Fast large-scale optimal power flow analysis for smart grid through network reduction

Yi Liang, Deming Chen

Abstract: Optimal power flow (OPF) plays an important role in power system operation. The emerging smart grid aims to create an automated energy delivery system that enables two-way flows of electricity and information. As a result, it will be desirable if OPF can be solved in real time in order to allow the implementation of the time-sensitive applications, such as real-time pricing. In this paper, we present a novel algorithm to accelerate the computation of alternating current optimal power flow (ACOPF) through power system network reduction (NR). We formulate the OPF problem based on an equivalent reduced system and interpret its solution and the detailed optimal dispatch for the original power system is obtained afterwards using a distributed algorithm. Our results are compared with two widely used methods: full ACOPF and the linearized OPF with DC power flow and lossless network assumption, the so-called DCOPF. Experimental results show that for a large power system, our method achieves 7.01X speedup over ACOPF with only 1.72% error, and is 75.7% more accurate than the DCOPF solution. Our method is even 10% faster than DCOPF. Our experimental results demonstrate the unique strength of the proposed technique for fast, scalable, and accurate OPF computation. We also show that our method is effective for smaller benchmarks.