Research On Transmission Network Expansion Planning Based On Improved Artificial Bee Colony Algorithm

It is very necessary to do transmission network planning with intelligent optimization algorithm, which with the growing size of the transmission network and increasing complexity of transmission network planning.. Artificial bee colony algorithm is a new intelligent optimization algorithm, which has the advantages of quick calculating speed, less control parameters, robustness and combining with other algorithms easily. Artificial bee colony algorithm has been widely applied in many fields recently. In this paper, artificial bee colony algorithm is improved, and utilized it in transmission network planning. Moreover, this improved algorithm opens up a brand new way for research transmission network planning.In this paper, we focus on shortcomings of standard artificial bee colony algorithm, including low calculating accuracy, being ease to fall into local optimum and computing slowly in later optimization stage. This paper provided six strategies to improve this algorithm. Concrete improvement strategies include:(1) Generate initial position of honey by the method of chaos initialization and reverse learning strategy in the initial stage.(2) Introduce mutation factor and cross factor to update the position of honey; and accept a new honey source by the annealing selection strategy in the employed bee stage;(3) Compute the probability of what every honey is selected by onlookers with tournament selection strategy in the selection stage.(4) Introduce a learning factor to update the position of honey; and accept a new honey source by the annealing selection strategy in the onlooker stage.(5) In order to improve the quality of the best honey. Use dynamic chaos search locally.(6) Use chaotic search on honey that stops to evolve in the scout stage. The experimental results on three typical testing functions indicate that improved artificial bee colony algorithm can accelerate the convergence rate and improve the searching precision. Improved artificial bee colony is better than the standard artificial bee colony algorithm outstandingly.This paper established the model which minimizes sum of construction cost and network loss cost in single objective planning for transmission network. The effectiveness of improved artificial bee colony algorithm has been demonstrated on the Garver-6-bus-system and Garver-18-bus-system. At last, This paper established model whose objective functions is construction cost, power losses,the cost of transmission corridor area and cost of transmission surplus capacity in multi-objective planning for transmission networks and solve this model with improved artificial bee colony algorithm. The example verified that the model of multi-objective planning is correct and effective.