In the past three decades, there has been a sweeping trend in Western and developed countries worldwide to transform the vertically integrated electricity supply chain into competitive electricity markets to diversify investment in the system and ultimately drive down operation costs. Nonetheless, due to some geopolitical and economic reasons, many developing countries adopted a modestly liberalized version of the power market (imperfect market). With the trend of privatization, specifically at the generation level, to leverage the hypothetical competitiveness, countries that did not adopt a full-fledged market structure face a dilemma. The system operators of incumbent imperfect market models find it increasingly difficult to deal with multiple private ownership of Independent Power Producers who are unwilling to share their detailed operational parameters for long-term generation scheduling (lasting for years). In this paper, Blockchain (BC) is being advocated as a platform that simulates a virtual market environment to address such issues. The proposed BC-based structure allows generators to participate in the short-term scheduling mechanism (such as day-ahead) in a trust-free environment without sharing their vital data yet achieving efficient, market-grade solutions. The feasibility of this new proposition is demonstrated through three different application scenarios, utilizing real-world load and renewable generation profiles sourced from the respective Grid System Operators databases. Python library (PYPSA) and Ethereum Testnet are being used for grid simulation and BC platform implementation respectively. The results of BC-assisted generation scheduling are presented and compared with the imperfect market model to highlight the viability of the proposed new approach.
Alexander A. Hernandez, Sook Yee Yip, Kam Meng Goh, Arlene R. Caballero · 5 authors
Standalone energy supply system has been in the limelight, especially in rural areas since it can provide cheap electrical supply to the local communities. Energy supply is always a problematic condition in many developing countries that significantly affects progress and inclusive economic development. Furthermore, excess energy generated is wasted due to insufficient battery storage. To confront these recurring problems, peer-to-peer energy resource sharing has emerged as a promising solution, offering a decentralized system and approach to reduce reliance on the grid energy resource. The system enables prosumers to share excess energy with consumers, reducing dependence on grid and lowering the cost of electricity. However, peer-to-peer energy resource sharing is still a relatively new technology in developing countries, especially in rural communities, needing enabling technologies, support, and sustainable policies and financing models. To this end, this study aims to provide comprehensive understanding of the state of the art of peer-to-peer energy resource sharing in rural communities.
Arvind Singh, R. Seshu Kumar, K. Reddy Madhavi, Faisal Alsaif · 6 authors
The integration of Electric Vehicles (EVs) into power grids introduces several critical challenges, such as limited scalability, inefficiencies in real-time demand management, and significant data privacy and security vulnerabilities within centralized architectures. Furthermore, the increasing demand for decentralized systems necessitates robust solutions to handle the growing volume of EVs while ensuring grid stability and optimizing energy utilization. To address these challenges, this paper presents the Demand Response and Load Balancing using Artificial intelligence (DR-LB-AI) framework. The proposed framework leverages Artificial intelligence (AI) for predictive demand forecasting and dynamic load distribution, enabling real-time optimization of EV charging infrastructure. Furthermore, Blockchain technology is employed to facilitate decentralized, secure communication, ensuring tamper-proof energy transactions while enhancing transparency and trust among stakeholders. The DR-LB-AI framework significantly enhances energy distribution efficiency, reducing grid overload during peak periods by 20%. Through advanced demand forecasting and autonomous load adjustments, the system improves grid stability and optimizes overall energy utilization. Blockchain integration further strengthens security and privacy, delivering a 97.71% improvement in data protection via its decentralized framework. Additionally, the system achieves a 98.43% scalability improvement, effectively managing the growing volume of EVs, and boosts transparency and trust by 96.24% through the use of immutable transaction records. Overall, the findings demonstrate that DR-LB-AI not only mitigates peak demand stress but also accelerates response times for Load Balancing, contributing to a more resilient, scalable, and sustainable EV charging infrastructure. These advancements are critical to the long-term viability of smart grids and the continued expansion of electric mobility.
This study presents a distributed electricity trading system using smart contracts to improve transaction efficiency and reduce costs in power markets. Three trading models are analyzed: centralized trading, blockchain-based decentralized trading, and smart contract-driven automated trading. The advantages and challenges of each model are examined, focusing on factors like node inclusion time, transaction costs, and price stability. The results show that the smart contract-driven model outperforms the others by increasing market efficiency, lowering transaction costs, and reducing price fluctuations. Through simulations and real-world analysis, this study provides support for using blockchain technology in power markets and offers practical advice for improving electricity trading systems. The findings suggest that the proposed system could greatly enhance transparency, efficiency, and cost-effectiveness in distributed energy markets, even in uncertain market conditions.
Amit Kumar Vishwakarma, Pratyush Kumar Patro, Adolf Acquaye, Raja Jayaraman · 5 authors
In attempts to progress the transition towards decarbonizing energy systems and achieving sustainable development goals linked to energy, renewable energy sources (RES), which are decentrally deployed, are being widely promoted. However, RES are constrained by many barriers, some of which are technical in nature, such as energy losses. For instance, traditional peer-to-peer (P2P) trading systems have been used as a mechanism to advance RES because they offer a promising solution for decentralized energy trading without third-party intermediaries. However, it faces significant challenges, notably in accounting for energy losses during transmission and distribution, thus reducing overall system efficiency. We propose an end-to-end blockchain-based solution to provide traceability of energy loss in a distributed network and enhance transaction security, trust, and operational efficiency by linking prosumers and consumers. Our solution uses smart contracts to automate business transactions among the stakeholders. We develop six algorithms and deploy the smart contracts to demonstrate the successful deployment of the P2P energy trading process. Our solution effectively provides traceability of energy loss and reliably secured P2P energy transaction verification. We also present the cost and security analysis to demonstrate the affordability and reliability of our solution. We make our smart contract codes publicly available on GitHub.
Md. Mainul Islam, Rachad Atat, Muhammad Ismail, Katherine Davis · 5 authors
Abstract Enhancing the resilience and reliability of power grids is crucial amid rising cyber threats and system complexities. To address these challenges, this paper proposes an energy‐efficient, consortium blockchain‐based global alarm system for power grid management. Using smart contracts and the proof of‐authority consensus algorithm, the alarm system triggers global alarms upon detecting local anomalies, ensuring a prompt response to partition the power grid and mitigate failures. The effectiveness is validated by simulating the Iberian power system with 15 providers from various regions. Key metrics, such as load shedding, damage reduction, energy consumption, latency, and transaction costs, are used to assess the performance. Through simulations, we show that the blockchain‐based system effectively limits the damage propagation and the load shedding during cascading failures by delaying the onset of instability and maintaining lower damage levels compared to non‐blockchain scenarios. Our investigations reveal that the proposed global alarm mechanism reduces the damage and load shedding by up to 29% and 87%, respectively, showcasing its potential for preventing widespread outages.
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.
• This paper reviews an overview of Energy Trading using Conventional and Advance method. • The application of Blockchain Technology in a de-regulated environment for peer-to-peer energy trading is discussed. • This article mentions existing financial models for implementing blockchain-based systems. • Blockchain Technology explores distributed architecture, security, privacy, economic effects, legal challenges, transaction object matching, and cost reduction in Energy Trading Platforms. • Review on Smart energy meter and IOT technology usage for Energy trading platform. Abstract Energy commerce models have changed due to the proliferation of renewable energy sources, moving from centralized to decentralized systems. Although a crucial element of these schemes is blockchain technology, problems with poor productivity, expensive transaction fees, and privacy and security concerns still exist. Although new ideas and techniques have been proposed to address these problems, unsolved regulatory frameworks and environmental difficulties still exist. Power sales agreements, electrical marketplaces, the benefits and drawbacks of energy trading, the design of trading platforms, market participant responsibilities, and energy trading are all covered in this article. Blockchain technology may improve security, lower prices, lessen the impact of distributed generation on the electrical grid, boost the advantages of energy storage, and attract social capital for shared energy storage. Because of the worldwide energy issue, automation must be included in the electrical industry. Energy waste may be decreased, and energy conservation can be aided via the Internet of Things (IoT). This paper investigates a blockchain-controlled microgrid model for smart meters and IoT, particularly on peer-to-peer interactions in conventional and non-conventional energy sources.
Abstract To improve the security and efficiency of smart grid power trading, this study designs a smart grid trading system based on blockchain technology. The transaction module of the system composed of smart contracts and order-matching algorithms is proposed. The credit consensus mechanism of the system adopts a designed credit-based hybrid consensus mechanism. The experimental results showed that under this system, the successful transaction prices of consumers bidding were between the grid price and the recycling price, making bidding more reasonable. The highest power utilization rate was 0.84, the lowest was 0.67, and the transaction throughput exceeded 350TPS. The highest CPU usage was 0.74, the lowest was 0.5, and the highest memory usage was only 0.44. Compared with other systems, the order matching of this system was more reasonable and its performance was better. Furthermore, the system has been subjected to rigorous security testing and is reliable, thereby providing a secure and efficient trading platform for smart grid electricity trading.
We propose an extended demand response program, based on ancillary service for supplying flexible electricity demand. In our proposed scheme, we suggest a broader management model to control the scheduling and power consumption of Bitcoin mining machines. The main aspect that we focus on is suppressing the power ramping and related transient effects. We extend previous works on the subject, that study the impact of incorporating cryptocurrency mining machines into existing power grid, and explore the potential profit of exploiting this flexible load in the Israeli electricity market. We analyze a trend based on historical data, of increasing electricity prices and ramping costs due to the increasing penetration of renewable energy sources. We suggest an extension to the unit commitment problem from which we obtain the scheduling scheme of the Bitcoin mining machines. We use simulation and the real-world data acquired from the "Noga" grid operator to verify the proposed ancillary service and test its practical limits for reducing the ramping costs, under changing ratio of energy production from renewable sources. Out results suggests that the machine price and ratio of production from renewable sources plays a significant role in determining the profitability of the proposed demand-response program.
This research investigates the integration of blockchain technology into smart grids, focusing on optimizing both electrical billing and peer-to-peer energy trading between producers and consumers. Using blockchain smart contracts, the system automates and secures energy consumption recording, bill calculation, payment processing, and energy transactions. In the electrical billing framework, a blockchain-based approach was developed to model these functionalities, utilizing an EnergyBilling smart contract to calculate bills and an EnergyPayment smart contract to ensure payment accuracy. Validation using actual consumption data from Sinoma Handan’s project site confirmed the system’s accuracy and reliability when cross-verified with mathematical models. Simultaneously, the study explores peer-to-peer energy trading, where producers (represented by Askari Cement Plant.Nizampur, Pakistan) and consumers (Sinoma Handan Ltd, Handan, China.) conduct automated, transparent transactions. Blockchain’s decentralized nature ensures transparency, data immutability, and a secure, tamper-proof record of transactions. The system eliminates intermediaries, enhancing operational efficiency and reducing costs. Key outcomes demonstrate successful transaction execution with detailed settlements, ensuring financial accountability. Our research highlights blockchain’s transformative potential in revolutionizing electrical billing and energy trading. It offers a secure, transparent, and efficient solution while acknowledging scalability, transaction costs, and regulatory hurdles. Future work could focus on real-world implementation, integration with IoT devices for real-time data collection, and scaling these technologies for broader industrial applications in global energy markets.
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.
Chinonso Nwanevu, Segun Samuel Oladipo, Abidemi Obatoyinbo Ajayi
Designing effective policy frameworks for the implementation of microgrids in developing countries is crucial for advancing sustainable energy access. Microgrids offer a decentralized and resilient solution to energy challenges, particularly in regions with limited grid infrastructure. However, the successful deployment of microgrids requires a nuanced understanding of the opportunities, challenges, and pathways to integration within the unique contexts of developing nations. This study explores the key factors influencing the design and implementation of microgrid policies, including regulatory environments, financial incentives, and technological innovations. It highlights the potential of microgrids to enhance energy security, reduce carbon emissions, and support economic development. At the same time, it addresses the challenges related to financing, regulatory compliance, and the need for capacity-building in local communities. By analyzing case studies from various developing countries, the study identifies best practices and strategic recommendations for policymakers to create supportive frameworks that encourage the adoption of microgrids. The research underscores the importance of international collaboration, public-private partnerships, and adaptive policy mechanisms that can respond to the evolving needs of the energy sector. Ultimately, the study provides a roadmap for leveraging microgrids as a key component of sustainable energy strategies in developing countries, contributing to broader goals of energy equity, environmental sustainability, and economic resilience.
• Different digitalization techniques in the energy sector is briefly outlined • Impacts of digitalization in smart grids is critically analyzed. • Integration of the renewable energy sources are given priority during the selection of digitalization methods. • Maximum energy efficiency attainment using demand response is ensured in the processes. Decarbonization, decentralization, and digitalization are essential for advanced energy systems (AES), which encompass smart grids, renewable energy integration, and demand response initiatives. Digitalization is a significant trend that transforms societal, economic, and environmental processes globally. This shift moves us from traditional power grids to decentralized, intelligent networks that enhance efficiency, reliability, and sustainability. By integrating data and connectivity, these technologies optimize energy production, distribution, and consumption. This article presents a comprehensive literature review of four closely related emerging technologies: Artificial Intelligence (AI), Internet of Things (IoT), Blockchain, and Digital Twin (DT) in AES. Our findings from the previous works indicate that AI significantly improves Demand Response strategies by enhancing the prediction, optimization, and management of energy consumption. Techniques like linear regression effectively predict power demand and aggregated loads, while more complex methods such as Support Vector Regression (SVR) and reinforcement learning (RL) optimize appliance scheduling and load forecasting. The integration of IoT technologies into Energy Management Systems (EMS) further enhances efficiency and sustainability through real-time monitoring and automated control. Additionally, DT technology aids in simulating energy scenarios and optimizing consumption in both residential and commercial smart grids. Our findings also emphasize blockchain’s role in creating decentralized energy trading platforms, facilitating peer-to-peer transactions, and enhancing trust through smart contracts. The insights gained from this review highlight the essential role of these emerging technologies in supporting decentralized, intelligent energy networks, offering valuable strategies for stakeholders to navigate the complexities of the evolving digital energy landscape.
Christian Winzer, Héctor Ramírez-Molina, Lion Hirth, Ingmar Schlecht
Decarbonization involves a large-scale expansion of low-carbon generators such as wind and solar and the electrification of heating and transport. Both space heating and battery-electric cars have significant embedded flexibility potential. Granular price signals that convey abundance or scarcity of electricity are a precondition for customers or aggregators acting on their behalf to exploit this flexibility. However, unmitigated real-time prices expose customers to electricity price risks. To tackle the dual need of providing flexibility incentives while protecting customers from cost shocks, real-time tariffs with a hedging component can be a solution. In such contracts customers pre-agree an amount of energy and a consumption profile, while hourly deviations are charged at spot prices. In this paper we analyze design options by using a dataset of anonymized smart meter data and show that profile tariffs can bring electricity bill volatility to similarly low levels as fixed tariffs while providing full flexibility incentives from spot prices. • Profile contracts reduce bill volatility to similar levels as fixed price contracts. • Profile contracts restore flexibility incentives suppressed by fixed price contracts. • Profile contracts may reduce bill of flexible customers compared to fixed prices. • Demand for profile contracts expected to increase as load flexibility increases.
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.
G.B. Bhavana, R. S. Anand, J. Ramprabhakar, Veerpratap Meena · 6 authors
Countries all over the world are shifting from conventional and fossil fuel-based energy systems to more sustainable energy systems (renewable energy-based systems). To effectively integrate renewable sources of energy, multi-directional power flow and control are required, and to facilitate this multi-directional power flow, peer-to-peer (P2P) trading is employed. For a safe, secure, and reliable P2P trading system, a secure communication gateway and a cryptographically secure data storage mechanism are required. This paper explores the uses of blockchain (BC) in renewable energy (RE) integration into the grid. We shed light on four primary areas: P2P energy trading, the green hydrogen supply chain, demand response (DR) programmes, and the tracking of RE certificates (RECs). In addition, we investigate how BC can address the existing challenges in these domains and overcome these hurdles to realise a decentralised energy ecosystem. The main purpose of this paper is to provide an understanding of how BC technology can act as a catalyst for a multi-directional energy flow, ultimately revolutionising the way energy is generated, managed, and consumed.
• The article highlights the significance of smart grids in managing energy demand, addressing environmental issues, and ensuring energy security, emphasizing the need for blockchain technology and AI. • The study discusses data collection from smart city power consumption in a smart grid, utilizing techniques like Z-Score normalization and Spatial Temporal Correlation, and suggests blockchain technology for secure data transmission and storage. • The paper proposes a LSTM-RNN-ISSA for improved load forecasting accuracy and discusses the use of Blockchain-Based Smart Energy trading for effective communication in the smart grid. • Task-Oriented Communication facilitates real-time demand response, balancing electrical load and supply, outperforming existing approaches in smart city energy management. A smart grid (SG) is the financial benefit of a complicated and smart power system that can keep up with rising demand. It has to do with saving energy and being environmentally friendly. Growing populations and new technologies have caused a big rise in energy use, causing big problems for the environment and energy security. It is essential and significant to use blockchain technology and artificial intelligence (AI) to solve problems with power control. Data can be collected using a smart city in a power-consumed smart grid data and pre-process using a Z-Score normalization technique. It can extract features using a Spatial-Temporal Correlation (STC) to assess smart grid power usage within the context of a smart city using large-scale, high-dimensional data. Ensuring data integrity, privacy, and trust among grid applicants, transmit the data securely and reliably to a centralized or distributed cloud platform utilizing blockchain technology—a secure transmission and storage using Distributed Authentication and Authorization (DAA) protocol. To achieve precise load forecasting, a short-term recurrent neural network with an improved sparrow search algorithm (LSTM-RNN-ISSA) is incorporated. The smart grid may then record the projected results. Communication can be done on a smart grid with the users; the Blockchain-Based Smart Energy Trading with Adaptive Volt-VAR Optimization (BSET-AVVO) algorithm can be used for effective communication—a quick balancing electrical load and supply via a task-oriented communication mechanism in real-time demand response. Finally, our proposed method performs successfully better than the existing approaches.
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.
Mithul Raaj A T, B. Saravana Balaji, Sai Arun Pravin R R, Rani Chinnappa Naidu · 10 authors
In response to the growing need for enhanced energy management in smart grids in sustainable smart cities, this study addresses the critical need for grid stability and efficient integration of renewable energy sources, utilizing advanced technologies like 6G IoT, AI, and blockchain. By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. Additionally, long short-term memory recurrent neural networks analyze weather data to forecast solar energy production accurately, enabling better energy consumption planning. For microgrid management within individual buildings or clusters, deep Q reinforcement learning dynamically manages and optimizes photovoltaic energy usage, enhancing overall efficiency. The integration of a sophisticated visualization dashboard provides real-time updates and facilitates strategic planning by making complex data accessible. Lastly, the use of blockchain technology in verifying energy consumption readings and transactions promotes transparency and trust, which is crucial for the broader adoption of renewable resources. The combined approach not only stabilizes grid operations but also fosters the reliability and sustainability of energy systems, supporting a more robust adoption of renewable energies.
Microgrids are regarded as vital components in contemporary realm of energy system improvement, resilience, and sustainability. In this paper a novel decentralized peer-to-peer energy trading system leveraging blockchain technology is proposed. The proposed model not only demonstrates the implementation of blockchain technology in microgrids but also transforms the energy sector by emphasizing decentralization, security and efficiency. This research aims to enhance the energy trading system by including smart contracts written on the Ethereum network. Energy Token and Demand Response contracts are integrated to enable dynamic interactions inside microgrids, which leads to a transparent, secure, and efficient energy trading system. Moreover, automation of energy transactions and elimination of intermediaries ensure cost effectiveness and utilization of excess energy potentially reduce dependency on the main grid. A microgrid system is designed in Simulink for distributed energy trading. Energy credits are represented by standard ERC-20 digital token and then demand response contract dynamically adjusts rewards and energy prices based on energy production and consumption. the ERC-20 contract manages the token transaction and then demand response contract enforces governance rules. The purpose of a demand response contract is to enable the automatic and efficient control of energy usage in response to changing demand and supply conditions. The use of the Web3 library further facilitates a direct and smooth connection between the blockchain network and microgrids. The results demonstrate the successful implementation of both smart contracts, making trading possible at noon when the combined generation from solar array and energy management system exceed the energy demand. • A P2P decentralized energy trading model leveraging blockchain technology to enhance microgrid efficiency and sustainability. • ERC-20 tokens are used for trading purpose. • Demand Response contract is written for balanced microgrid operation. • Employs Web3 library for direct and smooth interaction between microgrid and blockchain.