The economics of energy saving

Dr Afshin Afshari is professor of practice in engineering systems and management at the Masdar Institute of Science and Technology

ABU DHABI // There are no free lunches in economics. In the same way that a debt-based global financial system is doomed to fail, the dream of unlimited economic growth sustained by abundant and cheap energy from fossil fuels is turning into an environmental nightmare.

For the first time in its history, mankind is facing a formidable test of its resolve to coexist harmoniously with its environment. The next decade will be decisive and the outcome likely irreversible.

There are two important aspects to sustainable and environmentally friendly energy use.

One is the provision of "clean" energy that does not damage the environment or contribute to global warming, which is achieved mainly through the development of renewable energy.

The other is demand-side management (DSM), which seeks to reduce our overall ecological footprint by limiting energy consumption. This is often the more cost-effective of the two.

DSM strategies generally fall into one of three categories: energy conservation, energy efficiency and peak-load shedding/shifting.

The last category matters because renewable energy sources are often intermittent or unpredictable, while nuclear plants cannot modulate the amount of energy they generate in response to demand fluctuations.

Even if all our power supply comes from fossil fuels it is useful to be able to reduce or displace peak demand, as "peaking units" - plants that are called upon only when demand is at its highest, typically for 300 to 400 hours a year - are generally expensive and polluting.

DSM can be incredibly effective, especially in countries that are just starting their transition from old era usage patterns to more environmentally conscious behaviour.

In these initial stages there are more cheap, effective steps that can be taken - so well designed and executed interventions easily pay for themselves.

Why then is DSM not more widely used, instead sometimes lagging behind more risky or expensive supply-side options, even when there are still plenty of demand-side alternatives?

From the economic point of view, a major hurdle is the principal-agent problem, whereby the upfront costs of the DSM measure befall the owner while the benefits of energy savings accrue to the tenant.

Another is the difficulty of knowing in advance how much a particular measure will cost, and how much energy and money it will eventually save. These are not insurmountable problems.

The principal-agent problem is relatively easily solved, by drawing up the right financial framework.

The lack of a proper framework for selecting, implementing and verifying DSM measures is trickier, though. The bewildering variety of buildings and energy-consuming systems have so far prevented it from being addressed. Cities are complex, and people and weather are unpredictable - all of which makes good data and reliable models hard to come by.

Researchers at the Masdar Institute are working on a project that aims to address this need.

It has three long-term goals.

The first is to sustain Abu Dhabi's efforts to make its buildings more energy efficient and reduce their peak load. The latter will involve retrofitting existing buildings with advanced controls that incorporate peak-load signals in the control scheme, to shave or shift a portion of the peak demand.

Simply put, we want buildings to use less energy overall and to control their own power use, so power-hungry functions are carried out at times of less overall demand.

A second application of the tool will be in new developments such as Masdar City, where energy efficiency will be a key consideration right from the very start. This means taking into account not only individual buildings but also factors such as transportation, microclimate and district cooling in the overall decision-making.

The third application is in the day-to-day operation of a city. Once a sustainable city is built, the tool will be able to provide an accurate "live" model of its energy use, continuously analysing energy consumption data from all metered points within the city.

That model could then be used for forecasting, benchmarking, fault detection and diagnosis as well as continuous improvement of the demand-side energy performance.

Working hand in hand with the smart grid, that would allow us to "close the loop" and automate city processes. And that kind of convergence of the real and the virtual worlds would truly be an embodiment of the "internet of things" imagined by pioneering researchers as long ago as the 1980s.