A new method for trouble call analysis in low voltage distribution systems based on set covering theory and the tabu search (TS) technique is proposed. First, a new mathematical model for the trouble call analysis is developed based upon customer trouble calls, taking into account the protective device location, selectivity and operation possibility in the distribution network protection schema. Secondly, a new method is presented to solve this problem using a TS-based method to efficiently search for the protective device(s) that was the most likely to have operated. Finally, a sample distribution system is used to demonstrate the feasibility and efficiency of the developed method. Test results suggest that the developed TS-based method is efficient.

A systematic and mathematically sound model and a refined genetic algorithm (RGA) based method for fault-section estimation in power systems is proposed. First, the probabilistic causality relationship among section fault, protective relay action and circuit breaker trip is formulated as a probabilistic causality matrix. Secondly, the well-developed parsimonious set covering theory is applied to the fault-section estimation problem, and a 0-1 integer programming model is then obtained. Thirdly, a RGA-based method for fault-section estimation is developed by using information on operations of protective relays and circuit breakers. The proposed method is versatile and can deal with uncertainties in fault-section estimation, such as protective relay failures and/or malfunction and circuit breaker failures and/or malfunction. Test results for a sample power system have shown that the probabilistic approach developed for fault-section estimation is feasible and efficient.

stochastic optimization formulation is developed and two methods proposed for describing and solving this problem. A numerical example serves to illustrate the essential features of the approach and the results are used to investigate the potential market power.

The emerging electricity market behaves more like an oligopoly than a perfectly competitive market due to special features such as, a limited number of producers, large investment size (barrier to entry), transmission constraints, and transmission losses which discourage purchase from distant suppliers. This makes it practicable for only a few independent power suppliers to service a given geographic region and in this imperfect market each power supplier can increase its own profit through strategic bidding. The profit of each supplier is influenced to varying extents by differences in the degree of imperfection of knowledge of rival suppliers. A new framework to build bidding strategies for power suppliers in an electricity market is presented in this paper. It is assumed that each supplier bids a linear supply function, and that the system is dispatched to minimize customer payments. Each supplier chooses the coefficients in the linear supply function to maximize benefits, subject to expectations about how rival suppliers will bid. A

A method to predict the optimal energy production of a profit maximizing power producer in an oligopoly electricity market is presented. The cases where producers have incomplete information about the operating costs of rival producers are highlighted. Cournot noncooperative game theory is used to determine the oligopoly electricity market equilibrium state that corresponds to the optimal supply quantity of each power producer.