{"id":438,"date":"2020-12-23T16:58:40","date_gmt":"2020-12-23T16:58:40","guid":{"rendered":"http:\/\/www.gecad.isep.ipp.pt\/ERM-competitions\/?page_id=438"},"modified":"2023-12-14T10:36:22","modified_gmt":"2023-12-14T10:36:22","slug":"2021-2","status":"publish","type":"page","link":"https:\/\/www.gecad.isep.ipp.pt\/ERM-competitions\/2021-2\/","title":{"rendered":"Call for Competition on Evolutionary Computation in the Energy Domain: Smart Grid Applications 2021"},"content":{"rendered":"\n

IEEE PES GM 2021<\/a>, IEEE CEC 2021<\/a> & GECCO 2021<\/a> (Joint competition)<\/p>\n\n\n\n

26th-29th July 2021; 28th June-1st July 2021, Krak\u00f3w (Poland); 10th-14th July 2021, Lille (France)<\/p>\n\n\n\n

Organized<\/strong> by Fernando Lezama, Joao Soares, Bruno Canizes, Zita Vale, Ruben Romero<\/p>\n\n\n\n

Final Rank Results<\/h2>\n\n\n\n

Track 1<\/h3>\n\n\n\n
Final Rank<\/strong><\/td>Name<\/strong><\/td>Affiliation, Country<\/strong><\/td>Algorithm<\/strong><\/td>Rank Index<\/strong><\/td><\/tr>
1
IEEE CIS Prize \u2013 500 $<\/span><\/strong><\/td>		Junpeng Su, Han Huang<\/td>South China University of Technology, China<\/td>Cooperative Co-evolution Strategies with Time-dependent Grouping (CCS-TG)

Presentation<\/a>
Algorithm (link)<\/a><\/td>		1.4785<\/td><\/tr>
2<\/td>Ansel Yoan Rodr\u00edguez Gonz\u00e1lez, Samantha Barajas, Ram\u00f3n Aranda, Yoan Mart\u00ednez L\u00f3pez, Julio Madera<\/td>Unidad de Transferencia Tecnol\u00f3gica Tepic del Centro de Investigaci\u00f3n Cient\u00edfica y de Educaci\u00f3n Superior de Ensenada, M\u00e9xico<\/td>Hill Climbing to Ring Cellular Encode-Decode Univariate Marginal Distribution Algorithm (HC2RCEDUMDA)

Presentation<\/a>
Algorithm (link)<\/a><\/td>		1.5443<\/td><\/tr>
3<\/td>Rasul Esmaeilbeigi, Vicky Mak-Hau<\/td>Deakin University, Australia<\/td>Population REgeneration STar-guided Optimization (Presto)

Presentation<\/a>
Algorithm (link)<\/a><\/td>		1.5486<\/td><\/tr>
4<\/td>Diego Roberto Midence,
Nader Aljohani, and
Mario D. Baquedano-Aguilar<\/td>		Ente Operador Regional (EOR) for the Central American Electricity Market, El Salvador.
University of Florida – Power Lab (UF Power Lab), USA.
University of Florida – Laboratory for Cognition and Control for Complex Systems (UF CCC), USA.<\/td>		Memory JADE (MJADE)<\/td>1.5495<\/td><\/tr>
5<\/td>Fabricio Loor<\/td>National University of San Luis (UNSL), Argentina<\/td>Gaining Sharing Knowledge – Influence Factor (GSK-IF)<\/td>1.5527<\/td><\/tr>
6<\/td>Yoan Mart\u00ednez L\u00f3pez,
Julio Madera Quintana,
Miguel Bethencourt,
Ansel Rodr\u00edguez Gonz\u00e1lez<\/td>		Camag\u00fcey University, Mexico<\/td>Cellular UMDA with Normal-Gamma distribution (CUMDANGamma)<\/td>1.5678<\/td><\/tr>
7<\/td>Andres Angulo, Wilmer Garzon, Diego Rodriguez, David Alvarez, Ameena Al-Sumaiti, Sergio Rivera<\/td>Universidad Nacional de Colombia, Colombia<\/td>Harris hawks optimization + Differential Evolutionary Particle Swarm Optimization + Hybrid-adaptive differential evolution with decay function (HHO-DEEPSO-HyDE-DF)<\/td>1.6361<\/td><\/tr>
8<\/td>Kartik S. Pandya,
Dharmesh A. Dabhi,
Hans-Georg Beyer<\/td>		CHARUSAT, India<\/td>fastMAES<\/td>1.6373<\/td><\/tr>
9<\/td>Vasundhara Mahajan, Mahshooq Majeed<\/td>Sardar Vallabhbhai National Institute of Technology, India<\/td>GASAPSO<\/td>1.6599<\/td><\/tr>
10<\/td>Vasundhara Mahajan,
Mahshooq Majeed, Abhishek Saini,
Shreyas More,
Suraj Hajari<\/td>		Sardar Vallabhbhai National Institute of Technology, India<\/td>SaGaPSO<\/td>1.6599<\/td><\/tr>
11<\/td>Ricardo Faia<\/td>University of Salamanca, Spain<\/td>Success-History based Adaptive Differential Evolution (SHADE)<\/td>1.6876<\/td><\/tr>
12<\/td>Vasundhara Mahajan,
Mahshooq Majeed, Abhishek Saini,
Shreyas More,
Suraj Hajari<\/td>		Sardar Vallabhbhai National Institute of Technology, India<\/td>GAPSO<\/td>1.7721<\/td><\/tr>
13<\/td>Vasundhara Mahajan,
Mahshooq Majeed, Abhishek Saini,
Shreyas More,
Suraj Hajari<\/td>		Sardar Vallabhbhai National Institute of Technology, India<\/td>ABC<\/td>1.8012<\/td><\/tr>
14<\/td>Kartik S. Pandya,
Dharmesh A. Dabhi,
Hans-Georg Beyer<\/td>		CHARUSAT, India<\/td>Fast Covariance Matrix Adaptation Evolution Strategy (FC-MAES)<\/td>1.8952<\/td><\/tr>
15<\/td>Kartik S. Pandya,
Dharmesh A. Dabhi,
Hans-Georg Beyer<\/td>		CHARUSAT, India<\/td>Levy Fast Covariance Matrix Adaptation Evolution Strategy (LFC-MAES)<\/td>1.9339<\/td><\/tr>
16<\/td>Vasundhara Mahajan,
Mahshooq Majeed, Abhishek Saini,
Shreyas More,
Suraj Hajari<\/td>		Sardar Vallabhbhai National Institute of Technology, India<\/td>DE-TLBO<\/td>2.0352<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Track 2<\/h3>\n\n\n\n
Final Rank<\/strong><\/td>Name<\/strong><\/td>Affiliation, Country<\/strong><\/td>Algorithm<\/strong><\/td>Rank Index<\/strong><\/td><\/tr>
1
IEEE CIS Prize \u2013 500 $<\/span><\/strong><\/td>		Ansel Yoan Rodr\u00edguez Gonz\u00e1lez, Samantha Barajas, Ram\u00f3n Aranda, Yoan Mart\u00ednez L\u00f3pez, Julio Madera<\/td>Unidad de Transferencia Tecnol\u00f3gica Tepic del Centro de Investigaci\u00f3n Cient\u00edfica y de Educaci\u00f3n Superior de Ensenada, M\u00e9xico<\/td>Hill Climbing to Ring Cellular Encode-Decode Univariate Marginal Distribution Algorithm (HC2RCEDUMDA)

Presentation<\/a>
Algorithm (link)<\/a><\/td>		3.4932<\/td><\/tr>
2<\/td>Junpeng Su, Han Huang<\/td>South China University of Technology, China<\/td>Cooperative Co-evolution Strategies with Time-dependent Grouping (CCS-TG)

Presentation<\/a>
Algorithm (link)<\/a><\/td>		4.3492<\/td><\/tr>
3<\/td>Fabricio Loor<\/td>National University of San Luis (UNSL), Argentina<\/td>Gaining Sharing Knowledge – Influence Factor (GSK-IF)

Presentation<\/a>
Algorithm (link)<\/a><\/td>		4.7952<\/td><\/tr>
4<\/td>Rasul Esmaeilbeigi, Vicky Mak-Hau<\/td>Deakin University, Australia<\/td>Population REgeneration STar-guided Optimization (Presto)<\/td>5.7899<\/td><\/tr>
5<\/td>Andres Angulo, Wilmer Garzon, Diego Rodriguez, David Alvarez, Ameena Al-Sumaiti, Sergio Rivera<\/td>Universidad Nacional de Colombia, Colombia<\/td>Harris hawks optimization + Differential Evolutionary Particle Swarm Optimization + Hybrid-adaptive differential evolution with decay function (HHO-DEEPSO-HyDE-DF)<\/td>6.9199<\/td><\/tr>
6<\/td>Kartik S. Pandya,
Dharmesh A. Dabhi,
Hans-Georg Beyer<\/td>		CHARUSAT, India<\/td>First Coordinate Improvement Evolution Strategy and Enhance Levy Particle Swarm Optimization (FCI_ES-ELPSO)<\/td>7.1407<\/td><\/tr>
7<\/td>Diego Roberto Midence,
Nader Aljohani, and
Mario D. Baquedano-Aguilar<\/td>		Ente Operador Regional (EOR) for the Central American Electricity Market, El Salvador.
University of Florida – Power Lab (UF Power Lab), USA.
University of Florida – Laboratory for Cognition and Control for Complex Systems (UF CCC), USA.<\/td>		Memory JADE (MJADE)<\/td>7.1466<\/td><\/tr>
8<\/td>Vasundhara Mahajan,
Mahshooq Majeed, Abhishek Saini,
Shreyas More,
Suraj Hajari<\/td>		Sardar Vallabhbhai National Institute of Technology, India<\/td>SaGaPSO<\/td>7.3417<\/td><\/tr>
9<\/td>Vasundhara Mahajan,
Mahshooq Majeed, Abhishek Saini,
Shreyas More,
Suraj Hajari<\/td>		Sardar Vallabhbhai National Institute of Technology, India<\/td>GAPSO<\/td>7.6434<\/td><\/tr>
10<\/td>Kartik S. Pandya,
Dharmesh A. Dabhi,
Hans-Georg Beyer<\/td>		CHARUSAT, India<\/td>First Coordinate Improvement Evolution Strategy (FCI_ES)<\/td>7.6601<\/td><\/tr>
11<\/td>Vasundhara Mahajan,
Mahshooq Majeed, Abhishek Saini,
Shreyas More,
Suraj Hajari<\/td>		Sardar Vallabhbhai National Institute of Technology, India<\/td>ABC<\/td>7.9677<\/td><\/tr>
12<\/td>Mark Bezmaslov<\/td>St. Petersburg State University, Russia<\/td>Contribution-based cooperative co-evolution recursive differential grouping (CBCC-RDG3)<\/td>8.3167<\/td><\/tr>
13<\/td>Yoan Mart\u00ednez L\u00f3pez,
Julio Madera Quintana,
Miguel Bethencourt,
Ansel Rodr\u00edguez Gonz\u00e1lez<\/td>		Camag\u00fcey University, Mexico<\/td>Cellular UMDA with Normal distribution (CUMDAN(Simple))<\/td>8.4843<\/td><\/tr>
14<\/td>Ricardo Faia<\/td>University of Salamanca, Spain<\/td>Success-History based Adaptive Differential Evolution (SHADE)<\/td>10.4291<\/td><\/tr>
15<\/td>Kartik S. Pandya,
Dharmesh A. Dabhi,
Hans-Georg Beyer<\/td>		CHARUSAT, India<\/td>Fast Covariance Matrix Adaptation Evolution Strategy (FC-MAES)<\/td>10.7768<\/td><\/tr>
16<\/td>Kartik S. Pandya,
Dharmesh A. Dabhi,
Hans-Georg Beyer<\/td>		CHARUSAT, India<\/td>Levy Fast Covariance Matrix Adaptation Evolution Strategy (LFC-MAES)<\/td>10.8969<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

IEEE CIS PRIZE: <\/strong>We are glad to announce that our competition will offer a IEEE Computational Intelligence Society (CIS) prize of 1000 $ (500 $ for the winner of each track). Good luck and stay tuned. Thanks!<\/p>\n\n\n\n

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Competition Outline<\/h2>\n\n\n\n

Following the success of the previous editions (CEC, GECCO, WCCI), we are launching a more challenging competition at major conferences in the field of computational intelligence. This GECCO 2021 competition proposes two tracks in the energy domain:<\/p>\n\n\n\n

Track 1) Bi-level optimization of bidding strategies in local energy markets (LEM). This track is constructed under the same framework of the past competitions (therefore, former competitors can adapt their algorithms to this new track), 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 LEM), thus resulting in a strong interdependence of their decisions.<\/p>\n\n\n\n

Track 2) Flexibility management of home appliances to support DSO requests. A model for aggregators flexibility provision in distribution networks that takes advantage of load flexibility resources allowing the re-schedule of shifting\/real-time home-appliances to provision a request from a distribution system operator (DSO) is proposed. The problem can be modeled as a Mixed-Integer Non-Linear Programming (MINLP) in which the aggregator strives to match a flexibility request from the DSO\/BRP, paying remuneration to the households participating in the DR program according to their preferences and the modification of their baseline profile.<\/p>\n\n\n\n

Note: Both tracks are developed to run under the same framework of past competitions.<\/p>\n\n\n\n

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Competition goals<\/h2>\n\n\n\n
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The IEEE PES GM, CEC & GECCO 2021 competition on \u201cEvolutionary Computation in the Energy Domain: Smart Grid Applications\u201d has the purpose of bringing together and testing the more advanced Computational Intelligence (CI) techniques applied to energy domain problems, namely the optimal bidding of energy aggregators in local markets and the Flexibility management of home appliances to support DSO requests. 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 each independent problem since we understand the validity of the \u201cno-free lunch theorem\u201d, 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.<\/p>\n\n\n\n

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Rules<\/h2>\n\n\n\n

– 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. <\/p>\n\n\n\n

– The organizers provide a framework, implemented in MATALAB\u00a9 2014b 64 bits (Download here)<\/a>, in which participants can easily test their algorithms (we also provide a differential evolution algorithm implementation as an example). The guidelines  (<\/a>Download<\/a> here)<\/a> 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.<\/p>\n\n\n\n

– Since the proposed algorithms might have distinct sizes of population and run for a variable number of iterations, a maximum number of \u201c10,000 function evaluations\u201d is allowed for track 1 and “100,000” function evaluations are allowed for track 2. The convergence properties of the algorithms are not a criterion to be qualified in this competition.<\/p>\n\n\n\n

– Only random seed initial solutions are allowed in this competition. Heuristics and special tweaks for initial solutions are not accepted.<\/p>\n\n\n\n

– 20 independent trials should be performed in the framework by each participant.
<\/p>\n\n\n\n

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How to submit an entry<\/h2>\n\n\n\n

–The winner will be the participant with the minimum ranking index in each independent track<\/strong>, which is calculated as the average value over the 20 trials of the expected fitness value plus. We will make an independent rank for each track (i.e., we will have a winner for each track).<\/p>\n\n\n\n

– 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 CEC2021_trackX_AlgorithmName<\/em>_ParticipantName<\/em>.zip (e.g. CECI2021<\/em>_track2_DE<\/em>_Lezama<\/em>.zip).<\/p>\n\n\n\n

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Important Remarks<\/h2>\n\n\n\n

– Notice that submission of papers or assistance to PES GM, CEC, or GECCO by competition participants is not mandatory.<\/p>\n\n\n\n

– You can submit a paper to the special session on Evolutionary Algorithms for Complex Optimization in the Energy Domain (CEC SS-44). Submit it here<\/a> – select SS-44 in the research topic. (already closed<\/strong>)<\/p>\n\n\n\n

– You are welcome to submit short descriptions of your algorithms and results as 2-page papers to be included in the GECCO Companion. This is voluntary \u2014 Submission deadline is April 12, 2021<\/strong>. Submit it here<\/a> (already closed<\/strong>)<\/p>\n\n\n\n

Submit your results by June 15, 23:59 (GMT)<\/strong><\/p>\n\n\n\n

<\/p>\n\n\n\n

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Further related bibliography<\/h3>\n\n\n\n