Sharif Digital Repository

Dynamic economic dispatch (DED) determines system generation schedule in a specified time interval to meet load demand at each hour while satisfying generation system constraints. DED considering valve-point effects is a non-convex optimization problem and therefore conventional mathematical methods fail to solve this problem. This paper presents a hybrid gradient search - near optimal search method to solve the DED problem considering valve-point effects of generating units. Local minima obtained by Gradient Search Method (GSM) are utilized by a novel Near Optimal Search (NOS) method to find the global optimum of the DED problem. The efficiency and feasibility of the proposed method and its... 

Dynamic economic dispatch (DED) aims to schedule the committed generating units' output active power economically over a certain period of time, satisfying operating constraints and load demand in each interval. Valve-point effect, the ramp rate limits, prohibited operation zones (POZs), and transmission losses make the DED a complicated, non-linear constrained problem. Hence, in this paper, imperialist competitive algorithm (ICA) is proposed to solve such complicated problem. The feasibility of the proposed method is validated on five and ten units test system for a 24 h time interval. The results obtained by the ICA are compared with other techniques of the literature. These results... 

The objective of the dynamic economic dispatch (DED) problem is to schedule power generation for the online units for a given time horizon economically, satisfying various operational constraints. Due to the effect of valve-point effects and prohibited operating zones (POZs) in the generating units cost functions, DED problem is a highly non-linear and non-convex optimization problem. The DED problem even may be more complicated if transmission losses and ramp-rate constraints are taken into account. This paper presents a novel and heuristic algorithm to solve DED problem of generating units, by employing time varying acceleration coefficients iteration particle swarm optimization...