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An Enhanced Blockchain-Based Data Management Scheme for Microgrids
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DOI: 10.1007/978-3-030-44038-1_70
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An Enhanced Blockchain-Based Data
Management Scheme for Microgrids
Bacem Mbarek, Stanislav Chren, Bruno Rossi, Tom´as Pitner
Abstract Trading of distributed energy resources is an important aspect to fully
achieve energy efficiency. Modern microgrids and consumer/prosumer energy transactions
are such kind of enablers. The blockchain has been proposed as a solution
to aid microgrid applications with the support of a decentralized trading model, operations
processing, computation and storage. However, microgrids trading is still
vulnerable to so-called False Data Injection (FDI) attacks, that is the attempt by
malicious participating nodes to distribute false measurements to the peers to gain
personal advantages. In this paper, we propose an enhanced blockchain mechanism
to counteract possible FDI attacks by means of mobile software agents to control
and detect malicious activities of sellers nodes.
Keywords: Microgrid, Blockchain, FDI attacks.
1 Introduction
Blockchains have emerged over time as a reliable and secure way to record transactions
in an immutable manner, with their applicability that has been extended to
many domains. In the smart grids context, blockchains have been adopted also for
sharing and protecting electricity suppliers due to their scalability, interoperabilBacem
Mbarek
Faculty of Informatics, Masaryk University, Brno, Czech Republic, e-mail:
bacem.mbarek@mail.muni.cz
Stanislav Chren
Faculty of Informatics, Masaryk University, Brno, Czech Republic, e-mail: chren@mail.muni.cz
Bruno Rossi
Faculty of Informatics, Masaryk University, Brno, Czech Republic, e-mail: brossi@mail.muni.cz
Tomas Pitner
Faculty of Informatics, Masaryk University, Brno, Czech Republic, e-mail: tomp@fi.muni.cz
1
2 Bacem Mbarek, Stanislav Chren, Bruno Rossi, Tom´as Pitner
ity and sustainability when connected to the microgrid [13]. The blockchain has
been used to manage energy transactions between energy suppliers and neighbouring
houses connected to the microgrid distribution system. We can exemplify how
microgrids transactions have been adopting blockchain-based systems. Households
can be considered as either energy consumers or prosumers (both providing excess
energy and demanding energy). Each household has a quantity of electricity that it
can sell to other members of the channel. Each seller sends to the blockchain its
current state of stored energy reserves.
However, one of the well-known attacks in the microgrids context is the False
Data Injection (FDI) attack [26], in which an attacker tries to inject false data
within the system, for example by altering a subset of measurements either from
sensors/devices or from the network. The final goal of the attacker is either to observe
the behaviour based on the injected data or to force actors to take specific
actions based on the tampered data, in this case selling/buying energy. Thus, several
blockchain approaches have been proposed to support microgrids trading (e.g.,
[17]). However, such approaches focus more on the whole blockchain platform and
are not specifically targeted at the prevention and detection of FDI attacks.
In this paper, we address the issue of FDI attacks by proposing a secure and
efficient blockchain scheme for the microgrids, specifically tailored to identify and
counteract FDI attacks. In particular, we present a distributed blockchain platform
based on mobile agents to efficiently detect FDI attacks in microgrids. The mobile
agent helps the blockchain peers to improve the identification of malicious nodes
during the trading process.
The remainder of the paper is organized as follows. Section 2 presents an
overview of microgrids systems with common definitions. Section 3 proposes our
approach of integrating the blockchain with an agent-based system. To evaluate the
proposed solution, Section 4 discusses the state-of-the-art solutions that have been
proposed to implement blockchain in microgrids and approaches adopted for FDI
attacks identification. Finally, Section 5 concludes the paper and outlines future directions
for this work.
2 Background - Microgrids
The traditional power grid faced difficulties with issues such as increasing demand
for uninterrupted power supply, pressure on environment protection and efficiency
of power distribution. To tackle these challenges, the traditional power grid has begun
its transformation to a smart grid. A smart grid (SG) is an infrastructure which
utilizes the existing power network, enhanced with modern information and communication
technologies. SG covers all the segments of power infrastructure from
power generation, power distribution to power consumption in household, business
and industry premises. One of the integral part of SG are Distributed Energy Resources
(DERs). DERs are typically represented by renewable power sources, such
as photovoltaics (PV)s or wind turbines which have become more accessible also
to the household customers. Being able to locally produce and consume the electric
power has lead to the introduction of new localised power grid infrastructure called
An Enhanced Blockchain-Based Data Management Scheme for Microgrids 3
microgrid which allows to move the power generation closer to the loads [16]. The
benefits of microgrids include the reduction of power losses in energy distribution,
local regulation of power load, increased reliability and reduction of the high investments
for network upgrades [16]. Microgrids can be controlled autonomously,
and may operate in both grid-connected and self-sufficient modes [20]. Surveys of
approaches to microgrid management can be found in [10], [15]. The overall architecture
of a microgrid is shown in Fig. 1.
PV Wind
Generation
Controller
DER
Battery Capacitators
Storage
Controller
Storage
Load
Smart
Meter
ConsumerConsumer
Microgrid
Controller
PCC
Public electrical
grid
Electrical
power flow
Control
connection
Fig. 1 Microgrid architecture
The main components of a microgrid are [12]: i) the power distribution system,
ii) the DERs, iii) energy storage units and iv) communication and control modules.
The responsibility of the distribution system is to transmit the power from the power
between power generation, storage units, consumers as well as provides connection
to the external power infrastructure. The structure of the distribution system depends
on the type of the electrical current that is being transmitted: the alternating current
(AC), the direct current (DC) or the combination of both. The storage component
usually represents different types of batteries, capacitators or fuel cells. Their main
purpose is to balance the load in the grid to increase its stability. The potential imbalance
can be caused by the fluctuations of the power production output from DERs,
such as PVs or wind turbines that are largely dependent on the current weather conditions.
Additional fluctuations can be related to variety of consumer behaviour, i.e.
their power consumption profile may vary depending on the time period in a day,
week, year etc. There are many open issues in microgrids energy trading. Wang et
al. [21] cover several of these aspects. On one side, the centralized management
may lead to a single point of failure, and other risks related to transparency and data
tampering. Furthermore, there can be privacy-related issues, as transaction traces
can be used to derive patterns about energy generation and usage, especially if big
data analysis platforms are adopted. Other issues can be related to the security of
the communication of the infrastructure during energy transaction, as well as the resilience
to cyber attacks. Other aspects might be related to encouraging the support
of renewable resources, and granting flexibility to meet power demand.
The operation of microgrid depends on the up-to-date measurements of energy
supply and demand. The measurements are collected from smart meters, sensors
and controllers which are deployed at the customer premises. These components are
4 Bacem Mbarek, Stanislav Chren, Bruno Rossi, Tom´as Pitner
often deployed in open environment and communicate with each other using wireless
technologies which makes them vulnerable to potential cyber attacks. From the
power grid perspective, false data injection (FDI) attacks can have a serious impact
on stability of power network [8]. In general, injecting false data may increase
the risk of power grid instability by allowing injection (or consumption) of more
or less energy than is safe – based on decisions made on the basis of falsified data
[18]. In the microgrid context, the FDI attack can be viewed as forgery or modification
of measurements for energy supply and demand from customers. For example,
a compromised customer can claim less energy available than what can be provided,
or they can claim more energy needed than what is required or combination
of both [26].
3 Microgrid Electricity Transaction Mode Based on Blockchain
3.1 Overview of our layered design
Each household has specific quantities of energy that can be produced and stored and
will bid excess energy to be traded by means of microgrid energy transactions. Each
seller responds to the blockchain by letting it know the current amount of stored
energy. As discussed, False Data Injection (FDI) attacks are one of the main threats
faced by microgrids, making the whole transaction process less trustful, as they
introduce uncertainty about the values of energy bids. We therefore describe an enhanced
blockchain-scheme called Microgrid Blockchain Platform (MBP) based on
mobile agents to enable the trusted and secure settlement of electricity trading transactions.
Each agent monitors the activities of sellers in the network. The blockchain
peer nodes use the mobile agent report to improve the decision making process, and
make better decisions in the verification of the sellers declared information.
3.1.1 System Design
In our proposal, the blockchain is composed of three elements: 1) endorsing peers,
2) the ordering service, and 3) committing peers. As shown in Figure 2, each householder
is a transmitter/receiver that sends requests/information to neighbour houses
in the channel through the blockchain platform. In blockchain, the smart contract is
a code fragment that executes the terms of a contract [25]. A channel can be defined
as a sub-network for peers communication, if it is necessary to divide transactions
according to different boundaries according to some service logic. Channels can
represent others groups of neighbouring electricity producers and consumers.
• Endorsing peers are a predefined number of households that will endorse a
transaction proposal, as defined in specified policies. When enough endorsing
peers support a transaction proposal, it can be submitted to the ordering service
to be added as a block. During the commissioning and configuration of the
An Enhanced Blockchain-Based Data Management Scheme for Microgrids 5
blockchain network, the developers should select a number of households that
are defined as the endorsing peers.
• Ordering service collects transactions for a channel into proposed blocks for
distribution to peers. Blocks can be delivered for each defined channel. The task
of the ordering service is to gather all the endorsed transactions, perform the
ordering in a block, and then send the ordered blocks to the committing peers.
• Committing peers (including endorsing peers) run the validation and update
their copy of the blockchain and status of transactions. Each peer receiving the
block, as a committing peer, can now attach the new block to its copy of the transactions.
Committing peers have also the responsibility of updating the shared
ledger with the list of transactions.
3.1.2 Order-execute architecture in microgrid blockchain platform
When a seller agrees to enter into a transaction, he determines the parameters of
this transaction by specifying its energy supply value, its location, price. Each transaction
is stored in a smart contract and transmitted to the endorsing peers of the
blockchain platform. The received transaction is verified by checking their smart
contract. After the reception of N providers transactions, the endorsing peers can
choose one provider by their own preferences (provider/consumer locations and
distance report, supply value, price) to serve a consumer. Then, the endorsing peers
verifies the selected provider by using a mobile agent that attempts to detect the
quantity of the electricity supply stored in the selected seller and will be acting as
a local FDI detector. Moreover, the mobile agent will migrate from the Blockchain
platform to the selected seller to check the storage, power balance, smart meters
behaviour, and historical transactions. To improve the decision making process, the
endorsing peers use the mobile agent for detecting the wrong declared power supply
by the selected seller. Any malicious activity will be sent to the endorsing peers.
The mobile agents are able to autonomously migrate from the peers to a selected
house, transferring their code and data, which allows them to efficiently perform
computations, gather information and accomplish tasks.
Figure 2 depicts an example of execution of transaction in the blockchainenabled
microgrid system. The seller adds the current stored amount of energy to
a smart contract and sends it to the blockchain platform. Once the smart contract
is received by the blockchain platform, the endorsing peers checks and verifies the
authenticity of the smart contract and sends a mobile agent to the selected seller.
The mobile agent will migrate to seller and verifies its data (battery storage, meters
functionalities, encryption protocols, network communication, historical transactions)
and creates a reports about the gathered information and returns back to
the endorsing peers. When the report is received, the endorsing peers start the verification
of the received information and then decide to accept or reject the seller’s
transaction.
6 Bacem Mbarek, Stanislav Chren, Bruno Rossi, Tom´as Pitner
Fig. 2 Order-execute architecture in microgrid blockchain platform
3.1.3 Agent-based policy for an enhanced blockchain management
In our context, a mobile agent is a standalone software entity that can start various
tasks when visiting different computing nodes: such as collecting data, doing some
computation, as well as visiting other computing nodes [1].
The Battery Management System (BMS) sensors are coupled to all batteries storage
in the smart grids to monitor and control power density, battery life such as
charge and discharge process, which all are important parameters for battery storage.
BMS sensor consists of measuring devices to collect parameters such as battery
voltage, current, and temperature. These parameters can be used to calculate an estimation
of batteries state of charge (SOC) and state of health (SOH) [5] [22]. The
mobile agent can visit each of the microgrid nodes involved in energy transactions
and execute customized code on each BMS sensor. The mobile agent can detect
anomalies in the communicated data, compared to what detected by the BMS sensor.
Of course, such approach cannot detect cases in which the BMS sensor itself
has been tampered with, like modifications of firmwares or complete substitution
of the equipment (as long as the tampered hardware complies with the contract to
interface with the software compoments).
An Enhanced Blockchain-Based Data Management Scheme for Microgrids 7
3.1.4 System Implementation
In the implemented platform, the blockchain platform will create a mobile agent
dedicated to every selected seller. The mobile agent will migrate to the selected
seller. Then, the mobile agent will execute its code in the battery management sensor.
The mobile agent computes the volume of energy V that is produced by each
seller and reported by the sensors. Then the mobile agent compares V with D, where
D is the declared value of power supply submitted to the blockchain. If D < V ±ε,
then a potential FDI attack is suspected. The mobile agent also checks if the seller
is a member of other blockchain channels, and computes the supply transactions
C which will be delivered to those channels. If D < (V −C) ± ε, then a potential
FDI attack can be reported. The mobile agent detects the FDI attacks as depicted in
Equation (1), where ε represents some measurement error threshold.
FDI =
1, if D < V ±ε or D < (V −C)±ε
0, otherwise
(1)
We have implemented a preliminary prototype of our approach in the open source
blockchain platform called Hyperledger Fabric [7] which has been reported as one
of the most suitable platforms for the smart grid sector [24]. All nodes (both sellers
and buyers) on the network use the SHA-1 hash algorithm and 2MB as data
block sizes. With Battery Management home devices linked by the hyperledger
blockchain, the foundation of blockchain equipped smart home was designed to
control electricity trading between neighbour houses. For this system to work, sellers
should be a blockchain channel member. Besides that, there are two ways for
endorsing peers to verify and check the authenticity of the sellers transactions, by
their smart contract and by the mobile agent report. We created a smart contract
program that is used by the seller to send its transaction to the endorsing peers. We
deployed the initial prototype to test the feasibility of the solution, we planned further
tests by running simulations of different scenarios of energy trading and data
tampering activities.
4 Related Work
Related works go into two directions: the application of blockchains in the context
of microgrids, and the detection of FDI attacks disrupting microgrids transactions.
Usage of the Blockchain in Microgrids. The decentralized nature of the blockchain
has gathered wide interest for its application in the smart grids context, making energy
trading the most interesting area of adoption, with several solutions that were
proposed over time [4]. Blockchains have been applied to overcome many of the
issues in energy trading. According to Mengelkamp et al. [14], the adoption of
blockchains can bring a series of advantages. First of all, building distributed systems
that are more secure and built bottom-up, increasing transparency, reliability
and equality between different actors. Another advantage is no need for central in-
8 Bacem Mbarek, Stanislav Chren, Bruno Rossi, Tom´as Pitner
termediaries, as well as cost-efficient micro-transactions in a fully distributed and
decentralized system with transactions that are irreversible (which can sometimes
be considered an issue). On the other side, the correct application of blockchains
can also bring drawbacks such as scalability issues, high energy consumption, and
potentially the adoption of technologies that are not yet mature for a wide adoption.
Several platforms for the adoption of the blockchain in microgrids and energy
trading have been proposed over time. Mengelkamp et al. [14] provide the simulation
of a decentralized energy platform based on the Ethreum blockchain. Agentbased
systems are used to model prosumers bidding energy. Overall, the simulated
scenarios show the real-time behaviour of such energy trading mechanisms, however,
more concrete implementations are necessary to evaluate the feasibility. Kang
et al. [9] propose an energy trading platform based on the Ethereum blockchain.
Authors discuss the implementation of smart contracts, to allow prosumers perform
transactions. The scenario shown applies to only two nodes, so scalability of the
solution is a future work. While previous work focuses on the general application of
the blockchain for energy trading in the migrogrids, our research focuses more on
how the inclusion of an agent-based system, together with the blockchain, can bring
benefits in terms of control of data tampering activities.
Microgrids FDI Attacks Identification & Countermeasures. The identification
of FDI activites and application of proper countermeasures in the area of microgrids
is a widely discussed topic (e.g., [26, 8]). False data injection attacks have gathered
large attention in the context of the Smart Grids infrastructure, as false data
can propagate through the network bringing to wrong decisions and failures. In the
context of microgrids, FDI attacks can be exploited by malicious users to gather
the maximum benefits from the transactions between authorized participants [8].
Countermeasures about FDI go typically in three directions: protecting critical measurements,
detecting FDI attacks, and increasing uncertainties in power systems (to
make data injection activities more difficult) [11].
Various approaches have been proposed to deal with FDI attacks in microgrids.
Beg et al. formalized the problem as properties that do not change over time in microgrids
(invariants), with dynamic analysis aimed at identifying mismatches [2].
Chlela et al. proposed a real-time hardware-in-the-loop testing platform to detect
FDI attacks and study mitigation strategies [3]. Talebi et al. proposed to address
coordinated FDI attacks by dynamically changing the information structure of microgrids
[19]. Yu et al. coupled a threshold-based anomaly detection approach with
a watermarking-based scheme to avoid data tampering of measurement information
[23]. Furthermore, many authors focused on different machine learning approaches
for the identification of FDI attacks, like He et al. examining the application
of deep learning for real-time injection events identification [6]. While various
FDI detection techniques in the literature address the problem of false data injection
in the microgrids, there is still a lack on providing practical and secure FDI detection
methods. Our blockchain agent-based proposal is more based on the higher level,
but some of the approaches from related works can be applied either at the single
agent level or at the level of information aggregation by several agents.
An Enhanced Blockchain-Based Data Management Scheme for Microgrids 9
5 Conclusion
In this paper, we proposed a new blockchain mechanism based on a mobile agent
system to address the problem of False Data Injection (FDI) attacks in microgrids
energy trading. The proposed platform allows to detect FDI attacks as the
blockchain system does not have to wait for the distribution of the sharing supply
and the verification of the channel peers. The blockchain platform in collaboration
with endorsing peers sends a mobile agent to detect the possible FDI attacks in
each household location. By using and integrating mobile agents in the blockchain,
the FDI attacks are identified and isolated from the network. Thus, this will significantly
reduce the impact of tampered data and help the endorsing peers to make
a decision about the distributed transactions. As a future work, we plan to to conduct
set of experiments of simulated runs according to various microgrid topologies
and type of data tampering attacks, testing how the blockchain platform can behave.
Furthermore, we plan to handle the challenge of interoperability and the cost of
implementing such new framework compared to traditional approaches.
Acknowledgements The research was supported from ERDF/ESF ”CyberSecurity, CyberCrime
and Critical Information Infrastructures Center of Excellence” (No. CZ.02.1.01/0.0/0.0/16 019/
0000822).
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