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The document provides technical instructions for using auxiliary fuels in LEAP's Transformation processes. It distinguishes auxiliary fuels from feedstock fuels by noting they are not converted and do not affect overall process energy efficiency. The guide details how to configure 'own use' consumption via module properties and explains how LEAP handles fuel requirements through its hierarchical module structure and iterative calculations for feedback flows.

The document outlines the calculation methodology for stock analysis of demand technologies, detailing how stock turnover, energy intensity, energy consumption, and emissions are derived using variables such as vintage, survival profiles, and degradation factors.

The document describes the optimization capabilities of LEAP, which are powered by the open-source NEMO framework. It details the difference between partial cost optimization for single modules and full energy system optimization (ESO) across all transformation modules and selected demand end-uses. The guide explains that optimization aims to find the least-cost capacity expansion and dispatch by minimizing the total net present value of social costs, subject to constraints like emission caps. It also covers the implicit Reference Energy System (RES) in LEAP and the options for perfect versus limited foresight optimization.

The document defines useful energy demand as the portion of final energy demand available for work after device losses, noting that while typically smaller than final energy demand, it can be larger for devices like heat pumps. It also describes the software settings required to calculate these demands across all branches.

The 'Minimum Share of Production' variable ensures a specific process supplies at least a defined percentage of a fuel's total annual production, regardless of cost-effectiveness, to model policy targets or contractual obligations.

The document provides technical instructions for setting up Transformation analyses in LEAP, detailing the hierarchical structure of modules and processes used to model energy conversion sectors. It outlines the four-step setup process—identifying modules, sequencing them by energy flow, setting properties, and entering technology data—while explaining the roles of feedstock, auxiliary, and output fuels in calculating primary resource requirements.

The document outlines the technical setup for optimization in LEAP, including solver selection, required settings, and data input requirements for transformation modules and energy storage.

The document provides technical instructions for displaying environmental results in LEAP, detailing the use of Global Warming Potential (GWP) factors based on IPCC reports and the four available reporting formats for GHG emissions. It specifies that LEAP employs a territorial accounting approach aligned with IPCC and UNFCCC guidelines for national inventories.

The document provides technical instructions for displaying LEAP results on maps, which requires the installation of MapWinGIS. It details two primary map types: Region Maps, which use gradient, bar chart, or pie chart formats to show results for multi-region areas, and Gridded Maps, which allocate energy use and emissions to grid squares using proxy GIS data. The guide also describes map navigation tools, categorization methods for gradient maps (such as Natural Breaks and Equal Intervals), and formatting options including background image selection and transparency settings.

The document describes an optional methodology in LEAP for allocating emissions to the area producing the resource rather than where it is combusted. This approach helps analyze a country's dependence on fossil fuel production and its vulnerability to a global low-carbon transition. Users can specify emission factors via the Resources\Primary branches, using manual entries, mathematical formulae, or links to the Technology and Environmental Database (TED).

The Geography variable allows users to allocate energy use and emissions data among map squares within a current area or region. This process requires the use of four specific allocation functions: Allocate, AllocateEvenly, AllocateNone, and PointSource. Geographic mappings are configured in the General: Geographic Mapping screen, and the resulting data is displayed on gridded maps in the Results View.

The document explains the two types of Lifecycle Profiles in LEAP: Aging Profiles, which track how variables like fuel economy change as devices age relative to a new device, and Distribution Profiles, which describe vintage distributions within a stock. It details the software interface for creating, editing, and importing these profiles, including the option to define values via manual data points or an exponential curve formula.

The document outlines how to input process efficiency data in LEAP, detailing the use of percentage efficiency, heat rates, and energy losses. It provides typical values for steam generation and hydropower and specifies the application of these methods to storage and distribution systems.

The document defines capacity credit as the fraction of rated capacity considered firm for calculating module reserve margins. It notes that thermal plants typically do not exceed average annual Maximum Availability, while intermittent renewable plants have lower values, and some sources, such as imported electricity, may have zero capacity credit. For endogenous capacity planning, the document specifies that at least some plants must have a capacity credit greater than zero to contribute to the reserve margin, and suggests a first-order approximation for renewable plants based on the ratio of their availability to that of a standard thermal plant.

The document details the calculations for transport analysis, focusing on how vehicle stock is tracked by vintage and how fuel economy, mileage, and emissions degrade over a vehicle's lifecycle. It specifies that stock is determined by sales and survival profiles, while energy and pollutant emissions are calculated based on distance traveled and age-related degradation factors.

The document describes how LEAP uses scenarios as self-consistent storylines to model future energy systems under various socioeconomic and policy conditions. It details the relationship between scenarios and Current Accounts data, the inclusion of sensitivities, and the use of a hierarchical expression inheritance system to streamline data management.

The document describes the functionality of LEAP's Cost-Benefit View, which calculates capital, operating, and maintenance costs across energy systems, including optional environmental externalities. It emphasizes that the tool focuses on social opportunity costs rather than consumer prices or financial viability, and allows users to define costing boundaries to accommodate data limitations regarding upstream technologies.

The document defines feedstock fuels as those converted within a process, noting that process efficiency is the ratio of total output energy to total feedstock energy. It distinguishes feedstock fuels from auxiliary fuels, which are subsidiary energy inputs not converted in the process and therefore excluded from efficiency calculations. Most processes typically use a single feedstock fuel with a 100% share.

The document provides technical instructions for using the LEAP software's 'Non Energy Sector Effect Loading' feature to track non-energy greenhouse gas emissions. It describes how to enable the feature via the Scope tab, build a hierarchical structure of category and effect branches, and input annual total emissions loadings, which can be entered as static values or calculated using expressions.

The document describes the application of Sales and Sales Share variables in stock-turnover analyses to model how new energy efficiency or emissions standards for new technologies impact the overall average values of a total stock as older devices are replaced. It outlines two analysis types—Transport Analysis for vehicles and standard Stock Analysis for other devices—and explains that data can be entered via bottom-up technology branches or a top-down approach using shares. It further notes that Sales variables operate from a specified First Sales Year and are paired with Survival Profiles to model device retirement.