimize the associated risk. Based on the mean-variance portfolio theory, this paper proposes a sequential optimization approach to electric energy allocation between spot and contract markets, taking into consideration the risks of electricity price, congestion charge, and fuel price. Especially, the impact of the fuel market on electric energy allocation is analyzed and simulated with historical data in respect of the electricity market and other fuel markets in the U.S. Simulation results confirm that the proposed analytic approach is consistent with intuition and therefore reasonable and feasible for a Genco to make a trading plan involving risks in an electricity market. [ABSTRACT FROM AUTHOR]

This paper examines the efficiency of electric power generation plants in the United States. A 1996 data set from the Utility Data Institute and county-level wage data from the Bureau of Labor statistics provide the information needed to construct measures of cost, output, and input prices for 78 steam plants using natural gas as the primary fuel. This paper uses a Bayesian stochastic frontier model that imposes concavity and monotonicity restrictions implied by microeconomic theory to measure efficiency, price elasticities, and returns to scale of these plants. Results indicate that plants on average could reduce costs by up to 13% by eliminating production inefficiency. Results also indicate that most plants operate at increasing returns to scale, suggesting further cost savings could be achieved through increasing output. [ABSTRACT FROM AUTHOR]

We estimate the effect of the natural gas fuel cost on the choices for a new power generator; the energy source of gas, wind, or solar; and its capacity. Higher fuel costs increase odds of more renewable capacity, as opposed to gas, with large, significant effects for both wind and solar. As the United States has yet to implement nationwide carbon policies, we use fuel cost to proxy for a carbon tax. Our scenarios of $10–$50 per ton of carbon dioxide reduce emissions from new generators by 22%–47%, equivalent to 2%–4% of total electricity emissions. [ABSTRACT FROM AUTHOR]

Electric motor-driven fan, pump, and compressor systems accounted for 27% of the electricity usage in commercial sectors in United States in 2013. Therefore, even small efficiency improvements will produce very large energy savings. In fact, the efficiency of these systems deteriorates over their lifetime. The efficiency of motors could degrade by up to 5% while the efficiency of pumps could degrade by up to 25%. Since variable frequency drives (VFDs) are widely applied on electric motors, the efficiency degradation is more often concealed by the significant energy reduction introduced by VFDs. The purpose of this paper is to develop and demonstrate an approach, which can effectively detect the efficiency degradation of VFD-motor-pump systems by simply measuring the pump water flow rate and head as well as the system input power. First, the ideal system efficiency was modeled by combining the pump, motor, and VFD efficiencies and applied to correlate the pump water flow rate and head to the ideal system input power. Then, an experiment was conducted on a ten-year-old VFD-motor-pump system to quantify the efficiency degradation by comparing the measured and ideal system input power. The results of the experiment reveal that the efficiency of the studied system degraded by 19%. [ABSTRACT FROM AUTHOR]

This paper estimates the economically achievable potential for improving electricity end-use efficiency in the USA from a sample of policies. The approach involves identifying a series of energy efficiency policies tackling market failures and then examining their impacts and cost-effectiveness using Georgia Institute of Technology's version of the National Energy Modeling System. By estimating the policy-driven electricity savings and the associated levelized costs, a policy supply curve for electricity efficiency is produced. Each policy is evaluated individually and in an integrated policy scenario to examine policy dynamics. The integrated policy scenario demonstrates significant achievable potential: 261 TWh (6.5 %) of electricity savings in 2020 and 457 TWh (10.2 %) in 2035. All 11 policies examined were estimated to have lower levelized costs than the average electricity retail price. Levelized costs range from 0.5 to 8.1 cents/kWh, with the regulatory and information policies tending to be most cost-effective. Policy impacts on the power sector, carbon dioxide emissions, and energy intensity are also estimated to be significant. [ABSTRACT FROM AUTHOR]

The cost Malmquist productivity index (CMPI) has been proposed to capture the performance change of cost minimizing Decision Making Units (DMUs). Recently, two alternative uses of the CMPI have been suggested: (1) using the CMPI to compare groups of DMUs, and (2) using the CMPI to compare DMUs for each output separately. In this paper, we propose a new CMPI that combines both procedures. The resulting methodology provides group-specific indexes for each output separately, and therefore offers the option to identify the sources of cost performance change. We also define our index when input prices are not observed and establish, in that case, a duality with a new technical productivity index, which takes the form of a Malmquist productivity index. We illustrate our new methodology with a numerical example and an application to the US electricity plant districts. [ABSTRACT FROM AUTHOR]

Title IV of the 1990 Clean Air Act Amendments implemented a pollution permit system for sulfur dioxide (SO2) emissions that departs from state command-and- control regulations that were in place prior to and during its implementation. This paper develops a technology diffusion model relying on rank effects or firm heterogeneity, to explain the adoption of scrubbers, a SO2 abatement technology. Using survival analysis, empirical results show that generating units regulated under Phase I of Title IV, units facing more stringent state regulations, and units with low expected installation costs are much more likely to install a scrubber. In addition, there is evidence of positive scale effects of adoption, meaning larger units are more likely to adopt because expected installation costs are lower Tradable permit systems should give firms more flexibility in choosing abatement technologies, but results show that when command-and-control regulations overlap with a market-based system, the technology choice is restricted. [ABSTRACT FROM AUTHOR]

This paper examines the effects of market restructuring initiatives that introduced competition into the United States electricity industry on the thermal efficiency of electricity generation. An empirical model is estimated on annual data for over 950 plants from 1996 to 2006. Model estimates show that access to wholesale electricity markets and retail choice together increased the efficiency of investor-owned plants by about nine percent and that these gains stem from organizational and technological changes within the plant. Although not directly targeted by restructuring initiatives, similar efficiency gains are also found for municipality-owned plants. This result suggests that the potential benefits from competition have spilled over to public electricity generation. [ABSTRACT FROM AUTHOR]

This paper discusses historical and technical events in the U.S. and Europe over the last few years that are aimed at modernizing the electric power grid. The U.S. federal government has ratified the “smart grid initiative” as the official policy for modernizing the electricity grid including unprecedented provisions for timely information and control options to consumers and deployment of “smart” technologies. European countries are unified in researching and developing related technologies through various structures supported by the European Union. This paper presents the development of smart grids and an analysis of the methodologies, milestones, and expected evolutions of grid technologies that will transform society in the near future. [ABSTRACT FROM AUTHOR]

Data centers are a significant and growing component of electricity demand in the United States. This paper presents a bottom-up model that can be used to estimate total data center electricity demand within a region as well as the potential electricity savings associated with energy efficiency improvements. The model is applied to estimate 2008 U.S. data center electricity demand and the technical potential for electricity savings associated with major measures for IT devices and infrastructure equipment. Results suggest that 2008 demand was approximately 69 billion kilowatt hours (1.8% of 2008 total U.S. electricity sales) and that it may be technically feasible to reduce this demand by up to 80% (to 13 billion kilowatt hours) through aggressive pursuit of energy efficiency measures. Measure-level savings estimates are provided, which shed light on the relative importance of different measures at the national level. Measures applied to servers are found to have the greatest contribution to potential savings. [ABSTRACT FROM PUBLISHER]

Buildings are the dominant driver of daily and seasonal electric load cycles, and account for 40 percent of U.S. final energy use. They account for roughly 10 percent of direct U.S. CO2 emissions and roughly 40 percent once indirect emissions from electricity generation are included. This paper explores possible evolution of this sector over the coming century, its potential role climate action and response to climate policies, and the potential benefits advances in building technologies for addressing climate change. The paper presents a set of scenarios based on a detailed, service-based model of the buildings sector that is embedded within a long-term, global, integrated assessment model, MiniCAM. Eight scenarios are created in total, combining sets of assumptions regarding U.S. building service demand growth, two assumptions regarding the improvements in building energy technologies, two assumptions regarding long-term U.S. climate action --- a no-climate-action assumption and an assumption of market-based policies to reduce U.S. emissions consistent with a 450 ppmv global target. Through these eight scenarios, the paper comments on the implications of continued growth in building service demands, the ability of efficiency measures to reduce emissions, the strong link between decarbonization of electricity generation and building sector emissions. [ABSTRACT FROM AUTHOR]

In this paper a dynamic DEA-model (data envelopment analysis) is developed that takes up Nemoto and Goto's (Econ Lett 64(1):51-56, 1999) concept of quasi-fixed inputs but can dispense with price-information as it solely deals with technical efficiencies that are derived in an additive-model type of setting. This model is applied to the data of 50 of the largest US electric transmission system operators in the period 2000-2006 in order to show that the static DEA-models that are usually employed by electricity regulators can lead to wrong conclusions about the enterprises' efficiency since they ignore the short-run fixity of essential inputs like transmission lines. [ABSTRACT FROM AUTHOR]

Greenhouse gas emission is a mounting problem that threatens the future production of electricity from both coal and natural gas. In 2006, 70% of domestic electricity generation relied upon fossil fuels. Projections by the Energy Information Agency1 show U.S. demand for electricity increasing 30% to 40% by 2030. Today nuclear power is the largest source of non-greenhouse-gas emitting energy generation and will be an important source of energy production in the future. This paper considers potential construction of new plants as well as the long-term prospects of existing nuclear power plants in the United States. [ABSTRACT FROM AUTHOR]

This paper proposes a practical and effective model for the generation forecasting of a wind farm with an emphasis on its scheduling and trading in a wholesale electricity market. A novel forecasting model is developed based on indepth investigations of meteorological information. This model adopts a two-stage hybrid network with Bayesian clustering by dynamics and support vector regression. The proposed structure is robust with different input data types and can deal with the nonstationarity of wind speed and generation series well. Once the network is trained, we can straightforward predict the 48-h ahead wind power generation. To demonstrate the effectiveness, the model is applied and tested on a 74-MW wind farm located in the southwest Oklahoma of the United States. [ABSTRACT FROM AUTHOR]

This paper focuses on the implications of multi-agency review of electricity mergers. It highlights fundamental differences between regulatory and antitrust merger enforcement procedures–ranging from standards of review to data collection, economic analysis, and remedies. Such disparities potentially introduce costly and potentially conflicting redundancy into the merger review process. Illustrative examples are drawn from a number of cases, including apparent inconsistencies across applicant-filed analyses in FERC merger cases. Absent the delegation of competition analysis to the antitrust agencies, the paper makes the case for harmonizing review across regulators and antitrust enforcers. [ABSTRACT FROM AUTHOR]

The evaluation of locational marginal prices or LMPs, which constitute the basis of the new generation of market design in the U.S., is a critical need in the side-by-side operation of electricity markets and power systems. Their use in various applications such as congestion management and hedging requires specific LMP components in the computations. We provide a general formulation for the evaluation of the LMP components by making explicit use of the important role played by the nodes with generators with free capacity. On the other hand, the LMPs of the nodes without generation or with the generators at their limits are a function of the LMPs at the marginal nodes and the impacts of the network constraints, including the losses in the network. In the formulation, we pay special attention to the importance of the assumptions and specifications—the so-called policy specification—in the determination of the components. We obtain a very general formulation that brings under a single framework the different decomposition approaches that exist. The formulation's comprehensiveness brings numerous insights into the various decompositions, provides a platform for their comparative analysis, and allows us to understand the direct implications and the role of the policy specified. Moreover, the formulation reveals the limitations of any decomposition into the components due to the underlying structural interdependencies among them, which is clearly shown in this paper. We provide an example to illustrate the capabilities and the insights obtainable with the use of this formulation. [ABSTRACT FROM AUTHOR]

This paper presents a coherent demodulation scheme that reduces the multiple-access interference (MAI) considerably in a band-limited direct-sequence code-division multiple-access system. The despreading signal is obtained from a combination of the conventional despreading signal and its weighted early and late versions that are used in the delay-locked loop (DLL). It is shown that with appropriate choice of the weighting parameter of the early-late signals of the DLL, the signal-to-interference-plus-noise ratio of the decision variable is increased, which leads to improvement in the bit-error performance and hence the potential increase in the system capacity by about 25%. The analyses are performed in the frequency domain. The effect of imperfect synchronization on the system performance is also analyzed. [ABSTRACT FROM AUTHOR]

This paper examines the volatility of wholesale electricity prices for five US markets. Using data covering the period from May 1996 to September 2001, for the California-Oregon Border, Palo Verde, Cinergy, Entergy, and Pennsylvania-New Jersey-Maryland markets, we examine the volatility of electricity wholesale prices over time and across markets. We estimate volatility using a TARCH model to study the differences among markets and the seasonal characteristics of each market. For all markets, we find strong evidence for a downward trend in the ARCH term and a significant negative asymmetric effect over the sample period. We also document important differences among the regional electricity markets not only with respect to wholesale price volatility and seasonal variations, but also with respect to asymmetric properties and persistence of volatility. [ABSTRACT FROM AUTHOR]

The electricity industry is undergoing a substantial process of restructuring, with an emphasis on the introduction of competition in the generation sector. Traditional planning methods are not necessarily appropriate for this new environment. This paper extends a previously developed linear programming model to the problem of optimal expansion planning in the face of uncertainty. The model explicitly accounts for equipment availability and load duration curves in selecting optimal investment. The use of the model is illustrated with a simple 2-region problem calibrated with data from Hydro-Québec and the northeastern United States that suggests how the LP model can help decision makers. [ABSTRACT FROM AUTHOR]

This paper examines the hypothesis that, during merger waves, a bidder's actions provide information for other bidders and the market. I develop a real options model to explore the interplay between acquisition timing and the market reaction to these events. The model predicts a pattern of declining announcement returns along the merger wave and various forms of contagion returns. Consistent with the model's predictions, in a sample of U.S. mergers, I find that the dispersion in bidders' post-acquisition performance declines along the merger wave and that the start of a merger wave is associated with an increase in the conditional correlation of a bidder's stock returns and the stock returns of other bidders. [ABSTRACT FROM AUTHOR]