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Sandwell P, Winchester B, Beath H, et al., 2023, CLOVER: A modelling framework for sustainablecommunity-scale energy systems, The Journal of Open Source Software, Vol: 8, Pages: 1-5, ISSN: 2475-9066
Sustainable Development Goal 7 aims to provide sustainable, affordable, reliable and modernenergy access to all by 2030 (United Nations, 2015). In order for this goal to be achieved,sustainable energy interventions in developing countries must be supported with design toolswhich can evaluate the technical performance of energy systems as well as their economic andclimate impacts.CLOVER (Continuous Lifetime Optimisation of Variable Electricity Resources) is a softwaretool for simulating and optimising community-scale energy systems, typically minigrids, tosupport energy access in developing countries (Winchester et al., 2022). CLOVER can be usedto model electricity demand and supply at an hourly resolution, for example allowing users toinvestigate how an electricity system might perform at a given location. CLOVER can alsoidentify an optimally-sized energy system to meet the needs of the community under specifiedconstraints. For example, a user could define an optimum system as one which provides adesired level of reliability at the lowest cost of electricity. CLOVER can provide an insightinto the technical performance, costs, and climate change impact of a system, and allow theuser to evaluate many different scenarios to decide on the best way to provide sustainable,affordable and reliable electricity to a community.CLOVER can be used on both personal computers and high-performance computing facilities.Its design separates its general framework (code, contained in a source src directory) fromuser inputs (data, contained in a directory entitled locations) which are specific to theirinvestigations. The user inputs these data via a combination of .csv and .yaml files. CLOVER’sstraightforward command-line interface provides simple operation for both experienced Pythonusers and those with little prior exposure to coding. An installable package, clover-energy, isavailable for users to download without needing to become familiar with GitHub’s interface.Informat
Winchester B, Huang G, Sandwell P, et al., 2022, Integrated simulation and optimisation of hybrid photovoltaic-thermal (PV-T) and photovoltaic systems for decentralised rural hot water provision and electrification, The 35th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, Publisher: Danmarks Tekniske Universitet (DTU)
Demands for electricity and hot water continue to rise worldwide, with many people in low-income countries, especially in rural areas, lacking access to these basic services. Decentralised minigrids, capable of powering small off-grid communities, are increasingly used in low-income countries as a means of providing power to the 13% of people globally without access to electricity. Hybrid solar photovoltaic-thermal (PV-T) collectors combine both photovoltaic (PV) cell and solar-thermal absorbers and, therefore, output both electricity and heat from a single collector with efficiency benefits over standalone PV panels and solar-thermal collectors. Despite this, no models have yet been developed capable of assessing the performance of PV-T collectors generalisable across a range of off-grid settings. We present an integrated model for simulating and optimising combined systems comprising PV panels and PV-T collectors, accurate to within +/- 5% rms error, connected to wider electrical and hot water systems, and employ this to evaluate their potential to meet both electrical and hot-water demands of rural communities. We provide a tool for simulating the lifetime output fromcombined PV and PV-T systems, assessing their economic and environmental impact, and for optimising the systems to meet the needs of specific communities. We carry out simulations for a case study of a combined PV and PV-T system in Uttar Pradesh, India, and find that the system is able to meet 59.3% and 33.5% of hot water demand for upper and lower bounds for installed capacity. We carry out optimisations for static high demand and growing low-demand scenarios and find that that 35 kWpel and 5 hot-water tanks and 75 kWpel and 15 hot-water tanks are needed to meet these demand scenarios respectively.
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