'Renewables investors should look to Africa and AI-led projects to maximise returns'

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Smart data analytics for distributed energy resources can cut risk as the continent provides electricity access to millions, writes Hanno Schoklitsch

With the corporate world now taking climate change more seriously, and clean-energy investments yielding handsome returns, where should potential investors and relevant policymakers turn their attention?

Wind farms, solar parks, storage systems, including virtual power plants, are among the usual targets. However, there’s an argument to be made for considering alternative uses of capital.

The developing nations of the world offer perhaps the greatest opportunities. Among other reasons, this is due in large part to the low electrification rate in these regions. In sub-Saharan Africa for instance, only 45% of the population had reliable access to electricity in 2017 whereas the US and EU each had rates of 100%, according to the World Bank.

As one might expect, such circumstances weigh significantly on education, health, economic and environmental outcomes that, in turn, restrain social and national growth, development and progress.

The solution seems simple at first. Surely the same centralised electricity generation and distribution model that powered American and European homes in the 19th and 20th centuries could be transplanted to sub-Saharan Africa with similar success.

While that may be true to an extent, the exorbitant capital expenditures, time and organisational capacity needed to accomplish as much would discourage even the most intrepid investors, not to mention politically exposed lawmakers.

More to the point, each day that a centralised generation and distribution scheme is in progress is one where the anticipated environmental, social and governance (ESG) gains are diminished. In other words, closing the developing world’s electrification gap is a time-sensitive mission.

For these and other reasons, energy supply decentralisation ought to be the focus of global ESG-conscious investors, lenders and governments. Indeed, in the US and other industrialised nations, the products, processes and systems that comprise decentralised grids are readily available and rapidly improving.

These innovations, more commonly referred to as distributed energy resources (DERs), include solar-plus-storage systems and distributed ledger technologies, and are of increasing interest to the industrialised world’s investors, governments and consumers.

Combinations of these technologies and processes are already in operation in many developing states in sub-Saharan Africa and elsewhere, albeit with varying degrees of success. According to the International Energy Agency, while growth of electricity access in sub-Saharan Africa has begun to outpace population growth, the progress is uneven, and the total number of people without electricity has grown since 2000.

And by 2030 , up to 90% of the 650 million people without access to electricity globally will live in sub-Saharan Africa.

Combating these divergent trends will undoubtedly require more robust government and investor support for existing DERs. That much is clear. Yet even with decentralised systems, time is of the essence.

Artificial intelligence (AI), machine learning and the Internet of things (IoT) afford energy producers, their investors and policymakers a powerful catalyst.

Power generation and other energy assets equipped with modern computation devices can be connected to cloud-based AI platforms that in turn will aggregate, clean and analyse the various assets’ complex and unstructured data streams.

The information relevant in these processes is extensive, and includes real-time and historical performance data, enriched with meteorological and set into relation with financial data.

The potential value here cannot be overstated. Rather than letting these datasets lie fallow, cloud-based AI allows energy asset managers, whether they’re public utility companies or investors, to extrapolate performance trends that can then be used to minimise risks and maximise returns thanks to predictive analytics that makes early failure detection possible.

The bottom line for ESG-conscious investors is this: renewable energy investments are made more reliable and therefore more profitable by using data intelligence to supervise them.

And for the developing regions of the world, the insights generated by cloud-based AI systems shorten the time needed to bring DERs online and mitigate countless transition and installation risks.

Over time these technologies can accelerate both the proliferation of micro-grids and their consolidation into larger utility-scale grids, precipitously improving these regions’ electrification rates.

With this in mind, investors should perhaps direct their energy towards engaging with governments of the world, and determine what mechanisms and incentives may lead them and their organisations to improve their environmental, social and governance performance. Though it bears repeating, that time for action is running out.


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