Distributed optimal power flow for smart grid transmission system with renewable energy sources

Utilizing renewable energy sources to reduce carbon emission and minimizing the fuel cost for energy saving in the OPF (optimal power flow) problem will contribute to reducing the global warming effect from the power generation sector. In this paper, we propose a DPOPF (distributed and parallel OPF) algorithm for the smart grid transmission system with renewable energy sources to account for the fast variation of the power generated by renewable energy sources. The proposed DPOPF algorithm is a combination of the recursive quadratic programming method and the Lagrange projected gradient method; it can achieve the complete decomposition and can be executed in the smart grid transmission system to make distributed and parallel computation possible. We also propose Petri nets to control the computational synchronization of the DPOPF algorithm under the asynchronous data arrival in the smart grid transmission system. The proposed DPOPF algorithm is applied to solve OPF problems in a smart grid transmission system with renewable energy sources on a 26-bus test system. The test results demonstrate the computational efficiency of the proposed DPOPF algorithm, which is fast enough to cope with the fast variation of the power generated by renewable energy sources, and justify the accuracy of the obtained solutions.