Reinforcement Learning for Bidding Strategy Optimization in Day-Ahead Energy Market
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
In a day-ahead market, energy buyers and sellers submit their bids for a particular future time, including the amount of energy they wish to buy or sell and the price they are prepared to pay or receive. However, the dynamic for forming the Market Clearing Price (MCP) dictated by the bidding mechanism is frequently overlooked in the literature on energy market modelling. Forecasting models usually focus on predicting the MCP rather than trying to build the optimal supply and demand curves for a given price scenario. Following this approach, the article focuses on developing a bidding strategy for a seller in a continuous action space through a single agent Reinforcement Learning algorithm, specifically the Deep Deterministic Policy Gradient. The algorithm controls the offering curve (action) based on past data (state) to optimize future payoffs (rewards). The participant can access historical data on production costs, capacity, and prices for various sources, including renewable and fossil fuels. The participant gains the ability to operate in the market with greater efficiency over time to maximize individual payout.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 30 references that are in the map. First posted on arXiv 25 Nov 2024.
Builds on
5 of 30 references.
References · 5
- 2015Continuous control with deep reinforcement learningcited by 3,407
- 2017Multi-agent Reinforcement Learning in Sequential Social Dilemmascited by 209
- 2015Multiagent Cooperation and Competition with Deep Reinforcement Learningcited by 184
- 2019Optimization for Reinforcement Learning: From Single Agent to Cooperative Agentscited by 20
- 2022Multi-Agent Reinforcement Learning with Graph Convolutional Neural Networks for optimal Bidding Strategies of Generation Units in Electricity Marketscited by 3
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2026State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading
- 2026Evaluation of Electricity Market Clearing Mechanisms via Reinforcement Learning: Prices, Remuneration and Competitive Dynamics
- 2024Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets
- 2026PowerMarketJax: A JAX Benchmark Suite for Multi-Agent Reinforcement Learning in Power Markets
- 2026A Dual-Positive Monotone Parameterization for Multi-Segment Bids and a Validity Assessment Framework for Reinforcement Learning Agent-based Simulation of Electricity Markets
- 2025Arbitrage Tactics in the Local Markets via Hierarchical Multi-agent Reinforcement Learning ↗
- 2025Assessing Long-Term Electricity Market Design for Ambitious Decarbonization Targets using Multi-Agent Reinforcement Learning
- 2025Deep Reinforcement Learning-Based Bidding Strategies for Prosumers Trading in Double Auction-Based Transactive Energy Market