Intraday Battery Dispatch for Hybrid Renewable Energy Assets

2025-03-16

AI Summary

This paper addresses the challenge of intra-day power output tracking—ensuring wind-battery co-located assets meet their day-ahead generation targets under wind uncertainty. Method: We propose a stochastic optimal control framework tailored to grid-side operational compliance. It derives, for the first time, an analytical intra-day dispatch solution under quadratic tracking objectives and linear battery dynamics. Integrating Gaussian process regression with Monte Carlo simulation, we design a rolling optimization algorithm robust to wind forecast uncertainty. The framework further incorporates an asymmetric loss function and a physics-informed battery cycle degradation model. Results: Evaluated on over 140 wind-storage projects in Texas, the method significantly improves output compliance rates. Quantitative results demonstrate simultaneous reductions in both imbalance penalty costs and battery aging rate, thereby achieving high tracking accuracy while ensuring economic sustainability.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Optimizing intraday battery dispatch for wind-battery hybrid energy systems
• Developing stochastic control models for grid production firming targets
• Evaluating economic benefits through realistic energy market simulations

Innovation

• Mathematical model for co-located wind-battery dispatch
• Closed-form solution for quadratic objective case
• Gaussian Process-based Regression Monte Carlo adaptation