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

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

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Privacy Aware Energy Management in Smart
Communities by Exploiting Blockchain
1
Presented by : Omaji Samuel
CIIT/FA17-PCS-013/ISB
PhD (Scholar)
Supervisor : Dr. Nadeem Javaid
Co-Supervisor : Dr. Sohail Iqbal
“Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain ”
PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019

Agenda
Introduction
Related Work
Problem Statement
Problem Formulations
Proposed Solutions
System Models
Results
2
“Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain ”
PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019

Introduction (1/2) 3
According to World energy outlook 2018 [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 (August 5, 2019), Available:
http://www.worldenergyoutlook.org/resources/energydevelopment/energyaccessdatabase/.
Fig. 1: Energy crises
“Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain ”
PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019

Privacy and security
issues.
What else?
Neighbor
oriented energy
trading issues
Is the blockchain solution
good enough?
What is the possible
solution?
Introduction (2/2) 4
A centralized system
Fig. 2: Privacy issues in the energy management system and the solution
“Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain ”
PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019

Related Work (1/3)
5
[2]. Liu, N., Cheng, M., Yu, X., Zhong, J., & 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.
[3]. Liu, N., Yu, X., Wang, C., Li, C., Ma, L., & 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.
[4]. 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.
[5]. Devine, M. T., & 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 [2]
Maximize prosumers’ utility
Minimize prosumers’ energy cost
Energy sharing
Analytic Insecure environment
Delay in energy supply
Distributed iterative algorithm [3] Prosumers’ energy cost savings
Energy sharing
Analytic Insecure environment
Delay in energy supply
Game theory and differential
privacy [4]
Data sharing
Aggregator’s profit maximization
Privacy preserving of user’s data
Negotiation Insecure environment
Dishonesty
Mixed complementarity algorithm
[5]
Energy trading
Prosumer’s cost minimization
Demurrage
Time of use Privacy issue
Table. 1: Energy trading for prosumers

Related Work (2/3)
6
[6]. Aujla, G. S., Jindal, A., & Kumar, N. (2018). EVaaS: Electric vehicle-as-a-service for energy trading in SDN-enabled smart
transportation system. Computer Networks, 247-262.
[7]. Aujla, G. S., Kumar, N., Singh, M., & Zomaya, A. Y. (2019). Energy trading with dynamic pricing for electric vehicles in a
smart city environment. Journal of Parallel and Distributed Computing, 169-183.
[8]. Liu, C., Chai, K. K., Zhang, X., Lau, E. T., & Chen, Y. (2018). Adaptive blockchain-based electric vehicle participation scheme
in smart grid platform. IEEE Access, 25657-25665.
[9]. Su, Z., Wang, Y., Xu, Q., Fei, M., Tian, Y. C., & 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 [6] Energy trading between electric vehicles
(EVs) and charging stations (CSs)
Utility maximization
Analytic Insecure environment
Privacy issue
Stackelberg game [7] Energy trading between EVs and CSs Dynamic Insecure environment
Privacy issue
Iceberg order execution
algorithm, genetic algorithm
and blockchain [8]
Minimize power fluctuation level
Minimize overall charging cost of Evs
Price bidding Privacy issue
High computational cost
Contract theory and
blockchain [9]
Utility maximization
Energy allocation
Energy trading
Analytic Privacy issue
Table. 2: Energy trading for EVs

Related Work (3/3)
7
[10]. Li, L., Liu, J., Cheng, L., Qiu, S., Wang, W., Zhang, X., & Zhang, Z. (2018). Creditcoin: A privacy-preserving blockchain-
based incentive announcement network for communications of smart vehicles. IEEE Transactions on Intelligent Transportation
Systems, 2204-2220.
[11]. Guan, Z., Si, G., Zhang, X., Wu, L., Guizani, N., Du, X., & Ma, Y. (2018). Privacy-preserving and efficient aggregation
based on blockchain for power grid communications in smart communities. IEEE Communications Magazine, 82-88.
[12]. Gai, K., Wu, Y., Zhu, L., Qiu, M., & Shen, M. (2019). Privacy-preserving energy trading using consortium blockchain in
smart grid. IEEE Transactions on Industrial Informatics, 1-10.
Technique(s) Objective(s) Price Tariff Limitation(s)
Honest-but-curious adversary
model and blockchain [10]
Privacy preserving of EVs
Use different IDs of each
transaction
Dynamic tariff based
on bidding
Privacy issue based on
location
Traceability issue due to
different identities (IDs)
Price bidding is inefficient in
real time context
Bloom filter and blockchain
[11]
Privacy preserving of users’
consumption
Dynamic price Multiple pseudonyms create
traceability issue for single
user
High computational cost
Bound detection algorithm
and blockchain [12]
Privacy of users’ trade data Computational cost due
mapping of dummy account
and real account
Table. 3: Blockchain

Problem Statement (1/3) 8
The issues of insecure, and non-trusted environment of [2] discourage prosumers from data sharing. Generating new key pairs for each
changed ID and multiple pseudonyms for each user of [10] and [11] create traceability issues for blockchain’s transactions audit.
Mapping of the dummy and real account of [12] will lead to the additional computational burden as the proposed system requires extra
gas consumption for both transactions. Double auction mechanism proposed by [13] involves entities that act as sellers and buyers, who
can auction price within the localized trading environment. However, the proposed system takes much computational time and it
becomes impractical when one auctioneer is not willing to participate in the cumbersome auctioning at every hour in a day and with
insufficient power capacity. Furthermore, the authors in [14] propose non-monetary incentive as high priority ranking to users for using
more energy. However, authors fail to consider that the hoarding of energy by high priority users that is supposed to be shared with other
users results in dissatisfaction and resentment.
Authors in [1] provide a setup of a centralized server coordinating the energy trading between EVs and CSs to minimize cost. However,
trading is made in a non-trusted and insecure environment. Authors in [8], [9] and [13] propose a setup of blockchain based energy
trading between EVs and CSs to maximize the social welfare and enhance EVs' satisfactions. However, they do not consider the pricing
policy and privacy of EVs in terms of their locations. Authors in [15] provide privacy preserving for prosumers about their energy
consumption, energy prices and social welfare maximization; however, authors do not consider that the insecure environment and
disclosure of prosumers’ locations also raise privacy concerns.
[13]. Kang, J., Yu, R., Huang, X., Maharjan, S., Zhang, Y., & Hossain, E. (2017). Enabling localized peer-to-peer electricity trading
among plug-in hybrid electric vehicles using consortium blockchains. IEEE Transactions on Industrial Informatics, 3154-3164.
[14]. Zhang, T., Pota, H., Chu, C. C., & Gadh, R. (2018). Real-time renewable energy incentive system for electric vehicles using
prioritization and cryptocurrency. Applied energy, 582-594.
[15]. Zhao, C., Chen, J., He, J., & Cheng, P. (2018). Privacy-preserving consensus-based energy management in smart grids. IEEE
Transactions on Signal Processing, 6162-6176.
“Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain ”
PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019

Problem Statement Analogy (2/3) 9
Fig. 3: Limitation of a centralized system Fig. 4: Limitation of blockchain based
decentralized system
“Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain ”
PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019

Problem Statement (3/3) 10
[16]. Lee, J. T., & Callaway, D. S. (2018). The cost of reliability in decentralized solar power systems in sub-Saharan Africa. Nature Energy, 1-10.
[17]. Ferrag, M. A., & Maglaras, L. (2019). DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange Framework for Smart
Grids. IEEE Transactions on Engineering Management, 1-13.
Authors in [16] propose levelized cost of energy for decentralized solar power systems in SSA. In their framework, a standalone solar
home system (SHS) is considered with solar and battery capacities using a fraction of demand serve (FDS). Also, FDS is used to
measure the reliability via a multi-step optimization to derive the least-cost system and energy costs minimization using Newton’s
method. However, the authors do not consider energy reliability in terms of security, privacy and energy exchange for the decentralized
SHS. Also, Newton’s method requires the use of a second order partial derivative, which is tedious to compute and time-consuming. In
addition, the authors do not consider the working capital, and service level to quantify the reliability on generation costs. Furthermore,
they do not include the payment system such as pay-as-you-go and also do not address behaviors of electricity user with regard to non-
payment of electricity bill.
Authors in [17] propose a deep learning as an intrusion detection system using the recurrent neural network (RNN) for detecting
network attacks and fraudulent activities within the blockchain network. The authors use bilinear mapping, short signatures and hashes
to provide privacy preserving of energy trading transactions. However, the proposed scheme does not address privacy threat from
differential attack, collusion attack and dishonest behavior in updating and gradient collecting parameters of RNN. Also, they do not
consider privacy preserving of energy users from re-identification attack and method of data collection.
“Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain ”
PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019

11
Proposed proof of authority using PageRank
Mechanism [18] :
Problem Formulation (1/3)
[18]. Samuel, O., Javaid, N., Muhammad, A., Zeeshan, A., Muhammad, I., & Guizani, M. (2019). A Blockchain Model for Fair
Data Sharing in Deregulated Smart Grids. Accepted for publication in 2019 IEEE Global Communications Conference:
Communication & Information Systems Security, USA: 1-7.
[19]. Pagerank Algorithm [Online], Accessed on: (April. 10, 2019), Available: http://pr.efactory.de/e-pagerank-algorithm.shtml.
PR(A)= ,…, d
Fig. 5: Demonstration of the PageRank mechanism [19]
PR, address A’s page rank
, damping factor
C(), number of out-bound links on the
nth address B
PR(Bn), page rank of B link to A

12
Formulation of sub-problem 1 [13]:
Problem Formulation (2/3)
(h)) ,… (2)
Such that:
… (2.1)
… (2.2)
, … (2.3)
, ith buying prosumers’ utility at given time h
(h), ith selling prosumers’ energy cost at a given time h
, ith minimum prosumers’ adjusted power at a time h
, ith maximum prosumers’ adjusted power at a given time h
, ith prosumers’ generated energy supply from rooftop photovoltaic at a
given time h
, ith prosumers’ energy demand at a given time h
O1, Objective function 1; social welfare maximization
“Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain ”
PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019

13
Problem Formulation (3/3)
Formulation of sub-problem 2
[2] :
O2(t)), … (3)
Such that:
,… (3.1)
,… (3.2)
, … (3.3)
, nth EVs ‘ utility at given time t
(t), kth CSs’ energy cost at a given time t
, nth EVs’ satisfaction factor
, nth EVs’ required energy demand
, kth CSs’ available energy supply
, number of EVs
O2, Objective function 2; social welfare maximization
“Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain ”
PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019

Sub-problem 1
-differential privacy
PageRank mechanism
Security analysis
Characteristics
Blockchain energy trading
DRR
Demurrage mechanism
Sub-problem 2
Blockchain energy trading
MPPS
CEMA
(,-differential privacy

Proposed Solutions
14
MPPS, Multi-parameter pricing scheme
CEMA, Consensus energy management algorithm
, Epsilon
, Delta
DRR, Demand response ratio
An iterative method is used to solve the proposed sub-problem 1 and 2

Sub-problem 3
Quasi-Newton’s method
Multi-step optimization method
Gantt task incentive method
Blockchain energy trading
Working capital ratio
Blockchain levelized cost of energy
Fill rate and service level
Sub-problem 4
Deep learning
Energy exchange and incentive mechanism
Privacy preserving
Clustering
Attribute based encryption
Proposed Solutions
15

System Model (1/2) 16
Fig. 6: System model for sub-problem 1
Prosumers behave as buyer or seller on the basis of
available energy generation
Data sharing is done on the basis of negotiation
with respect to privacy
Energy trading between prosumers using
blockchain
“Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain ”
PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019

System Model (2/2) 17
Fig. 7: System model for sub-problem 2 Fig. 8: A directed graph for sub-problem 2
“Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain ”
PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019

Sub-problem 1
Five prosumers having rooftop solar energy [20] and loads [21]
Consider 100 prosumers for data sharing on the basis of load
categorization
Privacy costs ranges from 1 to 5 cents [4]
Prosumers “a” that valued their privacy against their reward have privacy
risk values ranges from 1 to 6 [4]
Prosumers “b” that valued their reward against their privacy have privacy
risk values ranges from 0.1 to 0.6 [4]
Consider 40 blocks to evaluate the blockchain’s computational cost
Consider 500 blocks to evaluate the blockchain’s gas consumption
18
Parameter Value
0.4 (cents)
1 (cents)
48 hours
0.2
0.1 and 0.2
and 1 hour
0.4 (cents)
100
0.01
Parameter Value
0.4 (cents)
1 (cents)
48 hours
0.2
0.1 and 0.2
1 hour
0.4 (cents)
100
0.01
Table. 4: Parameters of energy trading
for prosumers [3]
Results and Discussion (1/16)
[20]. Solar radiation research laboratory (SRRL) [Online], Accessed on: (April. 10, 2019), https://www.nrel.gov/esif/solar-
radiation-research-laboratory.html .
[21]. Bingtuan, G. A. O., Xiaofeng, L. I. U., Cheng, W. U., & Yi, T. A. N. G. (2018). Game-theoretic energy management with
storage capacity optimization in the smart grids. Journal of Modern Power Systems and Clean Energy, 656-667.

19
Results and Discussion (2/16)
Results includes
Blockchain costs of gas and computation
Privacy
Energy trading
Fig. 9: Solar dataset [20]
Fig. 10: Load data [21]
100 150 200 250 300 350 400 450 500
Peer number
0
0.5
1
1.5
2
2.5
3
3.5
4
G
a
s
c
o
n
s
u
m
p
t
i
o
n
10
8
PoW
Proposed PoA
Fig. 11: Comparison of gas
consumption

20
Results and Discussion (3/16)
0 5 10 15 20 25 30 35 40
Height of block
0
0.5
1
1.5
2
2.5
3
3.5
C
o
m
p
u
t
a
t
i
o
n
a
l
c
o
s
t
(
H
a
s
h
e
s
)
10
10
PoW
Proposed PoA
Fig. 12: Comparison of computational cost
Blockchain cost of gas and computation
Fig. 13: Effect of parameter “b” on the
number of prosumers
0.1 0.2 0.3 0.4 0.5 0.6
Parameter b
0
1
2
3
4
5
6
7
8
9
N
u
m
b
e
r
o
fp
r
o
s
u
m
e
r
s
Number of prosumers for = 0.1
Number of prosumers for = 0.2
Number of prosumers for = 0.1 (proposed)
Number of prosumers for = 0.2 (proposed)
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21
Results and Discussion (4/16)
1 2 3 4 5 6
Parameter a
0
10
20
30
40
50
60
70
80
90
N
u
m
b
e
r
o
f
p
r
o
s
u
m
e
r
s
Number of prosumers for = 0.1
Number of prosumers for = 0.2
Number of prosumers for = 0.1 (proposed)
Number of prosumers for = 0.2 (proposed)
Fig. 14: Effect of parameter “a” on the
number of prosumers
Privacy
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
Number of query
4
4.2
4.4
4.6
4.8
5
5.2
5.4
A
v
e
r
a
g
e
d
a
t
a
DP for original data
DP for original data with one added value
Fig. 15: Original data versus original data
with added noise; DP: Differential privacy
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22
Results and Discussion (5/16)
0.1 0.2 0.3 0.4 0.5 0.6
Privacy level
0
2000
4000
6000
8000
10000
12000
A
g
g
r
e
g
a
t
o
r
p
r
o
f
i
t
(
c
e
n
t
s
)
0.1 0.2 0.3 0.4 0.5 0.6
Privacy level
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
R
i
s
k
r
e
v
e
a
l
i
n
g
Fig. 16: Privacy level versus aggregator’s
profit
Fig. 17: Privacy level versus risk revealing
Privacy
“Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain ”
PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019

23
Results and Discussion (6/16)
Energy trading
Fig. 18: Internal prices versus grid prices Fig. 19: Internal prices deviation versus the
number of iterations
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24
Results and Discussion (7/16)
Energy trading
0 5 10 15 20 25 30 35 40 45 50
Time (hours)
0.4
0.5
0.6
0.7
0.8
0.9
1
P
r
i
c
e
(
C
e
n
t
s
)
Day-ahead sell price
Day-ahead buy price
Hour-ahead sell price
Hour-ahead buy price
Fig. 21: Internal prices versus operation day
Fig. 20: Net power versus operational day
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PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019

25
Results and Discussion (8/16)
Energy trading
Fig. 23: Prosumers’ social welfare
Fig. 22: Prosumers’ total utility
“Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain ”
PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019

Sub-problem 2
Consider 20 EVs and 4 CSs [7]
Comparison with MPPS without blockchain, fixed price
scheme and ToU scheme [7]
26
Parameter Value
0.8
2
3, 0.8
1, 2, 3, 4, 5 and 6
1,2,3,4,5 and 6
0.6, 0.5, 0.1, 0.1
12, 10, 17.87, 55 (kW)
,, 75 (kW), 7 (USD), 150
(kW)
Parameter Value
0.8
2
3, 0.8
1, 2, 3, 4, 5 and 6
1,2,3,4,5 and 6
0.6, 0.5, 0.1, 0.1
12, 10, 17.87, 55 (kW)
75 (kW), 7 (USD), 150
(kW)
Table. 5: Parameters of energy trading of
EVs and CSs [4] [7]
Results and Discussion (9/16)
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27
Results and Discussion (10/16)
Results includes
Energy trading
Evaluation of EVs’ assignment and CSs’ selection
probability
Privacy
Fig. 24: EVs’ demand [7]
Fig. 25: Distance of EVs from
CSs [7]
2 4 6 8 10 12 14 16 18 20
EVs
0
2
4
6
8
10
12
14
16
18
D
i
s
t
a
n
c
e
(
m
i
l
e
s
)
CS
1
CS
2
CS
3
CS
4
Fig. 26: CSs’ offered price

28
Results and Discussion (11/16)
Energy trading
0 2 4 6 8 10 12 14 16 18 20
EVs
0
1
2
3
4
5
6
7
P
r
i
c
e
(
U
S
D
)
Case 1 (Blockchain)
Case 2 (MPPS)
Case 3 (Fixed)
ToU
Fig. 27: Prices of various schemes
1234
CSs
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
A
v
e
r
a
g
e
p
r
i
c
e
(
U
S
D
)
Outside EV
Case 1 (Blockchain for urban)
Case 2 (MPPS for urban)
Case 1 (Blockchain for rural)
Case 2 (MPPS for rural)
Case 1 (Blockchain for special EV)
Case 2 (MPPS for special EV)
Fig. 28: Average prices for types of EV
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29
Results and Discussion (12/16)
Energy trading
0 2 4 6 8 10 12 14 16 18 20
EVs
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
S
a
t
i
s
f
a
c
t
i
o
n
f
a
c
t
o
r
Case 1 (Blockchain)
Case 2 (MPPS)
Case 3 (Fixed)
ToU
Fig. 29: EVs’ satisfaction factor; ToU: Time
of use
0 2 4 6 8 10 12 14 16 18 20
EVs
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
U
t
i
l
i
t
y
CS
1
CS
2
CS
3
CS
4
Fig. 30: EVs’ utility
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PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019

30
Results and Discussion (13/16)
Energy trading
0 2 4 6 8 10 12 14 16 18 20
EVs
0
1000
2000
3000
4000
5000
6000
7000
A
v
e
r
a
g
e
c
o
s
t
(
U
S
D
)
Case 1 (Blockchain)
Case 2 (MPPS)
Case 3 (Fixed)
ToU
Fig. 31: EVs’ average cost Fig. 32: CSs’ average cost
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31
Results and Discussion (14/16)
Energy trading
2 4 6 8 10 12 14 16 18 20
EVs
-40
-20
0
20
40
60
80
P
r
o
f
i
t
/
L
o
s
s
(
U
S
D
)
CS
1
CS
2
CS
3
CS
4
Fig. 33: Aggregator’s profit/loss Fig. 34: Social welfare
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32
Results and Discussion (15/16)
CS’s assignment and EV’s acceptance probability
0 2 4 6 8 10 12 14 16 18 20
EVs
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
A
c
c
e
p
t
a
n
c
e
p
r
o
b
a
b
i
l
i
t
y
Case 1 (Blockchain)
Case 2 (MPPS)
Case 3 (Fixed)
ToU
Fig. 36: CSs’ assignment probabilityFig. 35: EVs’ acceptance probability
“Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain ”
PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019

33
Results and Discussion (16/16)
Privacy
123456
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
R
i
s
k
r
e
v
e
a
l
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n
g
Case 1 (Blockchain)
Case 2 (MPPS)
123456
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
R
i
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k
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v
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Case 1 (Blockchain)
Case 2 (MPPS)
Fig. 38: Risk reveal versus privacy level
based on location
Fig. 37: Risk reveal versus privacy level
based on offered price
“Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain ”
PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019

34
Journal Publications (1/2)
Samuel, O., Javaid, N., Ashraf, M., Ishmanov, F., Afzal, M., & Khan, Z. (2018). Jaya based Optimization Method with
High Dispatchable Distributed Generation for Residential Microgrid. Energies, 11(6), 1513.
Samuel, O., Javaid, S., Javaid, N., Ahmed, S., Afzal, M., & 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),
3155.
Samuel, O., Javaid, N., Adia, K., Muhammad., K, A., Muhammad, U., & Kashif, B. Location Privacy-Preserving
based Energy Trading using Blockchain for Electric Vehicles in Smart Community. IEEE Transaction on Industry
Application (Conditionally accepted)
Samuel, O., & Javaid, N. Achieving Energy Trading Reliability: A Levelized Cost of Energy and Future Evolution
with Blockchain. IEEE Transaction on Power Systems (Submitted)
Samuel, O., & Javaid, N. A Blockchain Model for Energy Trading under Privacy-Preserving and Demurrage. IEEE
Transactions on Consumer Electronics (Submitted)
“Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain ”
PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019

35
Journal Publications (2/2)
Samuel, O., & Javaid, N. Dynamic Energy Management of a Microgrid: A Deep Learning Approach. IEEE
Transaction on Industrial Informatics (Submitted)
Samuel, O., & Javaid, N. A Robust Model to Forecast Medium-Term Load using Data Analytic in Smart Homes.
Journal of Intelligent Material Systems and Structures (Submitted)
Samuel, O., & Javaid, N. A game-theoretic approach for energy management within distribution systems in microgrid.
Concurrency and Computation: Practice and Experience (Under review)
“Privacy Aware Energy Management in Smart Communities by Exploiting Blockchain ”
PhD Synopsis Presentation by Omaji Samuel on August, 30, 2019