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Deregulation of the electric power industry has introduced new uncertainties for market participants and made planning of transmission expansion more difficult. More flexible transmission expansion plans are needed, to cope with the increased risks. In this paper, a novel planning approach is proposed to meet the above challenge. In our approach, the planning process is modeled as a mixed integer nonlinear programming (MINLP) problem, so that conflicting objectives can be optimized simultaneously. To minimize planning risks, our method identifies several scenarios based on statistics and expert knowledge; the most flexible expansion plan is selected as the plan that has least adaptation cost. The proposed method is tested with the IEEE 14-bus system. Promising results are obtained to demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Electricity price forecasting is a difficult yet essential task for market participants in a deregulated electricity market. Rather than forecasting the value, market participants are sometimes more interested in forecasting the prediction interval of the electricity price. Forecasting the prediction interval is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions. In this paper, a novel data mining-based approach is proposed to achieve two major objectives: 1) to accurately forecast the value of the electricity price series, which is widely accepted as a nonlinear time series; 2) to accurately estimate the prediction interval of the electricity price series. In the proposed approach, support vector machine (SVM) is employed to forecast the value of the price. To forecast the prediction interval, we construct a statistical model by introducing a heteroscedastic variance equation for the SVM. Maximum likelihood estimation (MLE) is used to estimate model parameters. Results from the case studies on real-world price data prove that the proposed method is highly effective compared with existing methods such as GARCH models. [ABSTRACT FROM AUTHOR]

This paper presents a study on the effects of winding tension on the characteristic resistance of a no-insulation (NI) coil. Two ReBCO NI test pancake coils, having the same winding i.d. (60 mm), o.d. (67.6 mm), and number of turns (60), were sequentially prepared in a way that the first test coil was wound with a winding tension of 12-N, tested, and then rewound with a new winding tension of 20-N for the same tests. In each test, the test coil was energized at a target current, the power supply was “suddenly” disconnected, and then the temporal decay of the coil center field was measured, from which the time constant of the test coil and the consequent characteristic resistance were obtained. To check the reproducibility of experimental data, each test was repeated four times and each time the test coil was unwound and rewound with a given winding tension. The experimental results were analyzed with equivalent circuit analyses. Correlation between the winding tension and the characteristic resistance was discussed in detail. [ABSTRACT FROM PUBLISHER]

Emission trading is widely considered to be the most effective policy to minimize the overall costs for CO2 abatement. However, the political feasibility of an emission trading scheme may crucially depend on the free initial allocation of emission permits to carbon-intensive industries in order to offset the reduction in profits.This paper aims to analyze these potential profit impacts and the possible compensation to affected generation compaflies through modeling the Australian National Electricity Market under a potential emission trading scheme. Historical emissionbased and historical generation-based allocation approaches are used in this paper to calculate and compare the percentages of carbon permits that should be freely allocated. Two carbon permit price scenarios are -used to analyze the sensitivity of the optimal percentage of free allocation to carbon permit price. [ABSTRACT FROM AUTHOR]