What do you need to profit from flexibility?

Five steps to market your flexible assets

One of the hottest topics in energy trading today is the marketing of flexibilities. Energy providers and public utilities can and should make the most of their flexible assets in order to maximize their revenue potential. The first decision they face is whether they should set up their own algo-trading operations, or go with a service provider who trades on their behalf. This decision can have major financial and operational impacts. 

Nearly every energy provider has flexible assets, which we’ve discussed in detail in a previous article. These include most forms of power plants and energy storage facilities, but also demand-side management. There is even flexibility in the charging of electric vehicles. So you might have more flexibility than you realize.

There are many options to profit from flexibility, but the most common today are balancing markets and, more recently, continuous intraday markets. The latter is highly attractive due to lower barriers to entry and its transparent nature.

However, low barriers to entry do not mean there are no barriers. If you implement software to manage your own trading, you have to understand that it’s not simply a plug-and-play scenario. And building your own software is even more complex. Based on our own experience with market entry as well as years of experience working with energy providers, we will give you an idea of what you will need in order to develop successful algo-trading operations. Granted, this assumes starting from scratch; you might have a few of these elements in place already. Still, to make an informed decision you need to be aware of how much effort is involved.

To be successful you will need five things: 

1. Access to short-term markets
2. Automated trading setup
3. Data and quantitative analytics
4. People with the right mindset & skills
5. The organization to support it all
   
   

1. Market access to get involved in power trading

In order to trade on the intraday market, of course, you must have access to the market. This is not for the faint of heart! You can expect this process alone to take around four to six months, including

• Get the energy market to know you - Energy Identification Code (EIC), register with ACER and national regulatory authorities, sometimes market-specific codes
• Apply for membership with a spot exchange (e.g. EPEX SPOT, Nord Pool) - choose your markets, technical setup and get some trader licenses (even if trading is 100% automated)
• Find a clearing bank and apply for membership with the Central Counterparty (CCP) - set up accounts, post collaterals, and agree on limits
• Register with TSO(s) for balancing group(s) - sign balancing contract, provide expected MW & MWh, post collaterals, and implement market communication processes

Be prepared to sign lots and lots of documents from KYCs to REMIT reporting services.

2. Software that makes the magic happen

In order to take advantage of your flexible asset, you may need an automated trading solution. With an ever-growing number of products and increasing volatility, today’s intraday market can hardly be managed effectively with manual trading. The majority of transactions on European markets now take place using APIs - for instance back in 2019, EPEX already reported the number at 65% - which means that a large portion of market participants are using algorithmic trading tools. A human trader simply can’t match the speed of a bot, and will not be able to react fast enough when market expectations change.

Several components are needed to perform the core function of automated execution. In the case of purchased software, these are typically provided by the vendor. First of all you’ll need an API interface to the exchange, which has been thoroughly conformance tested and certified by the exchange. The intelligence of the system is provided by trading algorithms, which are trading strategies turned into code. They make decisions about when to buy and sell how much at which price, taking into account a number of factors from market restrictions to technical factors of the physical power assets. For risk management, you must also have volume and price limits as well as automated shutoff, which provide a watchdog function to keep your market activities under your control. And of course, a user interface is needed to monitor trading activity and to configure or customize your strategies.

Whether you build or buy, your in-house trading operation will need some substantial elements beyond the core functionality that you’ll have to address yourself. Most critical are a large number of interfaces: automated trading needs to connect directly with a wide variety of different systems, and the complexity of this task is easy to underestimate. Algorithms make decisions based on huge volumes of data, so you’ll need interfaces for each of the data sources described in more detail below. Not only that, but your algo-trading system needs to talk to your ETRM system, to power plant management, to your scheduling system and much more. You will probably want a customized dashboard to monitor and report results. Finally, you need a backtesting environment, fed with the necessary data and evaluation tools, to validate and test algorithms before they go live and make sure they behave as expected. 

3. Data to feed your algos

Algorithms are only as smart as the data that feeds them. Successful trading strategies need a variety of data. This begins with order book and price data. The data itself comes as part of market access but needs to be fed into the algorithms. Purchased software comes with the interface, but if you build your own algo-trader it’s up to you. Other fundamental data include wind and photovoltaic forecasts, power consumption and load forecasts, import/export capacities, ancillary services data and power plant availabilities. To boost algo performance even further, more advanced data may be employed, such as satellite images, infrared imaging of power plants or magnetic field measurements of interconnectors for real-time grid data.

Why is all this data so important? Because you’re teaching a system to make a decision like a human trader. And a normal trader is also immersed in data, probably getting input from many different screens simultaneously to make an informed decision.

The key to successful trading is to identify signals that predict price trends, the reasons behind short-term price movements and the volatility that can lead to significant loss or profit. A trader looks at data to explain market behavior, then forms and tests hypotheses. Why did markets trade up yesterday but stayed level today? Which drivers are causing prices to go up? Is this a valid signal? How often is it correct?

In order to accomplish this level of analysis with automated trading, you really need data analytics. Markets change; a strategy that performs well today might lag behind tomorrow. Signals and the analysis behind them can represent a strong competitive advantage. So a system to sift through vast amounts of data and test potential signals is a significant and vital ongoing process. 

4. People with the right skills

Automated trading solutions require a team of specialists to operate, with skills that the average energy company might not have in place.

Traders, freed from the repetitive work of manual execution, together with quantitative analysts, monitor algo behavior and watch the market in order to develop new strategies. They then work with algorithm developers and data scientists to test and implement these strategies. It’s important to understand that algo-trading is a commitment in development: the markets are constantly changing, and trading strategies must evolve to match.

IT support is not such an issue for the trading software itself, since most are now delivered as SaaS, but all the interfaces must be supported and data must be stored, cleaned, aggregated and made useful for algo development and backtesting.

Risk managers have to understand how algo trading is different from manual trading, and that risk management must be tailored accordingly with sound policies and compliance frameworks.

Since trading algorithms tend to make a much larger number of smaller trades than a human trader, finance and back-office will suddenly face many more transactions that must be reconciled against data from the exchange, clearing and nominations. They need to prepare for the extra workload in advance.

And finally, even though algo-trading means you might not need a whole trading team 24/7, you will at least need someone on call for dispatch and dealing with exchange outages.

5. Organization for high-volume trading

You might now start to realize that all of this requires organizational support. Algo-trading actually needs a different mindset across the entire business. Every process in the trading value chain will have to be aligned with algo-trading: power plant dispatch, lead times, nomination, and on and on, will all need to be evaluated and possibly re-engineered.

A common sticking point when implementing algo-trading is risk management. Decisions are carried out in a different way. You will have to define a very precise risk management policy and put suitable compliance and risk methods in place.

An approval process for new algorithms will also be needed with criteria, a detailed test process, and a simulation environment.

Last but not least there’s the sticky issue of PnL and incentive structure. Who gets credit for the success of an algorithm?

Choose your destiny!

The decision of how to market your flexibilities should be made carefully and deliberately. While the advantages of trading on intraday markets are clear, it’s all too easy to underestimate the effort required, especially when you choose to run your own trading setup, as well as the impact it will have on the entire organization. This does not mean that making or buying software is the wrong choice; it’s a trade-off between simplicity and control and ultimately depends on your strategic goals.

However, you don’t necessarily have to trade everything the same way. Many companies use software for some cases but a service provider for others. Power, gas, both? Intraday markets only or also balancing, auctions, etc.? All your assets or just certain ones? There are many ways to distribute your flexibility marketing to suit your needs.

As you can see, implementing algo-trading in your organization, whether you purchase software or build your own, can get very complicated. If you are starting from scratch, the full cost can be around €1M/year or more. You’ll also need a team to support it: enough traders / on-call dispatchers to ensure 24/7 coverage, plus IT resources, at least one quantitative analyst and a data scientist.

One other factor in the decision might not be so obvious. Service providers who focus on trading are experienced traders themselves. As a result, they can offer valuable advice and knowledge exchange with their customers. This cooperative approach can have a significant impact on the success of your flexibility marketing and your bottom line.

In some cases running your own trading operations will be the right decision. If you choose a service provider you can outsource 80% or more of the complexity. In exchange, you might feel like you’re losing some control over your trading behavior, but at the same time you’re handing it over to a highly experienced partner who is motivated to make you successful, and who should be transparent about their approach.

Choosing to monetize your flexible assets is a wise decision that can deliver a great deal of added value to your organization. Just be sure that you plan well and do your homework. And if you have any questions, we’re happy to advise you. Book a non-binding call today to learn more.