<p>Intermittent energy resources, including wind and solar power, continue to be rapidly added to the generation fleet domestically and abroad. The variable power of these resources introduces new levels of stochasticity into electric interconnections that must be continuously balanced in order to maintain system reliability. Energy storage systems (ESSs) offer one potential option to compensate for the intermittency of renewables. ESSs for long-term storage (1-hour or greater), aside from a few pumped hydroelectric installations, are not presently in widespread use in the U.S. The deployment of ESSs would be most likely driven by either the potential for a strong internal rate of return (IRR) on investment and through significant benefits to system reliability that independent system operators (ISOs) could incentivize.</p><p>To assess the potential of ESSs three objectives are addressed. (1) Evaluate the economic viability of energy storage for price arbitrage in real-time energy markets and determine system cost improvements for ESSs to become attractive investments. (2) Estimate the reliability impact of energy storage systems on the large-scale integration of intermittent generation. (3) Analyze the economic, environmental, and reliability tradeoffs associated with using energy storage in conjunction with stochastic generation.</p><p>First, using real-time energy market price data from seven markets across the U.S. and the physical parameters of fourteen ESS technologies, the maximum potential IRR of each technology from price arbitrage was evaluated in each market, along with the optimal ESS system size. Additionally, the reductions in capital cost needed to achieve a 10% IRR were estimated for each ESS. The results indicate that the profit-maximizing size of an ESS is primarily determined by its technological characteristics (round-trip charge/discharge efficiency and self-discharge) and not market price volatility, which instead increases IRR. This analysis demonstrates that few ESS technologies are likely to be implemented by investors alone.</p><p>Next, the effects of ESSs on system reliability are quantified. Using historic data for wind, solar, and conventional generation, a correlation-preserving, copula-transform model was implemented in conjunction with Markov chain Monte Carlo framework for estimating system reliability indices. Systems with significant wind and solar penetration (25% or greater), even with added energy storage capacity, resulted in considerable decreases in generation adequacy.</p><p>Lastly, rather than analyzing the reliability and costs in isolation of one another, system reliability, cost, and emissions were analyzed in 3-space to quantify and visualize the system tradeoffs. The modeling results implied that ESSs perform similarly to natural gas combined cycle (NGCC) systems with respect to generation adequacy and system cost, with the primary difference being that the generation adequacy improvements are less for ESSs than that of NGCC systems and the increase in LCOE is greater for ESSs than NGCC systems.</p><p>Although ESSs do not appear to offer greater benefits than NGCC systems for managing energy on time intervals of 1-hour or more, we conclude that future research into short-term power balancing applications of ESSs, in particular for frequency regulation, is necessary to understand the full potential of ESSs in modern electric interconnections.</p> / Dissertation

Il est connu qu’avoir un système d’observation de la pluie de haute résolution spatio – temporelle est crucial pour obtenir de bons résultats dans la modélisation pluie – écoulement. Le radar est un outil qui donne des estimations quantitatives de precipitation avec une très bonne résolution. Lorsqu’il est fusionné avec un réseau des pluviomètres les avantages des deux systèmes sont obtenus. Cependant, les estimations fournies par le radar ont des incertitudes différentes à celles qui sont obtenus avec les pluviomètres. Dans le processus de calcul pluie – écoulement l'incertitude des précipitations interagit avec l'incertitude du modèle hydrologique. L’objectif de ce travail est d’étudier les méthodes utilisées pour quantifier l'incertitude dans l'estimation des précipitations par fusion radar – pluviomètres et de l'incertitude dans la modélisation hydrologique, afin de développer une méthodologie d'analyse de leurs contributions individuelles au traitement pluie - écoulement.Le travail est divisé en deux parties, la première cherche à évaluer: Comment peut-on quantifier l'incertitude de l'estimation des précipitations par radar? Pour répondre à la question, l'approche géostatistique par Krigeage avec Dérive Externe (KED) et Génération Stochastique de la précipitation a été utilisée, qui permet de modéliser la structure spatio – temporaire de l’erreur. La méthode a été appliquée dans la région des Cévennes - Vivarais (France), où il y a un système très dense d'observation. La deuxième partie explique: Comment pourrais être quantifiée l'incertitude de la simulation hydrologique qui provient de l'estimation de précipitation par radar et du processus de modélisation hydrologique? Dans ce point, l'outil de calcul hydrologique à Mesoéchelle (HCHM) a été développé, c’est un logiciel hydrologique distribuée et temps continu, basé sur le Numéro de Courbe et l’Hydrographe Unitaire. Il a été appliqué dans 20 résolutions spatio - temporelles allant de 10 à 300 km2 et 1 à 6 heures dans les bassins de l’Ardèche (~ 1971 km2) et le Gardon (1810 km2). Apres une analyse de sensibilité, le modèle a été simplifié avec 4 paramètres et l’incertitude de la chaîne de processus a été analysée: 1) Estimation de precipitation; 2) Modélisation hydrologique; et 3) Traitement pluie - écoulement, par l’utilisation du coefficient de variation de l'écoulement simulé.Il a été montré que KED est une méthode qui fournit l’écart type de l’estimation des précipitations, lequel peut être transformé dans une estimation stochastique de l’erreur locale. Dans la chaîne des processus: 1) L'incertitude dans l'estimation de précipitation augmente avec la réduction de l’échelle spatio – temporelle, et son effet est atténué par la modélisation hydrologique, vraisemblablement par les propriétés de stockage et de transport du bassin ; 2) L'incertitude de la modélisation hydrologique dépend de la simplification des processus hydrologiques et pas de la surface du bassin ; 3) L'incertitude dans le traitement pluie - écoulement est le résultat de la combinaison amplifiée des incertitudes de la précipitation et la modélisation hydrologique. / It is known that having a precipitation observation system at high space - time resolution is crucial to obtain good results in rainfall - runoff modeling. Radar is a tool that offers quantitative precipitation estimates with very good resolution. When it is merged with a rain gauge network the advantages of both systems are achieved. However, radars estimates have different uncertainties than those obtained with the rain gauge. In the modeling process, uncertainty of precipitation interacts with uncertainty of the hydrological model. The objective of this work is: To study methods used to quantify the uncertainty in radar – raingauge merge precipitation estimation and uncertainty in hydrological modeling, in order to develop a methodology for the analysis of their individual contributions in the uncertainty of rainfall - runoff estimation.The work is divided in two parts, the first one evaluates: How the uncertainty of radar precipitation estimation can be quantified? To address the question, the geostatistical approach by Kriging with External Drift (KED) and Stochastic Generation of Precipitation was used, which allows to model the spatio - temporal structure of errors. The method was applied in the Cévennes - Vivarais region (France), where there is a very rich observation system. The second part explains: How can it be quantified the uncertainty of the hydrological simulation coming from the radar precipitation estimates and hydrological modeling process? In this point, the hydrological mesoscale computation tool was developed; it is distributed hydrological software in time continuous, within the basis of the Curve Number and the Unit Hydrograph. It was applied in 20 spatio-temporal resolutions ranging from 10 to 300 km2 and 1 to 6 hours in the Ardèche (~ 1971 km2) and the Gardon (1810 km2) basins. After a sensitivity analysis, the model was simplified with 4 parameters and the uncertainty of the chain of process was analyzed: 1) Precipitation estimation; 2) Hydrological modeling; and 3) Rainfall - runoff estimation, by using the coefficient of variation of the simulated flow.It has been shown that KED is a method that provides the standard deviation of the precipitation estimation, which can be transformed into a stochastic estimation of the local error. In the chain of processes: 1) Uncertainty in precipitation estimation increases with decreasing spatio-temporal scale, and its effect is attenuated by hydrological modeling, probably due by storage and transport properties of the basin; 2) The uncertainty of hydrological modeling depends on the simplification of hydrological processes and not on the surface of the basin; 3) Uncertainty in rainfall - runoff treatment is the result of the amplified combination of precipitation and hydrologic modeling uncertainties.