Liaqat Ali, M. Imran Azim, Jan Peters, Nabin B. Ojha · 10 authors
This paper presents a local energy market (LEM) model to conduct peer-to-peer (P2P) energy trading between a number of participants by dint of the Ethereum-based blockchain technology. The proposed LEM mechanism is structured by considering relevant functional constraints while energy trading is arranged between several participants in the presence of other stakeholders including energy retailer and network operator. LEM participants’ mutual bidding intended P2P trading, actual settlement, and final billing are executed using the smart contracts in Ethereum blockchain to record LEM transactions and related data in an unchangeable and distributed fashion. Lastly, a case study is performed in an Australian suburb with 300 LEM participants, and the simulation results are benchmarked with an existing business-as-usual (BAU)scenario. The simulation results outline that the formulated LEM mechanism 1) reduces the electricity cost of participants remarkably while improving their self-sufficiency, 2) minimises power grid export and import, and 3) retains income margins for the energy retailer and network operator.
Abstract Distributed renewable energy supply (RES) is a new pattern for the transformation of power grids. As a characteristic case of RES, microgrids have an advantage in convenient operation. However, the energy management of microgrids remains as a major concern. With the emergence of the decentralized paradigm, blockchain potentially provides a reliable energy data metering and payment for the whole life cycle of energy management. In particular, the demand response (DR) in the microgrid can stimulate demanders to spontaneously manage their load consumption and maintain the balance of the energy trading market. To achieve optimal DR, a dynamic pricing strategy under the blockchain and game‐theoretic approach is proposed. First, the blockchain‐based architecture is applied to ensure the reliability of energy data and lay a foundation of binding agreements for games. Then, the pricing mechanism under the cooperative game is formulated to optimize DR. Moreover, to help resolve the optimal response quantity and reduce the supply punishment of the RES providers, the DR requires accurate forecasting of the energy generation and consumption profiles. Therefore, an ensemble method Long Short‐Term Gate Support (LSTGS) is designed to forecast the RES and load power for intelligent agent to make decision on effective energy scheduling and DR. Taking the classic distributed energy context as a case study, we demonstrate the effectiveness of our approach and show that it can achieve DR profits maximized and improve the stability of the energy‐trading market.
In recent years, user-side energy storage has begun to develop. At the same time, independent energy storage stations are gradually being commercialized. The user side puts shared energy storage under coordinated operation, which becomes a new energy utilization scheme. To solve the many challenges that arise from this scenario, this paper proposes a community power coordinated dispatching model based on blockchain technology that considers shared energy storage and demand response. First of all, this paper analyzes the operating architecture of a community coordinated dispatching system under blockchain. Combined with the electricity consumption mode of communities using a shared energy storage station service, the interactive operation mechanism and system framework of block chain for coordinated dispatching are designed. Secondly, with the goal of minimizing the total cost of coordinated operation of the community alliance, an optimal dispatching model is established according to the relevant constraints, such as the community demand response, shared energy storage system operation and so on. Thirdly, the blockchain application scheme of community coordinated dispatching is designed, including the incentive mechanism based on the improved Shapley value allocation coordination cost, and the consensus algorithm based on the change rate of users’ electricity utilization utility function. Finally, the simulation results show that the proposed community coordinated dispatching strategy in this paper can effectively reduce the economic cost, reduce the pressure on the power grid, and promote the consumption of clean energy. The combination of the designed cost allocation and other methods with blockchain technology solves the trust problem and promotes the innovation of the power dispatching mode. This study can provide some references for the application of blockchain technology in user-side energy storage and shared energy storage.
Anu G Kumar, M R Sindhu, Vivek Mohan, Rekha Viswanathan · 5 authors
Rooftop solar PV in India has seen good progress in the Commercial and industrial sectors, but the progress in the domestic sector is relatively slow due to the high initial installation cost. Thus, there arises the need for good market models for Rooftop Solar (RTS) implementation. This paper conducts a comparative study of workable RTS market models by employing the discounted cash flow method, as per the recent regulatory guidelines. Market models are formulated and tested for a typical residential high-rise apartment complex in India comprising 15 storied buildings with a combined maximum demand of 180kVA. The results suggest that the centralized community RTS model of 80kWp capacity with upfront financing is suitable when compared to the decentralized individual model, as it has the lowest levelized cost of 3.39 ₹/kWh and a payback period of 5.5 years. With the federal subsidy, the prosumer levelized cost reduces to 2.06 ₹/kWh with a payback period of 3.3 years. Thus grid parity is achieved for all tariff tier rates. With adaptive staggering strategy, this scheme is validated to be more attractive for the urban residential microgrids, as the solar installation of 80kWp and its cost can be staggered and even reduced over the planning period. Hence capital installation and operation costs can be distributed over the stipulated time interval. The study result gives RTS stakeholders insight into selecting the most cost-effective market model to suit their requirements. Financial analysis of the proposed models provides input to the customers, developers, and policymakers to assess the financial merit of adopting the suitable business model for RTS development. The proposed analysis can be replicated for high-rise residential buildings, especially in cities with high electricity tariffs. With time, a decrease in solar PV installation price and an increase in grid price are expected; hence, the overall investment cost gets reduced and staggered.
Piyush Kumar Yadav, Rajnish Bhasker, Albert Alexander Stonier, Geno Peter · 6 authors
Abstract Many progressed information scientific strategies, particularly Artificial Intelligence (AI) and profound learning methods, have been proposed and tracked down wide applications in our general public. This proposition creates information driven arrangements by utilizing the most recent profound learning and AI innovation, including outfit learning, meta‐learning and move learning, for energy the executives framework issues. Genuine world datasets are tried on proposed models contrasted and best in class plans, which exhibit the predominant presentation of the proposed model. In this proposition, the engineering of the Smart Grid testbed is additionally planned and created by using ML calculations and true remote correspondence frameworks to such an extent that constant plan necessities of Smart Grid testbed is met by this reconfigurable system with stacking of full convention in medium access control (MAC) and physical layers (PHY). The proposed engineering has the reconfiguration property in view of the organization of remote correspondence and trend setting innovations of Information and communication technologies (ICT) which incorporates Artificial Intelligence (AI) calculation. The fundamental plan objectives of the Smart Grid testbed is to make it simple to construct, reconfigure and scale to address the framework level prerequisites and to address the ongoing necessities.
Digitalization will play a vital role in the achievement of successful transitions towards more flexible, reliable and sustainable energy systems. The progressive availability of data from meters and sensors deployed across end-users and energy supply-chains constitute a first step towards this direction. Leveraging on such cross-sectorial trend, business developers find in decentralized ledger technologies (DLTs) – such as the blockchain – opportunities for value creation across a broad range of participants in energy systems. Peer-to-peer energy trading platforms interconnect legacy and novel participants leveraging renewable and storage capacity (small and utility-scale) with grid operators and within local microgrids or markets. In this way, near real time (bi-directional) exchanges can be secured and validated via tokenization and smart contracting. The proposed configurations enable governance arrangements between legacy and novel participants at the platform and sectorial level. This paper focuses on investigating the decentralized governance characteristics proposed by peer-to-peer energy trading platforms and briefly discusses their implications on legacy systems from a broad ‘relational’ (i.e. actor network, sociotechnical transitions) and ‘normative’ (i.e. energy justice) set of theories. The paper draws upon prior surveys and publicly available whitepapers from business initiatives (start-ups). For the purposes of this study, whitepapers constitute official written texts and potential sources of information for the study of emergent ways of governance and collective action leveraging this technology. Results from ground theory method (qualitative) leveraging an inductive content analysis approach, built four (4) main categories that explain: (1) the digital features of these platforms, (2) the participants and incentives involved (3) the purposes and benefits of the solutions and, (4) the governance dynamics arising from trading tokenized energy via smart contracting. Results reveal complex multi-level socio-technical interrelations at different layers and points towards opportunities for private and community-based solutions, end-user empowerment, democratic energy systems and polycentric governance while considering fractal planning in policy design.
Demand flexibility plays a pivotal role in modern power systems with high penetration of variable energy resources. In recent years, one of the fastest-growing flexible energy demands has been proof-of-work-based cryptocurrency mining facilities. Due to their competitive ramping capabilities and demonstrated flexibility, such fast-responding loads are capable of participating in frequency regulation services for the grid while simultaneously increasing their own operational revenue. In this paper, we investigate the physical and economic viability of employing cryptocurrency mining facilities to provide frequency regulation in large power systems. We quantify mining facilities' operational profit, and propose a decision-making framework to explore their optimal participation strategy and account for the most influential factors. We employ real-world ERCOT ancillary services data in our case study to investigate the conditions under which provision of frequency regulation in the Texas grid is profitable. We also perform transient level simulations using a synthetic Texas grid to demonstrate the competitiveness of mining facilities at frequency regulation provision.
Decentralized energy management can preserve the privacy of individual energy systems while mitigating computational and communication burdens. However, most decentralized energy management methods are partially decentralized and cannot ensure information exchange security. Therefore, this paper provides a secure fully decentralized energy management by using blockchain. First, a fully decentralized energy management framework using the optimality condition decomposition (OCD) is provided, in which individual energy system operators only exchange the boundary information with their peers rather than submitting proprietary information to a centralized system operator. Then, an asynchronous mechanism is proposed for updating the information exchange in OCD, enabling the proposed decentralized management to work under potential communication latency or interruption. Furthermore, the blockchain-based framework with state machine replication (SMR) based consensus algorithm is provided to safeguard the information exchange among individual energy systems in a secure and tamper-proof manner. The proposed decentralized energy management is tested on a multi-energy system with seven subsystems and a real-world multi-energy system in North China. The numerical results demonstrate the effectiveness of the proposed method in privacy protection and data security enhancement. The proposed method can prevent the cost increase caused by cheating activities, which in some subsystems can reach 17.6%. Additionally, the proposed fully decentralized method outperforms the partially decentralized method by 37.7% in reducing computation time. Also demonstrated are the computational precision, scalability and adaptability of the proposed method.1
Seyed Amir Alavi, Mehrnaz Javadipour, Ardavan Rahimian, Kamyar Mehran
Abstract The privacy of electricity consumers has become one of the most critical subjects in designing smart meters and their proliferation. In this work, a multilayer architecture has been proposed for anonymous data collection from smart meters, which provides: (1) The anonymity of information for third‐party data consumers; (2) Secure communication to utility provider network for billing purposes; (3) Online control of data sharing for end‐users; (4) Low communication costs based on available Internet of things (IoT) communication protocols. The core elements of this architecture are, first, the digital twin equivalent of the cyber‐physical system and, second, the Tangle distributed ledger network with IOTA cryptocurrency. In this architecture, digital twin models are updated in real‐time by information received from trusted nodes of the Tangle distributed network anonymously. A small‐scale laboratory prototype based on this architecture has been developed using the dSPACE SCALEXIO real‐time simulator and open‐source software tools to prove the feasibility of the proposed solution. The numerical results confirm that after a few seconds of anomaly detection, the microgrid was fully stabilized around its operating point with less than 5% deviation during the transition time.
Younes Zahraoui, Tarmo Korõtko, Argo Rosin, Hannes Agabus
Electricity generation using distributed renewable energy systems is becoming increasingly common due to the significant increase in energy demand and the high operation of conventional power systems with fossil fuels. The introduction of distributed renewable energy systems in the electric grid is crucial for delivering future zero-emissions energy systems and is cost-effective for promoting and facilitating large-scale generation for prosumers. However, these deployments are forcing changes in traditional energy markets, with growing attention given to transactive energy networks that enable energy trading between prosumers and consumers for more significant benefits in the cluster mode. This change raises operational and market challenges. In recent years, extensive research has been conducted on developing different local energy market models that enable energy trading and provide the opportunity to minimize the operational costs of the distributed energy resources by promoting localized market management. Local energy markets provide a stepping stone toward fully transactive energy systems that bring adequate flexibility by reducing users’ demand and reflecting the energy price in the grid. Designing a stable regulatory framework for local electricity markets is one of the major concerns in the electricity market regulation policies for the efficient and reliable delivery of electric power, maximizing social welfare, and decreasing electric infrastructure expenditure. This depends on the changing needs of the power system, objectives, and constraints. Generally, the optimal design of the local market requires both short-term efficiencies in the optimal operation of the distributed energy resources and long-term efficiency investment for high quality. In this paper, a comprehensive literature review of the main layers of microgrids is introduced, highlighting the role of the market layer. Critical aspects of the energy market are systematically presented and discussed, including market design, market mechanism, market player, and pricing mechanism. We also intend to investigate the role and application of distributed ledger technologies in energy trading. In the end, we illuminate the mathematical foundation of objective functions, optimization approaches, and constraints in the energy market, along with a brief overview of the solver tools to formulate and solve the optimization problem.
Samuel Karumba, Subbu Sethuvenkatraman, Volkan Dedeoglu, Raja Jurdak · 5 authors
The increasing adoption of clean energy technologies, including solar and wind generation, demand response, energy efficiency, and energy storage (e.g. batteries and electric vehicles) have led to the evolution of the traditional electricity markets from centralised energy trading systems into Distributed Energy Trading (DET) systems. Consequently, savvy business executives are exploring how blockchain might impact their competitive advantage in the emerging DET markets. Due to its salient features of distributed ledger, consensus mechanisms, cryptography, and smart contracts, blockchain technology is being used to provide decentralised trust, immutability, security and privacy, and transparency in DET system. However, integrating blockchain in DET systems is facing technical, administrative, standardisation and economic barriers. Consequently, we seek to conduct a comprehensive market analysis to identify the specific challenges hindering the integration of blockchain in DET systems. Nonetheless, we noticed that there isn't any evaluation and review framework for conducting a systematic literature review on blockchain-based DET systems. Therefore, in this work we first proposed a conceptual evaluation and review framework for conducting a systematic literature review on blockchain-based DET systems. Then, using the proposed framework, we reviewed the current studies on blockchain-based DET systems to the identify specific challenges hindering the adoption of blockchain and their proposed solutions. Our review found that, although there has been tremendous progress in addressing the technical barriers, the administrative, standardisation and economic barriers have grossly been under reviewed.
In the current era, the skyrocketing demand for energy necessitates a powerful mechanism to mitigate the supply–demand gap in intelligent energy infrastructure, i.e., the smart grid. To handle this issue, an intelligent and secure energy management system (EMS) could benefit end-consumers participating in the Demand–Response (DR) program. Therefore, in this paper, we proposed a real-time and secure incentive-based EMS for smart grid, i.e., RI-EMS approach using Reinforcement Learning (RL) and blockchain technology. In the RI-EMS approach, we proposed a novel reward mechanism for better convergence of the RL-based model using a Q-learning approach based on the greedy policy that guides the RL-agent for faster convergence. Then, the proposed RI-EMS approach designed a real-time incentive mechanism to minimize energy consumption in peak hours and reduce end-consumers’ energy bills to provide incentives to the end-consumers. Experimental results show that the proposed RI-EMS approach induces end-consumer participation and increases customer profitabilities compared to existing approaches considering the different performance evaluation metrics such as energy consumption for end-consumers, energy consumption reduction, and total cost comparison to end-consumers. Furthermore, blockchain-based results are simulated and analyzed with the help of deployed smart contracts in a Remix Integrated Development Environment (IDE) with the parameters such as transaction efficiency and data storage cost.
While blockchain and distributed ledger technology offer immense potential for applications in transparency, security, efficiency, censorship resistance, and more, they have been criticized due to the energy-intensive nature of the proof of work consensus algorithm, particularly in the context of Bitcoin mining. We systematically explore the state-of-the-art regarding the relationship between Bitcoin mining and grid decarbonization. We specifically focus on the role of flexible load response through proof of work mining as a potential contributor to renewable energy penetration and net decarbonization of the energy grid. The existing literature has not comprehensively examined this area, leading to conflicting views. We address the gap, analyzing the capabilities and limitations of Bitcoin mining in providing flexible load response services. Our findings show that renewable-based mining could potentially drive a net-decarbonizing effect on energy grids, although key adaptations in mining practices are needed to fully realize this potential. Overall, the paper suggests a re-evaluation of the environmental impact of Bitcoin mining, highlighting its potential role as a facilitator for renewable energy expansion, and decarbonization more broadly.
Ayushi Sharma, Pratham Sharma, Harsh Bamotra, Vibha Gaur
A nation cannot sustain a highly productive and efficient population without smart cities. Due to their significant reliance on digital technologies, these cities require a high level of cybercrime protection. Cryptocurrencies have gained significant attention due to their secure and reliable infrastructure. The decentralised cryptocurrency operates in a trust-less environment known as the blockchain, where each network participant has a ledger copy of all transactions. Blockchain technology employs a proven consensus mechanism without requiring establishment of a central authority. But the consensus mechanism requires miner to solve a cryptographic problem by generating random hashes until one of them matches the desired one. This procedure is energy-intensive, and when thousands of miners repeat it to verify a single transaction, a substantial amount of electricity is consumed. Moreover, electricity produces a significant amount of carbon footprint. Patch methodology utilises the data of all hashes created per year and the efficiency of mining hardware over a 10-year period to calculate the Bitcoins energy consumption. Due to a large number of unknown and uncertain factors involved, it is difficult to precisely calculate a single value for electricity consumption and carbon footprint as reported by Patch methodology. The proposed method extends the Patch methodology by adding a maximum and minimum limit to the hardware efficiency as well as the sources of power generation, which can help refine estimates of electricity consumption and carbon emissions for a more accurate picture. Using the proposed methodology, it was estimated that Bitcoin consumed between 38.495 and 120.72 terawatt hours of electricity in 2021 and released between 2.12 and 45.37 million metric tonnes of carbon dioxide. To address the issue of excessive energy consumption and carbon emissions, a significant number of individual miners and mining pools are relocating to energy-intensive regions, such as aluminium mining sites that rely on hydroelectricity for energy generation.
In order to promote the value circulation of energy resources and improve energy efficiency, credible energy sharing between Internet of Things Devices (IoTDs) came into being. However, sometimes IoTDs do not obtain the required energy in the required time period, resulting in less active participation in energy sharing. To address these challenges, this paper first proposes a credible energy transaction model based on the distributed ledger blockchain at the Edge of the Internet of Things, where the Edge Cloud Server (ECS) can collect a large number of surplus energy resources of IoTDs in a secure and credible energy sharing environment and share them with other IoTDs in urgent need of charging. Meanwhile, in order to attract IoTDs to participate in energy sharing for a long time and meet the energy demand of ECS to the maximum extent, a smart contract-based Expected Social Welfare Maximized double auction incentive mechanism of Single ECS to Multi-IoTDs (ESWM-StM) is proposed to enable dynamic and adaptive energy sharing from multiple IoTDs to a single ECS. In addition, this paper compares the proposed algorithm with the benchmark method in terms of energy-sharing cost and long-term utility. The simulation results show that the proposed incentive mechanism can enable IoTDs to provide more surplus energy per unit cost to meet the energy demand of ECSs, and can sustainably attract more energy trading participants to enhance the expected social welfare in the long term.
Kevin P. Hallinan, Haoliang Lu, Rydge B. Mulford, Lauren Bower · 7 authors
Despite the climate commitments made by countries in the Paris Climate Agreement adopted in 2015 and reinforced during COP 21 and with notably less success during COP 22, world carbon emissions increased in both 2021 and 2022. It is increasingly unlikely that the world will achieve the targeted 50% carbon reduction by 2030, the reduction approximately needed for reducing global temperature rise since the beginning of the Industrial Revolution to less than 1.5 deg. C. At the same time, there remain nearly 2 billion people in the world who have no or highly unreliable access to power. In developed countries, access to both clean energy and energy efficiency investment in residences within low to moderate income communities has also lagged. This paper provides a review of the “Productive Use of Energy (PUE)”, which is a means to add value to solar energy mini- and micro-grids to ensure investment worthiness and add more value to the communities being served. In this context, it posits an opportunity to leverage Bitcoin mining as a common PUE strategy applicable to new solar installations. Several actual pilot cases are described to demonstrate this potential throughout the world and at multiple scales. These include: (i) existing micro-grids with significant stranded energy to generate income that could be used to reduce the cost per kWh for the community; (ii) new solar micro-grids optimized to meet community load and mining operations; (iii) dedicated solar-powered Bitcoin mining mini-grids developed solely to create a funding stream for self-investment by communities for their benefit; and (iv) a low-income residential solar-powered Bitcoin miner to reduce the energy cost burden for residents. Several of these scenarios show significant potential to aid investment worthiness.
Tao Zhang, Jianhua Yang, Kaiyuan Jin, Tianjun Jing
Abstract With the increasing penetration of distributed photovoltaic generation (DPVG) in the rural distribution network, some problems such as abandoning solar energy and increasing voltage fluctuation have become more and more prominent. In order to promote local or nearby power consumption of the photovoltaic (PV) generation in the distribution network, the profit strategy is given with an optimal electricity price based on blockchain technology (BCT) by considering the topology, the load fluctuation of the distribution network and the reputation value of agricultural park consumers. The electricity price in the blockchain (BC) transaction is used as an intermediate variable to establish the revenue model for the PV generation, the agricultural park consumers and the distribution network. The power trading mechanism of the PV generation is constructed in the decentralized management agency of the BC. The maximum overall comprehensive income of the PV generation and the park consumers is used as the objective function, and the Stackelberg game theory is applied to prove the existence of the optimal game strategy in the transaction. The local power consumption method of the PV generation is simulated with the optimal electricity price in the IEEE 33‐node distribution network. The problem of abandoning solar energy is effectively relieved within an appropriate voltage limitation, and the comprehensive benefit of the PV generation and the park consumers is raised in the distribution network.
Bozhi Wang, Jinfei Xu, Ke Jin, C. L. Philip Chen · 9 authors
With the spread of distributed renewable energy, residents are shifting from being mere consumers to being energy producers and consumers. This role shift poses challenges to the electricity trading mechanism that connects distributed renewable energy sources to the grid. In this paper, a new efficient and secure blockchain-based distributed community energy trading mechanism is proposed, called CE-SDT. Our system is proved to be stable and scalable. It can also help shift loads and power peaks and reduce customer costs by 60%. As a result, our proposed blockchain-based trading mechanism, as compared to the centralized trading mechanism, is applied to microgrids formed by distributed renewable energy sources, not only obtaining greater economic benefits but also reducing the carbon footprint of residents, and furthermore, it promotes low or zero-carbon configurations of the power system, thereby achieving certain environmental benefits.
Energy is a major driver of human activity. Demand response is of the utmost importance to maintain the efficient and reliable operation of smart grid systems. The short-term load forecasting (STLF) method is particularly significant for electric fields in the trade of energy. This model has several applications to everyday operations of electric utilities, namely load switching, energy-generation planning, contract evaluation, energy purchasing, and infrastructure maintenance. A considerable number of STLF algorithms have introduced a tradeoff between convergence rate and forecast accuracy. This study presents a new wild horse optimization method with a deep learning-based STLF scheme (WHODL-STLFS) for SGs. The presented WHODL-STLFS technique was initially used for the design of a WHO algorithm for the optimal selection of features from the electricity data. In addition, attention-based long short-term memory (ALSTM) was exploited for learning the energy consumption behaviors to forecast the load. Finally, an artificial algae optimization (AAO) algorithm was applied as the hyperparameter optimizer of the ALSTM model. The experimental validation process was carried out on an FE grid and a Dayton grid and the obtained results indicated that the WHODL-STLFS technique achieved accurate load-prediction performance in SGs.
Abstract This research presents a decentralised incentive‐based demand response (DR) program using blockchain technology. Consumers self‐report baseline to the system operator (SO), the smart contract confirms the validity of the data to execute transactions and finally the validators record the information on the blockchain network. During the DR event, a set of consumers are randomly selected to deliver the required load reduction. The signalled consumer who delivers the load reduction is rewarded, and the non‐called consumers who diverge from their reported baseline are penalised. The randomness of choosing the consumers and penalty function restrict the baseline inflation. Here, we create a blockchain network and deploy a smart contract on the Ethereum build platform. A DR event scenario is adopted with residential houses data sets, all consumers report their baseline information to the SO through the Internet and smart meter. The SO calls four users to deliver the essential load reduction according to the probability of choosing a consumer. The smart contract verifies the received information to start transaction execution. We use proof of authority mechanism to select validation nodes from the participants using voting system. They validate each block before adding it to the blockchain. Last, the monetary transactions settle in participants' wallets. Our results confirm that decentralised systems like blockchain can significantly improve transparency, openness, and customer participation in the DR program. Also contributes to the security and privacy of user information with a minimal investment in new infrastructure.
Nihar Ranjan Pradhan, Akhilendra Pratap Singh, S. Sudha, K. Hemanth Kumar Reddy · 5 authors
With the electric power grid experiencing a rapid shift to the smart grid paradigm over a deregulated energy market, Internet of Things (IoT)-based solutions are gaining prominence, and innovative peer-to-peer (P2P) energy trading at a micro level is being deployed. Such advancement, however, leaves traditional security models vulnerable and paves the path for blockchain, a distributed ledger technology (DLT), with its decentralized, open, and transparency characteristics as a viable alternative. However, due to deregulation in energy trading markets, most of the prototype resilience regarding cybersecurity attack, performance and scalability of transaction broadcasting, and its direct impact on overall performances and attacks are required to be supported, which becomes a performance bottleneck with existing blockchain solutions such as Hyperledger, Ethereum, and so on. In this paper, we design a novel permissioned Corda framework for P2P energy trading peers that not only mitigates a new class of cyberattacks, i.e., delay trading (or discard), but also disseminates the transactions in a optimized propagation time, resulting in a fair transaction distribution. Sharing transactions in a permissioned R3 Corda blockchain framework is handled by the Advanced Message Queuing Protocol (AMQP) and transport layer security (TLS). The unique contribution of this paper lies in the use of an optimized CPU and JVM heap memory scenario analysis with P2P metric in addition to a far more realistic multihosted testbed for the performance analysis. The average latencies measured are 22 ms and 51 ms for sending and receiving messages. We compare the throughput by varying different types of flow such as energy request, request + pay, transfer, multiple notary, sender, receiver, and single notary. In the proposed framework, request is an energy asset that is based on payment state and contract in the P2P energy trading module, so in request flow, only one node with no notary appears on the vault of the node.Energy request + pay flow interaction deals with two nodes, such as producer and consumer, to deal with request and transfer of asset ownership with the help of a notary. Request + repeated pay flow request, on node A and repeatedly transfers a fraction of energy asset state to another node, B, through a notary.
Mirza Jabbar Aziz Baig, M. Tariq Iqbal, Mohsin Jamil, Jahangir Khan
Renewable energy resources have been gaining ground in recent years and we are on the verge of a decentralized energy market with consumers becoming prosumers. Platforms that facilitate peer-to-peer (P2P) sale or purchase of energy are therefore essential. This paper presents a way to trade energy across P2P networks using blockchain technology. The main server is a Raspberry Pi 4 Model B (Pi4B), on which the user interface (UI) as well as the private Ethereum blockchain are configured. The blockchain also implements a smart contract. For the purpose of developing the UI that provides assistance in conducting trading activities, an open-source Angular framework is used. Also explored in the study is the development of an Internet of Things (IoT) server using the latest ESP32-S3 microcontroller. The field instrumentation devices (FIDs) are connected to the microcontroller for the purpose of data acquisition and for subsequent transmission to an IoT server. The blockchain network maintains a record of all transactions in an immutable manner. Assuring security is achieved through a local configuration of the system, hosted on a private network with restricted access. For the purposes of information security and data integrity, additional security measures are also considered, such as a secret recovery phrase, firewalls, login credentials and private key. Among the servers and clients, there is an implementation of a Hypertext Transfer Protocol. The P2P energy trading approach involving renewable energy designed for remote communities is explained and illustrated in this paper.
Mingxing Guo, Ke Zhang, Su Wang, Jinlei Xia · 7 authors
Traditional centralized transactions require a control center for user demand matching, settlement and other processes. However, with the increase in the penetration rate of distributed energy in the community, the explosive increase in the number of transactions leads to a decrease in efficiency and it is difficult to guarantee user privacy and information security. The smart contract technology based on blockchain technology has the characteristics of decentralization, traceability and tamper resistance, and these key factors show unique advantages in distributed energy transactions. This paper explores Ethereum and smart contract technology, designs a peer-to-peer energy sharing mechanism with reward and punishment incentives and establishes a smart contract trading platform for smart community-based virtual power plant (CVPP). This paper verifies the functionality and effectiveness of smart contract. The results show that when the supply and demand ratio changes, the user can conduct energy transactions according to the contract without a third-party organization, which solves the problem of trust between the two parties and achieves the expected effect and runs successfully. In addition, the simulation results show that the peer-to-peer transaction based on smart contracts reduces the energy cost per household and increases the total benefit of CVPP.
Developments such as the increasing electrical energy demand, growth of renewable energy sources, cyber–physical security threats, increased penetration of electric vehicles (EVs), and unpredictable behavior of prosumers and EV users pose a range of challenges to the electric power system. To address these challenges, a decentralized system using blockchain technology and machine learning techniques for secure communication, distributed energy management and decentralized energy trading between prosumers is required. Blockchain enables secure distributed trust platforms, addresses optimization and reliability challenges, and allows P2P distributed energy exchange as well as flexibility services between customers. On the other hand, machine learning techniques enable intelligent smart grid operations by using prediction models and big data analysis. Motivated from these facts, in this review, we examine the potential of combining blockchain technology and machine learning techniques in the development of smart grid and investigate the benefits achieved by using both techniques for the future smart grid scenario. Further, we discuss research challenges and future research directions of applying blockchain and machine learning techniques for smart grids both individually as well as combining them together. The identified areas that require significant research are demand management in power grids, improving the security of grids with better consensus mechanisms, electric vehicle charging systems, scheduling of the entire grid system, designing secure microgrids, and the interconnection of different blockchain networks.