Research On Voltage Optimization Control Method Of Distribution Network With High PV Penetration

In recent years,with the shortage of traditional energy sources and the growth of power demand,the power grid has been driven toward green,safe,efficient and intelligent gird.As a clean and renewable energy source,large-scale photovoltaic(PV)are connected to the distribution network to mitigate energy shortage and reduce power supply pressure.However,PV brings many challenges to the safe and economic operation of the distribution network due to its characteristics of fluctuation and uncertainty the randomness.In this paper,considering the case of a large number of PVs connected to the distribution network,the PV and traditional equipment adjustment capabilities are fully explored,and the distribution network optimization control method is studied.It is designed to solve the voltage over-limit problem in active distribution network and improve the ability to accept PV.The following research work is carried out around multi-level optimization control method of distribution network with high PV penetration:1)Taking voltage as dividing standard,the distribution network is divided into high/medium/low voltage distribution networks.The adjustment devices at different voltage levels are introduced.Firstly,the adjustment principles of PV,capacitor banks(CB)and on-load tap-changers(OLTC)are analyzed and modeled separately.Secondly,several methods for establishing the load model are presented,and the constant power model is selected as the load model for the subsequent optimization control method.Then,taking the medium voltage distribution network as an example,the distribution network model is established to analyze the influence of different adjustment equipment on the voltage and the reactive power of the system.Finally,the sensitivity calculation method is introduced to analyze the influence of the adjustment equipment on the distribution network.2)In distributed PV optimization control,due to the lack of data collection and real-time statistics,the established low-voltage distribution network optimization control model is not accurate,and the calculated adjusted amount of PV has errors.Based on the extreme learning machine,an optimal PV control method considering model error in distribution network is proposed.Firstly,with the minimum voltage deviation and the lowest adjustment cost as the optimization objectives,a rough calculation model of PV regulation is established considering the safety constraints of the distribution network operation.Secondly,using the online sequential extreme learning machine,the PV control assistant decision model is established.Based on the above two models,the process of PV optimization control with assistance of artificial intelligence is designed.Finally,the proposed control method is verified in a distribution network system with PVs.The results show that the proposed control method can improve the control precision.3)In the multi-device coordination optimization control,the traditional control method is mostly based on the nine-zone control strategy.Due to the discreteness of traditional equipment and slow response,it is difficult to quick response and meet the requirement with high precision.Taking not changing original control strategy of substation as goal,a nonintrusive control strategy of voltage and reactive power based on nine-zone diagram control strategy for distribution systems is developed in this paper.Firstly,the PV control system is added on the basis of the nine-zone control system,and the non-intrusive reactive voltage control framework is established.Secondly,by using the sensitivity values Q-U curve of multiple devices,the reactive power output of PVs is calculated in the PV control time domain.Simultaneously,the voltage reference value in PV inverter control(PVC)horizon can be tracked based on feedback.Finally,a case study is carried out on an IEEE 33-node system to illustrate the effectiveness of the proposed method.It is shown that PV can be used as a continuous reactive power source to realize fast and accurate regulation and the proposed method can improve adjustment accuracy and reduce the operation of conventional equipment.