Search Results (29)

Distribution transformers serve as critical nodes in smart grids, and management of their recycling plays a vital role in the full life-cycle management for electrical equipment. However, the traditional manual dismantling methods often exhibit a low metal recovery efficiency and high levels of hazardous substance residue. To facilitate green, cost-effective, and fine-grained recycling of distribution transformers, this study proposes a fine-grained dismantling decision-making system based on a knowledge graph subgraph comparison and multimodal fusion perception. First, a standardized dismantling process is designed to achieve refined transformer decomposition. Second, a comprehensive set of multi-dimensional evaluation metrics is established to assess the effectiveness of various recycling strategies for different transformers. Finally, through the integration of multimodal perception with knowledge graph technology, the system achieves automated sequencing of the dismantling operations. The experimental results demonstrate that the proposed method attains 99% accuracy in identifying recyclable transformers and 97% accuracy in auction-based pricing. The residual oil rate in dismantled transformers is reduced to below 1%, while the metal recovery efficiency increases by 40%. Furthermore, the environmental sustainability and economic value are improved by 23% and 40%, respectively. This approach significantly enhances the recycling value and environmental safety of distribution transformers, providing effective technical support for smart grid development and environmental protection. 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

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

The emergence of computing power networks has improved the flexibility of resource scheduling. Considering the current trading scenario of computing power and network resources, most resources are no longer subject to change after being allocated to users until the end of the lease. However, this practice often leads to idle resources during resource usage. To optimize resource allocation, a trading mechanism is needed to encourage users to sell their idle resources. The Myerson auction mechanism precisely aims to maximize the seller’s benefits. Therefore, we propose a resource allocation scheme based on the Myerson auction. In the scenario of the same user bidding distribution, we first combine the Myerson auction with Hyperledger Fabric by introducing a reserved price, which creates conditions for the application of blockchain in auction scenarios. Regarding different user bidding distributions, we propose a Myerson auction network model based on clustering algorithms, which makes the auction adaptable to more complex scenarios. The experimental findings show that the revenue generated by the auction model in both scenarios is significantly higher than that of the traditional sealed bid second-price auction, and can approach the expected revenue in the real Myerson auction scenario. Full article

Along with the prevalence of photovoltaic (PV) procurement contracts, the corruption between auctioneers and potential electricity suppliers has attracted the attention of energy regulators. This study considers a corruption-proof environment wherein corruption is strictly suppressed. It elaborates a mechanism to explore the impact of corruption-proof measures on PV procurement auctions. It adopts incentive compatible constraints based on revelation principle to reflect PV firms’ optimal utilities. It employs first-price and first-score auctions and uses the Bayesian Nash equilibrium to provide a description of market outcomes. The results show that several strategies have different impacts on social welfare, PV firms’ utility, and the benefits of corruption. First, a first-price auction cannot act as a suitable policy because it may encourage corruption. Second, the first-score choice is desirable for social welfare to fit the forthcoming high-quality and low-price surroundings. Third, the first-score strategy maximizes PV firms’ utility and total income. The implications suggest that regulators ought not to employ first-price auctions in the future PV market from the perspective of social welfare. Another disadvantage of the first-price approach is that it enables the PV firm to maintain the utmost benefit from corruption. Full article

We consider the problem of allocating heterogeneous objects to agents with money, where the number of agents exceeds that of objects. Each agent can receive at most one object, and some objects may remain unallocated. A bundle is a pair consisting of an object and a payment. An agent’s preference over bundles may not be quasi-linear, which exhibits income effects or reflects borrowing costs. We investigate the class of rules satisfying one of the central properties of fairness in the literature, egalitarian-equivalence, together with the other desirable properties. We propose (i) a novel class of rules that we call the independent second-prices rules with variable constraints and (ii) a novel condition on constraints that we call respecting the valuation coincidence. Then, we establish that the independent second-prices rule with variable constraints that respects the valuation coincidence is the only rule satisfying egalitarian-equivalence, strategy-proofness, individual rationality, and no subsidy for losers. Our characterization result implies that in the case of three or more agents, there are few opportunities for agents to receive objects under a rule satisfying egalitarian-equivalence and the other desirable properties, which highlights the strong tension between egalitarian-equivalence and efficiency. In contrast, in the case of two agents and a single object, egalitarian-equivalence is compatible with efficiency. Full article

Green hydrogen has become the key to social low-carbon transformation and is fully linked to zero carbon emissions. The carbon emissions trading market is a policy tool used to control carbon emissions using a market-oriented mechanism. Building a modular carbon trading center for the hydrogen energy industry would greatly promote the meeting of climate targets. Based on this, a “green hydrogen market—national carbon trading market–electricity market” coupling mechanism is designed. Then, the “green hydrogen market—national carbon trading market–electricity market” mechanism is modeled and simulated using system dynamics. The results are as follows: First, coupling between the green hydrogen market, carbon trading market and electricity market can be realized through green hydrogen certification and carbon quota trading. It is found that the coupling model is feasible through simulation. Second, simulation of the basic scenario finds that multiple-market coupling can stimulate an increase in carbon price, the control of thermal power generation and an increase in green hydrogen production. Finally, the proportion of the green hydrogen certification, the elimination mechanism of outdated units and the quota auction mechanism will help to form a carbon pricing mechanism. This study enriches the green hydrogen trading model and establishes a multiple-market linkage mechanism. Full article

Per capita apple consumption is decreasing in many European countries due to the perception that apples have inferior sensory traits and to the dissatisfaction of some consumers with mainstream apple cultivars. Previous studies on the consumers’ acceptance of apples state that sensory attributes have a large influence on consumers’ willingness to pay (WTP). Following this previous evidence and with the aim of reversing this decreasing consumption trend, our objective was to study the effect of apple sensory attributes on consumers’ WTP for a mainstream (‘Golden Delicious’) and a local traditional apple cultivar (‘Reineta’), grown at two different altitudes (high and low). A total number of 195 apple consumers participated in one experiment with two tasks. In the first task, participants were asked to rate five external and internal sensory attributes, and in the second, to indicate their maximum WTP in an experimental auction for the four apple samples. Results indicated that the WTP and the effect of sensory attributes on the WTP were different among the four apple samples. Our findings indicate that sweeter and juicer apples might be marketed at higher prices and that firmness is only appreciated in the case of the local cultivar grown at low altitudes. Full article

We study asymmetric second-price auctions under incomplete information. The bidders have two potentially different, commonly known, valuations for the object and private information about their entry costs. The seller, however, does not benefit from these entry costs. We calculate the equilibrium strategies of the bidders and analyze the optimal design for the seller in this environment in terms of expected entry and the number of potential bidders. Full article

In ad-hoc vehicle networks (VANETs), the random mobility causes the rapid network topology change, which leads to the challenge of the reliable data transmission. In this paper, we propose a hybrid-price auction-based secure routing (HPA-SR) protocol using advanced speed and cosine similarity-based (ASCS) clustering to establish a secure route to avoid sinkhole attacks and improve connectivity between nodes. The main features and contributions of the proposed HPA-SR protocol are as follows. First, the HPA-SR protocol is employed by the first- and second-price auctions to avoid sinkhole attacks. More specifically, using the Markov decision process (MDP), each node can select a kind of auction method to establish the secure route by avoiding the sinkhole attack. Second, the advanced speed cosine similarity clustering protocol that is considered as underlying structure is presented to improve the connectivity between nodes. The ASCS is constructed based on the cosine similarity and distance between nodes using the speed and direction of the nodes. The results of the performance show that the proposed HPA-SR protocol can establish the secure route avoiding the sinkhole attack while the proposed ASCS clustering can support the strong connectivity. Besides, the HPA-SR with ASCS protocol can show better performance than the benchmark protocol in terms of the routing delay, packet loss ratio, number of packet loss, and control overhead. Full article

In this paper, we compare a multi-round, second-price, sealed-bid bundle auction and a single-item, sequential, second-price, sealed-bid auction for berth slot leasing for vessels (roll-on/roll-off passenger vessels and/or cruise ships) at a public marine terminal. The bundle auction mechanism is designed to maximize port operator profits by auctioning berth (time) slots in groups. The framework is tested using simulation by varying: the number of roll-on/roll-off passenger/cruise ship operating companies; the number of slots they bid for; and the mechanism design with regards to the winner determination, slot valuation, and max to min slot bid ratio among the bidders. The results indicate that neither auction type is a clear winner, and depending on the assumptions, a terminal operator should choose one over the other. The results from this study can be used by terminal operators, given their knowledge and/or assumptions on slot valuations and demand, to select a winner-determination policy and the minimum number of slots they allow players to bid for when designing the auction of their berth capacity to maximize their profits. Full article

Land use change has become the second-largest source of greenhouse gas emissions after fossil energy combustion. In the context of developing a low-carbon economy, it is important to study how to achieve energy savings and emission reduction by adjusting land prices, and transforming land trading methods and land use types. Utilizing a balanced panel dataset about 291 sample cities in China, during the period of 2010–2016, this paper divided land transactions into three dimensions: land transaction price, land transaction modes, and land transfer structure; then employed a fixed-effect model to investigate the relationship between land transactions and carbon emissions. On top of this, we further analyzed the moderating role of economic development level and emission reduction policy. This study found that land transaction price can significantly inhibit carbon emissions; the amount of land sold by auction and listing has a stronger inhibitory effect on carbon emissions than by bidding; the higher the transfer proportion of industrial land, the higher the carbon emissions, while the transfer proportion of residential land is significantly negatively correlated with carbon emissions; the moderating mechanism shows that the level of economic development and emission reduction policy can play a moderating role in the relationship between land transactions and carbon emissions, but the moderating effect of emission reduction policy is limited, only existing in the relationships between land transaction price, the amount of listed land, and carbon emissions. Full article

In this paper, to balance power supplement from the solar energy’s intermittent and unpredictable generation, we design a solar energy generation and trading platform (EggBlock) using Internet of Things (IoT) systems and blockchain technique. Without a centralized broker, the proposed EggBlock platform can promote energy trading between users equipped with solar panels, and balance demand and generation. By applying the second price sealed-bid auction, which is one of the suitable pricing mechanisms in the blockchain technique, it is possible to derive truthful bidding of market participants according to their utility function and induce the proceed transaction. Furthermore, for efficient generation of solar energy, EggBlock proposes a Q-learning-based dynamic panel control mechanism. Specifically, we set the instantaneous direction of the solar panel and the amount of power generation as the state and reward, respectively. The angle of the panel to be moved becomes an action at the next time step. Then, we continuously update the Q-table using transfer learning, which can cope with recent changes in the surrounding environment or weather. We implement the proposed EggBlock platform using Ethereum’s smart contract for reliable transactions. At the end of the paper, measurement-based experiments show that the proposed EggBlock achieves reliable and transparent energy trading on the blockchain and converges to the optimal direction with short iterations. Finally, the results of the study show that an average energy generation gain of 35% is obtained. Full article

The paper generalizes the models of the standard first- and second-price auctions (FPA and SPA) by using the Cobb-Douglas function to evaluate bidders’ two conflicting bidding preferences for the probability of winning the item (C_pwin) and the profit conditional on winning (C_profit). Compared with the popular expected profit function, this function has the advantage of allowing the bidders to equally or differently value C_pwin and C_profit from the point of view of multiple criteria decision making, and then can capture bidders’ bidding behavior in reality. Introduction of the Cobb-Douglas function into the FPA allows us to provide new interpretations for overbidding (underbidding) behavior and the dependence of the optimal reserve price on the number of bidders. Our new interpretations are all attributed to the bidders’ different preferences for C_pwin and C_profit. In contrast, this introduction into the SPA does not matter. Our findings suggest that the seller should prefer the FPA (SPA) over the SPA (FPA) and set a lower (higher) optimal reserve price if he conjectures that the bidders have a stronger desire to win an auction (to obtain a profit). The above results help the seller to select between the two auctions and set a reserve price in practice. Full article

Recently, the development of distributed renewable energy resources, smart devices, and smart grids empowers the emergence of peer-to-peer energy trading via local energy markets. However, due to security and privacy concerns in energy trading, sensitive information of energy traders could be leaked to an adversary. In addition, malicious users could perform attacks against the energy market, such as collusion, double spending, and repudiation attacks. Moreover, network attacks could be executed by external attackers against energy networks, such as eavesdropping, data spoofing, and tampering attacks. To tackle the abovementioned attacks, we propose a secure and privacy-preserving energy trading system (SPETS). First, a permissioned energy blockchain is presented to perform secure energy transactions between energy sellers and buyers. Second, a discrete-time double auction is proposed for energy allocation and pricing. Third, the concept of reputation scores is adopted to guarantee market reliability and trust. The proposed energy system is implemented using Hyperledger Fabric (HF) where the chaincode is utilized to control the energy market. Theoretical analysis proves that SPETS is resilient to several security attacks. Simulation results demonstrate the increase in sellers’ and buyers’ welfare by approximately 76.5% and 26%, respectively. The proposed system ensures trustfulness and guarantees efficient energy allocation. The benchmark analysis proves that SPETS consumes few resources in terms of memory and disk usage, CPU, and network utilization. Full article