Large-scale Photovoltaic Penetration Research Articles

Integration method of large-scale photovoltaic system in distribution network based on improved multi-objective TLBO algorithm

The increasing integration of distributed generations brings great challenges to the power grid. In this paper, a distributed photovoltaic (PV) integration methodology in distribution network is established for large-scale PV penetration. Firstly, a PV integration model was formulated with the aim of maximizing PV integration capacity and enhancing the voltage profile. Specially, the PV large-scale integration model for county-wide promotion is proposed by considering various typical integration scenarios. Additionally, a novel improved multi-objective Teaching-Learning based optimization (TLBO) algorithm, namely, IM-TLBO, was proposed to seek an optimal Pareto front of the PV integration model. The IM-TLBO algorithm innovatively incorporates the elite reverse learning search strategy to enhance exploration in the solution space. Moreover, the differentiated teaching guided by optimal individual and central location is employed to improve the efficiency of the “teaching” process. Meanwhile, a cyclic crowded sort deletion based on crowding distance is developed to enhance the diversity of elite individuals and the distribution characteristics of the Pareto Frontier. Finally, the performance of IM-TLBO is tested in benchmark functions. Also, a simulation case in IEEE 33 bus system is performed to verify the proposed PV integration method. It is observed that the proposed method in this paper can not only realize the overall optimal integration of roof distributed PV, but also improve voltage profile. The results of IM-TLBO are compared to other classical algorithms, and it is shown that IM-TLBO outperformed them in terms of convergence, distribution and diversity.

Stability of distribution network with large-scale PV penetration under off-grid operation

Large-scale photovoltaic (PV) penetration reduces system damping and causes stability problems on off-grid distribution systems. The single-machine equivalent method is typically used to simplify the full-order model by ignoring the differences in PVs. However, this results in substantial errors. When the differences between the control models of PVs are greater, the calculation results lead to greater error. Then the stability of the PV system cannot be judged. This study validated that the oscillation mode of the PV in the subsynchronous frequency band has a linear correlation with the operating conditions and control parameters. Accordingly, the authors proposed to divide the oscillation mode interval by taking the maximum and minimum PV operating conditions as the boundary to assess the stability of the system. Considering the difference in the operating conditions of the PVs, the average power was used to dynamically aggregate the PV electric farm to determine whether the system was at risk of instability. Finally, the effectiveness of the proposed method was verified through case studies on a 12-PV off-grid distribution system and a large-scale PV farm system. The verification results show that the proposed method can determine the stability of the distribution system accurately. The proposed method is significant for developing strategies to enhance the stability of off-grid distribution systems.

Application of PMUs to monitor large-scale PV penetration infeed on frequency of 60 Hz Japan power system: A case study from Kyushu Island

Recently, the large-scale photovoltaic (PV) penetration brings serious issues for system operators in monitoring and maintaining grid stability. The intermittent power output and loss of inertia property from high PV penetration critically affect the balance between generation and demand. These issues lead to larger frequency oscillations, generation tripping, and cascading failures. To overcome these challenges, this paper proposes an advanced application of phasor measurement units (PMUs) for monitoring dynamics of the system in the presence of high PV penetration. During such penetration, the data-driven analysis of the rate of change of frequency (ROCOF) is of great importance, reflecting the system stability. This approach is first demonstrated on Kyushu Island, and PMUs are installed at the domestic supply side. The contrastive scenarios of high-resolution PMUs recorded in each region are analyzed against different levels of PV penetration. The results reveal that monitoring system frequency and ROCOF variations not only indicate the oscillatory behavior of the grid, but also provide useful information for the wide-area dynamic stability. This paper also offers a recent overview of large-scale PV penetration infeed on frequency of the 60 Hz Japan power system, and it can be useful for improving a range of diagnostic/monitoring and control functionalities.

Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production

In recent years, the penetration of photovoltaic (PV) generation in the energy mix of several countries has significantly increased thanks to policies favoring development of renewables and also to the significant cost reduction of this specific technology. The PV power production process is characterized by significant variability, as it depends on meteorological conditions, which brings new challenges to power system operators. To address these challenges, it is important to be able to observe and anticipate production levels. Accurate forecasting of the power output of PV plants is recognized today as a prerequisite for large-scale PV penetration on the grid. In this paper, we propose a statistical method to address the problem of stationarity of PV production data, and develop a model to forecast PV plant power output in the very short term (0–6 h). The proposed model uses distributed power plants as sensors and exploits their spatio-temporal dependencies to improve forecasts. The computational requirements of the method are low, making it appropriate for large-scale application and easy to use when online updating of the production data is possible. The improvement of the normalized root mean square error (nRMSE) can reach 20% or more in comparison with state-of-the-art forecasting techniques.