Modeling and management of dynamic loads in power systems

Load modeling;Model identification;Dynamic load;Clustering;Nonlinear least-squares;Electric vehicle charging;Demand response;Dual-decomposition

Zhang, Kaiqing ; Zhu ; Hao

关键词: Load modeling;    Model identification;    Dynamic load;    Clustering;    Nonlinear least-squares;    Electric vehicle charging;    Demand response;    Dual-decomposition;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/99097/ZHANG-THESIS-2017.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Recent advances in power systems have led to the proliferation of dynamic, diverse, and even flexible loads in the system operations. An accurate as well as identifiable model that is able to characterize the dynamics of loads is of paramount importance for various power system operational tasks. Towards the goal of advanced load modeling, we are particularly interested in modeling this type of dynamic load, a diverse category of loads that pose different challenges in different contexts of power system operations. In this thesis, improved dynamic load modeling approaches are developed and analyzed for two critical operational tasks in power systems: transient stability analysis and demand side management.As regards transient stability analysis, one newly proposed load model structure, the WECC composite load model (CMPLDW), is investigated for its complexity with an large number of parameters to identify. We verify the underlying parameter redundancy stemming from the insensitivity and interdependency of these parameters. A general framework is then put forward to effectively visualize the redundancy and exhibit the identifiability issues of this load model. Furthermore, an improved parameter estimation scheme is developed by regularizing the nonlinear least squares error objectives in the measurement-based modeling approach. The effectiveness of the proposed dependency analysis and parameter estimation scheme is validated using both synthetic and real measurement data.In demand side management, one appealing objective of load modeling is to explore its spatio-temporal variability andflexibility for socially economic benefits. To this end, the demand of loads can be managed by pricing signals, i.e., the loads are modeled as price-responsive ones. In particular, here we consider one primary type of dynamic load, the charging load of electric vehicles (EV) en route. To comply with the time-varying property of EV travel demand, we integrate the characterization of EV traffic flow into the modeling of charging loads. Therein the power and transportation networks are coupled to jointly maximize the total social welfare of both systems. Additionally, to achieve the maximum social welfare, an optimal pricing scheme that preserves the privacy of the two infrastructure networks is developed. Through extensive numerical tests, the proposed pricing is shown to outperform other pricing schemes that fail to consider either the interaction of the two networks or the time-varying property of EV travel demand.