Optimization models and algorithms for demand response in smart grid.

optimization;demand response;distributed models;load control;pricing schemes

Guangyang Xu

University:University of Louisville

Department:Industrial Engineering

关键词: optimization;    demand response;    distributed models;    load control;    pricing schemes;   

Others  :  https://ir.library.louisville.edu/cgi/viewcontent.cgi?article=3660&context=etd

美国|英语

来源: The Universite of Louisville's Institutional Repository

【 摘 要 】

For demand response in smart grid, a utility company wants to minimize total electricity cost and end users want to maximize their own utility. The latter is considered to consist of two parts in this research: electricity cost and convenience/comfort. We first develop a system optimal (SO) model and a user equilibrium (UE) model for the utility company and end users, respectively and compare the difference of the two. We consider users' possible preference on convenience over cost-saving under the real-time pricing in smart grid, and each user is assumed to have a preferred time window for using a particular appliance. As a result, each user in the proposed energy consumption game wishes to maximize a payoff or utility consisting of two parts: the negative of electricity cost and the convenience of using appliances during their preferred time windows. Numerical results show that users with less flexibility on their preferred usage times have larger impact on the system performance at equilibrium. Second, we found that instead of minimizing total cost, if utility company is regulated to maximize the social welfare, the user equilibrium model can achieve identical optimal solution as the system optimal model. We then design a demand response pricing frame work to accomplish this goal under alternative secondary objectives. We also investigate the non-uniqueness of the user equilibrium solution and prove that there exist alternative user equilibrium solutions. In this case, robust pricing is considered using multi-level optimization for the user equilibrium. Third, we study empirical data from a demand response pilot program in Kentucky in an attempt to understand consumer behavior under demand response and to characterize the thermo dynamics when set point for heat, ventilation and air conditioning (HVAC) is adjusted for demand response. Although sample size is limited, it helps to reveal the great variability in consumers' response to demand response event. Using the real data collected, we consider to minimize the peak demand for a system consisting of smart thermostats, advanced hot water heaters and battery systems for storage. We propose a mixed integer program model as well as a heuristic algorithm for an optimal consumption schedule so that the system peak during a designated period is minimized. Therefore, we propose a consumption scheduling model to optimally control these loads and storage in maximizing efficiency without impacting thermal comfort. The model allows pre-cooling and pre-heating of homes to be performed for variable loads in