Research On Substation Planning Based On Particle Swarm Of Culture Algorithm

With the rapid development of our country's economic and the improvement of people's living standards, we demand much more electricity and better quality. But our country's current power grid has some shortcomings, such as lack of distribution capacity, low capacity-load ratio of substation, vulnerable of high-voltage grid, low reliability. To some certain extents, it restricts economic and other aspects of development. In recent years people are generally aware that urban distribution network planning is becoming an urgent task. Substation Locating and Sizing is a core step of the urban distribution network planning between Load Forecasting and Network Planning, whose result will affect many aspects directly such as power line routing, network structure, power network investment, operation economy level and power supply reliability.According to the structure of cultural algorithm based on characteristic of substation optimal planning, this paper analyses the possibility of a number of intelligent algorithms as an underlying algorithm of cultural algorithm's framework. Eventually it selects PSO, and designs the cultural source knowledge of the upper space constraints. Based on the above analysis, this paper presents a novel method for substation planning--Particle Swarm of Culture Algorithm, which can optimize the quantities, locations, sizes and power supply areas of substation. Firstly, this method obtains the number of new substations in accordance with capacity-load constraints. Secondly, it uses 0-1 type integer programming approach to obtain the optimal combination of capacity and several groups of sub-optimal solution. Thirdly, under the load density function, this algorithm gets a group of substations'location coordinates, which satisfy the number of new-to-built substations. Fourthly, it puts the relevant information of initial particle swarm into the Particle Swarm of Culture Algorithm to compute and evolve. Finally, it completes the location, capacity, supply area of substation, and other requirements of the planning area.This paper applies Particle Swarm of Culture Algorithm to the actual project examples, it compares and analyses in the aspects of the annual costs, convergency, and the computing time with other algorithm. The results show that the planning algorithm has advantages in the above-mentioned three indicators, especially in the aspect of annual cost. This method inherits the advantages from PSO, which is not sensitive to the initial solution. This strengthens its operability and practicality in Substation optimal planning, which makes it adapt to the actual projects, provides a suitable method to planning the substation.