Energy Swarm is a software platform containing an energy model, a control panel from which you can create new projects with up to 4 scenarios per project and invite collaborators, and a dashboard for setting targets and goals and understanding technology lead times for the scenarios you have modelled.
The platform offers
Energy Swarm is built to make the exercise of energy modelling more accessible and user-friendly, but also, to make the modelling outputs simpler to communicate to increase the potential impact of the work.
You will find
The final dashboard of Energy Swarm is ‘Monitoring Progress’.
Permanently saved for each scenario, here any project participant can access a framework to monitor transformations in the local energy system from now until 2050 with:
Below are some further resources categorized by subject area, relevant to Energy Swarm users and those wanting to go deeper into the field.
The energy model embedded in Energy Swarm was developed by Dr Quiggins at Chatham House with the brief of creating a multiple-user friendly model which can be used as an interactive tool in groups representing the energy system at the sub-national level. Dr Quiggins describes the model below (see the dropdown sections for content):
The Energy Swarm model is a prototype energy system model designed for participatory workshops with non-technical local planners and engaged citizens at the municipal scale. The objective of the model is to aid small groups to explore and contrast possible energy scenarios relevant to the constraints of their locality. Energy Swarm is not designed as a standalone model, rather its use is intended to be undertaken with the aid of workshop facilitators, whose role is to guide the small groups through an exploratory and discursive process of municipal energy scenario development. All models, without exception, have limitations, especially those concerned with modelling future scenarios. Energy Swarm is unique in that from conception it has been designed for use within this participatory process, where this process could be undertaken across the diversity of municipalities, the world over.
The possible application of Energy Swarm across global municipalities implicitly requires simplifications. Without such simplifications, users would be required to define an unmanageable quantity of data and decisions, which runs the risk of the non-technical user being locked-out and disempowered from the process. This is complicated still further by the availability of energy system, climatic and socio-economic data across nation states. Hence, Energy Swarm attempts to strike a balance between completeness, user friendless, global applicability and the limitations of data availability.
The principal objective of Energy Swarm is to enable users to create 2050 energy scenarios in which CO2 emissions are minimised; aligned with the global energy sectors carbon budget. Given the differences in countries historic emissions and current levels of energy access, Energy Swarm allows the user to define municipal per capita energy sector CO2 emissions targets, based on being guided through these differences. This allows the user the option to increase emissions – an important consideration for countries with limited energy access. Alongside emission target setting, Energy Swarm guides the user to set the speed of action. In essence, will the municipality begin deployment of technologies in the near term with a relatively gradual increase, or delay to benefit from price reductions but have to ramp up deployment much faster? The global energy sector carbon budget extends to the end of century, however like many other energy scenario models, Energy Swarm considers out to 2050.
Energy Swarm focuses on the electricity sector for two principle reasons. First, the majority of renewable and low carbon energy technologies produce or consume electricity. Secondly, it is these technologies that have exhibited the greatest price reductions of the last two decades. Further, in the majority of countries the main source of energy sector emissions originate from the power sector. Based on these trends, Energy Swarm allows the user to explore energy scenarios centred on the decarbonisation of power and electrification of domestic transport and heating. Energy Swarm confines its scope to residential heating and passenger vehicles. As such, Energy Swarm does not allow the user to investigate changes in fuel inputs to the industrial sector, such as high temperature processes. For instance, steel manufacturing shifting towards electric arc furnaces or other fuel substitutions, such as hydrogen. Transportation sectors such as aviation, shipping and trains are out of the scope of Energy Swarm.
In order to ensure usability across different global municipality types, Energy Swarm contains a significant amount of backup data. This is data that can be updated by the Energy Swarm team on a regular basis, and in advance of participatory workshops, such that the model reflects the real world as best possible. This backup data is crucial to minimise the requirement of users sourcing such data, which therefore increases the usability of Energy Swarm as a participatory energy scenario tool. This backup data includes, but is by no means exclusive to, country and regional Levelised Cost of Electricity (LCOE) for the various renewable energy technologies, capacity factors, capital expenditure (CAPEX) requirements, heating degree days, demand data, land areas by use type, populations and national energy scenario data. Please see the inputs list here for further information.
Electricity systems require near instantaneous supply-demand balancing, without which they are liable to experience blackouts and brownouts. As such, many electricity system models build in an hourly (or sub-hourly) resolution to modelling the supply from variable renewables, as well as the electricity demand from end users. This hourly supply-demand modelling approach enables the proportion of dispatchable generators and storage to be defined, and planned for. In order to achieve this hourly modelling, historic hourly weather and electricity demand data is required, including temperature data in the case of defining the hourly heating and cooling loads. Given the objective of running participatory workshops within municipalities spread across the globe, availability of data precludes this approach,at this early stage in Energy Swarm’s development.
Instead, Energy Swarm applies methodologies that allow applicability across municipalities. Renewable energy capacity factor data is now readily available for most countries, if not a nearby country. These capacity factors, combined with standardised load profiles and seasonally weighted, regionally specific, generation profiles and capacity margins allow quantification of storage requirements and limitations to be applied to the deployment of variable renewables.
Biomass: Energy Swarm includes biomass as a sustainable and low cost fuel source. Areas from forests and agriculture that would need to be devoted to growing bio crops and woody biomass up to land availability limits set by the user are included. Biomass here does not include municipal, agricultural or forest waste as data on municipality waste production flows is usually non existent or poor. ‘Bio crops’ are not specified as to type (e.g. willow, miscanthus etc), and types and quality of woody biomass are also not specified. Energy Swarm will automatically use a 50:50 split between woody biomass and bio crops, until a land availability limit is hit.
Although biomass will typically be selected as a low-cost dispatchable generator, neither will be selected if biomass is economically sub optimal based on the optimisation algorithm and parameters (which includes the LCOEs for all the different generator types, and the optimization constraints set to ensure it picks a mixtures to ensure diversity, enough storage, some dispatchable generators etc.).
The dynamics between national and municipal electricity systems requires careful consideration. Energy Swarm is designed to work for municipal planners and citizens, as such Energy Swarm allows the user to define the boundary of the model around the geography of that municipality. By virtue of the fact that most municipalities are located in a wider national electricity system, Energy Swarm accounts for the supply of electricity from the national system, if such a system exists for the particular municipality in question. This connection with the national system can also result in limitations to the scenario the user can generate at the municipal level. The main limitation results from the connection between municipality land availability and electricity demand in relation to national electricity embodied CO2 emissions. Energy Swarm accounts for the land footprint of generators in relation to either user defined land segmentations, or UN FAO statistics. If insufficient land is available and the national electricity system scenario has a high proportion of fossil fuel generators, the municipality may be limited in its ability to meet the user defined emission targets.
This connection between municipal and national scenarios results in a scale limitation to Energy Swarm. The objective of Energy Swarm and the participatory workshops is to provide a means by which municipal planners and engaged citizens can assess the municipality’s agency in furthering climate objectives, relative to the plans at a nation state level. As such, the point at which the population of the municipality represents a significant proportion of the nation states population; the embedded assumptions of the model become invalid. As such, Energy Swarm should only be used with municipalities representing up to 10% of the nation states population. For countries where a city represents a high proportion of the country’s population (such as Doha, Qatar – around 50%) Energy Swarm should be used for smaller segmentations of the city. In order for the standardised load profiles of Energy Swarm to be applied robustly, the minimum size of the municipality is assumed to be 5,000 households. Below this size the aggregation of individual building loads are unlikely to result in a smooth and predictable diurnal profile. Hence, quantification of storage is likely to be an underestimate. Whilst there are a growing number of electricity storage options available, Energy Swarm principally focuses on battery storage, specifically lithium-ion. Having said this, if the user specifies that geography suitable to pumped hydro is available within the municipal boundary, pumped hydro is available to the model. Questions associated with scale also extend downwards, depending on the level of development and access to energy the municipality has. In many non-OECD countries, national electricity grids do not yet extend into many rural areas. For this reason, Energy Swarm constrains certain elements of the model if the municipality isn’t connected to a national grid. In this instance the municipality is defined as being offgrid. As a result, electrification of heating and transport is considered beyond the technical feasibility of the electricity system and focus is given to the provision of traditional electricity services. Further, the facility to define a number of current and future refugee camps within municipality boundaries is built into Energy Swarm, along with the ability to explore electricity provision at Tier 4 of access to electricity in these camps.
Alongside the requirement to ensure a balanced supply-demand electricity system, including sufficient capacity of storage, and the minimisation of CO2 emissions (defined by life-cycle assessments of each generation technology) Energy Swarm attempts to optimise the scenario towards the least cost generation mix. This is integral to maintaining affordable electricity for households. Hence, LCOE is utilised to perform this optimisation and allows an estimation of the change in electricity price the municipality can expect under such a scenario. It should be noted that whilst CAPEX for generator types is quantified, it is outside the scope of Energy Swarm to quantify the grid infrastructure CAPEX requirement as detailed geographic data (beyond land areas) and national grid system topologies are not embedded within Energy Swarm. Having said this, the CAPEX requirement for generators does allow the user (under the guidance of the workshop facilitators) to explore the financing that would be necessary to meet such a scenario. It should be noted that Energy Swarm allows the CAPEX of chosen generators to exceed currently available sources of capital, within limits. This is an example of how Energy Swarm has been designed to be iterative – encouraging the groups of scenario planners to adjust inputs and decisions based on the outputs Energy Swarm provides. Another example of this iterative design pertains to subsidies. Energy Swarm allows the user to decide on the level of electric vehicles (EVs) to be deployed. One of the outputs illustrates the proportion of household’s income that would be spent on EVs, allowing the user to explore the subsidies that would need to be provided by the municipality to reduce this proportion to an acceptable level.
In order to allow the user to compare scenarios, a set of outputs for a Business as Usual scenario is provided. This allows comparison of output metrics such as: emission reductions, forecasted electricity price changes, dependency on fossil fuels, per capita energy demand and CAPEX investment requirements. Energy Swarm also allows the user to investigate the pipeline of generator projects that would be required to deliver the energy scenario. In essence this is a visual representation of the planning, tendering and investment, construction and commissioning timelines implicit in the delivery of the scenario.
Further to the outputs already outlined and standard outputs such as the generation mix, sectoral demand and emissions, a set of secondary outputs focus on the impacts on human and environmental systems, looking at; air pollution, water consumption, climate and cybersecurity vulnerabilities, and scales of governance for implementing the scenario’s energy mix.
Energy Swarm uses a combination of historical data inputs, and forward-looking decisions about the future to develop its optimized outputs.
The list of data inputs required to use Energy Swarm is detailed below, for more information on each input download the spreadsheet ‘Municipality Inputs’ below. It also acts as a framework for collecting the data in preparation for a Making Pathways workshop. Using the spreadsheet will make inputting the data into Energy Swarm straight-forward, saving time for the important discussions and decisions.
Below you will also find a spreadsheet detailing ‘National Data Inputs’; this is for those who want to know what national data Energy Swarm uses and is already built in to the model. If you would like to challenge, amend or use different data for your workshop and modelling activities, use the spreadsheet as a framework for sending it to us ahead of the workshop for inputting into Energy Swarm.
Isabel came up with the idea for Energy Swarm when building energy scenarios in Egypt. She wanted to create a platform where scarce funding and technical expertise was not a barrier to good policy and planning or catalyzing big visions. With the help of this team she’s guiding the overall design and prototyping process.
Dr Quiggins has been constructing and deconstructing energy models for years. He likes to create elegant solutions for the mammoth task of transforming our energy systems and his algorithms help us plan for that.
Dody is the founder and director of Silverkey Technologies based out of Cairo. He’s used to building complex internal systems for governments and interested in being part of the worldwide energy system transformation so he offered up his expertise and skill to develop the Energy Swarm platform.
The Making Pathways programme has been developed with Reos Partners to deliver a truly multi-stakeholder, long-term process of engagement to sustainably transform energy systems to be less harmful on natural systems, to build-in human co-operation across sectors, with principles of energy democracy, access and participation at the heart of the vision for sustained impact.
To do this Making Pathways uses three methods to be a partner and guide in transformation: Transformative Scenario Processes, Energy Dialogues, Social Innovation Labs.
The Energy Swarm software specifically developed for the Making Pathways programme is the primary tool used to test the plausibility, costs and impacts of the scenarios developed during Transformative Scenario Processes.
Making Pathways methods bring the energy system into the room through diverse participation of actors, new ways of seeing the system and it’s potential, and an integrated approach to complexity, supported by Energy Swarm as an accessible energy modelling tool to explore new pathways for the energy system’s future.
Face to face gatherings are carefully designed and facilitated by Reos Partners to be adapted to the local context and needs. We aim to bring a sense of perspective, potential and collaboration to a multi-stakeholder group interested in sustainably transforming their energy system.
This site is the home of Energy Swarm Beta, we are already imagining what versions 1 and 2 can bring to the table and we’re already thinking about how to bring that to you in the near future. Funding to enable Energy Swarm to be responsive to the needs and requests of its users, and the maintenance costs of up-to-date data sets, cloud storage and the domain hosting, will always be welcomed. We want Energy Swarm to remain free and in the hands of those who need it most. If you are interested in hearing more about the future plans for Energy Swarm get in touch, we’re always open to hearing from users and interested parties.
For those working in large groups needing extra features, or wanting to put their own touches to how it looks, the outputs generated and the monitoring and evaluation framework, Reos Partners can work with you to bring your idea to reality.
Energy Swarm exists because of the generous support of the Rockefeller Brothers Fund for its incubation. We are looking for the next set of funders and collaborators to evolve Energy Swarm further - we have ideas and we'd love to share them with you.
As this is software, it is always in development and made it available early so that we could start learning from people's different experiences in using it. If you have suggestions, problems or potential development ideas, please get in touch.