Without Transparent Energy Planning, the Lights Could Go Out

Why It Matters

Transparent, open modelling exposes the true value of storage, preventing costly over‑building and ensuring reliable, low‑carbon power systems.

Key Takeaways

• Open models increase transparency in energy planning
• Multi‑day storage could add up to 70 GW in Europe
• PyPSA‑Eur enables granular, cost‑optimal storage analysis
• Open tools reduce reliance on proprietary software
• Transparency improves security and stakeholder trust

Pulse Analysis

The energy sector is at a crossroads where the complexity of renewable integration demands more than proprietary, black‑box simulations. Open‑source frameworks like PyPSA, GenX and OSeMOSYS provide full visibility into assumptions, algorithms and data inputs, allowing utilities, regulators and investors to validate outcomes independently. This transparency not only builds trust among diverse stakeholders but also accelerates model iteration, reducing the time and cost associated with traditional licensing models. As grid operators confront variable generation and the need for flexible resources, open tools become essential for rapid scenario testing and collaborative decision‑making.

A landmark study by OET and Form Energy applied an open, multi‑year climate dataset to evaluate multi‑day storage (MDS) requirements for Germany’s 2035 clean‑energy target. By modelling 16 scenarios that incorporated gas phase‑outs, transmission delays and price volatility, the research identified up to 31 GW of storage capacity needed within Germany and 70 GW across the broader European context. The findings demonstrate that MDS—particularly iron‑air batteries—can dramatically cut curtailment, smooth electricity prices and lower dependence on fossil fuels, thereby delivering both economic and emissions benefits. The granular temporal resolution of the open model was crucial for capturing these cost‑optimal investment signals, which many conventional tools overlook.

Beyond the German case, the open‑model paradigm promises a scalable pathway for global energy transition planning. By licensing models under permissive MIT terms and hosting code on public repositories, OET enables rapid adaptation to local market conditions, regulatory frameworks and emerging technologies. This collaborative ecosystem not only enhances cybersecurity—through community‑driven vulnerability detection—but also democratizes access to high‑quality analysis for smaller utilities and developing economies. As policymakers strive to meet net‑zero commitments, embracing open, transparent modelling will be pivotal in aligning investment, policy and technology choices toward resilient, low‑cost power systems.