Abstract
The optimal power flow (OPF) problem plays an important role in power system operation and control. The problem is nonconvex and NP-hard, hence global optimality is not guaranteed and the complexity grows exponentially with the size of the system. Therefore, centralized optimization techniques are not suitable for large-scale systems and an efficient decomposed implementation of OPF is highly demanded. In this paper, we propose a novel and efficient method to decompose the entire system into multiple sub-systems based on automatic regionalization and acquire the OPF solution across sub-systems via a modified MATPOWER solver. The proposed method is implemented in a modified solver and tested on several IEEE Power System Test Cases. The performance is shown to be more appealing compared with the original solver.
Highlights
Optimal power flow (OPF) was first formulated by Carpentier [1], and has since become a crucial task in power system operation and control
Compared with existing distributed OPF solutions which usually conduct local optimization within each subsystem, but involve few interactions among different subsystems, we introduce two additional layers of iterations to search for the optimal solution: (i) for a single strategy, based on multiple regionalization strategies, we iteratively search for better OPF solutions across different subsystems; (ii) for multiple strategies, combinations of strategies will be created based on previously obtained optimization results and time consumption for convergence, we iteratively search for better OPF
We present the implementation of simulations to our proposed decomposed method and compare the results with centralized OPF using the solver provided by MATPOWER [24,25,26]
Summary
Optimal power flow (OPF) was first formulated by Carpentier [1], and has since become a crucial task in power system operation and control. One possible and popular way to address the non-convexity nature of the problem and approach the global optimality is to first convexify the problem via relaxation techniques so that a lower bound of the OPF could be found This lower bound can be used to evaluate and guide the search for a feasible solution (see e.g., [6,7,8,9,10,11,12,13,14]). Automatic: an automatic regionalization technique is proposed to decompose the original large system into smaller subsystems, which will balance the computational loads of the resultant subsystems and achieve the maximum independence of the variables in different subsystems to accelerate the convergence speed in iterative optimization. Symbols in bold capital letter such as Y, G and B are matrices and the symbols in bold lowercase letters such as i and v are vectors
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