Adaptive Barrier Filter Line-search ipm for Optimal Power Flow with facts devices

Fig. 6. Number of Iteration to Convergence with Different


Fig. 7. Number of Iteration to Convergence with Different

4. 
   Relationship between System Scale and Performance
   

We generate another set of P1 type problems on 14-, 30-, 39-, 57-, 118-, 300-, 2736-, 3012-, and 3120-bus systems. With the proposed ABFLS IPM applied to those problems, the numbers of iterations and time for all problems are presented in Table 8.

Table 8 Performance of the ABFLS IPM on Different Systems

Systems	14	30	39	57	118	300	2736	3012	3120
iter	16	18	29	15	44	39	37	36	50
time (s)	0.8	1.0	1.3	0.9	3.1	4.8	113.3	181.0	261.8

The number of iterations is not sensitive to problem scale, while the time consumed per iteration is nearly propositional to the problem scale. Hence there is roughly a linear or at least a super-linear relationship between the overall time and the problem scale.

5. Conclusion

In the engineering computation practice, the convergence reliability of exist IPM algorithms is not high enough when they are applied to solve the OPF problems, especially there are a large quantity of FACTS devices in power systems. In view of this, three measures, namely the adaptive barrier update strategy, the filter line-search method and the feasibility restore phase, have been simultaneously introduced in the conventional primal-dual interior point method framework to enhance the robustness of OPF algorithms in this paper. About ten thousands of numerical tests on both standard systems and large-scale real-world systems for this ABFLS IPM have demonstrated its convergence reliability. Comparative case studies show that the performance of the PC and MCC algorithms degrades as system scale and the FACTS device number increase. The proposed ABFLS algorithm is reliable and efficient, and outperforms the famous PD, PC and MCC algorithms in both robustness and efficiency.

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