The convergence of financial technology and sustainability has given rise to green fintech, an innovative field leveraging cutting-edge technologies to address environmental challenges through financial solutions. This review explores the evolution of green fintech, focusing on the transformative roles of Artificial Intelligence (AI), Internet of Things (IoT), and smart contracts in developing sustainable financial services. Through a comprehensive analysis of recent literature and case studies, we examine how AI enhances ESG assessments, enables data-driven sustainable investment strategies, and facilitates green lending practices. We investigate IoT applications in environmental monitoring, supply chain transparency, and smart grid integration, highlighting their contributions to sustainable finance. The implementation of smart contracts for sustainability is explored, discussing their potential in green bonds, carbon credit trading, and renewable energy markets. The paper addresses key challenges facing green fintech, including data quality issues, privacy concerns, and regulatory uncertainties, proposing future directions for research and development. Our findings suggest that the integration of AI, IoT, and smart contracts in green fintech has significant potential to accelerate the transition to a sustainable global economy by embedding environmental considerations into financial decision-making at all levels. This article contributes to the growing body of literature on sustainable finance, providing insights for practitioners, policymakers, and researchers. It underscores the need for a multidisciplinary approach to overcome technological, regulatory, and socio-economic barriers, paving the way for a more sustainable and technologically advanced financial ecosystem.
This study proposes a novel framework for smart homes to optimize energy consumption and production, leading to reduced costs and a more reliable grid. The framework schedules the use of controllable appliances and renewable energy sources while considering uncertainties in production, real-time market prices, and uncontrollable household loads. By incorporating both incremental and real-time pricing models, the system discourages excessive consumption during peak hours. The core innovation lies in a two-stage scheduling approach implemented using GAMS software. This method minimizes the expected total cost while accounting for limitations on controllable loads, power supply, production resources, battery performance, and overall home energy balance. Additionally, the framework leverages the previous day’s bilateral contract and allows residents to adjust desired lighting levels based on current market fluctuations. Simulations demonstrate the program’s effectiveness in reducing both net energy costs and peak load on the electricity grid.
The rapid growth of electric vehicles (EVs) and the deployment of vehicle-to-grid (V2G) technology pose significant challenges for distributed power grids, particularly in fostering trust and ensuring effective coordination among stakeholders. Establishing a trustworthy V2G operation environment is crucial for enabling large-scale EV user participation and realizing V2G potential in real-world applications. In this paper, an integrated scheduling and trading framework is developed to conduct transparent and efficacious coordination in V2G operations. In blockchain implementation, a cyber-physical blockchain architecture is proposed to enhance transaction efficiency and scalability by leveraging smart charging points (SCPs) for rapid transaction validation through a fast-path practical byzantine fault tolerance (fast-path PBFT) consensus mechanism. From the energy dispatching perspective, a game-theoretical pricing strategy is employed and smart contracts are utilized for autonomous decision-making between EVs and operators, aiming to optimize the trading process and maximize economic benefits. Numerical evaluation of blockchain consensus shows the effect of the fast-path PBFT consensus in improving systems scalability with a balanced trade-off in robustness. A case study, utilizing real-world data from the Southern University of Science and Technology (SUSTech), demonstrates significant reductions in EV charging costs and the framework potential to support auxiliary grid services.
Around the world policymakers and regulators are struggling with the question of how to design retail electricity tariffs in the face of increasing penetration of local generation (e.g., solar PV), smart appliances, local storage, and electric vehicles. There is a widespread recognition that retail tariffs should vary dynamically across time and space, reflecting the changing conditions (congestion and losses) on the underlying networks. But, at the same time, there is recognition that such tariffs potentially expose retail customers to substantial risk. Risk averse retail customers desire protection against price spikes and volatile wholesale spot prices. This paper seeks to derive the optimal retail contract in the special case in which the uncertainty in the market is contractible (in the sense defined here). We show that the optimal retail contract exposes the prosumer to the wholesale spot price at the margin, but also perfectly insulates the customer from risk, achieving the first-best outcome. We show how the hedge component of this retail contract can be constructed from standard-form hedge contracts. We draw out several lessons for policymakers.
Meeting the targets of Sustainable Development Goal (SDG) 7, which focuses on ensuring access to affordable, reliable, sustainable, and modern energy for all, poses significant challenges. Overcoming these hurdles requires innovative solutions that can bridge the gap between current capabilities and future needs. Swarm electrification emerges as a promising concept that could accelerate progress towards achieving SDG 7 goals by leveraging the collective power of decentralized energy resources. This paper presents a literature review on swarm electrification and related insights from case studies. The study delves into the concept of swarm electrification, placing it within the context of the prevailing trends in the power system sector: decentralization, decarbonization, and digitalization. It examines the role of digital technologies in enhancing swarm electrification and categorizes application areas according to the phases of swarm electrification. Particular attention is given to the technologies underpinning Deep Digitalization, such as distributed ledger technology, notably blockchain, and artificial intelligence, with a focus on machine learning. These technologies play pivotal roles in advancing swarm electrification. The review demonstrates how deep digitalization can facilitate the improvement of swarm electrification and ultimately support the integration of bottom-up initiatives with top-down grid expansion efforts over time.
Dynamic energy contracts, offering hourly varying day-ahead prices for electricity, create opportunities for a residential Battery Energy Storage System (BESS) to not just optimize the self-consumption of solar energy but also capitalize on price differences. This work examines the financial potential and impact on the self-consumption of a residential BESS that is controlled based on these dynamic energy prices for PV-equipped households in the Netherlands, where this novel type of contract is available. Currently, due to the Dutch Net Metering arrangement (NM) for PV panels, there is no financial incentive to increase self-consumption, but policy shifts are debated, affecting the potential profitability of a BESS. In the current situation, the recently proposed NM phase-out and the general case without NM are studied using linear programming to derive optimal control strategies for these scenarios. These are used to assess BESS profitability in the latter cases combined with 15 min smart meter data of 225 Dutch households to study variations in profitability between households. It follows that these variations are linked to annual electricity demand and feed-in pre-BESS-installation. A residential BESS that is controlled based on day-ahead prices is currently not generally profitable under any of these circumstances: Under NM, the maximum possible annual yield for a 5 kWh/3.68 kW BESS with day-ahead prices as in 2023 is EUR 190, while in the absence of NM, the annual yield per household ranges from EUR 93 to EUR 300. The proposed NM phase-out limits the BESS’s profitability compared to the removal of NM.
The role of prosumers who are consumers who produce, store, and consume energy is vital to the uptake of renewable energies in Local Energy Communities (LEC). However, the integration of prosumers in the smart grid to facilitate bidirectional flows of energy and information depends on intelligent operations of energy systems and flexible structures of the existing energy markets. But existing energy trading mechanisms are faced with issues of trust, privacy, security, and energy pricing determination. Also, there are fewer studies based on a citizen-centric prosumer approach. Thus, there is need to provide reliable solutions that addresses the aforementioned challenges faced by prosumers in LEC. Advancements in disruptive technologies, such as Distributed Ledger Technologies (DLT), Artificial Intelligence (AI), and the Internet of Things (IoT) have transformed a broad spectrum of intelligent systems in smart cities. Therefore, this study examines the integration of AI and IoT as AIoT and DLT towards a citizen-centric prosumer approach for decentralized energy markets trading. Additionally, this article develops an architectural model for energy prosumption in LEC using design science approach based on a user-centred design method that shows a possible implementation concept to support energy sharing and trading in LEC. The architectural model supports trust, data privacy, security, and energy pricing determination using AI and smart contracts to provides real-time energy trading monitoring, easy access, control, and immutable logs to unearth underlying energy demand and supply patterns thereby supporting citizen-centric prosumer approach. Finally, a use case scenario of DLT and AIoT for prosumption operations is presented. • Integrates AIoT and DLT towards a citizen-centric prosumer approach for decentralized energy markets trading. • Develops an architectural model for energy prosumption using design science approach based on a user-centred design method. • Supports trust, data privacy, security, and energy pricing determination using AIoT and smart contracts. • Employs AIoT for real-time energy demand and supply patterns for energy trading monitoring, control, and decision support. • Models a possible implementation concept to support energy sharing and trading in local energy communities.
Muhammad Rizwan, Mudassir Ali, Ammar Hawbani, Xingfu Wang · 8 authors
Vehicle-to-grid (V2G) energy trading based on distributed ledger technologies (DLT), such as blockchains, has attracted much attention due to its promising features, including ease of deployment, decentralization, transparency, and security. However, existing DLT-based models do not support microtransactions due to the low value of such transactions relative to the incentives offered to transaction verifiers. To address this issue, we propose an IOTA DLT-based efficient and secure energy trading model for V2G networks, where electric vehicles (EVs) and grids negotiate energy prices in an off-chain manner. The proposed model utilizes a privacy-preserving protocol to prevent real-time tracking of EV locations. We develop a Stackelberg game model to represent the interactions between the EVs and grids, from which we derive a pricing scheme and propose a deposit mechanism to prevent fake energy trading between the EVs and grids. Extensive simulations demonstrate that our proposed scheme outperforms existing V2G energy trading mechanisms regarding transaction efficiency, provides enhanced EV privacy, and improves resilience against fake energy trading. Offering robust computational performance and addressing computational complexity (time, space, and message), our model presents a comprehensive V2G energy trading solution, balancing efficiency, security, and privacy.
The integration of smart contracts within water distribution networks presents a transformative approach to addressing challenges in water management. In this context, we propose a pioneering tool aimed at streamlining the design and implementation of smart contracts tailored specifically to smart water distribution networks. This tool allows stakeholders to input essential parameters such as water sources, distribution points, consumption patterns, and contractual stipulations. Through the utilization of predefined templates and adaptable contract logic, the tool automates critical processes including water allocation, usage monitoring, and penalty imposition based on predefined criteria. Furthermore, seamless integration with blockchain technology ensures the security and integrity of contract execution. By addressing scalability, compliance, and regulatory considerations, this tool represents a significant advancement in empowering stakeholders to optimize water management practices through the deployment of efficient and transparent smart contracts.
In this thesis, we categorize the challenges that Distribution System Operators (DSO) are facing into two separate sets of articles. After the introduction, the initial set of articles (chapters 2, 3, and 4) focuses on network operation, addressing challenges, and suggesting creative solutions to enhance the resilience and effectiveness of decentralized energy systems. The subsequent set of articles (chapters 5,6,7 and 8) redirects attention to the exploration of energy communities, unveiling the potential of localized, participatory energy ecosystems. Chapter 2 can be summarized as follows. In an electrical system where decentralized and embedded productions are becoming increasingly important, it is essential to ensure a good understanding of their behavior at their operating limits. One of the most important operating limits is when the system frequency approaches 50.2 Hz. At this frequency, following the old requirements, many existing European PV inverters have to be disconnected. In such situations, we demonstrate that the variance of the frequency measurement taken at every PV inverter plays a key role. It has been demonstrated that this variance is a good thing from the system's point of view as it allows for a gradual disconnection, leading to a controlled variation of the frequency. To address the challenges due to decentralized energy generation and emerging loads like electric vehicles, DSOs implement Active Network Management (ANM) as a short-term strategy to manage efficiently power injection and consumption, avoiding congestion without the need for heavy infrastructure investment. ANM requires knowledge of the system state, necessitating the placement of measurement devices throughout the network to ensure accurate estimates. In that context, chapter 3 introduces a new method for placing measurement devices in distribution networks. In contrast to the previous research works which rely on objectives for the placement such as state estimation accuracy, the proposed method incorporates ANM considerations in the process of determining the optimal locations, aiming to enhance ANM quality. Simulation results on a test distribution network demonstrate the superiority of this approach, leading to reduced curtailment of generators and improved overall performance. Grid monitoring strategies, like the one presented in Chapter 3 is the process of collecting data from sensors across a distribution grid and sending it to a central system (SCADA) to identify and diagnose problems, improve reliability, and save energy and money. The increasing complexity of power flows and the need to manage them using ANM strategies requires accurate data and strong defenses against cyber attacks. A proof-of-concept software called "MonitORES" was developed using Hyperledger Fabric to demonstrate how a distributed ledger technology (DLT) such as blockchain can be used to monitor and control generation units within ANM schemes, with improved resilience against cyberattacks. It is this work that is presented in the chapter 4. Chapter 5 opens the second set of articles aiming to explore renewable energy communities (REC). The main goal of the E-Cloud, one of the first projects of energy communities in Wallonia, as with every microgrid, is to maximize the consumption of energy produced locally. To reach this goal, based on consumption profiles of customers willing to participate in the E-cloud and given some local restrictions (e.g. wind turbines cannot be put everywhere), an optimal mix of green generation sources (in kW) and local storage (in kWh) needs to be computed. Then according to this computation, the required generating units and storage devices are installed. A repartition mechanism grants the customer a share of the generated electricity and storage capacity. These shares are either computed offline or dynamically adapted online. The project aimed to test two models: either the DSO or a producer owns and operates the storage device. Two information flows (real-time for the operation of the storage facility and ex-post for its settlement) are needed to ensure correct information exchange with the wholesale market. These information flows are completed thanks to a forecast that provides members of the E-Cloud the full capability to anticipate and obtain the maximum benefits of the local generation. The expected benefits for the customer are a reduction of their electricity bill by a minimum of 10\%. Societal benefits should also arise: 1) easing the technical integration of renewables generation embedded in the distribution network, and 2) avoiding extra investment in the DSO network. The next chapter proposes that the success of local REC, now foreseen by the European Union directives but also growing worldwide, will rely on the appetite of consumers and investors. This is not obvious when the target local area is a residential community where people have varying expectations. Based on Bayesian game theory (also called a game of incomplete information), the purpose of this paper is to define an approach for determining, from the point of view of the renewable energy investor, the level of production capacity and energy price that needs to be offered to the consumers. Chapter 7 explores how the blockchain approach can be employed to foster this REC market. The goal is to determine the design that should allow a DSO to accept peer-to-peer energy exchanges based on a distributed ledger supported by blockchain technology. To this end, an evaluation is conducted integrating several designs based on criteria such as acceptance of the wholesale/retail market, the resilience of the consensus to approve a block, the accuracy, traceability, privacy, and security of the proposed schemes. Chapter 8 poses that, despite its success and large use in other crypto-currencies, Proof of Work's disadvantages are high latency, a low transaction rate, and a high energy expenditure, making it a less-than-perfect choice for many applications. In addition, the validation of transactions is not carried out with a definite temporality. However, for certain use cases such as auctions or the exchange of energy in the REC context, there is a need for this temporality. The purpose of this article is to propose a new type of consensus that is faster, less energy-consuming and that can be synchronized with a time reference. The core of the reflection is the use of the Condorcet voting mechanism to determine the miner. The last chapter sets the main conclusion of this research. Two appendixes show other works conducted with fellow researchers during this PhD research journey.
Precious Kgomotso Maine, Collins Achepsah Leke, Omowunmi Mary Longe
The Gautrain railway link in the Gauteng province of South Africa is a crucial transportation facility that has faced considerable energy-related difficulties and hence a dependable power supply is essential to ensure the continuous operation of railway processes. The total number of passenger trips on the Gautrain, in a year, is two million plus. The railway system, which has always relied on grid electricity to power trains, stations, lights, security, and operations is facing increasing difficulties because of rising electricity demand and costs, and power-related outages. To address these issues, the study designed an optimised power generation system for the Gautrain railway link (GRL) using a photovoltaic (PV) system with energy storage connected to the grid to lower energy costs, promote economic growth, and satisfy its energy demand. The Advanced Interactive Multidimensional Modelling System (AIMMS) tool was used to optimise the incorporation of solar energy generation and battery energy storage into the GRL power system. This study contributes to sustainable energy development by leveraging renewable solar energy, optimising the GRL power system, and integrating blockchain technology for peer-to-peer (P2P) energy trading. A smart contract is compiled and deployed within the Remix Online IDE to apply a logic that manages energy generation, consumption, and P2P energy trading amongst prosumers. It furnishes a replicable model for the advancement of electrical energy systems within diverse transportation networks, therefore fostering the adoption of energy-efficient practices.
In the ever-changing global energy landscape, the emergence of ‘prosumers’, individuals who both produce and consume energy, has blurred traditional boundaries. Driven by the growing demand for sustainability and renewable energy, prosumers play a critical role in bridging the gap between energy production and consumption. They can generate their own energy through decentralized sources like solar panels and wind turbines, and sell excess energy back to the grid. However, tracking carbon emissions and pricing strategies for prosumers pose challenges. To address this, we developed an innovative blockchain-driven peer-to-peer (P2P) trading platform for carbon allowances. This platform empowers prosumers to influence pricing and promotes a more equitable distribution of energy. The P2P platform leverages blockchain technology, a decentralized digital ledger, to provide transparency and security in carbon emission tracking and energy transactions. By eliminating intermediaries, blockchain ensures the accuracy of data and creates a tamper-proof record of energy production and consumption. This study employed a modified IEEE 37-bus test system to evaluate the efficacy of the proposed blockchain-based trading framework. The IEEE 37-bus system is a well-established benchmark for power system analysis, comprising 37 nodes, 13 generators, and 37 transmission lines. By leveraging this test system, this study demonstrated the framework’s ability to optimize energy consumption patterns and mitigate carbon emissions, highlighting the transformative potential of blockchain technology in the energy sector. The proposed P2P trading platform offers several benefits for prosumers: (1) Transparency: The blockchain-based platform provides a transparent record of all energy transactions, ensuring that prosumers are compensated fairly for the energy they produce. (2) Security: Blockchain technology makes it impossible to tamper with or counterfeit carbon allowances, ensuring the integrity of the trading system. (3) Efficiency: The P2P trading platform eliminates the need for intermediaries, reducing the cost and complexity of energy transactions. (4) Empowerment: The platform gives prosumers a greater say in how their energy is priced and distributed, promoting a more equitable energy system.
Given the complexity of issuing, verifying, and trading green power certificates in China, along with the challenges posed by policy changes, ensuring that China's green certificate market trading system receives proper mechanisms and technical support is crucial. This study presents a green power certificate trading (GC-TS) architecture based on an equilibrium strategy, which enhances the quoting efficiency and multi-party collaboration capability of green certificate trading by introducing Q-learning, smart contracts, and effectively integrating a multi-agent trading Nash strategy. Firstly, we integrate green certificate trading with electricity and carbon asset trading, constructing pricing strategies for the green certificate, carbon, and electricity trading markets; secondly, we design a certificate-electricity-carbon efficiency model based on ensuring the consistency of green certificates, green electricity, and carbon markets; then, to achieve diversified green certificate trading, we establish a multi-agent reinforcement learning game equilibrium model. Additionally, we propose an integrated Nash Q-learning offer with a smart contract dynamic trading joint clearing mechanism. Experiments show that trading prices have increased by 20%, and the transaction success rate by 30 times, with an analysis of trading performance from groups of 3, 5, 7, and 9 trading agents exhibiting high consistency and redundancy. Compared with models integrating smart contracts, it possesses a higher convergence efficiency of trading quotes.
Recent years have witnessed a significant dispersion of renewable energy and the emergence of blockchain-enabled transactive energy systems. These systems facilitate direct energy trading among participants, cutting transmission losses, improving energy efficiency, and fostering renewable energy adoption. However, developing such a system is usually challenging and time-consuming due to the diversity of energy markets. The lack of a market-agnostic design hampers the widespread adoption of blockchain-based peer-to-peer energy trading globally. In this paper, we propose and develop a novel unified blockchain-based peer-to-peer energy trading framework, called BPET. This framework incorporates microservices and blockchain as the infrastructures and adopts a highly modular smart contract design so that developers can easily extend it by plugging in localized energy market rules and rapidly developing a customized blockchain-based peer-to-peer energy trading system. Additionally, we have developed the price formation mechanisms, e.g., the system marginal price calculation algorithm and the pool price calculation algorithm, to demonstrate the extensibility of the BPET framework. To validate the proposed solution, we have conducted a comprehensive case study using real trading data from the Alberta Electric System Operator. The experimental results confirm the system’s capability of processing energy trading transactions efficiently and effectively within the Alberta electricity wholesale market.
Peer-to-peer (P2P) energy trading has attracted a lot of attention and the number of electric vehicles (EVs) has increased in the past couple of years. Toward sustainable mobility, EVs meet the standard development goals (SDGs) for attaining a sustainable future in the transport sector. This development and increasing number of EVs creates an opportunity for prosumers to trade electricity. Considering this opportunity, this review article aims to provide an in-depth analysis of P2P energy trading of EVs using blockchain in centralized and decentralized networks, which enables prosumers to exchange energy directly with one another. The paper is aimed to provide the reader with a state-of-the-art review on the P2P energy trading for EVs, considering different blockchain algorithms that are practically implemented or still in the research phase. Moreover, the paper presents blockchain applications, current trends, and future challenges of EVs’ energy trading. P2P energy trading for EVs using blockchain algorithms can be successfully implemented considering real-time scenarios and economically benefits smart sustainable societies.
Carbon footprint reduction can be achieved through various methods, including the adoption of renewable energy sources. The installation of such sources, like photovoltaic panels, while environmentally beneficial, is cost-prohibitive for many. Those lacking photovoltaic solutions typically resort to purchasing energy from utility grids that often rely on fossil fuels. Moreover, when users produce their own energy, they may generate excess that goes unused, leading to inefficiencies. To address these challenges, this paper proposes innovative blockchain-enabled energy-sharing algorithms that allow consumers -- without financial means -- to access energy through the use of their own energy storage units. We explore two sharing models: a centralized method and a peer-to-peer (P2P) one. Our analysis reveals that the P2P model is more effective, enhancing the sharing process significantly compared to the centralized method. We also demonstrate that, when contrasted with traditional battery-supported trading algorithm, the P2P sharing algorithm substantially reduces wasted energy and energy purchases from the grid by 73.6%, and 12.3% respectively. The proposed system utilizes smart contracts to decentralize its structure, address the single point of failure concern, improve overall system transparency, and facilitate peer-to-peer payments.
Peer-to-peer (P2P) renewable energy trading, facilitated by designing P2P market smart contracts on blockchain servers, is a promising approach to increase investments in cleaner energy generation. To enhance trading efficiency and pricing fairness, as the major challenges of P2P market designs, this study introduces two new market mechanisms, Hybrid Auction Coalition (HAC), and innovative coalition business model (ICBM), respectively. The performance of these market mechanisms is contrasted with existing mechanisms from the literature with respect to electricity bills, market efficiency, fairness, and blockchain feasibility using a comprehensive list of indicators including costs and profits, fairness, market efficiency, and technical viability. Compared to traditional billing, ICBM and HAC increase sellers’ profit by 88% and 66% respectively, while both impose 13% more costs on buyers. ICBM and HAC also set the fairest prices compared to the existing markets. ICBM is shown to improve blockchain feasibility and market efficiency due to lighter on-chain calculations, and absolute clearing mechanisms. The results also demonstrate that HAC outperforms standalone auctions in every aspect which endorses the hybridization benefits. • Novel and efficient P2P Models, and blockchain-based mechanisms for P2P renewable energy trading. • Enhanced fairness concerns in P2P trading through a novel pricing mechanism. • Improved P2P trading efficiency & potentially increased local grid self-sufficiency. • Technical comparison of state-of-the-art blockchain-based P2P trading markets.
Renewable energy trading could be considered the next step in power trading's development. It is probable that individuals currently involved in power trading will need to upgrade their data collection, processing, and reporting systems. This article provides a comprehensive evaluation of renewable energy trading utilizing Blockchain technology. Initially, the paper examines country-specific renewable energy trading with a focus on India, China, the US, France, and Germany's renewable energy policies. Moreover, the paper presents potential renewable energy trading markets such as peer-to-peer, over the grid, and partially or fully independent microgrid's. This paper shows the appraisal of bond, commodity, derivative, and algorithm-based renewable energy trading using different Blockchain methods, including Ethereum and R3 Corda. It is find out during the renewable energy trading, proposers of bid, also include capital cost of the renewable energy power plant, salvage value after useful life of different component of renewable energy power plant. It is also find out proper trading is to be done with offering subsidies of up to 70% of the capital cost, and with a 30% viability gap finance (VGF) at this cost.
There are a series of challenges in microgrid transactions, and blockchain technology holds the promise of addressing these challenges. However, with the increasing number of users in microgrid transactions, existing blockchain systems may struggle to meet the growing demands for transactions. Therefore, this paper proposes an efficient and secure blockchain consensus algorithm designed to meet the demands of large-scale microgrid electricity transactions. The algorithm begins by utilizing a Spectral clustering algorithm to partition the blockchain network into different lower-level consensus set based on the transaction characteristics of nodes. Subsequently, a dual-layer consensus process is employed to enhance the efficiency of consensus. Additionally, we have designed a secure consensus set leader election strategy to promptly identify leaders with excellent performance. Finally, we have introduced an authentication method that combines zero-knowledge proofs and key sharing to further mitigate the risk of malicious nodes participating in the consensus. Theoretical analysis indicates that our proposed consensus algorithm, incorporating multiple layers of security measures, effectively withstands blockchain attacks such as denial of service. Simulation experiment results demonstrate that our algorithm outperforms similar blockchain algorithms significantly in terms of communication overhead, consensus latency, and throughput.
Moein Qaisari Hasan Abadi, Russell Sadeghi, Ava Hajian, Omid Shahvari · 5 authors
The escalation of energy prices and the pressing environmental concerns associated with excessive energy consumption have compelled consumers to adopt a more optimal approach towards energy usage and an advanced infrastructure such as smart grids. Blockchain technology significantly improves energy management by creating supply chain resiliency in a distributed smart grid. This study proposes a blockchain-based decision-making framework with a dynamic energy pricing model to manage energy distributions, particularly during an energy crisis. Empirical data from U.S. consumers are employed to show the applicability of the proposed model. We include price elasticity to address changes in energy market prices. Findings revealed that the proposed framework reduces total energy costs and performs better when a disruption has occurred. This study provides a post hoc analysis in which four machine learning algorithms are used to predict energy consumption. Results suggest that the Autoregressive Integrated Moving Average (ARIMA) algorithm has the highest accuracy compared to other algorithms.
Tianqi Jiang, Haoxiang Luo, Kun Yang, Gang Sun · 7 authors
The energy market encompasses the behavior of energy supply and trading within a platform system. By utilizing centralized or distributed trading, energy can be effectively managed and distributed across different regions, thereby achieving market equilibrium and satisfying both producers and consumers. However, recent years have presented unprecedented challenges and difficulties for the development of the energy market. These challenges include regional energy imbalances, volatile energy pricing, high computing costs, and issues related to transaction information disclosure. Researchers widely acknowledge that the security features of blockchain technology can enhance the efficiency of energy transactions and establish the fundamental stability and robustness of the energy market. This type of blockchain-enabled energy market is commonly referred to as an energy blockchain. Currently, there is a burgeoning amount of research in this field, encompassing algorithm design, framework construction, and practical application. It is crucial to organize and compare these research efforts to facilitate the further advancement of energy blockchain. This survey aims to comprehensively review the fundamental characteristics of blockchain and energy markets, highlighting the significant advantages of combining the two. Moreover, based on existing research outcomes, we will categorize and compare the current energy market research supported by blockchain in terms of algorithm design, market framework construction, and the policies and practical applications adopted by different countries. Finally, we will address current issues and propose potential future directions for improvement, to provide guidance for the practical implementation of blockchain in the energy market.
This paper investigates a double auction-based peer-to-peer (P2P) energy trading market for a community of renewable prosumers with private information on reservation price and quantity of energy to be traded. A novel competition padding auction (CPA) mechanism for P2P energy trading is proposed to address the budget deficit problem while holding the advantages of the widely-used Vickrey-Clarke-Groves mechanism. To illustrate the theoretical properties of the CPA mechanism, the sufficient conditions are identified for a truth-telling equilibrium with a budget surplus to exist, while further proving its asymptotical economic efficiency. In addition, the CPA mechanism is implemented through consortium blockchain smart contracts to create safer, faster, and larger P2P energy trading markets. The proposed mechanism is embedded into blockchain consensus protocols for high consensus efficiency, and the budget surplus of the CPA mechanism motivates the prosumers to manage the blockchain. Case studies are carried out to show the effectiveness of the proposed method.
Nikolas Schöne, Tim Ronan Britton, Edouard Delatte, Nicolas Saincy · 5 authors
Off-grid electrification planning increasingly recognizes the importance of productive use of electricity (PUE) to promote community value creation and (financial) project sustainability. To ensure a sustainable and efficient integration in the community and energy system, PUE assets must be carefully evaluated to match both the community needs and the residential electricity demand patterns. We propose a novel methodology interlinking qualitative interviews, statistical analysis and energy system modeling to optimize decision making for PUE integration in off-grid energy systems in rural Madagascar by aligning relevant PUE effectively with anticipated residential electricity demand patterns based on socio-economic determinants of the community. We find that a possible contribution of the PUE to reducing the electricity costs depends significantly on three factors: (1) The residential electricity consumption patterns, which are influenced by the socio-economic composition of the community; (2) The degree of flexibility of (i) PUE assets and (ii) operational preferences of the PUE user; and (3) The capacity of community members to finance and operate PUE assets. Our study demonstrates that significant cost reductions for PUE-integrated off-grid energy systems can be achieved by applying our proposed methodology. When matching PUE and residential consumption patterns, the integration of PUE assets in residential community energy systems can reduce the financial risk for operators, provided the PUE enterprise operates reliably and sustainably. We highlight that the consideration of local value chains and co-creation approaches are essential to ensure the energy system is addressing the community’s needs, creates value for the community, enhances the project’s financial sustainability and is achieving the overall objectives of decentralized energy system planning.