Behavior Driven Predictive Energy Management System for Residential Buildings Within a Smart Grid

Date
2017
Authors
Mirakhorli, Amin
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Abstract

The inelastic energy consumption of buildings as the major consumer of electricity has forced the generation side to take all the control responsibilities in regulating voltage, frequency, and load. This is while the generators have limited capabilities and rates in load shifting. This dissertation introduces a single family residential building to grid integration method starting with individual appliance control and people behavior. This dissertation tries to close the gap between three paths of research which are: occupancy based appliances control, model predictive control (MPC) building energy management system (BEMS) and building-to-grid (B2G) integration. Firstly, a building energy management solution is introduced considering price and behavior in controlling major consumers of electricity in a residential building. An air conditioner (AC), water heater, electric vehicle (EV), and battery storage are controlled in a PV equipped building considering peoples' behavior in using these devices. Model predictive control is configured in a linear format to minimize operating cost considering operational constraints and system model in each device. A centralized and decentralized configuration of MPC for building energy management is formulated and simulated for a residential building using smart meter data. These two MPC configurations were put in contrast for the time of use pricing, hourly pricing, and five-minutes pricing. Simulation results show that real-time five minutes pricing can achieve 20% to 42% savings, time of use 13% to 26% savings and hourly 7% to 17% savings in different appliances operation. Beside the higher saving in five minutes pricing, the average building load is more smoothed for this pricing scheme. Later, an aggregation method is introduced for distribution grid nodal pricing for voltage and load control. 15000 buildings behavior were inverse sampled from annual smart meter data of 104 buildings and simulated on a 342-node networked distribution grid in an aggregated community. The results of this simulation show 21% generation cost reduction; 17% peak load shaving and reduced voltage drop at critical nodes. Stability of such a price and behavior driven integration is studied and finally, a software implementation is introduced. This dissertation shows capabilities of price and behavior driven BEMS in participating in an aggregated load and voltage control in a distribution grid while maintaining people satisfaction for each individual appliances control.

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This item is available only to currently enrolled UTSA students, faculty or staff.
Keywords
Building to grid integration, Electric vehicle, HVAC, Model predictive control, Smart building, Smart grid
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Department
Mechanical Engineering