Optimal Smart Inverters Volt-VAR Curve Selection with a Multi-Objective Volt-VAR Optimization using Evolutionary Algorithm Approach

Jafari, Mohamadsaleh; Olowu, Temitayo O.; Sarwat, Arif I.

doi:10.1109/naps.2018.8600542

Cited by 51 publications

(27 citation statements)

References 9 publications

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“…Smart grid is the advanced power and energy system that has been transformed from unidirectional power flow to bidirectional power flow. Moreover, it is employed with information communication technology among its entities for electricity supply to the consumers with reinforced control and efficiency [1,2,3,4]. Advanced metering infrastructure (AMI) is an critical building block of smart grid as it builds communication bridge between metering data management service (MDMS) and consumer meters for consumption data transfer utilizing wireless networks [5,6,7,8,9].…”

Section: Introductionmentioning

confidence: 99%

A Spectrum Sharing based Metering Infrastructure for Smart Grid Utilizing LTE and WiFi

Parvez¹,

Sarwat²

2019

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

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GHz AMI cognitive radio LTE-U CBRS spectrum sharing Internet of Things (IoT) smart meter licensed assisted access (LAA) WiFiIn advanced metering infrastructure (AMI) of smart grid, WiFi is an appropriate choice for its bidirectional communication requirement to transmit data to the billing center. But, WiFi functions in the free spectrum bands and LTE also requires to use the same free bands for its network expansion being licensed spectrum is limited and expansive. LTE and WiFi can operate simultaneously in the 3.5 GHz band (also known as citizen broadband radio service (CBRS)), which has large amount of free and clean spectrum. In this paper, we propose a smart grid metering infrastructure based on fixed duty cycled LTE and WiFi, where smart meters and its' data collectors (known as Access Point) use WiFi and LTE, respectively, for transferring data. Under a system level simulation environment, we investigated the LTE-WiFi coexistence performance in CBRS band considering a time division duplexing (TDD)-LTE associated with FTP traffic, and IEEE 802.11n (WiFi). The simulation performance demonstrates a good neighborhood coexistence between WiFi and LTE, which makes it a potential communication solution for the AMI.

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Section: Introductionmentioning

confidence: 99%

A Spectrum Sharing based Metering Infrastructure for Smart Grid Utilizing LTE and WiFi

Parvez¹,

Sarwat²

2019

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

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“…Of these issues, voltage control is one of the most important and of concern to utility companies. Traditionally, utility companies uses the legacy devices such as reactors, voltage regulators (VR), ON/OFF load tap changers (OLTC), and switched capacitor bank for voltage control [13,14]. With the integration of intermittent renewable energy resources such as PV systems, the need for fast and more effective voltage control becomes imperative.…”

Section: Introductionmentioning

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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“…In [29], the volt-var-watt function was employed to improve system performance; although the curve parameters were optimized, the performance improvement was limited because the system factors were not considered via a multiobjective function. Four objective functions and a genetic algorithm were used for VVC optimization in [30], but the optimization settings were not maximized because the weights were inappropriate.…”

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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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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Optimal Smart Inverters Volt-VAR Curve Selection with a Multi-Objective Volt-VAR Optimization using Evolutionary Algorithm Approach

Cited by 51 publications

References 9 publications

A Spectrum Sharing based Metering Infrastructure for Smart Grid Utilizing LTE and WiFi

A Spectrum Sharing based Metering Infrastructure for Smart Grid Utilizing LTE and WiFi

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

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

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