SCG introduces new renewable energy technologies and complete, end-to-end, energy solutions. They are economically and ecologically affordable road-maps to a carbon-free energy future. SCG implements comprehensive solutions for monitoring electrical power consumption, optimizing power delivery, and predicting power requirements at a local (micro) and large-scale (macro) level through custom AI technologies. SCG makes extensive use of its Quantum Relation Technology (QRT).
The goals of this technology project are:
-Development and delivery of tools for monitoring electricity usage;
-Delivery of methods and techniques for predicting energy usage
-Analysis and prevention of failure modes for electrical power delivery systems
-Substantial savings on costs of providing electrical power
The solution uses artificial intelligence technologies to develop and refine a model for electrical power consumption, based on actual measurements. As more experience is gained by the system, the model is more precisely tuned,
and becomes better at predicting power consumption at different locations. The model is used to control power delivery, thus providing substantial cost savings.
The model can be run in advance to make power consumption predictions, and to test “what-if” scenarios, including conversion of grid-provided power to locally generated power (local solar technologies, for example), and to test which of these technologies actually provide power savings sufficient to justify their costs.
Motivations for the Project
Much recent research on actual electrical power grid performance has shown a surprising result: Many power grids run dangerously close to the critical edge of failure. This has real consequences—when power grids pass the critical edge, one has unexpected blackouts and system failure. For example, a 2002 study of the power grid system in the United States demonstrated that this system suffers from exponentially grouped blackouts, meaning that it is, at best, barely adequate for power requirements; moreover, subsequent studies have shown that it is both inefficient and inadequate at delivering power where and when it is required.
A correct strategy of projecting future demand and costs, given perfect knowledge, would therefore dramatically decrease both capital and operating costs, increasing the profit for the reseller. In an ideal system with multiple sources, energy could be purchased at the point where its price is the lowest. This strategy would favor contracting for energy at a time quite far in advance of the actual delivery. But knowledge of the market is never perfect in actual practice and, therefore, there are dangers in such a strategy: First, prices may drop even lower. Second, demand for electricity may drop below projections, leaving the purchaser with excess capacity for which the purchaser must still pay. Third, the purchaser may underestimate demand, in which case energy costs may be unacceptably high, but the purchaser is nevertheless bound by regulations and by its own contracts to resell at a low price. These risks can be decreased by purchasing energy later, nearer the time of actual delivery, but of course at a higher cost. Thus, the overall economics are quite complex and difficult, requiring a careful and delicate balance between acceptable risk levels and restraining costs. SCG offers, through its Intelligent Energy Management, a viable response to this complex set of problems and can add substantial value to power delivery grids.
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