THE ENERGY INDUSTRY TIMES - DECEMBER 2017 Industry Perspective 13 The perfect storm for M&A Renewables will be the fastestgrowing source of electricity generation over the next five years, according to the International Energy Agency. In 2016 some 138 GW of new power capacity came online – almost 11 GW more than the previous 12 months. Already in 2016 wind, solar, biomass, wasteto energy, geothermal, small hydro and marine energy sources between them added 138.5 GW, up from 127.5 GW in 2015. The 2016 figure was the highest proportion in any year to date, equivalent to 55 per cent of all the generating capacity added globally. However, renewables fundamentally transform the electricity sector. Fluctuating feed-ins, changed load curves and low electricity prices all present substantial challenges for incumbent players. In these uncertain times, energy businesses are adapting their strategies and are looking to artificial intelligence (AI) and big data to improve energy forecasts. As a result, the 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, and one that often leads to quick-fire mergers and acquisitions. Over the first half of 2017 the M&A trend gathered momentum, with energy and renewables enterprises acquiring big data and (AI) companies. Data from BDO’s M&A database shows how the average deal value for these kinds of mergers and acquisitions has shot up from $500 000 to $3.5 million, while deal numbers have been climbing steadily through the last couple of years. And these are the early stages of an acquisition trend that will continue for years to come. 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. The way to generate such insights is through using big data analytics and AI to radically improve prediction models. But 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, wind turbines, solar panels) and distribution sides (e.g. energy grid and storage) are adapting and starting to integrate new technologies. AI will allow a transition to an energy portfolio with increased renewable resource production and minimal disruptions from the natural intermittency that comes with these sources due to variable sunlight and wind intensity. 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 the output of energy generated from multiple sources to match social, spatial, and temporal variations in demand in real-time. AI can screen large stacks of data for a wide range of factors that may impact performance, e.g. layout and location of a site, contractual offtake 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 the UK could be a better market than, for instance 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 $2 million of replacement parts in the next five years. It could indicate that in year seven, the probability that something is going to fail, potentially resulting in a shutdown 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 and pave the way for a cleaner, more affordable, and more secure energy system. The key lies in unlocking and using demandside flexibility so that consumers are not impacted and are appropriately rewarded. In the USA, 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 also 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. And IBM is far from the only big company pursuing these solutions. Google, for example, has launched its Project Sunroof. Data from CB Insights shows how the two, along with other big tech 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 $330 million. 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 2595 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&As to avoid losing market share to the new kids on the block. Jakob Sand is a Partner at BDO, Leader of BDO’s global technology, life sciences, media & entertainment and telecommunication transactions practice. BDO’s report titled: “Why big data, AI and renewables are the perfect M&A storm” can be downloaded at www.BDO.global. As renewables continue to grow in the generation mix, energy businesses are turning to artificial intelligence and big data to improve forecasting. According to a recent report by BDO, this is creating a perfect storm for merger and acquisition activity. Jakob Sand Big data analytics and renewable energy M&A deals 2016 - 2017 Q2 Sand: large companies may have to resort to M&As to avoid losing market share A selection of 2017’s significant energy and AI/big data M&A August 2017: Wildan Group buys Integral Analytics for $30 million Sub-trend: demand forecasting/smart grid Why it is interesting: Integral Analytics has built a software suite that helps utilities integrate distributed energy resources. Its solutions tap sources of data like econometrics and customer-owned power assets to help understand how customers use power and how that usage could change. This helps the power utilities plan the right level of resources and be well positioned for the future. May 2017: Itron acquires Comverge for $100 million Sub-trend: demand forecasting/smart grid Why it is interesting: Comverge uses machine learning to improve demand forecasts. It uses data to ‘train’ its prediction models to find the rules themselves. Over time, as the models ‘learn’ from more experience (more DR events), the forecasts become more accurate. Itron strengthens its portfolio of grid solutions with the industry’s leading demand response offering while also paving the way for gamechanging distributed energy management applications. February 2017: Vepos acquires Utopus (amount undisclosed) Sub-trend: big AI moving into renewables placement and smart grid forecasting Why it is interesting: A merger involving IBM’s clean energy research team. It forms one of the premier and likely best funded and technologically advanced start-ups. The company can rely on IBM’s Watson systems to create weather predictions. February 2017: Castrol buys Romax technology (amount undisclosed) Sub-trend: AI used to reduce maintenance costs Why it is interesting: Castrol and Romax partner to grow wind-turbine predictive maintenance business. The lubrication and maintenance of a wind turbine’s expensive gearbox is critical to optimising its performance and reliability, and Romax’s technology helps this through data-driven O&M, reducing O&M costs significantly.
Energy Industry Times December 2017
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