Abstract
A hybrid algorithm, biogeography based optimization with simulated annealing (SABBO), is proposed in this paper by integrating the SA strategy into a basic BBO algorithm to solve combined heat and power unit economic dispatch (CHPED) problem. In this modified algorithm, a simulated annealing operator is used to randomly perturb the best individual retains after migration and mutation operating in the basic BBO. Elitism solution is further optimized to enhance the algorithm convergence capacity, and to prevent the algorithm from getting trapped into local optimum. Several benchmark functions are then taken to test the proposed method's optimal capacity, and the results show that SABBO could significantly outperform the basic BBO algorithm and mostly as the best solution listed BBO algorithms with different versions. Then, various systems are taken to tested SABBO algorithm, compared with the basic BBO and algorithms from references. Test results indicate that the SABBO is better than others to solve CHPED problems including large-scale ones. Further, the energy saving potential is calculated based on the whole plant's feasible operating region to work as guidance for economic operation.
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Notes on contributors
Hui Gu
Hui Gu received the Ph.D. degree from the Southeast University and is now a lecturer in the Nanjing Institute of Technology. His research focuses on the optimal operation of generation units.
Hongxia Zhu
Hongxia Zhu is an associate professor in the Nanjing Institute of Technology. His researches research focuses on the operation monitoring of generation units.
Pan Chen
Pan Chen received the Ph.D. degree from the Southeast University. She is now a lecturer in the Nanjing Institute of Technology, and studies the characteristics of generation units.
Fengqi Si
Fengqi Si is a professor in Southeast University, and his research interests are monitoring and optimization studies on power plants.