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基于多智能体深度策略梯度的离网型微电网双层优化调度

Two-layer Optimization Scheduling for Off-grid Microgrids Based on Multi-agent Deep Policy Gradient

  • 摘要: 针对高渗透率分布式可再生能源并网引发的电压越限、双向潮流等问题,提出一种双层有功无功协同优化方法,实现离网型微电网有功无功协调优化调度,保证系统安全稳定运行并提升运行的经济性。下层模型基于混合整数二阶锥规划优化慢速调节离散设备,上层模型基于多智能体深度策略梯度算法优化快速调节连续设备。双层模型同时调节微电网的有功和无功潮流,能够实时观测微电网状态,在线决策调节设备的优化方案,且不依赖精确的潮流模型和复杂的通信系统。最后,在改进IEEE 33节点微电网系统中验证双层优化模型的可行性和有效性。

     

    Abstract: To address the voltage limit violations and bidirectional power flow problems arising from high-penetration integration of distributed renewable energy, this paper proposes a two-layer active-reactive power cooperative optimization method to achieve cooperative optimal dispatch of active and reactive power in off-grid microgrids, ensuring the secure and stable operation of the system while enhancing operational economy. The lower-level model optimizes slow-regulating discrete devices based on mixed-integer second-order cone programming, while the upper-level model optimizes fast-regulating continuous devices using a multi-agent deep policy gradient algorithm. The two-layer model coordinates both active and reactive power flows of the microgrid, enabling real-time monitoring of the microgrid's status and online decision-making for the optimization of device regulation, without reliance on precise power flow models or complex communication systems. Finally, the feasibility and effectiveness of the two-layer optimization model are validated in the improved IEEE 33-bus microgrid system.

     

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