In this Ph.D. thesis, the research work presented provides a contribution to the development of the required tools and frameworks to enable the integration into smart grids of Renewable Energy Sources and bidirectional electrical loads such as Electric Vehicles (EVs). The main aim is to improve the forecast accuracy of the RES available energy and of the electrical load energy demand. Following a critical analysis of the state of the art, improvements are proposed to increase both the reliability and reduce the computational burden required. A specific focus on the EV charging profiles and requirements is posed. This leads to the definition and development of a novel EV forecasting methodology that allowed to switch from the typical time series forecast to a probabilistic estimation of the EV charging and the relative energy requirements by comparing the load power curve with the contribution of the expected cluster of EV owners. All the proposed methodologies, PV forecast, load forecast, and EV forecast, are first developed and later implemented and tested on real case studies. In particular, the effectiveness of the RES forecast is firstly assessed in the SolarTechLab facility and later implemented in an operating multi-good microgrid, the MG2LAB. Moreover, they have been tested in the framework of Pilot 4a of the EU Horizon 2020 project “Digital PLAtform and analytic TOOls for eNergy” (PLATOON - Grant agreement ID: 872592).
Il lavoro di ricerca svolto durante il percorso di dottorato e qui presentato fornisce un contributo allo sviluppo degli strumenti necessari all'integrazione di generazione elettrica da fonti energetiche rinnovabili (FER) e carichi elettrici bidirezionali come i veicoli elettrici all’interno di reti intelligenti di nuova concezione. L'obiettivo principale è quello di migliorare l'accuratezza previsionale dell'energia resa disponibile dalle FER nonché della richiesta di energia da differenti tipologie di carichi elettrici. Un'attenzione particolare è stata posta al caso dei veicoli elettrici, per i quali è stata concepita una nuova metodologia di stima dei profili di ricarica. Tutte le metodologie proposte sono state inizialmente studiate e validate e successivamente implementate all'interno di casi studio reali. In particolare, tutti i modelli sviluppati sono attualmente funzionanti nel laboratorio SolarTechLab, all'interno del laboratorio di microreti MG2LAB e infine all'interno del Pilot 4a del progetto EU Horizon 2020 “Digital PLAtform and analytic TOOls for eNergy” (PLATOON - Grant agreement ID: 872592).
Computational intelligence enabling energy management systems for effective integration of V2G and renewable energy sources
Nespoli, Alfredo
2022/2023
Abstract
In this Ph.D. thesis, the research work presented provides a contribution to the development of the required tools and frameworks to enable the integration into smart grids of Renewable Energy Sources and bidirectional electrical loads such as Electric Vehicles (EVs). The main aim is to improve the forecast accuracy of the RES available energy and of the electrical load energy demand. Following a critical analysis of the state of the art, improvements are proposed to increase both the reliability and reduce the computational burden required. A specific focus on the EV charging profiles and requirements is posed. This leads to the definition and development of a novel EV forecasting methodology that allowed to switch from the typical time series forecast to a probabilistic estimation of the EV charging and the relative energy requirements by comparing the load power curve with the contribution of the expected cluster of EV owners. All the proposed methodologies, PV forecast, load forecast, and EV forecast, are first developed and later implemented and tested on real case studies. In particular, the effectiveness of the RES forecast is firstly assessed in the SolarTechLab facility and later implemented in an operating multi-good microgrid, the MG2LAB. Moreover, they have been tested in the framework of Pilot 4a of the EU Horizon 2020 project “Digital PLAtform and analytic TOOls for eNergy” (PLATOON - Grant agreement ID: 872592).File | Dimensione | Formato | |
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https://hdl.handle.net/10589/198410