Application of autonomous smart inverter Volt-VAR function for voltage reduction energy savings and power quality in electric distribution systems

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

doi:10.1109/isgt.2017.8085991

Cited by 29 publications

(19 citation statements)

References 6 publications

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“…The study was done to minimize the active power loss of the system while keeping the operation of the voltage control devices and the total harmonic distortion within limits. An NREL report and publication by Ding et al [21,22] developed an iterative optimization algorithm that coordinates the use of SI, LTCs and capacitor banks for optimal voltage regulation while implementing conservative voltage reduction technique as a means of mini zing the total system losses. The impact of the proposed optimization algorithm on the system's power quality was also analyzed.…”

Section: Voltage Control and Optimization Methodsmentioning

confidence: 99%

A Multi-Objective Voltage Optimization Technique in Distribution Feeders with High Photovoltaic Penetration

Olowu¹,

Jafari²,

Sarwat³

2019

Adv. sci. technol. eng. syst. j.

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With increasing photovoltaic (PV) penetration on distribution feeders, voltage functions is always a challenge. To control these variations due to the intermittent nature of PV generation,many utility companies use the traditional voltage regulating devices such as ON/OFF load tap changers, voltage regulators, switched capacitor banks and reactors. The use of smart inverters (SI) has been reported to provide a more effective and economical way of voltage regulation on distribution feeder. It then becomes necessary to optimally control the operation of these traditional voltage regulating devices and the SIs. This paper presents a multi-objective technique that minimizes the voltage fluctuation (by implementing conservative voltage reduction), the overall system active power losses and the amount of reactive power injection from the capacitor banks with various constraints on the smart inverter reactive power injection and voltage regulator switching. The proposed algorithm is tested on an IEEE 34 node system with six units of 300kW (400kV A SIs) PVs integrated using real data from an existing 1.4MW PV plant located at FIU. The Pareto optimization results of the proposed algorithm show the various optimal values of the PVs power factor, the voltage regulator settings and the capacitor reactive power injection. Using one of the Pareto optimal solutions, the results show that the system bus voltage profiles, total system power losses and capacitor reactive power injection were effectively optimized.

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Section: Voltage Control and Optimization Methodsmentioning

confidence: 99%

A Multi-Objective Voltage Optimization Technique in Distribution Feeders with High Photovoltaic Penetration

Olowu¹,

Jafari²,

Sarwat³

2019

Adv. sci. technol. eng. syst. j.

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“…Therefore, the sampling time must be chosen as high as compatible with no significant loss of information on the voltage fluctuation. In addition, a global metrics that quantifies the voltage fluctuation of the grid is presented in [32], the system average voltage fluctuation index (SAVFI):…”

Section: Voltage Fluctuation Metricsmentioning

confidence: 99%

Voltage Impact of a Wave Energy Converter on an Unbalanced Distribution Grid and Corrective Actions

et al. 2017

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Renewable energy is steadily increasing its penetration level in electric power systems. Wind and solar energy have reached a high degree of maturity, and their impacts on the grid are well known. However, this is not the case for emerging sources like wave energy. This work explores the impact of the fluctuating power injected by a wave energy converter on the distribution grid voltage and proposes a strategy for mitigating the induced voltage fluctuations. The paper describes the mechanics of how a fluctuating active power injection leads to grid voltage fluctuations and presents an unbalanced three-phase power flow tool that allows one to quantitatively analyze the voltage evolution at every phase and bus of a distribution grid driven by this power injection. The paper also proposes a corrective action for mitigating the voltage fluctuations that makes use of the hardware resources already available in the wave energy converter, by means of a control strategy on the reactive capability of the grid-side inverter. The use of a STATCOM as additional reactive compensation equipment is also explored. The effectiveness of the proposal is assessed in the IEEE 13-bus test feeder showing that, in some cases, the wave energy converter by itself is able to mitigate the voltage fluctuations that it causes. If not, a STATCOM can provide the extra reactive capability needed.

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“…Control strategies of smart inverters connected to numerous distributed PV systems have been proposed, and the same VVC was assigned to multiple PV systems [18]; however, the effect of the service transformer on the voltage was not considered when analyzing the VVC performance. The VVC was also analyzed for energy saving and power quality [19]; however, the overall performance was not obtained. VVC effects on a system with high PV penetration and smart inverters have been analyzed [20]; however, default settings were used for the impact analysis in the study.…”

mentioning

confidence: 99%

“…The proposed algorithm optimizes the VVC to set the parameters of the smart inverters in PV systems devoted to distributed generation. [19], [21]- [25], [28], [30], [32], [34], [36], [42])…”

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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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Application of autonomous smart inverter Volt-VAR function for voltage reduction energy savings and power quality in electric distribution systems

Cited by 29 publications

References 6 publications

A Multi-Objective Voltage Optimization Technique in Distribution Feeders with High Photovoltaic Penetration

A Multi-Objective Voltage Optimization Technique in Distribution Feeders with High Photovoltaic Penetration

Voltage Impact of a Wave Energy Converter on an Unbalanced Distribution Grid and Corrective Actions

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

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