Clean Energy News: Q&A Open Energi discusses the future of demand response

Following the launch of the follow-up to its popular demand response product Dynamic Demand, Clean Energy News caught up with Open Energi technical director Michael Bironneau and commercial director David Hill to discuss the platform’s development, demand response’s role in the energy transition and how it will change in the future.

Q: How has Open Energi looked to develop Dynamic Demand 2.0, and what’s contributed to it?

Michael Bironneau (MB): Historically Open Energi has been involved in the control of thousands of distributed assets, and in order to do that we often had to do a lot of very manual work to model the asset or understand its control philosophy. Once we’d done that, we still had to understand how to predict its performance characteristics and forecast when it would be available to us. That’s why our data science team is one of the largest in the business.

Read the full article here.

How Artificial Intelligence is shaping the future of energy

Artificial Intelligence can unlock demand side flexibility for end users

Across the globe, energy systems are changing, creating unprecedented challenges for the organisations tasked with ensuring the lights stay on. In the UK, large fossil fuelled power stations are being replaced by increasing levels of widely distributed wind and solar generation. This renewable power is clean and free at the point of use but it cannot always be relied upon. To date National Grid has managed this intermittency by keeping polluting power stations online to make up the difference but Artificial Intelligence offers an alternative approach.

What’s needed is a smart grid which can integrate renewable energy efficiently at scale without having to keep polluting power stations online to manage intermittency. This requires energy storage to act as a buffer, reducing demand when supply is too low or increasing it when it is too high. Most people associate energy storage with batteries, but the cheapest and cleanest type of energy storage comes from flexibility in our demand for energy.

This demand-side flexibility takes advantage of thermal or pumped energy stored in everyday equipment and processes, from an office air-con unit, supermarket fridge or industrial furnace through to water pumped and stored in a local reservoir. The electricity consumption patterns of these types of devices are not necessarily time-critical. Provided they operate within certain parameters – such as room temperature or water levels – they can be flexible about when they use energy.

This means that when electricity demand outstrips supply, instead of ramping up a fossil fuelled power station, certain types of equipment can defer their electricity use temporarily. And if the wind blows and too much electricity is being supplied instead of paying wind farms to turn off we can ask equipment to use more now instead of later.

Making our demand for electricity “intelligent” in this way means we can provide vital capacity 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 demand-side flexibility so that consumers are a) not impacted and b) appropriately rewarded.

At Open Energi, we’ve been exploring how artificial intelligence and machine learning techniques can be leveraged to orchestrate massive amounts of demand-side flexibility – from industrial equipment, co-generation and battery storage systems – towards the one goal of creating a smarter grid.

We have spent the last 6 years working with some of the UK’s leading companies to manage their flexible demand in real-time and help balance electricity supply and demand UK-wide.  In this time, we have connected to over 3,500 assets at over 350 sites, operating invisibly deep with business processes, to enable equipment to switch on and off in response to fluctuations in supply and demand.

Already, we are well on the way to realising a smarter grid, but to unlock the full potential of demand-side flexibility, we need to adopt a portfolio level approach. Artifical intelligence and machine learning techniques are making this possible, enabling us to look across multiple assets on a customer site, and given all the operational parameters in place, make intelligent, real-time decisions to maximise their total flexibility and deliver the greatest value at any given moment in time.

For example, a supermarket may have solar panels on its roof and a battery installed on site, as well as flexibility inherent in its air-con and refrigeration systems. Using artificial intelligence and machine learning means we can find creative ways to reschedule the power consumption of many assets in synchrony, helping National Grid to balance the system while minimising the cost of consuming that power for energy users.

Lack of data is often an obstacle to progress but we collect between 10,000 and 25,000 messages per second relating to 30 different data points and perform tens of millions of switches per year. This data is forming the basis of a model which can look at a sequence of actions leading to the rescheduling of power consumption and make grid-scale predictions saying “this is what it would cost to take these actions”. The bleeding edge in deep reinforcement learning shows how, even with very large scale problems like this one, there are optimisation techniques we can use to minimise this cost beyond what traditional models would offer.

Artificial Intelligence model learning to control the electricity consumption of a portfolio of assets

Graph of AI model

More rapid progress could be made across the industry if energy companies made more anonymised half-hourly power data available. It would enable companies working on smart grid technologies to validate these ideas quickly and cheaply. In the same vein, it would be a major breakthrough for balancing electricity supply and demand if energy companies made available APIs for reporting and accessing flexibility; it would allow companies like Open Energi to unlock enormous amounts of demand-side flexibility and put it to good use balancing not just the grid but also helping to optimise the market positions of those same energy companies.

In the UK alone, we estimate there is 6 gigawatts of demand-side flexibility which can be shifted during the evening peak without affecting end users. Put into context, this is equivalent to roughly 10% of peak winter demand and larger than the expected output of the planned Hinkley Point C – the UK’s first new nuclear power station in generations.  Artificial Intelligence can help us to unlock this demand-side flexibility and build an electricity system fit for the future; one which cuts consumer bills, integrates renewable energy efficiently, and secures our energy supplies for generations to come.

Michael Bironneau is Technical Director at Open Energi. He graduated from Loughborough University in 2014 with a PhD in Mathematics and has been writing software since the age of 10.

Can a sharing economy approach to energy deliver a more sustainable future?

Sunshine through tree tops - green energy

As global demand for electricity grows, are there alternatives to building more power stations which make smarter use of existing infrastructure? And in an industry renowned for high levels of consumer mistrust, could an Airbnb of energy finally deliver a consumer-centric energy market?

Technology is shaping our lives like never before, making our world smarter, more efficient and more connected. In the last decade, it has fuelled an explosion of sharing economy business models — adopted by the likes of Uber, Airbnb and Zipcar — who in just a few short years have revolutionised established industries. But can a sharing economy approach help to tackle one of man-kind’s greatest challenges and deliver clean, affordable and secure energy to all?

Sharing economies are a consumer-led phenomenon which work by exploiting excess capacity or inefficiencies in existing systems for mutual benefit. Take Airbnb for example. The wasted asset is your property and the excess capacity is the space you are not using. By creating a user-friendly platform and giving homeowners the security they need Airbnb have built the biggest hotel chain in the world, surpassing the Intercontinental Group in less than four years. They have achieved this because they haven’t needed to construct a single thing.

So how could this apply to the energy industry? As global demand for electricity grows, are there alternatives to building more power stations which make smarter use of existing infrastructure? And in an industry renowned for high levels of consumer mistrust, could an Airbnb of energy finally deliver a consumer-centric energy market?

Since the world’s first power station was built in 1882 the global energy system has worked on the basis that supply must follow demand. Consumers — businesses and households — have been passive users of power, paying to use what they want when they want, whilst electricity supply has adapted to ensure the lights stay on. This has created inefficient systems built for periods of peak demand — in the UK this is typically between 4–7pm on a cold winter evening — which most of the time are massively underused.

But this is no longer the case. Today, our ability to connect and control anything from anywhere means we can manage our demand for electricity in previously unimaginable ways, and consumers are emerging as a driving force for change.

By connecting everyday equipment to a smart platform (just as you might upload your property to Airbnb), it’s now possible for consumers to take advantage of small amounts of “flexible demand” in their existing assets and processes — be it a fridge, a water pump, or an office air con unit — and sell it to organisations tasked with keeping the lights on — like National Grid.

Applying artificial intelligence and machine learning to govern when and for how long assets may respond gives consumers confidence their equipment’s performance will not be affected, and in return for sharing their “flexible demand”, they benefit from cost savings or direct payments.

This sharing economy approach relies on the power of tech and our ability to orchestrate many thousands of consumer devices at scale. Any one piece of equipment can only make small changes to the timing of its electricity consumption — e.g. delaying when a fridge motor comes on for a few minutes during a spike in electricity demand at the end of a football match — but collectively, the impact is transformational.

It means that when electricity demand is greater than supply, we don’t need to fire up fossil-fuelled power stations. Instead, we can reduce demand by asking non-time critical assets to power down for a short while.

If the wind is blowing and too much electricity is being supplied, we don’t need to let this clean, abundant power go to waste, but can ask equipment to shift its demand and make use of this power as it is available.

And we don’t need to keep building more power stations to meet occasional peaks in demand. Instead, we can distribute demand more intelligently throughout the day, reducing the size of these peaks and making better use of existing capacity.

In the UK, Open Energi’s analysis suggests there is 6 gigawatts of peak demand which can be shifted for up to an hour without impacting end users. Put into context, this is equivalent to roughly 10% of peak winter demand and larger than the expected output of the planned Hinkley Point C — the UK’s first new nuclear power station in generations.

This doesn’t make it easy. Unlike other sharing economy success stories, energy is a public good. The need for incredibly robust solutions means the barriers to entry are high. But, if we can get it right, the prize is enormous; a cleaner, cheaper, more secure energy system which gives consumers control of how, when, and from where they consume their energy.

Businesses have already recognised the power they hold and the benefits it can bring, with the likes of Sainsbury’s, Tarmac, United Utilities and Aggregate Industries adopting the tech and demonstrating what’s possible. Households look set to follow, but wherever the flexibility comes from, it’s clear that consumers and the environment will benefit from a sharing economy approach to energy.

David Hill is strategy director of Open Energi. He is an expert on electricity markets and demand-side flexibility, including demand-side response and energy storage. He joined Open Energi in 2010 after completing an MSc Energy, Trade & Finance at Cass Business School.