Swing contract pricing: with and without Neural Networks

ArXiv ID: 2306.03822 “View on arXiv”

Authors: Unknown

Abstract

We propose two parametric approaches to evaluate swing contracts with firm constraints. Our objective is to define approximations for the optimal control, which represents the amounts of energy purchased throughout the contract. The first approach involves approximating the optimal control by means of an explicit parametric function, where the parameters are determined using stochastic gradient descent based algorithms. The second approach builds on the first one, where we replace parameters in the first approach by the output of a neural network. Our numerical experiments demonstrate that by using Langevin based algorithms, both parameterizations provide, in a short computation time, better prices compared to state-of-the-art methods.

Keywords: Swing Contracts, Stochastic Gradient Descent, Neural Networks, Langevin Algorithms, Energy Markets, Commodities (Energy)

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics such as stochastic optimal control, dynamic programming, and Langevin dynamics for optimization. It provides strong empirical validation through numerical experiments that compare performance against state-of-the-art methods with specific computational time and price improvements.

  flowchart TD
    A["Research Goal: Evaluate Swing Contracts with Firm Constraints<br>using Parametric Approaches"] --> B["Methodology: Two Parametric Approaches"]
    
    B --> C["Approach 1: Explicit Parametric Function<br>Parameters via Stochastic Gradient Descent"]
    B --> D["Approach 2: Neural Network<br>Replaces Parameters in Approach 1"]
    
    C & D --> E["Computational Process: Langevin-based Algorithms<br>for Fast Numerical Experiments"]
    
    E --> F["Key Findings/Outcomes:<br>• Better Prices vs. State-of-the-Art<br>• Short Computation Time<br>• Optimal Energy Purchase Control"]