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Discusses a study which presented an adaptive distance protection scheme using artificial neural networks. Highlights of the study. INSET: Closure to Discussion of 'Stand Alone Intelligent Digital....

The authors have proposed Flexible, Reliable and Intelligent ENergy Delivery System (FRIENDS) as a future power distribution system. In the FRIENDS, new facilities called quality control centers (QCCs), which consist of power electronics devices, distributed generators (DGs), energy storage systems (ESSs), etc., are installed between distribution substations and customers. By controlling and operating those devices in QCCs adequately, various functions can be realized. Particularly, when a power supply from the transmission network is interrupted, isolated local networks which consist of some QCCs are composed in order to realize uninterruptible power supply. In the isolated local network, the DG and ESS in QCCs are employed as backup generators. This paper proposes a novel distributed and autonomous method for balancing the supply and demand during isolated operation. This paper also investigates the effectiveness of the proposed method through some computational simulations using PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

Price forecasting in competitive electricity markets is critical for consumers and producers in planning their operations and managing their price risk, and it also plays a key role in the economic optimization of the electric energy industry. This paper explores a technique of artificial neural network (ANN) model based on similar days (SD) method in order to forecast day-ahead electricity price in the PJM market. To demonstrate the superiority of the proposed model, publicly available data acquired from the PJM Interconnection were used for training and testing the ANN. The factors impacting the electricity price forecasting, including time factors, load factors, and historical price factors, are discussed. Comparison of forecasting performance of the proposed ANN model with that of forecasts obtained from similar days method is presented. Daily and weekly mean absolute percentage error (MAPE) of reasonably small value and forecast mean square error (FMSE) of less than 7$/MWh were obtained for the PJM data, which has correlation coefficient of determination (R²) of 0.6744 between load and electricity price. Simulation results show that the proposed ANN model based on similar days method is capable of forecasting locational marginal price (LMP) in the PJM market efficiently and accurately. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to the fuzzy unit commitment problem using the absolutely stochastic simulated annealing method. In every iteration, a solution is taken with a certain probability. Typically in the simulated annealing minimization method, a higher cost feasible solution is accepted with temperature-dependent probability, but other solutions are accepted deterministically. However, in this paper, all the solutions, both higher and lower cost, are associated with acceptance probabilities, e.g., the minimum membership degree of all the fuzzy variables. Be- sides, the number of bits flipping is decided by the linguistic fuzzy control. Excess units with system-dependent distribution handle constraints efficiently and reduce overlooking the optimal solution. To reduce the economic load dispatch calculations, a sign bit vector is introduced with imprecise calculation of the fuzzy model as well. The proposed method is tested using the reported problem data sets. Simulation results are compared to previous reported results. Numerical results show an improvement in solution cost and time compared to the results obtained from powerful algorithms. [ABSTRACT FROM AUTHOR]

This paper presents an approach for short-term load forecast problem, based on hybrid correction method. Conventional artificial neural network based short-term load forecasting techniques have limitations especially when weather changes are seasonal. Hence, we propose a load correction method by using fuzzy logic approach in which a fuzzy logic, based on similar days, corrects the neural network output to obtain the next day forecasted load. An Euclidean norm with weighted factors is used for the selection of similar days. The load correction method for the generation of new similar days is also proposed. The neural network has an advantage of dealing with the nonlinear parts of the forecasted load curves, whereas, the fuzzy rules are constructed based on the expert knowledge. Therefore, by combining these two methods, the test results show that the proposed forecasting method could provide a considerable improvement of the forecasting accuracy especially as it shows how to reduce neural network forecast error over the test period by 23 % through the application of a fuzzy logic correction. The suitability of the proposed approach is illustrated through an application to actual load data of the Okinawa Electric Power Company in Japan. [ABSTRACT FROM AUTHOR]

Proposes a unit commitment method which applies fast extended priority list (EPL) method in electric power system operation and planning. Explanation of the EPL method; Details of the generation techniques for plurality of initial solutions obtained by EPL; Conditions for the simulation of the proposed method.

Proposes a one-hour-ahead load forecasting method using neural network. Significance of load forecasting to electric power system planning and operation; Evaluation of the similarity between a forecast day and a searched previous day; Structure of neural network; Learning and forecasting procedures for the proposed neural network.

This article discusses various issues related to the article "A Fast Technique for Unit Commitment Problem by Extended Priority List," T. Senjyu, K. Shimabukuro, K. Uezato, and T. Funabashj, that was previously published in the May 2003 issue of the periodical "IEEE Transactions on Power Systems." Since the extended Priority List (EPL) creates the priority list using a simple index, it may not provide the sufficient information for determining the optimal or a near-optimal priority commitment order. However, EPL can compensate the drawback by applying some suitable heuristics, which consider the cost characteristics of unit and prevent load base unit from operating at peak load. It is possible to apply EPL to multi-area unit commitment.