Here is a summary of the approach we take:
1. Mapping buildings with high accuracy – existing sources of building location data reviewed and via spot checks an estimate of accuracy made. Should the existing accuracy not be sufficient (generally 50-90%) then we can offer to manually map the area with our mapping team. We provide this service at $0.05/building and can map at up to 1 million buildings per month. A crosscheck against Census data may also be helpful at the end of this process.
2. Transformer / powerhouse positioning – once the buildings have been mapped, a set of trial transformer locations are generated via cluster analysis algorithms, such that no building is more than a service radius of 1km from a transformer, which is roughly the maximum distance a low “mains” voltage line of 240-400V AC can travel before exceeding a voltage drop limit of 10%, without resorting to thick expensive conductors (or an agreed alternative should a different voltage be used). From this first trial set of transformer locations, only those having 10 or more buildings allocated are retained (or a different number if the client prefers). This correlates to 200-500W of peak demand per building requiring a 2-5kW transformer, the smallest transformer generally available. Households allocated to discarded transformer locations will be allocated to the solar home system (SHS) solution if another acceptable transformer cannot be found within service radius.
3. Low voltage network analysis – each building is then connected to its transformer with the least length low voltage (LV) line via a minimum spanning tree algorithm (or similar). The length and cost of these lines is compared to the cost of providing an equivalent service from a solar home system. Our usual suggestion is that a 1kWh/day SHS of 250Wp costing around $1000 is compared to a $10,000/km cost to yield a 100m Critical Distance (CD) of line length per building. Thus, where the cumulative length of the network exceeds this CD limit for cumulative downstream households, such lines will be flagged as infeasible and the buildings assigned to be SHS buildings. If this causes a transformer to drop below 10 buildings per transformer, all buildings for that transformer will be assigned as SHS.
4. Medium voltage network analysis – Each transformer is then connected to its nearest neighbouring transformer, or to the existing grid, or to existing micro hydro minigrids located nearby. A similar Critical Distance analysis is performed, taking into account the length of line that is already used by the LV lines (eg. for CD=100m and an average LV length of 30m per building, the allowable maximum MV_CD remaining is 70m). It would therefore be helpful to have the location of the existing grid and existing minigrids to ensure all possibilities are considered. Unusual distribution voltages on LV lines can also be considered, as has been used in Nepal and Finland.
5. Demand side management analysis – if some solutions involves extending existing grids, a recommended extra task would be to explore how demand side management could reduce the peak power and daily energy demand from existing grids or minigrids, to free up more power that can then be used for adding more households. Examples would include using relamping with LED lights instead of incandescent and fluorescent lamps, energy efficient cookers that include pot insulation, and using brushless DC motors instead of normal motors on mills and other productive uses. We have direct experience in all these technologies, and often up to 50% of peak power and energy consumption can be saved quite easily, and the cost of this is often cheaper than making new power generation from solar or hydro and upgrading distribution networks to handle more load.
6. Cost and power generation analysis – The cost of the recommended solution can then be calculated, and each minigrid sized. This also needs an assumption of load per building – we often start with 100W average 200W peak x 10 hours = 1kWh/day of energy demand, but the other values can be used. Other assumptions can be added to size battery storage systems for minigrids and solar home systems, solar panel installations required, powerhouse/generator/inverter/transformer capacity and many more characteristics. By agreeing on unit costs for these materials, a capital and lifetime cost analysis can be performed on all options, and the most cost-effective option selected to then propose for construction.
7. Loadflow analysis – the above first 6 steps are mostly an exercise in geometry and mathematical costs. The electrical design work really starts with voltage drop analysis, but some reasonable cost estimates can be made without such loadflow calculations. Our software is capable of doing such calculations for all huge numbers of households much faster than the usual methods that might still being used today, and can even include conductor size optimization rather than the usual manual trial-and-error approach. This optional task would then result in a more refined engineering solution and cost estimate than if this task is omitted.
8. Productive use analysis – using census or building location data, land area calculations, and other agricultural output data, it is also be possible to estimate potential demand for productive uses of electricity, such as mills needed to process crops, irrigation water pumping demand, ice demand for fishing, health centre equipment, and many other uses of electricity. Spatial analysis can allow the optimum placement of such services to minimize the travel distance of the population to reach such services, helping to investigate how highly centralized or decentralized service models might compare. Existing service levels compared to the levels required to meet 100% of demand can quantify and cost any infrastructure gaps that might exist, or illustrate the potential for adding more services to what currently exists as a means of generating additional revenue.
Generally speaking, each of these levels of analysis cost around $0.05/building, so for less than $1 per connection which costs $500-1000, a highly detailed feasibility analysis can be made that helps developers and governments plan their infrastructure deployments at scale, at a very cost-effective rate that is less than 0.2% of the capital cost of the project. We typically find we can save clients 10-50% of the capital costs they had expected to need, giving a return on investment of 50x or more.
Yes. At a lower cost, you can share an instance of the software with us but still have 95% control over all content and data, or at a higher cost you can have your own stand-alone instance with 100% control. Prices start at $50/month – please contact us for further details about what you need.