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

Abstract

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

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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 Synopsis Presentation by Adamu Sani Yahaya on January, 25, 2021
PhD Synopsis Presentation
Blockchain based Privacy Aware Energy
Trading in Electric Vehicles and Smart Homes

Agenda
Introduction
Related Work
Problem Statement
Proposed Solutions
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“Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes ”
PhD Synopsis Presentation by Adamu Sani Yahaya on January, 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 Synopsis Presentation by Adamu Sani Yahaya on January, 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 Synopsis Presentation by Adamu Sani Yahaya on January, 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 Synopsis Presentation by Adamu Sani Yahaya on January, 25, 2021

Related Work (1/2)
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[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)
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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.

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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)
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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 Synopsis Presentation by Adamu Sani Yahaya on January, 25, 2021

Problem Statement (2/3)
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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.
[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 Synopsis Presentation by Adamu Sani Yahaya on January, 25, 2021

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Fig. 3: Proposed Combined System Model
The proposed combined
system model for static and
mobile energy prosumers is
presented in Fig. 3
Proposed Solution (1/18)
 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 Synopsis Presentation by Adamu Sani Yahaya on January, 25, 2021

Proposed Solution (2/18) 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 Synopsis Presentation by Adamu Sani Yahaya on January, 25, 2021

Proposed Solution (3/18) 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 Synopsis Presentation by Adamu Sani Yahaya on January, 25, 2021

Proposed Solution (4/18) 14
Pricing model:
SDR:
Selling price without demurrage :
Buying price without demurrage :
Prices based demurrage mechanism :

Proposed Solution (5/18) 15
Energy trading based demurrage
smart contract functions:

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Fig. 5: 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 (6/18)
“Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes ”
PhD Synopsis Presentation by Adamu Sani Yahaya on January, 25, 2021

Proposed Solution (7/18) 17
Solution for EVs Model:
Energy Trading based Smart Contract
“Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes ”
PhD Synopsis Presentation by Adamu Sani Yahaya on January, 25, 2021

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Solution for EVs Model:
EVs charging forecasting based on
Multiple Linear Regression
Fig. 6: EVs Charging Forecasting based on Multiple Linear Regression Model
Proposed Solution (8/18)

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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 (9/18)

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Fig. 7: 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. 7
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 (12/18)
“Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes ”
PhD Synopsis Presentation by Adamu Sani Yahaya on January, 25, 2021

Proposed Solution (14/18) 21
Fig. 8: Energy Trading Between Various Domains.
Fig. 8 shows how energy is trade 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 Synopsis Presentation by Adamu Sani Yahaya on January, 25, 2021

Proposed Solution (13/18) 22
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 Synopsis Presentation by Adamu Sani Yahaya on January, 25, 2021

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Fig. 9: 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 (15/18)
“Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes ”
PhD Synopsis Presentation by Adamu Sani Yahaya on January, 25, 2021

Proposed Solution (16/18) 24
Fig. 10: 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 Synopsis Presentation by Adamu Sani Yahaya on January, 25, 2021

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Fig. 9: 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 AA and
prosumers
Proposed Solution (17/18)
“Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes ”
PhD Synopsis Presentation by Adamu Sani Yahaya on January, 25, 2021

Proposed Solution (18/18) 26
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. 10
A dynamic pricing scheme based SDR and contract
based theory mechanism is proposed in the model
Fig. 10: The Proposed Consensus Process and Validators
Selection.
“Blockchain based Privacy aware Energy Trading in Electric Vehicles and Smart Homes ”
PhD Synopsis Presentation by Adamu Sani Yahaya on January, 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.
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List of Publications