trust and reputation in the smart energy grid

A research performed at Logica Groningen

Trust and Reputation in the Smart Energy Grid

byAlexander Bograd

A thesis submitted to the Faculty of Mathematics and Natural sciences ofRijksuniversiteit Groningenin partial fulfillment of the

requirements for the Degree ofMaster of Science

Computer Science

Groningen

2012

SUPERVISED BY:

Prof. Dr. ir. Marco Aiello (RuG)Prof. Dr. ir. Paris Avgeriou (RuG)

Margriet Meerholz (Logica)

TABLE OF CONTENTS

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Research question . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.3 Thesis contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.4 Thesis organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Chapter 2 Microgrids and distributed generation . . . . . . . . . . . . . . . . . . 92.1 Introduction and driving forces . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2 Demand side management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.3 The case of electric vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Chapter 3 Multi-agent systems in the Smart Energy Grid . . . . . . . . . . . . . 143.1 Who are the agents? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143.2 The potential benefits and the use of MAS technology in the Smart Grid . . . . . 15

3.2.1 Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.2.2 Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.2.3 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.3 Why not Web services or grid computing? . . . . . . . . . . . . . . . . . . . . . . 183.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Chapter 4 Trust and reputation management . . . . . . . . . . . . . . . . . . . . . 214.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214.2 Background of trust and reputation . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.2.1 Reputation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234.2.2 Reputation computation models . . . . . . . . . . . . . . . . . . . . . . . 264.2.3 Additional mechanisms for decentralized reputation management . . . . . 264.2.4 Classification of trust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274.2.5 Trust acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284.2.6 Main types of trust and reputation approaches . . . . . . . . . . . . . . . 294.2.7 Unfair ratings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294.2.8 Comparing of different trust and reputation mechanisms . . . . . . . . . . 30

4.3 The potential benefits and the use of trust and reputation management in theSmart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304.3.1 Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.3.2 VPP and coalition formation . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

ii

TABLE OF CONTENTS

Chapter 5 Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345.1 Tropos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

5.1.1 Tropos phases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355.1.2 Modeling activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355.1.3 The meta model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365.1.4 Modeling tools for i*/Tropos . . . . . . . . . . . . . . . . . . . . . . . . . 37

5.2 Applying Tropos methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385.2.1 Early requirements analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 385.2.2 Late requirements analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 415.2.3 Architectural design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Chapter 6 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456.1 Powermatcher architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456.2 Powermatcher and trust and reputation system . . . . . . . . . . . . . . . . . . . 476.3 Architecture of trust and reputation system for Powermatcher . . . . . . . . . . . 486.4 Extra features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Chapter 7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507.1 Research approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517.2 Future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

iii

LIST OF TABLES

Table 5.1 Activities and diagrams produced during the analysis process . . . . . . . 36

Table 6.1 Powermatcher devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46Table 6.2 Powermatcher original agents vs. proposed . . . . . . . . . . . . . . . . . . 48

iv

LIST OF FIGURES

Figure 1.1 End-to-end Smart Grid [41] . . . . . . . . . . . . . . . . . . . . . . . . . . 2Figure 1.2 Realized amount of electricity is higher than estimated . . . . . . . . . . 3Figure 1.3 Realized amount of electricity is lower than estimated . . . . . . . . . . . 3

Figure 2.1 The Smart Grid [47] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Figure 2.2 Demand side management techniques [43] . . . . . . . . . . . . . . . . . . 12

Figure 4.1 Relationship between trust and reputation (adopted from [77]) . . . . . . 24Figure 4.2 eBay feedback profile [20] . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Figure 5.1 Comparison of Tropos with the other methodologies . . . . . . . . . . . . 34Figure 5.2 The actor concept in the Tropos metamodel [9] . . . . . . . . . . . . . . . 37Figure 5.3 The stakeholders of the grid . . . . . . . . . . . . . . . . . . . . . . . . . 39Figure 5.4 Tropos symbols legend . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39Figure 5.5 Customers of the grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40Figure 5.6 Actor diagram modeling the stakeholders of TRS . . . . . . . . . . . . . 40Figure 5.7 A portion of the actor diagram including TRS and Consumer and goal

diagram of TRS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Figure 5.8 Soft goals decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . 42Figure 5.9 Architectural styles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Figure 6.1 Powermatcher architecture [39] . . . . . . . . . . . . . . . . . . . . . . . . 46Figure 6.2 Aggregation of utility functions [39] . . . . . . . . . . . . . . . . . . . . . 47

v

Chapter 1

Introduction

The illiterate of the 21st century will not be those

who cannot read and write, but those who cannot

learn, unlearn, and relearn.

—Alvin Toffler

The electric grid in the Netherlands and other countries in the world are aging and need

to be upgraded [28, 55]. In order to make use of electricity more efficient and to keep the

electric grid reliable there is a need to make some changes. The power system community has

proposed to extend the communications layer in the electric grid all the way from utilities to

the consumers [41], Figure 1.1. Historically, this layer was extended only from the utilities

to the infrastructure. Extending this layer opens a great opportunity for the creation of the

Smart Grid applications layer and extending the power layer with the distributed generation

and storage.

The term Smart Energy Grid has been in use since at least 2005 [50], but until now it is

still not very obvious what exactly the Smart Energy Grid is. There are numerous pilots going

on around the world, but they all have different vision, use different approaches and it is still

not clear when we will be able to call the electric grid ”Smart”. For example, the US and the

EU are two big players in the Smart Grid market. The US is richer than Europe in resources,

so its policies are more concerned with clean coal technologies, and distributed generation has

a lower priority. The EU, on the other hand, is more concerned with distributed generation

and usage of renewable energy sources. The US is highly concerned with security issues, such

as possible terrorist attacks and blackouts. The EU is concerned with the interconnection of

infrastructures between different countries of the union [17]. However, the main idea of the

Smart Grid is similar in the US and the EU: IT will control the production, distribution and

flow of energy, thus the existing infrastructure of power grid will not need to be drastically

upgraded despite the growth of the energy consumption. As a consequence of this, there will

1

1. Introduction

Figure 1.1: End-to-end Smart Grid [41]

be a reduction of energy loss, reduced CO2 emission, the grid itself will be more secure, fault

tolerant, etc. The power system overall will evolve from the hierarchical top-down control to

distributed control [32].

Nowadays, most of the energy is produced by large power stations, but this is supposed to

be changed and every household should be able to produce energy and sell the surplus of it.

Every consumer that is be able to sell energy as well as to consume it is called a prosumer.

”Prosumer” is a new word, but the concept of consumers becoming producers at the same time,

has been known for decades. Marshall McLuhan and Barrington Nevitt have talked about this

idea in their 1972 book Take today: The executive as dropout (p. 4). Since then, many famous

scientists, philosophers and even politicians have discussed this idea.

Let us look at the way how electricity is produced nowadays. The main load of electricity

2

1. Introduction

is produced by load power plants. This type of plant is usually represented by nuclear, coal,

or renewable energy source generators. For nuclear and coal power plants it takes a lot of

time to change the magnitude of their output and they are considered to be environmentally

unfriendly. However, the cost of the energy that they produce is considerably low. Renewable

energy is also cheap, but it is not controllable, since wind and solar energy is variable at all

times. Load following power plants are used during the peak hours, which may vary by region,

but usually it is considered to be morning and afternoon hours. Load following power plants can

quickly adjust their generation power and they are represented by hydro, oil and gas generators.

Peaking power plants do not produce energy all the time, they run only when there is a need

to change the amount of supplied energy very quickly. Usually, these plants use gas turbines

and can change their output in a fraction of a second.

Electric energy is a resource that cannot be stored efficiently in large amounts and for a long

time, this is why there is a 24 hour prediction for the consumption. Based on this prediction,

energy is produced by load power plants and load following power plants. In Figure 1.2 we

see that sometimes the amount of realized energy is higher than predicted, but it can also be

lower, Figure 1.3 (Dutch electricity market: information from TenneT1). This shows us that

these predictions are not very accurate.

Figure 1.2: Realized amount of electricity ishigher than estimated

Figure 1.3: Realized amount of electricity islower than estimated

With the introduction of more and more renewable energy, it will be even more difficult to

predict the amount of consumed energy. This is how it would work if the system would operate

in the same way as it operates now, but there is a way not only to predict the amount of energy

that would be consumed, but also to manage the consumption with the help of demand-response

technique, which is discussed in the later chapters.

1TenneT B.V. is the national electricity transmission system operator of the Netherlands

3

1. Introduction

1.1 Research question

We can model the whole energy grid as a graph, where every node in the graph can be seen

as a prosumer. Every prosumer in the grid has its own goal, such as, consume electricity at

the lowest possible price and sell electricity, when it is available, at the highest possible price.

However, the whole system has also its own goal; it has to be in equilibrium, because the energy

that is produced needs to be consumed immediately, since the electric grid does not provide

elasticity.

Some researchers are arguing that a multi-agent system as an approach to the construction

of robust, flexible and extensible systems is the technology to be adopted by the Smart Energy

Grid [34, 53, 54]. A multi-agent system (MAS) is a system composed of multiple interacting

intelligent agents and in the case of the Smart Energy Grid we can say that every node in

the system could be represented by a smart intelligent agent. However, the problem is that

these agents can be selfish or even malicious, thus making the whole system unstable. Another

problem arises when we start thinking about the number of agents communicating with each

other. All of them are as different as people are different in the society. In the area of Web

Services there is such an aspect as Quality of Service. Web Services can be invoked based on

their qualities, such as cost, availability, etc. Multi-agent systems are somewhat similar to the

service-oriented systems in this aspect; therefore, there may be a need to introduce such metrics

to the agents which are used in the Smart Grid. For these reasons we propose to include a trust

and reputation system in the Smart Energy Grid.

In the last few years a lot of research papers were published that investigated MAS based

approaches to the power system problems [57, 58, 68]. However, up to just recent times, quite

a few of these papers have touched the security aspect [40], which may also be improved by

reputation management. In our research, we study the state of the art in this area and propose

our view to this problem. Reputation management is a widely studied topic in multi-agent

systems. It is especially studied in the area of e-commerce, but up to now we have not found

much research in this field with respect to the Smart Energy Grid.

The main question for our research is:

What is the added value of including trust and reputation management in the

Smart Energy Grid specifically with respect to a prosumer energy trading system?

To answer the main research question, different sub-questions have to be answered. The

research sub-questions are detailed next.

1. What is the state of the art in the Smart Energy Grid in the given context?

4

1. Introduction

Before discussing trust and reputation, it is important to discuss the reasons for adopting

the Smart Energy Grid and the problems that it may solve as well as the current state of

the art in the field.

2. What are multi-agent systems and how can they add value to the Smart Energy Grid in

the given context?

The current energy system does not utilize smart agents as proposed by the current

studies and research. However, adoption of such agents promises to add some benefits to

the Smart Grid. This opinion is supported by different studies which can be discussed.

3. What is a Trust and Reputation Management System and how is it used in computer

science?

Before trying to discuss the importance of these systems in the Smart Grid, it is important

to research the state of the art of this field in other contexts of computer science.

4. What are the most applicable methods for trust and reputation in the Smart Energy Grid

in the given context?

There are different possible methods to adopt trust and reputation systems. Not all of

them may be applicable to the given context. Advantages and disadvantages of such

systems need to be analyzed.

1.2 Background

This research covers a topic which is based on a few scientific areas. In this section we briefly

introduce them, give some definitions and describe their applicability to our research. These

scientific areas also need to be defined in order for us to successfully answer all of our sub-

questions.

Multi-Agent systems (MAS):

MAS are software systems which consist of multiple interacting intelligent agents. In the case of

the Smart Energy Grid, an agent could represent a house, an electric car, an electric transformer,

etc.

Multi-agent systems can be used to solve problems that are difficult to solve by a single

application. These systems are used in a variety of different fields. Some of these fields are

closely related to the application of multi-agent systems in the Smart Energy Grid. One example

of such application lies in the area of e-commerce, where agents are used to aid customers to

find the best products based on some criteria. We think that it will be possible to draw a

parallel between these applications and partially apply it to the Smart Energy Grid.

5

1. Introduction

Peer-to-peer(P2P) and client-server systems:

We consider a system to be P2P if there is a collection of autonomous hosts that are con-

nected through a computer network. They execute computations, coordinate their activities

via message-passing and failure of one of the hosts does not lead to the failure of the whole

system [2].

Pure P2P systems do not have any preferred nodes and no central management. Centralized

systems on the other hand have a central node which plays important role and has a global

view on the system.

Advantages and disadvantages of these systems will be addressed in our research with respect

to the SEG.

Smart Energy Grid:

A Smart Grid is an energy network where bidirectional flow of information was added to connect

all participating actors. The energy in a Smart Grid does not necessarily need to be electric

energy but can also be heat or gas. In our research we will concentrate on electric energy only.

A Smart Grid is able to respond intelligently to the energy supply and demand. This means

that both local generators and smart devices are able to respond to the needs of energy market

and to other areas within the Smart Grid.

SEG is discussed in the context of energy trading between the prosumers, represented by

households or electric vehicles.

Trust and Reputation management:

SEG is a distributed and collaborative networked computing system. Trust and reputation

management is essential for establishing efficient collaboration among participants that might

not have sufficient prior knowledge about each other. And even if they do have such knowledge,

then it needs to be regularly monitored and updated as the behavior of the participants may

change over time. This is a field of science which can be applied to many areas, including human

behavior. Advantages and challenges in implementing trust and reputation management in

computer science and especially SEG will be addresses in our research.

Advantages of using trust and reputation management are seen in enhancing reliability and

robustness of the SEG. Also, there are three areas that present important technical challenges

in effectively utilizing trust and reputation management [42] and we will look at all of them in

more detail:

• Trust and reputation representation: most of the existing systems represent repu-

tation as a numerical value, for example, the larger the number, the larger the reputation

is. But in the case of SEG, this may not be enough. The entity may have a really good

reputation as a seller of energy, but a bad reputation as a buyer.

6

1. Introduction

• Recommendation aggregation: it is important not only to get recommendations from

other agents, but it is important to combine these recommendations in the right way.

• Attack resilient reputation systems: if reputation is important in SEG, then there

is always a chance that there may be some dishonest attempts to manipulate it. Thus

defense against these attempts is very important.

Tropos:

Tropos is a requirement-driven software methodology for building agent-oriented systems [14].

This methodology is used in this research to build a model of the Smart Energy Grid which

incorporates a trust and reputation system. The advantages of this methodology are discussed

in the thesis.

1.3 Thesis contribution

This thesis introduces a model for intelligent agents, which incorporates trust and reputation

mechanisms. This model introduces the concept of trust between the stakeholders, such as

prosumers, of the Smart Energy Grid. This concept can be used during the development of a

multi-agent system which is used for the trading of energy between different actors.

Our research is built on previous and ongoing work in several areas of computer science.

The novelty of our model is in our idea. At the time when our research started, there were

no publications discussing the use of trust and reputation systems in the Smart Energy Grid

in the context of trading electricity between prosumers. However, this idea has been recently

outlined by some scientists as a topic for future research.

In this thesis, we present a generic model of trust and reputation system in the Smart

Energy Grid, which would have to be adjusted to the specific system. However, we outline

the challenges and problems that have to be solved during the design of such systems. The

proposed model is based on the Tropos modeling language.

1.4 Thesis organization

The remainder of this thesis is organized as follows.

Chapter 2. Microgrids and distributed generation: introduces the concept of the

Smart Grid, driving forces for such a grid and some concepts of the grid, such as: demand-side

management, microgrids, electric vehicles, etc.

Chapter 3. Multi-agent systems in the Smart Energy Grid: discusses the applica-

bility of multi-agent system in the smart grid. It starts with the introduction and definition of

7

1. Introduction

multi-agent systems in general and goes into more detailed discussion on how they can be used

in the Smart Energy Grid.

Chapter 4. Trust and reputation management: is dedicated to trust and reputation

management. First, it is described in general terms, but later it goes into the discussion on

how and where it could be applied in the Smart Energy Grid.

Chapter 5. Modeling: is dedicated to the modeling of the proposed solution by using

Tropos methodology.

Chapter 6. Evaluation: introduces one of the most famous concepts for controlling

microgrids - Powermatcher. The model proposed in Chapter 6 is discussed with respect to the

Powermatcher.

Chapter 7. Conclusion: research sub-questions are restated. Answers to the questions

point to the chapters which discuss the problem in detail. Moreover, every sub-question is

followed by a brief discussion.

8

Chapter 2

Microgrids and distributed

generation

Almost every way we make electricity today, except

for the emerging renewables and nuclear, puts out

CO2. And so, what we’re going to have to do at a

global scale, is create a new system. And so, we

need energy miracles.

—Bill Gates

2.1 Introduction and driving forces

First of all, we define what a microgrid is and why there is research in that area. Just like

in a regular grid, a microgrid is a combination of distributed energy providers and consumers

interconnected on the electric and informational levels. A microgrid involves a low voltage

electric grid, loads, controlled and uncontrolled microsources, storage devices and a control

schema supported by a communication system [44].

A microgrid is supposed to be able to work while connected to the main grid and also it

should be able to isolate itself and still continue functioning in the case if the main grid is not

delivering energy, in so-called islanding mode. This concept has been outlined as one of the

key aspects of the Smart Grid [61]. Very important role in microgrids plays distributed energy

generation. Kok et al. [38] have summarized the advantages of distributed generation as: ”the

driving forces for distributed energy generation”:

• Environmental concerns: most of the green energy comes from solar panels and wind

turbines, which are distributed and supported by the governments of many countries.

9

2. Microgrids and distributed generation

• Open energy markets: deregulation of the energy market makes long-term investments

in large-scale power generation not very interesting and at the same time it opens many

possibilities in investment into the smaller generation facilities.

• Diversification of energy sources: fossil fuels are becoming more and more expensive, so

a lot of effort goes into diversification of energy sources to reduce dependability on other

countries.

• Energy autonomy: sufficient generation of energy at the point where it is produced allows

operating in islanding mode, for example when there is a failure in the external connection

or generation facilities.

• Energy efficiency: energy does not have to be transported, so there will not be any loses

in the long-range transmission.

All of these forces can also be seen as advantages of the Smart Grid over the traditional

grid.

If we look at hospitals, data centers or even some office buildings which have generators

on the premises, then we can say that microgrids have been existing for some time now. All

of the buildings listed above share the same characteristics as microgrids: low voltage electric

grid, loads, microsources, storage devices and control schema’s supported by a communication

system. Thus the biggest challenge now is how to make all of these microgrids work together.

Overall, microgrids can be seen as building blocks for the Smart Grid, Figure 2.1, where every

neighborhood, little town or even office building can be seen as a microgrid.

2.2 Demand side management

The laws of physics state that in order to keep frequency of electricity at the specified level,

the demanded amount of energy has to be very close to the amount of supplied energy. It

is very important to keep this balance. Nowadays this balance is kept by central generating

facilities, they adjust the generation in the matter of a fraction of a second. With the growing

prices for fossil fuels and with the growing concerns over the pollution that is produced by using

these fuels, scientists talk more and more about demand-side management. The original idea

was discussed more than 30 years ago by Schweppe [70, 71] and his colleagues. In their work

they discussed the possibility to change the demand for power in the peak hours by changing

customer attitude towards the consumption of energy. They proposed algorithms for changing

the demand for energy with price signals. They noted that by introducing this strategy it will

be possible to make the demand function more flat, thus it will reduce the total consumption

of energy overall and the need for peak power plants in particular.

10


Figure 2.1: The Smart Grid [47]

With the introduction of more and more renewable energy generators the demand response1

approach seems to be even more important, since it is very difficult to control how much

electricity is produced by this kind of generation. For example, we can look at windmills.

They can produce energy only when the wind has optimal strength. Too little wind and there

is no power produced, too much wind and there is also no power produced. Even rain and

temperatures below freezing may affect the production due to icing of the blades [76]. There is

also a possibility that too much energy is being produced, so by lowering prices, it is possible

to stimulate consumers to use more energy at that specific moment.

There are two main approaches in controlling of the demand: pricing strategy and the way

of directly controlling energy consuming devices. Even though it may not sound very realistic,

but direct control is already being used. For example, in the USA there are several energy

companies that have the ability to directly control the air conditioning units of their clients;

for doing this, they provide some discounts or rebates [75].

Logenthiran et al. summarized in their work [43] several methods of how to adjust the load

balance (Figure 2.2). Peak Clipping, Valley Filling and Load Shifting are mostly concerned

with the reducing of the amount and the height of the peaks. Peak Clipping and Valley Filing

are direct load control techniques. Load Shifting takes the advantage of time independence of

loads and uses demand side management to adjust them: shift loads from peak time to off-peak

time. Load Building and Conservation are used to increase or decrease the total consumption

of electricity. Conservation of energy is always important, but Load Building is more important

1Demand response is a mechanism to change the demand for power in short terms.

11


only when there is a great amount of renewable energy being produced, but the consumption

is very low. These two techniques are associated with the demand side management. Flexible

Load Shape is related to directly controlling customers electricity use by the energy companies,

for example, air conditioning.

Figure 2.2: Demand side management techniques [43]

We have described the general idea of how to control the demand with prices, but in reality

it is a lot more complex, since just changing the price for everyone at the same time may create

even more trouble due to many devices being switched on or off at the same time.

2.3 The case of electric vehicles

Integrating electric vehicles in the electric grid is one of the most discussed topics in the area

of the Smart Energy Grid. These vehicles can be used as a flexible storage of electricity. With

the smart charging and the vehicle-to-grid concept, it is possible to effectively use them in the

load balancing of the electric grid.

The term electric vehicle (EV) is sometimes applied to three types of vehicles which use

electricity to drive:

• Hybrid electric vehicles (HEV): internal combustion engine is used to power the electric

motors which run the vehicle.

• Pure electric vehicles (EV): electric motor is used to run the vehicle.

• Plug-in hybrid electric vehicles (PHEV): these vehicles can be charged the same way as

EVs and internal combustion engine is used to extend the driving range and/or increase

12


the power of the vehicle. PHEV vehicles can also serve as power generators in case there

is a power outage and the vehicle is plugged-in to the grid.

We are mainly concerned with the pure electric vehicles; they are the ones that may greatly

affect the grid. The average daily consumption of electricity by one household in Europe is

somewhere between 10-15kWH, but modern EV can hold up to two times or even more of that

amount (Nissan Leaf 24 kWh). This means that more EVs are on the street more effect they

will have on the grid [27]. This impact can be looked at in several different ways, some of them

are positive, but some are negative for the regular electric grid. However, it can be turned

around to have positive impact on the Smart Energy Grid.

Most of the electric energy is not evenly distributed throughout the day, EVs may make this

distribution even more uneven. For instance, after work people drive home to their residential

areas and plug-in their EVs. During evening hours there is already a spike in the use of energy,

but EVs will make this spike even larger. In the morning these people drive to work and

plug-in their vehicles in commercial areas, so the demand shifts geographically. Similar things

will happen if a lot of people attend some social event or even during shopping days at large

shopping malls.

The positive side of EVs is their ability to be used as a mean of increasing elasticity of the

grid by intelligently distributing power, or charging these vehicles. Even greater advantage lies

in the ability to provide energy back to the grid if the demand is too large and the car is parked

for a long time. This idea is called Vehicle-2-Grid (V2G) [19].

13

Chapter 3

Multi-agent systems in the Smart

Energy Grid

There is no such thing as society. There are only

individuals.—Margaret Thatcher

We don’t live alone. We are members of one body.

We are responsible for each other.

—J. B. Priestley (An Inspector Calls)

3.1 Who are the agents?

A software agent is a computer system that is situated in some environment and is capable of

autonomous action in this environment in order to meet its delegated objectives [79].

Even though there are many definitions of an Agent, we will not discuss all of them, but

rather define the most important properties:

• Autonomy: agents can function without human intervention and have control over their

own actions and internal state [15]. To achieve autonomy, they have means of evaluating

their behavior in terms of the environment and pre-set motivations[45]. For an agent which

represents a heater, the motivation could be to buy electricity at the lowest possible price,

but at the same time to keep the house no colder than it is specified by the user.

• Social ability: agents can interact with other agents and another very important feature

is the ability not only to interact, but also to negotiate with the other agents.

14

3. Multi-agent systems in the Smart Energy Grid

• Reactivity: agents can react to the changes in the environment and take some actions

based on the occurred changes.

• Pro-activeness: agents react to the changes not whenever they want, but more in order

to achieve their own goal. They can take initiative to achieve the preset goal.

A multi-agent system consists of at least two agents. The whole system may not have an

overall system goal, but agents or groups of agents may have a goal which they will try to

achieve. The behavior of every agent is formed by its goals and the context.

3.2 The potential benefits and the use of MAS technology in

the Smart Grid

Multi-agent systems are widely used in research related to the Smart Grid. They can provide

a way of building systems for the support of electric grids and also provide a way to model and

simulate such systems. This section describes some possible uses of MAS in the Smart Energy

Grid.

3.2.1 Market

We have discussed the possibility to use agents in an online market place or for trading stock.

It is also possible to use a similar technology for trading electricity between power generating

companies, power consuming companies and energy brokers as it was proposed by Jia-hai et

al. [34]. In their work they proposed an agent-based contract negotiation platform for the

electricity market. The main goal of their research was to present the architecture and the

negotiation strategy of the agents.

On the lower level, trading of energy between consumers and prosumers can be seen as an

agent-based market with electricity as the main commodity.

3.2.2 Control

The current electric grid is based on a centralized control. When a line goes down, everything

on the other side goes dark. However, in the Smart Grid it has been proposed to move towards

distributed control and to utilize smart distributed agents for this task. They can cooperate to-

gether during the normal and emergency situations. During a normal situation they control the

balance in the net by negotiating with each other and exporting/importing electricity to/from

different microgrids to keep the balance [18]. As soon as these agents sense an emergency situ-

ation, they break the connection with the event source, which could be another microgrid or a

15


powerline, try to restore the balance within its own microgrid and after that start monitoring

the possibility to reconnect to the main grid.

Ranganathan and Nygard have proposed to use agents to control functions of the grid

which are responsible for generating electricity, especially for directing the flow of energy from

distributed energy resources. They argue that this could prevent the grid from cascading

blackouts [65].

Ramchurn et al. have proposed a model of decentralized demand side management based

on smart agents, which represent individual smart meters. These agents react to the different

prices by adjusting energy usage of home appliances. Their model is able to reduce the peaks

in demand by up to 17% [64]. This solution has also proven to be very scalable by being able

to be used by up to 5000 households.

Most of the modern systems for charging electric cars are centralized, but there is also a lot

of research in adapting smart agents for this task. Belgian scientists have proposed a solution

for coordinating charging of EVs by integrating agents. The goal of their research was to show

that by smart scheduling of charging EVs it is possible to reduce the imbalance in the electric

grid. The simulation of their solution has proven to decrease the imbalance cost by 14-44%

[74].

In the V2G concept every car is unable to sell a lot of energy back to the market, but when

a lot of cars are parked in a geographically small area, they can form a coalition and can have

a great influence on the electric net. This idea is somewhat similar to virtual power plants, but

the main difference is that cars can come and go at any time, so it brings additional challenges.

Kamboj et al. have implemented such a system and tested it with 5 cars. They predicted that

the owners of electric vehicles participating in the system can earn between 100 and 200 dollars

a month [37].

An example of a centralized system, but using smart agent as a middleman between the

electricity market and electric vehicles is a simulation of the Iberian market [10]. The middleman

or aggregator in that simulation is an intelligent agent that buys electricity at the open market

and offers ancillary services, but does not directly control every individual EV. The research

has shown that intelligent charging can decrease the total cost, compared to the normal dumb

charging.

3.2.3 Simulation

Multi-agent systems provide a great way for simulating the behavior of entities, such as appli-

ances, households, electric vehicles, etc. Thus, even though some scientists propose centralized

mechanisms in the area of the Smart Grid, they still use MAS for simulating their models [24].

Multi-agent systems provide a great way of simulating complex software systems. However,

16


the Smart Energy Grid is not only software, but also electric hardware. Matlab [52] provides

a way for simulating electric circuits; so, the combination of Matlab and multi-agent systems

provides even deeper understanding of simulated models of the Smart Energy Grid. MACSimJX

toolbox provides an interface enabling JADE agents to interact with systems developed in

MATLAB.

Pipattanasomporn et al. [59] in their work used collaboration of agents to detect outages

in the microgrid, so that it can go into the islanding mode. The simulation of hardware is

done in Matlab, which simulates a simplified distribution circuit consisting of grid generator,

grid interface, loads and load circuit breakers. The results of the simulation indicated that the

proposed multi-agent system can disconnect and stabilize the microgrid. This simulation could

have been done without the use of Matlab, but then the results may have been different due to

the physical nature of electric hardware, which is not taken into account without the tools like

Matlab or by using real hardware.

3.2.4 Summary

To summarize the use of MAS in the Smart Energy Grid, we provide a list of possible applica-

tions which utilize MAS that have been proposed so far [82]:

• Electricity markets: MAS is very popular solution for simulating and building electricity

markets.

• Control of distributed energy resources.

• Microgrid management and automation: the agent based management system allows to

use different algorithms and strategies for research without changing the main manage-

ment system.

• Condition monitoring and maintenance: agents can effectively monitor the performance

of generating devices, such as gas turbines or circuit breakers.

• Protection management and fault diagnosis: by communicating with each other, the

agents, which represent generating facilities, relays or some other equipment can effectively

monitor the stability of the net.

• Reconfiguration and restoration: agents controlling microgrids can monitor the state of

the main grid and automatically reconnect to it.

• Security: by monitoring each others behavior, agents can improve the overall security of

the grid.

17


To conclude the summary we can say that multi-agent system technology has a big potential

in power engineering and particularly in the Smart Energy Grid.

3.3 Why not Web services or grid computing?

IEEE Power & Energy society’s Intelligent System Subcommittee has set up the IEEE Power

and Energy Society multi-agent systems working group. This working group has identified

multi-agent systems as a promising distributed control approach in power engineering [53, 54].

In this section we define other technologies, such as web services and grid computing techniques

and discuss their suitability in the Smart Energy Grid:

1. Web Services

The World Wide Web Consortium (W3C) defines a Web service (WS) as a ”software

system designed to support interoperable machine-to-machine interaction over a network”.

A Web service is an abstract notion that must be implemented by a concrete agent. So,

an agent is a computational resource that may realize or request zero or more services

[29].

Just by looking at the definition of a WS it seems like using it in SEG would be an ideal

solution, but after doing some investigation we see that this is not the case. WSs are

lacking some functionality that agents have. A WS knows only about itself, but nothing

about its environment. It stays inactive until it is invoked, also, web services are not

autonomous [31]. Social ability and pro-activeness gives agents a big advantage in SEG.

Web services are very flexible and they can provide interfaces for the agent, so maybe

there is a possibility to use agent-based Web-services. The use of Web services in SEG

has been considered or even used in several SEG projects [3, 73].

2. Grid Computing

Grid computing is used to effectively utilize hardware resources in scientific calculations

which are based on complicated mathematical foundation. Almost a decade ago, Irving

et al. [33] argued that grid computing could have very significant benefit for power

systems. They motivated their argument by comparing similarities between the Smart

Energy Grid and the scientific applications of grid computing. In science, grid computing

is used to utilize the resources (i.e. data stores, algorithms, and processing power) of all

participating scientists to create a virtual organization. The Smart Energy Grid, where

every smart meter, generator, transformer, etc. is equipped with a computer would not

be able to use existing control technology due to its poor scalability. However, grid

computing would be suitable for this task.

18


The IEEE Power and Energy Society multi-agent systems working group motivates their

argument of not choosing grid computing due to the missing requirement for nodes in

computational grids to exhibit autonomy and social ability.

3.4 Conclusion

Currently there are many research projects going on in the field of the Smart Energy Grid. The

common problem that we have noticed with all the projects is that they do not try to consider

the social aspect of the Smart Grid. By this we mean that they look at all the nodes of the

grid as reliable and trusted.

Even though we can look at these nodes as reliable and trusted in the simulation mode, we

cannot assume the same thing for the real world environment. There could be many reasons

for that, but we will mention just a few.

In the Smart Grid, the nodes do not just exchange information; they also exchange money

for service. In our case the service is electricity. Alice has agreed to sell some electricity to Bob,

so she wants to receive money for the provided electricity. If Bob is not very reliable payee,

Alice may want to stop selling electricity to Bob.

Let us look at another scenario. During every bid or negotiation, Alice promises that she

will use a certain amount of electricity, because all the devices in Alice’s house have agreed on

the amount of electricity that they will consume. But Alice is just a computer at a house and

the owners of the house, even though they have installed a home management system, do not

like it when the system controls the devices and manually override the systems decision. From

the neighborhood point of view, Alice should be treated as an unreliable neighbor and should

not be trusted when calculating the consumption of the whole neighborhood system. The idea

of penalizing for not meeting what was promised is not novice and is considered to be important

in, for example, coalition formation [36].

For the kinds of scenarios described in the previous paragraph, humans use their own rea-

soning. In computer science, on the other hand, decision making can be based on trust and

reputation systems. Some examples of such systems are: Pagerank, which is used by Google to

measure relative importance of links in the internet. Pagerank is a reputation system; it ranks

pages from a search result based on public information. Every incoming link can be looked at

as a positive rating and every outgoing link as a negative rating. eBay uses reputation to help

their customer in choosing reliable customers. However, choosing a reliable partner is done

manually, where reputation plays a very important role, but trust may be even more impor-

tant. For example, a seller may have a great reputation, but during a previous interaction he

did not deliver the promised resource, so it does not matter how high the reputation is, trust

may overrule the overall decision. Even in human society there exists some kind of reputation

19


system. If we have a question, we do not just run to some random person, but first think who

may be the most competent in giving us the right answer.

We propose to adapt trust and reputation system for the Smart Energy Grid. But before

we do that, in the next chapter we look at the available techniques for managing trust and

reputation.

20

Chapter 4

Trust and reputation management

Every kind of peaceful cooperation among men is

primarily based on mutual trust and only

secondarily on institutions such as courts of justice

and police

—Albert Einstein

The potential benefits of using MAS in power industry was discussed in the previous chap-

ters. This chapter introduces the concepts of trust and reputation management and describes

some models and approaches in the field.

4.1 Introduction

Trust and reputation systems have been a big part of scientific research since the introduction

of e-commerce, web-auctions and other virtual marketplaces where people can exchange goods

and services. Other fields for application of such systems include peer-to-peer networks (P2P),

grid computing, mobile networks, semantic web and web-services. One of the commonalities

between all of these systems is that they are open and distributed. Behind some of these

systems, such as web-auctions, there are real people and real money. These people do not

meet before the transaction, during it or after, but they have to trust each other. For some

systems, such as P2P file sharing, there is no physical exchange of goods, but just an exchange

of information, however, trust in the partner is still very important at least for security reasons.

In order, for the systems such as web-auctions, virtual marketplaces peer-to-peer networks

to be successful, all transaction parties have to trust each other. Before the era of the inter-

net, reputation was provided by past personal experience, vendors and buyers provided their

experience by word of mouth or specially published media. The standing of a businessman in

21

4. Trust and reputation management

the community could also provide valuable information. However, in the modern digital society

this task becomes more difficult.

The most popular electronic commerce systems, such as eBay, have adapted trust and

reputation systems in order to facilitate their users in choosing the most reliable partner. Every

transaction can be rated by both parties in an uncomplicated manner. All the ratings are

summed together and are available to all parties of the system. The idea behind this system is

very simple, but it has proven to work very well.

4.2 Background of trust and reputation

The difference between trust and reputation is:

Trust - a peer’s trust in other peers based on its own past experience.

Reputation - a peer’s trust in other peers based on experience of other peers.

The main difference is that in the first case, the peer’s trust is based on its own past

interactions and in the second scenario it is based on the interactions of other peers. The trust

itself is used in many scientific areas, such as sociology, psychology, economics, philosophy, etc.

In computer science this subject is predominantly studied in the area of multi-agent systems.

In psychology, trust is defined as a mental state or attitude of an agent towards another

agent [16]. Every decision that the agent makes is based on the trust in the opponent, so all

risks have to be calculated before delegating some operations. The delegation will be decided

by calculating what risk is greater, to delegate the task or not.

Sociologists look at trust as a social construct, and all human relationships are dependent

on trust. Without the notion of trust, human relationships may be too complex, so trust is

needed to reduce this complexity by increasing the possibility for experience and interaction

[26]. The more information is available from the past interactions, the less likely it is to make

incorrect decision in the future.

In economics, trust is described from the perspective of maximizing one’s utility. Game

theory and Prisoners dilemma are widely used to associate trust with economic utility.

For the definition of trust in computer science we take a definition of Grandison and Sloman

[25]. They define trust as the firm belief in the competence of an entity to act dependably,

securely and reliably within a specified context. Trust is a very important characteristic of

decision making for distributed systems and greatly influences the specification of security

policy.

Trust by itself is a very big research area and there are multiple definitions of trust for every

field of research. We are not interested as much in definitions of trust as we are interested in

key characteristics, such as [77]:

22


• Trust is subjective and depends on the opinion of the entity making the decision. It also

depends on the previous knowledge and experience.

• Trust is dynamic; it changes over time in either direction depending on the success of the

previous interactions. If there were no interactions for a long time, then trust can decay.

A typical example of this would be eBay, where the ratings are valid for only up to 12

months.

• Trust is context-dependant. A person can trust another person as his banker for his

savings account, but he would prefer another banker for every day transactions due to

the transaction costs.

• Trust is multi-faceted; for example a person could trust a car-body mechanic to paint his

car, but trust in the ability of the same mechanic to fix engine or the electric equipment

of the car would be a lot different.

• Trust is tightly coupled to uncertain situations, where the outcome of interaction could

go any way. This uncertainty is related to the identity of interaction party, uncertainty

in its behavior, uncertainty in observation and uncertainty in second-hand experience.

• The notion of trust is applied only in uncertain situations, where the outcome of interac-

tion could go any way.

After taking into consideration all the characteristics of trust, we can say that trust is used to

reduce the complexity [46] of decision making, even though there are some uncertainties in the

possible outcome. It is mostly used when there is limited information, knowledge, computing

capacity and limited time available to make an in-depth analysis for a decision [30]. Trust can

be measured or quantified, so the decision made is based on the threshold level specified prior

to the decision. The interaction gets a Go if the threshold level is lower and it gets a No Go if

it is higher.

4.2.1 Reputation

Reputation is a communal recommendation which is combined of individual recommendations.

It plays a very important role in open systems where entities had no previous interactions,

but need some help in making decision. A very good illustration of such a situation in real

life would be an example where a much respected auto magazine conducts tests of cars and

shares its results with the readers. The reader may never have had a previous experience with

a certain car or even brand, but he can make his decision based on the ratings provided by the

magazines.

23


Figure 4.1 shows the relationship between trust and reputation. Reputation is based on the

experience of all users (persons 2..n) and it may affect trust, however trust is subjective and it

reflects individual experiences (person 1).

Figure 4.1: Relationship between trust and reputation (adopted from [77])

In large open systems the benefits of good reputation can be looked at in two different ways:

the individual and collective point of view. In the first case, reputation helps entities to discover

reliable partners to interact with. In the second case, reputation systems encourage entities to

behave in a good way and provide only high quality resources, so that in the future they will

have a better chance of receiving interaction.

All reputation systems can be classified by the way they collect and share ratings as cen-

tralized and distributed. Centralized systems are widely used in e-commerce and eBay is the

most famous example of such a system. After every sale, the system gives a possibility to rate

the other party in very simple way: if the experience was positive, then 1 is added to the rating

and if it was negative, then -1. The overall rating gives other members a good idea of what to

expect when dealing with other members, Figure 4.2 [20].

All centralized systems share some common characteristics:

• A central entity collects all ratings and they are public and visible to the whole community.

• Values are built by the system

24


Figure 4.2: eBay feedback profile [20]

• Less communication is required (than in distributed systems). It is only needed to contact

the central entity to find out the reputation of another entity.

The typical example of a distributed reputation system would be of Abdul-Rahman and

Hailes [1]. In this system, every entity keeps track of all the entities that it had contact with

and the knowledge of previous interactions can be shared with other entities. All distributed

reputation systems also share some common characteristics:

• No central system

• Trust and reputation are subjective, based on previous interactions between entities.

• There is no global trust or reputation.

• Extra communication is needed to share the ratings (compared to the centralized system).

Generally, centralized systems are more simple and communication efficient than the dis-

tributed ones, but they provide a general public opinion, which may not be very good for

minorities if they do not share the public opinion.

Decentralized systems on the other hand are more flexible and every entity can decide by

itself how will it judge the opposing party and what rating has more or less value. By gaining

flexibility, decentralized systems lose some efficiency. For example, since every agent keeps its

own reputation score, there is a need to find it and convert it so it can be useful. Another issue

is with the agents staying online. If an agent stays online, then its reputation can be used, but

there is also possibility that an agent can be offline or just temporary unavailable.

25


4.2.2 Reputation computation models

There are multiple methods of aggregating reputation scores. In this section we will describe

the most common ones as they were outlined in [35].

Simple summation or average: we have briefly described this method in the previous

section with the example of e-Bay. In [67] this method is described in details. Amazon provides

the rating in the similar way. The sellers rating is 1 to 5 stars, with 5 stars being the best. The

average score is displayed for the buyers.

Bayesian systems: the final score is determined by combining previous reputation score

with the new ones after it was computed by statistical updating of beta probability density

functions. The score is represented in the form of the probability expectations value of the beta

probability density functions. The main disadvantage of this method is that it is very complex

for a regular person to understand [78].

Discrete trust models: one of the most famous examples is the model of Abdul-Rahman

[1]. It is based on sociological characteristics of trust. Trust degree in the proposed method

has four levels: very trustworthy, trustworthy, untrustworthy and very untrustworthy. The

grade of outcome of an experience is a member of the ordered set, with possible values as: very

good, good, bad and very bad. After an experience of one agent with another, the appropriate

member of the set gets incremented. If the experience came from another agent as a reputation,

then it is updated by also including the weight of the recommender. The final reputation of

each agent is given by the level corresponding to the column of the highest sum value.

Belief model: this model is related to probability theory. The possible outcome value

looks similar to the probability value, but the main difference is that Dempster’s rule is applied

in combining all related probabilities to compute the final reputation score [35].

Fuzzy models: in this model trust and reputation are represented as fuzzy concept. Fuzzy

logic [83] is used to compute the final reputation score [35].

Flow models: a good example of this model is Google’s Pagerank algorithm, where the

reputation score is higher when there are more incoming links [35]. Every incoming link increases

and every outgoing link decreases the reputation value.

4.2.3 Additional mechanisms for decentralized reputation management

In a completely distributed p2p system every peer is equal. But a problem may arise when

some peers that do not have memory or CPU capability try to take over the large load of

work. By introducing Super-Agents, we can get rid of these small bottle-necks and let the

super-peers, with enough capability, handle the most complex tasks, for example to manage

trust and reputation.

The problem is how to choose a super-agent to contact with. In order to overcome this

26


problem, there exists a community formation mechanism. This method is closely related to

super-agent based management. The only difference is that an agent has to choose a super-peer

which is closely related to the agent. If an agent joins a community of a super-agent which does

not reflect the opinion of this agent, then it will become a minority. Therefore, in this case,

the reputation will not reflect the opinion of the minor agent. The agents that want to join the

system may look at the public opinion to decide which community to join.

Super agents are not just regular agents, but they may still want to be selfish and provide

unreliable information and other agents should keep this in mind. This brings us to the need

of a reward mechanism for the super-agents, so that they will provide truthful information.

4.2.4 Classification of trust

In the previous sections we have described several kinds of trust systems, but there exist a lot

more. On a large scale, they all can be classified as either individual-level or system-level

trust systems. In the first case, every individual agent is more reliant on itself and it decides for

itself whether to trust other agents or not. In the second case, the trustworthiness is enforced

directly by the system. Both of these classifications are usually seen as being complementary of

each other [63], because the use of just one method does not always help to achieve the desired

outcome or even if it does, then it may not be the most efficient one.

As for individual level-trust, Wang [77] outlines several possible types:

Trust between a user and his/her agent: this type mainly deals with how much

freedom a user would give to the agent. There is a possibility that the agent will not operate

as expected by the user, and then the amount of resources that are traded by the agent can

be limited. The same could be applied to the Smart Grid. For example, would a user trust its

home automation system to completely charge/discharge an electric vehicle or just to use that

energy partially?

Trust in provider/consumer: this type is more about the honesty of agents with respect

to another agent. A provider may advertise high quality product for a low price, but deliver a

product with a lower quality or not deliver it at all. A consumer on the other hand may agree

to the financial terms and never fulfill them or fulfill with a significant delay.

Trust in references: agents may communicate with each other in order to find out the

reputation of a third agent. This kind of trust measures whether an agent can provide reliable

recommendations.

Trust in groups: this kind of trust relates to community formation. Once an agent joins

a community, it is important to trust it and to know that all entities in this community will be

trustworthy with each other. Only then will it be beneficial to join this group.

Ramchurn [63] proposes to subdivide system level trust in three terms:

27


Truth-eliciting interaction protocols: these protocols are aimed to make sure that the

agents stay truthful and are not speculating. They do this by imposing certain rules and steps

that the agents have to take while interacting.

Reputation mechanisms: these mechanisms are making sure that the agents behave in

the truthful way by revealing the reputation. In a centralized system these reputations may be

visible for all parties, but even in a decentralized system, where they are not clearly visible to

all entities, there is a way to share reputation between some agents.

Security mechanism: authentication by trusted third parties is the most famous example

of such a mechanism. Even though this mechanism does not completely guarantee the truth-

fulness or the ability of the agent to perform as it should, but at least the identity of that agent

is known.

4.2.5 Trust acquisition

Humans acquire trust by direct communication with other humans, they have a possibility to

ask around or to use specialized services, such as magazines. Computer systems behave in

a similar way. Scientists [21, 77] describe several mechanisms to acquire trust in multi-agent

systems:

Observation: this method is not commonly used in multi-agent systems because most of

the operations are private and it is not always possible to find the outcome of interaction of the

other entities. One of the examples where this method is used is a robot soccer game. Robots

can observe how other robots behave and based on those observations they can make their own

decisions.

Interaction: this is the most reliable technique, where a positive trust is acquired only

after a successful outcome of the interaction.

Using institutions: this mechanism is easy to describe from the perspective of human

society, where a human could trust another human just because he wears a police uniform and

has a badge. A similar idea could be adapted in multi-agent systems, where institutions could

be build just for the purpose of collecting and distributing fair reputation ratings.

Reputation: this is somewhat similar to institutions, but the main difference is that in

this case, reputation is collected from other regular agents, which do not necessarily specialize

in providing such a service.

Presumptions: sometimes it could be assumed that the agent is reliable only because it

belongs to a group which is known for its reliability. The same as humans who trust well known

brands.

28


4.2.6 Main types of trust and reputation approaches

Various trust and reputation mechanisms have been proposed by the scientific community.

These approaches are not only different in the type of algorithm that they use, but they are

also different in the type of applications that they are used in, for example: multi-agent system,

p2p network, etc. In this section we provide some of the main approaches and their brief

descriptions.

• Centralized vs. decentralized: centralized is easier to implement, but requires a powerful

central entity.

• Agent vs. resources: trust and reputation of the agent or the resources or service that

those agents provide. To provide an example of these two approaches, we will go back to

eBay and Amazon. The eBay system provides reputation of the agent and its qualities,

Amazon, on the other hand, uses a system to provide ratings of the products.

• Global vs. personalized: in a global system, the reputation is based on public opinion and

visible to all members. In a personalized system, the reputation is calculated separately for

every entity. It is merely impossible to design a system with global reputation mechanism

which will make all the agents in the system happy.

4.2.7 Unfair ratings

Most of the reputation systems use ratings from the agents to determine the overall reputation

value. Calculating an overall trust value based on these ratings is one story, but trying to

determine which ratings were truthful and which were not is a completely different story. Agents

are selfish or they could be malicious, so using the ratings that they provide needs to be

integrated with caution.

Basically, there is only one method to determine if agents are lying, this method is a sta-

tistical analysis of the previous interactions. However, this method can be divided into two

groups: endogenous and exogenous [78]. The first group uses pure statistical methods; they

filter out unfair ratings by assuming that they greatly deviate from the truthful ratings. The

second group also takes into account some other factors, such as the rating of the rater. It is

assumed that the agent with low rating will not be truthful when rating other agents.

Whitby et al. [78] use the first approach. They assume that the ratings provided by

different agents on a given agent follow a similar probability distribution. They compare the

overall reputation score with the reputation provided by each rater. The ratings which are

above or below a specified threshold are excluded. Recent ratings have a higher value than the

ratings that were given a long time ago. This method assumes that dishonest agents mostly

provide dishonest ratings, but if this is not the case, then this method may not work properly.

29


The model of Yu and Singh [80] on the other hand, uses the exogenous approach. It

adapts the algorithm to predict the trustworthiness of agents based on a set of testimonies from

witnesses. Each agent keeps a weight for all other agents that it gets the reputation score from.

This weight estimates how truthful the rating party is.

Just to summarize the problem of filtering out unfair ratings, we can say that both of these

approaches work well only up to the certain level, but they cannot provide a 100% guarantee.

It is also possible to combine both of these approaches, thus the filtering can be improved [?].

4.2.8 Comparing of different trust and reputation mechanisms

Comparing and evaluating different trust and reputation algorithms is not a simple task and the

methods to do that are not standardized. However, there are some proposals on how to evaluate

the effectiveness of such algorithms. Fullam et al. [23] have launched a testbed for agent trust

and reputation related technologies. There are two main goals for this testbed: a competition,

in which different scientists can test their solutions against each other, and the second goal is

to serve as a tool for running simulations in which different parameters can be easily adjusted.

The authors have noted that it is not an easy task to compare different approaches, so they

have proposed a competition mode, in which the agents try to maximize their bank account

balance. Some of the metrics that they have used are used to measure how:

Accurate: how accurate the prediction of reputation is, which can be easily calculated by

comparing calculated trust and the true value.

Adaptive: how fast the algorithm adapts to the dynamic trustworthiness of other agents.

Quickly converging: how fast does the algorithm learn the trustworthiness of a new agent.

Efficient: computational costs.

Marti and Garcia-Molina [48] have added a few more metrics which were not mentioned in

the testbed:

Effective: the probability that an agent can locate the trustworthy interaction partner.

Some other metrics include: message traffic, network load, robustness, etc. In conclusion

we can say one more time that the metrics mentioned above are just proposals and there is no

unified method to compare different trust and reputation mechanisms.

4.3 The potential benefits and the use of trust and reputation

management in the Smart Grid

The electric grid is considered to be a critical system, meaning, it has a large impact on society

and if it fails, it can result in loss of life or damage to the environment [72]. Therefore, one

of the most important requirements for such a system is dependability [6]. Since the Smart

30


Grid is still an energy grid it has the same requirements for dependability. In order to build

a dependable system, there needs to be an analysis of possible failures. Fault Tree Analysis is

widely used in systems engineering, but there are also other methods. Asnar et al. [5] have

proposed a model to analyze the risks by modeling the trust relationship in the system among

its actors. Every actor in the system depends on other actors to fulfill their goal. By using the

trust metrics it can decide whether to depend on the other actor or not. As we have described

before, there are many possible ways to use agents in the Smart Grid, but in this project we

are concerned with agents which trade energy, a prosumer-consumer relationship.

4.3.1 Security

We see the benefits of using trust and reputation management in the Smart Energy Grid

in providing soft security [66], meaning that the agents can detect malicious agents among

themselves. And another advantage is in promoting honesty between selfish agents.

Security is the most discussed use for trust and reputation management in the Smart Grid.

And it is understandable why, because some of the most important characteristics of the Smart

Grid are the improved reliability, security and efficiency with full cyber security. And addition-

ally to that, security is one of the most challenging tasks in this concept. The energy grid is

controlled by SCADA1 systems, which receive measurements from different parts of the grid,

computes and estimates its state and, based on that, implements a control strategy. As it

has been proposed so far, all measuring devices are represented by intelligent agents. In open

cyber space, these agents are vulnerable to attacks, so if they were hacked, they should not

be treated by the SCADA system as reliable nodes. Matei et al. [51] have proposed to use

trust-based filtering to decrease the vulnerability of SCADA system due to false data injection

from malfunctioning equipment or cyber attacks. In their algorithm they assign trust metrics to

every agent in the system that is controlled or monitored by SCADA. Since the electric grid is

geographically distributed, all agents are also distributed. Every agent in the system computes

its value based on its own measurement and the measurements of nearby agents. If these values

are very different, an agents’ reputation is lowered and in the future it will not influence much

the overall value.

In his PhD thesis [22], Fadul has proposed a somewhat similar idea to the one from the

previous paragraph. He advocates the use of reputation-based trust for the defense of the Smart

Grid from malicious and non malicious malfunctions. He has proposed a Trust Management

Toolkit to detect malfunctioning sensors. His approach has also improved the decision making

process’s response time and accuracy in preventing detected faults.

The work of Pradhan et al. [60] is also related to security and malfunctioning of devices,

1Supervisory Control And Data Acquisition

31


but they are more concerned with the advanced metering infrastructure and smart meters.

They apply a reputation to every smart meter in the system. It is supposed to prevent them

from being tampered with. Their system measures the overall consumption of electricity in the

neighborhood and compares this data to the sum of reported usage by every individual meter.

If there is a difference in these values, then they are able to predict which meter is not reporting

the true data, thus it was hacked or is fraudulent.

4.3.2 VPP and coalition formation

Ramchurn et al. [62] have outlined in their work the main challenges for artificial intelligence

in the Smart Energy Grid. They have noted that trust and reputation management may play

a big role in virtual power plant (VPP) communities. They argue that in these communities,

competing actors may be selfish and not always report their true consumption or production

of electricity. This may destabilize the whole system. The report concludes that there is a

need for a mechanism which would form trust measures for energy providers and automatically

reason about their ability to form VPPs.

The definition of a VPP is closely related to the definition of a microgrid. They both share

some of the main characteristics. VPP and microgrids are: a collection of distributed energy

generators, storage devices and a control schema supported by a communication system. A key

distinction of a microgrid and a VPP is that a VPP is not limited by geographical location.

Moreover, microgrids can work while connected to the main grid as well as disconnected, but

VPPs are always part of the main grid [4].

4.4 Conclusion

We could not find a lot of research in the area of trust and reputation applied to the Smart

Energy Grid. There might be multiple reasons for that. One of them is that it is mostly used

to facilitate the learning of agents and to reduce the complexity of decision making. But most

of the research projects are applied to considerably small pilots. For example, the pilot in

Hoogkerk [69] is considered to be one of the larger ones, but it only involves 25 households.

Another reason is that most of the projects are not considering the security aspect, they also

assume that the agents are always cooperating in the truthful way. But we think that we will

see more and more of this kind of research as we see it right now applied to other areas of the

use of MAS. For example, it has been proposed to use an agent-based trust and reputation

management scheme for wireless sensor networks in the work of Boukerche and Li [13]. They

have noted that the wireless sensors do not have a lot of computing power and there is also a

bandwidth constraint. By applying a trust measure to every node in the network, it is possible

to reduce message flooding, thus conserve energy, which is a very crucial thing for wireless

32


sensors. These kinds of ideas have not been studied in the Smart Grid, because it is not known

what kind of hardware will be used to run agents and what kind of infrastructure will be used

to enable agent communication.

33

Chapter 5

Modeling

Many agent oriented modeling methodologies have been proposed in the past. Some of them

deal mostly with architectural and detailed design, Figure 5.1; other ones are only concerned

with the early requirements stage. Detailed design is out of the scope for this project, but we

still would like to have this option opened for future research, so we have chosen Tropos as the

modeling framework, it supports all the stages, starting with the early requirements and going

all the way to the detailed design.

Figure 5.1: Comparison of Tropos with the other methodologies

5.1 Tropos

Tropos is a requirement-driven software development methodology for building agent-oriented

systems [14]. It gives the capability to describe the organizational environment of the system and

34

5. Modeling

the system itself. Tropos is based on i* [81], which is a framework for modeling and reasoning

about organizational environments and their systems. The main concept of the framework

is based on the intentional actor. The actors in the system have goals, beliefs, abilities and

commitments. Actors depend on each other to achieve goals which are too difficult to achieve

alone, but at the same time the actors become vulnerable if they depended on other actors

which did not fulfil their intentions.

5.1.1 Tropos phases

Tropos is composed of 4 different phases [14]:

Early requirements: concerned with the understanding of a problem by studying orga-

nizational settings, why there is a need for such a system and how the stakeholders’ needs and

concerns are addressed. Eric Yu argues that in most of the traditional frameworks the impor-

tance of this phase is underestimated and is done informally. So, in this phase all actors of the

system are identified as well as the dependencies of the actors, their goals, plans and resources

which are used. It lets us clearly see the main functionalities of the system.

Late requirements: the model is extended with a new actor: the system itself. The

final goal of the requirements analysis is to provide a set of functional and non-functional

requirements for the system-to-be.

Architectural design: an architectural style is selected. The global architecture is repre-

sented in terms of subsystems interconnected through data and control flows. Subsystems are

represented as actors and interconnections are represented as dependencies.

Detailed design: a definition of how the components will fulfill their responsibilities. In

this phase all the components of the system are defined in term of their inputs, outputs, controls

and other relevant information. AUML [7] can be used to describe the interactions of the agents.

In this section we have mentioned only 4 phases of modeling and design even though in

the original description of Tropos, there are 5 phases. The last phase is concerned with the

implementation of the system, which is out of the scope for this project.

5.1.2 Modeling activities

There are 5 different modeling activities in Tropos which contribute to the complete model of

the system. All these activities and diagrams that they produce are summarized in Table 5.1

at the end of this section. But before that, we provide a brief description of all of them.

Actor modeling: in the early requirements it consists of identifying and analyzing actors

of the environment or the main stakeholders and their intentions as social actors. In the late

requirements, actor modeling consists of analyzing the system-to-be and its relationship with

the other actors. In the architectural design, the focus is on specifying the system-to-be in

35

5. Modeling

terms of subsystems, interconnected through data and control flows.

Dependency modeling: in the early requirements phase it consists of identifying actors

which depend on each other to achieve goals, plans to be performed and resources to be supplied.

In the late requirements phase, the same dependencies are analyzed for the system-to-be actor.

In the architectural design phase, data and control flows are modeled between subsystems of

the system-to-be.

Goal modeling: goals are analyzed from the point of view of the actor by using means-end

analysis, contribution analysis and AND/OR decomposition. The aim of goal modeling is to

refine and elicit new dependencies and also it helps to decompose the system-to-be into a set

of sub-actors.

Plan modeling: can be look at as complementary modeling to goal modeling and it

provides decomposition of a root plan into sub-plans.

Capability modeling: capabilities of sub-actors are defined and specified. Capabilities

can be presented in a table and UML can be used for plan diagrams.

# Activity Diagrams produced

1 Actor modeling Actor diagrams: identified and analyzed actors ofthe environment and the system

2 Dependency modeling Actor diagrams: dependencies between actors3 Goal modeling Goal diagrams: analyzed goals of the actors4 Plan modeling Goal diagrams: goal decomposed in plans5 Capability modeling Capability and plan diagrams: capabilities presented

in a table

Table 5.1: Activities and diagrams produced during the analysis process

5.1.3 The meta model

The abstract syntax of Tropos is given in terms of a UML meta-model. A Tropos model is

a directed labeled graph whose nodes are instances of meta-classes of the metamodel, namely

actor, goal, plan and resource, and whose arcs are instances of the meta-classes represent-

ing relationships between them, dependency, means-end analysis, contribution and AND/OR

decomposition[14] . In this section we provide the main language concepts and an example of

one of the concepts as a UML class, as shown in diagram of Figure 5.2.

Actor: active entity that carries actions to achieve goals. Actors can be agents, positions

or roles. Each actor has 0..n goals and each goal is wanted by 1 actor. Actors also have

36

5. Modeling

Figure 5.2: The actor concept in the Tropos metamodel [9]

dependencies.

Dependency: describes the relationship between actors. One actor (depender) depends

on another actor (dependee) on something (dependum). The dependum is the type of the

dependency.

Goal: a condition that the stakeholders would like to achieve or their strategic interest.

The way how it is achieved is not specified.

Softgoal: softgoal is similar to the goal, but there is no clear criteria to tell whether this

goal was achieved or not, because some goals could be subjective. Softgoals are typically used

to model non-functional requirements.

Resource: resource can be physical or informational and the main concern about it is

availability.

Task: task specifies a way of doing something. Task dependencies are used in situations

where the dependee is required to perform a given activity.

Plan: a way of doing something.

5.1.4 Modeling tools for i*/Tropos

It is possible to draw diagrams by using any drawing tool or specialized tools like Visio or Dia,

however those tools are not always optimized for this task, plus what is really important is that

those tools do not check whether the model was built accordingly to the meta-model. There

are a few tools for Tropos, such as TAOM4e[8], Si*-Tool[12], OME[11].

Before we started with the modeling, we have tried to choose a tool which we could use.

37

5. Modeling

This task turned out to be a little more challenging than we expected. First of all, the list of

tools is rather large, but many of them have not been supported for many years. Installation

of most of them is not very straightforward, which is OK if there is a good manual on how to

do it, which is not the case for most of the tools. We had luck in running only one tool: Sectro

2.0, which is still in Beta version. The tool seemed to work fine in the beginning, but after a

few days of using it we have noticed that sometimes it cannot open previously created project.

Because of this, we could not use all the functionalities of the tool. Plus it has additional bugs,

such as: after creating wrong dependency, the tool gives an error, but the dependency still stays

on the diagram and it is not possible to delete it, so there is a need to start modeling all over

again.

5.2 Applying Tropos methodology

In this section we apply the Tropos methodology to model the system-to-be. It covers all

relevant phases in different levels of detail and includes descriptions on how it is done together

with some diagrams.

5.2.1 Early requirements analysis

The electric grid is a very complex system with many stakeholders, components, subsystems

and their dependencies. Modeling the whole system is out of the scope of this project, so we

just concentrate on the main stakeholders for the problem that we are trying to model. The

stakeholders in Tropos are represented by Actors in the actor diagram. Before we start with

the modeling of the system-to-be, we provide a graphical representation of the environment of

the system. The environment of the system includes, among others, the following stakeholders:

Customers are the households or small businesses that consume energy or in case if they have

electricity generators, they can sell their surplus electricity back to the supplier.

Suppliers produce electricity, which is sold to the customers. We simplify our model with the

assumption that if there is an excess of electricity from the customers, then the suppliers

will buy it back.

Distributor is mainly responsible for making sure that the supply and demand in the net is

in constant equilibrium. Distributors are the distribution system operators (DSO) as well

as the transmission system operators (TSO).

These actors, along with their goals, are shown in Figure 5.3. The distributor actor has its

main goal to keep an equilibrium in the system, meaning supply has to be equal to demand. In

38

5. Modeling

Figure 5.3: The stakeholders of the grid

Figure 5.4: Tropos symbols legend

order to achieve this goal, the Distributor depends on Suppliers to adjust their production.

Customers have two hard goals: to receive electricity from the Supplier and to sell the excess

of electricity back to the Supplier. The soft goal for the Customers is to minimize the total

cost: buy when the energy is cheap and sell when the prices are high. The softgoal of Supplier

is to maximize its profit. Figure 5.4 provides a legend for the diagram.

The early requirements analysis phase consists of identifying and analyzing the stakeholders

and their intentions. Our stakeholders are the customers that can play at least three different

roles, Figure 5.5: prosumer, consumer and evaluator.

We start by informally describing these stakeholders:

Prosumer: an entity that produces energy as a byproduct. Its objective is to maximize

the profit and sell its energy at the maximum price.

Consumer: an entity that consumes energy, its objective is to buy the product at the

lowest price from the reliable prosumer.

Evaluator: an entity that provides reputation values of its past interaction with the pro-

sumer. The evaluator can be another consumer or some agency specializing in providing non-

functional aspects of the prosumer. Such aspects may include: type of energy, price, reliability,

etc.

39

5. Modeling

Figure 5.5: Customers of the grid

TRS: trust and reputation system. Even though this actor is supposed to be introduced

in the late requirements, we included it in the diagram of early requirements for clarity of the

picture. The TRS does not have any soft goals, but it depends on the consumer and evaluator

for trust and reputation values.

Figure 5.6: Actor diagram modeling the stakeholders of TRS

Figure 5.6 shows the actor diagram for the TRS domain. The prosumer and the consumer

are associated with softgoals: maximize profit and minimize costs. The consumer depends on

the prosumer for energy, as a resource. And it also depends on the TRS for the trust value,

which could be used as a guide in deciding on whether to continue interaction with the current

prosumer. The TRS, in order to provide the trust value, needs to receive the personal experience

value from the consumers and as many as possible reputation values from the evaluators.

40

5. Modeling

5.2.2 Late requirements analysis

The late requirements analysis phase focuses on the TRS within its operating environment. It

is represented as one actor which has dependencies on other actors. These dependencies define

the systems functional and non-functional requirements.

The actor diagram in Figure 5.7 includes the TRS system and shows its goal which the

Consumer delegates to it. The goal is to provide a trust value for the specified agent. The goal

is decomposed into two sub-goals, which are: provide a direct trust value in case if there were

previous interactions and to provide a reputation value from the other agents.

Figure 5.7: A portion of the actor diagram including TRS and Consumer and goal diagram ofTRS

As we have discussed it in the previous chapters, it is not an easy task to compare trust

and reputation systems. In order to build a usable and useful system to facilitate the agent’s

decisions in the Smart Grid, we believe that such a system should have a number of features

that will have a positive contribution, Figure 5.8

Multiple rating approach: rating an agent as bad or good may not be enough in the

electric grid. Agents need to have ratings based on at least 2 criterias: as a prosumer and as a

consumer. We can probably go even further and analyze each type of agent on the time of the

day, day of the week, and so on, but it will bring additional complexity and it can negatively

affect the performance of the system.

Multiple information sources: direct experience is a very good source, but witness

observation provides additional value and minimizes the chances of making a bad contact.

Additional sources can also be a certified reputation and role-based trust.

41

5. Modeling

Figure 5.8: Soft goals decomposition

Adaptability: this quality is concerned with the bootstrapping and dynamism in open

multi-agent systems. Due to the openness of multi-agent systems, agents can join or leave at

any time, so it is very important to rate the newly arrived agents accordingly. It is important

to keep in mind the dynamism of multi-agent systems, where agents can change their behavior

and the number of participating agents can also change.

Reliability and honesty assessment: there are many tricks that can be used by malicious

agents. It is not possible to know all of them and predict when they will be used. However,

there are many known tricks that are widely used and there is a need to check for as many of

them as possible. For example, ballot box stuffing is not only a favorite scheme in government

elections in some countries. Agents may behave in the similar way: they may form a chain of

trust relationship. This coalition may share their ratings in a truthful way about all agents,

but one, which will always receive negative ratings.

5.2.3 Architectural design

There are 3 possible architectural styles that can be chosen for the trust and reputation systems

in MAS: centralized, decentralized and hybrid. In this section we analyze how they impact some

of the qualities of the system, Figure 5.9:

Scalability: a centralized architecture contradicts with the very nature of multi-agent sys-

tems; they are very dynamic and have a high possibility for growth in the number of agents.

With the number of agents growing, the costs of running a centralized system grow tremen-

dously.

42

5. Modeling

Figure 5.9: Architectural styles

Single point of failure: with the failure of a centralized system, no agent will be able to

use it. In the decentralized way, every agent always has access to its own system and even if it

fails, other agents will still be able to use their own systems.

Reliability: in this context, we use reliability as the measure of how difficult it is to cheat

the system. Centralized systems are more vulnerable to be cheated since they are never part

of the transaction and submitted ratings cannot be easily checked.

Adaptability: a decentralized system can be adopted to the need of every agent, which is

virtually impossible to do in centralized systems.

Hybrid systems try to adopt the benefits of both centralized and decentralized approaches.

Even though this style is not widely studied and was not used in the given context, it may be

adaptable by the Smart Energy Grid in the context of virtual power plants, where a limited

number of agents work together on the same goal.

5.3 Conclusion

The Tropos methodology provides a way to model systems from the early requirements phase,

to the late requirements, architectural design and to the detailed design. This process is done

in iterations, where it is possible to go back to the previous phase and refine the model. We

are not able to fully model the whole system and there is a clear reason for it: we still do

not know how exactly the Smart Grid will operate. At this stage we can only see how some

design decisions may affect the system in the future. For example, let us look at the scenario

where we want to motivate agents to truthfully reveal their capability of producing energy in

the really small microgrid, with the agents running on a closed platform. For this scenario,

including a basic central trust and reputation mechanism would be enough. The value could be

43

5. Modeling

a percentage of the successful interactions, which are revealed to the whole community. In this

scenario, the central system may work just fine, but in the case when there is a huge number

of agents interacting at a global scale, a simple central system may not work as well.

In the late requirements analysis we discussed some of the features that will have a positive

contribution to the trust and reputation system. However, there are also some decisions that

need to be made and it is really difficult to say how will they impact the system. For example,

a decision needs to be made on which reputation computation engine to use: fuzzy approach,

Bayesian model or the belief model. This question is not easily answered by modeling the

system; implementation is needed to answer this question.

44

Chapter 6

Evaluation

In order to evaluate our model we analyze it with respect to one of the most famous approaches

of controlling microgrids, the Powermatcher [38].

The Powematcher concept uses principles of agent technology that allows software agents,

representing real-world entities, to interact with each other for the purpose of negotiation and

trading on an electronic market. This concept was developed in 2004 and since that time it has

been tested in several field experiments.

The latest field-test of Powermatcher is still in progress and is expected to end in 2013. It

is the largest field-test for the Powermatcher and some extra features which were not tested

before, will be implemented in Hoogkerk. For example, one of these features is billing. The

field test consists of 25 interconnected households and two nearby labs at the site of KEMA1.

Every household is equipped with the smart meter and Table 6.1 provides an overview of all

the devices which are connected via a Powermatcher.

The goal of the current experiment is to test the scalability of the system as well as to see

how residents react to the system. For instance, how much comfort they are willing to sacrifice

in order to lower their energy bill.

6.1 Powermatcher architecture

The agents in Powermatcher are organized in a logical tree [39], Figure 6.1. The root of the

logical tree is an Auctioneer agent. This agent collects all bids from the leaf functions and forms

the market price. The bids are represented in the form of utility functions and the market price

is formed by summing up all the utility functions, Figure 6.2.

More detailed description of the agent roles:

1A global energy consultancy company headquartered in Arnhem, Netherlands

45

6. Evaluation

Amount Device Type

12 mCHP with gas boiler Producer13 Hybrid heat pump Consumer25 Solar panel Producer10 Smart washing machine and dishwasher Consumer1 PEHV Prosumer2 EV Prosumer1 Battery for electricity storage Prosumer1 Gas turbine Producer1 Windmill Producer

Table 6.1: Powermatcher devices

Figure 6.1: Powermatcher architecture [39]

• Device agent: represents a device, which could be a washing machine, a refrigerator

or some other device. All agents send their bids for buying or selling energy to the

concentrator agent, which determines the equilibrium price and sends it back to device

agents.

• Concentrator agent: concentrates the market bids of the agents into one bid and passes

it to the auctioneer.

• Auctioneer agent: performs the price-forming process based on all the incoming bids

from the lower agents and communicates the equilibrium price back to them.

• Objective agent: is used to give a cluster its purpose. Based on the purpose, different

46

6. Evaluation

Figure 6.2: Aggregation of utility functions [39]

business logic can be used behind this agent.

The agents in Powermatcher update their bids only when there is a significant change in the

utility functions. The advantage of this method is a reduced amount of communicated data.

Agent strategies in Powermatcher are based on short-term economics, so all decisions are

based on true marginal costs of the individual device. From the micro-economic point of view

it is assumed that all devices are participating in a competitive market. In this kind of market,

the best strategy for every device is to bid its true marginal cost [49].

From Table 6.1 we can see that the the field test in Hoogkerk has at least 25 Concentrator

agents (1 for each household). The total amount of device agents depends on the amount of

devices. It is not specified anywhere how many Auctioneer agents are in the system, so we

assume that there is only one. In order to test the system for scalability, it is possible to

simulate some of the devices.

6.2 Powermatcher and trust and reputation system

In Powermatcher it is assumed that the agents will always submit true marginal costs. This

assumption is valid in a simulated environment, but this is not always the case in the real

world, where agents can malfunction. Trust and reputation system can facilitate in the task of

47

6. Evaluation

determining how truthful the bids were. This is very important because the more uncertainty a

supplier faces, the more it will try to increase its utility function in order to increase the profit

[56].

In Powermatcher every auctioneer agent depends on the concentrator agent for truthful bids.

The simplest way to determine if the bids were truthful is to receive the amount of provided

electricity from a smart meter. If the supplied energy was not at the promised level, then there

is a need to take this into account for the next bidding round. This is where the trust and

reputation system comes in.

Now we will look how our model fits into the Powermatcher concept:

Every leaf of the tree in Powermatcher can be a consumer (home appliances) or a prosumer

(microCHP2), so all parties rely on each other for the truthful bids. The role of evaluator can

be played by a smart meter, which submits consumed or supplied amount of energy back to

the trust and reputation system.

6.3 Architecture of trust and reputation system for Power-

matcher

Every leaf node of Powermatcher passes its bid to the concentrator agent and all the leaf nodes

do not know about each other. Therefore, adopting a centralized trust system seems to be very

logical. This system should be a part of every concentrator and auctioneer agent, see Table 6.2.

The reputation approach is not possible because every concentrator agent only knows about its

own leafs.

Level Powermatcher (original) Powermatcher (proposed)

1 Device agent Device agent2 Concentrator agent Concentrator agent+TRS3 Auctioneer agent Auctioneer agent+TRS

Table 6.2: Powermatcher original agents vs. proposed

6.4 Extra features

Some of the important features of trust system for Powermatcher should include:

2Micro combined heat and power

48

6. Evaluation

Multiple rating approach: some of the devices can play a role of a consumer or a

prosumer, so it is important to keep both ratings separately.

Multiple information sources: this feature is not possible to use in Powermatcher since

leaf agents submit their bids only to their central agent, so it is the responsibility of the central

agent to accumulate a trust value.

Adaptability: Powermatcher is designed to be a closed system with agents being constantly

present. A bootstrapping feature would provide additional value to the system, but is not

critical. Dynamism though is important due to possible changes in an agent’s behaviour.

Reliability and honesty assessment:For the case of Powermatcher, there is no need to

implement a distributed trust and reputation system, since the architecture of Powermatcher is

a hierarchical distributed system and from the perspective of an auctioneer agent it looks like

a client-server type. For this kind of system implementing a centralized trust system would be

sufficient.

Powermatcher has a relatively simple architecture. Consequently, trust and reputation sys-

tem for it is also relatively simple and reduced to only trust system. A centralized architecture

seems to be a simple solution and it has an advantage over a distributed architecture in lower

communication, which is seen as one of the advantages of Powermatcher.

Security is enforced already on the system-level, since Powermatcher is a closed system

where agents are not designed to freely come and leave. They have to be manually registered

in the system.

49

Chapter 7

Conclusion

For the conclusion the research questions are restated and the answers to the question point to

the chapters where the answer can be found.

1. What is the state of the art in the Smart Energy Grid in the given context?

Chapters 1, 2 and 3 discuss the Smart Energy Grid and some of the problems that need

to be solved. Microgrids are discussed as building blocks of the Smart Grid and demand

side management as a technique which is supposed to be used to control the consumption

of energy by different entities. There is no clear vision yet on how exactly Prosumers will

be able to negotiate to trade energy. A lot of research is mostly concerned with how to

reduce imbalance in the grid produced by the renewable energy sources. And only some

scientists are are actually trying to take into account the financial aspect of prosumers.

For example, Kamboj et al. investigate how much money an owner of an electric car

would make in the V2G concept.

2. What are multi-agent systems and how can they add value to the Smart Energy Grid?

Chapter 3 introduces the concept of multi-agent systems and discusses the use of them in

the Smart Grid. It concludes that multi-agent systems are widely used in research related

to the Smart Grid. They can provide a way of building systems for the support of electric

grids and also provide a way to model and simulate such systems. It has been proposed

to use multi-agent systems in: electricity markets, control of distributed energy resources,

microgrid management and automation, and condition monitoring and maintenance of

generating devices and circuit breakers.

3. What is a Trust and Reputation Management System and how is it used in computer

science?

50

7. Conclusion

Chapter 4 is completely dedicated to this question. Trust and reputation systems are used

to facilitate their users in choosing the most reliable partner and to promote honesty in

agents. These systems have been a big part of scientific research since the introduction of

e-commerce, web-auctions and other virtual marketplaces as well as peer-to-peer networks,

grid computing, mobile networks, semantic web and web-services, which are all open and

distributed systems.

4. What are the most applicable methods for trust and reputation in the given context?

Chapters 4 and 5 discuss the issues related to this question. They conclude that there

is no general solution to this question. For every application there is a need to design a

specific trust and reputation system. For example, throughout this thesis we mostly talk

about distributed trust and reputation systems. However, in the case of Powermatcher a

centralized trust system would be more suitable.

After combining all the sub questions, we can give the answer on the main question. The

approach of including trust and reputation mechanisms in the Smart Energy Grid based on a

multi-agent system has several advantages, which are discussed throughout this thesis. However,

the main conclusion is that it will make the system more stable and fault tolerant. We have also

discovered that most of the research in the area of the Smart Energy Grid is not considering

trust and reputation. As we have stated before, we believe that this is the case because the

whole idea of the Smart Grid is already complex, so most of the scientists are trying to solve

other problems first, thus they start with the late requirements and do not even consider the

early requirements phase, where dependencies between all actors of the system are analyzed.

7.1 Research approach

The research approach of the thesis consists of a literature study in combination with modeling

and analyzing a trust and reputation system approach to facilitate agents in choosing a reliable

partner for the trading of energy. The goal of the research is not to come up with something

innovative, but rather to analyze already existing technologies and see whether they fit into

something new, which does not even exist yet: the Smart Energy Grid.

7.2 Future research

In this thesis we have modeled a trust and reputation system as an extra entity, with some other

actors of the system depending on it. This is one way to do it. However, currently Tropos is

being extended with trust modeling activities based on the trust-based concepts. This extension

will give a possibility to model the system and analyze dependencies of the actors based on trust

51

7. Conclusion

from the beginning of the modeling process. Moreover, we feel that it will be beneficial to model

the Smart Grid concept starting from analyzing early requirements by using Tropos. It would

give a great input for developing a secure Smart Grid by analyzing all the stakeholders and

their dependencies. This input would be beneficial not only for the technical architects, but

also for understanding the social concept of the Smart Grid.

Future research also needs to be done on the inclusion of trust and reputation systems

to some already proposed techniques for managing the Smart Grid. For example, previously

discussed methods for managing the charging of electric vehicles is done with the assumptions

that the cars will always follow their charging plan, so if it promised to stay there for one more

hour, then it will do so, but this is not always the case in real life. Research needs to be done

to evaluate how efficient it would be to include trust and reputation system to facilitate the

decision making for the agents.

The most interesting cases for studying trust and reputation in the Smart Energy Grid

would be the cases where control of the entities is completely distributed, ad hoc and involves

thousands of agents. This kind of case would be the most difficult to implement, but this is

where trust and reputation systems are the most useful.

In this thesis we only considered an example of where entities have to trust each other on

delivering promised amount of electricity. However, this may not be the only case and other

kinds of trust issues may arise, such as trust in the type of energy, financial trust, etc. All these

things could be very interesting for future research.

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