微电网能量管理难题本质上是一个多时间尺度下、须同时满足多冲突性能指标要求、具有多不确定性因素和非线性约束条件的动态高维多目标约束优化问题，已成为智能电网领域的研究热点之一。本项目针对现有建模和在线优化策略研究大多忽略或简化了这一本质特征而导致的时间尺度和优化模型过于简化、分层协同优化机制缺乏、能量优化模式粗放等缺陷，依托在微电网、进化计算和智能控制等领域的前期研究积累，拟从动态高维多目标约束优化和分数阶预测控制的新视角深入研究微电网能量管理难题，旨在提出基于动态高维多目标分数阶预测控制的微电网多时间尺度分层能量管理新方法，预期在微电网多时间尺度下的分层多目标能量管理建模、极值优化框架下动态高维多目标约束优化算法设计、动态高维多目标分数阶预测控制方法设计与在线实现等方面有所创新和突破，并通过仿真和实物实验验证以上创新成果的有效性，从而为智能微电网优化管理与高效运行提供理论依据和技术支撑。

The energy management issue for microgrids is essentially a dynamic many-objective constrained optimization problem which has multi-time scale, many conflict performance indices satisfied simultaneously, multiple uncertain factors, and nonlinear constraints. It has been a hot research topic in the field of smart grid recently. In order to deal with these defects such as oversimplified time scale and optimization model, deficiency of cooperative optimization mechanism and extensive mode of energy optimization, all of which arise from most of existing research works concerning modeling and on-line optimization strategies have ignored or oversimplified this essential characteristics, this project is to study deeply this energy management issue for microgrids from the novel perspectives of dynamic many-objective optimization and fractional-order model predictive control (MPC) based on the previous research works in regard to microgrids, evolutionary computation and intelligent control. The project is aimed at proposing a novel dynamic many-objective fractional-order MPC-based multi-time scale hierarchical energy management method for micogrids. More specifically, there are some main expected innovations and breakthroughs including the multi-time scale many-objective-based hierarchical energy management model for microgrids, dynamic many-objective constrained optimization algorithm design based on extremal optimization framework, the design and on-line implementation of dynamic many-objective fractional-order MPC methods. Moreover, simulation and physical experiments will demonstrate the effectiveness of the above innovation achievements. In conclusion, our research works of the proposed project will provide strong theoretical and technical supports for optimal management and efficient operation of smart microgrids.