24th International Conference on Electricity Distribution Glasgow, 12-15 June 2017

Paper 0678

CIRED 2017 1/5

DISTRIBUTION VOLTAGE MONITORING AND CONTROL UTILIZING SMART METERS

Yoshihito. KINOSHITA Kazunori. IWABUCHI Yasuyuki. MIYAZAKI Toshiba – Japan Toshiba – Japan Toshiba – Japan yoshihito.kinoshita@toshiba.co.jp kazunori.iwabuchi@toshiba.co.jp yasuyuki.miyazaki@toshiba.co.jp

ABSTRACT Conventional distribution voltage control system does not monitor and control wide area of low voltage (LV). In the case that LV near the end of the distribution line is deeply dropped due to heavy load, there is the potential that LV surpasses the upper or lower limit of distribution voltage. To regulate LV within the limits, the voltage control is required to improve its performance. On the other hand, smart meter which is device reporting the voltage and energy consumption at each customer is installed in various regions. To regulate LV within the voltage limits, the control which utilizes LV measured by smart meters is proposed in this paper. As a problem about the proposed control, the control requires to select meters for monitoring and controlling the voltages in all LV networks. Therefore, the selection method of smart meters and voltage control utilizing smart meters is studied, and the performance of the voltage control is evaluated.

INTRODUCTION There is a problem that LV near the end of the distribution line is deeply dropped due to heavy load. Conventional control without measuring the LV drop near the end of the line is not able to resolve the LV problem. On the other hand, smart meter which is device reporting voltage and energy consumption at each customer is installed in various regions. In this paper, to resolve the LV problem, the voltage control considering LV measured by smart meters is studied. As a problem about the proposed control, the sampling time to collect the measured voltages from meters depends on the number of the meters applied to the control. To shorten the sampling time, several tens of representative meters for the control are selected under constraints to enable the control to observe all LV conditions. To select the meters, a method to select the meters having measured the highest voltage or the lowest voltage at the end of network is proposed [1]. However, the meters do not always measure the highest or the lowest voltage in the meters and the voltage control does not measure the voltage profile of the other meters. Hence, this voltage control might not regulate the all voltages in all LV networks and be not able to optimize the outputs of voltage controllers such as transformer and static condenser. Thus, in this study, several tens of meters are selected as representative meters, and all voltages of the meters are estimated by the measured voltages of the representative meters. In addition, the voltage control regulates all estimated voltages. In a simulation, the performance of the voltage control is evaluated.

VOLTAGE CONTROL UTILIZING SMART METERS

A Concept of selection method of smart meters and voltage control Voltage trends measured by the nearby meters have similar voltage characteristics because impedances between the meters are low due to short distance. Therefore, the representative meters are selected from the meters with similar voltage characteristics. These representative meters enable to estimate the all voltage in LV networks without the others by similar characteristics. Here, Process of the meter selection and the voltage control is described as follows and the contents of each process are described from next section in detail. ・Process of meter selection method (Figure 1) (This process is executed about once a month.) Step S1: Previous voltages measured by all meters are

collected in a database. Step S2: The time series of LV in the database are

categorized on the basis of similarity of wave form.

Step S3: Representative meters for the voltage control are selected from each category in step S2.

Step S4: To estimate the voltages of the other meters from the voltages of the representative meters, coefficients of voltage relational equations between the representative meter and the others are calculated.

・Voltage control utilizing representative meters (This control is executed at all times.) Step V1: The voltages of the representative meters are

only collected. Step V2: The voltages of the other meters are estimated

by using the voltage equations from the voltages of the representative meters.

Step V3: The voltage control regulates the estimated voltage to design range and minimizes the output deviations of the voltage controllers.

Figure 1. Concept of selection method for smart meter

Vol

tage

Time

Step S2 Representative

Representative

Step S1

Others

Vi=αiVR+βi

Step S4

Step S3

24th International Conference on Electricity Distribution Glasgow, 12-15 June 2017

Paper 0678

CIRED 2017 2/5

Categorizing method based on similarity of voltage wave form (step S1~S2 in meter selection) As a categorizing method based on similarity of the voltage wave form, the voltage similarity between meters is evaluated by the correlation defined in (1). The voltages of the meters are categorized by hierarchical clustering on the basis of the correlation. Image of the hierarchical clustering is shown in Figure 2. This clustering method categorizes hierarchical. In the higher hierarchy, meters with similar wave form in the lower hierarchy are categorized to the same category. Here, because the representative meters are selected in each category, the number of representative meters is equal to the number of categories. Therefore, the number of the categories is designed to meet constrains of the control system.

∑∑

∑

==

=

−−

−−

=n

kjjk

n

kiik

n

kjjkiik

ij

vvvv

vvvvR

1

2

1

2

1

)()(

))((

(1)

i,j : Meter number (1~p) n: Number of time series data vin: Voltage data of meter i at n v : Mean value of v

Figure 2. Image of hierarchical clustering

Meter selection method by disturbance evaluation based on factor analysis (step S3 in meter selection) As shown in Figure 3, the voltage trend measured by smart meter consist of the common voltage trends f between some meters and the unique voltage trend ei caused by local demand fluctuation in each consumer. To estimate LV of the other meters by representative meter in the voltage control, desirable voltage characteristics of the representative meters are not to include the unique voltage trend as a disturbance but the common voltage trends. To select the meters with the desirable characteristics as representative meter, factor analysis is applied. Here, a definition of the voltage is described in (3) normalized by (2). As assumptions, f and ei meet the constraint conditions in (4).

)2(

)(1

2∑=

−

−=

n

kiik

ii

vv

vv

iV

(4)

Under the constraint conditions in (4), correlation of the voltages in (3) is shown in (5). In (5), the diagonal elements σi

2+Σaik2 are equal to 1. In addition, the

correlation in (5) is equal to the correlation calculated by actual value of time series voltage data. Therefore, aik and σi are calculated from these conditions. The σi is the variance of the ei, and the comparison of σi between meters enables to evaluate the magnitude of the unique voltage trend. To select the meter with desirable voltage characteristics as representative meter, the voltages in each category are applied the factor analysis and σi of the unique voltage trend are calculated. The control system selects the meter whose 3σi is less than a design value in each category as representative meter. In case that there are some meters having 3σi less than the design value, a meter whose voltage frequently reaches minimum voltage is selected.

Figure 3. Definition of voltage trends measured by meters

][][][ 221

122

1 1

1 112

12

1

pT

mk pkp

mk kpk

mk pkk

mk k

diag

aaa

aaa

σσ

σ

σ

+=

+

+=

∑∑

∑∑

==

==

p1p1 ΛΛΛΛ

R

(5)

1 2 3 4 5 Meter number

categories:3

Higher

Lower

Hierarchy

correlation Low

correlation High

Number of

categories:2 Number of

Meters with

similar wave form are categorized.

The common voltage trends f between some meters

Voltage trend f1 in medium voltage

distribution network Voltage trend f2 in MV/LV transformer

Voltage trend f3 in LV line

Unique voltage trend ei caused by local demand fluctuations in each consumer

Meter voltage

Assumptions of ei and f

ii efV += iΛ (3)

: Time series voltage data of meter i [ ]ini vv 1=iV

][ 1 imi aa =iΛ : Coefficients of f

: Common voltage trends

][ 1 ini ee =ie : Unique voltage trend

2

1

2

11

2

1

11

1,01,11,01

,01,01

in

iik

n

kik

n

kmk

n

kmk

n

ijkik

n

kmkik

en

en

fn

fn

jieen

fen

σ====

≠==

∑∑∑∑

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====

==

][,][ 1 mnmT ff == m1 ffff m

m is the number of the trends

24th International Conference on Electricity Distribution Glasgow, 12-15 June 2017

Paper 0678

CIRED 2017 3/5

Voltage relational equations between the representative meter and the others (step S4 in meter selection) The representative meters are selected from categories consisted of meters with similar voltage trend. From this voltage similarity, the voltage relations between representative meter and the other meters in same category are approximated as (6). In (6), αn and βn is calculated by regression analysis.

iRiiest VV βα +≈ (6) VR : Voltage of representative meter Vest : Estimated voltage, αn, βn：Coefficients In operating the voltage control, the voltages are estimated by substituting the voltage of the representative meter in the voltage relational equations. However, the voltage estimation includes estimation error due to approximation in (6). To consider the error, probability distribution ƒ(Eest) of the estimation error Eest in each meter are calculated as Figure 4. In Figure 4, τM is range which includes probability of the estimation error from ELest to EHest. By setting τM to compensate the estimation error in voltage control, ELest and EHest are derived as margins for voltage control in next section.

Figure 4. Probability distribution of estimation error Eest

Voltage control Voltage sensitivity of meters to voltage controller In the previous section, the representative meters are selected and the voltage conditions in all networks are estimated from the voltages of representative meters. In order to regulate these voltages, the control system has to determine which voltage controllers are able to regulate the estimated voltages efficiently. In particular, there is a case that network configuration is unclear. Hence, voltage sensitivity of the estimated voltages to the controllers is calculated under unclear network configuration, and the sensitivity is used in the voltage control for determination of the voltage controllers. The method to calculate the voltage sensitivity is shown in Figure 5. From Figure 5, the sensitivity is calculated by voltage derivation of the meter to the output of the voltage controller at operation of the voltage controller.

Figure 5. Evaluation method of voltage sensitivity

Optimal outputs of voltage controllers (step V1~ V3 in Voltage control) The control outputs of the voltage control are adjusted when the estimated voltages of the meters surpass design range. Then, its control regulates LV to minimize ΔV and Δu. Here, ΔV are the differences between the estimated voltages and reference voltage. Δu are the changing values of the control output such as static condenser and transformer. To evaluate ΔV and Δu, constraint conditions with regard to the design range of V and u are defined in (7). In addition, objective function is defined in (8). The voltage control calculates the control output u to meet (7) and to minimize (8).

Constraint conditions

maxmin

estestmax

estestmin

uΔuuuEHDΔuVVELDΔuVV

<+<++>−+<

(7)

Objective function

)(

||||minimize 11DΔuVVΔV

ΔuΔV

estref

kk

+−=

+ ∑∑ ==lk

pk η

(8)

Where,

=

=

=

lpp

l

lpest

est

dd

dd

u

u

V

V

1

11111,, DuVest

(9)

Vest : Estimated voltages in (6) Vref : References of voltages Vmax: Upper limits of voltages Vmin : Lower limits of voltages u : Control output of voltage controller D : Voltage sensitivity for voltage controller(ΔV=DΔu) EHest, ELest: Margin for compensation of estimation error

in Figure 4 η :Weighting coefficient With regard to minimization for the objective function in (8) under the constraint conditions in (7), these equations are linear inequality. To resolve quickly the minimum control outputs u for the linear inequality, linear programming is applied.

Time

Vol

tage

of

smar

t met

er

Out

put o

f vol

tage

co

ntro

ller

Measured V Actual V

The derivation of voltage before and after operation of voltage controller

dV du

u

V

24th International Conference on Electricity Distribution Glasgow, 12-15 June 2017

Paper 0678

CIRED 2017 4/5

SIMULATION VERIFICATION

Simulation conditions Distribution network In order to evaluate performance of the voltage control, benchmark model for medium voltage (MV) distribution network [3] in Figure 6(a) and that for LV distribution networks [4] in Figure 6(b) are applied. As a distribution network configuration, in the MV network, its capacitances are ignored, and its each node is connected the low voltage distribution network. In addition, each load in MV node is divided into the connected LV nodes. Then, to simulate different characteristics in each LV consumer, load with randomly fluctuation is added to each LV consumer. In this simulation, there are about a thousand consumers. As an example, voltages of meters are shown in Figure 7.

Figure 6. Simulation model

Figure 7. Low voltages of smart meters

Table 1. Specification of smart meter [5]

Rated 240V 200A 60Hz Accuracy 0.5%

Measurable items

Integral power consumption Instantaneous voltage Instantaneous current etc.

Sampling time 15, 30, 60min

Smart meter The specification of smart meter is shown in Table 1. It is assumed to enable to measure active power and instantaneous voltage every 15~60 minutes. In the proposed method, the control system corrects the voltages of the representative meters at all time and the voltages of the other meters low frequently. Thus, the sampling time to correct the voltage of representative meters is set to 15min.

Simulation results Selection of representative meters The meter selection method is applied to the voltage data in Figure 7. The result of categorizing is shown in Figure 8. From Figure 8, it is confirmed that the meters are categorized by similarity of the voltage wave form. Here, the representative meters are selected from each category by disturbance evaluation based on factor analysis. For example, the result of disturbance evaluation for category 1 in Figure 8 is shown in Figure 9. In Figure 9, the variance σ of the unique voltage trend in each meter is calculated. The control system select the meters whose 3σ is less than a design value in each category. In addition, because there are the meters having 3σi less than the design values, a meter whose voltage frequently reaches minimum voltage is selected finally. In Figure 9, the meter of number 11 is selected. Voltage estimation and voltage control For the voltage control, in Figure 6, the tap of distribution transformer (TR) and reactive power of static condenser (SC) set on the MV nodes N06 and N11 are utilized. The results of the voltage estimation and control are described in Figure 10. To compare the control performances, results of the conventional control that a tap of TR are operated on schedule is shown in Figure 11. As an evaluation of the voltage estimation, from LV measured by meters and estimated voltages in Figure 10, LV of some meters drop by 0.04p.u. from 16h to 20h. On the other hand, the estimated voltages drop by 0.022p.u. LV of some meters which drop by 0.04p.u. fluctuate widely and these wave forms are different from LV measured by the other meters. Thus, these voltages include large unique voltage trend in each meter defined in Figure3. The unique voltage trend is not estimated by estimation based on the voltages of the representative meter. On the other hand, the voltage control does not require measuring some unique voltage trends but all common voltage trends. Therefore, these estimation errors are not important for the voltage control. Here, the maximum root mean square (RMS) estimation errors for 24h in each category in Figure 8 are described as follows. The maximum RMS errors are about 0.016p.u. and the errors are not greatly affected to the voltage control due to small error.

( )∑ = −= nkRMSerror n

V 121]p.u.[ ikestik VV (10)

Category1=0.014 Category2=0.018 Category3 =0.011 Category4=0.018 Category5=0.015 Category6 =0.016

(b)Low voltage(LV) (a)Medium voltage(MV)

N01

N02

N03

N04

N05 N10 N07

N06 N09

N11

L04: Single residential consumer

L05: Apartment building

L03: Apartment building

L01: Single residential consumer

Distribution transformer TR1

distribution network

distribution network

N08

L02: Group of 4 residences

0 3 6 9 12 15 18 21 240.94

0.97

1

1.03

1.06

1.09

Vol

tage

[p.u

.]

Time [h]

24th International Conference on Electricity Distribution Glasgow, 12-15 June 2017

Paper 0678

CIRED 2017 5/5

In the voltage control, the conventional control regulates the voltages from -0.08 to +0.05p.u. The proposed control regulates LV from -0.04 to +0.05p.u. by considering LV conditions. As a result, the proposed control improves LV drop by 0.04p.u. Therefore, it is confirmed that the proposed voltage control is able to improve the voltage control performance.

Figure 8. Categorizing results of LV

SUMMARY This study proposes the selection method of representative meters, and the voltage control utilizing the meters to estimate and to control LV. Consequently, it is confirmed that the voltage estimation and the voltage control utilizing smart meters is able to estimate all LV and to improve the voltage control performance. As future works, we will evaluate this proposed voltage control by more realistic conditions such as actual measured voltage by smart meter and actual network. REFERENCES [1]Melissa A.Peskin, 2012, “Conservation Voltage

Reduction with Feedback from Advanced Metering Infrastructure”, IEEE Transmission and Distribution Conference and Exposition, Oriando

[2]N. Nakamura, 2009, “Methods for multidimensional data analysis”, Kyoritsu shuppan, Japan, 143-158.

[3]K. Rudion, 2006, “Design of Benchmark of Medium Voltage Distribution Network for Investigation of DF Integration”, IEEE Power Engineering Society General Meeting, 2006

[4]Stavros Papathanassiou, 2005, “A Benchmark Low Voltage Microgrid Network”, Cigre symposium Power systems with dispersed generation: technologies impacts on development operation and performance

[5]K. Nitta, 2010, “Advanced metering infrastructure for smart grid”, Toshiba review, vol.65, No.9

Figure 9. Disturbance evaluation results

Figure 10. Results of the voltage control utilizing the

smart meters

Figure 11. Results of the conventional voltage control

(Scheduled operation of a tap of distribution transformer)

0 3 6 9 12 15 18 21 240.94

1

1.06

Vol

tage

[p.u

.] V

olta

ge [p

.u.]

Vol

tage

[p.u

.]

0 3 6 9 12 15 18 21 240.94

1

1.06

0 3 6 9 12 15 18 21 240.94

1

1.06

0 3 6 9 12 15 18 21 240.94

1

1.06

0 3 6 9 12 15 18 21 240.94

1

1.06

Representative Meter

Category 1

Category 5 Time [h]

Time [h]

Category 2

Category 3 Category 4

0 3 6 9 12 15 18 21 240.95

1

1.05

0 5 10 15 200

0.5

1

1.5

2

2.5

3

3.5

independent factor

3σ o

f ei [

%]

Voltages of meters in category 1

Disturbance evaluation

Volta

ge [p

.u.]

Time [h]

Meter number

The meter of No.11 is selected as representative meter.

A design value for disturbance evaluation …

Time[h]

Tap

of T

R

-0.03

Estim

ated

LV

-0.04

LV

+0.03

MV

-0.02

[p.u

.] [p

.u.]

[p.u

.]*

pow

er o

f SC

+0.05

TR1

- N06 - N11

Rea

ctiv

e [p

.u.]

*Base of p.u. is 10MVA

+0.05

[p.u

.]

-0.022

Time[h]

TR

+0.05

+0.01

-0.03

-0.08

[p.u

.] M

V

Tap

of T

R

[p.u

.] LV

[p.u

.]