Intraday Battery Dispatch for Hybrid Renewable Energy Assets

Paper: arXiv 2503.12305

Abstract

We develop a mathematical model for intraday dispatch of co-located wind-battery energy assets. Focusing on the primary objective of firming grid-side actual production vis-a-vis the preset day-ahead hourly generation targets, we conduct a comprehensive study of the resulting stochastic control problem across different firming formulations and wind generation dynamics. Among others, we provide a closed-form solution in the special case of a quadratic objective and linear dynamics, as well as design a novel adaptation of a Gaussian Process-based Regression Monte Carlo algorithm for our setting. Extensions studied include an asymmetric loss function for peak shaving, capturing the cost of battery cycling, and the role of battery duration. In the applied portion of our work, we calibrate our model to a collection of 140+ wind-battery assets in Texas, benchmarking the economic benefits of firming based on outputs of a realistic unit commitment and economic dispatch solver.

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced stochastic control, SDEs, Gaussian Processes, and derives closed-form solutions under specific conditions, indicating high mathematical density. It also demonstrates high empirical rigor through calibration to 140+ real wind-battery assets in Texas and benchmarking against a realistic unit commitment solver.

Research Flowchart

  flowchart TD
  A["Research Goal: Optimal Intraday Battery Dispatch<br>for Co-located Wind-Battery Assets"] --> B{"Methodology: Stochastic Control Formulations"};
  B --> C["Primary: Firming w.r.t. Day-Ahead Targets"];
  B --> D["Extensions: Peak Shaving & Battery Cycling"];
  C & D --> E{"Solution Approaches"};
  E --> F["Closed-Form Solution: Quadratic Objective"];
  E --> G["Algorithm: GP-Adapted Regret Monte Carlo"];
  H["Data: 140+ Texas Wind-Battery Assets"] --> I["Calibration & Simulation"];
  F & G & I --> J{"Benchmarking"};
  J --> K["Economic Analysis: Unit Commitment & Economic Dispatch Solver"];
  K --> L["Key Findings: Economic Benefits of Firming & Optimal Dispatch Strategies"];

Cited References (94)

1. Regression M onte C arlo for Microgrid Management (2019) — link
2. Model-Free reinforcement learning with continuous action in practice (2012) — link
3. Partial differential equation methods for stochastic dynamic optimization: an application to wind power generation with energy storage (2017) — link
4. Integrating Sequential Hypothesis Testing into Adversarial Games: A Sun Zi-Inspired Framework (2025) — link
5. 2023 Special Report on Battery Storage (2024)
6. A model predictive control approach to the problem of wind power smoothing with controlled battery storage (2010) — link
7. Valuing American Options by Simulation: A Simple Least-Squares Approach (2001)
8. Simulation Methods for Stochastic Storage Problems: A Statistical Learning Perspective (2020) — link

1 of 94 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.