Hybrid DE-TLBO algorithm for solving short term hydro-thermal optimal scheduling with incommensurable objectives

In this paper, a hybrid differential evolution and Teaching-Learning-Based optimization (hDE-TLBO) algorithm is proposed to solve the multi-objective short-term optimal hydro-thermal scheduling (MOSOHTS). In the MOSOHTS model, the total fuel cost and emission effects are optimized as two conflict objectives, the water transport delay between reservoirs and the valve-point effect of thermal units are also taken into consideration. In the proposed method, Differential Evolution (DE) and Teaching-Learning-Based Optimization (TLBO) algorithm are combined together; DE plays the role of “Teachers Refresher” in TLBO and enhances the overall efficiency of the algorithm. In addition, an effective constraint handling approach is presented to deal with the complex constraints. The effectiveness of the proposed method is tested on three scenarios of a hydro-thermal power system with four cascaded hydro plants and three thermal units. The results show that hDE-TLBO is a viable alternative way to generate optimal non-dominated scheduling schemes for the MOSOHTS.