Unit commitment is a complex problem which, until now and for real size systems, has been solved using heuristic methods only. This paper proposes a new Lagragian relaxation method, which produces ??-optimal solutions. The flexibility of the algorithm is such that numerous developments can be envisaged, such as simultaneous management of pumping units, probabilistic determination of the spinning reserve, etc.

A New Method for Unit Commitment at Electricite De France

Increasing interest has been seen in applying fuzzy set theory to power systems problems from the number of publications on this topic. As a relatively new research topic a need is felt to pay more attention to the understanding of the basic principles of the theory and the identification of problems suitable for solving by this method. This paper presents a survey of publications on applications of fuzzy set theory to power systems and the basic procedures for fuzzy set based methods to solve specific power systems problems. Simple numerical examples are used to show the practical procedures of problem formulation and solution. Theses examples are: generator maintenance scheduling, dynamic programming, and power system stabiliser. >

Overview and literature survey of fuzzy set theory in power systems

The thermal power plant generator maintenance scheduling problem is addressed. The problem is formulated as a mixed-integer programming problem, and it is solved by using an optimization method known as simulated annealing. Since the simulated annealing method assumes an analogy between a physical multiparticle system and a combinatorial optimization problem, a global minimum can be found with high probability through a careful annealing process. Numerical results on a real-scale test system are given, and the effectiveness of the proposed method is demonstrated. >

Maintenance scheduling by using simulated annealing method (for power plants)

The incorporation of local search operators into a genetic algorithm has provided very good results in certain scheduling problems. The resulting algorithm from this hybrid approach has been termed a memetic algorithm. This paper investigates the use of a memetic algorithm for the thermal generator maintenance scheduling problem. The local search operators alone have been found (in earlier work by the authors and others) to produce good quality results. The main purpose of this paper is to discover whether a memetic approach can produce better results. We describe the approach taken and highlight the variety of local search algorithms that were employed. We compare the memetic algorithms with a variety of algorithms that include the local search operators on their own and a range of algorithms that apply the local search operator to randomly generated solutions. We see that, for the problems tested, the memetic algorithms produce better quality solutions (although they do take more time about it). Of course, in practice, for a problem like this, the time taken to produce a solution is not a major issue. What is far more important is the quality of the solution. We conclude that the most effective method (of the ones tested here) is a memetic approach that employs a tabu-search operator.

Hybrid evolutionary techniques for the maintenance scheduling problem

This paper addresses generation maintenance scheduling in a competitive electric energy environment. In a centralized setting, the system operator derives a maintenance scheduling plan that attains the desired reliability while minimizing cost and imposes it to all producers. In a competitive environment, this is not possible because the operator is still in charge of maintaining an adequate level of reliability, but the target of each producer is to maximize its own profits, which conflicts in general with the reliability objective of the operator. This paper proposes a technically sound coordinating mechanism based on incentives/disincentives among producers and the operator, which allows producers to maximize their respective profits while the operator ensures an appropriate level of reliability.

Generation maintenance scheduling in restructured power systems

Host currently used thermal-generator maintenance scheduling methods schedule each unit at a time, leading in many cases to a poor local optimum or to an unfeasible solution. Considering simultaneously all units in the scheduling process, a computationally impractical method results. In this paper a piece-wise compromising method of solution is presented, which conveniently groups generating units and uses successive approximations dynamic programming. The optimal maintenance scheduling problem is formulated by employing a suitable state-space representation; most common constraints used in practice are described and a multiple objective function is presented. The effectiveness of the optimizing method is illustrated by solving a problem with data obtained from a real system. The grouping criterion presented appears to be the cornerstone of the superior performance of the proposed method.

Generator maintenance scheduling via successive approximations dynamic programming

A number of objective criteria for optimal maintenance scheduling of thermal generators is discussed. The criteria are based on generation operating cost, reliability indices, deviations from a desired schedule and/or constraints violation penalties. A comparison of the performance of all these criteria is presented by maintenance scheduling a realistic 30 thermal-unit system. Also, the relationships between the several realibility indices are dicussed.

H.H. Zurn

Papers

Generator maintenance scheduling via successive approximations dynamic programming

Several objective criteria for optimal generator preventive maintenance scheduling