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Impact of High Solar Photovoltaic Penetration on
Power System Operations
By
Obaid Ur Rehman
CIIT/SP16-PEE-003/ISB
PhD Thesis
In
Electrical Engineering
COMSATS University Islamabad
Islamabad Campus - Pakistan
Spring, 2022
COMSATS University Islamabad
Impact of High Solar Photovoltaic Penetration on
Power System Operations
A Thesis Presented to
COMSATS University Islamabad
In partial fulfillment
of the requirement for the degree of
PhD (Electrical Engineering)
By
Obaid Ur Rehman
CIIT/SP16-PEE-003/ISB
Spring, 2022
ii
Impact of High Solar Photovoltaic Penetration on
Power System Operations
A Post Graduate Thesis submitted to the Department of Electrical and Computer
Engineering as partial fulfillment of the requirement for the award of Degree of
Ph.D in Electrical Engineering
Name Registration No.
Obaid Ur Rehman CIIT/SP16-PEE-003/ISB
Supervisor
Prof. Dr. Shahid Ahmed Khan
Professor,
Department of Electrical and Computer Engineering,
COMSATS University Islamabad.
Co-Supervisor
Prof. Dr. Nadeem Javaid
Professor,
Department of Computer Science,
COMSATS University Islamabad.
iii
Certificate of Approval
This is to certify that the research work presented in this thesis, entitled “Impact of
High Solar Photovoltaic Penetration on Power System Operations” was conducted by
Mr. Obaid Ur Rehman, CIIT/SP16-PEE-003/ISB, under the supervision of Prof. Dr.
Shahid Ahmed Khan and co-supervision of Prof. Dr. Nadeem Javaid. No part of this
thesis has been submitted anywhere else for any other degree. This thesis is submit-
ted to the Department of Electrical and Computer Engineering, COMSATS University
Islamabad in the partial fulfillment of the requirement for the degree of Doctor of Phi-
losophy in the field of Electrical Engineering.
Student Name: Obaid Ur Rehman Signature:
Examinations Committee:
Prof. Dr. Sohail Aftab Qureshi Prof. Dr. Mohammad Ahmad Choudhry
Ex-Professor, Ex-Professor,
Faculty of Electrical Engineering, Department of Electrical Engineering,
UET, Lahore UET, Taxila
Prof. Dr. Shahid Ahmed Khan Prof. Dr. Shurjeel Wyne
Supervisor, HoD,
Department of Electrical and Department of Electrical and
Computer Engineering, Computer Engineering,
CUI, Islamabad CUI, Islamabad
Prof. Dr. Laiq Khan Prof. Dr. Shahzad A. Malik
Chairperson, Dean,
Department of Electrical and Faculty of Engineering,
Computer Engineering, CUI
CUI
iv
Author’s Declaration
I Obaid Ur Rehman, CIIT/SP16-PEE-003/ISB, hereby state that my PhD thesis titled
“Impact of High Solar Photovoltaic Penetration on Power System Operations” is my
own work and has not been submitted previously by me for taking any degree from this
University i.e. COMSATS University Islamabad or anywhere else in the country/world.
At any time if my statement is found to be incorrect even after I graduate the University
has the right to withdraw my PhD degree.
Date:
Obaid Ur Rehman
CIIT/SP16-PEE-003/ISB
v
Plagiarism Undertaking
I solemnly declare that research work presented in the thesis titled “Impact of High So-
lar Photovoltaic Penetration on Power System Operations” is solely my research work
with no significant contribution from any other person. Small contribution/help wher-
ever taken has been duly acknowledged and that complete thesis has been written by
me.
I understand the zero tolerance policy of HEC and COMSATS University Islamabad
towards plagiarism. Therefore, I as an author of the above titled thesis declare that no
portion of my thesis has been plagiarized and any material used as reference is properly
referred/cited.
I undertake if I am found guilty of any formal plagiarism in the above titled thesis even
after award of PhD Degree, the University reserves the right to withdraw/revoke my
PhD degree and that HEC and the university has the right to publish my name on the
HEC/university website on which names of students are placed who submitted plagia-
rized thesis.
Date:
Obaid Ur Rehman
CIIT/SP16-PEE-003/ISB
vi
Certificate
It is certified that Obaid Ur Rehman, CIIT/SP16-PEE-003/ISB has carried out all the
work related to this thesis under my supervision at the Department of Electrical and
Computer Engineering, COMSATS University Islamabad and the work fulfills the re-
quirement for the award of PhD degree.
Date:
Supervisor:
Prof. Dr. Shahid Ahmed Khan
Professor
Head of Department:
Prof. Dr. Shurjeel Wyne
Department of Electrical and
Computer Engineering
vii
DEDICATION
I would like to dedicate my thesis to my wife : “Sunbal
siddique”, and my parents.
viii
ACKNOWLEDGMENTS
First and foremost, I would like to praise Allah the Almighty, the Most Gracious, and
the Most Merciful for His blessing given to me during my study. I also express all my
respects to the greatest reformer, Prophet Mohammad, Peace Be Upon Him, the most
perfect and elevated among those ever born on the surface of the Earth, for enlightening
our conscience, and whose love is the true accomplishment of my life.
I am very obliged to my mentors and research advisors, particularly Prof. Dr. Shahid
Ahmed Khan (Supervisor) and Prof. Dr. Nadeem Javaid (Co-Supervisor), for their
invaluable advice, strong encouragement, and generous support towards my research
and studies. I admire Prof. Dr. Shahid Ahmed Khan for his broad knowledge, cre-
ative thinking, and deep insight into the subjects of power transmission and distribution
systems. I feel so proud and fortunate to have had him as my research and academic
supervisor.
I would like to thank my committee members: Prof. Dr. Shahzad A. Malik, Dr. Qadeer
Ul Hasan, and Dr. Junaid Ikram, for their time, insightful suggestions, and serving in
my dissertation committee. I also want to express my gratitude to Prof. Dr. Liaq Khan
for his valuable suggestions to improve the quality of Ph.D dissertation .
Lastly, I acknowledge the intimate support of my wife, who kept me motivated and
assured me every moral and financial support throughout my doctoral studies. I have
immense regard for my parents, who always prayed for my success at all times. It would
be impossible to accomplish my Ph.D study without them being supporting me.
Obaid Ur Rehman
CIIT/SP16-PEE-003/ISB
ix
ABSTRACT
Impact of High Solar Photovoltaic Penetration on Power System
Operations
Distributed photovoltaic (PV) systems are growing rapidly owing to considerable re-
duction in PV panel prices, renewable energy supporting policies, and technological
advancements in inverter and controller designs. The variability and non-dispatchability
of PV energy generation affect the reliability and stability of the electricity grid, lead-
ing to PV energy generation curtailment and its integration to power system. High
penetration of PV systems in an electricity distribution grid causes various issues re-
garding voltage fluctuation, violation and unbalance. Installations of PV systems at
transmission/sub-transmission level affect the power system frequency more than nodal
voltages. Frequent changes in PV energy generation caused by passing clouds badly
affect the grid frequency. As, penetration of PV systems in the electricity grids is accel-
erating, therefore, flexibility options to tackle the challenges of PV energy generation
needs to be ensured/exercised.
Amongst the available flexibility options, demand side management (DSM) seems to
have considerable potential to cope with uncertain PV energy generation in an effective
manner. This strategy refers to modulate the energy consumption patterns of flexible
loads subject to time varying electricity pricing signals. DSM can provide regulatory
services to the grid through the application of demand response (DR). DR enabled smart
buildings have a great potential to provide ancillary services for accommodating PV
energy generation in a power system. Grid service provision through smart buildings
must give explicit consideration to occupants’ preferences. A paradigm that ensures
the integrated operation of buildings and grid is named as building-to-grid (B2G) sys-
tem. In this research work, B2G frameworks are further classified as: (1) building-
to-distribution-network (B2DN) for distribution system operation control (2) building-
to-transmission-network (B2TN) for transmission system operation control. Model pre-
dictive control (MPC) strategy embeds real-time disturbances i.e., PV energy generation
and/or weather variations while performing optimization. This feature makes the MPC
best fit to optimize the integrated operations of the buildings and a grid. Therefore,
MPC based B2DN and B2TN are developed for distribution and transmission networks
x
operations control.
Existing B2DNs consider only best case PV generation scenario, ignoring the effects
of PV generation variations. Therefore, to compensate for PV generation variations,
the MPC has to be operated for real-time supply-demand balancing which requires a
large potential of load flexibility and accurate prediction of PV energy generation. PV
curtailment is believed to be an instant solution to rectify the variations in PV energy
generation, however, resulting in wastage of PV energy generation resources. This
research work focuses on an integrated solution of DR and PV energy generation cur-
tailment in a B2DN paradigm. In this context, a two-stage B2DN optimization frame-
work is developed. In first stage, PV energy generation is shaped through an on-site
PV energy generation utilization parameter ‘g’ to solve intermittency issue. In second
stage, MPC based optimization is performed that uses the synergy of energy storage
system (ESS) and heating ventilation and air conditioning (HVAC) systems to extend
the demand flexibility to provide voltage regulatory service. These two operational
strategies, (1) PV self-consumption curtailment without DR (first stage operation only)
and, (2) PV self-consumption curtailment with DR (two-stage operation) are compared
in terms of B2DN’s performance parameters including building’s electricity cost, PV-
self consumption ratio (PV-SCR), grid’s load factor (LF), ramp rate control, and voltage
regulation. Moreover, different PV energy generation scenarios are analyzed showing
the effect of PV energy generation variations on B2DN performance parameters.
Regarding transmission network operation control, existing B2TNs lack the integration
of PV energy generation resources in electricity grids. In this context, this research
work develops a decoupled B2TN integration mechanism based on frequency based
real-time pricing (RTP) signals that facilitates the application of DR service and PV sys-
tems integration for the provision of frequency regulation. Separate MPC controllers are
developed to control the dynamics of building and transmission networks. Building’s
dynamics are modeled using grey box model (Resistor-capacitor network). Building’s
MPC embeds real-time pricing (RTP) and real-time disturbances to the HVAC system
such as outdoor temperature, solar irradiance and internal heat gains while performing
optimization. The behaviour of power system frequency is described by swing equa-
tion at transmission system level. Grid’s MPC embeds load and PV energy generation
variations in the optimization process. The interaction between buildings’ and grid’s
xi
MPCs is formulated through frequency based RTP. Three frequency based RTP signal
generators namely;linear, hyperbolic tangent and inverse hyperbolic tangent are used
to map frequency deviations to RTP signals. To enable DR service, a price responsive
MPC for optimal scheduling of HVAC load is devised where reference temperature set
point is dynamically adjusted with respect to RTP. The performance of the proposed
B2TN is investigated in terms of frequency deviations suppression in clear and cloud
covered sky conditions.
xii
Publications
1 Rehman, Obaid Ur, Shahid A. Khan, and Nadeem Javaid. “Impact of photo-
voltaic self-consumption curtailment on building-to-grid operations.” Interna-
tional Journal of Electrical Power and Energy Systems 124 (2021): 106374.
[IF=4.630].
2 Rehman, Obaid Ur, Shahid A. Khan, and Nadeem Javaid. “Decoupled building-
to-transmission-network for frequency support in PV systems dominated grid.”
Renewable Energy 178 (2021): 930-945. [IF= 8.001].
xiii
TABLE OF CONTENTS
1 Introduction ................................................................................................ 1
1.1 Chapter Summary ................................................................................. 2
1.2 PV deployment growth......................................................................... 2
1.2.1 Growth over time...................................................................... 2
1.2.2 Country-wise growth ................................................................ 4
1.2.3 Growth over locations .............................................................. 5
1.3 Impacts of solar PV generation on power system operations ............ 6
1.3.1 Impacts of small/medium solar PV systems ........................... 7
1.3.2 Impacts of large solar PV systems........................................... 9
1.4 Grid supportive technologies for PV integration ................................ 12
1.4.1 Voltage regulation technologies............................................... 12
1.4.2 Frequency regulation technologies .......................................... 15
1.5 Demand side management ................................................................... 17
1.5.1 A brief overview of DR: An ancillary service to the system
operators (SOs) .......................................................................... 18
1.5.2 DSM and DR ............................................................................ 18
1.6 Research Contributions ........................................................................ 20
1.6.1 Research problem statement-1 ................................................. 21
1.6.2 Research problem statement-2 ................................................. 22
1.6.3 List of publications ................................................................... 23
1.7 Thesis Organization.............................................................................. 24
2 Literature Review ....................................................................................... 25
2.1 Chapter Summary ................................................................................. 26
2.2 DR-enabled buildings as a gird resource............................................. 26
2.2.1 Research studies........................................................................ 27
2.2.2 Field studies .............................................................................. 34
2.3 Conclusion of the chapter..................................................................... 40
3 Model Predictive Building Control Framework .................................... 41
3.1 Chapter Summary ................................................................................. 42
3.2 Model predictive building framework ................................................. 42
xiv
3.2.1 MPC optimizer.......................................................................... 43
3.2.2 Building dynamics model ........................................................ 46
3.3 Classification of MPC problem............................................................ 48
3.4 MPC design tools.................................................................................. 49
3.5 MPC solvers.......................................................................................... 49
3.6 MPC control configuration................................................................... 50
3.7 Conclusion of the chapter..................................................................... 51
4 Building-to-distribution-network for distribution system operation
control ........................................................................................................... 53
4.1 Chapter Summary ................................................................................. 54
4.2 B2DN framework ................................................................................. 56
4.3 PV generation and curtailment ............................................................ 58
4.3.1 PV generation ........................................................................... 58
4.3.2 PV curtailment .......................................................................... 59
4.3.3 PV generation utilization model .............................................. 64
4.4 Building load modeling........................................................................ 64
4.4.1 ESS modeling ........................................................................... 65
4.4.2 BDC modeling .......................................................................... 65
4.5 ESS control strategy ............................................................................. 67
4.5.1 Critical hours operation ............................................................ 67
4.5.2 Non critical hours operation..................................................... 69
4.5.3 Actual BDC calculation using Rain Flow (RF) algorithm ..... 69
4.6 Building predictive controller .............................................................. 70
4.7 Simulations and results......................................................................... 72
4.7.1 Simulation setup and parameters ............................................. 72
4.7.2 Simulation results ..................................................................... 74
4.8 Conclusion of the chapter..................................................................... 82
5 Building-to-transmission-network for transmission system opera-
tion control ................................................................................................... 86
5.1 Chapter Summary ................................................................................. 87
5.2 Brief description of B2TN interaction mechanism............................. 88
xv
5.3 B2TN modeling .................................................................................... 90
5.3.1 Frequency based RTP signal generators.................................. 90
5.3.2 Grid models............................................................................... 93
5.3.3 PV generation model ................................................................ 97
5.4 Model predictive controllers (MPCs) .................................................. 97
5.4.1 Building MPC ........................................................................... 97
5.4.2 Grid MPC .................................................................................. 99
5.5 Simulation test bed ............................................................................... 100
5.6 Simulation results ................................................................................. 102
5.6.1 Performance comparison of bang-bang and ordinary MPC...103
5.6.2 Impact of PV energy generation patterns on B2TN opera-
tions ............................................................................................105
5.7 Conclusion of the chapter.....................................................................115
6 Conclusion and Future Work ...................................................................116
6.1 Conclusion ............................................................................................117
6.2 Future work...........................................................................................119
6.2.1 B2DN perspective..................................................................... 119
6.2.2 B2TN perspective .....................................................................120
6.2.3 MPC perspective.......................................................................120
7 References ....................................................................................................122
xvi
LIST OF FIGURES
1.1 Net energy generation addition from 2018-2020 by major technologies [1]... 3
1.2 Percentage addition of net energy generation from 2018-2020 by major
technologies .................................................................................................. 3
1.3 Top five nations in PV energy generation 2018-2020 [1–3] ........................... 4
1.4 Top five PV market shares 2018-2020............................................................ 4
1.5 Global installed rooftop and utility-scale PV energy generation from 2018-
2020 [1–3].................................................................................................... 5
1.6 Impacts of PV systems on the electricity grid operations................................ 6
1.7 Expected duck curve for Florida Reliability Coordinating Council (FRCC)
with steep ramp (top) and peak load (bottom) [4].......................................... 11
1.8 Grid supportive technologies for PV system integration................................. 12
3.1 Model predictive building control framework ................................................ 44
3.2 3R-2C thermal network. ................................................................................ 47
4.1 Proposed B2DN framework........................................................................... 57
4.2 PV generation scenarios ................................................................................ 59
4.3 Impact of ‘g’ on Ot........................................................................................ 62
4.4 Load curve to duck curve transformation as a function of ‘g’. ........................ 63
4.5 Flowchart of BEMS operation for battery cyclic dispatch............................... 68
4.6 Time series plots of ToU pricing scheme and building load profile. ................ 73
4.7 One line diagram of IEEE-33 node radial distribution feeder ......................... 74
4.8 Distribution of PV energy among different system components. .................... 75
4.9 Power from different energy resources to the building.................................... 77
4.10 Zone temperature and HVAC power consumption profile............................... 79
4.11 SoC profile of battery in all PV power generation scenarios ........................... 80
4.12 Voltage profile of distribution nodes connected with buildings in multiple
PV energy generation scenarios. .................................................................... 81
5.1 Schematic of decoupled B2TN optimization framework. ............................... 89
5.2 Conceptual representation of B2TN system including TSO, PV systems,
DSO and buildings. ....................................................................................... 90
xvii
5.3 Frequency based RTP functions..................................................................... 91
5.4 Dynamic price-to-temperature set point model............................................... 96
5.5 Working mechanism of proposed B2TN optimization framework. ................. 100
5.6 One line diagram of IEEE 30-bus system.......................................................101
5.7 Generation profiles of under-study PV systems. .............................................102
5.8 Building states: performance comparison of bang-bang controller and or-
dinary MPC...................................................................................................104
5.9 Grid states: performance comparison of bang-bang controller and ordinary
MPC..............................................................................................................106
5.10 Clear sky impact of PV energy generation on B2TN operations .....................107
5.11 Performance of tanh function based price-to-temperature set point model ...... 110
5.12 Performance of linear function based price-to-temperature set point model....111
5.13 Performance of tanh−1function based price-to-temperature set point model . 112
5.14 Impact of under-study RTP models on RTP and frequency deviations............ 113
xviii
LIST OF TABLES
2.1 Overview of research studies on B2DNs........................................................ 32
2.2 Overview of research studies on B2TNs......................................................... 33
2.3 Worldwide leading projects on DR for DSOs and TSO. ................................. 37
2.4 Technology related information of worldwide leading projects on DR ........... 38
3.1 Nomenclature ................................................................................................ 43
3.2 Overview of the soft-wares for formulating and solving MPC problems [5]. . 50
3.3 Overview of the optimization solvers for MPC problems [5]......................... 51
4.1 Nomenclature ................................................................................................ 55
4.2 Probability matrix of PV generation scenarios ............................................... 60
4.3 Battery cyclic lifetime ................................................................................... 70
4.4 Performance comparison of optimized and un-optimized B2DNs based on
different values of g....................................................................................... 83
4.5 Percentage improvement in performance parameters of the optimized B2DN
with reference to un-optimized B2DN ........................................................... 84
5.1 Nomenclature ................................................................................................ 87
5.2 Generation limits and cost coefficients of standard IEEE-30 bus system ........102
5.3 Clear sky and cloud cover impacts of PV power generation on B2TN ............114
xix
LIST OF ABBREVIATIONS
ANN Artificial neural network
ANSI American National Standards Institute
AR Auto regressive
ARMA Auto regressive with moving average
ARMAX Auto regressive with moving average and exogenous input
ARX Auto regressive with exogenous inputs
ASHRE American society of heating refrigerating and air condi-
tioning engineers
BDC Battery degradation cost
BEMS Building energy management system
BESS Building energy storage system
B2G Building-to-grid
B2DN Building-to-distribution-network
B2TN Building-to-transmission-network
CPP Critical Peak Pricing
D-STATCOM Distributed static synchronous compensates
DERs Distributed energy resources
DLC Direct load control
DoD Depth of discharge
DSM Demand side management
DR Demand response
DSO Distribution system operator
DVR Dynamic voltage regulator
ESS Energy Storage System
FACTS Flexible AC transmission system
FRCC Florida reliability and council
HVAC Heating Ventilation and Air Conditioning System
IDLC Indirect load control
xx
LP Linear Programming
LV Low volatge
LMPC Linear model predictive controller
MILP Mixed Integer Linear Programming
MINLP mixed-integer nonlinear Programming
MIQP Mixed-integer quadratic programming
MPC Model Predictive Controller
NLP Non-linear programming
MV Medium voltage
OLTC On-load tap changer
OPF Optimal Power Flow
PCC Point of common coupling
PF Power factor
PV Photo-voltaic
QP Quadrature Programming
RC Resistor-capacitor
RERs Renewable energy resources
RF Rain-flow
RTP Real time pricing
RTT Real time trading
SoC State of charge
SOs System operators
SPC Synchronous power converter
SQP sequential quadratic programming
SST Solid-state transformer
SVC Static VAR compensator
THD Total harmonic distortion
ToU Time of use
TSO Transmission system operator
xxi
Chapter 1
Introduction
1
1.1 Chapter Summary
Ever increasing energy demand and depletion of fossil fuel resources forced the re-
searches and policy makers to explore alternate energy resources particularly photo
voltaic (PV) system. Although PV deployment is a clean and cost effective choice,
but, it is challenging to incorporate PV systems in a power network owing to its nature
of volatility and variability. Partial shading caused by moving clouds and temperature
variations are main factors that affect PV power production, causing frequent fluctua-
tions in its output power. Installation of PV in distributed manner adds more difficulty
in managing grid operations since distributed PV generation is usually not controlled
by system operators (SOs) as reported in literature. Therefore, SOs require flexibility
options to tackle the challenges of PV generation. Amongst the available flexibility
options, demand side management (DSM) seems to have considerable potential to cope
with uncertain PV production in an effective manner. This strategy refers to modulate
the energy consumption patterns of flexible loads upon different contingencies such as
PV generation volatility. DSM can provide regulatory services such as voltage and fre-
quency regulations to the grid through applications of demand response (DR); refer to
modify the energy consumption pattern of demand flexible loads subject to time varying
electricity pricing signals. To enable DR service, SOs have to modify their infrastruc-
ture from communication and control perspectives. The highlights of this chapter are:
• Growth of PV systems over time, country-wise and locations.
• Impacts of small, medium and large PV systems on power system operations.
• Grid supportive technologies for PV integration, their challenges and limitations.
• A brief overview of DSM and DR.
1.2 PV deployment growth
1.2.1 Growth over time
Worldwide installed solar PV generation increased over 770 GW by 2020. It is expected
that net global installed PV generation capacity will achieve following milestones in
next 3 years: 971 GW in 2021, 1.0 TW in 2022 and 1.2 TW in 2023 [1].
2
Solar PV is intensely installed power source with net generation of 138 GW in 2020. In
the years before (2018 and 2019), PV added 102 GW and 117 GW, respectively. These
figures are not only more than combined coal and gas power generation capacities,
it also observed approximately twice as much power installed as all other renewables
together (Figure 1.1). Besides these achievements, PV power generation share reached
to 39%of all newly added power capacities in 2020, followed by wind 32%and gas
17%depicted in Figure 1.2.
2018 2019 2020
Years
0
50
100
150
Installed capacity (GW)
7 5
18
21 15 21
50
18
1
46
30
59
49
61
114
102
117
138
Other RERS
Hydro
Coal
Gas
Wind
Solar
Figure 1.1: Net energy generation addition from 2018-2020 by major technologies [1].
2018
22%
20% 2% 9%
3%
44%
2019
25%
7%12% 6% 2%
48%
2020
32%
< 1% 17%6%
5%
39%
Wind Coal Gas Hydro Other RES Solar
Figure 1.2: Percentage addition of net energy generation from 2018-2020 by major
technologies
The levelised cost of electricity (LCOE) is considered one of the key factors in adoption
of generation technologies. In June 2020, the international renewable energy agency
(IREA) reported power generation cost comparisons of various power generation tech-
nologies from 2009 to 2019. According to IREA, the highest reduction (82%) in LCOE
3
is observed for utility scale solar PV over 2009-2019 [6]. It is believed that shares of
PV in the future power grids will be increased. Based on projections 2050, worldwide
PV market share will approach 25%of total generation.
1.2.2 Country-wise growth
Trends in PV system deployment are observed globally, however, leading countries in
PV system adoption include: China, United States, Germany, Japan and India. Figure
1.3 shows the country-wise statistics of installed PV energy generation from 2018-2020
where China dominated the world up to 175 GW, 205 GW and 253 GW net PV energy
generation by 2018, 2019 and 2020, respectively. 2024 projection shows that china will
achieve total PV capacity beyond 286 GW.
As shown in Figure 1.4, China leading the world with 35%PV market shares by 2020
which is 3%higher than 2019 when China’s contributed to 32%of global solar energy
generation. China was followed by United States with 14%PV market shares by 2020
and 12%PV shares in precious two years (2018 and 2019).
2018 2019 2020 2024 (Predicted)
Years
0
100
200
300
400
500
Installed PV capacity (GW)
175 205
253
486
62 76 96
179
56 63 71 95
46 50 55 79
27 42 46
112
China
United States
Japan
Germany
India
Figure 1.3: Top five nations in PV energy generation 2018-2020 [1–3]
2018
34%
11%5%
29%
12%
9%
2019
32%
10% 7%
31%
12%
8%
2020
35%
6% 3%
38%
14%
4%
China Japan India Rest of world USA Germany
Figure 1.4: Top five PV market shares 2018-2020
4
Other leading PV energy generation countries from 2018 to 2020 are as follows:
• Japan with 11%, 10%and 6%market shares, respectively.
• Germany with 9%, 8%and 4%market shares, respectively.
• India with 5%, 7%and 3%market shares, respectively.
In 2020, declining trends in percentage ratio of PV generation are observed in Japan,
India and Germany. However, when looking at installed PV capacity in rest of the
world, the picture looks very different. Global share of solar market reached to 38%in
2020, this percentage was 31%by 2019 and 29%by 2018, respectively. Looking at the
bigger picture, PV market share increased at a good pace in rest of the world revealing
that solar PV installation is a global phenomenon.
1.2.3 Growth over locations
Solar PV systems are mainly installed at building’s rooftops (including residential, com-
mercial and industrial) and utility-scale. Solar PV generation either at rooftop or utility
scale is not controlled by grid operators. Main characteristic of rooftop PV generation
is that it is geographically located at the electric load points. According to Global Solar
Market Insight report (2021-2025), in 2020, rooftop solar PV generation capacity was
61 GW, compared to 42 GW and 30 GW in previous two years, respectively (Figure
1.5).
2018 2019 2020 2025 (Predicted)
Years
0
50
100
150
Gobal installed
PV capacity (GW)
30 42
61
96
73 75 78
139
Rooftop solar
Utility-scale solar
Figure 1.5: Global installed rooftop and utility-scale PV energy generation from 2018-
2020 [1–3].
In 2020, net PV energy generation of utility-scale PV systems is observed 78 GW, com-
pared to 75 GW and 73 Gw in previous two years (2019 and 2018). 2025 projections
5
for both rooftop and utility-scale PV systems are 96 GW and 139 GW, respectively. The
growth rate of both rooftop and utility scale PV systems depends on various factors such
as government policies, business opportunities and technological developments [7].
1.3 Impacts of solar PV generation on power system operations
Growing penetration of PV systems in electric-power networks is adversely affecting
the grid reliability. The size and location of the PV system greatly impact the power
system operations at transmission and distribution levels [8]. IEEE standard 929–2000
classifies the PV systems into three distinct categories: (i) Small PV systems with instal-
lation capacity up to 10 kW, (2) intermediate PV systems with installation capacity be-
tween 10 to 500 kW, and (3) large PV systems with installation capacity above 500 kW.
PV systems lie in first two categories are installed at the distribution level, while the PV
systems belonging to third category are installed at the sub-transmission/transmission
system level. The impacts of small/medium and large PV systems on the electric-power
network are further classified in Figure 1.6.
Impacts of PV
systems
Impacts of small
and medium PV
systems
Impacts of Large
PV systems
Frequency stability
Rotor angle stability
Voltage
uctua
ons
Voltage magnitudes
(Over and under
voltages)
Voltage unbalance
Harmonics
Ramp Rate
Figure 1.6: Impacts of PV systems on the electricity grid operations.
6
1.3.1 Impacts of small/medium solar PV systems
Small and medium PV systems are usually installed at the load premises. More pre-
cisely, these are installed behind the meter, therefore invisible to the grid operators
and non-dispatchable. According to a report published by European network of trans-
mission system in 2012, almost 80%of PV systems are installed at low voltage (LV)
distribution network [9]. High penetration of small/medium PV systems on LV grids
causing various issues regarding voltage fluctuation, violation and unbalance [10].
Voltage fluctuation
The most visible and prominent effect of Voltage fluctuation is flicker, however, in
flickering the magnitude of voltage fluctuations remain in defined limits [11]. However,
voltage flicker appears in the form of fluctuating luminance that not only affects the
lights, but it may also damage the sensitive electronic devices operated on constant
voltage such as medical devices [12].
PV systems installed at distribution network may cause noticeable voltage flicker due
to cloud movements [13]. The severity of the voltage flicker varies in different studies.
For instance, authors found that flicker does not have potential to violate any power
quality standard [14]. Another study reported that 20%PV penetration at a single
location resulted in severe flicker and voltage limit violation. On the other hand, when
the PV systems are well distributed, the voltage violations are not even found at 50%
PV penetration level [15].
Voltage magnitude
In a Power system, voltage is regulated to keep it within operational limits. Accord-
ing to the American National Standards Institute (ANSI) C84.1, voltage magnitude at
distribution network must be within ±10%of its reference value [16]. High voltage
variations leading to frequent operation of voltage regulation devices resulting in addi-
tional step-voltage changes. Further, frequent operation of voltage regulation devices
shorten their life cycles and increase the maintenance requirements.
In normal distribution network operation, the direction of power flow is from medium
voltage (MV) to the LV networks. However, when PV generation exceeds the net de-
mand, the power flows from LV to MV networks [17]. This reverse power flow might be
7
a problem because power systems are designed to operate in unidirectional power flow.
Besides that, several challenges arise due to reverse power flow including overload-
ing of distribution feeders, excessive power losses and over-voltage along distribution
feeders [18].
Researchers reported that MV networks are often experienced over-voltage issue at 20%
PV penetration level. It is due to the lack of transmission system flexibility to deal with
power fluctuations caused by cloud movement. Similarly, under-voltage limit might
collapse when PV generation drops abruptly as a consequence of cloud movement [19].
Voltage Unbalance
LV distribution networks operate on three phases, however majority of the households
have single phase connections. Voltage unbalance is a result of unbalanced loading
of the phases. Unbalanced installations of PV systems across the phases can further
deteriorates voltage balance. The voltage unbalance causes overheating of transformers,
three phase generators and induction motors [20, 21].
In LV networks, under-voltage and over-voltage issues might happen simultaneously
with voltage unbalance. For instance, when over-voltage is detected at any phase, the
tap-changing transformer adjusts its tap setting to balance the voltage. As a conse-
quence, other two phases might experience under-voltage [22]. In addition, the voltage
unbalance caused by PV generation can also effect the capabilities of voltage regula-
tion in the distribution network [23]. Reactive power injection or absorption at one
phase effects the voltages of other two phases as well. Thus, it is required to develop an
effective coordination mechanism among the three phases to solve voltage unbalance
problem effectively.
Further, power quality issue arises in terms of total harmonic distortion (THD) of cur-
rent and voltage at the point of common coupling (PCC) in the distribution network.
PV inverters produce THD of both voltage and currents at the PCC.
Harmonics
Harmonics are the components of frequency other than power system operating fre-
quency, usually integer multiples of fundamental frequency. In normal practice, the
current and voltage vary in sinusoidal manner despite that load is resistive, capacitive
or inductive. However, non-linear loads (e.g, Light Emitting Diodes (LEDs) or fluo-
8
rescent lighting) or power converters generate frequency harmonics. These harmonics
give rise to overheating of generators and motors, voltage stress in capacitors and mal-
functioning of electronic equipment [24–26].
IEEE 519-2014 standard set the limits on voltage and current harmonics in a power
system. Literature reported that harmonic limits are more likely to be violated at higher
PV penetration. Authors reported that 60%PV penetration violated the THD limit in a
LV distribution system [27]. Likewise, in [28] authors simulated the THD in 96-house
LV distribution network and concluded that THD limit violated at 100%PV penetration,
Single-phase PV inverter contributes to supraharmonics at 15–20 kHz and three-phase
inverter around 20 kHZ [14,24]. Authors observed the supraharmonics at 25 sites and
reported that the lowest and highest supraharmonics are found at the LV side of the
transformers and inverters’ connections, respectively [24].
1.3.2 Impacts of large solar PV systems
The over-voltage and under-voltage issues induced by PV systems are unlikely to hap-
pen in transmission networks since voltage is mainly affected by the reactive power
owing to low reactance values [10]. Nevertheless, PV power fluctuations probably af-
fect the frequency at transmission system level . This section addresses the effects of
large PV systems (500 kW or above) on frequency, rotor angle stability and generator
ramp rate.
Frequency stability
Frequency is a function of supply and demand. An increase/decrease in demand must
be accomplished through generation. The supply-demand mismatch resulting in system
frequency fluctuations. Conventionally, the frequency stability is preserved through in-
ertial and primary frequency supports. The system inertia provides fastest frequency
support through kinetic energy of the rotating generators. Besides inertia, primary fre-
quency support uses generator governor control to preserve system frequency.
As PV systems replace fossil fuel generators, the proportion of the governor responsive
generators is reduced for frequency regulation. Further, PV systems do not provide any
inertial support, therefore, severe frequency stability issues are expected in the future
electric grids. Although researchers agree that PV systems exacerbate the frequency
stability, however the exact share of PV systems which may cause the problem of fre-
9
quency instability is not clear. For instance, authors have tested the frequency stability
at 5%, 10%, and 20%PV penetration concluded that the frequency stability of the grid is
adversely affected at 20%PV penetration. Another study reported frequency deviations
59.37 to 60.54 Hz from nominal frequency (60 Hz) at 50%PV penetration level [29].
Similarly, the frequency stability of an interconnected grids system was assessed up to
65%PV penetration level and simulation results showed that the frequency response
of the interconnected system decreased dramatically when PV penetration increased to
60%[30].
Rotor angle stability
It refers to preserve synchronism of an electric generator by means of electromagnetic
and mechanical torques [31]. Rotor angle stability is further classified as: (1) small-
signal (steady-state) stability and (2) transient stability. Steady-state stability refers
to the ability of the power generators to preserve synchronism during gradual load-
changing conditions whereas transient stability concerns with system behaviour during
abrupt change in system conditions such as faults, sudden loss of interconnection lines,
sudden loss of power generation, or abrupt change in load. The time frame of transient
stability is usually 3-5 seconds.
An increase in PV systems penetration causes to reduce the proportion of synchronous
generators in an electric power system. Therefore, it is challenging for remaining syn-
chronous generators to preserve synchronism [32]. The severity of the rotor angle sta-
bility depends on the PV penetration level and location of the PV systems. At higher PV
penetration, rotor angles experience larger oscillations which makes the power system
unstable [33–35]. Researchers reported that higher PV penetration resulted in transient
instability at critical locations in the power network. When the PV share reached to
45%, the system collapsed owing to rotor angle instability [36].
Ramp rate
Ramp rate refers to the ability of an electric power generator to respond to sudden
changes in demand. The impacts of high PV penetration on power system having low
daytime load and peak evening load can be assessed through renowned ‘duck curve’
[37]. Duck curve is a graph that shows a change in the shape of net load curve as a
result of timing imbalance in electricity demand and PV generation on the course of a
10
day. The load curve experiences steep ramps during sunrise and sunset, a valley at noon
and a peak in the evening. Hence, duck curve will cause extensive stress of ramping
and peaking regulation on the conventional generators. Electric grid require fast re-
sponse and high capacity power generators to counteract ramp rate problem. Amongst
the available power generators, the fossil fuel-fired generators provide fast ramp rate
regulation. However, the fossil fuel-fired generators generate electric power at the ex-
pense of high electricity generation cost. Figure 1.7 depicts the expected change in
Florida Reliability and Council (FRCC) net load curve during high ramps and peak
load periods.
Figure 1.7: Expected duck curve for Florida Reliability Coordinating Council (FRCC)
with steep ramp (top) and peak load (bottom) [4]
As viewed, at the top portion of Figure 1.7, duck curve experiences high ramps during
sunrise and sunset, a dip in noon. The bottom part of Figure 1.7 represents the change
in the peak load at different PV penetration levels. An increase in PV penetration even-
tually delays the peaks of net load curve attains a maximum delay beyond 45%. Hence,
such net load patterns result in operational challenges for grid as well as reduce the
PV installation capacity in the power system. Duck curve will cause extensive stress
of ramping and peaking regulation on conventional generators. Therefore, power gen-
11
eration plants require fast response generators with high ramp up and down rates to
accommodate high PV penetration. Thus, increasing cycle cost of generators owing
to frequent operations, high ramp requirements and minimum generation levels need
increasing PV curtailment. These issues can be addressed by increasing power system
flexibility.
1.4 Grid supportive technologies for PV integration
This section discusses the main technologies for voltage and frequency regulation for
the PV penetrated grids. The potential grid supportive technologies for the PV integra-
tion are shown in Figure 1.8 and discussed in subsequent sections.
Grid suppor
ve
technologies for PV
system integra
on
Grid side
technologies
Demand side
technologies
OLTCS FACTS
Capacitor
banks
PV
invertors
Flexible
Loads
Large
capaicity
ba
ery
storage
Flexible
generators
PV
invertors
Voltage
regula
on
technologies
Frequency
regula
on
technologies
Demand side
technologies
Grid side
technologies
Flexible
Loads
HVAC
systems
Ba
ery
Storage HVAC
systems
Electric
vehicles
Figure 1.8: Grid supportive technologies for PV system integration.
1.4.1 Voltage regulation technologies
The common issues in LV distribution networks caused by high PV penetrations in-
clude: voltage fluctuations, voltage magnitude (over/under voltage) and voltage phase
are discussed in subsection 1.3.1. Various solutions have been suggested in the literature
for voltage regulation in PV coupled LV distribution networks. This section discusses
conventional and emerging technologies installed at grid side including fixed/switched
12
capacitors and on-load tap changers (OLTC). In addition, the emerging technologies in-
clude solid-state transformers (SSTs), solid-state OLTCs and flexible AC transmission
system (FACTS) devices. The other technologies installed at the customer side include
reactive power management and energy storage.
Grid side technologies
Capacitor banks are commonly used to address the voltage issues. Fixed/switched
capacitors offer fixed amount of reactive power compensation which might be insuf-
ficient when variations in PV power and load are high. This solution might result in
overcompensation problem [38].
Conventional OLTCs are one of the widely adopted technologies for solving the volt-
age problems. The frequent and swift operation of OLTCs is required to tackle voltage
variations caused by intermittent PV generation, which may shortens the life cycles of
OLTCs and increases the maintenance requirements [39].
Solid-state OLTCs equipped with power electronic devices are more useful owing to
its swift response to voltage fluctuations as compared to conventional OLTCs [40,
41]. However, it requires number of switches and their step-wise control to maintain
operational continuity [42]. SST [43–45] is another available technology eliminating
the requirement of line-frequency transformers and tap changers for the voltage control
in LV grids. However, the feasibility of SSTs deployment at wider range has yet to be
analyzed.
D-FACTS devices are other emerging solution for voltage control and categorized
into static VAR compensators (SVCs), distributed static synchronous compensators (D-
STATCOMs), dynamic voltage regulators (DVRs) and unified power flow controllers
(UPFCs). SVCs are installed at transmission lines to reduce the line losses, increase
the power transmission and voltage regulation capabilities, and improve the load power
factor (PF). However, their potential in LV distribution networks with DERs has yet
to be explored. DVRs are also used for voltage regulation because of their smaller
power ratings amongst all other D-FACTS devices [46–48]. However, ideal DRVs are
inefficient for tackling transients in non-linear loads. A comparative study between D-
STATCOMS and DVRs concluded better performance of D-STATCOMS in terms of
voltage unbalance mitigation [49]. Various studies use D-STATCOMs devices as volt-
13
age regulator to increase the hosting capacity of distributed PV systems. [50–53]. Al-
though D-STATCOMs mitigate the voltage unbalance in LV distribution network ,but,
these devices require additional transformer to compensate the voltage harmonics [54].
UPFCs have been used as voltage regulator in LV distribution networks [55,56]. Other
than voltage control, UPFCs are also efficient in series compensation and power flow
control.
Demand side technologies
Reactive power support: Conventionally, the reactive power is provided through ca-
pacitor banks and STATCOM at distribution and transmission levels, respectively. Re-
cent technological advances in inverters have led to reactive power support in PV sys-
tems.
To date, almost all PV inverters only generate active power because PV systems’ owners
are paid only for active power generation. Further, PV systems operated on unity power
factor that do not add reactive power which is demanded by the loads. Authors found
that seven 5-kWp PV units connected to a 400 kVA network resulted in decrease of
PF from 0.86 to 0.80 [57]. Reactive power support of PV systems can reduce under-
voltage and over-voltage problems and system operational costs [33, 58, 59]. Another
study reported that PV reactive power support can increase the share of PV generation
from 150%to 350%in a distribution network [60].
Despite the advantages of PV reactive power support, there are some disadvantages in
the form of reduced inverter’s lifetime and increased power losses [61, 62]. Moreover,
inappropriate control of reactive power causing severe voltage violations and imbalance
in highly PV penetrated grids [10, 23]. However, PV inverters are not very common
and effective as capacitor banks for reactive power provision [63].
Energy storage system (ESS) is a potential and convenient solution to mitigate the
over-voltage problem by absorbing excess PV power generation in LV distribution net-
work. Among the available ESS technologies, e.g., super-capacitors, battery storage,
pumped storage and compressed air storage; battery storage can be easily equipped
with PV inverters to tackle the problems associated with PV power generation. Further,
cycling (charging/discharging) of battery storage can be easily controlled to smooth the
PV power fluctuations.
14
Besides smoothing the PV power, battery storage can regulate the voltages alone or
in coordination with other technologies. For instance, a coordinated control strategy
for battery storage and OLTCs was developed to mitigate the over-voltage problem in
PV coupled distribution networks [64]. Another study developed a two level control
strategy for optimizing the combined operation of battery storage, OLTC and PV in-
verters for voltage regulation. In the first level, the battery storage and the tap position
of OLTCs are optimally controlled to mitigate the over-voltage problem by considering
the uncertainties in the PV generation. In the second level, reactive power compen-
sation through PV inverters is employed to further improve the voltage profile of LV
distribution network for the subsequent operating hour [65]. A voltage control strat-
egy combining distributed energy resources (DERs) such as , electric vehicles (EVs),
PV and wind systems have been developed in [66]. Recently, model predictive con-
trol (MPC) based battery storage control strategies have gained significant attentions of
researchers for voltage regulation [67–70].
1.4.2 Frequency regulation technologies
The utility-scale integration of PV systems affects the power system frequency more
than nodal voltages. The frequent and sudden ramp-up/down of PV power output
caused by passing clouds badly affect grid frequency. Moreover, trending in converter
interfaced (CI) technologies such as PV systems, storage devices and electric vehicles
(EVs) may cause full or partial desertion of synchronous generators that adversely af-
fect the frequency stability [32]. Although, these CI devices provide both active and
reactive power support, however, their response to the fast frequency dynamics are not
up to the mark. It is reported that east China electric grid experienced 25%reduction
in rotating inertia where synchronous generators were replaced with high-voltage direct
current (HVDC) lines. Under such circumstances, electric grids must be equipped with
additional frequency supportive devices for frequency regulation [71]. Some frequency
supportive technologies are discussed in the subsequent paragraphs.
PV controllers: Frequency deviation can be guided as an input signal to the PV con-
troller to emulate frequency response. Many studies have merely focused on inertial
response of PV controllers without considering any external energy storage [72–74].
Authors developed a virtual synchronous power converter (SPC) to improve the system
15
stability in high PV penetrated network [74]. The developed SPC effectively mitigates
the frequency deviations by allowing the PV system to respond power imbalances re-
sulting in frequency and rotor angle stability improvement. Likewise, another study
designed a PV plant controller with multiple capabilities including voltage control, real
and reactive power control, and frequency droop control [72].
Battery energy storage system (BESS): The role of BESS integrated with large PV
system for grid’s frequency support has been reported in literature [75, 76]. For in-
stance, authors proposed a rule-based strategy for distributed control of BESS for fre-
quency regulation [77]. In another study, authors developed an optimization control
framework for BESS and its effectiveness was demonstrated in terms of peak shaving
and frequency regulation [78]. Aiming for frequency control, a dynamic programming
based optimization strategy for BESS was developed for PV-dominated grids [79].
Simulation results demonstrated the superiority of the proposed method to other con-
trol strategies such as fuzzy logic control and proportional integration differentiation
control. Though frequency control can be emulated by appropriate control and sizing
of BESS, however, it is a costly solution.
BESS and PV converters when operated together act as a virtual synchronous genera-
tor (VSG) to provide external inertia for frequency control [80]. Many studies have
reported the work on VSGs particularly for frequency control in PV penetrated grids
[81–83].
Flexible conventional generation: It is realized that virtual inertia provided by VSGs
is not enough to maintain frequency stability. Therefore, a proportion of synchronous
system is required in a power system to provide inherent inertia along with virtual inertia
for a guaranteed stable grid operation [32, 35, 84–86].
The optimal proportion and dispatch ratio of conventional generators without compro-
mising the power system stability at various PV penetration levels has been investigated
by grid operators of various countries such as China and India [87,88]. Economics and
stability are the two key factors of an electric grid that must not be compromised while
planning the economic dispatch studies. For instance, authors deduced that high shares
of PV is substantial for economical and environmental benefits, however causing to
reduce the proportion of power generated from synchronous generators leading to un-
desirable curtailment of the system inertia [89]. Thus, there exists a trade-off between
16
stability and economics which is considered one of the main challenges for incorporat-
ing high shares of PV in the electric grids.
Increasing the flexibility of traditional power plants is believed as a short-term solution
for consolidating the increasing penetration of PV systems. In 2019, IRENA reported
the key characteristics of flexible power plants which mainly include: quick start-up
time, and high ramp up/down to comply with sudden load changes. The objective of
reducing PV curtailment through power plant flexibility enhancement has been achieved
by many nations such as China, Germany, India, Denmark and United States.
Existing grid support technologies with challenges and limitations were elaborated in
this section. Demand side Management (DSM) in combination with exiting solutions
can solve the grid reliability and stability issues caused by PV systems. DSM is gaining
the attention of researchers for increasing the hosting capacity of PV.
1.5 Demand side management
It is believed that future electrical grids where supply side will remain the only source
of RS may not be capable to incorporate high shares of PV generation. Unlike power
generators, demand side resources can provide quick response to the sudden change in
load or generation. The flexible operations of both supply and demand sides can ac-
commodate higher PV penetration than could be achieved by relying on conventional
generation alone. Fortunately, flexible loads with superior regulation capabilities can
promote the renewable energy penetration through demand response (DR). Particu-
larly, thermostatically controlled loads such as heating, ventilation and air-conditioning
(HVAC) systems are flexible loads and account for a significant portion of commercial
and residential buildings load [90].
For instance, demand side flexibility of residential heating system was used to absorb
surplus renewable power generation [91]. An optimization framework for optimal
scheduling of hydro-thermal generation and demand flexible loads was developed con-
sidering uncertainty and outage of wind turbine and solar PV power generation to en-
sure stable grid operation [92]. Likewise, a mixed integer linear programming (MILP)
based optimization technique was adopted for combined scheduling of PV system, bat-
tery storage and deferrable appliances meanwhile ensuring the grid’s reliability [93].
Another study proposed a coordinated scheduling of demand flexible heating system
17
and distributed energy resources (DERS) including gas turbines, gas storage and solar
PV system to increase the shares of renewable energy in a power system [94]. A mixed
integer non-linear problem was formulated to improve the resiliency of an electrical
distribution grid considering demand side management and optimal placement of solar
PV system in a distribution network. [95].
1.5.1 A brief overview of DR: An ancillary service to the system operators
(SOs)
To accommodate high shares of solar PV in a power system, the flexibility options
has gained significant attention of policy makers and researchers. Flexibility can be
provided in the forms of fast ramping generators, reserves, battery storage and demand
response (DR).
Adoption of flexibility options maily depends on the conditions and locations where
they are deployed. For instance, the flexibility requirements of distribution system op-
erators (DSOs) and transmission system operator (TSO) are different. TSO is mainly
responsible for maintaining supply-demand balance (frequency regulation), to achieve
this, it involves the operation of several competitive energy markets for ancillary ser-
vices. These services are acquired through centralised and decentralised power gen-
eration plants. Some of these power plants are being replaced with PV systems, thus
demanding more ancillary services to maintain supply-demand balance. It is also ex-
pected that cost of ancillary services will increase in the near future, therefore, TSO
may adopt flexible load as a cost effective ancillary service provider. On the other hand,
DSOs guarantee the continuous power supply to electricity customers by maintaining
the nominal voltage within ±10%(voltage regulation). Thus, DSO has to avoid Black-
outs (tripping of feeders owing to line overloading) and Brownouts (voltage instability
owing to increase in local energy consumption or generation). In this regard, acquir-
ing DR through flexible load is believed to be an economic choice for DSO rather than
grid reinforcements such as installation of new MV/LV transformers and replacement
of existing cables with high current carrying capacity cables.
1.5.2 DSM and DR
These two are almost same applications with a slight difference. DSM refers to modu-
late the energy consumption patterns of flexible loads, typically at LV distribution net-
18
work, upon different contingencies such as generation volatility. Whereas, DR refers to
modify the energy consumption patterns of electric consumers’ subject to grid’s signals
such as electricity price volatility. This research work focuses on enabling the appli-
cations of DR as an ancillary service for DSOs and TSO. This DR application can be
enabled at LV networks through building energy management systems (BEMS). The
provision of DR through BEMS requires a certain reaction to grid signals, utilities or
SOs.
Control paradigms for DR
It is important to mention control perspectives of DR programs. In this context, the
DR programs are classified into direct load control (DLC) and indirect load control
(IDLC). In DLC, the aggregator has direct access to the individual appliances. On the
other hand, the