Multi-objective-based robust unit commitment using hydro-thermal-wind: a hybrid technique

Abstract

Purpose

The purpose of this paper is to solve economic emission dispatch problem in connection of wind with hydro-thermal units.

Design/methodology/approach

The proposed hybrid methodology is the joined execution of both the modified salp swarm optimization algorithm (MSSA) with artificial intelligence technique aided with particle swarm optimization (PSO) technique.

Findings

The proposed approach is introduced to figure out the optimal power generated power from the thermal, wind farms and hydro units by minimizing the emission level and cost of generation simultaneously. The best compromise solution of the generation power outputs and related gas emission are subject to the equality and inequality constraints of the system. Here, MSSA is used to generate the optimal combination of thermal generator with the objective of minimum fuel and emission objective function. The proposed method also considers wind speed probability factor via PSO-artificial neural network (ANN) technique and hydro power generation at peak load demand condition to ensure economic utilization.

Originality/value

To validate the advantage of the proposed approach, six- and ten-units thermal systems are studied with fuel and emission cost. For minimizing the fuel and emission cost of the thermal system with the predicted wind speed factor, the proposed approach is used. The proposed approach is actualized in MATLAB/Simulink, and the results are examined with considering generation units and compared with various solution techniques. The comparison reveals the closeness of the proposed approach and proclaims its capability for handling multi-objective optimization problems of power systems.

Citation

Jain, A. and Huddar, A.P. (2019), "Multi-objective-based robust unit commitment using hydro-thermal-wind: a hybrid technique", International Journal of Energy Sector Management, Vol. 13 No. 4, pp. 804-827. https://doi.org/10.1108/IJESM-07-2018-0015