Optimal Volt–Var Curve Setting of a Smart Inverter for Improving Its Performance in a Distribution System

Abstract

We propose an algorithm to set the parameters of volt-var curves to improve the performance of distributed generation. Specifically, we optimally set the volt-var curves of the smart inverter for proper control of the distributed generation output. To improve the overall distribution system performance, we consider the minimization of voltage deviation, system loss, and peak of reactive power in an objective function, thereby providing optimal volt-var curve parameters. The proposed algorithm overcomes various limitations of existing studies. First, we use distribution system factors in a multiobjective optimization function to adjust volt-var curve parameters that deviate from the default settings. Second, we consider the service transformers that affect the system voltage according to the photovoltaic generation output in the test model during optimization. Third, we analyze the system improvement according to the number of parameters to be optimized. Fourth, we analyze the implementation of the optimized volt-var curve according to the practical periods when the smart inverter settings can be updated. Fifth, we evaluate different suitable voltvar curves for multiple photovoltaic generators grouped using clustering. Using the proposed algorithm, the distribution system operator can set the optimal volt-var curve according to the system conditions and requirements. We conducted simulations of the proposed method using OpenDSS, a quasistatic time-series simulation, on a test model reflecting the characteristics of a South Korean distribution system. The simulation results confirm that the overall system performance increases when the optimal volt-var curve settings obtained from the proposed algorithm are used. INDEX TERMS Distribution system, parameter optimization, smart inverter, volt-var curve. NOMENCLATURE i Bus index t Time (h) (year: 8760 h; spring: 2208 h; summer: 2208 h, fall: 2184 h; winter: 2160 h) vvnew Parameter (gene) for newly updated volt-var curve (Vvv2, Vvv3, Qvv1, Qvv4) vvcon Conventional volt-var curve parameter in Fig. 5 Pt(vv) System loss at time t according to volt-var curve parameter Vref Reference voltage, 22.9 kV, 1.00 pu ω Weight for system factor (voltage deviation, system loss, peak of reactive power) Vi,t Voltage of bus i at time t according volt-var curve parameter δVi,t(vv) − , () , voltage deviation index of bus i at time t Qi(vv) Reactive power output at smart inverter of bus i according to volt-var curve parameter Nconst Constraint condition ωc Penalty coefficient PFc Constraint weight E Disjoint subset of input pattern l Clusters index k Number of clusters Si Set of points belonging to cluster i µi Center of cluster i O• Objective function I. INTRODUCTION

Efficient Day-Ahead Scheduling Voltage Control Scheme of ULTC and Var of Distributed Generation in Distribution System

“…The reactive power control range of DG is determined by the inverter capacity of DG and the power factor limit magnitude. Regardless of the active power, the reactive power can be controlled independently within the setting range, and active power control of DG is impossible [31]. In addition, in this paper, the reactive power control variables of DG are divided according to sections.…”

Appropriate Volt–Var Curve Settings for PV Inverters Based on Distribution Network Characteristics Using Match Rate of Operating Point

“…360 candidate patterns are defined by the central voltage V ref , dead zone, and slope, and cover almost all the volt-var curves that may be applied in practice. As another solution, the volt-var curve can be determined by solving the volt-var curve optimization problem [24][25][26][27], which is one of state-of-the-art counterparts to determine the volt-var curve. However, the solution obtained should be equal to or very close to one of the 360 patterns.…”

“…The specification of the volt-var curve has been discussed as an evaluation index for the hosting capacity of PV generation based on the computer simulation using actual distribution lines in California [23]. An optimization method based on a genetic algorithm has been proposed to optimize the setting of the volt-var function, and it was shown that the control characteristics of the distribution system can be improved [24][25][26]. A method to determine the volt-var curve based on the relationship between the optimum reactive power and the inverter voltage using the interior point method has been proposed [27].…”

Distributed Volt-Var Curve Optimization Using a Cellular Computational Network Representation of an Electric Power Distribution System

“…The current state of art optimizes the volt-var curves over a long time horizon, but the optimization is not able to address the temporal dynamics in a minutes time frame [22]. Additionally, the state-of-the-art approaches are based on central optimization [23], which limits the applicability of such a method [24] in many practical systems. The proposed enhances the flexibility of operation by considering the spatial and temporal dynamics of the distribution system and optimizing the system in a granular, distributed manner.…”