<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>

This dissertation presents a comprehensive investigation into the performance optimization of a smart energy system called the Integrated Community Energy and Harvesting (ICE-Harvest) system, designed to optimize energy utilization in dense communities in cold climates. This system comprises a single-pipe variable-temperature micro-thermal network, a micro-electrical network, and distributed energy resources such as combined heat and power units, boilers, heat pumps, short-term storage systems, and long-term storage system. The objective of this research is to develop an optimal operation strategy for the system, considering the coordination of its components to realize its full potential including achieving demand management while ensuring occupants' comfort, harvesting and sharing waste energy, and facilitating energy arbitrage and taking advantage of energy price fluctuations, among other benefits. For this aim, the study begins by formulating precise quasi-dynamic mathematical representations of the system, considering the physical and operational limitations to capture the system's intricacies. The resultant optimization problem is a mixed integer nonlinear programming model that commercial solvers could not solve. To make the nonlinear models more tractable and solvable, various mathematical techniques are employed to linearize them. It is worth noting that many of these formulations are original contributions to the field. Given the specific configuration of the system with components requiring short-term and long-term operation scheduling and the large-scale nature of the optimization problem, a decomposition algorithm is proposed that breaks down the problem into three sequential layers: long-term, short-term, and ultra-short-term. Each layer addresses specific planning horizons, time resolutions, and optimization models, enabling effective optimization of the system's operation. The proposed optimization algorithm offers an effective framework for planning and optimizing ICE-Harvest operation at various time horizons and resolutions. It demonstrates the system's flexibility in performing waste energy harvesting and sharing, demand management, and dynamic switching between energy carriers based on real-time prices. / Dissertation / Doctor of Philosophy (PhD) / This dissertation aims to develop an energy management system for an integrated smart energy system, called integrated community energy and harvesting (ICE-Harvest). The ICE-Harvest system is envisioned as the future of energy systems for dense com munities in cold climates. This system comprises a single-pipe variable-temperature micro-thermal network, a micro-electrical network, and distributed energy resources. The goal is to coordinate all the variables and assets so that the system’s capabilities in harvesting waste energy to offset the community’s thermal demands, performing demand management without affecting occupants’ comfort, and realizing energy arbi trage are realized. For this aim, a hierarchical decision-making framework is developed in which three sequential layers are integrated. The three layers determine the long term, short-term, and ultra-short-term optimal operation of the ICE-Harvest system. The layers are differentiated by their objective, planning horizon, time resolution, and optimization models.

abstract: One of the key infrastructures of any community or facility is the energy system which consists of utility power plants, distributed generation technologies, and building heating and cooling systems. In general, there are two dimensions to “sustainability” as it applies to an engineered system. It needs to be designed, operated, and managed such that its environmental impacts and costs are minimal (energy efficient design and operation), and also be designed and configured in a way that it is resilient in confronting disruptions posed by natural, manmade, or random events. In this regard, development of quantitative sustainability metrics in support of decision-making relevant to design, future growth planning, and day-to-day operation of such systems would be of great value. In this study, a pragmatic performance-based sustainability assessment framework and quantitative indices are developed towards this end whereby sustainability goals and concepts can be translated and integrated into engineering practices. New quantitative sustainability indices are proposed to capture the energy system environmental impacts, economic performance, and resilience attributes, characterized by normalized environmental/health externalities, energy costs, and penalty costs respectively. A comprehensive Life Cycle Assessment is proposed which includes externalities due to emissions from different supply and demand-side energy systems specific to the regional power generation energy portfolio mix. An approach based on external costs, i.e. the monetized health and environmental impacts, was used to quantify adverse consequences associated with different energy system components. Further, this thesis also proposes a new performance-based method for characterizing and assessing resilience of multi-functional demand-side engineered systems. Through modeling of system response to potential internal and external failures during different operational temporal periods reflective of diurnal variation in loads and services, the proposed methodology quantifies resilience of the system based on imposed penalty costs to the system stakeholders due to undelivered or interrupted services and/or non-optimal system performance. A conceptual diagram called “Sustainability Compass” is also proposed which facilitates communicating the assessment results and allow better decision-analysis through illustration of different system attributes and trade-offs between different alternatives. The proposed methodologies have been illustrated using end-use monitored data for whole year operation of a university campus energy system. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2018