Jiang Chuanwen

The application of multi agent system in microgrid coordination control

Microgrid, a new type of power-supplying mode, has promising future. But it can hardly be controlled in a traditional way due to its distributed characteristics and considerable control capabilities over the network operation. In order to solve this problem and improve the efficiency and reliability level of Microgrid, a Multi Agent-based control framework is originally presented in this paper. A three-layer Multi Agent System (MAS), including main grid agents, Microgrid agents and component level agents, is established based on the proposed hierarchical coordinated control strategy. Thereafter, an agent control model, aiming at maximum efficiency of MAS, is introduced under the premise of MASs voltage stability and power balance. Accordingly, the special functions and control strategies of each agent are discussed in details as well. A simulation example of a particular Microgrid is performed to demonstrate the effectiveness of MAS.

Economic dispatch and optimal power flow based on chaotic optimization

By means of the ergodicity, regularity and intrinsic stochastic property of chaos, one improved chaotic optimization algorithm is introduced to solve an economic dispatch problem for power plant and the optimal power flow problem for power system. It utilizes chaotic variables to search the global optimal solution. It can gradually reduce the search space of optimized variables in order to improve the precision of solution. In addition, it can accelerate the convergence with the descending information of the object. Several numerical examples are tested with the proposed method. The simulation results show that the method being put forward is high effective and high precision and easy to use.

Combination of Data Mining and Ant Colony Algorithm for Reactive Power Optimization

The management of reactive resources plays an important role in maintaining voltage stability and system reliability. This paper presents a new method to find the optimal solution to reactive regulation in power system, using the daily data collected in power substations. The new algorithm is combined with improved ant colony algorithm and Apriori data mining technique. The mathematic models of reactive optimization are described and applied to the reactive optimal compensation in an example electric system. Test results show that the application of the new algorithm proposed in this paper for determining the plan of reactive optimization operation can raise the system’s operation efficiency and reduce the power loss.