• Energy Research
• Journal of Open Source Software close

OpenTerrace is a simulation framework for the prediction of transient temperature responses in thermal energy storage systems. Being able to store energy for extended periods of time is important for modern societies where increasing amounts of energy stem from renewable sources with time-varying production. Many storage technologies exist, each with their own setof disadvantages and advantages. Storing energy in the form of thermal energy is a promising solution because it is cheap and can be scaled up easily. While the total energy content of thermal energy storage systems can be estimated easily, the transient response requires solving partial differential equations in space and time. Using OpenTerrace the transient response of a wide range of thermal energy storage systems can be simulated easily. The storage systemcontains a storage tank filled with a carrier fluid and an optional bed phase. OpenTerrace comes with a wide range of predefined substances to be used as either fluid or bed material.Also, OpenTerrace ships with a set of primitive, predefined shapes that act as either storage tank or bed material. Besides that a set of predefined boundary conditions and source terms cover many different thermal energy storage applications.OpenTerrace is built to be:• Fast by making use of modern compilers and optimised tri-diagonal matrix solvers.• Flexible for easy integration in system models and optimisation loops.• Extendable by allowing new modules for new materials such as non-spherical rocks or exotic Phase Change Materials (PCM) to easily be plugged into the OpenTerrace framework.More information about how to get started, along with a user guide, can be found in the OpenTerrace documentation. Users may send pull requests to have their contributions with new functionality added to the official OpenTerrace GitHub repository. A set of tutorials is also provided within the framework to highlight its current functionality and to ease the learning curve for new users. Also, various unit tests are provided to verify different parts of the codein some well-defined benchmark cases.

OpenTerrace is a simulation framework for the prediction of transient temperature responses in thermal energy storage systems. Being able to store energy for extended periods of time is important for modern societies where increasing amounts of energy stem from renewable sources with time-varying production. Many storage technologies exist, each with their own setof disadvantages and advantages. Storing energy in the form of thermal energy is a promising solution because it is cheap and can be scaled up easily. While the total energy content of thermal energy storage systems can be estimated easily, the transient response requires solving partial differential equations in space and time. Using OpenTerrace the transient response of a wide range of thermal energy storage systems can be simulated easily. The storage systemcontains a storage tank filled with a carrier fluid and an optional bed phase. OpenTerrace comes with a wide range of predefined substances to be used as either fluid or bed material.Also, OpenTerrace ships with a set of primitive, predefined shapes that act as either storage tank or bed material. Besides that a set of predefined boundary conditions and source terms cover many different thermal energy storage applications.OpenTerrace is built to be:• Fast by making use of modern compilers and optimised tri-diagonal matrix solvers.• Flexible for easy integration in system models and optimisation loops.• Extendable by allowing new modules for new materials such as non-spherical rocks or exotic Phase Change Materials (PCM) to easily be plugged into the OpenTerrace framework.More information about how to get started, along with a user guide, can be found in the OpenTerrace documentation. Users may send pull requests to have their contributions with new functionality added to the official OpenTerrace GitHub repository. A set of tutorials is also provided within the framework to highlight its current functionality and to ease the learning curve for new users. Also, various unit tests are provided to verify different parts of the codein some well-defined benchmark cases.

Sustainable Development Goal 7 aims to provide sustainable, affordable, reliable and modern energy 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 tools which can evaluate the technical performance of energy systems as well as their economic and climate impacts. CLOVER (Continuous Lifetime Optimisation of Variable Electricity Resources) is a software tool for simulating and optimising community-scale energy systems, typically minigrids, to support energy access in developing countries (Winchester et al., 2022). CLOVER can be used to model electricity demand and supply at an hourly resolution, for example allowing users to investigate how an electricity system might perform at a given location. CLOVER can also identify an optimally-sized energy system to meet the needs of the community under specified constraints. For example, a user could define an optimum system as one which provides a desired level of reliability at the lowest cost of electricity. CLOVER can provide an insight into the technical performance, costs, and climate change impact of a system, and allow the user 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) from user inputs (data, contained in a directory entitled locations) which are specific to their investigations. The user inputs these data via a combination of .csv and .yaml files. CLOVER’s straightforward command-line interface provides simple operation for both experienced Python users and those with little prior exposure to coding. An installable package, clover-energy, is available for users to download without needing to become familiar with GitHub’s interface. Information about CLOVER and how to use it is available on the CLOVER wiki pages.

Sustainable Development Goal 7 aims to provide sustainable, affordable, reliable and modern energy 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 tools which can evaluate the technical performance of energy systems as well as their economic and climate impacts. CLOVER (Continuous Lifetime Optimisation of Variable Electricity Resources) is a software tool for simulating and optimising community-scale energy systems, typically minigrids, to support energy access in developing countries (Winchester et al., 2022). CLOVER can be used to model electricity demand and supply at an hourly resolution, for example allowing users to investigate how an electricity system might perform at a given location. CLOVER can also identify an optimally-sized energy system to meet the needs of the community under specified constraints. For example, a user could define an optimum system as one which provides a desired level of reliability at the lowest cost of electricity. CLOVER can provide an insight into the technical performance, costs, and climate change impact of a system, and allow the user 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) from user inputs (data, contained in a directory entitled locations) which are specific to their investigations. The user inputs these data via a combination of .csv and .yaml files. CLOVER’s straightforward command-line interface provides simple operation for both experienced Python users and those with little prior exposure to coding. An installable package, clover-energy, is available for users to download without needing to become familiar with GitHub’s interface. Information about CLOVER and how to use it is available on the CLOVER wiki pages.