IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE (WCCI) 2020

GECCO 2020 @ Cancun – GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE

#### 8th – 12th July 2020, Cancún (Mexico) & 19th – 24th July 2020, Glasgow (UK)

**Organized** by Joao Soares, Fernando Lezama, Bruno Canizes, Zita Vale

## Competition Outline

Following the success of the previous editions at WCCI 2018 (Rio de Janeiro, Brazil) and CEC/GECCO 2019 (New Zealand and Prague, Czechia) we are launching a more challenging algorithm competition at major international conferences in the field of computational intelligence. This WCCI & GECCO 2020 competition proposes two testbeds in the energy domain:

Testbed 1) optimization of a centralized day-ahead energy resource management problem in smart grids under environments with uncertainty. This testbed is similar to the past challenge using a challenging 500-scenario case study with a high degree of uncertainty. We also add some restrictions to the initialization of the initial solution and the allowed repairs and tweak-heuristics.

Testbed 2) bi-level optimization of end-users’ bidding strategies in local energy markets (LM). This test bed is constructed under the same framework of the past competitions (therefore, former competitors can adapt their algorithms to this new testbed), representing a complex bi-level problem in which competitive agents in the upper-level try to maximize their profits, modifying and depending on the price determined in the lower-level problem (i.e., the clearing price in the LM), thus resulting in a strong interdependence of their decisions.

Check former competitions in http://www.gecad.isep.ipp.pt/WCCI2018-SG-COMPETITION/ and http://www.gecad.isep.ipp.pt/ERM2019-Competition

## Competition goals

The WCCI & GECCO 2020 competition on “Evolutionary Computation in the Energy Domain: Smart Grid Applications” has the purpose of bringing together and testing state-ot-the-art Computational Intelligence (CI) techniques applied to energy domain problems, namely the energy resource management problem under uncertain environments and the optimal bidding of energy aggregators in local markets. The competition provides a coherent framework where participants and practitioners of CI can test their algorithms to solve two real-world optimization problems in the energy domain. The participants have the opportunity to evaluate if their algorithms can rank well in both problems since we understand the validity of the “no-free lunch theorem”, making this contest a unique opportunity worth to explore the applicability of the developed approaches in real-world problems beyond the typical benchmark and standardized CI problems.

## Rules

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

– Only random seed initial solutions are allowed in this competition. 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 in both testbeds, which is calculated as the average value over the 20 trials of the expected fitness value plus the standard deviation. Considering the framework is the same for both testbeds, algorithms should be able to run in both problems.

– 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

WCCI2020_testbedX_*AlgorithmName*_*ParticipantName*.zip (e.g. *WCCI2020*_testbed2_*DE*_*Lezama*.zip).

## Important Remarks

– Notice that submission of papers or assistance to CEC or 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 (CEC-26).

Submit your results by** May 31**^{th}**, 29:59 (GMT) **

**(If you plan to submit a paper to CEC or GECCO it must be submitted in the regular paper submission system but results of your algorithm can be improved and sent to us by May 31**^{th}** 2020)**

### Further related bibliography

- [1] 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).
- [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] 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.
- [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.