Thesis

Micro-réseaux d'électricité 100% solaire et isolés en Afrique. Eléments de dimensionnement, coût de l'électricité, dépendance au climat régional et au profil de demande

Thesis

Micro-réseaux d'électricité 100% solaire et isolés en Afrique. Eléments de dimensionnement, coût de l'électricité, dépendance au climat régional et au profil de demande

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Abstract

La réalisation commune de l'objectif du développement durable n°7 des Nations Unies visant à l'accès à une énergie propre et fiable pour tous d'ici 2030 et aux objectifs climatiques de l'accord de Paris nécessite le développement de micro-réseaux (MG), alimentés par des ressources énergétiques renouvelables locales, pour les zones isolées qui ne peuvent pas être connectées au réseau. C'est particulièrement le cas en Afrique subsaharienne où 600 millions de personnes, principalement dans les zones rurales reculées, n'ont pas accès à l'électricité. Cette thèse se concentre sur l'analyse des MG solaires non connectés au réseau (MGSI) pour répondre aux enjeux de la production d'électricité dans les zones isolées du continent africain. La faisabilité technico-économique propre à ces MG repose sur une forte adéquation temporelle entre la ressource solaire et la demande tout en limitant le coût de l'électricité fournie aux consommateurs.Nous explorons d'abord la variabilité temporelle multi-échelle de la ressource solaire en Afrique et son implication sur le dimensionnement des MGSI, en utilisant des données satellitaires à haute résolution de l'irradiance horizontale globale pour une période de 21 ans (1995-2015). La prise en compte des périodes de faibles ressources conduit à surdimensionner la surface photovoltaïque (PV) d'un facteur 1,3 à 4. Avec un tel surdimensionnement, il est possible d'assurer une bonne qualité de service sans dépendre d'un volume de stockage important. Pour certaines zones, une flexibilité de la demande pendant les périodes de faibles ressources permettrait de réduire significativement le dimensionnement.Nous analysons ensuite comment la saisonnalité potentielle de la demande électrique affecte la taille des MGSI, à travers l'analyse de la structure de co-variabilité entre la ressource solaire et la demande. Nous considérons que le MG doit répondre à une demande quotidienne totale d'au moins 95% des jours et à une variation saisonnière de la demande pouvant aller jusqu'à 30%. Alors que dans certaines régions d'Afrique, la taille requise pour répondre à la demande saisonnière est inférieure de 20% à ce qui est nécessaire pour répondre à la demande non saisonnière, elle peut également être supérieure de 20%. Nous explorons également dans quelle mesure l'effet de l'angle d'inclinaison des panneaux PV pourrait réduire l'inadéquation offre-demande et le dimensionnement. Généralement, l'angle d'inclinaison est égal à la latitude. Pour une demande quotidienne constante, le gain de taille obtenu en optimisant l'angle d'inclinaison est inférieur à 4%, mais pour des schémas de demande saisonniers spécifiques, il peut atteindre 9%.Enfin, le coût de l'électricité nécessaire pour assurer une bonne qualité de service est un facteur déterminant du déploiement potentiel des MGSI. Nous évaluons la sensibilité du coût actualisé de l'électricité (LCOE) et de la configuration optimale MG (c'est-à-dire avec le LCOE le plus bas) aux coûts des panneaux PV, des batteries et à d'autres paramètres économiques. Si la sensibilité du LCOE aux coûts actualisés est évidemment importante, la configuration optimale (surface des panneaux PV et capacité de stockage) est très robuste. La configuration optimale est presque uniquement déterminée par la structure de co-variabilité temporelle entre la ressource et la demande. Elle est donc dépendante d'une part du climat régional, et d'autre part de la structure temporelle de la demande. La variable d'ajustement est essentiellement le surdimensionnement des panneaux PV, qui est basé sur les faibles jours de ressource solaire tandis que le stockage a pour fonction principale de gérer l'inadéquation entre demande et ressource au niveau infra-journalier. Un résultat intéressant est que le LCOE est plus faible pour des utilisations productives de l’électricité comparé aux utilisations domestiques uniquement du fait de la capacité de stockage inférieure requise pour les utilisations productives.

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G.J. Prinsloo, A.A. Mammoli, R.T. Dobson. Discrete cogeneration optimization with storage capacity decision support for dynamic hybrid solar combined heat and power systems in isolated rural villages. Elsevier Journal Energy 133(1), pp 1051–1064. DOI: 10.1016/j.energy.2016.10.026 Pre-packaged cogeneration systems are popular as lead supply in distributed and isolated energy systems aimed at empowering remote off-grid communities and in supporting eco-villages in nature reserves. Energy cooperatives and independent power producers deploy cogeneration systems in community shared microgrid configurations as building blocks in smart localised multi-carrier energy grids. Small-scale renewable energy and distributed energy resources in the energy mix of multi-carrier combined heat and power microgrids call for the development of intelligent control automation solutions. This paper relates to the field of intelligent energy systems and describes a discrete-time multi-objective optimization solution where hierarchical digital microgrid control is integrated into solar powered micro-cogeneration. The control solution includes an integrated cost-competitive economic and environmental optimization algorithm customized around the needs of small off-grid isolated village settlements. A secondary stage optimization loop functions as a storage capacity decision support system, to alert the community when operating costs can be reduced through an investment into increased storage capacity for the village microgrid. This cost-aware control optimization algorithm is tested in a rural village microgrid by using parametric computer simulation models of a hybrid residential solar cogeneration system. Digital simulation experiments evaluate the operational plans and operational cost performances of energy management for different storage scenarios at an isolated remote rural location in Africa. Keywords: optimizing combined cogeneration and thermal storage optimal design management of cogeneration system automated intelligent system renewable energy remote power station solar transverter solar heat transverter thermal interface thermal smart-grid transverter alternative economy complimentary control social impact energy passage-way energy carrier power carrier demand curve active fuel management solution computer engineering data science energy dispensing fuel management system energy economy solar+ open source content management system optimal control scheme source code DC microgrid power inverter Uninterruptible Power Supply UPS embedded microprocessor distributed processing cybernetics smart distribution grids solar-plus power invertor net-metering smart thermostats demand side integration remote community electrification community micro-grid islanded solar-diesel solution demand response numerical optimization mixed integer linear programming remote control wii remote operation system Arduino micro-controller proportional-integral- derivative controller resource light-emitting diode coordination renewable energy preservation unit commitment problem energy efficiency energy supply policy dispatch regulation resource scheduling optimization distributed generation solar cogeneration predictive control intelligence energy balancing architecture cooperative control participatory control automation intelligent energy smartgrid storage optimization energetic exergy demand side management customer engagement rural electrification transactive energy systems red inteligente price characteristics energía transactivo pre-paid electricity pay-as-you-go basic free electricity societal impacts cultural control aspects participatory community psychology in control marshall energy resources orchestrated microgrid control family of heterogeneous devices multi-tier priority multi-level hierarchy machine learning re-enforced deep learning recurrent deep learning self-learning recurrent neural networks deep learning unsupervised feature learning solutions probalistic modeling probabilistic modelling Transactive energy source code repository dynamical systems flexible energy demand energy neutral microgrid energy neutral smartgrid EMS software central autonomous management controller CAMC coordinated control distributed generation DG units distribution management systems DMS transient response dynamic equivalents emergency functions black start microgrid central controller MGCC steady-state response multi-microgrids DERMS distributed energy resource management system cooperative multi-microgrid multi DC grid system distributed generation multi-microgrid management and control low-voltage microgrids MG combined heat and power CHP energy on demand irrigation equipment active power management multiple microgrids multi-agent coordination and control of single or multiple microgrids hierarchical control strategy of multi-channel microgrids prevent blackouts service restoration multi-source micro-grids load sharing load shedding granular computing smart energy system pervasive computing transactive energy system technique decentralized information processing embedding microprocessor control load estimation supply prediction load prediction data science power computer science computer science applications computer modeling statistics engineering advanced sensors smart electric meter economy analytics machine learning machine optimization LNG solutions offerings for cleaner power generation new wind power products distributed energy systems intelligent and efficient power transmission energy efficiency as well as customized financial solutions clear sky model all-weather global and beam irradiance model performance modeling and monitoring Energiewende solar resource data solar resource uncertainty forecasting for PV cogenerative microgrid integration solar PV modeling applications solar simulation modeling tools synthesis model updates moderated solar panel Discussion soiling and effects on performance concentrated solar and PV performance modeling solar spectrum Angle-of-Incidence module temperature prediction accuracy on PV performance advanced modeling ray-tracing bifacial PV arrays shade n panel shading field monitoring model validation home energy management Research Interests: Engineering, Electrical Engineering, Electronic Engineering, Mechanical Engineering, Agricultural Engineering, Mathematics, Applied Mathematics, Computer Science, Human Computer Interaction, Thermodynamics, Computer Engineering, Power Electronics, Mechatronics, Community Development, Optimization (Mathematics), Computer Networks, Electronics, Power System, Rural Development, Agriculture, Optimization techniques, Modeling and Simulation, Mathematical Modelling, Operations research and Optimization, Smart Grid, Computational Mathematics, Irrigation Systems Design, Microcontrollers, Microgrid, Optimization, Solar Energy, Smart Home Technology, Solar Power, Smart Home, Thermodynamic analysis, Community participation and engagement, Technology for Community Development, Eco-Village, Electrical and Computer Engineering (ECE), and Green Smart Village