基于边缘计算的能量自治区域调度策略

为了提升配电物联网基础数据海量增长背景下能量调度的效率，提出了基于边缘计算的能量自治区域调度策略，构建能量自治系统边缘计算架构和优化调度模型。该方法将大部分数据处理和储存工作下放至网络边缘侧，降低网络传输成本并且减少调度中心计算量，边缘侧采用基于莱维飞行的改进粒子群算法进行优化调度，既可以独立进行优化工作，也可以边云交互完成整体调度。相比于传统集中式调度策略，该方法可节省约90%的系统整体优化时间，且云层以及边缘层所进行的总迭代次数较集中式调度减少60%以上。最后以某地区电力负荷及分布式电源预测出力数据为算例进行仿真计算，结果表明基于边缘计算的优化策略有效地提高了能量调度的效率。

Energy Autonomous Region Scheduling Strategy Based on Edge Computing

In order to improve the efficiency of energy scheduling under the background of basic data massive growth of distribution Internet of things，energy autonomous regional scheduling strategy is proposed based on edge computing， the energy autonomous system edge computing architecture and optimal scheduling model are constructed. Compared with the traditional centralized scheduling strategy，this method decentralizes most of the data processing and storage work to the edge side of the network to reduce the network transmission cost and the calculation of the scheduling center. At the edge side，the improved particle swarm optimization algorithm based on Levy flight is used to optimize the scheduling，which can not only carry out the optimization work independently， but also complete the overall scheduling with the edge-cloud data interaction. This method can save about 90% of the optimization time， and the total number of iterations in the cloud layer and edge layer is more than 60% less than the centralized scheduling. Finally，the simulation results of the method applied in power load and distributed generation forecast output data in a certain area shows that the optimization strategy based on edge computing can effectively improve the efficiency of energy scheduling.