Review of bio-inspired optimization applications in renewable-powered smart grids: Emerging population-based metaheuristics
The management of renewable-powered smart grids deals with nonlinear optimization problems featuring a variety of linear or nonlinear constraints, discrete or continuous optimization variables, involving high dimensionality of the solution space, and strict time requirements to identify the optimal...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722017486 |
| _version_ | 1797901884421308416 |
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| author | Cristina Bianca Pop Tudor Cioara Ionut Anghel Marcel Antal Viorica Rozina Chifu Claudia Antal Ioan Salomie |
| author_facet | Cristina Bianca Pop Tudor Cioara Ionut Anghel Marcel Antal Viorica Rozina Chifu Claudia Antal Ioan Salomie |
| author_sort | Cristina Bianca Pop |
| collection | DOAJ |
| description | The management of renewable-powered smart grids deals with nonlinear optimization problems featuring a variety of linear or nonlinear constraints, discrete or continuous optimization variables, involving high dimensionality of the solution space, and strict time requirements to identify the optimal or near-optimal solution. One promising approach for addressing such optimization problems is to apply bio-inspired population-based optimization algorithms, many such metaheuristics emerging lately. In this paper, we have identified the metaheuristics with the highest impact published recently and reviewed their applications in the management of renewable-powered smart energy grids using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology and the Web of Science Core Collection as the reference database. Four main smart grid application domains we been analyzed: (i) energy prediction models’ optimization to reduce uncertainty (ii) energy resources coordination to handle the stochastic nature of renewables, (iii) demand response using controllable loads and flexibility while considering the consumers’ needs and constraints and (iv) optimization of grid energy efficiency and costs. The results showed the advantages of such metaheuristics for decentralized optimization problems with low computational time and resource overhead. At the same time, several issues need to be addressed to increase their adoption in the smart grid management scenarios: the lack of standard testing methodologies and benchmarks, efficient management of exploration and exploitation of the optimization search space, guidelines for metaheuristics application with clear links to the type of optimization problems, etc. |
| first_indexed | 2024-04-10T09:09:01Z |
| format | Article |
| id | doaj.art-9cc2481537fd43989d0a4c6a185f0768 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T09:09:01Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-9cc2481537fd43989d0a4c6a185f07682023-02-21T05:13:22ZengElsevierEnergy Reports2352-48472022-11-0181176911798Review of bio-inspired optimization applications in renewable-powered smart grids: Emerging population-based metaheuristicsCristina Bianca Pop0Tudor Cioara1Ionut Anghel2Marcel Antal3Viorica Rozina Chifu4Claudia Antal5Ioan Salomie6Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, RomaniaCorresponding author.; Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, RomaniaComputer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, RomaniaComputer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, RomaniaComputer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, RomaniaComputer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, RomaniaComputer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, RomaniaThe management of renewable-powered smart grids deals with nonlinear optimization problems featuring a variety of linear or nonlinear constraints, discrete or continuous optimization variables, involving high dimensionality of the solution space, and strict time requirements to identify the optimal or near-optimal solution. One promising approach for addressing such optimization problems is to apply bio-inspired population-based optimization algorithms, many such metaheuristics emerging lately. In this paper, we have identified the metaheuristics with the highest impact published recently and reviewed their applications in the management of renewable-powered smart energy grids using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology and the Web of Science Core Collection as the reference database. Four main smart grid application domains we been analyzed: (i) energy prediction models’ optimization to reduce uncertainty (ii) energy resources coordination to handle the stochastic nature of renewables, (iii) demand response using controllable loads and flexibility while considering the consumers’ needs and constraints and (iv) optimization of grid energy efficiency and costs. The results showed the advantages of such metaheuristics for decentralized optimization problems with low computational time and resource overhead. At the same time, several issues need to be addressed to increase their adoption in the smart grid management scenarios: the lack of standard testing methodologies and benchmarks, efficient management of exploration and exploitation of the optimization search space, guidelines for metaheuristics application with clear links to the type of optimization problems, etc.http://www.sciencedirect.com/science/article/pii/S2352484722017486Bio-inspired optimizationPopulation-based metaheuristicsSmart gridsEnergy managementOptimizationRenewable energy integration |
| spellingShingle | Cristina Bianca Pop Tudor Cioara Ionut Anghel Marcel Antal Viorica Rozina Chifu Claudia Antal Ioan Salomie Review of bio-inspired optimization applications in renewable-powered smart grids: Emerging population-based metaheuristics Energy Reports Bio-inspired optimization Population-based metaheuristics Smart grids Energy management Optimization Renewable energy integration |
| title | Review of bio-inspired optimization applications in renewable-powered smart grids: Emerging population-based metaheuristics |
| title_full | Review of bio-inspired optimization applications in renewable-powered smart grids: Emerging population-based metaheuristics |
| title_fullStr | Review of bio-inspired optimization applications in renewable-powered smart grids: Emerging population-based metaheuristics |
| title_full_unstemmed | Review of bio-inspired optimization applications in renewable-powered smart grids: Emerging population-based metaheuristics |
| title_short | Review of bio-inspired optimization applications in renewable-powered smart grids: Emerging population-based metaheuristics |
| title_sort | review of bio inspired optimization applications in renewable powered smart grids emerging population based metaheuristics |
| topic | Bio-inspired optimization Population-based metaheuristics Smart grids Energy management Optimization Renewable energy integration |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722017486 |
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