Designing a zero-order energy transition model: How to create a new Starter Data Kit

Cannone, Carla; Allington, Lucy; Barron, Karla Cervantes; Charbonnier, Flora; Walker, Miriam Zachau; Halloran, Claire; Yeganyan, Rudolf; Tan, Naomi; Cullen, J. Michael; Harrison, John; To, Long Seng; Howells, Mark

doi:10.1016/j.mex.2023.102120

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…Given the commitments made by many countries to combat climate change, today, it is expected to find long-term studies related to carbon neutrality or net zero emissions by 2050, for example, [19] , [20] , [21] .…”

Section: Value Of the Datamentioning

confidence: 99%

Techno-economic dataset and assumptions for long-term energy systems modelling in the Dominican Republic (2024–2050)

Quevedo,

Moya,

Mariano-Hernandez

et al. 2024

Data in Brief

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Section: Value Of the Datamentioning

confidence: 99%

Techno-economic dataset and assumptions for long-term energy systems modelling in the Dominican Republic (2024–2050)

Quevedo,

Moya,

Mariano-Hernandez

et al. 2024

Data in Brief

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“…Hands-on 3: Instructions on how to save, run, and visualise results using the Microsoft Access Database and the Excel Template provided with the software. Another asset of the clicSAND 1.0. software lies in its compatibility with a related project to create Starter Data Kits (SDKs) [14] [15]. These are national datasets and simple energy system models, which are currently available for 69 countries in Africa, East Asia, and South America (Annex A).…”

Section: Software Descriptionmentioning

confidence: 99%

clicSAND for OSeMOSYS: a user-friendly interface using open-source optimisation software for energy system modelling analysis.

Cannone,

Allington,

de Wet

et al. 2023

Preprint

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Energy models are crucial for helping governments and policymakers plan long-term investments in the energy transition context. One of the most established open-source optimisation models is OSeMOSYS—the Open-Source Energy Modelling System. This paper presents a new user interface—clicSAND—for OSeMOSYS, which shortens the learning curve and supports beginner energy modellers to perform long-term investment analyses efficiently. The freely available and open-source clicSAND software consists of a user-friendly Excel interface for entering data, integrated solvers, and a dashboard for visualising results. The results, which extend to 2070, can inform policy decisions and mobilise financial resources for sustainable development measures, for example, ensuring affordable and secure energy supply and mitigating the effects of climate change. This paper describes clicSAND's main benefits, architecture, and functionalities. A review of the evolution from clicSAND 1.0 to clicSAND 3.0 is also discussed. Furthermore, a South-African case study undertaken by participants of the international capacity-building event—the EMP-A (Energy Modelling Platform for Africa) 2021 shows the results achieved by inexperienced users following a three-week training course. Finally, current applications and future extensions of the software are also presented.

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“…Additionally, around 5 million people were without access to electricity in 2019 [4]. The electricity sector experiences a substantial demand and supply gap, plagued by distribution losses reaching up to 50% [5]. Results from the 2018 household survey indicated that poverty is still high across Sierra Leone, particularly in rural areas, with 10.8% of the population living in extreme poverty and 57% in poverty [6].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Long-Term Forecasting: A MAED Application for Sierra Leone’s Electricity Demand (2023–2050)

Fields,

Collier,

Kiley

et al. 2024

Energies

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Sierra Leone is an electricity-poor country with one of the lowest electricity consumption per capita rates across sub-Saharan Africa. Yet, with ambitious targets to transform and stimulate its economy in the coming decades, energy demand forecasting becomes an integral component of successful energy planning. Through applying the MAED-D (version 2.0.0) demand software, this research study aims to generate Sierra Leone’s electricity demand forecasts from 2023 to 2050. Three novel scenarios (baseline-, high-, and low-demand) are developed based on socio-economic and technical parameters. The baseline scenario considers the current electricity sector as business-as-usual; the high-demand scenario examines an ambitious development future with increased economic diversification and mechanisation, and the low-demand scenario examines more reserved future development. The modelled scenario results project an increase in electricity demand ranging from 7.32 PJ and 12.23 PJ to 5.53 PJ for the baseline-, high-, and low-demand scenarios, respectively, by 2050. This paper provides a base set of best-available data needed to produce an electricity demand model for Sierra Leone which can be used as a capacity-building tool for in-country energy planning alongside further integration into data modelling pipelines.

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Designing a zero-order energy transition model: How to create a new Starter Data Kit

Cited by 8 publications

References 13 publications

Techno-economic dataset and assumptions for long-term energy systems modelling in the Dominican Republic (2024–2050)

Techno-economic dataset and assumptions for long-term energy systems modelling in the Dominican Republic (2024–2050)

clicSAND for OSeMOSYS: a user-friendly interface using open-source optimisation software for energy system modelling analysis.

Long-Term Forecasting: A MAED Application for Sierra Leone’s Electricity Demand (2023–2050)

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