The main objective of this study is to analyze the impact of the stochastic renewable distributed generation (DG) system on the urban distribution network. Renewable DG systems, particularly photovoltaic (PV) systems, dispersed on the distribution network may, in spite of their relatively small individual capacities, change the behavior of such a network. Therefore, this study (1) developed tools and algorithms useful for planning, designing, and operating such a network, (2) addressed some of the issues in the analysis of the impact of renewable DG systems on such a network, and (3) designed a framework for streamlining the future development and the smooth integration of renewable DG systems into the urban distribution network. For this purpose, in Task 1, using the backward and forward sweep method implemented in MATLAB, this study developed an algorithm for three-phase power flow that models power system components, including distribution systems, transformers, and PV systems. To model the influence of the inherent uncertainty of the input, the location, and the capacity of the PV system, this study implemented a stochastic simulation algorithm combined with the power-flow algorithm. It also accelerated the stochastic algorithm using a method of variance reduction, including importance sampling, and the sampling of representative clusters and extreme points, which reduced the extremely heavy computational burden that the stochastic simulation inevitably imposed. Then this study analyzed inherent uncertainties such as the inputs, the locations, and the capacities of residential PV systems stochastically installed on urban distribution networks by performing several stochastic simulations. In Task 2, this study developed a genetic algorithm in MATLAB that solves an optimization problem that maximizes the reliability (or minimizes the frequency and the duration of failure) of urban distribution networks enhanced by protection devices (i.e., the recloser, the fuse, and the switch) and renewable DG. Using the backward and forward method, this study implemented an analytical method that simulates all possible permanent and transient faults and evaluated the reliability of an urban distribution network housing a combination of fuses, switches, reclosers, and DG systems. Then it analyzed the impact of both the DG system, including the effect of the islanded operation of the DG system, and the protection device, on the reliability of the urban distribution network. The objective of Task 3 of this study was to present a useful method for analyzing the impact of geographically dispersed DG systems, particularly PV systems, on statewide and nationwide power grids. Using the methods of Lagrangian optimization and hydrothermal coordination, this study developed an algorithm for environmentally constrained generation resource allocation that minimizes both fuel costs and ecological impact, including the cost and the impact of water consumption. Then, this study (1) analyzed, as an example of the statewide power grid of the future, the power system of the state of Georgia in 2010, (2) modeled the load consumption and the water inflow of the power system, (3) synthesized third-order power output functions for costs, emissions, and water consumption from actual heat-rate data, and (4) estimated the power output of PV systems geographically dispersed throughout the state and hydroelectric resources of the state in hourly intervals. Lastly, it performed simulations for the generation resource allocation of the power system in hourly and minute intervals.

Il est prévu que la génération distribuée par l’entremise d’énergie de sources renouvelables (DG) continuera à jouer un rôle clé dans le développement et l’exploitation des systèmes de puissance électrique durables, efficaces et fiables, en vertu de cette fournit une alternative pratique de décentralisation et diversification de la demande globale d’énergie, bénéficiant de sources d’énergie plus propres et plus sûrs. L’intégration de DG renouvelable dans les réseaux électriques existants pose des défis socio–technico–économiques, qu’ont attirés de la recherche et de progrès substantiels.Dans ce contexte, la présente thèse a pour objet la conception et le développement d’un cadre de modélisation, simulation et optimisation pour l’intégration de DG renouvelable dans des réseaux de puissance électrique existants. Le problème spécifique à considérer est celui de la sélection de la technologie,la taille et l’emplacement de des unités de génération renouvelable d’énergie, sous des contraintes techniques, opérationnelles et économiques. Dans ce problème, les questions de recherche clés à aborder sont: (i) la représentation et le traitement des variables physiques incertains (comme la disponibilité de les diverses sources primaires d’énergie renouvelables, l’approvisionnement d’électricité en vrac, la demande de puissance et l’apparition de défaillances de composants) qui déterminent dynamiquement l’exploitation du réseau DG–intégré, (ii) la propagation de ces incertitudes sur la réponse opérationnelle du système et le suivi du risque associé et (iii) les efforts de calcul intensif résultant du problème complexe d’optimisation combinatoire associé à l’intégration de DG renouvelable.Pour l’évaluation du système avec un plan d’intégration de DG renouvelable donné, un modèle de calcul de simulation Monte Carlo non–séquentielle et des flux de puissance optimale (MCS–OPF) a été conçu et mis en oeuvre, et qui émule l’exploitation du réseau DG–intégré. Réalisations aléatoires de scénarios opérationnels sont générés par échantillonnage à partir des différentes distributions des variables incertaines, et pour chaque scénario, la performance du système est évaluée en termes économiques et de la fiabilité de l’approvisionnement en électricité, représenté par le coût global (CG) et l’énergie non fournie (ENS), respectivement. Pour mesurer et contrôler le risque par rapport à la performance du système, deux indicateurs sont introduits, la valeur–à–risque conditionnelle(CVaR) et l’écart du CVaR (DCVaR).Pour la sélection optimale de la technologie, la taille et l’emplacement des unités DG renouvelables,deux approches distinctes d’optimisation multi–objectif (MOO) ont été mis en oeuvre par moteurs de recherche d’heuristique d’optimisation (HO). La première approche est basée sur l’algorithme génétique élitiste de tri non-dominé (NSGA–II) et vise à la réduction concomitante de l’espérance mathématique de CG et de ENS, dénotés ECG et EENS, respectivement, combiné avec leur valeurs correspondent de CVaR(CG) et CVaR(ENS); la seconde approche effectue un recherche à évolution différentielle MOO (DE) pour minimiser simultanément ECG et s’écart associé DCVaR(CG). Les deux approches d’optimisation intègrent la modèle de calcul MCS–OPF pour évaluer la performance de chaque réseau DG–intégré proposé par le moteur de recherche HO.Le défi provenant de les grands efforts de calcul requises par les cadres de simulation et d’optimisation proposée a été abordée par l’introduction d’une technique originale, qui niche l’analyse de classification hiérarchique (HCA) dans un moteur de recherche de DE.Exemples d’application des cadres proposés ont été élaborés, concernant une adaptation duréseau test de distribution électrique IEEE 13–noeuds et un cadre réaliste du système test de sous–transmission et de distribution IEEE 30–noeuds. [...] / Renewable distributed generation (DG) is expected to continue playing a fundamental role in the development and operation of sustainable, efficient and reliable electric power systems, by virtue of offering a practical alternative to diversify and decentralize the overall power generation, benefiting from cleaner and safer energy sources. The integration of renewable DG in the existing electric powernetworks poses socio–techno–economical challenges, which have attracted substantial research and advancement.In this context, the focus of the present thesis is the design and development of a modeling,simulation and optimization framework for the integration of renewable DG into electric powernetworks. The specific problem considered is that of selecting the technology, size and location of renewable generation units, under technical, operational and economic constraints. Within this problem, key research questions to be addressed are: (i) the representation and treatment of the uncertain physical variables (like the availability of diverse primary renewable energy sources, bulk–power supply, power demands and occurrence of components failures) that dynamically determine the DG–integrated network operation, (ii) the propagation of these uncertainties onto the system operational response and the control of the associated risk and (iii) the intensive computational efforts resulting from the complex combinatorial optimization problem of renewable DG integration.For the evaluation of the system with a given plan of renewable DG, a non–sequential MonteCarlo simulation and optimal power flow (MCS–OPF) computational model has been designed and implemented, that emulates the DG–integrated network operation. Random realizations of operational scenarios are generated by sampling from the different uncertain variables distributions,and for each scenario the system performance is evaluated in terms of economics and reliability of power supply, represented by the global cost (CG) and the energy not supplied (ENS), respectively.To measure and control the risk relative to system performance, two indicators are introduced, the conditional value–at–risk (CVaR) and the CVaR deviation (DCVaR).For the optimal technology selection, size and location of the renewable DG units, two distinct multi–objective optimization (MOO) approaches have been implemented by heuristic optimization(HO) search engines. The first approach is based on the fast non–dominated sorting genetic algorithm(NSGA–II) and aims at the concurrent minimization of the expected values of CG and ENS, thenECG and EENS, respectively, combined with their corresponding CVaR(CG) and CVaR(ENS) values; the second approach carries out a MOO differential evolution (DE) search to minimize simultaneously ECG and its associated deviation DCVaR(CG). Both optimization approaches embed the MCS–OPF computational model to evaluate the performance of each DG–integrated network proposed by the HO search engine. The challenge coming from the large computational efforts required by the proposed simulation and optimization frameworks has been addressed introducing an original technique, which nests hierarchical clustering analysis (HCA) within a DE search engine. Examples of application of the proposed frameworks have been worked out, regarding an adaptation of the IEEE 13 bus distribution test feeder and a realistic setting of the IEEE 30 bussub–transmission and distribution test system. The results show that these frameworks are effectivein finding optimal DG–integrated networks solutions, while controlling risk from two distinctperspectives: directly through the use of CVaR and indirectly by targeting uncertainty in the form ofDCVaR. Moreover, CVaR acts as an enabler of trade–offs between optimal expected performanceand risk, and DCVaR integrates also uncertainty into the analysis, providing a wider spectrum ofinformation for well–supported and confident decision making.

Power losses are always a cause of worry for any power grid. In India, the situation is even worse. Though recent reports by Ministry of Power shows that Aggregate Technical and Commercial losses (AT &C losses) have come down from 36.64% in 2002-03 to 27% in 2011-12, yet they are much higher than the losses seen in many of the developed nations. The reduction shown in power loss is because of the Electricity Act, 2003 and the amendments made to it in 2007 which controlled the commercial losses rather than the technical losses. According to Ministry of Power, technical losses (Transmission & Distribution losses or T&D losses) in India are reported to be 23.65% in 2011-12. However, according to the study done by EPRI, for systems deployed in developed countries, these losses are estimated to be in the range of 7-15.5%. T & D losses occur in four system components namely step-up transformers and high voltage transmission (0.5-1%), step down to in intermediate voltage, transmission and step down to sub transmission voltage level (1.5-3%), sub-transmission system and step down to low voltage for distribution (2-4.5%), and distribution lines (3-7%). 1% of power loss is approximately equivalent to annual loss of Rs 600 million for a single state. Hence, in a year, loss in distribution line alone causes approximate loss of Rs 1.8-4.2 billion per state. Understanding and reducing power losses in distribution lines which contribute nearly 50% of the total T&D losses assume significance and has formed the motivation for the work reported in the thesis. In recent years, the trend has been to encourage users to generate solar power predominantly at residential complexes and captive power plants at industrial complexes. It has been suggested in the literature that Distributed Generation (DG) can not only reduce the load demanded from the power grid but also the power loss. In this thesis, it has been shown that by the choice of proper size and location of DG, the power loss can be reduced substantially as compared to unplanned deployment of DGs. The objective of the thesis is to design strategy for location of distributed user generated power to maximize the reduction in power loss. The thesis begins with a study of distributed generation in primary distribution networks and proceeds to problem formulation, with the aim being to develop an algorithm that can find out the optimal locations for DG allocation in a network. A greedy approximation algorithm, named OPLODER (i.e. Optimal Locations for Distributed Energy Resources), is proposed for the same and its performance on a benchmark data set is observed, which is found to be satisfactory. The thesis then moves on to describe the actual data of 101,881 commercial, residential and industrial consumers of Bangalore metropolitan area. A loss model is discussed and is used to calculate the line losses in LV part of the grid and loss is estimated for the said actual data. The detailed analysis of the losses in the distribution network shows that in most cases the losses are correlated with the sanctioned load. However there are also some outliers indicating otherwise. The analysis concludes that the distributed generated sources need to be optimally located in order to benefit fully. Also presented thereafter is a study about the impact of electrical properties and the structure of the network on power loss. In the second part of the thesis, OPLODER was again used to process the BESCOM data of 101,881 consumers by modeling them to be connected in three topologies namely Bus (i.e. linear structure), Star (i.e. directly connected) and Hybrid (i.e. tree structure). In case of Bus topology, when DG capacity available is 5% of the demand in substation, OPLODER reduced the loss from 14.65% to 10.75%, from 11.63% to 7.71% and from 13.33% to 9.24% for IISc, Brindavan, and Gokula substations respectively. Similarly, for the same amount of DG in case of star topology, OPLODER reduced loss from 1.75% to 1.26%, from 3.39% to 2.59% and from 2.96% to 1.99% for IISc, Brindavan, and Gokula substations respectively. Thereafter, the available real world data is re-modeled as a tree-type structure which is closer to the real world distribution network and OPLODER is run on it. The results obtained are similar to those presented above and are highly encouraging. When applied to the three substations viz. IISc, Brindavan and Gokula, the power loss dips from 9.95% to 7.42%, from 6.01% to 4.44% and from 8.07% to 5.95%, in case of DG used is 5% of the demand in substation. For the optimal strategies worked out in the thesis, additional overheads will be present. These overheads are studied and it has been found that the present infrastructure and technologies will be sufficient to handle the smart distribution network and the optimal strategy for distributed sources.