Two-phase multi-objective evolutionary approach for short-term optimal thermal generation scheduling in electric power systems



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Kansas State University


The task of short-term optimal thermal generation scheduling can be cast in the form of a multi-objective optimization problem. The goal is to determine an optimal operating strategy to operate power plants, in such a way that certain objective functions related to economic and environmental issues, as well as transmission losses are minimized, under typical system and operating constraints. Due to the problem’s inherent complexity, and the large number of associated constraints, standard multi-objective optimization algorithms fail to yield optimal solutions.
In this dissertation, a novel, two-phase multi-objective evolutionary approach is proposed to address the short-term optimal thermal generation scheduling problem. The objective functions, which are based on operation cost, emission and transmission losses, are minimized simultaneously. During the first phase of this approach, hourly optimal dispatches for each period are obtained separately, by minimizing the operation cost, emission and transmission losses simultaneously. The constraints applied to this phase are the power balance, spinning reserve and power generation limits. Three well known multi-objective evolutionary algorithms, NSGA-II, SPEA-2 and AMOSA, are modified, and several new features are added. This hourly schedule phase also includes a repair scheme that is used to meet the constraint requirements of power generation limits for each unit as well as balancing load with generation. The new approach leads to a set of highly optimal solutions with guaranteed feasibility. This phase is applied separately to each hour long period. In the second phase, the minimum up/down time and ramp up/down rate constraints are considered, and another improved version of the three multi-objective evolutionary algorithms, are used again to obtain a set of Pareto-optimal schedules for the integral interval of time (24 hours). During this phase, the hourly optimal schedules that are obtained from the first phase are used as inputs. A bi-objective version of the problem, as well as a three-objective version that includes transmission losses as an objective, are studied. Simulation results on four test systems indicate that even though NSGA-II achieved the best performance for the two-objective model, the improved AMOSA, with new features of crossover, mutation and diversity preservation, outperformed NSGA-II and SPEA-2 for the three-objective model. It is also shown that the proposed approach is effective in addressing the multi-objective generation dispatch problem, obtaining a set of optimal solutions that account for trade-offs between multiple objectives. This feature allows much greater flexibility in decision-making. Since all the solutions are non-dominated, the choice of a final 24-hour schedule depends on the plant operator’s preference and practical operating conditions. The proposed two-phase evolutionary approach also provides a general frame work for some other multi-objective problems relating to power generation as well as in other real world applications.



optimal generation scheduling, multi-objective optimization, evolutionary algorithms

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Doctor of Philosophy


Department of Electrical and Computer Engineering

Major Professor

Sanjoy Das; Anil Pahwa