Economic dispatch using modified bat algorithm

Abstract

Due to the frequent opening and shutting of turbine valves in the power system, valve point effect (VPE) that makes the economic dispatching (ED) problem non-linear, non-smooth and non-convex may be generated. Moreover, various constraints appear in the operation process, such as network transmission loss, and power balance during unit operation, which make it more difficult to find the global optimum through traditional mathematical methods. Nowadays, intelligent algorithms have successfully become a useful optimization tool to deal with nonlinear problems. In this paper, an improved bat algorithm (IBA), into which random black hole strategy and Gaussian mutation are introduced, is proposed to solve the ED problem. Furthermore, the random black hole strategy can enhance the diversity of the population and improve the convergence speed of IBA. Gaussian mutation is adopted to help jumping out of the local optimum. IBA is tested in 50 and 100 dimensions on 10 sets of well-known benchmark functions respectively, and compared with the methods in literature to verify its feasibility. Then, three different scales economic dispatching problems (3 units, 13 units, 40 units) are solved by this method, which further proves its effectiveness. The results show that IBA has obvious advantages and practical application value compared with other optimization methods.

Vectorized Lambda Iteration Method for Swift Economic Dispatch Analysis

“…If we consider 40 units taking 32.5984 seconds, then a 42-unit Surrogate-Assisted Adaptive Bat Algorithm would exceed 32.5984 seconds 34) . Finally, if 40 units necessitate 5.0169 seconds, a 42-unit Improved Bat Algorithm would require more than 5.0169 seconds 35) . If we determine that a 40-unit VLIM necessitates 20 seconds for computation under the compulsion time, it can be inferred that a 42-unit Hybrid Artificial Algae Algorithm would require more than 20 seconds for the same task 36) .…”