Bai, for her continued support, encouragement and guidance throughout this research, as a mentor and a friend. Without her enduring patience and supervision, this milestone would not have been possible. My sincere gratitude also goes to Dr. Gerald Evans for constantly helping me, especially at the beginning of my graduate study as my first advisor. I am also grateful to Glasgow Electric Plant Board for important technical support on behalf of the TVA grant project. Being able to gain access to invaluable data helped me greatly. The knowledge and experience gained while working on the GEPB project is greatly appreciated and valued. Most importantly, I would like to thank my parents, Roshan Khadgi and Rita Khadgi, for their unconditional love and support. I thank my family for their prayers and their belief in my ability. The constant love and support from my beloved, Agrima Koirala, has been instrumental in my success and I thank her for inspiring me to always achieve greater heights. I wouldn"t be in this position without her. Lastly, I thank all my friends who have supported me along the way throughout this unforgettable journey.

When consumers' use of electricity is mainly driven by convenience, coincident demand occurs, resulting in electric load peaks. Consequently, the undesirable large gaps between peak and off-peak loads will adversely affect the system's efficiency due to unused capacity during off-peak hours and extra ancillary generators required during peak hours. Demand response (DR) has long been proposed to reduce peak load by providing incentives to encourage consumers to shift their peak loads to off-peak periods. In most DR literature, incentive schemes are purely financial in assuming that cost is the only parameter to influence consumers' load-shifting behavior. In this paper, we assume that in addition to cost, convenience of energy usage is also an important factor when consumers respond to DR programs. Hence, we use multi-attribute utility functions consisting of both cost and convenience factors to model consumer behavior on energy consumption for home appliances. The "convenience" herein is defined as being able to use an appliance during one's preferred time window. A simulation model is developed to study a residential population consisting B Lihui Bai 123 Khadgi et al.of heterogeneous households with varying preference of convenience over cost. We study the effects of time-of-use pricing structure on users' utility-based load shifting behaviors and subsequently on system-wide performances such as peak to average ratio (PAR) and load variance (LV). We also describe a method of design of experiment (DOE) for determining an optimal time-of-use rate structure that minimizes both PAR and LV for the system.

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In the interest of increasing energy efficiency and avoiding higher generation costs during peak periods, utility companies adopt various demand response (DR) methods to achieve load leveling or peak reduction. DR techniques influence consumer behavior via incentives and cause them to shift peak loads to off-peak periods. In this paper we study the energy consumption behavior of residents in response to a variable real-time pricing function. We consider thermostatic loads, specifically air conditioning, as the primary load and apply the model predictive control (MPC) method to study the behavior of consumers who make consumption decisions based on a trade-off between energy cost and thermal comfort. An agent-based simulation is used to model a population where each household is an agent embedded with the MPC algorithm. Each household is associated with a multi-attribute utility function, and is uniquely defined via the use of stochastic parameters in the utility function. INTRODUCTIONResidential electricity consumption behavior is by nature unpredictable and thus raises much interest in demand response (DR). In power economics literature, DR has long been proposed for incentivizing consumers to change their energy consumption behavior in achieving load leveling. Energy efficiency in a grid network can be achieved if the system load can be accurately predicted and balanced. DR tries to change the energy consumption behavior of consumers by providing them with financial incentives and education, encouraging them to use less energy during peak hours and more energy during off-peak hours in an attempt to level the system load. However, most DR programs provide financial incentives and assume that consumer behavior is driven primarily by cost. Fahrioglu and Alvarado (2000) applied game theoretical principles to study the interaction between the utility company and its customers. They obtained load relief during peak times by designing incentive compatible contracts that used nonlinear cost functions. Mohsenian-Rad et al. (2010) discussed the use of a distributed algorithm on smart meters to find optimal consumption schedules for subscribers. They achieved peak load reduction by using a pricing scheme based on non-linear cost functions and game theory analysis. Samadi et al. (2010) proposed a real-time pricing algorithm based on utility maximization. In our research, we study consumer behavior as a function of their comfort as well as cost incentives, because it is unrealistic to assume that all people value these incentives equally. The Annual Energy Outlook 2012 report from the US Energy Information Administration (EIA 2012) indicated that residential customers contributed about 37% of the total energy used in 2011. Thermostatically controlled loads (TCL) make up about 45% of the total residential energy use; 23% is attributed to air conditioning alone. Therefore, in this paper we focus mainly on the consumer behavior of 288 978-1-4799-7486-3/14/$31.00 ©2014 IEEE