Search Results (84)

Traditional electricity market bidding typically focuses on unilateral structures, where independent energy storage units and flexible loads act merely as price takers. This reduces bidding motivation and weakens the balancing capability of regional power systems, thereby limiting the large-scale utilization of renewable energy. To address these challenges and support sustainable power system operation, this paper proposes a double-sided auction market strategy for heterogeneous multi-resource (HMR) participation based on multi-agent reinforcement learning (MARL). The framework explicitly considers the heterogeneous bidding and quantity reporting behaviors of renewable generation, flexible demand, and energy storage. An improved incentive mechanism is introduced to enhance real-time system power balance, thereby enabling higher renewable energy integration and reducing curtailment. To efficiently solve the market-clearing problem, an improved Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithm is employed, along with a temporal-difference (TD) error-based prioritized experience replay mechanism to strengthen exploration. Case studies validate the effectiveness of the proposed approach in guiding heterogeneous resources toward cooperative bidding behaviors, improving market efficiency, and reinforcing the sustainable and resilient operation of future power systems. 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

Unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) systems have developed rapidly in the sixth generation (6G) era. However, factors such as the mobility of ground users and malicious jamming pose significant challenges to systems’ performance and reliability. Against this backdrop, this paper designs a multi-UAV-assisted ISAC system model under malicious jamming environments. Under the constraint of sensing accuracy, the total communication rate of the system is maximized through joint optimization of user association, UAV trajectory, and transmit power. The problem is then decomposed into three subproblems, which are solved using the improved auction algorithm (IAA), dream optimization algorithm (DOA), and rapidly-exploring random trees-based optimizer algorithm (RRTOA). The global optimal solution is approached through the alternating optimization-based predictive scheduling algorithm (AOPSA). Meanwhile, this paper also introduces a long short-term memory (LSTM) network to predict users’ dynamic positions, addressing the impact of user mobility and enhancing the system’s real-time performance. Simulation results show that compared with the baseline scheme, the proposed algorithm achieves a 188% improvement in communication rate, which verifies its effectiveness and superiority. Full article

With the support of the dual-carbon strategy and related policies, China’s offshore wind power has experienced rapid development. However, constrained by the inherent intermittency and volatility of wind power, large-scale expansion poses significant challenges to grid integration and exacerbates government fiscal burdens. To address these critical issues, this paper proposes a multi-market coupling trading model integrating energy storage-equipped offshore wind power into electricity–carbon–green certificate markets for large-scale grid networks. Firstly, a day-ahead electricity market optimization model that incorporates energy storage is established to maximize power revenue by coordinating offshore wind power generation, thermal power dispatch, and energy storage charging/discharging strategies. Subsequently, carbon market and green certificate market optimization models are developed to quantify Chinese Certified Emission Reduction (CCER) volume, carbon quotas, carbon emissions, market revenues, green certificate quantities, pricing mechanisms, and associated economic benefits. To validate the model’s effectiveness, a gradient ascent-optimized game-theoretic model and a double auction mechanism are introduced as benchmark comparisons. The simulation results demonstrate that the proposed model increases market revenues by 17.13% and 36.18%, respectively, compared to the two benchmark models. It not only improves wind power penetration and comprehensive profitability but also effectively alleviates government subsidy pressures through coordinated carbon–green certificate trading mechanisms. Full article

UAV-assisted mobile edge computing combines the flexibility of UAVs with the computing power of MEC to provide low-latency, high-performance computing solutions for a wide range of application scenarios. However, due to the highly dynamic and heterogeneous nature of the UAV environment, the optimal allocation of resources and system reliability still face significant challenges. This paper proposes a two-stage optimization (DSO) algorithm for UAV-assisted MEC, combining Stackelberg game theory and auction mechanisms to optimize resource allocation among servers, UAVs, and users. The first stage uses a Stackelberg game to allocate resources between servers and UAVs, while the second stage employs an auction algorithm for UAV-user resource pricing. Blockchain smart contracts automate task management, ensuring transparency and reliability. The experimental results show that compared with the traditional single-stage optimization algorithm (SSO), the equal allocation algorithm (EAA) and the dynamic resource pricing algorithm (DRP), the DSO algorithm proposed in this paper has significant advantages by improving resource utilization by 7–10%, reducing task latency by 3–5%, and lowering energy consumption by 4–8%, making it highly effective for dynamic UAV environments. Full article

The rapid global shift toward renewable energy necessitates innovative solutions to address the intermittency and variability of solar and wind power. This study presents a comprehensive review and framework for deploying Integrated Energy Storage Systems (IESSs) to enhance grid efficiency and stability. By leveraging a Multi-Criteria Decision Analysis (MCDA) framework, this study synthesizes techno-economic optimization, lifecycle emissions, and policy frameworks to evaluate storage technologies such as lithium-ion batteries, pumped hydro storage, and vanadium flow batteries. The framework prioritizes hybrid storage systems (e.g., battery–supercapacitor configurations), demonstrating 15% higher grid stability in high-renewable penetration scenarios, and validates findings through global case studies, including the Hornsdale Power Reserve (90–95% round-trip efficiency) and Kauai Island Utility Cooperative (15,000+ cycles for flow batteries). Regionally tailored strategies, such as Kenya’s fast-track licensing and Germany’s H2Global auctions, reduce deployment timelines by 30–40%, while equity-focused policies like India’s SAUBHAGYA scheme cut energy poverty by 25%. This study emphasizes circular economy principles, advocating for mandates like the EU’s 70% lithium recovery target to reduce raw material costs by 40%. Despite reliance on static cost projections and evolving regulatory landscapes, the MCDA framework’s dynamic adaptation mechanisms, including sensitivity analysis for carbon taxes (USD 100/ton CO₂-eq boosts hydrogen viability by 25%), ensure scalability across diverse grids. This work bridges critical gaps in renewable energy integration, offering actionable insights for policymakers and grid operators to achieve resilient, low-carbon energy systems. Full article

The transition towards renewable energy has increased the importance of virtual power plants (VPPs) in integrating distributed energy resources (DERs). However, questions remain regarding the most appropriate auction mechanisms (pay-as-bid (PAB) versus pay-as-clear (PAC)) and imbalance penalty structures, which significantly influence VPP bidding strategies and market operations. This study employs a three-stage stochastic programming model to evaluate VPP bidding behaviors under these auction mechanisms while also considering the effects of imbalance penalty structures. By simulating various market scenarios, the results reveal that PAC markets offer higher VPP revenues due to settlement at the market-clearing price; they also exhibit greater volatility and elevated imbalance penalties. For instance, power deviations in PAC markets were 52.60% higher than in PAB markets under specific penalty structures, and imbalance penalty cost ranges differed by up to 82.32%. In contrast, PAB markets foster stable, stepwise bidding strategies that minimize imbalance penalties and improve renewable energy utilization, particularly during high- and moderate-generation periods. The findings emphasize the advantages of the PAB mechanism in electricity markets with substantial renewable energy integration, providing significant insights for the design of auction mechanisms that facilitate reliable and sustainable market operations. Full article

While tradable green certificates (TGCs) and carbon emission trading (CET) play key roles in achieving peak carbon and carbon neutrality, the coupling effects between these two policies on the medium- and long-term electricity market and the spot market are still uncertain. In this study, we firstly construct a multi-scale market trading framework to sort out the information transfer of four markets. Secondly, we establish a multi-scale market system dynamics-coupled trading model with five sub-modules, including the medium- and long-term power markets, the spot market, and the carbon market. Subsequently, we adjust the policy parameters (carbon quota benchmark price, carbon quota auction ratio, and renewable energy quota ratio) and set up five policy scenarios to compare and analyze the impacts of the CET and TGC mechanisms on the power market and carbon emission reduction when they act alone or in synergy, in order to provide a theoretical basis for the adjustment of strategies of market entities and the setting of parameters. The results show that CET can increase spot electricity prices and promote renewable energy to enter the spot market, while TGCs can promote a high proportion of renewable energy consumption but lower spot electricity prices for a long time. The coordinated implementation of the CET and TGC mechanisms can improve the power market’s adaptability to high renewable energy penetration, but it may also result in policy redundancy. Full article

Brazil’s wind power industry (WPI) has thrived since the early 2000s, driven by a successful auction-based expansion plan. However, the recent rise of cost-competitive solar power and policy shifts favoring other energy sources, such as natural gas, have created uncertainty about the future of wind energy in Brazil and reduced the wind sector’s legitimacy. Additionally, the cancellation of wind power auctions and support for other energy sources (evidenced by the new regulation for natural gas) has sent mixed signals to the market. These actions have sparked concerns regarding the future trajectory of the WPI. This paper focuses on the long-term effects of this energy policy decision on the so-called legitimacy function of the technological innovation systems (TIS) for the case of WPI in Brazil. The study aims to identify challenges arising from the growing appeal of solar power that may hinder wind energy adoption and to offer policy recommendations to strengthen the wind sector’s legitimacy. A system dynamics model is proposed to quantify such impacts in the long run, showing the interactions between the wind power capacity, wind generation costs, and the legitimacy function of the TIS. Results show the importance of policy consistency and institutional support in fostering a stable environment for renewable energy technologies like wind power to flourish. Full article

An efficient energy management system (EMS) enhances microgrid performance in terms of stability, safety, and economy. Traditional centralized or decentralized energy management systems are unable to meet the increasing demands for autonomous decision-making, privacy protection, global optimization, and rapid collaboration simultaneously. This paper proposes a hierarchical multi-layer EMS for microgrid, comprising supply layer, demand layer, and neutral scheduling layer. Additionally, common mathematical optimization methods struggle with microgrid scheduling decision problem due to challenges in mechanism modeling, supply–demand uncertainty, and high real-time and autonomy requirements. Therefore, an improved proximal policy optimization (PPO) approach is proposed for the multi-layer EMS. Specifically, in the centrally managed supply layer, a centralized PPO algorithm is utilized to determine the optimal power generation strategy. In the decentralized demand layer, an auction market is established, and multi-agent proximal policy optimization (MAPPO) algorithm with an action-guidance-based mechanism is employed for each consumer, to implement individual auction strategy. The neutral scheduling layer interacts with other layers, manages information, and protects participant privacy. Numerical results validate the effectiveness of the proposed multi-layer EMS framework and the PPO-based optimization methods. Full article

To address the centralized trading demand within industrial parks and the scattered peer-to-peer trading demand outside industrial parks, this paper proposes a blockchain-based joint auction architecture for distributed energy in microgrids inside and outside industrial parks. By combining blockchain technology and auction theory, the architecture integrates the physical energy transactions within industrial parks with the distributed transactions in external microgrids to meet the centralized trading demand within industrial parks and the scattered peer-to-peer trading demand outside industrial parks, optimizing resource allocation and improving system resilience. In the microgrid auction mechanism for industrial parks, considering distributed energy providers (sellers) and distributed energy buyers, an auction mechanism with power transmission distance, average electricity price, and enterprise nature as its main attributes was constructed to maximize social welfare, realizing efficient energy flow in a multi-microgrid environment and enabling coordinated mutual benefits for producers and consumers within the region. Finally, a case study was conducted on the joint auction mechanism for microgrids inside and outside industrial parks, including the impacts of market dynamics and user preferences on electricity prices using different trading methods, the computational results using different trading matching methods (comparing single-attribute and multi-attribute methods), and multi-dimensional verification of user satisfaction with peer-to-peer transactions in a blockchain environment. The effectiveness of the joint trading between physical energy transactions within industrial parks and external microgrids was demonstrated, which could efficiently coordinate energy allocation inside and outside the parks and reduce the cost of energy configuration. Full article

Every year, many horses are transported by air. Alongside sport horses traveling to tournaments worldwide, mainly breeding horses, such as shuttle stallions and broodmares, thoroughbreds traded at auctions, and leisure horses are transported by air. Research in veterinary science has highlighted welfare concerns during air transportation. Equine welfare is constituted in the language and discourse evolving from social, political, and ethical views about the treatment of horses. Consequently, this study targets power in creating equine welfare by analyzing the welfare discourses, transportation practices that generate welfare, and their impact on horses and humans in the transportation of horses by air. In detail, this research uses a Foucauldian discourse analysis to examine how welfare discourses and linked transportation practices constitute horses and humans using disciplinary power. The empirical material consists of 81 newspaper articles about horse transportation by air, five video clips, and four interviews with representatives of horse transport agencies that have set standards for the transportation of horses by air. The analysis discovers four different welfare discourses and various practices that guide the carrying of horses by air. The discourses have created inactive horses and human professionals in the business of horse transportation by air. Full article

Renewable energy, such as wind power and photovoltaic power, has uncertain and intermittent characteristics and zero marginal cost characteristics. The traditional power market mechanism is difficult to adapt to the new power system with a high proportion of renewable energy, and the original market system needs to be reformed. This paper discusses the application of a VCG auction mechanism in the electricity market, proposes a two-stage VCG market-clearing model based on the VCG mechanism, including the day-ahead market and the real-time market, and discusses the nature of the VCG mechanism. In order to address the discrepancy between the actual output of stochastic generator sets in the real-time market and their pre-scheduled output in the day-ahead market due to prediction deviations, a method for calculating punitive costs is proposed. A reallocation method based on market entities’ contributing factors to budget imbalance is proposed to address the issue of budget imbalance under the VCG mechanism, in order to achieve revenue and expenditure balance. Through an example, the incentive compatibility characteristics of the VCG mechanism are verified, the problems of the locational marginal pricing (LMP) mechanism in the stochastic electricity market with a high proportion of renewable energy are analyzed, the electricity prices of the LMP mechanism and the VCG mechanism under different renewable energy proportions are compared, and the redistribution of the budget imbalance of the VCG mechanism is analyzed. Full article

As transportation evolves with greater adoption of electric vehicles (EVs), vehicle-to-vehicle (V2V) energy trading stands out as an important innovation for managing energy resources more effectively as it reduces dependency on traditional energy infrastructures and, hence, alleviates the pressure on the power grid during peak demand times. Thus, this paper conducts a systematic review of the V2V energy trading frameworks. Through the included article analysis (n = 61), this paper discusses the state-of-the-art energy trading frameworks’ structure, employed methodologies, encountered challenges, and potential directions for future research. To the best of the authors’ knowledge, this is the first review explicitly focused on V2V energy trading. We detail four critical challenges to face while establishing the framework in current research, providing an overview of various methodologies, including auctions, blockchain, game theory, optimisation, and demand forecasting, that are used to address these challenges and explore their integration within the research landscape. Additionally, this paper forecasts the evolution of V2V energy trading, highlighting the potential incorporation of advanced and established technologies like artificial intelligence (AI), digital twins, and smart contracts. This review aims to encapsulate the existing state of V2V energy trading research and stimulate future advancements and technological integration within the field. Full article

IoT (Internet-of-Things)-powered devices can be exploited to connect vehicles to smart city infrastructure, allowing vehicles to share their intentions while retrieving contextual information about diverse aspects of urban viability. In this paper, we place ourselves in a transient scenario in which next-generation vehicles that are able to communicate with the surrounding infrastructure coexist with traditional vehicles with limited or absent IoT capabilities. We focus on intersection management, in particular on reusing existing traffic lights empowered by a new management system. We propose an auction-based system in which traffic lights are able to exchange contextual information with vehicles and other nearby traffic lights with the aim of reducing average waiting times at intersections and consequently overall trip times. We use bid propagation to improve standard vehicle trip times while allowing emergency vehicles to free up the way ahead without needing ad hoc system for such vehicle, only an increase in their budget. The proposed system is then tested against two baselines: the classical Fixed Time Control system currently adopted for traffic lights, and an auction strategy that does not exploit traffic light coordination. We performed a large set of experiments using the well known MATSim transport simulator on both a synthetic Manhattan map and on a map we built of an urban area located in Modena, Northern Italy. Our results show that the proposed approach performs better than the classical fixed time control system and the auction strategy that does not exploit coordination among traffic lights. Full article