Demand-response is an increasingly valuable resource option that play a significant role in the reliable operation of electric grid by modifying the consumers' electricity usage especially during peak periods. Time-base tariff or other forms of financial incentives are used as methods of engaging end-users in demand-response program. In this thesis, we develop system models for smart buildings that involve reduction of energy consumption for acceptable levels of occupants' comfort.

Initially, we apply a framework for the simultaneous control of temperature, illumination and window roller blind position in a building. The occupants are allowable to adjust their comfort preference to a strict, mild or loose level. The cost function has two parts including energy consumption an...[ Read more ]

Demand-response is an increasingly valuable resource option that play a significant role in the reliable operation of electric grid by modifying the consumers' electricity usage especially during peak periods. Time-base tariff or other forms of financial incentives are used as methods of engaging end-users in demand-response program. In this thesis, we develop system models for smart buildings that involve reduction of energy consumption for acceptable levels of occupants' comfort.

Initially, we apply a framework for the simultaneous control of temperature, illumination and window roller blind position in a building. The occupants are allowable to adjust their comfort preference to a strict, mild or loose level. The cost function has two parts including energy consumption and comfort dissatisfaction, each of which is expected to be minimized based on the users' comfort settings. The control strategy is Model Predictive Control (MPC) and it computes a trajectory of future manipulated variables to optimize future room temperature, illumination and outside view along with the minimum possible departure from the desired level. Weather data like solar radiation, solar illumination and outside temperature are considered in the model with the aim of taking advantage of daylight without disrupting other comfort levels. Simulation analyses are performed for the summer and winter days revealing the influence of the roller blind position on the building total energy consumption.

Later, we go further and study an aggregation of buildings and consider demand-side flexibility in proving the frequency regulation service. We particularly focus on the flexibility of thermal systems in the buildings and propose a hierarchical demand-response market with a three-step algorithm to model the interactions between the three entities: Independent Service Operator (ISO), aggregators, and end-users. In step1, a robust optimization approach is examined to improve the user's decision making subject to the electricity price uncertainty. The deterministic and robust solutions are compared to explain the influence of price uncertainty on the users' contribution in the frequency regulation service and daily energy payment. The importance of comfort weight factor on the demand-side power consumption profile as well as the corresponding up and down reserve are also investigated. In step 2, to model the interaction between ISO and aggregators, a bi-level optimization problem is solved, in which ISO seeks to minimize its cost, while the aggregators maximize their benefits in a day-ahead market. In step 3, each aggregator allocates its successful trading reserve among end-users based on their performance score. Test results show that the performance-based allocation of reserve may be a good scheme to motivate participant resources to respond accurately to the real-time frequency regulation signal.