power systems: secure control, generation modeling and...

Zhou Peng (Speaker)

Du Dajun, Wang fei

School of Mechatronic Engineering and Automation

Shanghai University

Power systems: secure control, generation

modeling and photovoltaic applications

Barhrain-Shanghai Intercultural Communication Conference

Renewable Energy

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01

02

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Secure control for power systems

Probabilistic power generation modeling

Photovoltaic applications

3

01Secure control for power systems PART ONE

Zhou Peng

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立项依据

Smart grid as an example

A power system is a complex control system with advanced communication and computing technologies

背景问题挑战现状

Controlcenter Industrial

Customer

ElectronicVehicle

Wind

Photovoltaic

Smart meter3G/4G

Wireless

WIFIWiMAX

TCP/IP

NetworkManagement

EmbeddingComputing

Cloud computing

5

Secure control Background Current research

Cyber-security events in China

Power system is becoming the new battle field in the cyber space

China economic weekly ： ICSsecurity events affect 28.6%industries, and even worse 19.1%are shutdown

Cambridge reports：damage of50 power generation units willinduce more than 200 billiondollars financial lost.

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Our past research: vulnerability mining

Secure control Current researchBackground

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Power system security: beyond network security

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140

198

256 245

295

0

50

100

150

200

250

300

350

2013 20152010 2011 2012 2014

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206180

130147

125

0

50

100

150

200

250

300

350

2013 20152010 2011 2012 2014

Security events Control system vulnerabilities

Traditional security solutions encounter challenges due to the lack of knowledge for control internals


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Power system security: beyond safe control

Fault diagnosis and fault-tolerant control cannot work since adversaries can purposely evade detection


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Power system security: secure control


Feature

Boundary is uncertainty

Devices are heterogeneous

Faults are coupled

Hard to

protect

Hard to

detect

Hard to

control

Challenge

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Game theory for

arms race analysis

Adversarial learning

for attack detection

Threat modeling

for power system

Our solution

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Control center

Secure control experiment platform in our lab

Micro-grid platform supporting attack-defense experiments

Power devices

1500 square meters, 200KW, and connected tocampus power supply

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02PART TWO

Probabilistic power generation modeling

Du Dajun

13Shanghai University

Contents

1 Background

2 Probabilistic Modeling of Wind/Photovoltaic

Generation and Electric Vehicles

3 General Scheme and Probabilistic Load Flow Algorithms

4 Simulation


Background

Wind power generation

Photovoltaic power generation

Electric vehicles


Background

1. Randomness

2. Regularity (day and night; mid-day)Photovoltaic

Power Generation

1. Randomness

2. VolatilityWind

Power Generation

1. Charging (load)

2. Discharging (energy storage)Electric Vehicles

Characteristics

Probabilistic Modeling of

Wind/Photovoltaic Generation and

Electric Vehicles


2.1 Photovoltaic Power Generation

➢ Photovoltaic power generation is influenced by natural

conditions to a great extent. The output power varies with the

intensity of sunlight.

1 1

max max

1

S Sf S

S S

/ ,

,

r r r

r r

P S S S SP

P S S

The light intensity in a short time

scale (hours or a day) can be best

described by the Beta distribution.

The PDF can be described as

The relationship of the

output power and the light

intensity is described as


2.2 Wind Power Generation

➢ A large amount of measured data show that the curve of wind

speed can be generally best described by the Weibull distribution.

➢ The wind power curve between the output active power and the

wind speed can be described as follows.

1

exp

k kk v v

f vc c c

The PDF for the two-parameter

Weibull distribution can be

described as

P v

vin

vout

vN

v

NP

0


2.3 Electric Vehicles Charging and Discharging

The power demand of electric

vehicles charging and discharging is

best described by normal

distribution. Three cases are

considered:

➢ Charging without control

➢ Charging with control

➢ Charging/Discharging with

control

The corresponding curves of power

demand are shown as follows.

0

0.2

0.4

0.6

0.8

1

1 3 5 7 9 11 13 15 17 19 21 23

功率

/kW

时间段

00.20.40.60.8

11.21.41.6

1 3 5 7 9 11 13 15 17 19 21 23

功率

/kW

时间段

-5-4-3-2-1012345

1 3 5 7 9 11 13 15 17 19 21 23

功率

/kW

时间段

General Scheme and

Probabilistic Load Flow Algorithms


3.1 General Scheme

➢ 2m+1 point estimate

method is adopted to

analyze randomness

and obtain the data of

the node voltage.

➢ The independence of

random variables is

suitable for the 2m+1

point method.

Correlated non-normal random vector space

Independent standard normal random vector space

Correlated standard normal random vector space

(1)

CNNRVS

(2)

CSNRVS

(4)

CSNRVS

(3)

ISNRVS

(5)

CNNRVSPLF

Nataf

ET Inverse ET

Inverse Nataf

Calculate the coefficients of position and corresponding probability .Construct 2m+1 estimation points.

,k k


3.2 2m+1 Point Estimate Method


4.1 IEEE-33 System

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

19 20 21 22

23 24 25

26 27 28 29 30 31 32 33

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➢ The IEEE-33 example is adopted to verify the proposed method.

The correlation coefficient of photovoltaic and wind generation

is shown as follows.

Wind power generation

Photovoltaic power generation


4.2 Results Analysis

➢ The results of 2m+1 point estimate method are compared with those

of Monte Carlo Simulation. The PDF and CDF of bus voltage are

shown as follows.

Fig.1 The PDF of bus 22 in period 8(electric vehicles charging without control)

Fig.2 The CDF of bus 22 in period 8(electric vehicles charging without control)

0.9935 0.994 0.9945 0.995 0.99550

200

400

600

800

1000

1200

Bus Voltage（p.u.）

PD

F

MCS

2m+1

0.9943 0.9943 0.99441050

1100

1150

1200

0.9935 0.994 0.9945 0.995 0.99550

0.2

0.4

0.6

0.8

1

Bus Voltage（p.u.）

CD

F

MCS

2m+1


4.2 Results Analysis

➢ The results with photovoltaic generation are compared with those

without photovoltaic generation.

0 5 10 15 20 25 30 350.91

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

Bus V

oltage（

p.u

.）

Bus

Without PV

With PV

0 5 10 15 20 25 30 350.93

0.94

0.95

0.96

0.97

0.98

0.99

1

Bus V

oltage（

p.u

.）

Bus

Without PV

With PV

Fig.3 Average voltages of each bus in period 12

(electric vehicles charging without controlling)

Fig.4 Average voltages of each bus in period 15

(electric vehicles charging without controlling)

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03 Photovoltaic applicationsPART THREE

Wang Fei

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R&D – PV Applications

Project 1：

DC-DC converters applied for DC Micro-grid

Voltage Balancer (Prototype )

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Project 2：

Mitigation of Low-frequency Current Ripple for Enhancing The Performance in Single-

phase PV Inverters

• Current Ripple Reduce the Performance

of PV & Fuel Cell

Dual-Boost Based Inverters

DC/DC

变换器DC/AC

iL iinv

ic

iin io

uo

ω2ω2ω

iin iinv io

Uin 电网

A

C DB

• Mitigation methods in a summary

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Project 3：

Solar Pump for Irrigation

Typical example : Control

Diagram

Solar Pump Platform (in the lab)

逆变器 M

光伏阵列

三相异步电机水泵

UDC

MPPT控制器

PI控制器

PWM发生器

UgUDC

UI

P

MPPT解算器

乘法器

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Project 4：

Design & Optimization of PV Power Stations

Analysis of the key factors on system

efficiency

光伏阵列

电网

汇流箱Combiner Box

直流配电柜(根据电站类型规

模选配)DC Distributor

（Optional)

逆变器DC/AC Inverter

升压或隔离变压器

（根据入网需要）交流配电柜

(选配)DC Distributor

（Optional)

交流线损

变压器效率逆变器效率

交流线损交流线损

直流线损直流线损

汇流箱Combiner Box

组件并联失配；

汇流箱并联失配；光伏组件效率

折损

Optimization based on efficiency modelling

Platform: PV Module Level

Platform: Power Station Level

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Project 5 ：

MPPT Controller for PV Systems

MPPT Controller

光伏板 DC/DC变换器蓄电池

• Applications of MPPT Controller

• System integration

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Project 6:

System-level Research: Stability, Security, & Energy Management of Micro-grid Systems

Micro-grid Platform

Energy Router based on Solid-state Transformer

LVACHVAC

LVDC

MGE-routerMGCC

MVDCSST

PEMGi,t

Grid

PENGk,t

Nano grid k

PENGj,t

DC

NGCC Nano grid j

LD

DC/ACPD,t

BAT

DC/DCPEBAT,t

PV

DC/DCPGPV,t

PV Emulat

or

3-Ph PV

Inverters

(5pcs)

Load

s

1-ph PV Inverte

rs (6pcs)

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Thank you for your comments!

power systems: secure control, generation modeling and...

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