Photovoltaic Impact Assessment of Smart Inverter Volt-VAR Control on Distribution System Conservation Voltage Reduction and Power Quality

Ding, Fei; Nagarajan, Adarsh; Chakraborty, Sudipta; Baggu, Murali; Nguyen, Andu; Walinga, Sarah; McCarty, Michael; Bell, Frances

doi:10.2172/1337541

Cited by 48 publications

(21 citation statements)

References 7 publications

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“…In this problem, only one load scaling factor has been used, and clusters were created over year‐long hourly data given in [14]. Similarly, the cluster was created from year‐long hourly data of solar irradiation given in [15]. The number of clusters was estimated by the elbow point method, and it was found as seen in Fig.…”

Section: Solution Methodologymentioning

confidence: 99%

An intelligent methodology to improve distribution system operational parameters utilising smart inverter functionalities of PV sources

Verma

Eluvathiangal

2019

J. eng.

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Section: Solution Methodologymentioning

confidence: 99%

An intelligent methodology to improve distribution system operational parameters utilising smart inverter functionalities of PV sources

Verma

Eluvathiangal

2019

J. eng.

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“…With intrusions, the normal operating limits of the grid can be above or below the limit. The upper and lower limits for voltage stability endorsed by American National Standards Institute (ANSI) are 1.05 pu and 0.95 pu, respectively [37]. As shown in Figure 9, bus 6 is clearly below the limit while bus 2 and bus 3 are very close to the lower standard limit for Scenario B. IEEE guidelines and operational safety require a response and action to be taken by the operators to rectify such situations.…”

Section: Comparison Of the Two Scenariosmentioning

confidence: 99%

Real Time Security Assessment of the Power System Using a Hybrid Support Vector Machine and Multilayer Perceptron Neural Network Algorithms

2019

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In today’s grid, the technological based cyber-physical systems have continued to be plagued with cyberattacks and intrusions. Any intrusive action on the power system’s Optimal Power Flow (OPF) modules can cause a series of operational instabilities, failures, and financial losses. Real time intrusion detection has become a major challenge for the power community and energy stakeholders. Current conventional methods have continued to exhibit shortfalls in tackling these security issues. In order to address this security issue, this paper proposes a hybrid Support Vector Machine and Multilayer Perceptron Neural Network (SVMNN) algorithm that involves the combination of Support Vector Machine (SVM) and multilayer perceptron neural network (MPLNN) algorithms for predicting and detecting cyber intrusion attacks into power system networks. In this paper, a modified version of the IEEE Garver 6-bus test system and a 24-bus system were used as case studies. The IEEE Garver 6-bus test system was used to describe the attack scenarios, whereas load flow analysis was conducted on real time data of a modified Nigerian 24-bus system to generate the bus voltage dataset that considered several cyberattack events for the hybrid algorithm. Sising various performance metricion and load/generator injections, en included in the manuscriptmulation results showed the relevant influences of cyberattacks on power systems in terms of voltage, power, and current flows. To demonstrate the performance of the proposed hybrid SVMNN algorithm, the results are compared with other models in related studies. The results demonstrated that the hybrid algorithm achieved a detection accuracy of 99.6%, which is better than recently proposed schemes.

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“…Fig. 5 shows a generic VVC that provides reactive power (Qs,i) according to the voltage at the point of common coupling (V(t)) as detailed in (1) [22], [26], [33], [48]. When the voltage exceeds an upper level, the PV system prevents further voltage increase by absorbing reactive power.…”

Section: B Smart Inverter Vvcmentioning

confidence: 99%

“…Without the VVC, the voltage at the feeder end most affected by the PV system surpasses the upper (1.039 pu) and lower levels (0.908 pu). The VVC default setting (level 1) in the smart inverter ( [22], [33], [48]) facilitates the mitigation of the deviations in voltage, which remain within the normal operating range. However, as the VVC not only affects the voltage but also the system loss and PV output, the optimal setting considering tradeoffs between various factors should be determined, as detailed in Section II-C. , ( ( ) A PV system produces energy according to the solar irradiance and weather conditions.…”

Section: B Smart Inverter Vvcmentioning

confidence: 99%

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

2020

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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

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Photovoltaic Impact Assessment of Smart Inverter Volt-VAR Control on Distribution System Conservation Voltage Reduction and Power Quality

Abstract: NREL prints on paper that contains recycled content. 1. Providing an easy-to-use tool for modeling the impact of distributed PV with smart inverters in a utility voltage reduction scheme.

Cited by 48 publications

References 7 publications

An intelligent methodology to improve distribution system operational parameters utilising smart inverter functionalities of PV sources

An intelligent methodology to improve distribution system operational parameters utilising smart inverter functionalities of PV sources

Real Time Security Assessment of the Power System Using a Hybrid Support Vector Machine and Multilayer Perceptron Neural Network Algorithms

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

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