Call for Competition on Evolutionary Computation in the Energy Domain: Optimal PV System Allocation

IEEE WCCI 2024 & GECCO 2024 (Joint competition)

June 30 – July 5 – Yokohama, Japan (WCCI 2024) | July 14 – July 18 – Melbourne, Australia (GECCO 2024)

Organized by Polytechnic of Porto (ISEP), Oracle America Inc., and Baylor University

Polytechnic of Porto (ISEP): João Soares, Fernando Lezama, José Almeida, Bruno Canizes, Zita Vale

Oracle America Inc.: Wenlei Bai

Baylor University: Kwang Y. Lee

Deadline extension: The deadline to send contributions was extended by three weeks until 21st June 2024.

28/02/2024: Our guidelines and platform have been updated to address encountered problems and implement necessary corrections. Please review the changes to ensure a smooth experience. Thank you for your understanding and cooperation.

Important: The use of the software platform is prohibited for purposes other than competition without a prior authorization of the organizing team.

Results Published: 19th of July 2024

Final Rank Results

Final RankNameAffiliation, CountryAlgorithmRI submittedRI validated
IEEE CIS Prize – 500 $
Yoan Martínez-López, Miguel Bethencourt, Julio Madera, Ansel RodriguezCamaguey University, Cuba; CICESE, MexicoVIEDA++Pro

Algorithm (link)
IEEE CIS Prize – 300 $
Tianyu Gao, Yi Xiang, Haoxiang QinSouth China University of Technology, ChinaQDDE

Algorithm (link)
IEEE CIS Prize – 200 $
Jakub KůdelaInstitute of Automation and Computer Science, Faculty of Mechanical Engineering, Brno University of Technology, Czech RepublicDE_isl

Algorithm (link)
4Marcelo Marrugo, Sergio Rivera, Sebastian Krumscheid, Ameena Al Sumaiti, Kannappan ChettiarUniversidad Nacional de Colombia; KARLSRUHE INSTITUTE OF TECHNOLOGY (KIT); Khalifa University; SWITCHING BATTERY companyESMBLED_METHOD​-7428.5036-7428.3405
5Dr. Vasundhara Mahajan; Mr. Maxwell Mendonca; Miss Shalini RamanDepartment of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat – 395007, IndiamHyDE-7429.1845-7428.0343
6Edgar G. Morquecho, Santiago P. TorresDepartment of Electrical, Electronics, and Telecommunications Engineering (DEET), University of Cuenca, Cuenca, EcuadorDE-PSO-BBBC-7323.3276-7416.3309
7Haoxiang Qin, Yi Xiang, Tianyu Gao, Yuyan Han, Yuting WangSouth China University of Technology, China; Liaocheng University, ChinaIHDEA​-7377.8496-7221.4425
8Rui Qi, Yahui JiaSouth China University of Technology, ChinaHyDE-LS-7433.6220-7210.3156
9Dr. Vasundhara Mahajan, Mr. Maxwell Mendonca, Miss Shalini RamanDepartment of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat – 395007, IndiaGA-PSO​-6138.4571

Competition Outline

Following the success of the previous editions at IEEE PES; CEC; GECCO, WCCI, we are launching another challenging edition of competition at major conferences in the field of computational intelligence. This edition of GECCO 2024 competition proposes one track in the energy domain:

Track 1) Optimal PV systems allocation in an unbalanced distribution network. As photovoltaic (PV) penetration into distribution networks continues to grow, the transition from passive to active networks has brought about a new level of complexity in terms of planning and operation. The optimal PV allocation (sizing and location) is challenging because it is mixed-integer non-linear programming with three-phase non-linear unbalanced power flow equations. The objective is to find the optimal PV systems allocation that maximizes the PV penetration within a predefined planning horizon while satisfying operation constraints such as voltage limits. The IEEE 37-bus test feeder is considered as case study.

Note: The track is developed to run under the same framework as past competitions.

Competition goals

The competition has been held since 2017 at major conferences (the first competition was launched at IEEE PES GM) – Website:

The WCCI/GECCO 2024 competition on “Evolutionary Computation in the Energy Domain: Optimal PV System Allocation” has the purpose of bringing together and testing the more advanced Computational Intelligence (CI) techniques applied to energy domain problems, namely an optimal PV allocation problem considering three-phase unbalanced power flow constraints due to the unbalanced distribution network. The competition provides a coherent framework where participants and practitioners of CI can test their algorithms to solve a real-world optimization problem in the energy domain. The participants have the opportunity to evaluate if their algorithms can rank well in the proposed problem since we understand the validity of the “no free lunch theorem,” making this contest a unique opportunity worth exploring the applicability of the developed approaches in a real-world problem beyond the typical benchmark and standardized CI problems.


-Participants will propose and implement metaheuristic algorithms (e.g., evolutionary algorithms, swarm intelligence, estimation of distribution algorithm, etc.) to solve the proposed track problem in the energy domain.

-The organizers provide a framework (Download codes), implemented in MATALAB© 2023a 64 bits, in which participants can easily test their algorithms (we also provide a hybrid-adaptive differential evolution algorithm implementation as an example). The guidelines (Download) include the necessary information to understand the problem, how the solutions are represented, and how the fitness function is evaluated. Also, we provide information on the mathematical formulation regarding the objective function value and problem constraints. Those elements are common for all participants.

-Since the proposed algorithms might have distinct population sizes and run for a variable number of iterations, a maximum number of “5000 function evaluations” is allowed in each trial for all participants. The convergence properties of the algorithms are not a criterion to be qualified in this competition.

-10 independent trials should be performed in the framework by each participant.

28/02/2024: Our guidelines and platform have been updated to address encountered problems and implement necessary corrections. Please review the changes to ensure a smooth experience. Thank you for your understanding and cooperation.

How to submit an entry

-The winner will be the participant with the minimum ranking index in the proposed track, which is calculated as the average and standard deviation values of the objective function value over the 10 trials.

– Each participant is kindly requested to put the text files corresponding to final results (see guideline document), as well as the implementation files (codes), obtained by using a specific optimizer, into a zipped folder named: (e.g.)

The zipped folder must be summited to;,
by 21 June 2024 (anywhere on Earth)

Important Remarks

– Notice that submission of papers or assistance to CEC and GECCO by competition participants is not mandatory.

– You can submit a paper to the

CEC-2024 Special Session on Computational Intelligence for Control and Optimization of Complex Energy Systems (Submit it here) – select Special Session: Computational Intelligence for Control and Optimization of Complex Energy Systems.

– You are also welcome to submit short descriptions of your algorithms and results as 2-page papers to be included in the GECCO Companion. This is voluntary — The submission deadline is April 2024. Submit it here (Competition Entry Submissions)


We are glad to announce that our competition will offer an IEEE Computational Intelligence Society (CIS) prize of a total of 1000 $, consisting of 500 $ for the participant with the best rank, 300 $ for the second best, and 200 $ for the third best. Good luck, and stay tuned. Thanks!

Submit your results by June 21st 2024 (anywhere on earth)

Further related bibliography

  • [1] F., Lezama, J. Soares, Z. Vale, J. Rueda, S. Rivera, & I. Elrich, 2017 IEEE Competition on modern heuristic optimizers for smart grid operation: Testbeds and results. Swarm and evolutionary computation, 44, 420-427, 2019
  • [2] F. Lezama, J. Soares, P. Hernandez-Leal, M. Kaisers, T. Pinto, and Z. Vale, Local Energy Markets: Paving the Path Towards Fully Transactive Energy Systems, IEEE Transaction on Power Systems, IEEE (2018).
  • [3] Joao Soares, Bruno Canizes, M. A. Fotouhi Gazvhini, Zita Vale, and G. K. Venayagamoorthy, “Two-stage Stochastic Model using Benders’ Decomposition for Large-scale Energy Resources Management in Smart grids,” IEEE Transactions on Industry Applications, 2017.
  • [4] F. Lezama, J. Soares, E. Munoz de Cote, L. E. Sucar, and Z. Vale, “Differential Evolution Strategies for Large-Scale Energy Resource Management in Smart Grids,” in GECCO ’17: Genetic and Evolutionary Computation Conference Companion Proceedings, 2017. 
  • [5] Joao Soares, Mohammad Ali Fotouhi Ghazvini, Marco Silva, Zita Vale, “Multi-dimensional signaling method for population-based metaheuristics: Solving the large-scale scheduling problem in smart grids”, Swarm and Evolutionary Computation, 2016.
  • [6] Joao Soares, Hugo Morais, Tiago Sousa, Zita Vale, Pedro Faria, “Day-ahead resource scheduling including demand response for electric vehicles”, IEEE Transactions on Smart Grid 4 (1), 596-605, 2013.
  • [7] F. Lezama, J. Soares, B. Canizes, Z. Vale, Z., Flexibility management model of home appliances to support DSO requests in smart grids. Sustainable Cities and Society, 55, 102048, 2020.


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