Managing Post-Storm and Disaster Power Outages: Is Distributed Generation a Solution?

The Eastern United States was sweltering at the end of June. The record-high temperatures caused a rare storm called a “derecho” which produces straight-line winds across a wide front of near-hurricane speeds (while a hurricane produces circular winds). The winds created havoc in the Eastern states and a state of emergency was declared in Washington DC, Maryland, Ohio, Virginia and West Virginia. Across seven states, three million homes were left without power. Emergency 911 services failed in certain areas and popular Internet services such as Netflix, Pinterest and Instagram were also affected. In the case of this derecho, the major challenge for mobile communication was not with the systems themselves, but with power cuts caused by the storm.

Power failure is the major reason for breakdown of communication networks after disasters and it affects both developed and developing countries alike. Perhaps surprisingly, developing countries often have the edge as their networks are mostly designed to accommodate poor electricity supplies and their systems have a greater degree of power autonomy. Developed nations have become used to reliable power distributed from large centralised generating facilities via high voltage transmission lines to low voltage distribution systems and then to the end user. When disaster strikes, failure in high voltage transmission lines can leave huge areas without power; because these lines spread out over wide areas, the vulnerability is especially high. One way of reducing the vulnerability would be to identify local means of securing the provision of power to vital services such as schools, cell sites, gas stations, etc.

Distributed Generation (DG) could provide an answer to sustaining critical services during extended power outages following a disaster. Rather than relying on large centralized power facilities, DG collects and distributes electricity from many small energy sources, (small generators, gas turbines, or renewables, like solar and wind). A recent study from Carnegie Mellon University researchers Anu Narayanan and M. Granger Morgan looked at the possibility of creating energy “islands” by using DG units together with distribution automation and smart meters that will disconnect non-essential loads and optimise power usage. Their strategy looked at the cost of providing critical essential services to an area that serves 5,000 households via DG, services which consisted of 1 police station, 1 grocery store, 4 gas stations, 10 cell towers, 1 school and a number of streetlights for a total power requirement of 350 kW.

The added cost to each household was calculated to be a maximum of $22 per year for implementing the various DG scenarios. This may look like good value, but in these challenging economic times, consumers may not be willing to cover the cost to insure against what we all hope will be infrequent events. Likewise, it is uncertain if a utility company would cover the investment costs alone. Instead the responsibility could be shared: utilities companies, government and commercial companies (for example the Mobile Network Operators) could work out a strategy to cover both the capital and operational costs.

The paper suggests that constructing regional DG circuits may reduce the effects of mains electricity failure resulting from disasters, meaning that the critical services necessary for the health and safety of communities stay up and running during long power outages.

This may or may not turn out to be a practical solution, but is clear that the centralised electricity generating and distribution systems that we currently rely on to supply our ever increasing demands for power,  are not resilient to disasters. We do need to rethink the way that we distribute electricity to vital services, especially in disaster prone areas.

Note: For more information see:

Sustaining Critical Social Services During Extended Regional Power Blackouts Anu Narayanan*, M. Granger Morgan published in –  Risk Analysis Volume 32, Issue 7, pages 1183–1193, July 2012 and is available from:

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