On January 5, 2026, Dilip Pandit and a team of researchers at Sandia National Laboratories released a Python-based open source software tool called QuESt PCM: A Production Cost Modeling Tool with High-Fidelity Models of Energy Storage Systems, now available on GitHub. This tool is designed for evaluating power system operations with advanced modeling of diverse energy storage technologies. Production cost models (PCM) are computational tools that simulate power system operations by optimizing the commitment and dispatch of generation resources to meet demand at least cost, while respecting technical and reliability constraints. QuESt PCM is an open-source power system production cost modeling tool designed for high-fidelity representation of energy storage systems (ESS). Built in Python, it uses the Pyomo optimization interface to formulate technology-specific storage models and to capture diverse storage operational constraints. The tool also models market participation capabilities of storage systems, helping assess their impacts on day-ahead and real-time price signals. This tool is part of QuESt 2.0: Open-source Platform for Energy Storage Analytics.
Accurate analysis of system operations under a diverse portfolio of energy storage technologies and their varying market participation strategies requires detailed techno-economic modeling. QuESt PCM provides a market simulation framework that enables users to represent different storage technologies with high fidelity and evaluate their operational behavior across multiple market participation modes. By capturing the technical constraints and economic drivers of storage, the tool supports a comprehensive assessment of power systems with significant storage penetration. QuESt PCM can assist regulators, utilities, state agencies, and independent system operators in evaluating long-term storage investments that are both economically viable and aligned with evolving grid reliability and flexibility needs. As an open-source platform, it also serves the broader research community by enabling transparency, customization, and continued methodological advancement.
This material is based upon work supported by the U.S. Department of Energy, Office of Electricity (OE), Energy Storage Division.