The South African Department of Science and Technology is striving to develop a means of producing hydrogen gas in remote and civil areas through the use of renewable energy sources. For the purposes of creating such mobile hydrogen production facilities, a small-scale hydrogen production system based on renewable energy sources needs to be developed and modelled. This system is to serve as a pilot plant for further development of a large scale mobile hydrogen production facility. This work focuses on the characterisation of sizing algorithms for renewable energy sources which can determine component configurations that satisfy power requirements of the system. Additionally, optimal sizing techniques must be developed which can output an optimal plant configuration to a user based on cost and efficiency. To this end, a literature study was done on all the components that make up a renewable energy hydrogen system. The techniques researched were then applied to create algorithms capable of correctly sizing the required components of such a plant. These techniques were integrated into an application created in the LabVIEW environment, which is capable of outputting an optimal plant configuration based on the specific needs of a client. A case study was defined with which the results of the simulation models were verified. Using this work, a future, more comprehensive system may be developed and commercialised, building from the techniques implemented here. / Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013

The South African Department of Science and Technology is striving to develop a means of producing hydrogen gas in remote and civil areas through the use of renewable energy sources. For the purposes of creating such mobile hydrogen production facilities, a small-scale hydrogen production system based on renewable energy sources needs to be developed and modelled. This system is to serve as a pilot plant for further development of a large scale mobile hydrogen production facility. This work focuses on the characterisation of sizing algorithms for renewable energy sources which can determine component configurations that satisfy power requirements of the system. Additionally, optimal sizing techniques must be developed which can output an optimal plant configuration to a user based on cost and efficiency. To this end, a literature study was done on all the components that make up a renewable energy hydrogen system. The techniques researched were then applied to create algorithms capable of correctly sizing the required components of such a plant. These techniques were integrated into an application created in the LabVIEW environment, which is capable of outputting an optimal plant configuration based on the specific needs of a client. A case study was defined with which the results of the simulation models were verified. Using this work, a future, more comprehensive system may be developed and commercialised, building from the techniques implemented here. / Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013

Pakistan has been riddled with energy shortage crisis. Long hours of load shedding have caused major economic setbacks in urban areas and rural areas do not even make the cut. Some rural parts, which are connected to the grid, suffer major load shedding and so economic growth is minimal. Most energy is directed towards industrial demand; hence the domestic demand suffers and causes long hours of load shedding. To aid this supply-demand gap, microgrids can be helpful in relieving some of the domestic load on the grid. A microgrid may be more economical only as a support for the main grid in an area, depending on its configuration. Since microgrids are generally composed of renewable energy sources like wind or solar or a combination of both, the supply from just these sources may result in high intermittency. To allow uniform supply, a backup energy source or energy storage is included with the renewable sources. Sizing a microgrid for the targeted region is critical. Some major sizing factors include the availability of renewable resource, load profile of the region, land availability, grid availability, etc. For this thesis, a region near Gharo, a town in Thatta District in Sindh, Pakistan, is selected to deploy the microgrid with a wind farm and battery energy storage system. The microgrid is connected to the main feeder, which supplies grid electricity to a small town of 30 small homes, a school and a small hospital. Hourly wind speed data and an annual load profile is used to calculate the most economic size of the microgrid, depending on the energy dispatch philosophy. To find the most economical solution, this thesis incorporates a stochastic technique, known as the Particle Swarm Optimization (PSO), which is a powerful intelligence evolution algorithm for solving optimization problems. Over the years, PSO has gained popularity due to its simple structure and high performance in solving linear or non-linear objective functions with any number of constraints. In this case, the objective function to be minimized is the net present cost of the microgrid, which comprises of annual capital cost, annual operation and maintenance cost, annual replacement cost of all equipment involved and the annual net cost of buying/selling electricity from/to the grid, respectively.

Combined Cooling Heating and Power (CCHP) systems have been recognized as a viable alternative to conventional electrical and thermal energy generation in buildings because of their high efficiency, low environmental impact, and power grid independence. Many researchers have presented models for comparing CCHP systems to conventional systems and for optimizing CCHP systems. However, many of the errors and uncertainties that affect these modeling efforts have not been adequately addressed in the literature. This dissertation will focus on the following key issues related to errors and uncertainty in CCHP system modeling: (a) detailed uncertainty analysis of a CCHP system model with novel characterization of weather patterns, fuel prices and component efficiencies; (b) sensitivity analysis of a method for estimating the hourly energy demands of a building using Department of Energy (DOE) reference building models in combination with monthly utility bills; (c) development of a practical technique for selecting the optimal Power Generation Unit (PGU) for a given building that is robust with respect to fuel cost and weather uncertainty; (d) development of a systematic method for integrated calibration and parameter estimation of thermal system models. The results from the detailed uncertainty analysis show that CCHP operational strategies can effectively be assessed using steady state models with typical year weather data. The results of the sensitivity analysis reveal that the DOE reference buildings can be adjusted using monthly utility bills to represent the hourly energy demands of actual buildings. The optimal PGU sizing study illustrates that the PGU can be selected for a given building in consideration of weather and fuel cost uncertainty. The results of the integrated parameter estimation study reveal that using the integrated approach can reduce the effect of measurement error on the accuracy of predictive thermal system models.

The aim of this work is to research the issue of optimal sizing and location of photovoltaic distributed generation (PVDG) units on radial distribution feeders, and develop new procedures by which the optimal location may be determined. The procedures consider the concept that the PVDG production varies independently from changes in feeder load demand. Based on that, the developed procedures deal with two performance curves; the feeder daily load curve driven by the consumer load demand, and the PVDG daily production curve driven by the solar irradiance. Due to the mismatch in the profile of these two curves the PVDG unit might end up producing only part of its capacity at the time the feeder meets its peak load demand. An actual example of that is the summer peak load demand in Abu Dhabi city that occurs at 5:30 pm, which is 5 hours after the time the PV array yields its peak. Consequently, solving the optimization problem for maximum line power loss reduction (∆PPL) is deemed inappropriate for the connection of PVDG units. Accordingly, the procedures have been designed to solve for maximum line energy loss reduction (∆EL). A suitable concept has been developed to rate the ∆EL at one time interval over the day, namely feasible optimization interval (FOI). The concept has been put into effect by rating the ∆EL in terms of line power loss reduction at the FOI (ΔPLFOI). This application is deemed very helpful in running the calculations with no need to repeat the energy-based calculations on hourly basis intervals or even shorter. The procedures developed as part of this work have been applied on actual feeders at the 11kV level of Abu Dhabi distribution network. Two main scenarios have been considered relating to the avoidance and allowance of reverse power flow (RPF). In this course, several applications employing both single and multiple PVDG units have been solved and validated. The optimization procedures are solved iteratively. Hence, effective sub-procedures to help determine the appropriate number of feasible iterative steps have been developed and incorporated successfully. Additionally, the optimization procedures have been designed to deal with a 3-phase feeder under an unbalanced load condition. The line impedances along the feeder are modeled in terms of a phase impedance matrix. At the same time, the modeling of feeder load curves along with the power flow calculations and the resulting losses in the lines are carried out by phase. The resulting benefits from each application have been evaluated and compared in terms of line power loss reduction at the FOI (∆PLFOI) along with voltage and current flow profile.

L'objectif de cette thèse est la gestion et le dimensionnement optimaux d'un Système de Stockage d'Énergie (SSE) couplé à une production d'électricité issue d'Énergies Renouvelables Intermittentes (EnRI). Dans un premier temps, un modèle technico-économique du système SSE-EnRI est développé, associé à trois scénarios types d'injection de puissance au réseau électrique : lissage horaire basé sur la prévision J-1 (S1), puissance garantie (S2) et combiné (S3). Ce modèle est traduit sous la forme d'un programme d'optimisation non linéaire de grande taille. Dans un deuxième temps, les stratégies heuristiques élaborées conduisent à une gestion optimisée - selon les critères de fiabilité, de productivité, d'efficacité et de profitabilité du système - de la production d'énergie avec stockage, appelée “charge adaptative” (CA). Comparée à un modèle linéaire mixte en nombres entiers (MILP), cette gestion optimisée, applicable en conditions opérationnelles, conduit rapidement à des résultats proches de l'optimum. Enfin, la charge adaptative est utilisée dans le dimensionnement optimisé du SSE - pour chacune des trois sources : éolien, houle, solaire (PV). La capacité minimale permettant de respecter le scénario avec un taux de défaillance et des tarifs de revente de l'énergie viables ainsi que les énergies conformes, perdues, manquantes correspondantes sont déterminées. Une analyse de sensibilité est menée montrant l'importance des rendements, de la qualité de prévision ainsi que la forte influence de l'hybridation des sources sur le dimensionnement technico-économique du SSE. / This thesis aims at presenting an optimal management and sizing of an Energy Storage System (ESS) paired up with Intermittent Renewable Energy Sources (IReN). Firstly, wedeveloped a technico-economic model of the system which is associated with three typical scenarios of utility grid power supply: hourly smoothing based on a one-day-ahead forecast (S1), guaranteed power supply (S2) and combined scenarios (S3). This model takes the form of a large-scale non-linear optimization program. Secondly, four heuristic strategies are assessed and lead to an optimized management of the power output with storage according to the reliability, productivity, efficiency and profitability criteria. This ESS optimized management is called “Adaptive Storage Operation” (ASO). When compared to a mixed integer linear program (MILP), this optimized operation that is practicable under operational conditions gives rapidly near-optimal results. Finally, we use the ASO in ESS optimal sizing for each renewable energy: wind, wave and solar (PV). We determine the minimal sizing that complies with each scenario, by inferring the failure rate, the viable feed-in tariff of the energy, and the corresponding compliant, lost or missing energies. We also perform sensitivity analysis which highlights the importance of the ESS efficiency and of the forecasting accuracy and the strong influence of the hybridization of renewables on ESS technico-economic sizing.

Le transport routier est au cœur des enjeux environnementaux actuels. Les véhicules électriques hybrides (VEH) sont une alternative intéressante, notamment en milieu urbain. Cependant, la conception de tels systèmes est complexe car la chaîne de traction (composants et architecture), la mission et la gestion énergétique du véhicule sont intimement liées.Les travaux de V. Reinbold ont permis de mettre au point une méthodologie de dimensionnement de VEH. Les composants sont optimisés conjointement avec la gestion énergétique sur un cycle de fonctionnement afin de minimiser la consommation de carburant du véhicule. Une attention particulière est portée à la conception fine de la machine électrique via un modèle électromagnétique adapté à l’optimisation.Dans la suite de ces travaux, nous approfondissons plusieurs aspects du dimensionnement des VEH. Tout d’abord, nous introduisons la possibilité de gérer des paramètres discrets de la machine, comme le nombre de paires de pôles. Dans un second temps, nous développons un modèle thermique de la machine prenant notamment en compte les échanges autour des têtes de bobine. Ce modèle analytique par réseau de résistances thermiques est intégré puis utilisé dans le processus de dimensionnement par optimisation. Il a été préalablement validé via un modèle par éléments finis. Des méthodes d’analyse d’incertitude et de sensibilité sont appliquées afin de quantifier l’influence de certains paramètres thermiques. Enfin, nous appliquons la méthodologie de dimensionnement par optimisation à une architecture série/parallèle, intégrant deux machines électriques. / Road transportation has a huge impact on the environment. Hybrid electric vehicles (HEV) are an interesting alternative, especially for urban uses. However, HEV are complex systems to design because of the strong interaction between the component sizing, the energy management and the driving cycle.V. Reinbold previously developed a sizing methodology for HEV. The components and the energy management are simultaneously optimized to reduce the fuel consumption of the vehicle over a driving cycle. A specific electromagnetic model is used during the optimization process to describe accurately the electrical machine.As a continuation, we introduce firstly the possibility to deal with discrete variables such as the pole number of the electrical machine. Then, we develop a thermal model of the machine considering the convection exchanges around the end-windings. This analytical lumped parameter thermal network is used during the optimization process after being validated thanks to a finite element model. Uncertainty and sensitivity analysis are used to check the influence of some of the thermal parameters. Finally, the sizing methodology is applied to a series/parallel HEV including two electrical machines.

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