MULTI AGENT BASED CONTROL AND PROTECTION FOR … Thesis.pdf · MULTI AGENT BASED CONTROL AND PROTECTION FOR AN INVERTER BASED MICROGRID Asitha Lakruwan Kulasekera (108020B) Thesis

MULTI AGENT BASED CONTROL AND PROTECTION

FOR AN INVERTER BASED MICROGRID

Asitha Lakruwan Kulasekera

(108020B)

Thesis submitted in partial fulfilment of the requirements for the degree Master of

Science

Department of Mechanical Engineering

University of Moratuwa

Sri Lanka

February 2012

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DECLARATION

I declare that this is my own work and this thesis does not incorporate without

acknowledgement any material previously submitted for a Degree or Diploma in any

other University or institute of higher learning and to the best of my knowledge and

belief it does not contain any material previously published or written by another

person except where the acknowledgement is made in the text.

Also, I hereby grant to University of Moratuwa the non-exclusive right to reproduce

and distribute my thesis/dissertation, in whole or in part in print, electronic or other

medium. I retain the right to use this content in whole or part in future works (such as

articles or books).

Signature: Date:

The above candidate has carried out research for the Masters/MPhil/PhD thesis under

my supervision.

Signature of the supervisor: Date:

Signature of the supervisor: Date:

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Table of Contents

List of figures ........................................................................................................... v

List of tables ........................................................................................................... vi

List of abbreviations ............................................................................................... vii

1. Introduction ...................................................................................................... 1

1.1. Emergence of smarter electrical grids and microgrids ................................. 1

1.1.1. Smart grid ........................................................................................... 2

1.1.2. Smarter microgrids.............................................................................. 3

1.2. Use of multi agent systems ......................................................................... 4

1.3. Thesis statement ......................................................................................... 5

1.3.1. Methodology ....................................................................................... 6

1.4. Contributions ............................................................................................. 8

1.5. Organization .............................................................................................. 8

2. Literature Review ............................................................................................. 9

2.1. A Multi agent system ................................................................................. 9

2.1.1. Features of a multi agent system ....................................................... 10

2.2. Multi agent systems and applications ....................................................... 11

2.3. Toolkits/frameworks for development of multi agent systems .................. 12

2.4. JADE and Zeus – a comparative overview ............................................... 12

2.5. JADE agent building toolkit ..................................................................... 14

2.5.1. JADE architecture ............................................................................. 14

2.6. Recent applications of multi agent systems in microgrids ......................... 17

2.6.1. Distributed control ............................................................................ 17

2.6.2. Electricity trading ............................................................................. 19

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2.6.3. Optimization ..................................................................................... 20

2.6.4. Power restoration .............................................................................. 21

2.7. Control architectures ................................................................................ 21

3. Conceptual design of dual-layered MAS ......................................................... 24

3.1. Dual layered architecture ......................................................................... 24

3.2. Agents used in the dual-layered MAS ...................................................... 26

4. Implementation of dual-layered MAS ............................................................. 28

4.1. Dual-layered Multi agent system implementation in JADE ...................... 28

4.1.1. Agent specification ........................................................................... 28

4.1.2. Application analysis .......................................................................... 29

4.1.3. Application design ............................................................................ 30

4.1.4. MAS algorithm implementation ........................................................ 34

4.2. Running multi agent system application ................................................... 36

5. Simulations and Results .................................................................................. 37

5.1. Description of simulation circuit .............................................................. 38

5.2. Intentional islanding during a fault ........................................................... 39

5.2.1. Messages exchanged among agents and physical entities .................. 40

5.2.2. Updates to the ontology .................................................................... 42

5.2.3. Results and discussion ...................................................................... 43

5.3. Protecting critical loads during intentional islanding ................................ 45




5.4. Managing critical loads during islanded operation .................................... 49


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6. Conclusions and Future Directions ................................................................. 55

6.1. Conclusions ............................................................................................. 55

6.2. Future work ............................................................................................. 56

References ............................................................................................................. 57

Section 4: Impact of Research results ...................... Error! Bookmark not defined.

Section 5: Publications ............................................. Error! Bookmark not defined.

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LIST OF FIGURES

Figure 1.1 A smart grid application .......................................................................... 2

Figure 1.2 Conceptual model of a microgrid ............................................................. 4

Figure 2.1 Example of a multi agent system ............................................................. 9

Figure 2.2 JADE architecture ................................................................................. 15

Figure 2.3 Basic multi agent system structure ......................................................... 22

Figure 2.4 Single layered multi agent system architecture ...................................... 23

Figure 3.1 Proposed dual layered MAS architecture ............................................... 24

Figure 3.2 Interaction between agents in the dual layered multi agent architecture .. 25

Figure 4.1 Dual-layered MAS ontology .................................................................. 31

Figure 4.2 Control algorithm for multi agent system operation ............................... 34

Figure 4.3 Establishing communication between simulation environment and MAS36

Figure 5.1 Simulated microgrid test-bed ................................................................. 38

Figure 5.2 Test-bed set-up for case study 1 ............................................................. 39

Figure 5.3 Control flow chart for intentional islanding ........................................... 40

Figure 5.4 Key messages exchanged between agents during islanding .................... 41

Figure 5.5 Line to line voltages during intentional islanding ................................... 43


Figure 5.7 Line to line voltages during securing critical loads ................................ 48


Figure 5.9 Control flow chart for load management during islanded operation ....... 50

Figure 5.10 Line to line voltages during islanded operation .................................... 52

Figure 6.1 Proposed microgrid test-bed .................................................................. 56

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LIST OF TABLES

Table 2.1 A comparison between JADE and ZEUS platforms ................................ 13

Table 4.1 Microgrid domain knowledge, attributes and initial values ...................... 33

Table 5.1 Updated concepts, initial and updated values for case study 1 ................. 42



Table 5.4 Priority revision during islanded mode .................................................... 54

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LIST OF ABBREVIATIONS

Abbreviation Description

DG Distributed Generation

DSM Demand Side Management

FIPA Foundation for Intelligent Physical Agents

FOSS Free and Open Source Software

GHG Green House gasses

GUI Graphical User interface

JADE Java Agent Development Framework

MAS Multi Agent Systems

NCRES Non-conventional Renewable Energy Sources

OS Operating Systems

PC Personal Computer

PCC Point of Common Coupling

PV Photovoltaic (cell)

PWM Pulse Width Modulation

SCADA Supervisory Control and Data Acquisition

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SECTION 3

1. INTRODUCTION

An agent is a software entity which can operate on its own by responding and

reacting with its environment. A collection of such agents collaborating to achieve an

overall goal can be considered as a multi agent system (MAS). Such systems are

ideal for the complex real-time control problems that are faced with today.

Multi agent systems are being widely used in computer systems and e-commerce

applications [1], [2]. The collaborative social ability of agents is enhanced by the

presence of fast communication capabilities in such applications (i.e. web/internet).

With the integration of fast communication capabilities into power grids in building

future-proof smart(er) grids, the possibility of integrating multi agent systems to

power engineering is becoming real [3]. The unique feature set available in multi

agent systems is proving to be the correct fit in terms of the requirements in power

engineering applications.

This section provides an introduction to the research area, which the application of

multi agent systems in microgrid applications. The importance of this work is briefly

explained and is followed by the thesis statement.

1.1. Emergence of smarter electrical grids and microgrids

The ever increasing demand for electrical power has been a major challenge for the

electrical supply authorities worldwide [4]. The present grid systems which have

mostly been built over the last century are rapidly aging [4]. These legacy grid

infrastructures are becoming increasingly congested and are seen as incapable of

meeting the future energy needs of an information economy. The renewed interest in

climate change mitigation through reduced Green House Gas (GHG) emissions has

ushered in a need of greener generation and utilizing renewables in the energy mix.

Therefore, these power systems have to become smarter, more reliable and more

robust in taking on renewable energy sources without losing stability and efficiency.

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1.1.1. Smart grid

A smart grid is the use of sensors, communications, computational capabilities and

control to enhance the overall functionality of the electric power grid [4]. This

concept is depicted in Figure 1.1.

Figure 1.1 A smart grid application

Smarter grids are a step forward in overcoming the limitations of legacy electricity

infrastructure[4]. They must be automated and be able to behave as a distributed

energy delivery system, that incorporates two-way flow of electricity & information

including advanced sensing & monitoring [4] along with the following

functionalities [5].

• Visualizing the power grid in real time

• Increasing system capacity

• Relieving bottlenecks

• Enabling a self-healing network

• Enabling enhanced connectivity to consumers

• Allowing incorporation of distributed non-conventional renewable energy

sources (NCRES)

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One rising feature integrated into smart(er) grids is the microgrid concept [6]. It

allows for far greater control over improving grid reliability [5], resilience towards a

wide range of threats to the electrical grid [7], allow for easy incorporation of

distributed generation [8], reduce GHG emissions etc. Therefore, in developing the

future smarter grid, implementation of successful microgrids are vital.

1.1.2. Smarter microgrids

Smarter microgrids are part of the solution to the problems faced by legacy electrical

infrastructure. Their capability to house local embedded generators as distributed

generation and be locally controlled offers various advantages over legacy systems.

In order to realize the potential of such smarter systems, a high degree of distributed

control is required [6]. As a novel method in providing this flexible distributed

control requirement, Multi Agent Systems (MAS) are stepping forward.

Centralized bulk generation facilities are giving way to, more distributed small scale

generation. Distributed generation (DG) introduces the capability of embedding a

wide range of low emission and potentially lower cost generation options. Such

options can include internal combustion engines, micro-turbines and non-

conventional renewable energy sources such as solar photo voltaic, wind, biomass,

mini/micro-hydro and even waste-to-energy systems. These smaller generation

technologies allow the sources to be placed optimally with respect to the loads, thus

reducing emissions and transmission losses, while also allowing for local control

instead of central dispatching [6].

Such DG systems provide the potential to take a sub-system approach in working

with microgrids. During a disturbance in the main grid, local generation and their

loads can be separated from the utility and isolate the local loads from the

disturbance, while maintaining the integrity of the main utility grid. This isolation is

carried out at a single point of connection to the main utility known as point of

common coupling (PCC). This capability, known as islanding, requires high

reliability and flexibility from the microgrid. These requirements can be met by

having peer-to-peer control and plug-and-play capabilities for each entity in the

microgrid.

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A conceptual microgrid model is shown in Figure 1.2. This can be either a small

scale distribution network or an electrical system of a building. Certain loads,

considered critical, will have local power sources. Non-critical loads will have no

local supply. The critical loads can be isolated from upstream outages by islanding at

the PCC and shedding the excess demand of non-critical loads.

Figure 1.2 Conceptual model of a microgrid

1.2. Use of multi agent systems

Most conventional power systems rely on Supervisory Control and Data Acquisition

(SCADA) systems for control and communications. The control and communication

architecture provided by such SCADA systems is built upon proprietary

communication and signalling protocols. In terms of a microgrid, the aim is to allow

for flexible integration of a multitude of different distributed energy resources (DER)

and/or non-conventional renewable energy sources (NCRES). However the vendor

specific control protocols used in commercially available SCADA systems for

microgrids (i.e. RSView32 [9], Networked Distributed Resource™ and Central

Operation Management System) restrict the interconnectivity and interoperability of

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diverse systems. Such restrictions can result in increased deployment costs and

prevent microgrid rollout.

Use of multi agent systems to provide distributed control capabilities to the

microgrid applications offers various advantages over conventional SCADA systems.

Multi agent system development platforms are available as Free and Open Source

(FOSS) applications and most of them are based on Java, making them platform

independent. They can also be combined with external programming to interface

with different external hardware and software systems [1]. These advantages make

multi agent systems more suited for microgrid control. In addition, multi agent

systems also have the following advantages in context of microgrid control:

i. Multi agent systems are inherently flexible and extensible.

ii. Local loads and sources will have different specifications and systems.

Therefore, an intelligent distributed control system is the best solution.

iii. The microgrid will have to take rapid autonomous decisions regarding

seamless transition to islanded mode from grid connected mode and

protecting critical loads by load shedding.

iv. Multi agent systems allow for a complex task to be broken down into several

smaller tasks assigned to a team of agents. This allows for easier handling of

a larger problem.

1.3. Thesis statement

The objective of this research is to design, develop and simulate a multi agent system

that enables real-time energy management of a microgrid. This will include the

management and control procedures during transition from grid-connected to

islanding mode; the algorithms to manage priority levels of the connected loads and

protect critical loads utilizing available limited capacity from local embedded

generators during outages.

Novel MAS which provides scalable and robust distributed control architecture for

microgrid application is presented in the thesis. The MAS model is designed using

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the JADE (Java Agent Development framework) platform and is implemented on a

simulated test-bed in MATLAB/SIMULINK.

The steps taken to realize the objectives of the thesis statement are listed below, and

are further detailed in Section 1.3.1:

1. Identification of suitable agent building framework

2. Design of Multi agent system architecture

3. Development of Multi agent system

4. Integration of the multi agent system and the microgrid simulation

5. Validation of results using simulation studies

1.3.1. Methodology

the methodology consists of the following five steps:

Step 1: Identification of suitable agent building framework

Several commercial and open-source multi agent system building toolkits are

currently available today for developing complex multi agent system in which the

design phase has been simplified [10], [11]. Such tool kits have been assessed in

literature in terms of time required for analysis and design, code generation,

integration with external code, response time, the number of messages exchanged

among agents etc. Further details regarding the selection of a suitable toolkit is

discussed in Section 2.4.

Step 2: Multi agent system architecture Design

The multi agent system aims to provide control and protection system for an inverter

based microgrid. The aim is to break down a larger goal into several smaller tasks

and delegate them to individual entities rather than to a single controller. Further

details on designing the multi agent architecture is presented in Section 3.

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Step 3: Development of dual-layered multi agent system

Developing a multi agent system using a selected tool kit consists of following steps.

Initially, the abilities of each agent have to be specified, followed by identifying each

of their roles and responsibilities, through building role models and defining social

and domain responsibilities. Then the ontology of the system has to be created based

on modelling their knowledge or facts. All of these are concluded by creating the

agents and generating the code. This process is detailed in Section 4.

Step 4: Integration of the multi agent system and the microgrid simulation

The developed multi agent system attempts to control physical entities or a

simulation of the same. The multi agent system and the simulation are inherently in

two domains. The multi agent system runs on a JAVA platform while the simulation

is run on a simulating environment (i.e. MATLAB/SIMULINK). Therefore, in order

to establish communication between the two domains, for sensing and control

TCP/IP is used. A third party TCP/IP server is utilized as an interface to MATLAB

and it connects to another middle server which allows multiple simultaneous TCP/IP

connections. The middle server is required because; the interface allows only a single

connection at a time. Details regarding the creation of this interface are given in

Section 4.2.

Step 5: Validation of results using simulation studies

Three case studies are carried out to verify the implementation of the multi agent

system within a simulated microgrid test-bed environment. They demonstrate the

functionality of the system in islanding, protecting/securing supply to critical loads

and managing load priorities during islanded mode operation. The case studies and

their results are presented in detail in Section 5. Details of the microgrid test-bed

used for simulations are given in Section 5.1.

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1.4. Contributions

In summary, the proposed multi-agent system control architecture is designed,

developed and simulated in several simulation case studies. It is expected that this

research will provide an insight into the design and development of a multi-agent

system. Furthermore, this research also contributes novel control architecture to

increase effectiveness of multi-agent system in the context of microgrids.

Specifically, this comprises control algorithms for managing the available limited

supply from local embedded sources to protect local critical loads during fault/s in

the main grid.

1.5. Organization

Rest of the thesis is organized as follows. Section 2 summarizes related work.

Sections 3 and 4 explain the methodology presented in Section 1 in more detail.

Section 5 presents the case studies and results, and the final section presents

conclusions and future work.

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2. LITERATURE REVIEW

An agent system is hardware or software entity that possesses social coordination

and communication capabilities and capable of achieving a larger overall goal by

tackling smaller individual tasks. Agents have the ability to operate within its

environment to achieve its goal by sharing knowledge with other agents or taking

initiative.

Even a single agent can operate as a system, by reacting and interacting with its

environment. Such an agent will require specific programming to provide itself with

individual knowledge, actions and goals. Any other agents in the same environment

will not have any effect on the operation of such an agent.

2.1. A Multi agent system

A collection of social, collaborative agents constitute a multi agent system. All the

agents will inherit a set of common goals, and react to change in the environment,

make decisions to achieve those goals, or help other agents in achieving their goals

[1]. In contrast to single agent systems, other agents will also be a part of any agent’s

environment.

An example multi agent system is described in Figure 2.1. It shows a community of

agents in a stock market environment.

Figure 2.1 Example of a multi agent system

Source: Manage the Complexity with Intelligent Agent Technology

(http://home1.gte.net/pfingar/eba.htm)

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The selling agent only has direct knowledge of potential buyers and maximizing the

profit. Other agents will know about their own domains. The trading rules agent

knows of all the rules and regulations; the risk assessment agent is able to assess the

risk associated with any stock; Data mining agent is able to access and understand

patterns in the sales data and the forecasting agent is able to forecast the rise and fall

of stock prices. The selling agent can make a more educated decision on trading a

particular stock by collaborating with his fellow agents, than on his own. He can

communicate with the other agents in his environment and ask for any supporting

information that will enable him to maximize his sales and/or profits.

2.1.1. Features of a multi agent system

As the aim of this thesis is to implement a multi agent system for distributed control

(intelligence), the following available feature set is ideally suited for such

applications.

Autonomy – agents are capable of operating without human supervision.

Social ability – agents are able to communicate with each other as well as human

operators using a common language.

Reactivity – agents are able to identify and react to changes in the environment.

Proactivity – agents take decisions or initiatives based on their assigned

objectives/goals.

Agents also hold certain knowledge or beliefs regarding their existence or their

environment. This knowledge and beliefs enable the agents to take decisions

regarding changes in their environment and seek to fulfil their objectives. In addition

to these features, agents have all or some of the following capabilities as well.

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Data collection – an agent contains or will be able to collect data to build-up

knowledge regarding its environment, and assist fellow agents

making their decisions.

Social knowledge – the multi agent system contains an agent who helps the other

agents to know about each other’s existence and share their

capabilities (i.e. yellow pages service).

Learning – agents have learning capabilities to update their knowledge

and beliefs based on changes in the environment, and updates

from fellow agents.

Communication – all agents in the multi agent system should have a clearly

defined set of protocols regarding their communication with

humans or other agents.

2.2. Multi agent systems and applications

Multi agent systems are becoming more and more ubiquitous in the world. Recently,

multi agent systems are being implemented in a many fields such as mathematics,

physics, engineering sciences, and social science communities [12]. They have been

used in a wide array of applications ranging from computing and processing [13–16],

robotics and control systems [17–21], transportation [22–25], aerospace and nuclear

engineering in recent literature. There is a rising trend of applying multi agent

systems in power engineering applications for microgrids which is discussed in detail

in Section 2.4.

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2.3. Toolkits/frameworks for development of multi agent systems

A multi agent system can be built from base coding. However, this is not necessary

as there are many easily available agent development tool kits where the agent

development process has been simplified. There are a number of open-source agent

development toolkits available, such as: Aglets software development kit [26],

Voyager [27], Zeus [11], JADE [10], Tracy [28], SPRINGS [29], and Skeleton [30].

Performance analysis of these agent development toolkits has been comprehensively

discussed in [31–38] in terms of time taken for analysis and design, code generation,

integration with external code, response time, the number of messages exchanged

among agents etc.. Based on these evaluations JADE and ZEUS are initially short

listed as potential contenders. Further analysis carried out to select the best suited

platform is presented in the following Section 2.4.

Step 1: Identification of suitable agent building framework

2.4. JADE and Zeus – a comparative overview

In selecting an agent tool kit / platform for an open-source development application it

is important to select one which is based on a common standard. The established

standard for agent systems is IEEE’s standard on Foundation for Intelligent Physical

Agents (FIPA) [39]. Based on the development platform of a well-known standard

will allow for easier inter-operability between different systems, resulting in

universal applicability. Therefore, the main criteria in selecting the best suited toolkit

for this application are compliance with FIPA standards.

Several studies in the literature [31], [33], [37] have compared the performance of

JADE and ZEUS in terms of availability, design and analysis, number of lines of

code, response time etc. A comparison of JADE and ZEUS platforms are given in

Table 2.1.

.

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Table 2.1 A comparison between JADE and ZEUS platforms

JADE ZEUS

Free and open source Free and open source

FIPA Compliant FIPA Compliant

JAVA Based JAVA Based

Agent Communication Language (ACL) Communication

ACL and Knowledge Query

Manipulation Language (KQML)

Provides authentication Some security capabilities

Decent GUI User friendly GUI

Lower latency in complex systems Higher latency in complex systems

JADE and Zeus along with JATLite and Skeleton are comprehensively evaluated in

[31], grouping them together based on Java and availability of GUI. These four are

evaluated for use in a web-based news retrieval system. The author has evaluated

their performance based on run-time and software building capabilities. The

evaluated parameters in terms of software building characteristics are: time required

for analysis and design of multi-agent system; time required for code generation;

time required for integration of agent with external code; time required for testing

and debugging the system; number of lines in the code and number of new Java

classes and reused classes to build news agents in the system. Selected parameters

for evaluating run-time characteristics are: response time and number of messages

exchanged among agents. While the results show both to be similar in terms of

software building, JADE outperforms ZEUS in terms of run-time performance.

JADE, ZEUS and Skeleton are compared in terms of number of documents requested

from a web server versus response time while varying the number of web-agents in

[33], which show JADE outperforms all others for complex applications.

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In applying a multi agent system for a microgrid or power application, complex

systems maybe required with (near) real-time performance. Therefore response time

and the number of messages exchanged become critical. A delay of 0.1 s caused by

20-25 excess messages will be visible on the power distribution. Therefore JADE

becomes the most suitable tool for the application.

2.5. JADE agent building toolkit

JADE (Java Agent Development Framework) is an open source agent development

toolkit developed and distributed by Telecom Italia under the LGPL (Library Gnu

Public License) license [10]. It is a software development platform which provides

middleware functionalities independent of application and simplify agent creation

[1]. This middleware functionality [40] is provided by:

i. run-time environment where the agents exist

ii. vast class library for simplifying agent development

iii. suite of graphical tools that allow for simple administration and monitoring

of agents

JADE is based on the ubiquitous JAVA platform which makes it platform

independent and highly versatile. Details of the JADE architecture are presented in

Section 5.2.1.

2.5.1. JADE architecture

Each JADE run-time environment in operation is called a container, which can

contain several agents. A set of containers are called a platform. Each platform must

have a primary or main container, and all other secondary containers must register

with it as soon as they are created / initialized. These secondary containers are called

normal containers and must be told where to find the main container in their

platform. If another main container is created it will constitute a different platform.

The agents in these containers can communicate with one another, even if they are in

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the same container, different containers in the same platform or on different

platforms, using the network protocol stack provided by JADE. These notions are

depicted in Figure 2.2 [40].

When the main container is started, it also includes the following two special agents

(these are automatically started):

AMS (Agent Management System) Agent: this agent provides a unique name (Agent

ID or AID) to each agent in the platform (naming service) and represents the

authority in the platform to create/kill agents on remote containers.

DF (Directory Facilitator): this agent provides a Yellow Pages service, using which

an agent can find other agents with capabilities / information it requires in order to

achieve its goals.

Figure 2.2 JADE architecture

In Figure 2.2, A1 to A5 denote agents in two platforms 1 and 2. Agents A2 and A3

are in the same container, Agents A1 and A4 are in different containers, while all of

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them are in the same platform. Agent A5 is in the main container of a different

platform. Each main container hosts its own AMS and DF.

Agents may need to carry out parallel negotiations and each negotiation will need to

proceed at its own pace. Normally for a software entity/program this can be done by

assigning a thread. Modern OS designs limit the number of concurrent threads.

Therefore, in order for efficient parallel operation of agents, JADE provides a

concept called behaviours [41].

Behaviour represents a task that an agent can carry out and is implemented as an

object of a class that extends jade.core.behaviours.Behaviour. A behaviour is

essentially an event handler [42], a method which defines how an agent will react to

a given event. An event can be defined as a change of state in the environment. In

JADE, behaviours are classes and any code written to handle a given event will be

placed in a method called action. As behaviours are methods, they are processed one

after the other by an agent’s thread. Normally, this requires complex programming to

allow threads to pause in the middle of execution to listen for a response and

continue without losing context (active and passive phases). JADE simplifies this

process by allowing the easy creation of different behaviours for all active phases in

an activity, and arranging them to be created and triggered in the correct sequence

[43]. Each behaviour class must implement the following abstract methods:

Action( ) – actual task to be accomplished by the specific behaviour class

Done( ) – returns true when the behaviour has finished and false when the behaviour

has not and re-executes action

Reset( ) – restarts a behaviour from the beginning

An agent can be created by extending the agent class and providing agent specific

capabilities by programming one or more behaviour subclasses [41]. JADE provides

a wide range of ready-made behaviours for common agent tasks such as sending and

receiving ACL messages, and breaking down complex tasks as collections of simpler

tasks. Behaviours can be added to the agent by the addBehaviour( ) method of the

agent class. Behaviours can also be added at any time when an agent starts up [using

the setup( ) method] or from within other behaviours.

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As FIPA specifications [39] define, in order for agents communications to be

successful, they must share the same language, vocabulary and protocols. JADE

simplifies this by allowing the creation of Agent ontologies. A JADE ontology

defines the vocabulary and semantics for the content of messages exchanged between

agents, and is built up of three main interfaces (components); Concept, AgentAction

and Predicate [43].

2.6. Recent applications of multi agent systems in microgrids

Analysis of recent literature revealed that the majority of the work has focused on

distributed microgrid control, while the other categories included: electricity trading

(market model analysis), optimization and restoration.

2.6.1. Distributed control

The work concentrated on distributed control amounts to more than half of the recent

literature [44–66]. This depicts the recent trend in using multi agent systems for

development of distributed control for microgrids. Among the research, various

architectures and frameworks are presented with simulations showing their

effectiveness in controlling a microgrid.

A basic hierarchical MAS which will act as a basis for further development is

presented in [44] to demonstrate the applicability of MAS as a distributed control

framework. Artificial neural networks and fuzzy systems have been used for

generation planning and load forecasting for operation planning and an adaptive

control based on traditional PI (Proportional-Integral) control that has been used in

MAS-based microgrid control model [57]. A real-time power management and

control system proposed in [52] is capable to optimize microgrid systems based on

multiple objectives, such as power demands, fuel consumption, environmental

emissions, costs and dispatchable loads. In [49] a control architecture is given which

is based on a hierarchically equal agent structure without a central agent able to

resolve real and reactive power mismatch within microgrids. Though there is no

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central agent, a neighbour-to-neighbour three-step communication algorithm is used

to determine the real and reactive power mismatches.

Autonomous operation in island mode is proposed using a Multi-Agent

Reinforcement Learning Algorithm that utilizes the concept of layered learning in

[53], [56]. The concept of layered learning is used to group various controls and

actions of the agents depending on their effect on the environment and for the agents

to ultimately coordinate in achieving their goals. MAS controller for the MAS

control architecture is given in [47], [50]. An intelligent load controller is described

in [53] based on the same system. Further, the ability of MASs to achieve efficient

use of NCRES and green technologies is also described.

In [65], a three tiered hierarchical coordinated control strategy which aims to utilize

the MAS most efficiently for voltage stability and power balance in the microgrid

has been proposed. A controller for bilateral switching between grid-connected

operation and islanded operation is simulated in [55] for monitoring and control of a

microgrid. Intelligent Distributed Autonomous Power Systems (IDAPS) of Virginia

Tech [63] illustrates the capability of MAS’s to be considered as a software

alternative to traditional hardware-based zonal protection that can be used for

effective islanding. Their system demonstrates the MAS’s ability to disconnect and

stabilize the microgrid while being able to allow for redefinition of zonal boundaries

on the fly [63].

A control algorithm comprising of an intelligent connection agent and grid

synchronization system is used in [59] to ensure the appropriate microgrid

disconnection from the main grid. This is used to improve the performance of

microgrid and its interaction with the main network, or another microgrid, under grid

voltage transients.

MAS based DG controllers have been presented in [46], [51], [54] and [61]. A multi-

agent based DG microgrid control framework is presented in [46] for a DC

distributed energy system that can be used in a microgrid as a modular power

generation unit. Two-layered control architecture is presented in [51] to achieve local

19

autonomy and global optimization respectively, operating in both grid-connected

mode and islanded mode. A multi-agent models used in [54], [61] take each element

in the microgrid as a separate autonomous intelligent agent and are simulated using

the Real Time Digital Simulator (RTDS) and the ‘PowerWorld’ Simulator

respectively.

A smart grid (SG) should ideally be built up of microgrids. Therefore a multi-

microgrid system is essentially a SG system. Multi-microgrid systems inherit self-

healing, intentional islanding, and the anticipation of the entire distribution network

from SGs and the ability for dynamic behaviour from microgrids. MAS based control

systems have been proposed for multi-microgrids in [60], [64].

The incorporation of Service Oriented Architecture (SOA) approaches such as web

services into MASs are proposed in [58], [66]. The architectures presented are able to

dynamically adapt to changes, work together to solve complicated problems and be

flexible to accommodate for new behaviours with greater agility.

A hybrid MAS is presented in [45] where the controller is able to intelligently choose

the operation model, self-control the optimal operation of microgrid, monitor real-

time data and provide autonomous local protection.

The several MAS control architectures found in the literature contained a similar

basis or basic arrangement. A summary of these control architectures is discussed in

Section 2.4.5.

2.6.2. Electricity trading

MASs have been applied for electricity trading or market model analysis in [67–72].

A power market model is introduced in [67] for the effective operation of the

microgrid. A MAS electricity trading algorithm is proposed in [68] to maximize the

revenue from the microgrid. A case study is used in [69] on price determination

based on demand and supply side bidding strategies to propose a pricing mechanism

for Micro Grid energy in the competitive electricity market. The same mechanism is

20

developed in [70] by using a microgrid central controller to participate in the bidding

process to settle the market-clearing price (MCP). The need to include economic and

political measures, rather than only technological parameters in the future electricity

architecture is elaborated in [71]. This is done using a MAS which is able to

incorporate the mechanisms of a prevailing electricity market for high integration of

NCRES into microgrids. A retailing spot market of electric energy proposed in [72]

is able to interoperate with other stakeholders in the utility grid using a cooperating

MAS.

2.6.3. Optimization

MAS based controllers have been used in [48], [73–76] for optimizing microgrid

operations. As loads and sources within a microgrid can be diverse and distributed,

real-time reaction and DG source management is critical in preventing local power

outages. It is also essential that this is done most efficiently and cost effectively as

possible for the microgrid to be economically viable. The systems presented in [48],

[76] consider the characteristics of the source or load types and self-regulates with

other agents in order to globally optimize in terms of cost and efficiency. The

optimization MAS in [73] is able to determine the optimal operation of a solar-

powered microgrid considering the load demand, environmental requirements and

PV panel and battery capacities. A competitive pricing mechanism is also presented,

using auction theory and two bidding techniques (i.e. single bidding and

discriminatory bidding) in order to serve the consumers at a reduced price and to

provide better revenues. In [74], power production is maximized with respect to

production cost and DG unit constraints. Demand Side Management (DSM) is also

utilized via a load control agent. The MAS is able to monitor energy resources and

schedule generation (dispatch control) for optimized microgrid operation. An

artificial immune system based algorithm is used by the MAS in [75] to optimize the

production of the local DGs in order to optimize the microgrid operation.

21

2.6.4. Power restoration

MASs have been used for power restoration of microgrids in [77–80]. The load

restoration algorithm in [79] consists of agents that make synchronized load

restoration decisions according to information learned from their direct neighbours.

Though only the direct neighbours are contacted, global information is discovered

based on the Average-Consensus theorem [81], [82]. Thereafter, the load restoration

problem is modelled and solved using algorithms for the 0-1 Knapsack problem.

Power restoration in the case of a large-scale blackout in a microgrid with DGs is

discussed in [77], [80]. The models used the available capacity of DGs to reduce the

blackout area. A MAS using the Agent-Environment-Rules (AER) ontology and the

Constraint Satisfaction Problem (CSP) based model is used in [77]. An asynchronous

backtracking algorithm is used to solve the AER model based on CSP. In [80], a

multi-agent immune algorithm is used for rapid restoration of power. A method

called a ‘random tree’ method is used to code antibodies into the immune algorithm

to prevent unfeasible solutions. High frequency mutation is carried out for the

antigens to be dynamically changed in order to rapidly converge to the solution. A

hierarchical control strategy for power restoration is given in [78] targeting swift

power restoration to the most critical loads.

2.7. Control architectures

Intelligent agents can be assigned to each entity in a microgrid; i.e. circuit breaker,

generation unit, loads, users etc. This assignment will determine the MAS

architecture. Several different architectures for microgrid applications have been

proposed up to now. Almost all available architectures can be termed single layered

architectures. Especially, as an example, single layered architectures have been

proposed for microgrid applications such as resolving real and reactive power

mismatch [49] and load restoration [79].

Several multiple level control architectures have been presented in the literature [51],

[65]. However, in these multi layered topologies, the layers address different strata of

the distribution grid and not of the control architecture.

22

A summarization of the basic MAS structure used in distributed control approaches

in the literature is given in Figure 2.3. Three main agents shown are common in all

related work. A DG agent handles the addition, operation and control of all local DG

units. A user agent acts as or connects to a user interface where utility/consumer

users interact with the system. Last, but not least, a control agent will oversee all

these and any other related control tasks, while mainly connecting to/operating

circuit breakers. Two main types of control agents are seen; overall MAS controllers

[47], [50] and load controllers [53].

Figure 2.3 Basic multi agent system structure

This basic architecture can be implemented over a conceptual microgrid consisting of

several different DG units and critical and non-critical local loads as shown in Figure

2.4. Database agent facilitates message parsing (exchange) and storage, acting as the

access point for data for all the agents and users. Here the control agent is tasked

with islanding as well as load management.

Some drawbacks of the single layered architecture can be listed as:

• Certain agents get overloaded with objectives

• Such agents become critical, and their failure can be catastrophic

• Lower flexibility in terms of expanding the microgrid and/or controller

23

Figure 2.4 Single layered multi agent system architecture

24

3. CONCEPTUAL DESIGN OF DUAL-LAYERED MAS

This section presents the conceptual design of multi agent architecture in detail.

Step 2: Multi agent system architecture conceptual design

3.1. Dual layered architecture

The single layered multi agent systems have the drawback of agents needing cater

multiple objectives. This drawback can be overcome by properly delegating the

multiple objectives to different agents. The proposed multi agent architecture

achieves this task delegation by creating multi-layered multi agent system

architecture. This novel architecture is presented in Figure 4.1.

Figure 3.1 Proposed dual layered MAS architecture

The control hierarchy is split in to two, creating dual-layered control architecture.

The primary layer (or the strategic layer) comprises the Control agent who makes the

run-time decisions. The secondary (tactical) layer contains the User Agent, DG

Strategic (Primary) Level

Tactical (Secondary) Level

Utility (Background) Operations

� Makes run-time decisions

� Execution of run-time decisions

� I/O operations

� Stores, and shares data

� Data access point for all

agents

Control Agent

User Agent

DG Agent

LV Agent

Load Agent

DB Agent

25

Agent, LV Agent and Load Agent. This layer carries out the execution of the run-

time decisions of the strategic level, which mainly comprises input/output operations.

Both these layers are ultimately supported by a service (utility) provided by a DB

(Database) agent, running in the background. This utility agent stores system

information and data and messages shared between the agents and acts as a data

access point for all agents.

The agent assignment of the novel dual-layered architecture is given in Figure 3.2.

This introduces the new secondary control layer in comparison to the single layered

architecture given in Figure 2.3. The agents depicted in the figure are briefly

introduced in Section 3.2.

Figure 3.2 Interaction between agents in the dual layered multi agent architecture

The process of developing this dual-layered multi agent system and implementing it

on the simulation test-bed is given in Section 4. The different case studies carried out

and their respective results are given in Section 5.

26

3.2. Agents used in the dual-layered MAS

The proposed dual-layered MAS shown in Figure 3.2 comprise the control agent, LV

agent, Load agent, DG agent, User agent and DB agent:

Control agent: This agent monitors the main grid for faults and/or outages and

controls the secondary layer in the MAS. It oversees the operation of the secondary

layer which is tasked with handling loads and the low voltage (LV) connection. It

also informs the agents in the MAS whether the system is islanding capable or not.

Ultimately, the control agent will be the centre of the primary layer holding influence

over the DG agent and controlling the LV (Low Voltage) agent and Load agents.

LV agent: This agent controls the islanding operation by opening the circuit breaker

at the PCC. It stores the islanded or grid-connected status of the PCC. The LV agent

can also be given metering capabilities if a ‘net-metering’ scheme is used for the

microgrid.

Load agent: Each individual load has an individual load agent. It stores information

regarding each load and control the connect/disconnect operation. Information such

as load identification number, load priority and current demand are stored for each

load. The LV agent can also be given metering capabilities if DSM (Demand Side

Management) schemes are to be used within the microgrid.

DG agent: This agent is responsible for storing information regarding the DG unit,

metering and controlling the output and connecting/disconnecting the unit to/from

the microgrid. The information stored regarding the DG includes;

i. Identification number

ii. Connection status (connected/disconnected)

iii. Type of equipment (IC engine, micro-turbine, Solar PV, Fuel cell etc.)

iv. Rated power (in kW)

v. Output power (in kW)

vi. Local energy source (Fuel) availability (given as infinite)

vii. Unit availability (given as always available – not under maintenance)

viii. Energy cost (in Rs./kW)

27

User agent: This agent behaves as the gateway for the user to interact with the

system, to obtain real-time information and set system goals. This agent collects

information from other agents regarding demand and supply and makes it available

to the system users. The user agent can also interface with a GUI (Graphical User

Interface) for easy interaction.

Database Agent: stores system information and data and messages shared between

the agents. The priority list used for load management is also hosted by the database

agent. The database agent acts as the access point for data for all the agents and

users.

These agents operate collaboratively to achieve the overall goals of the system (i.e.

island the microgrid during a fault, protect supply to critical loads and provide

dynamic load management.)

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4. IMPLEMENTATION OF DUAL-LAYERED MAS

Following section describes the process of implementing the dual-layered

architecture introduced in Section 3.1. The methods and process of developing and

implementing the dual-layered multi agent system using JADE is explained

including features of multi-agent systems and the JADE toolkit. This section also

describes the process of establishing TCP/IP communication for running the multi

agent system on the simulated test-bed.

Step 3: Developing the dual-layered multi agent system

4.1. Dual-layered Multi agent system implementation in JADE

The multi-agent system implementation using JADE can be broken into the

following main steps:

i. Agent specification

ii. Application analysis

iii. Application design

iv. Implementation

4.1.1. Agent specification

Here, the specifications, requirements and tasks for the agents in multi agent system

are defined. The agents to be used in the multi agent systems were introduced in

Section 3.2.

29

4.1.2. Application analysis

The operation of the multi agent system is defined in this step. The target is to

develop a multi agent system that achieves the objectives put forward in Section 1.3.

The MAS is expected to provide the following capabilities to the microgrid:

i. Dynamic interaction with the microgrid (Rapid islanding in case of an

upstream fault)

ii. Sharing limited capacity of local DG unit/s between multiple loads

iii. Load management and prioritization

In order for the agents to follow the objectives, how they collaborate with one

another must be clearly defined. The agent collaboration process is described below.

Agent collaboration

1 Main-grid / microgrid Stimulus control agent receives information from main

grid and microgrid

2 Respond to microgrid main Control agent responds the microgrid

3 Inform all agents control agent informs all registered subscribers

about the information from microgrid

4 Command loads sends control commands to its corresponding

loads

5 Command DG send control commands to the DG unit

6 Load information receives load information from their

corresponding loads

7 Control DG Power production control agent informs DG agent of how much

power to produce

8 Information on DG output

DG unit informs DG agent about the amount of

power produced

9 Manage loads control agent manages its corresponding load

circuitry based on locally produced power

30

4.1.3. Application design

Using the JADE toolkit to allow for agent to communication with each other and

reason about facts and knowledge in relation to the microgrid domain is carried out

by the following steps [1]:

i. Define an ontology including the schemas for the types of predicate, agent

action and concept that are pertinent to the microgrid domain.

ii. Develop proper Java classes for all types of predicate, agent action and

concept in the ontology.

iii. Register the defined ontology and the selected content language with the

agent.

iv. Create and handle content expression as Java objects that are instances of the

classes developed in step ii and let JADE translate these Java objects to/from

strings or sequences of bytes that fit the content slot of ACL Messages.

Details of the ontology creation and concept identification are discussed as the first

step in application design.

31

Ontology creation

The detailed structure of the ontology for the dual-layered MAS is given in Figure

4.1. The ontology, as an example, has a concept named LV (LV Agent), with a

predicate called Islanded (addressing whether grid connected or not) and an agent

action called Disconnect (to open the circuit breaker at PCC).

Figure 4.1 Dual-layered MAS ontology

Concepts can either be a role of an entity or a common property. Here the concepts

represent the agents that make up the multi agent system. A concept can include

subconcepts as shown in Figure 4.1 (i.e. the concept, Load, includes the subconcepts,

Load ID, Demand, Priority, and Connected).

Predicates house the status of different concepts that can either be true or false. Four

predicates are defined in the ontology; Islanded, Connected, SupplyGTDemand and

32

CapacityGTDemand. Islanded and Connected predicates are used by LV agent and

Load agents respectively to define the state of their circuit breakers as open or

closed. SupplyGTDemand verifies whether supply greater than or equal (>=) demand

in the case of controlling DG output during load management in islanded mode.

CapacityGTDemand is used for load shedding for load management to verify if the

available local capacity is greater than or equal to the total local demand.

Agent actions are special concepts that indicate actions that can be performed by

particular agents. For example, the agent actions, connect and disconnect are used by

LV and Load agents to close and open their circuit breakers.

Software based development of the ontology is specific to the toolkit and detailed

information regarding this process can be found in the online tutorial [43]. The

process consists of the following steps.

i. Define the vocabulary of the agents communication space

ii. Define the java class that specifies the structure and semantic of the object

MakeOperation ( )

iii. Define the schema of the object

iv. Register the ontology and the language that are used for assembling and

parsing (or coding and decoding) the content of messages with the agent's

content manager

33

Concept identification

The next step in the application design consists of modelling the knowledge that will

be used by the agents. This knowledge is prescribed as the concepts used in the

ontology, their attributes and initial values. The multi agent system carries physical

as well as several abstract concepts. Being in islanded mode is an abstract concept,

and power demand/available power is a physical concept. A list of concepts defined

is given in Table 4.1.

Table 4.1 Microgrid domain knowledge, attributes and initial values

Concept Attributes Initial Value

islanded islanded: Boolean

pcc_state: Boolean

islanded: false

pcc_state: true

DG_info dg_id: String

connection_state : Boolean

type : String

rated_power : float

local_fuel : Integer

unit_availability : Integer

energy_cost : float

dg_id: 01

connection_state : true

type : IC

rated_power : 15

local_fuel : 100

Unit_availability : 100

energy_cost : 0

Load_x _info

(x = 1 or 2)

load_id: String

load_priority : float

power_demand: float

connected : Boolean

load_id: 1 / 2

load_priority : 0.7 / 0.4

power_demand: 7.5 / 7.5

connected : true / true

DG_Ctrl output_power : float output_power : 7.5

Ctrl_DG power_reqd: float power_reqd:

Ctrl_Load_x shed_loadx : Boolean shed_loadx : false

CB_A (Circuit Breaker A) cbA_state : Boolean cbA_state : true

CB_B cbB_state : Boolean cbB_state : true

CB_C cbC_state : Boolean cbC_state : true

CB_D cbD_state : Boolean cbD_state : true

The technical/programing aspects of next steps (steps ii – iv) are not discussed here

and they can be found in detail with examples in pages 82-90 in [1].

34

4.1.4. MAS algorithm implementation

The multi agent system implementation is discussed in this section. The multi agent

system allows the microgrid to operate in grid connected mode as well as islanded

mode. The multi agent system is implemented by developing the control algorithm

depicted below in Figure 4.2 .

Figure 4.2 Control algorithm for multi agent system operation

Assumptions: It is assumed that unlimited power is available from the grid and

local embedded generator has unlimited fuel to withstand an outage of any duration.

The embedded generator is assumed to be always online supplying part of the local

demand, and capable of instantly changing the output depending on changes in local

critical demand.

35

The control agent measures the main grid voltage and frequency, and detects any

fault or outage based on the measurements. If the measurements exceed the standard

thresholds in ±10% for voltage and ±0.5 Hz for frequency, the control agent switches

the microgrid to islanded mode operation. When the decision to switch to island

mode is taken, all the agents are notified and they cooperate and coordinate with the

control agent to achieve the objectives set forth. Based on the prescribed

MAS_Ontology, the agents can easily start their interactions and accomplish their

goals through the exchange of ACL messages. Three main objectives the multi agent

system has to meet (which are defined in Section 1.3) can be simplified as;

i. dynamic interaction with the microgrid (Rapid islanding in case of an

upstream fault)

ii. sharing limited capacity of local DG unit/s between multiple loads

iii. load management and securing supply based on load priority

Operation of each of the objectives is simulated and the success of the multi agent

system is verified in Section 5. The control algorithm is explained in detailed in the

respective simulation section.

36

Step 4: Integration of the multi agent system and the microgrid simulation

4.2. Running multi agent system application

The multi agent system attempts to control physical entities or a simulation of the

same. This puts the multi agent system and the simulation into two different

domains. In order for the multi agent system to work on the simulated environment

establishing communication between the two domains is required.

The multi agent system runs on a JAVA platform while the simulation is run on a

simulating environment (i.e. MATLAB/SIMULINK). Therefore, in order to establish

communication between two domains, for sensing and control TCP/IP is used. A

third party TCP/IP server [83] is utilized as an interface to MATLAB and it connects

to another middle server which allows multiple simultaneous TCP/IP connections.

Each agent requires a separate connection to the test-bed. The interface allows only a

single connection at a time; therefore a separate middle server is required. This

process is depicted in Figure 4.3.

Figure 4.3 Establishing communication between simulation environment and MAS

The middle server allows various agents in the multi agent system to establish

multiple simultaneous TCP connections and multiplexes all of them through the

single TCP connection to the TCP server. Both servers can be implemented on the

same PC running the simulation or on a separate PC on a connected network.

37

5. SIMULATIONS AND RESULTS

Step 5: Result validation using simulation studies

Three case studies are developed to verify the capability of the proposed MAS to

provide control and protection for an inverter based microgrid. Within conventional

power systems, frequency control is handled by rotating inertial masses of large

generators. This causes a problem within smaller microgrids. Therefore, an inverter

based system is selected, where the frequency control is handled by the inverter

interface. Details of the simulation test-bed used in the case studies are presented in

Section 5.1.

Case study 1: Intentional islanding during an upstream fault

In this case study, an upstream fault is introduced on the main grid system at 0.05 s

into the simulation. Upon the detection of the fault, the MAS switches from grid

connected mode to islanded mode of operation. The transition is carried out within

0.02 s of detecting the fault. This verifies the capability of the MAS in rapidly

initiating a seamless transition. This is further discussed in Section 5.2.

Case study 2: Protecting critical loads during intentional islanding

During an outage or fault on the main grid, the microgrid has to cater local demand

based solely on the local sources. In order to meet the local demand and maintain the

stability of the system at the same time, some loads have to be shed. This is done by,

shedding lower priority non-critical loads and protecting the higher priority critical

loads. This is completed within 0.02 s of detecting the fault and initiating islanding

transition. This verifies the capability of the MAS in rapidly protecting critical loads

during intentional islanding. This is further discussed in Section 5.3.

Case study 3: Managing critical loads during islanded operation

In the islanded mode, as the locally available DG capacity is limited, some of the

local loads have to be shed in order to maintain/protect the supply to higher priority

(critical) loads. During such islanded operation, if power needs to be re-routed to a

lower priority load by raising its priority, the MAS system allows for on-the-fly

38

priority revisions. This allows power to be re-connected to a previously shed load.

This is also completed within 0.02 s of detecting priority revision. This verifies the

capability of the MAS in rapidly managing load priorities during islanded operation.

This is further discussed in Section 5.4.

5.1. Description of simulation circuit

The simulations are carried out on an inverter-based microgrid test-bed developed

using MATLAB/SIMULINK. The test-bed is built mainly using the SimPower

Systems block set available in SIMULINK.

The test-bed based on the simplified block diagram, shown in Figure 5.1, is used for

the simulation case studies. The test-bed comprises an embedded generator, acting as

a DG unit, supplying part of the local demand, and some critical and non-critical

loads. It is assumed that the DG unit can operate at full capacity without fuel

limitations during any outage. It is also assumed that the DG unit can instantly

increase output using battery storage.

As an inverter based microgrid is considered, a grid interface unit comprising an

inverter, low pass filter, PWM (Pulse Width Modulation) and circuit breaker is used

in the case of a DC source. The microgrid is connected to a main utility grid (11 kV)

across a transformer (11kV/400V) and a main circuit breaker.

Figure 5.1 Simulated microgrid test-bed

39

5.2. Intentional islanding during a fault

The first case study is carried out to verify the effectiveness of the MAS in rapidly

islanding the microgrid upon the detection of an upstream outage on the main grid.

The ability of MAS to sense the upstream outage by detecting the voltage droop and

initiate agent actions to seamlessly transition to islanded mode from grid-connected

mode is addressed here. The test-bed used for the simulation is shown in Figure 5.2.

Figure 5.2 Test-bed set-up for case study 1

The test-bed comprises a 15kW DG unit producing 480V, connected to a 400V,

50Hz microgrid, a transformer (480V/400V) connecting the DG to the microgrid,

two 7.5kW local loads, with a connection to a 11kV main utility grid fed through

another transformer (11kV/400V), all of which are connected across circuit breakers.

Total local demand is 15 kW, 50% of which (7.5 kW) is supplied by the DG unit,

while the rest is supplied by the main grid. It is assumed that the DG unit can

instantly increase output using battery storage. An upstream fault is introduced to the

system at t = 0.05 s into the simulation. At the beginning of the simulation breakers

A, B, C and D are all closed.

40

5.2.1. Messages exchanged among agents and physical entities

The control agent detects the fault in the upstream, takes the strategic decision to

switch to island mode and informs the other agents to switch to islanded mode. The

control flow chart used in the process is given in Figure 5.3. This is a reduced

version of the algorithm given in Section 4.1.4. Upon receiving this ‘island’

command, the LV agent opens the main circuit breaker at PCC (point A) to isolate

the microgrid from the utility.

Figure 5.3 Control flow chart for intentional islanding

41

The control agent then queries local demand and available capacity data from the

Load and DG agents respectively. As the current local DG supply is enough to cater

the local demand the DG agent is asked to run at the present demand (increase output

to 15kW from 7.5 kW).

The messages exchanged between the agents are shown in Figure 5.4.

Figure 5.4 Key messages exchanged between agents during islanding

42

5.2.2. Updates to the ontology

The concepts known to the multi agent system is updated with every step. The agents

are able to identify the changes in the environment and react accordingly based on

these changes to the ontology. The updates to the ontology are:

Table 5.1 Updated concepts, initial and updated values for case study 1

Concept Initial Attribute/s Updated Value

islanded islanded: true

pcc_state: true

islanded: true

pcc_state: false

Load_1_info

load_id: 1

load_priority : 0.7

power_demand: 7.5

connected : true

load_id: 1

load_priority : 0.7

power_demand: 7.5

connected : true

Load_2_info

load_id: 2

load_priority : 0.4

power_demand: 7.5

connected : true

load_id: 2

load_priority : 0.4

power_demand: 7.5

connected : true

DG_Ctrl output_power : 15 output_power : 15

Ctrl_DG power_reqd: 15 power_reqd: 15

Ctrl_Load_1 shed_load1 : false shed_load1 : false


CB_A (Circuit Breaker A) cbA_state : true cbA_state : false

43

5.2.3. Results and discussion

The test-bed shown in Figure 5.2 is simulated for 0.1 s. The simulation results

presented in Figure 5.5 depicts the successful islanding process.

During grid connected mode:

While the microgrid is in grid-connected mode, the total demand is 15 kW. During

the grid-connected mode the DG unit only provides 7.5 kW output, 50% of the total

demand. Another 10 kW is required from the main grid to supply the microgrid. The

main grid experiences a fault at t = 0.5 s.

Figure 5.5 Line to line voltages during intentional islanding

During transition:

When the upstream outage is detected at t = 0.05 s the control agent informs the LV

agent and the Load agents to switch to island mode operation. Upon receiving the

islanding order the LV agent trips the main circuit breaker A, at the PCC, isolating

(a) Line to line voltage across the non-critical load at ‘A’, (b)

Line to line voltage at breaker ‘B’

(a) Main grid voltage supply

(b) Load voltage

44

the microgrid from the utility (Figure 5.5(a)). The Control agent then queries the DG

agent and the load agents regarding their available capacities the DG can provide to

the microgrid and the power requirements of the connected loads.

As the available maximum capacity of 15 kW is able to meet the total demand of 15

kW, the control agent commands the DG agent to increase DG output to match the

demand. Soon as the microgrid is put to island mode the supply to the loads are

maintained (see Figure 5.5(b)).

Islanded mode:

At t=0.05s the microgrid is separated from the main grid and the local loads are

secured. After the microgrid switches to island mode the total local demand is met by

the DG unit by supplying 15 kW.

All agent operations are carried out rapidly, from detecting fault, opening the main

breaker, connecting the local source and shedding loads, to stabilize the microgrid

within 0.02 s. Therefore the system is able to disconnect from the main utility grid

and maintain the supply to the critical loads without suffering a brownout and/or

blackout.

45

5.3. Protecting critical loads during intentional islanding

The second case study addresses the capability of the MAS to protect/secure power

to critical loads during intentional islanding. This should happen immediately

following the transition to islanded mode. If power supply from available local DG

unit is limited and insufficient to supply the total local demand, load shedding has to

be carried out rapidly in order to secure supply to the most critical loads. The test-

bed used for the simulation is shown in Figure 5.6.


The test-bed is same as the one used in case study 1, with only the following

changes. Two local loads are 15 kW each with assigned priorities of 0.7 and 0.4

respectively. The total local demand is 30 kW; initially the DG unit supplies 10 kW

while the other 20 kW is provided from the main grid. Similar to case study 1, an

upstream fault is introduced to the system at t = 0.05 s in to the simulation. At the

beginning of the simulation breakers A, B, C and D are all closed.

46


The control process and messages exchanged between agents are based on the details

given in Section 5. Further details regarding the control process can be found in

Figure 5.3. Messages exchanged between the agents are as shown in Figure 5.4. As

the local DG unit is unable to meet the total demand, load shedding is initialized

based on the assigned priorities. This makes load connected to point C with the

higher priority, the critical load. Therefore, a load_shed command is issued to the

load agent for the lower priority, non-critical load at point D.






Concept Initial Value Updated Value

islanded islanded: false

pcc_state: true

islanded: true

pcc_state: false

Load_1_info

load_id: 1

load_priority : 0.7

power_demand: 15

connected : true

load_id: 1

load_priority : 0.7

power_demand: 15

connected : true

Load_2_info

load_id: 2

load_priority : 0.4

power_demand: 15

connected : true

load_id: 2

load_priority : 0.4

power_demand: 15

connected : false




Ctrl_Load_2 shed_load2 : false shed_load2 : true


CB_D cbD_state : true cbD_state : false

47


The test-bed shown in Figure 5.6 is simulated for 0.12 s. The simulation results

shown in Figure 5.7 depict the successful islanding and subsequent securing of

critical loads.

Grid connected mode:

While the microgrid is in grid-connected mode, the total demand is 30 kW, with 15

kW critical loads and 15 kW non-critical loads. During the grid-connected mode the

DG unit provides 10 kW output and the other 20 kW is provided by the main grid.

The main grid experiences a fault at t = 0.5 s.

Transition period:

When the upstream outage is detected at t = 0.05 s the control agent informs the LV

agent and the Load agents to switch to island mode operation. Upon receiving the

islanding order the LV agent trips the main circuit breaker A, at the PCC, isolating

the microgrid from the utility (Figure 5.7(a)). The control agent then queries the DG

agent and the load agents regarding their available capacities that the DG can provide

to the microgrid and the power requirements of the connected loads.

As the total load of 30 kW exceeds the available maximum capacity of 15 kW, the

control agent commands the load agents to shed the 15 kW non-critical loads to

match the DG capacity and the DG unit to increase output to 15 kW. Soon as the

microgrid is put to island mode the supply to the critical loads is maintained (see

Figure 5.7(b)). The load agent at the non-critical loads sheds them from the system

by opening breaker D (see Figure 5.7(c)).

Islanded mode:

At t=0.05s the microgrid is separated from the main grid and the load agents balance

the local demand. After the microgrid switches to island mode the total local demand

is met by the embedded generator supplying 15 kW.

48

Figure 5.7 Line to line voltages during securing critical loads

All agent operations are carried out rapidly; from detecting fault, opening the main

breaker, connecting the local source and shedding loads, to stabilize the microgrid

within 0.02 s. Therefore the system is able to disconnect from the main utility grid

and maintain the supply to the critical loads without suffering a brownout and/or

blackout.

(a) Line to line voltage across the non-critical load at ‘A’, (b) Line

to line voltage at breaker ‘C’, (c) Line to line voltage at breaker ‘D’

(a) Main grid voltage supply

(b) Critical load voltage

(c) Non-critical load voltage

49

5.4. Managing critical loads during islanded operation

The third case study addresses the capability of the MAS to provide load

management capabilities during islanded operation. As the available local capacity is

limited and insufficient to cater total local demand during islanded mode, non-critical

(lower priority) loads have to be dynamically shed in order to protect the stability of

the microgrid. This simulation shows the ability of the MAS to allow for dynamic

update of load priority levels. The test-bed used for the simulation is shown in Figure

5.8.


The same test-bed used in Section 5.3 is used to simulate case study 3. The local

loads are given two priority levels as shown in Figure 5.8. Local load 01 is given a

priority level of 0.7, and load 02 is given a priority level is 0.4. This initial setting

designates load 01 as a higher priority load while load 02 is considered a lower

priority, by their respective load agents. At the beginning of the simulation breakers

A, B, C and D are all closed. An upstream fault is introduced to the system at t = 0.05

s into the simulation, and the microgrid switches to islanded operation. During

islanded operation the load 02 priority is raised to 0.9 at t = 0.14s.

50


The objective of the test setup is to demonstrate the ability of the proposed MAS to

island the microgrid after an upstream fault is detected, and maintain supply to the

most critical loads with dynamic priorities. The control flow chart used in the process

is given in Figure 5.9.

Figure 5.9 Control flow chart for load management during islanded operation

51






Concept Initial Attribute/s Updated Value

islanded islanded: false

pcc_state: true

islanded: true

pcc_state: false

Load_1_info

load_id: 1

load_priority : 0.7

power_demand: 15

connected : true

load_id: 1

load_priority : 0.7

power_demand: 15

connected : false

Load_2_info

load_id: 2

load_priority : 0.4

power_demand: 15

connected : true

load_id: 2

load_priority : 0.9

power_demand: 15

connected : true



Ctrl_Load_1 shed_load1 : false shed_load1 : true



CB_C cbC_state : true cbC_state : false

CB_D cbD_state : true cbD_state : true

52


The test-bed shown in Figure 5.8 is simulated for 0.2 s. the simulation results shown

in Figure 5.10 depict the successful islanding and subsequent securing of critical

loads.

Initially the microgrid is operating in the grid-connected mode, with the embedded

generator is supplying only part of the local loads while the rest is supplied by the

utility grid. The demands supplied initially by the DG and the grid are depicted in

Figure 5.8.

Figure 5.10 Line to line voltages during islanded operation

(a) Main grid voltage at breaker ‘A’, (b) load 01 measured

at ‘C’: switches from high priority to low priority, (c) load

02 measured at ‘D’: switches from low priority to high

priority.

53

Grid connected mode:

While the microgrid is in grid-connected mode, the total demand is 30 kW,

comprising two 15 kW local loads. During the grid-connected mode the embedded

generator only provides 10 kW, while the other 20 kW is supplied by the main grid

to the microgrid.

Transition period:

The proposed dual layered MAS is able to successfully island the microgrid during

the upstream outage within 0.02 s of detecting the fault in the main utility grid. As

the local generation capacity is unable to meet the total local demand, the MAS

initiates load shedding upon initially set priority levels of the loads as in case study 2

(see Section 5.3). This enables the MAS to maintain supply to the most critical load,

load 01, by shedding the least critical load, load 02.

Islanded mode:

At t = 0.05 s the microgrid is separated from the main grid and the load agents

balance the local demand as in case study 2 (see Section 5.3). After the microgrid

switches to island mode the total local demand is met by the embedded generator

supplying 15 kW.

During the island operation, the user agent can revise the initial priority assignment.

This can be pre-initiated or a user can change the priorities during the islanded mode.

At t=0.14, the user agent revises the priority value of load 02 from p=0.4 to p=0.9

and communicates the revise_request to the load agent at load 02. This update is

forwarded to other agents via the directory agent, and the control agent reinitiates

load management procedures. As the DG unit is still unable to meet the total demand

of 30 kW, and has to shed non-critical (lower priority) loads to maintain power to the

new highest priority load, load 02.

Therefore, the control agent commands the load agents to shed the load 01 of 25 kW

to match the DG capacity. Thus, the supply to the new most critical load, load 02 is

54

provided by re-closing ‘D’ (see Figure 5.10(c)). The load agent at the new lower

priority load, load 01, sheds it from the system by opening breaker ‘C’ (see Figure

5.10(b)). This process is shown in Table 5.4.

Table 5.4 Priority revision during islanded mode

Grid Connected mode Islanded mode

Initial priority Revised priority

Load 01 Priority = 0.7

Connected

Priority = 0.7

Connected

Priority = 0.7

Disconnected

Load 02 Priority = 0.4

Connected

Priority = 0.4

Disconnected

Priority = 0.9

Connected

When the load priorities are revised by a user via the user agent, the MAS is able to

reconfigure the system, in order to provide power to the new most critical load. The

reconfiguration is also done within 0.02 s of the revision and successfully reconnects

the new critical load. The results show the capability of the MAS to safely island and

maintain the supply to its critical loads, while allowing for the critical loads to be

dynamically revised.

55

6. CONCLUSIONS AND FUTURE DIRECTIONS

6.1. Conclusions

The developed MAS based control architecture for control and protection of an

inverter based microgrid has been successfully verified using a simulated microgrid

test-bed in MATLAB/SIMULINK. The MAS has been developed using the JADE

platform using its available programming techniques and communication facilities.

The control system has been endowed with seamless islanding, protection of critical

loads and load management capabilities during islanded operation.

Existing MAS based controllers for microgrid applications relied on a single layered

architecture which tasked agents with multiple objectives. This drawback was

overcome by developing novel dual layered architecture, where all tasks were

properly delegated to individual agents.

The MAS controller development phase involved application specification, analysis,

design and implementation. This process created the agents that sense the changes in

the environment and take initiative to achieve the overall goal put before them. This

was achieved by creation of five main agents; control agent, user agent, DG agent,

LV agent and load agent. These agents were supported by the DB agent running in

the background. The agents were then connected to a simulated microgrid test-bed in

MATLAB/SIMULINK over a third party TCP/IP server. Information regarding

agent development and server implementation has been discussed. The MAS

controller was simulated on a test-bed to demonstrate its ability to seamlessly island

the microgrid upon the detection of an upstream fault, protect critical loads during

islanding and manage critical and non-critical loads during islanded operation.

This incursion into multi agent based control system development will act as a

stepping stone to develop more intelligent distributed control systems in the future.

The novel concept of multi–layered control architectures will increase the impact

MAS has in microgrid control.

56

6.2. Future work

Multiple DG units and storage elements (i.e. battery storage, water pumping,

hydrogen generation etc.) can be integrated into the microgrid to increase reliability,

reduce emissions and to improve sustainability. As most NCRES tend to be of a

highly fluctuating output, energy storage would smooth-out the variation. With the

integration of multiple/various DG units, the user (utility or consumer) can select the

source which offers the optimal energy solution. These improvements will require

the development of DG agent’s responsibilities.

The following improvements can also be incorporated in future.

i. Agent behaviours can be improved by utilizing fuzzy / neural control logic

ii. The Effect of increasing number of agents on controller stability and service

latency can be studied to evaluate long-term extensibility of MAS

iii. With multiple DG units vying to service any given microgrid, a MAS based

energy bidding system can be developed for energy auctioning from

independent power producers (IPPs)

iv. The requirements of communication network redundancy and aspects of how

to improve data network security and preventing cyber-attacks and intrusions

should also be addressed in future work.

The developed multi agent system is to be implemented on the physical test-bed

shown in Figure 6.1 comprising rotating machines and solar PV panels.

Figure 6.1 Proposed microgrid test-bed

57

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