Optimization and Control of Transmission and Distribution System with High Integration of Distributed Energy Resources

With the modernization of power grids, high penetration of distributed energy resources (DERs), and modern state-of-the-art loads are increasing to the grid, optimal power flow (OPF) analysis is one of the essential tools for reliable power system planning, operation, and control. This research proposes novel OPF power distribution and transmission network models using non-linear programming (NLP). The advantages of NLP-based OPF models are that they are accurate and give optimal global solutions for both transmission and distribution networks. To confirm solution accuracy, the necessary conditions for the formulations of power flow models are addressed in this research work for the proposed OPF models. In this dissertation, a centralized and distributed model based on NLP is proposed for OPF analysis in power distribution networks, solved with the Sequential Quadratic Programming (SQP) algorithm. This work proposes the method and illustrates the necessary conditions for the global optimality of the solution. The main advantages of the methods are obtaining global optimal solutions in less than a minute (for more than 2000 nodes with high penetration of DERs) without approximations and relaxations in power flow equations and improving scalability by reducing the number of iterations significantly for both centralized and distributed OPF. This research also proposes an SQP-based centralized and distributed optimal power flow (D-OPF) method for bulk power transmission grids with a modified equal network approximation framework (MENA). Moreover, a method is proposed for integrated transmission-distribution (T&D) OPF, and the efficiency of the integrated (T&D) is shown. This article also proposed a novel volt-var-optimization (VVO)-based centralized closed-loop voltage management co-simulation methodology that uses an NLP-based optimization (OPF) model and a real-time Opal-RT simulator. This research developed a real-time model of an integrated T\&D network with fully detailed and dynamic models of energy sources such as synchronous generators and inverter-based resources (IBRs) to fully understand the voltage profile of both the transmission and distribution system, with penetration of IBRs on both sides, and a volt/var optimization framework is developed to control the distribution side voltage. The models are validated using different IEEE test cases consisting of both transmission and distribution networks. The simulated results prove the accuracy and efficiency of the models.