• Energy Research

Abstract Nowadays, more than 620 million people in Africa live without access to electricity. Nowhere in the world is the chasm between available energy resources and access to electricity greater than on the African continent. With the exception of conventional hydropower, the share of renewable energy in the electricity mix remains insignificant, despite the considerable untapped renewable energy potential in the region. A critical issue in the development of renewable energy sources is the cost, which is a function of the resource availability, the geographic and topological characteristics of a studied area as well as the selected energy conversion technologies. This paper applies a detailed Geographic Information Systems (GIS) approach in order to identify the cost of generating electricity using onshore wind turbines considering several localization criteria. The levelized cost of wind generated electricity is calculated geospatially and shall be used as an indicator to compare different suitable sites at the pre-feasibility stage. The levelized cost of generating electricity varies between 0.04 and 0.17 USD/kWh, placing wind power in a cost competitive position in the electricity market of the continental countries.

Abstract Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to energy system modelling, causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Malaysia, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and two stylized scenarios (Fossil Future and Least Cost) for 2020–2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.

The Climate, Land, Energy and Water systems (CLEWs) approach guides the development of integrated assessments. The approach includes an analytical component that can be performed using simple accounting methods, soft-linking tools, incorporating cross-systems considerations in sectoral models, or using one modelling tool to represent CLEW systems. This paper describes how a CLEWs quantitative analysis can be performed using one single modelling tool, the Open Source Energy Modelling System (OSeMOSYS). Although OSeMOSYS was primarily developed for energy systems analysis, the tool’s functionality and flexibility allow for its application to CLEWs. A step-by-step explanation of how climate, land, energy, and water systems can be represented with OSeMOSYS, complemented with the interpretation of sets, parameters, and variables in the OSeMOSYS code, is provided. A hypothetical case serves as the basis for developing a modelling exercise that exemplifies the building of a CLEWs model in OSeMOSYS. System-centred scenario analysis is performed with the integrated model example to illustrate its application. The analysis of results shows how integrated insights can be derived from the quantitative exercise in the form of conflicts, trade-offs, opportunities, and synergies. In addition to the modelling exercise, using the OSeMOSYS-CLEWs example in teaching, training and open science is explored to support knowledge transfer and advancement in the field.

Abstract Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to starting energy system modelling in developing countries, thereby causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Ethiopia, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and two stylized scenarios (Fossil Future and Least Cost) for 2020–2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.

Abstract At present there is no binding agreement (at a global level) to address the risk of anthropogenic climate change after 2012. Disagreements abound with respect to a post-2012 climate change agreement, on issues such as economic development, policy criteria, environmental effectiveness, cost-effectiveness, equity, dynamic flexibility, complementarity, enforceability and so on. One such disagreement is whether or not nuclear power should play a role in a post-2012 climate change agreement. This qualitative analysis explores the conditions under which nuclear power could contribute to addressing climate change in post-2012 architectures. It reveals that – given the right framework conditions – some architectures, like ‘cap and trade’ regimes or ‘policies and measures’ can improve the competitiveness of nuclear power plants, while others are unlikely to provide incentives for nuclear energy development in the short to medium term, such as adaptation and technology cooperation. Overall, the study concludes that post-2012 climate change policy should aim at providing policy flexibility without compromising technology flexibility. For example, the provision of long-term commitment periods has the potential to enable better investments in existing low-carbon technologies but stifle the policy flexibility that political decision makers are often keen to retain so that they can respond more quickly to new scientific evidence or advances in clean technology development.

Abstract Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to starting energy system modelling in developing countries, thereby causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Sudan, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020-2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.

Abstract Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to starting energy system modelling in developing countries, thereby causing delays. This article therefore provides data that can be used to create a simple zero order energy system model for Angola, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and two stylized scenarios (Fossil Future and Least Cost) for 2020–2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.

Abstract For decades, electrification planning in the developing world has often focused on extending the national grid to increase electricity access. This article draws attention to the potential complementary role of decentralized alternatives – primarily micro-grids – to address universal electricity access targets. To this aim, we propose a methodology consisting of three steps to estimate the LCOE and to size micro-grids for large-scale geo-spatial electrification modelling. In the first step, stochastic load demand profiles are generated for a wide range of settlement archetypes using the open-source RAMP model. In the second step, stochastic optimization is carried by the open-source MicroGridsPy model for combinations of settlement size, load demand profiles and other important techno-economic parameters influencing the LCOE. In the third step, surrogate models are generated to automatically evaluate the LCOE using a multivariate regression of micro-grid optimization results as a function of influencing parameters defining each scenario instance. Our developments coupled to the OnSSET electrification tool reveal an important increase in the cost-competitiveness of micro-grids compared to previous analyses.