On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Mathematics and Computers in Simulation
A modified particle swarm optimization for economic dispatch with non-smooth cost functions
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Solution of economic dispatch problems by seeker optimization algorithm
Expert Systems with Applications: An International Journal
Artificial bee colony algorithm solution for optimal reactive power flow
Applied Soft Computing
Evolutionary programming techniques for economic load dispatch
IEEE Transactions on Evolutionary Computation
Artificial bee colony programming for symbolic regression
Information Sciences: an International Journal
| Hi-index | 12.05 |
The current energy consumption in most of the countries is weighing heavily on fossil fuels, which account for about 70-90% of total energy used. The ecological concerns about air pollution and global warming are encouraging wider use of clean renewable technologies such as wind and solar energy. In this paper, Gbest guided artificial bee colony algorithm (GABC) is applied to optimize the emission and overall cost of operation of wind-thermal power system. The random nature of wind power is modeled using weibull probability distribution function (PDF). Moreover, the uncertainty in wind power is considered in the cost model by including the power imbalance terms such as overestimation and underestimation costs of available wind power. To validate the effectiveness of proposed method, it is first applied to three standard test systems considering different technical constraints such as valve loading effect, prohibited zones, ramp rate limits, etc. In second part, the effect of wind power generation on dispatch cost and emission is analyzed for IEEE-30 bus test system. A comparative analysis with other similar optimization techniques reveals that the proposed technique has better solution accuracy and convergence results.