GECAD – Polytechnic of Porto –, in collaboration with Delft and Adelaide Universities, proposes the optimization of a centralized day-ahead energy resource management problem in smart grids under environments with uncertainty.

The CEC/GECCO competition on “Evolutionary Computation in Uncertain Environments: A Smart Grid Application” has the purpose of bringing together and testing the more advanced Computational Intelligence (CI) techniques applied to an energy domain problem, namely the energy resource management problem under uncertain environments. 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 with uncertainty consideration, which makes the problem more challenging and worth to explore.

In this CEC/GECCO 2019 edition, we propose a more challenging case study in comparison with WCCI 2018 competition. The uncertainty is now represented by 500 scenarios (the last edition was 100 scenarios) with a higher level of uncertainty on the photovoltaic (PV) production. We also decreased the amount of energy that the aggregator can buy in the energy market, thus limiting the available options to find a feasible solution. Good luck to all the participants!

-Participants will propose and implement metaheuristic algorithm (e.g., evolutionary algorithms, swarm intelligence, estimation of distribution algorithm, etc.) to solve the energy resource management problem under uncertainty.

-The organizers provide a framework, implemented in MATALAB© 2014b 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 problem, 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 “50000 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.

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

-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 (over the considered uncertain scenarios) plus the standard deviation.

- 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

ERM2019_SG_Algorithm_Participant.zip

(e.g. ERM2019_SG_DE_Lezama.zip).

[1] Fernando Lezama, Joao Soares, Zita Vale, Jose Rueda, SG-ERM-Uncertainty framework, 2018, (Download here).

[2] Fernando Lezama, Joao Soares, Zita Vale, Jose Rueda, “Guidelines for the CEC & GECCO 2019 competition on Evolutionary Computation in Uncertain Environments: A Smart Grid Application”, December 2018.

[1] Fernando Lezama, Joao Soares, Zita Vale, Jose Rueda, Sergio Rivera, Istvan Elrich, “2017 IEEE competition on modern heuristic optimizers for smart grid operation: Testbeds and results,” Swarm and Evolutionary Computation, 2018.

[2] 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.

[3] Fernando Lezama, Joao Soares, Enrique Munoz de Cote, L. E. Sucar, and Zita Vale, “Differential Evolution Strategies for Large-Scale Energy Resource Management in Smart Grids,” in GECCO ’17: Genetic and Evolutionary Computation Conference Companion Proceedings, 2017.

[4] João 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.

[5] 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.