SMART GRIDS AND SOLAR ENERGY: ROLE OF ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT

Abstract

Smart grid integration with solar energy has enormous promise for efficient and sustainable energy systems. Artificial intelligence (AI) is key in maximizing smart grids' performance, dependability, and control with solar energy integration. The seamless integration of solar energy sources is the main topic of this chapter's exploration of the many uses of AI in grid management. The main functions of AI in energy storage management, grid monitoring, defect detection, optimization, and security are explored, along with load forecasting and demand response. Energy grid operators may successfully balance energy supply and demand, improve grid stability, and lower operating costs using AI's capabilities, leading to robust, secure, and sustainable smart grid systems.

Full text

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 97
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID
MANAGEMENT
Abstract
Smart grid integration with solar
energy has enormous promise for efficient and
sustainable energy systems. Artificial
intelligence (AI) is key in maximizing smart
grids’ performance, dependability, and control
with solar energy integration. The seamless
integration of solar energy sources is the main
topic of this chapter’s exploration of the many
uses of AI in grid management. The main
functions of AI in energy storage management,
grid monitoring, defect detection, optimization,
and security are explored, along with load
forecasting and demand response. Energy grid
operators may successfully balance energy
supply and demand, improve grid stability, and
lower operating costs using AI’s capabilities,
leading to robust, secure, and sustainable smart
grid systems.
Keywords: Smart grids; Solar energy;
Artificial intelligence; Solar forecasting; Load
forecasting; Demand response; Energy storage
management; Grid control.
Authors
Swapandeep Kaur
Department of Electrical Engineering
Guru Nanak Dev Engineering College
Ludhiana, India.
eeswapandeep@gndec.ac.in
Raman Kumar
Department of Mechanical and
Production Engineering
Guru Nanak Dev Engineering College
Ludhiana, India.
sehgal91@gndec.ac.in
Kanwardeep Singh
Department of Electrical Engineering
Guru Nanak Dev Engineering College
Ludhiana, India.
kds@gndec.ac.in

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 98
I. INTRODUCTION
The adoption of solar energy, generated from the sun’s abounding and restorative
rays, has increased significantly during the past few decades. As a potential answer to the
problems caused by traditional fossil fuels, this clean and sustainable energy source has
attracted the attention of researchers, decision- makers, and everyone else. Technology
improvements, declining costs, environmental concerns, and a worldwide push for energy
diversity and sustainability are a few reasons for the rising interest in solar energy.
 Technological Advancements: Significant advancements include the development of
photovoltaic (PV) cells, which convert sunlight directly into power. PV technology
has advanced from basic silicon- based solar cells to thinner-film and multi-junction
solar cells that are more effective and can produce power even in areas with poor
lighting [1].
 Falling Costs: The idea of economies of scale has been crucial in lowering the price
of solar energy. The price of solar panels and related equipment has decreased even
more due to mass manufacturing, which has been made possible by increased
demand, financial support from the government, and government subsidies.
 Environmental Issues: An important benefit of using solar energy to generate power
is that it helps to fight environmental problems. Solar energy is a carbon-neutral
source since it does not create greenhouse gases, unlike typical fossil fuel-based
power generation. Additionally, since solar energy doesn’t emit hazardous pollutants
like sulphur dioxide, nitrogen oxides, or particulates, it aids in the battle against air
pollution [2].
 Energy diversification and security: Countries may use solar resources to lessen
their reliance on limited fossil fuels, which are prone to price swings and depletion.
Solar energy also lessens reliance on erratic energy imports, providing a more steady
and dependable home energy source. A more robust energy infrastructure is ensured
by this diversity, which increases energy security and lessens susceptibility to
geopolitical conflicts or interruptions in energy supply chains [3].
 Economic Growth and Job Creation: Communities and nations profit economically
from expanding the solar sector. Government funding of solar energy projects and
incentives for using renewable energy attract private capital, which helps the solar
industry develop economically. Job possibilities are created due to the construction
and growth of solar power plants in several industries, including manufacture,
installation, maintenance, and research. Local economies are boosted, labour skills
are improved, and this employment creation aids general economic development.
 Decentralization and Independence: By permitting local electricity generation by
households, companies, and communities, solar energy encourages energy
decentralization. Individuals and groups can produce electricity by installing solar
panels on roofs or in community solar farms, decreasing dependency on centralized
power plants. This decentralized strategy promotes energy independence by partially
enabling communities to fulfil their own energy demands. Distributed solar power
systems can also continue to generate electricity during blackouts or grid
interruptions, boosting energy resilience and guaranteeing an uninterrupted power
supply [4].
 Government Policies and Incentives: Many countries have enacted pro-solar laws

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 99
and provided financial incentives to hasten the uptake of solar energy and foster its
expansion. These incentives may include tax breaks, gifts, financial aid, feed-in
tariffs, and net metering plans. Such regulations provide a favourable environment for
investments and promote industrial innovation. Financial incentives increase the
affordability and viability of solar energy for individuals, companies, and industries,
promoting its wider adoption and hastening the growth of the solar industry [3].
1. The Emergence of Smart Grids: Introducing smart grids represents a fundamental shift
in how the conventional energy grid operates. Smart grids use cutting-edge technology to
build a highly linked and intelligent energy network, unlike conventional power grids,
which function with a unidirectional flow of electricity and limited communication
capabilities. These cutting-edge grids use automation, real-time data analytics, digital
communication systems, and automation to monitor, regulate, and optimize energy
production, distribution, and use [5].
The issues brought on by rising energy consumption, the incorporation of
renewable energy sources, and the need for increased energy efficiency are addressed
explicitly by smart grids. Smart grids, which incorporate distributed energy resources
(DERs) like solar panels, wind turbines, and energy storage systems, can effectively
manage the variability of renewable energy, balance the supply and demand for
electricity, and promote energy independence.
Additionally, deploying smart grids gives users more influence over energy use.
With real-time data and smart metres, customers may learn about their power usage and
make decisions to optimize their energy use and lower their expenditures. Demand
response programmes, assisted by smart grids, persuade customers to change how they
use power at peak times, reducing system stress and improving overall grid stability [6].
Furthermore, incorporating electric vehicles (EVs) into smart grids creates chances
for improved energy management. By allowing EVs to charge during off-peak hours
when energy demand is lower, and prices are lower, smart charging systems contribute
to a more sustainable transportation ecology.
2. Integration of Artificial Intelligence (AI) in Energy Systems: An innovative
development with enormous promise for the whole energy industry is incorporating AI
into energy systems. Energy systems may become more productive, sustainable, and
adaptive with the help of AI’s remarkable skills in data analysis, pattern identification, and
decision-making, successfully addressing the complexity of today’s energy concerns. AI
is key in optimizing renewable energy production and increasing total energy productivity
by precisely forecasting output and enabling predictive maintenance.
Additionally, real-time monitoring and analysis are possible with AI-driven smart
grids, assuring effective load balancing, effective demand response, and lower costs and
emissions. Furthermore, AI enables utilities to efficiently manage electricity demand and
ease grid stress while empowering customers with real-time energy information, boosting
energy efficiency and informed decision- making. AI also assists energy market
forecasting and trading, improves grid security, and optimizes energy storage systems.
The use of AI promotes an innovation and research-based culture in the field of

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 100
renewable energy, resulting in ongoing advancements and environmental
sustainability[7].
Figure 1: Development of energy systems and units: Adapted from [8] (CC BY 4.0).
II. UNDERSTANDING SOLAR ENERGY
1. Solar Photovoltaic (PV) Technology: Solar photovoltaic (PV) technology, which
transforms sunlight directly into electricity, is the most advanced renewable energy
technology that is expanding quickly. It uses the photovoltaic phenomenon identified in
the 19th century and explains how some materials produce an electric current when
exposed to sunshine. The fundamental components of the technology, photovoltaic cells,
are the solar panels that make up solar PV systems [9].
2. Working components of Solar PV technology
 Solar Panels: Photovoltaic cells are joined to form solar panels, sometimes called
solar modules. These panels are often mounted on roofs, open spaces, or other
suitable sites with plenty of sunshine.
 Photovoltaic Cells: The brains of solar PV technology are photovoltaic cells. The
semiconductor materials used to create these cells, such as silicon, have unique
qualities that enable them to turn sunlight into energy. Photovoltaic cells produce an
electric current when photons (light particles) from the sun impact them. This electric
field is created throughout the cell.
 Electricity Generation: Direct current (DC) is the kind of electricity that solar cells
produce when they produce electricity. This power must be transformed into
alternating current (AC) via an inverter for homes and businesses. The AC electricity
is routed into the building’s electrical system or the power grid, depending on the
configuration [10].
 Off-Grid and Grid-Tied Systems: Solar PV systems come in grid-connected and
off-grid varieties. Grid-tied systems are plugged into the power grid, allowing extra
electricity to be sent back into the system and refunded to the owner via net metering.
Off-grid systems, in contrast, are independent systems that store extra power in
batteries for usage when the sun isn’t shining [11].

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 101
3. Benefits of Solar PV Technology
 Renewable Energy Source: Solar PV technology uses the sun’s energy, a plentiful
and regenerative resource. Solar power can be constantly produced as long as the sun
shines, making it a sustainable option for generating electricity. In contrast to limited
fossil fuels, solar energy does not decrease with usage, guaranteeing a long-term and
eco-friendly energy option.
 Lower Carbon Emissions: The capacity of solar PV systems to produce power
without releasing greenhouse gases is one of its most important benefits. Carbon
dioxide and other dangerous pollutants are released when fossil fuels are burned to
produce electricity, contributing to global warming and climate change [1].
 Limited Operating Expenses: Solar PV systems have very low operating expenses
once installed. Solar PV systems rely on the sun’s energy, which is free, as opposed to
traditional power plants, which need a constant fuel supply and continual
maintenance. The operational expenses of solar PV systems stay cheap throughout
their lengthy lifespans with adequate maintenance, such as routine inspections and
cleaning of the solar panels.
 Energy Independence: Solar PV systems can provide energy independence,
particularly in off-grid applications. Solar PV systems can offer a dependable and self-
sufficient energy source in isolated areas or places with restricted access to centralized
power networks. Solar PV systems boost energy security and decrease dependency on
centralized power sources by producing electricity locally, guaranteeing a steady
power supply even in remote or difficult situations [12].
 Scalability: Solar PV systems may be scaled up and down to meet various energy
demands. Solar PV technology may be adapted to fulfil various energy demands, from
modest installations on rooftops of private residences to enormous utility-scale solar
farms. Due to its adaptability, solar PV systems may be used for a wide range of
purposes, from generating huge amounts of electricity on a big scale to supplying
power to a single household.
 Decentralisation of Power Generation: Solar PV technology decreases transmission
and distribution losses typically associated with centralized power plants by
promoting decentralized power generation. The demand for long-distance power
transmission is reduced when electricity is produced near the point of consumption,
increasing the energy supply’s reliability and efficiency. Decentralized solar PV
systems also support a distributed energy network and help communities become self-
sufficient in supplying their energy demands [13].
4. Challenges and Considerations: While solar PV technology offers numerous benefits, it
also faces some challenges and considerations:
 Intermittency: Solar PV generation is erratic and unpredictable since it depends on
sunshine, especially in cloudy or dark situations.
 Energy Storage: To assure a constant supply of electricity, efficient energy storage
technologies are crucial for off-grid applications or grid-tied systems with few net
metering opportunities.
 Upfront Expenses: Although solar PV systems have low running expenses, the
upfront investment can be rather substantial, although prices have dropped
dramatically over the years.

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 102
 Urban Planning and Aesthetics: Utility-scale solar projects could need much space,
and visual concerns might arise in certain residential or commercial settings [14].
5. Solar Thermal Power Generation: Utilizing the sun’s heat to create electricity is known
as solar thermal power generation, commonly called concentrated solar power (CSP).
Solar thermal power generation uses mirrors or lenses to focus sunlight onto a receiver,
heating a fluid that powers a turbine to create electricity, as opposed to solar photovoltaic
(PV) technology, which directly transforms sunlight into energy using semiconductor
materials. Solar thermal power plants are a potential renewable energy option for utility-
scale electricity generation since they are built to operate at large sizes and can provide a
steady amount of power even when the sun is not shining [15].
 Solar Collectors: Solar thermal power plants use Large mirrors or lenses to focus
sunlight onto a receiver. These solar collectors are carefully positioned to follow the
sun’s path throughout the day, providing optimum efficiency and sunlight capture.
 Receiver: The solar receiver is a component that has been carefully developed to
capture concentrated sunlight and transform it into heat. Depending on the technology
employed, the receiver may be a central tower, a linear trough, or a parabolic dish
[15].
 Central Tower: In central tower systems, a receiver is situated on top of a tall tower
and illuminated by a heliostats mirror. The receiver includes a heat-absorbing
substance that absorbs the concentrated sunlight and heats to high temperatures,
such as molten salt or a heat- transfer fluid.
 Linear Trough: Linear trough systems focus sunlight onto a receiver tube that runs
along the focal line of the parabolic trough using a line of long parabolic mirrors. A
heat-transfer fluid in the receiver is heated as it passes through the tube.
 Parabolic Dish: In parabolic dish systems, sunlight is focused onto a receiver at the
focal point using a reflector with a parabolic shape. The dish’s centre-mounted
receiver, which absorbs focused sunlight, warms a fluid to a high temperature.
 Heat Transfer: The receiver’s heat-absorbing substance achieves hot temperatures,
usually exceeding 500 degrees Celsius (932 degrees Fahrenheit). The heat is
subsequently transferred to a fluid that conducts heat, such as molten salt or oil, and
circulates inside the receiver[14].
 Heat Exchange and Power Generation: After being pumped through a heat
exchanger, the hot heat-transfer fluid transmits heat to an additional water-based fluid.
The water-based substance is transformed into steam, which powers a generator and
turbine to generate energy.
 Energy Storage: Some solar thermal power facilities add energy storage technologies
to store extra heat produced during prime sunlight hours. Using the heat that has been
stored, electricity may still be produced at night or during overcast days when there is
little sunshine [9].
6. Solar Energy Pros and Cons

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 103
Table 1: Solar Energy Pros and Cons.
Sr.
No.
Pros
Cons
Reference
1
Sustainable and
Renewable
Weather dependence
and sporadic Ness
[16]
2
Carbon Footprint
Reduction
Energy Storage
Challenges
[17]
3
Lower Costs of
Energy
High Initial Costs
[18]
4
Independence
from energy
Use of Land and
Aesthetics
[19]
5
Low Operational
Costs
Regional restrictions
[20]
6
Distributed
Generation
Environmental Impact of
Production
[13]
7
Job Creation
Constraint on Night
time Generation
[19]
8
Technological
Developments
Integration of the
Energy Grid
[18]
9
Flexibility and
diversity
geographic
considerations
[17]
III. SMART GRIDS AND THEIR SIGNIFICANCE
Electricity sector paradigms are being changed by smart grids, reinventing how
energy is produced, delivered, and used. Smart grids have evolved as a game-changing
solution to the issues presented by conventional power grids and to meet the rising need for
efficient and sustainable energy systems by merging cutting-edge digital technology, real-
time data analytics, and two-way communication. Smart grids are the key to a more
dependable, eco-friendly, and intelligent energy future because they can incorporate
renewable energy sources, improve system resilience, and give customers greater control over
their energy use [7].
1. Smart Grid-Definition: Modern energy distribution networks known as “smart grids”
optimize power generation, transmission, distribution, and consumption by utilizing
cutting-edge digital technology, communication systems, and real-time data analytics.
Smart grids enable bidirectional communication between various grid components,
enabling smooth coordination and control, unlike traditional power networks that function
with limited automation and one-way electrical flow [21].
Modern electrical distribution networks called “smart grids” use real-time data
analytics, communication systems, and advanced digital technologies to optimize power
production, transmission, distribution, and consumption. Smart grids enable bidirectional
communication between components, allowing smooth coordination and control. This
contrasts traditional power networks with minimal automation and one-way electrical
flow.

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 104
With real-time information on electricity usage provided by smart grids, customers are better
equipped to optimize their energy use and cut expenses. Customers can actively engage in
load balancing and demand response initiatives through demand-side management, helping to
promote more sustainable and proportionate energy use [22].
2. Benefits of Smart Grid Implementation: Implementing smart grids offers many
benefits and challenges across the entire electricity ecosystem. Some of the key benefits
of smart grid implementation are:
 Improving dependability and Energy Efficiency: Smart grids provide real-time
monitoring and control, increasing grid dependability and reducing downtime. Data
analytics is used in smart grids to increase efficiency and decrease energy losses.
 Integration of Renewable Energy: Renewable energy sources are smoothly
incorporated into smart networks, which lowers emissions and encourages
sustainability [23].
 Demand Response and Load Shaping: Dynamic energy management during peak
hours is made possible by smart grids, which improve grid performance.
 Energy Storage and EV integration: Smart networks promote adequate energy
storage and utilization, improving overall system stability. EV charging and grid
assistance for sustainable mobility are made possible by smart grids.
 Decentralized Power Generation: Distributed energy resources may be
accommodated by smart networks, improving system resilience.
 Environmental Advantages and Cost Savings for Utilities: Smart grids optimize
resources, resulting in cost savings for utilities and customers. Smart grids help the
energy sector become more environmentally friendly by lowering its effect [24].
 Prospects and difficulties
 High Initial Investment: Securing funds for large-scale implementation is difficult
due to the high upfront expenses of installing smart grid technology.
 Legacy Infrastructure Compatibility: It can be difficult to integrate new smart
grid technologies with the infrastructure of the current electricity grid and may need
replacing or upgrading older equipment, which impedes smooth integration [25].
 Risks to Cybersecurity: Smart grids require increasing connection and data
interchange, which increases their susceptibility to cyberattacks. As a result, they
need comprehensive cybersecurity solutions to fend off possible dangers and
guarantee grid security [26].
3. Opportunities for Smart Grid Implementation
 Integration of Renewable Energy: Smart grids make it easier to seamlessly
incorporate renewable energy sources like solar and wind power into the electrical
system. Smart grids enable a bigger proportion of clean energy in the energy mix,
lowering greenhouse gas emissions and fostering sustainability by skilfully managing
the fluctuation of these resources.
 Demand Response Programmes: Smart grids enable customers to actively engage in
these initiatives by enabling them to modify their energy usage in response to current
grid circumstances and prices. This enables utilities to manage electricity demand

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 105
during peak hours better, enhancing grid efficiency and obviating the need for costly
peak power plants. It also benefits customers by lowering their energy bills [27].
 Decentralized Energy Generation: Smart grids enable the integration of DERs, such
as home energy storage devices and rooftop solar panels. By allowing users to
produce their power locally and sell any extra energy back to the grid, this
decentralized method of energy production encourages energy independence and
resilience, especially in the event of grid outages or natural catastrophes.
 Energy Storage and Grid Flexibility: The flexibility and stability of the grid are
improved by smart grids that include energy storage devices like batteries. These
systems provide a steady power supply and eliminate the need for expensive grid
improvements by storing extra electricity produced during times of low demand and
supplying it during peak hours [28].
IV. AI APPLICATIONS IN SOLAR ENERGY
Table 2: AI Applications in Solar Energy
Sr.
No.
AI Applications
in Solar Energy
Description
Reference
1
Solar Prediction
AI systems estimate solar irradiance by
examining weather patterns, historical data, and
satellite images. This enables precise
projections of solar energy production.
[29]
2
Panel
Enhancement
AI uses real-time sun monitoring data to arrange
solar panels in the best possible way,
maximizing their exposure to
sunlight and overall energy production.
[30]
3
Maintenance and
Fault
Investigation
AI-driven monitoring systems find and diagnose
solar panel defects, allowing for preventative
maintenance and decreased
downtime.
[3]
4
Energy Storage
Optimization
AI improves grid stability and effective energy
use by
optimizing energy storage devices’ charging and
discharging cycles.
[2]
5
Demand
Response
AI makes possible demand response
programmes, which
coordinate energy use based on grid
circumstances and customer preferences, to
balance supply and demand.
[27]
6
Energy Trading
and Market
Forecasting
AI analyses energy market data, pricing trends,
and consumer behaviour to make data-driven
energy trading and market operations decisions.
[14]
7
Microgrid
Management
AI regulates microgrids by maximizing energy
distribution, promising grid stability, and
promoting effective energy use
inside the microgrid.
[31]

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 106
Certain applications have been mentioned below in detail: -
1. Solar Panel Placement and Sun Tracking: Photovoltaic (PV) system location and sun
tracking are essential elements in maximizing the efficiency of solar energy output. Solar
panels must be carefully placed in areas with the best sun exposure to capture the most
sunlight throughout the day. Usually, this entails positioning the panels to face the sun’s
direction, which is south in the Northern Hemisphere and north in the Southern
Hemisphere. The panels also capture the maximum quantity of solar energy by altering
the tilt angle to correspond with the latitude of the installation site [15].
By continually changing the position of the panels to face the sun as it travels
across the sky, sun- tracking devices improve the performance of solar PV systems. These
devices, which have motors and sensors, carefully watch the sun’s position to ensure the
solar panels get the most sunlight possible throughout the day. While dual-axis systems
additionally consider variations in the sun’s angle over the year, single-axis sun tracking
systems track the sun’s east-west motion. Strategically positioned solar panels and sun-
tracking technologies considerably boost energy output, improving overall effectiveness
and making solar energy more reasonably priced [13].
2. Predictive Maintenance for Solar Farms: Sensors continually gather real-time
temperature, voltage, and irradiance data in solar farms. This data is analyzed using
machine learning techniques and AI-driven data analytics to look for trends and
anomalies that could indicate shading or panel deterioration. The system offers maintenance
suggestions and predictive warnings, allowing operators to plan and optimize maintenance
schedules while lowering costs and minimizing downtime [14].
Benefits of Predictive Maintenance for Solar Farms:
 Increased Reliability
 Enhanced Performance
 Cost Savings
 Extended Equipment Lifespan
 Efficient Resource Allocation
 Data-Driven Decision Making
3. AI-Based Solar Forecasting
AI and machine learning algorithms are used in AI-based solar forecasting to
provide precise, real-time predictions about solar energy production. Solar forecasting is
necessary to efficiently integrate solar energy into the electrical grid and maximize its
utilization. AI-based solar forecasting’s main goal is to produce accurate and trustworthy
estimations of solar irradiance and energy production, as shown in Figure 1, to enhance
grid management, energy trading choices, and integrating solar power into the overall
energy mix.
Real-time solar irradiance measurements, satellite images, and historical
meteorological data train machine-learning models in AI-based solar forecasting. These

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 107
models use this data to identify trends, patterns, and connections in solar irradiance while
considering a variety of variables that impact the production of solar energy, such as
cloud cover, meteorological conditions, and seasonal fluctuations [21].
Figure 2: Benefits of AI-based Solar Forecasting
V. AI-DRIVEN GRID MANAGEMENT
AI-driven grid management refers to the application of AI technologies in optimizing
the operation and control of electrical grids. By leveraging AI algorithms, machine learning,
and data analytics, grid management systems can make data-driven decisions, improve grid
efficiency, enhance reliability, and facilitate the integration of renewable energy sources and
other distributed energy resources (DERs). AI-driven grid management is crucial in
modernizing and transforming traditional power grids into more intelligent, flexible, and
sustainable energy ecosystems[32].
1. Grid Monitoring and Control with AI: AI technology to continually monitor, analyse,
and improve electrical grids’ performance is called “grid monitoring and control with AI.”
AI-driven grid monitoring and control systems use data from various sources, including
smart metres, sensors, and SCADA (Supervisory Control and Data Acquisition) systems,
to make real-time decisions, increase grid resilience, and improve efficiency. Two types of
learning techniques, i.e., supervised and unsupervised, have been presented in Figure 3.

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 108
Figure 3: Modern learning techniques in the smart grid: Adapted from [33] (CC by 4.0)
2. Load Balancing and Demand Response: The comparison of two crucial methods for
controlling the supply and demand of electricity in the power sector, i.e. load balancing
and demand response. Have been represented in Table 3. Load balancing matches
electricity generation with consumption in real time to guarantee grid stability and
resource efficiency. In response to changes in demand, grid operators keep an eye on and
modify the output of power plants and renewable resources [34].
Demand Response, on the other hand, is a system where grid management
interacts with customers, sending messages during times of high demand or grid strain
and rewarding them for adjusting their power use appropriately. Consumers actively alter
their usage by cutting back on non-essential power use or planning energy-intensive
activities for off-peak hours. Load balancing and demand response improve the energy
grid system, making it more dependable, sustainable, and responsive [26].
Table 3: Load balancing and demand response
Sr.
No.
Load
Balancing
Demand
Response
Reference
1
Definition: The process of real-
time demand-supply matching
in the power industry to
preserve grid stability and
avoid over- or under- utilizing
system resources.
Definition: A technique
employed by grid managers
to control the demand for
power during periods of high
usage or when the grid is
under strain. It entails
providing incentives for
individuals and organizations
to modify their power
consumption in response to
signals from the grid
operator.
[35]

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 109
2
Grid operators regularly monitor
the generating and consumption
trends of power.
Grid operators communicate
with consumers by sending
signals alerting them to times
of high demand or
Dangerous grid conditions.
[36]
3
Grid operators modify the
output of power plants,
renewable energy
sources, and other production
facilities based on real-time
data to adapt to changing
demand.
Consumer Reaction:
Consumers alter their power
usage on their initiative by
cutting back on non-
essential usage or scheduling
energy-intensive activities at
off-peak times.
[37]
4
Power distribution:
To maintain an equitable
electricity distribution across
the system, grid managers may
distribute power
From various sources to
regions with higher demand.
To promote participation,
consumers may get cash
incentives or discounted
power prices during demand
response events.
[35]
3. AI-Enabled Fault Detection and Self-Healing Grids: Without human interaction, AI-
driven systems may automatically respond to grid problems by enacting self-healing
procedures to restore electricity and maintain grid stability. The objectives include
reduced downtime, increased grid resilience, and enhanced overall grid performance.
Significant progress has been achieved in AI-enabled grid management in recent years,
especially in defect detection and self-healing. The creation of improved machine
learning algorithms, especially for fault detection in power grids, is one prominent area of
advancement in this field. These complex algorithms can correctly identify and categorize
different defect types, yielding more accurate fault detection with fewer false positives.
This increase in fault detection precision improves grid dependability and makes
responding quickly to possible problems easier. Various key components and
functionalities are shown in as shown in Figure 4 [38].

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 110
Figure 4: Components and functions of AI-enabled grids
Researchers are investigating decentralized decision-making in self-healing grids
by dispersing AI capabilities across substations and distributed energy resources (DERs).
This strategy improves grid resilience and adaptation even in the face of communication
breakdowns. By proactively addressing possible equipment failures, predictive
maintenance, enabled by AI algorithms analyzing sensor data, decreases downtime and
increases grid dependability. AI optimization in grid management supports a sustainable
energy future since it enables the seamless integration of energy storage and renewable
energy sources while boosting system stability and self-healing capacities.
VI. ENHANCING SOLAR PANEL EFFICIENCY
The performance and energy production of photovoltaic (PV) systems may be
significantly improved by increasing solar panel efficiency using AI-driven panel
performance analysis. Solar panel operators and system owners may improve energy
production, extend the life of the solar array, and obtain useful insights into the performance
of individual panels by utilizing AI and data analytics.
1. AI-Driven Panel Performance Analysis: Some key ways AI-driven panel performance
analysis enhances solar panel efficiency:
 Continuous Real-Time Monitoring and Anomaly Detection: AI algorithms
continually track each solar panel’s performance in real-time, looking for
abnormalities or departures from normal behaviour. Operators can rapidly spot
problems including panel deterioration, shadowing, dirt buildup, or other defects
that might impede energy production [39].
 Predictive Maintenance and Fault Identification: AI-driven analysis is able to
identify faults and forecast possible equipment breakdowns. Solar panel
operators can minimize downtime, maximize energy production, and increase
the lifespan of the panels by resolving problems before they become more
serious.

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 111
 Optimum Panel Tilt and Orientation: AI-driven analysis can suggest
modifying the tilt and orientation of solar panels in response to shifting weather
conditions and seasonal fluctuations. Maximizing the panel’s exposure to
sunlight enables maximum energy absorption and better efficiency.
 Integration with Energy Management Systems: Enabling more effective
energy use and grid integration, AI-driven panel performance analysis may be
integrated with energy management systems. The output of solar panels may be
synchronized with electrical demand patterns to optimize energy use, minimize
waste, and increase self-consumption [40].
 Weather Prediction and Energy Prediction: Through weather and energy
forecasting, AI systems are a key component in predicting the performance of
solar panels. AI algorithms can predict future weather conditions, including solar
irradiance and ambient temperature, by examining previous weather data and
using meteorological predictions. Operators can properly estimate variations in
solar energy generation thanks to this knowledge. Energy grid operators and
system administrators can better plan and manage energy resources if they
anticipate variations in energy production [2].
 Continuous Learning and Adaptation: AI models change their analyses to
consider ageing panels and changing environmental circumstances as they
continuously learn from fresh data. The panel performance analysis will always
be accurate and timely because of its versatility.
2. Dust and Dirt Mitigation Techniques with AI: Modern methods for mitigating dust and
dirt that use AI to maximize solar panel cleaning operations are known as AI-driven dust
and dirt mitigation approaches, shown in Figure 5. These approaches determine the ideal
time and frequency for cleaning operations by analysing various data sources, including
meteorological information, local environmental factors, and solar panel performance.
Resources are used more efficiently, and manual labour needs and costs are decreased by
scheduling cleaning activities during anticipated high dust and dirt collection periods.
Additionally, solar panel photos are taken using AI-powered remote sensing technologies,
such as satellites or drones with cameras, and then analysed by AI algorithms to find
panels that require immediate cleaning because of an excessive buildup of dust[41].

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 112
Figure 5: Dust mitigation strategies: Adapted from [42] ( CC by 4.0).
Table 4: Dust and dirt mitigation techniques
Sr.
No.
AI-Driven Dust
And Dirt Mitigation
Techniques
Description
Reference
1
Automated
Cleaning
Scheduling
To choose the best time and frequency for
cleaning operations, AI algorithms
evaluate previous weather data, local
environmental variables, and panel
performance. This guarantees that
resources are used effectively and lowers
manual labour requirements and
expenses.
[43]
2
Remote Sensing
and Image Analysis
Solar panels are photographed by AI-
powered remote sensing technology, such
as satellites or drones with cameras. These
photos are analysed by AI algorithms to
find panels that need to be cleaned right
away because of excessive levels of dust
and filth accumulation.
[44]
3
Predictive Dust
Accumulation
Based on past data and future weather
predictions, AI algorithms estimate dust
buildup patterns. Maintenance crews can
avert a large decline in energy production
by foreseeing when and where dust is
likely to build.
[45]
4
Machine Learning-
Enhanced Cleaning
Patterns
Cleaning robots powered by AI use
machine learning algorithms to gain
knowledge from previous cleaning tasks.
The cleaning patterns of the robots are
continuously improved based on
feedback data, making them more
effective and efficient over time.
[23]
3. Temperature Regulation and Cooling Strategies: To maintain optimal performance and
avoid overheating, temperature regulation and cooling techniques are crucial components
of managing electronic equipment, power systems, and industrial processes. Excessive
heat can result in decreased performance, more wear and tear, and even system
breakdowns in the context of electronics and electrical systems. Several temperature
control and cooling strategies are used to solve these issues, including passive cooling
techniques like heatsinks and thermal paste as well as active cooling strategies like fans,
liquid cooling systems, and phase-change cooling. These methods ensure the
dependability and lifespan of crucial parts and systems by dissipating surplus heat and

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 113
keeping temperatures within safe operating ranges [26].
VII. CYBERSECURITY IN AI-ENABLED SMART GRIDS
Smart grids are more susceptible to cyberattacks that might jeopardize the grid’s
integrity, stability, and security as they grow more dependent on AI, data analytics, and digital
communication technologies. Utilities and grid operators may safeguard vital infrastructure,
defend grid dependability, and preserve public confidence in the security of the energy supply
by giving cybersecurity a high priority in AI- enabled smart grids. Strong cybersecurity
measures are even more important as the grid grows networked and AI-dependent to battle the
changing panorama of cyberattacks. The role of cybersecurity in AI-enabled grids has been
shown in Figure 6 [31].
Figure 6: Cybersecurity in AI grids
1. Potential Security Risks and Threats: Grid operators and utilities need to carefully
assess and manage a number of possible security issues and threats in AI-enabled smart
grids. An increased attack surface for cyber adversaries is produced by the growing
adoption of digital technology, networked devices, and dependence on AI algorithms and
data analytics. One of the biggest concerns is from cyberattacks, where threats like
ransomware, distributed denial-of-service (DDoS) assaults, and phishing scams may
compromise the grid’s control systems, interrupt the flow of energy, and have serious
operational and financial repercussions [7].
Smart grid AI algorithms are susceptible to adversarial assaults, in which
attackers modify data inputs or the AI models themselves to trick the system and provide
choices that are incorrect. It is also essential to ensure the physical security of key grid
infrastructure, such as substations and control centres, because unauthorized physical
entry might result in grid operations being tampered with or disrupted. Furthermore,
introducing erroneous or damaged data into the grid’s data streams might confuse AI
systems and result in poor judgement, eventually impairing grid performance and

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 114
stability [46].
2. AI-Based Security Solutions: Main points are: -
 AI-driven threat detection for tracking and identifying cyberthreats in real-time.
 Analysis of behaviour to identify typical patterns and spot abnormalities for early
danger identification.
 Automated incident response to block malicious traffic and isolate impacted systems.
 Predictive cybersecurity to foresee upcoming risks based on global intelligence and
historical data.
 AI-enhanced endpoint security to stop ransomware and malware assaults.
 Using Natural Language Processing (NLP) to analyse unstructured security data and
improve insights.
 User authentication enabled by AI that uses behavioural biometrics for more precise
verification.
 AI-based phishing detection to find fraudulent URLs and phishing attempts.
 Security orchestration and automation powered by AI to speed up incident response
processes.
3. Ensuring Data Privacy and Protection: Sensitive data must be protected from
unauthorized access, breaches, and abuse in order to retain the publics and stakeholders’
confidence. The following are the main steps to take to ensure data privacy and
protection[7].
Figure 7: Data privacy and protection
VIII. FUTURE PROSPECTS AND CHALLENGES
1. Future Prospects
 Enhanced Energy Efficiency: AI in solar energy systems is anticipated to increase
energy production and efficiency. Improved system efficiency will arise from
advanced AI algorithms optimising solar panel performance, tracking, and
maintenance [21].

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 115
 Improved Grid Integration: AI-driven smart grids will make it easier to integrate
renewable energy resources into current energy infrastructures, including solar. This
integration will make better load balancing, demand response, and control of
distributed energy resources possible, making the energy system more dependable and
environmentally friendly [4].
 Energy Storage Optimization: Using AI’s predictive powers, energy storage systems
will undergo a revolution to maximize the efficiency of batteries and other storage
technologies. AI algorithms will streamline the charging and discharging process,
prolonging battery life and enhancing energy storage system effectiveness [47].
 AI for Solar Policy and Planning: Governments and policymakers may leverage AI-
based data analytics to create successful solar energy policies, incentives, and planning
strategies. AI may analyze large datasets to find places for solar systems that take into
consideration environmental conditions, energy consumption, and weather patterns.
2. Challenges
 Data Security and Privacy Concerns: Much private data must be handled as AI is
increasingly used in solar energy systems. It is essential to ensure data security and
privacy to protect against potential data breaches and unauthorized access. Strong
cybersecurity measures must be put in place to safeguard this important data and
preserve stakeholder confidence in AI-driven systems [36].
 Reliability and Trust: The precision and dependability of AI algorithms in solar
energy systems determine their efficacy. Gaining the trust of stakeholders requires
developing reliable AI models. Since they help users understand how AI makes
choices and guarantee that AI-driven energy systems can be depended upon for
important activities, transparent and answerable AI models are crucial.
IX. CONCLUDING REMARKS
1. The Transformative Power of AI in Solar Energy: The adoption of AI in solar energy
systems is revolutionizing the field of renewable energy and ushering in a new period of
opportunities and development. The impact of AI on solar energy is changing how we
produce, distribute, and manage renewable energy, which has several advantages for a
sustainable and effective future. AI optimizes energy generation, lessens reliance on
traditional sources, and improves grid stability by enabling accurate solar energy
forecasts[23]. Additionally, AI- driven analytics continually track the performance of
solar panels to guarantee peak performance, longer equipment lifespans, and better energy
outputs. Additionally, AI supports intelligent energy storage, dynamic grid management,
and decentralized energy solutions, speeding innovation, lowering prices, and increasing
accessibility and inclusivity of renewable energy.
2. A Brighter Future: AI and Sustainable Energy Solutions: AI and green energy
technologies can create a better and more sustainable future for our world. The landscape
of renewable energy is being transformed by AI’s transformational powers, which also
drive the adoption of clean energy sources and tackle urgent environmental issues. We are
advancing towards a more effective, resilient, and environmentally responsible energy
ecosystem by using the potential of AI in all facets of energy generation, distribution, and

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 116
consumption.
Optimizing energy generation is the first step in AI’s participation in
renewable energy. To anticipate energy output precisely, AI-driven systems examine
enormous volumes of data, including weather patterns and sun irradiation. With the
help of this forecasting accuracy, solar, wind, and other renewable energy sources
may function at their most effective levels, cutting waste and improving overall grid
stability. AI-enhanced energy storage systems ensure a continuous and sustainable
energy supply, which maximizes the use of batteries and other storage technologies
by holding extra energy during peak production and releasing it when demand is
high [24].
Despite their enormous promise, AI and sustainable energy solutions still
have several limitations to overcome. The challenges include addressing data
security and privacy issues, assuring transparent and moral AI use, and negotiating
legal complexity. In addition, equal access to AI-driven energy solutions is necessary
to guarantee that all communities get the rewards of clean energy. AI and eco-
friendly energy options work together to create a powerful transformational force
that can lead us to a better future. Using AI’s potential, we can hasten the transition
to a more resilient, carbon-neutral, and sustainable energy environment, resulting in
a world where cleaner, greener energy fuels a sustainable future for future
generations [25].
REFERENCES
[1] Finke, S., et al., Transition towards a full self-sufficiency through PV systems integration for sub-
Saharan Africa: a technical approach for a smart blockchain-based mini-grid. Renewable Energy and
Environmental Sustainability, 2022. 7: p. 8.
[2] Chaudhary, P. and M. Rizwan, Energy management supporting high penetration of solar
photovoltaic generation for smart grid using solar forecasts and pumped hydro storage system.
Renewable Energy, 2018. 118: p. 928-946.
[3] Asgher, U., et al., Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with
Integration of Solar Energy Sources. 2018. 11(12): p. 3494.
[4] Mostafa, N., H.S.M. Ramadan, and O. Elfarouk, Renewable energy management in smart grids by
using big data analytics and machine learning. Machine Learning with Applications, 2022. 9: p.
100363.
[5] Gungor, V.C., et al., Smart Grid Technologies: Communication Technologies and Standards.
a. IEEE Transactions on Industrial Informatics, 2011. 7(4): p. 529-539.
[6] Gercek, C. and A. Reinders, Smart Appliances for Efficient Integration of Solar Energy: A Dutch
Case Study of a Residential Smart Grid Pilot. 2019. 9(3): p. 581.
[7] Kulkarni, G., et al., The Role of Artificial Intelligence (AI) in Creating Smart Energy Infrastructure
for the Next Generation and Protection Climate Change. 2023. p. 457-464.
[8] Stecyk, A. and I. Miciuła, Harnessing the Power of Artificial Intelligence for Collaborative Energy
Optimization Platforms. 2023. 16(13): p. 5210.
[9] Dubey, B., S. Agrawal, and A. Sharma, Effect Of Solar Irradiance On Different Solar Pv Module
Technologies For Maximum Efficiency Extraction. 2023.
[10] Dubey, B., et al., Efficient Utilization of Different Solar PV Module Technologies for Maximum
Power Extraction. 2023: p. 2023.
[11] Ghatak, S.R., S. Sannigrahi, and P. Acharjee, Multi-Objective Approach for Strategic Incorporation
of Solar Energy Source, Battery Storage System, and DSTATCOM in a Smart Grid Environment.
IEEE Systems Journal, 2019. 13(3): p. 3038-3049.
[12] Nassar, Y., et al., Mapping of PV Solar Module Technologies Across Libyan Territory. 2022.

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 117
[13] Panagoda, S., et al., Advancements In Photovoltaic (Pv) Technology for Solar Energy Generation.
2023. 43: p. 30-72.
[14] Said, Y. and A. Alanazi, AI-based solar energy forecasting for smart grid integration. Neural
Computing and Applications, 2023. 35(11): p. 8625-8634.
[15] Vilasboas, I., J.A. da Silva, and O. Venturini, On the Summarization of Meteorological Data for
Solar Thermal Power Generation Forecast. Energies, 2023. 16: p. 3297.
[16] Abid, M., et al., Environmental Impacts of the Solar Photovoltaic Systems in the Context of
Globalization. Ecological Engineering & Environmental Technology, 2023. 24: p. 231-240.
[17] Sanyal, A., Ahmed, and J. Mohanta, Recent Developments and Challenges in Solar Harvesting of
Photovoltaic System: A Review. 2023. p. 251-275.
[18] Liaqat, K., Design and optimization of CSP power plants for Pakistan: a comparative study.
a. Clean Energy, 2023. 7.
[19] Enrick, H. and D. Pretzell, An in-depth study on solar photovoltaic: Design, operation, and
maintenance. International Journal of Multidisciplinary Research and Growth Evaluation, 2023. 4:
p. 1029-1037.
[20] Shamshirband, S., T. Rabczuk, and K.W. Chau, A Survey of Deep Learning Techniques: Application
in Wind and Solar Energy Resources. IEEE Access, 2019. 7: p. 164650-164666.
[21] Mikut, R., Computational Intelligence in Smart Grids. 2015.
[22] Najah, A.-M., et al., Call for Paper: Unleashing the Power of AI: Transforming Renewable Energy
with Artificial Intelligence. Computer Systems Science and Engineering, 2023.
[23] Nazary, F., Trustworthy Machine Learning in Smart Grids. 2023.
[24] Shahinzadeh, H., et al. IoT Architecture for Smart Grids. in 2019 International Conference on
Protection and Automation of Power System (IPAPS). 2019.
[25] Shi, Z., et al., Artificial intelligence techniques for stability analysis and control in smart grids:
Methodologies, applications, challenges and future directions. Applied Energy, 2020. 278: p.
115733.
[26] Venayagamoorthy, G.K. Potentials and promises of computational intelligence for smart grids. in
2009 IEEE Power & Energy Society General Meeting. 2009.
[27] Wurtz, F. and B. Delinchant, “Smart buildings” integrated in “smart grids”: A key challenge for the
energy transition by using physical models and optimization with a “human-in-the-loop” approach.
Comptes Rendus Physique, 2017. 18.
[28] Tuballa, M.L. and M.L. Abundo, A review of the development of Smart Grid technologies.
a. Renewable and Sustainable Energy Reviews, 2016. 59: p. 710-725.
[29] Arumugham, V., et al., An Artificial-Intelligence-Based Renewable Energy Prediction Program for
Demand-Side Management in Smart Grids. 2023. 15(6): p. 5453.
[30] Camarinha-Matos, L.M., Collaborative smart grids – A survey on trends. Renewable and
Sustainable Energy Reviews, 2016. 65: p. 283-294.
[31] Nair, D., M. Nair, and T. Thakur, A Smart Microgrid System with Artificial Intelligence for Power-
Sharing and Power Quality Improvement. Energies, 2022. 15: p. 5409.
[32] Liu, Z., Y. Gao, and B. Liu, An artificial intelligence-based electric multiple units using a smart
power grid system. Energy Reports, 2022. 8: p. 13376-13388.
[33] Omitaomu, O.A. and H. Niu, Artificial Intelligence Techniques in Smart Grid: A Survey. 2021.
a. 4(2): p. 548-568.
[34] Xu, Y., et al., Electricity Market Empowered by Artificial Intelligence: A Platform Approach.
a. 2019. 12(21): p. 4128.
[35] Puttamadappa, C. and P. B D, Demand Side Management of Small Scale Loads in a Smart Grid
using Glow-worm Swarm Optimization Technique. Microprocessors and Microsystems, 2019. 71: p.
102886.
[36] Mujeeb, S. and N. Javaid, Data Analytics based Efficient Energy Management in Smart Grids. 2021.
[37] Dai, S. and F. Meng, Online Model-based Functional Clustering and Functional Deep Learning for
Load Forecasting Using Smart Meter Data. 2022.
[38] Bakkar, M., et al., Artificial Intelligence-Based Protection for Smart Grids. Energies, 2022. 15: p.
4933.
[39] Abdel-Basset, M., et al., Responsible Artificial Intelligence for Smart and Climate-friendly Energy
Production Verantwortungsbewusstes System auf der Grundlage Künstlicher Intelligenz für eine
intelligente und klimafreundliche Energieerzeugung. 2022.
[40] Abo-Elyousr, F.K. and A. Elnozahy, Bi-objective economic feasibility of hybrid micro-grid systems

Futuristic Trends in Renewable & Sustainable Energy
e-ISBN: 978-93-6252-436-2
IIP Series, Volume 3, Book 1 , Part 1 ,Chapter 8
SMART GRIDS AND SOLAR ENERGY: ROLE OF
ARTIFICIAL INTELLIGENCE IN GRID MANAGEMENT
copyright © 2024 Authors Page | 118
with multiple fuel options for islanded areas in Egypt. Renewable Energy, 2018. 128: p. 37-56.
[41] K, J., et al., AI Based Solar Panel Cleaning Robot. international journal of engineering technology
and management sciences, 2023. 7: p. 313-318.
[42] Vedulla, G., A. Geetha, and R. Senthil, Review of Strategies to Mitigate Dust Deposition on Solar
Photovoltaic Systems. 2023. 16(1): p. 109.
[43] Bölke, D., Design Study for Dust Mitigation Solutions at the Tether Unit of the Nanokhod
Microrover. 2023.
[44] Pathmakumar, T., et al., Toward a Comprehensive Domestic Dirt Dataset Curation for Cleaning
Auditing Applications. Sensors, 2022. 22: p. 5201.
[45] Araujo Costa Silva, L., et al., Assessing the impact of soiling on photovoltaic efficiency using
supervised learning techniques. Expert Systems with Applications, 2023. 231: p. 120816.
[46] John, L., An Overview of Artificial Intelligence, Big Data, and Internet of Things for Future Energy
Systems. 2023. p. 25-48.
[47] Merrad, Y., et al., Cost-Effective Energy Demand Response Management System with a Smart
Contracts-Based Optimal Power Flow Solution for Smart Grids. Energies, 2022. 15.