India the world’s fifth-largest electricity generation capacity and faces acute power shortage due to the with outdated power infrastructure to meet the growing demand from residential as well as the Commercial/industries. India has suffered consistent grid failures and power black outs since independence. In 2012, the failure of northern, eastern and north-eastern grids left many parts of the country in the dark. The country remains energy deficient despite 15 percent or more of federal funds being allocated to the power sector. what can be the reasons the answer is according to industry experts, bankrupt state-run electricity boards, an acute shortage of coal, skewed subsidies which end up benefiting rich farmers, power theft, and under-performing private distribution agencies are to blame. The Aggregate Technical & Commercial (AT&C) losses are expected to be around 33 percent. In fact, it is estimated that the AT&C loss levels in some states are as high as 70 percent because of antiquated grids, power theft and faulty meters.
The problem of power shortage in India is persistent though we have all kinds of resources In July last year, the country faced the largest power outage in the country affecting 22 states and 620 million people. The outage was a wakeup call to modernize the power infrastructure in the country.
With huge losses of ATC , leakages and theft there is strong push for the deployment of smart meters and smart grid technologies to plug this gap. The Ministry of Power recently approved pilots for smart grids in the country and private players like Tata Power, Reliance Infrastructure are already running smart meter and smart grid led project. Recently State Pondicherry is successful in pilot implementation of Smart Grid .
The resulting transformation is generating tremendous volumes of data. The frequency and volume of data that is emerging from smart meters, grid devices and other network controls and sensors is pushing the sector to deploy analytics to accommodate the emerging opportunities around customer demand ,Distributed Generation,
Around the world, utilities are under pressure.
Financial stakeholders look for operational efficiency at a time when aging workforces and infrastructure
Regulators require compliance and detailed reporting on operations.
Operators seek action on smart grid and smart metering initiatives that add intelligence to infrastructure.
Customers seek choice and convenience—at affordable costs.
TO overcome all the difficulties today’s utilities to re-examine every aspect of their business process, Meter to Bill and Meter to Cash scenarios.
Big Data Analytics Benefits
Utilities are rolling out smart grid and smart metering projects to address some of these challenges. These deployments are underway in India are at a nascent stage and mature in due course of time. They are creating exponentially more data for distribution companies and giving them access to information they’ve never had before. Accessing, analyzing, managing, and delivering this information can help them optimize business operations and enhance customer relationships.
They can perform continuous analytics against this data to look for anomalies, patterns and trends that might indicate an opportunity for them to make actionable decisions on both supply and demand. Integration into outage and distribution management applications allows for further development of business capabilities such as distribution, load management switching, etc. Protocols can be established to move customers to alternate feeders during times of over capacity.
Analytical information also allows utilities to look at granular use and consumption patterns for neighborhoods, districts, or cities to facilitate better supply planning and load forecasting in these service territories.
Big Data can also help distribution companies achieve and maintain the levels of satisfaction desired by customers and by regulators. For instance, by integrating advanced metering, grid devices and network management systems, they can address more proactively outages and other system conditions that exist within their territories. This allows them to be much more proactive in the provision of network condition information to customers and other stakeholders.
Big Data and Analytics can help the companies move away from “one size fits all” services. For example, at the customer premise level, they can analyze usage patterns at the meter level and provide this usage information back to consumers with the intent of developing market driven and customized pricing offers that reflect individual consumption characteristics.
Many companies also have geo-spatial data available from their equipment, diagrams and vehicles. This data can be used to deliver real-time analytics to pin-point the need for a maintenance person, when a network is down, overloaded or reaching capacity.
In mature markets like North america and Europe, Big Data solutions is also helping utility companies determine competitor strengths and weakness, enabling them to exploit competitive strongholds and target marketing programs towards specific customers or segments of customers.
Big Data and analytics can also give an impetus to the adoption of renewable sources of energy. Traditional power generation investments involve large amounts of property to build a large plant on, but newer renewable sources like wind and solar energy can be located closer to demand sources. Big Data solutions can look at all of factors of a city, from standard utility ones like load profiles and capacity to more unstructured ones from city demographics.
Traditional utility data, demographic information and new sensor data can therefore be combined to provide the optimal investment scenarios necessary to meet growing renewable energy portfolio requirements. This can then be used to make smarter investment decisions.
Scenario where data on wealth distribution in office spaces, commuter congestion and electric vehicle population history combined with current load profiles and capacity is combined to predict which buildings will have the highest growth in electric vehicles over the next two decades. This data can feed portfolio planning decisions like deciding where to invest in solar panels – to help source cheaper and cleaner local energy to charge those vehicles instead of transporting it in from a remote fossil plant at high cost.
Predictive Analytics in Big Data will Forecast customer revenue, energy consumption, maintenance costs, outages, reliability, energy procurement costs
Conclusion
An optimized power generation and distribution system with Big Data analytics can complement new additions to power generation to meet the power deficit in the country.
