quantitative analysis of distributed energy resources in...

Degree project in

Quantitative analysis of DistributedEnergy Resources in Future Distribution

Networks

Xue Han

Stockholm, Sweden 2012

XR-EE-ICS 2012:004

ICSMaster Thesis

Abstract

There has been a large body of statements claiming that the large scaledeployment of Distributed Energy Resources (DERs) will eventually reshape

the future distribution grid operation in numerous ways. However, there is

a lack of evidence specifying to what extent the power system operation will

be alternated. In this project, quantitative results in terms of how the future

distribution grid will be changed by the deployment of distributed genera-

tion, active demand and electric vehicles, are presented. The quantitative

analysis is based on the conditions for both a radial and a meshed distri-

bution network. The input parameters are on the basis of the current and

envisioned DER deployment scenarios proposed for Sweden.

The simulation results indicate that the deployment of DERs can signif-

icantly reduce the power losses and voltage drops by compensating power

from the local energy resources, and limiting the power transmitted from the

external grid. However, it is notable that the opposite results (e.g., severe

voltage fluctuations, larger power losses) can be obtained due to the inter-

mittent characteristics of DERs and the irrational management of different

types of DERs in the DNs. Subsequently, this will lead to challenges for the

Distribution System Operator (DSO).

Keywords: Distribution Network, Distributed Generation, Electric Vehi-

cle, Active Demand, Power Losses, Voltage Profile

Acknowledgements

The thesis has been implemented in cooperation with Vattenfall Research

and Development and was approved by the Department of Industrial Infor-

mation and Control Systems at KTH - Royal Institute of Technology. This

project would have not been completed without all those who helped me

with difficulties and problems.

First and foremost, I would like to show my gratitude to my supervisor

Claes Sandels, who provides the basic idea of this thesis and offers me the

opportunity to work on it. I am also grateful for the support and fruitful

discussion from my co-supervisor, Kun Zhu. All their contributions of time,

ideas, and important feedback throughout the whole period of thesis work

make me accumulate the experience and knowledge in a stimulating envi-

ronment.

I am especially grateful for the encouragement and suggestions on future

plans from Prof. Lars Nordstrom.

I would like to thank Arshad Saleem, Nicholas Honeth, Yiming Wu,

Davood Babazadeh for their kind help on modelling of DERs and designing

of DNs. I also want to show my appreciation to Aquil Amir Jalia, Quentin

Lambert, and Ying He for their help when collecting data, and their valuable

advice.

Thanks to all the people at Vattenfall who share their insightful ideas

with me and all my friends in ICS who always support me.

In the end, I do hope my thesis could help Claes and Kun with their PhD

study in ICS and could give some interesting ideas to Vattenfall for their

research.

Xue Han

Stockholm, March

Contents

List of Figures iv

List of Tables vi

Abbreviation vii

1 Introduction 1

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Goals and Delimitations . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2.1 Goals and Objective . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2.2 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2.3 Delimitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2.4 Definitions and Nomenclature . . . . . . . . . . . . . . . . . . . . 5

1.3 Outline of the Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 Method 8

2.1 Study Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2 Mathematical Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3 Theory 11

3.1 Basic Power System Theory . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.1.1 DN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.1.2 Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.1.3 Calculations in Power System . . . . . . . . . . . . . . . . . . . . 13

3.2 Comparison of Network Topologies . . . . . . . . . . . . . . . . . . . . . 15

3.2.1 Description of Several Networks . . . . . . . . . . . . . . . . . . . 16

3.2.2 Comparison of Key Parameters . . . . . . . . . . . . . . . . . . . 17

3.3 Wind Power as DGs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.3.1 Operation Mechanism . . . . . . . . . . . . . . . . . . . . . . . . 19

3.3.2 Historical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.4 EV Fleets and Behaviours of Customers . . . . . . . . . . . . . . . . . . 21

3.5 Load Profiles in the MV Level DNs . . . . . . . . . . . . . . . . . . . . . 23

i

CONTENTS

3.5.1 Conventional Residential Load . . . . . . . . . . . . . . . . . . . 25

3.5.2 Other Types of Loads . . . . . . . . . . . . . . . . . . . . . . . . 25

3.5.3 Actions Applied in AD Dimension . . . . . . . . . . . . . . . . . 26

3.6 Estimation of the Development of DERs and the Changes of Activities in

DNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4 Construction of the Simulation Toolbox 31

4.1 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.2 DG Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.2.1 Wind Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.3 EV Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.3.1 Algorithm of Modelling . . . . . . . . . . . . . . . . . . . . . . . 33

4.3.2 Parameter Sets for Simulations . . . . . . . . . . . . . . . . . . . 35

4.3.3 Individual Results . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4.4 AD Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4.4.1 Price Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

4.4.2 Energy Efficiency Actions . . . . . . . . . . . . . . . . . . . . . . 38

4.4.3 Small Scale Productions . . . . . . . . . . . . . . . . . . . . . . . 39

4.4.4 Individual Results . . . . . . . . . . . . . . . . . . . . . . . . . . 40

4.5 Summary of the Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 42

5 Results and Analyses 43

5.1 Simulation Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5.2 Phase 1 Simulation of Individual Dimensions . . . . . . . . . . . . . . 45

5.3 Phase 2 Estimated Use Cases . . . . . . . . . . . . . . . . . . . . . . . 47

5.3.1 Results of Cases in the Radial Network . . . . . . . . . . . . . . 47

5.3.2 Results of Cases in the Meshed Network . . . . . . . . . . . . . . 51

5.3.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

5.4 Phase 3 Sensitive Analysis and Extreme Cases . . . . . . . . . . . . . 57

5.4.1 DG Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.4.2 EV Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

5.4.3 AD Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

6 Discussion and Future Work 61

6.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

7 Conclusion 63

References 64

ii

CONTENTS

A Topologies and Description of Test Networks 69

B Flow charts of models 76

C Load Profile of AD 79

D Pre-study on impacts of DERs 81

E Matlab GUI 83

iii

List of Figures

1.1 Background of the thesis project . . . . . . . . . . . . . . . . . . . . . . 1

1.2 The scenario space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.1 The Project Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.1 Typical network topologies . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.2 -equivalent circuit of lines . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.3 Equivalent circuit of transformer . . . . . . . . . . . . . . . . . . . . . . 13

3.4 General Structure in WindTurbine Block . . . . . . . . . . . . . . . . . . 19

3.5 Typical power curve of wind turbine . . . . . . . . . . . . . . . . . . . . 20

3.6 Total production of wind turbines on Gotland in 2010 . . . . . . . . . . 21

3.7 Starting time for different types trips in 24-hour period . . . . . . . . . 22

3.8 Typical charging curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.9 Structure of the hourly load curve of apartments . . . . . . . . . . . . . 24

3.10 Structure of the hourly load curve of houses . . . . . . . . . . . . . . . . 24

3.11 Aggregated load profiles on a random bus of other types of loads . . . . 26

3.12 The acceptance of customers on different appliances . . . . . . . . . . . 27

3.13 The Equivalent Circuit Diagram of Photovoltaic Cell . . . . . . . . . . . 28

3.14 V-I Feature Curve of a PV cell . . . . . . . . . . . . . . . . . . . . . . . 29

3.15 Historical data of Clearness Index on Gotland . . . . . . . . . . . . . . . 30

4.1 Wind power production on 24-hour base . . . . . . . . . . . . . . . . . . 32

4.2 Characteristics of EVs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.3 Individual Results of the model of EVs . . . . . . . . . . . . . . . . . . . 37

4.4 Typical structure of a house as a flexible demand . . . . . . . . . . . . . 37

4.5 Appliances investigated in the strategy . . . . . . . . . . . . . . . . . . . 38

4.6 Structure of the difference between original and reshaped hourly load curve 41

5.1 The organization of scenarios . . . . . . . . . . . . . . . . . . . . . . . . 43

5.2 The study procedure of simulations . . . . . . . . . . . . . . . . . . . . . 44

iv

LIST OF FIGURES

5.3 The simulation process . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5.4 Allocation of different load profiles . . . . . . . . . . . . . . . . . . . . . 45

5.5 Total wind production in the radial network . . . . . . . . . . . . . . . . 46

5.6 Total consumption of EVs in the network . . . . . . . . . . . . . . . . . 46

5.7 Electricity price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

5.8 Total consumption of residential customers (radial network) . . . . . . . 47

5.9 Voltage condition in Radial Network . . . . . . . . . . . . . . . . . . . . 48

5.10 Voltage on Bus5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

5.11 Total power losses in the network . . . . . . . . . . . . . . . . . . . . . . 49

5.12 Voltage condition in Meshed Network . . . . . . . . . . . . . . . . . . . 50

5.13 Voltage on Bus SS10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

5.14 Total power losses in the network . . . . . . . . . . . . . . . . . . . . . . 52

5.15 Total wind production in the network . . . . . . . . . . . . . . . . . . . 57

5.16 Voltage on Bus5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.17 Equivalent load curve of EVs in the network . . . . . . . . . . . . . . . . 58

5.18 The Voltage on Bus5 and power losses in the network . . . . . . . . . . 58

5.19 Total consumption of the ADs in the network . . . . . . . . . . . . . . . 59

5.20 Consumption of all kinds of loads in the network . . . . . . . . . . . . . 59

5.21 Voltage on Bus5 and power losses in the network . . . . . . . . . . . . . 60

A.1 Topology of the Rural Bornholm MV Feeder. . . . . . . . . . . . . . . . 69

A.2 Topology of the IEEE Test Feeder. . . . . . . . . . . . . . . . . . . . . . 70

A.3 Topology of the Rural network from the Swedish reliability report. . . . 71

A.4 Topology of the Urban network from the Swedish reliability report. . . . 72

A.5 Topology of radial network . . . . . . . . . . . . . . . . . . . . . . . . . 73

A.6 Topology of meshed network . . . . . . . . . . . . . . . . . . . . . . . . 73

A.7 Network Description of the Radial Network. . . . . . . . . . . . . . . . . 74

A.8 Network Description of the Mesh Network. . . . . . . . . . . . . . . . . 75

B.1 Detailed description of blocks in the flowchart. . . . . . . . . . . . . . . 76

B.2 Flowchart of EVs algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 77

B.3 Flowchart of load generation procedure . . . . . . . . . . . . . . . . . . . 78

C.1 Load profiles of apartments and houses . . . . . . . . . . . . . . . . . . . 80

D.1 Impacts of DGs and EVs on DNs . . . . . . . . . . . . . . . . . . . . . . 81

D.2 Impacts of DGs and EVs on DNs . . . . . . . . . . . . . . . . . . . . . . 82

E.1 GUI application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

v

List of Tables

3.1 Different voltage levels in DNs . . . . . . . . . . . . . . . . . . . . . . . 11

3.2 Comparison of demonstrative parameters of test network . . . . . . . . . 17

3.3 Swedish fleet in traffic in 2010 . . . . . . . . . . . . . . . . . . . . . . . . 22

3.4 Average Commuting distances and time . . . . . . . . . . . . . . . . . . 22

3.5 Comparison of capacities of different types of EV . . . . . . . . . . . . . 22

3.6 Different types of charing . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.7 Seasonal Coefficients of Appliances . . . . . . . . . . . . . . . . . . . . . 25

3.8 Estimation of evolution of DERs . . . . . . . . . . . . . . . . . . . . . . 30

4.1 Comparison of demonstrative parameters of DN topologies . . . . . . . . 31

4.2 Location and the penetration level of wind power . . . . . . . . . . . . . 32

4.3 Allocation of characteristics of the EV fleet . . . . . . . . . . . . . . . . 35

4.4 Capacity of Battery of each PHEV/BEV[kWh] . . . . . . . . . . . . . . 35

4.5 Trip Types for different types of EV . . . . . . . . . . . . . . . . . . . . 36

4.6 Price sensitivity strategy for appliances . . . . . . . . . . . . . . . . . . 39

4.7 Modelling Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5.1 Simulation Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

5.2 Summary of Voltage Fluctuation . . . . . . . . . . . . . . . . . . . . . . 53

5.3 The Extent of Voltage Fluctuations in Radial Network . . . . . . . . . . 55

5.4 Summary of Average Power Losses . . . . . . . . . . . . . . . . . . . . . 56

vi

Abbreviations

AD Active DemandAVR Automatic Voltage RegulatorBEV Pure Battery Electric VehicleCHP Combined Heat and Power (plant)CV Commercial VehicleDER Distributed Energy ResourceDG Distributed GenerationDN Distribution NetworkDSM Demand Side ManagementDSO Distribution System OperatorEV Electric VehicleHV High Voltage (level)LV Low Voltage (level)MV Medium Voltage (level)PHEV Plug-in Hybrid Electric VehiclePSS Power System Stabilizerp.u. per unitPrV Private VehiclePV Photovoltaic panel (cells)RES Renewable Energy SourceSOC State of Charge

vii

Chapter 1

Introduction

In this chapter, the scopes and aims of the project are described. The preliminary

study on both the characteristics of DNs and the potential problems of DERs are pre-

sented as well. Furthermore, the outline of the thesis report is given in the last section.

1.1 Background

Figure 1.1: Background of the thesis project - [1]

A series of environmental goals, such as [2][3][4], are proposed worldwide, which will

lead to the changes of policies and legislations in different countries [5][6]. These adjust-

ments result in a dramatically increased penetration of DERs in the conventional DNs.

The continuously growing DG, especially powered by intermittent energy resources,

poses a potential risk on the power system operation (especially in the situations of the

mismatches between the generation and the demand of customers in the DNs [7]). EVs,

as the stars in the future transportation sector, are expected to reduce the dependency

in fossil fuels. The introduction of EVs does not only cast a burdens on the electricity

grid but also imply a new load pattern which is consumer driving behaviour dependent.

Meanwhile, the widely use of advanced metering technology gives the opportunity for

customers, especially households, to respond on price signals from the electricity mar-

1

1.1 Background

ket. These modifications result in challenges and problems in terms of the operation

and planning of the DNs for the DSOs [8][9][10][11]. Fig. 1.1 illustrates the discussion

above [1].

Some related concepts such as DER, Smart Grid, and EV, have been drawn

a lot of attention among engineers, academic researchers, and energy companies. A lot

of projects are organized to study DERs[12]. However, most of the projects focused

on reliability issues of the operation [13][14], and analysing the features of one specific

category of DERs [8][15][16]. Therefore, it is hard to see a global view on interpreting

the changes in a quantitative way[17]. So in this project, we try to use the concrete and

quantitative results to indicate the impacts of DERs in DNs, and to consult the DSO

into further research domains.

The introduction of DERs will significantly influence the operation of the whole

network (see Fig. D.1 and Fig. D.2 in Appendix). The impacts are classified below:

Power Flow and Power Losses. DERs at the terminal of feeders can change theoriginal power flow, even result in a bidirectional power flow to some extent [8][18].

The capacity of transmission lines is released by DGs and the peak load may be

reduced by Demand Side Management (DSM), i.e., strategically managing the

active demand [11][19]. However, renewable energy production is hard to predict

and control comparing to the conventional generation sources. In some conditions,

power losses may be larger.

Power Quality. Power quality includes the following aspects: voltage fluctuationsand harmonics [8][20]. Large deviations of the production or consumption of DERs

in each hour, such as the removal of a certain load or generator, cause voltage sag

or swell [8][18]. Power electronics configured in the DNs inject some high frequency

harmonics into the DNs as well [8][18].

Reliability and Availability. The integration of DGs reduces power transmis-sion and improves the availability of grid and power supply in general. It also

benefits to the island operation and black-out start when a large disturbance oc-

curs [13]. Some DGs, equipped with Automatic Voltage Regulators (AVR) and

Power System Stabilizers (PSS), can help to stabilize the power system frequency

and voltage, which improve the reliability of the DNs [11]. However, large pene-

tration of DGs may trigger the instability of the whole system and give rise to a

poor power factor, a poor frequency stability, and a strong chance of short-circuit

[18].

Thus, some critical problems may occur in the power system, especially in DNs.

2

1.2 Goals and Delimitations

Some immediate questions can be addressed, such as:

What happens if a large-scale wind production is introduced in a given DN? How big are the consequences? Will it affect the voltage profile in the DNs? How much power is saved in the DNs by applying energy efficiency actions? How large load profile will a given EV fleet introduce to the DN?

For Vattenfall, the assessment of theses questions is urgently needed to be answered

to maintain a strong grid operation. At the same time, by analysing different energy

resources available in terms of all the roles in DNs (including DSO), some business

cases (e.g., Aggregators [15][21]) appear very interesting which could provide economical

profits and strengthened electricity supply facing the changes in DNs. For example, the

isolated grid of Gotland (connected to mainland Sweden with a HVDC line), with a high

penetration of wind energy, requires an extensive upgrade (e.g., the implementation of

advanced grid management system according to smart grid concept) to enhance the

security of the present grid and the quality of power supply. It is therefore interesting

from the DSO point of view to look upon the most possible critical problems in the DNs

of Gotland. For more information about Gotland, see [22] and [23].


It is obvious that all the DER components own their unique and very complicated sys-

tems and can be modelled in different ways for various purposes. Considering the size of

this project, all components are simplified and integrated as loads or generators in MV-

DNs. The dynamic behaviour is neglected during the modelling. In the static analysis

of power system, power losses and voltage profiles are the two primary concerns on the

grid operation and planning. The reason is that they are directly relevant to the opera-

tional investments and are sensitive to changes in the network [8][15][16][19][24][25][26].

Thus, the main task of the thesis project is to interpret changes in the power system

onto different scenarios in the future environment of the Swedish DNs. The quantita-

tive results with regard to the voltage profiles and power losses are expected from the

simulations. Subsequently, the analysis on the challenges caused by the changes can be

assessed based on these simulation results.

1.2.1 Goals and Objective

The master thesis project has three goals:

1. The first goal is to design and implement two reference DNs, a meshed and a radial

network, see Section4.1) in Simulink.

3


2. The second goal is to construct a toolbox that consists of different DER models.

This is done in order to make it possible for simulating and analysing different

scenarios. A scenario is further defined in Section 1.2.4.

3. The third and final goal is to analyse different technical problems arising from the

scenarios, i.e., voltage problems and power losses in the DNs.

1.2.2 Research Questions

After the thesis work, the following questions should be answered:

Which technical problems are observed in the worst case? And in which condition? How does different factors (e.g., season, network, penetration level, etc.) affect the

DN? Are the impacts beneficial or harmful?

And some more questions could be discussed:

Is it necessary to apply some aggregators to improve the behaviour of DNs? What type of aggregators are needed?

1.2.3 Delimitation

Initial delimitations are summed up below:

Data sets are based on the real conditions of Sweden (specifically, solar irradiationand historical wind production data from Gotland).

Only two specific networks (i.e. radial and meshed) are modelled for the simula-tions and are designed to represent the condition in Sweden, especially in Gotland

to some extent.

Only two interesting factors are selected as targets of simulations and analyses,i.e., voltage fluctuations and power losses in the network.

Dynamic behaviour and protection issues are not considered in the thesis. All the models are integrated at a medium voltage (MV) DN, i.e., 10kV. Only consider customers behaviour in a random weekday. Wind power is the only component in the DG dimension. Two types of vehicles, i.e., private vehicles (PVs) and commercial vehicles (CVs),

with certain behaviour are simulated.

Two types of electric vehicles, i.e., pure battery vehicles (BEVs) and plug-in hybridelectric vehicles (PHEVs), are taken into account.

Charging infrastructure is only available at home and at work. The regulating actions of the DSO is out of the scope of this study. The price

sensitivity is only concerning the day ahead prices (not any time of use tariffs etc.)

4


1.2.4 Definitions and Nomenclature

To clarify the contribution and delimitation of the thesis, some important definitions

in the whole thesis work are presented.

1.2.4.1 Distributed Energy Resources

The concept of Distributed Energy Resources is not clearly defined either by an

authoritative organization, nor by an academic project team. Yet it is widely used in a

lot of papers, reports, and books. In our project, the definition of DER is given as:

Definition 1 DERs are regarded as the electric equipment installed in DNs, which pro-vide energy or participate in the operation of the power system, regardless of producingor consuming power. The deployment of DERs in the grid reflects a paradigm shiftregarding how electricity power is in a power system [11][17].

1.2.4.2 Scenario

A scenario is composed by different DERs in a certain DN during a certain period

(e.g. a random weekday in the winter). The integration of DERs are influenced by some

external forces, such as opinions of policy makers and consumers, and the parameters of

the DN, i.e., the peak load of the specific DN. DERs are grouped into three dimensions,

DGs, EVs and ADs. To some extent, if the integration levels of DERs hit the threshold

values, the DSO will have no choice but to either: (i) refurbish the network (e.g.,

replace wires, install bigger transformers, etc.), or/and (ii) contract ancillary services

from generators and loads, to secure the network operation. (i) - (ii) will require some

kind of investments from the DSO, and how he should act towards this issue is part of

an optimal decision policy problem. The DSO is supposed to minimize the overall cost

with respect of keeping the system operation safe, reliable and efficient. To simplify

the model, we assume that the dimensions are independent from one another. Hence, a

scenario is defined as follows:

Definition 2 A scenario is a specific future plan in terms of amount of DERs in acertain DN. DERs are defined by three dimensions (DG, AD, and EV, viz. the scenariospace), and their potential success is only dependent on external factors, such as opinionsfrom policy makers and consumers. Furthermore, the scenario space is variable, i.e.,the dimension can be assigned different numbers. Meanwhile, the properties of the DNis fixed. In the end, the scenario should reflect some kind of technical issue of theoperation, that the DSO should solve from an optimal decision making policy in networkinvestments.

1.2.4.3 Electric Vehicle

Continuous improvements of storage technologies and a dedicated support from policy

makers promise a bright future for EVs. From the power systems perspective, EVs are

5


batteries embedded in transportation facilities, charged and discharged in some cases

when connected to the grid.

How much, when and where EVs are charged are mostly depending on: (i) driving

patterns (e.g., the behaviour of commuters), (ii) the type of EVs (e.g., PHEVs), (iii)

charging availability (e.g., at home). For example, a private car runs two or three trips

per day within the time periods around 9:00 - 17:00 respectively. The length of each

trip is about 20 km in cities. The locations available for charging are at home and at

work. If the car is a BEV, the possible driving range is very limited (typically 20 - 25

km before a recharge is needed [27][28]).

Definition 3 EVs are modelled as portable batteries whose behaviour and properties aredetermined by the vehicle types, driving patterns and availability of charging facilities.

1.2.4.4 Active Demand

The improved technologies, such as dispatched smart-metering system, enable the

customers to track the price and consumption in households. The trends of of market

deregulation provide much more opportunities of their participation in the system. At

the same time, new feed-in tariffs and plans to increase energy efficiency lead to a

strong will of advanced activities on the demand side (e.g., installation of solar panels,

new appliances with an energystar label, etc). Three segments are set to define AD,

regardless of the effects of DSOs (e.g., cutting off the peak load, compensating power to

the grid when isolated). AD dimension is consequently defined as:

Definition 4 Active Demand is residential consumers who change their load by either(i) shifting consumption with respect to the electricity price on the day-ahead market,(ii) producing their own energy by installing PV panels on their roofs, or (iii) updatinghousewares with the purpose of improving energy efficiency. In the end, these changeswill reshape the load profiles of the households.

1.2.4.5 Dimension 3 Distributed Generation

The installed capacity of DGs is growing stably, especially powered by renewable

energy, such as wind, solar, biomass, etc. Reasons to introduce them are listed in

the book [8], such as the trends of deregulation of Electricity Market, environmental

concerns, and enhancing the margin between peak load and available production, etc.

According to [24], the definition of DG is given as:

Definition 5 Distributed generation is an electric power source directly connected tothe MV-DNs within the scale of 1.5 MW. Specifically in this project, the wind powergeneration is studied due to its popularity and notable intermittent nature.

Obviously, the operations of different kinds of DGs are independent on their primal

drivings and the interfaces with the electricity grids, which will be detailed described in

6

1.3 Outline of the Report

latter part of this section. In view of the development of DG technologies in Sweden,

wind power is considered as the only resource in the DG dimension to reduce the contents

of the models. Some micro or small size production (e.g. solar panels) are regarded

as part of AD dimension due to the reason that they are directly connected to the

consumers, i.e., on the customer side of the meters.

DG

AD EV

Figure 1.2: The scenario space - DERs are defined by three dimensions (DG, AD, andEV, viz., the scenario space)

1.3 Outline of the Report

In chapter 2, common methods used in the thesis work are listed and explained.

Furthermore, some general knowledge of power system theory which is the basis of sim-

ulation is introduced.

In chapter 3, introduction and explanations of different components in the DN are

given (e.g. mechanism of regulation of power production of DGs, load profiles of con-

ventional residential loads, state of art of the EV technology, etc.). Different network

topologies are compared, two of which are selected as the networks for simulation.

In chapter 4, models of different components participating in the networks are de-

scribed with their mathematical algorithms on the basis of their behaviour. Models in

the three dimensions are illustrated independently with the rational level of integration

of individual components.

In chapter 5, some results of the performed simulations are presented and analysed.

A short summary of conclusions obtained in the thesis work is presented in chapter 6.

In Chapter 7, an outlook of potential future work can be found.

7

Chapter 2

Method

In the first section, the study approach of the thesis work is presented, followed by

some basic mathematical methods.

2.1 Study Approach

The procedure of the thesis is presented as follows in Fig. 2.1:

In the pre-study phase, some potential problems in the DNs are studied based on

the fundamental power system theory (e.g., power flow calculation, mathematical mod-

els of the DERs and the DN components in power system). Two target factors, i.e.,

power losses and voltage, are studied by simulating the scenarios based on the under-

lying models and theoretical algorithms. The parameter inputs are from the empirical

observations and the statistics in the related materials, such as [29][28]. The models

are improved by detecting the difference between expectation and simulation results,

e.g., the availability of vehicles in the network at a certain time. The parameters and

algorithms are adjusted until the simulation results meet the real condition based on

historical data. The problems revealed from the simulations could further be studied

and solved by, for instance, aggregators.

Figure 2.1: The Project Procedure - The process of simulation repeats until the perfor-mances of models are close to the results in pre-study literatures and outputs of simulationsare sufficient for the analyses.

8

2.2 Mathematical Method

Other than the method used in the thesis project, i.e., modelling and simulation,

some other applied procedures can be found in other studies. In the book [8], the author

collected a large amount of reliable data of different DERs, analysed the data by using

statistic methods, and in the end got the conclusions based on the observations. In the

large project Microgrid[13], some real cases are studied, for example a low voltage

level (LV) network study in Portugal. Electricity prices, line characteristics, and their

reliability are studied on the basis of comparison of different countries. In the report

[30], the author modelled a fleet of EVs in Monte Carlo simulation models and drew

some conclusions regarding their impact on, e.g. the peak load.

To help the DSO to make decisions in network investments, the quantified results are

necessary. These results could only be accessed by either simulations or study on real

cases. Since the pilot network is costly and is absent of existence, the only way is to do

the simulations based on the real conditions and available data.


In this section, some methods based on probability theory and used in specific algo-

rithms of DERs are described. These mathematical methods are carried out to construct

models with specific random factors following certain distributions [31].

Random variable A variable X is a random variable when X is a numerical function

on a probability space , function X : R, i.e. X is measurable. The distributionof X is described by giving its probability function, F (x) = P (X x). When theprobability function F (x) has the form of F (x) =

x f(y)dy, X has the density function

f .

Normal distribution Normal distribution describes the empirical measurements of

experiments are normally and continuously distributed. The density function is given

as following:

f(x) =1

2e (x)

2

(22) (2.1)

is the mean value and is the standard deviation.

Lognormal distribution If the random variable is normally distributed, X =

exp() follows the lognormal distribution. The estimation of X, EX = e+2

2 .

9


Uniform distribution f(x) = 1 in a certain range (a, b), and 0 otherwise.

F (x) =

0 , x ax , a x b1 , x > 1

(2.2)

Binomial Distribution If the random variable X is said following a Binomial (n, p)

distribution if

P (X = m) =

(nm

)pm(1 p)nm (2.3)

Weibull distribution The density function of a Weibull random variable X is:

f(x;, ) =

{0 , x < 0(

x)1e(

x

) , x 0 (2.4)

where > 0 is the shape parameter and > 0 is the scale parameter. = 1 indicates

the exponential distribution, while = 2 indicates the Rayleigh distribution.

Monte Carlo method Some mathematical results could not be strictly proved but

could be derived or estimated when facilitated Monte Carlo methods. Applications are

utilized in a lot of fields, from decisions making to simulations of complicated systems.

Monte Carlo methods are those methods which use random samples in a calculation

that has a structure of a stochastic process (i.e. a sequence of states whose evolution

is determined by random events) [32]. If there are an amount of variables (i.e. system

inputs), and the function of these inputs are complicated, Monte Carlo methods can be

used to get a good estimation of the observation (i.e. system output). It is formulated

as:

E(Y ) =1

N

Ni

Yi (2.5)

where, Y is the observation, and implemented in Monte Carlo methods for N times.

Each observation Yi is independent and determined by functions and constrains of vari-

ables x1,2,...,M .

10

Chapter 3

Theory

The basic knowledge in power system analysis is introduced in this chapter. For the

simulation purpose, the underlying foundation of models are presented in the following

sections. Data sets used to model elements in the toolbox are proposed.

3.1 Basic Power System Theory

This section is a brief digest from some power system analysis books.

3.1.1 DN

Voltage level DN is the last stage in electricity delivery path, which carries electricity

from transmission networks to the end users. The whole DN can be split up into three

voltage levels corresponding to the nominal value of the voltage (see Table 3.1) [33]:

As mentioned in Chapter 1, MV is selected as the voltage level for simulation and

modelling. Usually, customers in the low voltage levels are connected to MVs with

step-down transformers in substations. Some consumers with large consumptions are

directly connected to MVs. Common voltages in MV-DNs in Sweden are 10 kV and 20

kV. There are voltage level 3, 6, and 33 kV, but these are rare [34].

Network topology The simulation approach requires the first step that typical DN

topologies should be identified. Usually, the MV-DNs in urban area are with loop or

mesh topologies, while in rural area with mesh or radial topologies (see Fig. 3.1) .

System Nominal Voltage [kV]

LV 1MV 1 35HV 35

Table 3.1: Different voltage levels in DNs[33]

11


However, considering the breakers in networks are normally open, most networks are

operating as radial ones [13].

(a) Loop (b) Mesh (c) Radial

Figure 3.1: Typical network topologies[13]

3.1.2 Components

3.1.2.1 Transmission lines

A considerable share of power in MV-DNs are consumed by the transmission lines

instead of loads due to lower voltage and higher current. In MV-DNs, both overhead

lines and underground cables are commonly used [13]. In Sweden, the ratio between

these two types of transmission lines is 3:1 [34]. Overhead lines are less expensive than

underground cables, but more space consuming [34][35].

Transmission lines are commonly characterized as model by their resistance Rs,

Rs Xs

Bsh

2

Bsh

2

Figure 3.2: -equivalent circuit of lines - -equivalent of transmission line with Rs,Xs, and Bsh

series inductance Ls, and shunt capacitance Csh, as shown in Fig. 3.2. Reactance Xs

is obtained by Ls, while subceptance Bsh is obtained by Csh. Thus, the equivalent

model of the transmission line is derived as:

Zs = Rs + jXs (3.1)

Ysh = jBsh (3.2)

where,

is the angular frequency of power system,

12


Zs is the series impedance of the line,

Ysh is the shunt admittance of the line.

The fact that underground cables have larger capacitance and smaller inductance

compared with overhead lines may lead to novel phenomena of voltage and power losses

in feeders [35].

3.1.2.2 Transformer

Transformers as the interface between MV-DNs and HV-DNs or between LV-DNs

and MV-DNs bring the voltage levels down, e.g., from 60 kV to 10 kV. Usually, on-load

tap-changers are only utilized on HV/MV transformers to keep the voltage constant

without disconnecting any loads, while manually tap-changers are utilized on MV/LV

transformers and adjusted only once during installation [36].

The equivalent circuit (referring to the primary side) of a transformer as shown in Fig.

3.3 consists of in-series resistance Rp and Rs representing power losses on each side, in-

series reactance Xp and Xs resulted from flux leakage, as well as the magnetizing branch,

reactance Xm in parallel with the iron losses component Rc (see Fig. 3.3).

Figure 3.3: Equivalent circuit of transformer - Secondary impedance is referred tothe primary side [36]

3.1.3 Calculations in Power System

3.1.3.1 Power flow

Power flow calculations, in general, enable a certain power system to indicate voltage

magnitude and angle in each bus, active and reactive power flow between each bus. The

identified known and unknown quantities in the system determine types of buses (i.e.

PQ bus, PV bus, slack bus). Then, calculations could be implemented by power balance

equations:

0 = Pi +Nk=1

| Vi || Vk | (Gik cos ik +Bik sin ik) (3.3)

13


0 = Qi +Nk=1

| Vi || Vk | (Gik sin ik Bik cos ik) (3.4)

where Gik and Bik can be obtained from the admittance matrix, YBUS .

3.1.3.2 Power losses

The reason to choose which voltage level of the grid much depends on power losses,

which are transformed into heat. When power losses decrease, the maximal capacity on

lines, mainly due to the limit of thermal capacity, could increase. Power losses on the

level of 10 kV are about 3 times of those on the level of 20 kV [34]. Other than the

technical factors, the design of system, operation could also affect the total losses in the

system. Power losses, Ploss, in power system follow such formulas:

Ploss = I2R (3.5)

I =S

U(3.6)

where,

R is the resistance of the transmission line, [],

I is the current flowing through the line, [A],

U is the voltage difference between two ends of the line, [V],

S is the absolute value of complex power given byP 2 +Q2, [VA].

3.1.3.3 Power factor

In power flow calculation, two components are obtained as active power [W], P , which

transfer energy, and reactive power [Var], Q, which move no energy to the loads. Hence,

power factor is defined as:

powerfactor =| cos |= PS

=P

P 2 +Q2(3.7)

When powerfactor = 0, the power flow is purely reactive. When powerfactor = 1,

only active power generated by the source is consumed. Power factors are expressed as

leading or lagging to show the phase angle .

3.1.3.4 Voltage variation and weakest point

For safety concerns, there are limits of network voltage to protect equipments con-

nected to the system. According to the European Standard EN50160 [20], the voltage

magnitude variation should not exceed 10% for 95% of one week, measured as mean10 minutes root-mean-square (RMS) values. The consumption will result in a voltage

drop on the feeder. The further away from the substation bus, the lower the voltage

14

3.2 Comparison of Network Topologies

will be. On the contrary, the connection of generations will lead to a voltage rise on

the feeder. The interaction of consumption and generation in MV-DNs therefore causes

voltage variations.

The voltage variation in steady state is approximately equal to:

U

U=RlinePG +XlineQG

U2 100% (3.8)

With a given limit on the variation of the voltage, Umax, the maximum allowed

produced active power from a connected generator can be derived as:

Assume that the power factor is constant. Then, Qmax = Pmax.According to the equation (3.8),

Umax =(R+ X)PG,max

U(3.9)

PG,max =Umax

(R+ X)U(3.10)

From Equation 3.9, we can derive that when the nominal voltage value and feed-in

power are constant, larger equivalent impedance leads to larger variation in voltage. In a

power system, the weakest point is the load point where the equivalent impedance is the

largest. Usually, the most critical voltage problem appears in the weakest point. The

equivalent impedance is called Thevenin equivalent impedance, which is usually decided

by the distance from the slack bus, material of transmission line, and the topology of

the network.

3.1.3.5 Penetration levels

The penetration levels of DER components indicates the share of them in the whole

network. The penetration level is expressed as the ratio between the installed capacity

of DER components and the peak load value:

pen = 100%

(PnomG

P peakL

)(3.11)


The DNs directly connect to the end users. Their topologies and sizes are different

among areas depending on the operation concepts, utilized devices, and the load profiles.

Thus, voltage quality, reliability, and power losses in this stage are interesting aspects to

study in DNs. Several electrical network models are developed as test systems to analyse

one or more specific characteristics (e.g., voltage regulation, unbalanced load, reliability

15


of system, etc). However, a majority are developed to study power system reliability,

which indicates that they are not of sufficient details to the level upon which our research

questions could be addressed. Additionally, there are some network models for power

flow analysis but their sizes are too large to be implemented in Simulink Matlab. For the

purpose of investigating the integration of DERs in a future DN, reasonable test networks

are required for a sufficient analysis of the simulation results and a correct direction for

further network studies. The DNs are studied at a medium voltage level, i.e., 10 kV.

From literature review study, several typical existing test networks are acknowledged,

described and compared [13][37][38][39]. Their topologies can be found in the Appendix.

3.2.1 Description of Several Networks

IEEE Test Feeder IEEE Comprehensive distribution test feeder developed byIEEE Working Group is presented on the IEEE website. It consists of 55 buses,

two voltage levels, and 25 loads, which represent a wide variety of components

in one circuit [40]. It is a large radial network. It is possible to connect some

switches in the network to form a small loop. Some other types of modules (e.g.,

identical feeders connected to the same bus, radial structure with loads distributed

on feeders) are presented in the network. Examples of all the components in the

network are provided in the work document. The network allows all the standard

components of a distribution system to be tested (See Fig. A.2 in appendix for

the topology).

Bornholm Test Network The Bornholm Test Network is modelled based onthe real distribution network in Bornholm, an island of Denmark, with 2 voltage

levels, 11 buses, 9 transmission lines, 7 loads, and 3 distributed generations. The

detailed parameters of the network are not available. However, the general network

data can be found in [37] (refer to the topology in Fig. A.1 in the appendix).

Swedish Test Systems The Swedish Test Systems are developed by Elforsk,a Swedish industry research association, to analyse the reliability of Swedish net-

works and the electricity market in Sweden. The test systems include two distri-

bution systems, one for the urban area (i.e., SURTS) and the other one for the

rural area (i.e., SRRTS). Loads, component data are well represented by these test

systems due to the support from and involvement of Swedish power distribution

companies. SURTS is a 10 kV system with 10 identical feeders in cable-loop de-

sign, while SRRTS consists of two modules, both of which are radial structure.

Reliabilities, load data and outage costs are specified in the model [34] (refer to

the topologies in Fig. A.3 and Fig. A.4 in the appendix).

16


The Gotland Network - An isolated mixed network, i.e., both urban and ruralnetworks, with a big share of wind power generation [41]. A typical feeder in

the network consists at least 20 load points. Shunt capacitors are installed in

the network at the MV level. Wind power is directly and particularly connected

to the substation bus with a transformer. Load data and parameters of network

components are available. It is accessible for the simulation in future work. The

edge of the feeder could be regarded as a normally open switch or a big load. Some

typical structures of feeders could be selected to represent the whole MV network

after analyse both topologies and load characteristics of the network.

The German Network - The German test network is provided for the EU projectMore Microgrids. The description is in the work package G, deliverable 1 of More

Microgirds. The test system consists of two MV networks (i.e. urban and rural)

and a detailed LV network for study. Line parameters and load profiles are well

presented in the document. The MV networks are taken from existing networks

considered to be typical. The LV network is an artificial network with different

structure for different load segments. The structure of urban MV network is similar

to the Swedish one, but the loop circuit is closed by two paralleled feeders with a

normally open switch in between. The rural MV network is a 20kV mesh system

with 2 external connection points and 14 buses. 15 loads (with static state values

in the figure) and 2 generations are involved in the network. Initial power-flow

calculation is described in the figure for different lines and loads. Compared with

Swedish rural one, the topology is more complicated but with less components in

the network [13].

3.2.2 Comparison of Key Parameters

A discussion on the listed test networks is conducted by comparing their complexity

and representativeness. Table 3.2 shows some key parameters of these networks.

Table 3.2: Comparison of demonstrative parameters of test network

Network Voltage Level Buses Loads GenerationsTopology

IEEE N/A 55 25 5 meshBornholm 60 kV/10 kV 11 7 3 radialSURT 130 kV/10 kV 62 60 0 loopSRRT 40 kV/10 kV 19 many 0 radialGerman urban ? kV/20 kV 83 81 N/A loopGerman rural ? kV/20 kV 14 15 2 meshGotland 70 kV/11 kV hundreds mesh

17


Complexity: From the description part, it is shown that Bornholm test network

and German rural network are much simpler than the other ones. The Bornholm net-

work does only consist of 11 buses and German one of 14 buses, which simplifies the

quantities of parameters and the analysis of phenomena in simulations. Part of the Ger-

man network is selected as mesh network for simulation, with 11 buses, 8 load points,

and two locations for generations. The other part of the network is connected with

another substation bus and is isolated to the selected part during normal conditions.

Seven or eight load points in the network are sufficient to implement different consumer

categories (i.e. residential, commercial, industrial) and to assign EVs together with

loads randomly. Some large scale distributed generation, such as wind power can be

directly connected to the 10 kV level or a even higher voltage level. Those generations

in small scales, such as Photovoltaic (PV) panels can be connected in parallel with the

residential load, denoting that connecting points are in lower voltage level.

Some other simple test systems, developed by IEEE [40], are proposed to study unbal-

anced systems and voltage regulators in a very long feeder which do not fit the condition

in this project and the characteristics of real Swedish DNs.

Representativeness: The network condition in Gotland is similar to that in Born-

holm. The restructured German rural network and the DNs in Gotland have some

mutual features. To obtain a better representation, typical parameters are chosen ac-

cording to the real condition in Gotland (e.g., the proportions of cables and overhead

lines, the distance between buses, the voltage levels). The radial network could repre-

sent a typical radial feeder and the first part of a loop feeder with normally open switch.

Besides, some features of it are set for comparison. Feeder B2 is quite short while feeder

B3 is longer, which means that buses in B2 can be regarded as strong grid compared

with those in B3. In addition, B4 and B5 are compared to show the difference between

cables and overhead lines. Thus, most of technical effects due to the topology of the

network are considered in the test system. Since the meshed network is well presented

in the report [13], all properties and characteristics of the cables are modelled based on

the real condition.

The IEEE test system, SURT, and SRRT are all intended for reliability analysis which

indicates that there is no detailed parameters within the documentation. In addition,

the size of these test systems is a way to large and is not appropriate for the simulation

in Simulink.

18

3.3 Wind Power as DGs


Wind energy is one of the major growing renewable energy technologies in the recent

years. Wind power is intermittent and hard to manage, which may lead to the voltage

sag or swell usually in a short period. The deviation between the real wind production

and and the predicted production may result in grid outages. For those wind turbines,

equipped with induction generators, reactive power is absorbed by them, which bring

about a poor power factor and less active power capacity of the transmission line. From

an economic perspective, additional reactive power compensating devices are costly and

mostly installed in HV networks (to secure the reliable system operation). Wind speed

always reaches the peak value in the winter. However, for safety concerns, a strict

temperature limit (e.g. 40C) and speed limit (e.g., 25 m/s) will force the turbinesto cut off their whole production. The extracted power curve fits the demand curve in

Sweden, because the peak load in Sweden also appears in the winter time. Aggregation

of a large geographically spread wind power can reduce the impact of meteorological

influence and improve the accuracy of the prediction significantly [26].

Figure 3.4: General Structure in WindTurbine Block

3.3.1 Operation Mechanism

The wind turbine is the device that extract mechanical power from wind power going

through its swept area and then to transfer energy to electricity (see Fig. 3.4). The

power output of the turbine is given by the following equation [42]

Pm = Cp(, )A

2v3w (3.12)

Tm =Pmel

(3.13)

19


where,

Cp is coefficient of extracted wind power,

is tip speed ratio of rotor blade tip speed and wind speed,

is blade pitch angle,

is air density, (kg/m3),

A is the tip swept area, (m2),

vw is wind speed, m/s,

el is the rotation speed of generator, (rad/s).

A generic equation is used to model Cp [43]:

Cp(, ) = c1

(c2i c3 c4

)e

c5i + c6 (3.14)

in which,1

i=

1

+ 0.08 0.0353 + 1

. (3.15)

Power curves of different type of turbines are unique due to different control methods,

different blade lengths, different tower heights, etc, in which cut-in speed, cut-off

speed, rated speed, and rated power are the main features of the power curve. A

typical power curve is shown in Fig. 3.5 [44].

Figure 3.5: Typical power curve of wind turbine - Power curves and other technicaldata of wind turbines are given in the product brochures[44]

3.3.2 Historical Data

The occurrence of wind speed follows a Weibull distribution and there is little corre-

lation of wind speeds in neighbouring hours. Meanwhile, the direction and turbulence

of wind are other two main factors which affect the production of electricity. Except for

these factors, the turbine structures, regulation strategies, location of the wind farm,

and the distribution of wind turbines, have a large impact on the production. Hence,

as a static model, the aggregated wind power is depicted as the generation changing on

20

3.4 EV Fleets and Behaviours of Customers

an hourly basis in a MV-DN at the generation point in the network. Fig. 3.6 shows the

variation of total wind production on Gotland throughout the whole year 2010 [41].

0 30 60 90 120 150 180 210 240 270 300 330 3600

10

20

30

40

50

60

70

80

90

Day

Tota

l W

ind P

roduction [M

W]

Figure 3.6: Total production of wind turbines on Gotland in 2010 - The datarecords production in each hour in the whole year [41]


EVs are vehicles that at least are partly powered by electricity or use electric motors

as the direct propulsion. Transport sector is one of the most important and largest part

of energy consumption and the energy use in transport sector is dominated entirely by

oil products (e.g. petrol, diesel, etc.) in Sweden.[45]. For the purpose of reducing the

emission of CO2, and improving energy efficiency, the deployment of a large amount of

EVs can be envisioned in a near future.

On-board batteries, the core of the EV technology, introduce a large amount of

possibilities to the power system to interact with them, via charging facilities (e.g.,

plug-in electric outlets). Without considering the effects of management and control,

the charging behaviour is mainly dependent on three factors:

I. Driving patterns. In our case, only Private Cars (PrV) and Commercial Vehi-

cles (CV) are considered. The statistics of travelling behaviour in Sweden (e.g.,

numbers of trips per day, the distance covered by on e trip, hours of the trips,

etc) are collected based on [28] and [29]. Table 3.3 implies that each household in

Sweden own one car in average, and EVs are not commonly deployed until now.

Table 3.4 presents the general trip length in different areas, and the time consumed

in each trip. In Fig. 3.7, the blue curve and the black one illustrate two types

of driving patterns in a day. It gives the primary understanding on how vehicles

behave in Sweden.

II. Types of EVs. Among various types of EVs, we took Plug-in Hybrid Electric

Vehicles (PHEVs) and Pure Battery Electric Vehicles (BEVs) into our considera-

21


Table 3.3: Swedish fleet in traffic in 2010 [29]

Type of Vehicles Private Cars Commercial Cars Heavy Vehicles Others

No. of Vehicles 3 446 517 1 376 358 526 441 1 869 683

Type of fuel Petrol Diesel Electricity Other

No. of Cars 3 479 607 606 570 190 248 815

Table 3.4: Average Commuting distances and time[28]

Municipality Groupings Distance [km] Time [mins]

Urban Area 20 1 42 3Suburban Area 25 2 40 2Rural Area 28 2 41 1Total 23 41

Figure 3.7: Starting time for different types trips in 24-hour period, (103) [28]

Table 3.5: Comparison of capacities of different types of EV[27] - shown in differentcoloured curves.

Type of Vehicles Battery Capcity [kWh]

BEV 25 35City-BEV 10 16PHEV90 12 18PHEV30 6 12

tion. Compared to PHEVs (with internal combustion system), BEVs (with pure

electricity propulsion) need a larger battery capacity. According to the report [27],

typical capacities are shown in Table3.5

III. Charging patterns. Charging facilities for different purpose in different area are

assumed planned well to fulfil the basic requirements of EV charging. Depending

22

3.5 Load Profiles in the MV Level DNs

on the preferences of car owners, three possible charging approaches1 are proposed,

as listed in Table 3.6 [27].

In reality, the charging curve is not a straight line, but with some variations

Table 3.6: Different types of charging [27]

Type of Charge Load Charging time

Slow 5 kW 4 hoursStandard 5 kW - 20 kW 1 hour - 4 hoursFast 40 kW 1 hour

of the slope along the charging process. Fig. 3.8 shows one typical real charging

curve. For simplicity, we assume that the charging current is constant (i.e., the

curve is a straight line).

Three stages of charging places are stated in [27] as (1) at home, (2) at home

and at work, (3) everywhere. Different charging approaches correspond to different

charging places.

Figure 3.8: Typical charging curve - This is a charging curve of Li-Ion batteries whichcould be idealized or linearised[27]


Flexible residential loads is an important part of our scope. The conventional load

profile is reshaped by modifying actions. Loads of other sectors are excluded, which are

assumed that they have already made their own optimized modifications on their loads.

1Charging time depends on the size of battery.

23


Figure 3.9: Structure of the hourly load curve of apartments - Apartment withtwo people (25-65 years old), Workdays

Figure 3.10: Structure of the hourly load curve of houses - House with two people(25-65 years old), Workdays)[46]

24


3.5.1 Conventional Residential Load

According to the report Statistics Sweden 2011 [29], the distribution of houses and

apartments are quite even, especially in cities. According to [46], the most representa-

tive pattern is a two persons apartment or house. Thus, the load profiles on an hourly

basis of these patterns are collected as the original residential load profiles in our study.

To perform price sensitive strategies, different type of household appliances are classified

(e.g. Dish washer, heating, white goods, etc). Usually, heating and water heating in

apartment buildings are not accounted into the consumption of electricity, while direct

electric heating is (e.g. from heat pumps). Subsequently, the profiles are presented in

Fig. 3.9 and Fig. 3.10, with different appliances in different colors.

As described in [34], the density of residential customers at the LV-DNs are 20/(km

of line) in the urban area, and less than 10/(km of line) in the rural area respectively.

Thus, the numbers of customers in each load points in the network model are set to

be around 500 for the urban area, and 200 for the rural area. The load curve of an

apartment is based on the data set in [47], with 120 units aggregated (i.e. a whole

building). The load curve of a house is based on the data set for a two person (25-65

years old) household (with direct electric heating), measured and presented in the report

[46]. In general, one appliance of the data set refers to an average value of a group of

appliances in the households.

Temperature is an important factor influencing the consumptions of different ap-

pliances, of which the seasonality effects coefficients are assigned in the report [46] as

well. To simplify the model, coefficients of four seasons are selected to represent the

temperature factors (see Table 3.7).

Table 3.7: Seasonal Coefficients of Appliances[46]

Appliances Spring Summer Autumn Winter

White goods 0.9 1.0 0.9 0.7Lighting 1.4 1.0 2.2 1.6Audio and TV 1.3 1.0 1.5 1.9Cooking 1.3 1.0 1.3 1.7Heating 1.0 0.3 1.0 2.5

3.5.2 Other Types of Loads

In [34], some real load profiles of different load types are given. Since, only the

residential loads are interesting, the combination of other types are distributed in the

network. As an example, Fig. 3.11 shows different loads profiles (industrial, public and

agricultural).

25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

100

150

200

Hour

Consum

ption [kW

]

Industrial

Public

Agriculture

Figure 3.11: Aggregated load profiles on a random bus of other types of loads -The curve is consistent with the information in report [34]

3.5.3 Actions Applied in AD Dimension

In the future DN, a large amount of smart metering devices are installed, which

provides customers the possibility to behave more positive and to get involved in the

energy markets. At the same time, customers are more aware of their cost of electricity

as well as the relevant environmental issues, especially after suffering some extreme high

prices in recent years (e,g. price spikes of 1 000 e/MWh on 22nd Feb of 2010). A survey

conducted at Maingate shows that a significant portion of the Swedes would change

their load profile if the electricity price and their consumption were displayed on an

hourly basis [1]. New legislations and plans in Sweden could increase the interests of

interaction even further. To evaluate the consequences of these changes 1, a model of the

flexible load is implemented by performing three main factors (i.e. price sensitivities,

small-scale productions, energy efficiency actions) [1]. These factors are described more

fine grained below.

3.5.3.1 Price sensitivity

In our case, price sensitivity implies that retailers could shift or shed the load of different

appliances in households with respect to the electricity price. Whether the price sensi-

tivity action can be performed depends on two factors: one is how large the electricity

price varies; the other one is the acceptance of the customers to shift or shed one or

more appliances. Fig. 3.12 shows the assumed customer acceptance.

3.5.3.2 Energy efficiency actions

The Swedish Energy Agency, Energimyndigheten, initiated a program that helps some

companies to be successful in their efforts to perform energy efficiency actions. There is

no doubt that these actors can both save money and energy by executing these actions.

A series of new measures to improve energy efficiency are proposed by European

Commission in June 2011 [7]. To save energy and to reduce the emission of greenhouse

1We do not study the exact impacts of demand tariffs.

26


Figure 3.12: The acceptance of customers on different appliances - [19]

gases, some products and actions are recommended to improve the efficiency of energy

consumption. For example, to replace light bulbs with Compact Fluorescent Lamps

(CFLs) at home or in public areas, to use energy-efficient housewares, to enhance the

insulation of buildings. Subsequently, the reduction of consumption can be ob-

tained. However, different actions (like the change of lamps) may lead to other negative

results (e.g. change of power factor, harmonics introduced in the network, etc). When

considering an individual household, the change is tiny, which can be ignored, compared

with consumption on the bus.

3.5.3.3 Small scale production PV panels

Small scal production is considered as the production units that have a power output

of typically a couple of kWs, and are connected directly to the loads (like PV panels,

micro CHPs).

A few decades ago, limited by high cost, solar photovoltaic is not that prevalent as

nowadays. The price of PV cells has significantly been reduced while the efficiency is

increased due to the new technology advancements and the manufacturing process [48].

According to [45], the electricity production of solar in Sweden is about 200 MWh in

2009. And a variety of forms of grants and subsidies for solar cells in Sweden promise

a blooming trend of their development. To interpret this trend into the thesis work,

the PV model is created as the small scale production to indicate its influence on the

residential load profile. Data are collected from [49], where the sizes of PV panels on the

roofs are determined as 20 m2 for each house and 3 m2 for each apartment respectively.

A PV cell can produce current under its exposure to the sun. Thus, it is reasonable

to model it as a diode together with an equivalent current source depending on the

27


Figure 3.13: The Equivalent Circuit Diagram of Photovoltaic Cell

environment temperature and irradiation of the sun (see Fig. 3.13). In some papers,

instead of a current source, the author modelled the PV cell as a voltage source of which

the output voltage is stablized around the system voltage and the output current are

influenced by the environmental temperature and the irradiation of the sun [48]:

VC = CV

(AkTref

eln

(Iph + Io IC

Io

)RsIC

)(3.16)

Iph = CI Isc (3.17)

IPV = IC NS (3.18)

VPV = VC NP (3.19)

where,

NS is the amount of series cells in a panel,

NP is the amount of paralleled cells in a panel,

VC is the output voltage of a PV cell,

IC is the output current of a PV cell,

Iph is the photocurrent produced by the semiconductor in the PV cell,

Isc is the short-circuit current of the equivalent circuit,

Io is the reverse satiation current of the diode (0.0002A),

RS is the series resistor in the equivalent circuit to reshape the I V curve according to

the open circuit voltage,

A is a curve adjusting factor to fit the model curve to the real one,

k is the Bolzmann constant as 1.38 1023 J/K,Tref is the reference operation temperature given by the manufacturer,

e is the quantity of a single electron as 1.602 1019 C,

28


CV is the coefficient of voltage caused by the temperature and the irradiation,

CI is the coefficient of current caused by the temperature and the irradiation.

From [50], the coefficients of the voltage and the current, CV and CI , can be described

by two decoupled part, temperature dependent coefficients and irradiation dependent

coefficients as:

CTV = 1 + T (Tref Tx) (3.20)

CTI = 1TSref

(Tref Tx) (3.21)

CSV = 1 TS(Sref Sx) (3.22)

CSV = 11

Sref(Sref Sx) (3.23)

where,

Sref is the reference operation irradiation given by the manufacturer,

T is the PV cell output voltage versus the temperature coefficient,

S is the slope of the change in the temperature due to a change of the solar irradiation

level,

T is the module efficiency.

The V-I characteristics curve is shown in Fig. 3.14.

Figure 3.14: V-I Feature Curve of a PV cell

It is obvious that the PV production is mostly affected by the solar irradiation

and temperature. The production in the winter is very low compared to the sum-

mer conditions. Some seasonal factors are introduced in the simulation to reflect these

effects. The PV panel production is estimated by applying historical irradiation data

from NASA, as shown in Fig. 3.15 [51].

29

3.6 Estimation of the Development of DERs and the Changes of Activitiesin DNs

1983 1987 1991 1995 1999 20030

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Day (1983.07.01 ~ 2005.06.30)

Cle

arn

ess I

nd

ex

Figure 3.15: Historical data of Clearness Index on Gotland - Clearness Index isproportional to the production of PV cells [kW/m2/d]

3.6 Estimation of the Development of DERs and the Changesof Activities in DNs

As mentioned, a lot of targets related to energy and environment are set. The most

important target is the EU 20/20/20 targets (see [2]). The targets as the short-term

plan are applied and need to be met in 2020, while the long-term planning can be ex-

tended to 2050. Thus, it is necessary to estimate how the electricity market and the

future DNs look like.

In the annual report Energy in Sweden in 2010 [45], the new climate and energy

policies are presented. These are: (i) The proportion of energy supplied by RES should

be 50% larger than the annual energy consumption in Sweden by 2020, (ii) Vehicles in

Sweden should be independent of fossil fuels by 2030, (iii) The efficiency of energy use is

required to be improved to reduce 20% in energy consumption between the years 2008

to 2020, (iv) 40% reduction in greenhouse gas emissions needs to be met by 2020 in

comparison with 1990, while the target, no greenhouse gas emission by 2050, is set for

a longer vision.

Aiming at these targets, the changes in the future DNs are implemented in accor-

dance with the three dimensions in scenarios, Table 3.8 gives a first idea of these future

developments.

Table 3.8: Estimation of evolution of DERs

DER Short-term Long-term

DG 30% of the peak load 50% of the peak loadEV 20% of total vehicles 50% of total vehiclesAD 20% of total customers 50% of total customersADenergyefficiency 20% reduction of load

30

Chapter 4

Construction of the SimulationToolbox

This chapter focuses on the modelling of DNs and DERs based on the theory stated in

the previous chapter. The radial and meshed network models are presented in the first

section, where some important parameters are listed in table form. The algorithms and

parameters for the DER models are given in the following sections.

4.1 Network Model

In Simulink, two different network models are constructed. To capture their static

characteristics, both generation and loads are constituted as Load blocks in Simulink,

i.e., generation is indicated as the negative input, and the consumption as the positive

one. To detect the voltage and power flow on each bus, Scopes are added with some

sufficient Transformation blocks. Other components (e.g., switches) and functions can

be joined into the model for further study. The network parameters are based on the

report [13] (see Chapter 3) and summarized in Section 4.5. The topologies of these

networks are shown in Fig. A.5, and Fig. A.6 respectively. In these two networks,

the number of individual residential loads are about 3800 (radial) and 1800 (meshed) 1.

Detailed data concerning these two networks can be found in Table 4.1, Fig. A.7 and

Fig. A.8 in Appendix.

Table 4.1: Comparison of demonstrative parameters of DN topologies

Topology Voltage Level Buses Loads Generators Referring Network Peak Load

Radial 60 kV/10 kV 11 7 3 Bornholm [37] 15MWMeshed 60 kV/10 kV 11 8 2 German rural [13] 20MW

1Numbers of loads are defined according to the report [13] corresponding to the real conditions indifferent types of Swedish areas, in which about 400 residential loads are connected to each load point.

31

4.2 DG Model

4.2 DG Model

4.2.1 Wind Power

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sprin

g

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sum

mer

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Autu

mn

Time [Hr]

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Win

ter

Time [Hr]

Figure 4.1: Wind power production on 24-hour base

A cluster of wind power plants from one wind wind park is modelled as an aggre-

gated generation unit connected to the MV-DNs at the production points. Their pro-

duction profiles are depicted on an hourly basis, and their volumes are collected from

[41] (source: wind production data of Gotland in year 2010). To capture the features

of wind generation, the mean values and deviations are calculated and it is assumed

to follow a normal distribution with high deviations [52]. An example of production

profiles in four different seasons are shown in Fig. 4.1. The potential production of

wind power is determined by the penetration level of DG, DGpen , i.e., the ratio between

the installed DG capacity and the peak load. The aggregated wind generation and their

sizes are presented in Table 4.2 with respect to the peak load. The unit names can be

found in the topologies of network models (See Fig. A.6 and Fig. A.5).

Table 4.2: Location and the penetration level of wind power

Network Radial network Meshed network

Unit Name G1 G2 G3 G1 G2Penetration 27

DGpen

37

DGpen

27

DGpen

13

DGpen

23

DGpen

32

4.3 EV Model

4.3 EV Model

4.3.1 Algorithm of Modelling

EV Fleet

Urban Rural

Private Vehicles

Commercial Vehicles

Private Vehicles

Commercial Vehicles

BEV PHEV

SOCTrip Type

Battery Size...

70% 30% 90% 10%

50% 50%

1

*

... ... ...

1

*

Figure 4.2: Characteristics of EVs - Assign characteristics by different classes

There are two types of EVs studied in this paper, i.e., Pure Battery Electric Vehi-

cles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs). The total number of EVs

is in proportion to the number of households in the studied area, defined by EVpen. It

is assumed that if one EV leaves the geographic area reflecting the studied distribution

networks, an EV with the same features will enter.

The EV fleet is defined by driving patterns, type of vehicles , and the availability of

charging facilities, of which the statistics of the parameters are described in the section

3.4. The EV fleet is randomly split into different classes as shown in Fig. 4.2. With the

categorized properties, the EVs are performed on the state transitions.

Three states for EVs are set: Running (0), Charging (1), and Parking (2).

The initial states of all EVs are assumed to be at home and charging. In each time

step, according to the last state of the EVs, different transition procedures are applied.

The State of Charge (SOC), power consumption duo to charge, remaining trip length

and numbers of completed trips are updated at the beginning of each iteration. The

33

4.3 EV Model

algorithm is described by the flowchart in Fig. B.2.

In the block Power and SOC update (Fig. B.1 (d)), the following steps are imple-

mented:

if the EV is charging,

PCharging(busi,t+1, t+ 1) = PCharging(busi,t, t) + Chargingi

SOC(i, t+ 1) = SOC(i, t) + Charging Chargingi t/Capi

if the EV is running,

SOC(i, t+ 1) = SOC(i, t) vi t Consi/Capi

RTL(i) = RTL(i) vi t

where,

vi is the speed of the EV i,

PCharging(busi,t, t) is the equivalent load of charging, on busi,t at time t,

Chargingi is the load of EV i, according to the type of charging now,

SOC(i, t) is the State of Charge of EV i, at time t. Capiand Consi are the Capacity

[kWh] and Consumption [kW/ km] of EV i, respectively,

RTL(i) is the remaining trip length [km] of EV i in current trip.

In the block Transition of States, different strategies are applied to different original

state (transitions in Fig. B.1 ).

To make the algorithm more easy to follow, some boolean indices are set:

prtl is 1 when RTL(i) is larger than 0, psocfull is 1 when SOC(i, t) is smaller than 1, psoc is 1 when the vehicle can support the remaining length of the trip (if the

vehicle is PHEV, psoc = 1.),

ptime is 1 when the departure time is arriving.

In each cycle, the state Running is prioritized. When prtl, psoc, and ptime are 1 (i.e.,

when a EV is supposed to start and there is enough power to run the remaining trip),

then the next state of EV i is Running. Charging will be applied when either of

the aforementioned premises is not satisfied, if there is charging facilities available and

psocfull is 1. In cases that EV does not have sufficient power to drive to the destinations,

it is either in the state of Charging, if there is charging facilities available on sites,

or in the state of Parking if not. The charging time is dependent on the remaining

time before the next trip and the type of charging facilities. If the EV cannot run or

34

4.3 EV Model

get charged, then the state of EV i will be set as Parking. For observations, states,

location, and SOC in each time instant for every EV are recorded in matrices together

with the equivalent load matrix.

4.3.2 Parameter Sets for Simulations

Table 4.3: Allocation of characteristics of the EV fleet

Network PrV/CVSpeed Consumption Trip Length Trip Type[km/h] [kW/km] [km] 1 2 3 4

Urban

PrV40 0.12

20(std)0.2 0.3 0.4 0.1

(70%) 1CV

25 0.18100 (std)

0.5 0.45 0.05(30%) 5

Rural

PrV60 0.18

28(std)0.2 0.3 0.4 0.1

(90%) 1.5CV

30 0.22100 (std)

0.5 0.45 0.05(10%) 10

Allocation of parameters for different types of EVs

The percentage of PrV/CV and PHEV/BEV are random numbers following a normal

distribution, so as departure time of each trip and the capacity of battery in each vehicle.

Trip lengths of all trips follow a log-normal distribution. Table 4.3 and Table 4.4 shows

characteristics of the EV fleet.

Due to the effect of temperature, the consumption of the EVs vary. Hence, the

common seasonal factor is introduced to reflect the effect of temperature to some extent.

Possibility of charging and charging type

The availability of the charging facilities determine the possibilities to be charged in

Table 4.4: Capacity of Battery of each PHEV/BEV[kWh]

Type Cap. Mean Cap. Dev

PHEV 9 3BEV 25 5

the model. If the available charging duration is less than 1 hour, there is no chance for

charging (i.e. no fast charging utility); if the available charging duration is less than 4

hours and more than 1 hour, the possibility of charging is 0.3 and the charging type is

standard charging; if the available charging duration is even longer or the EV is at home

( the initial place), the charging possibility is 1 and the charging type is slow charging.

Commuting patterns and corresponding probabilities (see Table 4.5).

After studying the customers behaviour, a number of trip types are designed with

different possibilities as shown in Table 4.3.

35

4.4 AD Model

For PrVs, four possibilities are considered:

Two trips a day. One is in the morning, and the other one is in the afternoon (goto work and go back home).

Three trips a day. Except for those two trips, there is one extra trip for leisureafter work.

Four trips a day. Except for those two trips, there are two extra trips for leisureat noon.

No trip in the whole day.

For CVs, three possibilities are considered:

One trip a day, running since morning. Two trips a day. One is in the morning, the other one is in the afternoon. No trip in the whole day.

Table 4.5: Trip Types of different types of EV[27]

Type of Vehicles Type of trips Departure time Possibility

PrV

1 9 12 13 17 0.22 9 17 24 24 0.33 9 17 19 24 0.44 24 24 24 24 0.1

CV

1 9 24 24 24 0.52 9 13 24 24 0.453 24 24 24 24 0.05

4.3.3 Individual Results

By applying the algorithm stated above, the simulation result can be derived as shown

in Fig. 4.3. In Fig. 4.3 (b), we can observe that the critical situation appears in the

evening around 8 p.m, when most of the vehicles drive back home and start to charge.

There are two dips of the equivalent load curve at noon, since some vehicles drive off

for lunch and cannot connect to the grid.

4.4 AD Model

To evaluate the consequences of the changes on the demand side, a general model of

the flexible loads is developed according to the discussion in Chapter 3 [1], with the con-

sequence as a reshaped load profile (see Fig. 4.4). Smart meters control the operation

of the appliances by reading the electricity price on an hourly basis. DC/AD converters

transmit the power production of PV panels to supply consumption of appliances, and

36

4.4 AD Model

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241

2

3

4

5

6

7

8B

us

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

0.10.20.30.40.50.60.70.80.9

1

SO

C [%

]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

run(0)/charge(1)/park(2)

Sta

te

(a) The state of a certain EV in the simulation

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

10

20

30

40

50

60

70

80

Eq

uiv

ale

nt

loa

d p

rofile

of

EV

s [

kW

]

Time [Hr]

(b) Equivalent load curve of EVs in Bus4

Figure 4.3: Individual Results of the model of EVs

Figure 4.4: Typical structure of a house as a flexible demand - residential applianceswith smart meters, PV panels, and DC/AC Converters [49]

to feed power back to the grid to some extent.

The original residential load profiles are given by [12]. The structure of the mathe-

matical model of AD is given in Fig. B.3. Properties, such as location (at which bus),

type (apartment or house), are assumed to be random. The original load profiles are

reshaped if and only if the three independent factors affect the customers behaviour:

4.4.1 Price Sensitivity

AD will change the electricity consumption (e.g., from heat pumps) with respect

to the electricity price from the day-ahead market, with a purpose to reduce energy

bills. We assume that consumers are price sensitive, and proper metering technology

and market contracts are available. The elect

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