中文摘要：

为了解决力量的协作和优化，联网有效地计划，根据介绍力量的概念影响产生的情报中心(照片)，关键因素力量流动，线投资和负担部门，在照片的传播部门和派遣中心被分析并且多客观的协作为新力量情报中心( NPIC )的最佳的模型被建立。保证力量格子和还原剂投资费用的可靠性和协作，二个方面被优化。进化算法被介绍解决最佳的力量流动问题，健康功能被改进保证发电的最小的费用。灰色的粒子群优化(GPSO ) 算法被用来精确地预报负担，它能与高可靠性保证网络。在这个基础上，多客观的协作更实际并且与电的需要一致出售的最佳的模型被建议，然后，协作模型有效地通过改进粒子群优化算法被解决，并且相应算法被获得。IEEE30 节点系统的优化证明进化算法罐头有效地解决最佳的力量流动的问题。GPSO 预报的平均负担是 26.97 MW，它与实际负担相比有 0.34 MW 的一个错误。算法更高让预报精确性。为 NPIC 的最佳的模型能有效地处理的多客观的协作协作和力量的优化问题联网。

英文摘要：

In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center （PIC）, the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center （NPIC） was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization （GPSO） algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network.