Customers respond to fluctuating energy prices in real time

With customers resond to fluctuating prices In real time to encourage the demand side response may reduce the need for investment in grid reinforcement and infrastructure.

pilot rojects are underway by a sample group of households which are installed a smart addition to their electricity meters that collects, analyses and redistributes usage data minute-by-minute. At the same time, the system displays the cost of the electricity, allowing consumers to control their consumption in response to changing prices and target spend thresholds that they have set themselves.

cloud-based information management service, provided by energy management system which willreceive, store, and process consumption data and then present it to customers via browser, tablet or smart phone. It also alerts customers via email or sms when they are approaching their target spend, allowing them to change their behaviour accordingly.

The goal is to see how customers react to energy prices in real time and how they relate to the spend targets that they have set for themselves

Aim of pilot is to create tariffs that contribute to more consistent electricity consumption. Customers will be encouraged to save money by using less electricity during peak periods in the morning and afternoon, which will in turn reduce the load on the grid.

“There is a lot of interest in dynamic pricing and the contribution it can make to demand side response solutions. achieving both simplification and time-of-use tarrifs so that all stakeholders share the benefits. 

Trends in Utilities/Smart Grid and its impact

A mix of smart technologies, customer engagement, and demand response will help bring electricity production and consumption into the precise alignment that the grid requires to function properly. While innovative energy storage approaches may play a future role in managing this exacting dance between power supply and demand, other more proven and more cost-effective options will be required in the near term.

The impressive ability of demand response (DR) to reliably stabilize electric systems under pressure has been on full display in the past year: DR helped keep the lights on during hours of record-breaking summer power demand in New York last July, and also during hours of record-breaking winter power demand in Texas just this month.

It appears that nimble DR mechanisms (e.g. dynamic pricing and real-time customer engagement) will become increasingly valuable assets for utilities as a low-cost strategy to manage not just weather-driven peaks, but also the day-to-day patterns associated with a cleaner and smarter electric grid.

1. Energy efficiency policies worldwide.

More than half of US states have now officially enacted quantitative energy efficiency targets, and around 30 states offer concrete incentives to utilities that drive reductions in energy demand. Yet even more states have instituted a framework for severing the tie between utility energy sales and revenue, thereby removing the disincentive for utilities to help electric and gas customers lower their bills. Mississippi and Louisiana are the latest players to join the energy efficiency policy landscape.

Across the pond, European member states recently formalized their action plans to achieve an EU-wide 20 percent reduction in energy consumption by 2020, as part of a sweeping Energy Efficiency Directive.

And in Asia — where it’s forecasted that more than half of annual global energy consumption will be consumed with a few decades by 2035 — several countries are becoming more aggressive with efficiency policies. Japan, Singapore, China, and many developing nations in the region are finding efficiency to be one of thecheapest and cleanest energy resources at their disposal.

2. Natural gas and renewable energy keep chipping away at coal

America’s energy portfolio is changing. Natural gas — along with clean power — is persisting in chipping away at coal’s segment of the US energy generation mix.

Much of this shift is due to the expansion of oil and natural gas production here at home. Domestic natural gas production is projected to grow 56 percent between 2012 and 2040. And by 2040 — if not earlier — natural gas will displace coal as the primary fuel for US electricity generation. The shift is already under way: in November, the Tennessee Valley Authority — located in one of the top coal-burning states — announced its plans to shutter eight coal plants representing 3,300 megawatts of capacity.

At the same time, the share of renewable energy in the US generation mix continues to grow rapidly.

3. Innovative utilities are exploring ways to thrive in a distributed-generation world

How should a bakery respond when, each year, more and more of its customers want to start baking their own cookies?

Electric utilities will confront a similar situation in 2014, as tens of thousands of additional homes and businesses will start buying less electricity from traditional retailers, instead opting to produce power from their own solar panels. Rooftop solar installations have reached a furious pace in the US: a new system is now brought online every four minutes. And all other things equal, an increase in behind-the-meter distributed generation (DG) means a decrease in sales and revenue for utilities.

This DG-driven revenue curtailment could produce a frightening cycle for the power industry: reduced sales revenues could lead to less system-wide investment, which could lead to a less cost-effective electric grid, which could in turn lead to an increase in rates for consumers, and that could drive more high-value consumers opting to produce their own power . . . which could all lead to further reduced sales revenue for utilities. You get the picture. Rinse and repeat, until the days of a centralized utility give way to a distributed generation world.

The challenge for utilities in the coming year and beyond will hinge on ensuring that they can constructively participate in this trend, rather than sit on the sidelines. An innovative pack of utilities are already seizing upon such opportunities, which include utilities’ leasing solar panels to ratepayers and creating subsidiaries that install rooftop solar outside their regulated service territory.

In parallel to finding an optimal role in distributed generation, utilities are naturally suited to further unlock the potential of large-scale solar. It still accounts for the majority of installed solar electric capacity in the US, and it is set to take off in a big way in the next few years.

4. A critical mass of smart meter infrastructure is paving the way for dynamic pricing programs

In the last 6 years, the number of smart meters in the US has grown more than sixfold. There are now more than 46 million smart meters installed nationwide — enabling real-time communication of energy data between customers and their service providers.

This trend isn’t about to slow down. Worldwide, the installed base of smart meters will triple from 313 million in 2013 to nearly 1.1 billion within ten years, according to a November report by Navigant Research.

But while smart meter deployments are becoming widespread, the use of dynamic pricing — which better matches energy supply and demand through real-time price changes — is not as prevalent. However, some utilities are emerging as leaders in applying dynamic pricing to better engage their customers and ensure system reliability — just as the concept has picked up steam in the transportation sector.

Programs like Pacific Gas and Electric’s SmartRate and Baltimore Gas and Electric’s Smart Energy Rewards are at the forefront of the utility industry’s adoption of dynamic pricing mechanisms. Their focus is on using time-varying energy prices to keep a grid-friendly balance between electricity supply and electricity demand, and on designing easy-to-understand rates and rebates that help customers manage their consumption in a personalized and energy efficient way.

5. Demand response will aid the grid’s transition toward renewable energy supply

Fundamental changes in the electric grid’s supply and demand profile are requiring utilities to think creatively about how to manage this transition.

On the supply side, deep investments in utility-scale renewables like solar and wind are bringing into focus the intermittency of these sources. It’s no secret that solar electricity production grinds to a halt in the evening, and that wind speeds often pick up after electricity consumers have gone to sleep. And on the demand side, the rise in electric vehicles — the most energy-intensive appliances in the history of the home — could put substantial pressure on the grid at certain times of day.

Solving the consumer conundrum

Once upon a time, electric utilities and their customers lived in a world with little need to interact except over billing and power outages. Over the last few years, that has changed dramatically. Government mandates to reduce peak loads, modernize the grid and engage customers have utilities scrambling to become more dynamic and better support their customers’ lives.

Customer engagement is not just a valuable end in itself. It also serves as the engine for powerful demand response and energy efficiency programs. Yet consumers aren’t accustomed to building relationships with their power providers, beyond calling to dispute a bill or report an outage.

1. Story Telling 

Package energy data which are consumed by househould can be determined by using language that consumers understand that relates those stories directly to their lives. Better to say  how many dollars of energy a bedroom is using every day than grasping the concept of kilowatt-hours used per month for the entire house. 

Always better to avoid industry jargon and introduce the concets which every common man understand the terminology for better benefits to the consumer

2 Fun learning 

      With the advent of gadgets and apps on tablets and mobile it become very easy to present the data clear and intertesting way . Better way to engage customers is making enjoyable experience with introduction of data in visual format dashboards and Messaging  With this type of learning customers care about and invite it into their lives

       3. Action items

       Smart meters make real-time data possible. Now, customers can see the impact based on what they’re doing right now, enabling them to draw a clear connection between cause and effect. Presenting just the right data, at the right time, and in the right context is vital to drive customer behavior change.

    

       4.Respect the customer:

      Don’t force customers to do anything they don’t want to do. Instead, give customers the power to join programs, be honest and thoughtful with them about the benefits and drawbacks, and let them opt-out without hassle. Said another way: don’t demand response, ask nicely.

 5. Empower customers: People change behavior when they have knowledge that makes action irresistible. Key to this is enabling technology like smart thermostats or in home displays which present easy-to-access, understandable energy information. Recently, one of our customers commented, “Before, I didn’t make any changes because I was not aware of the impact of everything I did—now I know!”

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Big Data and Demand Response

Utilities are collecting unprecedented amounts of information from millions of smart meters installed in recent years, but the capacity to collect data seems largely to have overwhelmed companies and regulators. Processes designed for a world run on paper billing and planned excess capacity are of diminishing effectiveness in one defined by interactive usage and distributed generation.

This opened door for data scientists and data startups to compete with some of the established business service providers themselves rushing to better understand, analyze and use the insights raw data can provide.  Most of the vendors advice and analytics for power bills the old fashioned way – door-to-door – and is taking the insight it has into customer priorities and matching that to generator priorities as market rules and operating realities shift.

power tariffs enable customers to save money by limiting use during certain peak energy use “events,” the kind of program that has been in place around the country for years, but adoption has been limited in part because “the conversation about tariffs at commissions and in utilities is very divorced from the reality on the ground, which is focused 

The installation of smart meters is set to accelerate and provide ever more detailed information to a wide variety of firms and customers in energy and beyond, and companies  will be providing the next level of analysis and engagement based on that data to help companies and regulators move forward with segmenting, prioritizing and putting into action their plans and objectives based on data-driven insights.

Digital transformation in Utilities through Analytics

Analytics in Utilites will impact  the Operational and business models over the next few years

More and more data will be generated by Grid and Customer applications,Analytics play a vital role in deriving the efficiency by various tools.

1.       Long term analytic strategy : All the current systems in Utilities are project based  . Long term strategy would be to concentrate on the fusion of Opertationlal  technology with Information Technoglogy by leveraging analytics . Implemetation of Analytic tools will improve the business value

2.       Customer Engagement Models and services : The current model of communication should be changed to advanced Customer communication  with Multiple channels . Enablement of Social networking channels like facebook, twitter via mobile integration in utilities will have better insights and satisfcation results . Example Outages

3.       Optimize The Grid : Utilities must evaluate the Grid and Optimize for better efficiency. Advanced Analytics strategies and tools will stream line the data for better results.

Smart Grid Data Analytics – Players in the market

1.       IBM

2.       SAS

3.       Teradata

4.       EMC

5.       SAP

6.       ORACLE

All the players are promising to transform the way utilities mangaze with the huge flood of data which is big data coming through Smart Meters, sensors, relays ,substation control systems and any other grid technologies. Also there are some untapped resources like customer information data, Work force data with Mobile integration, social networking, business process data

Challenges and capabilities

Theft detection, outage restoration, Mobile workforce for utility field workers

Load balancing , line loss, volt/VAR Management and prective modeling for grid operators

"Big Data" in Green Building ,design, construction, and operation of the built environment.

The building industry often enters the “Big Data” conversation through energy efficiency, and Smart Grid and the proliferation of sensors and sub-meters. These provide unprecedented information about energy use over time and across spatial scales. This also reflects the long-standing, foundational importance of energy efficiency to the green buildingmovement. “Big Data” refers to mixtures of volume (scale of data), velocity (analysis of streaming data), variety (difference in forms of data), and veracity (uncertainty associated data). Relatively speaking, energy efficiency alone is modest by these measures. Green building changes things and brings true Big Data into play. Green building encompasses energy efficiency and adds many more dimensions of performance. LEED offers a definition of green building which includes location and transportation, energy and atmosphere, water efficiency, materials and resources, and indoor environmentalquality. This breaks out into hundreds of individual LEED credits and thousands of specific metrics. A Big Data story starts to emerge when tens of thousands of green buildings projects using thousands of metrics, generate data from tens or hundreds of thousands of automated “points”, and provide daily experiences for millions of occupants. Now, we’re talking Big Data: huge data volumes, streaming at different rates, taking a wide variety of forms, and varying dramatically in their accuracy, precision, and reliability.

Big Data Analytics in Utilities - Indian Perspective

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.