M&A deals between energy, renewables and AI/big data companies shot up 50 per cent over the past 12 months, with deal value jumping from US$500,000 to US$3.5 million, a new BDO paper shows. Here, the international business advisory explains why energy companies are increasingly on the hunt to acquire big data and artificial intelligence wizards.

 

Renewables will be the fastest-growing source of electricity generation over the next five years, according to the International Energy Agency.  Yet such diversification brings challenges as current energy infrastructure and management systems are unfit to accommodate both the rise and irregularities that characterise renewable power.  Renewables fundamentally transform the electricity sector: fluctuating feed-ins, changed load curves and low electricity prices all present substantial obstacles for incumbent players.  In these uncertain times, energy businesses are adapting their strategy and looking to artificial intelligence (AI) and big data to improve their forecasts.

 

The acquisitions, scores of energy start-ups and new solutions hitting the market make the marriage of machine learning and renewables a very promising space to watch.  This year we have seen an M&A trend gathering momentum – one that involves technologies like big data analytics and AI meeting renewable energy.  Data from the BDO M&A database shows how average deal value for these kinds of mergers and acquisitions has shot up, while deal numbers have been climbing steadily through the last couple of years – indications that we are in the early stages of what could become an M&A trend for years to come.

 

Disruptions and dips 

 

Investment in renewable energy capacity has outstripped that in fossil fuel generation for the fifth year in a row now.  A record 138.5GW of new renewable power capacity (excluding large hydro) came online in 2016 – almost 11GW more than in the previous 12 months.  Encompassing new wind, solar, biomass and waste-to-energy, geothermal, small hydro and marine sources, the 2016 gigawatt figure was equivalent to 55 per cent of all the generating capacity added globally – the highest proportion for renewables in any year to date.  

 

Contrasting this growth, we have seen a worrisome global drop in new investment in renewables, down 23 per cent to US$241.6bn – the lowest total since 2013.  However, acknowledging a funding slowdown in China, Japan and emerging markets, a ‘more for less’ dynamic has also played.  A technology cost plunge precipitated a further fall in investment in renewables in 2016, with average dollar capital expenditure per MW down by more than 10 per cent for solar photovoltaics, and for both onshore and offshore wind.  Nevertheless, availability of finance does not appear to bottleneck investment in renewables.  Purchases of assets such as wind farms and solar parks reached an all-time high of US$72.7bn, while corporate takeovers reached US$27.6bn – some 58 per cent more than in 2015.  Asset finance for smart meters and energy storage, plus equity raised for specialist companies in energy efficiency, storage and electric vehicles, totalled a record US$41.6bn last year – up 29 per cent.

 

Companies seeking a competitive edge will increasingly look towards new technologies such as AI and big data, as they strive to make both production and distribution of renewable energy more efficient.

 

Challenging the grid

 

The biggest challenge associated with increased renewables market share is, of course, the fluctuating levels of energy production tied to natural phenomena like sunshine and wind.  Operating power grids is complex, and handling uncertainty in a cost-effective manner lies at the core of the problem.  

 

When demand outpaces supply, utilities turn on backup fossil fuel-powered plants – known as ‘peaker plants’ – at a minute’s notice to avoid black-out.  This procedure – the most expensive and wasteful part of the business for such companies – manifests itself in higher electricity bills for consumers and enhanced greenhouse gas emissions into the atmosphere.  Improved energy management is expected to contribute to average net cost savings of e53bn (US$62bn) per year to 2035 and average CO2 emissions savings of 165 million tonnes of CO2 per year.  The industry and service sectors could save over 25 per cent of their energy by 2035 by adopting energy management systems.

 

Congestion relief

 

Transmission congestion is a shortage of electricity transmission capacity to supply a waiting market.  The unpredictable nature of renewables like wind and solar present a challenge to Transmission System Operators (TSOs) who experience difficulties forecasting energy inflow from such intermittent sources.

 

Attempting to operate a transmission system beyond its rated capacity is likely to result in line faults and electrical fires.  To maintain safety, TSOs curtail production of generators to avoid local congestion.  

 

Renewable producers with support schemes bear the inherent cost of congestion when they are re-dispatched.  This reduces the revenue of renewable producers and limits green energy development.  In 2015, remedial actions to relieve grid congestion hit an all-time high of almost 20TW/h, amounting to costs of over e800 million (US$942m).  Grid adaptation is key to successfully implement energy transition.  

 

High-quality power forecasts are becoming increasingly important for maintaining a financially viable and secure energy system, as the proportion of weather-dependent energy sources to total power production rises.  In Germany, the goal is to produce 35 per cent of all power production from renewable sources by 2020.  In the US, 30 per cent of the electricity consumed by the federal government is to come from renewable energy sources by 2025.  Meanwhile, the EU is aiming to draw 20 per cent of energy requirements from renewable sources by 2020.  

 

An additional challenge is the rise of distributed generation, where private users generate and use their own electricity from renewable sources, such as wind and solar.  In certain regions, this complicates supply and demand, and forces utility companies to buy excess energy from private users, who generate more electricity than they use and send the excess energy back to the grid.  Since 2010, solar use has more than tripled – and this trend is poised to continue into the future, as photovoltaic cells decrease in cost and increase in efficiency.

 

Adopting insight

 

To operate the grid more efficiently and keep fossil reserves at a minimum, operators need to have a better idea of how much wind and solar power to expect at any given time.  Such insights are generated by big data analytics and AI – technology that can radically improve prediction models.  

 

Yet adoption is slow: utilities understandably are not the fastest-moving sector in the world, given the vast scale and complexity of energy grids and power plants, tied to cross-border political negotiations.  However, we are starting to see a marked shift where both the production (power plants, windmills, solar panels, and so on) and distribution sides (energy grid and storage) are adapting and beginning to integrate new technologies.  

 

In Europe, grid operators are currently finalising plans to launch a digital information exchange platform that will serve as a basis for developing new digital applications to manage electricity flows and take up growing amounts of renewable energy.  In the meantime, many of the 2,595 clean energy start-ups tracked by AngelList are already bringing their products and services to market.  It leads to a situation where many large companies may have to resort to M&A to avoid losing market share to young upstarts.  

 

Blockchain-based applications are also entering the fray, adding flexibility to the new energy market model.  A blockchain project sponsored by E.ON aims to provide prosumers with an application to balance out the market for a profit while grid operators use the software to mitigate congestion problems.

 

In the US, PowerScout uses machine learning and big data to find smarter ways to sell solar panels to customers, while kWh Analytics offers risk management solutions to protect investments in solar.  Again, AI plays a central role in their solutions.  

 

Major tech companies are likewise investing and working to establish themselves in the space.  For example, IBM Research has already partnered with 200 companies that use its solar and wind forecasting technology, while Google has launched its Project Sunroof – a solar calculator that helps you map your roof’s solar savings potential.  Moreover, data from CB Insights shows how the two tech giants, alongside other such companies, have been making scores of AI acquisitions.  

 

The same goes for some of the companies specialising in technological solutions for renewable energy, such as NEXTracker, which acquired the start-up BrightBox Technologies to ‘enable smart and connected solutions for the renewable energy market’.  NEXTracker was itself acquired by Flextronics International for US$330m.  

 

The acquisitions, scores of start-ups and new solutions hitting the market underline how the marriage of machine learning and renewable tech is still a relatively immature space – albeit a very promising one – and it is such conditions that often lead to quick-fire M&A.  

 

AI’s promise to renewables

 

AI will allow a transition to an energy portfolio with increased renewable resource production and minimal disruptions from the natural intermittency inherent with such sources.  For example, when renewables are operating above a certain threshold, either due to increases in wind strength or sunny days, AI-powered energy management software would automatically reduce production from fossil fuels, thus limiting harmful greenhouse gas emissions.  The opposite would be true during times of below-peak renewable power generation, thus allowing all sources of energy to be used as efficiently as possible and only relying on fossil fuels when necessary.  Additionally, producers will be able to manage in real-time the output of energy generated from multiple sources to match social, spatial and temporal variations in demand.

 

Furthermore, AI can screen large stacks of data for a wide range of factors that may impact performance: layout and location of a site, contractual off-take agreements, type of equipment, grid connection, weather, and operation and maintenance costs can all help predict a possible financial rate of return.  For example, consider a wind farm: with location data, the software can use public data sets to calculate the last few decades of wind speed and determine the project’s overall performance.  Location can also help determine the project’s profitability in the market – California or Denmark could be a better market than, say, Texas.  

 

Specific types of equipment and manufacturer matter, too.  If an investor considers a certain type of wind turbine, data can be pulled to determine that the turbine in a given location will need US$2 million of replacement parts in the next five years.  It could indicate that in year seven, probability that something is going to fail (potentially resulting in a shut-down of the site) will be 50 per cent.  Making the demand for electricity ‘intelligent’ means that vital capacity can be provided when and where it is most needed, thus paving the way for a cleaner, more affordable, and more secure energy system.  The key lies in unlocking and using demand-side flexibility so that consumers are not impacted and are appropriately rewarded.

 

BDO International

BDO (Binder Dijker Otte) provides accountancy and business advisory services in 158 countries, with 68,000 people working out of 1,400 offices worldwide.  It has revenues of US$7.6bn.

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