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.

Energy systems modelling plays a pivotal role in understanding and optimizing complex energy systems. By integrating various factors such as energy demand, supply, infrastructure, and environmental considerations, energy systems modelling provides valuable insights for policymakers, industry stakeholders, and researchers. This can be key to informing stakeholder and policy decisions and facilitate the mobilisation of capital and market development to support the development of the energy sector. This article presents the data, assumptions, and related calculations used for the development of a national scale power system model for Sierra Leone. The focus of the model was a techno-economic analysis of specific hydropower expansion plans. Where possible, this data has been collected from publicly available sources such as scientific literature, feasibility studies, international databases, data from existing modelling efforts in Sierra Leone, and local stakeholders. The collection of the data, development of the model, and creation of scenarios was done in collaboration with the Ministry of Energy in Sierra Leone and verified by a broad range of stakeholders. The following paper outlines the key data presented in the full database which can be accessed through the link found in the Specifications Table. The link also contains the Reference Energy System (RES) used in this study.

Energy systems modelling plays a pivotal role in understanding and optimizing complex energy systems. By integrating various factors such as energy demand, supply, infrastructure, and environmental considerations, energy systems modelling provides valuable insights for policymakers, industry stakeholders, and researchers. This can be key to informing stakeholder and policy decisions and facilitate the mobilisation of capital and market development to support the development of the energy sector. This article presents the data, assumptions, and related calculations used for the development of a national scale power system model for Sierra Leone. The focus of the model was a techno-economic analysis of specific hydropower expansion plans. Where possible, this data has been collected from publicly available sources such as scientific literature, feasibility studies, international databases, data from existing modelling efforts in Sierra Leone, and local stakeholders. The collection of the data, development of the model, and creation of scenarios was done in collaboration with the Ministry of Energy in Sierra Leone and verified by a broad range of stakeholders. The following paper outlines the key data presented in the full database which can be accessed through the URL found in the Specifications Table. The URL also contains the Reference Energy System (RES) used in this study.

Energy planning under uncertainty remains a critical challenge in developing economies, where data limitations and climate variability complicate long, term investment decisions. In Sierra Leone, these challenges are particularly acute given low electricity access 36% and heavy reliance on hydropower with strong seasonal variability. This study addresses the limitations of deterministic energy modelling, which typically assumes perfect foresight and produces single optimal pathways that may not remain valid under uncertain futures. To overcome this, we integrate an OSeMOSYS, based technoeconomic optimization model with a Robust Decision Making (RDM) framework to evaluate resilient energy strategies. The approach is applied to the case of the Bumbuna Hydroelectric Expansion, including the proposed upstream Yiben reservoir. Key contributions include the systematic validation of least, cost model outputs under deep uncertainty through hundreds of parameter variations, and the identification of critical uncertainty drivers using scenario discovery techniques (PRIM). Results show that scenarios incorporating the Yiben reservoir (Scenarios 6 and 7) achieve up to ,100% renewable electricity generation by 2030, reduce reliance on heavy fuel oil generation, and deliver lower system costs and Levelised Cost of Electricity compared to alternatives without storage expansion. Furthermore, these scenarios consistently demonstrate robustness across a wide range of futures, clustering in low, cost, high, renewable outcome spaces. The findings highlight hydropower storage as a key enabler of system reliability and decarbonization. From a policy perspective, the study underscores the importance of integrating uncertainty analysis into national energy planning and supports prioritising cascade hydropower development to enhance energy security