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Decision-Making Systems in Power System Planning and Operation in the Presence of High Shares of Renewable Energies

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 18038

Special Issue Editors

Dr. Alfredo Vaccaro

E-Mail Website
Guest Editor
Department of Engineering, University of Sannio, Piazza Roma 21, 82100 Benevento, Italy
Interests: power systems analysis; reliable computing; decentralized optimization; self-organizing sensor networks; renewable power generators
Special Issues, Collections and Topics in MDPI journals
Dr. Fabrizio de Caro

E-Mail Website
Guest Editor
Department of Engineering, University of Sannio, Piazza Roma 21, 82100 Benevento, Italy
Interests: power systems analysis; reliability and resilience in power systems; electricity markets; wind power forecasting; machine learning; stochastic modeling; decision making tools in the presence of uncertainty

Special Issue Information

Dear Colleagues,

The massive penetration of not-programmable Renewable Energy Sources (RESs) in modern power systems still represents a challenge for system operators. Despite the promising results of the last years, many questions remain unanswered. In particular, how to harness the power of Big Data for developing reliable decision-making tools is one of them. In this context, the developed frameworks should deal with the effect of uncertain sources, promoting renewable energy integration in Smart Grids.

Potential topics include but are not limited to the following:

  • Deterministic and probabilistic multi-step ahead wind/solar power forecasting, preferably over large scales.
  • Novel metrics to assess the impact of renewable energy uncertainty in power systems.
  • Data-driven techniques for power system operation and control.
  • Decentralized computing in power systems.
  • Methodologies for uncertainty management and characterization in power system applications.
  • Decision-making tools for planning and operation of power systems in the presence of uncertainty.

Dr. Alfredo Vaccaro
Dr. Fabrizio de Caro
Guest Editors

Manuscript Submission Information

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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

  • decision-making tools
  • renewable energies
  • wind power forecasting
  • solar power forecasting
  • data-driven techniques
  • planning and operation of power systems
  • decentralized computing in smart grids

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Published Papers (7 papers)

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Research

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20 pages, 6355 KiB  
Article
Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis through Temperature Data Analysis
by Alessandro Murgia, Robbert Verbeke, Elena Tsiporkova, Ludovico Terzi and Davide Astolfi
Energies 2023, 16(2), 620; https://doi.org/10.3390/en16020620 - 4 Jan 2023
Cited by 16 | Viewed by 2480
Abstract
Wind turbines are expected to provide on the order of 50% of the electricity worldwide in the near future, and it is therefore fundamental to reduce the costs associated with this form of energy conversion, which regard maintenance as the first item of [...] Read more.
Wind turbines are expected to provide on the order of 50% of the electricity worldwide in the near future, and it is therefore fundamental to reduce the costs associated with this form of energy conversion, which regard maintenance as the first item of expenditure. SCADA-based condition monitoring for anomaly detection is commonly presented as a convenient solution for fault diagnosis on turbine components. However, its suitability is generally proven by empirical analyses which are limited in time and based on a circumscribed number of turbines. To cope with this lack of validation, this paper performs a controlled experiment to evaluate the suitability of SCADA-based condition monitoring for fault diagnosis in a fleet of eight turbines monitored for over 11 years. For the controlled experiment, a weakly supervised method was used to model the normal behavior of the turbine component. Such a model is instantiated as a convolutional neural network. The method, instantiated as a threshold-based method, proved to be suitable for diagnosis, i.e. the identification of all drivetrain failures with a considerable advance time. On the other hand, the wide variability between the time the alarm is raised and the fault is observed suggests its limited suitability for prognosis. Full article
(This article belongs to the Special Issue Decision-Making Systems in Power System Planning and Operation in the Presence of High Shares of Renewable Energies)
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18 pages, 721 KiB  
Article
Multivariate Data-Driven Models for Wind Turbine Power Curves including Sub-Component Temperatures
by Davide Astolfi, Ravi Pandit, Andrea Lombardi and Ludovico Terzi
Energies 2023, 16(1), 165; https://doi.org/10.3390/en16010165 - 23 Dec 2022
Viewed by 1618
Abstract
The most commonly employed tool for wind turbine performance analysis is the power curve, which is the relation between wind intensity and power. The diffusion of SCADA systems has boosted the adoption of data-driven approaches to power curves. In particular, a recent research [...] Read more.
The most commonly employed tool for wind turbine performance analysis is the power curve, which is the relation between wind intensity and power. The diffusion of SCADA systems has boosted the adoption of data-driven approaches to power curves. In particular, a recent research line involves multivariate methods, employing further input variables in addition to the wind speed. In this work, an innovative contribution is investigated, which is the inclusion of thirteen sub-component temperatures as possible covariates. This is discussed through a real-world test case, based on data provided by ENGIE Italia. Two models are analyzed: support vector regression with Gaussian kernel and Gaussian process regression. The input variables are individuated through a sequential feature selection algorithm. The sub-component temperatures are abundantly selected as input variables, proving the validity of the idea proposed in this work. The obtained error metrics are lower with respect to benchmark models employing more typical input variables: the resulting mean absolute error is 1.35% of the rated power. The results of the two types of selected regressions are not remarkably different. This supports that the qualifying points are, rather than the model type, the use and the selection of a potentially vast number of input variables. Full article
(This article belongs to the Special Issue Decision-Making Systems in Power System Planning and Operation in the Presence of High Shares of Renewable Energies)
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17 pages, 1421 KiB  
Article
Discussion of Wind Turbine Performance Based on SCADA Data and Multiple Test Case Analysis
by Davide Astolfi, Ravi Pandit, Ludovico Terzi and Andrea Lombardi
Energies 2022, 15(15), 5343; https://doi.org/10.3390/en15155343 - 22 Jul 2022
Cited by 20 | Viewed by 2577
Abstract
This work is devoted to the formulation of innovative SCADA-based methods for wind turbine performance analysis and interpretation. The work is organized as an academia–industry collaboration: three test cases are analyzed, two with hydraulic pitch control (Vestas V90 and V100) and one with [...] Read more.
This work is devoted to the formulation of innovative SCADA-based methods for wind turbine performance analysis and interpretation. The work is organized as an academia–industry collaboration: three test cases are analyzed, two with hydraulic pitch control (Vestas V90 and V100) and one with electric pitch control (Senvion MM92). The investigation is based on the method of bins, on a polynomial regression applied to operation curves that have never been analyzed in detail in the literature before, and on correlation and causality analysis. A key point is the analysis of measurement channels related to the blade pitch control and to the rotor: pitch manifold pressure, pitch piston traveled distance and tower vibrations for the hydraulic pitch wind turbines, and blade pitch current for the electric pitch wind turbines. The main result of this study is that cases of noticeable under-performance are observed for the hydraulic pitch wind turbines, which are associated with pitch pressure decrease in time for one case and to suspected rotor unbalance for another case. On the other way round, the behavior of the rotational speed and blade pitch curves is homogeneous and stable for the wind turbines electrically controlled. Summarizing, the evidence collected in this work identifies the hydraulic pitch as a sensible component of the wind turbine that should be monitored cautiously because it is likely associated with performance decline with age. Full article
(This article belongs to the Special Issue Decision-Making Systems in Power System Planning and Operation in the Presence of High Shares of Renewable Energies)
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17 pages, 1101 KiB  
Article
A Digital Twin Approach for Improving Estimation Accuracy in Dynamic Thermal Rating of Transmission Lines
by Gian Marco Paldino, Fabrizio De Caro, Jacopo De Stefani, Alfredo Vaccaro, Domenico Villacci and Gianluca Bontempi
Energies 2022, 15(6), 2254; https://doi.org/10.3390/en15062254 - 19 Mar 2022
Cited by 14 | Viewed by 3088
Abstract
The limitation of transmission lines thermal capacity plays a crucial role in the safety and reliability of power systems. Dynamic thermal line rating approaches aim to estimate the transmission line’s temperature and assess its compliance with the limitations above. Existing physics-based standards estimate [...] Read more.
The limitation of transmission lines thermal capacity plays a crucial role in the safety and reliability of power systems. Dynamic thermal line rating approaches aim to estimate the transmission line’s temperature and assess its compliance with the limitations above. Existing physics-based standards estimate the temperature based on environment and line conditions measured by several sensors. This manuscript shows that estimation accuracy can be improved by adopting a data-driven Digital Twin approach. The proposed method exploits machine learning by learning the input–output relation between the physical sensors data and the actual conductor temperature, serving as a digital equivalent to physics-based standards. An experimental assessment on real data, comparing the proposed approach with the IEEE 738 standard, shows a reduction of 60% of the Root Mean Squared Error and a decrease in the maximum estimation error from above 10 °C to below 7 °C. These preliminary results suggest that the Digital Twin provides more accurate and robust estimations, serving as a complement, or a potential alternative, to traditional methods. Full article
(This article belongs to the Special Issue Decision-Making Systems in Power System Planning and Operation in the Presence of High Shares of Renewable Energies)
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18 pages, 30354 KiB  
Article
Global vs. Local Models for Short-Term Electricity Demand Prediction in a Residential/Lodging Scenario
by Amedeo Buonanno, Martina Caliano, Antonino Pontecorvo, Gianluca Sforza, Maria Valenti and Giorgio Graditi
Energies 2022, 15(6), 2037; https://doi.org/10.3390/en15062037 - 10 Mar 2022
Cited by 8 | Viewed by 2546
Abstract
Electrical load forecasting has a fundamental role in the decision-making process of energy system operators. When many users are connected to the grid, high-performance forecasting models are required, posing several problems associated with the availability of historical energy consumption data for each end-user [...] Read more.
Electrical load forecasting has a fundamental role in the decision-making process of energy system operators. When many users are connected to the grid, high-performance forecasting models are required, posing several problems associated with the availability of historical energy consumption data for each end-user and training, deploying and maintaining a model for each user. Moreover, introducing new end-users to an existing network poses problems relating to their forecasting model. Global models, trained on all available data, are emerging as the best solution in several contexts, because they show higher generalization performance, being able to leverage the patterns that are similar across different time series. In this work, the lodging/residential electricity 1-h-ahead load forecasting of multiple time series for smart grid applications is addressed using global models, suggesting the effectiveness of such an approach also in the energy context. Results obtained on a subset of the Great Energy Predictor III dataset with several global models are compared to results obtained with local models based on the same methods, showing that global models can perform similarly to the local ones, while presenting simpler deployment and maintainability. In this work, the forecasting of a new time series, representing a new end-user introduced in the pre-existing network, is also approached under specific assumptions, by using a global model trained using data related to the existing end-users. Results reveal that the forecasting model pre-trained on data related to other end-users allows the attainment of good forecasting performance also for new end-users. Full article
(This article belongs to the Special Issue Decision-Making Systems in Power System Planning and Operation in the Presence of High Shares of Renewable Energies)
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22 pages, 1895 KiB  
Article
Hybrid Forecast and Control Chain for Operation of Flexibility Assets in Micro-Grids
by Hamidreza Mirtaheri, Piero Macaluso, Maurizio Fantino, Marily Efstratiadi, Sotiris Tsakanikas, Panagiotis Papadopoulos and Andrea Mazza
Energies 2021, 14(21), 7252; https://doi.org/10.3390/en14217252 - 3 Nov 2021
Cited by 2 | Viewed by 2037
Abstract
Studies on forecasting and optimal exploitation of renewable resources (especially within microgrids) were already introduced in the past. However, in several research papers, the constraints regarding integration within real applications were relaxed, i.e., this kind of research provides impractical solutions, although they are [...] Read more.
Studies on forecasting and optimal exploitation of renewable resources (especially within microgrids) were already introduced in the past. However, in several research papers, the constraints regarding integration within real applications were relaxed, i.e., this kind of research provides impractical solutions, although they are very complex. In this paper, the computational components (such as photovoltaic and load forecasting, and resource scheduling and optimization) are brought together into a practical implementation, introducing an automated system through a chain of independent services aiming to allow forecasting, optimization, and control. Encountered challenges may provide a valuable indication to make ground with this design, especially in cases for which the trade-off between sophistication and available resources should be rather considered. The research work was conducted to identify the requirements for controlling a set of flexibility assets—namely, electrochemical battery storage system and electric car charging station—for a semicommercial use-case by minimizing the operational energy costs for the microgrid considering static and dynamic parameters of the assets. Full article
(This article belongs to the Special Issue Decision-Making Systems in Power System Planning and Operation in the Presence of High Shares of Renewable Energies)
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Review

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28 pages, 807 KiB  
Review
Hosting Capacity Assessment Strategies and Reinforcement Learning Methods for Coordinated Voltage Control in Electricity Distribution Networks: A Review
by Jude Suchithra, Duane Robinson and Amin Rajabi
Energies 2023, 16(5), 2371; https://doi.org/10.3390/en16052371 - 1 Mar 2023
Cited by 7 | Viewed by 2254
Abstract
Increasing connection rates of rooftop photovoltaic (PV) systems to electricity distribution networks has become a major concern for the distribution network service providers (DNSPs) due to the inability of existing network infrastructure to accommodate high levels of PV penetration while maintaining voltage regulation [...] Read more.
Increasing connection rates of rooftop photovoltaic (PV) systems to electricity distribution networks has become a major concern for the distribution network service providers (DNSPs) due to the inability of existing network infrastructure to accommodate high levels of PV penetration while maintaining voltage regulation and other operational requirements. The solution to this dilemma is to undertake a hosting capacity (HC) study to identify the maximum penetration limit of rooftop PV generation and take necessary actions to enhance the HC of the network. This paper presents a comprehensive review of two topics: HC assessment strategies and reinforcement learning (RL)-based coordinated voltage control schemes. In this paper, the RL-based coordinated voltage control schemes are identified as a means to enhance the HC of electricity distribution networks. RL-based algorithms have been widely used in many power system applications in recent years due to their precise, efficient and model-free decision-making capabilities. A large portion of this paper is dedicated to reviewing RL concepts and recently published literature on RL-based coordinated voltage control schemes. A non-exhaustive classification of RL algorithms for voltage control is presented and key RL parameters for the voltage control problem are identified. Furthermore, critical challenges and risk factors of adopting RL-based methods for coordinated voltage control are discussed. Full article
(This article belongs to the Special Issue Decision-Making Systems in Power System Planning and Operation in the Presence of High Shares of Renewable Energies)
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