Combination of Site-Wide and Real-Time Optimization for the Control of Systems of Electrolyzers

TL;DR

This paper tackles the challenge of integrating renewable energy volatility with green hydrogen production by proposing a two-stage optimization framework that unifies site-wide optimization (SWO) and real-time optimization (RTO) for systems of electrolyzers. It reuses an existing static optimization model in a dual-use configuration and couples long-horizon planning with short-horizon, real-time adjustments through a hierarchical temporal structure and forecast-informed decisions. The method is evaluated on a case study with electrolyzers powered by grid and wind, showing that SWO provides robust long-term plans while RTO adapts to real-time fluctuations, reducing mismatch with renewable availability and improving hydrogen production efficiency. The approach offers a robust, scalable framework for integrating flexible energy resources into power systems, with potential extensions to other technologies and decentralized optimization.

Abstract

The rapid expansion of renewable energy sources has introduced significant volatility and unpredictability in the energy supply chain, necessitating advanced control strategies to ensure grid stability and reliability. Green hydrogen production via electrolysis offers a viable solution for converting and storing this volatile renewable energy. However, the inherent fluctuations of renewable energy sources present challenges for consistent utilization and integration of green hydrogen. This work proposes a two-stage optimization approach, combining site-wide optimization and real-time optimization for managing systems of electrolyzers. By adapting an existing static optimization model, dual use is achieved in both site-wide optimization and real-time optimization. The hierarchical optimization structure, characterized by distinct temporal resolutions, enables effective responses to both dynamic changes and long-term trends. The side-wide optimization layer generates long-term plans based on forecast data, while the real-time optimization layer refines these plans in real-time, accommodating immediate fluctuations and ensuring efficient operation. The results from the case study on a system of electrolyzers demonstrate the method's effectiveness in aligning electrolyzer operation with actual availability of renewable energy. This approach offers a robust framework for optimizing the operation of electrolyzers but also other types of flexible energy resources, contributing to sustainable and economically viable energy management.

Figures (8)

• Figure 1: Control Hierarchy (adopted from Sko23)
• Figure 2: Schematic Representation of the Concept
• Figure 3: Flowchart for the use of an existing optimization model in RTO
• Figure 4: Forecast and their uncertainty (data by ALW+16)
• Figure 5: Electricity price EPEb