提出一种基于改进天鹰优化算法的配电网分布式光伏并网功率控制方法。利用物联网技术实时采集分布式光伏并网与配电网的关键数据,包括电流、电压、天气状况以及负荷情况等。采用广义预测控制方法,结合这些关键数据,构建功率预测控制模型,并充分考虑光伏系统动态特性和电网的波动等因素,通过运用丢番图方程等推导出光伏并网系统未来时刻的输出预测值,实现对未来一段时间内光伏系统的电压波动与闪变的有效预测。基于预测结果和电网的实际需求,设计滚动优化策略,进一步优化功率控制,以应对电网的高度动态性和不确定性。引入改进天鹰优化算法,根据预测结果和电网的实际需求,优化逆变器的调控参数。通过调整这些参数,减少输出功率的波动,进而降低电压波动与闪变的程度,实现分布式光伏并网功率的精确预测与控制。实验结果表明：相较于后置性控制的对比方法,所提方法在配电网分布式光伏并网功率控制方面表现出更优越的效果,且适用于实际应用场景。
Abstract
This paper proposes a power control method for distributed photovoltaic （PV） grid-connected systems in distribution networks based on an improved aquila optimizer. This method uses internet of things （IoT） technology to collect real-time key data from distributed PV grid connection and distribution networks, including current, voltage, weather conditions, and load status. A generalized predictive control method is adopted to construct a power prediction control model by integrating these key data. The method fully considers the dynamic characteristics of PV systems and grid fluctuations, and derives future output predictions of the PV grid-connected system by employing the Diophantine equation, thereby achieving effective prediction of voltage fluctuations and flicker over a future period. Based on the prediction results and actual grid requirements, a rolling optimization strategy is designed to further optimize power control in response to the high dynamics and uncertainty of the power grid. An improved Aquila optimizer is introduced to optimize the regulation parameters of the inverter according to the prediction results and actual grid demands. By adjusting these parameters, the method reduces output power fluctuations, subsequently alleviates voltage fluctuations and flicker, and achieves accurate prediction and control of distributed PV grid-connected power. Experimental results show that, compared with the proactive control method, the proposed method exhibits superior performance in power control of distributed PV grid connection in distribution networks and is suitable for practical application scenarios.
关键词
功率控制 /
预测控制 /
优化算法 /
天鹰算法 /
配电网 /
物联网技术
Key words
power control /
predictive control /
optimization algorithms /
aquila optimizer /
distribution network /
internet of things technology
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基金
国网江苏省电力有限公司孵化科技项目（JF2023026）
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