Electric power unit commitment scheduling using a dynamically evolving mixed integer program
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 unit commitment schedules for a one week time span. This sophisticated, yet computationally feasible, method is used to develop the hourly 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 generation schedule is formulated which considers fossil, nuclear, hydroelectric, gas turbine and pumped storage generation facilities; power demands, reliabilities, operating constraints, startup and shutdown factors, geographic considerations, as well as various contracts such as interregional power exchanges, interruptible loads, gas contracts and nuclear fuel optimum batch utilization. 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 minimal computational effort. A discussion of other existing and potential solution techniques is included, with an example of the proposed solution technique used as a scheduler. Although the inputs are precisely defined, this paper does not deal with the explicit fabrication of inputs to the model, such as e.g. river flow prediction or load forecasting. Rather, it is meant as a method of incorporating those inputs into the optimum operation scheduling process.
Prepared in association with Electric Power Systems Engineering Laboratory and Dept. of Civil Engineering, M.I.T.
MIT Energy Lab
Production scheduling, Integer programming, Electric power systems -- Mathematical models
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