Jihen
Elkhaldi
ENICathage,
University of Carthage, Tunisia
E-mail: jihenelkhaldi@yahoo.com
Lotfi
Bouslimi
ENICarthage, University of Carthage, Tunisia
E-mail: lotfi.bouslimi@enicarthage.rnu.tn
Hamza
Wertani
ENICarthage, University of Carthage, Tunisia
E-mail: hamzawertani22@gmail.com
Mohamed
Najeh Lakhoua
ENICarthage, University of Carthage, Tunisia
E-mail: MohamedNajeh.Lakhoua@ enicarthage.rnu.tn
Submission: 6/25/2020
Revision: 7/30/2020
Accept: 9/8/2020
ABSTRACT
The concept of Smart Grids refers to a complex
ecosystem that can be described as a combination of systems to capture its most
structural elements. When studying these complex systems, the traditional tools
of the Cartesian methods have shown their limits. There is therefore a need to
resort to other methods to model them.
These modeling methods are generally classified and
grouped into five families: functional modeling, decision modeling, resource
modeling, information modeling and mixed modeling. This review provides an overview of the state
of the art of an intelligent network. The classification of modeling methods is
also presented. Then an application of the bond graph approach will be
explained. Finally we describe a general idea on the management of smart grids.
Keywords: Smart Grid, modeling
methods, classification, management
1.
INTRODUCTION
Around
the globe an adjustment of electric energy is required to limit CO2
gas emission, preserve the greenhouse, limit pollution, fight climat exchange and increase energy security (Ourahou et al., 2018). The solution will come from
renewable natural energies considered inexhaustible, and in addition they produce
neither toxic waste, greenhouse gas nor nuclear wast,
etc.
By consequences, most of
the current electricity grids have to change if they are to support the
transition to a renewable based energy system.
This change will be
based on the progressive development of a new "intelligence" of the
electrical system, based on a greater penetration of new technologies, which
may be the solution for the above problem: smart grid technologies (Weedall, 2000).
The smart grid
technologies can also allow the allocation of large amounts of renewable-based
power generation.
Despite the many
benefits they bring, renewable energy has also some flaws that should not be
neglected. The most important is the irregularity of electricity production
over time. This problem with variable and unsecured power can be solved by a
coupling of supply sources and the formation of a hybrid system.
2.
GENERALTIES ON SMART GRIDS
Smart grids can be
defined as the integration of electric grids, communications networks, specific
hardware and computational intelligence (algorithms) to monitor, control, and
manage the generation, distribution, storage and consumption of energy. The
smart grid of the future will be distributed, interactive, self-healing and
communicating with every device (Carvallo &
Cooper, 2011).
A smart grid is an
umbrella term that covers modernization of both of the transmission and
distribution grids (Figure 1).
Figure1: Model of a smart grid
Source: Gungor
et al. (2011).
The
architecture of Smart Grids can be divided into three levels:
· A first layer of infrastructure
composed of equipment used to carry electricity (lines, transformers, etc.).
· A second level formed by
communication architectures (multi-media and multi-technologies) collecting
data from different network sensors.
· A final level consisting of
applications and services, such as monitoring, remote intervention systems, and
automation of electricity demand responses using real-time information.
The
main objectives expected of smart grids are (Jabban,
2013):
· A high capacity to introduce new
services.
· Reduced time and cost of
development.
· New functions introduced within the
network to allow each user to personally manage their data.
Like any complex system,
an intelligent network consists of a large number of interacting entities. It
adapts to external or internal pressures to maintain its functionality. This
complexity contributes to the emergence of the modeling.
In order to ensure
efficient modeling and rapid optimization of Smart Grids, we need to study them
systemically to define and understand the characteristics and behaviors of each
component of the global system.
And
to do that, there are
different methods, and a multitude of application domains.
3.
MODELING METHODS
Modeling
a system before it is made allows a better understanding of how the system
works. It is also a good way to control its complexity and ensure its
consistency (Figure 2).
Figure 2:
Example of modeling a smart grid
Modeling
methods are generally classified and grouped into five families: functional
modeling, decision modeling, resource modeling, informational modeling and
mixed modeling.
Function-oriented modeling describes the
functions, activities, and designed process of a system. Functional modeling
methods represent the interactions between functions and activities by
describing the information exchanged between them and the resources used and
proceed to their decomposition in an organized and detailed manner to better
understand the functioning of a system (Rahmouni
& Lakhoua, 2010).
Different methods have
been used for the analysis and modeling of functions such as: SADT, IDEF family
and Petri nets (Figure 3).
Figure 3: Example of a Petri
net
Source: Rahmouni
and Lakhoua (2010)
This approach aims to provide a detailed description of the decisions to
be made within a clearly defined time horizon and according to the activities.
The best-known decision-oriented modeling methods are GIM and its GRAI
origin (Figure 4), originally developed by Professors Pun and Doumeingts at the GRAI research laboratory of the
University of Bordeaux in the early 1980s (Hassan, 2010).
Figure 4: The GRAI reference
model
Source: Kromm,
Christophe and Deschamps (2004)
Resource-oriented modeling methods allow
the description of the resources required to carry out an activity by taking
into account the constraints of allocation of these resources. They are
specialized in the management of resources from its acquisition until its
exploitation but without understanding their operation (Darras,
2004).
Among the methods of
resource modeling, we consider the multi-agent approach, PERA (Figure 5) and
MOVES.
Figure 5: Skeleton of the PERA
methodology
Source: Kosanke,
Vernadat and Williams (1997).
Informational
modeling methods and process are intended to model the information system. They
ensure the flow of information about the processes, functions, resources,
organization, etc of a system. Some languages have
been developed (Figure 6) to meet this need such as: UML, UEML and IEM.
Figure 6:
History of UML
A methodological
approach is qualified as mixed when the researcher combines quantitative and
qualitative data / methods in the same study.
This modeling technique
studies the organization, the resources, and the information process models and
quantitatively analyzes the running of a system.
Although they cover
several aspects and study various systems (functional, informational, resource
and organizational). Mixed modeling methods cannot model decision-making
systems.
In the
face of very rapid scientific progress, some modeling methods (such as Merise, Booch, etc.) have quickly shown
certain limits. This evoked the birth of other methods (such as UML, SysML,
etc.).
Others have shown great reliability in studying
systems systematically, in order to extract the main characteristics and
behaviors of the different elements making up the overall system.
In
the next section, an approach to modeling an energy source (Wind Turbine) that
is part of the global smart grid system to be studied will be presented.
4.
WIND TURBINE MODELING APPROACH
Bond graph is a graphical technique used to model systems with a unified
language for all areas of the physical sciences (Dauphin-Tanguy, 2000).
We can combine different types of model systems such as electrical, mechanical,
hydraulic, thermal in the same Bond Graph, allowing a graphic visualization of
cause and effect, and provides power conservation (Table 1).
Table 1: List of
applications and their benefits
|
Applications |
Advantages |
|
Modelization |
Makes the energy study possible |
|
Analysis |
Simplifies model building for multidisciplinary
systems |
|
Control |
Leads to a systematic writing of mathematical
models (linear or non-linear associates) |
|
Identification |
Model estimation and identification of slow and
fast variables |
|
Surveillance |
Study
of structural properties |
|
Simulation |
Possibility to build a state observer from the
model |
A wind
turbine (WT) is a machine that transforms the kinetic energy of the wind into
mechanical or electrical energy. The power recoverable by a WT is a function of
the square of its diameter and the cube of the wind speed. This current is
supplied to a converter which transforms the variable direct current into
stable alternating current and supplies the building's electrical network or is
stored in batteries.
The
modeling of a WT (Figure 7 and 8) can be divided into several sub-models as
shown in figure 9. This includes the aerodynamic model, drive train model, and
the generator model. The model of the generator is generally a PMSG type as
induction type generators would require field supply from the grid. In terms of
control, wind turbines are normally equipped with pitch angle control for
maximum power extraction and converter control to provide control of output
power (Lakhoua, Naouali
& Chakroun 2014).
Figure 7:
Bloc diagram of the WT
The aerodynamic model of the WT gives the amount of
energy that can be extracted by the turbine from the wind. This is given in
terms of the mechanical power extracted by the WT which is given as (Diaf, Belhamel & Haddadi,
2007):
The blades of the WT extract the kinetic energy from the wind and
converted mechanical energy.
The kinetic energy is equal to the mass of air m and the wind speed in
the equation (2):
The moving airpower is equal to:
Where m is the mass flow rate per second. The air passes across an area
A. From equation (4):
Where 𝜌 is the
air density (𝜌 = 1.225𝑘𝑔/𝑚2). The power extracted from the wind by the
blades:
Where 𝐶𝑝 is the
power coefficient. The power coefficient is given to the function. 𝛽 (in degree) is the pitch angle of the rotor
blades. 𝜆 defines the
tip speed:
Figure 8 shows a sketch of a WT. It consists of six
inertias which are; the three blades, hub, gearbox and generator. The inputs
are wind speed and electromagnetic torque. To derive the dynamic equations for
this model using Newton’s second law can be quite hard, and one can easily make
some mistakes (Diaf, Belhamel & Haddadi, 2007). This is why the
differential equations are derived for the simplified case. The different
parameters are explained in Figure 9 shows a three-mass sketch of a WT.
Figure 8:
Model of the WT
The sketch consists of a hub, gearbox, and generator. Inputs are
aerodynamic torque and electromagnetic torque.
Figure 9:
Assemblage bond graphs of the WT
A model of a flexible WT is built by using the bond graph method; we
introduced in this model the flexibility of
blades, shafts, and the tower we finally got a complete model that describes
the behavior of wholes the essential elements of the system and less difficult
than other methods, in the future work we will analyze the behavior of the
system and we will compare it with a classical method to show the efficiency of
this method and to study the interaction of the WT with the other systems.
The simulation step
is done on a specific bond graph software 20-Sim, which is an object-oriented
hierarchical modeling software. It allows users to create models using bond
graphs, block-diagram, and equation models.
In this simulation model case, a list of numerical values of WT system
parameters is introduced in order to ensure a best simulations result. The
different speeds (Hub, gearbox and generator) of the WT are presented in figure
10.
Figure 10:
Results of the WT system speeds with respect to time(s)
The parameters
introduced for the wind model are the pitch angle, the reference power and the
wind speed. The simulations are made with a maximum pitch angle, a maximum wind
condition, a maximum power and with initial conditions on the rotor and the wind
generator.
The result obtained is not so significant; it is
just to validate part of the modeling approach by bond graph. There are still
many future improvements in the coupling of the turbine with the converter and
the intelligent control system to improve the performance of the electrical
parameters (current, voltage and frequency) in the output of the system to be
well usable to smart grids and consumers (demand side).
In the next section
we will explore the concept of management and its importance between the
distribution and the demand sides in the smart grid.
5.
MANAGEMENT OF SMART GRIDS
The
transformation of today’s grid towards smart grid imposes modifications on the
management of resources. A large part of
the energies used by smart grids are renewable energies, which are
characterized by their unpredictability. Consequently the distribution of
energy becomes more difficult. Therefore control methods are necessary to
achieve implementation.
Demand side management (Rahman
& Rinaldy, 1993; Cohen & Wang, 1988) is an important function in energy
management of the future smart grid, which provides support towards smart grid
functionalities in various areas such as electricity market control, and
management, infrastructure construction, and management of decentralized energy
resources.
The primary objective of the demand
side management techniques presented in the literature is reduction of system
peak load demand and operational cost.
Demand side management
commonly refers to programs implemented by utility companies to control the
energy consumption at the customer side of the meter (Logenthiran, Srinivasan
& Zong Shun, 2012).
There
are several demand side management techniques and algorithms used in the
literature (Cohen & Wang, 1988; Hsuand & Su, 1991).
Distributed Side Management (DSM)
control actions are implemented with the aim of reducing peaks in energy demand
and to make the energy demand pattern flatter, either at a per-house or at a
grid level. This is mainly obtained by avoiding the synchronous activation of
appliances or loads and by optimizing their activation in order to achieve the
overall better energy efficiency and by doing this in the most
transparent way with respect to the end-users (Miceli, 2013).
In order to be deployed,
these strategies have to be supported by specific appliances or house
infrastructures, by suitable control algorithms.
6.
CONCLUSIONS
Smart grids are the expression of the
digital revolution in our energy grids and it is certain that they have started
and will continue to change the entire value chain. They are not intended to replace the existing electrical
network, but to improve it. The Smart Grid must reconcile internal emergence
and self-organization by external factors in order to find the most optimal
balance of energy distribution in real time.
The work carried out
should make it possible to create a general model of smart grids. The analysis
and optimization of these complex systems opens up new perspectives.
Starting from this study
of overview on modeling and management of Smart Grids presented in this paper,
we will study and model an example of multi-source Smart Grid with different
methods with the aim of optimising the consumption of
electrical energy and improving the management of this energy from production
to the consumer.
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