The global energy landscape has achieved significant progress toward achieving SDG7 by 2030 and ensuring access to affordable, reliable and sustainable energy for all. Despite this, 675 million people are still without electricity, 80 percent of whom live in Sub-Saharan Africa. This problem is influenced by the great lack of energy demand data that limits the proper sizing of off-grid systems; in particular, this paper focuses on the data needed for the development of mini-grids. The purpose of the thesis is to map state-owned mini-grids operating in rural areas of Kenya and create a systematic database in order to develop a predictive model that estimates component sizing based on the relationship between energy demand-users and installed component capacities. The database includes data on the 130 operational and planned mini-grids, with information regarding their technical specifications and operational parameters where present. The data were collected from government reports, environmental and social impact assessments (ESIAs) of the projects, and information gathered from research in Kenya in collaboration with REREC, a promoter of rural electrification in Kenya. Subsequently, data analysis allowed significant relationships to be found among the sizing variables. The patterns identified thus allowed the predictive model to be developed. Thus, starting from the number of users, it is possible to make an estimate on the average daily energy demand, peak power required, and PV, storage battery, and diesel capacities. This approach aims to provide an additional tool that can speed up and improve energy access in rural and underserved areas of Kenya. The study includes an application case using MicroGridsPy software, which involves testing the predictive model through hybrid sizing of an existing mini-grid on Faza Island, where part of the diesel generation is replaced with PV panels and storage batteries. Finally, we highlight how accurate data collection and management are essential for effective energy planning and policy and emphasize the need for supporting investments in order to collect useful data to address the energy needs of rural populations. The work was coordinated under the LEAP-RE project funded by Horizon 2020.
Il panorama energetico globale ha raggiunto progressi significativi per il raggiungimento dell'SDG7 entro il 2030 e garantire a tutti l'accesso a un'energia economica, affidabile e sostenibile. Nonostante ciò, 675 milioni di persone sono ancora prive di elettricità, l'80% delle quali vive nell'Africa Subsahariana. Questo problema è influenzato dalla grande mancanza di dati relativi alla domanda energetica che limita il corretto dimensionamento dei sistemi off-grid, in particolare, in questo elaborato, si focalizza sui dati necessari per lo sviluppo delle mini-grids. Lo scopo della tesi è di effettuare una mappatura delle mini-grids statali che operano in aree rurali del Kenya e creare un database sistematico al fine di sviluppare un modello predittivo che stimi il dimensionamento dei componenti sulla base del rapporto tra domanda energetica-utenti e le capacità dei componenti installati. Il database include dati sulle 130 mini-grids operative e pianificate, con informazioni in merito alle loro specifiche tecniche e i parametri operativi ove presenti. I dati sono stati raccolti da relazioni governative, valutazioni di impatto ambientale e sociale (ESIA) dei progetti e informazioni raccolte da una ricerca in Kenya in collaborazione con la REREC, ente promotrice dell’elettrificazione delle zone rurali in Kenya. Successivamente l’analisi dei dati ha permesso di riscontrare delle forti relazioni tra le variabili per il dimensionamento. I pattern identificati hanno così permesso di sviluppare il modello predittivo. In tal modo, partendo dal numero di utenti, è possibile effettuare una stima sulla domanda energetica media giornaliera, la potenza di picco richiesta e le capacità di fotovoltaico, batteria di accumulo e diesel. Questo approccio ha l'obiettivo di fornire uno strumento aggiuntivo che può velocizzare e migliorare l'accesso all'energia nelle aree rurali e poco servite del Kenya. Lo studio comprende un caso di applicazione utilizzando il software MicroGridsPy, che prevede la verifica del modello predittivo attraverso il dimensionamento ibrido di una mini-grid esistente sull'isola di Faza, dove parte della generazione diesel viene sostituita con pannelli fotovoltaici e batterie di accumulo. Infine, si evidenzia come la raccolta e la gestione accurata dei dati siano essenziali per una pianificazione e una politica energetica efficaci e si sottolinea la necessità di investimenti di supporto al fine di raccogliere dati utili per rispondere ai bisogni energetici delle popolazioni rurali. Il lavoro è stato coordinato nell’ambito del progetto LEAP-RE finanziato da Horizon 2020.
Systematic mapping of rural mini-grids in Kenya for consistent off-grid energy system modelling
Brasile, Alessandro
2023/2024
Abstract
The global energy landscape has achieved significant progress toward achieving SDG7 by 2030 and ensuring access to affordable, reliable and sustainable energy for all. Despite this, 675 million people are still without electricity, 80 percent of whom live in Sub-Saharan Africa. This problem is influenced by the great lack of energy demand data that limits the proper sizing of off-grid systems; in particular, this paper focuses on the data needed for the development of mini-grids. The purpose of the thesis is to map state-owned mini-grids operating in rural areas of Kenya and create a systematic database in order to develop a predictive model that estimates component sizing based on the relationship between energy demand-users and installed component capacities. The database includes data on the 130 operational and planned mini-grids, with information regarding their technical specifications and operational parameters where present. The data were collected from government reports, environmental and social impact assessments (ESIAs) of the projects, and information gathered from research in Kenya in collaboration with REREC, a promoter of rural electrification in Kenya. Subsequently, data analysis allowed significant relationships to be found among the sizing variables. The patterns identified thus allowed the predictive model to be developed. Thus, starting from the number of users, it is possible to make an estimate on the average daily energy demand, peak power required, and PV, storage battery, and diesel capacities. This approach aims to provide an additional tool that can speed up and improve energy access in rural and underserved areas of Kenya. The study includes an application case using MicroGridsPy software, which involves testing the predictive model through hybrid sizing of an existing mini-grid on Faza Island, where part of the diesel generation is replaced with PV panels and storage batteries. Finally, we highlight how accurate data collection and management are essential for effective energy planning and policy and emphasize the need for supporting investments in order to collect useful data to address the energy needs of rural populations. The work was coordinated under the LEAP-RE project funded by Horizon 2020.| File | Dimensione | Formato | |
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https://hdl.handle.net/10589/223751