Keywords: Network design, medium-voltage network planning, ... type of chromosome, christened the pointer-based chromosome (PBC), and the particular ...
costs are considered taking into account the constraints of conductor capacities and voltage drop. The investment costs will take into account that some cables can be lying in the same trench. The process was applied for a Spanish city of 200,000 inhabitants. Keywords: Network design, medium-voltage network planning, urban distribution network, evolutionary algorithm. Preprint Order Number: PE-134PRS (04-2002) Discussion Deadline: September 2002
Generation Expansion Planning: An Iterative Genetic Algorithm Approach Kazay, H.E; Legey, L.F.L. Author Affiliation: Brazilian Electric Power Research Center, Brazil; Federal University of Rio De Janeiro, Brazil Abstract: The generation expansion planning problem (GEP) is a large-scale stochastic nonlinear optimization problem. To handle the problem complexity, decomposition schemes have been used. Usually, such schemes divide the expansion problem into two subproblems: one related to the construction of new plants (investment subproblem) and another dealing with the task of operating the system (operation subproblem). This paper proposes an iterative genetic algorithm (IGA) to solve the investment subproblem. The basic idea is to use a special type of chromosome, christened the pointer-based chromosome (PBC), and the particular structure of that subproblem, to transform an integer-constrained problem into an unconstrained one. IGA's results were compared to those of a branch and bound algorithm (provided by a commercial package) in three different case studles of growing complexity, respectively containing 144, 462, and 1845 decision variables. These results indicate that the IGA is an effective altemative to the solution of the investment subproblem. Keywords: Genetic algoritluns, integer programming optimization methods, planning, power systems, uncertainty. Preprint Order Number: PE-249PRS (04-2002) Discussion Deadline: September 2002
Artificial Neural Network-Based Peak Load Forecasting Using Conjugate Gradient Methods Saini, L.M.; Soni, M.K. Author Affiliation: Regional Engineering College, India Abstract: Daily electrical peak load forecasting has been done using the feed forward neural network based upon the conjugate gradient back propagation methods by incorporating the effect of eleven weather parameters, the previous day's peak load information, and the type of day. To avoid the trapping of the network into a state of local minima, the optimization of user-defined parameters viz., leaming rate and error goal has been performed. The training data-set has been selected using a growing window concept and is reduced per the nature of the day and the season for which the forecast is made. For redundancy removal in the input variables, reduction of the number of input variables has been done using the principal component analysis method of factor extraction. The resultant data set is used for the training of a three-layered neural network To increase the leaming speed, the weights and biases are initialized according to the Nguyen and Widrow method. To avoid overfitting, an early training is stopped early at the minimum validation error. Keywords: Back propagation, gradient methods, load forecasting, neural networks. Preprint Order Number: PE-255PRS (04-2002) Discussion Deadline: September 2002
A Heuristic Meter Placement
Method for Load Estimation Yu, D.C.; Liu, H.; Chiang, H.D. Author Affiliation: Cooper Power Systems; University of Wisconsin-Milwaukee; Comell University, Ithaca, NY IEEE Power Engineering Review, July 2002
Abstract: A heuristic method of optimal meter placement for load estimation in distribution systems is presented in this paper. The approach can be used to efficiently find the meter location candidates for load estimation. The meter placement method presented in this paper has a two-stage approach. In the first stage, meters are placed using a heuristic method. In the second stage, the confidence interval is calculated to determine if the meters give satisfactory results when loads vary between the maximum and minimum. Sample system analysis and testing results show the approach is efficient for finding tentative meter locations. Real application constraints such as meter failure backup, availability of space, automated switch locations, and unbalanced systems are also considered. The meter placement method for load estimation can be easily extended to place meters for circuit state estimation. Keywords: Power distribution planning, heuristic method, load estimation, meter placement. Preprint Order Number: PE-337PRS (04-2002) Discussion Deadline: September 2002
Incorporating Aging Failures in Power System Reliability Evaluation Li, W Author Affiliation: BC Hydro, Canada Abstract: This paper presents a method for incorporating aging failures in power system reliability evaluation. It includes development of a calculation approach with two possible probability distribution models for unavailability of aging failures and implementation in reliability evaluation. The defined unavailability of aging failures has a consistent form as that for repairable failure. This allows aging failures to be easily included in existing reliability evaluation techniques and tools. Differences between the two models using normal and Weibull distributions have been discussed. The BC Hydro north metro system was used as an example to demonstrate an application of the proposed method and models. The results indicate that aging failures have significant impacts on system reliability, particularly for an "aged" system. Ignoring aging failures in reliability evaluation of an aged power system will result in an overly underestimation of system risk and most likely a misleading conclusion in system planning. Keywords: repairable failure, aging failure, aged system, power system reliability, unavailability. Preprint Order Number: PE-414PRS (04-2002) Discussion Deadline: September 2002 Neural Network Load Forecasting with Weather Ensemble Predictions Taylor, J.W; Buizza, R. Author Affiliation: University of Oxford, Oxford, U.K.; European Center for Medium-Rang Weather Forecasts, Reading, U.K. Abstract: In recent years, a large literature has evolved on the use of artificial neural networks (NNs) for electric load forecasting. NNs are particularly appealing because of their ability to model an unspecified non-linear relationship between load and weather variables. Weather forecasts are a key input when the NN is used for forecasting. This study Investigates the use of weather ensemble predictions in the application of NNs to load forecasting for lead times from 1 to 10 days ahead. A weather ensemble prediction consists of multiple scenarios for a weather variable. We use these scenarios to produce multiple scenarios for load. The results show that the average of the load scenarios is a more accurate load forecast than that produced using traditional weather forecasts. We use the load scenarios to estimate the uncertainty in the NN load forecast This compares favourably with estimates based solely on historical load forecast errors. Keywords: Load forecasting; neural networks weather ensemble
predictions. Preprint Order Number: PE-567PRS (04-2002) Discussion Deadline: September 2002
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