Solar Power Forecasting

The problems with solar power:

A major concern surrounding solar power is the variability and unpredictability of sunlight. If it is overcast or cloud cover present during the day, then the photovoltaic cells are unable to produce electricity, or will do so inefficiently. This inherent variability poses issues with grid reliability and the expenses associated with operating the solar units. Moreover, peak electricity demand usually occurs when it is dark outside, when solar production is zero. All of these factors make it difficult to predict the photovoltaic output of solar panels, and photovoltaic forecasting is a method used to address this issue.

How can solar forecasting help?

With an increasing number of installed utility-scale PV plants and a growing need for predictable energy generation, the solar industry has started paying attention to solar forecasting. The reasons behind this are:

1. Solar generation is variable in nature.
2. Being able to predict solar output will make the electric grid work better under variable conditions.

Essentially, solar forecasting provides a way for grid operators to predict and balance energy generation and consumption. Capabilities will vary from grid to grid, but the application of a solar forecast largely remains the same.

Methods:

A few key components make up solar forecasting tools. First, there is the weather model. As mentioned, solar generation is variable by nature. Cloud cover causes this variability by impeding sunlight from hitting the solar panels. If you can predict the weather with a great amount of certainty, you’re already one step ahead.

The second factor in a solar forecast is the model used to convert the weather into utility plant power output. The solar industry uses these “PV simulation” models to predict the performance of a PV plant under environmental conditions like irradiance, wind speed, temperature and relative humidity. PV simulation models also incorporate important plant behaviours such as tracking, which predicts the orientation of the PV panels mounted on single- or dual-axis tracking hardware.

Ultimately, accurate weather models and PV simulation tools are both needed to produce an accurate and useful solar energy forecast.

It’s all about the risk

Errors in solar forecast accuracy arise either from the weather prediction or PV simulation step. Forecasting is just one part of the puzzle – “what next” is the key issue. If you deviate from the forecast, should you be penalized, and by how much? These are key issues. There is inherently a limit to perfection in forecasting. On the other hand, tools are improving, so not only are average errors decreasing; the time-periods of confidence are improving as well.

For example, multiple weather forecast techniques can be used to predict cloud cover and irradiance. These could include:

• Numerical weather prediction (NWP) models, which use physical relationships to predict large-scale atmospheric trends and are good for longer (one day ahead and further) forecast horizons. NWP models have a limited capability to predict smaller clouds.
• Satellite cloud motion vector forecasts, which use satellite imagery to predict near-term cloud motion from geostationary satellites. Satellite forecasts are dominant in the short-term, typically up to 4-6-hours ahead, and are the best method to detect small clouds such as the ones.

Current status – steps taken:

Andhra Pradesh recently became the latest state to notify forecasting, scheduling and deviation settlement regulations for solar and wind power generation. It joins Karnataka, Chhattisgarh, Jharkhand and Uttarakhand, who have already announced these regulations. Six other states including Rajasthan, Gujarat, Madhya Pradesh, Tamil Nadu, Odisha and Manipur have announced draft regulations. Together, these states account for about 70% of operational and under development solar capacity. The primary objective of the new regulations is to make generators more accountable through enhanced forecasting requirements and penalizing them for deviation. Once operational, this should help facilitate large scale grid integration of intermittent renewable power while maintaining grid stability.