Search Results (61)

Ukraine’s renewable electricity transition combines decarbonisation with wartime resilience and post-war reconstruction. This study examines the relationship between installed renewable capacity and realised electricity generation using a structured institutional diagnostic; a same-year 2023 benchmark with Poland, Romania, and Slovakia; the capacity-realisation index (CRI); the capacity-deficit ratio (CDR); and the exploratory k-means clustering of 42 European electricity systems. The clustering model uses four non-overlapping 2023 capacity variables—renewable hydropower, onshore wind, solar PV, and bioenergy—while treating pure pumped storage separately as a flexibility characteristic. Renewables accounted for 24.8% of Ukraine’s installed electricity capacity and 18% of generation in 2023, yielding a CRI of 0.73, compared with 0.59 in Poland, 0.76 in Romania, and 0.68 in Slovakia. Ukraine therefore does not exhibit uniquely weak capacity–generation alignment within the selected benchmark. Nevertheless, the summer 2024 CDR of 19.2% and Energy Community scores of 72% for decarbonisation, 56% for markets, and 26% for energy security document substantial system stress. In the exploratory k = 3 solution (silhouette = 0.645), Ukraine belongs to a 33-case cluster and lies 0.563 standardised units from its centroid. The combined evidence is consistent with, but does not prove, constraints related to settlement discipline, grid evacuation, balancing, project bankability, and wartime security. Policy priorities include audited arrears resolution, financeable auctions and PPAs, curtailment and war-risk provisions, targeted grid reinforcement, competitive flexibility procurement, and solar-plus-storage systems for critical facilities. 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

Quantum Secure Multi-party Summation (QSMS) is a fundamental primitive of Quantum Secure Multi-party Computation (QSMC), enabling multiple participants to jointly compute the sum of their private inputs without disclosing individual data. However, most existing QSMS protocols require all participants to possess full quantum capabilities and often rely on pre-shared keys, auxiliary mask transmission, or multiple trusted third parties, resulting in high communication overhead and limited practicality. To address these limitations, we propose an efficient Semi-Quantum Secure Multi-party Summation (SQSMS) protocol based on d-dimensional n-particle entangled states. By exploiting the global correlation properties of high-dimensional entangled states, the proposed protocol generates correlated random masks directly from quantum measurement outcomes. These masks cancel automatically during the aggregation process, eliminating the need for additional mask distribution and transmission. Compared with existing QSMS schemes, the proposed protocol reduces communication overhead, improves quantum efficiency, and avoids reliance on pre-shared keys or multiple trusted third parties. Moreover, only simple measurement operations are required from classical participants, making the protocol more practical for semi-quantum environments. We further provide formal correctness and security analyses of the proposed protocol and conduct quantum circuit simulations using the IBM Qiskit platform to demonstrate its feasibility. Moreover, based on the proposed summation protocol, we design several extended application protocols, including anonymous voting, anonymous auction, and anonymous ranking, which further illustrate the scalability and practical applicability of the proposed scheme. Full article

The rapid growth of renewable energy and the inherent volatility of wind power grid integration have imposed stringent requirements on power system security and economic operation. To address this challenge, energy storage systems (ESSs) are widely adopted as flexible regulation tools; however, their high capital costs make the shared energy storage model a more efficient and viable solution. This paper proposes an optimal configuration model for wind farms participating in shared energy storage (SES) based on cooperative game theory. First, integrating wind power output forecasting data and market electricity price information, a wind-storage combined optimization model accounting for wind power uncertainty is first established. Subsequently, a core pricing strategy integrating the core allocation rule with the Vickrey–Clarke–Groves (VCG) auction mechanism is proposed to realize the fair allocation of energy storage resources and effective revenue incentives. Finally, comparative experiments between the proposed core pricing mechanism and the fixed pricing mechanism verify its superiority in terms of social welfare, budget balance, and allocation fairness. The results demonstrate that the proposed mechanism not only enhances the overall social benefits of the wind-storage system but also effectively ensures the incentive compatibility of all participants and the stability of the alliance, providing feasible theoretical and methodological support for the economic dispatch of wind-farm-shared energy storage. Full article

Blockchain-based auctions often utilize smart contracts to automate auction rules, with much research focusing on enhancing privacy and fairness through cryptographic techniques. However, the authenticity of external data input into these systems is frequently overlooked. In particular, rational nodes may manipulate bidding data by submitting false types to maximize their utility, compromising market fairness and the reliability of auction outcomes. The aim of this study is to propose an alternative blockchain-based auction mechanism to incentivize nodes to report types honestly. We propose the Mixed-Node Advertising Auction (MNAA) mechanism for digital advertising auctions on blockchain systems. MNAA integrates quasi-linear and value maximization utility models to design allocation and pricing rules that eliminate nodes’ incentives to misreport their types, ensuring the authenticity of data submitted to the auction. To enhance efficiency, MNAA employs state channel technology and off-chain smart contracts, reducing main chain interactions. Theoretical analysis confirms that MNAA incentivizes truthful behavior and ensures security and correctness. Simulation results show that MNAA outperforms Generalized Second Price (GSP), Mixed Bidders with Private Classes (MPR), and Vickrey–Clarke–Grooves (VCG) auctions in terms of liquid social welfare (LSW), publisher revenue, and allocation efficiency, while also improving the transaction throughput and showing good performance in terms of transaction costs and latency. Full article

In the Internet of Vehicles (IoV), connected vehicles generate high-load perception tasks with large-scale and multimodal sensitive data, imposing strict requirements on latency, computing, and privacy. Existing solutions still suffer from high task service latency and privacy risks. To address these issues, this paper proposes an integrated framework that jointly considers multi-flow task offloading, adaptive privacy preservation, and latency-aware resource incentive mechanism. Specifically, we propose a Location-Aware and Trust-based (LA-Trust) dual-node task offloading algorithm based on deep reinforcement learning (DRL), which treats pre-partitioned subtasks as multiple parallel flows and enables flow-level collaborative offloading optimization across neighboring nodes, allows subtask data uploading and processing to proceed concurrently, and incorporates node security into decision making. To further enhance privacy protection, a Distribution-Aware Local Differential Privacy (DA-LDP) algorithm is designed to adaptively inject artificial noise according to data heterogeneity, balancing privacy protection and task execution accuracy. In addition, a Delay-Cost Reverse Auction (DC-RA) algorithm is proposed to further reduce latency by introducing wireless channel modeling between idle vehicles and edge nodes into the incentive mechanism. Experimental results show that the proposed framework improves task execution accuracy by 38% and reduces offloading cost, delay, incentive cost, and auction communication latency by 64.41%, 64.64%, 19%, and 44%, respectively, while more than 60% of tasks are offloaded to high-trust nodes. Full article

Simultaneous ascending auctions find extensive applications in spectrum licensing and advertising space allocation. However, existing quantum sealed-bid auction protocols suffer from dual limitations: they cannot support multi-item simultaneous bidding scenarios, and their reliance on complex quantum resources along with requiring full quantum operational capabilities from bidders fails to accommodate practical constraints of quantum resource-limited users. To address these challenges, this paper proposes a multi-party semi-quantum simultaneous ascending auction protocol based on single-particle states. The protocol employs a trusted honest third party (HTP) responsible for quantum state generation, distribution, and security verification. Bidders determine their groups through quantum measurements and privately encode their bid vectors. Upon successful HTP authentication, each bidder obtains a unique identity code. During the bidding phase, HTP dynamically updates quantum sequences, allowing bidders to submit bids for multiple items by performing only simple unitary operations. HTP announces the highest bid for each item in real time and iteratively generates auction sequences until no new highest bid emerges, thereby achieving simultaneous ascending auctions for multiple items. It acts as a quantum-secured signaling layer, ensuring unconditional security for bid transmission and identity verification while maintaining classical auction logic. Quantum circuit simulations validate the protocol’s feasibility with current technology while satisfying critical security requirements, including anonymity, verifiability, non-repudiation, and privacy preservation. It provides a scalable semi-quantum auction solution for resource-constrained scenarios. Full article

As electric vehicles (EVs) gain popularity, the existing public charging infrastructure is struggling to keep pace with the rapidly growing demand for the immediate charging needs of EVs. V2V power trading has gradually attracted widespread attention and development. EVs need to transmit sensitive information, such as transaction plans, through communication entities in the Internet of Vehicles (IoV). This could lead to leaks of sensitive information, thereby threatening the fairness of transactions. In addition, due to the differences in the cryptographic systems of entities, communication between entities faces challenges. Therefore, a privacy-preserving scheme for V2V double auction power trading based on heterogeneous signcryption and IoV is proposed. Firstly, a heterogeneous signcryption algorithm is designed to realize secure communication from certificateless cryptography to identity-based cryptography. Secondly, the scheme employs a pseudonym mechanism to protect the real identities of EVs. Furthermore, a verification algorithm is designed to verify the information sent by EVs and ensure the traceability and revocation of malicious EVs. The theoretical analysis shows that the proposed scheme could serve common security functions, and the experiment demonstrates that the proposed scheme reduces communication costs by about 14.56% and the computational cost of aggregate decryption by 80.51% compared with other schemes in recent years. Full article

With the rapid advancement in artificial intelligence, autonomous driving has emerged as a prominent research frontier. Autonomous vehicles rely on high-precision high-definition map data, necessitating timely map updates by map companies to accurately reflect road conditions. This paper proposes an efficient and fair map-data-sharing mechanism for vehicular networks. To encourage vehicles to share data, we introduce a reputation unit to resolve the cold-start issue for new vehicles, effectively distinguishing legitimate new vehicles from malicious attackers. Considering both the budget constraints of map companies and heterogeneous data collection capabilities of vehicles, we design a fair incentive mechanism based on the proposed reputation unit and a reverse auction algorithm, achieving an optimal balance between data quality and procurement costs. Furthermore, the scheme has been developed to facilitate mutual authentication between vehicles and Roadside Unit(RSU), thereby ensuring the security of shared data. In order to address the issue of redundant authentication in overlapping RSU coverage areas, we construct a Merkle hash tree structure using a set of anonymous certificates, enabling single-round identity verification to enhance authentication efficiency. A security analysis demonstrates the robustness of the scheme, while performance evaluations and the experimental results validate its effectiveness and practicality. Full article

Our research analyzes the design of an auction model for railway transportation on the South-East Railway of Peru, managed by Ferrocarril Transandino S.A. (Fetransa) and operated by PeruRail. Initially, the regulatory framework aimed to promote competition in railway transportation through timetable auctions and infrastructure access. However, the concession has resulted in a vertically integrated structure that favors PeruRail, which faces minimal direct competition, controls high-demand time slots, and hinders the entry of other operators due to strategic and structural access barriers. To address these distortions, we propose reforming the auction mechanism to neutralize these advantages and enhance competition. In this revised framework, the track usage fee will serve as the competitive factor, with the highest bid above a minimum base rate securing the allocation. Additionally, we propose the implementation of asymmetric tariffs to compensate for the higher costs faced by operators with fewer economies of scale, technological optimizations to facilitate equitable access to time slots, and stricter oversight mechanisms to ensure transparency in timetable allocation. These measures aim to balance the market and safeguard competition through a more equitable and efficient auction design. Full article

With The integration of renewable energy sources into smart grids and electric vehicle (EV) charger-sharing networks is essential for achieving the goal of environmental sustainability. However, the uneven distribution of distributed energy trading among EVs, fixed charging stations (FCSs), and mobile charging stations (MCSs) introduces challenges such as inadequate supply at FCSs and prolonged latencies at MCSs. In this paper, we propose a multi-agent deep reinforcement learning (MADRL)-based auction algorithm for energy trading that effectively balances charger supply with energy demand in distributed EV charging markets, while also reducing total charging latency. Specifically, this involves a MADRL-based hierarchical auction that dynamically adapts to real-time conditions, optimizing the balance of supply and demand. During energy trading, each EV, acting as a learning agent, can refine its bidding strategy to participate in various local energy trading markets, thus enhancing both individual utility and global social welfare. Furthermore, we design a cross-chain scheme to securely record and verify transaction results of energy trading in decentralized EV charger-sharing networks to ensure integrity and transparency. Finally, experimental results show that the proposed algorithm significantly outperforms both the second-price and double auctions in increasing global social welfare and reducing total charging latency. Full article

A time-lock puzzle encapsulates a secret message such that the receiver needs to perform a sequential computation, which takes a specified amount of time, to recover the message. Time-lock puzzles can be used in various scenarios, such as sealed-bid auctions, fair contract signing, and so on. The time required to generate a time-lock puzzle and the time needed to solve it are asymmetric, making the verification of a time-lock puzzle crucial. Before solving the puzzle, the solver needs to verify the validity of the puzzle to avoid computing invalid time-lock puzzles. After the puzzle has been solved, it is essential for a third party to confirm the correctness of the solution. This paper proposes a framework for time-lock puzzles, providing both pre-verification and post-verification functionalities, and outlines the security requirements of this framework. Furthermore, we present a practical construction based on iterated squaring in the RSA group and analyze the security of the specific construction. Finally, we implement this construction in Python and demonstrate its efficiency in different settings when implemented in practice. Full article

The transactive energy market (TEM) is a recent development in energy management that enables prosumers to trade directly, promising many environmental and economic benefits. Prosumer trading necessitates sharing information to facilitate transactions. Additionally, many TEMs propose using blockchains to manage auctions and store transactions. These facts introduce privacy concerns: consumption data, trading history, and other identifying information pose risks to users if leaked. Anonymity by trading under a pseudonym is commonly presented as a solution; however, this creates risks for market participants: scammed users will not have recourse, and users with innocent malfunctions may be banned from trading. We propose the Individually Linkable Pseudonymous Trading Scheme (ILPTS), which enables users to trade under a pseudonym, protecting their identity, while a smart contract monitors reputations and can temporarily deanonymize a user, ensuring market integrity. ILPTS was developed in stages. Examination of existing TEM literature was performed to identify desirable features. Analysis of cryptography literature was performed to identify techniques that may confer certain features. It was found through formal analysis that ILPTS adheres to identified design goals, improves upon existing solutions, and resists common attacks against TEMs. Future work includes software simulation and on-device implementation to further verify security and feasibility. Full article

In this paper, we present our work on developing a Smart Pricing Policies module specifically designed for individual users and Mobile Network Operators (MNOs). Our framework will operate in a multi-MNO blockchain radio access network (B-RAN) and is tasked with determining prices for resource sharing among users and MNOs. Our sophisticated adaptive pricing system can adjust to situations where User Equipment (UE) shifts out of the coverage area of their MNO by immediately sealing a contract with a different MNO to cover the users’ needs. This way, we aim to provide financial incentives to MNOs while ensuring continuous network optimization for all parties involved. Our system accomplishes that by utilizing deep reinforcement learning (DLR) to implement a reverse auction model. In our reinforcement learning scenario, the MNOs, acting as agents, enter a competition and try to bid the most appealing price based on the user’s request, and based on the reward system, agents that do not win in the current round will adjust their strategies in an attempt to secure a win in subsequent rounds. The findings indicated that combining DRL with reverse auction theory offers a more appropriate method for addressing the pricing and bid challenges, and additionally, administrators can utilize this strategy to gain a notable edge by dynamically selecting and adjusting their methods according to the individual network conditions and requirements. Full article

This work presents a private consortium blockchain-based automotive data monetization architecture implementation using the Substrate blockchain framework. Architecture is decentralized where crowd-sourced data from vehicles are collectively auctioned ensuring data privacy and security. Smart Contracts and OffChain worker interactions built along with the blockchain make it interoperable with external systems to send or receive data. The work is deployed in a Kubernetes cloud platform and evaluated on different parameters like throughput, hybrid consensus algorithms AuRa and BABE, along with GRANDPA performance in terms of forks and scalability for increasing node participants. The hybrid consensus algorithms are studied in depth to understand the difference and performance in the separation of block creation by AuRa and BABE followed by chain finalization through the GRANDPA protocol. Full article