Call for Competition on Evolutionary Computation in the Energy Domain: Risk-based Energy Scheduling

GECCO 2022 & IEEE WCCI 2022 (Joint competition)

18-23 July – Padua, Italy (WCCI 2022) | 9-13 July – Boston, USA (GECCO 2022)

Organized by João Soares, Fernando Lezama, José Almeida, Bruno Canizes, Zita Vale

IEEE CIS PRIZE: 500$ IEEE Computational Intelligence Society (CIS) prize for the first ranked contestant. Good luck and stay tuned. Thanks!

UPDATES: The deadline for submitting the results is now extended to 30 June 2022.

The rule for the initialization of the initial solution has been updated (30.03.2022). See the Rules section.

Competition Outline

Following the success of the previous editions at IEEE PES; CEC; GECCO, WCCI, we are launching another challenging edition of the competition at major conferences in the field of computational intelligence. This edition of the WCCI/GECCO 2022 competition proposes one track in the energy domain:

Track 1) Risk-based optimization of aggregators’ day-ahead energy resource management (ERM) considering uncertainty associated with the high penetration of distributed energy resources (DER). This testbed is constructed under the same framework of past competitions (therefore, former competitors can adapt their algorithms to this new track), representing a centralized day-ahead ERM in a smart grid with a 13-bus distribution network using a 15-scenario case study with 3 scenarios considering extreme events (high impact, and low probability). A conditional value-at-risk (CVaR) mechanism is used to measure the risk associated with the extreme events for a confidence level (α) of 95%. We also add some restrictions to the initialization of the initial solution and the allowed repairs and tweak heuristics.

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

Competition goals

The WCCI/GECCO 2022 competition on “Evolutionary Computation in the Energy Domain: Risk-based Energy Scheduling” has the purpose of bringing together and testing the more advanced Computational Intelligence (CI) techniques applied to energy domain problems, namely the risk-based optimal day-ahead ERM considering the uncertainty associated with high penetration of DER. 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 complex problems in the energy domain by offering two distinct tracks.

– The organizers provide a framework, implemented in MATALAB© 2018a 64 bits (Download here), in which participants can easily test their algorithms (we also provide a differential evolution algorithm implementation as an example). The guidelines  (Download here) include the necessary information to understand the problems, how the solutions are represented, and how the fitness function is evaluated. Those elements are common for all participants.

– Since the proposed algorithms might have distinct sizes of population and run for a variable number of iterations, a maximum number of “5,000 function evaluations” is allowed for track 1. The convergence properties of the algorithms are not a criterion to be qualified in this competition.

– The initial solutions should be initialized randomly between the upper and lower bounds of the variables. However, one solution can be initialized with the variable’s lower bounds since we found it can significantly help to find a better solution in this problem. Heuristics and special tweaks for initial solutions are not accepted.

– 20 independent trials should be performed in the framework by each participant.

How to submit an entry

The winner will be the participant with the minimum ranking index, which is calculated as the average value over the 20 trials of the expected fitness value plus.

– 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 .zip named: (e.g.

Important Remarks

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

– You can submit a paper to the special session on Evolutionary Algorithms for Complex Optimization in the Energy Domain (WCCI SS-20). Submit it here – select SS-20 as the primary subject area.

– 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 11, 2022. Submit it here (Competition Entry Submissions)

Submit your results by June 30 2022 (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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