Description
Affordable housing is a serious issue in certain counties in Maine. High utility costs are one aspect of the problem which have been controlled historically through federal and state assistance. This thesis proposes an alternative method: reducing utility costs through wind renewable energy and implementation of a residential energy management system. The scenario modeled is an apartment complex located near the coast in winter. The building is separately metered and electricity costs are categorized as owner or tenant expenses. Property management has recently upgraded the heating system and appliances in all apartments and common areas to energy efficient electric models. An energy management system is installed in the apartments and office. This system enables cost savings through appliance load shifting, which requires an electricity pricing agreement which varies cost by time of day. Two electricity pricing schemes are examined. One plan, which is currently offered by the utility, varies pricing by time of day. The cost of electricity in the second plan is higher, and cost varies in both power and time. The energy management system has both forecasting and optimization capabilities. An Adaptive Neuro Fuzzy Inference System (ANFIS) in each unit predicts a schedule for certain appliances. The unit controller calculates the cost to operate the appliances. A branch and bound optimization program then provides the user with a less expensive schedule. The complex property is ideal for wind energy, and property management investigates the option of installing a wind turbine and sharing energy produced among tenants and landlord. Cost analysis is performed on a 10 kW and 50 kW turbine. Analysis shows the 10 kW turbine saves tenants and landlord on average roughly fifteen percent. The 50 kW turbine provides nearly eighty percent savings for all units under one pricing plan and exceeds energy needs under the other plan. Even though the 50 kW turbine provides greater savings, certain metrics favor purchasing the 10 kW turbine. The energy management system provides two percent savings under one pricing plan and six percent savings under the other plan each month. Wind energy combined with the energy management system provides savings beyond what may be achieved with either system alone under the time and power-varying pricing plan.
