Post on 13-Jan-2022
Agent Based Modeling of Electricity Market for
Sustainable Energy in a Smart Grid
Environmentby
Abdel Rahman Karam Ibrahim Al-Ali
Matric No: 170096
Progress Presentation
Doctor of Philosophy
Supervisor
Dr Danial Md Nor
Co-Supervisor
Dr. Norfaiza Fuad
Panels: Ts.DR. Khalid Isa
Dr. Nan Mad Sahar
1
PRESENTATION OUTLINE
Research Problem
Objectives
Literature review
Methodology
Conclusion
References
Research Problem
Retail customers use the extensive set of information
provided by their ICT equipment to review and choose
the appropriate tariff from the retail market offered by
energy companies.
The wholesale market represents a deregulated market
that is used by competitive energy companies that want
to obtain the necessary capacity for their customers.
3
OBJECTIVES
1. To develop a relationship between the energy
market layer and the electricity markets.
2. To model the complex environment and
market demand based on three sets of agents,
economic, social and contextual.
3. To test and evaluate the market design prior
to its real-world deployment using agent-based
modeling.
Literature Review on the use of
IoT to collect dataSNo
.
Researc
her
Application Communicatio
n Method
Limitation Future work
1. [14] Smart Meter IoT-wireless
communication
There is no error or fault
detected in this system.
More functionality can be
added to improve the
system
2. [15] Smart Meter IoT Bills are generated for
every instant of usage.
Notification can be
developed.
2. [16] Smart Meter IoT – Thing Speak System measured only the
power consumed.
Addition of relay switches
to control the loads
remotely can be done.
3. [19] Smart Meter IoT-Mobile
application
Method used to measure
energy is conventional.
Conventional methods can
be replaced.
4. [20] Smart meter IoT- GSM
webpage
There is no fault detection
or any notification in the
system
Fault detecting system can
be implemented.
Literature Review on Simulation of
Electrical Vehicle and Energy Network
using Agent Based TechniqueSNo Resea
rcher
Technique Used Results achieved Gap Identified or
drawbacks and Future
work
1 [23] Agent Based Modelling Smart grid is modelled as multi
agent. High confidence of
application.
Lack of test results
2 [24] Agent Based Modelling &
Monte Carlo Simulation
Algorithm used.
The outcome inferred was that
there was no significant impact on
the smart charging strategy
especially during the off peak
period and further some MV/LV
transformers exceed the nominal
power without control.
A more efficient
algorithm must be
developed for power
control.
3 [25] Agent Based Modelling - micro
and macroscopic modeling &
used NetLogo Software
Observed that the model
overcame all the disadvantages of
the existing models by considering
the human aggregate behavior on
the overall charging demand of
EVs.
To select a wider range of
variables for
comprehensive sensitivity
analysis using the fuzzy
membership functions.
4 [26] Agent Based Modelling -
Integrated analytic framework
& Monte Carlo Simulation
Algorithm used.
Charging demand is observed to
be highly dependent on the PEVs
evolution scale.
More efficient method
using Artificial Intelligence
could be used.
Literature Review on Simulation of
Electrical Vehicle and Energy Network
using Agent Based TechniqueSNo Research
er
Technique Used Results achieved Gap Identified or drawbacks
and Future work
5 [33] Multi Agent
Simulation Method
Resulted in lowering the
clearing prices.
The future work is to improve
the agent model by considering
more factors that could affect
the response characteristics
namely customer interaction and
user satisfaction.
6 [34] Integrated system of
combining Electric
Vehicles (EVs) and
the intermittent
renewable energy
sources
The results indicate that
the higher scores are
associated with self-
sufficiency and self-
consumption indicators
which uses battery as the
energy source.
The gap in this work is that the
researcher has focused on five
factors. Future work is to analyse
the model through Discrete
Choice Experiments or Conjoint
analyses for sustainable charging.
7 [35] zip code is
considered as the
agent and each
agent has the
threshold adoption
defined by the
Roger’s model
It is observed that the
energy consumption
increases gradually as the
number of EVs has
increased over the years.
This model has considered only
the energy demand rather not
considered the supply side. Also,
further to consider the street
block level distribution of EVs ,
to predict more accurately which
could be reliable.
Literature Review on Simulation of
Electrical Vehicle and Energy Network
using Agent Based TechniqueSNo Research
er
Technique Used Results achieved Gap Identified or
drawbacks and
Future work
8 [36] Agent Based
Simulation
This model predicts the charging
infrastructure EV adoption
relationship and compared
various charging technologies
Lack of efficient method
using Artificial
Intelligence.
9 [37] Impact of the Plug in
Electric Vehicles
(PEVs) that are
integrated into the
power distribution
system
The PEV model proposed had a
lower impact on the power grid
as compared to the conventional
load
The future work is to
analyse the PEVs effect
depending on the
conventional or complex
type loads
10 [38] Proposed agent
based simulation
employing the
Disruptive
Innovation Theory
(DIT)
It is observed from the simulation
results that the market entry
order has a very crucial success,
while the RET diffusion is highly
impacted due to lower price-
higher consistency of consumer’s
preferences
Lack of efficient method
using Artificial
Intelligence.
LITERATURE REVIEW - SUMMARY
1. IoT can be used to collect data and display in the webpage with the help of server and can store the data incloud data storage .
2. Agent based simulation model is best suited forElectricity market for a sustainable energy for EVs
3. There are many software tools used for simulation of theagent based model, while the most common based on theliterature review is Monte Carlo SimulationsoftwareLength.
4. Further, many models have been used for the agent basedmodel like zip-code, Roger’s model, microscopic ¯oscopic level, and so on.
5. The latest techniques of artificial intelligence, such asNeural Networks or Fuzzy logic has not been used toanalyse and model the same
Methodology
1. First , the method to construct wholesalemarket trading needs to be incorporated.
2. Secondly the method to model the complexenvironment and market demand based onthree sets of agents, economic, social andcontextual must be modelled.
3. Finally the method to test and evaluate themarket design prior to its real-worlddeployment using agent-based modeling. Thisrequires an open and rich test bed thatspecializes in simulating the structure andoperation of innovative retail markets.
Progress Updates
1. Till date literature review of 45 journals has been
done, and reviewed & summarised the methodology
and techniques employed by other researchers, along
with the outcomes and results achieved.
2. A review paper titled “The Review of Simulation of EV
and EN using ABT” was drafted, submitted and
presented as an article to Global Research Conference
2020.
3. The methodology implementation is started and the
initial results will be shared during the next progress
updates.
CONCLUSION
Generally, electricity prices go up along withdemand by providing consumers withinformation on current consumption and smartgrid prices power management service help toreduce consumption during high cost time andpeak demand.
Further, complexity of the smart grid achievingoptimization is not an easy task, even usingcomputer models and this have the power tomanage power by generation better thanintermittent power source
Hence, agent based modelling isproposed for this task.
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