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...
3Rs for Power And Demand
Dynamic monitoring and decision systems maximize energy resources.
The operations and planning rules for integrating variable resources aren’t the same across the electric power industry in the United States at present. Opinions are somewhat divided about what these should be, as well as the assessments of potential benefits and costs. In order to support sustainable deployment of variable resources at value, it’s critical to identify major sources of potential problems and to proactively design and implement a systematic framework for managing their unique characteristics as reliably and efficiently as possible. It would be possible to efficiently and reliably integrate relatively large-scale wind capacity in the existing electric power grids provided that this is done in coordination with responsive demand. However, in order for this to happen, it’s critically important to manage industry risk in qualitatively different ways than is done at present.
In particular, the rules, rights and responsibilities (the 3Rs) need to be established to provide incentives to all for sharing the right information and for self-managing their resources dynamically in the face of seemingly large variability. Short-, mid- and long-term physical and financial risks must be aligned and distributed among many power producers, end users, aggregators, delivery providers, system operators and system-wide coordinators. A multi-layered, multi-directional and interactive IT architecture that dynamically supports the aligning of physical and financial risks according to the 3Rs is the key to distributing risks among states, utilities, emerging aggregators, and individual producers and end users.
An implementation comprising a physical power grid with its resources, IT-enabled communications and embedded decision-making intelligence would become a much talked-about smart grid capable of managing distributed risk at value. Instead of planning and operating for meeting system-wide reliability criteria centered around grid integrity, load-serving entities would be required to provide information about the short- and long-term characteristics of their customers and resources. Moreover, they would have to specify their willingness to respond to system conditions at the value pre-specified by themselves. Much complex decision making and autonomy would be left to the system users and this would reduce the need for complex decisions by the system operators.
A proof-of-concept implementation example of large-scale wind power dispatch in coordination with price responsive demand is shown for illustrative purposes on an IEEE RTS Test System (see Figure 1) .