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Abstract: Distributed generator (DG) resources are small, self-contained electric generating plants that can provide power to homes, businesses or industrial facilities in distribution feeders. They can be used to reduce power loss and improve service reliability. However, the values of DGs are largely dependent on their types, sizes and locations as they were installed in distribution feeders. A value-based method is proposed in this paper to enhance the reliability and obtain the benefits for DG placement. The benefits of DG placement described in this paper include power cost saving, power loss reduction, and reliability enhancement. The costs of DG placement include the investment, maintenance and operating costs. The proposed value-based method tries to find the best tradeoff between the costs and benefits of DG placement and then find the optimal types of DG and their corresponding locations and sizes in distribution feeders. The derived formulations are solved by a genetic algorithm based method. Test results show that with proper types, sizes and installation site selection, DG placement can be used to improve system reliability, reduce customer interruption costs and save power cost; as well as enabling electric utilities to obtain the maximal economical benefits. [Copyright &y& Elsevier]

Abstract: In a competitive electric energy market, service quality and reliability are two of the essential issues of the business. Distribution automation has been chosen by many utilities around the world as one of the most reliable measures for reducing outage time in their distribution networks. Considering network reliability data and the customer interruption costs, a value-based planning method is proposed in this paper to find the optimal numbers and locations of switches in feeder automation systems. The proposed method takes reliability costs, maintenance and investment costs into account to obtain a feeder automation plan that has a maximum benefit and a proper system reliability requirement. Three stages are involved in the search for optimal solution. Using minimum feeder data and assume equally distributed feeder loads, stage 1 gives initial estimates of reliability indices and costs, and benefit/cost ratios of different feeder automation options. To gain the maximum benefits from feeder automation with reasonable costs, results of stage 1 are used to select feeders with highest priorities for automations. Stage 2 determines the optimum locations of switches that minimize feeder outage costs, and in stage 3, the best locations for tie switches in the automated network are determined. Numerical processing procedure is described and the solution efficiency and results are compared with those obtained from a genetic algorithm. [Copyright &y& Elsevier]

Demand Response (DR) provides an opportunity for customers to reduce their loads during times of high prices and therefore to shave the peak loads. The power outputs of large-scale generator units can be predicted and controlled easily; therefore, the pricing and reliability of conventional power utilities can be assessed straightforwardly. However, the customer loads are very variable and difficult to predict and control; therefore, the integration of DR might cause uncertainty issues on pricing and reliability and is essential to be further investigated. A novel uncertainty model for load reduction is proposed in this paper. The probability intensities of load reduction are first estimated from the measured load reduction variations. A multi-state probability model is then proposed for load reduction and the Markov process is used to calculate the state probabilities. A stochastic analysis scheme using Monte Carlo simulation for pricing and reliability taking the DR uncertainty into account is then investigated. Several cases are designed to compare the effects of DR uncertainty. Simulation results show that the proposed uncertainty model can be integrated into conventional economic dispatch to precisely evaluate the DR uncertainty on system operation and reliability. [ABSTRACT FROM AUTHOR]