Search Results (135)

Peer-to-peer (P2P) energy trading combines local energy resources, market coordination, data-driven decisions, and transaction management. This review examines how machine learning (ML) and blockchain are used across these functions and separates market and ledger processes from physical electricity delivery. A structured review procedure was applied to a corpus of 52 peer-reviewed journal articles, including the core P2P energy-trading evidence and a limited number of closely related contextual studies, supplemented by 10 non-journal or foundational sources, using defined search families, screening criteria, and qualitative synthesis. The literature is organized by the functional role of ML and compared across architecture, market operation, trust, consensus, privacy, and implementation. The consensus discussion considers practical Byzantine fault tolerance, Istanbul Byzantine fault tolerance, proof-of-authority, and application-oriented Byzantine-fault-tolerance variants, while the privacy discussion distinguishes federated learning, differential privacy, zero-knowledge proofs, and secure multiparty computation. Two deterministic MATLAB examples are included only for illustration. In the five-prosumer forecasting example, regression reduced mean absolute error (MAE) from 0.4240 to 0.2219 kWh and the hourly grid-import mismatch from 30.3529 to 7.9029 kWh. In the 10-peer workflow, five trades settled 7.7587 kWh, corresponding to 59.35% of the horizon-level surplus–deficit denominator defined in the simulation. These examples do not validate feeder feasibility, consensus performance, cryptographic security, or deployment readiness. Full article

The growing adoption of renewable energy and small-scale power producers has increased the need for reliable and transparent peer-to-peer (P2P) energy trading. Traditional centralized markets often struggle with high transaction fees, limited transparency, and a greater risk of manipulation, which restrict efficient energy distribution. To overcome these issues, this study presents a decentralized P2P trading framework that implements a fully functional blockchain-based trading system with smart grid simulation and demonstrates a prototype machine learning forecasting module (Random Forest, 84% accuracy) designed for future integration. The trading mechanism is developed using Ethereum smart contracts and a custom ERC-20 token, the TUM Energy Coin (TEC), enabling secure and traceable energy exchange. System security is strengthened through dual confirmation steps, role-based access control, and consensus-driven market clearing. A double-sided auction model is used to match buyers and sellers fairly. Real-time grid behavior such as fluctuating loads, prosumer generation, and consumer demand is modeled using MATLAB Simulink to reflect realistic operating conditions. To enhance decision-making, a Random Forest model is integrated for load forecasting and dynamic pricing, achieving an accuracy of 84%. The simulation results show improved transaction throughput, more stable pricing, and strong resilience against false-data injection attacks. The primary novelty of this work lies in (1) an entirely operational and validated blockchain-trading system simulation with synchronized time using Simulink, (2) a working Random Forest forecasting tool demonstrating feasibility for incorporation in the future, and (3) an analysis of the system’s robustness in the case of FDIA attacks. The authors point out that the ML component used is a prototype and not yet integrated into the functioning block chain. Full article

Adoption of peer-to-peer (P2P) trading is very challenging, mainly due to numerous issues and limitations such as lack of trust in the concept and awareness of the technical, economic and social benefits. This paper aims to understand how blockchain technology can address the current issues/limitations of P2P distributed solar energy (DSE) trading. A series of semi-structured interviews were conducted with 23 community energy stakeholders to confirm and expand the stakeholder issues identified in the literature review. A case representing community energy projects was selected to (1) develop and implement a blockchain system and (2) evaluate its ability to eliminate (or reduce) stakeholder issues and meet stakeholder expectations. A blockchain-enabled P2P trading platform was developed using an Ethereum backend. The system clearly demonstrated its ability to deliver full or partial solutions to 12 stakeholder issues. Two stakeholder issues are unable to be addressed via the blockchain platform since they uncovered the weaknesses of blockchain technology. The P2P trading platform has also demonstrated its ability to facilitate decentralized trading and data management. The outcome of this study indicates the areas of P2P trading projects that can be improved by the application of blockchain technology. Full article

Blockchain-based peer-to-peer (P2P) energy trading enables prosumers to sell surplus electricity directly to one another without a central intermediary, but its open ledger reveals each participant’s bid price to every blockchain node, including the distribution system operator (DSO) that settles the trades. Prior work has addressed this concern by using inner product encryption (IPE) to encode each bid as a vector and perform matching directly over ciphertexts, yet the resulting pairing-based comparison and heavy on-chain heap restructuring still incur substantial gas costs. To resolve this issue, this paper proposes two orthogonal optimization methods. First, we replace the pairing-based IPE of the prior baseline with a matrix-based IPE, which substitutes pairing operations with matrix multiplication and a trace evaluation. This allows compact ternary encoding of integers and enables ciphertext entries to be packed into narrower Solidity integer types. Second, we introduce two heap-management policies: (i) the index-based heap, which exchanges integer indices instead of full ciphertexts during swaps, and (ii) the root-retention heap, which eliminates redundant heap restructuring under residual rebidding. Our performance analysis shows that the proposed optimization strategies substantially reduce the total gas consumption of the energy trading system compared with the pairing-based baseline, and the heap-management policies deliver consistent savings regardless of the underlying cryptographic primitive. Full article

Distributed integrated energy microgrids support the low-carbon transition of regional energy systems. However, multi-agent trading among microgrids still faces insufficient cross-market coordination, weak low-carbon incentives, and difficulties in fair benefit allocation. To address these issues, this paper proposes a cooperative-game-based low-carbon optimal operation strategy for multi-microgrids using multi-agent deep reinforcement learning. First, an energy-carbon-green certificate peer-to-peer coordinated trading mechanism and a green-carbon offsetting-based dual-incentive model are developed to link energy exchange, carbon quota adjustment, and green certificate circulation. Second, a Nash bargaining-based cooperative game model is formulated for multi-commodity P2P trading to maximize coalition benefits and ensure a fair allocation of surplus. Finally, the cooperative game is transformed into a Markov decision process, and a centralized training and decentralized execution framework with homogeneous agents is constructed based on the multi-agent soft actor-critic algorithm. Case studies using data from the Yangtze River Delta region of China show that the proposed method achieves a 1.25% optimality gap compared with the centralized MILP benchmark and reduces the coalition operating cost by 8.19% relative to independent operation. The carbon trading costs of the three microgrids are reduced by 37.27%, 40.13%, and 33.82%, respectively, verifying the economic applicability of the proposed method. Full article

The rapid growth of electric vehicle (EV) adoption presents significant challenges for power system stability while creating new opportunities for decentralized energy management. Peer-to-peer (P2P) energy networks have emerged as a promising approach for transforming EVs from passive loads into coordinated grid assets. This paper presents a comprehensive review of EV-P2P integration through a three-layer architectural framework that systematically connects physical infrastructure, market mechanisms, and intelligent control strategies. The Physical Layer reviews how V2X technologies and bidirectional charging enable EVs to operate as flexible storage resources and ancillary service providers. The Transactional Layer reviews on blockchain-based platforms, auction mechanisms, and game-theoretic models for secure energy trading. The Intelligence Layer reviews advanced control strategies, including decentralized optimization methods such as the Alternating Direction Method of Multipliers (ADMM) and Deep Reinforcement Learning. Collectively, the reviewed studies demonstrate that these approaches enable EVs to operate as flexible loads, distributed storage resources, and ancillary service providers, while improving energy trading efficiency, reducing operating costs, and alleviating network congestion under simulated operating conditions. Despite these promising results, a substantial gap remains between simulation-based studies and practical implementation. Future research should prioritize integrated pilot projects to evaluate scalability, interoperability, cybersecurity, and regulatory compliance under realistic operating conditions. Full article

Energy industrial zones can act as a transformative model for industrial decarbonization by integrating renewable energy infrastructure directly with industrial production. By combining energy industrial zones with the energy community framework and peer-to-peer (P2P) energy trading, this study proposes a pathway toward 100% renewable energy sources. The model was tested using a techno-economic assessment applied to the Bravar-Jasenice case study in Croatia featuring 12 MW of solar PV, 10 MW of wind power, and a 9.3 MW biogas cogeneration plant. This integrated approach can achieve 80–90% energy self-sufficiency and reduce electricity expenditures for participating enterprises by approximately 15%. Furthermore, the system facilitates an annual reduction of roughly 20,000 tonnes of CO₂ emissions, thus directly supporting European Green Deal objectives. The study also highlights the potential for industrial symbiosis, including green hydrogen production, data centre integration, and waste heat recovery. Ultimately, the proposed framework provides a robust strategy for enhancing industrial competitiveness and ensuring energy security through localized, sustainable energy management. Full article

This paper proposes a regional distributed energy operation framework that integrates Power-to-X (P2X)-based sector coupling with Distributionally Robust Optimization (DRO) for distribution network operation environments in special zones established under South Korea’s Special Act on the Promotion of Distributed Energy. The conventional South Korean electricity market has primarily operated under a centralized Cost-Based Pool (CBP) structure, where the participation of small-scale renewable energy providers has been limited due to requirements for centralized dispatch generators. To address these structural limitations, the South Korean government introduced the distributed energy special zone policy and has promoted a Peer-to-Peer (P2P)-based electricity trading mechanism that enables direct electricity transactions between renewable energy providers and consumers within regional distribution networks. As a result of these policy initiatives, investment in small-scale renewable energy projects within designated special zones is expected to increase significantly; however, the limited local demand capacity of regional distribution networks simultaneously imposes clear constraints on the accommodation of renewable energy. Therefore, this study applies P2X-based sector coupling technologies to improve the capability to accommodate renewable energy within special zones while simultaneously establishing new energy business models. In addition, DRO is incorporated into the proposed framework to demonstrate the system’s economic feasibility and operational robustness under high uncertainty in electricity prices. Full article

With the increasing penetration of distributed renewable energy, coordinated operation between distribution networks and multiple integrated energy microgrids (IEMs) has become increasingly important for improving operational economy and maintaining system security. To address the insufficient integration of network constraints, P2P energy sharing, and contribution-based benefit allocation, this paper proposes a hybrid game-based optimal scheduling model for multi-IEM systems under distribution network constraints. In the upper level, a Stackelberg game is established between the distribution system operator (DSO) and the multi-IEM alliance to coordinate electricity trading and distribution network operation. In the lower level, an asymmetric Nash bargaining-based cooperative game is developed to enable peer-to-peer (P2P) energy sharing and allocate cooperative benefits according to the actual energy-sharing contributions of individual IEMs. Furthermore, a distributed solution framework combining the Success-History Adaptive Differential Evolution (SHADE) algorithm and an improved alternating direction method of multipliers (ADMM) is adopted to preserve data privacy and improve computational efficiency. Case studies based on the modified IEEE 33-bus distribution system demonstrate that the proposed method eliminates voltage violations and reduces network losses by 9.0%. Meanwhile, the proposed cooperative mechanism decreases the total operating cost of the IEM alliance by 7815.8 CNY and yields a more contribution-consistent profit allocation among participating microgrids. In addition, the improved ADMM reduces computation time by 42.1% compared with the conventional serial ADMM. The results demonstrate the effectiveness of the proposed method in enhancing distribution network security, promoting renewable energy sharing, and improving the economic performance of multi-IEM systems. Full article

With increasing penetration of distributed energy resources, peer-to-peer (P2P) electricity trading has attracted attention for locally utilizing surplus renewable energy. This paper proposes a distributed consensus-based P2P electricity trading method that explicitly considers prosumer equipment operation constraints. Each prosumer autonomously solves a daily scheduling problem considering electricity demand, PV generation, battery operation, grid purchase and sale, and P2P trades with neighboring prosumers. P2P prices and desired trading quantities are iteratively adjusted through local information exchange. After convergence, bidirectional trades are converted into net one-way trades, and the final feasible daily schedule is obtained by re-optimizing with fixed trading quantities. Numerical simulations were conducted for six low-voltage prosumers using annual residential demand data and a representative daily PV generation profile. In the base case, the proposed method reduced annual electricity cost by 13.7% compared with the no-P2P case, while its total cost was only 2.3% higher than that of the centralized benchmark. Unlike the centralized benchmark, which increased costs for some prosumers, the proposed method reduced costs for all prosumers. Wheeling-charge sensitivity analysis showed that the charge affects P2P trading volume and benefit allocation. Future work will address tariff design, PV uncertainty, scalability, and distribution-network constraints. Full article

Modern smart cities increasingly depend on resilient and intelligent energy infrastructures to maintain critical urban services during large-scale disturbances and multi-fault conditions. Conventional restoration approaches are often limited by centralized operation, delayed response, and inadequate coordination of distributed energy resources (DERs) under emergency conditions. To address these challenges, this paper proposes a Federated AI-Driven Urban Energy Resilience Framework for Smart City Critical Infrastructure Restoration using Virtual Power Plant (VPP) coordination, blockchain-enabled peer-to-peer (P2P) energy trading, and intelligent distributed energy management. The proposed framework is validated on the IEEE 118-bus radial distribution system under severe dual-fault outage conditions, representing urban disaster-induced infrastructure interruptions. Critical urban service zones, including healthcare support systems, emergency loads, smart residential sectors, and EV charging corridors, are considered during the restoration process. The Seagull Optimization Algorithm (SOA) is employed to optimize DER dispatch and improve restoration performance under operational constraints. A progressive restoration strategy comprising conventional outage conditions, VPP-assisted restoration, blockchain-enabled decentralized energy trading, and AI-driven coordinated restoration is analyzed. Simulation results demonstrate that the proposed framework significantly enhances urban energy resilience by increasing load restoration from 55.05% to 94.20%, reducing Energy Not Supplied (ENS), improving voltage stability, and lowering interruption-related economic losses. The minimum bus voltage improves to 0.965 p.u. under the proposed coordinated restoration strategy. The results show that coordinated VPP operation and blockchain-based energy sharing can support reliable restoration of critical urban infrastructure during major outage conditions. The results indicate that integrating AI-assisted VPP coordination with secure decentralized energy trading can effectively support smart city critical infrastructure continuity during extreme outage conditions. The proposed framework provides a scalable and resilient solution for future intelligent urban energy systems and disaster-resilient smart city applications. Full article

To achieve low-carbon and cost-effective operation of multi-park electricity–heat–hydrogen integrated energy systems (EHHSs), this paper proposes a low-carbon dispatch framework based on federated safe reinforcement learning. First, a multi-park EHHS dispatch model is established by considering heterogeneous park characteristics, electricity–heat–hydrogen coupling, stepped carbon trading, and peer-to-peer (P2P) energy trading. Then, to address the coupled challenges of privacy preservation, operational coupling, and safety constraints, the dispatch problem is formulated as a constrained Markov decision process (CMDP). On this basis, a federated safe proximal policy optimization algorithm (FedSafePPO) is developed by integrating PPO, Lagrangian-based safety constraint handling, and federated parameter aggregation. The proposed method enables each park to learn a local dispatch policy from private data while sharing global knowledge without exchanging raw operational data. In addition, an actor–dual-critic architecture is adopted to jointly evaluate economic returns and constraint costs, thereby improving convergence stability and dispatch feasibility. Case studies involving three heterogeneous parks—industrial, commercial, and residential—demonstrate that the proposed method effectively reduces total operating costs and carbon emissions while satisfying system constraints. Compared with PPO, FedPPO, and SafePPO, the proposed FedSafePPO achieves superior low-carbon economic performance, greater training stability, and better adaptability to heterogeneous operating conditions. The results verify the effectiveness and engineering applicability of the proposed method for the low-carbon dispatch of multi-park EHHSs. Full article

This study presents a comprehensive clustering analysis of residential prosumer profiles for optimizing control and peer-to-peer (P2P) energy trading in community renewable energy systems (CRES). Using data from 25 prosumer households equipped with rooftop solar photovoltaic (PV) systems and electric vehicle (EV) charging capabilities, this study implements and compares k-means and spectral clustering algorithms to identify optimal segmentation strategies for prosumer energy management. K-means clustering identifies seven practical prosumer categories with a silhouette coefficient of 0.17, while spectral clustering achieves superior mathematical separation with a silhouette coefficient of 0.275 in ten clusters, though producing six singleton outliers. The k-means solution demonstrates three primary prosumer categories: net producers, net consumers, and balanced profiles. Cluster size variation requires adaptive optimization, while singleton outliers need custom strategies. EV ownership impact consumption, so future proliferation demands dynamic clustering, and these findings will guide metaheuristic algorithms for energy trading and pricing. Full article

This study investigates how Climate Policy Uncertainty (CPU) influences investments in the Renewable Energy Transition (ET), a relationship widely presumed to be negative, despite the empirical literature reporting mixed and highly heterogeneous results. Using a preregistered systematic review following PRISMA guidelines, we identify seventeen peer-reviewed studies from Web of Science and Scopus. Their quantitative estimates are harmonized using Fisher z-transformations and analyzed within a meta-analytic framework. A global random-effects meta-analysis reveals a small and statistically insignificant average effect of CPU on ET-related investment outcomes, together with extremely high heterogeneity, indicating that a single pooled coefficient is not an informative universal summary. To examine whether part of this dispersion follows an interpretable pattern, we estimate an exploratory mixed-effects meta-regression based on a four-channel transmission framework derived from the reviewed literature. This model accounts for 50.4% of the between-study variance, and only the Macroeconomic channel shows a negative and statistically significant deviation from the reference category ($\beta$ = 1.0700, p = 0.0060). This result should be interpreted cautiously, however, given the small number of studies in each subgroup and the persistence of substantial residual heterogeneity. Overall, the evidence suggests that the CPU does not affect ET-related investment outcomes in a uniform way; rather, the reported relationship varies across contexts, with the strongest negative pattern appearing in studies that capture macroeconomic conditions related to the energy transition, such as foreign direct investment, trade openness, and aggregate green investment. By providing the first meta-analytic quantification of this relationship and a structured mapping of transmission mechanisms, this study offers novel empirical clarity to a fragmented literature. The policy implication is direct, and governments seeking to accelerate the energy transition must prioritize long-term credibility, regulatory stability, and macroeconomic predictability, as these are the domains through which climate policy uncertainty most severely constrains low-carbon investment. Full article

The transition toward capacity-based network tariffs shifts the primary role of battery energy storage systems (BESS) from traditional energy arbitrage to active peak shaving. This paper presents a mixed-integer linear programming (MILP) optimization model for the co-optimization of both BESS size and operation scheduling for multiple prosumers operating individually and within an energy community (EC). Battery aging is accounted for in the optimization model through the state of health (SOH). The framework is evaluated by a comprehensive techno-economic analysis of BESS integration under Slovenia’s multi-block tariff structure. The results demonstrate that while individual distributed BESS integration is highly profitable, centralized EC BESS financially underperforms. Because centralized BESS cannot directly reduce individual contracted power limits, its profitability relies on energy arbitrage, making the initial investment and double grid fees the primary barriers. Conversely, integrating distributed storage with peer-to-peer (P2P) trading minimizes the required BESS capacity while maintaining profitability. The evaluation also reveals that ECs do not automatically act as socio-economic equalizers, indicated by a stable Gini coefficient. However, a break-even analysis reveals the necessary reduction in capital costs to overcome these hurdles, confirming the strong future viability of centralized EC BESS. Full article