This dataset contains the benchmark files that used in "Reclaimer Scheduling in Dry Bulk Terminals", IEEE Acccess, 2020.

Instructions: 

The data files are in IBM CPLEX Optimization Studio format. For parameter names, please refer to the article.

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This work focuses on using the full potential of PV inverters in order to improve the efficiency of low voltage networks. More specifically, the  independent per-phase control capability of PV three-phase four-wire inverters, which are able to inject different active and reactive powers in each phase, in order to reduce the system phase unbalance is considered.  This new operational procedure is analyzed by raising an optimization problem which uses a very accurate modelling of European low voltage networks.

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This report outlines the derivation of the first-, second-, and third-order Taylor series expansions of the power flow solution; it is the Electronic Companion of the following paper:

R. A. Jabr, “High-order approximate power flow solutions and circular arithmetic applications,” IEEE Transactions on Power Systems, vol. 34, no. 6, pp. 5053-5062, November 2019.

The derivation is carried out in complex variables via the use of Wirtinger calculus.

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1. Figure S1 shows the plasma frequency profile of the two-layer analytical model of the ionosphere, see Eq. (9) of the main text.

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These datasets include the results from the comparison of different traffic-free path planning strategies presented in the work entitled "A primitive comparison for traffic-free path planning",  Antonio Artuñedo, Jorge Godoy, Jorge Villagra. https://doi.org/10.1109/ACCESS.2018.2839884

Instructions: 

The dataset files are named as follows: p'x'.csv, where 'x' is the percentile used to filter data included in the file. Each file contains data of both considered scenarios.

 

The content of the datasets is organized in set a columns that represent the concrete test cases setup included in each row.

The columns order is:

  1. ID: Test case identifier.
  2. ID_num: Test case number.
  3. Scenario: Scenario number.
  4. RP select method: Referenc points selection method
  5. Primitive: Primitive used in the test case
  6. RP opt. method: Reference points optimization method
  7. RP opt. algorithm: Reference points optimization algorithm
  8. RP cost fcn.: Cost function used in reference points optimization
  9. SP opt. method: Seeding points optimization method
  10. SP opt. algorithm: Seeding points optimization algorithm
  11. SP cost fcn.: Cost function used in seeding points optimization
  12. Init. heading: Initial heading setting
  13. Final heading: Final heading setting
  14. Init. curv.: Initial curvature setting.
  15. Final curv.: Final curvature setting.
  16. K_t (exc. time): time KPI 
  17. K_kmax: Maximum curvature KPI
  18. K_k0: KPI related to curvature along the path
  19. K_k1: KPI related to the first derivative of curvature along the path
  20. K_k2: KPI related to the second derivative of curvature along the path
  21. K_cl: KPI related to centreline offset.

For further details please find the paper in this link: https://doi.org/10.1109/ACCESS.2018.2839884, or contact the corresponding author at antonio.artunedo@car.upm-csic.es

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