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Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain (PhD Thesis Presentation)

Authors:
  • Edo State University Iyamho

Abstract

Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain (PhD Thesis Presentation).
Omaji Samuel
CIIT/FA17-PCS-013/ISB
Supervisor: Dr Nadeem Javaid (Associate Professor)
Co-Supervisor: Dr Sohail Iqbal (Associate Professor)
Department of Computer Science
COMSATS University Islamabad, Islamabad
Privacy Aware Energy Management in Smart
Communities by Exploiting Blockchain
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
Outline 2
Introduction
Literature Review
Focused Problem
Objectives
Combined System Model and Proposed Solutions
Efficient Energy Management for Residential Microgrid using Jaya Algorithm
Efficient Energy Management based on Forecasting in Smart Grids
Efficient Energy Management of Multi-microgrid
Privacy Aware Energy Management for Prosumers in Smart Grids using Blockchain
Privacy Aware Energy Trading for Electric Vehicle in Smart Community using Blockchain
Privacy Aware Energy Trading for Prosumers in Sub-Sahara Africa using Blockchain
Privacy Aware Multi-data sharing in Smart Communities using Blockchain
Privacy Aware Energy Management for Multi-agent System in Smart Grids using Blockchain
Energy Management for Resource Constrained Prosumers using Blockchain and Artificial Intelligence
Privacy and Anonymity Management for Prosumers using Blockchain and Garlic Routing
Conclusion and Future Work
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
Introduction (1/3) 3
According to World energy outlook 2020 [1]
1.1 billion people with inaccessible electricity worldwide
Sub-Sahara Africa (SSA) (650 million)
Asia (350 million)
Caribbean and Latin America (20 million)
[1]. World energy outlook [online], Accessed on (May 10, 2021), Available:
https://www.iea.org/topics/energy-access
Fig. 1: Energy Crises.
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
Introduction (2/3) 4
Efficient energy management is a part of power systems that monitors, controls and regulates energy without affecting the
comfort of energy consumers
High energy demands from the increasing population have raised pressure on the traditional power grid. So, energy generations
from the power grid become insufficient; therefore,
Efficient energy trading becomes necessary, but highly challenging in a smart community
The challenges are due to the high penetration of Renewable Energy Resources (RES) into the power grid, inaccurate
load forecasting, load balancing problem, etc.
In retrospect, Centralized Energy System (CES) generates energy at a large scale and ensures economic of scales by reducing
energy setup cost. It also monitors and manages different energy generating plants. However, its benefits are not felt in rural
communities. Also, it is faced with high operation and maintenance cost, lack of trust, single point of failure, and untimely
response due to high traffic of energy requests. Therefore, there is a need for an alternative system
Recently, Decentralized Energy System (DES) is a part of the smart grid that provides independent and flexible sources of
energy to satisfy several communities. It uses RES to provide energy supply for critical facilities during emergency and outage,
reduces energy storage cost, and ensures fault tolerance and local accountability
Introduction (3/3)
5
However, frequent intermittency of RES at the participants level can make DES complex
Another challenge of a smart grid does not rely on the physical support of the grid, but it relies on both privacy and security,
which draws the attention of the research community in the cybersecurity context
Attacks can be launched by an attacker by disrupting the operation of the smart grid using fake data injection, denial of service,
data mining attacks, etc.
Therefore, it is important to provide a framework that protects the smart grid and at the same time, retains its underlying
objectives.
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
6
Literature Review (1/3)
[2] Liu, C., Chai, K. K., Zhang, X., Lau, E. T., and Chen, Y. (2018). Adaptive blockchain-based electric vehicle participation scheme in smart grid
platform. IEEE Access, 25657-25665.
[3] Aujla, G. S., Jindal, A., and Kumar, N. (2018). EVaaS: Electric vehicle-as-a-service for energy trading in SDN-enabled smart transportation
system. Computer Networks, 247-262.
[4] Liu, C., Chai, K. K., Zhang, X., Lau, E. T., and Chen, Y. (2018). Adaptive blockchain-based electric vehicle participation scheme in smart grid
platform. IEEE Access, 25657-25665.
[5] Su, Z., Wang, Y., Xu, Q., Fei, M., Tian, Y. C., and Zhang, N. (2018). A secure charging scheme for electric vehicles with smart communities in energy
blockchain. IEEE Internet of Things Journal, 1-14.
Technique(s) Objective(s) Price Tariff Limitation(s)
Stackelberg game [2] Energy trading between EVs and CSs Analytic Insecure environment
Privacy issue
Stackelberg game [3] Energy trading between EVs and CSs
Utility maximization
Analytic Insecure environment
Privacy issue
Iceberg order execution
algorithm, genetic algorithm
and blockchain [4]
Minimize power fluctuation level
Minimize overall charging cost of EVs
Price bidding Privacy issue
High computational cost
Contract theory and
blockchain [5]
Utility maximization
Energy trading
Analytic Privacy issue
Table. 1: Energy Trading for EVs.
EVs: Electric Vehicles
CSs: Charging Stations
7
Literature Review (2/3)
[6] Liu, N., Cheng, M., Yu, X., Zhong, J., and Lei, J. (2018). Energy-sharing provider for PV prosumer clusters: A hybrid approach using stochastic
programming and stackelberg game. IEEE Transactions on Industrial Electronics, 6740-6750.
[7] Liu, N., Yu, X., Wang, C., Li, C., Ma, L., and Lei, J. (2017). Energy-sharing model with price-based demand response for microgrids of peer-to-peer
prosumers. IEEE Transactions on Power Systems, 3569-3583.
[8] Yassine, A., Shirehjini, A. A. N., & Shirmohammadi, S. (2015). Smart meters big data: Game theoretic model for fair data sharing in deregulated smart
grids. IEEE Access, 2743-2754.
[9] Devine, M. T., and Cuffe, P. (2019). Blockchain Electricity trading under demurrage. IEEE Transactions on Smart Grid, 2323-2325.
Technique(s) Objective(s) Price Tariff Limitation(s)
Stochastic programming and
Stackelberg game [6]
Maximize prosumers’ utility
Minimize prosumers’ energy cost
Energy sharing
Analytic Insecure environment
Delay in energy supply
Distributed iterative algorithm [7] Prosumers’ energy cost savings
Energy sharing
Analytic Insecure environment
Delay in energy supply
Game theory and differential
privacy [8]
Data sharing
Aggregator’s profit maximization
Privacy preserving of user’s data
Negotiation Insecure environment
Lack of trust
Mixed complementarity algorithm
[9]
Energy trading
Prosumer’s cost minimization
Demurrage
Time of use Privacy issue
High computational cost
Table. 2: Energy Trading for Prosumers.
8
Table 3: Load Forecasting.
ANN: Artificial Neural Network
MI: Mutual Information
FS: Feature Selection
mEDE: Modified Enhanced Differential Evolution
Literature Review (3/3)
Technique(s) Objective(s) Limitation(s)
Times series analysis and regression
model [10]
To improve reliability of power system
Short-term forecasting
Not efficient for the large
households' loads
ANN-based strategy [11] Enhance the convergence rate of the model
Short-term load forecasting
Achieve low forecasting
accuracy
ANN-based strategy [12] To incorporate an optimizer
Short-term load forecasting
High execution time
ANN-based strategy [13] Short-term load forecasting
To improve MI based FS
Downsized inputs do not
further reduce the training time
Information loss and unstable
convergence of the mEDE
Inefficiency of the model to
train on massive amount of
data
[10] Amara, F., Agbossou, K., Dubé, Y., Kelouwani, S., Cardenas, A., and Hosseini, S. S. (2019). A residual load modeling approach for household short-term
load forecasting application. Energy and Buildings, 187, 132-143.
[11] Amjady, N., and Keynia, F. (2008). Day-ahead price forecasting of electricity markets by mutual information technique and cascaded neuro-evolutionary
algorithm. IEEE Transactions on Power Systems, 24(1), 306-318.
[12] Amjady, N., Keynia, F., and Zareipour, H. (2010). Short-term load forecast of microgrids by a new bilevel prediction strategy. IEEE Transactions on smart
grid, 1(3), 286-294.
[13] Ahmad, A., Javaid, N., Guizani, M., Alrajeh, N., and Khan, Z. A. (2016). An accurate and fast converging short-term load forecasting model for industrial
applications in a smart grid. IEEE Transactions on Industrial Informatics, 13(5), 2587-2596.
Problem Statement
9
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 

[14] Luo, F., Dong, Z. Y., Liang, G., Murata, J., and Xu, Z. (2018). A distributed electricity trading system in active distribution networks based on multi-agent
coalition and blockchain. IEEE Transactions on Power Systems, 34(5), 4097--4108.
Objectives 10
To solve the issues in the problem statement, this research analyzes and proposes a methodology for blockchain based
energy management system, and designs mechanisms that provide privacy, anonymity, trust, security, resource
management, and efficient energy pricing for energy users in a smart community. Other objectives achieve in this research
are as follows.
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
Minimization of production and fuel cost
Maximization of sales revenue
Accurate load forecasting
Energy cost savings
Resolve demurrage fee dispute
Social welfare maximization
Resolve problem of historical antipathy for regional energy trading
Cost effective energy planning model
11
System Model
SHS: Solar Home System
Fig. 1: Combined System Model.
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
12
Proposed Solutions (1/3)
Subproblems Limitations Addressed Proposed Solutions Validations
1Inefficient energy management for a
single microgrid
Efficient scheduling method using Jaya
optimization algorithm
Critical Peak Pricing (CPP) scheme
Tables 7-10
Figs. 4-11
2Inaccurate load forecasting Improved Conditional Restricted
Boltzmann Machine (CRBM)
Discrete Time Markov Chain (DTMC)
MI based FS
Tables 11-14
Figs. 14-16
3Inefficient energy management for multi-
microgrid
Deep Convolutional Neural Network
(CNN)
Column Generation Algorithm
Cooperative Game
Tables 15-19
4Order imbalance
Supply chain problem
Lack of privacy
Analytical pricing scheme based on
Demand Response Ratio (DRR)
Blockchain
Additive Homomorphic Encryption (AHE)
Demurrage mechanism
Differential privacy
Security analysis (passive attack, active
attack, etc.)
Figs. 21-28
Table. 4: Mapping of the Proposed Solutions with Limitations.
13
Proposed Solutions (2/3)
Subproblems Limitations Addressed Proposed Solutions Validations
5Linking and data mining attacks
Location privacy related attacks for EVs
Inefficient pricing schemes
Private and public blockchains
An improved Consensus Energy
Management Algorithm (CEMA)
Attacker models (Secure two party
background knowledge model, spatial and
temporal location based models)
Demand based pricing scheme
Figs. 31-40
6Inefficient regional energy trading
Inefficient cost effective planning model
for sub-Sahara Africa
Lack of security and privacy
Blockchain based levelized cost of energy
Regional energy trading method based on
demand and supply ratio
Table 23
Figs. 42-47
7Insecure multi-data sharing (load
consumption data and gradient parameters)
Lack of privacy
Blockchain based credibility management
system
Secure Recurrent Neural Network (RNN)
AHE and differential privacy
Security analysis (double spending attack)
Figs. 50-60
Table. 5: Mapping of the Proposed Solutions with Limitations.
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
14
Proposed Solutions (3/3)
Subproblems Limitations Addressed Proposed Solutions Validations
8Lack of trust for multi-agent system
Lack of privacy
Lack of security
Fictitious play and imitation learning
Blockchain based coalition system
AHE
Security analysis (passive, active attacks,
etc.)
Figs. 62-71
9Inefficient resource management for resource
constrained blockchain users
Lack of privacy
An improved Sparse Neural Network
(ISNN)
Multi-pseudonym mechanism
Blockchain based computation offloading
system
Security analysis (Sybil attack)
Figs. 73-85
10 Lack of anonymity
Lack of privacy
GarliChain (blockchain and garlic
routing)
Identity based encryption
Stochastic path selection mechanism
Reputation management system
Security analysis (passive, active, etc.)
Figs. 88-93
Table. 6: Mapping of the Proposed Solutions with Limitations.
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
The Proposed Solution 1:
Efficient Energy Management for Residential
Microgrids using Jaya Algorithm
Samuel, O, Javaid, N., Ashraf, M., Ishmanov, F., Afzal, M. K., and Khan, Z. A. (2018). Jaya based Optimization Method
with High Dispatchable Distributed Generation for Residential Microgrid. Energies, 11(6), 1513. [IF=2.702].
Solutions Proposed:
Energy Management based on Scheduling
Profit and Revenue Evaluations using Multi-objective Optimization
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
16
Efficient Energy Management for Residential Microgrids using Jaya
Algorithm (1/3)
System Model
Fig. 2: System Model 1. Fig. 3: The Proposed Flowchart.
WT: Wind turbine; MT: Microturbine; PV: Photovoltaic;
PCC: Point of common coupling; EMS: Energy management
system; BAT: Battery; MG: Microgrid; AC: Alternative
current
Schedule dispatchable generators using Jaya algorithm under
the CPP and Time of Use (ToU) pricing schemes
The EMS is required to handle
scheduling according to the power
capacity of the MG, the ON and OFF
status, price tariffs and load requirement.
17
1.2 Case 2
Grid connected microgrid: min( (1.2.1)
Sales revenue [15]: (t)) (1.2.2)
Total cost [15]: (t) (1.2.3)
: Production cost
: Operation and maintenance cost
: Fuel cost
: Start up cost
: Decision variables
: Operation and maintenance
: Diesel generator
: Wind turbine
: Photovoltaic
: Microturbine
: Energy storage
: Sales revenue
E: Energy generated
: Grid’s selling price
: Grid’s buying price
: Grind’s power
: Total cost
Efficient Energy Management for Residential Microgrids using Jaya
Algorithm (2/3)
1.1 Case 1
Standalone microgrid: (1.1.1)
Production cost: (1.1.2)
Maintenance cost:
(1.1.3)
)
[15] Li, H., Eseye, A. T., Zhang, J., and Zheng, D. (2017). Optimal energy management for industrial microgrids with high-penetration renewables. Protection and Control of Modern Power
Systems, 2(1), 1-14.
18
MaxIter: Maximum iteration
D: Set of Dispatchable generators (DGs)
T: Total time 24-h
P: Initial population
ESU: Energy storage unit
t: Time of scheduling
k: Set of sorted dispatchable generators
Efficient Energy Management for Residential Microgrids using Jaya
Algorithm (3/3)
Time complexity: O(T2)
Algorithm
Simulation Results
The Proposed Solution 1: Efficient Energy
Management for Residential Microgrids using Jaya
Algorithm
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20
Efficient Energy Management for Residential Microgrids using
Jaya Algorithm (1/4)
Table 7: Parameters of Algorithms Mapping EMS.
Same simulation setup is
followed for both cases.
While, datasets for the RES
are taken from [16]
H 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
ToU 8.7 8.7 8.7 8.7 8.7 8.7 13.2 13.2 13.2 13.2 18 18 18 18 18 13.2 13.2 8.7 8.7 8.7 8.7 8.7 8.7 8.7
CP
P11.4 11.4 11.4 11.4 11.4 11.4 11.4 11.4 11.4 11.4 123.4 123.4 123.4 123.4 123.4 123.4 11.4 11.4 11.4 11.4 11.4 11.4 11.4 11.4
GP 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
LD 720 960 640 180 480 720 260 1720 2120 1400 680 1160 640 320 180 720 720 200 220 689 780 640 500 160
Results and Discussions
Simulations are performed
in two cases:
Standalone mode
Grid connected mode
H: Time in hours CPP: Critical Peak Pricing (kWh) LD: Load Demand (kW) Nrunner: Length of runner SBA: Straw Berry Algorithm EMS: Energy Management System
ToU: Time-of-Use (kWh) GP: Selling Price to Grid (kWh) P: Population Droot: length of root N: Number of decisions EDE: Enhanced Differential Evolution
Jaya EDE SBA EMS Values
P P P Individual Solution 30
Modification
Functions
Mutant Vectors Growths Initial DGs States 2
N Trial Vectors Nrunner Number of DG 6
Selection Selection Droot Time Slots 24
Fitness Function Fitness Function Fitness Function Design Objective 1
Table 8: Pricing Schemes and Load Demands.
[16] Li, H., Eseye, A. T., Zhang, J., and Zheng, D. (2017). Optimal energy management for industrial microgrids with high-penetration renewables. Protection and Control of Modern Power
Systems, 2(1), 1-14.
21
Table. 10: Using ToU.
Efficient Energy Management for Residential Microgrids using
Jaya Algorithm (2/4)
Model Total Daily Fuel
Cost (c$/kWh)
Daily Energy Sales
Revenue (c$/kWh)
Total Daily Production
Cost (c$/kWh)
Execution
Time (s)
Standalone
Without
EMS
403,850 13,325,000 4,2495,000
Jaya 327,280 31,027,000 2,600,500 0.4505
EDE 152,800 31,125,000 3,015,600 0.8126
SBA 334,610 −60,302,000 2,686,000 0.5172
Grid Connected
Jaya 249,840 23,023,000 2,600,500 0.0488
EDE 838,520 −18,478,000 3,015,600 0.3275
SBA 381,320 −65,229,000 2,686,000 0.2598
Fuel, Production Cost and Selling Income
Table. 9: Using CPP.
Model Total Daily Fuel
Cost (c$/kWh)
Daily Energy Sales
Revenue (c$/kWh)
Total Daily Production
Cost (c$/kWh)
Execution
Time (s)
Standalone
Without
EMS
403,850 13,611,000 42,593,000
Jaya 401,380 22,376,000 24,998,000 0.4253
EDE 524,930 −7,369,700 27,972,000 0.6791
SBA 111,560 −45,810,000 24,157,000 0.5479
Grid Connected
Jaya 537,750 15,945,000 3,255,200 0.0551
EDE 552,070 17,002,000 2,691,000 0.3725
SBA 191,690 −5,992,000 2,697,000 0.3507
The negative selling revenue indicates the operation and maintenance costs are
greater than the production cost
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22
Fig. 7: Using ToU.
Fig. 6: Using CPP.
Efficient Energy Management for Residential Microgrids using
Jaya Algorithm (3/4)
Operation and Production
Cost
Fig. 4: Using CPP. Fig. 5: Using ToU.
23
Fig. 10: Using CPP for Grid Connected.
Efficient Energy Management for Residential Microgrids using
Jaya Algorithm (4/4)
Demand and Supply
Fig. 11: Using ToU for Grid Connected.
Fig. 8: Using CPP for Standalone. Fig. 9: Using ToU for Standalone.
Finally, simulation results show
that the Jaya-based optimization
method minimizes the fuel cost by
up to 38.13%, production cost by
up to 93.89% and yields a
monetary benefit of up to 72.78%
from sales revenue.
The Proposed Solution 2:
Efficient Energy Management based on Load
Forecasting in Smart Grids
Samuel, O., Alzahrani, F. A., Hussen Khan, R. J. U., Farooq, H., Shafiq, M., Afzal, M. K., and Javaid, N. (2020). Towards
Modified Entropy Mutual Information Feature Selection to Forecast Medium-Term Load Using a Deep Learning Model in
Smart Homes. Entropy, 22(1), 68. [IF= 2.494].
Samuel, O., Javaid, N., and Rafique, A. (2018, October). A New Entropy-based Feature Selection Method for Load
Forecasting in Smart Homes. In Proceedings of the International Conference on Cyber Security and Computer Science
(ICONCS), Safranbolu Karabuk University (KBU) Turkey, 185-192.
Solutions Proposed:
New Feature Selection Mechanism for Medium-term Load Foresting (MTLF) in Smart Grids
Method for Consumers’ Behaviour Dynamics on Load Forecasting
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25
Efficient Energy Management based on Load Forecasting in
Smart Grids (1/2)
Fig. 12: Customers’ Behaviour Dynamics.
The Proposed Model
CRBM
DTMC
MI based FS
Fig. 13: System Model 2.
CRBM: Conditional Restricted Boltzmann Machine
DTMC: Discrete Time Markov Chain
FS: Feature Selection
MI: Mutual Information
RMSE: Root Mean Square Error
We accelerate the convergence speed and minimize
the RMSE of the forecasting model using Jaya
optimization algorithm
We obtain four features, such as target
value, average value, moving average
value and random historical value
FS is proposed to eliminate
redundancy and irrelevancy
from the data
26
Efficient Energy Management based on Load Forecasting in Smart
Grids (2/2)
2.1 Problem Formulation
MI based FS:
(2.1.1)
Auxiliary variables:
(2.1.2)
State transition [17]:
(2.1.3)
Objective function:
(2.1.4)
Example:
: ith random input value
: jth target value
: mth average value
: qth moving average value
L: length of input data
: mth auxiliary value
pr : probability
: Maximum likelihood estimation
: Number of transition from state x to state I
: Total number of transitions from state x
A(k): kth actual input
: kth predicted load
N: Size of input
[17] Sigauke, C., and Chikobvu, D. (2017). Estimation of extreme inter-day changes to peak electricity demand using Markov chain analysis: A comparative analysis
with extreme value theory. Journal of Energy in Southern Africa, 28(4), 68–76.
To minimize
the RMSE
Simulation Results
The Proposed Solution 2: Efficient Energy
Management based on Load Forecasting in Smart
Grids
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28
Efficient Energy Management based on Load Forecasting in Smart
Grids (1/4)
Simulations and Discussions
Simulations are performed in three
cases:
MI based FS
Load forecasting and confidence
interval
Customers behaviors’ dynamics
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0 0 0 0 0.91 0.91 0.91 0.91 0 0 0 0 0.91 0.91 0.91 0.91
Binary F1 F2 F3 F4
0 0.6667 0.0000 0.4986 0.5002
1 0.6667 1.0000 0.5014 0.4998
It means that the
feature variables are
independent.
Table 11: MI for Four Joint Discrete Random Variables.
Table 12: Joint Probability of the Individual Value of FS.
It means that the
feature variables are
slightly related.
When features F1 and F2 are selected, F3 and F4 are evaluated by making an equivalent combination of C =
{F1,F2,F3,F4}. This study assigns 1= (0,0,0,0), 2=(0,0,0,1), 3=(0,0,1,0), 4=(0,0,1,1), 5=(0,1,0,0) and so on until
15=(1,1,1,1). If F3 > F4, then F3 will be selected when binary is 1; however, if MI(C,F3) = 0.0 and MI(C,F4) =
0.91, it implies that F4 is more relevant to C than F3. Nevertheless, F4 is redundant to the combination of C
while F3 and C are complement to each other (i.e., MI(C,F3) = F3 = 0.0).
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Efficient Energy Management based on Load Forecasting in Smart
Grids (2/4)
Simulations and Discussions
Simulations are performed in three
cases:
MI based FS
Load forecasting and confidence
interval
Customers behaviors’ dynamics
Z Proposed AFC-STLF SVR NB Ensemble KNN ANN
1 0.32 0.42 8.22 9.01 8.92 0.98 5.81
2 0.35 11.40 1.03 5.52 5.68 11.87 10.12
3 0.40 13.30 1.52 5.88 5.92 13.02 11.54
4 0.31 0.25 0.58 0.48 2.45 0.30 3.18
5 0.40 0.58 1.10 0.83 0.78 0.72 1.53
6 0.51 11.81 1.30 7.02 6.37 11.0 9.92
Table 13: Comparisons of Forecasting Accuracies for Selected Zones (Z1-Z6).
There is a trade-off
between accuracy and
execution Time.
Z Proposed AFC-STLF SVR NB Ensemble KNN ANN
1 100.00 125.50 0.4986 39.20 38.10 38.35 30.21
2 142.01 146.76 0.5014 39.27 39.32 37.22 35.90
3 161.32 172.34 35.89 38.23 38.322 35.22 30.21
4 158.23 170.39 42.91 43.82 42.49 40.21 35.32
5 178.39 184.87 38.21 3 39.88 40.44 37.92 30.77
6 150.45 160.76 39.87 40.32 40.88 38.22 30.55
Table 14: Comparisons of Execution Time in Seconds for the Selected Zones.
AFC-STL: Accurate Fast and Convergence Short-term Load Forecasting
SVR: Support Vector Regression
NB: Naïve Bayes
KNN: K-Nearest Neighbour
ANN: Artificial Neural Network
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Efficient Energy Management based on Load Forecasting in Smart
Grids (3/4)
Simulations and Discussions
Simulations are performed in three
cases:
MI based FS
Load forecasting and confidence
interval
Customers behaviors’ dynamics
Fig. 14: Seven Days of Consecutive MTLF Distribution of Load with
CI of 95%.
Fig. 15: Thirty Days of Consecutive MTLF Distribution of Load with
CI of 95%.
This research considers also the true distribution of the consumers’ electrical
load while the certainty of the future temperature is unknown. To resolve this
issue, this research makes an average between the consumers’ electrical load
and the temperature to derive a new MTLF distribution.
CI enables us to know the range of values for the
given distribution. Since the focus of this thesis is
concerned with capturing the true distribution of
the consumers’ electrical load consumption, a
wider interval would be much better
MTLF: Medium-term Load Forecasting
CI: Confidence Interval
AR-MTLF: Accurate and Robust Medium-term Load Forecasting
31
Efficient Energy Management based on Load Forecasting in Smart
Grids (4/4)
Simulations and Discussions
Simulations are performed in three
cases:
MI based FS
Load forecasting and confidence
interval
Customers behaviors’ dynamics
Fig. 16: : Probability of Transition using Edge Color.
As researchers are rarely concerned with the consumers’ load consumption
dynamic characteristics in all periods; this thesis concentrates on a specific
period of time. A DTMC process is employed on the sequence of demand
response of dynamic consumers’ load consumption behavior from one state to
another. This is possible only if the grouping of consumers’ consumption in
different adjacent periods is considered
Lowest consumption
Low consumption
Average consumption
High consumption
Extreme high consumption
DTMC: Discrete Time Markov Chain
The relations and transitions between consumption behaviors in adjacent
periods are known as dynamics
The Proposed Solution 3:
Efficient Energy Management for Multi-
microgrids in Smart Grids
Samuel, O., Khan, Z. A., Iqbal, S., and Javaid, N. (2018, November). An Efficient Energy Management in Microgrid: A
Game Theoretic Approach. In 2018 Fifth HCT Information Technology Trends (ITT), United Arab Emirates, 33-40.
Samuel, O., Javaid, N., Khalid, A., Wazir Z., K., Mohammed, Y., A., Muhammad, K., Afzal., and Byung-Seo K. (2020).
Towards Real-time Energy Management of Multi-microgrid using a Deep Convolutional Neural Network and Cooperative
Game Approach. IEEE Access. [IF= 3.745]
Solutions Proposed:
Energy Management for Coalition Microgrid using Cooperative Game
Optimum Cost Allocation for Multi-microgrid
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Efficient Energy Management for Multi-microgrids in Smart
Grids (1/3)
Fig. 17: The Proposed Deep Learning.
Fig. 18: System Model 3.
Energy management system performs energy cost savings and
real-time optimal scheduling policy of each microgrid.
BADC: Bidirectional AC/DC
PCC : Point of Common Coupling
h : Hidden neurons; ReLU: Rectified Linear Unit
v: Output layer
u: Input layer
a and b: Network biases
w: Weights of the network
In the proposed deep Convolution Neural Network (CNN), the
fully connected layer is enhanced using Conditional Restricted
Boltzmann Machine (CRBM).
34
Efficient Energy Management for Multi-microgrids in Smart Grids
(2/3)
3.1 The Problem Formulation
Allocation objective function:
(3.1.1)
Subject to: (3.1.2)
Operating cost: (3.1.3)
: Nucleolus solution
Z: Size of coalition
: Number of players
: Player
AS: Ancillary service cost
: Cost of dispatchable generators
: Decision variables
: Cost of dispatchable loads
: Cost of buying energy from the grid
: Individual allocation cost
: State at time t
: Action at time t
FP: Number of feasible policies
We use the column generation algorithm to minimize the maximum allocation cost for
each microgrid.
The optimal scheduling policy is defined as:
35
W: Weight of the Network
Efficient Energy Management for Multi-microgrids in Smart Grids
(3/3)
Time complexity: O(log n)
The Proposed Algorithm
Simulation Results
The Proposed Solution 3: Efficient Energy
Management for Multi-microgrids in Smart Grids
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Efficient Energy Management for Multi-microgrids in Smart Grids (1/4)
Simulations and Discussions
Simulations are performed in three
cases:
Analysis of average daily operating
cost of dispatchable generators
Accuracy of the model
Analysis of cost savings
Table 15. Analysis of the Average Daily Operating Cost of DGs.
Optimized ($) Greedy ($) MPC ($) ADP-1h ($) ADP-5h ($)
Grid 166.85 210.03 205.315 176.16 176.16
TG1 0.0699 0.7430 1.3746 1.5777 1.5777
TG2 0.0746 0.6411 1.1732 1.3427 1.3427
TG3 0.0746 0.6704 1.2061 1.3448 1.3448
WT 0.0986 0.6766 1.2295 1.3687 1.3687
PV 0.1012 0.6604 1.1061 1.2448 1.3008
SSH 0.1092 0.6040 1.2061 1.3448 1.3448
AS 1505.3 2247 2745.1 2465.7 2163.8
MPC: Model Prediction Control
ADP-h1h: Approximate Dynamic Programming with 1-hr Horizon
ADP-5h: Approximate Dynamic Programming with 5-hrs Horizon
TG: Thermal Generator
WT: Wind Turbine
PV: Photovoltaic
SSH: Small Hydro-Solar
87.86% cost
savings
79.52% cost
savings
73.94% cost
savings 79.42% cost
savings
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Efficient Energy Management for Multi-microgrids in Smart Grids (2/4)
Simulations and Discussions
Simulations are performed in three
cases:
Analysis of average daily operating
cost of dispatchable generators
Accuracy of the model
Analysis of cost savings
Table 16. MAPE and RMSE Analysis of Different Models.
RNN: Recurrent Neural Network
ANN: Artificial Neural Network
SVM: Support Vector Machine
Mean RMSE MAPE STD
Proposed 328.28 0.34 0.70 379.62
RNN 288.31 2.44 1.95 150.35
ANN 328.28 1.42 1.98 379.65
Encoder 327.73 1.76 1.86 379.19
SVM 285.58 1.94 1.79 231.90
Time (s)
Proposed 11.54
RNN 169.66
ANN 0.99
Encoder 12.18
SVM 0.62
Table 17. Computational Time.
In deep learning models, the maximum training
time goes to the backpropagation process and the
input sizes. Based on the proposed deep CNN
model, the number of calculations depends on
input and the number of filters
The smaller value of the error
provides better forecasting accuracy
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Efficient Energy Management for Multi-microgrids in Smart Grids (3/4)
Simulations and Discussions
Simulations are performed in three
cases:
Analysis of average daily operating
cost of dispatchable generators
Accuracy of the model
Analysis of cost savings
Table 18. Comparison of Energy Allocation Costs for Different Models
(IEEE 30-bus System).
MG Proposed
($)
Cost Savings
(%)
Bender
($)
Cost Savings
(%)
Shapley
($)
Cost Savings
(%)
Independent
($)
1 514.0497 3.33 529.4485 0.43 532.7427 -0.18 531.7794
2 701.6255 2.46 717.0243 0.32 731.2542 -1.65 719.3551
3 650.4024 2.65 665.8012 0.34 675.1302 -1.04 668.1321
4 210.4197 7.77 225.8185 1.02 227.5433 0.26 228.1493
5 22.8439 43.69 38.2427 5.74 50.1443 -23.58 40.5736
6 159.1966 10.02 174.5954 1.31 190.0583 -7.42 176.9263
The improvement is achieved because our
proposed model has the ability to minimize the
maximum expenses by eliminating negative
energy cost reductions
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Efficient Energy Management for Multi-microgrids in Smart Grids (4/4)
Simulations and Discussions
Analysis of cost savings
Table 5. Analysis of the Average Daily Operating Cost of DGs.
MG 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Proposed
($)
1070.0 277.9 881.0 177.9 287.6 70.33 101.2 300.0 336.2 338.4 318.07 324.9 118.5 86.86 89.32 534.1 287.8 335.0 341.5 91.76
Cost Savings
(%)
1.02 3.82 1.23 5.84 3.69 13.56 9.82 3.54 3.17 3.15 3.35 3.28 8.51 11.27 11.00 2.02 3.69 3.19 3.13 10.73
Bender ($) 1077.5 285.4 888.5 185.4 295.0 77.82 108.7 307.5 343.7 345.9 325.5 332.4 126.0 94.35 96.81 541.6 295.3 342.4 348.9 99.25
Cost Savings
(%)
0.32 1.22 0.39 1.87 1.18 4.36 3.16 1.14 1.02 1.06 1.07 1.05 2.73 3.62 3.53 0.65 1.18 1.02 1.01 3.45
Shapley ($) 1013.6 209.5 842.3 129.0 247.6 27.4 50.4 276.2 297.4 272.3 271.3 270.6 50.28 38.93 25.13 466.8 266.9 280.8 279.3 44.22
Cost Savings
(%)
6.23 27.47 5.57 31.69 17.09 66.32 55.11 11.20 14.34 22.07 17.5 19.4 61.20 60.23 74.95 14.37 10.66 18.84 20.75 56.98
Independent
($)
1081.0 288.9 892.0 188.9 298.6 81.37 112.3 311.0 347.3 349.4 329.1 335.9 129.6 97.90 100.3 545.2 298.8 346.0 352.5 102.8
The drastic reduction in energy allocation cost by Shapley model may create financial problems when the energy allocation cost, is set below
the corresponding expenses borne by each MMG
Execution time of
0.1955 s
Execution time of
41.3569 s
Execution time of
0.2920 s
Table 19. Comparison of Energy Allocation Costs for All Models (IEEE 118-bus System).
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
The Proposed Solution 4:
Privacy Aware Energy Trading for Prosumers
using Blockchain
Samuel, O., Javaid, N., Awais. M., Ahmed, Z., Imran, M., and Guizani, M. (2019, December). A Blockchain Model for
Fair Data Sharing in Deregulated Smart Grids. 2019 IEEE Global Communications Conference (GLOBECOM),
Waikoloa, HI, USA, 1-7.
Samuel, O., and Javaid, N. (2020). A Secure Blockchain-based Demurrage Mechanism for Energy Trading in Smart
Communities, International Journal of Energy Research, 45(1), 297-315. [IF=3.741].
Solutions Proposed:
Blockchain based Proof of Authority (PoA) Consensus Mechanism
Analytic Energy Pricing Scheme
Differential Privacy and Additive Homomorphic Encryption
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
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42
Privacy Aware Energy Trading for Prosumers using Blockchain
(1/3)
Fig. 19: The Proposed Demurrage Mechanism.
The proposed model
Fig. 20: System Model 4.
We perform localize
energy trading
We perform data sharing
with service provider
We secure the privacy
of prosumers using
encryption and
differential privacy
We minimize the
computation cost of
Ethereum blockchain
using PageRank
algorithm
We solve supply chain
problem using
demurrage mechanism
We analyse security and
privacy of the proposed
model
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Privacy Aware Energy Trading for Prosumers using Blockchain
(2/3)
4.1 The Problem Formulation
PageRank [18]: 56 55 74**5
Energy price:
74*85
74*,5
Demurrage [19]:
(4.1.4)
Privacy risk: (4.1.5)
(A): Page rank of node A
: Damping factor between 0 and 1
C(: Number of outbound links to node B
: Page rank of node B link to A
: Buying price at Time t
Selling price at Time t
: Demand response ratio
: Mid price of grid
: Selling price of grid at time t
: Buying price of grid at time t
D(t): Demurrage charges at time t
: Lay time at time t
: Berth waiting time at t
: Privacy concern within 0 and 1
SL: Sensitivity level within 0 and 1
: Private data
We use Laplacian distribution to add
noise to small portion of the dataset.
[18] Pagerank Algorithm [Online],http://pr.efactory.de/e-pagerank-algorithm.shtml, accessed April, 2019.
[19] Al-Harthy, M. H. (2008). Oil Export Tanker Problem-Demurrage and the Flaw of Averages. Energy Exploration \& Exploitation, 26(3), 143—156.
44
Privacy Aware Energy Trading for Prosumers using Blockchain
(3/3)
4.2 Security requirement of the proposed system
Similarity attacks
Birthday attacks
Passive attacks
Active attacks
4.3 Characteristic of the proposed system
Consistency
Availability
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
Simulation Results
The Proposed Solution 4: Privacy Aware Energy
Trading for Prosumers using Blockchain
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
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46
Privacy Aware Energy Trading for Prosumers using Blockchain (1/2)
Table 20: Parameters Used [20].
Parameters Value
0.4 Cents
1 Cents
0.4 Cents
1 Hour
1 Hour
Parameters Value
0.4 Cents
1 Cents
0.4 Cents
1 Hour
1 Hour
Results and Discussions
Blockchain computation
Energy trading
Fig. 21: Hash Power of the Network. Fig. 22: Encryption Vs Decryption
Fig. 24: System Overhead Cost.
[21] Guan, Z., Lu, X., Wang, N., Wu, J., Du, X., and Guizani, M. (2020). Towards secure and efficient energy trading in IIoT-enabled energy Internet: A blockchain approach. Future Generation
Computer Systems, 110, 686-695.
Fig. 23: Encryption Vs Decryption for Two Cryptosystems.
BC-ETS: Blockchain Energy Trading System [21]
[20] Liu, N., Yu, X., Wang, C., Li, C., Ma, L., and Lei, J.
(2017). Energy-sharing model with price-based demand
response for microgrids of peer-to-peer prosumers. IEEE
Transactions on Power Systems, 32(5), 3569–3583..
47
Privacy Aware Energy Trading for Prosumers using Blockchain (2/2)
Results and Discussions
Energy trading
Differential privacy
Fig. 25: : Internal Prices Vs Grid Price. Fig. 26: Internal Price Deviation Vs Number of Iterations.
Fig. 28: Effect of Parameter “a” on the Number of Prosumers.Fig. 27: Effect of Parameter “b” on the Number of Prosumers.
: Denotes the prosumers that valued their privacy more
than the
reward they get
b: Denotes the prosumers that valued the reward they get
against their privacy concerns.
: Probability breach event
C: Privacy cost
Q: Epsilon-differential privacy
r: Reward given to the prosumers
The Proposed Solution 5:
Privacy Aware Energy Trading for Electric
Vehicles using Blockchain
Samuel, O., Javaid, N., Shehzad, F., Iftikhar, M. S., Iftikhar, M. Z., Farooq, H., and Ramzan, M. (2019, November).
Electric Vehicles Privacy Preserving Using Blockchain in Smart Community. In International Conference on Broadband
and Wireless Computing, Communication and Applications, 67-80. Springer, Cham.
Samuel, O., and Javaid, N. A Secure Energy Trading System for Electric Vehicles in Smart Communities using
Blockchain, Future Generation Computer Systems, 1-19. (Under Review). [IF=6.125].
Solutions Proposed:
Demand based Pricing Policy
Location based Privacy of Electric Vehicles
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
49
Fig. 30: System Model 5.
Privacy Aware Energy Trading for Electric Vehicles using
Blockchain (1/4)
The Proposed Model
Fig. 29: The Proposed System
Network.
Charging station can
serve many EVs in
different areas
We propose demand
based pricing policy
We use am improved
CEMA to preserve
the privacy of EVs
We analyse privacy
and security of the
proposed system
We use private
blockchain to
preserve the privacy
of EVs
50
Privacy Aware Energy Trading for Electric Vehicles using
Blockchain (2/4)
5.1 Problem Formulation
Energy price:
73**5
73*85
Privacy protection metric [22]:
(5.1.3)
: Urban Area Energy Price at Time t
: Peak Energy Price at Urban Area
: Off Peak Energy Price at Urban Area
: Rural Area Energy Price
: Peak Energy Price at Rural Area
Off Peak Energy Price at Rural Area
: nth Electric Vehicle energy requirement
: kth Charging Station Energy Threshold
: Peak Time Energy Threshold
: Discount Factor
: Blockchain Incentive
: Charging Station Available Energy
: Peak Time Constant Factor
: Number of Trusted Nodes
: Number of inaccessible nodes
: Degree of Privacy breach
: Blockchain Transaction Information
[22] Wang, H., Huang, H., Qin, Y., Wang, Y., and Wu, M. (2017). Efficient location
privacy-preserving k-anonymity method based on the credible chain. ISPRS International
Journal of Geo-Information, 6(6), 1-19.
51
Privacy Aware Energy Trading for Electric Vehicles using
Blockchain (3/4)
(5.2.1)
(5.2.2)
A B C D 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
677521221222213122133113
677522422434113131221122
A B C D 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
677521221222213122133113
677522422434113131221122
Table 21: Cardinality for In-bound and Out-bound.
(5.2.3)
(5.2.4)
5.2 An Improved Consensus Energy Management Algorithm
: In-bound
: Out-bound
: Number of In-bounds
: Number of Out-bounds
: Minimum Distance from n to k
Maximum Distance from n to k
: Location of Electric Vehicle
: Distance Factor
52
Privacy Aware Energy Trading for Electric Vehicles using
Blockchain(4/4)
5.3 Security Attacks
51% attack
Impersonation attack
Double spending attack
5.4 Security Analysis
Temporal based attacks
Spatial attacks
Secure two party computation
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
Simulation Results
The Proposed Solution 5: Privacy Aware Energy
Trading for Electric Vehicles using Blockchain
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
54
Privacy Aware Energy Trading for Electric Vehicles using Blockchain (1/2)
Results and Discussions
Privacy preservation
Energy trading
Fig. 32: Relative Error Vs ε for Location. Fig. 33: Relationship between Ω and ϑ
Fig. 35: Analysis of Different Functions. (a) Shows the
Transaction Cost and (b) Shows the Execution Cost.
Fig. 31: Distances between EVs and CSs [2].
Fig. 34: Privacy Risk Revealing Vs Privacy Level for Energy Price.
55
Privacy Aware Energy Trading for Electric Vehicles using Blockchain (2/2)
Results and discussions
Privacy preservation
Energy trading
Fig. 37: Average Energy Prices Offered by CSs. Fig. 38: CS’s Energy Prices by the Proposed Scheme.
Fig. 36: Energy Demand [2].
Fig. 40: EVs’ Energy Cost based on Different Schemes.Fig. 39: Energy Prices of Various Schemes.
The Proposed Solution 6:
Privacy Aware Energy Trading in Sub-Sahara
Africa using Blockchain
Samuel, O., Javaid, N., Rabiya, K., Muhammad, I., and Moshen, G. (2020, June). Case Study of Direct Communication
based Solar Power Systems in Sub-Saharan Africa for Levelled Energy Cost Using Blockchain. In proceedings of the
IEEE International Conference on Communications, Dublin, Ireland, 1-6.
Samuel, O., Almogren, A., Javaid, A., Zuair, M., Ullah, I., and Javaid, N. (2020). Leveraging Blockchain Technology for
Secure Energy Trading and Least-Cost Evaluation of Decentralized Contributions to Electrification in Sub-Saharan Africa.
Entropy, 22(2), 226. [IF=2.494].
Solutions Proposed:
Cost-effective Energy Planning Model
Security and Privacy
Cross-border Energy Trading
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
57
Privacy Aware Energy Trading in Sub-Sahara Africa using Blockchain (1/3)
The Proposed Model
Fig. 41: System Model 6. (1) financing mode; (2,3) metering and trading platform; (4) industrial standard
We perform localize
energy trading
We perform cross-border energy
trading
We secure the privacy of
SHS using the blockchain
We perform crypto-
currency trading
We provide cost-effective
energy planning solution
1: Financial mode
2,3: Metering mode and trading platform
4: Industrial standard
Tx: Transaction
58
6.1 Problem Formulation
The proposed scheme:
7**5
Centralized scheme [23]:
(6.1.2)
Decentralized scheme [23]:
(6.1.3)
: Total capital cost of system
: Battery cost
: Solar cost
: Average load demand
: Capital cost per unit peak load
: System peak load capacity
: Annual discounting rate
: Project term
: Service level
: Fraction of demand served
: Blockchain incentive
: Transmission cost
N: Number of iterations
: Total yearly consumption
: Variable cost of energy generation
CRF: Capital recovery factor
(
Privacy Aware Energy Trading in Sub-Sahara Africa using Blockchain (2/3)
[23] Levin, T., & Thomas, V. M. (2012). Least-cost network evaluation of centralized and decentralized contributions to global electrification. Energy Policy, 41, 286-302.
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
59
6.2 Energy Trading
Regional trading:
78*5
7597885
: Total Cost of Energy Demand within the Region at Time h
: Total Cost of Excess Energy within the Region at Time h
: Total Energy Generated within the Region at Time h.
: Total Load within Region at Time h
: Selling Cost of Group to Group (g2g) within the Region r
: Buying Cost of Group to Group Within the Region r
Privacy Aware Energy Trading in Sub-Sahara Africa using Blockchain (3/3)
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
Simulation Results
The Proposed Solution 6: Privacy Aware Energy
Trading in Sub-Sahara Africa using Blockchain
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
61
Privacy Aware Energy Trading in Sub-Sahara Africa using Blockchain (1/2)
Table 22: Assumption of Economic Parameters [24].
2017 2022
Cost of Solar
Module + d.c Balance
of System
1.00 0.50
Charge Controller 0.20 0.10
Total ($/W) 1.20 0.60
Costs of Battery
Total ($/kw) 400 100
Costs of Load
Inverter 0.30 0.15
Soft Costs + d.c
Balance of System
1.00 0.50
Total ($/W) 1.30 0.65
Other Assumptions
Operation and
Maintenance
100 $/kw
Length of Project 20 years
Battery Replacement 10 years
Discount 10%
Results and Discussions
Cost-effective solution
Energy trading
Fig. 42: Energy Costs Vs Fill Rate.
Fig. 43: System Overhead Cost.
[24] Lee, J. T., and Callaway, D. S. (2018). The cost of
reliability in decentralized solar power systems in sub-
Saharan Africa. Nature Energy, 3(11), 960–968.
62
Privacy Aware Energy Trading in Sub-Sahara Africa using Blockchain (2/2)
Results and Discussions
Table 23: Comparison of the Selling and Buying Cost of Different Unmet Load Consumption.
Fig. 47: Decentralized based Energy System.
Fig. 46: Centralized based Energy System.
Unmet Loads Selling Cost
($/kWh)
Buying Cost
($/kWh)
Offer Price
($/kWh
Blockchain Price
($/kWh)
Constant Load 2.05 153.86 6.60 0.088
Medium Load 2.60 386.23 7.58 0.051
Heavy Night Load 4.53 226.91 9.84 0.096
Business Daily Load 8.82 119.94 23.63 0.095
Fig. 44: Offered Price Vs Consensus Price.
Fig. 45: Blockchain based Energy System.
The Proposed Solution 7:
Privacy Aware Multi-data Sharing in Smart
Communities using Blockchain
Solutions Proposed:
Secure Multi-data Sharing
Privacy Preservation
Credibility Management System
Samuel, O., Javaid, N. A Secure Blockchain based Multi-data Sharing System for Energy Users using Deep Learning in a
Smart Community, Journal of Ambient Intelligence and Humanized Computing, (Under Review). [IF=4.594].
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
64
Privacy Aware Multi-data Sharing in smart Communities using
Blockchain (1/2)
Proposed system model
KDC: Key Distribution Centre; sk: Private key
Ag: Aggregator; pk: Public key
Wj: jth worker node; H{}: Hash function
Nk: kth residential node
dk : residential nodes data
lj: blinded data
Fig. 49: System Model 7.
Fig. 48: Data Sharing and Collection Process.
Worker nodes are selected based on their
reputation scores and they are rewarded if
the provide an accurate feedback to the
blockchain
Privacy negotiation is performed before
data is shared and the residential nodes
are rewarded if they are willing to share
their data
We perform gradient sharing among
worker nodes for an efficient
implementation of the proposed deep
learning model
We evaluate the workers’ feedback based
on data distortion, data consistency, local
scoring and data reliability
65
.
: Local reputation score for jth node
: Election time for nodes
: Initial credit value of node
: Exponential decay time
: Data distortion
: Actual data of kth residential node
: Predicted data
: Standard deviation
: Data consistency
: Sensitivity parameter for data consistency
: Worker node
: Sensitivity parameter for local scoring
: Local scoring
: Data reliability
: Sensitivity parameter for data reliability
: Global reputation update
: Sensitivity parameter for local reputation
7.1 Problem Formulation
Local credibility: 7/**5
Credibility management:
7/*85
7/*,5
(7.1.4)
(7.1.5)
(7.1.6)
Privacy Aware Multi-data Sharing in Smart Communities using
Blockchain (2/2)
InterClass correction method is used, which
measures the degree in which two generated
datasets are identical [25]
+ = 1
[25] Qin, S., Nelson, L., McLeod, L., Eremenco, S., & Coons, S. J.
(2019). Assessing test–retest reliability of patient-reported outcome
measures using intraclass correlation coefficients: recommendations
for selecting and documenting the analytical formula. Quality of Life
Research, 28(4), 1029-1033.
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
Simulation Results
The Proposed Solution 7: Privacy Aware Multi-data
Sharing in Smart Communities using Blockchain
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
67
Privacy Aware Multi-data Sharing in Smart Communities using
Blockchain (1/3)
Results and Discussions
Reputation management
Computation cost
Fig. 50: Effect of on User Reputation (data distortion).
Fig. 51: Effect of βs on User Reputation (local scoring).
Fig. 53: Effect of γs on User Reputation (data reliability).
Fig. 52: : Effect of αs on User Reputation (data consistency).
Parameter Value
Size of key 2048-bit
ηr 0.001
Cpr 10 Cents
Wp 1, 2, 3, 4, 5, 6
Wr 0.1, 0.2, 0.3, 0.4, 0.5, 0.6
: 0.1, 0.2, 0.3, 0.4, 0.5, 0.6
Parameter Value
Size of key 2048-bit
ηr 0.001
Cpr 10 Cents
Wp 1, 2, 3, 4, 5, 6
Wr 0.1, 0.2, 0.3, 0.4, 0.5, 0.6
0.1, 0.2, 0.3, 0.4, 0.5, 0.6
Table 24: Proposed System Parameters [8].
68
Privacy Aware Multi-data Sharing in Smart Communities using
Blockchain (2/3)
Results and discussions
Reputation management
Computation cost
Fig. 55: Comparison between Honest and Malicious for Global Update. Fig. 56: Comparison between Different Bits.
p = 248, q = 277, pad = 15, pBits = 32. For the 940 input test set, 10 hidden
units, 20 outputs, the total test set gradient is 9630.i.e,. NGRD = (940 + 1) ×
10 + (10+1)×20.
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
Fig. 54: Privacy Level Vs Number of Houses.
69
Privacy Aware Multi-data Sharing in Smart Communities using
Blockchain (3/3)
Results and discussions
Double spending attack
Fig. 57: Probability of Double Spending Vs Number of valid Transaction. Fig. 58: Probability of Double Spending Vs Time
Disadvantage.
Fig. 59: Probability of Double Spending Vs Pre-mined Block. Fig. 60: Probability of Double Spending Vs Computing Power.
[26] Pinzón, C., and Rocha, C. (2016). Double-spend attack
models with time advantange for bitcoin. Electronic Notes in
Theoretical Computer Science, 329, 79–103.
The proposed double spending method is
compared with an existing method in [26]
The Proposed Solution 8:
Privacy Aware Energy Management for Multi-
agent System using Blockchain
Solutions Proposed:
Energy Pricing Policy for Multi-agent System
Privacy Preservation
Trust
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
Samuel, O., Sana, M., Javaid, N., Muhammad, S., and Jin-Ghoo, C. (2021). A Secure Multi-agent Coalition System for
Distributed Energy Trading in Smart Grids using Blockchain, Computers Materials & Continua, 1--15. (Under Review)
[IF=4.801].
71
Privacy Aware Energy Management for Multi-agent System using
Blockchain (1/2)
The Proposed System Model
Fig. 61: System Model 8.
We provide a secure coalition formulation mechanism for multi-agent
system
We provide an efficient energy trading pricing policy for multi-agent
system
We use imitation learning method to detect malicious behaviors of
agents in the multi-agent system
We use fictitious play method for efficient energy trading
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
72
: Utility of the ith agent
: Actions of the ith agent
: Actions of agents in coalition S
: Set of probability distribution random
Variables
: Agents’ buying price
: Buying constant factor
: Number of agents with energy deficit
: Agents’ energy deficit
: Agents’ surplus energy
: Selling constant factor
: Number of agents with surplus energy
: Buyer agents’ utility
: Revenue of buyer agent
8.1 Problem Formulation
Fictitious play: 72**5
Energy pricing:
72*85
 72*,5
(8.1.4)
Privacy Aware Energy Management for Multi-agent System using
Blockchain (2/2)
Fictitious play is a variant of learning models in the game theory, where
SCAs are facing unknown distribution of their opponent’s strategies.
Moreover, each SCA observes the strategies of their opponents’ play to
update its belief by choosing the best response to the opponents’ play.
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
Simulation Results
The Proposed Solution 8: Privacy Aware Energy
Management for Multi-agent System using
Blockchain
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
74
Privacy Aware Energy Management for Multi-agent System using
Blockchain (1/2)
Results and Discussions
Energy trading
Computation cost
Fig. 63: Variation in the Total Number of Tasks. Fig. 64: Energy Trading with SCAs.
Fig. 66: Maximizing Utility using Fictitious Play.
Fig. 65: : Average Imitated Rewards for SCAs.
Fig. 62: : Variation in Selling and Buying Vs Utility.
Total number of reward levels does not exceed 1.It
is assumed that the legitimate SCA’s reward level
will converge to 0. Based on this assumption, the
malicious nodes reward level will converge to 1 or
become unstable.
75
Results and Discussions
Reputation management
Computation cost
Fig. 68: Evaluating the Adaptivity of the System. Fig. 69: Evaluating the Robustness of the Proposed System.
Privacy Aware Energy Management for Multi-agent System using
Blockchain (2/2)
Fig. 67: Evaluating the Response Time Vs Key Sizes.
Fig. 71: Evaluating the Block Sizes based on Throughput.
Fig. 70: Evaluating the Various Parameters for the
Performance of the Proposed System.
D=C+T
ElaspseTime=
Vulnerability=
Capacity=
D: Total computational demand of request
C: Time of communication between SCAs
T: Time of delivery of energy
ST: System’s throughput
PrS: Probability of seller SCAs
PrB: Probability of buyer SCAs
Cass: Cost of reassigning upon failure of seller SCAS
Creq: Cost of losing all requests to a single buyer SCA
The Proposed Solution 9:
Energy Management For Resource Constrained
Prosumers using Blockchain and Artificial
Intelligence
Solutions Proposed:
Efficient Resource management
Privacy Preservation
Efficient Energy Pricing Policy
Samuel, O., Javaid, N., Turki, A. A., and Neeraj, K. (2021). Towards Sustainable Smart Cities: A Secure Trading System
for Residential Homes using Blockchain and Artificial Intelligence, Sustainable Cities & Society, 1–20. (Under Review)
[IF=5.268].
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
77
Energy Management For Resource Constrained Prosumers using
Blockchain and Artificial Intelligence (1/2)
The Proposed System Model
Fig. 72: System Model 9.
We propose an improved sparse neural network for minimizing the cost of the proposed
system model
We provide an efficient energy trading pricing policy prosumers
We propose a multi-pseudonym for privacy preservation of prosumers
We propose proof of computational closeness for the selecting of miners
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
78
: Utility of the ith seller
: Utility of the jth buyer
: Constant value
: Surplus energy of a seller
: Deficit energy of a buyer
: Satisfaction of buyer
: Proposed energy price
: Discount factor
: Blockchain incentive
and : Time for buyer and seller, respectively
: Charge imposed on a buyer for not remitting
energy token within the specified time
: Charge imposed on a seller for not delivering
energy to a buyer within the agreed time
: Reward given to a seller for energy trading
: Interest rate
: Decision of a buyer
: Decision of a seller
9.1 Problem Formulation
Energy pricing:
Pr 7**5
7*85
(9.1.3)
Energy Management For Resource Constrained Prosumers using
Blockchain and Artificial Intelligence (2/2)
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
Simulation Results
The Proposed Solution 9: Energy Management For
Resource Constrained Prosumers using Blockchain
and Artificial Intelligence
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
80
Results and Discussions
Energy trading
Computation cost
Sybil attack
Fig. 73: Analysis of Energy Price.
Fig. 74: Analysis of Prosumer’s Satisfaction.
Fig. 76: Evaluation of the Utility of Buyers.
Fig. 75: Evaluation of the Utility of Sellers.
Energy Management For Resource Constrained Prosumers using
Blockchain and Artificial Intelligence (1/3)
81
Results and Discussions
Energy trading
Computation cost
Sybil attack
Fig. 77: Analysis of Average System Cost. Fig. 78: Analysis of Average Transmission Time.
Fig. 80: Cost Evaluation of Blockchain.
Fig. 79: Cost Evaluation of the Proposed System.
Energy Management For Resource Constrained Prosumers using
Blockchain and Artificial Intelligence (2/3)
[27] Chen, W., Zhang, Z., Hong, Z., Chen, C., Wu, J., Maharjan, S.,
and Zhang, Y. (2019). Cooperative and distributed computation
offloading for blockchain-empowered industrial Internet of Things.
IEEE Internet of Things Journal, 6(5), 8433–8446
[28] Liu, S., Mocanu, D. C., Matavalam, A. R. R., Pei, Y., and
Pechenizkiy, M. (2020). Sparse evolutionary deep learning with
over one million artificial neurons on commodity hardware. Neural
Computing and Applications, 1–16.
The proposed model is compared with Deep
Reinforcement Learning Combined with Genetic
Algorithm (DRGO) [27] and Sparse Evolutionary
Training and Multi-layer Perceptron (SET-MLP) [28]
82
Results and Discussions
Reputation management
Computation cost
Sybil attack
Fig. 82: Convergence Analysis of ISNN. Fig. 83: Sybil Attack when N = 14 and m = 4.
Fig. 85: Sybil Attack when N = 200, and m = 4; 8 and 10.
Fig. 84: Sybil Attack when N = 200 and m = 4.
Energy Management For Resource Constrained Prosumers using
Blockchain and Artificial Intelligence (3/3)
Fig. 81: Convergence Analysis of Offloading Cost.
The Proposed Solution 10:
Privacy and Anonymity Management for
Prosumer using Blockchain and Garlic Routing
Solutions Proposed:
Stochastic path selection method
GarliChain
Reputation Management Method
Samuel, O., and Javaid, N. (2021). GarliChain: A Novel Privacy Preserving System for Smart Grid Consumers using
Blockchain, International Journal of Energy Research, (Accepted). 1–12. [IF=3.741].
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
84
Privacy and Anonymity Management for Prosumer using
Blockchain and Garlic Routing (1/2)
The Proposed System Model
Fig. 86: System Model 10.
To propose an improved identity based encryption for securing the data of
prosumers using a double encryption approach.
To propose a privacy preservation mechanism, which is based on garlic
routing and blockchain, known as GarliChain
To develop a dynamic path selection algorithm based on a stochastic
mechanism for routing data from the source node to
the destination node
To propose a reputation management system for ensuring trust and
credibility between nodes in the proposed system
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
To perform a security analysis of the proposed system for showing that the
system is safe from passive and active attacks
85
k: Number of path trajectories
: Number of nodes in the network
: Number of malicious nodes
: Number of trusted nodes
: Is an constant
: Maximum congestion
10.1 Problem Formulation
Path selection model: 7*+*5
Path set: (10.2)
Probability of successful path selection
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
Privacy and Anonymity Management for Prosumer using
Blockchain and Garlic Routing (2/2)
86
Fig. 87: Sequence Diagram of the Proposed System Model.
Simulation Results
The Proposed Solution 10: Privacy and Anonymity
Management for Prosumer using Blockchain and
Garlic Routing
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
88
Results and Discussions
Reputation management
Evaluation path selection
Fig. 88: Reputation Score Vs Number of Nodes. Fig. 89: Degree of Dishonest Node.
Fig. 91: Path Selection Model Vs the Number of Nodes.
Fig. 90: Path Selection Model Vs the Number of Trajectories.
Privacy and Anonymity Management for Prosumer using
Blockchain and Garlic Routing (1/2)
[29] Alnasser, A., and Sun, H. (2017). A fuzzy logic trust model for
secure routing in smart grid networks. IEEE Access, 5, 17896-
17903.
The proposed reputation model is compared with fuzzy
method[29]
89
Results and Discussions
Reputation management
Evaluation path selection
Fig. 92: Randomly Chosen Path Vs Path Chosen Only
Once. Fig. 93: Comparison of Cost of the System Vs
Transaction per Second.
Privacy and Anonymity Management for Prosumer using
Blockchain and Garlic Routing (2/2)
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
90
Conclusions
We have provided a solution for optimizing sales revenues and cost savings for electricity generation
by microgrids
We have proposed a dynamic pricing scheme that addresses the issue of fixed pricing and also resolved
conflicts over demurrage fees in the supply chain of prosumers
As EV is currently transforming the power grid sector as the flexible energy bank and distributed
mobile energy carriers, we have provided a solution that resolves the problem of location-related privacy
issues of EV owners during the charging and payment process
We have resolved the problem of historical antipathy and security of decentralized rural electrification
in Sub-Sahara Africa
We have ensured security and availability of data to train deep learning model, as a deep learning
system overrides accuracy achieved from human computation level
We have analyzed the privacy and trust related issues for the multi-agent system in smart grids
We have provided solutions that ensure anonymity and resource management of prosumers in smart
grids
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We hope to examine how socio-cultural background of prosumers in Sub-Sahara Africa may limit the
acceptance of the proposed framework as the adoption of blockchain is still in its infancy.
Additionally, the economic background of prosumers is another constraint that can prevent the
proposed system from being implemented, as prosumers need to be trained and well informed about
the emerging technology
By a employing reinforcement learning technique, we hope to examine how customers can learn in an
interactive environment using the feedback from their actions
The proposed consensus mechanism which uses the PageRank algorithm becomes inefficient as the
network links grow in infinite link cycles. There are also tendencies that certain nodes can deliberately
not take part in the ranking process, leading to a deadlock
The present architecture of blockchain does not support transaction reversibility as there is a short
time to mitigate and address any attack. Also, the immutability of a blockchain based transaction is a
double edge sword that leads to an increased effect of a defective and fraudulent transaction. Such a
transaction needs to be reversible to sustain a real life scenario
Future Work
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1. Samuel, O., and Javaid, N. (2021). GarliChain: A Novel Privacy Preserving System for Smart Grid Consumers using Blockchain,
International Journal of Energy Research, 1--12. (Accepted) [IF=3.741].
2. Rabiya, K., Samuel, O., Javaid, N., Aldegheishem, A., Shafiq, M., Nabil, A. (2021). A Secure Trust Method for Multi-agent
System in Smart Grids using Blockchain, IEEE Access, 1-16. [IF=3.745].
3. Samuel, O., and Javaid, N. (2021). A Secure Blockchain-based Demurrage Mechanism for Energy Trading in Smart Communities,
International Journal of Energy Research, 45(1), 297-315. [IF=3.741].
4. Samuel, O., Javaid, N., Khalid, A., Wazir Z., K., Mohammed, Y., A., Muhammad, K., Afzal., and Byung-Seo, K. (2020). Towards
Real-time Energy Management of Multi-microgrid using a Deep Convolution Neural Network and Cooperative Game Approach.
IEEE Access, 8, 161377-161395. [IF= 3.745].
5. Samuel, O., Almogren, A., Javaid, A., Zuair, M., Ullah, I., and Javaid, N. (2020). Leveraging Blockchain Technology for Secure
Energy Trading and Least-Cost Evaluation of Decentralized Contributions to Electrification in Sub-Saharan Africa. Entropy, 22(2),
1--29. [IF=2.494].
Publications (1/6) [Total IF= 25.364]
Journal publications (1/3)
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6. Samuel, O., Alzahrani, F. A., Hussen Khan, R. J. U., Farooq, H., Shafiq, M., Afzal, M. K., and Javaid, N. (2020). Towards
Modified Entropy Mutual Information Feature Selection to Forecast Medium-Term Load Using a Deep Learning Model in Smart
Homes. Entropy, 22(1), 1-31. [IF= 2.494].
7. Samuel, O., Javaid, N., Ashraf, M., Ishmanov, F., Afzal, M. K., and Khan, Z. A. (2018). Jaya based Optimization Method with
High Dispatchable Distributed Generation for Residential Microgrid. Energies, 11(6), 1-29. [IF=2.702].
8. Samuel, O., Javaid, S., Javaid, N., Ahmed, S. H., Afzal, M. K., and Ishmanov, F. (2018). An efficient power scheduling in smart
homes using Jaya based optimization with time-of-use and critical peak pricing schemes. Energies, 11(11), 1-27. [IF=2.702].
9. Samuel, O., Sana, M., Javaid, N., Muhammad, S., and Jin-Ghoo, C. (2021). A Secure Multi-agent Coalition System for Distributed
Energy Trading in Smart Grids using Blockchain, Computers Materials & Continua, 1-15. (Under Review) [IF=4.801].
10. Samuel, O., and Javaid, N. (2021). A Survey on Blockchain based Data Storage, Access Control and Data Sharing, Future
Generation Computer Systems, 1-42. (Under Review) [IF=6.125].
Publications (2/6) [Total IF=25.364]
Journal publications (2/3)
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11. Samuel, O., Javaid, N., Turki, A. A., and Neeraj, K. (2021). Towards Sustainable Smart Cities: A Secure Trading System for
Residential Homes using Blockchain and Artificial Intelligence, Sustainable Cities & Society, 1-20. (Under Review) [IF=5.268].
12. Samuel, O., and Javaid, N. (2021). Cloud based Resource Management of Electric Vehicles using Blockchain with Electric Curve Privacy,
IEEE Transactions on Systems, Man and Cybernetics: Systems. (Under Review), 1-8. [IF=9.309].
13. Samuel, O., Javaid, N., Aldegheishem, A., Alrajeh, N., and Shafiq, M. (2021). A Secure Blockchain based Multi-data Sharing System for
Energy Users in a Smart Community, Journal of Ambient Intelligence and Humanized Computing, (Under Review), 1-16. [IF=4.594].
14. Samuel, O., and Javaid, N. (2021). A Secure Energy Trading System for Electric Vehicles in Smart Communities using Blockchain, Future
Generation Computer Systems, 1-42. (Under Review) [IF=6.125].
15. Yahaya, A. S., Javaid, N., Samuel, O., Turki, A. A., and Neeraj, K. (2021). A Two-stage Privacy Preservation and Secure Energy Trading
Model using Blockchain and Cloud based Aggregator in Smart Grids, IEEE Transactions on Industrial Informatics, 1-8. (Under Review)
[IF=9.112].
Publications (3/6) [Total IF= 25.364]
Journal publications (3/3)
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1. Samuel, O., Javaid, N., Khalid, A., Imran, M., and Nasser, N. (2020). A Trust Management System for Multi-Agent System in Smart
Grids Using Blockchain Technology. In 2020 IEEE Global Communications Conference (GLOBECOM), Taipei, Taiwan, 1-6.
2. Ayesha, S., Javaid, N., Samuel, O., Adia, K., Noman, H., and Muhammad, I. (2020, June). Efficient Data Trading and Storage in
Internet of Vehicles using Consortium Blockchain. In the 16th International Wireless Communications and Mobile Computing
Conference (IWCMC), Byblos, Lebanon, 1-7.
3. Iftikhar, M. S., Javaid, N., Samuel, O., Muhammad, S., and Muhammad, I. (2020, June). An Incentive Scheme for VANETs based on
Traffic Event Validation using Blockchain. In the 16th International Wireless Communications and Mobile Computing Conference
(IWCMC), Byblos, Lebanon, 1-7.
4. Ashraf U., Javaid, N., Samuel, O., Muhammad, I., and Muhammad S. (2020, June). CNN and GRU based Deep Neural Network for
Electricity Theft Detection to Secure Smart Grid. In the 16th International Wireless Communications and Mobile Computing
Conference (IWCMC), Byblos, Lebanon, 1-7.
Conference proceedings (1/3)
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Publications (4/6) [Total IF= 25.364]
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5. Samuel, O., Javaid, N., Rabiya, K., Muhammad, I., and Moshen, G. (2020, June). Case Study of Direct Communication based Solar
Power Systems in Sub-Saharan Africa for Levelled Energy Cost Using Blockchain. In proceedings of the IEEE International
Conference on Communications, Dublin, Ireland, 1-6.
6. Samuel, O., Javaid, N., Awais. M., Ahmed, Z., Imran, M., and Guizani, M. (2019, December). A Blockchain Model for Fair Data
Sharing in Deregulated Smart Grids. 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 1-7.
7. Samuel, O., Javaid, N., Shehzad, F., Iftikhar, M. S., Iftikhar, M. Z., Farooq, H., and Ramzan, M. (2019, November). Electric Vehicles
Privacy Preserving Using Blockchain in Smart Community. In International Conference on Broadband and Wireless Computing,
Communication and Applications, 67-80. Springer, Cham.
8. Samuel, O., Javaid, N., and Rafique, A. (2018, October). A New Entropy-based Feature Selection Method for Load Forecasting in
Smart Homes. In Proceedings of the International Conference on Cyber Security and Computer Science (ICONCS), Safranbolu
Karabuk University (KBU) Turkey, 185-192.
Conference proceedings (2/3)
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Publications (5/6) [Total IF= 25.364]
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9. Samuel, O., Khan, Z. A., Iqbal, S., and Javaid, N. (2018, November). An Efficient Energy Management in Microgrid: A Game
Theoretic Approach. In 2018 Fifth HCT Information Technology Trends (ITT), United Arab Emirates, 33-40.
10. Samuel, O., Javaid, N., Aslam, S., and Rahim, M. H. (2018, March). JAYA optimization based energy management controller for smart
grid: JAYA optimization based energy management controller. In 2018 International Conference on Computing, Mathematics and
Engineering Technologies (iCoMET), Sukkur, Pakistan, 1-8.
11. Samuel, O., Javaid, N., Rahim, M. H., and Aslam, S. (2018, March). Harmony search optimization technique for home management
system in smart grid: HSA technique for HEMS. In 2018 International Conference on Computing, Mathematics and Engineering
Technologies (iCoMET), Sukkur, Pakistan, 1-9.
Conference proceedings (3/3)
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Publications (6/6) [Total IF= 25.364]
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Thanks for patient hearing
Questions and Answers
Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain
Final PhD Defense by Omaji Samuel, May, 18, 2021
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