• • • ENERGY EFFICIENCY • • •
Al-driven risk management for optimised energy trading
Wholesale electricity price volatility continues to increase as a result and corre- lated to the unprecedented changes in weather patterns and renewables penetra- tion, says Krishnan Kasiviswanathan, chief operating offi cer at Innowatts
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Energy traders and regulated utilities are experiencing an increasingly complex challenge in managing their market
positions to maintain profi tability and attain key performance metrics. One example from the retail energy side of the equation is the measurement of the mean absolute percentage error (MAPE). This is a measure the industry has become accus- tomed to leveraging when forecasting energy con- sumption and the related procurement of supply. The fact is wholesale electricity price volatility is a risk that carries the potential to cost millions in pounds/dollars in the event of uncovered market po- sitions – a reality that became all too real for several energy suppliers this year – and the consequences of which can be devasting.
ENERGY RISK – THE POTEN- TIAL COST OF RISK MIS- MANAGEMENT
In recent months, inadequate load forecasting and risk management has seen several energy suppliers over-exposed to volatile spot market prices. In Feb- ruary for example, severe weather caused ERCOT’s wholesale prices to soar to $9,500 (£6,735) per MWh. Following the event, Brazos Electric, alongside several other suppliers, fi led for bankruptcy after the company received $2.1 billion (£1.49bn) in unex- pected bills from the grid operator.
The ability to leverage near-real time meter-level customer data and other external data resources
electricalengieneeringmagazine.co.uk
through new, proven-at-scale AI-driven platforms, such as Innowatts, provides energy suppliers with the ability to enhance their risk management capabilities, reducing the potential for over-exposure. Alongside other related workfl ows such as load forecasting, meter-level insights and analytics it also better informs customer engagement and short- and long-term forecasting planning efforts. The Innowatts’ platform integrates directly with any energy supplier’s energy trade and risk management systems to provide traders with a holistic view of all of their positions. This capability coupled with the integration of AI technologies provides traders with the option to run a variety of ‘what-if’ scenarios on the data. This provides a much more informed trading position that enables data-driven decisions that better assess the various generation sources available, and positions taken or contemplated.
WHILE BENEFICIAL – OPTIMISE LOAD FORECAST- ING AS A FIRST STEP
A critical fi rst step to take before introducing more advanced risk management practices is to optimise load forecasting capabilities. Afterall, risk manage- ment insights are only as good as the load forecast- ing data that it resides upon; see Chart 1. Optimising load forecasting does not necessarily equate to acquiring new data, nor a need for smart meter data, rather, the primary focus is on exploiting what is already available to seek out additional gran-
ularity.
There are multiple sources of data that can create greater transparency and offer energy suppliers valua- ble insights to the data that currently exists. For exam- ple, substation and feeder metrics, meter data, billing history, wholesale pricing, socioeconomic and other related demographic data, alongside generation and weather forecasts can all be used by the Innowatts platform to create more accurate load forecasts. This provides energy suppliers and traders the fl exibility to build out the platform’s common data model, de- pending on the data that they are most interested in analysing.
For those energy suppliers that have a large port- folio of customers the AI driven algorithms can be executed on any customer segment(s) to reap the benefi ts of near real-time meter data. These options combined create a centralised forecasting analytical solution that is unprecedented.
A STRATEGIC AND COMPETITIVE EDGE
In an increasingly competitive and volatile landscape, accurate load forecasting and optimised risk manage- ment is an essential capability for all energy suppliers and vertically integrated utilities across the globe. With advanced data analytics, the most mature en- ergy supplier trading desks will not only be competing to secure the best possible price and market position but have a mission to drive down the margin of error and increase profi tability or rate payer returns.
ELECTRICAL ENGINEERING • SEPTEMBER 2021 35
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