<div>This dissertation develops an exclusive workflow driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System (IES). The objective of this research is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, coal, gas, wind, and solar). The main contribution of this dissertation addresses several major challenges in current optimization methods of the energy portfolios in IES. First, the feasibility of generating the synthetic time series of the periodic peak data. </div><div>Second, the computational burden of conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models.</div><div>Third, the inadequacies of previous studies about the comparisons of the impact of the economic parameters.</div><div><br></div><div>Several algorithmic developments are proposed to tackle these challenges. A stochastic-based optimizer, which employs Gaussian Process modeling, is developed. The optimizer requires a large number of samples for its training, with each sample consisting of a time series describing the electricity demand or other operational and economic profiles for multiple types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads limited set of historical data, such as demand and weather data from past years. To construct the Reduced Order Models (ROMs), several data analysis methods are used, such as the Auto Regressive Moving Average (ARMA), the Fourier series decomposition, the peak detection algorithm, etc. The purpose of using these algorithms is to detrend the data and extract features that can be used to produce synthetic time histories that maintain the statistical characteristics of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit (FOM), the specific cash flow stream for each energy producer and the total Net Present Value (NPV). The Screening Curve Method (SCM) is employed to get the initial estimate of the optimal capacity. Results obtained from a model-based optimization of the Gaussian Process are evaluated using an exhaustive Monte Carlo search. </div><div><br></div><div>The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The proposed workflow can provide a comprehensive, efficient, and scientifically dependable strategy to support the decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of IES.</div><div><br></div>