Chance-Constrained Optimization-Based Unbalanced Optimal Power Flow for Radial Distribution Networks

• Published in: IEEE transactions on power delivery 2013-07, Vol.28 (3), p.1855-1864
• Main Authors: Yijia Cao, Yi Tan, Canbing Li, Rehtanz, C.
• Format: Article
• Language: English
• Subjects: Applied sciences; Chance-constrained optimization; distributed generation (DG); Electric power plants; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; Generators; Miscellaneous; multi objective optimal power flow; Operation. Load control. Reliability; Pareto optimization; Power networks and lines; Reactive power; Security; Stochastic processes; unbalanced distribution networks
• ISSN: 0885-8977; 1937-4208
• DOI: 10.1109/TPWRD.2013.2259509

Optimal power flow (OPF) is an important tool for active management of distribution networks with renewable energy generation (REG). It is better to treat REG as stochastic variables in the distribution network OPF. In addition, distribution networks are unbalanced in nature. Thus, in this paper, a chance constrained optimization-based multiobjective OPF model is formulated to consider the forecast errors of REG in the short-term operation of radial unbalanced distribution networks. In the model, expected total active power losses of distribution lines, expected overload risk and voltage violation risk with respect to N-1 contingencies are minimized, and inequality constraints in the normal state are satisfied with a predefined probability level. Thus, the profitability and security can be balanced in the presence of stochastic REG. The proposed multiobjective OPF problem is solved by the multiobjective group search optimization and the two-point estimate method. Simulation results show that distribution network economy and postcontingency performance deteriorate with increased penetration level of REG, and the penetration level has a greater impact than the forecast errors of REG.