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Long-term fuel scheduling addresses the problem of optimum allocation of fuels to various generating units using mixed and shared fuels subject to yearly and monthly constraints, inventory constraints, generation availability constraints and load require ments (both energy and capacity).This problem is formulated as a network flow optimization problem. Feasibility of the resulting fuel consumption plan with respect to fuel constraints and its optimality with respect to cost of fuel purchases are assured by the network flow algorithm. Feasibility with respect to the load duration and generation availability are assured by defining appropriate fictitious branches in the network. The limits on these branches are calculated by the method of cumulants.The unit priorities in each month are modified by an iterative procedure, on the basis of both fuel price and fuel availability as seen by the network flow algorithm.Results for a 17 unit/17 contract test system over one year are presented to illustrate various aspects of the long-term fuel resource scheduling problem.

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In the unit maintenance scheduling (UMS) problem, fuel usage limitations at a unit affects the loading of the unit and hence the maintenance schedule of units. Accounting for this interaction of fuel limits at a unit with unit maintenance and system constraints greatly increases the complexity of the problem.Using duality theory, the UMS problem with fuel constraints (UMSFC) is decomposed into a master problem and subproblems. The definition and the coordination of master and subproblems gives rise to alternate algorithms to solve the original problem. p i s paper presents results of one such decomposition. In this approach, the master problem solves for a tiial maintenance schedule to satisfy unit maintenance constraints and unit fuel limits over all time intervals of the study. Given the maintenance schedule from the master problem, a subproblem calculates the minimum operating cost subject to system constraints for each interval of the study period. If one or more subproblems are infeasible, additional constraints are generated and then added to the master problem so that an improved maintenance schedule that satisfies the fuel and system constraints is obtained. The iteration between master and subproblems is continued until an optimal or nearoptimal solution is found.Key Wordr: Unit maintenance scheduling, fuel constraints, optimization in power systems, 0-1 integer programming, generalized Benden decomposition. . Intraducfion.Preventive maintenance scheduling of an electric utility's generating units plays a very important role in the economical and reliable operation of the system. The unit maintenance scheduling (UMS) problem minimizes the total operating cost over the operational planning period subject to unit maintenance, and system load and reliability constraints. F' apers presented at the Seventeenth PICA Conference at the Hyatt Regency Baltimore Hotel, Baltimore, Maryland, May 7 -10, 1991 Sponsored by the IEEE Power Engineering Society Several well-known optimization methods have been proposed in the literature for the UMS problem, induding integer programming, branch-and-bound, dynamic programming, and simulated annealing [4,8-12]. More recently, duality theory has been applied to decompose the problem into a master problem and a series of subproblems. The coordination of master and subproblems results in the solution of the UMS problem as reported in [l].For some utilities, contractual obligations limit the amount of fuel bum at a unit. The fuel consumption restrictions at a unit may indude yearly or monthly maximum and minimum limits. During actual operation, the units are loaded in decreasing order of operating cost efficiency. If the fuel to a particular unit is scarce, then the unit's operating cost is effectively higher. If the maximum amount of fuel is allocated to that unit in one time period, that fuel may become scarce in some other time period when its availability is even more important. Accounting for this interaction of the fuel restrictions with the unit and system constraints great...