摘要
The aim of this paper is simultaneous mini- mization of hydrothermal units to reach the best solution by employing an improved artificial bee colony （ABC） algorithm in a multi-objective function consisting of economic dispatch （ED） considering the valve-point effect and pollution function in power systems in view of the hot water of the hydro system. In this type of optimization problem, all practical constraints of units were taken into account as much as possible in order to comply with the reality. These constraints include the maximum and minimum output power of units, the constraints caused by the balance between supply and demand, the impact of pollution, water balance, uneven production curve con- sidering the valve-point effect and system losses. The proposed algorithm is applied on the studied system, and the obtained results indifferent operating conditions are analyzed. To investigate in various operating conditions, different load profiles in 12 h are taken into account. The obtained results are compared with those of the other methods including the genetfc algorithm （GA）, the Basu technique, and the improved genetic algorithm. Fast convergence is one of this improved algorithm features.
The aim of this paper is simultaneous mini- mization of hydrothermal units to reach the best solution by employing an improved artificial bee colony （ABC） algorithm in a multi-objective function consisting of economic dispatch （ED） considering the valve-point effect and pollution function in power systems in view of the hot water of the hydro system. In this type of optimization problem, all practical constraints of units were taken into account as much as possible in order to comply with the reality. These constraints include the maximum and minimum output power of units, the constraints caused by the balance between supply and demand, the impact of pollution, water balance, uneven production curve con- sidering the valve-point effect and system losses. The proposed algorithm is applied on the studied system, and the obtained results indifferent operating conditions are analyzed. To investigate in various operating conditions, different load profiles in 12 h are taken into account. The obtained results are compared with those of the other methods including the genetfc algorithm （GA）, the Basu technique, and the improved genetic algorithm. Fast convergence is one of this improved algorithm features.
