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PV Hosting Analysis and Demand Response Selection for handlingModern Grid Edge Capability

Sherin Ann Abraham

Thesis submitted to the Faculty of the

Virginia Polytechnic Institute and State University

in partial fulfillment of the requirements for the degree of

Master of Science

in

Electrical Engineering

Robert P. Broadwater, Chair

Virgilio A. Centeno

Jaime De La ReeLopez

May 09, 2019

Blacksburg, Virginia

Keywords: Smart grid, AMI data clustering, PV hosting

Copyright 2019, Sherin Ann Abraham


Sherin Ann Abraham

(ABSTRACT)

Recent technological developments have led to significant changes in the power grid. Increas-

ing consumption, widespread adoption of Distributed Energy Resources (DER), installation

of smart meters, these are some of the many factors that characterize the changing distribu-

tion network. These transformations taking place at the edge of the grid call for improved

planning and operation practices. In this context, this thesis aims to improve the grid edge

functionality by putting forth a method to address the problem of high demand during peak

period by identifying customer groups for participation in demand response programs, which

can lead to significant peak shaving for the utility. A possible demand response strategy for

peak shaving makes use of Photovoltaic (PV) and Battery energy storage system (BESS).

In the process, this work also examines the approach to computation of hosting capacity

(HC) for small PV and quantifies the difference obtained in HC when a detailed Low voltage

(LV) network is available and included in HC studies. Most PV hosting studies assess the

impact on system feeders with aggregated LV loads. However, as more residential customers

adopt rooftop solar, the need to include secondary network models in the analysis is studied

by performing a comparative study of hosting capacity for a feeder with varying loading

information available.


Sherin Ann Abraham

(GENERAL AUDIENCE ABSTRACT)

Today, with significant technological advancements, as we proceed towards a modern grid,

a mere change in physical infrastructure will not be enough. With the changes in kinds

of equipment installed on the grid, a wave of transformation has also begun to flow in the

planning and operation practices for a smarter grid. Today, the edge of the grid where the

customer is interfaced to the power system has become extremely complex. Customers can

use rooftop solar PV to generate their own electricity, they are more informed about their

consumption behavior due to installation of smart meters and also have options to integrate

other technology like battery energy storage system and electric vehicles. Like with any good

technology, adoption of these advancements in the system brings with itself a greater need

for reform in operation and planning of the system. For instance, increasing installation of

rooftop solar at the customer end calls for review of existing methods that determine the

maximum level of PV deployment possible in the network without violating the operating

conditions. So, in this work, a comparative study is done to review the PV hosting capacity

of a network with varying levels of information available. And the importance of utilities to

have secondary network models available is emphasized. With PV deployed in the system,

enhanced demand response strategies can be formulated by utilities to tackle high demand

during peak period. In a bid to identify customers for participation in such programs, in

this work, a computationally efficient strategy is developed to identify customers with high

demand during peak period, who can be incentivized to participate in demand response

programs. With this, a significant peak shaving can be achieved by the utility, and in turn

stress on the distribution network is reduced during peak hours.

Dedication

This is dedicated to my parents.

iv

Acknowledgments

I would like to thank my advisor Dr. Robert Broadwater for his guidance and support

throughout my work with him here at Virginia Tech. I would also like to thank Dr. De La

Ree and Dr. Centeno, for their encouragement and belief in me. Taking courses with them

helped me delve deeper into this field.

Also, I’m grateful for the opportunity that Suchi and I got to work together and what started

out as a small project has shaped into a good piece of research. I want to sincerely thank her

not only for her contributions in this work, but I also truly value and cherish our friendship.

I would not want to miss out on acknowledging all my fellow graduate students in the Power

and Energy Centre as well as my roommates Genesis and Grace, with whom I’ve shared

fun moments as well as meaningful discussions. A special mention to my friend Lari, who

despite being in another city, has always been there for me, supporting me through thick

and thin.

Finally, I am ever so grateful to my parents, and my brother Pramod for all their encour-

agement and support.

v

Contents

List of Figures viii

List of Tables x

1 Introduction 1

1.1 Motivation - Changing Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 PV Hosting Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Smart Meter Data Applications . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 Hosting Capacity Analysis for small PV sites 12

2.1 Circuit Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.1.1 Primary Feeder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.1.2 Secondary Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.1.3 Case Studies for analysis . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.2 PV Hosting Capacity Application on DEW . . . . . . . . . . . . . . . . . . . 16

2.3 Hosting Capacity Application Setup . . . . . . . . . . . . . . . . . . . . . . 18

2.4 Hosting Capacity Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

vi

3 Customer Identification for Demand Response 24

3.1 Demand Response Strategy for Peak Period Load Reduction . . . . . . . . . 24

3.2 Description of Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.2.1 Data Cleaning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.2.2 Data Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.3.1 K-means Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.3.2 Attribute Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.4.1 Optimum Number of Clusters . . . . . . . . . . . . . . . . . . . . . . 33

3.4.2 Ideal Cluster for Demand Response . . . . . . . . . . . . . . . . . . . 33

3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4 Summary and Future Work 37

Bibliography 39

vii

List of Figures

2.1 Circuit Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.2 Distribution Transformer Information . . . . . . . . . . . . . . . . . . . . . . 14

2.3 Flowchart of the DEW PV Hosting Capacity Application [20] . . . . . . . . 17

2.4 Scatter plot of random PV placements for customer overvoltage violation

criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.5 PV Generation profile for clear sky measurements . . . . . . . . . . . . . . . 19

2.6 Hosting Capacity for different times and violation criteria, run on a Primary

feeder with secondary network details . . . . . . . . . . . . . . . . . . . . . . 21

2.7 Comparative study of hosting capacity for three cases . . . . . . . . . . . . . 22

2.8 Comparison of voltage for 3 cases of PV penetration . . . . . . . . . . . . . 22

3.1 Comparison of voltage and loading parameters at the distribution transform-

ers for networks with and without PV . . . . . . . . . . . . . . . . . . . . . 25

3.2 Comparison of voltage and loading parameters at the customer end for net-

works with and without PV @ 1pm . . . . . . . . . . . . . . . . . . . . . . . 26

3.3 Analysis of aggregate consumption for the whole year . . . . . . . . . . . . . 27

3.4 Seasonal exploration of daily load profiles in 2010 . . . . . . . . . . . . . . . 28

3.5 Daily load profiles in 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.6 Average daily consumption of customers in 2010 . . . . . . . . . . . . . . . . 30

viii

3.7 Clustering Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.8 Normalized consumption at different time periods for cluster centroids . . . 35

3.9 Normalized average daily consumption of clusters . . . . . . . . . . . . . . . 35

3.10 Percentage peak load reduction achieved . . . . . . . . . . . . . . . . . . . . 36

ix

List of Tables

2.1 Secondary Network Information . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.2 PV Hosting Capacity Analysis Times . . . . . . . . . . . . . . . . . . . . . . 20

2.3 Violation criteria considered for PV Hosting Capacity Analysis . . . . . . . . 20

2.4 Comparison of Hosting Capacity obtained for the different cases . . . . . . . 21

3.1 Population distribution with respect to average daily demands . . . . . . . . 28

3.2 Time block definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

x

Chapter 1

Introduction

1.1 Motivation - Changing Grid

In recent years, the power grid has been rapidly evolving due to significant technological ad-

vancements in the area. A key aspect of this is widespread deployment of Distributed Energy

Resources (DER). As with other renewable sources, significant research in the field of pho-

tovoltaics has enabled this technology to be more accessible. So, there is an emerging trend

among residential customers to install rooftop solar panels due to reasons like accessibility,

increased awareness regarding the technology, environmental concerns towards traditional

power plants and the customer’s ability to reduce energy costs. Installation of these gener-

ation sources in the low voltage (LV) network near the load have introduced bi-directional

power flow in the distribution grid. This brings about new challenges in power system opera-

tion and planning that did not exist in the traditional network [1]. Also, excessive integration

of distributed generation can lead to problems due to violation of operating conditions. So,

utilities have turned to PV hosting capacity studies to determine the maximum possible PV

penetration that can be integrated without hampering system performance[2].

One other aspect that continues to change in the power system is the load. Demand has been

rising over the past years, and carries the potential to disrupt grid operation if not addressed

by infrastructure upgrades or other means. This has motivated utilities to introduce various

kinds of demand response programs to tackle rising demands during peak periods. Through

1

2 Chapter 1. Introduction

these programs, utilities manage loads by encouraging customers to reduce or shift their

consumption during peak hours, thereby lowering system stress. Today, with the advent of

behind-the-meter generation and storage, demand response programs have the capability to

extend beyond just reducing customer consumption.

The wave of change flowing in the power system has also brought about the technology of

advanced metering infrastructure (AMI). AMI enables bi-directional communication of data

between the customers and system operators. Smart meters, a part of this infrastructure,

have the capability to record and send different kinds of data, like energy consumption, at

specific time intervals to the utility. This functionality of recording intra-day consumption

data enables implementation of time-variant pricing schemes and the applications don’t

end there. This meter data can be utilized not only for billing customers, it can help

improve awareness of a customer regarding their consumption, hence equipping them to make

better decisions. Utilities can also leverage this data to analyze and improve operations and

planning. Today, since smart meters have been deployed on a large scale at the customer end,

utilities have access to huge amounts of consumption data. Dealing with huge amounts of

data pose a high computational burden and so this calls for efficient data mining techniques

to tackle the data.

1.2 PV Hosting Capacity

Increasing deployment of renewable energy has been encouraged due to its potential environ-

mental benefits as reliance on conventional sources of generation could gradually be reduced.

However, DG installations at different parts of the grid have changed how power flowed tra-

ditionally, hence calling for a new approach to operation and planning. In addition to this,

there are constraints on the capacity of DG that can be installed in the system. Excessive

1.2. PV Hosting Capacity 3

penetration can lead to problems and unacceptable system performance, like overvoltage,

thermal overloading and incorrect operation of protection infrastructure. So, this concept of

Hosting Capacity (HC) was introduced and it is the maximum amount of penetration allow-

able on the network beyond which it causes operational problems and deteriorates the system

performance [2]. Before HC calculations were started to determine the maximum allowable

PV penetration, a 15% rule was followed by some distribution system operators (DSO). This

stated that 15% of the peak load could be an allowable penetration level for DG. Anything

beyond that would need to be analyzed further before installation [3]. However, today, a lot

of work has been done in identifying techniques for determining the hosting capacity of a

network as well as finding measures for improving the maximum PV penetration limit.

Ismael et al. [2] have provided an overview of the research and advancements that’s been

done in the area of hosting capacity of distributed generation. This work is an extensive

review of papers across the entire domain of hosting capacity. It discusses the history, types

of hosting capacity methodologies, system parameters that limit DG penetration as well as

techniques to increase hosting capacity.

There have been various approaches to determine the hosting capacity of a system like

analytic, stochastic and streamlined methods [2]. In an analytic approach, each bus in the

network is tackled one at a time. At a particular bus, the penetration is increased in steps and

power flow calculations are run to determine the operating conditions. If these parameters

like voltage and loading are not within their acceptable limits, a violation is reported. The

penetration level before the system conditions are violated is determined to be the hosting

capacity of the bus. Since this takes into many scenarios, it is computationally intensive and

time consuming.

The other approach that has been used in HC calculations is the stochastic method [4]. In

such methods, the uncertainties in loading, DG rating and placement are simulated using


methods like Monte Carlo (MC). By modeling it in this way, the uncertainties are accounted

for and a number of scenarios are created and analyzed. The time, computational burden

and accuracy for this method is dependent on the level of complexity defined.

Another technique to compute the hosting capacity called Streamlined Hosting capacity

(SLHC) was developed by EPRI in 2014. This method aims to reduce the computational

burden and time of the calculations [5]. With SLHC, a quick and simple calculation on the

network can be done, before deciding to proceed with a more detailed set of studies.

Most of the utilities today do not have detailed and accurate secondary network models

available. So, a lot of the work has been focusing on running the analysis on primary feed-

ers. Hosting capacity maps are also made available showing the possible PV penetration on

primary feeders in the power system. This hosting capacity obtained can be used to deter-

mine the PV penetration for large scale PV systems which are deployed on MV networks.

As residential PV deployment is increasing, the need for inclusion of LV loads in the analysis

was identified and works to account for these were published, like [6], [7].

In [7], authors examine the interactions between MV and LV network while determining the

PV hosting capacity. Based on their analysis, it was observed that PV penetration in the

LV network did not highly impact the hosting capacity of MV network. However, HC on the

MV network was primarily limited due to violations reached at the LV network side. In their

approach, the authors modeled a LV network and first computed the LV HC. PV site for this

penetration level was then installed in all the service transformers and the MV network HC

was computed considering MV-LV interactions. In the methodology to determine the hosting

capacity, a single PV system is installed for all possible sizes and locations in the system and

the maximum penetration level for each possible location is obtained. This location-specific

approach to determine the hosting capacity is given by Estorque and Pedrasa [8]. In this

manner, the maximum possible penetration levels before violations are identified. While

1.2. PV Hosting Capacity 5

determining the LV HC, a 1 p.u. source voltage is connected to the distribution transformer

and the effect of the MV system is ignored. Maximum PV penetration levels are determined

one-by-one for all LV networks. The LV HC is then said to be the least PV penetration

level out of the determined penetrations for all LV networks. Similarly, the MV HC was

obtained wherein the maximum possible penetration without violations is determined for

all MV nodes, the minimum of which is said to be the HC of the MV network. In this

manner, the HC was obtained for the network. The impact of this HC was also studied on

the system in the following manner. The LV HC obtained was connected to the system as

one PV generator at the location identified during the analysis and its effect on the LV and

MV voltage was studied. Not all placements of PV were considered for the analysis. So, in

this work, location specific PV hosting is carried out to determine the HC.

In [4], Dubey et al. analyze distribution voltages due to installation of residential PV.

Deployment of residential PV systems was shown in the various scenarios by being connected

at the node in the primary feeder and the locations and size of PV systems were determined

by a stochastic approach. The HC analysis in this work is run on an actual primary feeder

and many scenarios of PV system deployment are studied. Substation loading data for 2013

was considered for the feeder and minimum and maximum loading conditions are identified

and studied. In this way, primary feeder hosting capacity was determined and conclusions

based on location of installed PV system and the effect of loading were drawn. Another

work that was done considering LV networks was done by Arshad et. al [9] where they

review the LV networks in Finland and analyze the limiting parameters for PV HC. The test

network cases were generated by a Monte Carlo based algorithm and scenarios for balanced,

unbalanced cases, PV connections on various phases, effect of On-Load Tap Changer (OLTC)

at the transformer were studied and violating parameters were described.

In the work by Ballanti and Ochoa [6], the impact of LV network PV penetration on MV


network HC is studied. It shows that voltage violations in the LV network were the primary

limiting factor in MV network HC. This was analyzed by taking an integrated MV-LV

approach to determining the HC. This integrated MV-LV HC approach considered total HC

possible, considering PV penetration on LV network and MV network. Primarily, the impact

of PV penetration at LV level was studied on the MV network HC. It was observed that

even for no PV penetration in the LV network, the MV HC was limited due to overvoltage

problems observed in the LV network. So, here, realistic loading and generation profiles were

considered and the effect of LV and MV-connected DG units was studied on the the integrated

MV-LV network. However, detailed secondary network models were not considered and

instead, LV loads were aggregated at the secondary substation. So, the HC obtained for the

LV side PV penetration might not be conservative.

Most of the methods to determine hosting capacities would run power flow analysis at every

step of PV deployment. However, Heslop et. al [10] identify an approach to determine the

maximum PV penetration limits without having to rely on running power flow for every

scenario of PV deployment. In this study, the load and PV are considered to be evenly

distributed. It identified a relationship between maximum allowable PV penetration with

number of customers, total feeder impedance for a particular loading condition, hence elim-

inating the need to run load flow calculations for all scenarios.

As discussed, a lot of work has been done to calculate the PV hosting capacity and various

HC methods have been deployed for the same. In most of the work, LV network details

are not included due to the unavailability of secondary network information. The secondary

network is mostly represented as an aggregated load connected to the distribution trans-

former. However, with increasing adoption of small PV in LV networks, in this thesis, work

is done to compare the current approach to determine the HC, and how that would fare with

secondary network models connected to the MV network.

1.3. Smart Meter Data Applications 7

1.3 Smart Meter Data Applications

The edge of the grid represents that part where the customers interface with the power

system [11]. As we proceed towards Grid Modernization, there are a lot of changes that

are taking place at the edge of the grid. Today, at the customer end, there are loads,

distributed energy resources, battery energy storage system, electric vehicles and also smart

meters that record information about energy consumption and other technologies. More and

more residential customers are installing rooftop solar, and so DER connections at the grid

edge is increasing. With this increasing generation in the LV network at the edge of the

grid, in this work, the existing hosting capacity approach is reviewed and analyzed to see

if the penetration value obtained is a good reference for deployment of small PV sites. As

technology advances, it has also led to increase in customer consumption, leading to high

demands during peak periods. However, utilities today find themselves in a position to tap

into the potential of the changes taking place at the edge of the grid. They can utilize the

technology changes and advancements which have surfaced at this grid edge to address these

issues. Smart meters, a part of advanced metering infrastructure is one such technology

which is being adopted at the customer end at a fast rate. Smarts meters record and provide

intra-day energy consumption details, and this capability of smart meters can be utilized to

propose a possible way of tackling high demand during peak time.

As utilities find themselves in possession of high resolution smart meter data, it opens up

huge avenues to improve customer experience as well as their own planning and operation

processes. In a bid to harness this huge potential, various data mining techniques have been

used to extract meaningful information for applications in power systems. A lot of effort

has resorted to various data mining techniques in order to exploit the prospective benefits of

this data without getting overwhelmed by its volume. This has led to identification of many


important applications of smart meter data analytics which can be applied in the system.

Due to many grid modernization initiatives underway, tremendous research has been carried

out in each of these areas in the recent years. Smart meter data analytics gives insight into the

present state of the system, provides capability to predict parameters and enable decision

making based on this information. These contributions are called descriptive analytics,

predictive analytics and prescriptive analytics, respectively. Data mining methods such

as anomaly detection, classification, clustering, regression analysis have been employed for

applications like energy theft detection, load forecasting, customer segmentation and load

profiling, outage management, demand response planning and implementation. The papers

in this application area have been reviewed by the authors in [12].

Load profiling looks into analyzing the customer consumption profiles and gives in-depth

insight into consumption behavior. It further allows grouping customers for applications

like demand response. In the past, since a major portion of the analysis was done on

higher voltage levels and not a lot of secondary information was available, aggregated load

of residential customers at the distribution transformer was analyzed. Work on identification

of customers for partaking in energy efficiency programs was also based on demographic and

survey data [13]. However, today, with installation of smart meters allowing access to high

resolution customer consumption data, and introduction of DER, there is a need, as well

as the capability, to include this information in the analysis carried out. With so many

possible approaches to cluster smart meter data, the methods adopted rely on the intended

application and outcome of the process. So, since this thesis focuses on customer grouping

for an application of demand response, a literature review is given in this section of works

that have approached the topics of load profiling, and identification of customers with similar

load shapes with intent of applications in demand response.

High variability in residential customer load makes clustering on this data difficult. Moreover,


consumption pattern for the same customer might also be different on different days in

the year. There have been works that have performed direct clustering wherein clustering

methods are applied directly on the time-series consumption data [14]. However, this would

have a high computational burden and lead to the same customers being placed in different

clusters, due to variance in daily load shapes through the year. To reduce this variability

i.e. same customer load profiles being placed in different clusters, authors in [15] based their

method on dynamic time warping (DTW). In DTW, best possible alignment between two

load shapes is found by stretching or contracting the time axis. For this work, all customer

load curves for every day in the analysis were taken and clustering was performed. This

might place load curves from the same house in different clusters, which was captured by

the measure of entropy.

Indirect clustering is another approach to cluster smart meter data, instead of taking all

daily load profiles for every customer like in direct clustering. In this method, there is a pre-

processing step for attribute selection and dimensionality reduction. Reducing the number of

attributes that represent a customer is favorable since it brings down the computational bur-

den of the process. However, while the dimensionality is reduced, care should also be taken

that useful information is not lost. Principal component analysis (PCA), Self Organizing

Maps (SOM) are some such preprocessing steps taken to reduce dimensions [16]. Statistical

parameters (mean, variance), specific features like ratios or magnitudes of peak loads, base

loads are also extracted from time-series data and chosen as attributes to represent each

customer[17]. So, domain knowledge can also be utilized to extract and compute relevant

parameters and prevent important information in time-series consumption data from being

lost.

In [18], attributes chosen for clustering were 30 minute energy readings averaged over the

entire time period to obtain mean daily consumption profile for each customer. The authors


also demonstrate the efficiencies of different clustering methods applied for selecting cus-

tomers for participation in demand response programs. With regard to chosen attributes,

once the mean daily consumption profile was obtained for each customer, they were then

normalized and a percentage of electricity consumption for every 30-min time period was

computed. Hence, each customer was characterized by a percentage load shape. The peak

and off-peak percentage consumption for every customer was also determined. Clustering

analysis was done on the percentage load shape for each customer. Four different data mining

techniques, namely random forests, k-nearest neighbors, decision tree and artificial neural

networks were deployed to perform the clustering and their performance was compared. This

is a load shape based method to identify customers for demand response and deploys indirect

clustering. However, averaging half hourly consumption over the entire year might lead to

losing information due to change in peak load occurrence times across seasons.

In [19], the authors have also extracted certain important attributes from the time-series

consumption data and have used the clustering technique of finite mixture modeling (FMM).

FMM gives the capability to cluster categorical data in addition to continuous data. So here,

the authors aim to identify customers for demand reduction and place an emphasis on choice

of appropriate attributes for clustering. The temporal resolution of data has been reduced

by determining four time periods for analyzing the data. The count of occurrence of different

size peak demand was studied in every time interval through the day for all customers and

this information was used as a basis to determine the time periods. A measure of variability

was also included to account for the variation observed within a single customer behavior.

Seasonal and weekday-weekend variations were considered as well by including parameters

for seasonal score and weekday-weekend difference attribute. The work primarily focused

on representing customers based on their load shapes, and FMM based clustering on those

attributes. Bootstrapping methods were employed to gauge the robustness of the system.


A similar approach of dividing the day into specific time ranges is done by Azaza and Wallin

[17]. However, after the authors determined specific times ranges in a day, they identified the

time of occurrence of system peak load for each of these periods. Responsibility factor was

computed by dividing the customer load at system peak time occurrence by the system peak

load. This value would give information on contribution of a customer to system peak load.

Since there is huge variation in customer’s daily consumption, variance was also considered

as a factor for the analysis. It is also mentioned that these factors could be detailed and

obtained over daily load profiles or could be computed over monthly aggregated values. Once

the indicators were determined, they were clustered using hierarchical and self organizing

maps (SOM).

In this thesis, primary focus is placed on obtaining representative characteristics of a cus-

tomer and k-means clustering technique is used as of means of demonstrating the effectiveness

of the chosen attributes. Seasonal variations in peak load occurrence was considered. In ad-

dition to having high peak period demand, an ideal customer for demand response should

also have a high magnitude. So consumption magnitude and load shape, both these fac-

tors are considered for representing customers. High consumption customers with high peak

period demand would be ideal for utilities to target for participation in demand response

programs. If these customers can reduce the peak by a particular percentage, a significant

peak shaving can be achieved by the utility by smaller number of such customers. Also, to

make the method computationally efficient, the dimensionality of data was reduced further,

at the same time ensuring relevant information isn’t lost.

Chapter 2

Hosting Capacity Analysis for small

PV sites

As the PV penetration on the LV network increases, it is important for utilities to have

accurate hosting capacity values to ensure smooth grid operation. DG penetration in the

network beyond a particular limit can lead to problems like overloading and overvoltage,

hence affecting system performance. So, in this chapter the approach for comparison of PV

hosting capacity techniques is described and the results obtained are discussed.

2.1 Circuit Setup

The network used for this comparative study and analysis is modeled on DEW, and details

on that are given in this section.

2.1.1 Primary Feeder

The primary feeder is modeled such that is has a voltage level of 12.47 kV L-L and 12

distribution transformers connected to it. There are no MV customers connected on this

feeder. The primary feeder model for this work is shown in Fig 2.1a.

12

2.1. Circuit Setup 13

2.1.2 Secondary Networks

Secondary models have been taken from the Pepco Holdings Database. 12 secondary models

with varying topologies have been chosen for this work, and they are shown in Fig 2.1b.

Information of loading and rating of distribution transformers for these secondary models is

shown in Fig 2.2. As can be observed, the topologies of the secondary models are different,

and the distribution transformers also have varied ratings.

For the purpose of loading, the customer loads connected in the LV network are consid-

ered to have a residential consumption profile, obtained from the DEW database. And the

magnitude of loads is obtained by calculations according to connected kVA of transformer.

(a) Primary feeder with distribution trans-formers

(b) Secondary Network Models

Figure 2.1: Circuit Information

14 Chapter 2. Hosting Capacity Analysis for small PV sites

(a) Ratings of distribution transformers (b) Max. kW loading of distribution trans-formers

Figure 2.2: Distribution Transformer Information

The number of loads for each secondary network, alongwith the kVA rating and maximum

kW loading of transformers, is given in table 2.1.

Table 2.1: Secondary Network Information

Circuit No. No.of Loads TransformerPhasing

Total TransformerkVA rating

Max. TransformerkW loading

1 1 ABC 225 5.842 2 AA’ 25 3.023 6 ABC 111 26.394 9 AA’ 37.5 15.685 3 CC’ 15 5.326 4 BB’ 50 3.047 10 BB’ 50 17.88 11 AA’ 37.5 15.529 12 CC’ 50 20.7910 16 CC’ 37.5 15.711 17 CC’ 50 20.8512 8 ABC 150 26.68

2.1. Circuit Setup 15

2.1.3 Case Studies for analysis

Hosting Capacity studies are performed on three cases, each with varying level of network

information availalble and they are listed below. All of these analyses are for identifying the

hosting capacity for small PV that is installed at customer residences.

Case 1: Primary feeder with LV loads aggregated at the distribution transformer

In this case, the scenario where detailed secondary network models are not available is

considered. So, LV loads are aggregated and connected to the secondary of the distribu-

tion transformers in the primary feeder being analysed, The hosting capacity obtained by

analysing this circuit will be referred to as HCLV agg.

Case 2: Primary feeder with detailed secondary network models connected

For this case, it is considered that the secondary network models are available. So, the pri-

mary feeder being analyzed for Hosting Capacity also contains the details of the LV network.

The hosting capacity obtained on the network for this case is referred to as HCLV detail.

Case 3: Secondary Network models only

In this case, the hosting capacity analysis is done separately for each secondary network.

Here, each secondary network is connected to a 1 p.u. source. So, for this case, the effect

of their location on the primary feeder was ignored. The sum of all the hosting capacities is

referred here as HCLV only.


2.2 PV Hosting Capacity Application on DEW

The hosting capacity for the circuits under analysis was found by running the PV Hosting

Capacity Application on the Distributed Engineering Workstation (DEW). This application

uses a stochastic PV allocation approach and finds the highest PV penetration possible on the

circuit before system performance is affected. Parameters like maximum penetration level,

step size for PV penetration, number of random placements for a particular penetration level

and size of PV site to be added can be defined by the user before running the application.

At the start, a base-case analysis is done and the circuit is analyzed for violations. Once the

base-case analysis is successfully passed, the application starts the process of determining the

PV hosting capacity by randomly allocating PV. Using the list of customers on the circuit,

the application randomly adds PV sites of the specified size. The size of the added PV site

is increased to reach the penetration level under consideration. With this newly allocated

PV, the circuit is now analyzed and checked for violations. For each penetration step, this

process is repeated for a user-specified number of times, and this is how the analysis is done

for a particular penetration level. The algorithm then proceeds to the next penetration level,

where the same sequence of steps is repeated, which goes on till the maximum penetration

level is reached. The list of violations is also customizable by the user [20]. This algorithm

to determine the hosting capacity of a circuit is shown in Fig 2.3.

The application, in a defined time range, identifies the times of occurrence of Peak PV

Generation, maximum difference in PV generation to Load, maximum PV to load ratio and

maximum native load. Out of these time points, the user can choose to run the analysis for

all or any of these times, or can also specify their own time for analysis.

Once the PV Hosting Capacity Application is run on DEW for a given circuit, the results

are stored in a database. This analysis gives two penetration limits for the circuit namely:

2.2. PV Hosting Capacity Application on DEW 17

Figure 2.3: Flowchart of the DEW PV Hosting Capacity Application [20]

Strict Penetration Limit and Maximum Penetration Limit. The strict penetration limit is the

penetration level above which the first violation is encountered. The maximum penetration

limit is the penetration level above which all the random PV allocations result in violations.

Fig 2.4 helps explain the concept of strict and maximum penetration limit even more clearly.

This scatter plot is created for all the cases of random placements for a given circuit, and the

overvoltage violation criterion is taken into consideration for an example here. The x-axis

is the penetration limit and the y-axis is the maximum calculated value of the violation

variable, which in this case is the customer voltage. Every random placement case in this


0 20 40 60 80 100 120 140PV Penetration (%)

122

124

126

128

130M

ax C

alc

Valu

e

Figure 2.4: Scatter plot of random PV placements for customer overvoltage violation criterion

analysis is represented by a point, and the two vertical lines show the strict and maximum

penetration limit. As can be seen, maximum customer voltages for all the PV deployments at

penetration levels below the strict penetration limit are less than the violation threshold. On

the other hand, any additional PV placement beyond the maximum limit leads to customer

overvoltage violation. For penetration levels in between these two limits, additional PV

placements are possible, but it would need further analysis, and would depend on size and

location of the placement [20].

2.3 Hosting Capacity Application Setup

Now that the circuit cases and PV hosting capacity application has been described, the

parameters considered for the analysis are detailed here. Starting with PV generation mea-

surements, clear sky PV measurements are considered for this analysis. The PV generation

2.3. Hosting Capacity Application Setup 19

profile for the day for a 2.5kW PV site is shown in fig 2.5. The customer loads taken for the

circuit have been explain in section 2.1.

Figure 2.5: PV Generation profile for clear sky measurements

Before entering into this comparison of hosting capacity for the three cases, a few decisions

on running the PV hosting capacity application had to be taken, like the time of analysis

and violation criteria. The DEW PV Hosting application has the capability to analyse

and identify crucial times, based on PV generation and loading, and determine the hosting

capacity at those times. However, since this work performs a comparative study of hosting

capacity analysis of the three cases described in section 2.1.3, the HC application is run for

one particular day in the year, on 2 June. As can be observed from Fig 2.5, the time range

of PV Generation is 07:00 - 19:00. In this range, the time corresponding to the occurrence

of certain quantities are identified and shown in table 2.2. Also, table 2.3 shows a list of

violations that are checked for during the PV Hosting Capacity Analysis [21]. The primary

violations obtained during the analysis were overvoltage and overload violations.

So, as can be seen in Fig 2.6, the Hosting Capacity is determined on the circuit with secondary

network details for the different times as shown in table 2.2, for two violation criteria. From

the figure, it was seen that the minimum hosting capacity was obtained at the time of


Table 2.2: PV Hosting Capacity Analysis Times

Parameter Time of OccurrenceMaximum PV Generation 13:00

Maximum Load during PV Generation 19:00Minimum Load during PV Generation 7:00

Table 2.3: Violation criteria considered for PV Hosting Capacity Analysis

S.No Parameter Criteria Threshold1 Customer Overvoltage Greater than 126 V2 Customer Undervoltage Less than 114 V3 Component Overvoltage Greater than 126 V4 Component Undervoltage Less than 114 V5 Transformer Overload Greater than 100 %6 Component Overload Greater than 100 %

occurrence of maximum PV generation. So, 13:00 was decided as the time for subsequent

hosting capacity analysis.

2.4 Hosting Capacity Results

The hosting capacity analysis was done on all the cases, the results of which are compared

and shown in table 2.4 and Fig 2.7a. As can be seen, the hosting capacity obtained on the

primary circuit without secondary network details (Case 1) is higher than that obtained for

a primary circuit with secondary network details (Case 2). The total hosting capacity for

Case 3, obtained for individual secondary sources with 1 p.u. voltage source is the highest

among all the three. The hosting capacity obtained when the analysis is run on Case 3 (i.e.

on individual secondary circuits) is shown in Fig 2.7b.

For deployment of small scale PV, this analysis shows that it is essential to incorporate

secondary network models in the study. Without inclusion of that, if the secondary loads

are only modeled as loads directly connected to the distribution transformer, the hosting

2.4. Hosting Capacity Results 21

Figure 2.6: Hosting Capacity for different times and violation criteria, run on a Primaryfeeder with secondary network details

Table 2.4: Comparison of Hosting Capacity obtained for the different cases

Case No. Case Name HC Hosting Capacity (kW)1 Primary Circuit with Sec Model HCLV detail 192.52 Primary Circuit without Sec Model HCLV agg 2453 Only secondary networks HCLV only 606.38

capacity obtained for this circuit is not conservative enough for installation of small-scale

PV. A hosting capacity of 245 kW was obtained for Case 2 when analysing the circuit without

secondary network models (HCLV agg). In practice, since secondary network detailed models

are not available for analysis, the hosting capacity is usually obtained in this manner.

To study the impact of Hosting Capacities studied on Cases 2 and 3 on the actual system,

a placement of PV at the corresponding penetration level is done on the secondary network

and the voltage is observed. So, to analyze the impact of Hosting Capacity obtained for Case

2 (HCLV agg), PV placement corresponding to a penetration level of 245 kW was done on the

secondary network. Similarly, for Case 3 (HCLV only), where hosting capacity was obtained

separately for all secondary networks, PV placement was done for each of these secondary

networks for their respective hosting capacity and these secondary networks were connected

to the primary feeder. These two PV placements are compared with the PV placement for


(a) Hosting Capacities for all circuits underconsideration

(b) Hosting Capacity obtained when individ-ual secondary circuits are analyzed with 1p.u. sources connected

Figure 2.7: Comparative study of hosting capacity for three cases

Figure 2.8: Comparison of voltage for 3 cases of PV penetration

HCLV detail (i.e. HC obtained for the Case 1 with secondary network models).

Voltages are plotted for the loads for these three cases, which is indicated in Fig 2.8. As can

be observed, for PV penetration for hosting capacity obtained from Case 2, 5 instances of

overvoltage violations at customer loads were encountered. For PV penetration for hosting

capacity obtained from Case 3, 8 cases of overvoltage violation cases were observed.

2.5. Conclusions 23

2.5 Conclusions

Most of the PV Hosting techniques assess the PV penetration on the primary feeder. Based

on these computed values, hosting capacity maps are also made available by certain utili-

ties. These maps gives an idea of the maximum penetration that an MV feeder can safely

and reliably have without needing infrastructure upgrades. Nowadays, as more and more

residential PV is getting deployed in the system, it was observed that PV hosting capacity

obtained for a circuit with only aggregated LV loads was not conservative enough for deploy-

ment of small PV. PV installed on the LV network does not impact the MV network hosting

capacity much. Small PV penetration is limited primarily by overvoltage violations encoun-

tered in the secondary network. In addition to this, hosting capacity was obtained for each

secondary network separately. However, this gives a high total hosting capacity which when

placed with the primary feeder led to overvoltage violations. So hosting capacity obtained

by considering secondary networks separately would also not be a good estimate for limits

on deployment of residential PV. Hence, an accurate hosting capacity for small PV can be

obtained only when secondary networks are considered in addition to the primary feeder.

So, it is essential for utilities to have with them, secondary network models available for

analysis. Hosting capacity obtained for large PV on the MV network is not a good estimate

of the hosting capacity of small PV.

Chapter 3

Customer Identification for Demand

Response

3.1 Demand Response Strategy for Peak Period Load

Reduction

Now that the PV hosting capacity for a network is obtained, the other aspect of modern

grid edge is addressed in this chapter. With increasing load, high Peak demand is one issue

that is faced by utilities today. However, due to technological advancements and changes

taking place at the edge of the grid like deployment of DERs, battery energy storage systems,

and electric vehicles, utilities have the capability to make use of these to introduce demand

response programs for tackling this issue.

The impact of the installation of DER at the grid edge is analysed here to see how this

capability, in conjunction with other technology like BESS can help improve the operation

and performance of the system. Installation of Battery Energy Storage Systems (BESS)

is one possible demand response strategy for peak period load reduction. When batteries,

along with PV are installed at the customer premises, it can help reduce peak period demand

by increasing self consumption of PV.

In the previous chapter, a comparative study of PV hosting capacities was presented to de-

24

3.1. Demand Response Strategy for Peak Period Load Reduction 25

termine an accurate PV hosting capacity approach for small PV deployment. Based on the

conclusion, the hosting capacity for a circuit with secondary models was obtained and the

highest possible PV penetration level without violations was identified. The strict penetra-

tion limit was obtained to be 74.4% beyond which overvoltage violations were encountered.

At this hosting capacity obtained, PV placements were done on the network and the voltage

and loading profile for all distribution transformers for that case of placement is shown in

Fig 3.1a and 3.1b.

(a) Comparison of Maximum voltage acrossall transformers on a 120V scale

(b) Comparison of distribution transformerloading

Figure 3.1: Comparison of voltage and loading parameters at the distribution transformersfor networks with and without PV

Peak PV generation is at 1pm and as can be seen in Fig 3.1b, there are reverse flows on

many of the distribution transformers. Also, observing the voltage profile, it can be seen

that the voltage is higher on the network with PV installed. The load and voltage profiles at

the customer ends are also shown in figures 3.2b and 3.2a. These figures show a comparison

of customer voltages and kW loading on the network before and after PV is installed.

As can be seen in Fig 3.2b, there are flows back into the grid from many of the loads with

installed PV. From Fig 3.2a, it can be observed that the voltages at the customers are

higher when PV is installed on them. So, with PV and BESS installed, the battery charging

schedule can be such that it can be charged during peak PV generation during the day. This

26 Chapter 3. Customer Identification for Demand Response

(a) Comparison of Voltage across all cus-tomers on a 120V scale

(b) Comparison of net loading at the cus-tomer ends

Figure 3.2: Comparison of voltage and loading parameters at the customer end for networkswith and without PV @ 1pm

stored energy can then be utilized at the time of peak demand, hence leading to reduced

stress on the existing infrastructure. So, this is one possible strategy for reducing the peak

period demand.

Out of all the customers in the utility, if a small set of customers can be identified such that

by targeting them for participation in demand response programs, then the utility can achieve

significant peak shaving. The next section discusses a computationally efficient strategy to

identify this small group of customers by determining certain attributes from their AMI data.

Note: The following sections of this chapter are reproduced from Reference [22].

3.2 Description of Dataset

The dataset used has real electricity consumption data from 4991 Irish homes and businesses,

recorded as a part of the electricity Customer Behavior Trial (CBT) project undertaken

by CER during 2009 and 2010. The participating residential and business customers had

3.2. Description of Dataset 27

smart meters installed in their premises and their half-hourly electricity consumption was

recorded. The resulting dataset has been accessed via the Irish Social Science Data Archive

- www.ucd.ie/issda.[23] These results are made available in an anonymous format with no

specific information of location. Information from July 14, 2009 to December 31, 2010 is

available, but for this work, only meter readings from 2010 have been used. Moreover, the

available metadata have not been considered in the scope of the present work.

3.2.1 Data Cleaning

The first step in this analysis was identifying meters with missing and spurious data. Missing

readings were observed in 1286 meters (about 26% of the population). In addition to this,

42 meters were suspected to be corrupt as more than 30% of their readings had zero values.

These meters were also eliminated from the present analysis. Data for meters with corrupt

or missing data could have been estimated using historical information, but was not included

in the scope of present work. Consumption data for 9 bank holidays in Ireland were also

removed from analysis to ensure that the clustering focused on consumer behavior on typical

days. Hence, the analysis was performed on 3554 meters for 356 days.

Jan March May Jul Sep NovMonth (2010)

250

300

350

400

450

500

Cons

umpt

ion

(kW

h)

Avg. half-hourly consumption-weekdaysAvg. half-hourly consumption-weekends

(a) Average half hourly demands in 2010


2000

2500

3000

3500

4000

4500

5000

5500

Cons

umpt

ion

(kW

h)

Max. half-hourly consumption-weekdaysMax. half-hourly consumption-weekends

(b) Maximum half hourly demands in 2010

Figure 3.3: Analysis of aggregate consumption for the whole year


12:00 AM 5:00 AM 10:00 AM 3:00 PM 8:00 PMTime of day

0

1000

2000

3000

4000

5000

6000Co

nsum

ptio

n (k

Wh)

Summer weekendsSummer weekdays

(a) Daily load profiles in summer of 2010

12:00 AM 5:00 AM 10:00 AM 3:00 PM 8:00 PMTime of day

0

1000

2000

3000

4000

5000

6000

Cons

umpt

ion

(kW

h)

Winter weekendsWinter weekdays

(b) Daily load profiles in winter of 2010

Figure 3.4: Seasonal exploration of daily load profiles in 2010

3.2.2 Data Exploration

Analyzing the aggregate daily profiles revealed some interesting trends about consumer be-

havior in Ireland. Figures 3.3a and 3.3b show variation in daily aggregate average and

maximum half-hourly demands throughout year 2010. It is interesting to note that the sum-

mer average and maximum demands are lower than their winter counterparts. This can be

explained by the fact that summers (May - September) in Ireland enjoy a pleasant weather

(mean daily temperatures range from 10.9 to 15.7 degrees Celsius) while winters (January

- April, October - December) are colder (mean daily temperatures range from 5.1 to 10.3

degrees Celsius) [24].

Table 3.1: Population distribution with respect to average daily demands

Percentile Average daily demand (kW)25 15.633750 23.498775 33.5844

Variations in load profiles with respect to different seasons as well as weekdays and weekends

were investigated. For every half hour interval in a day, the total energy consumption was

expressed as the sum of individual consumer meter readings. Daily aggregate load profiles

3.2. Description of Dataset 29

Figure 3.5: Daily load profiles in 2010

were constructed for each day in 2010 from these values. It was found that aggregate weekday

and weekend load profile shapes mostly agree with each other, although the magnitude is

lower for weekends (figures 3.4a-3.4b).

Seasonal variations were explored by dividing the year into four seasons- spring, summer,

autumn and winter; but spring and autumn displayed a mixture of both summer and winter

load patterns. Hence, the load profiles were demarcated by two broad seasons- summer

and winter using climate data from Ireland [24]. Figure 3.5 shows the daily aggregate load

profiles for every day in 2010. It is evident that seasonal variation in behavior is quite well

pronounced. For instance, it is apparent that in the two seasons, the peak period, or time at

which maximum consumption takes place, do not coincide. Thus, the seasonal peak period

variation was accounted for in the proposed clustering approach.

Figure 3.6 shows the distribution of average daily demands of customers in 2010, where

the black dotted lines indicate the 25th, 50th and 75th percentiles. It can be seen that the


Figure 3.6: Average daily consumption of customers in 2010

consumer population distribution is skewed towards lower consumption values. Table 3.1

shows the population distribution of customers on the basis of their average daily consump-

tion. Since the number of customers with significantly high average daily consumption is

low, utilities can explore targeting individual high consumption customers on a case-by-case

basis as well. But, for the purpose of present analysis, individual contractual agreements

were not taken into account.

3.3 Methodology

3.3.1 K-means Clustering

K-means [25] is one of the simplest and most popular partitional or non-hierarchical clus-

tering approaches that can deal with large datasets. Given a set of data points X and an

integer number k such that k is lesser than the total number of objects in X, k-means seeks

to iteratively divide X into k clusters that minimizes the intra-cluster Sum of Squared Er-

rors (SSE). At each iteration, every cluster is expressed by a centroid and each point in X

is assigned to that cluster whose centroid is closest to it. The next step is to recompute

3.3. Methodology 31

the cluster centroids. A cluster centroid is simply the average of all data points within the

cluster. The iterative process continues until the specified maximum number of iterations is

reached or the SSE value falls below a predetermined threshold value. The distance measure

used is usually the euclidean distance, and hence the k-means algorithm is best applicable for

datasets with continuous or ordered categorical attributes. However, extensions for k-means

using Manhattan distance for categorical attributes (not necessarily ordered) have also been

proposed in literature [26].

Although there are a few limitations associated with the algorithm (for instance, the number

of clusters has to be specified at the beginning and output depends on initialization), k-means

enjoys popularity due to its intuitiveness and simplicity.

3.3.2 Attribute Selection

In clustering, attributes or features are the co-ordinates by which a data point is described.

Attribute selection is a critical part of the clustering process. The number of attributes

selected should be optimal, high enough to capture maximal variability in the dataset and

low enough to be computationally inexpensive. This work aims to identify a small group of

customers that can be targeted by utilities to participate in demand response programs for

achieving significant peak shaving. The idea is that if a small number of customers who have

a high energy consumption during peak hours and low consumption during off-peak hours

can be incentivized to shift a percentage of their peak hour load, the aggregate peak load

can be reduced as well. The smaller the number of customers to be targeted, the better can

be the incentive program offered by utilities. Smaller groups of participants are also easier

to monitor for further analysis. Hence, the selected attributes should reflect both the load

profile shape and average annual consumption magnitude of customers.


Table 3.2: Time block definition

Time Period Summer WinterDay 08:00 - 15:59 hours 08:00 - 16:59 hours

Peak period 16:00 - 17:59 hours 17:00 - 18:59 hoursEvening 18:00 - 22:59 hours 19:00 - 22:59 hours

Overnight 23:00 - 07:59 hours 23:00 - 07:59 hours

Since we are using time-series data over a year, each customer is represented by 48 × 356

values, and an efficient strategy is needed to reduce the number of attributes used. Five

attributes are selected and they are listed below. Since the peak periods for summer and

winter seasons do not coincide (figure 3.5), the time block definitions used are different for

the two seasons. The final attribute computed is the weighted average of the summer and

winter values.

Attributes 1-4:

To capture the shape of load profile for each customer, their daily consumption is first 0-1

normalized. Next, 24 hours of a day are divided into four time blocks (day, peak period,

evening and overnight) and the seasonal mean consumption across each time block is calcu-

lated for every customer. The time periods in the Time of Use (ToU) tariff structure used

during the Irish smart meter trial [27] was chosen as a guide for defining the time blocks in

this study. The period definitions for summer and winter are shown in table 3.2. The final

values of attributes 1-4 calculated is the weighted average of the summer and winter values.

All attributes are 0-1 normalized.

In [19], the authors have also used time block definitions to extract attributes and have

assigned a seasonal score to each customer based on their seasonal consumption. However,

seasonal variation in peak hours is not considered.

3.4. Results 33

Attribute 5:

Attribute 5 is the mean daily consumption of each customer for year 2010 and it is 0-1

normalized.

3.4 Results

3.4.1 Optimum Number of Clusters

One major limitation of the k-means algorithm is that the number of clusters has to be spec-

ified at the beginning of the clustering process. Without a good idea of how the distribution

of data points looks like, determining the number of clusters required is a difficult task.

The number of clusters should be high enough to capture the difference in behavior within

consumer groups, but having too many clusters would necessitate dealing with too much

data. In this paper, the elbow criterion has been used to determine the optimal number of

clusters. The number of clusters was varied from 1 to 50 and Sum of Squared Errors (SSE)

was calculated for each of the scenarios. SSE is a cluster validation index that measures the

intra-cluster distance among points. Lower the SSE, better is the clustering. From figure,

3.7a, the optimal number of clusters chosen is 10. From figure 3.7b, it is evident that the

total population is spread among all the clusters and no one cluster dominates or is sparsely

populated.

3.4.2 Ideal Cluster for Demand Response

The idea here is to identify customers who can be given incentives to shift some of their

peak load consumption to off-peak periods. If the number of customers targeted can be kept


0 10 20 30 40 50Number of clusters

100

200

300

400

SSE

(a) SSE

0 1 2 3 4 5 6 7 8 9Cluster Number

0

100

200

300

400

500

600

Coun

t

(b) Distribution of customers among differ-ent clusters

Figure 3.7: Clustering Results

small, then utilities can offer larger incentives, thereby attracting participation in demand

response programs. Hence in this paper, the following points were considered when selecting

a cluster for peak-shaving-.

• Shape of load profile: High peak-hour consumption and low off-peak consumption.

• Average daily consumption: High average daily consumption is desired.

• Number of customers in cluster: This number must be low.

Figures 3.8 and 3.9 show the trend of normalized consumption during different time peri-

ods and relative normalized average daily consumption of the cluster centroids respectively.

Based on the criteria outlined above, cluster 8 was chosen as an ideal cluster for participating

in a demand-response program. Cluster 8 has 291 customers or 8.67% of the total consumer

population. Based solely on the shape of their load profiles, clusters 0 and 6 are probable

candidates for peak shaving as well, but were not chosen because they comprised mostly of

low-consumption customers. An utility can also choose to target these clusters via a tiered

incentive program.

3.4. Results 35

Day Peak Evening Overnight0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.80123456789

Figure 3.8: Normalized consumption at different time periods for cluster centroids

0 2 4 6 8Cluster number

0.02

0.04

0.06

0.08

0.10

Attri

bute

5

Figure 3.9: Normalized average daily consumption of clusters

In this work, the peak loads for customers in cluster 8 were reduced by 20%. Figure 3.10

shows the overall percentage peak reduction achieved as a result throughout 2010. The black

dotted line indicates the median value of percentage reduction achieved is 2.060654. It can

be seen that by targeting just about 8% consumers, a significant reduction in peak load can

be achieved. It is also interesting to note that higher percentage peak reduction is achieved

in the warmer months.



1.6

1.8

2.0

2.2

2.4

2.6

Perc

enta

ge p

eak

redu

ctio

n

Figure 3.10: Percentage peak load reduction achieved

3.5 Conclusion

This paper describes a computationally inexpensive generic clustering strategy that can be

used by utilities to recruit consumers for participation in demand response programs. The

approach used places emphasis on both the shape of load profiles and the magnitude of

energy used by individual customers. It is demonstrated that the clustering strategy is

able to identify small groups of customers, whose peak hour usage, if curtailed can lead to

significant reduction in aggregate peak hour demands. The methodology can be applied to

different distribution systems with minor modifications. We do not claim that the approach

described herein is the best clustering methodology for grouping electricity consumers, but

its flexibility, low computation burden and simplicity makes this a good starting point of

analysis for utilities planning to roll out demand-response programs.

Chapter 4

Summary and Future Work

As technological advancements trickle into the distribution network, a lot of effort has been

directed towards strategies and approaches to ensure continued smooth operation of the

grid. As PV penetration of small PV increases, a comparative study of hosting capacities

for networks with varying levels of circuit information available was performed to evaluate

if the system could ensure continued smooth operation by deployment of that level of PV

penetration. On analysis of the various cases, it was observed that hosting capacity obtained

for the case where LV loads were aggregated at the secondary of the distribution transformer,

was not a conservative value for PV penetration on the secondary network, and would lead

to overvoltage violations. So, possessing secondary network information is really essential

for utilities. With secondary network information available, one case of hosting capacity was

when secondary networks were analysed independently. In this case, with a 1p.u. source

connected for each secondary, since the impact of the primary feeder was not considered,

a very high hosting capacity was obtained. When this secondary PV deployment was con-

sidered in conjunction with the primary feeder, overvoltage violations were observed. So, it

was observed that for obtaining a good estimate of hosting capacity for small PV, secondary

networks were to be considered and connected to the primary feeder. In the future, work to

identify representative secondary networks can be done. By identifying a set of representa-

tive LV networks, various analyses, like PV hosting capacity and voltage control schemes,

can be implemented on this representative system for wholistic results.

37

38 Chapter 4. Summary and Future Work

As PV deployment on LV network increases, utilities find themselves in possession of more

strategies for implementation of demand response programs to achieve peak shaving. So,

in this work, a scheme for customer selection was also developed so that targeting those

customers for participation in demand response would lead to significant peak shaving. The

method aimed to represent each customer by their load shape and magnitude of consumption,

and using these attributes, the optimum set of customers were identified for participation in

demand response programs.

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