NREL/PR-5D00-70018

August 28, 2017

Predictive Analytics for Coordinated Optimization in Distribution Systems

Rui Yang

Research Engineer

Power Systems Engineering Center

National Renewable Energy Laboratory

Workshop on Data Analytics for the Smart Grid Pullman, WA

2

• Increased Amount of Data in Power Systems

2 Motivation

3

• Data

o Nonpervasive

o Heterogeneous

o Highly variable

o Different resolution

3 Motivation

Topology data

Operations Optimization Control Planning

DER

Distribution Transmission

Load Load

4

• Data

o Nonpervasive

o Heterogeneous

o Highly variable

o Different resolution

4

Topology data

How?

How?

Real-Time Operations

How to use the data?

DER

Distribution Transmission

Motivation

Load Load

How to facilitate the real-time decision-making?

5

5 Power System Situational Awareness

Distribution Transmission

DER

Nodal Voltage

Load

Load

Monitor current states

6

6 Power System Situational Awareness

Distribution Transmission

DER

Future

Future

Nodal Voltage

Load

Load

Future

Monitor current states

Forecast future states

Future

7

• Renewable with Smart Inverters

o Able to adjust power generation

o Providing grid services

• Smart Loads

o Smart appliances

o Flexible power consumption

• Challenge – Lack of Coordination

o Not necessary to benefit the overall system operations

o Not fully utilizing the flexibility brought by these resources

Flexible Resources

Source: PV Magazine

Source: Microchip Technology Inc.

8

8 Predictive System Operations

Distribution Transmission

DER

Future

Future

Nodal Voltage

Load

Load

Future

Future

Coordinated Optimization

Set Points of Resources

9

• State Forecasting-Based Voltage Regulation [1, 2]

Applications

• Consumer Behavior-Aided Dispatch [3-5]

Coordinated Optimization

2 PM1 PM 3 PM 4 PM 5 PM 6 PM 7 PM 8 PM

0.89

0.9

0.91

0.92

0.93

0.94

Errors

Vo

lta

ge (

p.u

.)

Training Forecasting

Training data

Forecast dataTarget data

10

• Goals

o Accurately forecasting system states in the near future

o Prioritizing the control needs

• Approach

State Forecasting-Based Voltage Regulation

Dynamic Weights

Load Deviation

Power Set Point

Load Flexibility

PV Inverters

OPF

Loads

Available Power

State Forecasting

Forecasting OPF

11

Dynamically Weighted OPF

Power balance

Voltage constraints

PV plant

Smart load

Dynamically determined by the forecasted voltages

12

12

Voltage Magnitude

Voltage Angle

Results – State Forecasting Error

Accurate state forecast with machine learning methods

13

13

Voltage Violation

Voltage violations reduced significantly with state forecasting-based optimal scheduling

Results – Voltage Regulation

14

• Goals

o Actively engaging electricity consumers

o Achieving system-level control objectives without sacrificing consumers’ needs

• Integrated Optimization Approach

Consumer Behavior-Aided Dispatch

Load Deviation

Power Set Point

PV

Load Flexibility

DMS

HEMS

PV Inverters

OPF

Smart Loads

PV

Smart Loads

Substation

Available Power

Transmission

Distribution

15

Model-Based Load Forecasting

HEMS

User Preference

Weather Forecast

Model Predictive Control

Weights

Model

Load Forecast

Weather

16

12am 4am 8am 12pm 4pm 8pm 12am(+1)-5

0

5

Time

Tota

l Lo

ad (M

W)

opt

w/o c

12am 4am 8am 12pm 4pm 8pm 12am(+1)-0.2

0

0.2

0.4

0.6

0.8

1

Time

Vo

ltag

e V

iola

tio

n (p

.u.)

opt

w/o c

Total Load

Voltage Violation

Results – Distribution System Level

17

• Example – One House

12am 4am 8am 12pm 4pm 8pm 12am(+1)-15

-10

-5

0

5

10

15

Time

Load

(kW

)

ref

min

opt

max

Load Consumption

System performance improved without significant load deviation

Results – Home Level

18

• Predictive Analytics for Coordinated Optimization

o Data analytics methods to facilitate the decision-making

o Optimal coordination of various resources

• Ongoing Work

o Data-driven, model-based, and hybrid methods for resource and load forecasts [6]

o Integrated framework for system state estimation and forecasting [7]

o Incentives to drive desirable behaviors of consumers [8]

Summary

19

[1] Huaiguang Jiang and Yingchen Zhang, “Short-term distribution system state forecast based on optimal synchrophasor sensor placement and extreme learning machine,” IEEE PES General Meeting, Boston, MA, July 2016.

[2] Rui Yang, Huaiguang Jiang, and Yingchen Zhang, “Short-term state forecasting-based optimal voltage regulation in distribution systems,” IEEE Innovative Smart Grid Technologies, Arlington, VA, April 2017.

[3] Rui Yang and Yingchen Zhang, “Coordinated Optimization of Distributed Energy Resources and Smart Loads in Distribution Systems,” IEEE PES General Meeting, Boston, MA, July 2016.

[4] Rui Yang, Yingchen Zhang, Hongyu Wu, and Annabelle Pratt, “Coupling energy management systems at distribution and home levels,” IEEE Transactions on Smart Grid, under review.

[5] Yingchen Zhang, Rui Yang, Kaiqing Zhang, Huaiguang Jiang, and Jun Jason Zhang, “Consumption behavior analytics-aided energy forecasting and dispatch,” IEEE Intelligent Systems, vol. 32, no. 4, pp. 59-63, 2017.

[6] Huaiguang Jiang, Yingchen Zhang, Eduard Muljadi, Jun Jason Zhang, and Wenzhong Gao, “A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization,” IEEE Transactions on Smart Grid.

[7] Yingchen Zhang, “Predictive Analytics for Energy Systems State Estimation,” panel presentation, IEEE PES General Meeting, Chicago, IL, July 2017.

[8] Rui Yang and Yingchen Zhang, “Three-phase AC optimal power flow based distribution locational marginal price,” IEEE Innovative Smart Grid Technologies, Arlington, VA, April 2017.

References

Collaborators:

Yingchen (YC) Zhang

Huaiguang Jiang

Andrey Bernstein

Contact:

Rui Yang

Email: Rui.Yang@nrel.gov

Thank You!