Blockchain based transactive energy systems for voltage regulation in active distribution networks

IET Smart GridVolume 3, Issue 5 p. 646-656 Research ArticleOpen Access Blockchain based transactive energy systems for voltage regulation in active distribution networks Shivam Saxena, Corresponding Author shivam@eecs.yorku.ca orcid.org/0000-0002-4280-1389 Electrical Engineering and Computer Science, York University, 4700 Keele St, Toronto, CanadaSearch for more papers by this authorHany E.Z. Farag, Electrical Engineering and Computer Science, York University, 4700 Keele St, Toronto, CanadaSearch for more papers by this authorHjalmar Turesson, Schulich School of Business, York University, 4700 Keele St, Toronto, CanadaSearch for more papers by this authorHenry Kim, Schulich School of Business, York University, 4700 Keele St, Toronto, CanadaSearch for more papers by this author Shivam Saxena, Corresponding Author shivam@eecs.yorku.ca orcid.org/0000-0002-4280-1389 Electrical Engineering and Computer Science, York University, 4700 Keele St, Toronto, CanadaSearch for more papers by this authorHany E.Z. Farag, Electrical Engineering and Computer Science, York University, 4700 Keele St, Toronto, CanadaSearch for more papers by this authorHjalmar Turesson, Schulich School of Business, York University, 4700 Keele St, Toronto, CanadaSearch for more papers by this authorHenry Kim, Schulich School of Business, York University, 4700 Keele St, Toronto, CanadaSearch for more papers by this author First published: 29 May 2020 https://doi.org/10.1049/iet-stg.2019.0286Citations: 7AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract Transactive energy systems (TES) are modern mechanisms in electric power systems that allow disparate control agents to utilise distributed generation units to engage in energy transactions and provide ancillary services to the grid. Although voltage regulation is a crucial ancillary grid service within active distribution networks (ADNs), previous work has not adequately explored how this service can be offered in terms of its incentivisation, contract auditability, and enforcement. Blockchain technology shows promise in being a key enabler of TES, allowing agents to engage in trustless, persistent transactions that are both enforceable and auditable. To that end, this study proposes a blockchain based TES that enables agents to receive incentives for providing voltage regulation services by (i) maintaining an auditable reputation rating for each agent that is increased proportionately with each mitigation of a voltage violation, (ii) utilising smart contracts to enforce the validity of each transaction and penalise reputation ratings in case of a mitigation failure, and (iii) automating the negotiation and bidding of agent services by implementing the contract net protocol as a smart contract. Experimental results on both simulated and real-world ADNs are executed to demonstrate the efficacy of the proposed system. 1 Introduction Under the smart-grid paradigm, legacy power systems are evolving from a centralised, fossil-fuel based architecture into a decentralised, highly flexible system that is served primarily by distributed generation units (DGs). Particularly in active distribution networks (ADNs), DGs are being deployed in close proximity to the end consumer, significantly reducing line losses and increasing grid resiliency by allowing end consumers to draw backup power from the DGs when an outage occurs within the main grid. With traditional end consumers now beginning to own and operate DGs, the role of the end consumer has evolved to that of an autonomous control agent that is capable of contributing ancillary grid services to improve and optimise power system operation [[1]]. Several grid services in ADNs include: black start, load balancing, congestion management, as well as voltage regulation [[2]]. Voltage regulation, in particular, is a critical grid service because the intermittent power output of renewable DGs (mostly solar) can push the voltage outside of allowable limits, thereby causing damage to existing infrastructure [[3]]. This shift to service-oriented smart grids is best exemplified by the concept of transactive energy systems (TES), which seeks to combine the economic objectives of all agents with distributed control techniques to improve grid reliability and efficiency. The guiding principles of TES place special emphasis on enabling non-discriminatory participation by qualified agents, providing observable and auditable interfaces, and ensuring that all agents are accountable for standards of performance [[4]]. While many of the aforementioned grid services have been explored in the context of transactive energy [[5]], voltage regulation has not been adequately explored and a review of previous work reveals two main shortcomings. First, the incentive calculation is typically controlled by a centralised entity and the incentive amount is kept constant, regardless of the level of contribution of the agent in mitigating a voltage violation [[6]-[8]]. Second, the concept of contracting, auditing, and enforcement of contracts is not mentioned, where prosumers must be held accountable when they accept a contract to resolve a voltage violation and should be penalised if they do not comply. Both these shortcomings prevent the realisation of a TES where a competitive marketplace can be established to enable agents to bid and negotiate for the right to procure services, which is how many deregulated energy and ancillary markets are executed in present times [[9]]. Considering these shortcomings, blockchain technology has emerged as a suitable platform for the implementation and auditable governance of decentralised marketplaces in the context of TES [[10]]. Blockchains are a type of distributed ledger, where each agent maintains a local copy of the ledger and a consensus algorithm is used to ensure consistency amongst all agents. Transactions submitted to the blockchain network during a given time period are collected in a discrete block of data, verified by agents against a set of rules that the network is governed by, and then appended to the end of the blockchain in an immutable, tamper-proof fashion. Transactions can be automatically enforced by the use of smart contracts, which are software applications that are deployed to the blockchain and auto-execute based on the state of the ledger. As such, the ethos of TES align well with the ethos of blockchains, and address the two aforementioned shortcomings in previous work. Namely, blockchains allow for the incentive scheme and bidding process for the voltage regulation service to be decentralised by executing it as a smart contract that is verified by all participating agents, thereby enabling non-discriminatory governance. The immutability property of the blockchain ledger enables auditability and trust. Finally, the smart contract can enforce the terms of a service contract by penalising an agent for not resolving a voltage violation in an acceptable manner. Blockchain based TES has proven popular in both academic and real-world initiatives. However, its main use case has been applied to the implementation of peer to peer (P2P) energy trading systems, which is the focus of ∼33% of all active projects [[10]]. The objective of such systems is to exemplify how the implementation of local energy markets can result in the mitigation of large power imbalances while also unlocking new revenue streams for agents. The most publicised example is the Brooklyn microgrid (developed by LO3), where a neighbourhood of ten homes can directly sell their excess solar energy to each other at a price and time of their choosing. The system is implemented via Ethereum-based smart contracts [[11]]. Further variations in academia can be found in [[12]-[14]], where all of the work utilises smart contracts to facilitate transactions amongst agents as well as to implement a mathematical function to ensure that the market price of electricity is fair and equitable (social welfare maximisation, double auction). However, the aforementioned work does not mention enforcement of unresolved contracts, nor does it discuss how blockchains or smart contracts could be utilised as a mechanism to penalise such occurrences. To that end, this paper proposes a novel, blockchain-based TES that enables agents to participate in a decentralised marketplace and offer voltage regulation as a grid service within ADNs. It is worthwhile to note that this paper considers grid-connected microgrids to be miniature versions of ADNs as in [[15]], and thus, without loss of generality, the proposed system can be applied to grid-connected microgrids as well. The implementation of the system is exemplified within an ADN that is divided into several zones, where each agent must regulate the voltage in its own zone. In the event of a voltage violation that the agent cannot resolve on its own, the agent can initiate the process of soliciting bids from neighbouring agents and awards a service contract to the most suitable bid. The responding agent must then resolve the voltage violation of the initiating agent using control actions of available DGs within its zone. The proposed system maintains a reputation rating for each agent on the ledger that is analogous to a credit score, and is increased or decreased proportionately for each successful or unsuccessful mitigation of a voltage violation, which directly impacts the ability of the agent to procure bids in the future to earn revenue. The agent negotiation process is implemented as a smart contract and is based on a modified version of the contract net protocol (CNP), where all service contracts are enforced by the smart contract through checking the latest power measurement values on the ledger to determine if the voltage violation has been resolved successfully. To validate the system as a proof of concept, two sets of experiments are presented. The first experiment utilises a modified IEEE-33 bus feeder as the system-under-test, and executes the proposed system to ensure that both overvoltage and undervoltage violations can be mitigated. The second experiment involves a real-world implementation at a small, low-voltage, grid-connected Canadian microgrid to mitigate overvoltage violations. Both experiments utilise the hyperledger fabric (HLF) framework for the implementation of the blockchain. The contributions of this paper can be summarised as follows: (i) Developing a novel implementation of a distributed voltage regulation algorithm using blockchain technology. To the best of the authors' knowledge, only one previous paper has explored voltage regulation in this context, but neglects to explore how smart contracts can enforce service contracts, nor does it explore incentive schemes for voltage regulation services provided. [[16]]. (ii) Modifying the existing CNP by adding an enforcement stage to be utilised as a smart contract in order to handle the initiation, negotiation, and finalisation of all service contracts between agents. (iii) Execution of a real-world experiment at a Canadian, grid-connected microgrid to demonstrate how voltage violations can be mitigated by the deployment of the proposed system. The organisation for the remainder of the paper is as follows. Section 2 provides a detailed overview of blockchain technology, while Section 3 presents the system modelling of a typical power distribution system and defines the mathematical relationship between a power injection of an DG and the system voltage. Section 4 introduces the design principles and implementation of the proposed TES. Section 5 presents the results of the simulated and real-world experiments, while Section 6 is reserved for conclusions. 2 Review of blockchain technology 2.1 Blockchain components At its core, a blockchain is a decentralised, transaction-based state machine that immutably tracks P2P transactions from different agents and utilises distributed consensus techniques to ensure that all agents are in agreement with the current system state [[17]]. All transactions are stored on a ledger that is shared and maintained collectively by all agents within the blockchain network, where transactions are any pieces of data or commands that might alter the current state of the system. Examples of transactions may be a money transfer between agents, or a measurement of a unit of renewable energy from the DG of an agent. All transactions are collected together into discrete blocks of data that are chained together in chronological order, such that the evolution of system state is transparent to all agents on the network over time. Fig. 1 shows a block diagram of a general blockchain system, where a ledger of discrete blocks store a set of transactions , where k is the discrete timestep and i is the index of the transaction within the block. A block has a maximum block size that defines the total number of transactions that can be stored within it, characterised by . The validity of each transaction is checked by agents within the blockchain network using distributed consensus techniques [[18]], and once verified, each block is timestamped and encoded with a unique identifier using a cryptographic hash function that is referred to as a block hash. The block hash is uniquely composed of all the data stored within the block , and as such, any tampering of the block contents will result in a completely different block hash that can be detected. Each previous block hash is also stored within the subsequent block of the ledger and chained together in sequence, resulting in ledger data that is tamper-proof and immutable. Fig. 1Open in figure viewerPowerPoint Generalised representation of a blockchain network Also depicted in Fig. 1 are smart contracts, which are software applications that automatically execute transactions amongst agents based on ledger data and an associated set of terms and conditions. Smart contracts are deployed to the blockchain in much the same manner as regular transactions and must be validated by agents on the network. As the smart contracts have access to the shared ledger, they have the ability to enforce the terms and conditions of the contract. An example of a smart contract could be the transfer of ownership of an asset contingent upon the transfer of a sum of money, or the checking of power measurements on the ledger to determine if an agent operated a DG correctly to resolve a voltage violation. The computational burden of the execution of the smart contract, as well its participation in the consensus procedure is taken on by a computing resource referred to as a node. An agent interfaces with the node (and by extension, the entire blockchain network), via an intuitive client application. In this way, an agent can define smart contracts using an instance of a client application, deploy the smart contract to the ledger via the node, and execute transactions based on the smart contract after other agents have also verified the transaction using their respective nodes. It is worthwhile to mention that each agent must fully authenticate their identity within the blockchain network by utilising public/private key pairs to digitally sign their transactions, such that each individual transaction can be uniquely identified as belonging to the correct agent. These public/private key pairs , along with the current account balance of the agent (whether in fiat currency or cryptocurrency) is stored in a data structure referred to as a wallet. 2.2 Consensus mechanisms As mentioned earlier, consensus mechanisms play a vital role in blockchains to verify the authenticity and validity of all transactions between agents. Due to the fact that the ledger is shared amongst all network participants and must reflect a single point of truth, there must be general consensus amongst all agents with respect to the current system state. In general, consensus mechanisms aim to be byzantine fault tolerant (BFT), which refers to the ability for a network of agents to reach consensus even if a number of agents experience communication loss, or intentionally send incorrect responses to other agents during the validation process [[19]]. To establish BFT, it has been mathematically proven that a minimum of two-thirds of the number of agents must return the same result when verifying a block of transactions (or the anticipated result of the execution of a smart contract) [[19]]. The most popular consensus algorithm in use today is proof of work (PoW), used in platforms such as Bitcoin and Ethereum [[20]]. In PoW, all agents are incentivised to participate in the consensus process, where agents leverage computational resources within their node to validate transactions for a monetary reward (often referred to as mining). The validation of an upcoming block of transactions involves solving a cryptographic puzzle that is composed of a unique combination of and , and the first miner to solve the puzzle receives a reward. Once a miner solves the puzzle, it broadcasts the solution to all other miners that verify its correctness and the new block is added to the chain. However, PoW relies on brute force methods to solve the puzzle, resulting in significant scalability concerns due to the computational complexity and energy expenditure required by the consensus process [[21]]. The energy consumed from the PoW consensus process on the Bitcoin network in the year 2017 was equivalent to the annual energy consumption of Ireland, which is in excess of 30 TWh [[22]]. In contrast, an example of a consensus algorithm that does not use mining techniques is practical byzantine fault tolerance (pBFT), which is used by platforms such as HLF [[23]]. Within pBFT, one agent is designated as a leader, and the other agents are designated as followers. During the validation process, the leader agent broadcasts the block of transactions (or proposed smart contract to be executed) to all other followers, who in turn, validate the transactions. The transactions are successfully confirmed only when more than two-thirds of the agents return an identical confirmation result. Compared to PoW, pBFT is minimally computationally intensive, however, its communication complexity is because every agent must communicate with every other agent during the validation process [[24]]. As a result, pBFT is ideally suited when there are a limited set of agents within the network, which is further discussed in the next subsection. 2.3 Permissioned and permissionless architecture Generally, blockchains can be divided into two categories: permissionless (public), or permissioned (private or hybrid) [[23]]. Permissionless blockchains, such as Bitcoin and Ethereum, are open to the public, and any agent can begin to make transactions after registering on the network. In contrast, permissioned blockchains, such as HLF, require an invitation to join the network. As a result, the identities of the agents within the network can be known. Permissioned blockchains have found success in use cases where there are shared business networks in which agents (i) might have conflicting incentives or (ii) do not necessarily trust each other, yet must still conduct transactions for a shared business objective [[25]]. Since an invitation is required to participate in a permissioned blockchain, the consensus algorithm does not require all agents to participate in the consensus process, thereby alleviating the concerns about excessive energy use associated with permissionless blockchains [[26]]. The requirement of the invitation also mitigates concerns of Sybil attacks, where a malicious agent creates fake identities, registers the identities onto the network, and attempts to undermine the consensus process [[27]]. Recent work has benchmarked permissioned blockchains (HLF) and permissionless blockchains (Ethereum) and has revealed that HLF consistently outperforms Ethereum within scalability metrics such as transaction throughput (transactions per second), average latency of transactions (total time required for transaction to be committed to the ledger) and computational burden (peak memory usage) [[28]]. At 16 nodes, HLF is capable of processing 1000 transactions per second at 50 s latency, while Ethereum processes ∼200 transactions per second at 75 s latency [[28]]. In terms of peak memory usage, one benchmark involved the sorting of an array of 10 million elements, which resulted in a peak memory usage of 473 MB for HLF and over 22,000 MB for Ethereum [[28]]. It should be mentioned, however, that HLF cannot support more than 16 nodes, while Ethereum does not have a theoretical limit [[29]]. 3 Problem of voltage regulation in ADNs and its application in distributed TES This section introduces the problem of voltage regulation in ADNs by presenting the mathematical modelling of ADN components, as well as the impact of DG control actions on the voltage within ADNs. This is followed by a subsection that explores how the voltage regulation problem can be addressed by blockchain-based systems. 3.1 Mathematical modelling of ADNs Fig. 2 shows a typical radial distribution feeder within an ADN, where DGs and loads are connected. Renewable DGs, primarily based on solar and wind energy, are typically preset to operate at maximum power point tracking mode [[30]]. However, the inverter of the DG is capable of setting operating points for active and reactive power to provide support for voltage at its point of common coupling (PCC) with the main grid. As such, active power (P) and reactive power (Q) represent the control variables that an agent can set when operating the DG. In general, at time instant t, the active and reactive power flow in branch i, and the voltage at bus i, could be represented with respect to bus j as follows [[31]]: (1) (2) (3)where n is the line iterator, are the output active/reactive powers, are the active/reactive loads, are the injected active/reactive powers from the DGs, is the voltage at each bus, and is the resistance/reactance of each line. From (1)–(3), it can be seen that changes in the injected/absorbed active and reactive power play a dominant role in shaping the voltage profile of the overall network. Assuming the system is in steady state allows the neglecting of the change in load demand, and further linearisation of (3) by neglecting the change in load demand can be expressed as (4)The voltage at the substation is typically very stiff given the overall impedance of the main grid. Given this, the substation voltage is typically held steady and can be set to zero. This results in (5)It is now possible to determine the sensitivity of each bus to any P/Q power injection/absorption as follows: (6)Further, the impact of any control action (changes in P/Q) at bus j on the voltage of bus i can be expressed by (7)From (7) it can be seen that the penetration of large amounts of DGs within power systems can both cause and mitigate voltage violations. Large injections of active power may result in an overvoltage violation, whereas the curtailment of active power and/or absorption of reactive power have the potential to reduce the voltage and resolve the violation. Fig. 2Open in figure viewerPowerPoint Example ADN with connected DGs and loads 3.2 Potential application of blockchain for voltage regulation Conventional approaches for voltage regulation within ADNs have followed two approaches: centralised and distributed (non-blockchain based, however) [[32]]. A centralised approach relies on a single, trusted authority to unilaterally control all agents within the entire ADN to globally regulate the system voltage. On the other hand, a distributed approach obviates the need for a central authority by spatially partitioning the ADN into several zones, where each zone is governed by an agent that regulates the zonal voltage by controlling the DGs in its own zone. As shown in Fig. 3, zonal partitioning requires agents to strategically coordinate their control actions via P2P exchanges to ensure that the overall system voltage remains within normal operating conditions. Fig. 3Open in figure viewerPowerPoint Example ADN partitioned into four zones, each governed by an agent that operates the DGs in the zone strategically. Measurements from smart meters allow agents to compute state estimates about the voltage at each bus in its zone Yet, both approaches do not provide explicit mechanisms for defining and maintaining trust among agents, where the notion of trust within the application of voltage regulation is composed of (i) validating that the incentive received by an agent for taking a control action is fairly calculated and delivered; (ii) validating that the agent indeed took the expected control action to mitigate a voltage violation; and (iii) maintaining an impartial, auditable interface to track the performance of the agent over time. A centralised approach can easily use a central controller to validate all control actions of the agents and track agent performance, but since its authority is unchallenged, the controller cannot be fully trusted to make equitable economic decisions for all agents [[33]]. A conventional distributed approach enables agents to formulate their own incentive scheme and make rational economic decisions via P2P negotiation, however, these approaches do not address how the agents will impartially track the performance of other agents. Blockchain technology can address these trust issues by using the immutability principles of shared ledgers and smart contracts. All agents would be required to store measurements of zonal voltage and power output of their DGs to the shared ledger, where the measurements would be stored as transactions as shown in Fig. 1. Smart contracts would enable agents to offer voltage regulation services to other agents in the form of a digital service contract, which would include terms and conditions such as: the bidding price of the service, the voltage deviation to be corrected, as well as the time expiry of the contract. Crucially, the smart contract would be able to enforce the terms and conditions of the service contract by querying the power quality measurements on the ledger to ensure that the voltage deviation was corrected within the specified time. In addition, the agents would collectively maintain an agent reputation rating on the ledger, which the smart contract would autonomously update when a voltage violation is resolved appropriately. In this way, the functions of calculating agent incentives, validating agent control actions, and maintaining auditable metrics of agent performance are performed collectively by all agents, since the agents must reach consensus to maintain the state of the ledger and execute smart contracts. 4 Blockchain implementation of proposed TES 4.1 Blockchain architecture design of proposed TES When considering design principles for designing blockchain-based systems, careful thought is given to its design architecture in terms of security and scalability [[34]]. In the context of TES, especially with the application of voltage regulation, a permissioned blockchain seems to be more suitable than a permissioned blockchain for two main reasons. First, participation of the agents within the system should be qualified, invite-only, and agreed upon by all agents within the network to decrease cybersecurity concerns with respect to malicious agents and Sybil attacks [[35]]. Thus, an invited agent would be assigned a specific ID , as well as a public/private key pair such that the agent would be able to digitally sign transactions within the blockchain network to prove their authenticity. Second, the scalability of the overall system in terms of transaction speed and computational burden should be kept as low as possible. As discussed earlier, a permissionless blockchain architecture achieves higher transaction throughput, lower latency, and significantly less computational burden when compared to permissioned blockchain architectures. In the case of HLF, a maximum constraint of 16 agents within a network is indeed a limiting factor, however, this limit may be overcome by assigning larger spatial zones of operation to an agent as in [[30]]. To further improve the scalability of blockchain-based systems, a common strategy is to minimise the data stored on the ledger, as well as to reduce the complexity of the logic executed by the smart contracts [[36]]. Since both these blockchain components require consensus, and therefore, heavy computational burden, only critical pieces of data that are required by the smart contract to enforce any service contracts between the agents should be stored on-chain. Any data, or logic that does not require intervention by other agents can be executed off-chain. For the application of voltage regulation, the required logic is defined in (7), where the data items include: measurements of the active/reactive power of the DG, a matrix of the sensitivity of each bus towards P/Q modulation, as well as measurements of voltage at every bus in the system. Additionally, the ledger would store the aforementioned reputation rating of each agent on the ledger, as well as service contract details such as the IDs of the agents within the contract, bid price, voltage deviation to