Generalized spatio-temporal model for the optimal sizing, operation, and location of energy system assets

Future energy systems will contain large amounts of distributed energy resources (DER). In DER-based energy systems asset related to energy demand, supply, transport, storage, conversion, and demand response will be deeply coupled. This thesis develops the Space-Time Energy Vector Flow Networks (STEVFNs; pronounced like the name “Steven”) unified asset model, framework, and tool for the optimal design of DER-based energy systems. This thesis is the first to develop a generalized spatiotemporal asset model that unifies energy demand, energy supply, energy transport, energy storage, energy conversion, and demand response assets. STEVFNs enables energy system design by co-optimizing the sizing, operation, and location of energy system assets. It utilizes modern advancements in convex programming and is the first to enable the optimization of energy system design with rapid scenario assessment. It is demonstrated using a case study of designing an energy system to satisfy Singapore’s hourly electricity and high temperature heating demand using intercontinental solar and wind farms, distributed batteries, HVDC lines, distributed ammonia storage, ammonia transport, and various conversion technologies. STEVFNs is available as an open-source tool on GitHub for the co-optimization, with rapid scenario assessment, of the sizing, operation, and location of energy system assets that can provide energy demand, supply, transport, storage, conversion, and demand response

Renewable energy in Morocco: assessing resource curse risks

Renewable energy development and export creates an opportunity for low- and middle-income countries to foster green economic growth while supporting global decarbonisation. However, without careful assessment of risks, a renewable energy boom could create a resource curse which paradoxically slows growth and development. Here, the likelihood of a resource curse driven by renewable energy development in Morocco is evaluated. Specifically, 14 potential negative impacts of the resource curse (i.e. “symptoms”) relevant to renewable energy are studied. Through surveys with 21 Moroccan energy experts, the highest-risk (i.e. most likely and highest-impact) symptoms are found to be: (1) increased economic dependence on other countries and international organisations, (2) increased dependence on other countries for technology and expertise, and (3) damage to local flora, fauna, and landscape. The risks associated with these symptoms, while serious, are preventable via policy strengthening or intervention. Through 10 follow-up semi-structured interviews and subsequent complex systems analysis, the following policy interventions are identified to mitigate resource curse risks: careful negotiation of robust co-funding arrangements to safeguard Moroccan autonomy; the development of local renewable energy innovation capability, including technology manufacturing and test-bedding; and continuation and enhancement of environmental protection mandates

Modified minimum spanning tree for optimised DC microgrid cabling design

The construction of low-cost nanogrids could help speed up the electrification rate in remote communities in sub-Saharan Africa. One of the challenges in nanogrid design for these communities is the uncertainty in future load demand profiles impacting the cabling topology. We propose two new wiring design approaches for radial direct current power distribution systems. The first one is a modified minimum spanning tree (MST) algorithm and the second one is an adaptive shuffled frog leaping algorithm (SFLA). Their objective is to identify the optimal cable path and the lowest cost wiring characteristics to electrify rural areas with poor infrastructural development. A comparative study of computation burdens has shown the applicability limits relative to the SFLA based approach and encouraged the implementation of the MST as it is faster and does not imply any limitation regarding the number of dwellings to electrify, while providing low-cost wiring design option. The latter is applied to a village located in Kenya and demonstrated more than 25% savings on the entire system cabling cost compared to a classical wiring design based on shortest-path calculation

Photoresponsive Liquid Marbles and Dry Water

Stimuli-responsive liquid marbles for controlled release typically rely on organic moieties that require lengthy syntheses. We report herein a facile, one-step synthesis of hydrophobic and oleophobic TiO₂ nanoparticles that display photoresponsive wettability. Water liquid marbles stabilized by these photoresponsive TiO₂ particles were found to be stable when shielded from ultraviolet (UV) radiation; however, they quickly collapsed after being irradiated with 302 nm UV light. Oil- and organic-solvent-based liquid marbles could also be fabricated using oleophobic TiO₂ nanoparticles and show similar UV-induced collapse. Finally, we demonstrated the formation of the micronized form of water liquid marbles, also known as dry water, by homogenization of the TiO₂ nanoparticles with water. The TiO₂ dry water displayed a similar photoresponse, whereby the micronized liquid marbles collapsed after irradiation and the dry water turned from a free-flowing powder to a paste. Hence, by exploiting the photoresponsive wettability of TiO₂, we fabricated liquid marbles and dry water that display photoresponse and studied the conditions required for their collapse

CCG Starter Data Kit: Papua New Guinea

A starter data kit for Papua New Guine

CCG Starter Data Kit: Uruguay

A starter data kit for Urugua

CCG Starter Data Kit: Bolivia

A starter data kit for Bolivi

CCG Starter Data Kit: Paraguay

A starter data kit for Paragua

Selected ‘Starter Kit’ energy system modelling data for Botswana (#CCG)

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 Botswana, 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

Selected 'Starter kit' energy system modelling data for selected countries in Africa, East Asia, and South America (#CCG, 2021).

Energy system modeling can be used to develop internally-consistent quantified scenarios. These provide key insights needed to mobilise finance, understand market development, infrastructure deployment and the associated role of institutions, and generally support improved policymaking. However, access to data is often a barrier to starting energy system modeling, especially in developing countries, thereby causing delays to decision making. Therefore, this article provides data that can be used to create a simple zero-order energy system model for a range of developing countries in Africa, East Asia, and South America, 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 organisations, journal articles, and existing modeling studies. This means that the datasets can be easily updated based on the latest available information or more detailed and accurate local data. As an example, these data were also used to calibrate a simple energy system model for Kenya using the Open Source Energy Modeling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020-2050. The assumptions used and the 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