Reinforcement Learning Based Bidding Framework with High-dimensional Bids in Power Markets

• URL: http://arxiv.org/abs/2410.11180v1
• Date: Tue, 15 Oct 2024 01:39:28 GMT
• Title: Reinforcement Learning Based Bidding Framework with High-dimensional Bids in Power Markets
• Authors: Jinyu Liu, Hongye Guo, Yun Li, Qinghu Tang, Fuquan Huang, Tunan Chen, Haiwang Zhong, Qixin Chen,
• Abstract summary: We propose a framework to fully utilize HDBs for RL-based bidding methods. First, we employ a special type of neural network called Neural Network Supply Functions (NNSFs) to generate HDBs in the form of N price-power pairs. Second, we embed the NNSF into a Markov Decision Process (MDP) to make it compatible with most existing RL methods.
• Score: 3.8066343577384796
• License:
• Abstract: Over the past decade, bidding in power markets has attracted widespread attention. Reinforcement Learning (RL) has been widely used for power market bidding as a powerful AI tool to make decisions under real-world uncertainties. However, current RL methods mostly employ low dimensional bids, which significantly diverge from the N price-power pairs commonly used in the current power markets. The N-pair bidding format is denoted as High Dimensional Bids (HDBs), which has not been fully integrated into the existing RL-based bidding methods. The loss of flexibility in current RL bidding methods could greatly limit the bidding profits and make it difficult to tackle the rising uncertainties brought by renewable energy generations. In this paper, we intend to propose a framework to fully utilize HDBs for RL-based bidding methods. First, we employ a special type of neural network called Neural Network Supply Functions (NNSFs) to generate HDBs in the form of N price-power pairs. Second, we embed the NNSF into a Markov Decision Process (MDP) to make it compatible with most existing RL methods. Finally, experiments on Energy Storage Systems (ESSs) in the PJM Real-Time (RT) power market show that the proposed bidding method with HDBs can significantly improve bidding flexibility, thereby improving the profit of the state-of-the-art RL bidding methods.

Related papers

• BOND: Aligning LLMs with Best-of-N Distillation [63.254031574394965]
  We propose Best-of-N Distillation (BOND), a novel RLHF algorithm that seeks to emulate Best-of-N but without its significant computational overhead at inference time. Specifically, BOND is a distribution matching algorithm that forces the distribution of generations from the policy to get closer to the Best-of-N distribution. We demonstrate the effectiveness of our approach and several design choices through experiments on abstractive summarization and Gemma models.
  arXiv  Detail & Related papers  (2024-07-19T18:38:25Z)
• AIGB: Generative Auto-bidding via Conditional Diffusion Modeling [26.283427427408085]
  This paper introduces AI-Generated Bidding (AIGB), a novel paradigm for auto-bidding through generative modeling. In this paradigm, we propose DiffBid, a conditional diffusion modeling approach for bid generation. Experiments conducted on the real-world dataset and online A/B test on Alibaba advertising platform demonstrate the effectiveness of DiffBid.
  arXiv  Detail & Related papers  (2024-05-25T09:21:43Z)
• Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets [13.03742132147551]
  We develop a novel BESS joint bidding strategy that utilizes deep reinforcement learning (DRL) to bid in the spot and contingency frequency control ancillary services markets. Unlike conventional "black-box" DRL model, our approach is more interpretable and provides valuable insights into the temporal bidding behavior of BESS.
  arXiv  Detail & Related papers  (2024-02-29T12:41:54Z)
• High-dimensional Bid Learning for Energy Storage Bidding in Energy Markets [2.1053035142861423]
  We propose a new bid representation method called Neural Network Embedded Bids (NNEBs) Our studies show that the proposed method achieves 18% higher profit than the baseline and up to 78% profit of the optimal market bidder.
  arXiv  Detail & Related papers  (2023-11-05T02:59:53Z)
• Proximal Policy Optimization Based Reinforcement Learning for Joint Bidding in Energy and Frequency Regulation Markets [6.175137568373435]
  Energy arbitrage can be a significant source of revenue for the battery energy storage system (BESS) It is crucial for the BESS to carefully decide how much capacity to assign to each market to maximize the total profit under uncertain market conditions. This paper formulates the bidding problem of the BESS as a Markov Decision Process, which enables the BESS to participate in both the spot market and the FCAS market to maximize profit.
  arXiv  Detail & Related papers  (2022-12-13T13:07:31Z)
• Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
  This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
  arXiv  Detail & Related papers  (2022-11-29T01:18:58Z)
• Dual Generator Offline Reinforcement Learning [90.05278061564198]
  In offline RL, constraining the learned policy to remain close to the data is essential. In practice, GAN-based offline RL methods have not performed as well as alternative approaches. We show that not only does having two generators enable an effective GAN-based offline RL method, but also approximates a support constraint.
  arXiv  Detail & Related papers  (2022-11-02T20:25:18Z)
• Data-Driven Online Interactive Bidding Strategy for Demand Response [0.30586855806896046]
  Demand response (DR) provides the services of peak shaving, enhancing the efficiency of renewable energy utilization with a short response period, and low cost. Various categories of DR are established, e.g. automated DR, incentive DR, emergency DR, and demand bidding. This paper determines the bidding and purchasing strategy simultaneously employing the smart meter data and functions. The results prove that when facing diverse situations the proposed model can earn the optimal profit via off/online learning the bidding rules and robustly making the proper bid.
  arXiv  Detail & Related papers  (2022-02-09T02:44:20Z)
• Adversarial Energy Disaggregation for Non-intrusive Load Monitoring [78.47901044638525]
  Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions. Recent advances reveal that deep neural networks (DNNs) can get favorable performance for NILM. We introduce the idea of adversarial learning into NILM, which is new for the energy disaggregation task.
  arXiv  Detail & Related papers  (2021-08-02T03:56:35Z)
• Demand Responsive Dynamic Pricing Framework for Prosumer Dominated Microgrids using Multiagent Reinforcement Learning [59.28219519916883]
  This paper proposes a new multiagent Reinforcement Learning based decision-making environment for implementing a Real-Time Pricing (RTP) DR technique in a prosumer dominated microgrid. The proposed technique addresses several shortcomings common to traditional DR methods and provides significant economic benefits to the grid operator and prosumers.
  arXiv  Detail & Related papers  (2020-09-23T01:44:57Z)
• A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding [69.37299910149981]
  A key component for the successful renewable energy sources integration is the usage of energy storage. We propose a novel modelling framework for the strategic participation of energy storage in the European continuous intraday market. An distributed version of the fitted Q algorithm is chosen for solving this problem due to its sample efficiency. Results indicate that the agent converges to a policy that achieves in average higher total revenues than the benchmark strategy.
  arXiv  Detail & Related papers  (2020-04-13T13:50:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.