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Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes (PhD Thesis Presentation)

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Abstract

Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes (PhD Thesis Presentation)
PhD Thesis Presentation
Blockchain based Privacy Aware Energy
Trading in Electric Vehicles and Smart Homes
1
Presented by: Adamu Sani Yahaya
PhD (Scholar)
CIIT/FA18-PCS-002/ISB
Supervisor: Dr. Nadeem Javaid
Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Agenda
Introduction
Related Work
Problem Statement
Proposed Solutions
Simulation Results
Conclusion
Future Work
2
Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Introduction (1/3) 3
Fig. 1: Smart City Components
Smart Energy Management (SEM) is one of the
constituents of a smart community that efficiently
Monitors, controls, and regulates the energy without
affecting the users ’ comfort
Example of SEM is Smart Energy Trading (SET)
It comprises of energy providers and consumers
Example of providers are utility
companies and local energy
prosumers while
consumers are residential homes,
transportation industrial domains,
etc.
Recently, the dramatic rise in the penetrations of Electric
Vehicles (EV) in the transportation has increased pressure on
the power grid
Furthermore, as the energy generation from power grid becomes
scare,
Efficient local trading of energy becomes necessary, but challenging in
the community
These challenges are caused due to:
The increased in the penetration of highly intermittent
distributed renewable energy sources in the power systems
Poorly coordinated EVs
Load balancing issues, etc.
Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Introduction (2/3) 4
In conventional energy system, thousands of energy storage devices and centralized generators are
required to balance energy
An alternative method is required
Introduction of DR with Internet connected EVs gives a better approach to manage the huge demand
without increasing energy storage and generation
Different SEM for DR management are deployed in the energy network with help of ICT
These are vulnerable to different forms of attacks in which a malicious user may take advantage of the
network security loopholes [1]
Privacy and security problems, etc.
Other issue that is caused when a centralized energy trading model is used
A single point of failure
A robust and secure energy management system is required.
[1]. Liang, Gaoqi, Steven R. Weller, Fengji Luo, Junhua Zhao, and Zhao Yang Dong. ``Distributed blockchain-based data
protection framework for modern power systems against cyber attacks.'' IEEE Transactions on Smart Grid 10, no. 3 (2018):
3162-3173
Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Privacy and security issues.
Lack of optimal scheduling
system.
What is the
possible solution?
Is the blockchain solution
good enough?
Introduction (3/3) 5
Energy management system
Fig. 2: Security threats in the energy management system and solution
No, Privacy leakages,
Low system efficiency, trust issues,
load balancing, pricing
scheme etc.
I have looked at
the system and
found a single
point of failure.
Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Related Work (1/2)
6
[2]. Liu, Nian, Minyang Cheng, Xinghuo Yu, Jiangxia Zhong, and Jinyong Lei. "Energy-sharing provider for PV prosumer
clusters: A hybrid approach using stochastic programming and stackelberg game." IEEE Transactions on Industrial
Electronics 65, no. 8 (2018): 6740-6750.
[3]. Yassine, Abdulsalam, Ali Asghar Nazari Shirehjini, and Shervin Shirmohammadi. "Smart meters big data: Game theoretic
model for fair data sharing in deregulated smart grids." IEEE access 3 (2015): 2743-2754.
[5]. Aujla, Gagangeet Singh, Anish Jindal, and Neeraj Kumar. "EVaaS: Electric vehicle-as-a-service for energy trading in SDN-
enabled smart transportation system." Computer Networks 143 (2018): 247-262.
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
Single point of failure
Game theory and differential
privacy [3]
Data sharing
Aggregator’s profit maximization
Privacy preserving of user’s data
Negotiation Insecure environment
Dishonesty
Stackelberg game [5] Energy trading between electric
vehicles (EVs) and charging stations
(CSs)
Utility maximization
Analytic Insecure environment
Privacy issue
Single point of failure
Table. 1: Energy trading for prosumers
Related Work (2/2)
7
Technique(s) Objective(s) Price Tariff Limitation(s)
Consortium blockchain approach
[7]
Privacy of energy users Price bidding Computational cost due
mapping of dummy
account and real account
Poor performance for
sudden changes and
creates generation spikes
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
of price bidding
Contract theory and blockchain [9] Utility maximization
Energy allocation
Energy trading
Analytic Trust issues
High energy consumption
at consensus
Table. 1: Blockchain based Energy
trading
[7]. Gai, Keke, Yulu Wu, Liehuang Zhu, Meikang Qiu, and Meng Shen. "Privacy-preserving energy trading using consortium
blockchain in smart grid." IEEE Transactions on Industrial Informatics 15, no. 6 (2019): 3548-3558.
[8]. Liu, Chao, Kok Keong Chai, Xiaoshuai Zhang, Eng Tseng Lau, and Yue Chen. "Adaptive blockchain-based electric vehicle
participation scheme in smart grid platform." IEEE Access 6 (2018): 25657-25665.
[9]. Su, Zhou, Yuntao Wang, Qichao Xu, Minrui Fei, Yu-Chu Tian, and Ning Zhang. "A secure charging scheme for electric
vehicles with smart communities in energy blockchain." IEEE Internet of Things Journal 6, no. 3 (2018): 4601-4613.
8
With the increase in distributed renewable energy resources and the existence of dispersed energy consumers in communities, local
energy trading between prosumers becomes essential. Nowadays, energy trading is executed using a centralized model [13][14].
However, this model is weak and vulnerable to different forms of attacks. For example, a single point of failure, security threats, and
privacy leakages are possible challenges that affect the central models' platform In this regard, there is a need for distributed and
decentralized secure energy trading between prosumers. Blockchain technology is one option that works in a distributed and
decentralized manner. The technology has been used in many applications and domains[15][16]. The authors in [17] propose a
decentralized market mechanism using a private blockchain and an auction method to determine the price. However, the proposed
system takes much computational time when the number of users increases and it becomes impractical when auctioneers are not willing
to participate in the cumbersome auctioning at every hour of the day. Also, the authors do not consider resolving the issues of energy
hoarders in peak generation hours.
The authors in [18] propose a distributed privacy-preserving and efficient matching of demander EVs with charging suppliers model.
The proposed model uses Bichromatic Mutual Nearest Neighbor (BMNN) to address the issue of exposing driving patterns, schedules,
and whereabouts of EVs users. However, the pieces of information transmitted or received are not verified and are not guaranteed to be
from legitimate users. Also, charging EVs is conducted in a non-trusted and insecure environment. Furthermore charging of EVs is time
consuming, therefore, an optimal scheduling is need.
[13]. Long, Chao, Jianhong Wu, Yue Zhou, and Nick Jenkins. “Peer-to-peer energy sharing through a two-stage aggregated battery control in a
community Microgrid.” Applied energy 226 (2018): 261-276.
[14]. Mengelkamp, Esther, Samrat Bose, Enrique Kremers, Jan Eberbach, Bastian Hoffmann, and Christof Weinhardt. “Increasing the efficiency of local
energy markets through residential demand response.” Energy Informatics 1, no. 1 (2018): 11.
[15]. Li, Xiaoqi, Peng Jiang, Ting Chen, Xiapu Luo, and Qiaoyan Wen.
“A survey on the security of blockchain systems.” Future Generation Computer Systems 107 (2020): 841-853.
[16]. Cong, Lin William, and Zhiguo He. “Blockchain disruption and smart contracts.” The Review of Financial Studies 32, no. 5 (2019): 1754-1797.
[17]. Mengelkamp, Esther, Benedikt Notheisen, Carolin Beer, David Dauer, and Christof Weinhardt. ``A blockchain-based smart grid: towards
sustainable local energy markets.'' Computer Science-Research and Development 33, no. 1-2 (2018): 207-214.
[18] .Yucel, Fatih, Kemal Akkaya, and Eyuphan Bulut. ``Efficient and privacy preserving supplier matching for electric vehicle charging.'' Ad Hoc
Networks 90 (2019): 101730-101740.
Problem Statement (1/3)
Problem Statement (2/3)
9
In [19], the authors examine the adaptability of the usage of consortium blockchain to set up a stable electricity trading network. The
blockchain in the trading network has distributed storage and authorized nodes. However, relying on the merits of consortium
blockchain cannot guarantee the reliability of the network security. Also, it does not prevent the information from internal attackers. The
authors in [20] propose an effective solution to reduce the excessive operational overhead. The overhead is created when entire nodes are
motivated to use local energy out of their self-interest. However, this mechanism decreases the economics and financial benefit of the
system. Also, the privacy and security of the data are overlooked.
In [21], the authors propose secure energy trading mechanism based on consortium blockchain for EVs. While in [22], the authors
further improve the efficiency of the consensus mechanism process. The proposed models help to balance load demand within a smart
community. However, the improvement of the model leads to an increase in computational and communication costs. The models use
Proof of Work (PoW) consensus mechanism, which requires enormous computational power during the process. In addition, the works
does not consider to allocate the available energy without starving consumers.
[19]. Li, Zhetao, Jiawen Kang, Rong Yu, Dongdong Ye, Qingyong Deng, and Yan Zhang. “Consortium blockchain for secure energy trading in industrial
internet of things.” IEEE transactions on industrial informatics 14, no. 8 (2017): 3690-3700.
[20]. Hou, Weigang, Lei Guo, and Zhaolong Ning. “Local Electricity Storage for Blockchain-based Energy Trading in Industrial Internet of Things.” IEEE
Transactions on Industrial Informatics (2019):3610 - 3619.
[21]. Zhou, Zhenyu, Bingchen Wang, Yufei Guo, and Yan Zhang. “Blockchain and computational intelligence inspired incentive-compatible demand
response in internet of electric vehicles.” IEEE Transactions on Emerging Topics in Computational Intelligence 3, no. 3 (2019): 205-216.
[22]. Zhou, Zhenyu, Bingchen Wang, Mianxiong Dong, and Kaoru Ota. “Secure and efficient vehicle-to-grid energy trading in cyber physical systems:
Integration of blockchain and edge computing.” IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, no. 1 (2019): 43-57.
Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Problem Statement (2/3)
10
Authors in [24] propose a P2P energy trading model where the energy prices in the market are fixed. However, this mechanism is
inefficient and unbeneficial for prosumers as prices of the energy are less than the grid pricing tariff. In another way, energy prices are
set based on auction or negotiation market approaches. The approaches are among the best techniques in solving the problem of pricing
determination [25]. However, both auction and negotiation mechanisms become complex and time-consuming when the number of users
grows. The auction mechanism takes more time for a matching process to converge. While the negotiation approach usually takes place
through an arbitrator, which makes the approach to lack trust and transparency. Furthermore, storage overhead and delay in
communication are challenges that need urgent attention, especially in resource constrained devices for sustainable and efficient
transactions.
[24]. Park, Lee Won, Sanghoon Lee, and Hangbae Chang. “A sustainable home energy prosumer-chain methodology with energy tags over the
blockchain.” Sustainability 10, no. 3 (2018): 658.
[25]. Samuel, Omaji, and Nadeem Javaid. “A secure blockchain‐based demurrage mechanism for energy trading in smart communities.” International
Journal of Energy Research (2020).
Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
11
Fig. 3: Proposed Combined System Model
The proposed combined
system model for static and
mobile energy prosumers is
presented in Fig. 3
Proposed Solution (1/51)
Note that:
LEM: Local Energy Market
HEM: Home Energy
Management
RSFEAP: Reputation-based
Starvation Free Energy
Allocation Policy
PoERG: Proof of Energy
Reputation Generation
PoERC: Proof of Energy
Reputation Consumption
SDR: Supply-Demand Ratio
Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Proposed Solution (2/51) 12
Fig. 4: SM1: Blockchain based LEM Considering HEM and
Demurrage Mechanisms
The proposed SM1 comprises of three
participants: prosumers, consumers, and the
main grid as depicted in Fig. 4
Energy Optimization using Earliglow-based
optimization algorithm: Jaya and strawberry
The pricing model is developed using SDR by
including the demurrage mechanism
Energy trading between prosumers is conducted
using blockchain and smart contracts
Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Proposed Solution (3/51) 13
Optimization formulation problem:
Appliances status at time t:
Cost of electricity:
Optimal energy formulation:
Objective function: is an aggregated function, which is formulated as multi-objective optimization techniques to
reduce energy cost and optimized energy consumption
Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Proposed Solution (4/51) 14
Pricing model:
SDR:
Selling price without demurrage :
Buying price without demurrage :
Prices based demurrage mechanism :
Proposed Solution (5/51) 15
Energy trading based demurrage
smart contract functions:
Simulation Results (6/51)
SM1: Blockchain based LEM Considering HEM and Demurrage MechanismsSM1:
BLOCKCHAIN BASED LEM CO
16
Fig. 6: Relationship between the Supply and Demand
Ratio and the Price with Compensation.
Fig. 5: Energy Generation and Demand for 24
hours from a Prosumer.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Simulation Results (7/51)
SM1: Blockchain based LEM Considering HEM and Demurrage MechanismsSM1:
BLOCKCHAIN BASED LEM CO
17
Fig. 7: (a) Internal Prices with Critical Peak Price (CPP) for Buying and Selling with Demurrage and without
Demurrage; (b) Internal Prices with Real-Time Price (RTP) for Buying and Selling with Demurrage and
without Demurrage.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Simulation Results (8/51)
SM1: Blockchain based LEM Considering HEM and Demurrage MechanismsSM1:
BLOCKCHAIN BASED LEM CO
18
Fig. 8 : Energy Demand for a Single Household with Scheduled and
Unscheduled Consumption.
Fig. 9 : Net Load of the Prosumer.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Simulation Results (9/51)
SM1: Blockchain based LEM Considering HEM and Demurrage MechanismsSM1:
BLOCKCHAIN BASED LEM CO
19
Figure 10: (a) Electricity Cost for a Single Household with Scheduled and Unscheduled Consumption using CPP;
(b) Electricity Cost of Scheduled, Scheduled with Demurrage, and Unscheduled without Demurrage using CPP.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Simulation Results (10/51)
SM1: Blockchain based LEM Considering HEM and Demurrage MechanismsSM1:
BLOCKCHAIN BASED LEM CO
20
Figure 11: (a) Electricity Cost for a Single Household with Scheduled and Unscheduled Consumption using RTP; (b)
Electricity Cost of Scheduled, Scheduled with Demurrage, and Unscheduled without Demurrage using RTP.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Simulation Results (11/51)
SM1: Blockchain based LEM Considering HEM and Demurrage MechanismsSM1:
BLOCKCHAIN BASED LEM CO
21
Fig. 12: (a) Comparison of Total P2P Cost with Demurrage, Total P2P and Scheduled Cost with Demurrage, Total Cost
Scheduled without Demurrage, and Total Unscheduled Cost using CPP; (b) Comparison of Consumer Cost, Prosumer Cost
and Profit, and Prosumer Profit using CPP.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Simulation Results (12/51)
SM1: Blockchain based LEM Considering HEM and Demurrage MechanismsSM1:
BLOCKCHAIN BASED LEM CO
22
Fig. 13: (a) Comparison of Total P2P Cost with Demurrage, Total P2P and Scheduled Cost with Demurrage, Total Cost
Scheduled without Demurrage, and Total Unscheduled Cost using RTP; (b) Comparison of Consumer Cost, Prosumer Cost
and Profit, and Prosumer Profit using RTP.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Simulation Results (13/51)
SM1: Blockchain based LEM Considering HEM and Demurrage MechanismsSM1:
BLOCKCHAIN BASED LEM CO
23
Fig. 14: (a) Electricity Cost for a Single Household with Scheduled and Unscheduled Consumption using RTP; (b) Electricity
Cost of Scheduled, Scheduled with Demurrage, and Unscheduled without Demurrage using RTP.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Simulation Results (14/51)
SM1: Blockchain based LEM Considering HEM and Demurrage MechanismsSM1:
BLOCKCHAIN BASED LEM CO
24
Table 2: Cases Formulated for CPP.
Cases Load Price CPP Cost CPP Price at
Agreed Time
Cost at
Agreed Time
Minimum load,
Maximum price
1.00 kWh 895.00 Cents 895.00 Cents 60.00 Cents 60.00 Cents
Minimum load,
Minimum price
1.00 kWh 11.40 Cents 11.40 Cents 10.40 Cents 10.40 Cents
Maximum load,
Minimum price
13.25 kWh 11.40 Cents 151.05 Cents 10.40 Cents 137.80 Cents
Maximum load,
Maximum price
13.25 kWh 895.00 Cents 11858.75
Cents
60.00 Cents 792.00 Cents
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Simulation Results (15/51)
SM1: Blockchain based LEM Considering HEM and Demurrage MechanismsSM1:
BLOCKCHAIN BASED LEM CO
25
Table 3: Cases Formulated for RTP.
Cases Load Price CPP Cost CPP Price at
Agreed Time
Cost at
Agreed Time
Minimum load,
Maximum price
1.00 kWh 390.00 Cents 390.00 Cents 30.00 Cents 30.00 Cents
Minimum load,
Minimum price
1.00 kWh 10.63 Cents 10.63 Cents 10.40 Cents 10.40 Cents
Maximum load,
Minimum price
13.25 kWh 11.40 Cents 140.85 Cents 10.40 Cents 137.80 Cents
Maximum load,
Maximum price
13.25 kWh 390.00 Cents 5167.50 Cents 30.00 Cents 397.50 Cents
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Simulation Results (16/51)
SM1: Blockchain based LEM Considering HEM and Demurrage MechanismsSM1:
BLOCKCHAIN BASED LEM CO
26
Fig. 15: Performance Metric with Varying Number of
Customers Participating in Energy Trading
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Simulation Results (17/51)
SM1: Blockchain based LEM Considering HEM and Demurrage MechanismsSM1:
BLOCKCHAIN BASED LEM CO
27
Table 4: Report of the Security Vulnerability Analysis using Oyente Tool for Energy
Trading Smart Contract. Ethereum Virtual Machine (EVM)
Parameters Energy Transaction Contract
EVM Code Coverage 42.4%
Integer Underflow False
Integer Overflow False
Parity Multisig Bug 2 False
Callstack Depth Attack Vulnerability False
Transaction-Ordering Dependency (TOD) False
Timestamp Dependency False
Re-entrancy Vulnerability False
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
28
Fig. 16: SM2: Energy Trading using Blockchain based Privacy
Preservation and Reward Mechanisms
The proposed SM2 is divided into two components: EVs’
and residential energy prosumers’ models.
The EVs’ model: reputation based privacy-preserving
search and match scheduling, energy trading based
blockchain, and EVs charging forecasting
Homomorphic encryption is used to preserved privacy of
the EVs location
Residential energy prosumers model: RSFEAP algorithm
considering starvation and malicious activities
Users privacy is protected using ID-based encryption
technique
Proposed Solution (18/51)
Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Proposed Solution (19/51) 29
Solution for EVs Model:
Energy Trading based Smart Contract
Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
30
Solution for EVs Model:
EVs charging forecasting based on
Multiple Linear Regression
Fig. 17: EVs Charging Forecasting based on Multiple Linear Regression
Model
Proposed Solution (20/51)
31
Solution for residential prosumers’ model:
An optimal energy allocation based on
users’ starvation, energy contributions, and
transaction conducted is shown in
Algorithm 4
Algorithm 4: RSFEAP Algorithm
Proposed Solution (21/51)
Simulation Results (22/51)
SM2: Energy Trading using Blockchain based Privacy Preservation and Reward Mechanisms
SM1: BLOCKCHAIN BASED LEM CO
32
Table 5: Report of the Security Vulnerability using Oyente Tool for Energy Trading and Reputation
Smart Contracts
Parameters Energy Trading Contract Reputation Contract
EVM Code Coverage 47.9% 42.5%
Integer Underflow False False
Integer Overflow False False
Parity Multisig Bug 2 False False
Callstack Depth Attack Vulnerability False False
Transaction-Ordering Dependency (TOD) False False
Timestamp Dependency False False
Re-entrancy Vulnerability False False
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
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Simulation Results (23/51)
SM2: Energy Trading using Blockchain based Privacy Preservation and Reward Mechanisms
SM1: BLOCKCHAIN BASED LEM CO
Security and privacy analysis:
Security of the secret key
Passive attack
Disguise attack
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
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Table 6: Energy Trading Smart Contract Cost
Simulation Results (24/51)
SM2: Energy Trading using Blockchain based Privacy Preservation and Reward Mechanisms
SM1: BLOCKCHAIN BASED LEM CO
Function Transaction
Cost (Gwei)
Execution
Cost (Gwei)
Actual Cost
(Ether)
GiveKwh 43257 20577 6.3834E-14
Create Storage 126906 104866 2.31772E-13
SellEnergy 23796 796 2.4592E-14
BuyEnergy 23574 574 2.4148E-14
Contract Creation 1826521 1335125 3.16165E-12
Function Transaction
Cost (Gwei)
Execution
Cost (Gwei)
Actual
Cost
(Ether)
Submit
feedback
440697 417057 8.57754E-
13
Contract
Creation
2017617 1478941 3.49656E-
12
Table 7: Reputation Smart Contract Cost
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
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Simulation Results (25/51)
SM2: Energy Trading using Blockchain based Privacy Preservation and Reward Mechanisms
SM1: BLOCKCHAIN BASED LEM CO
Figure 18: Total Convergences Duration of the Proposed
Matching of EBEV with ESEV Figure 19: Normalized EVs Energy Consumption.
Figure 20: Comparison of Actual and Forecasted EVs’
Energy Load Consumption. Figure 21: Forecasting Errors Comparison.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
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Simulation Results (26/51)
SM2: Energy Trading using Blockchain based Privacy Preservation and Reward Mechanisms
SM1: BLOCKCHAIN BASED LEM CO
Figure 22: Comparison of Allocated Energy to
Prosumers at T1 Time
Figure 23: Comparison of Allocated Energy to
Prosumers at T2 Time
Figure 24: Comparison of Allocated Energy to
Prosumers at T3 Time
Figure 25: Comparison of Allocated Energy to
Prosumers at T1 Time
Figure 26: Comparison of Allocated Energy to
Prosumers-1 for each Time Slot
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
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Simulation Results (27/51)
SM2: Energy Trading using Blockchain based Privacy Preservation and Reward Mechanisms
SM1: BLOCKCHAIN BASED LEM CO
Figure 27: Impact of Reward Index on Energy Allocation to Prosumer at T1
Time Slot.
Figure 28 Impact of Reward Index on Energy Allocation to Prosumer at T2
Time Slot.
Figure 29: Impact of Reward Index on Energy Allocation to Prosumer at T3
Time Slot.
Figure 30: Impact of Reward Index on Energy Allocation to Prosumer at T4
Time Slot.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
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Simulation Results (28/51)
SM2: Energy Trading using Blockchain based Privacy Preservation and Reward Mechanisms
SM1: BLOCKCHAIN BASED LEM CO
Figure 31: The Valid and Malicious Transactions in
the System.
Figure 32: Impact of Malicious Transactions in
the Energy Contribution for C2, C3, and C4
Figure 33: The Effects of Contributions on Energy
Allocation.
Figure 34: Average Convergence Time against Number of Prosumers.
Figure 35: Number of Maximum Iterations
against Number of Prosumers.
39
Fig. 36: SM3: Blockchain-based Energy Trading and Load
Balancing using Contract Theory and Reputation
There are four major energy domains in the proposed SM3: the
group of smart homes, industries, commercial buildings, and
EVs as shown in Fig. 36
In the proposed model, a novel reputation based contract theory
incentive mechanism is proposed
PoWR consensus mechanism is used to validate and add new
blocks in the blockchain
Energy trading between prosumers using consortium blockchain
Proposed Solution (29/51)
Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Proposed Solution (30/51) 40
Fig. 37: Energy Trading Between Various Domains.
Fig. 37 shows how energy traded between domains
EVs can be energy buyer or seller depending on their
available energy
Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Proposed Solution (31/51) 41
Algorithm 8: Shortest Distance Algorithm
A contract theory will be formulated to hide
the actual users energy and price
Shortest path algorithm is used to minimize
EVs traveling cost as shown in Algorithm 8
Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
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Simulation Results (32/51)
SM3: Blockchain based Energy Trading and Load Balancing using Contract Theory and
Reputation SM1: BLOCKCHAIN BASED LEM CO
Figure 38: Contract Feasibility: Supplied
Energy Vs Type of Energy Entity
Figure 39: Contract Feasibility: Reward
Vs Type of Energy Entity.
Figure 40: System Performance: Utility of
Entities Vs Type of Energy Entity.
Figure 41: System Performance: Utility of LEA
Vs Type of Energy Entity.
Figure 42: System Performance: Social
Welfare Vs Type of Energy Entity.
43
Simulation Results (33/51)
SM3: Blockchain based Energy Trading and Load Balancing using Contract Theory and
Reputation SM1: BLOCKCHAIN BASED LEM CO
Figure 43: SDCR Vs Time of Simulation; the
Ratio of Honest LEA is 0.5.
Figure 44: Difficulty Vs Reputation at
Consensus Mechanism.
Figure 45: Shortest Routes and Distance
Between EV and LEA.
Figure 46: Effects of Optimal Route
Analysis on EVs
Figure 47: Effect of DR Management on Smart
Homes Domain.
Figure 48: Comparison of PAR with DR and
without DR scenario.
44
Simulation Results (34/51)
SM3: Blockchain based Energy Trading and Load Balancing using Contract Theory and
Reputation SM1: BLOCKCHAIN BASED LEM CO
Security and privacy analysis:
Authentication and anonymity
Integrity and transparency
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
45
Fig. 49: SM4: Blockchain-based Secure Energy Trading with
Mutual-verifiable Fairness
The proposed SM4 consists of energy sellers
(prosumers), energy buyers (consumers), and a
local energy aggregator.
A local energy aggregator is an energy transaction
manager in the model.
New consensus mechanisms PoERG and PoERC
are used to validate and add blocks
Energy trading between prosumers is performed
using consortium blockchain
Proposed Solution (35/51)
Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Proposed Solution (36/51) 46
Fig. 50: The Demonstration of the Energy Trading in Two
Perspectives.
Mutual-verifiable fairness using timed-
commitment is proposed
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on January, 25, 2021
47
Simulation Results (37/51)
SM4: Blockchain based Secure Energy Trading with Mutual-verifiable Fairness SM1:
BLOCKCHAIN BASED LEM CO
Figure 51: Impact of Energy Generation using
PoERG Consensus Mechanism in Off-peak
Hours.
Figure 52: Impact of Energy Consumption using PoERC
Consensus Mechanism in Peak Hours.
Figure 53: Impact of Winning Factor (q) in Peak
Hours using PoERC Consensus Mechanism.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
48
Simulation Results (38/51)
SM4: Blockchain based Secure Energy Trading with Mutual-verifiable Fairness SM1:
BLOCKCHAIN BASED LEM CO
Figure 54: Illustration of Available Energy for
All of the Scenarios. Figure 55: Illustration of Grid and Energy
Trading Prices.
Figure 56: Comparison of Energy Cost with
the Grid Price and Energy Trading Price. Figure 57: PAR Comparison for all the scenarios.
49
Simulation Results (39/51)
SM4: Blockchain based Secure Energy Trading with Mutual-verifiable Fairness SM1:
BLOCKCHAIN BASED LEM CO
Security and privacy analysis:
Authentication and anonymity
Integrity and transparency
Mutual-verifiable fairness
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
50
Fig. 58: SM5: A Two-stage Peer-2-Peer Secure Energy Trading
using Blockchain.
The proposed SM5 is divided into two levels.
The first level provides the privacy-preserving
mutual authentication between the buying
prosumer and selling prosumer.
In the second level, a secured P2P energy trading
between the prosumers in the permissioned
blockchain network is implemented.
The proposed model consists of Agr and
prosumers
Proposed Solution (40/51)
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Proposed Solution (41/51) 51
Bilinear pairing techniques is used for the privacy
preserving Mutual authentication
Incentive–punishment algorithms is used to
encourage user to participate and perform non-
malicious transaction
Validators selection mechanism is used to reduce
the number of malicious validators at consensus
stage as shown in Fig. 59
A dynamic pricing scheme based SDR and contract
based theory mechanism is proposed in the model
Fig. 59: The Proposed Consensus Process and Validators
Selection.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
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Simulation Results (42/51)
SM5: Two-stage P2P Secure Energy Trading using Blockchain BLOCKCHAIN BASED LEM
CO
Schemes Scheme 1 Scheme 2 Proposed Scheme
Computational
Cost
2088.112 ms 13.4 ms 7.45 ms
Communication
Cost
1568 bits 3532 bits 1152 bits
Figure 60: Comparison of Communication
Cost between our Proposed Scheme and Other
Schemes
Table 8: Communication Cost and Computation Cost
Comparisons with Related Schemes
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
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Simulation Results (43/51)
SM5: Two-stage P2P Secure Energy Trading using Blockchain BLOCKCHAIN BASED LEM
CO
Figure 61: The Impact of Rewards on
Malicious and Non-malicious Activities. Figure 62: The Impact of Energy Contribution in the
Proposed Reward.
Figure 63: The Impact of Normalized Rating, Error
Rate, and Success Rate on Validators’ Selector Factor.
Figure 64: Comparison of the Validator Nodes Selection in
Traditional PoW and the Proposed Consensus Mechanism.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
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Simulation Results (44/51)
SM5: Two-stage P2P Secure Energy Trading using Blockchain BLOCKCHAIN BASED LEM
CO
Figure 65: The Effects of SDR=1 in the
Proposed Pricing Scheme. Figure 66: The Effects of SDR=0 in the
Proposed Pricing Scheme.
Figure 67: The Effects of SDR=0.5 in the
Proposed Pricing Scheme.
Figure 68: The Effects of SDR=1.5 in the
Proposed Pricing Scheme.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
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Simulation Results (45/51)
SM5: Two-stage P2P Secure Energy Trading using Blockchain BLOCKCHAIN BASED LEM
CO
Security and privacy analysis:
Impersonation
Transactional data validation and anonymity
Integrity and transparency
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
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Fig. 69: SM6: A Blockchain based Optimized Data Storage with Secure
Communication for Internet of Vehicles.
The proposed SM6 consists of IoV nodes, miner
nodes, controller nodes and a cloud server.
It also has two blockchains: local and main.
Smart contract is proposed where incentive and
rating are executed.
For data filtration, the Message Transfer Process-
Argon2 (MTP-Argon2) method is applied.
It is used in a cloud server to filter the raw data and
remove the duplicate data that resides in it.
Proposed Solution (46/51)
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
57
To minimize the service delay, the caching technique is
proposed, which ensures quick services.
Using an encryption and decryption technique, a reliable
communication channel is achieved.
The technique is used to prevent or reduce different types of
malicious attacks in the network, e.g., active, passive, and
double spending.
An authentication mechanism is proposed to prevent malicious
nodes from entering the network. Moreover, security analysis
is performed to assess the robustness of the network against the
mentioned attacks.
Proposed Solution (47/51)
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
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Simulation Results (48/51)
SM6: A Blockchain based Optimized Data Storage with Secure Communication for IOV
BLOCKCHAIN BASED LEM CO
Figure 70: Gas Consumption of Service Request and
Response.
Figure 71: Gas Consumption of Incentive and
Reputation.
Figure 73: Gas Consumption of Malicious Node Detection,
Authentication and Cache Memory.
Figure 72: Stored Data with Respect to Computational
Time.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
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Simulation Results (49/51)
SM6: A Blockchain based Optimized Data Storage with Secure Communication for IOV
BLOCKCHAIN BASED LEM CO
Figure 74: Comparison of Encryption Techniques with
Respect to Average Execution Time. Figure 75: Transactions with Respect to Request and
Response of Services.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
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Simulation Results (50/51)
SM6: A Blockchain based Optimized Data Storage with Secure Communication for IOV
BLOCKCHAIN BASED LEM CO
Figure 76: Double Spending Attack Probability Against Block
Advantage.
Figure 77: Double Spending Attack Probability Against
Computational Power.
Figure 78: Double Spending Attack Probability Against Acceptance
of Valid Transaction Confirmation.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
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Simulation Results (51/51)
SM6: A Blockchain based Optimized Data Storage with Secure Communication for IOV
BLOCKCHAIN BASED LEM CO
Security and privacy analysis:
Double Spending Attack
Passive and Active Attacks
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
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Conclusion (1/2)
BASED LEM CO
We propose an LEM using private blockchain within a small community with many PV systems
A dynamic pricing model and HEM system are introduced to increase economic benefits at
both the community and individual levels.
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
A blockchain based privacy preserving energy trading system is proposed. The proposed
system has two components:
EVs and residential prosumers.
A blockchain based secure DRM model for energy trading and load balancing in a smart grid
ecosystem is proposed.
Energy trading between energy entities and LEA is performed in a secure manner using
consortium blockchain.
Moreover, a contract theory based incentive mechanism is also introduced in this work to
encourage energy users’ participation.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
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Conclusion (2/2)
BASED LEM CO
A secure and privacy preserving energy trading model based on blockchain is proposed to
manage and supervise the trading process
PoERG and PoERC consensus mechanisms are proposed to eliminate the existing issues
created by PoW and PoS mechanisms
Mutual-verifiable fairness mechanism is proposed to solve the issue of cheating attacks launched
by both sellers and buyers in the energy systems
A two-layered P2P secure energy trading model using blockchain network is proposed
The layers are mutual authentication process, and secure and privacy preserving energy
trading
Afterwards, an incentive-punishment algorithm is presented to motivate energy prosumers to
contribute more energy in the system
Furthermore, a contract theory based SDR pricing mechanism is proposed to solve the issues
that are originated from auction, fixed, and negotiation pricing schemes
Also, the proposed system shows better performance in terms of security, privacy, and trust
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
64
Future Work
BASED LEM CO
In the future, penalty policy will be incorporated to evaluate prediction deviations in the forecast
profile and to improve trading within the neighborhoods. Additionally, a robust mechanism will be
developed to solve the issues of the creation of rebounce in off-peak hours
We intend to improve the EVs charging load forecasting efficiency as well as the prediction
accuracy. The increase in the number of charging EVs, increases their amount of data, which will be
used for improving EVs charging load forecasting. Furthermore, the performance of the proposed
system will be optimize and explore using hardware implementation
We aim to investigate a more complicated scenario where knowledge about the probability
distribution of the energy entity type is unknown. Machine learning techniques will be explored to
get the corresponding knowledge. In addition, the storage and scalability issues blockchain will also
be considered.
A complicated models will be explored and powerful techniques will be used to achieve good
results for the energy trading.
Blockchain based Privacy Aware Energy Trading in Electric Vehicles and Smart Homes
PhD Thesis Presentation by Adamu Sani Yahaya on July, 25, 2021
Journal Publications
Yahaya, Adamu Sani, Nadeem Javaid, Muhammad Umar Javed, Muhammad Shafiq, Wazir Zada Khan, and Mohammed Y.
Aalsalem.“Blockchain Based Sustainable Local Energy Trading Considering Home Energy Management and Demurrage
Mechanism.” Sustainability 12.8 (2020): 3385. IF=3.251 (Published)
Yahaya, Adamu Sani, Nadeem Javaid, Fahad A. Alzahrani, Amjad Rehman, Ibrar Ullah, Affaf Shahid, and Muhammad
Shafiq.“Blockchain-based Energy Trading and Load Balancing using Contract Theory and Reputation in a Smart
Community,” in IEEE Access, (2020). IF=3.367 (Published)
Conference Proceedings
Yahaya, Adamu Sani, Nadeem Javaid, Rabiya Khalid, Muhammad Imran, and Mohsen Guizani.“A Blockchain-based
Privacy-Preserving Mechanism with Aggregator as Common Communication Point.” In ICC 2020-2020 IEEE International
Conference on Communications (ICC), pp. 1-6. IEEE, 2020.
Yahaya, Adamu Sani, Nadeem Javaid, Rabiya Khalid, Muhammad Imran, and Nidal Naseer.“A Blockchain based Privacy-
Preserving System for Electric Vehicles through Local Communication.” In ICC 2020-2020 IEEE International Conference
on Communications (ICC), pp. 1-6. IEEE, 2020.
Yahaya, Adamu Sani, Nadeem Javaid, Kamran Latif, and Amjad Rehman.“An Enhanced Very Short-Term Load Forecasting
Scheme Based on Activation Function.” In 2019 International Conference on Computer and Information Sciences (ICCIS) pp
. 1-6. IEEE. 2020.
65
List of Publications
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