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Control and Optimization of Renewable Energy Systems

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 21783

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Special Issue Editors

Dr. Dimitrios Tzovaras

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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

E-Mail 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 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

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 (7 papers)

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Research

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20 pages, 5172 KiB

Open AccessArticle

Incentive-Based Demand Response Framework for Residential Applications: Design and Real-Life Demonstration

by Angelina D. Bintoudi, Napoleon Bezas, Lampros Zyglakis, Georgios Isaioglou, Christos Timplalexis, Paschalis Gkaidatzis, Athanasios Tryferidis, Dimosthenis Ioannidis and Dimitrios Tzovaras

Energies 2021, 14(14), 4315; https://0-doi-org.brum.beds.ac.uk/10.3390/en14144315 - 17 Jul 2021

Cited by 7 | Viewed by 1926

Abstract

In 2020, residential sector loads reached 25% of the overall electrical consumption in Europe and it is foreseen to stabilise at 29% by 2050. However, this relatively small increase demands, among others, changes in the energy consuming behaviour of households. To achieve this, Demand Response (DR) has been identified as a promising tool for unlocking the hidden flexibility potential of residential consumption. In this work, a holistic incentive-based DR framework aiming towards load shifting is proposed for residential applications. The proposed framework is characterised by several innovative features, mainly the formulation of the optimisation problem, which models user satisfaction and the economic operation of a distributed household portfolio, the customised load forecasting algorithm, which employs an adjusted Gradient Boosting Tree methodology with enhanced feature extraction and, finally, a disaggregation tool, which considers electrical features and time of use information. The DR framework is first validated through simulation to assess the business potential and is then deployed experimentally in real houses in Northern Greece. Results demonstrate that a mean 1.48% relative profit can be achieved via only load shifting of a maximum of three residential appliances, while the experimental application proves the effectiveness of the proposed algorithms in successfully managing the load curves of real houses with several residents. Correlations between market prices and the success of incentive-based load shifting DR programs show how wholesale pricing should be adjusted to ensure the viability of such DR schemes. Full article

(This article belongs to the Special Issue Control and Optimization of Renewable Energy Systems)

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31 pages, 10255 KiB

Open AccessArticle

Optimal D-STATCOM Placement Tool for Low Voltage Grids

by Gregorio Fernández, Alejandro Martínez, Noemí Galán, Javier Ballestín-Fuertes, Jesús Muñoz-Cruzado-Alba, Pablo López, Simon Stukelj, Eleni Daridou, Alessio Rezzonico and Dimosthenis Ioannidis

Energies 2021, 14(14), 4212; https://0-doi-org.brum.beds.ac.uk/10.3390/en14144212 - 12 Jul 2021

Cited by 7 | Viewed by 2979

Abstract

In low-voltage grids with a wide spread of domestic and/or small commercial consumers, mostly single-phase, problems can appear due to unbalanced power consumption between the different phases. These problems are mainly caused due to voltage unbalances between phases and the increase in distribution losses. This phenomenon occurs more frequently at the end of highly radial grids and can be stressed by the installation of renewable generators next to the consumers. Amongst the various techniques that have been proposed to solve this problem, this article explores the use of a D-STATCOM, presenting and testing a new method for the optimal location of this type of D-FACT. The developed method starts from a detailed analysis of the existing voltage unbalances in a distribution network and identifies the optimal location of the D-STATCOM (i.e., the one that reduces these unbalances while reducing energy losses). The developed method has been successfully tested for one year at four real European locations with different characteristics and different kinds of users. Full article

(This article belongs to the Special Issue Control and Optimization of Renewable Energy Systems)

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24 pages, 2388 KiB

Open AccessArticle

A Tri-Layer Optimization Framework for Day-Ahead Energy Scheduling Based on Cost and Discomfort Minimization

by Paraskevas Koukaras, Paschalis Gkaidatzis, Napoleon Bezas, Tommaso Bragatto, Federico Carere, Francesca Santori, Marcel Antal, Dimosthenis Ioannidis, Christos Tjortjis and Dimitrios Tzovaras

Energies 2021, 14(12), 3599; https://0-doi-org.brum.beds.ac.uk/10.3390/en14123599 - 17 Jun 2021

Cited by 18 | Viewed by 2376

Abstract

Over the past few decades, industry and academia have made great strides to improve aspects related with optimal energy management. These include better ways for efficient energy asset management, generating great opportunities for optimization of energy distribution, discomfort minimization, energy production, cost reduction and more. This paper proposes a framework for a multi-objective analysis, acting as a novel tool that offers responses for optimal energy management through a decision support system. The novelty is in the structure of the methodology, since it considers two distinct optimization problems for two actors, consumers and aggregators, with solution being able to completely or partly interact with the other one is in the form of a demand response signal exchange. The overall optimization is formulated by a bi-objective optimization problem for the consumer side, aiming at cost minimization and discomfort reduction, and a single objective optimization problem for the aggregator side aiming at cost minimization. The framework consists of three architectural layers, namely, the consumer, aggregator and decision support system (DSS), forming a tri-layer optimization framework with multiple interacting objects, such as objective functions, variables, constants and constraints. The DSS layer is responsible for decision support by forecasting the day-ahead energy management requirements. The main purpose of this study is to achieve optimal management of energy resources, considering both aggregator and consumer preferences and goals, whilst abiding with real-world system constraints. This is conducted through detailed simulations using real data from a pilot, that is part of Terni Distribution System portfolio. Full article

(This article belongs to the Special Issue Control and Optimization of Renewable Energy Systems)

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36 pages, 4484 KiB

Open AccessArticle

Multi-Step Energy Demand and Generation Forecasting with Confidence Used for Specification-Free Aggregate Demand Optimization

by Nikolaos Kolokas, Dimosthenis Ioannidis and Dimitrios Tzovaras

Energies 2021, 14(11), 3162; https://0-doi-org.brum.beds.ac.uk/10.3390/en14113162 - 28 May 2021

Cited by 4 | Viewed by 2242

Abstract

Energy demand and generation are common variables that need to be forecast in recent years, due to the necessity for energy self-consumption via storage and Demand Side Management. This work studies multi-step time series forecasting models for energy with confidence intervals for each time point, accompanied by a demand optimization algorithm, for energy management in partly or completely isolated islands. Particularly, the forecasting is performed via numerous traditional and contemporary machine learning regression models, which receive as input past energy data and weather forecasts. During pre-processing, the historical data are grouped into sets of months and days of week based on clustering models, and a separate regression model is automatically selected for each of them, as well as for each forecasting horizon. Furthermore, the multi-criteria optimization algorithm is implemented for demand scheduling with load shifting, assuming that, at each time point, demand is within its confidence interval resulting from the forecasting algorithm. Both clustering and multiple model training proved to be beneficial to forecasting compared to traditional training. The Normalized Root Mean Square Error of the forecasting models ranged approximately from 0.17 to 0.71, depending on the forecasting difficulty. It also appeared that the optimization algorithm can simultaneously increase renewable penetration and achieve load peak shaving, while also saving consumption cost in one of the tested islands. The global improvement estimation of the optimization algorithm ranged approximately from 5% to 38%, depending on the flexibility of the demand patterns. Full article

(This article belongs to the Special Issue Control and Optimization of Renewable Energy Systems)

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19 pages, 3813 KiB

Open AccessArticle

OptiMEMS: An Adaptive Lightweight Optimal Microgrid Energy Management System Based on the Novel Virtual Distributed Energy Resources in Real-Life Demonstration

by Angelina D. Bintoudi, Lampros Zyglakis, Apostolos C. Tsolakis, Paschalis A. Gkaidatzis, Athanasios Tryferidis, Dimosthenis Ioannidis and Dimitrios Tzovaras

Energies 2021, 14(10), 2752; https://0-doi-org.brum.beds.ac.uk/10.3390/en14102752 - 11 May 2021

Cited by 5 | Viewed by 3346

Abstract

As microgrids have gained increasing attention over the last decade, more and more applications have emerged, ranging from islanded remote infrastructures to active building blocks of smart grids. To optimally manage the various microgrid assets towards maximum profit, while taking into account reliability and stability, it is essential to properly schedule the overall operation. To that end, this paper presents an optimal scheduling framework for microgrids both for day-ahead and real-time operation. In terms of real-time, this framework evaluates the real-time operation and, based on deviations, it re-optimises the schedule dynamically in order to continuously provide the best possible solution in terms of economic benefit and energy management. To assess the solution, the designed framework has been deployed to a real-life microgrid establishment consisting of residential loads, a PV array and a storage unit. Results demonstrate not only the benefits of the day-ahead optimal scheduling, but also the importance of dynamic re-optimisation when deviations occur between forecasted and real-time values. Given the intermittency of PV generation as well as the stochastic nature of consumption, real-time adaptation leads to significantly improved results. Full article

(This article belongs to the Special Issue Control and Optimization of Renewable Energy Systems)

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16 pages, 3276 KiB

Open AccessArticle

Price Forecasting for the Balancing Energy Market Using Machine-Learning Regression

by Alexandre Lucas, Konstantinos Pegios, Evangelos Kotsakis and Dan Clarke

Energies 2020, 13(20), 5420; https://0-doi-org.brum.beds.ac.uk/10.3390/en13205420 - 16 Oct 2020

Cited by 27 | Viewed by 4936

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 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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Review

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13 pages, 1407 KiB

Open AccessReview

Centralized Control of Distribution Networks with High Penetration of Renewable Energies

by Fco. Javier Zarco-Soto, Pedro J. Zarco-Periñán and Jose L. Martínez-Ramos

Energies 2021, 14(14), 4283; https://0-doi-org.brum.beds.ac.uk/10.3390/en14144283 - 15 Jul 2021

Cited by 12 | Viewed by 2141

Abstract

Distribution networks were conceived to distribute the energy received from transmission and subtransmission to supply passive loads. This approach, however, is not valid anymore due to the presence of distributed generation, which is mainly based on renewable energies, and the increased number of plug-in electric vehicles that are connected at this voltage level for domestic use. In this paper the ongoing transition that distribution networks face is addressed. Whereas distributed renewable energy sources increase nodal voltages, electric vehicles result in demand surges higher than the load predictions considered when planning these networks, leading to congestion in distribution lines and transformers. Additionally, centralized control techniques are analyzed to reduce the impact of distributed generation and electric vehicles and increase their effective integration. A classification of the different methodologies applied to the problems of voltage control and congestion management is presented. Full article

(This article belongs to the Special Issue Control and Optimization of Renewable Energy Systems)

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