Photovoltaic power Forecasting and management of energy production based on meteorological data

Résumé

Energy management is an indispensable part of today’s electrical systems and Smart Grid (SG) paradigms, especially with the high penetration of Renewable Energy Sources (RES). Thus, significant attention is paid from academia and industry to foster the synergy between these two paradigms as a means to accelerate the transition to a more diverse generation portfolio that includes an unprecedented amount of RES such as Photovoltaic (often shortened as PV) energy. Due to the ever-growing electricity consumption, state-of-the-art Artificial Intelligence (AI)-based techniques play a central role in providing necessary system flexibility to deal with the bulk integration of the PV energy for power-and-energy-efficient computing. AI lies at the core of forecasting methods to enhance the power delivery service between the grid-connected PV stations and end-consumers. In other words, the futuristic power grid infrastructure should rely on accurate PV Power Forecasting (PVPF) methods as a cornerstone of achieving unit commitment and stable energy supply. Nevertheless, designing effective energy management systems is complex because it involves designing components and hardware-software interfaces across the computing stack. So ubiquitous and complex are energy management mechanisms requiring a high level of scalability and generalization potential to gratify the load needs and cope with the meteorological factors' stochastic nature in tandem with optimal power grid stability. In an effort to break this stalemate, this research aims to explore the potential AI techniques for energy management between the energy-mix on the supply side and the load side. Consequently, the present work proposes efficient techniques to tackle the instability and intermittency of PV power production and its significant impact on the load demand. First, a comprehensive overview of SG and energy management has been conducted. Next, various ML models-based PVPF have been introduced and applied to real-world scenarios to ensure an uninterrupted power supply. Afterward, innovative load forecasting methods have been proposed to cope with the volatile PV energy supply and accommodate the stochasticity of the customers' demand with efficient energy management strategies. Finally, SG stability methods have been proposed to predict the grid's state. This will aid in shaping the best strategies for preventive maintenance and risk hedging policies. This research thesis applies data science methods to the SG paradigm for effective dynamic control and management.

Citer ce document

Accès au document

Ce document est hébergé sur son dépôt institutionnel d'origine.

Auteur(s)

Statistiques

Consultations : 3

Téléchargements : 0