Optimal Scheduling of Multi-Chain Hydrothermal System Using Teaching-Learning Based Optimization

Abstract

The optimal, short term scheduling of hydrothermal plants is important in planning the operation of the power system. In this scheduling, the hydroelectric and thermal power generation is optimized to minimize the total operating cost (fuel cost) of the thermal plant. This problem of short term hydrothermal scheduling (STHTS) is complex due to consideration of equality and inequality constraints and nonlinearties like valve point loading and prohibited discharge zone.

In this dissertation, teaching - learning based optimization (TLBO) algorithm is used to solve the problem of short-term hydrothermal scheduling (STHTS) considering nonlinearities like valve point loading effects of the thermal unit and prohibited discharge zone of water reservoir of the hydro units. The proposed algorithm has been tested on the multi-chain test system having four hydro units and one thermal. The results are compared with results reported in literature and the implementation is found effective.