Now that wireless carriers are promoting their networks as a cost-effective communications platform for smart grid data, they face legitimate questions about fundamental performance issues. But if...
Intelligent networks support better decision making.
Yet maintaining a reliable grid is a challenge for many reasons.
For example, as many grid assets reach the end of their useful life and face an increased risk of failure, utilities cannot afford to replace all of the assets, so they must better understand and maintain their existing assets.
Further complicating this problem is the reality that utilities will have to maintain this infrastructure with a rapidly aging workforce. Many utilities ultimately want to replace their retiring workforce with fewer, more productive employees. However, new employees do not have the same extensive knowledge about the grid as longtime employees.
With the need to understand many complicated factors, such as power flows and voltage profiles, analyzing the grid is already difficult. New additions to the grid, such as distributed and renewable energy sources, smart metering, and demand response programs, are only going to increase the grid’s complexity. Energy trading also introduces more complexity because utilities have to deal with multiple power sources and providers.
The North American Electric Reliability Corporation (NERC) reliability standards cover a broad range of issues, from resource and demand balancing to personnel performance, training, and qualifications. With the enforcement of the NERC reliability standards, utilities can face fines of up to $1 million per event, per day.
Given these needs, utilities are finding that the intelligent grid is not just for making operating decisions about their transmission and distribution (T&D) assets. To fully understand their T&D systems, utilities are also looking at decisions they have to make about areas connected to the grid—generation ( e.g., energy storage, distributed and renewable generation, and new central generation plants), customers ( e.g., residential, commercial, industrial, and municipals), and staff ( e.g., mobile workforce) ( see Table 2 ).
Knowledge about the grid can help answer these sorts of questions. Intelligent grid analytics build this knowledge by bringing together complex data and turning it into information that utilities can then use to make well-informed, effective decisions about the grid. Some utilities are finding that faster, more real-time analytics are important not only in preventing grid failures, but also to support other grid initiatives such as demand response, real-time pricing, and more distributed and renewable generation. Yet despite these needs, today’s utilities cannot effectively make these decisions because they have limited, and mostly historical information. In terms of information, utilities must deal with:
– Few data points: Utilities generally have good data about transmission networks, but they collect little data about distribution networks beyond major substations. In particular, utilities are now collecting more metering information, but neglecting to monitor other critical assets between the substation and the meter, such as transformers. Furthermore, most utilities do not collect real-time data about their distribution networks, which is critical for making split-second decisions.
– Limited access to data: Even with the data utilities do collect, often they have limited access to this data. Personnel might maintain data in spreadsheets on their own computers, or the utility may separate data into silos across the company. For example, a utility may store meter data—which is