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.

Restructuring of the power industry worldwide has brought the issue of market competitiveness to the forefront. While "vertical" market power has been limited by disaggregating generation and transmission, nondiscriminatory access to the transmission system by power suppliers, and the creation of the Independent System Operator (ISO), "horizontal" and localized market power still survives. In the last few years, the issue of a small number of generators exercising market power has received a great deal of attention, and much has been published on how to identify and mitigate such abuse by market management measures. This paper focuses on the analysis and mitigation of market power in electricity supply.

The new electricity market behaves more like an oligopoly than a laissez-faire 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 possible for only a few generating companies to service a given geographic region. In an imperfect market each power supplier can increase its own profit through strategic bidding. The problem of building optimally co-ordinated bidding strategies for competitive suppliers in energy and spinning reserve markets is addressed. Each supplier bids a linear energy supply function and a linear spinning reserve supply function to the energy and spinning reserve markets, respectively, and the two markets are dispatched separately to minimise customer payments. Each supplier chooses the coefficients in the linear energy and spinning reserve supply functions to maximise total benefits, subject to expectations about how rival suppliers will bid. A stochastic optimisation model is first developed to describe this problem and a genetic algorithm based method is then presented to solve it. A numerical example is utilised to illustrate the essential features of the method.

A new approach to designing bilateral power transaction bargaining models between two independent utilities in a deregulated electricity market is proposed. In the paper it is assumed that each utility (a seller or a buyer) knows its own operating costs but does not know those of its opponent. The bilateral power transaction problem is then considered as nonco-operative bargaining under incomplete information. Each participant develops its own bargaining strategy based on estimates of the opponent's operating costs and bargaining strategy. Two bargaining models are developed and examples are employed for demonstration.

A tabu search (TS) based approach is proposed for alarming processing in power systems. First, several existing evaluation criteria describing the alarm processing problem are briefly discussed, and a new criterion is proposed. Secondly, a novel method is developed to solve this problem using a TS based method. Finally, two examples are used to demonstrate the feasibility and efficiency of the developed method. The paper also presents a comparison between the developed TS based and the more established genetic algorithm (GA) based approaches to the alarm processing problem. Many simulation results show that the TS-based approach is more efficient than the GA based approach. Key features of this proposed method are that it has solid mathematical foundation and can find multiple global optimal solutions directly and efficiently in a single run. This is very suitable for complex alarm processing problems especially for situations with missing or false alarms, because different combinations of events can produce the same set of alarms under these circumstances. The test results suggest that the developed TS based method is promising.

One of the challenges for power industry restructuring is to maintain sufficient generation installed capacity to meet demand in nowadays and in the future, and the electricity market failure in California has brought this subject to the forefront of extensive debates. This paper initiates a review, assessment and discussion on some important issues related to ensuring adequate generation capacity additions under a competitive electricity market environment.