Improvement of Power Quality and Optimization Techniques in Power Distributed Network using Photovoltaic Inverter Control

Abstract

Power quality is a critical aspect of any electrical distribution network, and with the increasing penetration of photovoltaic (PV) systems in power generation, ensuring optimal power quality becomes even more crucial. This abstract presents an overview of the improvement of power quality and optimization techniques in power distributed networks using photovoltaic inverter control. The integration of PV systems into the power grid brings several challenges, including voltage fluctuations, harmonic distortions, and reactive power imbalances. These issues can degrade the power quality and affect the performance of other connected loads. Therefore, effective control strategies are necessary to mitigate these problems and enhance the power quality in distributed networks.

Introduction

I. INTRODUCTION

As one of the greatest innovations in human history, the electrical grid is an interconnected network of generators, high voltage transmission lines, and distribution facilities for delivering electricity from producers to individual consumers. The distribution system is a part of the power system, existing between distribution substation and power consumers. The traditional distribution grids were originally built to unidirectional delivery power to cope with slowly-varying customer loads.

Due to the growing concern for the environment and the increasing load demand, more and more attention has been given to renewable energy sources. In the USA, at the end of the third quarter of 2017, there was 49.3 GW of cumulative solar electric capacity

The cost of solar photovoltaic (PV) is already competitive with fossil fuels in some markets around the world. As the solar PV industry scales, the price is expected to further decline in coming decades. The increasing penetration of distributed PV generation presents both challenges and opportunities for distribution networks [2].

Due to the cloud movement, the solar power is highly intermittent and hard to predict. Figure 1.1 shows plots of solar irradiance data for two typical clear and cloudy days. This kind of rapidly varying irradiance conditions introduces several challenges. For example, PV generation can cause node voltage rise.

Although the intermittent nature of the PV generation poses a number of significant challenges at the distribution level, at the same time, the PV generation units can actually contribute to the improvement of the power quality and also serve to save energy. If combined with appropriate Volt/Var control algorithms, the PV DC-AC inverters can address the challenges in high penetration of renewable resources. The DC-AC inverters are power electronic devices that are used to couple DC to the AC grid. The primary function of the inverter is to deliver the DC power to the AC side as efficiently as possible. At the same time, many inverters already deployed today can provide a new reactive power management by injecting or absorbing reactive power into or from the grid, respectively. When the capacity of the PV inverter is not fully used by the real power delivery, it can work as Var regulation device [5]. The reactive power of PV inverters can be changed continuously with extremely fast speed (in milliseconds). A large number of recent studies [81 in the literature have explored the possibility of utilizing PV inverters to improve the power quality of distribution systems with high renewable penetration levels. As the share of the intermittent sources increase in the future, PV inverters are likely to take over the grid tasks together with the conventional control devices, such as OLTCs and SCs.

The location and size of PV inverters play a vital role in voltage quality and power losses of distribution systems. The traditional Deterministic Load Flow (DLF) analysis, which uses specific values of power generation and load demand, is unable to capture the variations and uncertainties of the PV output and load demand.

The results calculated from DLF may be unrealistic. Contrast to the DLF, the Probabilistic Load Flow (PLF) is first proposed in 1974 [91 and has been applied in power system short-term and long-term planning as well as other areas [10]. The inputs of the PLF are formulated with Probabilistic Density Function (PDF) or Cumulative Distribution Function (CDF) to calculate system states. The outputs of PLF are in terms of PDF or CDF to include and reflect the uncertainties of the system. In this dissertation, the PLF method is used to assess the optimal location and capacity of the PV generation units.

II. LITRATURE REVIEW

It should be noted that the literature papers deals with the Distributed Generations (DGs) placement problem in two different ways: analytical and numerical. In [14], an analytical approach to place distributed generation (DG) to minimize the power loss of the system is presented. Because the load flow equations are non-linear, they must be linearized in order to make the convolution solvable. The common linearization methods are based on Gram-Charlier expansion and CornishFisher expansion. Due to the approximation process, the analytical method, while quite mathematically elegant, may not be suitable to perform on a complicated system with large nonlinearities.

The numerical method performs a large number of DLFs with inputs determined from the samplings of the random variables in the PLF. The most common numerical method is the Monte Carlo method. A Monte Carlo based method for optimum allocation of PV generation is presented in [15] to minimize the power loss. Given the time-varying nature of the load and the intermittent nature of solar energy, the Monte Carlo method is a good approach when uncertainties are involved in the optimal allocation of PV generation problems. In the Monte Carlo Simulation (MCS) method, every sample value is accurate without any approximation. The accuracy and convergence of MCS are guaranteed by the probabilistic limit theory. In some papers, the distributions of the PV generation output and load values are assumed to have a predefined probability density function, such as the uniform distribution in [151, the Gaussian distribution in [161, or the Weibull distribution in [17], which are difficult to perform on a realistic system. OPF problem seeks to optimize a certain objective function, such as voltage fluctuation, power loss, and/or utility elements cost, subject to power balance, Kirchhoff’s law, as well as capacity, stability and operation constraints on the voltage and power flows.

There has been a great deal of research on OPF since first formulated by Carpentier in 1962 [l I l. OPF problem is generally nonlinear, nonconvex, and NP-hard. A large number of relaxations and optimization algorithms have been proposed to solve this problem [18].

A popular approximation of OPF is the DC power flow problem. The OPF problem is linearized and therefore easy to solve [19], [20]. Nevertheless, this method is not adequate enough to solve the OPF problem with non-smooth objective functions. In [21], a coordination strategy to minimize the total number of tap changer operations is proposed. The interior point method is applied to solve this optimization problem. However, the interior point method is not suitable to solve the non-convex problems due to the global optimum cannot be guaranteed.

Instead of solving the OPF problem directly, the authors in [221 propose a method to solve the convex Lagrangian dual problem of the original OPF problem. This provide a way to determine for sure if a power flow solution is globally optimal for the nonconvex problem. The authors in [23] present a systematic approach to determine the active and reactive power set points for PV inverters. The sparsity promoting regularization and semidefinite relaxation techniques are applied to reformulate the original OPF problem. The limitation of semidefinite relaxation for OPF is presented in [24]. It is proved that the sufficient condition of [22] always holds for radial networks.

However, as a line flow constraint is tightened, the sufficient condition fails to hold for some mesh networks. Hence it is important to develop systematic methods for solving OPF involving radial networks and mesh networks. In [25], the backtracking search optimizer algorithm is applied to solve the reactive power dispatch problem, but only a single time-point is considered in the optimization problem.

III. METHODOLOGY AND DATA ANALYSIS

The procedure of identifying the optimal location of a PV plant is depicted in Figure. Identifying the optimal location of PV plant is considered as a system planning task which requires high accuracy, so we use full enumeration to ensure the global optimal result. Every possible location is tested in this simulation. The process of identifying the optimal PV location is repeated until the constraints on the bus voltages are satisfied.

Conclusion

The integration of photovoltaic (PV) systems into power distributed networks presents both opportunities and challenges. While PV systems offer clean and renewable energy generation, their integration can introduce power quality issues such as voltage fluctuations, reactive power imbalances, and harmonic distortions. This research focused on the improvement of power quality and optimization techniques by employing effective control strategies for PV inverters. Through the implementation of various optimization techniques, significant advancements have been achieved in power quality parameters. The use of maximum power point tracking (MPPT) algorithms ensures optimal power extraction from PV panels, maximizing energy generation and improving system efficiency. Voltage regulation techniques maintain a stable output voltage from the PV inverter, mitigating voltage fluctuations and ensuring a steady power supply. Reactive power control plays a crucial role in balancing the reactive power flow between the PV system and the grid. By dynamically adjusting reactive power injection, the power factor can be optimized, leading to efficient power transmission and reduced losses. Harmonic mitigation techniques effectively suppress harmonics generated by the PV inverters, minimizing distortion in voltage and current waveforms.

References

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Copyright

Copyright © 2024 Divyanshu Soni, Abhishek Arya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.