Energies

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24 pages, 7031 KiB

Open AccessArticle

Data-Driven Evaluation of Secondary- and Tertiary-Reserve Needs with High Renewables Penetration: The Italian Case

by Filippo Bovera, Giuliano Rancilio, Davide Falabretti and Marco Merlo

Energies 2021, 14(8), 2157; https://doi.org/10.3390/en14082157 - 13 Apr 2021

Cited by 12 | Viewed by 2607

The diffusion of nonprogrammable power plants, together with the decommissioning of conventional, rotating generators, is increasing the need for flexible resources to always ensure the safe and secure operation of the European electric-power system. Beyond technological advances, policy aspects also play a fundamental [...] Read more.

The diffusion of nonprogrammable power plants, together with the decommissioning of conventional, rotating generators, is increasing the need for flexible resources to always ensure the safe and secure operation of the European electric-power system. Beyond technological advances, policy aspects also play a fundamental role in the opening of electricity markets to new players; in this regard, System Operations Guideline EU 2017/1485 and Italian Regulatory Authority documents require the Italian transmission-system operator (TSO; Terna) to publish all exploited algorithms and methodologies for the management of market balancing. In this context, the present paper develops and presents a data-driven methodology to estimate secondary and tertiary reserve needs; a numerical real-life case study, focused on the North Italy geographical zone, is presented. Data for 2017, 2018, and 2019 on electricity consumption and production (forecasted and actual) were gathered. Following the European TSOs Organization (ENTSO-E) and the Italian TSO (Terna) prescriptions, methodology for the calculation of reserve needs was developed. Results are presented under graphical form and refer, among others, to spinning and nonspinning reserve duration curves, forecast error contribution to reserve calculation, and samples considered for analysis. While a comparison with available market observations is not very helpful, results suggest that the developed methodology could be useful for the evaluation of reserve needs in different control areas. Full article

(This article belongs to the Special Issue Emerging Technologies and Advanced Controls in Renewable-Energy-Based Power Generation Systems)

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16 pages, 2467 KiB

Open AccessArticle

Improving Wind Power Forecasts: Combination through Multivariate Dimension Reduction Techniques

by Marta Poncela-Blanco and Pilar Poncela

Energies 2021, 14(5), 1446; https://doi.org/10.3390/en14051446 - 6 Mar 2021

Cited by 8 | Viewed by 1900

Abstract

Wind energy and wind power forecast errors have a direct impact on operational decision problems involved in the integration of this form of energy into the electricity system. As the relationship between wind and the generated power is highly nonlinear and time-varying, and [...] Read more.

Wind energy and wind power forecast errors have a direct impact on operational decision problems involved in the integration of this form of energy into the electricity system. As the relationship between wind and the generated power is highly nonlinear and time-varying, and given the increasing number of available forecasting techniques, it is possible to use alternative models to obtain more than one prediction for the same hour and forecast horizon. To increase forecast accuracy, it is possible to combine the different predictions to obtain a better one or to dynamically select the best one in each time period. Hybrid alternatives based on combining a few selected forecasts can be considered when the number of models is large. One of the most popular ways to combine forecasts is to estimate the coefficients of each prediction model based on its past forecast errors. As an alternative, we propose using multivariate reduction techniques and Markov chain models to combine forecasts. The combination is thus not directly based on the forecast errors. We show that the proposed combination strategies based on dimension reduction techniques provide competitive forecasting results in terms of the Mean Square Error. Full article

(This article belongs to the Special Issue Emerging Technologies and Advanced Controls in Renewable-Energy-Based Power Generation Systems)

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15 pages, 2771 KiB

Open AccessArticle

Real-Time Sensorless Robust Velocity Controller Applied to a DC-Motor for Emulating a Wind Turbine

by Onofre A. Morfin, Riemann Ruiz-Cruz, Jesus I. Hernández, Carlos E. Castañeda, Reymundo Ramírez-Betancour and Fredy A. Valenzuela-Murillo

Energies 2021, 14(4), 868; https://doi.org/10.3390/en14040868 - 7 Feb 2021

Cited by 5 | Viewed by 2328

Abstract

The wind power systems of variable velocity using a doubly-fed induction generator dominate large-scale electrical generation within renewable energy sources. The usual control goal of the wind systems consists of maximizing the wind energy capture and streamlining the energy conversion process. In addition, [...] Read more.

The wind power systems of variable velocity using a doubly-fed induction generator dominate large-scale electrical generation within renewable energy sources. The usual control goal of the wind systems consists of maximizing the wind energy capture and streamlining the energy conversion process. In addition, these systems are an intermittent energy source due to the variation of the wind velocity. Consequently, the control system designed to establish a reliable operation of the wind system represents the main challenge. Therefore, emulating the operation of the wind turbine by means of an electric motor is a common strategy so that the controller design is focused on the induction generator and its connection to the utility grid. Thus, we propose to emulate the dynamical operation of a wind turbine through a separately excited DC motor driving by a sensor-less velocity controller. This controller is synthesized based on the state-feedback linearization technique combined with the super-twisting algorithm to set a robust closed-loop system in the presence of external disturbances. A robust velocity observer is designed to estimate the rotor velocity based on the armature current measuring. Furthermore, a robust differentiator is designed for estimating the time derivative of the velocity error variable, achieving a reduction in the computational calculus. Experimental tests were carried using a separately excited DC motor coupled with a dynamometer to validate the proposed wind turbine emulator. Full article

(This article belongs to the Special Issue Emerging Technologies and Advanced Controls in Renewable-Energy-Based Power Generation Systems)

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19 pages, 8177 KiB

Open AccessArticle

Optimal Voltage Control in MV Network with Distributed Generation

by Piotr Kacejko and Paweł Pijarski

Energies 2021, 14(2), 469; https://doi.org/10.3390/en14020469 - 17 Jan 2021

Cited by 11 | Viewed by 2094

Abstract

The article presents the concept of voltage control in a medium-voltage network, using the classical control of a HV/MV (High Voltage/Medium Voltage) transformer and active participation of distributed generation sources. The proposed solution is based on the results of the optimization process. The [...] Read more.

The article presents the concept of voltage control in a medium-voltage network, using the classical control of a HV/MV (High Voltage/Medium Voltage) transformer and active participation of distributed generation sources. The proposed solution is based on the results of the optimization process. The objective function is considered as a single-criterion—the voltage quality indicator or value of power losses in the network, and optionally as two-criteria (voltage quality and losses combined, with appropriately selected weight factors). The analysis carried out for random selection of independent variables, using the original heuristic algorithm, indicated a very high efficiency of the proposed control process, compared to the traditional approach. A significant improvement in the voltage quality index and reduction of losses was found, which justifies the advisability of looking for new solutions in the field of voltage control in MV networks, taking into account active participation of distributed generation. Full article

(This article belongs to the Special Issue Emerging Technologies and Advanced Controls in Renewable-Energy-Based Power Generation Systems)

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18 pages, 1256 KiB

Open AccessArticle

Hybrid PV Power Forecasting Methods: A Comparison of Different Approaches

by Alessandro Niccolai, Alberto Dolara and Emanuele Ogliari

Energies 2021, 14(2), 451; https://doi.org/10.3390/en14020451 - 15 Jan 2021

Cited by 41 | Viewed by 2842

Abstract

Accurate photovoltaic (PV) prediction has a very positive effect on many problems that power grids can face when there is a high penetration of variable energy sources. This problem can be addressed with computational intelligence algorithms such as neural networks and Evolutionary Optimization. [...] Read more.

Accurate photovoltaic (PV) prediction has a very positive effect on many problems that power grids can face when there is a high penetration of variable energy sources. This problem can be addressed with computational intelligence algorithms such as neural networks and Evolutionary Optimization. The purpose of this article is to analyze three different hybridizations between physical models and artificial neural networks: the first hybridization combines neural networks with the output of the five-parameter physical model of a photovoltaic module in which the parameters are obtained from a datasheet. In the second hybridization, the parameters are obtained from a matching procedure with historical data exploiting Social Network Optimization. Finally, the third hybridization is PHANN, in which clear sky irradiation is used as an input. These three hybrid methods are compared with two physical approaches and simple neural network-based forecasting. The results show that the hybridization is very effective for achieving good forecasting results, while the performance of the three hybrid methods is comparable. Full article

(This article belongs to the Special Issue Emerging Technologies and Advanced Controls in Renewable-Energy-Based Power Generation Systems)

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15 pages, 5142 KiB

Open AccessEditor’s ChoiceArticle

Forecasting Photovoltaic Power Generation Using Satellite Images

by Dukhwan Yu, Seowoo Lee, Sangwon Lee, Wonik Choi and Ling Liu

Energies 2020, 13(24), 6603; https://doi.org/10.3390/en13246603 - 14 Dec 2020

Cited by 23 | Viewed by 3707

Abstract

As the relative importance of renewable energy in electric power systems increases, the prediction of photovoltaic (PV) power generation has become a crucial technology, for improving stability in the operation of next-generation power systems, such as microgrid and virtual power plants (VPP). In [...] Read more.

As the relative importance of renewable energy in electric power systems increases, the prediction of photovoltaic (PV) power generation has become a crucial technology, for improving stability in the operation of next-generation power systems, such as microgrid and virtual power plants (VPP). In order to improve the accuracy of PV power generation forecasting, a fair amount of research has been applied to weather forecast data (to a learning process). Despite these efforts, the problems of forecasting PV power generation remains challenging since existing methods show limited accuracy due to inappropriate cloud amount forecast data, which are strongly correlated with PV power generation. To address this problem, we propose a PV power forecasting model, including a cloud amount forecasting network trained with satellite images. In addition, our proposed model adopts convolutional self-attention to effectively capture historical features, and thus acquire helpful information from weather forecasts. To show the efficacy of the proposed cloud amount forecast network, we conduct extensive experiments on PV power generation forecasting with and without the cloud amount forecast network. The experimental results show that the Mean Absolute Percentage Error (MAPE) of our proposed prediction model, combined with the cloud amount forecast network, are reduced by 22.5% compared to the model without the cloud amount forecast network. Full article

(This article belongs to the Special Issue Emerging Technologies and Advanced Controls in Renewable-Energy-Based Power Generation Systems)

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22 pages, 9862 KiB

Open AccessArticle

Short-Term Forecasting of Large-Scale Clouds Impact on Downwelling Surface Solar Irradiation

by Panagiotis Kosmopoulos, Dimitris Kouroutsidis, Kyriakoula Papachristopoulou, Panagiotis Ioannis Raptis, Akriti Masoom, Yves-Marie Saint-Drenan, Philippe Blanc, Charalampos Kontoes and Stelios Kazadzis

Energies 2020, 13(24), 6555; https://doi.org/10.3390/en13246555 - 11 Dec 2020

Cited by 14 | Viewed by 4052

Abstract

This study focuses on the use of cloud motion vectors (CMV) and fast radiative transfer models (FRTM) in the prospect of forecasting downwelling surface solar irradiation (DSSI). Using near-real-time cloud optical thickness (COT) data derived from multispectral images from the spinning enhanced visible [...] Read more.

This study focuses on the use of cloud motion vectors (CMV) and fast radiative transfer models (FRTM) in the prospect of forecasting downwelling surface solar irradiation (DSSI). Using near-real-time cloud optical thickness (COT) data derived from multispectral images from the spinning enhanced visible and infrared imager (SEVIRI) onboard the Meteosat second generation (MSG) satellite, we introduce a novel short-term forecasting system (3 h ahead) that is capable of calculating solar energy in large-scale (1.5 million-pixel area covering Europe and North Africa) and in high spatial (5 km over nadir) and temporal resolution (15 min intervals). For the operational implementation of such a big data computing architecture (20 million simulations in less than a minute), we exploit a synergy of high-performance computing and deterministic image processing technologies (dense optical flow estimation). The impact of clouds forecasting uncertainty on DSSI was quantified in terms of cloud modification factor (CMF), for all-sky and clear sky conditions, for more generalized results. The forecast accuracy was evaluated against the real COT and CMF images under different cloud movement patterns, and the correlation was found to range from 0.9 to 0.5 for 15 min and 3 h ahead, respectively. The CMV forecast variability revealed an overall DSSI uncertainty in the range 18–34% under consecutive alternations of cloud presence, highlighting the ability of the proposed system to follow the cloud movement in opposition to the baseline persistent forecasting, which considers the effects of topocentric sun path on DSSI but keeps the clouds in “fixed” positions, and which presented an overall uncertainty of 30–43%. The proposed system aims to support the distributed solar plant energy production management, as well as electricity handling entities and smart grid operations. Full article

(This article belongs to the Special Issue Emerging Technologies and Advanced Controls in Renewable-Energy-Based Power Generation Systems)

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37 pages, 4438 KiB

Open AccessFeature PaperArticle

Evaluation of Mathematical Model to Characterize the Performance of Conventional and Hybrid PV Array Topologies under Static and Dynamic Shading Patterns

by Manoharan Premkumar, Umashankar Subramaniam, Thanikanti Sudhakar Babu, Rajvikram Madurai Elavarasan and Lucian Mihet-Popa

Energies 2020, 13(12), 3216; https://doi.org/10.3390/en13123216 - 20 Jun 2020

Cited by 90 | Viewed by 5288

Abstract

The analysis and the assessment of interconnected photovoltaic (PV) modules under different shading conditions and various shading patterns are presented in this paper. The partial shading conditions (PSCs) due to the various factors reduce the power output of PV arrays, and its characteristics [...] Read more.

The analysis and the assessment of interconnected photovoltaic (PV) modules under different shading conditions and various shading patterns are presented in this paper. The partial shading conditions (PSCs) due to the various factors reduce the power output of PV arrays, and its characteristics have multiple peaks due to the mismatching losses between PV panels. The principal objective of this paper is to model, analyze, simulate and evaluate the performance of PV array topologies such as series-parallel (SP), honey-comb (HC), total-cross-tied (TCT), ladder (LD) and bridge-linked (BL) under different shading patterns to produce the maximum power by reducing the mismatching losses (MLs). Along with the conventional PV array topologies, this paper also discusses the hybrid PV array topologies such as bridge-linked honey-comb (BLHC), bridge-linked total-cross-tied (BLTCT) and series-parallel total-cross-tied (SPTCT). The performance analysis of the traditional PV array topologies along with the hybrid topologies is carried out during static and dynamic shading patterns by comparing the various parameters such as the global peak (GP), local peaks (LPs), corresponding voltage and current at GP and LPs, fill factor (FF) and ML. In addition, the voltage and current equations of the HC configuration under two shading conditions are derived, which represents one of the novelties of this paper. The various parameters of the SPR-200-BLK-U PV module are used for PV modeling and simulation in MATLAB/Simulink software. Thus, the obtained results provide useful information to the researchers for healthy operation and power maximization of PV systems. Full article

(This article belongs to the Special Issue Emerging Technologies and Advanced Controls in Renewable-Energy-Based Power Generation Systems)

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