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This paper proposes an auction-based market trade mechanism for the electric vehicle battery recycling (EVBR) problem, which aims to realise the optimal resource allocation and pricing of EVBR. The main motivation of this paper is to attempt to explore an approach to achieving efficient battery recycling. We first consider an EVBR market with m buyers and n sellers, and develop the multi-unit trade reduction (MTR) mechanism in the EVBR market. According to the supply and demand relationship in the EVBR market, we consider three market scenarios of supply and demand balance, oversupply, and overdemand, and formulated corresponding auction allocation rules. Numerical study results show that the proposed MTR mechanism can achieve efficient resource allocation. We also observed that not all results increased with the number of sellers/buyers. Second, considering the distance between sellers and buyers, we developed a stochastic multiple MTR (SM-MTR) mechanism to enable sellers and buyers within the region to conduct transactions. Finally, we propose an integrated MTR, SM-MTR and one-sided Vickrey--Clarke--Groves auction mechanism that is feasible in both one-sided and bilateral environments. Furthermore, our work can provide novel managerial implications for EVBR market stakeholders in terms of practical application. [ABSTRACT FROM AUTHOR]

In peer-to-peer (P2P) energy trading, members locally trade energy. Blockchain-based systems are employed for the above trading. These systems are limited in speed because of the time required in the consensus process to audit and verify transactions. Further energy is traded using auction, which also consumes time. In this paper, we propose a blockchain and machine learning-based system to speed up trading. This system uses Byzantine Fault Tolerance method to reduce consensus time and machine learning to reduce auction time. In addition, the proposed system provides loans to cash-deficient buyers thereby enabling them to purchase energy. Machine learning is used to arrive at optimal loan rates for buyers. All these claims have been verified by the simulation results. Simulation results show that the new mechanism finds the optimal value of the trader's quotes prices quickly by altering the value of the profit margin, and the new loan system increases the number of transactions in this blockchain-based system. Hence, the proposed system will promote P2P trading by making it faster and enabling greater participation of cash-deficient buyers in the trading. [ABSTRACT FROM AUTHOR]

The sensors available in the smartphones are useful to explore a diverse range of city dynamics (e.g. noise pollution, road condition, traffic condition, etc.). The potential of the smartphone sensors coupled with their widespread availability help to emerge a new paradigm of sensing known as participatory sensing. It uses the power of smartphone equipped sensors to collect, store, and analyze data with high spatiotemporal granularity. In a participatory sensing based system, a task provider (also known as a crowdsourcer) may have a set of sensing tasks regarding different dynamics of a city. Here, adequate users' participation is necessary to acquire a sufficient amount of data which is a key factor for the participatory sensing based systems to provide good service quality. The task providers appoint a set of task executors (smartphone users i.e. participants of crowdsensing tasks) to execute those sensing tasks. But, existing works on sensing task allocation suffer from lack of good incentive mechanisms that are attractive for the task executors. In order to address this issue, in this paper, a double auction based incentive mechanism called TATA (Truthful Double Auction for Task Allocation) is proposed for participatory sensing. TATA performs fair allocation of tasks which is leading to efficient incentive mechanism. In the case of TATA, the fair allocation of sensing tasks of the task providers to the task executers indicates that the proposed double auction mechanism is able to satisfy the truthfulness property in order to resist market manipulation (i.e., untruthful bidding and asking). Specifically, TATA achieves all the desirable properties like individual rationality, truthfulness (i.e. incentive compatibility), budget balance, etc. TATA is also computationally efficient and yields high system efficiency. Additionally, the performance of the proposed incentive mechanism is evaluated and compared with the existing mechanisms through extensive simulations based on the real-world data from Amazon Mechanical Turk. TATA yields high utility and satisfaction for the task providers and executors as compared to the existing mechanisms. [ABSTRACT FROM AUTHOR]

Multi-party carpooling emerges as a burgeoning shared transportation scheme whereby the trip shared by each driver is shared among multi-party riders whose itineraries coincide. Confronting the information asymmetry and the voluntary self-interested nature of bilateral participants in matching and pricing operations, this study designs M ulti-party c A rpooling S uppor T ed strat E gy-p R oof (MASTER) double auction mechanisms considering personalized carpooling constraints. First, in a scheduled carpooling scenario, two MASTER S mechanisms that masterfully blend the ideas of the famed trade reduction method and multi-stage approach are proposed which implement distinct group bid determination approaches for responding to different market conditions. Second, in an on-demand carpooling scenario, two parameterized MASTER O mechanisms that integrate frustration-based promotion to proactively prioritize matching and deferentially compensate riders based on their waits are contrived which also endow the platform with operational flexibility to agilely pursue alterable operational objectives by adjusting promotion strength. We prove theoretically that the proposed mechanisms satisfy strategy proofness, budget balance, individual rationality, and asymptotic efficiency under mild conditions. Experimental results reveal that multi-party carpooling constitutes a multi-win solution under higher rider-driver ratios whilst it could be detrimental to drivers otherwise, which can be ameliorated by favoring the driver side in determining promotion strength. Simulation studies manifest that our proposed auction mechanisms could bring benefits concerning allocation efficiency and service responsiveness compared with their academic and practical counterparts. We also shed light on choosing among alternative mechanisms according to market conditions and operational orientations. [ABSTRACT FROM AUTHOR]

Blockchain, a promising technology, has been extensively applied in numerous fields, such as network security, finance, and medical care. However, due to the low power consumption and weak computing power of the mobile environment, the application of blockchain in this environment still faces many challenges. Therefore, edge computing has been introduced to improve the computing power of mobile devices and encourage more mobile edge devices to join the blockchain network. In this paper, we propose a double auction model to address the issue of edge computing resource allocation in blockchain networks. Based on this auction model, we first propose a truthful double auction mechanism based on breakeven (TDAMB) to determine matched pairs of edge computing service providers (ECSPs) and miners. Furthermore, to improve the system efficiency, we propose a double auction mechanism based on a critical value (DAMCV). We also theoretically analyze the individual rationality, budget balance and truthfulness of the proposed mechanisms. Extensive experiments show that TDAMB and DAMCV have good effects on edge computing resource allocation in blockchain networks. [ABSTRACT FROM AUTHOR]

We investigate effects of tax reporting institutions on evasion and incidence using an experimental double auction market. We find that 28% of the sellers are truthful when only sellers report, but that 88% and 64% of them are truthful under costless and costly third-party reporting by buyers, respectively. Reporting behavior therefore responds to the intensity of deterrence. However, we find that prices do not fully reflect the lower taxes of the evaders. Thus, when sellers can unilaterally evade taxes, tax incidence deviates from the prediction of the standard model, and there is deadweight loss even if tax revenue is low. Pricing, incidence, and reporting patterns in all treatments are consistent with a model with heterogeneous lying costs, or a combination of lying costs and image concerns that give rise to a motivation to appear honest. [ABSTRACT FROM AUTHOR]

With development of integrated energy systems and energy markets, transactive energy has received increasing attention from society and academia, and realization of energy distribution and integrated demand response through market transactions has become a current research hotspot. Research on optimized operation of a distributed energy station as a regional energy supply center is of great significance for improving flexibility and reliability of the system. Based on retail-side energy trading market, this study first establishes a framework of combined electric and heating energy markets and analyses a double auction market mechanism model of interconnected distributed energy stations. This study establishes a mechanism model of energy market participants, and establishes the electric heating combined market-clearing model to maximize global surplus considering multi-energy storage. Finally, in the case study, a typical user energy consumption scenario in winter is selected, showing market-clearing results and demand response effects on a typical day. Impact of transmission line constraints, energy supply equipment capacity, and other factors on clearing results and global surplus are compared and analyzed, verifying the effects of the proposed method on improving global surplus, enhancing interests of market participants and realizing coordination and optimal allocation of both supply and demand resources through energy complementarity between regions.

External effects can be triggered through trade and depend on the locations of buyers and sellers who are matched. Inspired by electricity markets, we experimentally investigate markets in which net trades between two locations induce social costs. Based on a modified double auction setting, we compare the performance of market platforms that are location-blind with those where information on the location of (potential) trading partners or the level of the externality is given. We demonstrate that locational information is sufficient to reduce the externality. Imposing the full external costs on individual trades leads to maximal price differentiation between locations and further reduces net trades, while welfare improvements are limited. Reasons for not achieving the typically high efficiency of double auctions are discussed. [ABSTRACT FROM AUTHOR]

For a trading problem where a buyer is interested in an aggregate resource with fragmented ownership, the individually owned resources are perfect complements in trade. A double auction, chosen in accordance with a value alignment principle which we formulate, is shown to be strategy proof for owners. Since it also values the aggregate resource correctly, it mitigates the holdout problem by changing the source of inefficiency from complementarity on owners' side to lack of competition on buyer side. The value alignment principle implies that this double auction has a majority trading rule. With multiple buyers, a suitable modification makes the double auction strategy proof even for the buyers, thus mitigating the holdout problem by achieving approximate ex post efficiency when the number of owners is large. [ABSTRACT FROM AUTHOR]

In a model with correlated and interdependent values/costs, we identify for the buyer's bid double auction the asymptotic distributions of the price and of two order statistics in the first order conditions for optimal bidding/asking, all of which are normal. Substitution of the asymptotic distributions into the first order conditions can permit the solution for approximately optimal bids/asks that provide insight into what is " first order" in a trader's strategic decision-making, which has been difficult to obtain through analysis of equilibrium. [ABSTRACT FROM AUTHOR]

With the development of Big Data and the Internet of Things (IoT), the data value is more significant in both academia and industry. Trading can achieve maximal data value and prepare data for smart city services. Due to data's unique characteristics, such as dispersion, heterogeneity and distributed storage, an unbiased platform is necessary for the data trading market with rational trading entities. Meanwhile, there are multiple buyers and sellers in a practical data trading market, and this makes it challenging to maximize social welfare. To solve these problems, this paper proposes a Social-Welfare-Oriented Many-to-Many Trading Mechanism (SOMTM), which integrates three entities, a trading process and an algorithm named Many-to-Many Trading Algorithm (MMTA). Based on the market scale, market dominated-side and market fixed-side, simulations verify the convergency, economic properties and efficiency of SOMTM.

This study aimed to improve the truthfulness and efficiency of double auction mechanism in the cloud computing environment. In an auction, bidders have a high incentive to announce an unrealistic price, that is, each cloud user tends to announce a lower price and each cloud service provider tends to bid higher price than the actual one. This untruthful behavior will have catastrophic consequences to the market, leading to participants' fear of being misused. Therefore, it seems vital to provide a mechanism for the realization of truthfulness involving no unrealistic offer by the users. On the other hand, it has been proven that it is not possible to achieve the characteristics of truthfulness, budget balance and economic efficiency at the same time. There is a weak budget balance in a truthful mechanism with a price difference greater than zero between the total price paid by the cloud users and that received by the cloud service providers. In this paper, a mechanism is proposed to encourage participants to offer truthful prices by rewarding honest participants. The proposed called the reward mechanism uses a repository to store bidders' information, including the number of bids and the number of honest bids per auction as well as a ranked list of bidders based on their bidding history in offering honest prices. The results show that the proposed mechanism not only motivates the bidders to announce honest bids, but also solve the problem of weak budget balance by dividing the bid amount among the winning bidders. [ABSTRACT FROM AUTHOR]

The paper investigates two-player double-auction bargaining with private values in a setting with discrete two-point overlapping distributions of traders' valuations. We characterize parameter settings in which there exists a fully efficient equilibrium, and show that if there are traders that behave naively, i.e., set bid or ask equal to their valuation, then there is no equilibrium achieving full efficiency. We conduct an experiment to test the theoretical possibility that the presence of naive traders can reduce efficiency. We find, however, that efficiency is not lower in the presence of naive traders. Subjects mostly set bid/ask prices strategically but they do not coordinate on a fully efficient equilibrium and the extent of strategic behavior is not different in the presence of naive players. We show that a learning model of noisy strategy adjustment explains the observed behavior better than other (equilibrium or non-equilibrium) models. [ABSTRACT FROM AUTHOR]

Abstract By transferring power-hungry huge data centers to lightweight Internet of Things (IoT) mobile devices, mobile edge computing (MEC) has completely changed the IoT. For MEC, to optimize economic gains and motivate profit-oriented entities, the joint resource allocation and network economics problem must be solved, and the joint issue is limited by local constraints, namely, the edge server only serves multiple nearby mobile devices, which is restricted by its available energy. The article studies the jointly issue of network economics and energy allocation in MEC, where mobile device apply for offloading at a purported bid and an edge server supplies its restricted serving at an asking price. In particular, this paper puts forward two dynamic pricing double auction strategies in the MEC system, i.e., a double auction according to the break-even mechanism (DABM) and a more practical double auction based on dynamic pricing mechanism (DADPM) to decide the matching between mobile devices and edge servers, and the pricing strategy for high-priced economic profit in the case of local restricts. Theoretical analysis shows that the proposed two algorithms have properties such as budget balance, individual rationality, economic benefit, authenticity. Extensive simulation experiments evaluate the efficiency of the system, and results verify that the proposed two schemes will greatly make better the economic benefits of MEC.

By transferring power-hungry huge data centers to lightweight Internet of Things (IoT) mobile devices, mobile edge computing (MEC) has completely changed the IoT. For MEC, to optimize economic gains and motivate profit-oriented entities, the joint resource allocation and network economics problem must be solved, and the joint issue is limited by local constraints, namely, the edge server only serves multiple nearby mobile devices, which is restricted by its available energy. The article studies the jointly issue of network economics and energy allocation in MEC, where mobile device apply for offloading at a purported bid and an edge server supplies its restricted serving at an asking price. In particular, this paper puts forward two dynamic pricing double auction strategies in the MEC system, i.e., a double auction according to the break-even mechanism (DABM) and a more practical double auction based on dynamic pricing mechanism (DADPM) to decide the matching between mobile devices and edge servers, and the pricing strategy for high-priced economic profit in the case of local restricts. Theoretical analysis shows that the proposed two algorithms have properties such as budget balance, individual rationality, economic benefit, authenticity. Extensive simulation experiments evaluate the efficiency of the system, and results verify that the proposed two schemes will greatly make better the economic benefits of MEC. [ABSTRACT FROM AUTHOR]

In order to avoid the occurrence of transaction failure of allocated parking spaces due to users􀆳 breach of trust or default in the process of shared parking, and aim to improve the utilization rate of parking spaces, a berth allocation model considering the user􀆳s credit was proposed. Firstly, combined with the existing credit evaluation model, an allocation model considering the credit worthiness of users was built. Then, based on price and time constraints, a bidding mechanism was established for shared berth participants (owner, user, platform) based on the double auction model to determine the final transaction price. In addition, the total user utility value was calculated, the optimal allocation sequence based on the utility value was obtained, and the berth requests submitted by the user are allocated orderly according to the sequence. Finally, the proposed model was compared with the traditional auction model and first-come-first-stop through MATLAB software simulation. The results show that the proposed model is on the first-come, first-stop scheme and the traditional auction model, with revenue growth rates increasing by 2. 15% and 32. 07%, respectively, compared to the traditional auction model. The average utilization rate of berths increases by 0. 93% and 29. 99%, respectively. The research results can effectively circumvent the user default phenomenon and provide an effective way to optimize the existing berth allocation model. [ABSTRACT FROM AUTHOR]

We study minimal conditions for competitive behavior with few agents. We adapt a price-quantity strategic market game to the indivisible commodity environment commonly used in double auction experiments, and show that all Nash equilibrium outcomes with active trading are competitive if and only if there are at least two buyers and two sellers willing to trade at every competitive price. Unlike previous formulations, this condition can be verified directly by checking the set of competitive equilibria. In laboratory experiments, the condition we provide turns out to be enough to induce competitive results, and the Nash equilibrium appears to be a good approximation for market outcomes. Subjects, although possessing limited information, are able to act as if complete information were available in the market. [ABSTRACT FROM AUTHOR]

The present study contributes to the ongoing debate on possible costs and benefits of insider trading. We present a novel call auction model with insider information. Our model predicts that more insider information improves informational efficiency of prices, but this comes at the expense of reduced gains from trade. Testing these hypotheses in the lab, we find that insider information increases informational efficiency of call auction prices but does not decrease the realized gains from trade. We further find that the call auction does not perform worse than the continuous double auction. In fact, when the probability of insider information is high, the call auction has the most informative prices and highest realized gains from trade. Our experiment provides new evidence, from markets with very asymmetrically dispersed information, that lends support to the decision by many stock exchanges to use call auctions when information asymmetries are severe and the need for accurate prices is large, e.g. at the open or close of the trading day. [ABSTRACT FROM AUTHOR]

The energy trading market that can support free bidding among electricity users is currently the key method in smart grid demand response. Reinforcement learning is used to formulate optimal strategies for them to obtain optimal strategies. Non-etheless, the security problem raised by artificial intelligence technology has been paid more and more attention. For example, the neural network has been proved to be able to resist adversarial example attacks, thus affecting its training results. Considering that reinforcement learning is also widely used for training by neural networks, the security problem can not be ignored, especially in scenarios with high security requirements such as smart grids. To this end, we study the security issues in reinforcement learning-based bidding strategy method facing by the adversarial example. First of all, regarding to the electric vehicle double auction market, we formalize the bidding decision problem of EVs into a Markov Decision Process, so that reinforcement learning is used to solve this problem. Secondly, from the perspective of attackers, we have designed a local Fast Gradient Sign Method which affects the environment and the results of reinforcement learning by changing its own bidding. Then, from the perspective of the defender, we designed a reinforcement learning training network containing an attack identifier based on the deep neural network, so as to identify malicious injection attacks to resist against adversarial attacks. Finally, comprehensive simulations are conducted to verify our proposed method. The results shows that, our proposed attack method will reduce the auction profit by influencing reinforcement learning algorithm, and the protect method will be able to completely resist such attacks.

The development of local electricity markets (LEMs) and energy communities is accelerating the shift from consumerism to prosumerism. However, there is no concrete understanding of how electricity sharing in LEMs should be organized, a local wholesale market within or centralized sharing? This paper explores trading algorithms that can represent a competitive market and bidding conditions within a LEM. That is, how well trading algorithms can represent the wholesale market of an energy community?; What is a fair LEM reference price to create bidding simulations? How do the system characteristics affect the outcome of the trading algorithms? We address these questions by analyzing a community (residential buildings) in Steinkjer (Norway) and London (UK), including PV systems and wind turbines. We first determine bids and offers based on different bidding simulations and develop a market reference price. Afterward, we applied the trading algorithms Peer-to-Peer (P2P) and Multi-unit-Double-Auction (MUDA) for local electricity trading. We compared the results in selected KPIs such as self-sufficiency, traded energy, and curtailment. We find that P2P provides a more economically efficient trading algorithm than MUDA as it generally enables more trading and thus lowers grid imports. However, there are concerns that P2P brings disadvantages such as unfair trading. • Collaborative (energy community) and competitive (Peer-to-peer and Vickrey-MUDA) local electricity markets. • Understanding how a market algorithm will determine local prices in the UK and Norway. • Local market results based on different bidding simulations that reflect participants' behavior. • Developed a reference price index tailored for Local Electricity Markets. • P2P gives better results in terms of trading efficiency compared to MUDA. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) emerges as an appealing paradigm to provide time-sensitive computing services for industrial Internet of Things (IIoT) applications. How to guarantee truthfulness and budget-balance under locality constraints is an important issue to the allocation and pricing design of the MEC system. In this paper, we propose a truthful combinatorial double auction mechanism, which integrates the padding concept and the efficient pricing strategy to guarantee desirable properties in constrained MEC environments. This mechanism takes into account the locality characteristics of the MEC systems, where mobile devices (MDs) only offload tasks to edge servers (ESs) in the proximity with various requirements, and ESs only serve their neighboring MDs with limited resources. To be specific, for allocation, a linear programming (LP)-based padding method is used to obtain the near-optimal solution in the polynomial time. For pricing, a critical-value-based pricing strategy and a VCG-based pricing strategy are designed for MDs and ESs to achieve truthfulness and budget-balance. Our theoretical analysis confirms that TCDA is able to hold a set of desirable economic properties, including truthfulness, individual rationality, and budget-balance. Furthermore, simulation results validate the theoretical analysis, and verify the effectiveness and efficiency of TCDA. [ABSTRACT FROM AUTHOR]

In wireless communication networks, the stock of spectrum, computing, storage and other resources at the edge was limited. The traditional decentralized and exclusive resource allocation resulted in weak resource reuse capability and low utilization rate. At the same time, it was difficult to ensure the fairness of resource sharing in the traditional resource scheduling process due to the lack of trust between the owners. First, a double auction model of computing and wireless communication resource joint management based on blockchain was proposed. In the model, a resource market was established, in which resource buyers and sellers allocated resources through double auctions. Secondly, blockchain was used to store the resource information of buyers and sellers to solve the mutual trust problem of all parties in the network. Finally, the experimental simulation was carried out to verify that the proposed model effectively improved the system performance and resource utilization efficiency. [ABSTRACT FROM AUTHOR]

This paper studies the real-time energy trading problem in a smart community consisting of a group of grid-connected prosumers with controllable loads, renewable generations and energy storage systems. We propose a peer-to-peer (P2P) energy trading system, which integrates energy trading with energy management, enabling each prosumer to jointly manage its energy consumption, storage scheduling and energy trading in a dynamic manner for smart communities consisting of a group of grid-connected prosumers with controllable loads, renewable generations and energy storage systems. The proposed community-based P2P energy trading system combines an online energy control and trading algorithm with a double auction mechanism. The energy control and trading algorithm is designed based on the Lyapunov theory, allowing each prosumer to independently determine its bid in each time slot only based on its current energy supply condition, while the trading price, which is determined via the double auction mechanism, reflects the collective energy supply conditions of all prosumers participating in energy trading. The integration of the Lyapunov-based energy control and trading algorithm and the double auction mechanism yields a dynamic energy trading pricing mechanism that induces the prosumers to participate in energy trading in a coordinated manner by influencing the energy consumption, energy charging/discharging and energy trading decisions of the prosumers. Numerical simulation results demonstrate that energy exchange in the proposed scalable energy trading system yields significant improvements in terms of energy cost savings and renewable energy utilization efficiency, while ensuring the fair sharing of the benefits reaped from energy trading among the prosumers.

Peer-to-peer (P2P) energy trading, whereby customers can trade energy with one another rather than the energy supplier only, has the potential to save money for consumers whilst also incentivising more efficient and environmentally beneficial behaviour. Many existing models for P2P only consider a real-time or hour-ahead market, which does not allow proper scope for the planning of flexible demand or for energy storage. Accordingly, in this model we employ a day-ahead continuous double auction (CDA), in which all the upcoming timeslots are simultaneously open for trading. This allows schedules for device dispatch to be developed properly. We consider the flexibility and interdependence of bidding across different timeslots and develop strategies to address this. Furthermore, we consider the trade of heat as well as power, via a low temperature heat network. Heat and power trading interact due to the use of air source heat pumps (ASHPs) as well as reversible solid oxide cells (rSOCs), which can provide combined heat and power, or alternatively produce hydrogen via water electrolysis. In our case study, the P2P market is simulated with 25 houses participating, for two week-long periods in different climate conditions. P2P electricity trading is found to bring a marked reduction in reliance on grid electricity, and a reduction in peak grid load. This is brought about mainly by the incentive for rSOCs to generate at a higher average load factor, and the average house makes savings of ca. £10 / week in winter weather. Heat trading brings a further decrease in reliance on grid electricity, and largely eliminates the use of inefficient resistive heat. However, the heat trading may not be financially worthwhile in all conditions. • Simulation of peer-to-peer (P2P) energy trading via a continuous double auction • Heat and power both considered, with fuel cells and heat pumps coupling these • Auction format allows flexible devices to plan schedules in advance • With electricity trading, higher load factor for fuel cells, cuts reliance on grid • Heat trading brings extra technical benefits though cost savings are not certain [ABSTRACT FROM AUTHOR]

As a novel sensing paradigm, crowd sensing systems have gained great attention and been widely adopted in the environmental monitoring and calculation areas. In crowd sensing systems, mobile users provide their multiple resources to the requesters to execute tasks. Existing studies focus on the divisible task or one-to-one mapping for single resource allocation. However, this assumption does not hold for crowd sensing systems. Owing to the task attribute, some tasks cannot be divided into multiple parts to run on different devices. In addition, a high performance mobile device can execute multiple tasks simultaneously. We address the problem of multi-resource allocation in crowd sensing systems for the auction-based model considering many-to-one mapping for indivisible tasks, where many-to-one mapping allows one mobile device to provide multiple resources to execute one or more tasks. In this article, we study, for the first time to the best of our knowledge, a truthful mechanism that stimulates mobile users and requesters to declare their true values. We design a truthful double auction mechanism together with a payment scheme tailored to fit it that would help researchers understand how a truthful double auction mechanism can be designed. In addition, we prove that our proposed mechanism maintains budget-balance, individual rationality, and computational tractability. Furthermore, we analyze the approximation ratio of our proposed approximation algorithm. Experimental results demonstrate that our proposed mechanism has high computation efficiency and good performance. [ABSTRACT FROM AUTHOR]

The decentralization of power generation driven by the rise in the adoption of distributed energy resources paves the way for a new paradigm in grid operations. P2P energy trading is beneficial to the grid as well as the connected peers. A blockchain-based smart contract is well suited to transparently facilitate trades between energy consumers and producers without the services of intermediaries. In this paper, Ethereum-based smart contracts that facilitate double energy auction and spinning reserve trading are developed with Solidity, compiled, and deployed within the Remix IDE. Willing energy sellers/buyers submit offers/bids to a contract that implements the double auction procedure. In order to fulfil energy supply obligations, sellers are also able to purchase spinning reserves via another smart contract. The smart contracts' effectiveness in performing the auction procedure and making payments is confirmed using an energy/reserve market scenario. The proposed scheme encourages further adoption of distributed energy resources and participation in local P2P energy trading. [ABSTRACT FROM AUTHOR]

The unprecedented growth of demand for charging electric vehicles (EVs) calls for novel expansion solutions to today’s charging networks. Riding on the wave of the proliferation of sharing economy, Airbnb-like charger sharing markets open the opportunity to expand the existing charging networks without requiring costly and time-consuming infrastructure investments, yet the successful design of such markets relies on innovations at the interface between game theory, mechanism design, and large scale optimization. In this paper, we propose a price-based iterative double auction for charger sharing markets where charger owners rent out their under-utilized chargers to the charge-needing EV drivers. Charger owners and EV drivers form a two-sided market which is cleared by a price-based double auction. Chargers’ locations, availabilities, and unit time service costs as well as drivers’ time and location preferences are considered in the allocation and scheduling process. The goal is to compute social welfare maximizing schedules which benefit both charger owners and EV drivers and, in turn, ensure the continuous growth of the market. We prove that the proposed double auction is budget balanced and individually rational. In addition, results from our computational study show that the proposed auction achieves on average 94% efficiency compared with that of the optimal solutions and is suitable for a larger day-ahead charger sharing market setting in terms of running time. [ABSTRACT FROM AUTHOR]

This paper proposes a simple descriptive model of discrete-time double auction markets for divisible assets. As in the classical models of exchange economies, we consider a finite set of agents described by their initial endowments and preferences. Instead of the classical Walrasian-type market models, however, we assume that all trades take place in a centralized double auction where the agents communicate through sealed limit orders for buying and selling. We find that, under nonstrategic bidding, double auction clears with zero trades precisely when the agents' current holdings are on the Pareto frontier. More interestingly, the double auctions implement Adam Smith's "invisible hand" in the sense that, when starting from disequilibrium, repeated double auctions lead to a sequence of allocations that converges to individually rational Pareto allocations. [ABSTRACT FROM AUTHOR]

• We design double auction mechanisms for online freight platforms. • We propose two mechanisms with transaction costs and asymmetric demand information. • We show that the mechanisms have good properties. • Numerical studies show advantages of mechanisms over two commonly used mechanisms. In recent years, many online freight platforms have appeared to improve transport market efficiency. An important problem faced by such platforms is how to elicit the agents' information and to match their supplies and demands efficiently. To address the problem, we propose the modified Multi-unit Trading Reduction and Vickery‒Clarke‒Groves (MMTR-MVCG) mechanism for considering heterogeneous transaction costs. Under this mechanism, the shippers and carriers first decide their bids, and then the platform determines the trading agents, sets the transaction prices, and matches the shippers' demands and the carriers' supplies. We show that this mechanism is incentive-compatible, individually rational, budget-balanced, and asymptotically efficient. For the special case with homogeneous transaction costs, we first show that the MMTR-MVCG mechanism can be simplified. In addition, we design a new double auction mechanism for the case with asymmetric demand information and homogeneous transaction costs and show that this mechanism also is incentive- compatible, individually rational, budget-balanced, and asymptotically efficient. Moreover, we conduct numerical studies to assess the impacts of transaction costs and information asymmetry on the mechanisms and compare the performance of our mechanisms with those of two commonly used mechanisms. Our mechanisms can yield greater social welfare, more trading agents, and larger trading volumes than the other two mechanisms. We also discuss the impact of the subsidy strategy. [ABSTRACT FROM AUTHOR]

This paper investigates the existence of a nondegenerate pure-strategy Bayesian-Nash equilibrium in a double auction between one seller and several heterogeneous buyers in the independent private value framework. It begins with three examples describing some of the model's particular features. After studying a number of continuity-related properties of the interim payoff functions, we construct a sequence of strategy profiles that converges to a nondegenerate monotone Bayesian-Nash equilibrium of the game under some strict monotonicity conditions. The equilibrium existence result is applied to a double auction with risk-averse bidders. [ABSTRACT FROM AUTHOR]

With the rapid advancement of distributed energy resources (DERs), artificial intelligence, and smart meter technologies, traditional consumers are undergoing a paradigm shift towards 'prosumers'. In this context, peer-to-peer (P2P) energy trading emerges as an effective approach to enhance local energy utilization. Nevertheless, the inherent intermittency and forecasting challenges associated with renewable energy resources may magnify uncertainties in the markets, and pose a potential threat to destabilize the markets. To address this challenge, this paper presents a method to assess the economic impacts of forecasting errors and introduces a metric, the bill deviation index. Additionally, the consequences of forecasting errors on market outcomes are examined based on the mathematical model of three different pricing mechanisms. Our findings indicate that forecasting errors can lead to significant financial discrepancies, the magnitude of which is closely related to the pricing mechanisms and their dependency on energy quantity. The paper further underscores the role of variability in clearing price, balancing cost, and the supply–demand relationship in determining the economic fallout of forecasting errors. It concludes by providing insights for managing energy trading in markets marked by high forecasting errors and suggests strategies to mitigate the associated economic risks. • An economic impact assessment method considering pricing calculation principles is proposed. • The influence of forecasting errors on market outcomes is analyzed through three pricing mechanisms. • A P2P energy trading model is developed to evaluate crucial variables under various market conditions. [ABSTRACT FROM AUTHOR]