The best example of combined dynamic rates and smart billing is found in Ontario, Canada. It uses central MDM to produce time-differentiated customer bills.
AMI: Smart Enough?
Metering potential and limitations for smart-grid design.
There are many definitions of what constitutes a smart grid and many visions for how a more intelligent grid will enable the future energy economy. While these visions vary, they also have many characteristics in common—characteristics such as distributed intelligence, adaptive self-healing, and multi-way communications across the entire energy delivery chain. Some approaches to smart-grid design involve deployment of high-performance line sensors, while others rely upon use of the advanced meter infrastructure (AMI) to provide visibility into distribution grid state. While smart meters don’t provide support for the highest-performance smart-grid functions, they can provide significant capability when the AMI system is properly designed to support the evolution to a smart grid. Proper design for this purpose implies modifications to several aspects of the AMI system, including types of meters, specifications for the meter communication system, and design of the meter data management solution.
The question, then, is how far can smart metering take us toward realizing a smart grid, and what factors should a utility consider in the design of a smart meter system if the eventual goal includes smart grid?
A smart grid uses sensing, embedded processing, digital communications and software to integrate grid-derived information into utility processes and systems, thus making it observable (able to measure the states of all grid elements), controllable (able to affect the state of any grid element) and automated (able to adapt, self-adjust and correct). A smart grid supports the three main pillars of utility function: 1) delivery of reliable, high-quality, sustainable energy: 2) asset utilization optimization and asset lifecycle management; and 3) advanced customer services and choice enablement.
Given this definition, an AMI system can supply some, but perhaps not all of the capability around grid observability. Since residential meters are located on the low-voltage grids, they do provide some visibility to quantities such as voltage, but do not give us visibility into voltage and current phasors. And, while residential meters may have the capability of capturing some details of transient phenomena such as voltage sags, they may not capture proper parametric descriptions of these events. They also provide limited visibility into real and reactive power flows on the main feeders, something that becomes increasingly important as the penetration of distributed energy resources increases. Aggregation or roll-up strategies for computing power distributions from premise meter data have been suggested, but most AMI systems do not support data transfer at rates necessary to make this a viable alternative to sensing at the medium-voltage feeder level and most residential meters do not measure reactive power, although many commercial and industrial (C&I) meters do. New developments in metering, such as feeder meters, offer potential solutions to these limitations.