Truthful Bidding Research Articles

On market clearing of day ahead auctions for European power markets: Consumer payment minimisation versus social welfare maximisation

For inflexible demand and considering network constraints, we introduce a Consumer Payment Minimisation (CPM) market clearing mechanism and compare it with the standard Social Welfare Maximisation (SWM) used in European Day Ahead Electricity Markets. CPM presents a challenging optimisation problem, which we addressed by four numerical algorithms with varying computational costs and convergence guarantees, evaluated on synthetic data. We derive strong, partial analytical results and the concept of optimal zonal stack curves to facilitate efficient CPM solutions. Evaluations using 2019–2020 data from the Central Western European Day Ahead Power Market under the assumption of truthful bidding show that SWM models historical prices well, while CPM reduces producers’ market power and decreases zonal prices and total electricity procurement costs compared to SWM.

A novel experimental test of truthful bidding in second-price auctions with real objects

We present experimental evidence on bidding in second-price auctions with real objects. Our novel design, combining a second-price auction with an individual-specific binary-choice task based on the outcome of the auction, allows us to directly identify over- and under-bidding. We analyze bidding in real-object and induced-value auctions, and find significant deviations from truthful bidding in both. Overall, under-bidding is somewhat more prevalent than over-bidding; yet, the latter has a bigger magnitude, especially with induced values. At the individual level, however, we find that participants who over-bid in induced-value auctions are equally likely to over- or under-bid in real-object ones; moreover, there is no association in the size of the deviations from truthful bidding across the two types of auctions. Hence, we conclude that there is no relation between the tendency to deviate from truthful bidding in induced-value vs. real-object auctions.

EWA‐DA‐based resource allocation method for 5G networks

AbstractIn 5G networks, most D2D work focuses on optimizing system throughput without addressing economic constraints. This article encourages users to exchange resources and apply honest bidding tactics to attain high auction efficiency. This study describes an experience‐weighted attraction double auction (EWA‐DA) technique for allocating resources in 5G D2D networks. The suggested method classifies mobile users into seller cellular users (SCU) and buyer cellular users' (BCU). The SCU transmits the beacon packet, and the nearby cellular users create a D2D group in response to the beacon considered BCU. Then, the bid values of the buyer and seller cellular users are transmitted to the base station side. The BS uses an experience‐weighted attraction‐learning model to obtain accurate bidding prices. Lastly, an ask‐to‐mean‐value double auction model was utilized instead of accurate bid values. Extensive simulation results demonstrate that the suggested strategy delivers excellent auction efficiency and fair earnings for both seller and buyer users. In addition, we identified that the participating users accomplished truthful bidding and used fewer resource blocks than existing techniques.

The impact of core constraints on truthful bidding in combinatorial auctions

Combinatorial auctions (CAs) offer the flexibility for bidders to articulate complex preferences when competing for multiple assets. However, the behavior of bidders under different payment rules is often unclear. Our research explores the relationship between core constraints and several core-selecting payment rules. Specifically, we examine the natural and desirable property of payment rules of being non-decreasing, which ensures that bidding higher does not lead to lower payments. Earlier studies revealed that the VCG-nearest payment method – a commonly employed payment rule – fails to adhere to this principle even for single-minded CAs. We establish that when a single effective core constraint exists, the payment maintains the non-decreasing property in single-minded CAs. To identify auctions where such a constraint is present, we introduce a novel framework using conflict graphs to represent single-minded CAs and establish sufficient conditions for the existence of single effective core constraints. We proceed with an analysis of the implications on bidder behavior, demonstrating that there is no overbidding in any Nash equilibrium when considering non-decreasing core-selecting payment rules. Our study concludes by establishing the non-decreasing nature of two additional payment rules, namely the proxy and proportional payment rules, for single-minded CAs.

An optimization-based partial marginal pricing method to reduce excessive consumer payment in electricity markets

Among the pricing schemes, locational marginal pricing is the most widely adopted scheme in current electricity markets. Despite the nice properties, theoretical study and practical experience have shown that considering the strategic bidding behaviors of market participants, locational marginal pricing may result in excessive consumer payment and prejudices market efficiency. In this paper, we propose an optimization-based partial marginal pricing method to reduce consumer payment under strategic bidding. The advantages of the locational marginal pricing method are reserved to the best extent while the remaining problems are delicately handled. To achieve this, we first analyze the characteristics and problems of the locational marginal pricing scheme and present a partial marginal pricing structure. Second, we establish an optimization-based partial marginal pricing model, in which discriminatory price components are introduced in price formulation and the pricing properties including competitive equilibrium and revenue adequacy are formulated as constraints. Third, to verify the performance of the proposed pricing scheme considering the strategic bidding, a bi-level strategic bidding problem of the generator under the proposed pricing method is established and transformed into a mixed integer linear programming problem. The numerical results indicate that under the conventional pricing methods, the consumer payments under strategic bidding can be up to 16.67 times higher than those under truthful bidding, while the proposed method can effectively reduce excessive consumer payments by 92.1% while maintaining the desired pricing properties.

Edge–IoT Computing and Networking Resource Allocation for Decomposable Deep Learning Inference

Deep learning (DL) applications have attracted significant attention with the rapidly growing demand for Internet of Things (IoT) systems. However, performing the inference tasks for DL applications on IoT devices is challenging due to the large computational demands of DL models. Recently, edge computing has offered us a solution by deploying resources near the end users. However, resources at the edge are still limited; thus, management issues, such as allocating the networking resources as well as the computing capabilities and configuring the devices appropriately for different applications, become essential. For knobs in such edge management, we consider multiple application tasks with different options of DL models and different hyperparameter settings, along with possible decomposition points that utilize the split DL concept to design the configuration tables. Layer-level decomposition in split DL provides greater flexibility by splitting a single DL inference model into parts on different computing devices, and each part consists of several consecutive layers. We then propose the SplitDL-Image and the SplitDL-Video algorithms based on the Vickrey–Clarke–Groves (VCG) mechanism by considering model performance and frames per second (FPS) requirements with the preferences of the heterogeneous IoT devices. The proposed method allocates networking and edge server computing resources according to the designed configuration tables by assigning the appropriate configuration to each IoT device. Simulation results based on real-world applications show that the proposed method indeed allocates more resources to IoT devices with more urgent/important tasks, preference for better accuracy, or higher local computational cost. In addition, other desired properties, such as truthful bidding, individual rationality, and weakly budget balance, are also guaranteed.

Core-selecting auction-based mechanisms for service function chain provisioning and pricing in NFV markets

With the rapid development of network function virtualization (NFV), an ever-increasing number of enterprises, businesses and operators are resorting to network service provisioning by means of the service function chains (SFCs). Toward this trend, NFV markets are emerging, where users rent SFCs from the network service provider (NSP) to execute their tasks, and the NSP uses the computation resources across geo-distributed clouds to construct various SFCs. Such NFV markets need well-designed pricing mechanisms for SFCs since the users have motivations to strategize around their payments. The Vickrey–Clarke–Groves (VCG) mechanism is a natural fit here, which can ensure that truthful bidding is a dominant strategy. However, VCG mechanism is susceptible to the problems of collusion and shill bidding. To address these problems, in this paper, we design a core-selecting auction-based mechanism (CSAM) for SFC provisioning and pricing in the NFV market. This mechanism considers dynamic provisioning, flexible deployment and reservation prices across geo-distributed clouds. Specifically, we first formulate the core of NFV auction, and prove that an in-core outcome can prevent collusion and shill bidding. Next, we use a couple of correlated linear program (LP) and quadratic program (QP) to design an in-core VCG-nearest payment algorithm, aiming to acquire an in-core outcome and minimize users’ incentives to bid untruthfully. Simulation results verify the strict theoretical analysis, and validate the effectiveness and efficiency of our proposed CSAM.

An efficient multi-attribute multi-item auction mechanism with ex-ante and ex-post satisfaction for 4PL transportation service procurement

Reverse auction is an effective tool for a 4PL to purchase transportation services. This paper investigated a new transportation services procurement problem for 4PL, which involves three features: the 4PL’s loss-averse behavior, price and non-price attributes, and multiple transportation requests. An efficient multi-attribute multi-item reverse auction mechanism considering the 4PL ex-ante and ex-post satisfaction (EES-MMRA) is proposed to purchase transportation services for the 4PL. In the EES-MMRA, integrating the allocation rule with the 4PL ex-ante satisfaction, a 0-1 programming model is constructed to determine winning 3PLs and obtain efficient allocations. Then, a payment rule considering the 4PL ex-post satisfaction is established to ensure truthful bidding of 3PLs. And we discuss some desirable properties (e.g., incentive compatibility, individual rationality, efficiency, and budget balance properties) to justify the EES-MMRA mechanism, subsequently. Next, several numerical experiments are conducted to demonstrate the effectiveness and applicability of the EES-MMRA mechanism. Furthermore, sensitivity analysis presents the influences of the weights of the non-price attributes, risk attitude coefficients, and loss aversion coefficients. Finally, we conduct comparison analysis to show the advantages of the EES-MMRA mechanism over the known Vickrey–Clark–Groves (P-VCG) mechanism.

Pricing real-time stochastic storage operations

Pricing storage operation in the real-time market under demand and generation stochasticities is considered. A scenario-based stochastic rolling-window dispatch model is formulated for the real-time market, consisting of conventional generators, utility-scale storage, and distributed energy resource aggregators. We show that uniform pricing mechanisms require discriminative out-of-the-market uplifts, making settlements under locational marginal pricing (LMP) discriminative. It is also shown that the temporal locational marginal pricing (TLMP) that adds nonuniform shadow prices of ramping and state-of-charge to LMP removes the need for out-of-the-market uplifts. Truthful bidding incentives are also established for price-taking participants under TLMP. Revenue adequacy and uplifts are evaluated in numerical simulations.

Pricing incentive analysis under non-convexity in electricity market

The operational characteristics of generators cause non-convexity in the electricity market. The presence of non-convexity implies that market participants will bear lost opportunity costs under linear prices, which motivates them to violate the dispatch decisions or obtain excess profits through strategic bidding. To resolve this issue, finding a reasonable pricing scheme under non-convexity is a research field of great concern. Currently, most studies regard uplift payments as an indicator to quantify the effectiveness of a pricing scheme. It is usually recognized that convex hull pricing is a promising pricing method with nice incentive properties because it minimizes the uplift payment and thus improves market transparency. However, whether the pricing mechanism can guide truthful bidding has not been thoroughly investigated. In this paper, considering the complex market clearing process, we propose a framework with certain qualifications to analyze the incentive properties under different pricing schemes. Then, conclusions regarding the incentive property of different pricing schemes are provided. The effectiveness and correctness of the proposed method are validated using the numerical results of case studies.

A market mechanism for truthful bidding with energy storage

This paper proposes a market mechanism for multi-interval electricity markets with generator and storage participants. Drawing ideas from supply function bidding, we introduce a novel bid structure for storage participation that allows storage units to communicate their cost to the market using energy-cycling functions that map prices to cycle depths. The resulting market-clearing process – implemented via convex programming – yields corresponding schedules and payments based on traditional energy prices for power supply and per-cycle prices for storage utilization. We illustrate the benefits of our solution by comparing the competitive equilibrium – assuming price-taker participants – of the resulting mechanism to that of an alternative solution that uses prosumer-based bids. Our solution shows several advantages over the standard prosumer-based approach that prices energy per slot. It does not require a priori price estimation. It also incentivizes participants to reveal their truthful cost, thus leading to an efficient, competitive equilibrium. Numerical experiments using New York Independent System Operator (NYISO) data validate our findings.

BARA: A blockchain-aided auction-based resource allocation in edge computing enabled industrial internet of things

The recent emergence of Industrial Internet of Things (IIoT), a novel subset of Internet of Things (IoT), integrates sensors and intelligent devices with industry applications to develop a self-organizing system for creating enhanced and adaptive industrial environments. IIoT devices usually have limited computational power, while IIoT applications are mostly mission-critical and/or safety-critical. Therefore, these applications need computing resources that are closer to the devices. This work proposes a decentralized auction-based resource allocation mechanism in edge computing enabled IIoT using consortium blockchain and smart contract that shelves the involvement of a trusted third party, i.e. auctioneer. Various quality parameters are considered during resource allocation to address the mobility of both IIoT devices and edge resources, heterogeneity of edge servers, false assurance of edge servers, reliability, delay in results, and responsiveness of edge servers. Utility functions for IIoT devices are designed to calculate their degree of satisfaction depending on various quality parameters. The proposed blockchain-aided auction based mechanism fulfills various auction-based resource allocation requirements such as seal bidding, no impersonation of the bidder, no modification in any bid or result of allocation by the auctioneer, and proof for winners. The proposed work encourages edge servers for truthful bidding and furnishes the allocation results in polynomial time. Performance evaluation of the proposed model exhibits encouraging results.

EggBlock: Design and Implementation of Solar Energy Generation and Trading Platform in Edge-Based IoT Systems with Blockchain.

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.

An efficient, open-bid procurement auction for small-scale electricity markets

Despite early calls to investigate the application of Vickrey auctions in electricity markets, academic literature still provides little insight into the matter. Similarly, despite general auction-theoretic literature discussing various advantages of employing revealed rather than sealed bidding procedures, publications on electricity market auctions rarely even mention differences between these opposing features. This paper marries these neglected features into a new auction model for electricity markets. We propose a novel open-bid, descending-clock auction capable of handling inter-bidder dependencies such as thermal line limits. A novel set of termination criteria ensures that our proposed auction facing markets with power balance and generator capacity constraints and thermal line limits, preserves truthful bidding as a dominant strategy, which, if employed by all bidders, allocates supply efficiently constituting an ex-post perfect equilibrium. Furthermore, we discuss known disadvantages of Vickrey–Clarke–Groves type auctions in the context of our proposed auction applied in electricity markets.

Rebate Auction Mechanisms for Bidirectional Grid Balancing Using Cloud Workload Migrations

Increasing the power-grid’s flexibility is essential for expanding the integration of renewable energy sources in modern power grids. This work presents a new rebate auction framework that allows power grids to use cloud datacenters as managed loads to provide upward/downward grid flexibility. Since the energy consumption of datacenters is proportional to their computational workload, this paper presents a rebate auction framework that can induce cloud workload migrations between datacenters to correct energy imbalances. Unlike existing datacenter-based power grid balancing approaches that have only focused on providing downward flexibility and only considered owner-operated datacenters, the proposed framework provides bidirectional flexibility and accommodates both owner-operated and public cloud datacenters. Thus, providing a general framework for creating workload migrations between datacenters to balance the power grid. Because workloads on public cloud datacenters are managed by end-users, the proposed framework uses monetary incentives (auctioned rebates) to encourage large-scale end-users to migrate their cloud workloads between datacenters to correct energy imbalances. The use of monetary rewards as an incentive hides the complexity of grid-balancing from auction participants, who only participate in the auction to lower their cost, while grid-balancing happens as a result of workload migrations. This paper presents and compares two auction implementations under the proposed framework, a strategy-proof implementation that guarantees truthful bidding as a dominant strategy, but has NP-hard computational complexity, and an alternative implementation that does not guarantee truthful bidding, but has polynomial time complexity. Simulation results show that the proposed framework is effective in incentivizing cloud workload migrations to achieve the grid balancing goal and provides positive utility to all participants.

Design and regulation of balancing power auctions: an integrated market model approach

We present an integrated market model which considers the dependencies between the wholesale market and the highly regulated balancing power markets. This fosters the understanding of the mechanisms of these markets and, thus, allows the evaluation of the designs of these markets and their interplay. In contrast to existing literature, in our model the prices on the different markets are interdependent and endogenously determined, which also applies to the switch from inframarginal suppliers to extramarginal suppliers. Linked to this, the implementation of a specific assignment of the suppliers to the different markets is according to their production costs and their ability to provide balancing power. We prove the existence of a market equilibrium, analyze its outcome and contrast this with German market data. Based on this model, we assess design changes, partly stipulated by recent European regulation. This includes uniform pricing as a common settlement rule (effect: no truthful bidding in general), standardized prequalification criteria (promising measure for cost reduction), market flexibilization via “free energy bids” (no increased competition) and the alternative score “mixed-price rule” (no effect on the equilibrium).

Mechanism Design for Fair and Efficient DSO Flexibility Markets

The proliferation of distributed energy assets necessitates the provision of flexibility to efficiently operate modern distribution systems. In this article, we propose a flexibility market through which the DSO may acquire flexibility services from asset aggregators in order to maintain network voltages and currents within safe limits. A max-min fair formulation is proposed for the allocation of flexibility. Since the DSO is not aware of each aggregator's local flexibility costs, we show that strategic misreporting can lead to severe loss of efficiency. Using mechanism design theory, we provide a mechanism that makes it a payoff-maximizing strategy for each aggregator to make truthful bids to the flexibility market. While typical truthful mechanisms only work when the objective is the maximization of Social Welfare, the proposed mechanism lets the DSO achieve incentive compatibility and optimality for the max-min fairness objective.

An allocatively efficient auction for pollution permits

This article proposes a new auction design for the efficient allocation of pollution permits. We show that if the auctioneer restricts the bidding rule of the uniform-price auction—coupled with a simple ex-post supply adjustment rule—then truthful bidding is obtained. Consequently, the uniform-price auction is more allocatively efficient than conventional formats that are currently observed in pollution markets.

Multi-Block Ascending Auctions for Effective 5G Licensed Shared Access

Licensed Shared Access (LSA) is a new concept proposed by the radio spectrum policy group in order to optimize spectrum usage: a Mobile Network Operator (MNO) can access temporarily to other incumbent's spectrum after obtaining a license. The licensing process is made via an auction mechanism. The mechanisms proposed in the literature for the LSA context are one-shot auction mechanisms which allocate all the available spectrum as a unique block. In this paper, we first show how to increase the performance of those auctions (in terms of revenue, efficiency and fairness of the allocation) while preserving truthful bidding, by splitting spectrum and converting single block auctions into multi-block auctions. Simulation results illustrate how appropriately choosing the number of blocks allows to increase the aforementioned metrics. Second, we show how to convert one-shot mechanisms to equivalent ascending mechanisms (in terms of allocations and payments) so that we add transparency and privacy to the auction.

Cooperative Service Placement and Scheduling in Edge Clouds: A Deadline-Driven Approach

Mobile edge computing enables resource-limited edge clouds (ECs) in federation to help each other with resource-hungry yet delay-sensitive service requests. Contrary to common practice, we acknowledge that mobile services are heterogeneous and the limited storage resources of ECs allow only a subset of services to be placed at the same time. This paper presents a jointly optimized design of cooperative placement and scheduling framework, named JCPS, that pursues <i>social cost minimization</i> over time while ensuring diverse user demands. Our main contribution is a novel perspective on cost reduction by exploiting the <i>spatial-temporal diversities</i> in <i>workload and resource cost</i> among federated ECs. To build a practical edge cloud federation system, we have to consider two major challenges: <i>user deadline preference</i> and <i>ECs’ strategic behaviors</i> . We first formulate and solve the problem of spatially strategic optimization without deadline awareness, which is proved <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math></inline-formula> -hard. By leveraging user deadline tolerance, we develop a Lyapunov-based <i>deadline-driven</i> joint cooperative mechanism under the scenario where the workload and resource information of ECs are known for one-shot global cost minimization. The <i>service priority</i> imposed by deadline urgency drives time-critical placement and scheduling, which, combined with cooperative control, enables workloads migrated across different times and ECs. Given selfishness of individual ECs, we further design an auction-based cooperative mechanism to elicit <i>truthful bids</i> on workload and resource cost. Rigorous theoretical analysis and extensive simulations are performed, validating the efficiency of JCPS in realizing cost reduction and user satisfaction.