977 resultados para Electric power.


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Includes bibliography.

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"OTA-E-246"--P. [4] of cover

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This research employs econometric analysis on a cross section of American electricity companies in order to study the cost implications associated with unbundling the operations of integrated companies into vertically and/or horizontally separated companies. Focusing on the representative sample average firm, we find that complete horizontal and vertical disintegration resulting in the creation of separate nuclear, conventional, and hydro electric generation companies as well as a separate firm distributing power to final consumers, results in a statistically significant 13.5 percent increase in costs. Maintaining a horizontally integrated generator producing nuclear, conventional, and hydro electric generation while imposing vertical separation by creating a stand alone distribution company, results in a lower but still substantial and statistically significant cost penalty amounting to an 8.1 % increase in costs relative to a fully integrated structure. As these results imply that a vertically separated but horizontally integrated generation firm would need to reduce the costs of generation by 11% just to recoup the cost increases associated with vertical separation, even the costs associated with just vertical unbundling are quite substantial. Our paper is also the first academic paper we are aware of that systematically considers the impact of generation mix on vertical, horizontal, and overall scope economies. As a result, we are able to demonstrate that the estimated cost of unbundling in the electricity sector is substantially influenced by generation mix. Thus, for example, we find evidence of strong vertical integration economies between nuclear and conventional generation, but little evidence for vertical integration benefits between hydro generation and the distribution of power. In contrast, we find strong evidence suggesting the presence of substantial horizontal integration economies associated with the joint production of hydro generation with nuclear and/or conventional fossil fuel generation. These results are significant because they indicate that the cost of unbundling the electricity sector will differ substantially in different systems, meaning that a blanket regulatory policy with regard to the appropriateness of vertical and horizontal unbundling is likely to be inappropriate.

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This paper analyzes the impact of load factor, facility and generator types on the productivity of Korean electric power plants. In order to capture important differences in the effect of load policy on power output, we use a semiparametric smooth coefficient (SPSC) model that allows us to model heterogeneous performances across power plants and over time by allowing underlying technologies to be heterogeneous. The SPSC model accommodates both continuous and discrete covariates. Various specification tests are conducted to compare performance of the SPSC model. Using a unique generator level panel dataset spanning the period 1995-2006, we find that the impact of load factor, generator and facility types on power generation varies substantially in terms of magnitude and significance across different plant characteristics. The results have strong implication for generation policy in Korea as outlined in this study.

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This paper analyzes the impact of load factor, facility and generator types on the productivity of Korean electric power plants. In order to capture important differences in the effect of load policy on power output, we use a semiparametric smooth coefficient (SPSC) model that allows us to model heterogeneous performances across power plants and over time by allowing underlying technologies to be heterogeneous. The SPSC model accommodates both continuous and discrete covariates. Various specification tests are conducted to assess the performance of the SPSC model. Using a unique generator level panel dataset spanning the period 1995-2006, we find that the impact of load factor, generator and facility types on power generation varies substantially in terms of magnitude and significance across different plant characteristics. The results have strong implications for generation policy in Korea as outlined in this study.

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A tanulmány az aukciós villamosenergia-tőzsdéken kialakuló óránkénti árak statisztikai jellemzőivel foglalkozik. Célja, hogy egyes legújabb kutatási eredmények alapján új megvilágításban mutassa be a villamos energia óránkénti árára jellemző főbb megállapításokat, amelyek a későbbiekben az ár modellezésének alapjául szolgálhatnak. A jelenségeket az EEX és Nord Pool áramtőzsdén kereskedett termékek árainak adatain szemlélteti. Látni fogjuk, hogy át kell értékelnünk több, a villamosenergia-árak statisztikai viselkedéséről alkotott meggondolást. / === / The article concerns the statistical features of the hourly prices on auction-based markets for electric power. The purpose is to use the latest research findings to present the main statements about the hourly price for electric power in a new light, so that they can serve later as a basis for price modelling. The phenomena are viewed through the price data of products traded on the EEX and Nord Pool power exchanges. It emerges that several ideas about the statistical behaviour of electric power prices have to be reviewed.

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Power system engineers face a double challenge: to operate electric power systems within narrow stability and security margins, and to maintain high reliability. There is an acute need to better understand the dynamic nature of power systems in order to be prepared for critical situations as they arise. Innovative measurement tools, such as phasor measurement units, can capture not only the slow variation of the voltages and currents but also the underlying oscillations in a power system. Such dynamic data accessibility provides us a strong motivation and a useful tool to explore dynamic-data driven applications in power systems. To fulfill this goal, this dissertation focuses on the following three areas: Developing accurate dynamic load models and updating variable parameters based on the measurement data, applying advanced nonlinear filtering concepts and technologies to real-time identification of power system models, and addressing computational issues by implementing the balanced truncation method. By obtaining more realistic system models, together with timely updated parameters and stochastic influence consideration, we can have an accurate portrait of the ongoing phenomena in an electrical power system. Hence we can further improve state estimation, stability analysis and real-time operation.

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Transportation system resilience has been the subject of several recent studies. To assess the resilience of a transportation network, however, it is essential to model its interactions with and reliance on other lifelines. In this work, a bi-level, mixed-integer, stochastic program is presented for quantifying the resilience of a coupled traffic-power network under a host of potential natural or anthropogenic hazard-impact scenarios. A two-layer network representation is employed that includes details of both systems. Interdependencies between the urban traffic and electric power distribution systems are captured through linking variables and logical constraints. The modeling approach was applied on a case study developed on a portion of the signalized traffic-power distribution system in southern Minneapolis. The results of the case study show the importance of explicitly considering interdependencies between critical infrastructures in transportation resilience estimation. The results also provide insights on lifeline performance from an alternative power perspective.

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Two trends are emerging from modern electric power systems: the growth of renewable (e.g., solar and wind) generation, and the integration of information technologies and advanced power electronics. The former introduces large, rapid, and random fluctuations in power supply, demand, frequency, and voltage, which become a major challenge for real-time operation of power systems. The latter creates a tremendous number of controllable intelligent endpoints such as smart buildings and appliances, electric vehicles, energy storage devices, and power electronic devices that can sense, compute, communicate, and actuate. Most of these endpoints are distributed on the load side of power systems, in contrast to traditional control resources such as centralized bulk generators. This thesis focuses on controlling power systems in real time, using these load side resources. Specifically, it studies two problems.

(1) Distributed load-side frequency control: We establish a mathematical framework to design distributed frequency control algorithms for flexible electric loads. In this framework, we formulate a category of optimization problems, called optimal load control (OLC), to incorporate the goals of frequency control, such as balancing power supply and demand, restoring frequency to its nominal value, restoring inter-area power flows, etc., in a way that minimizes total disutility for the loads to participate in frequency control by deviating from their nominal power usage. By exploiting distributed algorithms to solve OLC and analyzing convergence of these algorithms, we design distributed load-side controllers and prove stability of closed-loop power systems governed by these controllers. This general framework is adapted and applied to different types of power systems described by different models, or to achieve different levels of control goals under different operation scenarios. We first consider a dynamically coherent power system which can be equivalently modeled with a single synchronous machine. We then extend our framework to a multi-machine power network, where we consider primary and secondary frequency controls, linear and nonlinear power flow models, and the interactions between generator dynamics and load control.

(2) Two-timescale voltage control: The voltage of a power distribution system must be maintained closely around its nominal value in real time, even in the presence of highly volatile power supply or demand. For this purpose, we jointly control two types of reactive power sources: a capacitor operating at a slow timescale, and a power electronic device, such as a smart inverter or a D-STATCOM, operating at a fast timescale. Their control actions are solved from optimal power flow problems at two timescales. Specifically, the slow-timescale problem is a chance-constrained optimization, which minimizes power loss and regulates the voltage at the current time instant while limiting the probability of future voltage violations due to stochastic changes in power supply or demand. This control framework forms the basis of an optimal sizing problem, which determines the installation capacities of the control devices by minimizing the sum of power loss and capital cost. We develop computationally efficient heuristics to solve the optimal sizing problem and implement real-time control. Numerical experiments show that the proposed sizing and control schemes significantly improve the reliability of voltage control with a moderate increase in cost.

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This thesis attempts to find the least-cost strategy to reduce CO2 emission by replacing coal by other energy sources for electricity generation in the context of the proposed EPA’s regulation on CO2 emissions from existing coal-fired power plants. An ARIMA model is built to forecast coal consumption for electricity generation and its CO2 emissions in Michigan from 2016 to 2020. CO2 emission reduction costs are calculated under three emission reduction scenarios- reduction to 17%, 30% and 50% below the 2005 emission level. The impacts of Production Tax Credit (PTC) and the intermittency of renewable energy are also discussed. The results indicate that in most cases natural gas will be the best alternative to coal for electricity generation to realize CO2 reduction goals; if the PTC for wind power will continue after 2015, a natural gas and wind combination approach could be the best strategy based on the least-cost criterion.