Spatiotemporal optimization of peer-to-peer energy transactions in sustainable ecosystem clusters with blue-green hydrogen integration and e-transportable storage under carbon neutrality goals

Purpose This study aims to explore the market factors of carbon trading in the Australian construction industry and further develops an index for overall market factor level. Design/methodology/approach Thirty market factors were identified from literature and ranked by experts in an expert forum. Fuzzy synthetic evaluation was used in developing the market index. Analyses were conducted using statistical package for social sciences version 27 and R software. Findings Factor analysis was used to cluster the factors into five components, and they were used as the input variables for fuzzy analysis. The respective components were: gross domestic product factors, demand for carbon credits factors, profits and energy gap factors, trading factors and transaction costs factors. Originality/value The index developed can exist as a multidimensional framework for measuring market factors that relate to carbon trading in construction projects. For stakeholders and policymakers, the market model serves as a guide for the critical market factors to prioritise in carbon trading projects.

The advancement of information technology has encouraged organizations to transform their data management practices, including those within the electrical power sector. PT PLN (Persero) UP3 Lubuk Pakam previously utilized Google Sheets to store and manage electrical energy loss data. However, this approach posed data security risks, as the files could be accessed and modified by unauthorized individuals. To overcome these limitations, this study developed a web-based information system specifically designed for the Electricity Energy Transaction Division (TEL) to replace Google Sheets as a platform for managing energy loss data. The proposed web application integrates a Role-Based Access Control (RBAC) mechanism, ensuring that only administrators are authorized to input, edit, or delete data, while other users can only view validated records. Furthermore, the system incorporates interactive data visualization through real-time charts, facilitating effective monitoring and analysis of electrical energy losses. The implementation of this system is expected to enhance data security, accuracy, and management efficiency within the TEL Division of PT PLN UP3 Lubuk Pakam.

The strong uncertainty of renewable energy poses significant reliability and safety challenges for the coordinated operation of multi-integrated energy systems (MIES). To address this, a data-driven two-stage distributed robust collaborative optimization scheduling model for MIES is proposed, based on a spatiotemporal fusion conditional diffusion model (STF-CDM). First, to more accurately capture the uncertainty in renewable energy output, the model utilizes a scenario set generated by the STF-CDM model and reduced via the K-means clustering algorithm as the initial renewable energy scenarios for the distributed robust optimization set. The STF-CDM model employs a Temporal module component (TMC) unit composed of Transformer time-series modules and a Spatial module component (SMC) unit composed of CNN neural networks for feature extraction and fusion of time-series and spatial-series data. Second, a benefit allocation method based on multi-energy trading contribution rates is proposed to achieve equitable distribution of cooperative gains. Finally, to protect participant privacy and enhance computational efficiency, an alternating direction multiplier method (ADMM) coupled with parallelizable column and constraint generation (C&CG) is employed to solve the energy trading problem. The case analysis results demonstrate that the STF-CDM model proposed in this study exhibits superior performance in addressing the uncertainty of renewable energy output. Concurrently, the asymmetric Nash game mechanism and the ADMM-C&CG solution algorithm proposed in this study achieve a fair and reasonable distribution of benefits among all participants when handling energy transactions and cooperative gains. This is accomplished while ensuring system robustness, economic efficiency, and privacy.

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.

Hierarchical Control Strategy for Electric Vehicles: Managing Travel Uncertainty and Engaging EV Owners in Transactive Energy Markets

A scalable decentralized framework for transactive energy management in low-voltage residential community

Nash bargaining-based game for transactive energy of multi-microgrids with dynamic carbon emission factor

Blockchain for transactive energy management in networked neighborhood microgrids

Advances in the application of model-free reinforcement learning in protecting transactive energy systems against cyberthreats: A review

Double objective decentralized transactive energy market framework for multi-energy microgrid

Effective energy management is essential for the sustainability of rapidly growing smart cities. Traditional centralized systems, which often rely on Artificial Neural Networks (ANNs), struggle with scalability and vulnerability to cybersecurity threats for IoT-enabled networks. To remedy these concerns, this study introduced a Spatiotemporal Blockchain Network (SBN) and a Blockchain-Integrated Transformer-based Energy Prediction Model (TEPM). The SBN provides transparency, immutability, and added protection for sensitive energy transactions and the TEPM utilizes sophisticated attention mechanisms to accurately predict energy using spatiotemporally dynamic characteristics. Overall, the future incorporation of the SBN and TEPM would allow for real-time energy optimization and corresponding reduction in wasted energy and operational efficiency maximization. The framework was implemented and validated as a case study in MATLAB/Simulink with Hyperledger Fabric, achieving a 22% improvement in energy efficiency, a forecasting error of 3.2% (MAPE), a throughput rate of 450 transactions per second and supported a scale of over 15,000 IoT nodes. These results indicate the system’s ability to revolutionize urban energy management by improving the capacities for secure, scalable and adaptive energy distribution, and therefore support sustainable and resilient smart city infrastructures.

Distributed and adaptive robust energy management of an active distribution network with multi-microgrid considering transactive energy

Transactive energy and peer-to-peer energy trading based on blockchain: A comprehensive review and a generalized cyber-physical framework

Distributionally robust framework for decentralized P2P energy trading in modern transactive energy markets

The transition towards sustainable and low-carbon energy systems highlights the crucial role of buildings, microgrids, and local communities as key actors in enhancing resilience and achieving decarbonization targets. The application of artificial intelligence (AI) is of paramount importance as it enables accurate prediction, adaptive control, and optimization of distributed resources. This paper reviews recent advances in AI applications for transactive energy (TE) and dynamic energy management (DEM), focusing on their integration with building automation, microgrid coordination, and community energy exchanges. It also considers the emerging role of life cycle-based methods, such as life cycle assessment (LCA) and life cycle cost (LCC), in extending operational intelligence to long-term environmental and economic objectives. The analysis is based on a curated set of 97 publications identified through structured queries and thematic filtering. The findings indicate substantial advancement in methodological approaches, notably reinforcement learning (RL), hybrid model predictive control, federated and edge AI, and digital twin applications. However, this study also uncovers shortcomings in the integration and interoperability of sustainability. This paper contributes by consolidating fragmented research and proposing a multi-layered AI framework that aligns short-term performance with long-term resilience and sustainability.

The increasing demand for grid stability and energy efficiency has propelled the advancement of intelligent control and monitoring technology for smart metering systems. This study examines scholarly publications, research papers, technical reports, and industry publications over a fifteen-year period (2010–2025). The evaluation emphasizes significant technological advancements, such as blockchain-enabled energy transactions, improved bidirectional communication protocols, automated demand response systems, and the integration of IoT with artificial intelligence. Despite these advancements, ongoing challenges encompass insufficient load separation, absence of intelligent load management, difficulties in energy supply-demand equilibrium, restricted bidirectional communication, and deficiencies in device classification. The results indicate the need to include edge computing, predictive load management driven by artificial intelligence, interoperability with hybrid energy systems, and improved cybersecurity. These gaps can be addressed through multidisciplinary cooperation among engineers, data scientists, legislators, and energy companies. Future smart meters should, according to the study, utilize cutting-edge technologies to offer sustainable grid management, enhance consumer satisfaction, and advance the energy economy. By incorporating sophisticated communication protocols, load management optimization, and intelligent control mechanisms, smart metering systems may significantly improve sustainable energy solutions and modernize smart grids.

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.

MG-OPT: intelligent multi-objective Pareto-based optimization framework and transactive energy for Hybrid Renewable Energy Systems with hydrogen integration

Multiperiod Equilibrium in Coupled Transportation System and Transactive Energy Community