From the development of an open-source energy modelling tool to its application and the creation of communities of practice: The example of OSeMOSYS

Abstract

In the last decades, energy modelling has supported energy planning by offering insights into the dynamics between energy access, resource use, and sustainable development. Especially in recent years, there has been an attempt to strengthen the science-policy interface and increase the involvement of society in energy planning processes. This has, both in the EU and worldwide, led to the development of open-source and transparent energy modelling practices. This paper describes the role of an open-source energy modelling tool in the energy planning process and highlights its importance for society. Specifically, it describes the existence and characteristics of the relationship between developing an open-source, freely available tool and its application, dissemination and use for policy making. Using the example of the Open Source energy Modelling System (OSeMOSYS), this work focuses on practices that were established within the community and that made the framework's development and application both relevant and scientifically grounded.

OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling

“…A new software system was developed, the so-called "OSeMOSYS-PuLP". It is a new code implementation of the open source energy modeling system (the OSeMOSYS modeling framework) [10,11] with the substantial extension of Monte Carlo simulations (MCS). Other code implementations of OSeMOSYS (i.e., GNU MathProg, GAMS, and Python using the software library Pyomo [12,13]) have been extensively used for modeling and analysis of long-term energy planning scenarios in the scientific literature, e.g., see References [14][15][16][17][18][19][20][21].…”

“…Both imply that there is a key limitation in the field of energy research. Only a few active academic open-source models have existed for more than a few years including Balmorel [45], TEMOA [46], and OSeMOSYS [10,11]. Preceded by DEECo in 2004, and GnuAE in 2005, OSeMOSYS-with an easy to decipher code base-sparked a new wave of open-source endeavors [47].…”

“…Furthermore, a sensitivity analysis cannot quantify the uncertainty caused by random errors in the input data [49]. For instance, the two academic deterministic energy system modeling framework Balmore [45] and OSeMOSYS [10,11] typically (except Ref. [19]) assume perfectly forecasted demands and do not capture stochastic properties of their parameters [10,11,45].…”

“…For instance, the two academic deterministic energy system modeling framework Balmore [45] and OSeMOSYS [10,11] typically (except Ref. [19]) assume perfectly forecasted demands and do not capture stochastic properties of their parameters [10,11,45]. Examples for varying parameters include solar power [60] and wind power [61] that both depend on weather conditions.…”

Sedimentary basin geothermal favourability mapping and power generation assessments

“…This model is well suited to investigate supply-side capacity expansion and dispatch in the context of land, demonstrated in studies that e.g. link sustainable energy constraints to available agricultural land and water resources in Uganda (Gardumi et al, 2018), investigate land use strategies in the context of bioenergy crops in post-conflict Columbia (Gonzalez-Salazar et al, 2017), and quantify the land area impacts of strict emission reductions in Alberta, Canada (Palmer-Wilson et al, 2019). To enable the investigation described in this study, the OSeMOSYS model is modified in three ways.…”