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Editorial

Distributed Power Generation Scheduling, Modeling, and Expansion Planning

by
Javier Contreras
* and
Gregorio Muñoz-Delgado
Escuela Técnica Superior de Ingeniería Industrial, Universidad de Castilla—La Mancha, Campus Universitario s/n, 13071 Ciudad Real, Spain
*
Author to whom correspondence should be addressed.
Energies 2021, 14(22), 7757; https://doi.org/10.3390/en14227757
Submission received: 26 August 2021 / Accepted: 1 November 2021 / Published: 19 November 2021
This volume contains the successful invited submissions [1,2,3,4,5,6,7,8] for a Special Issue of Energies on the subject area of “Distributed Power Generation Scheduling, Modelling, and Expansion Planning”.
Distributed generation is becoming more important in electrical power systems due to the decentralization of energy production. Within this new paradigm, new approaches for the operation and planning of distributed power generation are yet to be explored. Thus, this Special Issue deals with distributed power generation, considering its operation, scheduling, and planning. Topics of interest include, but are not limited to, the following:
  • Distributed power generation modeling;
  • Integration of distributed generation in distribution systems and smart grids;
  • Distributed power generation expansion planning;
  • Optimal scheduling of distributed power generation;
  • Distributed generation in a transactive energy framework.
Published submissions for this Special Issue include the most important topics applied to distributed power generation, such as:
  • Benders decomposition for renewable generation investment;
  • Micro-grid management;
  • Economic dispatch for hybrid micro-grids;
  • Energy storage and curtailment;
  • Distributed generation capacity allocation and control;
  • Charging of electric vehicles;
  • Environmentally-based economic dispatch and demand response;
  • Battery energy sources usage.
Responses to our call for papers had the following statistics:
  • Submissions (19);
  • Publications (8);
  • Rejections (11);
  • Article type: review article (1) and research article (7).
Authors’ geographical distribution (for published papers):
  • Korea (2);
  • Spain (1);
  • China (1);
  • Poland (1);
  • Croatia (1);
  • Portugal (1);
  • Italy (1).
We found the editions and selections of papers for this book very inspiring and rewarding. We also wish to thank the editorial staff and reviewers for their efforts and help during the process.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Montoya-Bueno, S.; Muñoz-Hernandez, J.; Contreras, J.; Baringo, L. A Benders’ Decomposition Approach for Renewable Generation Investment in Distribution Systems. Energies 2020, 13, 1225. [Google Scholar] [CrossRef] [Green Version]
  2. Tightiz, L.; Yang, H.; Piran, M. A Survey on Enhanced Smart Micro-Grid Management System with Modern Wireless Technology Contribution. Energies 2020, 13, 2258. [Google Scholar] [CrossRef]
  3. Jiang, K.; Wu, F.; Shi, L.; Lin, K. Distributed Hierarchical Consensus-Based Economic Dispatch for Isolated AC/DC Hybrid Microgrid. Energies 2020, 13, 3209. [Google Scholar] [CrossRef]
  4. Andrychowicz, M. Comparison of the Use of Energy Storages and Energy Curtailment as an Addition to the Allocation of Renewable Energy in the Distribution System in Order to Minimize Development Costs. Energies 2020, 13, 3746. [Google Scholar] [CrossRef]
  5. Čađenović, R.; Jakus, D. Maximization of Distribution Network Hosting Capacity through Optimal Grid Reconfiguration and Distributed Generation Capacity Allocation/Control. Energies 2020, 13, 5315. [Google Scholar] [CrossRef]
  6. Gomes, I.; Melicio, R.; Mendes, V. Comparison between Inflexible and Flexible Charging of Electric Vehicles—A Study from the Perspective of an Aggregator. Energies 2020, 13, 5443. [Google Scholar] [CrossRef]
  7. Ryu, H.; Kim, M. Combined Economic Emission Dispatch with Environment-Based Demand Response Using WU-ABC Algorithm. Energies 2020, 13, 6450. [Google Scholar] [CrossRef]
  8. Celli, G.; Pilo, F.; Pisano, G.; Ruggeri, S.; Soma, G. Relieving Tensions on Battery Energy Sources Utilization among TSO, DSO, and Service Providers with Multi-Objective Optimization. Energies 2021, 14, 239. [Google Scholar] [CrossRef]
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Share and Cite

MDPI and ACS Style

Contreras, J.; Muñoz-Delgado, G. Distributed Power Generation Scheduling, Modeling, and Expansion Planning. Energies 2021, 14, 7757. https://doi.org/10.3390/en14227757

AMA Style

Contreras J, Muñoz-Delgado G. Distributed Power Generation Scheduling, Modeling, and Expansion Planning. Energies. 2021; 14(22):7757. https://doi.org/10.3390/en14227757

Chicago/Turabian Style

Contreras, Javier, and Gregorio Muñoz-Delgado. 2021. "Distributed Power Generation Scheduling, Modeling, and Expansion Planning" Energies 14, no. 22: 7757. https://doi.org/10.3390/en14227757

APA Style

Contreras, J., & Muñoz-Delgado, G. (2021). Distributed Power Generation Scheduling, Modeling, and Expansion Planning. Energies, 14(22), 7757. https://doi.org/10.3390/en14227757

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