Special Issue "Control and Optimization of Renewable Energy Systems"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Sustainable Energy".

Deadline for manuscript submissions: 31 May 2021.

Special Issue Editors

Dr. Dimitrios Tzovaras
Website
Guest Editor
Center for Research and Technology Hellas / Information Technologies Institute (CERTH/ITI), Thermi, Greece
Interests: machine learning for forecasting services; visual analytics for big data analysis; artificial intelligence including embedded systems and mobile environments; decision support systems for demand-side management and building facility management
Dr. Dimosthenis Ioannidis
Website
Guest Editor
Center for Research and Technology Hellas / Information Technologies Institute (CERTH/ITI), Thermi, Greece
Interests: smart grid monitoring and control; optimization and scheduling of distributed assets; fog-enabled intelligent devices; demand response programs planning and optimization; demand-side management; smart charging platform for electric vehicles; deep neural networks for prediction and forecasting on demand and generation; building performance analysis

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Energies Special Issue on “Control and Optimization of Renewable Energy Systems”.

The aim of this Special Issue is to deliver to the research community the latest accomplishments in the smart grid ecosystem, targeting new and emerging technologies for advanced infrastructure and assets monitoring and control, multifactor energy management, fully addressing all countermeasures to support resilience of the smart grid to attacks through novel cybersecurity mechanisms. Domain applications include, among others, innovative technologies for local energy communities, novel methods for boosting energy management in smart cities as well as research areas for energy efficiency in industrial environments, with special focus on distributed renewable energy systems.

 Indicatively topics to be addressed for this Special Issue are outlined below:

  • Control and Optimization of Energy Systems
    • Control optimization of various RES and other assets, collaboration of RES with distribution network capabilities, load RES generation forecasting, indicatively:
  • Distributed, semi-autonomous control of field RES assets;
  • Optimization of microgrid operation for several assets that include PVs, BESS and CHP, both in islanded and grid connected mode;
  • Optimization of distribution network operation using network reconfiguration for improving RES integration and operation;
  • Power flow and quality, inverter control and black start methods;
    • Advanced state monitoring (including disaggregation) and estimation of grid assets with heavy RES penetration:
  • Voltage harmonics produced by RES in distribution grids;
    • Local RES, storage, and self-consumption maximization:
  • Novel RES/thermal storage design for optimal integration in urban environments;
  • Optimization of EV charging schedule under consideration of different variable and dispatchable RES;
  • Electrical and thermal load profiling;
  • Holistic system sizing planning tools;
    • Microgrid-enabled frameworks and methods for participation in demand response programs;
  • Solutions for Buildings and Next-Generation Smart Infrastructures
    • Building flexibility (taking into account RES and storage);
    • RES energy production forecasting through advanced machine learning and deep neural networks;
    • BMS utilizing RES;
    •  Demand flexibility profiling at building level (Building-as-a-Battery, P2H), including human-centric approaches;
  • Ancillary Services
    • Ancillary services (DR-based) directly from RES inverters;
    • Market energy price forecast for demand response services taking into account the volatile nature of distributed RES (i.e., Wind, PV, etc.);
    • Methods and tools for supporting local energy communities for participation in the energy market;
    • Fog-enabled embedded devices supporting decentralized architectures and emerging ancillary services;
  • Protocol connecting gateways focusing on emerging demand response standards;
    • Predictive maintenance methods for distribution grid assets with heavy RES penetration;
    • Platform architectures for the uniform utilization of diverse flexibility resources by aggregators. Diverse virtual power plants for implicit and explicit DR scenarios;
  • Security-related/Cybersecurity
    • Secure distributed asset management through decentralized architectures (i.e., application to demand response, demand-side management);
    • Cybersecurity methods for monitoring RES against attacks through IoT architectures.

The potential advantages, impacts, and limitations of the works presented in the Special Issue need to be coupled with pilot studies in real-life environments, next generation simulation studies (i.e., introducing as much as possible co-simulation whenever applicable), and/or in-depth evaluation tests in relation to the role of renewable energy systems in facilitating smart grid management.

Dr. Dimitrios Tzovaras
Dr. Dimosthenis Ioannidis
Guest Editors

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 papers will be 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 1800 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

  • Distributed RES assets monitoring and control
  • Microgrid operation and control (including energy storage)
  • Smart building management systems exploiting RES assets
  • Distribution network operation involving RES integration
  • Demand-side management
  • AI for generation/demand forecasting with online learning capabilities
  • Cybersecurity for energy infrastructures
  • Integration of fog computational intelligence (FI) and lightweight neural networks for smart grid asset management and control
  • Interoperability of standards and protocols in future energy market scenarios.

Published Papers (1 paper)

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Research

Open AccessArticle
Price Forecasting for the Balancing Energy Market Using Machine-Learning Regression
Energies 2020, 13(20), 5420; https://doi.org/10.3390/en13205420 - 16 Oct 2020
Abstract
The importance of price forecasting has gained attention over the last few years, with the growth of aggregators and the general opening of the European electricity markets. Market participants manage a tradeoff between, bidding in a lower price market (day-ahead), but with typically [...] Read more.
The importance of price forecasting has gained attention over the last few years, with the growth of aggregators and the general opening of the European electricity markets. Market participants manage a tradeoff between, bidding in a lower price market (day-ahead), but with typically higher volume, or aiming for a lower volume market but with potentially higher returns (balance energy market). Companies try to forecast the extremes of revenues or prices, in order to manage risk and opportunity, assigning their assets in an optimal way. It is thought that in general, electricity markets have quasi-deterministic principles, rather than being based on speculation, hence the desire to forecast the price based on variables that can describe the outcome of the market. Many studies address this problem from a statistical approach or by performing multiple-variable regressions, but they very often focus only on the time series analysis. In 2019, the Loss of Load Probability (LOLP) was made available in the UK for the first time. Taking this opportunity, this study focusses on five LOLP variables (with different time-ahead estimations) and other quasi-deterministic variables, to explain the price behavior of a multi-variable regression model. These include base production, system load, solar and wind generation, seasonality, day-ahead price and imbalance volume contributions. Three machine-learning algorithms were applied to test for performance, Gradient Boosting (GB), Random Forest (RF) and XGBoost. XGBoost presented higher performance and so it was chosen for the implementation of the real time forecast step. The model returns a Mean Absolute Error (MAE) of 7.89 £/MWh, a coefficient of determination (R2 score) of 76.8% and a Mean Squared Error (MSE) of 124.74. The variables that contribute the most to the model are the Net Imbalance Volume, the LOLP (aggregated), the month and the De-rated margins (aggregated) with 28.6%, 27.5%, 14.0%, and 8.9% of weight on feature importance respectively. Full article
(This article belongs to the Special Issue Control and Optimization of Renewable Energy Systems)
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