Rishab Shrestha, Oorja’s Project Analyst responsible for energy demand data collection and analysis, has been in our rural communities installing smart meters over the last two months. Here, he explains why this field work is important to design an affordable solution to provide electricity to off-grid communities.
Anyone that has conducted surveys among rural low-income households would have faced the difficulty in finding out the income of the surveyed participants accurately. Similar is the case for estimating electricity access and demand. By knowing the power of the appliances used and the duration and timing of their usage, we can estimate the energy demand. Alternatively, we can use smart meters to measure and record the actual consumption accurately. Such demand information is critical for sizing of decentralised energy systems. Optimal sizing of the systems can have considerable impact on cost reduction of providing energy access, thereby making it more affordable.
An important element of calculating energy demand is the peak demand. This is the maximum cumulative power that a village consumes. For example, imagine that there is a village of 100 households with each household electrified with 2 lights LED bulbs of 10 watts each. If every household switched on their light bulbs simultaneously, the maximum power consumption in this case would be 2000 watts. However, it is very unlikely that all of the consumers use all their lights at the same instant. Some may have only one light bulb on or none at a given time. Hence, the peak demand will be less than the maximum possible and the energy supply system can be sized accordingly.
Despite the variability of individual household energy demand, one can still estimate the demand for an entire village through sample data collection. This is what most rural energy suppliers have done. Based on this data, energy service providers can determine power generation capacity of the plant and batteries to be installed. If costs are determined to be affordable both for the project developer and the community, then the project gets a green light.
Are surveys accurate enough for estimating demand?
We don’t know yet, but we hope to compare the results of surveys with smart meter data to find out. Additionally, we are developing a computer model to estimate demand for comparison with the smart meter data. Oorja has currently deployed 5 smart meters, supplied by Maven Systems, to get real-time electricity use data. These smart meters measure a single household’s entire load and transmit the power consumption every minute to the cloud server via a sim card, which is later used for analysis . We plan on installing 15 more smart meters among different types of users, including SMEs, schools, dispensaries and households. This smart metering project has been funded by SUPERGEN SuperSolar Hub and is being carried out with researchers including PhD student Philip Sandwell at Imperial College London. We believe this effort will enable us to have a clearer picture of demand diversity and help us in designing the most optimal, reliable and cost-effective decentralised energy systems.
From the smart meter data, we will also be able to ascertain at what times grid outages in Uttar Pradesh occur and for how long. This information is useful to compare ourselves with the national grid and it allows us to become a more reliable provider. While the grid is cost competitive (on account of subsidies), it is extremely unreliable. Preliminary smart meter data has informed us that in some rural sites in our pilot location, the probability of grid being available is only 20% from 7 pm-11 pm, when the demand for energy is critical and the highest. This agrees with recent government reports, which state that only 23% of households in Uttar Pradesh receive power supply during the critical evening hours. The certainty, or rather uncertainty, of these data enables us to understand the risks associated with implementing the project and also comprehensively assess the feasibility of our initiative.
Why all the fuss about accurate demand estimation?
Oversizing and undersizing of decentralised energy systems can be a drain on limited resources. When capacity utilisation of generation technology is not adequate due to oversizing, you get diminishing financial returns. Add to that the cost of excess land which would have been leased for a bigger plant. These unnecessary expenses, as a result of inaccurate due diligence, translate to higher tariffs for consumers and hence lower adoption of energy services. In cases where supply is not able to fulfill the demand due to undersizing, the customers would be paying expensive tariffs for unreliable power supply. Add to that the recurring replacement cost of batteries as they deplete much quickly, as a result of overuse. This would be a recipe for failure. Once one takes these nuances into consideration, it becomes quite evident why accuracy in demand estimation is of importance for finding the suitable size of generation.
That’s not all, there’s more to the smart meter data. Hard energy data from typical customers enables us to classify end-users into various groups. We could then formulate various tiers of tariffs and energy plans targeted to different user group’s needs and affordability. And the data can then be published within scientific publications, making it available to a wider range of people, such as other project developers, development practitioners, researchers, and governments.
Overall, the data from smart metering will allow Oorja to validate several demand assumptions. Investing in this smart metering project will help us design the most cost-effective configuration of the energy supply systems. By embracing technology, we hope to understand the true behaviour of consumers in energy use. Through data driven decision-making, we hope to create a sustainable impact in the lives of rural communities.
Rural bank where a smart meter has been installed.
Electricity is essential for this photocopy shop. Hard energy data from typical customers enables us to classify end-users into various groups.
We could then formulate various tiers of tariffs and energy plans targeted to different user group’s needs and affordability. In this photo, a tailor shop which we are "smart metering".
We are also measuring the energy consumption of essential services as health clinics
and local yummy samosa's restaurants!
