Planning Future Grids with Combined Transmission and Distribution Grid Infeasibility Analysis
Conference Year
2024
Abstract
Integration of Distributed Energy Resources (DERs) like photovoltaics, battery storage systems, heat pumps, and electric vehicles has transformed the electric grid architecture and made it more heterogenous, distributed, and sustainable. Although the changing grid architecture offers many benefits, recognizing its inherent complexities is critical, especially in the context of co-dependency between the transmission and distribution grid operation and planning. With the proliferation of DERs, there is an increasing need for a novel approach that enables the integrated operation and control of Transmission and Distribution (T&D) networks. Infeasibility analysis helps in identifying the weak spots (buses which need support e.g. voltage support) in the grids, providing valuable information to system planners about potential requirements for new or upgraded infrastructure. Additionally, it aids grid operators in identifying areas of the system that may face reliability challenges during extreme conditions. The objective of the research is to efficiently model and simulate both large-scale transmission networks and three-phase distribution networks within a unified solution algorithm. This includes incorporating infeasibility analysis studies to quantify and localize weak points. In the initial phase of the research, the focus lies on modeling the combined Transmission and Distribution (T&D) network. This involves incorporating a coupling port that facilitates the exchange of information between the transmission and distribution networks. Subsequently, a distributed optimization technique is applied to execute the infeasibility analysis. This integrative approach not only ensures effective communication within the T&D networks but also employs optimization methods to assess and address potential infeasibilities. In conclusion, the proposed approach will undergo testing in larger networks to ensure scalability.
Primary Faculty Mentor Name
Amritanshu Pandey
Status
Undergraduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Electrical Engineering
Primary Research Category
Engineering and Math Science
Planning Future Grids with Combined Transmission and Distribution Grid Infeasibility Analysis
Integration of Distributed Energy Resources (DERs) like photovoltaics, battery storage systems, heat pumps, and electric vehicles has transformed the electric grid architecture and made it more heterogenous, distributed, and sustainable. Although the changing grid architecture offers many benefits, recognizing its inherent complexities is critical, especially in the context of co-dependency between the transmission and distribution grid operation and planning. With the proliferation of DERs, there is an increasing need for a novel approach that enables the integrated operation and control of Transmission and Distribution (T&D) networks. Infeasibility analysis helps in identifying the weak spots (buses which need support e.g. voltage support) in the grids, providing valuable information to system planners about potential requirements for new or upgraded infrastructure. Additionally, it aids grid operators in identifying areas of the system that may face reliability challenges during extreme conditions. The objective of the research is to efficiently model and simulate both large-scale transmission networks and three-phase distribution networks within a unified solution algorithm. This includes incorporating infeasibility analysis studies to quantify and localize weak points. In the initial phase of the research, the focus lies on modeling the combined Transmission and Distribution (T&D) network. This involves incorporating a coupling port that facilitates the exchange of information between the transmission and distribution networks. Subsequently, a distributed optimization technique is applied to execute the infeasibility analysis. This integrative approach not only ensures effective communication within the T&D networks but also employs optimization methods to assess and address potential infeasibilities. In conclusion, the proposed approach will undergo testing in larger networks to ensure scalability.