Open Energi’s software platform designed to ‘flex’ the consumption of loads and optimise energy storage assets lets commercial energy consumers take a proactive role in controlling energy costs.
Dynamic Demand 2.0, the latest software platform from UK aggregator Open Energi, uses artificial intelligence to coordinate and optimise distributed energy assets in real-time.
The company initially developed technology for monitoring and remotely controlling domestic fridges, which it then extended to other loads such as water pumps, air conditioning, generators and more recently battery storage and electric vehicle (EV) charging.
For energy storage the company has developed its own suite of controls – as part of our Dynamic Demand 2.0 technology platform – working with providers like Tesla.
“This is beneficial for customers that might have several battery systems from different suppliers, because Open Energi’s platform connects the different battery assets, providing one interface, simplifying their operation,” says commercial director David Hill.
Most battery storage systems in the UK are deployed by third party developers, installing behind the meter batteries and structuring power purchase agreements around various savings and revenue streams.
Open Energi has partnered with several of these to help optimise batteries. Dynamic Demand 2.0 includes state of charge (SoC) management, to optimise the battery’s performance depending on what functions or tasks it has to do.
“Dynamic Demand 2.0 is designed to eke out as much value from the battery every hour of the day,” Hill says.
One of the key functions of Dynamic Demand 2.0 is the software platform’s ability to operate fleets of assets, enabling flexibility in their consumption. A fleet could comprise several batteries at a few sites, or a mix of different loads and generation sources, including batteries, belonging to a customer, across multiple locations.
If, for example, the customer is contracted to supply a megawatt (MW) to the grid operator then the platform can shift this commitment around depending on various factors, such as the asset’s demand profile.
For the longevity of the battery it may be best not to meet that MW commitment by discharging it, so other assets in the fleet can be called upon.
The degree of machine-learning that Dynamic Demand 2.0 deploys has been developed on the back of seven years of data monitoring and controlling different assets for various customers at different locations in the UK, so the platform is continually learning how best to flex a fleet of assets.
Complex business case for energy storage in the UK
The business case for energy storage in the UK is complicated. In the US, for example, demand charge reduction has been a key driver for energy storage uptake among commercial energy users. For instance, a retailer might have energy storage installed at its locations to optimise onsite solar self-consumption, using energy from the battery to avoid incurring peak demand charges.
This type of driver is not so acute in the UK. The only way behind-the-meter batteries will pay for themselves is to generate revenues from grid services, as well as savings, through load shifting or peak shaving. These differ based on the constraints of each site.
However, as more renewable generation is integrated into the UK power system, users will incentivised to take demand off the grid. That means identifying how much demand can be defined as flexible – turned up or down, or shifted in time, which batteries can enable.
UK retailer Marks & Spencer is a recent example of one large commercial energy user committed to making as much as 50% of its peak energy demand flexible, helping save money in terms of avoiding demand related charges.
Energy suppliers are now starting to incentive customers to flex their energy consumption, through turning loads up or down, or time-shifting, which having battery storage installed can enable.
This is due to suppliers themselves are being more heavily penalised for missing forecast targets when purchasing power. It is cheaper for a supplier to get a customer to take a MW off the grid than it is to go and purchase that MW generated by a power station.
Hill says: “Flexibility will increase in value, but to convert more peak demand to flexible demand – 20% and beyond – requires more complex management of generation assets, such as onsite combined heat and power (CHP), battery storage, generators, as well as loads like water heating, refrigeration and other appliances and equipment.”
Key customers of Open Energi, including Aggregate Industries, Sainsbury’s and Tarmac, are fully aware that to be able to ‘flex’ more of the consumption is an important trend that will increase as more intermittent renewable energy generation is brought online.
Rather than be affected by the impact of exposure to higher costs, from the investment that will be required to build more expensive gas peaker capacity in future, large energy consumers can be part of the solution.
Offshore wind is both large-scale generation and is also getting cheaper. Open Energi has been working with Ørsted, which owns the biggest offshore wind portfolio, mostly in UK waters, to link its software technology to Ørsted’s new product for energy customers called Renewable Balancing Reserve (RBR).
Energy customers of the energy supplier wanting to consume electricity with higher renewables content, but with flexibility in supply and consumption can participate in RBR.
Dynamic Demand 2.0 can dispatch the flexibility in a reliable and accurate manner. As generation becomes increasingly volatile, it will become harder for suppliers to forecast supply and demand in real-time, so machine learning platforms like Dynamic Demand 2.0 will help ensure that flexibility from the customers’ side is properly exploited at any given moment.