Enhancing Singapore’s Solar Adoption and Grid Reliability with Solar Forecasting Tools
03 Nov 2022
The trial for a Solar Forecasting Model
to anticipate solar intermittency and enhance Singapore’s power grid resilience
has been successfully completed.
2 Supported by the Energy Market
Authority (EMA) and the Meteorological Service Singapore (MSS) of the National
Environment Agency (NEA), and developed by the Solar Energy Research Institute
of Singapore (SERIS) at the National University of Singapore (NUS), the model completed
its one-year trial at EMA’s Power System Control Centre in September this year.
First of its kind, the model is able to forecast Singapore’s island-wide solar
irradiance up to one hour ahead, with an average error rate lower than 10%, one of the lowest for solar forecasting in the tropics.
3 Unlike power generation
plants, solar power generation cannot be moderated according to energy demand.
Its power generation is dependent on Singapore’s tropical weather conditions
which fluctuate depending on environmental factors such as cloud cover, rain,
and humidity. This can lead to imbalances between electricity demand and supply
output from solar photovoltaic (PV) systems.
4 The Model would allow
EMA, as Singapore’s power system operator, to anticipate the solar power output
ahead of time and take pre-emptive actions to manage solar intermittency and
balance the power grid. This is another step towards maintaining grid
reliability as we scale-up solar deployment in Singapore. It also allows the electricity market to procure
additional reserves or adjust the output of power generation plants and energy
storage systems to increase electricity supply ahead of time to meet demand.
5 “In tandem with the
Singapore Green Plan 2030 and to advance Singapore’s energy transition,
Singapore aims to deploy at least 2 gigawatt-peak (GWp) of solar capacity by
2030. A reliable solar forecasting model to predict solar irradiance, will enhance
Singapore’s grid resilience and flexibility while supporting the deployment of
additional solar capacity. This goes a long way in supporting our solar
ambition and enhancing the resilience of our power grid.” said Mr Ngiam Shih
Chun, Chief Executive, EMA.
6 Dr Thomas Reindl, Deputy
Chief Executive Officer at SERIS, who led the project team said: “Forecasting
of solar irradiance is increasingly being required by Asian power grid
operators from owners of large-scale solar power systems. Therefore, the developed
model has strong potential to be scaled up and commercialised to support the
operations of solar farms across the region.”
7 Following the completion
of the trial, EMA is upgrading its Energy Management System (EMS) to
incorporate solar generation forecasts produced by the Solar Forecasting Model
by 2023. These forecasts would also be provided to the Energy Market Company
(EMC), Singapore’s wholesale electricity market operator, to be factored into
the market clearing process to ensure more accurate dispatch schedules for
power generators to meet power system demand. For more details, please refer to
EMC’s information here: https://www.emcsg.com/f2165,162493/EMC371-EMA-CY.pdf.
Annex A: Images of Solar Forecasting Model
 The Solar Forecasting Model utilises data from real-time irradiance sensors installed on rooftops of buildings and electrical substations across Singapore. It also incorporates numerous dynamic solar forecasting techniques such as satellite imagery and machine learning algorithms. Combining outputs from MSS’ numerical weather prediction system, known as SINGV, the Solar Forecasting Model is able to aggregate the various types of data to generate round-the-clock solar irradiance forecasts at regular intervals from 5 minutes to 24 hours ahead of schedules.
 The Solar Forecasting Model has a nRMSE (normalised root mean square error) of lower than 10% up to 1 hour ahead, on average.
Back to Top