Accounting for Optimal Control in the Sizing of Isolated Hybrid Renewable Energy Systems Using Imitation Learning

2026-01-07

arXiv.org

AI Summary

This study addresses the common oversight in capacity planning for off-grid hybrid renewable energy systems, where the impact of finite-horizon optimal control on wind and energy storage sizing is often neglected, leading to biased carbon emission estimates. To remedy this, the authors propose a novel capacity configuration framework that integrates imitation learning with stochastic neural model predictive control (MPC). This approach explicitly models optimal feedback policies under limited prediction horizons during the design phase, enabling co-optimization of battery energy storage systems (BESS), wind power, and gas turbine capacities. By uniquely combining imitation learning with stochastic neural MPC for system sizing, the method effectively captures the nonlinear trade-off between capital investment and fuel consumption reduction. Case studies on representative offshore systems quantify the emission reductions and economic performance across configurations, demonstrating that embedding optimal control significantly enhances design accuracy.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• 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
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Decarbonization of isolated or off-grid energy systems through phase-in of large shares of intermittent solar or wind generation requires co-installation of energy storage or continued use of existing fossil dispatchable power sources to balance supply and demand. The effective CO2 emission reduction depends on the relative capacity of the energy storage and renewable sources, the stochasticity of the renewable generation, and the optimal control or dispatch of the isolated energy system. While the operations of the energy storage and dispatchable sources may impact the optimal sizing of the system, it is challenging to account for the effect of finite horizon, optimal control at the stage of system sizing. Here, we present a flexible and computationally efficient sizing framework for energy storage and renewable capacity in isolated energy systems, accounting for uncertainty in the renewable generation and the optimal feedback control. To this end, we implement an imitation learning approach to stochastic neural model predictive control (MPC) which allows us to relate the battery storage and wind peak capacities to the emissions reduction and investment costs while accounting for finite horizon, optimal control. Through this approach, decision makers can evaluate the effective emission reduction and costs of different storage and wind capacities at any price point while accounting for uncertainty in the renewable generation with limited foresight. We evaluate the proposed sizing framework on a case study of an offshore energy system with a gas turbine, a wind farm and a battery energy storage system (BESS). In this case, we find a nonlinear, nontrivial relationship between the investment costs and reduction in gas usage relative to the wind and BESS capacities, emphasizing the complexity and importance of accounting for optimal control in the design of isolated energy systems.

Problem

• optimal control
• system sizing
• hybrid renewable energy systems
• energy storage
• emission reduction

Innovation

• imitation learning
• stochastic model predictive control
• hybrid renewable energy systems
• optimal control
• system sizing