Yes / Traditionally, the electricity industry is fully regulated with a centrally controlled structure. The power system operator has full technical and costing information as well as a full control over the operation and maintenance of power system equipment. Recently, many countries have gone through privatization of their electricity industries unbundling the integrated power system into a number of separate deregulated business entities. The preventive maintenance of power system equipment in the restructured electricity industries is no longer controlled centrally, and none of these entities currently have explicit accountability for maintenance activities. The approaches used to schedule the maintenance activities in the centralized system are not ideal for addressing the new deregulated environments. In recent years a few research publications has been reported in this area. This paper presents a review and analysis of these reported maintenance scheduling approaches for power system equipment in the changed environment.

With the deregulation of power industry and the market competition, reliable power supply and secured system operation are major concerns of the independent system operator (ISO) or decision-maker (DM). Power dispatch under deregulated environment is complicated with various possibilities of decisions involved. Without the assistance of Energy Management System (EMS), it is not easy for ISO or DM to operate with pure experiences. To enhance the operational efficiency, EMS involves the state-of-the-art control technology, and the fast and effective computer assisted decision support system will help dispatch the power. A conventional EMS has a few major tasks, among them, the ¡§network analysis¡¨ task and the ¡§forecast and scheduling¡¨ task are the most important in assisting the on-line power dispatch. In dealing with the new deregulated environment, an ¡§operational planning¡¨ has to be added to aid the EMS for more security. There are significant changes on EMS after deregulation. This dissertation will focus on the changes and new functions, in the ¡§network analysis¡¨ and the ¡§forecast and scheduling¡¨ tasks of an EMS, which supports the operation in the competitive environment. In the ¡§network analysis¡¨ task, we discuss the real and reactive power dispatch and congestion management with AC optimal power flow (OPF). In this task, the formulation of AC OPF with deregulation issues and the effect of flexible AC transmission systems (FACTS) devices are presented. A predictor-corrector interior-point nonlinear-programming (PCIPNLP) algorithm has been developed to solve the problem. The model involves only slight modification to the present OPF for social welfare maximization to obtain the optimized bid-based dispatch and nodal spot prices. The incorporation of FACTS devices for system operations can ease the difficulties caused by transmission congestion. It is found that PCIPNLP technique is very effective for the modified OPF solution for congestion relief under deregulation. In the ¡§forecast and scheduling¡¨ task, we discuss the resource scheduling for bid-based dynamic economic dispatch and spot dispatch for power and reserve. They can be formulated for social welfare maximizing problem that is solved by using an efficient interior point method. And the optimal resource allocation and nodal spot price can be given from the various test results. We have also proposed a fuzzy based strategic gaming method to determine the GenCo¡¦s bidding strategy. Based on fuzzy set theory, a multi-criteria decision-making method is used to obtain the optimal strategy combination, bids and expected payoffs. Decision maker can find the optimal strategy combination by using weight vector to represent his subjective attitude about the structure of multi-objectives. The advantages have also been demonstrated through the numerical examples. Compared with the classical (¡§nonfuzzy¡¨) game theory, the proposed approach could help the decision maker to obtain higher expected payoffs, and make his choices easily. Possible applications of the proposed fuzzy method can be suggested for other decision-making problems in the power systems