capSpire Blog

Especially in Europe, renewable energy generation—specifically, wind and solar energy—comprises a significant percentage of the overall energy-generation mix. According to Bloomberg New Energy Finance, newly installed renewable capacity was at 138.5 gigawatt (GW) in 2016—an increase from 127.5 GW in 2015 and 106 GW in 2014.

Electricity prices, which already exhibit large intraday variations because of the inelastic nature of the market, will show increasing pricing volatility as a result of this influx of renewables. The implication of this trend is that traders, risk managers, and modelers in electricity markets face major challenges when pricing renewables:

• How do you view or quantify the uncertainty around renewable forecasts of megawatt (MW) output over the next 48 hours? (This uncertainty is a combination of factors such as fluctuating weather and variances in generator efficiency levels.)
• If you participate in a day-ahead market, how much volume do you offer? Similarly, for intraday auctions and continuous trading scenarios, which trading strategy do you pursue given volume and delivery risks?

Managing Uncertainty: Study Historic Trading Data

All market participants need to formulate their trading and risk-mitigation strategies taking into consideration historic data:

• What is the correlation between observed renewable-generation levels and observed market prices?
• Are there any discernible grouping or clustering effects between wind levels and market prices? If so, what impact do other factors such as demand levels and thermal-generation margins—particularly the running schedules of thermal generators—have?
• Are there any noticeable behaviors or patterns around renewable-generation levels and bid/offer data?

Evaluating Uncertainty: Model Market Scenarios and Pricing

Once market participants start delving into and gaining insights from this data, the next step is to simulate market prices and then develop trading and risk-mitigation strategies around this modeling.

Although improvements in forecast accuracy relating to renewable output may not be on the immediate horizon, given the continued build-out of renewable energy generation in the marketplace and hence more volatile electricity-market prices, an increasing percentage of market participants will begin migrating toward dynamic trading and modeling strategies. For example, market participants will spend more time and effort trying to quantify pricing uncertainty.

What is clear is that market participants will need to invest in their IT infrastructure and their people. To participate effectively in the renewable-generation market, organizations will need to provide access to trading data in as close to real time as possible. They will also need trading and risk-management systems to enable seamless interaction with the market once data has been ingested and processed. Without a modeling team that has the skillset and expertise to ask the right questions and a trading/risk team to take advantage of gained insights, the aforementioned IT investment will be, to a large extent, negated.

With a unique combination of deep technical and business experience, capSpire is actively engaged in the entire pre-trade analysis lifecycle, from designing IT infrastructure to implementing and organizing data in warehouses, building analytics solutions and modeling simulations, and reporting. To learn more about how capSpire can assist your organization in pre-trade analysis, email info@capspire.com.