PDF(1578 KB)
PDF(1578 KB)
PDF(1578 KB)
考虑光伏波动性与负荷时变性的ADN多故障修复策略
Multi-Fault Repair-Recovery Strategy for Active Distribution Network Considering Volatility of Photovoltaic and Time Variation of Load
在主动配电网(active distribution network, ADN)中,光伏出力具有波动性,且负荷功率具有实变性,当配电网发生多个故障时,修复时间较长,光伏在某些时段可能会出现出力不足的情况。为保证故障后对非故障失电区内尽可能多的重要负荷恢复供电,结合短时间尺度优化调度方法建立基于多代理系统(multi-agent system,MAS)的ADN分区域、分场景、分时段的动态修复模型。该模型以恢复全网负荷价值最大和开关次数最少为上层目标,以各非故障失电区内不同恢复时段、不同开关动作次数下失电负荷恢复价值最大为前提,总供电量最大为下层目标,并且光储、分布式电源(distributed generation, DG)共同参与联合寻优。提出一种孤岛路径寻优方法,可快速求得非故障失电区内各时段最优恢复路径。设计多目标蚁群算法获取全网最优抢修方案,以IEEE 69节点配电系统为例,证明所提策略的可行性和有效性。
In the active distribution network (ADN), the photovoltaic (PV) output has fluctuations and the load power has real degeneration. When multiple faults occur in the distribution network, the repair time is longer, and PV may have insufficient output during certain periods of time. The power supply is restored to as many important loads as possible in the non-faulty power loss zone. This paper combines the short-time scale optimization scheduling method to establish the dynamic repair model for the ADN sub-area, sub-scenario, and time-segment based on a multi-agent system (MAS). In the model, the maximum load value of the network and the minimum number of switching times are the upper targets; the maximum recovery power of the different recovery periods and different switching times in the non-faulty power loss zone is the premise, and the maximum total power supply is the lower target; and PV and battery storage and distributed generation participate in joint optimization. An island path optimization method is proposed, which can quickly find the optimal recovery path in each period of non-faulty power loss zone. The multi-objective ant colony algorithm is designed to obtain the optimal repair scheme for the whole network. The IEEE 69-point distribution system is taken as an example to prove the feasibility and effectiveness of the proposed strategy.
主动配电网(ADN) / 多故障 / 动态修复 / 多代理技术
active distribution network (ADN) / multiple faults / dynamic repair / multi-agent technology
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