Design and Simulation Issues for Secure Power Networks as Resilient - - PDF document

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Design and Simulation Issues for Secure Power Networks as Resilient - - PDF document

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November 2015 Design and Simulation Issues for Secure Power Networks as Resilient Smart Grid Infrastructures Prof. O. A. Mohammed mohammed@fiu.edu Tel: 305-321-5622 Energy Systems Research Laboratory Department of Electrical & Computer

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November 2015

  • Prof. O. A. Mohammed, IEEE SmartGridComm_Keynote, Miami, FL USA

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Energy Systems Research Laboratory, FI U

Design and Simulation Issues for Secure Power Networks as Resilient Smart Grid Infrastructures

  • Prof. O. A. Mohammed

mohammed@fiu.edu Tel: 305-321-5622 Energy Systems Research Laboratory Department of Electrical & Computer Engineering Florida International University Miami, Florida

Keynote Presentation at IEEE SmartGridComm 2015 November 5, 2015 Miami, Florida

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA
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November 2015

  • Prof. O. A. Mohammed, IEEE SmartGridComm_Keynote, Miami, FL USA

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Energy Systems Research Laboratory, FI U

  • Management of increased levels of distributed and

renewable energy sources. (control challenge)

  • Integrating a wide variety of systems governed by

different regulations and owned by different entities.(interoperability challenge)

  • The variable nature of renewable energy sources.

(Generation uncertainty )

  • Real time energy forecasting and energy management

system for generation and demand balancing. (Demand uncertainty)

  • New distributed architectures with many microgrids.

(Resiliency)

Challenges of integrating distributed resources

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

  • Interoperability between different protocols

and applications (in software layer).

  • Identify the communication network and

bandwidth required to collect measurement and control remote sites (Distributed control).

  • Data availability (Delay, corrupted data, denial
  • f service,…etc.)
  • Data security and privacy
  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA
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  • Prof. O. A. Mohammed, IEEE SmartGridComm_Keynote, Miami, FL USA

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Energy Systems Research Laboratory, FI U

20 40 60 80 100 Power Grid Water Nuclear Government Facilities Healthcare Percentage (%) Sectors

Power Grid Cyber Attack Risk

Trustworthy Critical Infrastructures

Critical infrastructures increasingly targeted by cybercriminals

Some Governmental initiatives

– Identified by NSF as a key research area – Critical infrastructure protection (CIP) set by the US Presidential Directives – North American electric reliability corporation (NERC) CIP requirements, 2013

50 100 150 200 250 300 2009 2010 2011 2012 2013 Number of Attacks Year

28X

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

Smart Grid Cyber Infrastructure

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA
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November 2015

  • Prof. O. A. Mohammed, IEEE SmartGridComm_Keynote, Miami, FL USA

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Energy Systems Research Laboratory, FI U

Smart Meter Security Threats

SMART Meter Vulnerability:

  • The AMI network is open to external unsecured

environments such as cellular channels, power line carriers and radio signals.

Cellular, Power line modem Radio Signal (900MHZ)

Adversary

  • The AMI can provide a communication path

to customer systems such as building management systems (BMS) through the customer gateway.

  • If the adversary succeeds in penetrating into

the AMI network and pretending to be a valid smart meter management system, he can easily send a disconnect signal to millions of customers. AMI: Advanced Metering Infrastructure

Disconnection signal. Incorrect price Incorrect Load Data

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA

ZigBee WiFi, etc.

Energy Systems Research Laboratory, FI U

Smart Meter Security Threats

  • In January 16, 2014 Proofpoint, Inc. uncovered

Cyber attack involving conventional household "smart" appliances. The global attack campaign involved more than 750,000 malicious communications coming from more than 100,000 everyday consumer gadgets such as home- networking routers, connected multi-media centers, televisions and at least one refrigerator that had been compromised and used as a platform to launch attacks. Secure Measures

  • The network topology should prevent interaction

between customers in the NAN.

  • Price signal should be authenticated
  • Smart meters use X.509 authentication certificate.
  • Most of the smart meters doesn't update the

certificate for life time (that is a problem). Example

latest discovered bug “Heartbleed” in OpensSSL used to compromise the certificate Zigbee, Wifi

X.509 Certificate is an Authentication Protocol Between Smart Meter and Utility. Uses SSL Certificate Example: an attack on a customer appliance

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA
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  • Prof. O. A. Mohammed, IEEE SmartGridComm_Keynote, Miami, FL USA

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Energy Systems Research Laboratory, FI U

Smart Grid Cyber Infrastructure

(FAN Threats)

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

Field Area Network (FAN)

  • FAN shared multi service IP

network cover Distribution automation, Integrated Distributed resources, Demand Response and field devices

  • Based on Broad Band wireless
  • resources. FAN routers has

WIFI interface for field technician.

  • Data integrity and

confidentiality should be ensured for smart meter data and field devices.

  • If adversary succeed to

compromise FAN router the intruder could easily send wrong signal to switches or field devices

NIST reference Model

NIST Publication 1108 Page 35

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA

FAN routers located on the pole

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  • Prof. O. A. Mohammed, IEEE SmartGridComm_Keynote, Miami, FL USA

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Energy Systems Research Laboratory, FI U

Smart Grid Cyber Infrastructure

(WAM Threats)

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

Security challenges:

  • Most of the protocols were developed for efficient data

transmission in isolated control network without considering the security required for wide spread and open system.

  • Phasor Measurement units PMU depend on external clock

source which can be spoofed or jammed.

  • PMU protocols ( C37.118 and IEEE 1334 ) doesn't support

authentication.

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA

Spoof Network Attack

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Energy Systems Research Laboratory, FI U

  • State estimator can detect

bad data form faulty meters or communication errors

  • Stealth attack can be

designed to be hidden from state estimator.

  • Several types of stealth

attack can be performed against state estimator such as (state, framing and topology attack)

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA

Bad data from faulty meter Bad data Identified State estimator stealth attack Bad data not identified Energy Systems Research Laboratory, FI U

To design security aware WAM, different factors should be considered in the communication and system design such as:

  • Data authentication (insure the source of the Data)
  • Data integrity (detect corrupted or changed data)
  • Proper location of highly secured and encrypt meters

to prevent state estimator attack.

  • Data mining techniques could be used to detect

altered data.

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA
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  • Prof. O. A. Mohammed, IEEE SmartGridComm_Keynote, Miami, FL USA

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Energy Systems Research Laboratory, FI U

  • Cyber Physical security should not only be

considered in the cyber component but also the power system network topology should be designed to be resilient in cases of attack.

  • The control system should be designed to

withstand cyber attack and cyber component failures.

  • Centralized control suffer from single point
  • f failure problems.
  • Successful attack against centralized

control system could lead to serious damage and loss of service

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

  • Decentralized

control reduce the risk of single point

  • f failures and loss
  • f service.
  • Risk of attacking

area and loss of service still high

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA
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  • Prof. O. A. Mohammed, IEEE SmartGridComm_Keynote, Miami, FL USA

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Energy Systems Research Laboratory, FI U

  • Distributed control minimize

the risk of cyber attack.

  • Each node exchange

information and cooperate with neighbor node to improve the system stability.

  • Attack detection can be

improved by data mining from different sources

completely distributed multi-agent control

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

  • The types and levels of data protection used to

encrypt or authenticate signals should be coordinated with signal sensitivity and impact on the system stability.

  • The attack detection should rely on physical

system characteristics as well as the cyber security rules

  • Cyber attack countermeasures should consider

the dynamics and the special nature of the power system.

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA
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  • Prof. O. A. Mohammed, IEEE SmartGridComm_Keynote, Miami, FL USA

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Energy Systems Research Laboratory, FI U

  • We need new modeling and simulation tools to

capture the dynamic nature of both cyber and physical components.

  • We need test bed facility that will provide the

ability to perform experiments involving integration of different technologies in one system (communication, software and physical components)

  • Simulation tools and test bed implementations

should provide the ability to test different types

  • f vulnerability and Launch attack scenarios
  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

  • Complex cyber physical infrastructure

environment is required for identifying possible vulnerabilities and testing solutions

  • The infrastructure should provide modular and

flexible structure to implement different physical topologies and operational scenarios

  • The test environment should seamlessly

integrate with different protocols and support interoperability (standards???).

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA
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  • Prof. O. A. Mohammed, IEEE SmartGridComm_Keynote, Miami, FL USA

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Energy Systems Research Laboratory, FI U

Our verification Platform– The Test Bed has the following capabilities and components:

 Phasor measurement Units

 monitoring, protection, and control

 Distributed and renewable energy sources

 wind, solar, Fuel cells, etc.

 New operational schemes

 protective digital relaying  Wide Area Protection

 Intelligent protection schemes and their application for

 Prevent cascading outages  Islanding situations  Grid blackout

 Emulation of Plug-In-Hybrid and Electric Vehicles (PHEVs) and (PEVs)

 Energy Storage systems, SOC and SOH for batteries

 Integration of Hybrid AC-DC systems

 micro grid solutions for residential and industrial applications.  Enhancement of Energy Efficiency and EMS

 Cyber infrastructure

 Communication network, servers, communication protocols, HIL

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

So, Various Composable Modules are Integrated in the Test Bed

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA
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Energy Systems Research Laboratory, FI U

IEC61850 Relays Transmission lines Buses Synchronizers SCADA

PMUs

Battery Charging And PEV system Pulse load/super cap. micro grid DC architecture Software platform Battery management system Wind, PV, Storage microgrid

  • Prof. O. A. Mohammed.

Energy Systems Research Laboratory, FI U

Configurable transmission Network Remote control Circuit Breaker Remote control load emulator Synchronous generator and control agent Hybrid Microgrid DC Microgrid Communication network

Linux Multi Agent Communication Protocols

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Energy Systems Research Laboratory, FI U

  • Communication middleware is required to

enable information exchange between different controllers.

  • should provide portability and interoperability

between different system component.

  • should provide time predictable performance,

low latency and overhead to meet the real time application requirements.

  • The communication middleware must support

large system expansion and adding new types of data.

  • The Communication Middleware could be

message centric or data centric

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

  • Message centric middleware enables

different nodes to send messages to each

  • ther without regard to physical location or

message contents.

  • Messages must be predefined based on

required data exchange and operation case which limit the system expansion.

  • The application is responsible of insuring

correct data format and parsing received information which add extra overhead in development control application.

  • Data filtering is done at the application level

which lead to poor utilization of network bandwidth.

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA
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Energy Systems Research Laboratory, FI U

  • Data centric middleware provide data

aware communication approach which

  • vercome the disadvantages of message
  • centric. The message is built by the

middleware to exchange and update the system state.

  • Messages are derived from the system data
  • model. (no predefined message structure)
  • Data format validation and parsing

information are done on the middleware

  • level. (reduce the complexity of control

application)

  • Data filtering is done at the middleware level

which improve the utilization of the network bandwidth.

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

Efforts saved By Data centric Middleware

Data centric is chosen to be Used at the smart grid Test bed

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA

Emphasized by Industry (see Smart grid Interoperability Panel)

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  • Prof. O. A. Mohammed, IEEE SmartGridComm_Keynote, Miami, FL USA

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Energy Systems Research Laboratory, FI U

  • Data Distribution service is a communication

standard based on data centric and publisher subscriber approach created by Object Management Group (OMG).

  • Supported By Standard application

Programming Interface API which simplify integration with different applications.

  • Utilize real time publisher subscriber protocol

(RTPS)

  • IEC 61850 implements RTPS

communication.

(provide interoperability between different vendors)

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

  • No message broker or server which avoid single

point of failure.

Communication failure

DDS DATA BUS Client server communication schema

  • Single point of failure
  • Low update rate
  • High latency

DDS publisher/subscriber communication schema

  • Reliable communication (no single point of failure)
  • High update rate
  • Low latency (no intermediate message broker)

Transmitting nodes Transmitting nodes Receiving nodes Receiving nodes

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA
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  • Prof. O. A. Mohammed, IEEE SmartGridComm_Keynote, Miami, FL USA

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Energy Systems Research Laboratory, FI U

Unicast communication

  • Multiple stream for multiple

destination

  • Consume high bandwidth
  • Not suitable for remote or

distributed control where multiple agents need to access the same data

  • Multiple copy of the same data

sent from the source to Each distention.

  • Consume high bandwidth
  • Not suitable for Wide area

measurement since allocated bandwidth for remote site usually low.

  • Transmitting multiple stream

add extra processing overhead

  • n the transmitting nodes and

reduce the update rate

Three copy of the same data

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

  • Single stream for all destination
  • Reduce the network bandwidth
  • Reduce the processor overhead
  • Suitable for low bandwidth link

and remote sites

  • Reach set of Quality of Services

profiles QoS (Predictable delivery)

  • QoS defines the data

transmission priority, life time,

  • rdering based on time stamp

and allowed latency

Multi cast communication

  • Single stream for multiple

destination

  • Optimize network bandwidth
  • suitable for remote or distributed

control where multiple agents need to access the same data Single copy of the data

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA
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Energy Systems Research Laboratory, FI U

DDS Gatewy DAQ/RTU DATABASE

Distributed Data service DDS

DDS Gatewy PROTECTION RELAYS DDS Gatewy LOAD EMULATORES DDS Gatewy GENERATION CONTROL DDS Gatewy

RENEWABLE ENERGY EMULATORS

DDS Gatewy Power system analysis SW packages DDS Gatewy Smart Meters DDS Gatewy ENERGY STORAGE MANAGMEN DDS Gatewy DEMAND SIDE MANAGMENT

The gateway is the bridge between different types of protocols and DDS

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

  • Performance tests were performed to measure the

latency and maximum possible update rate for the implemented DDS.

  • 10,000 messages were sent with different

message rate (from 50 to 1000 Message/sec) and the delay time is observed.

  • The test was repeated with different message

size (from 32 B-63 kB)

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA
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Energy Systems Research Laboratory, FI U

Performance Test for DDS Unicast and Best effort QoS. Performance Test for DDS Multicast and Best effort QoS Max 1.2 ms delay Max 1.4 ms delay Max 0.3 ms delay Higher message size Higher message size

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA

Max 0.35 ms delay

Energy Systems Research Laboratory, FI U

Micro Grid Component Infrastructure

  • DC Microgrids Components

– Modular MIBBC – Battery Bank – Programmable Load emulator – DC bus module – High Freq. Inverter – more

  • Hybrid PEVs Charging system

Components

– LC Filter – Bidirectional ac/dc converter – Bidirectional dc/dc converter – Hardware Overview – Lithium-ion Battery – Management, Diagnostics Software

  • AC/DC interconnected network

components

– Uncontrolled rectifier – Dynamic load emulation – AC Filter – DC Filter – DC/Dc Boost Converter – AC/DC Measurements and Protection – DC Power Module – Medium voltage DC Transmission line model

  • Components of Microgrids for pulse

load studies

– Super capacitor bank – Bidirectional converter – Multi port Boost Converter – Lead Acid Battery Bank – Programmable DC Load

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA
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Energy Systems Research Laboratory, FI U

Smart Grid Test-bed Components (The CPS infrastructure)

  • Distributed control components

– Embedded agent platform – HIL simulators – DDS infrastructure – Smart Meters

– Developed Interface library

  • Phasor measurement Components

– PMUs – PDCs – RTACs – Interface for control and protection modules – Real time phasor data server

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

Hybrid HW/SW CPS Simulation Environment

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA

High Level Architecture IEEE Standard for Federated Simulation

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  • Prof. O. A. Mohammed, IEEE SmartGridComm_Keynote, Miami, FL USA

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Energy Systems Research Laboratory, FI U

Test Bed Interface Library

  • Interface Library developed to

provide control function and data collection capability from local and remote network

  • Real time publisher subscriber

protocol (RTPS) insure interoperability

  • Provide Flexible environment

for Distributed control test and validation

  • Currently available interface for

generators, Micro Grids, CB, measurement nodes, PMU, Load emulators and renewable energy emulators

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

Generator block (slack mode) provide interface to

  • Start and shutdown the

generator

  • Control generator speed
  • Read terminal voltage, line

current and frequency in real time Generator block (Power control mode) provide interface to

  • Start, shutdown and

synchronize the generator

  • Control generator torque
  • Read terminal voltage, line

current and frequency in real time

Microgrid block provide interface to

  • Control active and

reactive power flow from the Micro grid.

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA
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  • Prof. O. A. Mohammed, IEEE SmartGridComm_Keynote, Miami, FL USA

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Energy Systems Research Laboratory, FI U

Wind turbine emulator provide interface to

  • Control wind speed
  • active and reactive

power reference

  • Voltage and current

feedback in real time PV emulator provide interface to

  • Control the PV panel

temperature and irradiance

  • Voltage and current

feedback in real time Load emulator provide interface to

  • Load active and reactive

power Network configuration block Bus measurement block Circuit Breaker control block

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

  • To ensure proper operation and

initialization an automated startup algorithm where developed using finite state machine.

  • The controller is built using state

flow toolbox

  • The automated startup algorithm is

divide into two layer.

  • The top layer (supervisory layer)

which monitor the state of the CB and generators and ensure correct startup sequence

  • The bottom layer which handle the

generators startup and synchronization process

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA
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Energy Systems Research Laboratory, FI U

Generator in P control mode

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

Control algorithm modeled by Simulink Developed interface library Control signal

Control signal Control signal Feedback Feedback Feedback

SIMULATION Real System

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA

Different types of attacks can be performed on actual comm. network Attack models can be simulated in real time (e.g. false data injection, delayed attack, …)

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Energy Systems Research Laboratory, FI U

Generator Interface Block Load emulators Interface Block Bus measurements Interface Block Control Model Load pattern

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

20 40 60 80 100 120 140 160 180 10 20 30 40 50 60 70 80 90 100 Frequency Time(sec) Frequency(Hz) 20 40 60 80 100 120 140 160 180 20 40 60 80 100 120 Load Bus Voltage Time (sec) Voltage (V) 20 40 60 80 100 120 140 160 180 100 200 300 400 500 600 700 800 900 1000 Generator Output Power Time (sec) Power (W) 20 40 60 80 100 120 140 160 180 0.5 1 1.5 2 2.5 3 Generator Current Time (sec) Current (Amp)

Bus voltage Generator Frequency Load Generator Current

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA
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Energy Systems Research Laboratory, FI U

Multi Agent Data Information Model for Power Systems

  • Decentralized control is

established using multi agent frameworks

  • Agents interact and cooperate

to achieve a global or local

  • bjective (through an
  • ptimization function)

In future active distribution networks, simultaneous power system operations will be controlled by the system operator and private microgrid operator entities:

  • Frequency and voltage support (Ancillary Service)
  • Online DER Scheduling (Optimal Dispatch)
  • Market models (Auctions, Dynamic Pricing)

There is a need to perform concurrent control. Multi agent control applications are required.

Agent Entity

Peer-to-peer Communication

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

  • An agent needs to interact with its

environment through sensors and actuators.

  • A sensor acquires the data from the
  • utside world and the actuator responds

according to the agent’s decision.

Agent Platform Sensors Decision Making Actuators Environment Perception Action

How Can We Link Power System Physical Objects to Agent Platforms?

  • For Actual Multi Agent Field

Implementation : Need to link agent objects to distributed industrial control systems.

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA
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25

Energy Systems Research Laboratory, FI U

Can IEC 61850 Meet Decentralized Control of Active Distribution Network Demands?

 The smart grid concept covers an extensive control, automation and protection applications.  IEC 61850 does not meet all the required forms

  • f monitoring and information exchange demands.

 Active distribution networks require dynamic adjustment of primary, secondary and tertiary control levels.

  • Frequency and voltage support

(Ancillary Service)

  • Online DER Scheduling

(Optimal Dispatch)

  • Market models

(Auctions, Dynamic Pricing) Advanced intelligent multi agent frameworks are necessary with a flexible ability to create tailor‐

made decentralized control schemes while allowing the legacy protocols.

AESPO Agent Microgrid Agent

DER Agent Microgrid Agent DER Agent Microgrid Agent DER Agent DER Agent DER Agent DER Agent

Tertiary Level Control Agent Communication Secondary Level Control Agent Communication

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

Agent codes are developed and performed in real‐time power system applications

OPC UA MIDDLEWARE Cloud Communication Interface

Client / Server

Java Client FIPA Messages IEC 61850 Manufacturing Message Specification (MMS)

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA

MULTI AGENT FRAMEWORK

The foundation for intelligent physical agents (FIPA)

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26

Energy Systems Research Laboratory, FI U

Proposed Multi Agent Framework

 The foundation for intelligent physical agents (FIPA) is an

  • rganization which intends to

evolve inter-operable agent communications with semantically meaningful messages, such as how messages are transferred and presented as objects.  Taking the specific benefits of two major frameworks, We want to provide a flexible framework for active distribution network application layers  Merging IEC 61850, FIPA and the open connectivity unified architecture (OPC UA) standards  OPC UA a middleware for abstracting various IED protocols into an interoperable interface for secure and reliable data exchange.

OPC UA MIDDLEWARE Cloud Communication Interface

Client / Server

Java Client FIPA Messages IEC 61850 Manufacturing Message Specification (MMS)

  • Prof. O. A, Mohammed, IEEE SmartGridComm, Miami, FL USA

Energy Systems Research Laboratory, FI U

Conclusions

  • The smart grid requires new set of modeling, simulation and

experimental tools to implement and validate new designs.

  • The smart grid test bed provide unique integrated environment for

testing developed techniques involving cyber and physical component

  • The DDS provide real time performance and distributed

architecture which simplify the data exchange and control implementation.

  • The RTPS ensure the interoperability between different nodes
  • The interface library for the smart grid test-bed provide flexible

and powerful tools to integrate with simulation packages.

  • The federation simulation provide flexible tool for co-simulation of

CPS system to identify full system behavior.

  • Agent based control schemes provide decentralized operation

capability for power systems with data information modeling and protocols.

  • Prof. O. A. Mohammed, IEEE SmartGridComm, Miami, FL USA