Tutorial - italy.ieeer8.org · Control System DMS/EMS DMS ICT System (ns-2) α, β, Δt ICT Distribution Network data Distribution System Load Flow calculation X S SSH/SCP X(t 0)

Emilio Ghiani - Cosimulation of power system and communications networks for smart distribution planning and operation studies

Research and Technologies for Society and Industry - RTSI 2015

Dr. Emilio Ghiani University of Cagliari - Italy

Tutorial

Cosimulation of power system and communications networks for smart

distribution planning and operation studies



Outline

Purpose of the tutorial

Basic co-simulation issues

Co-simulation of active distribution networks

Co-simulation platform

Centralized DMS model

Power system model

Communication system model

Application example

Smart distribution network

Discussion on simulation results



Purpose of the tutorial

To provide insight to co-simulation methods and

techniques as well as how they are being

applied in distribution systems

To assist smart grid simulator developers that

wish to gain insights and learn more about

simulator paradigms, architectures, etc

It is not our intention to provide a detailed

implementation guide for smart grid simulators




What is co-simulation?

Integration of software packages?

How can I create or retrofit a simulation program to

exchange information with other simulation programs?

Composition of models? How can I take two models that differ in their resolution of

time, aggregation of entities or phenomena, or in some other

way and put them together to create a useful whole?




The co-simulation approach usually involves

the integration of two or more simulators to

capture the cyber (multi)physical dependency

of a process/complex system.




Automobiles are typical examples of Cyber-Physical Systems

Chemical energy (gasoline, diesel, ethanol fuel...) or electrical energy is converted to kinetic energy.

Electronic controllers and networks present in vehicles interact with vehicles components that are sub-systems of multi-physical nature (mechanical, thermodynamic, electrical ...) and whose design involves multi-disciplinary teams.




The design of next generation high-tech smart

electricity grids requires a tight coordination between

computation, communication and control elements

(the cyber part) on the one hand, and physical

processes (the physical part) on the other hand.




Smartgrid co-simulation involves the

interaction of, at least, 2 models of sub-systems:

Power System and ICT System

Smart grid Power system

simulator

Information and Communication

Tecnology System simulator



Integrated simulation of smart distribution systems

Combined and Simultaneous simulation of Power and ICT system

Co-simulation

Comprehensive simulation

Systems are analysed by their

own dedicated simulators

Integration is obtained by

appropriate designed interfaces

as well as coordinated simulation

management



Integrated simulation of smart distribution systems

Combined and Simultaneous simulation of Power and ICT system

Co-simulation

Comprehensive simulation

The challenge is to bring

together both system models and

solving routines which leads either to

integrate power systems simulation

techniques into a communication

network simulator or vice versa

Analyzes both domains

combining power system and

communication network

simulation in one environment



Simulation of active distribution networks

Sensors for signal (voltage/current) acquisition

Elements for transmitting the information to the

command/control unit

Command/control units that take decisions and give

instructions based on the available information

Components transmitting decisions and instructions

Actuators that perform or trigger the required action

SDN will consist of diverse components that need

to be modelized in co-simulation:



The development of the future energy system

requires a radical change in the operation of the

electricity distribution network (smart/active grid

paradigm)

Smart distribution networks (SDN) have

systems in place to control a combination of

distributed energy resources (DERs), and

distribution system operators (DSOs) can

operate the electricity flows




Primary Substation

Secondary Substation


Tie Switch CB

SS SS ASS ASS SS SS

CB: Circuit Breaker

SS: Sectionalizing Switch

ASS: Automated Sectionalizing Switch

Fuses

Normally closed

Normally open

DG

DG

DG

Traditional Distribution Network




Primary Substation


Tie Breaker

CB

SS SS CB CB SS SS

CB: Circuit Breaker

SS: Sectionalizing Switch

CB

Normally closed

Normally open

IED

IED

IED

IED

MEMS

MEMS

SCADA

DMS

IED: Intelligent Electronic Device

DMS: Distribution Management System

MEMS: Microgrid Energy Management System

SCADA: Supervisory Control And Data Acquisition

MV Microgrid

DG

DG

DG

DES DES

DES

Communication link

MEMS

LV Microgrid Microgrid Communication link

MEMS

Smart Distribution Network




Intelligent Electronic Devices (IEDs)

• smart meters, sensor and PMUs

• two-way digital communication

• actuators

• able to perfom control commands to DER

• able to communicate with SCADA systems

• enable distributed intelligence to be applied to achieve faster self-healing methodologies

• fault location/identification




Introduction to simulation of active distribution network operation

A/D P - DSP

TX/RX

DMS

Power System

IED

P

Q

IEDs are going to be RTU/PMUs with control and communications capabilities Today RTU/PMU applications could be dedicated to the observability of the

distribution grid

DER



In SDN context, ICT is not a simple add-on of the electrical system, but its availability and efficiency is essential to the operation of the entire power distribution system

Co-simulation is then essential for analyzing different issues related to SDN implementations, since it allows to simulate the power distribution system and the ICT system behaviour simultaneously, taking into account the interdependences among the two systems.

Introduction to simulation of distribution network operation



Co-Simulation based approaches should be

utilized to develop, test and verify paradigms for

next generation monitoring, control and

operation of smart power systems

Co-simulation, is a potential avenue for

performing proper smart distribution planning

and operation studies




depending on the time scale different model representations are adopted

the time scale considered depends on the use case, related to a part of the

grid




Co-simulation Platform – Conceptual Scheme

Co-simulation is performed with an architecture in which “a single dedicated component is responsible for synchronizing and connecting all the different components offering a unified interface for the control logic (federated simulation).

Communication Network

Power System

Simulation Syncronization

Control

(ns-2) (OpenDss)

(Matlab)

(Matlab)



Power System Simulator – Open DSS

Designed to simulate utility distribution systems

In arbitrary detail, unbalanced power flow, 1-phase & unbalanced 3-phase modeling.

Distributed energy resources

For most types of analyses related to distribution system planning and analysis.

It performs its analysis types in the frequency domain,

Power flow, Harmonics and Dynamics.

It does NOT perform electromagnetic transients (time domain) studies.

Download from: http://sourceforge.net/projects/electricdss/



TLC network– network simulator 2

ns-2 is an open source (linux based) software designed to simulate:

Wired/wireless TLC networks.

Protocols

Traffic

Variable bit-rate and bandwidth

It permits to evaluate network perfomance, latency, errors, QoS

Large number of models available

Download from: http://nsnam.isi.edu/nsnam/index.php/User_Information



TLC network– network simulator 2 ns-2 is used in this application to simulate Wi-MAX

communication network

WiMAX (Worldwide Interoperability for Microwave Access) is a wireless communications standard designed to provide 30-40 Mbit/s data rates

The bandwidth and range of WiMAX make it suitable for the following potential applications:

Providing portable mobile broadband connectivity across cities.

Providing a wireless alternative to cable and digital subscriber line (DSL) for "last mile" broadband access.

Providing a source of Internet connectivity.

Smart grids and metering

Reference: V. C. Gungor e F. C. Lambert. A survey on communication networks for electric system automation. Comput. Netw., vol. 50, n. 7, pp. 877-897, May 2006.

http://en.wikipedia.org/wiki/Last_mile



t1

Time

t2 t5 t3

t0

(ns-2)

t0+t …………..

t4

Syncronization and OpenDSS call- -> Simulation of DSSE

DMS receives/sends control signals to active resources

Co-simulation Platform - Syncronization

Time

(OpenDss)

Power System

Communication Network

(ns-2)

Control system

(Matlab)

IED trigger

DMS Action

ok

IED trigger

DMS Action

fails

DMS Action

ok

Repeat DMS

Action



Co-simulation Platform

Control System DMS/EMS

ICT System

(ns-2)

α, β, ΔtICT

Distribution Network data

Distribution System

Load Flow calculation

LIN

UX

WIN

DO

WS

SSH/SCP

COM X(t0)

ΔP(t0) ΔQ(t0)

αΔP(t0 + ΔtDMS + ΔtICT) βΔQ(t0 + ΔtDMS + ΔtICT)

ΔtDMS

TLC Network data

(OpenDss)

(Matlab)

Geographic coordinates and meteorological

conditions

Tx Rx



Co-simulation Platform - Meteorological Model

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

00

:00

01

:15

02

:30

03

:45

05

:00

06

:15

07

:30

08

:45

10

:00

11

:15

12

:30

13

:45

15

:00

16

:15

17

:30

18

:45

20

:00

21

:15

22

:30

23

:45

[mm/h]

1 2 3 4 … … 30 31

Random extraction

[0,1]

0 0 0 0 1 0

Location meteo profile.xls

Hourly rain level

0

50

….

Hourly rain profile

[mm/h]



DMS/EMS

EMS optimizer

State Estimation

Forecast of local generation

and demand

Technical Constraints

Network data

Set point Distributed

Energy Resurces

Co-simulation Platform – DMS/EMS



Co-simulation Platform – DMS/EMS

Reference: F. Pilo, G. Pisano, G. G. Soma. Optimal coordination of energy resources with a two-stage online active management. Industrial Electronics, IEEE Transactions on, vol. 58, n. 10, pp. 4526-4537, 2011.

Optimal Power Flow for the management of Smart Distribution Networks

_min P GD Var AD DES lossesC C C C C

• 𝐶𝑃_𝐺𝐷 is cost for active power dispatching • 𝐶𝑉𝑎𝑟 is the cost for reactive power dispatching • 𝐶𝐴𝐷 is the cost for demand side integration • 𝐶𝐷𝐸𝑆 is the cost for distributed energy storage dispatching • 𝐶𝑙𝑜𝑠𝑠𝑒𝑠 is the cost for energy losses

Subject to technical (e.g. node voltages and branch power flows during normal and emergency conditions) and economic constraints (e.g. costs for dispatching active resources and joule losses).



Co-simulation Platform – Flow diagram

t = Synchronization time-step Start

State Estimation

Network Data Acquisition

t = t + t[s]

Network constraints

check No

violations

Violations

DMS optimization

New P/Q Settings

Signal Transmission

DMS Action check

Yes No New P/Q setpoints

t = 0

t ≤T

DG Disconnection



Co-simulation Platform – ICT Reliability

detailed modeling of the ICT network highest degree of detail and consistence

of simulation to reality greater trustworthiness of results easier examination on weaknesses in

the ICT structure

Tx Rx

Tx

Rx

Router Router

DMS/EMS

IED

DMS/EMS DER

DER

DER Latencies, Failures…

Ideal

communication link

communication link

DMS/EMS

Ideal Ideal

Tx Rx

ICT chain

ICT chain

ICT chain

black-box modeling of the ICT network useful to demonstrate the robustness

of the smart grid in conditions of non-delivery of the control signals

ideal modelling of ICT network co-simulation study allows to verify the

correctness and the efficiency of the control algorithms



Application example

Analysis of a centralized DMS/EMS actions

on active power distribution nework

No DMS/EMS intervention

P/Q control

P control

Q control

TLC network performance analysis



Application example - Software Interface



34 49 55 53 39 48

45 51 36 50 37

54

86 83 101 84 81 94 85 99 103 82

29 31 30 27 28

33

69 75 73 72 70 74 71

15 14 22 8 20 4 25 21 12 5 16

56

67 59 66 68 65 64 62 63

58 61 57

9 19 26 11

24 23

7 13 3 6

17

92 93 80

96 91

87 90

40

35 44

38 52

95 76

60

18

2

42

32

10

1 MW

1.75 MW

2 MW

3 MW

3 MW 1 MW

1 MW

41 46 43 47

3 MW

3 MW

3 MW

1 MW

3 MW 79

3 MW

3 MW

3 MW

3 MW

3 MW

88 98 100 102 97 89 78 77

SCADA

DMS

Application example – MV 103 Nodes

Feeder Wind [MW]

PV [MW]

LOAD [MW]

F1 0 6 4

F 2 0 4.75 2.5

F 3 1.75 14 4

F 4 0 0 0.3

F5 0 3 2.8

F 6 0 0 3.7

F7 6 4 2.5



Application example - DER with P/Q variable capability curves

P<400kW

P>400kW



V [pu] P [MW] Q[MVAR]

Time [h]

0

0.5

1

1.5

2

2.5

3

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

00:00 04:00 08:00 12:00 16:00 20:00

Overvoltage threshold

Summer day - No EMS intervention

@b

us

04

7

@b

us

04

7

@b

us

04

7



Summer day - No EMS intervention

DER @bus 047 – most critical node

Overvoltages and undervoltages are detected

by IED and transmitted to control center.

Day 10: 10 Jun 2020 00:15:00 00:30:00 [...] 10:45:00 11:00:00 Overvoltage on N_047 @ 11:00:00 detected by IED



Summer day - P control

Time

V [pu]

-0.5

0

0.5

1

1.5

2

2.5

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

00:00 04:00 08:00 12:00 16:00 20:00

P [MW] Q[MVAR]

Time [h]


DMS P correction

@b

us

04

7

@b

us

04

7



Summer day – P/Q control

-1

-0.5

0

0.5

1

1.5

2

2.5

3

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

00:00 04:00 08:00 12:00 16:00 20:00


Time [h]

DMS P/Qcorrection


DMS P/Qcorrection

Undervoltage threshold @b

us

04

7

@b

us

04

7



Summer day- Q control

Time


-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

0.9

0.92

0.94

0.96

0.98

1

1.02

1.04

1.06

00:00 04:00 08:00 12:00 16:00 20:00

Time [h]


DMS Q correction

DMS Q correction

Undervoltage threshold

@b

us

04

7

@b

us

04

7



In case of no ICT available DG

are automatically disconnected

from network in case of

contingencies (interface device)

Summer day – ICT not delivering signal

Voltage: 85% Vn V 110% Vn Fequency: 47.5Hz f 51.5Hz



Summer day – ICT not delivering signal

Time -1

0

1

2

3

4

5

6

7

8

9

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2V [pu] P [MW] Q[MVAR]

Time [h]


DG automatically disconnected

(300s automatic reclosing) @

bu

s 0

47

@b

us

04

7



Further analysis: influence of packet size on communication latency – 20kmx20km area

15

10

5

5 10 15 20

20

km

DER_1 DER_2

DER_4

DER_3

DER_8

DER_9

DER_5

DER_6

DER_7

DER_10

ns-2 ICT network analysis of WiMAX performances

10-30 IED/DER randomly spread along a 20kmx20km area

Variable bit packet size



Further analysis: influence of packet size on communication latency – 20kmx20km area

0

50

100

150

200

250

300

350

400

450

500

10 nodes 20 nodes 30 nodes

100 Bytes

500 Bytes

1000 Bytes

1500 Bytes

Latency [ms]

Number of nodes



Further analysis: influence of distance to cover on communication latency – point to point link

Variable distance – 10-30km

ns-2 ICT network analysis of WiMAX performances 10-30 km distance Variable bit packet size



Further analysis: influence of distance to cover on communication latency – point to point link

0

2

4

6

8

10

12

14

16

18

10 km 20 km 30 km

100 Bytes

500 Bytes

1000 Bytes

1500 Bytes

Latency [ms]

Distance



Co-simulation software output for for operation and planning analysis

Information available

Hourly/daily V/P/Q plots

.xls reports (long term analysis)

Single/average point to point communication latency

ICT reliability metrics

Number of DG disconnections

Energy curtailment evaluation

…..



Conclusions

This presentation has showed the capabilities of a co-simulation platform based on commercial and open source software for active management of distribution networks

In order to simulate all possible smart grid environments, different co-simulators need to be built

Steady state power system simulators are useful for control operation strategies of active distribution networks

Protection studies need co-simulators with power system transient analysis capabilities



Relevant Bibliography 1. K.Mets, J.A.Ojea, C.Develder. Combining power and communication network simulation for cost-

effective smart grid analysis. IEEE Communications Surveys and Tutorials, 16 (3), art. no. 6766918,

pp. 1771-1796. 2014.

2. G. Celli, P.A. Pegoraro, F. Pilo, G. Pisano, S. Sulis, DMS Cyber-Physical Simulation for Assessing

the Impact of State Estimation and Communication Media in Smart Grid Operation, IEEE

Transactions on Power Systems, vol.29, no.5, pp.2436,2446, Sept. 2014.

3. M. Garau, E. Ghiani, G. Celli, F. Pilo. S. Corti. A Co-simulation tool for active distribution networks.

Proc. of CIRED Workshop 2014. Rome 11-12 June 2014. Paper No 0198.

4. F. Pilo, G. Pisano, G. G. Soma. Optimal coordination of energy resources with a two-stage online

active management. Industrial Electronics, IEEE Transactions on, vol. 58, n. 10, pp. 4526-4537,

2011.

5. V. C. Gungor e F. C. Lambert. A survey on communication networks for electric system automation.

Comput. Netw., vol. 50, n. 7, pp. 877-897, May 2006.

6. X. Yuzhe; C. Fischione, "Real-time scheduling in LTE for smart grids," Communications Control and

Signal Processing (ISCCSP), 2012 5th International Symposium on , vol., no., pp.1,6, 2-4 May 2012.

7. Atlantide project network database. www.progettoatlantide.it

Documents

Tutorial - italy.ieeer8.org · Control System DMS/EMS DMS ICT System (ns-2) α, β, Δt ICT Distribution Network data Distribution System Load Flow calculation X S SSH/SCP X(t 0)