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Forecasting, Control and Optimization for Distributed Energy Resources in Future...
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Forecasting, Control and Optimization for Distributed Energy Resources in Future Power Grids

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 1863

Special Issue Editor

Dr. Daisuke Kodaira

E-Mail Website
Guest Editor
Department of Electrical Engineering, Faculty of Science and Technology, Tokyo University of Science, 2641 Yamazaki, Noda-shi, Chiba 278-8510, Japan
Interests: power systems; smart grid; electric vehicle; vehicle-to-grid; energy management system

Special Issue Information

Dear Colleagues,

The impact of distributed energy resources is nowadays unquestionable, especially at the distribution level, such as in photovoltaic (PV) generation, wind turbine (WT) generation, energy storage systems, and electric vehicles (EV). The forecasting methodologies for PV, WT, and EV charging demand is becoming more important to manage the voltage or even frequency in the network. Controlling distributed energy resources, such as through a demand response and peak shift, needs to be more sophisticated in the future grids. As the number of controlled units increases, the optimization of scheduling with forecasted information becomes more complex. Additionally, the traditional energy market is no longer appropriate for the prosumers who own distributed energy resources. The local market or P2P trade based on blockchain is promising in the future power grid.

This Special Issue will address the forecasting, control, and optimization of distributed energy resources. The focus will include methods and techniques to optimize operation, aggregate the resources, namely by virtual power players, and reimburse them. Integrating distributed resources in electricity markets will also be addressed as the main reason for their efficient use.

Dr. Daisuke Kodaira
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Forecasting generation or consumption from distributed energy resources
  • Control methods for distributed energy resources
  • Optimization methods to control distributed energy resources
  • Computational complexity and scalability
  • Demand response
  • Virtual power plant
  • Market mechanism design
  • P2P trade
  • Pilot programs and field tests
  • New business models

Published Papers (1 paper)

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Research

17 pages, 3779 KiB  
Article
Assessing the Impact of Features on Probabilistic Modeling of Photovoltaic Power Generation
by Hiroki Yamamoto, Junji Kondoh and Daisuke Kodaira
Energies 2022, 15(15), 5337; https://0-doi-org.brum.beds.ac.uk/10.3390/en15155337 - 22 Jul 2022
Cited by 4 | Viewed by 1286
Abstract
Photovoltaic power generation has high variability and uncertainty because it is affected by uncertain factors such as weather conditions. Therefore, probabilistic forecasting is useful for optimal operation and risk hedging in power systems with large amounts of photovoltaic power generation. However, deterministic forecasting [...] Read more.
Photovoltaic power generation has high variability and uncertainty because it is affected by uncertain factors such as weather conditions. Therefore, probabilistic forecasting is useful for optimal operation and risk hedging in power systems with large amounts of photovoltaic power generation. However, deterministic forecasting is the mainstay of photovoltaic generation forecasting; there are few studies on probabilistic forecasting and feature selection from weather or time-oriented features in such forecasting. In this study, prediction intervals were generated by the lower upper bound estimation (LUBE) using neural networks with two outputs to make probabilistic modeling for predictions. The objective was to improve prediction interval coverage probability (PICP), mean prediction interval width (MPIW), continuous ranked probability score (CRPS), and loss, which is the integration of PICP and MPIW, by removing unnecessary features through feature selection. When features with high gain were selected by random forest (RF), in the modeling of 14.7 kW PV systems, loss improved by 1.57 kW, CRPS by 0.03 kW, PICP by 0.057 kW, and MPIW by 0.12 kW on average over two weeks compared to the case where all features were used without feature selection. Therefore, the low gain features from RF act as noise and reduce the modeling accuracy. Full article
(This article belongs to the Special Issue Forecasting, Control and Optimization for Distributed Energy Resources in Future Power Grids)
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