Electric generation production scheduling using a quasi-optimal sequential technique

Abstract

A quasi-optimal technique ('quasi' in that the technique discards unreasonable optimums), realized by a dynamically evolving mixed integer program, is used to develop regional electric power maintenance and production schedules for a two to five year planning horizon. This sophisticated, yet computationally feasible, method is used to develop the bulk dispatch schedules required to meet electric power demands at a given reliability level while controlling the associated dollar costs and environmental impacts. The electric power system considered is a power exchange pool of closely coupled generation facilities supplying a region approximately the size of New England. Associated with a tradeoff between a given cost of production and the relevant ecological factors, an optimum production schedule is formulated which considers fossil, nuclear, hydroelectric, gas turbine and pumped storage generation facilities; power demands, reliabilities, maintenance and nuclear refueling requisites; labor coordination, geographic considerations, as well as various contracts such as interregional power exchanges, interruptible loads, gas contracts and nuclear refueling contracts. A prerequisite of the model was that it be flexible enough for use in the evaluation of the optimum system performance associated with hypothesized expansion patterns. Another requirement was that the effects of changed scheduling factors could be predicted, and if necessary corrected with a minimum computational effort. A discussion of other possible optimization techniques is included, however, this study was primarily intended as a development of a static procedure; a dynamic technique counterpart with a more probabilistic. approach is being undertaken as a Part II of this study and at its conclusion the two techniques will be compared. Although the inputs are precisely defined, this paper does not deal explicitly with any of the fabrications of the required inputs to the model. Rather, it is meant as a method of incorporating those inputs into the optimum operation schedule process.