Optimizing Energy Hubs to Reduce Grid Congestion in the Netherlands

: A Model-Based Approach to Support Energy Hub Initiators in Energy Hub Development

Abstract

Grid congestion in the Netherlands is an escalating challenge driven by increasing electricity demand and the growing integration of renewable energy sources. In this context, Energy Hubs (EHs) have emerged as a promising solution to locally manage electricity supply and demand, thereby alleviating the pressure on the national grid. However, the initial stages of EH development are confronted with uncertainties, insufficient data, and a lack of advanced calculation tools for EH initiators.

This research investigates the early-stage development of EHs in the Netherlands and presents an optimization model designed to support EH initiators. The study reveals that while current frameworks and government initiatives provide a foundational basis for EH development, they remain fragmented and offer limited accessibility and guidance. Similarly, existing calculation tools, although valuable for pre-development analysis, do not fully meet the tailored needs of EH initiators. Data availability further complicates the process due to challenges in stakeholder collaboration and data sharing, underscoring the necessity for safe
publicly accessible registers that can offer aggregated insights.

The analysis of electricity consumption and production patterns across different sectors highlights significant temporal and operational variations. Distinct consumption profiles, characterized by varying utilization rates and temporal fluctuations, are complemented by the production patterns of renewable assets such as solar panels and wind turbines. The complementary nature of these profiles suggests that integrating diverse business sectors into an EH can effectively balance overall electricity demand.

The optimization model developed in this study evaluates three scenarios: collaborative consump-tion, the integration of solar panels and wind turbines, and the addition of battery storage with a seasonal charging strategy. Results demonstrate that the most effective peak shaving—essential for alleviating local grid congestion—is achieved when all three elements are combined. Although model performance, expressed in terms of explained variance, is sensitive to the imposed constraints, the integrated approach consistently enhances grid stability and offers actionable insights for EH initiators. Overall, the research contributes to the transition towards a more resilient electricity grid by providing a robust calculation tool to EH initiators for optimizing EH development.

Date of Award	3 Apr 2025
Original language	English
Supervisor	Jetse Stoorvogel (Examiner), Lyana Curier (Co-assessor), Nicolaas Brouwer (External assessor) & Joris Miedema (External assessor)