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

Chowdhury, Moinul Morshed Porag; Kiekintveld, Christopher; Son, Tran Cao; Yeoh, William

doi:10.24963/ijcai.2018/23

Cited by 14 publications

(5 citation statements)

References 9 publications

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“…Periodic double auction allows the bidding only during a specified market clearing period [99]. A periodic double auctionbased bidding strategy was implemented in [100], which forecasts clearing prices for different time periods and perform Monte Carlo tree search to design the bids for multiple time periods. In k-double auction, the market clearing price is determined from the range between the highest seller bid and lowest buyer bid so that no seller/buyer will have to receive/pay less/more than their bids [101].…”

Section: Auction Methodsmentioning

confidence: 99%

A Review of Peer-to-Peer Energy Trading Markets: Enabling Models and Technologies

Islam

2024

Energies

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This paper presents a detailed review of the existing literature on peer-to-peer (P2P) energy trading considering market architectures, trading strategies, and enabling technologies. P2P energy trading enables individual users in the electricity network to act as sellers or buyers and trade energy among each other. To facilitate the discussion on different aspects of P2P energy trading, this paper focuses on P2P market mechanisms, relevant bidding strategies, and auction models. In addition, to solve the energy management problems associated with P2P energy trading, this paper investigates widely used solution methods such as game-theoretic models, mathematical optimisation, as well as more recent machine learning techniques and evaluates them in a critical manner. The outcomes of this investigation along with the identification of the challenges and limitations will allow researchers to find suitable P2P energy trading mechanisms based on different market contexts. Moreover, the discussions on potential future research directions are expected to improve the effectiveness of P2P energy trading technologies.

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Section: Auction Methodsmentioning

confidence: 99%

A Review of Peer-to-Peer Energy Trading Markets: Enabling Models and Technologies

Islam

2024

Energies

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“…First, we illustrate that the MDP based strategy indeed achieves the equilibrium strategy characterized for OBOS setting. Then, we conduct different experiments to compare MDPLCPBS with the following strategies: ZI (Gode and Sunder 1993), ZIP (Tesauro and Das 2001), TacTex (Urieli and Stone 2014), and MCTS (Chowdhury et al 2018). Our analysis shows that MDPLCPBS outperforms ZI, TacTex, and ZIP in all the cases, and closely matches with MCTS.…”

Section: Introductionmentioning

confidence: 90%

Bidding in Smart Grid PDAs: Theory, Analysis and Strategy

Ghosh

Gujar

Paruchuri

et al. 2020

AAAI

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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.

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“…For the first time, Schvartzman and Wellman combined empirical game theory with the Q-learning algorithm in reinforcement learning to analyze the optimal trading strategy of traders in the double auction market [32], but this algorithm is only suitable for a small and discrete space of bidding actions. Chowdhury et al proposed a trading strategy using Monte Carlo Tree Search (MCTS) [33]. However, this algorithm is suitable for discrete bidding sets and cannot deal with bidding problems with continuous types and action spaces.…”

Section: Related Workmentioning

confidence: 99%

Trading and Pricing Sensor Data in Competing Edge Servers with Double Auction Markets

Shi

Song

2021

Journal of Sensors

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With the development of the IoT (Internet of Things), sensors networks can bring a large amount of valuable data. In addition to be utilized in the local IoT applications, the data can also be traded in the connected edge servers. As an efficient resource allocation mechanism, the double auction has been widely used in the stock and futures markets and can be also applied in the data resource allocation in sensor networks. Currently, there usually exist multiple edge servers running double auctions competing with each other to attract data users (buyers) and producers (sellers). Therefore, the double auction market run on each edge server needs efficient mechanism to improve the allocation efficiency. Specifically, the pricing strategy of the double auction plays an important role on affecting traders’ profit, and thus, will affect the traders’ market choices and bidding strategies, which in turn affect the competition result of double auction markets. In addition, the traders’ trading strategies will also affect the market’s pricing strategy. Therefore, we need to analyze the double auction markets’ pricing strategy and traders’ trading strategies. Specifically, we use a deep reinforcement learning algorithm combined with mean field theory to solve this problem with a huge state and action space. For trading strategies, we use the Independent Parametrized Deep Q-Network (I-PDQN) algorithm combined with mean field theory to compute the Nash equilibrium strategies. We then compare it with the fictitious play (FP) algorithm. The experimental results show that the computation speed of I-PDQN algorithm is significantly faster than that of FP algorithm. For pricing strategies, the double auction markets will dynamically adjust the pricing strategy according to traders’ trading strategies. This is a sequential decision-making process involving multiple agents. Therefore, we model it as a Markov game. We adopt Multiagent Deep Deterministic Policy Gradient (MADDPG) algorithm to analyze the Nash equilibrium pricing strategies. The experimental results show that the MADDPG algorithm solves the problem faster than the FP algorithm.

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

Cited by 14 publications

References 9 publications

A Review of Peer-to-Peer Energy Trading Markets: Enabling Models and Technologies

A Review of Peer-to-Peer Energy Trading Markets: Enabling Models and Technologies

Bidding in Smart Grid PDAs: Theory, Analysis and Strategy

Trading and Pricing Sensor Data in Competing Edge Servers with Double Auction Markets

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