Given the variable nature of solar photovoltaics (PV) production, accurate forecasting becomes essential for energy system integration and control. This chapter provides a comprehensive exploration of different aspects associated with PV forecasting for energy system integration and control. In this regard, we first introduce different temporal resolutions in PV forecasting and then provide information on how PV is integrated into the energy system. Another topic discussed in this chapter is the use of PV forecasting to facilitate the integration of PV systems together with other renewable energy sources and energy storage into energy system. This chapter highlights how PV forecasting techniques can enhance grid flexibility, allowing infrastructure to respond to fluctuations in solar PV generation. It also explores the role of PV forecasting in energy management. By forecasting changes in PV output, system operators can better align energy demand with supply through demand-side measures such as load shifting and demand response, and generation-side strategies, including flexible dispatch and dynamic generation scheduling. Regulatory and policy frameworks also play a crucial role in enabling the integration of PV forecasting into energy systems control. This chapter also reviews policies and incentives that have been implemented to promote the adoption of PV forecasting technologies. Lastly, to illustrate the practical applications and benefits of PV forecasting, this chapter presents three case studies: a robust unit commitment model using uncertainty sets tailored to solar variability; a coordinated voltage control strategy using forecast-based scheduling of local assets; and an energy management system for an EV charging station, where forecast accuracy significantly impacts operational costs. Forecasting can also be embedded in a foundational model, a large, pre-trained model that serves as a base for many downstream tasks. Its detailed design and implementation lie beyond the scope of this book and will be addressed in a separate study.

Solar photovoltaic forecasting for energy system integration and control

Matrone, Silvana;Ogliari, Emanuele;Leva, Sonia;
2025-01-01

Abstract

Given the variable nature of solar photovoltaics (PV) production, accurate forecasting becomes essential for energy system integration and control. This chapter provides a comprehensive exploration of different aspects associated with PV forecasting for energy system integration and control. In this regard, we first introduce different temporal resolutions in PV forecasting and then provide information on how PV is integrated into the energy system. Another topic discussed in this chapter is the use of PV forecasting to facilitate the integration of PV systems together with other renewable energy sources and energy storage into energy system. This chapter highlights how PV forecasting techniques can enhance grid flexibility, allowing infrastructure to respond to fluctuations in solar PV generation. It also explores the role of PV forecasting in energy management. By forecasting changes in PV output, system operators can better align energy demand with supply through demand-side measures such as load shifting and demand response, and generation-side strategies, including flexible dispatch and dynamic generation scheduling. Regulatory and policy frameworks also play a crucial role in enabling the integration of PV forecasting into energy systems control. This chapter also reviews policies and incentives that have been implemented to promote the adoption of PV forecasting technologies. Lastly, to illustrate the practical applications and benefits of PV forecasting, this chapter presents three case studies: a robust unit commitment model using uncertainty sets tailored to solar variability; a coordinated voltage control strategy using forecast-based scheduling of local assets; and an energy management system for an EV charging station, where forecast accuracy significantly impacts operational costs. Forecasting can also be embedded in a foundational model, a large, pre-trained model that serves as a base for many downstream tasks. Its detailed design and implementation lie beyond the scope of this book and will be addressed in a separate study.
2025
AI-Based Forecasting of Solar Photovoltaics Power Generation
9781837240197
9781837240203
File in questo prodotto:
File Dimensione Formato  
PBPO268E_ch10.pdf

Accesso riservato

: Publisher’s version
Dimensione 9.26 MB
Formato Adobe PDF
9.26 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1309164
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
  • OpenAlex ND
social impact