scholarly journals Bidding in Smart Grid PDAs: Theory, Analysis and Strategy

2020 ◽  
Vol 34 (02) ◽  
pp. 1974-1981
Author(s):  
Susobhan Ghosh ◽  
Sujit Gujar ◽  
Praveen Paruchuri ◽  
Easwar Subramanian ◽  
Sanjay Bhat
Keyword(s):  
Smart Grids ◽  
Complete Information ◽  
Equilibrium Analysis ◽  
Equilibrium Strategy ◽  
Double Auction ◽  
Single Shot ◽  
Wholesale Market ◽  
Bidding Strategies ◽  
Double Auctions ◽  
Best Response

Periodic Double Auctions (PDAs) are commonly used in the real world for trading, e.g. in stock markets to determine stock opening prices, and energy markets to trade energy in order to balance net demand in smart grids, involving trillions of dollars in the process. A bidder, participating in such PDAs, has to plan for bids in the current auction as well as for the future auctions, which highlights the necessity of good bidding strategies. In this paper, we perform an equilibrium analysis of single unit single-shot double auctions with a certain clearing price and payment rule, which we refer to as ACPR, and find it intractable to analyze as number of participating agents increase. We further derive the best response for a bidder with complete information in a single-shot double auction with ACPR. Leveraging the theory developed for single-shot double auction and taking the PowerTAC wholesale market PDA as our testbed, we proceed by modeling the PDA of PowerTAC as an MDP. We propose a novel bidding strategy, namely MDPLCPBS. We empirically show that MDPLCPBS follows the equilibrium strategy for double auctions that we previously analyze. In addition, we benchmark our strategy against the baseline and the state-of-the-art bidding strategies for the PowerTAC wholesale market PDAs, and show that MDPLCPBS outperforms most of them consistently.

Fuzzy logic-based bidding strategies in dynamic double auctions

Kybernetes ◽  
2019 ◽  
Vol 48 (3) ◽  
pp. 612-635
Author(s):  
Baki Unal ◽  
Çagdas Hakan Aladag
Keyword(s):  
Fuzzy Logic ◽  
Bidding Strategy ◽  
Double Auction ◽  
Auction Markets ◽  
Content Type ◽  
Bidding Strategies ◽  
Double Auctions ◽  
Inference Systems ◽  
Zero Intelligence ◽  
Logic Inference

Purpose Double auctions are widely used market mechanisms on the world. Communication technologies such as internet increased importance of this market institution. The purpose of this study is to develop novel bidding strategies for dynamic double auction markets, explain price formation through interactions of buyers and sellers in decentralized fashion and compare macro market outputs of different micro bidding strategies. Design/methodology/approach In this study, two novel bidding strategies based on fuzzy logic are presented. Also, four new bidding strategies based on price targeting are introduced for the aim of comparison. The proposed bidding strategies are based on agent-based computational economics approach. The authors performed multi-agent simulations of double auction market for each suggested bidding strategy. For the aim of comparison, the zero intelligence strategy is also used in the simulation study. Various market outputs are obtained from these simulations. These outputs are market efficiencies, price means, price standard deviations, profits of sellers and buyers, transaction quantities, profit dispersions and Smith’s alpha statistics. All outputs are also compared to each other using t-tests and kernel density plots. Findings The results show that fuzzy logic-based bidding strategies are superior to price targeting strategies and the zero intelligence strategy. The authors also find that only small number of inputs such as the best bid, the best ask, reference price and trader valuations are sufficient to take right action and to attain higher efficiency in a fuzzy logic-based bidding strategy. Originality/value This paper presents novel bidding strategies for dynamic double auction markets. New bidding strategies based on fuzzy logic inference systems are developed, and their superior performances are shown. These strategies can be easily used in market-based control and automated bidding systems.

scholarly journals An equilibrium analysis of market selection strategies and fee strategies in competing double auction marketplaces

2012 ◽  
Vol 26 (2) ◽  
pp. 245-287 ◽  
Author(s):  
Bing Shi ◽  
Enrico H. Gerding ◽  
Perukrishnen Vytelingum ◽  
Nicholas R. Jennings
Keyword(s):  
Equilibrium Analysis ◽  
Double Auction ◽  
Market Selection ◽  
Selection Strategies

scholarly journals Bidding in Periodic Double Auctions Using Heuristics and Dynamic Monte Carlo Tree Search

2018 ◽  
Author(s):  
Moinul Morshed Porag Chowdhury ◽  
Christopher Kiekintveld ◽  
Son Tran ◽  
William Yeoh
Keyword(s):  
Monte Carlo ◽  
Single Type ◽  
Bidding Strategy ◽  
Multiple Time ◽  
Tree Search ◽  
Wholesale Market ◽  
Monte Carlo Tree Search ◽  
Bidding Strategies ◽  
World Energy ◽  
Heuristic Strategy

In a Periodic Double Auction (PDA), there are multiple discrete trading periods for a single type of good. PDAs are commonly used in real-world energy markets to trade energy in specific time slots to balance demand on the power grid. Strategically, bidding in a PDA is complicated because the bidder must predict and plan for future auctions that may influence the bidding strategy for the current auction. We present a general bidding strategy for PDAs based on forecasting clearing prices and using Monte Carlo Tree Search (MCTS) to plan a bidding strategy across multiple time periods. In addition, we present a fast heuristic strategy that can be used either as a standalone method or as an initial set of bids to seed the MCTS policy. We evaluate our bidding strategies using a PDA simulator based on the wholesale market implemented in the Power Trading Agent Competition (PowerTAC) competition. We demonstrate that our strategies outperform state-of-the-art bidding strategies designed for that competition.

scholarly journals A cloud priority-based dynamic online double auction mechanism (PB-DODAM)

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
S. M. Reza Dibaj ◽  
Ali Miri ◽  
SeyedAkbar Mostafavi
Keyword(s):  
Resource Allocation ◽  
Service Providers ◽  
Supply And Demand ◽  
Cloud Service ◽  
Time Constraints ◽  
Double Auction ◽  
Auction Mechanism ◽  
Double Auctions ◽  
Auction Models ◽  
Scheduling Mechanisms

AbstractDouble auctions are considered to be effective price-scheduling mechanisms to resolve cloud resource allocation and service pricing problems. Most of the classical double auction models use price-based mechanisms in which determination of the winner is based on the prices offered by the agents in the market. In cloud ecosystems, the services offered by cloud service providers are inherently time-constrained and if they are not sold, the allocated resources for the unsold services are wasted. Furthermore, cloud service users have time constraints to complete their tasks, otherwise, they would not need to request these services. These features, perishability and time-criticality, have not received much attention in most classical double auction models. In this paper, we propose a cloud priority-based dynamic online double auction mechanism (PB-DODAM), which is aligned with the dynamic nature of cloud supply and demand and the agents’ time constraints. In PB-DODAM, a heuristic algorithm which prioritizes the agents’ asks and bids based on their overall condition and time constraints for resource allocation and price-scheduling mechanisms is proposed. The proposed mechanism drastically increases resource allocation and traders’ profits in both low-risk and high-risk market conditions by raising the matching rate. Moreover, the proposed mechanism calculates the precise defer time to wait for any urgent or high-priority request without sacrificing the achieved performance in resource allocation and traders’ profits. Based on experimental results in different scenarios, the proposed mechanism outperforms the classical price-based online double auctions in terms of resource allocation efficiency and traders’ profits while fulfilling the double auction’s truthfulness pillar.

Equilibrium Analysis of Dual-Channel Supply Chain Under Retailer's Greening Cost Information Asymmetry

2020 ◽  
Vol 13 (4) ◽  
pp. 1-22
Author(s):  
Rofin T. M. ◽  
Biswajit Mahanty
Keyword(s):  
Supply Chain ◽  
Information Asymmetry ◽  
Complete Information ◽  
Equilibrium Analysis ◽  
Theoretic Model ◽  
Cost Information ◽  
Dual Channel ◽  
Game Theoretic ◽  
Dual Channel Supply Chain ◽  
The Impact

The purpose of this study is to investigate the impact of information asymmetry of retailer's greening cost on the performance of both the manufacturer and the retailer. The study considers a dual-channel supply chain comprising of a manufacturer and a retailer committed to green operations. The authors have employed sequential game theoretic model to derive the closed form expressions corresponding to the two cases under consideration, that is: (1) complete information and (2) asymmetric information. They have found that the sharing of greening cost information by the retailer can make both the manufacturer and the retailer better off in terms of profit. They have also found that the greening cost information sharing is all the more important when the greening cost efficiency is weak. The study helps retail managers to make a decision on whether to conceal or reveal the greening cost information with the upstream manufacturer.

Incomplete information and iterated strict dominance

2020 ◽  
Author(s):  
Christian W Bach ◽  
Andrés Perea
Keyword(s):  
Incomplete Information ◽  
Solution Concept ◽  
Complete Information ◽  
Common Belief ◽  
Person Perspective ◽  
Response Sets ◽  
Best Response ◽  
Necessary And Sufficient

Abstract The solution concept of iterated strict dominance for static games with complete information recursively deletes choices that are inferior. Here, we devise such an algorithm for the more general case of incomplete information. The ensuing solution concept of generalized iterated strict dominance is characterized in terms of common belief in rationality as well as in terms of best response sets. Besides, we provide doxastic conditions that are necessary and sufficient for modelling complete information from a one-person perspective.

scholarly journals Bounded Incentives in Manipulating the Probabilistic Serial Rule

2020 ◽  
Vol 34 (02) ◽  
pp. 2276-2283
Author(s):  
Zihe Wang ◽  
Zhide Wei ◽  
Jie Zhang
Keyword(s):  
Complete Information ◽  
Response Strategy ◽  
Individual Agent ◽  
Worst Case ◽  
Incentive Compatible ◽  
Best Response ◽  
Strategic Manipulation ◽  
Utility Gain ◽  
Serial Rule ◽  
Random Assignment Problem

The Probabilistic Serial mechanism is well-known for its desirable fairness and efficiency properties. It is one of the most prominent protocols for the random assignment problem. However, Probabilistic Serial is not incentive-compatible, thereby these desirable properties only hold for the agents' declared preferences, rather than their genuine preferences. A substantial utility gain through strategic behaviors would trigger self-interested agents to manipulate the mechanism and would subvert the very foundation of adopting the mechanism in practice. In this paper, we characterize the extent to which an individual agent can increase its utility by strategic manipulation. We show that the incentive ratio of the mechanism is 3/2. That is, no agent can misreport its preferences such that its utility becomes more than 1.5 times of what it is when reports truthfully. This ratio is a worst-case guarantee by allowing an agent to have complete information about other agents' reports and to figure out the best response strategy even if it is computationally intractable in general. To complement this worst-case study, we further evaluate an agent's utility gain on average by experiments. The experiments show that an agent' incentive in manipulating the rule is very limited. These results shed some light on the robustness of Probabilistic Serial against strategic manipulation, which is one step further than knowing that it is not incentive-compatible.

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