Blockchain Papers

Taner Çarkıt

No abstract is available for this record.

Komeil Moghaddasi, Raja Jurdak, Sara Khalifa, Yuchen Zhang · 7 authors

The rapid growth of distributed energy resources (DER) such as rooftop photovoltaics (PV), battery storage, electric vehicles (EV), and flexible loads, is shifting power system coordination from centralised control centres to millions of prosumers and local controllers at the distribution level. This transition has led to many new coordination approaches across control, market, and learning-based models, which are often described as decentralised. However, this term is applied inconsistently: it may refer to decomposed optimisation, edge computing, peer-to-peer (P2P) trading, or distributed ledger technology, obscuring what is actually being decentralised, authority, computation, information, or topology. Existing surveys typically address one such concept in isolation, for example, microgrid control structures, energy management system (EMS) topologies, or market designs, without providing a unified, multi-dimensional view across the full coordination landscape. In this survey, we propose a six-tier graduated decentralisation scale for distribution level coordination architectures, accompanied by a set of classification criteria that we apply to systematically map and compare recent architectures. We discuss how topology, decision-making, autonomy, intelligence, information flow, and coordination mechanisms evolve as architectures move from centralised to more decentralised operation. We further identify concrete research gaps, and outline future directions for deployment grade, multi-actor grid coordination.

Otilia Elena Dragomir, Florin Dragomir

Prosumer communities, aggregations of residential and commercial entities equipped with distributed energy resources (DER), including photovoltaic systems, battery storage, and flexible loads, are emerging as critical organizational units in decarbonising smart grid architectures. Managing these communities effectively requires balancing economic efficiency with equity, autonomy, and environmental sustainability, objectives that conventional centralized control methods and existing multi-agent reinforcement learning (MARL) implementations fail to address simultaneously. This article proposes a value-aligned hierarchical multi-agent reinforcement learning (VA-HMARL) framework as a formally unified architecture that embeds equity (Jain’s Fairness Index J ≥ 0.90), individual autonomy, and carbon sustainability as hard constraints within the MARL reward structure. The framework integrates: a multi-objective Value Alignment Module (VAM) combining economic, fairness, sustainability, and comfort objectives; attention-based implicit coordination for scalable agent interaction; and differentially private federated policy aggregation (ε = 1.0, δ = 10−5) for GDPR-compliant collaborative learning. Simulation on a 20-prosumer community modelled on the IEEE 33-bus feeder over 10 Monte Carlo runs (300 episodes each) demonstrates: a 6.2% energy cost reduction versus the Rule-Based baseline (p = 0.0004); a Jain’s Fairness Index of 0.912 ± 0.031 at policy convergence (final 50 episodes), satisfying the J ≥ 0.90 community equity floor; and an 18.0% reduction in CO2 emissions. The economic efficiency trade-off relative to performance-optimized MARL baselines is limited to 2.4%, within the 5% design target. These results establish VA-HMARL as a technically feasible and ethically grounded paradigm for autonomous decentralized energy governance.

Ghali Ahmad, Md Shafiullah, Mohammad A. Abido, Faris Aljehani

No abstract is available for this record.

Anjali Arora, Dr. Upendra Kumar Srivastava -

Distributed Energy Resources (DER) such as solar PV, wind micro-turbines, smart inverters, electric vehicles (EVs), and home energy storage systems are rapidly increasing in modern power systems. However, their decentralized nature introduces complexities in coordination, demand–supply balancing, and resilience. Existing blockchain-based DER frameworks primarily focus on peer-to-peer (P2P) trading, security, and certificate validation, but lack mechanisms for coordinated swarm-like behaviour among DER units. This paper introduces a novel concept—Blockchain-Based Swarm Coordination of DER Clusters, inspired by swarm intelligence principles such as self-organization, collaboration, local decision-making, and emergent global behaviour. The proposed system integrates blockchain, multi-agent coordination, and decentralized smart contracts to enable secure, autonomous, and scalable coordination of DER clusters. A layered architecture, cluster formation mechanism, consensus-driven decisioning, and energy-sharing algorithms are presented. The framework significantly enhances grid flexibility, improves energy distribution efficiency, reduces central-dependency, and enables real-time proactive response during grid fluctuations. Simulation-driven conceptual outcomes demonstrate improved DER responsiveness, fault tolerance, trust, and transparency. This work establishes a new research direction by merging blockchain with swarm intelligence for next-generation decentralized energy systems.

Nishkar R. Naraindath, Raj Naidoo, R. C. Bansal

This paper introduces a novel decentralized autonomous organization (DAO) framework for microgrid governance, specifically targeting diverse stakeholder ownership. It integrates principles of decentralization, democratization, and digitization to streamline the just energy transition. The multifaceted model synthesizes key DAO mechanisms with microgrid elements by incorporating tokenomics, fund management, actor reputation, decision-making, tender selections and dispute resolutions. Preliminary conceptual validation in Python case studies demonstrates the feasibility of the approach. However, further research and validation are needed to pave the way from centralized structures to a more empowered, equitable and resilient future.

Abdullah Umar, Prashant K. Jamwal, Deepak Kumar, Nitin Gupta · 6 authors

Renewable-driven microgrids require transparent and adaptive coordination mechanisms to manage variability in distributed generation and flexible demand. Conventional pricing schemes and centralized demand-side programs are often insufficient to regulate real-time imbalances, leading to inefficient renewable utilization and limited prosumer participation. This work proposes a blockchain-integrated Stackelberg pricing model that combines real-time price regulation, optimal demand-side management, and peer-to-peer energy exchange within a unified operational framework. The Microgrid Energy Management System (MEMS) acts as the Stackelberg leader, setting hourly prices and demand response incentives, while prosumers and consumers respond through optimal export and load-shifting decisions derived from quadratic cost models. A distributed supply–demand balancing algorithm iteratively updates prices to reach the Stackelberg equilibrium, ensuring system-level feasibility. To enable trust and tamper-proof execution, smart-contract architecture is deployed on the Polygon Proof-of-Stake network, supporting participant registration, day-ahead commitments, real-time measurement logging, demand-response validation, and automated settlement with negligible transaction fees. Experimental evaluation using real-world demand and PV profiles shows improved peak-load reduction, higher renewable utilization, and increased user participation. Results demonstrate that the proposed framework enhances operational reliability while enabling transparent and verifiable microgrid energy transactions.

Pavan Ramchandra Padghan, Vikash Rajak

No abstract is available for this record.

Ileana Maria Muntean, Radu Tîrnovan, Horia G. Beleiu

As renewable generation becomes increasingly deployed at the local level, the reliability of microgrids depends not only on physical infrastructure but also on the credibility of the measurement data driving energy control decisions. In conventional Energy Management Systems (EMS) architectures, monitoring is implicitly assumed to be correct, even though no mechanism exists to verify the authenticity or integrity of the received data. This gap can lead to suboptimal or misleading control actions, especially in distributed environments involving multiple stakeholders. This paper introduces a trust-by-design approach in which monitoring and energy management processes are natively supported by a lightweight Distributed Ledger Technology (DLT) layer embedded within the EMS. Rather than relying on external trust assumptions, the proposed mechanism ensures built-in traceability and tamper-evidence, enabling independent validation of the microgrid’s operational history. A simple renewable microgrid with battery storage is used as a demonstrative case study to show how a DLT-based ledger can safeguard measurement integrity and control decisions without adding technical complexity to the EMS itself. The results demonstrate that verifiable data flows and tamper detection significantly enhance the transparency and robustness of EMS architectures, while enabling future extensions towards predictive or AI-assisted control strategies.

Vidya Krishnan Mololoth, Christer Åhlund, Saguna Saguna

The integration of renewable energy sources (RES) into modern power grids has enabled decentralized energy generation at the community level, fostering peer-to-peer (P2P) energy trading among prosumers and microgrids. Accurate forecasting of household energy consumption and photovoltaic (PV) generation is critical for optimizing energy flows, enhancing grid reliability, and enabling cost-effective trading decisions. This paper presents an intelligent energy trading platform that integrates machine learning-based forecasting, battery-aware decision-making, and blockchain-enabled transactions to facilitate secure and efficient local energy exchange. Using historical smart meter and weather data from London households, multiple forecasting models including GRU, LSTM, Random Forest, and XGBoost were trained and evaluated. The GRU model achieved superior performance in predicting energy consumption, while Random Forest produced the most accurate PV generation forecasts. These predictions were combined with household battery levels to dynamically determine next-day operational roles: Buyer, Seller, Store, or Use Battery. Unlike conventional fixed-threshold approaches, the framework supports user-defined variable battery thresholds, allowing personalized energy management strategies. The proposed decision-making model achieved an accuracy of 90.72 % for one random block, and extended simulations across 29 different random household blocks confirmed its robustness with an average accuracy of 88.69 % (95 % CI: 87.9–89.6 %). In the trading phase, households participate in a decentralized energy trading platform powered by blockchain and smart contracts. Based on the next-day forecasts, a linear programming-based optimization algorithm matches buyer requests and seller offers to minimize the total system cost while ensuring fairness and efficient energy allocation. To assess its performance, the proposed optimization approach was compared against a greedy matching algorithm where sequential matching is done without a cost optimization and a grid baseline scenario where no storage/sharing of energy takes place. The optimized matching consistently achieved substantially lower trading costs across all households demonstrating superior efficiency, fairness, and scalability compared to the benchmark methods. All transactions are executed securely and transparently on the blockchain through Ethereum-based smart contracts, which automate energy trading, pricing, and settlement. A user-friendly web interface was developed to allow participants to monitor and interact seamlessly with the platform. Overall, this battery-aware, community-driven trading framework showcases how intelligent energy forecasting, cost-optimized decision-making, and blockchain-enabled trading can collectively enhance energy autonomy, cost savings, and renewable energy utilization at both the household and community levels.

Hongyan Sun, Tim Weingärtner

The integration of renewable energy sources (RES) and distributed energy resources (DER) into local energy markets is transforming modern power grids toward a decentralized architecture. To enhance the efficiency of decentralized energy trading, blockchain technology has been widely adopted in constructing peer-to-peer energy trading platforms, providing incentives for renewable energy generation and utilization. However, the rapid growth of small-scale suppliers and intermittent DERs introduces significant challenges to grid stability, including supply–demand imbalances and voltage fluctuations. To address these challenges, we propose a blockchain-based energy trading system architecture designed to enable a self-regulating, sustainable, and resilient grid. The proposed system architecture achieves grid stability through three key components: (i) precise endpoint control via AI Agents with lightweight forecasting models integrated into existing hardware systems, (ii) flexible distributed control through an efficient incentive mechanism, named Proof of Prediction, based on a blockchain-based automated trading process, and (iii) macro-level coordination via global regulation roles. We implemented a prototype of the proposed architecture on the Ethereum Blockchain and applied it to a microgrid-scale distributed automated trading environment. Our evaluation results show that using the architecture we proposed achieves a peak-shaving rate of up to 29.6%, while maintaining the overall supply–demand deviation of around 5% on average, demonstrating its strong potential as a foundation for building stable and modern power grids.

Sajarupan Tharumaraja, Madura Prabhani Pitigala Liyanage, Akila Wijethunge, Janaka Ekanayake

The integration of Distributed Energy Resources (DERs), such as rooftop photovoltaic (PV) systems and Battery Energy Storage Systems (BESS), enables peer-to-peer (P2P) energy trading in microgrids, enhancing grid flexibility and optimizing operational management. This study presents an automated, blockchain-enabled framework for very short-term (VST) P2P trading, tested using Sri Lanka's tariff data to harness the economic and operational potential of decentralized energy systems. The Intelligent Prosumer Energy Node (IPEN) facilitates autonomous energy trading through real-time monitoring, VST demand forecasting, recommendations from the OpenDSS Demand-Side Management (O-DSM), and userguided decisions. Similarly, the Intelligent Consumer Energy Node (ICEN) autonomously executes trading based on power demand monitoring, forecasting, and O-DSM guidance. The blockchain network, built on Hyperledger Fabric, secures and transparently manages transactions across five organizations, supported by multiple channels and smart contracts. Three trading models, Feed-in Tariff (FiT), P2P without storage, and P2P with BESS, were evaluated across prosumer-to-consumer ratios of$25:75,50:50$, and$75:25$. Results show that automated P2P trading outperforms FiT, with BESS providing the highest economic gains. Prosumers achieved up to 35.1% higher profits, while consumers reduced costs by up to 15.2%, demonstrating the system's potential for scalable microgrid deployment.

Robert Jahn, Alfio Spina, Julia Schmeing

The increasing integration of Distributed Energy Resources (DER) into modern power systems requires more flexible and decentralized approaches to improve operation and ensure system resiliency. In this scenario, the approach to provide ancillary services has a significant impact. Traditional centralized control schemes for ancillary services provision are increasingly challenged by issues of limited scalability, lack of transparency, and slower responsiveness. To address these challenges, this paper proposes a conceptual approach for the decentralized provision of ancillary services supported by Distributed Ledger Technology (DLT) and smart contracts. The proposed framework enables secure, transparent, and automated coordination among distributed assets without reliance on centralized intermediaries. This enables small-scale assets to access future ancillary service markets, supporting automated functions such as capability verification, streamlined access control, and instant verification and payment processes. The framework is fully implemented in a laboratory environment and validated using a Redox flow Battery Energy Storage System (BESS) and a DC charging station for Electric Vehicles (EVs). The experimental results highlight the feasibility and effectiveness of the DLT-based approach for Frequency Containment Reserve (FCR), laying the groundwork for deployments and extensions to additional ancillary services.

Farid Hamzeh Aghdam, Aleksandr Zavodovski, Mehdi Rasti, Éva Pongrácz

The energy domain worldwide is experiencing high transformative pressure due to the imperative of climate change and new opportunities brought by various rapidly evolving digital technologies. Particularly, this change is affecting smart grids (SGs), which are increasingly shifting toward utilizing renewable and distributed energy sources. The natural intermittency of these energy sources increases the complexity of SG operations, such as ensuring continuity of energy supply and demand response balancing. In mitigation of these challenges, the tools and complex approaches that digitalization can provide have shown themselves particularly advantageous. There is a solid body of work showing how technologies like the Internet of Things (IoT), distributed ledgers, edge and cloud computing, machine learning (ML), etc., can be applied to address a variety of technical and economic problems in the energy sector, emphasizing SGs. This paper presents the most comprehensive literature review to date on digitalization in renewable energy source-based SGs, synthesizing over 200 studies across data analytics, artificial intelligence and ML, digital twins, edge–fog–cloud computing, the IoT, advanced metering infrastructure, and distributed ledger technologies. Unlike previous reviews, which are often limited to a single technology or narrow application, this work provides a cross-technology synthesis linking technical and financial aspects, identifies consolidated research gaps, and proposes a unified research agenda. The review further highlights future trends, including large language models, 6G communications, and distributed autonomous organizations, and discusses their implications for both industry practice and academic research.

Jayesh Vijay Patil, Hanumantha Rao Bokkisam

This paper presents a blockchain-enabled decentralized autonomous organization (DAO) framework that leverages interconnected multilateral smart contracts to create revenue streams for decentralized token holders. The framework facilitates participation in energy arbitrage mechanisms using a battery energy storage system (BESS), enabling efficient and transparent value generation. In this framework, each token holder (shareholder) exercises their voting rights to manage the operation of the battery energy storage system (BESS) within the energy arbitrage mechanism. Decision-making is informed by historical electricity prices, forecasted prices, real-time market price, and the battery’s current state of charge. Token holders collectively control the battery through a consensus-based decision-making algorithm, determining its operational mode (charge, discharge, or float). This process is seamlessly facilitated by smart contracts, ensuring transparency and efficiency.

Muhammad Kazim, Harun Pirim, Om Prakash Yadav, Chau Le · 5 authors

No abstract is available for this record.

Dauren Amanbek, Akzhan Tursunbek, Balzhan Azibek, Nurkhat Zhakiyev

Achieving global sustainability goals requires integrating renewable energy sources (RES) into the electrical grid, but their intermittent nature poses challenges to grid stability and supply-demand balance. Energy storage systems (ESS) offer a solution, yet their deployment remains complex and costly. This study proposes an optimized conceptual model for ESS placement using blockchain-powered smart contracts to automate decentralized energy trading. The research methodology involves a comprehensive literature review and the development of a conceptual model for optimal storage placement. A thorough examination of the literature and the creation of a conceptual model for optimal storage location serve as the foundation of the research approach. Smart contracts are proposed to enhance energy trading and storage management with transparency and efficiency. Energy trading and storage management will be automated through the design and implementation of smart contracts. Expected outcomes include improved grid reliability, lower operating costs, and greater integration of renewable energy sources. A decentralized energy market is made possible by the model’s use of blockchain technology, which allows producers and consumers to exchange energy directly without the need for centralized utilities. This study advances blockchain applications in energy, offering innovative solutions and insightful information to grid operators, policymakers, and market participants, fostering a more sustainable and efficient electricity grid.

Ramin Sadooghi, Taher Niknam, Morteza Sheikh, Jamshid Aghaei · 7 authors

No abstract is available for this record.

Abdullah Umar, Sumit Kumar Jha, Deepak Kumar, T. K. Ghose · 5 authors

In isolated microgrids, distributed energy resources (DERs) such as small-scale generators, energy storage systems, and flexible loads operate independently from the main grid. The challenge is to optimize these resources to minimize user costs while ensuring microgrid stability and efficiency. This paper presents an optimization framework for DERs, leveraging a game-theoretical approach to demand-side management (DSM) in an isolated microgrid environment. Each participant aims to minimize their total cost by strategically managing renewable energy generation, storage, and consumption. The framework models the DSM problem as a noncooperative game, identifying equilibrium points where no user can unilaterally reduce costs. The proximal decomposition algorithm is employed to iteratively update user strategies, ensuring convergence to a Nash equilibrium. Furthermore, a blockchain-based system with smart contracts is integrated to automate critical processes, including registration, event detection, DSM actions, and incentive distribution. This integration enhances transparency, security, and efficiency in the microgrid. During the registration phase, all devices are authenticated and authorized through a secure, transparent blockchain ledger. Event detection is managed by the microgrid Energy Management System (EMS), which continuously monitors voltage and frequency levels, triggering predefined smart contract responses to maintain stability. DSM actions are automatically executed by smart contracts, adjusting energy loads, generation, and storage to balance supply and demand dynamically. The smart contracts also manage the economic incentives that drive participant engagement. They calculate and distribute incentives based on predefined criteria, ensuring accurate and prompt allocation. This process is recorded on the blockchain, providing an immutable and auditable trail of actions and rewards. By leveraging blockchain technology and a game-theoretical approach, the proposed framework ensures continuous optimal operation despite fluctuations in energy demand and renewable generation. This dynamic and adaptive model promotes decentralized and efficient energy management within the microgrid, fostering a resilient and sustainable energy ecosystem.

Shyam Agarwal, Shailesh Kapoor, Amit Jain

In recent years, various renewable energy sources along with energy storage systems for power generation have grown significantly due to fossil fuel depletion and increasing environmental concerns. Optimizing energy flow in this system is crucial for efficient resource utilization while adhering to system constraints. Conventional methods for energy optimization include operators or intermediaries which have their own limitations. By using blockchain technology in energy optimization, intermediaries/operators are avoided. Also, its use offers other advantages such as higher security, transparency and immutability of energy transactions. This paper introduces an approach for energy optimization using blockchain in smart microgrid system. A modified algorithm based on new particle swarm optimization (NPSO) is employed for this purpose. The algorithm is executed with the help of smart contract. It schedules, distributed energy sources and determines charging and discharging of energy storage system, while adhering to various system constraints. The algorithm is deployed and tested on a ganache platform which is based on Ethereum. The results obtained from this modified algorithm are compared with the standard PSO algorithm, demonstrating its effectiveness. The study is conducted on a typical smart microgrid system comprising three prosumers, and the results are presented.

Ernest Ozoemela Ezugwu, Samuel Okechukwu Okozi, Okonkwo S. Hilary, Edet G. Godwin · 6 authors

Blockchain technology, smart contract and microgrid systems have facilitated innovations and breakthroughs in the electricity industry. The once bundled electricity market dominated by a few key players is now gradually becoming unbundled to a more consumer-centric market due to these new technologies. To facilitate the use of community microgrids, this study develops a new trend in peer-to-peer energy trading. In this work, a model for a smart microgrid system, a decentralized energy trading platform based on blockchain, and smart contract technologies is proposed, considering an islanded community microgrid network of energy prosumers. Smart meters were used to ensure the bi-directional flow of data and power, thereby giving prosumers control over their power usage. Storage and validation of participants’ data are stored on the blockchain network, which has a strong feature of decentralization, transparency, security and data immutability. The smart contract automatically executes power delivery and transfer of tokens from the buyer’s wallet to the seller’s energy wallet based on the transaction logic and protocol. A web-user interface was designed to enable the ease of transactions by market participants and the web-user interface was designed on React.Javascript while the smart contract codes were done on the solidity programming language. Algorithms were also developed for the market trade operations in real time and sets of mathematical equations were formulated for energy pricing based on the supply and demand philosophies to curtail over-pricing and underpricing of energy.

Abdul Haseeb Tariq, Uzma Amin

Peer-to-peer (P2P) electricity trading has received a lot of attention in the last decade and recently there has been growing interest in the evaluation of market design, classical methods, and novel approaches for multi-energy trading. The existing literature only focused on electricity/electric-gas/electric-heat networks. Therefore, a comprehensive analysis of P2P multi-energy trading (P2P-MET) in a decentralized network is required for the multi-energy like electricity, hydrogen gas, and heat all in one framework for a sustainable future. This review study aims to provide an in-depth understanding of P2P-MET in a decentralized network including layers-defined multi-source network configurations, trading platforms, classical methods, novel approaches, and mechanisms. Considering MET, the rising number of prosumers take advantage of the opportunity and make this complicated decentralized network more complex. To handle the network complexities, smart contracts using distributed ledger-based blockchain, novel algorithm-based game theory, and some other approaches are reviewed. Furthermore, this review study covers the limitations, challenges, opportunities, and benefits of P2P-MET as well as current trends and future directions for a better understanding of the readers. It concludes that P2P-MET using blockchain/game theory in decentralized networks is alternatively better and more secure. • An overview of P2P multi-energy trading (MET) reviewed in a decentralized network. • This paper presents classical methods and core approaches for P2P-MET. • Numerous pilot and under-developed projects implemented globally are reviewed. • Simulating tools commonly used by researchers for P2P-MET are briefly discussed. • Current trends and future directions are reviewed for P2P-MET sustainable future.

Wentao Liu, Qian Ai

Park microgrids, valued for their efficiency and flexibility, require privacy-conscious energy management to ensure a trusted scheduling and trading environment. This paper, focusing on park microgrids with shared energy storage, designs an energy management strategy that comprehensively considers shared energy storage, scheduling transparency, and privacy security. First, a blockchain-based energy management platform is established, forming an energy dispatch consensus committee to execute decentralized scheduling management and decision-making. Next, an optimized energy scheduling smart contract for park microgrids is designed, considering Time-of-Use (ToU) pricing and storage arbitrage to formulate the day-ahead electricity purchase and sales plans as well as the shared energy storage operation plans. Then, a privacy protection strategy based on the Shamir secret sharing scheme is proposed, effectively preventing data leakage during blockchain interactions. Finally, through case analysis, the superiority of the proposed method in microgrid optimized scheduling, data tamper-resistance, and privacy protection is demonstrated.

Bokolo Anthony Jnr

Presently Rural Energy Communities (REC) are faced with challenges such as the inefficient distribution of energy from Renewable Energy Sources (RES), unfair pricing, and the inclusion of prosumers into the electricity market. Therefore, this article proposed an approach that employed enabling technologies such as Distributed Ledger Technologies (DLT), self-enforcing smart contracts-enabled Internet of Things (IoT), and Artificial Intelligence (AI) for sustainable energy sharing and tracking in REC. Additionally, a model is proposed based on key factors that influence the adoption of enabling technologies in REC. For the methodology qualitative data is collected from secondary sources and descriptive analysis is employed to present the key findings. Key findings from this study contributes to develop a decarbonized, decentralized, and digitized energy management approach to support the sustainability of REC. The deployment of AI can facilitate prediction short-term energy planning for RES production and consumption based on real-time data from IoT devices. More importantly, findings from this study presents use case scenarios of energy sharing and tracking, and green electric vehicle charging in REC suggesting that DLT based smart contracts, IoT, and AI offers an effective approach to accelerate the sharing and tracking of RES in REC. Besides, DLT and smart contracts enables real-time electricity consumption monitoring, energy trading management, and pricing.