How will the energy landscape change in 2019?

It’s 2019 ” that’s nearly 2020! It doesn’t seem long ago that the EU was setting what seemed like fairly radical targets for 2020; for decarbonization, for renewable energy use and for energy efficiency.

However, time runs fast and looking back, it was 12 years ago in 2007. Despite the good work by EU member states, concerns over climate change are growing and the expectation is these concerns will grow faster in the coming year.

Now we often talk of the three Ds of energy; decarbonization, decentralization and digitalization (sometimes a couple more Ds are added for good measure; deregulation and democratization). Do these three Ds have the same imperative?

Decarbonization of the power sector still seems fairly clear ” the options haven’t changed so much; energy efficiency, renewables, nuclear and CCS. Although, the costs and policies have changed enormously over time and renewables have made great advances.

For 2019 we can expect to see further advances and more geographies around the world in which renewables can out-compete traditional forms of generation and face the market more and more. This means renewables developers and investors getting comfortable with more market and less regulatory risk.

Decentralization and digitalization

Decentralization means different things to different people. For some its behind-the-meter investment, for others its generation and storage embedded in distribution networks. The drive for renewables produced a lot of energy from the latter as onshore wind and solar PV took off.

This decentralization trend is set to continue next year with a return in some countries to onshore wind. More battery storage will be built and it will be performing different roles. The first wave of batteries was fulfilling near instantaneous response requirements, but as that is saturated, batteries will provide response more generally and play a role in alleviating grid constraints. But pure energy arbitrage opportunities are some way off requiring greater levels of renewables on the system.

Behind-the-meter investment will continue into generation and storage but, as tariffs structures are reviewed and amended to be more cost-reflective, then care will be needed when making long-term investments. But decentralization is not an objective in itself, it is an outcome.

When thinking of the end user more corporate PPAs are expected, although solutions will need to be found to manage counterparty risks if these are going to form an enduring option for bankable renewable investments. In addition, some corporates need to understand better what they are signing up to. Ultimately it comes back to the market.

And so inevitably to digitalization

Over our working lives, change from digitalization has always been there. Laptops, email, the internet, vast improvements in computing power; plus and an increasing reliance on algorithms and access to more data than we can manage. And now the extremely high interest ” and some would say hype ” in digitalization.

But perhaps one’s hope should be tempered by, for example, the utilities failures in the past to combine and manage simple customer data and billing systems adequately. What will they make of big data?

In last year’s article looking to 2018, Steve Martin of GE quoted the IEAs Digitalization and Energy report as saying that digitalization could save 5 per cent of annual power generation costs.

Given the fall in renewable and battery technology costs we’ve witnessed, this doesn’t sound very exciting; nor in the light of increasing volatility of commodity prices. Perhaps those technology cost falls have themselves have resulted from greater digitalization, but does that make the energy sector largely a recipient rather than a participant in the digitalization journey?

What can we expect from digitalisation in 2019?

The step-change that we see is two-fold: on the one-hand, computing power increasing and enabling greater levels of AI and machine learning; and secondly, the amount of data available for those computers to analyse. These two combine to mean that better decisions are made and are also automated.

These are the key areas we expect to see further progress in 2019 in digitalization in energy:

Fault prediction and dynamic maintenance: This is one of the clearest uses of AI which enables operators to predict equipment failures by using sensor data from various units to significantly reduce their costs of downtime and maintenance. Pàƒ¶yry has an offering for this called KRTI4.0. On the retail side, a startup Verv is offering a meter device which identifies individual home appliances and tries to predict faults or a device being accidentally left on by building up individual profiles from the meter data.

Investment optimisation: BP’s venture arm invested in an AI startup called Beyond Limits to dig through seismic images and geological models to increase the chances of success when drilling wells. Another example of longer-term investment decisions is the US Department of Energy project where machine learning is being used on satellite imagery and operation data to prioritise reinforcement at vulnerable points of the grid to improve resiliency.

Energy efficiency: Deepmind, which is a part of Google, has championed the use of Reinforcement Learning to reduce energy use in its data centres by a claimed 15 per cent. The model learnt by looking at years of operational data and then issued changes to individual units within the operating constraints of the plant.

Better prediction: Deepmind is also currently in talks with National Grid of the UK to better forecast demand of the system with the stated goal of reducing the entire country’s energy usage by 10 per cent. Another example is improved prediction of wind power production to reduce imbalance costs by 50 per cent which was achieved by a company called Swhere.

Trading: According to the FT, systematic and algorithmic trading now account for nearly 60 per cent of the traded volume on just the CME energy product group ” highest level of any commodity group. Anecdotal evidence from mid-2018 is that over 50 per cent of trades on the EPEX Spot intraday market are algo-trades (although the total volumes are still smaller than trades executed by human). Sophisticated machine learning models are also being deployed by speculators which are relying on large streams of diverse data to respond to the market changes quickly.

A more commercial example is Origami Energy using machine learning to predict asset availability and balancing mechanism market prices in near real time to successfully bid into the Frequency Response markets. Pàƒ¶yry is exploring a deep learning algorithm to support trading and dispatch decisions for generation assets in the prompt trading markets, focusing on the issue ‘when should I commit a trade’ (to maximise the option value of flexible capacity).

Retail: retailers are using machine learning to understand patterns of customer behaviour, to attract and retain customers and even to predict bill (non)-payment. Customer call centres are being fronted by algorithms which chat to customers (verbally or online) and deal with queries.

Customers: For customers, AI solutions are also gaining traction, and many retailers are offering these systems as part of an integrated package. Devices such as Amazon’s Alexa enable the customer to seamlessly interact with their thermostat (such as Centrica’s Hive). This increasing customer interaction with the device leads to the development of a more personalised usage profile, which reduces bills for the consumer and also helps the energy provider to accurately forecast demand.

It would seem then that the digitalization opportunities in energy are large. It will be a vital enabler of decarbonization is some areas in the future such as flexible demand shifting to meet supply.

The opportunities available rely heavily though on sufficient volumes of good quality data being available. So, expect more sensors and more data acquisition throughout the energy sector in 2019. And in time with growing autonomy expect the focus to switch to the appropriate monitoring, alerts and controls.

As succinctly put by computer scientist Andrew Ng: “AI is the new electricity ” enabling us to do more.”

Matt Brown is Head of Western Europe, Middle East and Americas, and Ravi Mahendra is an Analyst, at Pàƒ¶yry Management Consulting.

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