Load Forecasts, Risk Management, and Utilities’ Billion Dollar Energy Procurement Losses
To hedge, or not to hedge?
For utilities, that’s a big question, with a complicated answer. Florida Power and Light has been in the news recently for losing an estimated $6Bn as a result of hedging it’s natural gas risk exposure. Puget Sound Energy lost $835MM from 2005-2015 by hedging gas prices as well.
Cue the uproar? Not so fast.
A quick definitional refresher is important to note here:
Hedging vs. Speculation
Hedging is described by regulators as trying to achieve price stability to protect customers from sharp run-ups in price.
“Hedging is a rational device to deter price spiking, like insurance against major catastrophic losses. It is a risk management device applied to assure steady pricing and the availability of supply at reasonable prices”. Washington Gas Light, Formal Case No. 874, Order No. 13221 (D.C. PSC 2004).
Taking a hedging position without a corresponding market to serve or trying to beat the market is speculation.
There is a laundry list of utilities that have gone bankrupt trying to beat the markets, but consumers absolutely need to be protected against unforeseen, short-term market price movements.
As just one of many examples, during the polar vortex in 2014, the wholesale price of electricity jumped from a nominal value of $30 / MWh to $2,600 – or roughly an 86X increase in electricity prices.
The only way to weather short-term fluctuations like these, and ensure long-term stability for all market participants (especially given the fixed-price nature of most residential electricity contracts today) is to work with regulators to develop a long-term risk management strategy for electricity procurement.
There are many factors and variables that go into this strategy, but at it’s core, the fundamental decisions are based on expected future load requirements.
But therein lies the problem.
Consumer patterns are changing, and predicting load requirements on a long-term basis is getting much more complex.
This has a huge impact on how much energy to buy, and at what price.
As we have discussed in two of our previous posts (see: The CFO Use Case Story, and the Impact of Load Analysis on Systems Planning), using a “top down”, or sampling approach to estimate future load is losing it’s effectiveness.
This is largely due to the adoption of rooftop solar panels, electric cars, increasingly energy efficient appliances, and demand response programs, which are accelerating and widening the gap between individual consumer’s demand profiles.
The key takeaway is that temperature, GDP, and market growth estimates only go so far at estimating load requirements.
- Now, utilities need to be able to factor in individual meter-level data with metrics beyond just temperature and dew point (i.e. sunlight and cloud cover – which are much more dynamic and complex).
- These factors impact both the consumer and utility generation side of the equation, causing steeper ramp-ups of demand, which can even lead to over-generation.
A great example of this is the “Duck Curve” in California, which highlights the significant changes in daytime load profiles caused by renewable energy.
- A recent UtilityDive report found that the Duck Curve’s belly is significantly outpacing projections, with the minimum daytime net load dropping 31% from 2011 to 2016, largely caused by solar, rather than distributed generation.
- Ramping hours, the “neck” of the Duck, were also greater than expected, with the three-hour ramp exceeding 5,000 MW 58% of the year in 2015, up from 6% in 2011.
The Answer is Clear
Thanks to the availability of much more granular data sources (like 15-minute interval data from smart meters and more robust weather data) along with advances in in-memory computing such as SAP HANA, utilities can now analyze their load in the granular and cost effective manner they need to combat and plan around these changing dynamics.
That’s why Utegration teamed up with one of the world’s preeminent data scientists to develop Enterprise Load Analysis on SAP HANA, which provides the ability to leverage all smart meter data and all other granular information such as hourly weather data.
Better Long-Term Trading Strategies Start With Better Inputs
By leveraging all this data, utilities can complete much more comprehensive what-if models and Monte Carlo simulations to identify how various inputs will impact future load requirements.
These future load profiles can be used to feed into energy procurement and risk management strategies, as well as ongoing monitoring, and compliance.
Said another way – from the utility’s trading perspective – you, your regulators, and your customers don’t want you to enter into a long-term contract to hedge $1Bn in natural gas exposure, if you only needed $950MM from the onset.
Getting it right
At the end of the day, the question is not “to hedge, or not to hedge”. The markets are unpredictable, and utilities shouldn’t be speculating. We all remember what happened to “The Smartest Guys in the Room”.
But regardless of if a utility’s management believes they need to hedge 50% or 40% of future demand, or simply enter into long-term forward contracts, the most critical factor in this equation is to get the future demand right – and regulators agree.
Either way, you want to be as through and comprehensive as possible to document your assumptions transparently – at the end of the day, it’s what differentiates between speculation, and hedging.
Click the link below to schedule a demo, and let show you how Enterprise Load Analysis will transform your business’ finance and regulatory process, energy procurement, and system planning. In the meantime, stay tuned for our upcoming white paper on The Modern Power Grid.