Search

Double auction provides a cost-effective manner for sellers/buyers in smart grid. Due to concerns about information leakage, the asks/bids from sellers/buyers are sealed, making it challenging to select potential winners. To address this problem, the concept of public key encryption with equality test is deployed in double auction, since it is able to perform information retrieval over secure asks/bids. However, previous solutions suffer from the following two limitations: (i) unable to check inconsistent secure asks/bids due to the lack of tester-verifiable mechanism; (ii) incurring high matching time costs caused by one-to-one secure asks/bids. Therefore, we propose the VerDA, a secure double auction retrieval system with verifiable equality retrieval towards multiple secure asks/bids. Technically, to achieve the property of consistency over secure asks/bids, we develop the tester-verifiable technology by combining the decryption module and test module. To improve the efficiency of retrieval, we introduce secure multi-asks/bids testing function by augmenting the number of inputs in a same retrieval process. Moreover, we implement VerDA based on the PJM dataset in real cloud environment, where the experimental results show practical performance with encryption and test costs amounting to only 57.4% and 18.7% compared to state-of-the-art solution. [ABSTRACT FROM AUTHOR]

With development of e-commerce and intelligent devices, large amounts of asset transaction data and traders' identity information have been generated. It poses huge challenges to the data security and privacy when data are stored in a centralized trading system. Here, considering the need of data sharing and data privacy in asset trading, we proposed a blockchain-based privacy protecting framework to accommodate both aspects of these requirements. The proposed framework combines a multi-channel access control model and smart contracts to enhance the security and transparency of asset trading within permissioned channels while maintaining privacy for transactions occurring outside of these channels. Furthermore, regarding market efficiency and transaction fairness, we design trading algorithm of double auction to make transaction more automated and intelligent. Finally, we implement asset trading prototype based on Hyperledger Fabric and evaluate the performance of model. The experimental results show that the proposed multi-channel blockchain-based framework realized intra-channel date sharing and extra-channel privacy protection. Our framework achieves enhanced security, high efficiency and strong traceability for assert trading. [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 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]

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]

Peer-to-peer (P2P) renewable energy trading, facilitated by designing P2P market smart contracts on blockchain servers, is a promising approach to increase investments in cleaner energy generation. To enhance trading efficiency and pricing fairness, as the major challenges of P2P market designs, this study introduces two new market mechanisms, Hybrid Auction Coalition (HAC), and innovative coalition business model (ICBM), respectively. The performance of these market mechanisms is contrasted with existing mechanisms from the literature with respect to electricity bills, market efficiency, fairness, and blockchain feasibility using a comprehensive list of indicators including costs and profits, fairness, market efficiency, and technical viability. Compared to traditional billing, ICBM and HAC increase sellers' profit by 88% and 66% respectively, while both impose 13% more costs on buyers. ICBM and HAC also set the fairest prices compared to the existing markets. ICBM is shown to improve blockchain feasibility and market efficiency due to lighter on-chain calculations, and absolute clearing mechanisms. The results also demonstrate that HAC outperforms standalone auctions in every aspect which endorses the hybridization benefits. • Novel and efficient P2P Models, and blockchain-based mechanisms for P2P renewable energy trading. • Enhanced fairness concerns in P2P trading through a novel pricing mechanism. • Improved P2P trading efficiency & potentially increased local grid self-sufficiency. • Technical comparison of state-of-the-art blockchain-based P2P trading markets. [ABSTRACT FROM AUTHOR]

This paper presents a new prosumer-centric peer-to-peer (P2P) energy trading framework incorporating the network-secure export–import limits, known as dynamic operating envelope (DOE), for prosumers considering their preferences to trade in the local market. In the proposed framework, a DOE-integrated energy management algorithm is developed for prosumers considering electric vehicles (EVs) charge–discharge and photovoltaic (PV) systems. The framework allows prosumers to operate within defined DOE, aligning with the preferences of EV-owning prosumers by managing the desired EV battery charge levels at their departure. An auction-based P2P energy trading mechanism for prosumers is proposed incorporating the allocated DOE to ensure secure network operation and valuing prosumers' preferences to benefit them. The proposed framework is implemented on a 78-node low-voltage (LV) residential distribution network to evaluate its performance. The results and comparative analyses verify the effectiveness of the proposed framework in addressing network issues such as voltage and line congestion problems, meeting prosumers' preferences in asset management, and providing benefits to all participants in the energy trading process. • A prosumer-centric P2P energy trading using network-secure export–import limits. • Hybrid DER marketplace model-based framework consisting of the DSO and the DMO. • Energy management within the limits considering EVs charge–discharge and PV systems. • DOE-integrated Double auction-based market mechanism for P2P energy. • Fairness in allocating DOEs with high RPI, PIEI, QoS, QoE and MiM values. [ABSTRACT FROM AUTHOR]

The intermittent nature of renewable energy sources challenges the ability of grid operators to maintain balance between supply and demand of electricity. Demand response, or flexibility, is the ability of a load to modify its consumption in response to a control signal. The necessary two-way communications between end-users and grid operators introduce new risks on consumer privacy and data security. Consumer engagement in such programs tends to be low not only for privacy issues, but also because of the inappropriate incentive schemes. In this work, secure Multiparty Computation is used in a flexibility market where end-consumers provide demand response to grid operators in exchange for a monetary compensation. Multiparty computation is a cryptographic method that performs operations over encrypted data. Users submit their offers in encrypted form, and a novel way to characterize flexibility as a commodity and to determine device constraints with very little information from the consumers is developed. The market is cleared via a double auction-like process. The method is validated using flexibility offers from 200 devices of different types. We demonstrate the possibility to clear the market and allocate resources with encrypted orders for sole information. • Private-by-design flexibility market for trading demand flexibility as a commodity. • Market orders for balancing services are submitted in encrypted form. • The market is cleared using secure Multiparty Computation. • Novel flexibility characterization method based on minimal information. • Priority-based, private dispatch algorithm to safely allocate demand-response. [ABSTRACT FROM AUTHOR]