Modeling and Controller Design of a Community Microgri

Microgrids are becoming increasingly popular in recent decades. The benefits of microgrids include accelerating improvement, increasing reliability, helping customers save money, reducing greenhouse gas emission, etc. Distributed energy resources (DER), e.g. combined heat and power (CHP) units, photovoltaics (PV), energy storage (ES) units, as well as thermal and electrical loads are commonly seen components in community-level microgrids. Commercial-residential hybrid buildings are commonly seen in high population density cities, especially in Asian countries, like China, Korea and Japan. With rapid growth of microgrid application, a community-level hybrid microgrid consisted of DG resources and demand response (DR) resources, commercial-residential hybrid buildings, will become possible in the future.;In this dissertation, four aspects of the modeling and controller design of a communitylevel hybrid microgrid are researched on. (1) A real-time dynamic CHP model for microgrid applications is presented. The CHP model includes three key components: generator, turbine, and absorption chiller. A new isochronous governor control strategy is proposed to provide zero-steady-state-error frequency regulation. The supply of building thermal loads is modeled to facilitate the calculation of the overall CHP system efficiency. The impact of ambient temperature on the maximum electrical output is considered. The developed model is implemented on OPAL-RTRTM for testing the microgrid controller performance in a microgrid system. (2) A data-driven, decoupled modeling method for Deriving model parameters of the thermostatically controlled appliance (TCA) is presented. HVAC is used as an example for demonstration. The method uses outdoor temperature and HVAC power consumption as inputs to estimate the parameters of the HVAC equivalent thermal parameter (ETP) model. A novel decoupled-ETP model is proposed to decouple the modeling of the "ON" and "OFF" periods of an HVAC unit to improve the modeling accuracy. Then, an adjusted decoupled-ETP model is developed to allow users to derive the model parameters using only the midnight data. The last method fits for cases where the daytime HVAC consumptions are heavily distorted by occupant activities. The methods are tested and validated on 100 houses using actual 1-minute HVAC consumption data. (3) A novel measurement-based, data-driven residential household load forecasting algorithm is presented. Compared with traditional forecasting techniques, the proposed method adds in more measurements of individual appliances in a house, such that measurable appliances are divided into different types based on their characteristics. Different forecasting algorithms are designed for each type of appliance. The proposed forecasting algorithm is verified by a series of simulation case studies, including model-generated and field-measurement individual appliance validation, total household forecasting validation and cost-benefit studies, comparison with other traditional forecasting algorithms, etc. (4) An optimal dispatch strategy called "Microgrid Commitment" (MC) algorithm is presented to optimally dispatch and schedule the commands of all DG and DR resources while supplying both electrical and thermal loads under islanding operations. The MC algorithm is composed of 2 optimization problem to deal with the conditions when DG and DR resources can or cannot supply baseloads. Three cases are performed to verify the effectiveness of the MC algorithm and the influence of forecasting accuracy on the MC algorithm.