Research On Reactive Power Optimization In Distribution Networks Based On Chaos Ant Colony Algorithm

Power industries are the most important basic energy industries of national economydevelopment basis for the energy industry in the development of national economy, the firstbasic industries of the national economy, the basic industries of the people’s livelihood, andthe priority industries in the countries of the world’s economic development strategy.Distribution networks which will consume some energy in the process of transmission ofelectricity are the carrier of transporting electricity. Therefore the reliability and economy ofdistribution networks have major impact on electricity supply.The reactive power optimization in distribution networks is not only an effective means toensure distribution networks’ safe and economic operation, but also an important measure toimprove the voltage quality of the distribution networks. It can improve the distribution of thepower distribution system voltage and the quality of the voltage, and reduce the energy loss ofpower transmission when we execute reactive power optimization of distribution networks.Thereby we can both reduce electricity costs and improve the allocation of powertransmission capacity and its level of stable operation of the distribution networks.As a global search algorithm, ant colony algorithm can effectively avoid local advantagesproblem. While there are still some problems with the basic ant colony algorithm, for example,fall into local optima and search with long time in multi-dimensional spatial. Chaos has thecharacteristics of sensitivity for initial value, randomness and ergodicity. Chaotic ant colonyalgorithm contains Chaos Theory and the basic ant colony algorithm. With the characteristicsof Chaos, Chaotic ant colony algorithm can reduce the overall search time and avoid fallinginto local optimum.This paper takes the IEEE-14system and IEEE-30system as examples which achieveChaotic Ant Colony Algorithm for Reactive Power Optimization by programming inMATLAB7.0. The results of the program are not only proved the feasibility of Chaotic AntColony Algorithm for Reactive Power Optimization, but also proved Chaotic Ant ColonyAlgorithm having better performance than the basic Ant Colony Algorithm.