Fast growing distributed resources create technical challenges for utilities. Advanced DMS technology promises to help keep local grids balanced.
Efficient Regulation, Efficient Grid
Intelligent infrastructure requires an intelligent policy framework.
expensive generation would be realized over the long-term planning process. The correlation between the cost and benefit of the new investment largely would be lost to the customers and would present a difficult political challenge to the regulator.
Increasing favorable economics associated with smart-grid technologies, even considering a relatively high price of a carbon tax (in excess of $50 per ton) likely will add to the cost of service resulting in a long series of rate increases piled upon other cost factors ( i.e., increasing the utilities’ cost of service via reduced load growth, higher operating costs, increasing fuel costs and aging infrastructure).
Even with $4 billion in stimulus funding targeted for the smart grid, the utilities’ matching portion of the monies must be added to the utility rate base, again raising rates.
Another significant barrier the utility must face is regulatory lag, or the time that elapses between the investment and the utility’s ability to recover the investment in rates. In the transformer example, the utility, its lenders and its stockholders are faced with significant financial risk as the large-scale financing of such an endeavor must consider the regulatory risk associated with the investment. If the new high-tech transformer investment is fully or partially disallowed in rate base, investors are left holding the bag. This regulatory conclusion takes time and might lag the investment by up to two years, further increasing uncertainty and risk.
As the current financial model is extrapolated across the “smart grid,” an important conundrum arises. The price tag will go up as utilities (understandably) fear their inability to recover the costs. As a case in point, AMI historically has made tremendous sense from a utility operating perspective, yet its greatest advantage may come in the form of customer engagement. However, capturing that value in the current business paradigm is challenging for consumer and utility. While the consumer might be able to reduce consumption (knowledge is power) and save on energy, the dollar savings in most rate schemes may not create sufficient incentive to motivate the majority of customers to do so.
In calculating benefits for systems such as AMI, there are more than 50 value attributes and related cost reductions that can be identified in supporting the investment decision. Yet it’s often challenging as utilities and regulators try to balance the operating needs of the utilities with the reality of short-term rate-hike aversion. This often results in the deferral of investments based on exogenous factors such as local politics, social-economic considerations and the fervor and strength of intervention during a rate case. The decision to defer isn’t based on the technical assessments or value of the investment, and it’s entirely possible that utilities don’t believe their own business cases. In effect, the electricity sector’s current destiny isn’t gated by technical limitations, but rather by limitations of the political system in which it operates.
The current methodologies for computing smart-grid value rely on business cases anchored in an aging and not necessarily robust business view of this changing world. Traditional business cases look at technologies