Manual vs Automated trading
In essence, optimising flexible assets in traded energy markets means trying to maximise (or minimise) the captured price for whatever energy can be sold (or bought) by the device in question: a gas power station, battery storage or just a single electric vehicle. In practice, it is often a highly complex exercise requiring processing and a combination of information from two distinct sources: the asset characteristics and market intel.
This cross-optimisation involves continuous calculation over different time horizons, as market opportunities (like Day Ahead auctions) and asset limits (such as ramp rates restrictions) must be planned against. A classic example would be a trader managing a gas power plant; assessing changing market conditions and working closely with plant operators to understand variable physical parameters, such as efficiency at different power outputs or the energy required to start up the generator.
This information must then be considered when selling energy into the power markets, and that involves a careful trade-off between physical and price considerations. For example, when low demand causes low prices overnight, plants must choose between shutting down in the evening then starting again in the morning or running through the night, selling power at a loss – whichever is the more economical.
Adapting to new technologies
However as storage, demand response and hydrogen production become the dominant tools for balancing the grid, this is also driving change in the methods for optimising assets. Novel technologies are being deployed at the megawatt or kilowatt level, not gigawatt, meaning many more assets will be involved in making up the required level of balancing capacity.
Each device has its own features and characteristics, such as power capacity or response time, as well as dynamically changing parameters impacting optimisation, eg state of charge (SoC) or energy recovery period. So it is clear when dealing with thousands of individual assets the complexity of the problem scales greatly.
Even for larger Front-of-the-Meter batteries, warranties are becoming increasingly complex to manage in real time; specifying rest periods or dynamically limiting depth of discharge, as opposed to simply warrantying a certain number of cycles per year. Also, many projects will be co-located with variable renewable power to exploit the benefits of a shared connection, effectively giving a dynamically varying export connection to factor into optimisation.
Pros and cons of automation
In this world, automation is fast becoming essential. Allowing the principles of continuous asset optimisation can be applied at a scale far below what would be economical for a human trader.
Open Energi has been trading fully algorithmically in the Day Ahead markets for over a year and has some key learnings.
On the face of it picking the highest and lowest hour in the day is simple (and fairly predictable). However, to maximise revenue you must also respond to more real time signals which occur within day (eg Triad), which alters your SoC from the planned schedule. This creates a problem when submitting bids in advance for the trading day before the current day has ended, as you do not yet know what SoC the battery will be at at the start of the trading day.
Automated fixes are able to easily correct for misalignments to get round this; however, doing so in the most economical fashion is harder. And the problem is exacerbated when dealing with more dynamically changing markets like the intraday continuous, which require thinking between different trading horizons.
Manual plus automated trading – a winning team
Overall, traders still have the upper hand on algorithms in areas like price formation, especially during extreme events like 4th March, and the best solutions will be ones that exploit the strengths of both. This is the principle of our solution Dynamic Demand 2.0 Trader, where full automation capabilities will perform the heavy lifting but oversight from Erova Energy’s 24hr trading desk provides the manual oversight and possibly intervention if greater opportunity is identified.
Ultimately, combining the strengths of power traders and algorithms provides the best optimisation. And ensuring each is capable of running independently provides the built-in resilience that proves its worth as COVID-19 pushes systems to their limits.
For a free consultation about automated trading, get in touch.
Written by Sebastian Blake.