Like it or not, the train has left the station and smart grids are becoming a reality. Whether by the carrot (ARRA Stimulus Funding in the US) or the stick (EU 20:20:20 initiative), utilities are undertaking significant investments into smart meters and smart grid. There is widespread recognition that this presents a seismic shift in the data volumes that utilities will need to manage and integrate into their business processes in the future. This tsunami of data1 is the core or enabling component of the "smart" in smart grid. Successfully harnessing and exploiting this data to better understand, forecast and ultimately influence consumption are some of the key business benefits that can be achieved.
Unfortunately, the rush to deploy and implement smart meters means that the necessary thought has not been given to ensure that the maximum benefit is gleaned from the investment, for the utilities, their customers and the legislators. The bulk of the benefit to be gained is the greater insight that smart meters/grid provide in terms of consumption, forecasting, demand response and trading. The necessary analytics to unearth these potential benefits have a significant latency requirement that will be explored in detail later in this article. The tendency to build further data silos with no connection to other systems such as SCADA, OMS, CIS, billing and trading means that the "smart" data cannot be used in a context that is relevant to the rest of the business.
The most obvious immediate benefit in terms of more accurate and granular billing has not been realized in many of the initial deployments as the initial response from many customers is negative rather than positive. This results in increased service costs due to more customer service calls from customers questioning their meter readings and bills. Customers are becoming more aware of how their consumption impacts their bill and are now exposed to some of the potential complexities that their provider billing processes impose. A significant cultural change is required before mainstream customers will fully appreciate the longer term benefits that they can enjoy as more accurate and timely bills represent the tip of the iceberg. Few utilities have made any significant change to their billing processes, instead attempting to insulate their tried and proven processes and billing techniques from the impact of the granular data they now or will soon have .
Exposing overall energy consumption without understanding what the main contributors are is akin to telling someone that their car isnít working but not being able to tell them that they have a puncture or have run out of gas. Itís vital that consumers are provided with the insight to identify which appliances or rooms are consuming the most power so that the appropriate corrective action can be taken. Thankfully, this requirement is being addressed with the emergence of Home Energy Management Systems with Home Area Networks (HANS), In Home Displays (IHD), smart appliances and thermostats that may be offered as an extension to the smart meter or can be a discrete and independent offer to the consumer.
The distribution function within the utility can also benefit by analyzing consumption data to forecast better load balancing across the grid, providing the necessary capacity where and when it is required more cost effectively.
In addition to consumption data, smart meters will also function as extended grid sensors enabling utilities to capture and correlate events to detect quality of service and failures on the grid as soon as they occur rather than depending on inbound calls from customers. This, combined with additional smart infrastructure, dramatically changes the scale of event management requirements. Such event detection goes far beyond the existing event management systems currently used by utilities since they would now have to deal with millions rather than hundreds or thousands of sensors. So, in theory, outage detection should be improved.
This, however, merely scratches the surface of the analytical capabilities that are potentially enabled by the smart grid. Smart grid analytics can provide business benefits such as:
Reduced capital expenditures
Reduced operating expenses
- Focus capital budgets on components that are approaching the end of their actual life span using improved asset management
- Keep existing components operating for as long as safely possible by using preventive maintenance
- Slow peak demand growth by using smart meters to implement time-of-use tariffs
- Delay or eliminate major capital investments in generation plants through a reduction in peak demand via demand response
Improved network reliability and operational efficiency
- Identify and reduce electricity theft/diversion, which will improve revenue and improve safety
- Avoid emergency maintenance and replacement of network components
- Use demand response regimes to change peak utilization to reduce dependency on spot energy trading
Improved customer service
- Improve network configuration in real time, which allows components to operate within their optimal ranges
- Prevent or minimize outages
- Optimize spot and day trading using demand response to reliably create excess tradable capacity
- Provide real-time pricing, allowing customers to control usage at peak times of power consumption and reduce their energy cost
- Determine optimal terms for commercial and residential micro-generation
These benefits are all theoretically achievable, but can only be realized if certain prerequisites can be satisfied. Letís return to the data currency/latency challenge mentioned previously. With smart meters, utility companies are suddenly data rich, but can they successfully exploit the value and benefits this data presents? The obvious solution is to implement a Meter Data Management System (MDMS) or data historian that can reliably capture and store the data produced by the AMI system. MDMS and data historians are normally designed and optimized for data capture and storage with very limited analytical capabilities. There is, therefore, a further requirement to extract the data from the MDMS and transform the data into a form that can then be loaded into a data warehouse or analytical package. This process introduces significant data latency that may adversely impact which analytics can be used. This problem can be somewhat eliminated by using an operational data store as an intermediate step between the MDMS and the data warehouse, enabling some operationally oriented analytics to be performed. This is illustrated in the analytical spectrum diagram below.
(Mouseover image to enlarge)
The lower part of the diagram illustrates the potential data movement flow from the grid into the MDMS into an operational data store and ultimately into the data warehouse. This represents a very significant amount of data movement on a daily basis, combined with multi-terabytes of data duplication. The diagram also illustrates the impact that data latency has on some of the analytical processes that utilities may want to exploit. The green-colored processes such as demand response, dynamic pricing and outage detection all require near-real time data to be of value and, therefore, need to run on the MDMS Ė which as discussed previously is unlikely given that the MDMS is optimized for data capture Ėor in an external complex event processing (CEP) environment. Such a CEP environment potentially introduces yet another area of data duplication.
The approach outlined above does not represent a "smart" architectural approach to the immediate smart grid data management challenges. As an alternative, HP recommends that utilities consider a connected intelligence approach to address their requirements:
- Utilizing a single common data store to underpin all analytical requirements
- Using the same real-time data ingestion, scalability and reliability provided by specialist MDMS stores
- Combining these capabilities with the ability to support the whole spectrum of smart grid analytics
By bringing the application and analytics to the data, the connected Intelligence approach significantly reduces the need for massive data movement and duplication while enabling the necessary near real-time analytical processes to be executed and their benefits realized.
In the next article, weíll explore in more detail the needed analytical processes and how the concept of virtual power plants can be realized and exploited.References:
- Smart meters are causing a seismic data management shift in utility companies that previously had to deal with at most 4 meter readings a year per customer to having 35,040 readings per year (assuming readings every 15 minutes) per customer.
SOURCE: Smart Grid ≠ Smart Architecture
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