How do weather forecasts improve renewable energy output?

How do weather forecasts improve renewable energy output?

Renewable energy can be a very volatile source of energy, with the unpredictability of the weather producing an unpredictable source of energy. Resource variability can result in production swings, higher costs, and negatively affect grid stability. This is where weather forecasting steps in the value of forecasting lies in helping to achieve higher energy yields, lower levels of imbalance and safer operations. High-resolution, probabilistic weather forecasts can convert uncertainty into actionable decisions for solar, wind, and hybrid sites. It addresses the accuracy of data, can action lower curtailment and tighter reserve needs, while encouraging smarter trading and making sense of data sources.

Weather forecasting is also a vital tool in the green energy transition, it can help to increase renewable energy output and revenue by improving operations, trading, and grid integration.

Forecast types and time horizons for trading and revenue optimisation

Nowcasting minutes is a crucial aspect of accurate forecasting, as it allows traders to forecast demand vs. energy generation down to as low as to 6 hourly increments for dispatch and intraday trading which is To make sure renewable sources are reliable for trading and to increase revenue, short-term day-ahead unit generation can be scheduled to be turned off or on for the next day, with for unit commitment and bids submitted by generators for at the least cost for those specific units.

To help manage risk, medium outlooks allow traders to look ahead in yearly increments for forward planning. In contrast, seasonal outlooks allow for monthly forecasts that might affect pricing, both of these methods are what’s known as hedging.

Imbalance and PPA risk mitigation can be achieved with continuous updates, usually via analysing unusual energy behaviours using real-time data. AI and market intelligence can also be harnessed to constantly update flexible PPAs to ensure the best rates for energy. 

Key variables that drive output

In the case of some renewable energy methods, output is driven by the sun, so an interruption of that source will produce variable levels of energy. 

Solar

When weather changes, such as cloud cover, occur, they can shield the sun from the photovoltaic panels, reducing the amount of power generated. Aerosols and soiling of the panels can have a similar effect, while high temperatures can prevent the solar panels from working altogether. Global horizontal irradiance refers to sunlight scattered by the atmosphere which reaches the surface of the photovoltaic panel, not enough, and the panel cannot generate the required levels of energy. 

Wind

The higher a wind turbine is, the faster the turbine blades turn, producing more energy. Hub-height wind measures the height of the point where the blades attach to the turbine. The problem with higher wind turbines is that they can experience shear, which is a non-uniform load of wind on the turbine blades, caused by turbulence, which is the random increase and decrease of wind speed, which overall damages the blades and can affect output. Wake effects can also affect output, this is when a turbine sitting in the slipstream of a turbine in front of it experiences turbulent wind sources in the ‘wind shadow’ of the turbine in front of it. 

Hydro and wave

Hydro is an energy source driven by water, so it makes sense that when there are changes in water behaviour, such as precipitation, rain, hail, sleet or snow, it affects output. Snowpack can affect hydro energy because it freezes available water sources and prevents water from reaching the hydroplants, preventing them from generating energy, but snow melting can also have an impact, as when snow melts, it can affect the catchment area from which the water enters the reservoir, potentially flooding the hydroplant with water. 

Using forecasts to lift solar output

To increase solar output, certain techniques can be employed to improve yield, such as trackers, which allow movement of solar panels so that they track alongside the direction of sunlight to increase the amount reaching the panels. You can also employ solar clipping, which involves overseeing a solar inverter in the fashion of a larger array to increase solar performance and generate more energy. Some plants may target overheating management, as solar panels' optimum operating temperature is 20-25 degrees, which involves improving elements of solar array design, such as improving expansion vessel components, or improving shading and insulation of panels.  

Using forecasts to lift wind output

Wind output can be increased by addressing the issues that prevent wind turbines from operating at optimum levels. This could be via optimising the yaw and pitch of turbines by looking at the position of rotors with the wind or addressing fatigue and damage associated with higher loads. Power operators could also reconfigure the positioning of wind turbines so that they are misaligned with the wind stream, away from the turbine, further down from them that are causing wake loss, this technique is called wind wake steering. 

Grid integration and balancing

For the grid to correctly integrate new sources of energy and balance these new sources, right-sizing reserves involves managing the operation of energy reserve procedures so that appropriate amounts of reserve energy are available if needed. Providers can also help to reduce curtailment by improving the electricity grid infrastructure, such as integrating smart grid technology and battery storage systems, as well as addressing demand vs response to make energy consumption more flexible for consumers.

Congestion forecasts allow for re-dispatch, which involves redistributing energy sources from overloaded areas to under-resourced areas to prevent congestion, and topology planning, which targets more long-term solutions, such as restructuring the grid by adding new infrastructure, preventing bottlenecks.

Implementation roadmap

There is hope on the horizon for the energy industry in the form of optimised data capture, analysis and implementation. Real-time monitoring can feed into a SCADA (Supervisory Control and Data Acquisition) system, which can help to balance grids via the collection and analysis of data from sensors on factors such as solar irradiance or wind speed, informing demand response programs. Machine learning may also be integrated for monitoring to help with retraining systems to optimise them, identifying and reacting to issues before they happen, such as drift alerts, which occur when a business exceeds its energy usage on a specific system. Power plant owners may also chose to streamline the data-gathering process and make forecasting more accurate by using APIs to gather source data.

In summary, better forecasts turn volatility into value: optimise solar and wind, reduce curtailment and imbalance risk, and integrate renewables more safely into the grid.