Hybridization and energy storage
high efficiency and low cost
2020 6th IEEE Congress on Information Science and Technology (CiSt) | 978-1-7281-6646-9/21/$31.00 ©2021 IEEE | DOI: 10.1109/CIST49399.2021.9357198
Khalil SAADAOUI
Laboratoire RITM
Ecole Supérieure de Technologie
Electrical Department-Hassan II
University
Casablanca, Morocco
k-saadaoui@hotmail.fr
The production of electricity must be constantly balanced
with the consumption of electricity and this work of forecast
and permanent adjustment between production and
consumption, it is the national society of regulation and
transport of electricity which is responsible.
Abstract—Hybrid energies interest many companies and
countries. No form of electricity production is optimal in all
situations. The wind and the sun are intermittent but do not
consume fuel and do not emit greenhouse gases. Natural gas
electricity production emits greenhouse gases but is
distributable (i.e. it has a yield that can be easily controlled
between maximum values of nominal capacity or reduced to
zero) to help balance supply and demand. Hydroelectric power
often requires and devotes large areas, but is renewable and
distributable. However, the realization of all these projects
remains dependent on the development of more efficient and
more economical electrical energy storage systems. Hybrid
power plants: a solution for the future? To provide energy that
is more affordable, more reliable, and more sustainable. AI,
smart grid [6] and Energy storage: The dynamics of new
energies, that is to say, local and renewable, are indeed
launched. To succeed in this revolution, the problem of storing
renewable energies, due to their intermittent nature, remains
to be resolved. Machine learning and neural networks play an
important role in improving forecasts in the energy industry.
Today the electrical networks of the countries are more
and more connected and the alternating electric current at a
frequency of 50 Hertz when one in a situation of balance
between production and consumption. If production exceeds
consumption, the frequency increases. Conversely, if
consumption increases and production is lower than
consumption, the frequency decreases. In one case as in
another, if the frequency variation is very large compared [2]
to the reference of 50 Hertz, this can result in incidents on
the network and cuts in localized or even generalized
electricity, hence the need for an almost perfect balance at all
times between production and consumption.
This is the problem of renewable, intermittent, or variable
energies. When you have controllable power plants [13] that
you can start-up in a few minutes, such as gas power plants,
there is no problem in balancing production with
consumption at the matching moment. On the other hand,
with certain variable electricity sources such as wind turbines
[3] [4] and photovoltaics, the production of which depends
on weather conditions, in this case, it becomes complicated.
Keywords—Hybrid, Energy, Modeling, Solar, Wind, Battery,
Matlab, Fuel cell, Intermittence, Fossil, Storage. AI, Discharge,
Production, frequency, renewable, photovoltaic panel, electrical
energy, Windfarm, Water, Tidal.
I.
Abderraouf ABOUDOU
Laboratoire RITM
Ecole Supérieure de Technologie
Electrical Department-Hassan II
University
Casablanca, Morocco
abderraoufaboudou@gmail.com
Kaoutar SENHAJI RHAZI
Laboratoire RITM
Ecole Supérieure de Technologie
Electrical Department-Hassan II
University
Casablanca, Morocco
senhaji.ksr@gmail.com
INTRODUCTION
A.
Intermittent energy:
Energy transition a question that comes up regularly
when we talk about the development of renewable energies,
particularly wind turbines and photovoltaic panels. This is
the question of their intermittence, we also talk about their
variability. When the weather calms down and there is no
wind or sun, it's a blackout. It is not likely to happen, because
indeed it seems logical to think that when there is no sun and
there is no more wind, the electrical network is no longer
supplied and like wind and solar have more and more room
in the electric energy [1] and electric mix from developing
countries and even at the global level, one could fear future
electricity shortages.
II.
STORAGE
A. Energy storage
To balance the supply and demand of electricity during
consumption peaks, we use electricity that would have been
produced by photovoltaic power plants or by a wind farm
[21] during the day when the demand for electricity was less
strong. The electricity that we stored.
Electricity storage can be done in different ways:
- Lithium-Ion batteries
- Vanadium batteries
B. The variability of certain energies
Renewable Electricity as such cannot be stored. The
batteries store electrical energy, energy converted into
electrochemical form, which electrical energy in the
electrochemical form will be further converted into
electricity. Electricity which is electron flow in the proper
sense of the term that cannot be stored is difficult in storage.
This implies that at all times, power stations should produce
enough electricity to serve our consumption requirements.
- PETS
PETS/STEP (Pumped Energy Transfer Station) is a kind
of two-way dam.
978-1-7281-6646-9/20/$31.00 ©2020 IEEE
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B. Developing Battery Management Systems with Simulink
and Model–Based Design
The Battery frameworks have now turn into an essential
instrument for designing battery-powered systems. Their
function contains the battery description and algorithm
development, and real-time simulation, state of health (SOH)
approximation, state-of-charge (SOC), and system-level
optimization, for the system planning of battery
management.
Their
uses
include
algorithm
development,
characterization, and health status assessment (SOH), battery
state-of-charge (SOC), real-time simulation, and design level
upgrading for the battery management system.
Fig1 Workflow of battery management system development with
Simulink and Model-Based Design.
The predominant energy storage systems in aircraft,
electric vehicles, portable devices, and other equipment
requiring a dependable, high-energy-density, low-weight
power reserve comprise Lithium-ion battery packs.
The Battery models which based on equivalent circuits
are usually preferred for system-level development and the
control applications due to their relative simplicity.
Equivalent circuits are used by engineers for modeling the
thermo-electric performance of batteries, defining their
nonlinear element parameters by using correlation methods
that combine frameworks and experiment-based measuring
through optimization.
Across industries, the growing dependence on battery
pack energy storage has underscored the importance of
battery management systems (BMSs) that can ensure
maximum performance, safe operation [25], and optimal life
span under diverse charge-discharge and environmental
conditions. To meet these objectives by designing a BMS,
engineers develop feedback and supervisory control
algorithms that:
-
Balance the state-of-charge of individual cells
-
Keep in line the battery charging pattern state-ofhealth
(SOH)
and
state-of-charge
(SOC)
approximation
-
Set apart the battery pack from source and load when
needed
-
Limit power input and output for thermal and
overcharge protection
-
Monitor cell voltage and temperature
Fig 2 Complete battery management system
This figure represents the complete battery management
system containing two or battery packs and connected with
the plant.The capabilities of modeling and simulation by
Simulation enable BMS growth, admitting automatic code
generation, control logic, electronic circuit design,singlecell-equivalent circuit formulation and parameterization, and
verification and validation. With Simulink, engineers can
design and simulate the battery management systems by:
Our basic purpose is to know how engineers develop
BMS algorithms and software systems by executing systemlevel models using Simulink. Simulation-Based Design by
utilizing Simulink empowers you to achieve awareness into
the dynamic performance of the battery pack, explore
software architectures, examine active cases, and begin
hardware tests in early, reducing design errors.
The development of the Battery management system with
Simulink depends upon 3 factors.
-
Desktop Simulation
-
Real-Time Simulation
-
Hardware Implementation
-
Modeling battery packs using electrical networks
that set topology mirrors in the real system and
balance with the number of cells.
-
Parameterizing equivalent circuit elements using test
data for an accurate representation of cell chemistry.
-
Designing the power electronics circuit that links the
pack to controls.
-
Preparing closed-loop control algorithms
supervisory and fault detection logic.
-
Working-out state perceivers for state-of-health and
state-of-charge on-line approximation.
for
But the battery management system can also consist of a
single battery pack controlling the charging and discharging.
It is also known as the Battery controller design. We can also
say it Battery management system of a small scale. It works
on the principle that when the voltage source is disabled, the
battery will supply the load. And when the voltage source is
enabled the battery will charge and the load will be supplied
from the voltage source.
The battery controller has two cases.
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-
Charging
-
Discharging
So the battery is charging in two modes.
-
Constant current (We should check the allowed
maximum current)
-
Constant voltage
Fig 6 Simulation results for battery in discharge mode
Up-to this point we have discussed the two modes of
battery separately. But we should combine this to make the
model more efficient. So to make it compatible for both
modes I have used the switch which will determine the
mode.
Fig 3 Simulation results for charging mode
From the figure, you can see that battery is in charging
mode. Because both SOC (state of charge) and voltage is
increasing but the current is decreasing. The voltage is
25.98V and the current is 16A. The reference current is at the
maximum levelwhichis -22A.
Fig 7 Combined Simulink Model for both charging and discharging
It works based on a voltage source because we determine
the modes based on the source. Now we can check the
effects by using simulation by keeping the switch on or off
(for charging and discharging mode).
Fig 4 Simulink model for charging of the battery
III. ARTIFICIAL INTELLIGENCE ENABLES ELECTRICAL
EFFICIENCY AND RELIABILITY
AI has transformed by now and will remain to transform
renewable energy companies on the supply-side. To provide
the supreme practical value out of a storage scheme, AI
optimizes through out multiple uses such as backup power,
management of demand charge, trade of energy, and market
involvement of wholesale. AI invariably brings in economic
[9] trade offs when finding out how much energy to
distribute or keep for the future.AI automatizes system
operation [25]. The platform comprehends market
complications, absorbing datasets such as market
participation rules, price signals, rate structures, and solar
production forecasts. It reacts nearly right away to these
contributions to determine the top worth for customers of
Stem.
This algorithm is designed in such a way that the battery
will charge up to 80% SOC level. To make the battery
efficient and enabled it to be charged up-to 100% we may
require a different algorithm. PI controller issued in this
Simulink model to determine the reference battery current.
For discharge mode, we have taken a different PI
controller and we have assumed the loadis 48V.
Artificial Intelligence is a set of technologies
"implemented with a view to achieving machines capable of
simulating human intelligence", such as reasoning or
learning.
Fig 5 Simulink model for battery in discharge mode
So in discharge mode, the voltage and SOC level should
decrease, and reference current should increase. So we
validate these results by simulating Simulink.
The main field of study in Artificial Intelligence is called
Machine Learning. It consists in teach computers to perform
three types of actions: predict, classify and group. The
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algorithms [22] or models performing these tasks are called:
regressions: Ridge, Lasso, forest of decision trees, gradient
boosting, neural networks, etc. classifiers: support vector
machine, decision trees, neural networks etc. data
partitioning algorithms or clustering : k-means, DBScan, etc.
These models can be used alone or in combination with other
methods such as optimization [8] to implement more
complex applications.
-
Supervised learning (regression and classifier)
-
Unsupervised learning (data partitioning)
Fig 8 iFORBES ENERGY STORAGE
Application cases of the technologies of Artificial
Intelligence in the sector of energy. The use of Artificial
Intelligence (AI) technologies already exists in the energy
sector. Generally demanding in quality data, these
applications have often focused on maintenance
optimizations in production or networks as well as on
maintenance algorithms for the forecast in particular for
energy exchange activities. Beyond that, the penetration of
AI technologies is still limited in the energy sector. This is
mainly because Machine Learning algorithms generally
require a more complex and more sophisticated the energy
sector has so far only had access to a large amount of highquality data, but the energy sector has only had little data
compared to other sectors (internet, telecom, mobility,
industrial). In addition, the marginal value of complexity
may be low in energy1)
IoT involves sensors or devices, connectivity, data
processing, and user interface for control. The sensors or
instruments will gather data from their surroundings, such as
the temperature of a cubicle.
Connectivity will send that piece of information, the data
containing the temperature, via the internet to the cloud.
Then, that data will be processed by software and lastly, this
information is made useful to the end-user on an easy to read
platform. AI plays a big role by being able to quickly gather
insights from the gathered data. This technology can create
analytics, identify patterns, and detect anomalies in the data
more Distributed Energy Resources (DERs), like battery
storage and solar panels, and have to learn how to optimize
the grid with these new forms of generation. Over the next
six years, the global DER market size is predicted to reach
over $570 billion by 2025 and expected to grow at a rate of
15% during that period. With DERs drastically on the rise,
AI can help utilities better manage and control [14] the grid.
.
This situation, perhaps less favorable than in other
sectors, doesn’t imply that the Machine Learning will not be
used in the energy sector. Regulatory changes and
technology are leading to an increase in the volume and
complexity of data (deployment of communicating meters,
BIM, etc.) which allows the development of solutions and
tools [5] that are all the more performance.
AI will help utilities leverage the disruption of
decarbonization, decentralization, and digitalization. Lastly,
digitalization allows utilities to make more sense of the vast
amounts of data collected and enable interoperability
between their physical assets and software via the cloud and
the Industrial IoT.
AI has been around for over half a century. However,
three essential components of AI technology have seen
significant growth over the past years, which has accelerated
and expanded the uses of AI. First of all is computation
power, which is crucial for AI to be massively scaled as
machines need to be able to process data quickly and
efficiently.
Secondly, access to big data helps drive AI technologies.
The world has seen tremendous growth in data and has
created nine times more data than it had from the beginning
of time up until 2015.7.Accompanying this growth in data, is
the endless storage accessible via the cloud. As per
Microsoft, the cloud is a worldwide network of servers that
are linked to functioning as a single ecosystem. These
servers can store and manage data, run applications, deliver
content, service office productivity software, and more. AI
goes hand in hand with the cloud and the Internet of Things
(IoT) and is functionally necessary to connect devices online
and make sense of all the data streaming from these IoT
devices. IoT is connecting any electronic device to the
Internet or one another.
Fig 9 Optimization Solar Microgrid
According to the World Economic Forum, the electricity
sector will capture over $1.3 trillion of value from
digitalization, AI can help save over $200 billion globally for
the energy industry. General Electric is studying how to use
technology to improve the flow of electricity from batteries
to consumption points. One small technology change can
unleash enormous savings for companies. Moreover, IDC
forecasts that by 2021, 40% of numerical revolution
creativities will habit artificial intelligence services and 75%
of business applications will use artificial intelligence by
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2025. With the current market conditions, utility companies
have much to gain by leveraging the massive computational
power and speed with the uses of AI technology. According
to BP’s Energy Outlook, by 2040, renewables will make up
over 20% of total global power generation, while hydro and
nuclear will make up about 10% and 8%, respectively.This
global generation mix will be the most diversified the world
has ever seen. Additionally, this growth in renewable is
predominant in developed and developing countries,
including OECD countries, China, and India AI can help
mitigate issues by creating forecasts for electricity demand,
generation, and weather, and predicting and managing
fluctuations.
Fig 11 PHOTO PETS
Two huge pools of water located at two different
altitudes. During the day when the electricity consumption is
low, we use the electricity produced by the wind turbines
solar panels to feed pumps which will raise the water from
the lower basin to store it in the upper basin. Use lost
electricity to pump water.
In the evening at the time of the peak of electric
consumption, it is enough to bring down the water and make
turn the turbines with alternators and thus to produce
electricity.
Today with the development of renewable energies, we
can very well imagine coupling a wind farm with a PTES.
Thus, the excess electricity produced during periods of
high winds will be stored to be used during periods of low
wind or high demand.
Fig 10 Predictive analytics
It is a hybrid power plant (Wind /Solar/ Water)
AI will reshape the relationship between the energy
consumer and supplier by individualizing the customer
offering and experience. The energy business is changing
rapidly as it confronts commotion from digitalization,
decentralization, and, decarbonization. AI can help utilities
success fully optimize the grid and maintain reliability and
resiliency. Massive computational power, the growth of big
data, and advanced algorithms have propelled AI technology
to solve numerous problems in every industry. Particularly
with utilities, AI can develop human-to-asset interactions
that improve asset management, routine operations, and field
service operations. Additionally, AI optimizes renewable
[27] resources on the grid and can improve reliability and
resiliency. It also offers an opportunity for utilities to create a
personalized customer experience.
PETS represents the most promising means of electricity
storage in the world today.
Lithium-Ion batteries are the technology
developing very quickly thanks in particular
development of electric vehicles. This technology
expensive but its price has dropped less than -85%
year since 2010.
that is
to the
is a bit
year on
So thanks to batteries, we can also develop hybrid power
plants. In this case, we couple a wind farm where
photovoltaic to batteries.
Carmaker Tesla and French renewable energy company
NEONE joined forces and installed in the state of South
Australia a lithium-ion battery storage plant to store part of
the electricity produced by wind, a 100 Megawatt power
plant that supplies 30,000 Australian homes during peak
consumption hours.
Utilihive empowers utilities to manage the data flow for
utilities. TAC™ is a protected channel concerning a firm’s
circulated statistics and professional experts. Such systems
work with worldwide power-driven functions and
administration organizations to assist them achieve approach
[23] to the AI consultants that make them able to regulate
considerable collection of data. A smart grid [6] driven by AI
will aid grid workers in proposing improved renewable
energy dispersal results to buyers on the request side.
Renewable energy will still be an affordable and more
reliable development that can only be considered a success
for sustainability.
This has enabled residents to make big savings on bills
and reduce the use of gas and oil plants in the region.
It is a hybrid power plant (Wind / Batteries)
NEONE commissioned a power plant in France in 2019
the largest power storage plant for a photovoltaic [26] farm
coupled with batteries Lithium-Ion. This 6 MW storage plant
Megawatt allows intervention in less than 30 seconds in the
event of an increase or drops in voltage on the French
electricity network
It is a hybrid power plant (Solar / Batteries)
Storing electricity thanks to hydrogen [16] when there are
sunshine and radiation, the panels capture a maximum of the
sun's rays and therefore produce a large amount of electricity
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which is lost. In this context, the surplus electricity can be
applied to run electrolysis and electrolyze the water.
The Electrolysis allows thanks to an electric current to
decompose the molecules of H2O water to recover on one
side oxygen and on the other of hydrogen that we will store
in a tank in winter when the days are shorter and less sunny
and the solar panels are less productive.
We will use the stored hydrogen to make electricity: it is
the fuel cell [11] which is a generator that converts chemical
energy in this case hydrogen into electric energy (the reverse
principle of electrolysis of water).It is a very expensive
technology.
Figure 12- Different Energy Sources: Fossils, Hybrids, Storage, and
Smart Grids.
It is a hybrid power plant (Solar / Hydrogen)
IV.
PV / autonomous fuel cell hybrid system for the city of
Brest in France
FOSSIL HYBRID
The principle of the Aïn Beni thermo-solar power plant is
that this power plant consists of two lines, two gas turbines,
two recovery boilers, a steam turbine and production and
evacuation lines for 'energy.
Best design [28] of a complete hybrid PV/FC power
system devoid of battery storage to supply the electric load
demand of the city of Brest. The optimization [8] study,
using the total net present cost, clearly showed that a hybrid
power supply system, especially fuel cells [11], is a viable
alternative to diesel [10][17] generators as a noncontaminating dependable power reserve at a low cost [18]
of gross up keep. Fuel cell generators could efficiently
complement a fluctuating renewable source like solar energy
to satisfy growing loads.
So steam is produced by two sources: there is natural gas,
as part of the normal combined cycle, and steam produced by
the solar field.
These two vapors converge on the steam turbine and
integrate at the same time to produce electrical energy. "The
total power of the plant is 472 MegaWatt, of which 20MW is
solar, which allows it to satisfy about 10 % of the country's
energy demand.
Storage solution for the future
It is a hybrid power plant (Thermo-Solar)
It is an alternative and innovative energy storage system.
An opportunity to present this new technology and the
industrialization potential in Morocco.
The Vanadium flow (VFB) battery
The large capacity of these batteries makes them well
suited to applications requiring significant storage, a
response to maximum consumption, or production equation
from variable sources such as solar or wind power plants.
Low self-discharge and limited maintenance led to their
adoption in some military applications2.
Figure 13-Floating Wind Turbines
These batteries allow us to respond quickly to demand,
they can also be used in applications ASI(actuator sensor
interface) where they replace the originators of lead-acid
batteries.
December 31, 20 kilometers from the coast of Viana do
Castelo in Portugal.At 4,000 GW in Europe, it is
significantly more than the resource potential.
The main advantages of this technique are:
-
modular capacity at will, using more or less large,
more or less full tanks;
-
The battery can be left discharged for long periods
without deteriorating. It can also be recharged by
replacing the electrolyte if no energy source is
available to charge it. This battery thus allows rapid
recharging by replacing the electrolyte through a
pump, or slow recharging, by connection to an
energy source;
-
If the electrolytes are mixed accidentally, the battery
suffers from no irreversible damage.
3.1 Gigantic Floating Wind turbines
Figure 14-Floating Water Turbine (Tidal network)
The potentially huge global tidal power industry.
Generating more than 18 MWh (megawatt hours) over 24
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hours. The leading tide related project around the globe is the
Sihwa Lake Tidal Power Station in South Korea, with a set
capability of 254MW.
3.2 DEFINITION OF A HYBRID SYSTEM
The problem with the variable and unsecured power
produced by renewable energy sources can be solved by
coupling supply sources and the shaping of analleged hybrid
system (SH). A hybrid system with renewable energy
sources (SHSER) is an electronic system that includes
multiple energy sources, at least one of which is renewable
(Lazarov et al, 2005).
Figure 15: Schematic diagram of a hybrid system.
VI.
The hybrid system may include a storage [15] device.
From a more global point of view, the energy system of a
given country can be considered as a hybrid system.
Hybrid Systems Autonomous hybrid systems allow the
production of electrical energy outside the public grid,
particularly in areas with limited accessibility. Energy
production is optimized by the use of several sources:
photovoltaic solar, wind, micro-hydraulic [7], and
methanation. The generator set is called upon from time to
time if necessary. Storage on batteries is still necessary and
makes it possible to optimize the use of the group and the
autonomy of the system. Hybrid systems thus allow the
supply of mini-grids from 1 kVA to more than 300 kVA in
the following sectors: rural electrification island
electrification
industries
tourism
agriculture
telecommunications. The advantages of hybrid systems:
BENEFITS OF RES Reduction of fuel consumption and
supply constraints Energy independence and long-term
visibility of energy costs longer life of generator sets reduced
maintenance Reduction of noise pollution and air pollution
of the site
Integration of wind energy into the electric transport
network, and their participation in ancillary services
and in particular the primary frequency and voltage
formalization apart from the obstruction to fall.
-
Integration of energy storage [20] to reduce
fluctuations in power due to the intermittent nature
and irregularity of wind generation.
Developing Battery Management Systems with Simulink
Then, dynamic and complete modeling of batteries is
established, taking into account some simplifying
assumptions. A simplified model of the charging and
discharging .It works based on a voltage source because we
determine the modes based on the source. Now we can check
the effects by using simulation by keeping the switch on or
off (for charging and up-to this point we have discussed the
two modes of battery separately. We combine this to make
the model more efficient to make it compatible for both
modes I have used the switch which will determine the
mode.
Thanks to these system services, the voltage and
frequency are maintained within ranges which guarantee a
sufficient level of safety and which are defined by
regulations [10].
MODELIZATION :
Tools [5] on the modeling [24] of storage solutions and
system services that they can provide to promote the
insertion of renewable energies on the networks.
1.
-
The work presented in this paper is part of the integration
of wind energy/ solar energy and various other sources into
the electric transport network, and their participation in
ancillary services and in particular the primary frequency and
voltage regulation, as well as the resistance to voltage-dips
coupled to Artificial Intelligence, smart grid [6] and storage
batteries.
Integration of hybrid energies in electrical networks
The desire to see the development of hybrid energy
sources will lead to the increasing integration [19] of solar
power plants, wind turbines, etc. into the electrical network
In the event of a fault occurring on the network, these wind
turbines are forced to disconnect since they cannot regulate
their active and reactive [12] production in order to provide
system services to the electrical network, and in particular
frequency regulation, voltage regulation and resistance to
voltage drops [8].
V.
CONCLUSION:
General conclusion and outlook
- The share of renewable energy mainly from wind
energy / solar energy in the energy mix in the world
and in particular in Morocco is always climbing. The
integration of this wind energy into the electrical
network implies compliance with technical
constraints in order to ensure the stability of the
electrical system and to assure users a dependable
and superior energy supply. This type of energy is
distinguished by its intermittent nature and which is
often the source of serious problems related to the
stability of networks.
Homer Software:
AI Efficiency, Electrical Efficiency, and Reliability
The Homer program was founded by the National
Renewable Energy Laboratory (NREL) in the United States.
It is a very powerful tool for simulating and calculating
intelligent electrical networks with a hybrid component.
AI constantly makes economic [9] trade offs when
determining how much energy to deploy or store up for
later.AI automatizes system function. The platform realizes
the complication of the market, compiling data sets such as
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price structures, solar production forecasts, price signals, and
market participation rules. It responds near-instantaneously
to these inputs to drive the most value for Stem customers.
inverter input voltageregulation features in compliance with electric
gridrequirements. Electric Power Systems Research, 79 (9): 1271-1285.
[2] Dursun, E .; Kilic, O. (2012). Comparative evaluation of different
power management strategies of a stand-alonePV / Wind / PEMFC hybrid
power system. Electrical Power and
Energy Systems, 34 (1): 81-89.
Other interesting perspectives could be envisaged to
make it even easier to insert AI in energy field. A first order
of ballpark figure of the technical feasibility and deployment
potential of these fields of application led to the selection of
five of them, which are:
-
Predictive maintenance applied to production assets:
example of wind turbines
-
Automatic detection and classification of defective
entities from the electricity grid
-
Knowledge and customer support tools
-
Intelligent consumption management
-
Detection and optimization of consumption
groupings and infrastructure deployments
[3] Technical brief on Wind Electricity Generation (2009):Retrieved on
May 25, 2019, from www.windpower.org
[4] Iheonu, EE; Akingbade, FOA; Ocholi, M. (2002). Wind Resources
Variations over selected sites in the West Africansub-region. Nigerian
Journal of Renewable Energy, 10, 43-47.
[5] Sengprasong, P .; Jean-Yves LC; Demba, D .; Ghislain, R .;Claude M.
(2010). Reviews on Micro-Grid Configuration andDedicated Hybrid
System Optimization Software Tools:Application to Laos. Engineering
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