research-article

Towards coding strategies for forecasting-based scheduling in smart grids and the energy lab 2.0

Authors:

J. Á. González Ordiano,

V. HagenmeyerAuthors Info & Claims

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1271 - 1278

https://doi.org/10.1145/3067695.3082481

Published: 15 July 2017 Publication History

Abstract

Development of the power supply system towards a more decentralized system with a growing share of renewable energies constitutes an additional complexity for its reliable, secure, and economic operation. This has a strong impact on a variety of optimization tasks, such as power plant resource scheduling, reactive power management, or the expansion of the system by additional transmission lines, power generators or storage systems. In particular, scheduling and expansion planning depend strongly on a reliable forecast of expected demands and electricity production, the latter being a demanding task for volatile sources, such as wind power plants or photovoltaic power generators (PV). For testing new approaches and strategies, the Karlsruhe Institute of Technology (KIT) develops a test bed comprising different energy grids called Energy Lab 2.0. This test bed will allow studying the effects of new tools, forecasting and scheduling techniques, and other algorithms aimed at managing a smart grid. The lab and applied forecasting techniques will be briefly introduced in the present contribution.

First ideas about metaheuristic scheduling of different energy sources based on production and demand forecasts with the aim of ensuring a reliable and economic energy supply are introduced. Appropriate representations for Evolutionary Algorithms (EAs) are discussed and some experience from earlier scheduling projects for fast scheduling of many jobs to heterogeneous resources are given.

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Cited By

Poppenborg RPhipps KBeichter MFörderer KMikut RHagenmeyer V(2024)Dynamic Phenotype Mapping in Evolutionary Algorithms for Energy Hub SchedulingEnergy Informatics10.1007/978-3-031-74741-0_14(205-223)Online publication date: 23-Oct-2024
https://dl.acm.org/doi/10.1007/978-3-031-74741-0_14
Poppenborg RPhipps KKhalloof HFörderer KMikut RHagenmeyer VSilva SPaquete L(2023)Dynamic Chromosome Interpretation in Evolutionary Algorithms for Distributed Energy Resources SchedulingProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3590666(755-758)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3590666
Poppenborg RBeisswanger KHotz CFörderer KKolb THagenmeyer V(2023)Dynamic Mapping for Evolutionary Algorithm Based Optimization of Energy Hub Gas Scheduling2023 IEEE 11th International Conference on Smart Energy Grid Engineering (SEGE)10.1109/SEGE59172.2023.10274571(206-211)Online publication date: 13-Aug-2023
https://doi.org/10.1109/SEGE59172.2023.10274571
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Index Terms

Towards coding strategies for forecasting-based scheduling in smart grids and the energy lab 2.0
1. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling

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cover image ACM Conferences

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2017

1934 pages

ISBN:9781450349390

DOI:10.1145/3067695

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 15 July 2017

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German Federal Ministry of Education and Research (BMBF)
Ministry of Science, Research and the Arts (MWK) of the State of Baden-Wuerttemberg
Helmholtz Association

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GECCO '17

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SIGEVO

GECCO '17: Genetic and Evolutionary Computation Conference

July 15 - 19, 2017

Berlin, Germany

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Poppenborg RPhipps KBeichter MFörderer KMikut RHagenmeyer V(2024)Dynamic Phenotype Mapping in Evolutionary Algorithms for Energy Hub SchedulingEnergy Informatics10.1007/978-3-031-74741-0_14(205-223)Online publication date: 23-Oct-2024
https://dl.acm.org/doi/10.1007/978-3-031-74741-0_14
Poppenborg RPhipps KKhalloof HFörderer KMikut RHagenmeyer VSilva SPaquete L(2023)Dynamic Chromosome Interpretation in Evolutionary Algorithms for Distributed Energy Resources SchedulingProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3590666(755-758)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3590666
Poppenborg RBeisswanger KHotz CFörderer KKolb THagenmeyer V(2023)Dynamic Mapping for Evolutionary Algorithm Based Optimization of Energy Hub Gas Scheduling2023 IEEE 11th International Conference on Smart Energy Grid Engineering (SEGE)10.1109/SEGE59172.2023.10274571(206-211)Online publication date: 13-Aug-2023
https://doi.org/10.1109/SEGE59172.2023.10274571
Khalloof HCiftci SShahoud SDuepmeier CFoerderer KHagenmeyer VWagner M(2022)Facilitating the hybridization of parallel evolutionary algorithms in cluster computing environmentsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533997(2001-2008)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3533997
Khalloof HMohammad MShahoud SDuepmeier CHagenmeyer VChbeir RManolopoulos YDamiani EBenslimane DBellatreche LMorzy T(2020)A Generic Flexible and Scalable Framework for Hierarchical Parallelization of Population-Based MetaheuristicsProceedings of the 12th International Conference on Management of Digital EcoSystems10.1145/3415958.3433041(124-131)Online publication date: 2-Nov-2020
https://dl.acm.org/doi/10.1145/3415958.3433041
Khalloof HJakob WShahoud SDuepmeier CHagenmeyer V(2020)A Generic Scalable Method for Scheduling Distributed Energy Resources Using Parallelized Population-Based MetaheuristicsProceedings of the Future Technologies Conference (FTC) 2020, Volume 210.1007/978-3-030-63089-8_1(1-21)Online publication date: 1-Nov-2020
https://doi.org/10.1007/978-3-030-63089-8_1
Khalloof HJakob WLiu JBraun EShahoud SDuepmeier CHagenmeyer VTakadama K(2018)A generic distributed microservices and container based framework for metaheuristic optimizationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208253(1363-1370)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3208253

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