Date of Thesis


Thesis Type

Masters Thesis

Degree Type

Master of Science in Electrical Engineering


Electrical Engineering

First Advisor

Philip Asare


Virtual energy storage (VES) refers to an indirect method of storing energy without using a battery. In a residential setting, VES uses the building structure interior appurtenances together with its physical properties as an energy storage device. It represents a methodology in energy storage mechanisms to help with load management in residential microgrids. It is an approach that is critical to the necessary paradigm shift from the less flexible and more costly "demand response" energy market of the present to the more flexible and potentially less costly "availability response" energy market of the future. This work quantifies VES monetary cost-savings potential for residential homes, as part of an effort to develop smart systems (using power sensors, and simple computation and control mechanisms) to assist individuals in making decisions about energy use that will save energy and, consequently, electricity costs. The project also compares the cost-effectiveness of VES to that of battery energy storage (BES)¿currently the more traditional and widely-advocated-for approach to energy storage for load management. In addition, this project devises a load management framework for a residential microgrid, where strategies that enable energy and cost savings for both utilities and consumers are tested. To make a home act as its own storage device, we need to intelligently control its heating, ventilation, and air conditioning (HVAC) system. Through this control, we can harness the house's thermal storage abilities by methods such as preheating or precooling the house (with due consideration to user comfort) during periods when energy is less expensive so that this heat or coolness will be retained during higher-cost periods. A well-insulated residential home equipped with sensing technology and intermittent generation resources will be utilized as a testbed for this project. Using a testbed is advantageous as it provides realistic results as well as a platform where behavior of the home can be learned. By combining modeling techniques with test results from a live testbed, cost-saving solutions can be simulated and later evaluated. This work provides a means to determine how to reduce peak demand and save costs for both utilities and consumers by changing consumer behavior, while respecting consumer thermal comfort preferences. Additionally, by creating the aforementioned modeling framework, we provide the load management community with tools by which they can readily test their optimization algorithms. By so doing, more efficient algorithms can be developed (potentially leading to increased residential energy efficiency).