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sensors
Review
Blockchain in Smart Grids: A Review on Different
Use Cases
Tejasvi Alladi 1, Vinay Chamola 1, Joel J. P. C. Rodrigues 2,3,4,∗and Sergei A. Kozlov 4
1Birla Institute of Technology and Science, Pilani 333031, India; p20170433@pilani.bits-pilani.ac.in (T.A.);
vinay.chamola@pilani.bits-pilani.ac.in (V.C.)
2Federal University of Piauí, Teresina-PI 64049-550, Brazil
3Instituto de Telecomunicações, 1049-001 Lisboa, Portugal
4International Institute of Photonics and Optoinformatics, ITMO University, St. Petersburg 197101, Russia;
kozlov@mail.ifmo.ru
*Correspondence: joeljr@ieee.org
Received: 30 September 2019; Accepted: 4 November 2019; Published: 8 November 2019
Abstract:
With the integration of Wireless Sensor Networks and the Internet of Things, the smart
grid is being projected as a solution for the challenges regarding electricity supply in the future.
However, security and privacy issues in the consumption and trading of electricity data pose serious
challenges in the adoption of the smart grid. To address these challenges, blockchain technology is
being researched for applicability in the smart grid. In this paper, important application areas of
blockchain in the smart grid are discussed. One use case of each area is discussed in detail, suggesting
a suitable blockchain architecture, a sample block structure and the potential blockchain technicalities
employed in it. The blockchain can be used for peer-to-peer energy trading, where a credit-based
payment scheme can enhance the energy trading process. Efficient data aggregation schemes based
on the blockchain technology can be used to overcome the challenges related to privacy and security
in the grid. Energy distribution systems can also use blockchain to remotely control energy flow to a
particular area by monitoring the usage statistics of that area. Further, blockchain-based frameworks
can also help in the diagnosis and maintenance of smart grid equipment. We also discuss several
commercial implementations of blockchain in the smart grid. Finally, various challenges to be
addressed for integrating these two technologies are discussed.
Keywords:
blockchain; smart grid; Internet of Things; security and privacy; Peer-to-Peer trading;
Wireless Sensor Networks
1. Introduction
Smart grids are currently advancing technologically at a very fast pace by leveraging the benefits
offered by Wireless Sensor Networks (WSNs) and the Internet of Things (IoT). They offer optimization
in energy production and consumption by the adoption of intelligent systems that can monitor and
communicate with each other [
1
–
3
]. Automation of the smart sensor-based metering system by using
Advanced Metering Devices (AMI) leads to a lesser requirement of manpower and more accuracy.
Thus, by making the grid more intelligent, efficient energy utilization is achieved [
4
,
5
]. Smart grids
also promise more efficient tapping of renewable sources of energy by offering technological support
for the transfer of energy between local energy producers and consumers. The consumers who
can harvest renewable sources of energy such as sunlight using rooftop solar panels can become
producers-cum-consumers (prosumers) by selling their surplus energy either to neighboring consumers
or to the grid. This promotes consumers to utilize renewable sources of energy [
6
]. Since the energy
demand is ever-growing and there are multiple sources of energy, the need for a decentralized
Sensors 2019,19, 4862; doi:10.3390/s19224862 www.mdpi.com/journal/sensors
Sensors 2019,19, 4862 2 of 25
energy management system has arisen [
7
]. The system should be able to manage the individual
transactions between the users as well as between the user and the grid without any tampering
of data or loss of information. Integrating distributed renewable energy resources whose power
generation is highly fluctuating makes it very challenging for the utilities to estimate the state of
the system. Some
works [8–10]
have proposed novel Kalman filter-based approaches for accurate
microgrid state estimation and control for the smart grids. Their models encourage consumers to use
environment-friendly renewable energy sources which will lead to many benefits such as line-loss
reduction, reliability, energy efficiency, etc. The authors of [
11
] discussed energy demand reduction
of the utilities and consumers and smart energy management while considering the ever-growing
renewable energy integration. Another issue that hinders an efficient grid management system is the
requirement of third parties for the supply and distribution of energy. Third-party involvement always
increases the cost of operation drastically and paves the way for erroneous transactions, intentionally
or otherwise. This is where blockchain offers a promising solution to these existing issues of the smart
grid [12,13].
The adoption of blockchain technology allows the grid network to decentralize its operations.
That means the decision making and the transaction flows do not need to be channeled through
a centralized system that is inclusive of third parties, e.g., mediators, banks, etc. The record of
transactions is stored in all or selected nodes involved in the operation of the network depending on the
type of blockchain used [
14
,
15
]. The transactions of buying and selling of energy across users no longer
needs to go through the procedures of a bank but rather can be done through a computer program by
validating the required pre-determined clauses of the transaction [
16
]. Blockchain technology among
various other benefits helps in setting up real-time energy markets and identity preserving transactions
at much lower costs due to a simplified trading
framework [17,18]
. The computation and power
consumption of IoT devices are important challenges restricting the application of blockchain in IoT
and smart grid. The authors of [
19
] proposed a decentralized on-demand energy supply architecture
for miners in the IoT network, using microgrids to provide renewable energy for mining in the IoT
devices. This paper identifies some of the various scenarios in which blockchain can be incorporated
in the smart grid, and discusses the various technological aspects about each scenario.
The main contributions of this paper are:
•
We discuss major applications of blockchain in smart grids, giving details such as blockchain
architecture, sample block structure and blockchain-related technologies employed in each
application area.
•
A table summarizing these application areas with important technical details is also presented
after a discussion of the application areas.
•We then discuss commercial implementations of blockchain in the smart grid.
•We also discuss existing challenges for incorporating blockchain into the smart grid and present
some future research directions.
The rest of the paper is organized as follows. Section 2gives a brief overview of blockchain
technology. In Section 3, important application areas of blockchain in the smart grid are discussed.
Section 4summarizes several commercial implementations of blockchain in the energy sector.
In Section 5
, practical challenges in the incorporation of blockchain into the smart grid are discussed.
Section 6suggests some future research directions. Finally, the paper is concluded in Section 7.
2. Blockchain Overview
Blockchain is a decentralized ledger meant for keeping a record of the various transactions carried
out in the network right from the beginning of the chain. The ledger is shared among different
nodes (also referred to as peers) that participate in the network, with each peer having its copy of
the ledger. Each block in the chain is connected to the previous one using cryptographic techniques,
which makes the system secure and resistive to malicious attacks and malpractices, as illustrated in
Sensors 2019,19, 4862 3 of 25
Figure 1. Each node can check for the validity of the transactions and reach a consensus before adding
the block to the blockchain, thus providing a high level of transparency and reliability.
Previous Block Hash
Nounce
Target Diff.
Version
Timestamp
Merkle Root Hash
Block Body
Block Header
Block 1
Previous Block Hash
Nounce
Target Diff.
Version
Timestamp
Merkle Root Hash
Block Body
Block Header
Block 2
Previous Block Hash
Nounce
Target Diff.
Version
Timestamp
Merkle Root Hash
Block Body
Block Header
Block 3
Previous Block Hash
Nounce
Target Diff.
Version
Timestamp
Merkle Root Hash
Block Body
Block Header
Block 4
Figure 1. Blockchain structure.
2.1. Composition of Blockchain
Each transaction in a blockchain is verified by the participating nodes using a consensus algorithm
and, if a consensus is reached upon its validity by the nodes, the transaction data are stored into
structures called blocks. Mining is the addition of the blocks into the blockchain while the Miners or
the Mining Nodes are the nodes involved in this process. A cryptographic hash function [
20
] links any
two adjacent blocks in the blockchain, with the hash of the previous block stored in the current block.
To carry out a successful attack, the attacker trying to modify a particular block in the blockchain has
to ensure that all the following blocks are also modified. Since the hash of the current block is stored
in the next block, modifying any field of the current block will also modify its hash. Thus, the older
the target block is, more challenging it is for an attacker to modify and update the block and all the
succeeding blocks until the newest block in the blockchain. Furthermore, the attacker also has to
ensure that no new block has been added into the blockchain by the time his changes are reflected in
the blockchain. This requires a much higher processing and hashing capability on the attacker’s end
compared to the combined capability of all the miners. Therefore, such an attack on the blockchain
network remains economically quite infeasible. In addition, since a copy of the complete blockchain is
available with each participating node of the network, any malpractice such as modification of a block
of the blockchain can be easily detected. These cryptographic security techniques thus provide data
immutability to the blockchain. Each block essentially comprises of a block header and a block body.
The block header contains various fields such as the previous hash, timestamp, etc. The timestamp
indicates the time of the creation of a block. Version denotes the type and format of data contained
in the block while the Merkle root hash is the combined hash of all the transactions that have been
Sensors 2019,19, 4862 4 of 25
added into that block. Merkle trees are generated by iteratively hashing pairs of transactions until
there is only one hash value left. The single hash value is called the Merkle root. Merkle root is the
digital fingerprint of all the transactions stored in a particular block. Using a Merkle root, a user can
securely and efficiently verify the presence of a particular transaction in a block. A nonce is an arbitrary
number used by the mining nodes to change the block’s hash value to satisfy the consensus criterion
of a blockchain. The block body comprising of the transaction information related to the block can be
divided into two parts. The first part of the block stores information about the transactions (amount,
date, time, etc.), whereas the other part stores information about the participants of the transactions.
All blocks are connected to form a chain having information about the transaction history of the whole
network and are shared with the whole network [21–24].
2.2. Classification of Blockchains
Blockchains are generally classified into three types, namely public, consortium and private
blockchain. A comparison of these three types based on different parameters is summarized in Table 1.
Table 1. Classification of blockchains [25–31].
Parameter Public Blockchain Consortium
Blockchain Private Blockchain
Receptivity Fully open Open to some nodes
Open to a person/entity
Access to Write Anyone Specific nodes Internally controlled
Access to Read Anyone Anyone Open to the public
Obscurity More Less Less
Speed of Transaction Low High Extremely high
Decentralization Fully decentralized Less decentralized Less decentralized
3. Applications of Blockchain in Smart Grid
Figure 2lists important applications of blockchain in the smart grid scenario. Based on the
existing surveys and reviews on blockchain applicability in IoT [
32
–
35
], in this paper, we focus on
these five important application areas in smart grids where blockchain technology has been extensively
researched. Each of these application areas is discussed below giving details of the blockchain
architecture employed, the structure of a sample block and the different blockchain technologies used.
Use Cases of Blockchain
in Smart Grid
Peer-To-Peer
Energy Trading
Infrastructure
Energy Trading
in Electric Vehicles
Security and Privacy
Preserving Techniques
Power Generation
and Distribution
Secure Equipment
Maintenance for
Smart Grids
Figure 2. Applications of blockchain in smart grid.
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3.1. Peer-To-Peer Trading Infrastructure
A major drawback in the existing grid networks is the lack of security regarding the transactions
caused by the involvement of mediators and other third parties. This hierarchical organizational
trading structure of the grid leads to heavy operating costs with low efficiency of operation [
36
,
37
].
On the other hand, a blockchain-based trading infrastructure offers a decentralized platform that
enables the Peer-to-Peer (P2P) trade of energy between consumers and prosumers in a secure manner.
The identity privacy and security of transactions is higher in the decentralized platform compared
to the traditional system. The P2P energy trade finds purpose in many applications including the
Industrial Internet of Things (IIoT) and enhances the possibility of developing micro-grids leading to
sustainable energy utilization [
38
,
39
]. The UK based Energy Networks Association has declared the
plan to invest 17 billion Euros in the local energy markets using the smart grid [
40
]. Various aspects of
P2P energy trade using blockchain are discussed below.
3.1.1. Blockchain Architecture
Based on the various state-of-the-art research works surveyed on P2P energy trading infrastructure
using blockchain, the blockchain architecture for a typical P2P energy trading system can be shown
as in Figure 3. This architecture is based on the reference model used in [
38
], in which the authors
used a consortium blockchain-based secure P2P energy trading system. A comparison of several such
research works is shown in Table 2. Depending on the market scenario, the required computational
power and the speed of transactions, the decision regarding the choice of blockchain type to be used
can vary.
EAG EAG
EAG EAG
EAG
Energy coins / Tokens
Energy
Energy buyers
Credit
Credit
EAG
Energy sellers
Energy blockchain
EAG
Figure 3. Architecture for P2P energy trading.
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Table 2. Comparison of state-of-the-art research papers on P2P energy trading using blockchain.
Ref. Cost and Energy
Optimized
Optimization
Applied
Secure against
Attacks
Security
Analysis Scalable Performance
Analysis
[
41
]
3 3 7 7 7 7
[
42
]
3 3 7 7 3 7
[
43
]
7 7 3 7 3 3
[
44
]
3 3 7 7 3 3
[
38
]
3 3 3 3 3 3
A public blockchain gives a high level of transparency by providing a copy of the distributed
ledger to each node, and the ability to perform consensus and validation of data. However,
the disadvantage comes in the form of energy consumption and performance. A consortium blockchain,
on the other hand, allows only a set of pre-authorized nodes to handle the distributed ledger or
the transaction database. Only these authorized nodes are allotted high computational capabilities
required to solve the consensus algorithm thereby reducing the overall power consumption and
facilitating faster transactions. The authors of [
38
] proposed a consortium blockchain platform for
facilitating a secure P2P system for energy trade in IIoT, called energy blockchain. The different energy
nodes comprising of small scale consumers, industrial consumers, electric vehicles, etc. are given the
flexibility to choose their roles as buyers/sellers or idle nodes can initiate transactions according to
their requirement. A record of these transactions is stored and managed by a special authorized set of
entities called Energy Aggregators (EAGs).
3.1.2. Block Structure
In the case of P2P energy transfer, a typical block in the blockchain network, as shown in
Figure 4, consists of data structures that include information regarding the amount of energy used
and the timestamp indicating the usage of energy usually dealing with a particular transaction [
45
].
The number of structures and the data included depends on the architecture adopted.
Block ID
Header
Transaction
Lock Time
Block n TID
MID
AER
AEG
ET
DSS
DSP
TR
TT
Block n+2
Block n+1
Block n
Block n-1
Block n-2
Figure 4. Block structure for P2P energy trading.
In a consortium blockchain with predefined processing and consensus nodes, the block structure
consists of the Block ID for unique identification; Header, which is hashed with a Secure Hash
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Algorithm (SHA); Lock Time, which indicates the time of addition of that particular block into the
network; and the transactions. Each transaction is generated when the buyer requests energy from the
transaction servers of the supervisory nodes. The transaction part of the block structure consists of data
specific to each transaction such as Transaction ID (TID), Meter ID (MID), Amount of Energy Requested
(AER), Amount of Energy Granted (AEG) for the requesting buyer by the supervisory nodes based on
the available energy from the sellers, Energy coins Transferred (ET) by the buyer for the transaction,
Digital Signature of the Seller (DSS) indicating a successful transaction, and Digital Signature of the
Processing node (DSP) indicating validation of the transaction. It also includes timestamps indicating
Time of Request (TR) and Time taken for Transaction (TT).
3.1.3. Technologies Used
•
Virtual currency: Using blockchain, a virtual currency can be created for representing each unit
of electricity. This system is highly useful in situations where renewable energy is generated
at the prosumer’s end. Surplus energy available to the prosumer can be sold by engaging in
transactions with other peers within the blockchain network and transferring this electrical energy
into the grid. The prosumer can earn virtual currency for the energy sale at a specified price while
the consumers with deficit can buy energy for their requirement with the virtual currency. The
true identity of both the buyer and the seller do not need to be disclosed in such transactions
using virtual coins [
39
,
46
]. Further, incentive schemes can be introduced for the promotion of
renewable energy. A set of peers who contribute the most to the trade of renewable energy can
be chosen by monitoring the transaction history from the blockchain ledgers and rewarded with
virtual currencies.
•
Credit-based transactions: Since there is some latency in the validation and addition of transactions
into the blockchain, which in turn delays the release of virtual currency for the respective user,
users might face a shortage of virtual currency temporarily. A credit-based transaction system
helps such users in purchasing the required energy without actual possession of virtual currencies
at that moment. Li et al.
[38]
utilized a credit-based payment scheme where each node is allotted
an identity, a set of public and private keys, a certificate for unique identification, and a set of
wallet addresses upon a legitimate registration onto the blockchain. Upon initialization, the
wallet integrity is checked and its credit data are downloaded from the memory pool of the
supervisory nodes (which store records on credit-based payments). The request from each node
for the release of credit-based tokens is validated by the credit bank managed by the supervisory
nodes and released if the requesting node meets the specified criteria. These tokens which are
then transferred to the wallet of the node can be used to buy the required energy from other
selling nodes [39,47].
•
Smart contracts: These are computer codes consisting of terms of agreements under which the
parties involved should interact with each other. They are finite state machines that implement
some predefined instructions upon meeting a particular set of conditions or certain specified
actions. Smart contracts associated with the smart meters in the grid are deployed in the
blockchain. They ensure secure transactions by allowing only authentic data transfers between the
smart meters and the supervisory nodes and report if any unauthorized and malicious tampering
of data has occurred [47,48].
3.2. Energy Trading in Electric Vehicles
Electric vehicles (EVs) play an important role in the smart grid infrastructure for distributed
renewable energy transportation [
49
,
50
]. There can be two sources to charge the EVs: using
vehicle-to-grid (V2G) and vehicle-to-vehicle (V2V) trading. In V2V trading, EVs can trade electricity in
the hotspots (charging stations or parking lots) in a P2P manner, where the discharging vehicles (with
surplus electricity) discharge their energy to fulfill the electricity demand of the charging vehicles
and thus balance the electricity supply–demand equilibrium. However, due to privacy concerns,
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discharging EVs tend to be reluctant to participate in the electricity trading market and consequently
the supply and demand equilibrium among EVs becomes unbalanced. These problems with the
traditional centralized electricity trading schemes which rely on intermediary parties are discussed
in [
51
]. Hence, there is a need to provide a secure electricity trading system that is decentralized and
preserves privacy for EVs during the electricity trade.
3.2.1. Blockchain Architecture
A blockchain-based solution to trade electricity brings with it the advantages of security,
decentralization, and trust. A system known as PETCON is designed by the authors of [
52
] to achieve
secure trading of electricity. Among the other existing works on energy trade in EVs, this architecture
is not only shown to be secure against cybersecurity attacks but also shown to be cost-optimized and
scalable for multiple nodes, as shown in Table 3. Further, it is based on the P2P architecture discussed
in the previous section on the P2P energy trade. Consortium blockchain technology is used here
because of the cost advantage compared to the existing blockchain methods employed in electricity
trading. Based on this model, a generic architecture for electricity trading among EVs is shown in
Figure 5.
A
A
Power
Grid
Grid
Substation
Grid
Substation
Local Aggregator 1
Local Aggregator
2
Energy Buffer
Energy Buffer
Discharging PHEVs
Social Hotspot 2
Charging PHEVs
Social Hotspot 1
Energy Coin DataEnergy ASmart Meter Switch
TS
MP AP
TS
AP
MP
A
A
A
A
A
A
A
A
A
A
Energy Coins
Social Hotspot 1 Social Hotspot 2
Energy Coins
Energy Blockchain
Figure 5. Architecture for energy trading in electric vehicles.
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Table 3.
Comparison of state-of-the-art research papers on energy trading in Electric vehicles (EVs)
using blockchain.
Ref. Cost and Energy
Optimized
Optimization
Applied
Secure against
Attacks
Security
Analysis Scalable Performance
Analysis
[
53
]
7 7 3 7 3 3
[
54
]
3 3 7 7 7 7
[
55
]
3 3 3 3 3 7
[
52
]
3 3 3 3 3 3
The EVs in this system are divided into three categories as follows:
•Discharging EVs, which sell surplus energy.
•Charging EVs, which demand energy.
•Idle EVs, which neither demand nor sell energy.
An EV joins the system after registering itself with the trusted authority and chooses its role
as a discharging/charging EV as per its current energy states and the future energy requirements.
The charging EV sends a request to the EAG, which broadcasts its demand to the discharging EVs.
Upon receiving their responses, the EAG performs bidding and transactions among the EVs. The
payment of energy coins is made by the charging EV to the discharging EV’s wallet address, which
verifies it using the last block in the memory pool of the EAG. The fastest EAG is considered as the
leader of the consensus process and sends data and timestamp of the block along with the PoW to other
EAGs for verification and auditing. Only when all the EAGs reach an agreement are the data stored as
a block in the blockchain. Since a consortium blockchain is being used here, the number of nodes in the
network is not expected to grow a lot unlike in a public blockchain. For networks with the exponential
growth of nodes, running PoW will require high energy consumption. Instead, other consensus
algorithms such as Proof-of-Stake (PoS) [
56
], Proof-of-Burn (PoB) [
57
] and Proof-of-Elapsed-Time
(PoET) [58] may be run.
The various entities used in the energy blockchain are discussed here. Borrowers are the energy
buyers borrowing energy coins from the credit banks. Transaction Servers (TS) are responsible for
collecting and counting the energy requests and matching the transaction pairs for energy trading.
Wallets are the entities that store the energy coins. Account pools (AP) are the entities that record the
wallets, the energy coin accounts, and the wallet addresses. The transaction records of the local EVs
are stored in the Memory Pools (MP). Credit banks are the entities through which borrowers borrow
energy coins based on their credit values.
3.2.2. Block Structure
Information about the traded electricity and the transaction records of the digital assets are stored
in a block of this blockchain. The transaction records are collected and managed by the local energy
aggregators (EAGs). These records are encrypted, signed with digital signatures and audited by
the rest of the EAGs using the consensus algorithm, PoW. The block structure is shown in Figure 6.
The first is PoW for EAGs, in which data auditing by authorized EAGs is done. Various EAGs compete
for the creation of blocks by finding PoW and the fastest one audits the transaction records and puts in
the block which is verified by other EAGs. Secondly, PoW for EVs is the amount of energy sold by that
EV, which is measured and recorded by the built-in smart meters.
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Block b-1
Block b+1
Block b
Header
Block ID
Lock Time
Transaction
Block b
Transaction ID
Energy transferred
Digital signature
of sellers
Digital signature
of LAGs
Figure 6. Block structure of energy trading in electric vehicles.
3.2.3. Technologies Used
•
Smart contracts: These are used for the commitment from the charging EVs before the local
aggregator (LAG) makes the contract with the discharging EVs. A deal is formed, which includes
the rates for the purchase of battery and discounts for charging and parking. Breaking the terms of
contracts will lead to penalties. Smart contracts are used for registering the EVs and for securing
the energy trade (e.g., detection of malpractices).
•
Digital currency: The NRGcoin is the digital currency used in energy trading between the EVs.
After the trading of electricity, there will be a transfer of NRGcoins from the wallet of charging EV
to the discharging EV’s wallet address.
•
Double auction mechanism: Double auction mechanism is used for energy negotiation, bidding,
and transactions between the EVs. This mechanism is used for price optimization and also
for optimizing the electricity units traded between EVs, thus maximizing social welfare along
with the privacy protection of EVs. LAG acts as an auctioneer and performs this mechanism
iteratively according to the selling prices of discharging EVs and buying prices of charging EVs.
The auctioneer will determine the final prices of the trading and the amount of electricity to be
traded, which will be useful to protect the EV information during the electricity trade [51].
3.3. Security and Privacy-Preserving Techniques
Smart meters in the smart grid are placed at every house to get information about electricity
consumption in real-time, which is used by the utilities for various purposes [
59
,
60
]. By analyzing
the electricity consumption profile of the users, malicious entities can track the electricity usage
pattern, thereby disclosing the users’ private information [
61
–
63
]. The authors of [
64
] proposed a
blockchain-based scheme for efficient data aggregation and privacy preservation. In their work, they
divided the users into many groups with each group using a blockchain for recording the users’ data.
Bloom filter is used by the scheme for fast authentication, to facilitate a quick check of the legality of
the user ID in the system. For the preservation of privacy within the group, pseudonyms are used by
the users. Although blockchain technology does not directly ensure privacy preservation, advanced
cryptographic mechanisms can be incorporated for enabling data privacy. Zero knowledge proof
(ZKP), Elliptic Curve Digital Signature Algorithm (ECDSA), and linkable ring signatures are some
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of the techniques which can protect the privacy of the devices involved. The privacy-preserving
blockchain architecture discussed here makes use of ZKP combined with pseudonyms for the users.
3.3.1. Blockchain Architecture
A typical blockchain-based architecture for the data aggregation scheme is described in Figure 7.
This is based on the reference model presented by Guan et al.
[64]
. We present a comparison of
blockchain-based state-of-the-art research works done on security and privacy-preserving techniques
in the smart grid in Table 4. Based on electricity consumption, users are divided into various
groups/neighborhood area networks (NANs). Multiple public and private key pairs are generated
by the key management center (KMC) for every user using RSA, a popular public-key cryptography
algorithm, with the pseudonym of the user being the public key. By collecting the pseudonyms, the
bloom filter is created by the KMC for every group and sent to all the users of the corresponding group.
The authenticity of the user pseudonym can be verified using zero-knowledge proof, a probabilistic
verification method in cryptography. At every time slot, a mining node is selected among the users of
the group based on the average consumption of the electricity data. The mining node aggregates the
electricity data consumed, records them in a private blockchain and sends it to the central unit with
the help of wide-area network (WAN). The central unit can extract the electricity consumption profile
in real-time for energy planning and dynamic pricing. The billing center on the arrival of the billing
date calculates the users’ electricity bill and records it in the blockchain.
ControlUnit BilingCentre Monitoring
CenterUnit
WAN
KMC
NAN
Blockchain Blockchain Blockchain
Block
Block
Block
Block
Block
Block
Figure 7. Architecture for data aggregation and privacy preservation scheme.
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Table 4.
Comparison of state-of-the-art research papers on security and privacy-preserving techniques
in smart grid using blockchain.
Ref. Cost and Energy
Optimized
Optimization
Applied
Secure against
Attacks
Security
Analysis Scalable Performance
Analysis
[
65
]
3 7 7 7 7 7
[
66
]
3 3 3 3 7 7
[
67
]
3 7 3 3 3 3
[
64
]
3 3 3 3 3 3
3.3.2. Block Structure
The block structure for this scheme is shown in Figure 8. The PoW consensus algorithm is adopted
for the selection of mining nodes among the different users [
64
]. The mining nodes record the electricity
consumption data in a Merkle tree. Block header records the hash value of the previous block, the root
hash value of the Merkle tree, timestamp, pseudonym and the average. Timestamp marks the time
at which each transaction occurs on the blockchain and indicates when and what happened in the
blockchain. A pseudonym is a public key for that user, generated by the KMC. Average provides the
value of the average consumption of electricity data.
Hash23Hash01
Hash2 Hash3Hash1Hash0
Pk0,m0 Pk1,m1 Pk3,m3Pk2,m2
Root hash
Average
Pki,mi
Timestamp
Previous block hash
Block Header
Block n
Block n+2
Block n+1
Block n
Block n-1
Block n-2
Figure 8. Block structure for data aggregation and privacy preservation scheme.
3.3.3. Technologies Used
The technologies used in this scheme can be discussed in two aspects, namely the preservation of
user identity and preservation of user data. User identity can be preserved using a virtual ring,
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where the control center validates the user’s identity using the ring signature without actually
knowing its identity. In smart contracts, the system can be protected against any theft by using
multiple independent parties to sign the transactions before considering them as valid (multi-signature
transactions). User identity can also be preserved using pseudonyms or based on household battery.
Depending on the consumption profile of the household, the battery will discharge (if household
consumption goes high). Hence, using the household battery, the electricity consumption profile of
the user can be balanced and at the same time, the privacy of the user can be protected. The other
technique to preserve the user data is based on authentication in which the credentials generated by
the consumer are sent for his/her proof of identity to the control center, which signs the credentials.
User data can also be preserved based on data aggregation, which employs data obfuscation and
homomorphic encryption techniques. In data obfuscation, noise is added to the original data to make
data of the user’s electricity consumption unclear, while, in homomorphic encryption, the intermediary
agent is allowed to operate on encrypted data without any information of the plaintext.
3.4. Power Generation and Distribution
Numerous cyberattacks on smart grids have been undertaken in the past where the malicious
attackers have used various methods such as Denial of Service (DoS), Data Injection Attacks (DIA),
etc. to manipulate data and gain control in the grid [
68
,
69
]. This has resulted in complications such as
regional power outages and even complete blackouts [
70
]. Incorporating blockchain into the power
generation and distribution systems help in the prevention of data manipulation since one of the prime
characteristics offered by the blockchain system is its ability to ensure data immutability.
3.4.1. Blockchain Architecture
Figure 9shows how a blockchain system can be incorporated into a power generation station
with a Single Machine Infinite Bus (SMIB) system and its distribution networks. This framework is
created based on the architectures discussed in other works on power generation and distribution
using blockchain [66,71].
An SMIB is constituted by a synchronous generator G, which is connected to the infinite bus
through a reactance Z; a load, which is fed through a load switch SL; a Power System Stabilizer (PSS)
used for damping the generator’s electro-mechanical oscillations to protect the shaft line and to provide
grid stabilization; and a control switch SC for the PSS, which takes its input from the load switch.
A cyber attacker can use a suitable attacking scheme to modify the conditions of the switches resulting
in the removal of load from the generator and leading to sudden transition in the terminal voltages to
very high values. Since the control switch, SC to PSS, is tampered with, automatic voltage regulation is
rendered unresponsive and damping of the oscillations does not occur. This leads to shaft damage and
loss of synchronization in the target generator. This can be avoided by incorporating the blockchain
into the power generation system [
71
]. The time-stamped values of each switch state and the target
generator can be stored as data in the blocks. Specific nodes can be given the privilege to validate and
mine the data into the blocks. In the event of an attack, violation in the current state of each switch
should be reported to the blockchain. A smart contract in the metering device would then identify the
violation and maintain the previous terminal value of the target generator by enforcing PSS to damp
the oscillations.
Sensors 2019,19, 4862 14 of 25
G
PSS
Infinite
bus
SC
SL
L
Supervisory nodes
Block n-2
Block n-1
Block n-2
Block n+1 Block n+2 Block n+3
Blockchain based decentralized platform
Power electronic
devices
DApp DApp
Distribution network
Target generator
Figure 9. Architecture for power generation and distribution.
3.4.2. Block Structure
The block body, as shown in Figure 10, includes measurements, switch states, violations and
timestamp. The measurements part of the block includes the frequency, voltage, and current generated
by the system. Switch States store the states of the switches SL, SC, and PSS and the measured value
of the target generator G. The failed status of the switches as reported by the respective metering
devices is stored in the violations part of the block. The timestamp indicates the time instant of the
measurement. This data is further utilized by the smart contract to take the necessary action.
Sensors 2019,19, 4862 15 of 25
Block n-2
Block n-1
Block n+1
Block n
Voltage
Frequency
Current
Header
Measurements
Switch states
Timestamps
Violations
Block n
PSS
SC
SL
G
Figure 10. Block structure for power generation and distribution.
3.4.3. Technologies Used
Decentralized Applications (dApps) are the applications running on a network of peers. They
can be utilized in blockchain-based smart grid systems to connect every prosumer, consumer and
energy substation in the grid [
72
]. Smart contracts are used to intelligently make decisions and to
monitor data. In a DoS attack on any node of the distribution system, a massive amount of data is sent
from multiple sources rendering the node unresponsive. If it occurs on a Remote Terminal Unit (RTU),
which is responsible for monitoring and control of the SCADA devices in the grid, the whole balance
in the distribution system is disrupted. Since the RTU will not be able to communicate with the master
controller in such circumstances, there may be an excess supply of power leading to blowing up of
fuses or to variations in frequency. The dApps provide a means of remotely monitoring the power
consumption metrics, to monitor the change in values of voltage and frequency measures by their
direct links to the blocks in the blockchain network. Since the measured metrics such as the voltage,
current, and frequency are stored in the blocks with timestamps, these values can be compared against
the measurements in the grid to identify the attack and verify the real power consumption.
3.5. Secure Equipment Maintenance for Smart Grids
Equipment health monitoring, fault diagnosis, and maintenance are integral parts of the smart
grid system. Traditional methods of diagnosis involve the necessity for technicians to visit the field for
diagnosis and maintenance. It also involves an investment of manpower and other expenses at the
risk of the client being unsatisfied with the services. This leads to a need for developing systems that
can reduce the maintenance time and are unaffected by regional restrictions. Smart grids encompass
several types of equipment ranging from substations to smart meters installed in homes. Such a
complex smart system, in turn, requires smart equipment maintenance measures to ensure high
efficiency and reliability [73,74].
3.5.1. Blockchain Architecture
A blockchain integrated framework, as shown in Figure 11, can be utilized to create a platform
for interaction among the vendors, diagnosis depots and clients in a secure manner to decide upon the
required maintenance measures for a mutually agreeable price. This framework has been chosen from
Sensors 2019,19, 4862 16 of 25
the existing works in smart grid equipment maintenance and monitoring using blockchain technology,
as discussed in Table 5. Zhang and Fan
[75]
used a consortium blockchain with pre-determined
book-keeping nodes to implement this system.
Block
Original Vendor
Failure node
Diagnosis node Consensus processing
Policy control
center
Smart contract
triggered
Consensus run
for block creation
Diagnosis requested
Figure 11. Architecture for secure equipment maintenance.
Table 5.
Comparison of state-of-the-art research papers on equipment maintenance and monitoring in
smart grids using blockchain.
Ref. Cost and Energy
Optimized
Optimization
Applied
Secure against
Attacks
Security
Analysis Scalable Performance
Analysis
[
45
]
7 7 3 7 7 7
[
66
]
3 3 3 3 7 7
[
67
]
3 7 3 3 3 3
[
75
]
3 3 3 3 3 3
Whenever a piece of electrical equipment exhibits some fault or abnormality in its operation,
a request for diagnostic services is sent in the network. These equipment are then labeled as failure
nodes in the network. Smart contracts are modeled to respond to the failure nodes and to lead the
flow of operation as decided. The failure node needs to deposit virtual currency for the maintenance
services to the smart contract. If the equipment is within its warranty period, its vendor will perform
the required services and the deposited currency will be returned to the node. However, if the device is
out of its warranty period, the vendor and the other verified maintenance teams who are willing to tend
to the diagnosed fault will now bid to obtain the tender. These nodes will form the diagnosis nodes.
Only the entities that are validated and registered in the system can compete as diagnosis nodes. They
compete fairly in the auction process and the smart contract decides upon who gets the bid depending
on the auction algorithm. When the deal is finalized, the smart contract broadcasts the transaction with
the details fed in the block format to the network. The bookkeeping nodes carry out the respective
consensus algorithm to add the block to the network. This prevents double-spending and once a
consensus is reached regarding the bid, neither of the parties can withdraw without depositing penalty,
making it a reliable system.
3.5.2. Block Structure
As shown in Figure 12, the block body consists of Device Type; Transaction Value denoting the
cost of the maintenance; Diagnosis Node, which responds to the request for diagnosis; Service Files
with information related to the failure node, failure type, time, etc.; Maintenance Mode, indicating
whether it is a remote or on-site maintenance; and Credit, which acts as a means for one node to assess
if they require the services of another.
Sensors 2019,19, 4862 17 of 25
Previous
block hash
State
root
Timestamp
Receipt
root
Tx
root
Register 1
Register 2
Register n
Number of
transactions
Credit
Service files
Transaction
value
Maintenance
mode
Maintenance
node
Block Header
Block Body
Device type
Block b
Block b+2
Block b+1
Block b
Block b-1
Block b-2
Timestamp
Figure 12. Block structure for secure equipment maintenance.
3.5.3. Technologies Used
To provide satisfactory customer service, through a smartphone application that communicates
with the blockchain network via the smart contract, one can connect and log into the network. The app
can receive the progress of the transaction regularly and can be used as a tool to bring about any
adjustments in the policy of operation as required. Information about the node initiating the diagnosis,
the diagnosis method adopted, the payment information, etc. can be relayed periodically to the app,
which leads to an even more reliable and secure transaction.
The comparison between the different use cases discussed above is described in Table 6.
Table 6. Summary of blockchain applications in the smart grid.
Application Problem Addressed
Preferred
Blockchain
Architecture
Sample Block Content Technologies Used
P2P energy
trading
Decentralized electricity
trade between prosumers
and consumers, promotion
of renewable energy
harvesting
Consortium
blockchain
Transaction ID, consumer meter
ID, amount of energy requested
and energy granted, a digital
signature of the seller and the
processing node
Smart contracts, virtual
currency, credit-based
e-wallet
Energy trade
between EVs
Buying and selling of
surplus energy between
EVs, privacy-preserving of
EVs
Consortium
blockchain
Transaction ID, EV’s meter
ID, charged energy, a digital
signature of the charging station
and the processing node
Smart contracts, energy coins
Security
and privacy-
preserving
techniques
To protect the application
usage pattern and the
privacy information of
users
Private
blockchain
Transaction ID, the energy
transferred, a digital signature of
the seller and the LAGs
Bloom Filter, data
aggregation, authentication
techniques
Power
generation
and
distribution
Protection from cyber
attacks, incorporation
of abnormality control
measures
Consortium
blockchain
Time of measurement,
measurement of frequency,
voltage and current, switch
states
Smart contract, dApps,
remote control of distortion
using power electronics
devices
Secure
equipment
maintenance
Platform for interaction
between vendor and client
for equipment diagnosis
and privacy preservation
Consortium
blockchain
Device ID, mode of maintenance,
service files and credits,
transaction value
Smart contracts, user
interaction using smart
phone app
Sensors 2019,19, 4862 18 of 25
4. Commercial Implementations of Blockchain in the Smart Grid
One of the foremost applications of blockchain in the smart grid is to incorporate virtual
currencies for payments. The first company to accept
Bitcoin
for payment of energy bills was
BASNederl and
[
76
]. This inspired several other companies to come up with cryptocurrency-based
solutions for billing and metering, and several of them providing incentives for users making payments
using cryptocurrency instead of those using fiat currencies [
77
,
78
]. Meanwhile, some other companies
such as the South Africa based startup
Bankymoon
are developing smart meters with integrated
payments using Bitcoin [
79
]. The Netherlands based companies
Spectral
and
Alliander
have developed
a blockchain-based token for energy sharing called
Jouliette
[
80
]. This token allows the P2P transaction
of electricity through spending the energy tokens from their e-wallets. Another company,
PowerLedger
,
an Australia based startup, developed a blockchain-based platform for P2P renewable energy transfer
between residential prosumers and consumers [
81
]. The platform makes use of a smart contract-based
system called
POWR
to enable the transfer of tokens called
Sparkz
. The company has demonstrated
its ability in saving significant revenue for the users and supplying additional incentives for renewable
energy producers.
The most significant implementation of blockchain in P2P decentralized energy trading and
creation of a local marketplace is the
Brooklyn
microgrid. It was launched by the US energy firm
LO
3
Energy
along with
ConsenSys
, a Blockchain company [
82
]. The first trial of the project, which
was carried out with five prosumers and five consumers, marked the first-ever recording of energy
transactions using blockchain. Ethereum-based smart contracts were used to architect the platform,
which facilitated the consumers to buy surplus renewable energy from the prosumers through a
token-based transaction system. The surplus energy tapped through the rooftop photovoltaic (PV)
panels by the prosumers is converted into tokens by the smart meters installed in their houses, which
can be directly used for trade in the energy market. This platform records the mode of transaction in
energy units or tokens as per the requirement of the user. The ledger stores, in chronological order,
details about each transaction, such as the parties involved, the amount of energy consumed/sold
and the related contract terms. The future developments in the
Brooklyn
microgrid system include
assigning the users with the ability to choose from the prospective buyers/sellers the required energy,
among other privileges such as the ability to decide the percentage of energy share needed to buy
from prosumers and the main grid. A bidding system will be used in which renewable energy will be
sold to the highest bidder. A mobile application is also being developed to provide users with easy
means of interaction with the platform. Such projects will change the face of energy transactions in the
coming future [83].
Share and Charge
is a blockchain-based platform developed jointly by
Innogy Motionwerk
,
a subsidiary of German energy conglomerate RWE, and a blockchain firm
Slock
. This platform allows
P2P energy trading among EVs and the private charging stations [
84
]. The users can use their e-wallets
to know about the real-time prices and carry out transactions on this public Ethereum-based platform,
which automatically manages certificates and billing.
JuiceNet
is yet another blockchain-based
platform deployed by a company called
eMotorwerks
in California for leasing out charging piles to EV
drivers for some time [85]. The platform maintains a record of the transactions and allows the owner
of the charging pod the required payment. Moreover,
JuiceNet
provides a mobile application for the
owners of the EVs to locate a charging pile from among the enlisted charging piles in the neighborhood.
5. Challenges for Blockchain Incorporation into Smart Grid
5.1. Scalability Issues
Transactions in a blockchain increase on a day-to-day basis, which calls for heavy storage
capabilities to accommodate the ever-growing number of transactions. Currently, the storage for
Bitcoin
has exceeded 200 GB while that for
Ethereum
has reached about 1 TB. Even though a
considerably high number of transactions are being carried out using
Bitcoin
, the processing rate of
Sensors 2019,19, 4862 19 of 25
data into blocks in a blockchain is estimated to be about seven per second. Meanwhile, the average
number of transactions in
Ethereum
is up to 15 per second. Such low rates of processing are attributed
mostly to the consensus mechanism, PoW, which is used in the
Bitcoin
technology. High processing
power and time are required by the nodes to compute the PoW algorithm to add the block into the
blockchain network. According to the report in [
86
], to process 30 million transactions, 30 billion kWh
of electricity was spent, which accounted for about 0.13 percent of global electricity consumption.
In the energy sector, for large scale operations, the number of transactions per second is very high since
thousands of users are simultaneously involved in the process of buying and selling energy. This creates
a large overhead upon the nodes involved in the consensus and validation process. This problem
can be addressed by replacing the PoW consensus algorithm either with the Proof-of-Stake (PoS) or
the Proof-of-Authority (PoA) algorithm. These algorithms require much less computing capacity and
support much higher rates of transactions. A new blockchain platform named
EnergyWeb
blockchain
is aimed specifically at the energy sector with transaction rates as high as a few thousand per second.
It uses the PoA consensus mechanism, which gives it such high processing rates. Further research and
innovations have to be carried out to find solutions to properly scale up the platform to accommodate
the requirements of the smart grid system without compromising on the security aspects [
13
]. Other
so-called “second-layer” solutions are intensely being researched by the community for addressing the
scalability issues [
32
,
87
]. Off-chain [
88
] and side-chain [
89
] techniques have been proposed for reducing
the number of transactions and for parallelizing the transaction validation, respectively. Research is
also leading to advancement in the enabling technologies such as Distributed Hash Table (DHT) [
62
],
InterPlanetary File System (IPFS) [
90
], and nonlinear block organizations such as Directed Acyclic
Graph-based chains (DAGchains) [
91
] to potentially address the scalability and
throughput challenges.
5.2. Chances of Centralization
Currently, blockchain application in the energy sector is still a budding technology and is prone
to attacks from the energy conglomerates who might exploit it for financial advantages. One of the
reasons for centralization is the clustering of mining nodes into mining pools for better computational
capacity. The only chance of changing the transactional data in a block is through the 51 % attack,
where the attacker controls 51 % of the computational capacity in the network. By clustering the mining
nodes into pools, there exists a risk of the mining pools acquiring enough resources to plot a malicious
attack. Another reason for centralization is the fact that much of the architecture in the energy sector is
based on consortium or private blockchains. The reason for their popularity is the problem of power
wastage and latency associated with public blockchain architectures. Since a predefined set of nodes
are responsible for validation and consensus in the public blockchains, chances of malpractice exist.
Therefore, strict supervision under governmental laws should be enforced especially in the beginning
stages to ensure security.
5.3. Development and Infrastructure Costs
Implementing blockchain in the smart grid requires high infrastructural costs for re-architecting
the current grid networks, upgrading smart meters to aid in transactions through smart contracts,
infrastructure for Information and Communication Technologies (ICT) specific for Blockchain
operations, other related Advanced Metering Interfaces (AMI) and software for development of
the whole platform. Such high infrastructure costs may dissuade grid operators from the incorporation
of blockchain into the grid structure. The current infrastructure of the grid has been adopted after
years of research and development and it yields optimal results with much less overall expenditure.
For example, the grid communication system currently employs technologies such as telemetry which
is more mature as well as much less expensive compared to blockchain.
Sensors 2019,19, 4862 20 of 25
5.4. Legal and Regulatory Support
The regulatory bodies do support the active participation of users in the energy market, and
the formation of community energy structures. However, when it comes to radical changes in the
main power grid framework, the current grid legal system does not support the trading of energy
from prosumers to consumers and does not endorse the adoption of the distributed ledger into the
framework. New types of contracts have to be developed especially for the P2P trading system and
changes in the energy tariffs need to be brought about to support such services. Such matters are
heavily regulated in the current grid system. For these reasons, even though blockchain technology
has proven its worth in the formation of microgrids, without amended legal structures, it is very
challenging to adopt the technology into the main grid framework.
6. Future Research Directions
•
Using blockchain technology, a decentralized computing platform can be created in addition
to the trading infrastructure. All the peers who participate in the network give a share of their
computational capacity, thus increasing the total capacity within the system, which will enable
efficient operation and control of microgrids. It will also increase trust among the owners and the
scalability of the grid. Even if an extra consumer is added, there will be no increase in complexity.
•
Any new technology needs to prove that it can offer the scalability, speed, and security required
before it can be widely accepted. The blockchain technology has already passed the proof
of concept but it still needs to be scaled up and be cost-efficient. For grid communication,
there already exists established solutions such as telemetry, which are significantly cost-efficient.
Blockchain technology also has to compete on all the above-mentioned aspects for wider utility
and acceptance. There is a scope in cutting the cost for data storage by storing actual data in the
sidechains (as subsidiary blockchains) and operating the main blockchain as a control layer rather
than as a storage layer.
•
In current large-scale blockchain networks such as Ethereum or Bitcoin, any upgrade to the
software code that runs in the participating nodes must be approved (via consensus algorithms)
to be affected throughout the network. Any disagreements to do so can lead to forking and
fragmentation of the network, compromising its security and data integrity. The design of these
blockchain networks for smart grids must be protected against such effects.
•
Blockchain-based solutions for various aspects of decentralized grid management and control,
e.g., improving demand–supply balance, automated verification of grid assets, forecasting grid
requirements, self-adjusting power consumption based on price surges or drops, etc., need to
be explored.
7. Conclusions
The smart grid is a booming technology in the energy sector and it essentially needs a reliable
and secure framework for operations. This paper discusses several use cases in which blockchain can
be incorporated into the smart grid to open the doors to a wide range of possibilities. P2P energy trade
is the need of the hour for promoting sustainable use of energy and for tapping renewable energy
sources. With the advent of using the blockchain in the smart grid scenario, V2V and V2G energy
transfer have become simpler and more reliable than ever. Although the privacy requirement is not
directly ensured by blockchain technologies, advanced cryptographic techniques such as ZKP and
ECDSA can be incorporated to enable privacy preservation. Further, this paper discusses the data
immutability aspect of blockchain, which can be used to prevent cyber attacks, especially in the power
generation and distribution systems. A secure equipment maintenance system that ensures efficient
diagnosis using smart contracts is also discussed. Along with discussing various application areas of
blockchain, this paper illustrates potential blockchain architectures and block structures for each of
Sensors 2019,19, 4862 21 of 25
these areas. However, for the widescale adoption of blockchain in the smart grid, the industry and
research community will have to work together to address the significant challenges that lay ahead.
Author Contributions: T.A. investigated the related literature on the topic, wrote the first draft of the document
and identified some open research challenges. V.C. supervised all of the study and reviewed the structure and the
first draft. J.J.P.C.R. and S.A.K. reviewed all the content carefully and edited the paper. T.A., V.C., J.J.P.C.R. and
S.A.K. contributed equally to the scope definition, motivation, and focus of the paper.
Funding:
This research/project was supported by DST-SERB, India funding ECR/2018/001479; by the National
Funding from the FCT—Fundação para a Ciência e a Tecnologia through the UID/EEA/50008/2019 Project; by
the Government of the Russian Federation, Grant 08-08; and by the Brazilian National Council for Scientific and
Technological Development (CNPq) via Grant No. 309335/2017-5.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Bayindir, R.; Colak, I.; Fulli, G.; Demirtas, K. Smart grid technologies and applications. Renew. Sustain.
Energy Rev. 2016,66, 499–516. [CrossRef]
2.
Kumari, A.; Tanwar, S.; Tyagi, S.; Kumar, N.; Obaidat, M.S.; Rodrigues, J.J. Fog Computing for Smart Grid
Systems in the 5G Environment: Challenges and Solutions. IEEE Wirel. Commun.
2019
,26, 47–53. [CrossRef]
3.
Chaudhary, R.; Aujla, G.S.; Garg, S.; Kumar, N.; Rodrigues, J.J. SDN-enabled multi-attribute-based secure
communication for smart grid in IIoT environment. IEEE Trans. Ind. Inform.
2018
,14, 2629–2640. [CrossRef]
4.
Ahsan, U.; Bais, A. Distributed big data management in smart grid. In Proceedings of the 26th Wireless and
Optical Communication Conference (WOCC), Newark, NJ, USA, 7–8 April 2017; pp. 1–6.
5.
Falvo, M.C.; Martirano, L.; Sbordone, D.; Bocci, E. Technologies for Smart Grids: A brief review.
In Proceedings of the 2013 12th International Conference on Environment and Electrical Engineering,
Wroclaw, Poland, 5–8 May 2013; pp. 369–375.
6.
Zhu, J.; Xie, P.; Xuan, P.; Zou, J.; Yu, P. Renewable energy consumption technology under energy internet
environment. In Proceedings of the 2017 IEEE Conference on Energy Internet and Energy System Integration
(EI2), Beijing, China, 26–28 November 2017; pp. 1–5.
7.
Cheng, L.; Qi, N.; Zhang, F.; Kong, H.; Huang, X. Energy Internet: Concept and practice exploration.
In Proceedings of the 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), Beijing,
China, 26–28 November 2017; pp. 1–5.
8.
Rana, M.; Li, L. An overview of distributed microgrid state estimation and control for smart grids. Sensors
2015,15, 4302–4325. [CrossRef]
9.
Rana, M.; Li, L. Microgrid state estimation and control for smart grid and Internet of Things communication
network. Electron. Lett. 2015,51, 149–151. [CrossRef]
10.
Rana, M.M.; Li, L.; Su, S.W. An adaptive-then-combine dynamic state estimation considering renewable
generations in smart grids. IEEE J. Sel. Areas Commun. 2016,34, 3954–3961. [CrossRef]
11.
Tom, R.J.; Sankaranarayanan, S.; Rodrigues, J.J. Smart Energy Management and Demand Reduction by
Consumers and Utilities in an IoT-Fog based Power Distribution System. IEEE Internet Things J.
2019
,
6, 7386–7394. [CrossRef]
12.
The Promise of the Blockchain: The Trust Machine. 2015. Available online: https://www.economist.com/
leaders/2015/10/31/the-trust-machine (accessed on 31 May 2019).
13.
Andoni, M.; Robu, V.; Flynn, D.; Abram, S.; Geach, D.; Jenkins, D.; McCallum, P.; Peacock, A. Blockchain
technology in the energy sector: A systematic review of challenges and opportunities. Renew. Sustain.
Energy Rev. 2019,100, 143–174. [CrossRef]
14.
Wang, K.; Hu, X.; Li, H.; Li, P.; Zeng, D.; Guo, S. A survey on energy internet communications for
sustainability. IEEE Trans. Sustain. Comput. 2017,2, 231–254. [CrossRef]
15.
Distributed Ledger Technology: Beyond Block Chain. 2015. Available online: https:
//assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/
492972/gs-16-1-distributed- ledger-technology.pdf (accessed on 31 May 2019).
16.
Tapscott, D.; Tapscott, A. How Blockchain Is Changing Finance. Available online: https://hbr.org/2017/03/
how-blockchain-is-changing- finance. (accessed on 31 May 2019).
Sensors 2019,19, 4862 22 of 25
17.
Hassija, V.; Bansal, G.; Chamola, V.; Saxena, V.; Sikdar, B. BlockCom: A Blockchain Based Commerce Model
for Smart Communities using Auction Mechanism. In Proceedings of the 2019 IEEE International Conference
on Communications Workshops (ICC Workshops), Shanghai, China, 20–24 May 2019; pp. 1–6.
18.
Bansal, G.; Hassija, V.; Chamola, V.; Kumar, N.; Guizani, M. Smart Stock Exchange Market: A Secure
Predictive Decentralised Model. In Proceedings of the 2019 IEEE Globecom, Big Island, HI, USA, 9–13
December 2019; pp. 1–6.
19.
Li, J.; Zhou, Z.; Wu, J.; Li, J.; Mumtaz, S.; Lin, X.; Gacanin, H.; Alotaibi, S. Decentralized On-Demand Energy
Supply for Blockchain in Internet of Things: A Microgrids Approach. IEEE Trans. Comput. Soc. Syst.
2019
.
[CrossRef]
20.
Nakamoto, S. Bitcoin: A Peer-To-Peer Electronic Cash System. Available online: https://bitcoin.org/bitcoin.
pdf (accessed on 31 May 2019).
21.
Swan, M. Blockchain: Blueprint for a New Economy. Available online: https://lib.hpu.edu.vn/handle/
123456789/28101 (accessed on 31 May 2019).
22.
Vranken, H. Sustainability of bitcoin and blockchains. Curr. Opin. Environ. Sustain.
2017
,28, 1–9. [CrossRef]
23.
Hassija, V.; Saxena, V.; Chamola, V. Scheduling drone charging for multi-drone network based on consensus
time-stamp and game theory. Comput. Commun. 2019,149, 51–61. [CrossRef]
24.
Li, X.; Jiang, P.; Chen, T.; Luo, X.; Wen, Q. A survey on the security of blockchain systems. Future Gener.
Comput. Syst. arXiv 2017, arXiv:1802.06993.
25.
Casino, F.; Dasaklis, T.K.; Patsakis, C. A systematic literature review of blockchain-based applications:
Current status, classification and open issues. Telemat. Inform. 2018,36, 55–81. [CrossRef]
26.
Puthal, D.; Malik, N.; Mohanty, S.P.; Kougianos, E.; Das, G. Everything you wanted to know about the
blockchain: Its promise, components, processes, and problems. IEEE Consum. Electron. Mag.
2018
,7, 6–14.
[CrossRef]
27.
Okada, H.; Yamasaki, S.; Bracamonte, V. Proposed classification of blockchains based on authority
and incentive dimensions. In Proceedings of the 2017 19th International Conference on Advanced
Communication Technology (ICACT), Bongpyeong, Korea, 19–22 February 2017; pp. 593–597.
28.
Zheng, Z.; Xie, S.; Dai, H.; Chen, X.; Wang, H. An overview of blockchain technology: Architecture,
consensus, and future trends. In Proceedings of the 2017 IEEE International Congress on Big Data (BigData
Congress), Honolulu, HI, USA, 25–30 June 2017; pp. 557–564.
29.
Ouyang, X.; Zhu, X.; Ye, L.; Yao, J. Preliminary applications of blockchain technique in large consumers
direct power trading. Proc. CSEE 2017,37, 3737–3745.
30. Yuan, Y.; Wang, F. Development status and prospect of blockchain technology. J. Autom. 2016,42, 481–494.
31.
Zheng, Z.; Xie, S.; Dai, H.N.; Chen, X.; Wang, H. Blockchain challenges and opportunities: A survey. Int. J.
Web Grid Serv. 2018,14, 352–375. [CrossRef]
32.
Xie, J.; Yu, F.R.; Huang, T.; Xie, R.; Liu, J.; Liu, Y. A Survey on the Scalability of Blockchain Systems.
IEEE Netw. 2019,33, 166–173. [CrossRef]
33.
Ali, M.S.; Vecchio, M.; Pincheira, M.; Dolui, K.; Antonelli, F.; Rehmani, M.H. Applications of blockchains in
the Internet of Things: A comprehensive survey. IEEE Commun. Surv. Tutor.
2018
,21, 1676–1717. [CrossRef]
34.
Ferrag, M.A.; Derdour, M.; Mukherjee, M.; Derhab, A.; Maglaras, L.; Janicke, H. Blockchain technologies for
the internet of things: Research issues and challenges. IEEE Internet Things J.
2018
,6, 2188–2204. [CrossRef]
35. Dai, H.N.; Zheng, Z.; Zhang, Y. Blockchain for internet of things: A survey. arXiv 2019, arXiv:1906.00245.
36.
Gartner Identifies Three Megatrends that Will Drive Digital Business into the Next Decade. 2018.
Available online: https://www.gartner.com/newsroom/id/3784363 (accessed on 31 May 2019).
37.
Abdella, J.; Shuaib, K. Peer to peer distributed energy trading in smart grids: A survey. Energies
2018
,
11, 1560. [CrossRef]
38.
Li, Z.; Kang, J.; Yu, R.; Ye, D.; Deng, Q.; Zhang, Y. Consortium blockchain for secure energy trading in
industrial internet of things. IEEE Trans. Ind. Inform. 2017,14, 3690–3700. [CrossRef]
39.
Mengelkamp, E.; Notheisen, B.; Beer, C.; Dauer, D.; Weinhardt, C. A blockchain-based smart grid: Towards
sustainable local energy markets. Comput. Sci. Res. Dev. 2018,33, 207–214. [CrossRef]
40.
Energy Networks to Unveil Plan for £17 Billion Smart Grid Boom. 2018. Available online: https://www.
telegraph.co.uk/business/2017/12/04/energy-networks-unveil-plan-17bn-smart- grid-boom/ (accessed on
31 May 2019).
Sensors 2019,19, 4862 23 of 25
41.
Dang, C.; Zhang, J.; Kwong, C.P.; Li, L. Demand Side Load Management for Big Industrial Energy Users
under Blockchain-Based Peer-to-Peer Electricity Market. IEEE Trans. Smart Grid
2019
,10, 6426–6435.
[CrossRef]
42.
Guerrero, J.; Chapman, A.C.; Verbiˇc, G. Decentralized p2p energy trading under network constraints in a
low-voltage network. IEEE Trans. Smart Grid 2018,10, 5163–5173. [CrossRef]
43.
Ferrag, M.A.; Maglaras, L. DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange
Framework for Smart Grids. IEEE Trans. Eng. Manag. 2019. [CrossRef]
44.
Wang, S.; Taha, A.F.; Wang, J.; Kvaternik, K.; Hahn, A. Energy Crowdsourcing and Peer-to-Peer Energy
Trading in Blockchain-Enabled Smart Grids. arXiv 2019, arXiv:1901.02390.
45.
Gao, J.; Asamoah, K.O.; Sifah, E.B.; Smahi, A.; Xia, Q.; Xia, H.; Zhang, X.; Dong, G. Gridmonitoring: Secured
sovereign blockchain based monitoring on smart grid. IEEE Access 2018,6, 9917–9925. [CrossRef]
46.
Leonhard, R. Developing Renewable Energy Credits as Cryptocurrency on Ethereum’s Blockchain. Available
online: https://ssrn.com/abstract=2885335 (accessed on 31 May 2019).
47.
Buterin, V. A Next-Generation Smart Contract and Decentralized Application Platform. Available online:
https://www.ethereum.org/pdfs/EthereumWhitePaper.pdf/ (accessed on 31 May 2019).
48.
Delmolino, K.; Arnett, M.; Kosba, A.; Miller, A.; Shi, E. Step by step towards creating a safe smart contract:
Lessons and insights from a cryptocurrency lab. In Proceedings of the International Conference on Financial
Cryptography and Data Security, Christ Church, Barbados, 22–26 February 2016; pp. 79–94.
49.
Münsing, E.; Mather, J.; Moura, S. Blockchains for decentralized optimization of energy resources in
microgrid networks. In Proceedings of the 2017 IEEE Conference on Control Technology and Applications
(CCTA), Mauna Lani, HI, USA, 27–30 August 2017; pp. 2164–2171.
50. Ipakchi, A.; Albuyeh, F. Grid of the future. IEEE Power Energy Mag. 2009,7, 52–62. [CrossRef]
51.
Mwasilu, F.; Justo, J.J.; Kim, E.K.; Do, T.D.; Jung, J.W. Electric vehicles and smart grid interaction: A review
on vehicle to grid and renewable energy sources integration. Renew. Sustain. Energy Rev.
2014
,34, 501–516.
[CrossRef]
52.
Kang, J.; Yu, R.; Huang, X.; Maharjan, S.; Zhang, Y.; Hossain, E. Enabling localized peer-to-peer electricity
trading among plug-in hybrid electric vehicles using consortium blockchains. IEEE Trans. Ind. Inform.
2017
,
13, 3154–3164. [CrossRef]
53.
Jiang, T.; Fang, H.; Wang, H. Blockchain-based internet of vehicles: Distributed network architecture and
performance analysis. IEEE Internet Things J. 2018,6, 4640–4649. [CrossRef]
54.
Zhou, Z.; Wang, B.; Guo, Y.; Zhang, Y. Blockchain and Computational Intelligence Inspired
Incentive-Compatible Demand Response in Internet of Electric Vehicles. IEEE Trans. Emerg. Top. Comput.
Intell. 2019,3, 205–216. [CrossRef]
55.
Zhou, Z.; Wang, B.; Dong, M.; Ota, K. Secure and Efficient Vehicle-to-Grid Energy Trading in Cyber Physical
Systems: Integration of Blockchain and Edge Computing. IEEE Trans. Syst. Man Cybern. Syst.
2019
.
[CrossRef]
56.
Kiayias, A.; Russell, A.; David, B.; Oliynykov, R. Ouroboros: A provably secure proof-of-stake blockchain
protocol. In Annual International Cryptology Conference; Springer: Santa Barbara, CA, USA, 2017; pp. 357–388.
57.
Slimcoin: A Peer-to-Peer Crypto-Currency with Proof-of-Burn. 2019. Available online: https://github.com/
slimcoin-project/slimcoin-project.github.io/blob/master/whitepaperSLM.pdf (accessed on 31 May 2019).
58.
Chen, L.; Xu, L.; Shah, N.; Gao, Z.; Lu, Y.; Shi, W. On security analysis of proof-of-elapsed-time (poet).
In Proceedings of the International Symposium on Stabilization, Safety, and Security of Distributed Systems
(SSS 2017), Boston, MA, USA, 5–8 November 2017; Springer: Berlin/Heidelberg, Germany, 2017; pp. 282–297.
59. Junior, W.L.R.; Borges, F.A.; Veloso, A.F.D.S.; de AL Rabêlo, R.; Rodrigues, J.J. Low voltage smart meter for
monitoring of power quality disturbances applied in smart grid. Measurement
2019
,147, 106890. [CrossRef]
60.
Avancini, D.B.; Rodrigues, J.J.; Martins, S.G.; Rabêlo, R.A.; Al-Muhtadi, J.; Solic, P. Energy meters evolution
in smart grids: A review. J. Clean. Prod. 2019,217, 702–715. [CrossRef]
61.
Kosba, A.; Miller, A.; Shi, E.; Wen, Z.; Papamanthou, C. Hawk: The blockchain model of cryptography and
privacy-preserving smart contracts. In Proceedings of the 2016 IEEE Symposium on Security and Privacy
(SP), San Jose, CA, USA, 22–26 May 2016; pp. 839–858.
62.
Zyskind, G.; Nathan, O. Decentralizing privacy: Using blockchain to protect personal data. In Proceedings
of the 2015 IEEE Security and Privacy Workshops, San Jose, CA, USA, 21–22 May 2015; pp. 180–184.
Sensors 2019,19, 4862 24 of 25
63.
Alladi, T.; Chamola, V.; Sikdar, B.; Choo, K.K.R. Consumer IoT: Security Vulnerability Case Studies and
Solutions. IEEE Consum. Electron. Mag. 2019,9, 6–14.
64.
Guan, Z.; Si, G.; Zhang, X.; Wu, L.; Guizani, N.; Du, X.; Ma, Y. Privacy-preserving and efficient aggregation
based on blockchain for power grid communications in smart communities. IEEE Commun. Mag.
2018
,
56, 82–88. [CrossRef]
65.
Wang, Y.; Luo, F.; Dong, Z.; Tong, Z.; Qiao, Y. Distributed meter data aggregation framework based on
Blockchain and homomorphic encryption. IET Cyber-Phys. Syst. Theory Appl. 2019,4, 30–37. [CrossRef]
66.
Kamal, M.; Tariq, M. Light-Weight Security and Blockchain Based Provenance for Advanced Metering
Infrastructure. IEEE Access 2019,7, 87345–87356. [CrossRef]
67.
Fan, M.; Zhang, X. Consortium Blockchain Based Data Aggregation and Regulation Mechanism for Smart
Grid. IEEE Access 2019,7, 35929–35940. [CrossRef]
68.
Hassija, V.; Chamola, V.; Saxena, V.; Jain, D.; Goyal, P.; Sikdar, B. A Survey on IoT Security: Application Areas,
Security Threats, and Solution Architectures. IEEE Access 2019,7, 82721–82743. [CrossRef]
69.
Mahmood, K.; Li, X.; Chaudhry, S.A.; Naqvi, H.; Kumari, S.; Sangaiah, A.K.; Rodrigues, J.J. Pairing based
anonymous and secure key agreement protocol for smart grid edge computing infrastructure. Future Gener.
Comput. Syst. 2018,88, 491–500. [CrossRef]
70.
Mo, Y.; Kim, T.H.J.; Brancik, K.; Dickinson, D.; Lee, H.; Perrig, A.; Sinopoli, B. Cyber–physical security of a
smart grid infrastructure. Proc. IEEE 2011,100, 195–209.
71.
Singh, K.; Choube, S. Using blockchain against cyber attacks on smart grids. In Proceedings of the 2018
IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), Bhopal,
India, 24–25 February 2018; pp. 1–4.
72.
What Is a dApp? Decentralized Application on the Blockchain. 2018. Available online: https:
//blockchainhub.net/decentralized-applications-dapps/ (accessed on 31 May 2019).
73.
Miller, D. Blockchain and the Internet of Things in the Industrial Sector. IT Prof.
2018
,20, 15–18. [CrossRef]
74.
Huh, S.; Cho, S.; Kim, S. Managing IoT devices using blockchain platform. In Proceedings of the 2017 19th
International Conference on Advanced Communication Technology (ICACT), Bongpyeong, Korea, 19–22
February 2017; pp. 464–467.
75.
Zhang, X.; Fan, M. Blockchain-Based Secure Equipment Diagnosis Mechanism of Smart Grid. IEEE Access
2018,6, 66165–66177. [CrossRef]
76.
Dutch Energy Supplier BAS to Accept BITCOIN. 2018. Available online: https://www.ccn.com/dutch-
energy-supplier-bas- to-accept-bitcoin/ (accessed on 31 May 2019).
77.
Enercity, Payment with Bitcoin. 2018. Available online: https://www.enercity.de/privatkunden/service/
bitcoin/index.html (accessed on 31 May 2019).
78.
Elegant, Wat Zijn Bitcoins? 2018. Available online: https://www.elegant.be/be/nl/bitcoins/ (accessed on
31 May 2019).
79.
Blockchain Powered Solutions and Services Leveraging Blockchain Based Infrastructure to Create and
Streamline Existing Processes. 2018. Available online: http://bankymoon.co.za/ (accessed on 31 May 2019).
80.
Spectral and Alliander Launch Blockchain-Based Renewable Energy Sharing Token. 2018. Available
online: https://www.metabolic.nl/news/spectral-and-alliander-launch-blockchain-based-renewable-
energy-sharing-token/ (accessed on 31 May 2019).
81.
Power Ledger White Paper. 2018. Available online: https://cdn2.hubspot.net/hubfs/4519667/Documents%
20/Power%20Ledger%20Whitepaper.pdf (accessed on 31 May 2019).
82.
Bruno, D. Brooklyn’s Latest Craze: Making Your Own Electric Grid. 2019. Available online:
https://www.politico.com/magazine/story/2017/06/15/how-a-street-in-brooklyn-is- changing-the-
energy-grid-215268 (accessed on 31 May 2019).
83.
Mengelkamp, E.; Gärttner, J.; Rock, K.; Kessler, S.; Orsini, L.; Weinhardt, C. Designing microgrid energy
markets: A case study: The Brooklyn Microgrid. Appl. Energy 2018,210, 870–880. [CrossRef]
84. Share and Charge. 2019. Available online: https://shareandcharge.com/ (accessed on 31 May 2019).
85.
JuiceNet: Vehicle-to-Grid Integration Platform. 2019. Available online: https://emotorwerks.com/products/
juicenet-software/juicenet (accessed on 31 May 2019).
86.
Bitcoin Energy Consumption Index. 2019. Available online: https://digiconomist.net/bitcoin- energy-
consumption (accessed on 31 May 2019).
Sensors 2019,19, 4862 25 of 25
87.
Wang, W.; Hoang, D.T.; Hu, P.; Xiong, Z.; Niyato, D.; Wang, P.; Wen, Y.; Kim, D.I. A survey on consensus
mechanisms and mining strategy management in blockchain networks. IEEE Access
2019
,7, 22328–22370.
[CrossRef]
88.
Poon, J.; Dryja, T. The Bitcoin Lightning Network: Scalable Off-Chain Instant Payments. Available online:
http://lightning.network/lightning-network-paper.pdf (accessed on 31 May 2019).
89.
Back, A.; Corallo, M.; Dashjr, L.; Friedenbach, M.; Maxwell, G.; Miller, A.; Poelstra, A.; Timón, J.; Wuille, P.
Enabling Blockchain Innovations with Pegged Sidechains. Available online: http://www.opensciencereview.
com/papers/123/enablingblockchain-innovations-with-pegged- sidechains (accessed on 31 May 2019).
90.
Klems, M.; Eberhardt, J.; Tai, S.; Härtlein, S.; Buchholz, S.; Tidjani, A. Trustless intermediation in
blockchain-based decentralized service marketplaces. In Proceedings of the International Conference on
Service-Oriented Computing, Malaga, Spain, 13–16 November 2017; Springer: Berlin/Heidelberg, Germany,
2017; pp. 731–739.
91.
Lewenberg, Y.; Sompolinsky, Y.; Zohar, A. Inclusive block chain protocols. In Proceedings of the International
Conference on Financial Cryptography and Data Security, San Juan, Puerto Rico, 26–30 January 2015;
Springer: Berlin/Heidelberg, Germany, 2015; pp. 528–547.
c
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