The case for committing to greener telecom networks
Courtesy of McKinsey & Company
By Richard Lee, Dickon Pinner, Ken Somers, and Sai Tunuguntla

Energy costs for telecom operators around the world are already high: at the end of 2018, they accounted, on average, for around 5 percent of operating expenditures. In emerging markets, where low grid coverage often means operators must supply their own power with a generator set, energy can account for as much as 7 percent of expenditures. And costs look set to rise further, putting greater pressure on margins at a time when the industry can scarcely handle any additional financial burden.

This growing energy challenge is, in large measure, a result of the exponential growth in traffic that new 5G services are likely to deliver. Although the 5G-new-radio standard is more energy efficient per gigabyte than are the 4G standards, the proposed 5G use cases and new spectrum bands will require many more mobile sites, outstripping potential energy efficiencies. Each 5G site will need two to three times more power than the 4G-equivalent site, according to industry estimates. At the same time, as more services are provided at the edge, the number of data centers will need to rise. By our calculations, these already account for 5 to 10 percent of a telecom operator’s energy costs.

Supply-side costs are also likely to increase. On average, global power prices have risen by about 1 percent a year over the past decade, although in Australia, Canada, Egypt, France, and South Africa, they have climbed by between 3 and 7 percent. There is no reason to believe this trend will abate, particularly given the likely shift to electric vehicles in many markets and the extra demand for grid power that this will unleash.

Costs are not the only concern, however. Telecom operators already account for 2 to 3 percent of total global energy demand, often making them some of the most energy-intensive companies in their geographic markets. As operators’ energy consumption expands, so will their carbon footprint, hurting not just the environment but also their reputation and standing, particularly among the expanding class of socially responsible investors.

But this does not have to be the case. All operators have considerable scope to cut energy costs and consumption. In current mobile networks, for example, transferring data only consumes around 15 percent of energy. Some 85 percent is wasted because of heat loss in power amplifiers, equipment kept idling when there is no data transmission, and inefficiency in systems such as rectifiers, cooling systems, and battery units.

Some savings lie in deploying artificial intelligence (AI) and the Internet of Things (IoT): some in structural and architectural transformations, and some in cheaper and more sustainable energy sourcing. The extent of the potential savings will vary by operator and market. Regulations governing distribution and retailing, green-energy incentives, OEM choice, and an operator’s starting point in energy efficiency will all make a difference. Nevertheless, our work suggests that many operators can reduce energy costs by at least 15 to 20 percent in the space of just one year—and more over a longer period.

Seizing these opportunities, however, requires major organizational and mind-set shifts. Energy costs may well be aggregated at company level, but responsibility for the cost drivers is split across many different functions and divisions, such as network and infrastructure planning, field operations, facilities management, procurement, and IT, making managing this level of change all the more challenging. Moreover, reducing energy costs requires resources (labor and capital) at a time when operators are concentrating their investments on expanding the capacity and reach of their networks. For both these reasons, energy consumption and costs are unlikely to fall without high-level recognition of the importance of reducing them and a comprehensive strategy for doing so.

Where the opportunities lie

The biggest opportunities for energy-consumption and cost reductions lie in four areas. Some are more complex than others to capture, and some require more capital spending than others.

Artificial-intelligence-driven sleep and shutdown

Running systems that are not in constant use consumes significant amounts of energy. Typically, radio access network (RAN) accounts for about 60 percent of the power used at a mobile site. Data-traffic loads are intermittent, though, so that different parts of RAN can be put briefly into sleep mode, even during periods of peak traffic. A mobile operator in Australia found that simply turning off the power-amplifier symbol at a site could cut consumption by more than 7 percent, without any service degradation.

AI expands the potential for such energy-saving opportunities across the network. The ability to analyze vast amounts of data relating to traffic patterns, real-time demand, and network-resource availability allows for quick, automated decisions on the parts of the system that can be put into sleep mode or shut down. For example, this could involve shutting down frequency carriers or shutting down a site momentarily in areas where there is overlapping coverage. We estimate that such energy-conserving AI tools can deliver 5 to 7 percent savings for some operators, in addition to savings that accrue from stand-alone, site-level efficiency measures. And the potential will surely rise further. As open and cloud-native approaches to building RAN gain ground, additional AI solutions that not only save energy but also minimize related customer-experience issues, such as latency, are emerging. They can also be used on all networks, from 2G to 5G.

Similar energy-saving advances are occurring with AI on fixed networks. For example, AI can reduce the energy cost of central offices by between 3 and 5 percent by continuously calibrating the optimal settings of chillers, pumps, and fans to guard against waste.

The AI tools for managing energy efficiency in data centers (where the cloud RAN will be located) are considerably more mature. Google, for example, has reported 30 percent energy savings using AI at its data centers, illustrating just how high telecom operators could set their sights.

Internet of Things–based energy optimization

It is hard, if not impossible, to reduce energy consumption and costs if you cannot measure consumption accurately. That is the starting point of any concerted energy-efficiency program. But until recently, accurate measurement on an industrial scale has been difficult. Old equipment does not always measure consumption, and even if it does, recording it requires hundreds of employees to make and submit accurate readings. The IoT, by contrast, uses sensors to track consumption.

That advance opens up all sorts of new ways for operators to save energy. Sensors that read consumption—essentially, smart meters—give companies access to time-of-usage discounts in markets where they are offered. One Southeast Asian operator saved 5 percent on its energy costs because of a differential pricing offer from the utility provider.

And the IoT can counter the theft of fuel and grid power, which is a serious concern in some developing markets, where it can raise energy consumption and costs for some operators by 10 to 15 percent. By placing sensors at various points to gauge grid-power input, fuel levels, the number of hours the generation set has been running, battery voltages, and consumption by different types of equipment and then analyzing the data, operators could uncover potentially costly anomalies. What’s more, the IoT platform could provide real-time alerts when they occur.

Like AI, the IoT also makes it much easier to optimize consumption—with air conditioners, for example. Many operators have already moved heat-generating equipment outdoors to take advantage of natural convection cooling. Nevertheless, our analysis shows that, on average, about 20 percent of a telecom operator’s sites and other facilities still use air conditioning. On mobile sites where this is the set case, air conditioning accounts for 30 percent of energy consumption. Here, the installation of sensors would allow monitoring and remote adjustment of the site temperature. The sensors could even alert the operations center if a door was left ajar or the maintenance crew altered the setting, forcing the air conditioning to work unnecessarily hard. The overall result could be a 1 to 3 percent reduction in a site’s energy consumption.

Importantly, companies can retrofit the IoT for use by old equipment, even as OEMs are integrating it into newer equipment.

Structural and architectural transformation

Structural and architectural changes can deliver sizeable energy savings. Energy is the primary source of cost savings when decommissioning legacy networks, for example. A Southeast Asian operator realized a 3 percent saving on its total energy bill when it decommissioned its stand-alone 2G network and moved to a single-RAN architecture, as the legacy 2G equipment, although underused, was more energy intensive. Similarly, migrating to architectures such as cloud-RAN and “clean cloud” data centers can deliver energy savings of more than 10 percent.

Another opportunity lies in customizing RAN as well as passive infrastructure specifications site by site, depending on load conditions. One size does not need to fit all. The size of battery trays for backup power can be custom designed, for example, as can multiple-input and multiple-output (MIMO) configurations. China Mobile says it is improving energy efficiency through site-specific MIMO configurations, using 32T32R arrays—or even 8T8R—in some locations instead of 64T64R at every site.

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About the author(s)

Richard Lee is a senior partner in McKinsey’s Seoul office, Dickon Pinner is a senior partner in the San Francisco office, Ken Somers is a partner in the Antwerp office, and Sai Tunuguntla is a partner in the Singapore office.