Optimal short-term hydro-thermal scheduling using multi-function global particle swarm optimization

Abstract

<p>An optimal short-term hydro-thermal scheduling (ST-HTS) problem is solved in this paper using the multi-function global particle swarm optimization (MF-GPSO). A multi-reservoir cascaded hydro-electric system with a non-linear relationship between water discharge rate, power generation and net head is considered in this paper. The ST-HTS problem determines the optimal power generation of hydro and thermal generators which is aimed to minimize total fuel cost of thermal power plants during a determined time period. Effects of valve point loading and prohibited operating zones in the fuel cost function of the thermal power plants is examined. Power balance, reservoir volume, water balance and operation constraints of hydro and thermal plants are considered. The effectiveness and feasibility of MF-GPSO algorithm is examined on a standard test system, and the simulation results are compared with other algorithms presented in the literature. The results show that the MF-GPSO algorithm appears to be the best in terms of convergence speed and optimal cost compared with other techniques reported in the literature.</p>

A Novel Hybrid Approach for Hydrothermal Scheduling Using Mathematical and Metaheuristic Search Methods

“…Salkuti et al [16] proposed multi-function global particle swarm optimization (MFGPSO) for optimal shortterm hydro-thermal scheduling (ST-HTS). This ideal ST-HTS optimizes hydro and thermal generator generating schedules to lower thermal power plant fuel costs.…”

“…From aforementioned literature, it is found that computation capacity and execution time is increased in [16], [17] breaks equality requirements while changing the violated dependent variables, [18] slows multimodal optimization convergence, in [19] swarm does not coalesce to a single spot, [21] cannot determine optimal scheduling, [22] does not address the objective function properly, in [23] algorithm is highly complex, [24] ignores social and cognitive factors for the PSO technique while [25] takes more computational time. The Hamilton and Egarch algorithm is a computational approach for optimizing the control of water flow in hydraulic systems based on certain criteria and constraints [26][27].…”