Transmission Network Planning Based On Mind Evolutionary Computation

Transmission network planning plays an important role in electric power system planning. Appropriately optimized transmission network is essential to the harmonious and healthy development of power system. Transmission network planning with security is a complex nonlinear combinatorial optimization problem, which cannot be well solved by traditional optimization methods. Mind Evolutionary Computation (MEC) is a novel kind of evolution algorithm. Employing operations of similartaxis and dissimilation, introducing information matrix and billboard, MEC possesses excellent performances due to three mechanisms: evolutionary directionality mechanism, memory mechanism and harmony mechanism between exploitation and exploration. MEC has successfully solved a series of combinatorial optimization problems, which shows prominent potential in this field.This paper introduces the development, principle, performance and applications of MEC in general, and applies it to power transmission network planning. According to MEC algorithm introduced for non-numerical optimization problems, this paper put forward the coding method, similartaxis operation and dissimilation operation for transmission network planning problems. Taking the minimization of invest and annual operation cost as objective function in single-stage planning model, analysis examples are presented which proves that MEC has successfully solved static transmission network planning problem considering"N-1"security constrains and the residual capacity of power network.Several parameters that influence the performance of algorithm and different dissimilation operations are analyzed to improve convergence rate of the algorithm. Simulating results show that MEC is reliable and effective for solving transmission network expansion planning problems.