[論文レビュー] AC Power Flow Data in MATPOWER and QCQP Format: iTesla, RTE Snapshots, and PEGASE

Cédric Josz, S. Fliscounakis+2 more|arXiv (Cornell University)|Mar 4, 2016

Power System Optimization and Stability参考文献 11被引用数 111

ひとこと要約

Nine realistic MATPOWER-format AC power flow test cases (iTesla, RTE snapshots, and PEGASE) and a MATLAB tool to convert them to a QCQP formulation for OPF analysis, with initial numerical results and discussion on optimality bounds.

ABSTRACT

In this paper, we publish nine new test cases in MATPOWER format. Four test cases are French very high-voltage grid generated by the offline plateform of iTesla: part of the data was sampled. Four test cases are RTE snapshots of the full French very high-voltage and high-voltage grid that come from French SCADAs via the Convergence software. The ninth and largest test case is a pan-European ficticious data set that stems from the PEGASE project. It complements the four PEGASE test cases that we previously published in MATPOWER version 5.1 in March 2015. We also provide a MATLAB code to transform the data into standard mathematical optimization format. Computational results confirming the validity of the data are presented in this paper.

研究の動機と目的

• Provide realistic AC power flow test cases for the research community to benchmark OPF methods.
• Offer data originating from iTesla, RTE snapshots, and PEGASE to reflect French and European grid characteristics.
• Deliver a MATLAB tool to transform MATPOWER data into a standard QCQP format for broader applicability.

提案手法

• Publish nine MATPOWER-format test cases (iTesla, RTE snapshots, PEGASE) with detailed origin and data conversion notes.
• Convert snapshots to MATPOWER by applying adjustments (e.g., zeroing Pmin for negative-generation units) to fit MATPOWER conventions.
• Provide a MATLAB QCQP converter (qcqp_opf.m) that maps MATPOWER data to a standard complex/real QCQP form with matrices C, Ak, Bk.

実験結果

リサーチクエスチョン

• RQ1 How realistic are the published test cases in capturing grid characteristics across French and European contexts?
• RQ2 Can a standard QCQP formulation faithfully represent the AC OPF problem for these datasets?
• RQ3 What initial feasible solutions and lower bounds can be obtained for OPF on these datasets using conventional and SDP-based methods?
• RQ4 What are the practical limitations of current SDP relaxations when applied to large-scale instances from PEGASE and RTE data?

主な発見

Case Name	nVAR	nEQ	nINEQ	Spa. (%)
case89pegase	178	154	380	5.23
case1354pegase	2708	2188	6612	0.19
case1888rte	3776	3222	9036	0.13
case1951rte	3902	3162	9580	0.12
case2848rte	5696	4904	11742	0.08

• Nine test cases are provided: four from iTesla (French VHV grid), four from RTE snapshots (full French VHV+HV grid), and the largest from PEGASE (pan-European fictitious grid).
• Knitro-based OPF can be solved for these cases, with parameter tuning (xtol adjustments) affecting convergence and feasibility.
• DCOPF provides a trivial lower bound; ACOPF (Knitro) solutions are feasible but locally optimal, with reported optimality gaps between 0.87% and 2.14% across cases.
• SDP-based lower bounds (SDPOPF with Sedumi or Mosek) produce variable results; Mosek yields self-consistent bounds while Sedumi often faces numerical issues; global optimality proofs range from 0.00% to 2.14%.
• Global optimality proofs using SDP relaxations are challenging for the largest cases (6k–13k buses), but the authors demonstrate scalability to large problems on their hardware, noting numerical cautions.
• A QCQP conversion tool demonstrates that OPF can be framed as a sparsity-aware QCQP, enabling broader methodological testing by the applied mathematics community.