RESEARCH
Forecasting Solar Power Generation:
Revise the Forecast when it requires
By Abhik Kumar Das, Del2infintity Energy Consulting Pvt. Ltd.
Forecasting and Scheduling (F&S) of solar power generation is an essential requirement of sustainable
energy mix for the stable grid system since the variability and unpredictability inherent to solar create a
threat to grid reliability due to balancing challenge in load and generation.
T
he integration of significant
solar energy into the existing
supply system is a challenge
for large scale renewable
energy penetration; hence
the day-ahead and short-term
renewable energy forecasting is
needed to effectively integrate
renewable power to the grid.
The most usable and
conventional strategies in F&S
of solar power generation are
predicting the weather parameters using of NWP (Numerical
Weather Prediction) models and converting the values of
weather parameters into power generation using thesolar
plant characteristics.Since the ensemble of daily solar power
generation shows a partial periodic nature, the use of ANN
(Artificial Neural Network) based forecasting strategies using NWP models is a widely accepted methodology in the
research community. The recent advancement of different
architectures of DNN (Deep Neural Network) has created a
huge scope in forecasting solar power generation with high
accuracy. Considering the uncertainty of different variables,
a good forecast does not produce a deterministic solution,
but produces different scenarios having different probability values and the scenario with optimum probability can
be considered minimizing the penalty due to the deviation.
Hence, fromtheoretical considerations, F&S of solar power
generation is best considered as the study of the temporal
evolution of probability distributions associated with variables
in the power generation.
Considering the regulations by CERC, FOR and other proposed regulations of different states of India, it is mandatory
requirements of power generators to submit the day-ahead
forecast of the power generation and if the error is more than
a specified limit, there exists a penalty due to the deviation.
For solar power forecasting 8 Intradayrevisions (one revision
in every 1 hour 30 minutes) are allowed and this revision is
effective from 4th time-block (1 time block = 15 minutes) onwards. It is interesting to see that the revision is not mandatory, but revision is permissible to tune the forecast accuracy.
Since solar power is variable in nature, the intraday revision in solar power is useful when there is an expectation
of unscheduled fluctuations due to which power generation
shows ramping behavior. The use of proper AI (artificial
intelligence) techniques using DNN and proper plant specific
localized solutions of NWP modelsmust be capable of forecasting not only solar power generation, but to decide when
the intraday revision in forecasting is required by predicting
and analyzing thesolar power ramping events.
A good forecasting methodology is not only a
solution having the high forecast accuracy and
low penalty due to the deviation, but a solution
to maintain the similar or better accuracy in the
minimum number of intra-day revisions. Multiple
revisions are useful when those are necessary, but
unnecessary submission of an intraday revision
in every one or two hours proves the incapability
of having proper pattern recognition techniques in
forecast models.When same or better forecasting
solution is possible in lesser number of revisions,
providing intraday revision in every one or two
hours can increase the indirect cost of power generators; and if this revision requires real time data
availability, multiple revisionsare not an economic
viable option for small capacity solar plants.
Though there is an argument that intraday revision increases the forecasting accuracy, but the proper generation
of patterns using DNN optimizes the number of revisions if
the expectation of unscheduled fluctuations is low. Intraday revisions are useful only when there is high possibility
of ramping events or the forecasting is using some simple
nonlinear models.
Hence power generators must utilize the ‘revision strategy’ intelligently using better forecast models which give
similar or better accuracy in the minimum number of intraday
revisions.