A Look-Ahead Energy Management System (EMS) Framework for Microgrids

Xianyong Feng, PhD

Center for Electromechanics (CEM)

University of Texas at Austin

Nov. 28, 2016

1

Outline

• Introduction

• Motivation

• Microgrid Control Operation

• Microgrid EMS Problem Description

• Case Studies

• Performance Analysis

• Conclusion

2

Introduction

• DOE Microgrid Definition: A group of interconnected loads and distributed energy resources within clearly defined electrical boundaries that acts as a single controllable entity with respect to the grid. A microgrid can connect and disconnect from the grid to enable it to operate in both grid-connected or island-mode.

3

PV system

ES system

~

Distributed

Generator M

~

Microgrid

Communication

link

Central

controller

Local

controller

Local

controller

Local

controller

Local

controller

Distributed

Generator 1

s1s2s3s4s5s6

s7

s8

s9

PCC

Utility

Grid

...

Controllable

load 2Critical

load Controllable

load N

Controllable

load 1

Local

controller

Local

controller

Local

controller

Local

controller

...

PCC

controller

· Utility electricity price signal

· Weather forecast

Introduction

• MW-level Microgrid at CEM in The University of Texas at Austin

4

~ ~

~ ~

M ~D

480 V AC source

3-ph

AC breaker

480 V AC source

3-ph

AC

breaker

490 kVA

480 V/808 V

transformer

DC

disconnectorAC

breaker

Diode rectifier

3.2 MVA

DC

disconnector

DC cable

DC

disconnector

DC

disconnector

DC

disconnector

DC

disconnector

1 MVA motor

drive

2 MVA

motor

5 MW

dynamometer

DC

disconnector

1.2 MVA

480 V/808 V

transformer

1.2 MVA

controlled

rectifier

2 MW

DC chopper

1.3 MW

resistive load

1120 VDC

1120 VDC

Zone 1

Zone 2

DC bus 1

DC bus 2

Motivation

• High Penetration Renewable• Accommodate high penetration

intermittent DERs in future grid

• Energy Efficiency• Reduce grid operation cost

• Reduce emission

• Grid Reliability and Resilience • Improve grid stability

• Reliable fault ride-through

• Seamless mode transitions

5

Time (sec)

Frequency (Hz)

60larger inertia

smaller inertia

LP is the same

1f

2f

n

i i

L

H

fP

dt

fd

1

rated

2

Total inertia

Stability and Resilience

PV generation (partial cloud day) Wind generation (daily curves)

Renewable Intermittency

10 hrs.

Microgrid Control Operation

• Tertiary Control • Economics and efficiency

• System security

• Secondary Control• System-level stability

• Power sharing

• Primary Control• Local stability

• Equipment protection

6

Tertiary

control

Secondary control

Primary control

Information flow

Decision signals

Economic dispatch

Unit commitment

Optimal power flow

Voltage var control

Time frame: seconds to minutes

Real-time load management

Secondary load-frequency control

Secondary voltage control

Automatic generation control

Time frame: 100s milliseconds

Droop control

Protection

Time frame: milliseconds

Look-ahead energy

management system

(EMS)

Microgrid EMS Problem Description

• Objective • Enable economic and secure

steady-state operation

• Seamless integration with local controllers

• Constraints• Power balance

• DG ramp rate and capacity

• ESS state of charge

• Operational constraint (voltage, current, and freq.)

7…

Mixed Integer Linear Programming (MILP)

problem

Microgrid EMS Problem Description

• Challenges• How to improve the predictive

control accuracy

• How to reduce computational complexity

• How to minimize ESS charging and discharging cycles

• How to integrate realistic operational constraints in real time economic dispatch

• How to minimize load interruptions during outage

8

• Solutions• Plan DERs using most recent

load and renewable forecasts

• Decoupled look-ahead DER schedule and real-time ED/OPF

• Designed a virtual cost associated with ESS

• Optimal power flow and sensitivity-based method are integrated in the ED

• Designed an optimal planned outage function

Case Study

• A modified IEEE 34-node system• Maximum load: 1.8 MW

• Rated voltage: 24.9 kV

• 1 diesel, 2 micro-turbine, 1 wind, and 1 PV/solar generators

• 2 energy storage systems

9

DG name DG 1 - diesel DG 2 – MT1 DG3 – MT2

Location Bus 824 Bus 840 Bus 854

No load cost ($/hour) 4 3 5

Fuel cost ($/kWh) 0.22 0.085 0.085

Start-up cost ($) 10 10 10

Shut-down cost ($) 10 10 10

Pmax (kW) 360 270 450

Pmin (kW) 72 54 90

Qmax (kvar) 174 130 217

Qmin (kvar) 0 0 0

Ramp rate (%/min) 40 30 30

ES name ES1 ES2

Location Bus 850 Bus 858

Maximum charging rate (kW) -20 -40

Maximum discharging rate (kW) 100 200

Maximum energy capacity (kWh) 200 600

Minimum energy capacity (kWh) 80 210

Charging efficiency (%) 80 85

Discharging efficiency (%) 85 90

Name Wind Solar

Location Bus 808 Bus 848

Capacity (kW) 200/400 100/200

Control type Continuous or discrete Continuous or discrete

Case Study

• Real-time simulator• Simulated the 34-node system

• Local controllers communicate with Opal-RT simulators

• Look-ahead microgrid EMS• Implemented on central

controller

• Dispatch signals are transferred to local controllers through LAN

10

Simulated 1.8 MW microgrid

Ethernet RT simulator

User interface

Local controllers for DERs

…

…

Local Area Network (LAN)

Central controller

Look-ahead microgrid

EMS

Measurement Control…

Hardware-in-the-loop test platform

Case Study

• Case 1: One-day look-ahead EMS in grid-connected mode operation with an outage• 2-step price signal

• A planned outage (4 am - 8 am)

• ESSs discharge to avoid using diesel generator (a higher-cost unit)

• ESSs charge before planned outage happens

• The ESS charge/discharge cycle is minimized

11

Diesel

MT 1MT 2

Case Study

• Case 2: One-day look-ahead EMS in island mode operation• ESSs discharge to avoid using

diesel generator (a higher-cost unit)

• ESSs charge before load is increased

• The ESS charge/discharge cycle is minimized

• Renewable energy is fully utilized

12

0 5 10 15 200

20

40

60

80

100

120

140

160

180

200

time (hour)

Win

d P

ow

er

(kW

)

0 5 10 15 200

20

40

60

80

100

120

time (hour)

Sola

r P

ow

er

(kW

)

0 5 10 15 200

200

400

600

800

1000

1200

1400

1600

1800

2000

time (hour)

Load D

em

and (

kW

)

0 5 10 15 200

50

100

150

200

250

300

350

400

450

500

time (hour)

Genera

tion P

ow

er

(kW

)

DG1

DG2

DG3

0 5 10 15 20-40

-20

0

20

40

60

80

100

120

time (hour)

ES

1 O

utp

ut P

ow

er

(kW

)

0 5 10 15 20-50

0

50

100

150

200

250

time (hour)

ES

2 O

utp

ut P

ow

er

(kW

)

Wind Power

Solar Power

Load Demand ESS2 Output Power

ESS1 Output Power

Generation Power

Diesel

MT 1

MT 2

Performance Analysis

• Performance Analysis• Each look-ahead DER

schedule problem (MILP problem) for the modified IEEE 34-node system with 7 DERs includes:• 1296 decision variables

• 2692 linear constraints

• The real-time ED/OPF is formulated as quadratic programming problem • The calculation can be finished

in 1 second on both platforms

13

Platform PC (Intel R Xeon E5606 2.13 GHz)

Embedded system (AM335x 1GHz ARM® Cortex-A8;

512MB RAM) Grid-connected

mode 9.8 sec 194 sec

Island mode 35.3 sec 554 sec

Performance of look-ahead DER schedule (averaged from 100 runs)

> 5 min

• A dedicated powerful computer is required to perform the central look-ahead EMS optimization in real time

Conclusion

• The look-ahead EMS approach fully utilizes the most recent load and renewable forecast to improve the predictive control accuracy

• The decoupled DER schedule and real-time ED approach significantly reduces the computational complexity

• The real-time OPF-based ED integrate operational constraints to generate realistic dispatch results

• The central EMS calculation can be accomplished in real time on a regular PC

14

Thank You

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