Evaluation and Improvement of the Residential Energy Hub Management System

• Main Author: Hashmi, Syed Ahsan
• Language: en
• Published: 2010
• Subjects: Conservation and Demand Management; Demand Response; Electrical and Computer Engineering
• Online Access: http://hdl.handle.net/10012/5571

Energy consumption in the residential sector of Ontario is expected to grow by 15%, most of which is expected to be from electricity use, with an annual average growth rate of 0.9% between 2010 and 2020. With Ontario government’s Integrated Power System Plan (IPSP) recommending phasing out coal fired generators by 2014, the execution of Conservation and Demand Management and Demand Response programs can have significant impact on reducing power consumption and peak demand in the province. Electricity generation, especially from fossil fuel, contributes 18% of total green house gas (GHG) emissions in Ontario. With climate change effects being attributed to GHG emissions and environmental regulations, it is necessary to reduce GHG emissions from power generation sector. In this context, the current Energy Hub Management System project, of which the work presented here is a part, may lead to the reduction of electricity power demand and GHG emissions in Ontario. This thesis presents the validation of Energy Hub Management System (EHMS) residential sector model. Performances of individual appliances and the results obtained from various case-studies considering the EHMS model are compared with respect to a base case representing a typical residential customer. The case-studies are carefully developed to demonstrate the capability of the EHMS model to generate optimum operational schedules to minimize energy costs, energy consumption and emissions based on user defined constraints and preferences. Furthermore, a forecasting methodology based on single variable econometric time series is developed to estimate day-ahead CO2 emissions from Ontario’s power generation sector. The forecasted emissions profile is integrated into the EHMS model to optimize a residential customer’s contribution to CO2 emissions in Ontario.