How Data Analytics can help tackle electricity power woes

Background

India is on the verge of becoming a power surplus country. Still many cities are facing frequent power breakdown. The reasons are mainly due to power theft, tampering of meter, and non-payment of electricity bills. Consequently, state distribution companies not purchasing enough power for uninterrupted power supply to consumers. For any consumer product, prices are governed by demand and supply. Currently, flat tariff structure is charged to the consumers and this doesn’t reflect the full effects of supply and demand. As a result, actual costs of generation, transmission and distribution are not recovered. Hence, distribution companies are facing financial losses, which in turn have slowed down investments in necessary infrastructure. The more they supply power to consumers, the more they are making losses. Also, fixed tariff system doesn’t incentivise customer for judicious use of energy. The solution to all these electricity woes is analytics and smart meters.

Dynamic tariff Mechanism for power consumption

The above issue can be mitigated by efficiently managing the peak load. Currently, the load demand is high during day time as there is a high utilization in industrial and commercial establishments and is low during night time. This leads to an inefficient and uneven distribution and utilization of electricity. Hence, huge generation capacity addition is required. Also, to avoid overload and load shedding, new equipment and transmission lines are required to be installed .

To evenly distribute the consumption of load by consumers, dynamic tariff system can be introduced based on load demand at any particular point of time. It is based on the principle that higher tariffs will be charged during day time at high overall load demand and lower tariff will be charged during night time when load demand is low.

After dynamic tariff system is introduced, residential consumers will consume power only when in need (say water heater) due to higher tariffs during day time and shift their energy usage for non-essential utility (say operating washing machine) to night time when tariffs are low.

Because of change in consumption pattern, consumers will get benefited on account of lower tariff at night time. This has been further explained in the form of graph below.

Figure-1: Efficient Distribution of peak load demand

Types of dynamic tariff 

  • Time of use pricing: Prices for specific hourly time are fixed and are known to consumers in advance. The consumers may accordingly plan their electricity consumption and may shift their usage accordingly. TOU pricing has been widely adopted in US and Canada.
  • Critical peak pricing: At critical load, prices will be exceptionally high
  • Real time pricing: Prices are purely on real time basis, based on cost of generation, power purchase and demand at that point of time.
  • Peak load reduction: Those consumers who get into an agreement for reducing peak load demand will get credits

Architecture

Millions of households can be connected through an IoT device called the “Smart Meter” with the local grids. A smart meter is an electrical meter to record energy consumption in intervals. It communicates the information back to the utility for monitoring and billing purposes on daily basis. Smart meters have real-time sensors, power outage notification, power quality monitoring, and generally can support two way communications.

This can help us to remotely control the power supply. It also ensures that the right supply is distributed at local grids and households and a differential rate can be charged to the customers based on their consumption. Further, the power utility will generate an efficient mix of renewable and non-renewable source of energy depending on load demand during the day.

efficient distribution
Fig: 2: Efficient Distribution of peak load demand

 

Cost Benefit analysis

Cost: Cost of equipment such as energy meter, hardware etc. including operation and maintenance

Benefits:

a) Power utilities no longer need to purchase power, since peak demand throughout the day will be lesser and will be catered with the existing generation.

b) Transmission companies no longer need to augment the capacity of cables and transmission line as peak load demand during the day reduced.

c) Power utilities can accordingly plan the % of conventional and non-conventional energy sources based on historical load consumption pattern.

Ultimately, the benefits to power utilities on account of the above factors will be passed on to consumers.

Still, on introduction of dynamic tariff mechanism, consumer should be willing to pay for associated infrastructure. Consumer should have an option of choosing smart meter system. Also, forecasting of demand also plays an important role in dynamic tariff system. From accurate forecasting, utilities will be in a better position to decide how much capacity to be added to the grid.

Road map for implementation comprises of the following:

  • Communication and awareness program on benefits of dynamic tariff system
  • Awareness on how low income customers are going to benefit from dynamic pricing
  • Begin with a pilot project for a small geographical region
  • Identify the target population
  • Enabling technologies on smart meter and observe the electricity usage pattern
  • Study the peak load pattern before and after implementation of dynamic pricing

Providing cheap and reliable power supply has become challenging due to increasing load demand. Using power digitisation, Internet of things and data analytics, the challenge can be addressed by efficient utilisation of power at consumer end and right mix of renewable and non-renewable source of energy at generation level. This will result in substantial value gains to both consumers and power utilities. Going forward, power digitisation will ultimately remove the bottleneck by reducing the peak demand during day time.

References:

https://web.iima.ac.in/assets/snippets/workingpaperpdf/167750922015-08-03.pdf

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