Options for reducing energy spend through active procurement strategies and monetising flexibility in near term markets are discussed by Paul Conlon, Head of Modelling and Forecasting at Grid Beyond.
Procurement of energy can be considered a complex and risky process. Potentially starting years ahead, a poorly managed procurement strategy could render a company’s budget inaccurate at best and at worse threaten the future viability of the business.
With unprecedented volatility in energy markets across the world since late 2021, managing energy expenditure has become a major concern.
This is unlikely to be a temporary situation and industry analysts are expecting that the situation could persist into the 2030s and beyond.
At the same time, there is growing pressure to act on emissions and climate change. With natural gas acting as a transition fuel in the journey to a low carbon future it is of added significance that much of the volatility seen in energy markets has occurred in the gas market, which in turn filters through to electricity prices.
Although the forward price for gas and electricity are currently in ‘backwardation’, i.e. lower in the future than the current spot market level, prices as far ahead as 2025 are likely to remain significantly above their long-term average price zone.
Companies need an energy strategy that tackles all of these challenges, protecting them from the risks that threaten their operations as well as addressing the climate issue.
The challenges are linked and can be solved together with the right energy procurement strategy and technology to actively manage risk exposure, regardless of whether your objective is budget certainty, budget improvement, price optimisation or a combination of all three.
Energy procurement should focus on controlling costs and managing risk. While exposure to energy price volatility cannot be fully eliminated, measures can be taken that effectively manage the risk to a business financial bottom line.
The first step to achieving this, is to understand how companies are affected by volatility in energy pricing. A well-defined energy risk management policy should protect businesses against the volatility of energy markets and set the framework for energy price management decisions.
Organisations may also have flexibility in their energy use through assets they may have at their disposal. This can lead to cost savings and potential revenue earning opportunities that can offset energy expenditure if managed in the correct way.
A modern procurement strategy requires the right combination of fixing (hedging) and flexing to maximise all opportunities across both long- and short-term markets. Such an approach is summarised in Figure 1.
Hedging via futures (forward) markets
The primary reason for hedging is to provide an acceptable level of certainty over future financial outcomes. Having a fixed proportion of energy purchased in advance at a price that meets budgetary needs can help protect businesses from unexpected changes in the market and prevent sleepless nights worrying about where prices might be going.
But no one likes paying more than they need to and this is where forecasting plays a part.
The most publicly accessible forecast of electricity and gas prices is the forward curve which is a condensation of market participants forecasts, weighted by their willingness to trade at prices diverging from those forecasts. In essence, it represents what both buyers and sellers agree (via transaction) that the future price of gas or electricity is at that instant in time, (though subject to change in the next instant). The curve offers a single price point for any given future period however it does not show the sensitivity of the pricing around that point.
A more useful forecast however would provide a probability distribution of prices at a future point in time. Such a forecast uses percentile lines to show both the level of sensitivity around the average price and where the outlying prices might land under less likely scenarios (Figure 2).
Probabilistic forecasting can help influence hedging decisions about when and how much to hedge. It can also feed into answering questions like: is it worth waiting for the market to move before hedging or should my position be left open to the spot market?
Active position management within risk limits
Once a decision to hedge has been executed it is not necessarily the end of the journey. An active trading strategy can allow market participants to take advantage of falling prices to buy or sell in smaller blocks, benefitting from price fluctuations in the market and locking in prices at close to optimum time, while managing risk within pre-agreed limits.
Figure 3 demonstrates how such an active trading strategy resulted in a customer saving 59% relative to the average market price over the period October 2021 to October 2022. Such a strategy maximises value arising from market volatility whilst all the time measuring and containing risk.
Optimised asset scheduling to reduce energy cost
Even then that is not the end. Typically, large energy users will leave 10% to 25% of their volume as unfixed MWh floating on the wholesale market. Companies can leverage this exposure, utilising existing and/or new assets to gain maximum benefit in near term markets, in the following ways:
- Flexible demand side assets can be turned down in response to market price signals from +24hrs down to near real time
- On-site back up generation (where it exists) can be run to avoid extreme peak prices
- Behind the meter battery storage assets can be charged at low price periods and discharged during the peaks to earn revenue from the price arbitrage available.
Automated asset control coupled with short term load and price forecasts is critical to achieving the best outcomes in terms of generating value from flexible assets, as is an understanding of the latent flexibility in such assets, allowing your facility to continue critical processes while benefitting from flexibility in less critical assets.Best results are achieved when an organisation leverages all three of the above options, which entails solving an optimisation problem across multiple markets and timeframes often requiring re-optimisation as new information becomes available closer to real time.
Such problems are best solved using technology and algorithmic recommendation engines, while still leaving the final decision-making step open to traders.
ABOUT THE AUTHOR
Paul Conlon heads up the modelling function at Grid Beyond. He is an expert in a broad spectrum of analytical techniques that are applied to gas and power markets and has worked in a variety of roles covering market design, regulation, and trading. Previously he worked with Ireland’s ESB Generation and Trading.