Optimal Energy Dispatch of Integrated Community Energy and Harvesting (ICE-Harvest) System

Abstract

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.