Peak power consumption has been consistently increasing over the years. The country’s peak demand grew from 90 GW in 2004-05 to 153 GW in 2011-12, recording a compound annual growth rate of 7.88 per cent. Energy usage has increased for all consumer segments. For instance, domestic users, who consume over 22 per cent of the total power produced, witnessed a consumption growth of nearly 73 per cent between 1989-90 and 2010-11. Consumption growth in the industrial segment, which uses over 45 per cent of the total power produced, stood at nearly 45 per cent during this period. Meanwhile, consumption by the agricultural and commercial consumer segments increased by 43 per cent and 88 per cent respectively over the last two decades.
An important trend in this regard is the changing energy mix due to renewable capacity addition. Renewable energy, which constitutes over 12 per cent of the total installed capacity, involves several challenges in integration with the national grid due to the uncertain nature of generation. Going forward, these challenges will grow as there are plans to enhance the share of renewables in the total installed capacity to about 20 per cent by 2020.
Besides, various new areas of power consumption like electric vehicles are emerging. Thus, in order to ensure quality and reliable power supply, we need to switch from the conventional method of varying generation to that of altering consumption. In this regard, load management through demand response (DR), forecasting and smart grid applications is gaining importance.
Load management
Load management helps utilities in matching the power supply and demand requirements in a cost-efficient manner. Meeting peak loads involves high expenditures for power utilities in the form of purchase of expensive short-term power and an increase in distribution infrastructure requirements. This can be reduced or eliminated by transferring non-emergency power demand from peak hours to off-peak hours, or by using the energy stored during off-peak hours at peak times. This can be achieved through real-time pricing of electricity, which encourages end-users to shift their consumption from peak hours to off-peak hours. This not only helps utilities to manage load but also helps consumers to reduce their power bills. On the supply side, accurate power demand forecast, load limiting based on a consumer cat-egory and credit profile, selective load shedding, brownouts, etc. are a few important techniques to ensure optimal power supply.
Accurate forecasting can assist in load management to a large extent. While short-term load forecasting involves forecasting for one to two days ahead, mid-term forecasting involves planning for a couple of months and long-term forecasting involves planning for a period of over a year. Forecasts for the short term are calculated on the basis of historical load, weather and local conditions/factors/events including public holidays and protests. Mid-term load forecasting is based on historical load and historical weather while long-term load forecasting is based, to a large extent, on growth plans like whether a new township is planned or a new city is coming up.
Demand response
The basic goals of DR programmes are demand reduction, peak load shifting, an improvement in asset utilisation and a reduction in spinning reserves. Economic as well as grid reliability considerations warrant the need for DR programmes. The reliability aspect can be addressed using techniques like load shedding or direct load control. Pricing techniques like time-of-use pricing, critical peak pricing and real-time pricing are also useful in DR.
Smart grid applications can significantly help in reducing demand. Besides making the grid interactive, intelligent and transparent, these solutions accord the consumer a larger role in the value chain. This can be done by incentivising consumers to shift their energy load from peak to off-peak hours.
Various DR programmes are operational across the world. While power flows from power producers at the top to customers at the bottom, the revenue moves in the opposite direction. This revenue chain has now been altered by the introduction of bilateral aggregators, DR service companies and technology providers. The bilateral aggregator has agreements with the utility, under which the former is responsible for reducing the load during grid-stress periods. The bilateral aggregator ensures this by reducing the load of consumers enrolled with it and compensates them in return. For this, it is paid by the utility per MW of the curtailable load provided.
This concept is being experimented with in various countries in the West and in India, and has proved to be very useful for Tata Power in Mumbai. DR, as compared to other initiatives like energy storage, involves a high rate of return and low capital expenditure.
Various levels of DR interactions take place through the smart grid-user interface. These include direct load control, which implies that the DR service provider has direct control on physical devices on the customer’s premises. Also, data is exchanged through this interface between the suppliers and the consumers, which includes price signals, DR event information, meter data and ancillary service (AS) signals from the suppliers’ side; demand forecast; and the customer response to DR and AS signals.
Demand-side management
Managing the energy consumption of various user segments can significantly help in reducing demand. For instance, there is significant scope for reducing energy usage in buildings which consume over 40 per cent of the global energy through smart grid applications. Today, smart buildings are increasingly being seen as a source of energy production and storage. A smart building typically enables two-way communication with utilities and proactive energy management/smart consumption, and also offers storage capacity for added flexibility and active carbon management.
Similarly, efforts are needed to reduce energy use in other consumer segments. To start with, replacement of inefficient pump sets in agricultural usage can go a long way in addressing this issue. Optimisation of the manufacturing processes and ensuring adherence to the Bureau of Energy Efficiency (BEE) standards can reduce power usage in the industrial segment, while green architecture, thermal storage and demand-side management (DSM) initiatives by discoms can help in reducing commercial and domestic consumption. Regulatory initiatives have been of great importance in this regard. The Maharashtra Electricity Regulatory Commission, for instance, has taken various steps in this regard – notifying DSM regulations for utilities, application of time-of-day tariffs for industrial and commercial consumers, and formation of an inter-utility DSM consultation committee and the Maharashtra Smart Grid Coordination Committee. States like Delhi and Gujarat are also taking steps in this direction.
The Working Group 5 under the India Smart Grid Forum has been constituted to recommend technologies that can be adopted in India for load control and consumption to enable peak load management through technology recommendations, energy controlling systems and devices, recommendations for selection of appropriate pilot projects and coordination with key institutions/groups like the BEE and the Bureau of Indian Standards.
Conclusion
Going forward, there is a need to move away from a regime wherein generation follows load to one under which load follows generation. In the coming years, more consumers are expected to shift from energy savings through conventional strategies to a reduction in energy consumption using advanced and dynamic strategies. The concept of “negawatt”, which is essentially power saved through conservation or efficiency measures, is gaining traction globally as a means of meeting the growing demand for power. The aim is to strike a sustainable balance between generation and consumption, and load management can significantly help in achieving this target.
Based on a presentation by Vikram Gandotra, General Manager, Marketing, Energy Automation Systems, Siemens, at a recent Power Line conference
