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.
In this article, load frequency control of an interconnected power system is achieved by two methods. The first is based on the classical H 1 control method and is subjected to linear matrix inequalities. The second uses a proportional-integral controller that is tuned by genetic algorithm optimization and is also subjected to the same linear matrix inequalities in order to obtain robustness against disturbances. Both controllers are tested on a two-area power system with three scenarios of load disturbances, as well as on power systems in a deregulated environment to demonstrate their robust performances.
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