Solar generation and intraday volatility: ramps, clouds and price shocks

One of these factors is ramps: when production drops off, requiring alternative sources to fill the demand shortfall created by solar. However, during the day, solar generation is at its highest, when demand tends to drop off. This can also cause issues for the grid, as prices can drop to very low or even negative levels, which is known as price cannibalisation. 

We can mitigate the downsides of solar in the intraday market through battery storage, simple trading strategies, and interconnected grids. However, it's worth noting that these can also cause geographic price shocks across side-by-side regions. Here, we explain how solar generation reshapes intraday price curves, creating predictable yet volatile ramps, particularly in summer months.

How solar changes the intraday price shape

The output profiles of solar and price are closely linked, as solar energy cycles follow a fairly repetitive pattern, with generation peaking during the midday sun. This high energy production affects the entire energy market, flooding the grid and driving prices down to levels below average. This is called the duck curve.

However, this predictable pattern is sometimes interrupted by cloudy cover or other mitigating weather factors. This causes forecasting errors when predicted solar levels are not met, requiring alternative power from other sources to stabilise the grid, which can drive up costs. But it may not have the grid that requires stabilising: pricing in the intraday market sees numerous small adjustments throughout the day to keep it stable, too. 

The midday collapse and evening ramp

This pattern of pricing falling due to overproduction of solar power is also known as midday collapse. The reason prices fall so drastically is not just the higher solar energy production midday, but also the lower demand from consumers. The morning, when industrial start-up occurs, is a high-demand period for power and becomes a pressure point, as does the evening, when residential demand tends to be the highest. This is particularly during the darker, colder winter months, when lighting and heating requirements are higher, and we call this the evening ramp. However, midday periods aren't classified as high demand, and these two patterns of high production vs. Low demand result in a specific curve known as a duck curve. 

Trading around these price distortions can be difficult, which is why many market participants use financial techniques to mitigate the risk of price drops and spikes, for example, through intraday trading, where pricing is continuously adjusted, and hedging, where forecasting is integrated into trading strategies.

Cloud-driven forecast error and local volatility

Cloud cover is a key challenge in intraday solar trading, as successful intraday trading requires pricing to be adjusted based on accurate short-term forecasting. Cloud cover can affect solar production, dropping up to 90% when sunlight is blocked, resulting in very short-term volatility and creating short-term forecast challenges for traders. This also means grids require a lot of expensive backup power in reserve. To mitigate this, some traders are integrating machine learning to automate core pattern recognition and forecasting. 

Local volatility can be managed through demand response techniques: this involves optimising grids with solar forecasting so that high-energy tasks, such as industrial processes, are scheduled to occur when solar is at its peak. 

Storage techniques such as quick-response batteries can also stabilise intraday pricing by supplementing high-demand periods when energy production is low, also known as shaving. This can still be quite costly, however. A more cost-effective option would be to co-locate solar and wind to exploit their differing optimum operating conditions. 

Seasonal solar volatility patterns

Certain regions see higher curves than others, for example, in very high-producing solar regions such as California. California's evening ramps are rapid, creating steep duck curves of up to 13GW in 4 hours. There are also effects from phenomena such as the Cloud Edge effect, where sunlight is refracted and concentrated at the edge of clouds, increasing output levels above those expected. Certain cloud formations found in conveyed clouds can have the opposite effect, causing downward ramps.

In Southern Europe, abundant sunlight creates many opportunities for solar generation. However, Europe is subject to differing geopolitics and legislative differences that make cross-border grid collaboration difficult. 

Trading implications for solar-heavy systems

All of these competing challenges can have measurable implications for trading. Factors such as forecasting errors can have an everyday impact on intraday pricing, increasing balancing costs and, in turn, increasing traders' risks. Strategies that traders can incorporate include advanced forecasting using high-resolution meteorological data at a granular level, as close as 10 minutes apart, and utilising AI. AI models can help to predict ramps before they occur, reducing risk. From a hardware perspective, trading can be improved by using more modern inverters and increasing the number of smart meters to better manage demand.