In competitive electricity market worldwide raises many challenging tasks related to the economic and optimal operation of electric power systems. In deregulated market structure, the generation is being despatched by means of hourly power delivery. The penalty is improved on power producers, if they fail to attain the planned energy delivery. The inadequate hydel resources associated with environmental constraints of thermal plants necessiates a precise scheduling system to satisfy the ever growing power demand. The power generator in a hydrothermal has to manage the conflicting objectives of profit maximization and emission minimization. Normally, the multi-objective optimization problem is tuned for optimising the two or more conflicting objectives subject to some constraints. Short-term hydrothermal scheduling (STHTS) problem deals with more objective functions such as profit maximization and emission minimization. Hence it is necessary to evolve a constructive framework based on intelligent techniques. In this paper, a stochastic multi-objective model is derived for the flexible scheduling of hydrothermal plants with valve-point loading effects. A non-dominated sorting teaching learning based optimization (NSTLBO) algorithm is presented for solving STHTS problem. The proposed algorithm is applied to derive a pair of non-dominated results and then the fuzzy based methodology has been argued to choose the best solution. It is tested on a three thermal and four hydro test system with twenty four hour time period. The results are extracted by means of total profit and emission from the plants. Comparative studies have also been done to validate the viability of the proposed method.

Deregulation, Hydrothermal Scheduling, Profit Maximization, Emission Limitations, Non-dominated Sorting TLBO algorithm

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