Abstract
A novel hybrid approach is developed based on the hybridization of Biogeography Based Optimization and Discrete Hopfield Neural Network. BBO algorithm is employed to tune for the optimal weights of discrete Hopfield Neural Network leading to the minimization of energy function. The proposed hybrid BBO-DHNN is implemented for 10, 20, 40 and 60 units power system under consideration. Based on the simulation results presented, it is clearly noted that the proposed HBDHNN approach results in better solutions for the unit commitment problem considered and this in turn reduces the computational burden to a significant extent. The proposed approaches are developed in MATLAB environment version 7.8.0.347 and executed in a PC with Intel core 2 Duo processor with 2.27 GHz speed and 2 GB RAM with 64 bit operating system.
Highlights
In general considering the electric power industry sector, key issues lie in the optimal planning and economic operation of the electric power generation systems
Solving Unit Commitment Problem (UCP) using proposed hybrid BBO-discrete hopfield neural network: It is been noted that the unit commitment problem cannot be handled effectively in an accurate manner within the framework of traditional Hopfield Neural Network
A novel hybrid approach is developed based on the hybridization of Biogeography Based Optimization and Discrete Hopfield Neural Network
Summary
In general considering the electric power industry sector, key issues lie in the optimal planning and economic operation of the electric power generation systems. Unit Commitment Problem (UCP) is a major component with regard to the resource management side of the generation part. UCP is an optimization problem to compute the schedule of the generating units within a power system so as to minimize the incurred fuel cost. On performing this UCP optimization process, certain number of constraints like ramp rate limits, unit capacity limit, minimum up time and down time constraints and spinning reserve constraints are to be satisfied. The proposed HBDHNN technique with the features of an evolutionary optimization approach and neural network is used in this study to determine the fuel operating cost so as to minimize it to solve for unit commitment problem. The developed HBDHNN technique is employed for 10, 20, 40 and 60 units system to solve UCP
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