2017 Atlanta Regional User Seminar - Real-Time Volt/Var Optimization Scheme for Distribution Systems with PV Integration

Grid-connected Advanced Power Electronic Systems

Real-time Volt/Var Optimization Scheme for Distribution Systems with PV Integration

02-15-2017

Presenter Name: Yan Chen (On behalf of Dr. Benigni)

22Date: 02/15/2017

Outline

Impacts of PV Integration on Distribution Grids

Solution: PV Inverter Control to Sustain High Quality of Service

A Top-level Day-ahead Control that Optimizes Voltage Deviations

and Power Losses

A Fast on-line Control that Compensates for PV Generation and

Load Variability

Communication Network Aware Distributed Voltage Control

Algorithms

Conclusion

33Date: 02/15/2017

PV Impact On Distribution Grids

Change in feeder voltage profiles, including voltage rise and unbalance

Deteriorated power quality: PV-DG intermittency may lead to rapid fluctuations in

bus voltage magnitudes

Frequent operation of voltage-control and regulation devices, such as on load tap

changers (OLTCs), line voltage regulators (VRs), and shunt capacitor banks

(SCBs)

Change in electric losses, where relatively large reverse power flow may

increase power losses

0:00 8:00 16:00 24:00

Time

0.94

0.96

0.98

1

1.02

1.04

1.06

1.08

1.1

1.12

Optim

al s

chedulin

g for

Tap1

44Date: 02/15/2017

Day-ahead Coordinated Optimal Control Objectives: Determine how to optimally control the related electric elements

to minimize the voltage fluctuation and power losses with constraints on the

OLTC and SC operations.

PV inverter

On-load tap changer

Shunt capacitor bank

PV Inverter VAR control: When the PV generation is not at the maximum

level, the unused converter capability can be used for reactive

compensation.

��(�) = �(�)� − �(�)�

� � ≤ ��(�)

55Date: 02/15/2017

Optimal Control Problem

Decision variables:

Reactive power of PV inverter (continuous variables)

OLTC tap position (discrete variables)

SC switch state (Boolean variables)

Objective function:

Total voltage deviation

Total power losses

Constraints:

Reactive power limit of PV inverter:

Limit of node voltage magnitudes (ANSI C 84.1):

Limit of tap positions of OLTC:

Limit of the tap operations of OLTC within a day:

Limit of the switch operations of shunt capacitor within a day:

[ , , ], 1, 2,...t t tpvQ Tap SC t T x

1 1 1

min ( (1 ) )node brN NT

t ti j

t i j

F w VD w PL

2 2 2 2( ) ( )t t tpv pv pv pv pvS P Q S P

L t UTap Tap Tap

maxTSC TSC

L t UiV V V

maxTTC TTC

66Date: 02/15/2017

Overall Process

Inputs:

Forecasted PV Generation

Forecasted Load Demand

Distribution Network Information

Optimization Process:

Pattern Search Algorithm

Genetic Algorithm

Treated as a black-box model

Outputs:

Reactive power of PV inverters

Tap position of OLTCs

Switch State of Shunt Capacitors

77Date: 02/15/2017

Case Study

IEEE 34 Node Test Feeder

Controlled Devices Location Decision variables

PV inverter Node 34− ��

� − ��

≤ �� ≤ ��

� − ��

On-load tap changer Node7-8 ��, ±10 taps with 1% voltage regulation per tap.

Shunt capacitor Node 27 ��, could be 0 (disconnected) or 1 (connected)

88Date: 02/15/2017

Results and Discussions

Constraint function TTC=23TSC=16

TTC=16TSC=16

TTC=12TSC=12

TTC=8TSC=8

TTC=4TSC=4

Objection function 49.32 52.70 56.51 75.69 85.71

99Date: 02/15/2017

Discussion

The performance of the day-ahead control method is affected by the forecast

errors.

Solar PV output: errors caused by actual irradiance Cloud cover

Aerosols and other atmospheric constituents

Temperature

Load demand: Temperature

Random (stochastic) customer behavior

Feeder outages

1010Date: 02/15/2017

Real-time Optimization We propose an online optimal reactive power control strategy to keep the total voltage

deviations and power losses to a minimum regardless of unpredicted changes.

In order to reduce the additional “wear and tear” on the physical voltage control devices,

the tap position of the OLTC and the switch state of the SC are controlled according to the

day-ahead optimal control scheme.

The reactive power of the PV is decided by the real-time system status.

Day D Day (D+1) t

Day-ahead scheduling for

OLTC, SC, and Qpv

PV output and load demand forecast

��,��

Real-time control of Qpv

Real-time system status

1111Date: 02/15/2017

Control Structure

Meter Meter

Meter

Meter

Meter

Meter

Measurements

(Pinj, Qinj, V, I)

Measurements

(Pinj, Qinj, V, I)

Control Center

1212Date: 02/15/2017

Control Structure

Meter Meter

Meter

Meter

Meter

Meter

Measurements

(Pinj, Qinj, V, I)

Measurements

(Pinj, Qinj, V, I)

State Estimation Optimization Algorithm

1313Date: 02/15/2017

Control Structure

Controller Controller

Controller

Controller

Controller

Controller

Control Signal Control Signal

(Qpv)

1414Date: 02/15/2017

Control Structure

Meter Meter

Meter

Meter

Meter

Meter

Measurements

(Pinj, Qinj, V, I)

Measurements

(Pinj, Qinj, V, I)

Control Center

1515Date: 02/15/2017

Distributed Control Lab Instructure

1616Date: 02/15/2017

Controller Board ODROID-U3+

Position Key Features

Upper layer • Low-cost, powerful computer• Ease of programming• Network capable• ARM Quad-core 1.7 GHz CPU and 2GB

RAM. Xubuntu 13.10 Operation System

U3 I/O Shield


Middle layer • 36 IO ports of GPIO/PWM/ADC

OPAL-U3-Shield


Bottom layer • Contains level shift, amplification, and filter circuitry for different signal requirements between OPAL (-10V-10V) and U3 I/O Shield.

• Allows access to all IO ports on the U3 I/O Shield

1717Date: 02/15/2017

Controller Board ODROID-U3+


Upper layer • Low-cost, powerful computer• Ease of programming• Network capable• ARM Quad-core 1.7 GHz CPU and 2GB

RAM. Xubuntu 13.10 Operation System

U3 I/O Shield


Middle layer • 36 IO ports of GPIO/PWM/ADC

OPAL-U3-Shield


Bottom layer • Contains level shift, amplification, and filter circuitry for different signal requirements between OPAL (-10V-10V) and U3 I/O Shield.

• Allows access to all IO ports on the U3 I/O Shield

1818Date: 02/15/2017

cRIO-9035 Embedded Controller

Xilinx FPGA for rapid signal processing

1.33 GHz Dual-Core allows wide range of computations

Digital and analog I/O modules

Analog I/O: 12-bit resolution bidirectional at 20 kS/s

Digital I/O: 8 bidirectional channels at 10 MHz

GPS module enables synchronous signal measurement

BA14

Slide 18

BA14 add a picture that show the full rack and add some detail on the IO modulesBENIGNI, ANDREA, 2/10/2017

1919Date: 02/15/2017

Network Emulator: Netropy N91

Test the effect of WAN:

Bandwidth

Latency and jitter

Loss

Other impairment

Congestion

Corruption

Queuing and Prioritization

Applications:

Throughput

Responsiveness

Quality

2020Date: 02/15/2017

Real-time Simulation of Distribution Grids


4 SSN nodes, 5 subsystems

Ts = 50us


7 SSN nodes, 8 subsystems

Ts = 50us

2121Date: 02/15/2017

Model Components

RT-LAB overview

ARTEMis State-Space Nodal (SSN)

The SSN algorithm creates virtual state-space partitions of the network that are

simultaneously solved using a nodal method at the partition points of connection. The

partitions can be solved in parallel on different cores of a PC without delays.

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Test Setup

HIL structure

2323Date: 02/15/2017

Case Study

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Case Study

Meter Meter

Meter

Meter

Meter

Meter

Measurements

(Pinj, Qinj, V, I)

Measurements

(Pinj, Qinj, V, I)

State Estimation Optimization Algorithm

Qpv

2525Date: 02/15/2017

Case Study

To evaluate the ANN approach, 17,520

samplings are generated from one

year’s historical record, and 15%, 15%,

10%, and 5% white noise is added to

the domestic load, commercial load,

industrial load and street light load,

respectively.

The comparison of the forecasted

results (in red curves) and the real

measurements (in blue curves) of the

PV output and the load demand.

2626Date: 02/15/2017

Real-time Qpv Control Results

The real-time reactive power control is applied to correct the forecast errors of PV

output and load demand. The overall objective function value is decreased from 102.30

to 89.82. The voltage magnitudes are more centralized to 1 PU. The power quality

improvement is apparent when there is dramatic uncertainty of the PV output.

2727Date: 02/15/2017

Distributed Control Algorithm

A modified IEEE-123 power distribution system is simulated on OPAL-RT in

real time

10 CompactRIO embedded controllers connected at various points in the

grid measure voltage phasors to determine reactive power flow

By communicating with one another, these controllers attempt to minimize

transmission losses by injecting and absorbing reactive power

The control algorithm adapts to the network conditions generated by the

network emulator; using either gossip-like or F-DORPF algorithm

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Distributed Control Algorithm

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Conclusions

The increasing penetration of distributed and renewable energy

resources introduces challenges to the distribution systems operation

and control

Real-time simulation (of power and communication networks) and

Hardware In the Loop simulation are fundamental tools for the design

and testing of innovative control solutions

3030Date: 02/15/2017

Thanks for your attention

Questions?

Dr. Andrea Benigni

Department of Electrical Engineering

[email protected]

Yan Chen, Ph.D. student

Department of Electrical Engineering

[email protected]