Introduction to Optimization

This guide explains how the Low Emissions Analysis Platform (LEAP) integrates with the open-source NEMO framework to perform energy system optimization. It details the capabilities for both partial and full energy system optimization, the costs and constraints considered in least-cost calculations, and the options for foresight modeling.

• LEAP performs optimization calculations by integrating with NEMO, an open-source framework written in the Julia programming language. NEMO is a separate, free installation that connects to LEAP to handle numerical computing without requiring the user to interact directly with its data or results files.
• The software supports two levels of optimization: partial cost optimization for a single Transformation module (such as electricity generation) and full energy system optimization (ESO). ESO allows for simultaneous least-cost calculations across all Transformation modules, optional optimization of demand end uses with useful energy demand, and the modeling of transmission systems like pipelines and electricity networks.
• Optimization is defined as finding the configuration with the lowest total net present value of social costs over the calculation period. The process accounts for capital costs, salvage values or decommissioning costs, fixed and variable O&M costs, fuel costs, and environmental externality values. Calculations are subject to constraints such as emission limits, energy demands, domestic primary resource reserves, and import/export levels.
• LEAP utilizes an implicit Reference Energy System (RES) that automatically creates connections between resources, processes, and end-use demands based on module properties and ordering. This prevents the optimization from ignoring inherent transmission and distribution losses, though users can override default fuel source assumptions using the Fuel Source variable.
• The system supports two foresight methodologies: perfect foresight, where all years are optimized simultaneously, and limited foresight, where years are divided into groups and optimized separately to simulate incomplete knowledge of future events. Limited foresight requires NEMO v2.2 or later.

APA

Stockholm Environment Institute (n.d.). Introduction to Optimization. https://leap.sei.org/help24/Optimization/OptimizationIntroduction.htm

Chicago

Stockholm Environment Institute. Introduction to Optimization. n.d. https://leap.sei.org/help24/Optimization/OptimizationIntroduction.htm.

Wikipedia

{{cite report |author=Stockholm Environment Institute |title=Introduction to Optimization |url=https://leap.sei.org/help24/Optimization/OptimizationIntroduction.htm |access-date=17 August 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{stockholmenvironmentinstitutendintroduction, author = {{Stockholm Environment Institute}}, title = {{Introduction to Optimization}}, institution = {Stockholm Environment Institute}, url = {https://leap.sei.org/help24/Optimization/OptimizationIntroduction.htm}, urldate = {2026-08-17}, note = {Indexed by Climate Insights Directory} }