# Reinforcement Learning for Bidding Strategy Optimization in Day-Ahead Energy Market

arXiv: https://arxiv.org/abs/2411.16519 · HTML: https://mlsanity.com/paper/2411.16519/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 5 of 5)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Multi-agent Reinforcement Learning in Sequential Social Dilemmas](https://mlsanity.com/paper/1702.03037/) (2017)
- [Multiagent Cooperation and Competition with Deep Reinforcement Learning](https://mlsanity.com/paper/1511.08779/) (2015)
- [Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents](https://mlsanity.com/paper/1912.00498/) (2019)
- [Multi-Agent Reinforcement Learning with Graph Convolutional Neural Networks for optimal Bidding Strategies of Generation Units in Electricity Markets](https://mlsanity.com/paper/2208.06242/) (2022)

## Built on by (most cited first, 2 of 2)

- [Natural-gas storage modelling by deep reinforcement learning](https://mlsanity.com/paper/2511.02646/) (2025)
- [Functional Architecture of European Electricity Trading Markets: Requirements for AI Supported Trading Systems under Regulatory Constraints](https://mlsanity.com/paper/2609.29108/) (2026)

## 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.