Madina Konyrova, Katipa Chezhimbayeva, Abdul Razaque, Dina S.M. Hassan
The integration of renewable resources and prosumers into smart grids poses challenges related to scalability, transparency, and transmission efficiency. Centralized routing frequently depends on expensive technology and experiences significant losses. This study presents a blockchain-based smart contract system (BSCS) that reduces transmission losses while guaranteeing secure and decentralized energy transfers. The smart grid is represented as a weighted directed graph, with edges denoting actual power losses. Dijkstra’s shortest path algorithm generates optimal paths from the generator to the consumer with minimal loss. These optimal pathways are permanently documented and regulated by permissioned blockchain smart contracts, ensuring tamper-proof and verifiable energy settlement. Validation is performed using an enhanced IEEE 58-bus test system, which is based on the standard IEEE 57-bus network, by incorporating an additional synthetic consumer node (Bus 58) linked to Bus 12 to simulate a flexible prosumer load of 1.5 MW + 0.5 Mvar. Additionally, the synthetic consumer node employs Ganache, Truffle, and Solidity for its implementation. This modification facilitates the assessment of dynamic energy routing and decentralized transaction settlement in extended topology scenarios. The proposed BSCS demonstrates substantial enhancements compared to baseline blockchain systems. Active power losses in transmission lines are diminished, gas consumption declines by approximately 12%, latency is enhanced by as much as 21%, and throughput increases by more than 30%. The rapid deployment and execution of smart contracts within sub-second intervals validate the system's appropriateness for real-time grid operations. The proposed technique combines graph-theoretic optimization with blockchain governance to provide a safe, scalable, and hardware-independent framework for decentralized energy markets.
Michele Fabi, Viraj Nadkarni, Leonardo Leone, Matheus V. X. Ferreira
<div> We develop an axiomatic theory for Automated Market Makers (AMMs) in local energy sharing markets and analyze the Markov Perfect Equilibrium of the resulting economy with a Mean-Field Game. In this game, heterogeneous prosumers solve a Bellman equation to optimize energy consumption, storage, and exchanges. Our axioms identify a class of mechanisms with linear, Lipschitz continuous payment functions, where prices decrease with the aggregate supply-to-demand ratio of energy. We prove that implementing batch execution and concentrated liquidity allows standard design conditions from decentralized finance-quasi-concavity, monotonicity, and homotheticity-to construct AMMs that satisfy our axioms. The resulting AMMs are budget-balanced and achieve ex-ante efficiency, contrasting with the strategy-proof, expost optimal VCG mechanism. Since the AMM implements a Potential Game, we solve its equilibrium by first computing the social planner's optimum and then decentralizing the allocation. Numerical experiments using data from the Paris administrative region suggest that the prosumer community can achieve gains from trade up to 40% relative to the grid-only benchmark. </div>
This paper presents a blockchain-based smart pricing framework designed to enhance traditional electricity markets by enabling decentralized, peer-to-peer (P2P) energy trading among distributed renewable energy producers and consumers. The proposed system, implemented on the Ethereum blockchain, introduces SmartPricingExchange. The system employs energy credits managed through an internal ledger to facilitate transparent and automated transactions. By removing conventional intermediaries such as DISCOMs, the framework promotes market liberalization, improves price transparency, and incentivizes small-scale producers through fair and trustless settlements. Deployed on the Ethereum Sepolia testnet using Remix IDE and MetaMask, the model demonstrates the feasibility of decentralized energy exchange and offers a scalable path for the modernization of future power markets.
Efficient energy sharing among solar-based microgrids was crucial for enhancing grid reliability, scalability, and sustainability in modern energy systems. This research presents a novel blockchain-powered decentralized energy trading framework that integrates Raspberry Pi 4, IoT-driven real-time monitoring, and Ethereum-based smart contracts to facilitate seamless and secure peer-to-peer (P2P) energy exchange. The proposed system enables real-time data acquisition and transmission of critical energy parameters, including current, voltage, and power generation, from five interconnected solar microgrids. Raspberry Pi 4 serves as the centralized edge computing node, aggregating and transmitting real-time energy data to the ThingSpeak IoT platform, where advanced AI-driven analytics optimize grid efficiency. Blockchain technology, specifically Ethereum with Ganache, was employed to create a tamper-proof, transparent, and trustless energy marketplace, eliminating reliance on centralized energy intermediaries. The incorporation of Solidity-based smart contracts automates transactions, ensuring secure, immutable, and fair energy trading while enabling dynamic pricing models based on real-time demand-supply conditions. Python, integrated with Web3.py, facilitates seamless interaction between Raspberry Pi 4 and the blockchain network, ensuring low-latency transaction execution and verifiable trade settlements. Through the integration of IoT-enabled smart grids, blockchain-based energy transactions, and AI-driven predictive analytics, the proposed system offers a scalable, autonomous, and energy-efficient solution for decentralized energy management. Experimental validation confirms the system's effectiveness, demonstrating its ability to achieve real-time energy balancing, seamless P2P trading, and enhanced security through blockchain immutability. This cutting-edge approach significantly advances the adoption of renewable energy sources, optimizes microgrid autonomy, and reinforces the resilience of next-generation smart power networks, paving the way for a sustainable and decentralized energy economy.
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.
Virtual Power Plant (VPP) trading mechanisms confront unprecedented challenges from behavioral complexities and technological uncertainties that conventional rational choice models inadequately address. This research develops an integrated framework combining prospect theory-driven decision modeling with evolutionary smart contracts and multi-stage negotiation protocols to enhance trading effectiveness in cross-regional energy markets. We establish mathematical foundations incorporating loss aversion, probability distortion, and reference-dependent preferences into VPP decision-making, while developing adaptive contracts capable of autonomous evolution responding to market changes. Through composite game-theoretic analysis examining nested interactions between contract evolution and negotiation dynamics, we validate the framework across three comprehensive scenarios: emergency dispatch under extreme weather, renewable energy integration, and cross-regional collaboration. Simulation results demonstrate 15–25% negotiation efficiency improvements compared to traditional mechanisms, with behavioral models capturing significant heterogeneity in loss aversion coefficients (2.1–3.4) across VPP configurations. The evolutionary contracts successfully adapt within 72-hour windows to policy changes and technological developments, while maintaining system stability. Cross-regional analysis reveals how cultural distance and information asymmetries influence trading outcomes, with the framework achieving superior market integration despite these barriers. These findings establish new paradigms for behaviorally-informed energy market design, offering transformative implications for renewable integration and decentralized electricity systems.
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.
The growth of distributed energy resources and local energy markets heightens the need for price formation that is transparent, privacy preserving, and compatible with network constraints. Blockchain provides a trust-minimized substrate for auditable clearing and settlement through consensus, tamperevident ledgers, and smart contracts. This survey organizes blockchain-enabled pricing into three families, namely auction-based, game-theoretic, and optimization-based, and links them to enabling techniques such as metering oracles, secure multiparty computation, zero-knowledge proofs, and verifiable optimality certificates. Applications span wholesale electricity, carbon and green certificates, distributed energy trading, ancillary services, and electric vehicles. Evidence indicates gains in auditability, privacy, network awareness, and automated settlement, alongside challenges in scalability, data protection, grid integration, and regulation. The survey distills design patterns and research directions toward verifiable, interoperable, and governable pricing modules that complement system-operator markets.
Ihunanya Udodiri Ajakwe, Victor Ikenna Kanu, Simeon Okechukwu Ajakwe, Dong‐Seong Kim
The Korean Emission Trading Scheme (K-ETS) is vital for reducing carbon emissions in South Korea. However, issues in transparency, security, and computational overhead limit its effectiveness. This work proposes an energy-efficient blockchain-based framework (eBCTC) to enhance the system with a decentralized blockchain architecture, Purechain. The framework leverages an improved consensus mechanism, the Proof of Authority and Association (PoA 2 ). This is to address key challenges in the current K-ETS, such as centralization, lack of transparency, and high energy consumption. The PoA 2 significantly reduces gas usage, with experimental results showing a 22 % reduction in gas consumption compared to traditional Proof of Work (PoW) and Proof of Authority (PoA) mechanisms. Also, PoA 2 recorded a ×6 and ×2 reduction in gas price compared to PoW and PoA. The system also achieves faster transaction finality and lower computational costs, with transaction costs reduced by up to 83 % across the key K-ETS activities, including emissions reporting, credit allocation, and trading. Also, the system achieved moderate throughput, high latency, doubling scalability, high reliability, and a high success rate compared with DPoS and PBFT based on transaction stress validation tests. With an improved smart contract, intelligent automation of key functions, the system achieved a high energy gain for improved incentives. The proposed framework not only enhances the scalability and transparency of K-ETS but also aligns with South Korea's carbon neutrality goals by minimizing the environmental impact of blockchain operations. This study provides a solid foundation for sustainable carbon trading systems and an accountable carbon economy, contributing to global efforts to combat climate change in achieving the 2050 net-zero carbon emissions goal. • Purechain PoA2 enables secure, low-energy carbon trading in K-ETS. • 22 % less gas usage and 83 % lower costs than PoW and PoA. • Smart contracts automate K-ETS compliance and incentives. • Improves scalability, transparency, and network reliability. • Supports South Korea's 2050 net-zero carbon goal.
The increasing complexity of urban energy systems requires decentralized, sustainable, and scalable solutions. The paper presents a new multi-layered framework for smart energy management in microgrids by bringing together advanced forecasting, decentralized decision-making, evolutionary optimization and blockchain-based coordination. Unlike previous research addressing these components separately, the proposed architecture combines five interdependent layers that include forecasting, decision-making, optimization, sustainability modeling, and blockchain implementation. A key innovation is the use of Temporal Fusion Transformer (TFT) for interpretable multi-horizon forecasting of energy demand, renewable generation, and electric vehicle (EV) availability which outperforms conventional LSTM, GRU and RNN models. Another novelty is the hybridization of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to simultaneously support discrete and continuous decision variables, allowing for dynamic pricing, efficient energy dispatching and adaptive EV scheduling. Multi-Agent Reinforcement Learning (MARL) which is improved by sustainability shaping by including carbon intensity, renewable utilization ratio, peak to average load ratio and net present value in agent rewards. Finally, Ethereum-based smart contracts add another unique contribution by providing the implementation of transparent and tamper-proof peer-to-peer energy trading and automated sustainability incentives. The proposed framework strengthens resilient infrastructure through decentralized coordination and intelligent optimization while contributing to climate mitigation by reducing carbon intensity and enhancing renewable integration. Experimental results demonstrate that the proposed framework achieves a 14.6% reduction in carbon intensity, a 12.3% increase in renewable utilization ratio, and a 9.7% improvement in peak-to-average load ratio compared with baseline models. The TFT-based forecasting model achieves RMSE = 0.041 kWh and MAE = 0.032 kWh, outperforming LSTM and GRU by 11% and 8%, respectively.
The hydropower fleet in Austria is ageing and needs to be modernised to adapt to changing conditions in national and international energy systems. The financial viability of hydropower repowering projects remains a challenge because of high investment costs and long payback periods. A part from additional revenuestreams, a Bitcoin mining operation has the potential to be used as a flexible demand source also for curtailment and grid stability services. This thesis provides quantitative evidence on whether a Bitcoin mining operation can serve as an additional revenue stream to improve the investment metrics of a hydro repowering project in Austria, using a dynamic investment calculation and sensitivity analysis.The results show that Bitcoin mining can improve the financial performance especially for run-of-river plants with higher full load hours. These positive effects are sensitive to the volatility of the Bitcoin price and the network hash rate, making long-term returns difficult to predict.
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.
The increasing decentralization of energy generation via home solar panels and microgrids necessitates safe, scalable, and autonomous peer-to-peer (P2P) energy trading systems. Conventional grid management technologies lack the adaptability and reliability necessary for decentralized contexts. This study presents the Blockchain-Enabled Energy Swarm Protocol (BESP), which combines Ethereum smart contracts with Particle Swarm Optimization (PSO) to enhance energy trade efficiency and enable the dynamic clustering of prosumers. The protocol guarantees safe, trustless communication, low-latency energy matching, and transparent transaction auditability without dependence on a central authority. The system is assessed using empirical data from the Pecan Street Dataport dataset, which includes high-resolution records of energy usage and solar output from more than 1,000 residences in Austin, Texas. Particle Swarm Optimization (PSO) was executed in MATLAB Simulink, whilst smart contracts were deployed and evaluated via Remix IDE and Ganache on a private Ethereum network. Experimental findings indicate that BESP decreases transaction latency by 35.2%, reduces communication overhead by 27.8%, and enhances energy cost efficiency by more than 60% relative to traditional P2P and centralized frameworks. These findings underscore BESP's efficacy in facilitating energy-efficient, secure, and decentralized communications inside smart grids, in accordance with future sustainable infrastructure objectives.
Maneesha Papireddygari, Xintong Wang, Bo Waggoner, David M. Pennock
Automated Market Makers (AMMs) are used to provide liquidity for combinatorial prediction markets that would otherwise be too thinly traded. They offer both buy and sell prices for any of the doubly exponential many possible securities that the market can offer. The problem of setting those prices is known to be #P-hard for the original and most well-known AMM, the logarithmic market scoring rule (LMSR) market maker [Chen et al., 2008]. We focus on another natural AMM, the Constant Log Utility Market Maker (CLUM). Unlike LMSR, whose worst-case loss bound grows with the number of outcomes, CLUM has constant worst-case loss, allowing the market to add outcomes on the fly and even operate over countably infinite many outcomes, among other features. Simpler versions of CLUM underpin several Decentralized Finance (DeFi) mechanisms including the Uniswap protocol that handles billions of dollars of cryptocurrency trades daily. We first establish the computational complexity of the problem: we prove that pricing securities is #P-hard for CLUM, via a reduction from the model counting 2-SAT problem. In order to make CLUM more practically viable, we propose an approximation algorithm for pricing securities that works with high probability. This algorithm assumes access to an oracle capable of determining the maximum shares purchased of any one outcome and the total number of outcomes that has that maximum amount purchased. We then show that this oracle can be implemented in polynomial time when restricted to interval securities, which are used in designing financial options.
Rooftop solar (RTS) represents a critical component of India’s clean energy transition, offering decentralized generation, reduced transmission losses, and potential resilience benefits. Yet, despite ambitious national targets and substantial technical potential, RTS adoption has lagged behind expectations. This narrative review synthesizes peer-reviewed literature (2019–2025), government program documents, and policy reports to examine the trends, barriers, enablers, economics, regional signals, stakeholder outcomes, and policy implications shaping rooftop solar adoption in India. Findings indicate that commercial and industrial consumers have historically dominated the sector due to favorable tariffs and access to credit, while residential uptake accelerated only after the launch of flagship initiatives such as PM Surya Ghar. Persistent barriers include high upfront costs, affordability gaps, regulatory uncertainty, procedural delays, information asymmetries, and built environment constraints. At the same time, innovations such as simplified subsidy pipelines, digitalized approval portals, DISCOM performance incentives, vendor certification, and emerging business models including RESCO/OPEX and group or virtual net metering demonstrate viable pathways to expand adoption. The review identifies future research needs in program evaluation, financial innovation, grid integration, apartment governance, and quality assurance, and emphasizes the importance of stable regulatory frameworks, inclusive finance, and community-oriented models.
J. Balamurugan, Devineni Poojitha, R Bindu, Archana Pallakonda · 8 authors
Decentralized energy trading has been designed as a scalable substitute for traditional electricity markets. While blockchain technology facilitates efficient transparency and automation for peer-to-peer energy trading, the majority of current proposals lack real-time intelligence and adaptability concerning pricing strategies. This paper presents an innovative machine learning-driven solar energy trading platform on the Ethereum blockchain that uniquely integrates Bayesian-optimized XGBoost models with dynamic pricing mechanisms inherently incorporated within smart contracts. The principal innovation resides in the real-time amalgamation of meteorological data via Chainlink oracles with machine learning-enhanced price optimization, thereby establishing an adaptive system that autonomously responds to fluctuations in supply and demand. In contrast to existing static pricing methodologies, our framework introduces a multi-faceted dynamic pricing model that encompasses peak-hour adjustments, prediction confidence weighting, and weather-influenced corrections. The system dynamically establishes energy prices predicated on real-time supply–demand forecasts through the implementation of role-based access control, cryptographic hash functions, and ongoing integration of meteorological and machine learning data. Utilizing real-world meteorological data from La Trobe University’s UNISOLAR dataset, the Bayesian-optimized XGBoost model attains a remarkable prediction accuracy of 97.45% while facilitating low-latency price updates at 30 min intervals. The proposed system delivers robust transaction validation, secure offer creation, and scalable dynamic pricing through the seamless amalgamation of off-chain machine learning inference with on-chain smart contract execution, thereby providing a validated platform for trustless, real-time, and intelligent decentralized energy markets that effectively address the disparity between theoretical blockchain energy trading and practical implementation needs.
The increasing integration of renewable energy into smart grids introduces challenges of demand-supply imbalance, peak load stress, and cyber-physical vulnerabilities. Existing demand response (DR) frameworks often lack scalability, privacy-preserving data sharing, and secure transaction mechanisms, which limit user participation and grid resilience. To address these challenges, this study proposes GridSyncNet, a blockchain-enabled multi-agent deep reinforcement learning framework for real-time demand response. The framework integrates federated learning to enhance decentralized forecasting accuracy, blockchain consensus to ensure transparent and tamper-proof energy trading, and actor–critic based DRL agents to dynamically optimize load scheduling and energy dispatch across prosumers. Extensive simulations demonstrate that GridSyncNet outperforms benchmark models such as OD-CNN, D-FCAS, and USTCF. Specifically, it achieves a 98.2 % demand response efficiency, 30.6 % reduction in carbon emissions, and 97.4 % forecasting accuracy. Comparative analysis with multi-agent DRL (MADRL) approaches further confirms that GridSyncNet provides superior scalability, privacy, and security in decentralized environments. The proposed framework contributes to the design of secure, resilient, and sustainable energy management systems, offering practical insights for accelerating the transition toward net-zero energy communities. By combining blockchain, federated learning, and multi-agent reinforcement learning, GridSyncNet establishes a comprehensive pathway for trustworthy and adaptive smart grid operations. • A multi-agent deep reinforcement learning framework for adaptive DR in smart grids. • Decentralized peer-to-peer energy trading to transparent, secure energy trading. • Renewable energy utilization 89 %, CO 2 reduction 30.6 % & forecasting accuracy 92.4 %. • Federated learning & improved system resilience against cyber threats for DSM. • Optimize load balancing, peak shaving & cost efficiency for distributed grid agents.
The smart grid (SG) plays a seminal role in the modern energy landscape by integrating digital technologies, the Internet of Things (IoT), and Advanced Metering Infrastructure (AMI) to enable bidirectional energy flow, real-time monitoring, and enhanced operational efficiency. However, these advancements also introduce critical challenges related to data privacy, cybersecurity, and operational balance. This review critically evaluates SG systems, beginning with an analysis of data privacy vulnerabilities, including Man-in-the-Middle (MITM), Denial-of-Service (DoS), and replay attacks, as well as insider threats, exemplified by incidents such as the 2023 Hydro-Québec cyberattack and the 2024 blackout in Spain. The review further details the SG architecture and its key components, including smart meters (SMs), control centers (CCs), aggregators, smart appliances, and renewable energy sources (RESs), while emphasizing essential security requirements such as confidentiality, integrity, availability, secure storage, and scalability. Various privacy preservation techniques are discussed, including cryptographic tools like Homomorphic Encryption, Zero-Knowledge Proofs, and Secure Multiparty Computation, anonymization and aggregation methods such as differential privacy and k-Anonymity, as well as blockchain-based approaches and machine learning solutions. Additionally, the review examines pricing models and their resolution strategies, Demand–Supply Balance Programs (DSBPs) utilizing optimization, game-theoretic, and AI-based approaches, and energy storage systems (ESSs) encompassing lead–acid, lithium-ion, sodium-sulfur, and sodium-ion batteries, highlighting their respective advantages and limitations. By synthesizing these findings, the review identifies existing research gaps and provides guidance for future studies aimed at advancing secure, efficient, and sustainable smart grid implementations.
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.