“Without integrating operational data with traditional IT data, I don’t think the industry would be any further along than it was five or 10 years ago.”
~Steve Ehrlich, Space Time Insight...
3Rs for Power And Demand
Dynamic monitoring and decision systems maximize energy resources.
by internalizing the cost of their own inter-temporal characteristics and uncertainties at risk levels they are comfortable with. If this is done and communicated to the system operators, most of the aggregate self-committed power, energy and capacity would become predictable and dispatchable with surprisingly high accuracy. This, in turn, would eliminate concerns regarding the volatility of variable resources. Having both conventional and new resources specify their ability to supply, and having users specify their willingness to adjust, would increase the number of possible providers and would, ultimately, reduce gaming to a significant degree.
This distributed risk-management concept contrasts sharply with today’s practice, in which the overall physical and financial risks associated with both long- and short-term uncertainties are borne by the customers, often after the fact. Moreover, today the effects of inter-temporal constraints are spread across all participants and there is simply no way to value different technologies based on the rate at which they could respond. This overall situation has resulted in both a lack of incentives to utilize resources efficiently—because those causing inefficiencies don’t pay for it—and also in a lack of interest by the customers to participate more pro-actively in decisions related to their electricity services; the signals to price-responsive demand aren’t strong enough to reflect the true value of their participation. 2 This also has led to the inability of specialized technologies to recover their costs. The system operator at present doesn’t have different rules for compensating expensive storage than it does for paying the combined-cycle power plant. This, in turn, requires subsidies to support the deployment of new technologies, such as storage. Moreover, electricity market derivatives aren’t sufficiently diverse technologies to be differentiated according to their rates of response.
The magnitude of these problems likely will grow as plans are made for deploying more varied resources—wind, PHEVs, fuel cells, batteries, and solar—and the gaps will increase between what customers want, what producers want and what is sustainable and good for the society as a whole. And, consequently, the problem of missing investment money will remain. While these gaps have existed in the past, they are becoming pronounced because of the unusually high uncertainties brought about by industry restructuring, environmental objectives and the influx of novel technologies. These range across regulatory, physical and financial uncertainties.
While some of these uncertainties are harder to manage, it’s possible to do significantly better with relatively modest regulatory and technical design of 3Rs for future energy systems. For example, using such possible simple 3Rs in support of large-scale wind integration and responsive demand yields quantifiable enhancements in system performance.
3Rs for Wind Power And Demand
Consider the problem of integrating large-scale wind capacity in operations without creating reliability problems. The first question is how to treat wind power. If it’s considered as a negative inelastic load, this right away results in higher volatility than today’s system load and requires even larger stand-by and capacity reserves than at present. NERC (N-1) reliability criteria aren’t explicit, at this time, about how to treat wind.
One possible design of the 3Rs would be to treat