---
title: "Open energy system models"
chunk: 15/16
source: "https://en.wikipedia.org/wiki/Open_energy_system_models"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:30.157219+00:00"
instance: "kb-cron"
---

In practical terms, the framework allows for modeling competition between technologies that can serve the same purpose, such as power generation, while also providing insights into when and where a specific technology is required. Additionally, it can be applied to transportation problems, where the optimal exchange of a commodity between at least two distinct regions needs to be determined. Furthermore, it addresses storage problems, where the optimal balance between production and consumption at different points in time is calculated.
REMix offers several key features that make it a robust tool for energy system modeling. It is designed to handle large-scale models with high spatial and technological resolutions, making it suitable for complex analyses. The framework also incorporates path optimization, allowing for multi-year analyses and strategic planning over extended periods. Ongoing work deals with very large instances involving path optimization using the parallel solver PIPS-IPM++. A notable feature is its custom accounting capability, provided through the indicator module, which enables flexible definitions of what contributes to the objective functions. Additionally, REMix supports flexible modeling, offering multiple approaches to integrate and model technologies, allowing users to tailor the framework to their specific needs. Finally, it supports multi-criteria optimization, where, beyond cost minimization, additional factors such as ecological impacts or resilience indicators can be considered in the objective function, providing a more comprehensive approach to system optimization.
In the past, the model has been used to investigate a wide range of research questions. In addition to detailed analyses of the integration of renewable energies into the electricity system, for example, the role of hydrogen in the energy system of the future has also been examined.
For the purpose of validating the REMix model, German Aerospace Center has participated in various model comparisons.

=== TEMOA ===

TEMOA stands for Tools for Energy Model Optimization and Analysis. The software is being developed by the Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina, USA. The project runs a website and a forum. The source code is hosted on GitHub. The model is programmed in Pyomo, an optimization components library written in Python. TEMOA can be used with any solver that Pyomo supports, including the open source GLPK solver. TEMOA uses version control to publicly archive source code and datasets and thereby enable third-parties to verify all published modeling work.
TEMOA classes as a modeling framework and is used to conduct analysis using a bottom-up, technology rich energy system model. The model objective is to minimize the system-wide cost of energy supply by deploying and utilizing energy technologies and commodities over time to meet a set of exogenously specified end-use demands. TEMOA is "strongly influenced by the well-documented MARKAL/TIMES model generators".
TEMOA forms the basis of the Open Energy Outlook (OEO) research project spanning 2020–2022. The OEO project utilizes open source tools and open data to explore deep decarbonization policy options for the United States.

From mid‑2021, an interactive interface located on the main website allows registered users to manipulate scenario data locally, upload structured SQLite files, and then run these scenarios using the TEMOA software. The service also provides some limited data visualization and project management functionality.

== Specialist models ==
This section lists specialist modeling frameworks that cover particular aspects of an energy system in more detail than would normally be convenient or feasible with more general frameworks.

=== RAMP ===

RAMP is an open-source software suite for the stochastic simulation of user‑driven energy demand time series based on few simple inputs. For example, a minimal definition of a user type — say, a particular category of household — requires only information about which energy-consuming devices they own, when they tend to use them on any typical day, and for how long in total. The software then leverages stochasticity to make up for the absence of more detailed information and to include the unpredictability of human behavior.
The RAMP software can then generate synthetic data wherever metered data does not exist, such as when designing systems in remote areas  or when looking forward to future electric-vehicle fleets.  The limited data requirements also allow for a greater flexibility in scenario selection and development than similar but more data-intensive characterizations.
RAMP has been used in scientific research for a variety of use cases, including the generation of electricity demand profiles for remote or residential communities, domestic hot water usage, cooking practices, and electric mobility.  Associated geographical scales can range from neighborhoods to continents.
RAMP has several dozen users worldwide.  In the early‑2020s, the software became part of a multi-institution software development effort, supported by TU Delft, VITO, Reiner Lemoine Institute, University of Liège, Leibniz University Hannover, and Universidad Mayor de San Simón.
RAMP runs on Python and requires input in tabular form. Graphical user interfaces (GUI) are available, allowing the software to be run from web browsers.

=== venco.py ===

The venco.py model framework can be used to investigate interactions between the uptake of battery electric vehicles (BEV) and the electricity system at large. More specifically, BEVs can usefully contribute to short‑haul storage in power systems facing high shares of fluctuating renewable energy. But unlike dedicated grid storage, BEV contributions are highly dependent on the connection and charging choices that individual vehicle owners might make.

Venco.py has been applied to various scenarios in Germany in 2030 using a projected 9 million BEVs in service and an annual fleet power consumption of 27 TWh. Simulations show that owner decisions are indeed significant and that some system design variables have more influence than others. For instance, aggregate fleet capacity and the availability of fast charging facilities appear to strongly impact the likely system contribution. Further work is needed to assess the influence of more resolved weather and demand patterns. The mathematical formulation is available. Venco.py builds on an earlier spreadsheet prototype.

== Project statistics ==
Statistics for the 30 open energy modeling projects listed (given sufficient information is available) are as follows: