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 &macroscopic 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.

REFERENCES[1] N.Apergis and J.Payne, “Renewable and non-renewable energy consumption-growth nexus:

Evidence from a panel error correction model”, Energy Economics,Vol. 34, No. 3, pp. 733-738, 2012.

[2] E.A.Chavez-Urbiola, Y.V.Vorobiev and L.P.Bulat, “Solar hybrid systems with thermoelectric

generators”, Solar Energy, 2012.

[3] H.L. Zhang, J. Baeyens, J. Degrève, G. Cacères, “Concentrated solar power plants: Review and

design methodology”, Renewable and Sustainable Energy Reviews,VOl. 22 , pp. 466-481, 2013.

[4] Feldman,“Solar Energy”, 2015

[5] B.I.Rani, G.S.Ilango and C.Nagamani, “Enhanced power generation from PV array under partial

shading conditions by shade dispersion using Su Do Ku configuration, IEEE transaction is sustainable

Energy,Vol. 4, No. 3, pp. 594-601, 2013.

[6] M.Seyedmahmoudian, S.Mekhilef, R.Rahmanu, R.Yusof and A.A.Shojaei, “Maximum power point

tracking of partial shaded photovoltaic array using an evolutionary algorithm: A particle swarm

optimization technique”, Journal of Renewable and Sustainable Energy,Vol. 6, pp. 1-13,2014.

[7] R. Rodriguez, M.Preindl, A.Emadi and J.Cotton, “Maximum Power Point Tracking for

Thermoelectric Generators with High Frequency Injection”, 41st Annual Conference of the IEEE

Industrial Electronics Society, Japan, 2015.

[8] S.Kolsi, H.Samet and M.B.Amar, “ Design Analysis of DC-DC converters connected to a

Photovoltaic Generator and Controlled by MPPT for optimal energy transfer throughout a clear

day”, Journal of Power and Energy Engineering,Vol.2, pp. 27-34, 2014.

[9] S.Siouane, S.Jovanovic and P.Poure, “Influence of contact thermal resistances on the Open Circuit

Voltage MPPT method for Thermoelectric Generators”, IEEE International Energy Conference, 2016.

[10] P.J. Stecanella, M.A.A.Faria, E.G.Domingues and P.H.G.Gomes, “Electricity generation using

Thermoelectric generator-TEG”, 15th International Conference on Environment and Electrical

Engineering, 2015.

REFERENCES[11] M.Mandic, U.Ivo and M. Viktor, “Design and testing of 25kV AC electric railway power supply systems”, Vol. 3, pp. 505-509, 2013.

[12] P.Garcia, J.P.Torreglosa, “Optimal energy management system for stand-alone wind turbine/photovoltaic/hydrogen/battery hybrid system with supervisory control based on fuzzy logic”, International Journal of Hydrogen Energy, Vol. 38, No. 33, pp. 14146-14158, 2013.

[13] P. Alotto, M. Guarnieri and F. Moro, “ Redox flow batteries for the storage of renewable energy: A review”, Renewable and Sustainable Energy Reviews, Vol. 29, pp. 325-335, 2014.

[14] K.Kewal, P. Bhagyashri and T. Sujata, “IoT Based Smart Energy Meter”, International Journal of Computer Engineering and Applications, Vol. 12, No. 1, pp. 1-3, 2016.

[15] N. Bui, A. Castellani, P. Casari, and M. Zorzi, “The Internet of Energy: A Web-Enabled Smart Grid System,” Network, IEEE, vol. 26, no. 4, pp.39–45, 2012.

[16] M.A.Virol, “IoT Power Consumption Monitoring System for Off-Grid Households”, International Journal of Electronic and Computer Science, Vol. 17, No. 2, pp. 65-71, 2017.

[17] H.P.Ashok and G.U.Kharat, “IoT based Smart Grid to remotely monitor and control Renewable Energy Sources”, International Journal of Innovative Research in Computer and Communication and Engineering, Vol. 4, No. 7, pp. 13164-13169, 2016.

[18] P.Sindhuja and M.S.Balamurugan, “Smart Power Monitoring and Control System through Internet of Things using Cloud Data Storage”, Indian Journal of Science and Technology, Vol. 8, No. 19, pp. 1-7 , 2015.

[19]A.J.Shreedhar, K.Srijay, Y.R.Rahul and S.Shashank, “IoT Based Smart Energy Meter”, International Journal of Research in Communication and Engineering, Vol. 6, No. 1, pp. 89-91, 2016.

[20] K.S.Birendra, R.Tejashree, J.T.Akib and P.Ranjeet, “IoT Based Smart Energy Meter”, international Research Journal of Engineering and Technology, Vol. 4, No.4, pp. 96-101, 2017.

[21] MACAL C.M. and NORTH M.J. Tutorial on agent-based modelling and simulation. Journal of Simulation, volume 4(3):pages 151–162, 2010.

[22] GILBERT N. Agent-Based Models (Quantitative Applications in the Social Sciences). Sage Publications, Inc, 2007.

REFERENCES[23] Saraansh Dave, Mahesh Sooriyabandra, Mike Yearworth, “A Systems Approach to the Smart Grid”, First International Conference on Smart Grids, Green Commuications and IT Energy-aware Technologies, Energy 2011, pp. 131-134, 2011.

[24] Pol Olivella-Rosell, Roberto Villafafila-Robles, Andreas Sumper, Joan Bergas-Jane, “Probabilistic Agent based model of Electric Vehicle Charging Demand to Analyse the impact on Distribution Networks”, Energies 2015, Vol. 8, pp: 4160-4187.

[25] Kapesh, C., Nandha Kumar, K., and Ashok, K., “Agent Based Aggregated Behaviour Modelling for Electric Vehicle Charging Load”, IEEE transaction on Industrial Informatics, 2018.

[26] W. Yang, J. Liu, Y. Xiang and C. Gu, “Agent-Based Modeling for Scale Evolution of Plug-In Electric Vehicles and Charging Demand”, IEEE Transactions on Power Systems, Vol. 33, No. 2, pp. 1915-1925, 2018.

[27] J. Babic, “Agent-based Modeling of Electricity Markets in a Smart Grid Enviornment”, Semantic Publisher, Computer Science, 2013.

[28] H. Gharavi and R. Ghafurian, “Smart Grid: The Electric Energy System of the Future,” Proceedings of the IEEE, vol. 99, no. 6, pp. 917–921, 2011.

[29] S. Shekara, S. Reddy, L. Wang, V. Devabhaktuni, and N. Gudi, “Smart Meters for Power Grid –Challenges , Issues , Advantages and Status,” Renewable and Sustainable Energy Reviews, vol. 15, no. 6, pp. 2736–2742, 2011.

[30] A. Ipakchi and F. Albuyeh, “Grid of the Future,” Power and Energy Magazine, IEEE, vol. 7, no. 2, pp. 52–62, 2009.

[31] M. G. Simões, S. Member, R. Roche, S. Member, E. Kyriakides, S.Suryanarayanan, B. Blunier, K. D. Mcbee, P. H. Nguyen, P. F. Ribeiro, and A. Miraoui, “A Comparison of Smart Grid Technologies and Progresses in Europe and the U.S.,” Industry Applications, IEEE Transactions on, vol. 48, no. 4, pp. 1154–1162, 2012.

[32] Demijan Grgic, Hrvoje Vdović, Jurica Babic, Vedran Podobnik, “CrocodileAgent 2018: Robust agent-based mechanisms for power trading in competitive environments” Journal of Computer Science and Information Systems, vol.16,pp.40, 2018.

REFERENCES[33] Z. Jiang and Q. Ai, “Agent-based simulation for symmetric electricity market considering price-based demand response”, Journal of Modern Power Systems and Clean Energy, Vol. 5, No. 5, pp. 810-819, 2017.

[34] Mart Van Der Kam, Annemijn Peters, WilfriedVan Sark and Floor Alkemade, “Agenet-Based Modelling of Charging Behaviour of Electric Vehicle Drivers”, Journal of Artificial Societies and Social Simulation, vol. 22, No. 4, pp. 1-27, 2019.

[35] A. Ahkamiraad and Y. Wang, “An Agent-Based Model for Zip-Code Level Diffusion of Electric Vehicles and Electricity Consumption in New York City”, Energies, Vol. 11, pp. 1-17, 2018.

[36] R. Wolbertus and R. Van Den Hoed, “Expanding Charging infrastructure for lage scale introduction of Electric Vehicles”, Electric Vehicle Symposium, France, 2019.

[37] Y. Kongjeen and K. Bhumkittipich, “Impact of Plug-in Electric Vehicles Integrated into Power Distribution System based on Voltage-Dependent Power Flow Analysis”, Energies 2018, Vol. 11, pp. 1-16, 2018.

[38] Y. Zeng, P. Dong, and Y. Shi, “On the Disruptive Innovation Strategy of Renewable Energy Technology Diffusion: An Agent based Model”, Energies 2018, Vol. 11, pp. 1-21, 2018.

Q&A

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