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In this paper, a chance-constrained (CC) framework is developed to manage the voltage control problem of medium-voltage (MV) distribution systems subject to model uncertainty. Such epistemic uncertainties are inherent in distribution system analyses given that an exact model of the network components is not available. In this context, relying on the simplified deterministic models can lead to insufficient control decisions. The CC-based voltage control framework is proposed to tackle this issue while being able to control the desired protection level against model uncertainties. The voltage control task disregarding the model uncertainties is firstly formulated as a linear optimization problem. Then, model uncertainty impacts on the above linear optimization problem are evaluated. This analysis defines that the voltage control problem subject to model uncertainties should be modelled with a joint CC formulation. The latter is accordingly relaxed to individual CC optimizations using the proposed methods. The performance of proposed CC voltage control methods is finally tested in comparison with that of the robust optimization. Simulation results confirm the accuracy of confidence level expected from the proposed CC voltage control formulations. The proposed technique allows the system operators to tune the confidence level parameter such that a tradeoff between operation costs and conservatism level is attained.
Bashir Bakhshideh Zad; Jean-François Toubeau; François Vallée. Chance-Constrained Based Voltage Control Framework to Deal with Model Uncertainties in MV Distribution Systems. Energies 2021, 14, 5161 .
AMA StyleBashir Bakhshideh Zad, Jean-François Toubeau, François Vallée. Chance-Constrained Based Voltage Control Framework to Deal with Model Uncertainties in MV Distribution Systems. Energies. 2021; 14 (16):5161.
Chicago/Turabian StyleBashir Bakhshideh Zad; Jean-François Toubeau; François Vallée. 2021. "Chance-Constrained Based Voltage Control Framework to Deal with Model Uncertainties in MV Distribution Systems." Energies 14, no. 16: 5161.
Low voltage distribution networks have not been traditionally designed to accommodate the large-scale integration of decentralized photovoltaic (PV) generations. The bidirectional power flows in existing networks resulting from the load demand and PV generation changes as well as the influence of ambient temperature led to voltage variations and increased the leakage current through the cable insulation. In this paper, a machine learning-based framework is implemented for the identification of cable degradation by using data from deployed smart meter (SM) measurements. Nodal voltage variations are supposed to be related to cable conditions (reduction of cable insulation thickness due to insulation wear) and to client net demand changes. Various machine learning techniques are applied for classification of nodal voltages according to the cable insulation conditions. Once trained according to the comprehensive generated datasets, the implemented techniques can classify new network operating points into a healthy or degraded cable condition with high accuracy in their predictions. The simulation results reveal that logistic regression and decision tree algorithms lead to a better prediction (with a 97.9% and 99.9% accuracy, respectively) result than the k-nearest neighbors (which reach only 76.7%). The proposed framework offers promising perspectives for the early identification of LV cable conditions by using SM measurements.
Egnonnumi Codjo; Bashir Bakhshideh Zad; Jean-François Toubeau; Bruno François; François Vallée. Machine Learning-Based Classification of Electrical Low Voltage Cable Degradation. Energies 2021, 14, 2852 .
AMA StyleEgnonnumi Codjo, Bashir Bakhshideh Zad, Jean-François Toubeau, Bruno François, François Vallée. Machine Learning-Based Classification of Electrical Low Voltage Cable Degradation. Energies. 2021; 14 (10):2852.
Chicago/Turabian StyleEgnonnumi Codjo; Bashir Bakhshideh Zad; Jean-François Toubeau; Bruno François; François Vallée. 2021. "Machine Learning-Based Classification of Electrical Low Voltage Cable Degradation." Energies 14, no. 10: 2852.
The current energy transition and the underlying growth in variable and uncertain renewable-based energy generation challenge the proper operation of power systems. Classical probabilistic uncertainty models, e.g., stochastic programming or robust optimisation, have been used widely to solve problems such as the day-ahead energy and reserve dispatch problem to enhance the day-ahead decisions with a probabilistic insight of renewable energy generation in real-time. By doing so, the scheduling of the power system becomes, production and consumption of electric power, more reliable (i.e., more robust because of potential deviations) while minimising the social costs given potential balancing actions. Nevertheless, these classical models are not valid when the uncertainty is imprecise, meaning that the system operator may not rely on a unique distribution function to describe the uncertainty. Given the Distributionally Robust Optimisation method, our approach can be implemented for any non-probabilistic, e.g., interval models rather than only sets of distribution functions (ambiguity set of probability distributions). In this paper, the aim is to apply two advanced non-probabilistic uncertainty models: Interval and ϵ -contamination, where the imprecision and in-determinism in the uncertainty (uncertain parameters) are considered. We propose two kinds of theoretical solutions under two decision criteria—Maximinity and Maximality. For an illustration of our solutions, we apply our proposed approach to a case study inspired by the 24-node IEEE reliability test system.
Keivan Shariatmadar; Adriano Arrigo; François Vallée; Hans Hallez; Lieven Vandevelde; David Moens. Day-Ahead Energy and Reserve Dispatch Problem under Non-Probabilistic Uncertainty. Energies 2021, 14, 1016 .
AMA StyleKeivan Shariatmadar, Adriano Arrigo, François Vallée, Hans Hallez, Lieven Vandevelde, David Moens. Day-Ahead Energy and Reserve Dispatch Problem under Non-Probabilistic Uncertainty. Energies. 2021; 14 (4):1016.
Chicago/Turabian StyleKeivan Shariatmadar; Adriano Arrigo; François Vallée; Hans Hallez; Lieven Vandevelde; David Moens. 2021. "Day-Ahead Energy and Reserve Dispatch Problem under Non-Probabilistic Uncertainty." Energies 14, no. 4: 1016.
Renewable Energy Communities consist in an emerging decentralized market mechanism which allows local energy exchanges between end-users, bypassing the traditional wholesale/retail market structure. In that configuration, local consumers and prosumers gather in communities and can either cooperate or compete towards a common objective, such as the minimization of the electricity costs and/or the minimization of greenhouse gas emissions for instance. This paper proposes data analytics modules which aim at helping the community members to schedule the usage of their resources (generation and consumption) in order to minimize their electricity bill. A day-ahead local wind power forecasting algorithm, which relies on state-of-the-art Machine Learning techniques currently used in worldwide forecasting contests, is in that way proposed. We develop furthermore an original method to improve the performance of neural network forecasting models in presence of abnormal wind power data. A technique for computing representative profiles of the community members electricity consumption is also presented. The proposed techniques are tested and deployed operationally on a pilot Renewable Energy Community established on an Medium Voltage network in Belgium, involving 2.25MW of wind and 18 Small and Medium Enterprises who had the possibility to freely access the results of the developed data modules by connecting to a dedicated web platform. We first show that our method for dealing with abnormal wind power data improves the forecasting accuracy by 10% in terms of Root Mean Square Error. The impact of the developed data modules on the consumption behaviour of the community members is then quantified, by analyzing the evolution of their monthly self-consumption and self-sufficiency during the pilot. No significant changes in the members behaviour, in relation with the information provided by the models, were observed in the recorded data. The pilot was however perturbed by the COVID-19 crisis which had a significant impact on the economic activity of the involved companies. We conclude by providing recommendations for the future set up of similar communities.
Zacharie De Grève; Jérémie Bottieau; David Vangulick; Aurélien Wautier; Pierre-David Dapoz; Adriano Arrigo; Jean-François Toubeau; François Vallée. Machine Learning Techniques for Improving Self-Consumption in Renewable Energy Communities. Energies 2020, 13, 4892 .
AMA StyleZacharie De Grève, Jérémie Bottieau, David Vangulick, Aurélien Wautier, Pierre-David Dapoz, Adriano Arrigo, Jean-François Toubeau, François Vallée. Machine Learning Techniques for Improving Self-Consumption in Renewable Energy Communities. Energies. 2020; 13 (18):4892.
Chicago/Turabian StyleZacharie De Grève; Jérémie Bottieau; David Vangulick; Aurélien Wautier; Pierre-David Dapoz; Adriano Arrigo; Jean-François Toubeau; François Vallée. 2020. "Machine Learning Techniques for Improving Self-Consumption in Renewable Energy Communities." Energies 13, no. 18: 4892.
This paper addresses the voltage control problem in medium-voltage distribution networks. The objective is to cost-efficiently maintain the voltage profile within a safe range, in presence of uncertainties in both the future working conditions, as well as the physical parameters of the system. Indeed, the voltage profile depends not only on the fluctuating renewable-based power generation and load demand, but also on the physical parameters of the system components. In reality, the characteristics of loads, lines and transformers are subject to complex and dynamic dependencies, which are difficult to model. In such a context, the quality of the control strategy depends on the accuracy of the power flow representation, which requires to capture the non-linear behavior of the power network. Relying on the detailed analytical models (which are still subject to uncertainties) introduces a high computational power that does not comply with the real-time constraint of the voltage control task. To address this issue, while avoiding arbitrary modeling approximations, we leverage a deep reinforcement learning model to ensure an autonomous grid operational control. Outcomes show that the proposed model-free approach offers a promising alternative to find a compromise between calculation time, conservativeness and economic performance.
Jean-François Toubeau; Bashir Bakhshideh Zad; Martin Hupez; Zacharie De Grève; François Vallée. Deep Reinforcement Learning-Based Voltage Control to Deal with Model Uncertainties in Distribution Networks. Energies 2020, 13, 3928 .
AMA StyleJean-François Toubeau, Bashir Bakhshideh Zad, Martin Hupez, Zacharie De Grève, François Vallée. Deep Reinforcement Learning-Based Voltage Control to Deal with Model Uncertainties in Distribution Networks. Energies. 2020; 13 (15):3928.
Chicago/Turabian StyleJean-François Toubeau; Bashir Bakhshideh Zad; Martin Hupez; Zacharie De Grève; François Vallée. 2020. "Deep Reinforcement Learning-Based Voltage Control to Deal with Model Uncertainties in Distribution Networks." Energies 13, no. 15: 3928.
This study intends to compare two different approaches for the stochastic analyses of low-voltage (LV) distribution networks. A quasi-sequential approach using a distribution based method and a sequential approach using seasonal auto-regressive moving average models time series for individual consumption and generation are benchmarked on a LV network. A qualitative and quantitative analysis of two scenarios (without and with storage) shows the advantages and limitations of both approaches. Additionally, it highlights the great potential of modelling sequentially as new load management techniques will be made available.
Martin Hupez; Zacharie De Grève; François Vallée. Comparative assessment of a quasi-sequential and a sequential approach for distribution network stochastic analysis. CIRED - Open Access Proceedings Journal 2017, 2017, 2161 -2164.
AMA StyleMartin Hupez, Zacharie De Grève, François Vallée. Comparative assessment of a quasi-sequential and a sequential approach for distribution network stochastic analysis. CIRED - Open Access Proceedings Journal. 2017; 2017 (1):2161-2164.
Chicago/Turabian StyleMartin Hupez; Zacharie De Grève; François Vallée. 2017. "Comparative assessment of a quasi-sequential and a sequential approach for distribution network stochastic analysis." CIRED - Open Access Proceedings Journal 2017, no. 1: 2161-2164.
The prediction task is attracting more and more attention among the power system community. Accurate predictions of electrical quantities up to a few hours ahead (e.g. renewable production, electrical load etc.) are for instance crucial for distribution system operators to operate their network in the presence of a high share of renewables, or for energy producers to maximise their profits by optimising their portfolio management. In the literature, statistical approaches are usually proposed to predict electrical quantities. In the present study, the authors present a novel method based on matrix factorisation. The authors' approach is inspired by the literature on data mining and knowledge discovery and the methodologies involved in recommender systems. The idea is to transpose the problem of predicting ratings in a recommender system to a problem of forecasting electrical quantities in a power system. Preliminary results on a real wind speed dataset tend to show that the matrix factorisation model provides similar results than auto regressive integrated models in terms of accuracy (MAE and RMSE). The authors' approach is nevertheless highly scalable and can deal with noisy data (e.g. missing data).
Fabian Lecron; Zacharie De Grève; François Vallée; Gerard Mor; Daniel Pérez; Stoyan Danov; Jordi Cipriano. Using matrix factorisation for the prediction of electrical quantities. CIRED - Open Access Proceedings Journal 2017, 2017, 2568 -2571.
AMA StyleFabian Lecron, Zacharie De Grève, François Vallée, Gerard Mor, Daniel Pérez, Stoyan Danov, Jordi Cipriano. Using matrix factorisation for the prediction of electrical quantities. CIRED - Open Access Proceedings Journal. 2017; 2017 (1):2568-2571.
Chicago/Turabian StyleFabian Lecron; Zacharie De Grève; François Vallée; Gerard Mor; Daniel Pérez; Stoyan Danov; Jordi Cipriano. 2017. "Using matrix factorisation for the prediction of electrical quantities." CIRED - Open Access Proceedings Journal 2017, no. 1: 2568-2571.
Following a call to foster a transparent and more competitive market, member states of the European transmission system operator are required to publish, among other information, aggregate wind power forecasts. The publication of the latter information is expected to benefit market participants by offering better knowledge of the market operation, leading subsequently to a more competitive energy market. Driven by the above regulation, we consider an equilibrium study to address how public information of aggregate wind power forecasts can potentially affect market results, social welfare, as well as the profits of participating power producers. We investigate, therefore, a joint day-ahead energy and reserve auction, where producers offer their conventional power strategically based on a complementarity approach and their wind power at generation cost based on a forecast. In parallel, an iterative game-theoretic approach (diagonalization) is incorporated in order to investigate the existence of an equilibrium for various values of aggregate forecast. As anticipated, variations in public forecasts will affect market results and, more precisely, underforecasts can mislead power producers to make decisions that favor social welfare, while overforecasts will cause the opposite effect. Furthermore, energy and reserve market prices can also be affected by deviations in aggregate wind forecasts altering, inevitably, the profits of all power producers.
Lazaros Exizidis; Jalal Kazempour; Pierre Pinson; Zacharie De Greve; Francois Vallee. Impact of Public Aggregate Wind Forecasts on Electricity Market Outcomes. IEEE Transactions on Sustainable Energy 2017, 8, 1394 -1405.
AMA StyleLazaros Exizidis, Jalal Kazempour, Pierre Pinson, Zacharie De Greve, Francois Vallee. Impact of Public Aggregate Wind Forecasts on Electricity Market Outcomes. IEEE Transactions on Sustainable Energy. 2017; 8 (4):1394-1405.
Chicago/Turabian StyleLazaros Exizidis; Jalal Kazempour; Pierre Pinson; Zacharie De Greve; Francois Vallee. 2017. "Impact of Public Aggregate Wind Forecasts on Electricity Market Outcomes." IEEE Transactions on Sustainable Energy 8, no. 4: 1394-1405.
In three-phase Low Voltage (LV) networks, distributed photovoltaic (PV) units can contribute to voltage unbalance mitigation in case they are connected with the use of three-phase inverters integrating unbalance mitigation control schemes. This paper presents a probabilistic framework that simulates the time-varying action of voltage magnitude and unbalance mitigation schemes, locally implemented by PV inverters in LV feeders. The scope includes evaluating the effect of such strategies in the context of a long term techno-economic planning of the LV network and characterizing LV network operation for increasing the observability of state estimation techniques applied in the Medium Voltage level. The presented framework evaluates the action of four distributed control schemes in an extensive range of possible network states assembled with the use of feeder-specific smart metering (SM) data. The simulation of a real LV feeder with distributed PV generation and long term SM measurements is presented. A control strategy that acts resistively towards the negative- and zero-sequence voltage components without modifying the total nodal injected power (three-phase damping control strategy) results to be more efficient compared with traditionally applied voltage control schemes.
Vasiliki Klonari; Bart Meersman; Dimitar Bozalakov; Tine L. VanDoorn; Lieven Vandevelde; Jacques Lobry; François Vallée. A probabilistic framework for evaluating voltage unbalance mitigation by photovoltaic inverters. Sustainable Energy, Grids and Networks 2016, 8, 1 -11.
AMA StyleVasiliki Klonari, Bart Meersman, Dimitar Bozalakov, Tine L. VanDoorn, Lieven Vandevelde, Jacques Lobry, François Vallée. A probabilistic framework for evaluating voltage unbalance mitigation by photovoltaic inverters. Sustainable Energy, Grids and Networks. 2016; 8 ():1-11.
Chicago/Turabian StyleVasiliki Klonari; Bart Meersman; Dimitar Bozalakov; Tine L. VanDoorn; Lieven Vandevelde; Jacques Lobry; François Vallée. 2016. "A probabilistic framework for evaluating voltage unbalance mitigation by photovoltaic inverters." Sustainable Energy, Grids and Networks 8, no. : 1-11.
Lazaros Exizidis; François Vallée; Zacharie De Grève; Jacques Lobry; Vasilis Chatziathanasiou. Thermal behavior of power cables in offshore wind sites considering wind speed uncertainty. Applied Thermal Engineering 2015, 91, 471 -478.
AMA StyleLazaros Exizidis, François Vallée, Zacharie De Grève, Jacques Lobry, Vasilis Chatziathanasiou. Thermal behavior of power cables in offshore wind sites considering wind speed uncertainty. Applied Thermal Engineering. 2015; 91 ():471-478.
Chicago/Turabian StyleLazaros Exizidis; François Vallée; Zacharie De Grève; Jacques Lobry; Vasilis Chatziathanasiou. 2015. "Thermal behavior of power cables in offshore wind sites considering wind speed uncertainty." Applied Thermal Engineering 91, no. : 471-478.
Vasiliki Klonari; Jean-François Toubeau; Tine L. VanDoorn; Bart Meersman; Zacharie De Grève; Jacques Lobry; François Vallée. Probabilistic framework for evaluating droop control of photovoltaic inverters. Electric Power Systems Research 2015, 129, 1 -9.
AMA StyleVasiliki Klonari, Jean-François Toubeau, Tine L. VanDoorn, Bart Meersman, Zacharie De Grève, Jacques Lobry, François Vallée. Probabilistic framework for evaluating droop control of photovoltaic inverters. Electric Power Systems Research. 2015; 129 ():1-9.
Chicago/Turabian StyleVasiliki Klonari; Jean-François Toubeau; Tine L. VanDoorn; Bart Meersman; Zacharie De Grève; Jacques Lobry; François Vallée. 2015. "Probabilistic framework for evaluating droop control of photovoltaic inverters." Electric Power Systems Research 129, no. : 1-9.
Electrical generation based on the use of renewable energies is emerging in modern grids. In that way, one of the most popular solutions as well in transmission as in distribution grids is certainly coming from wind energy. However, wind resources on a given location randomly fluctuate with time and have thus a major impact on the capacity of the electrical system to continuously face the load. In order to evaluate this impact and to consequently adapt required reinforcements, Monte Carlo simulations are often used. Those approach can be either sequential or not. Nowadays, load shifting solutions (storage, demand side management…) are practically set in order to adapt consumption to time varying generation without involving too consequent investments. In that way, sequential approach is currently preferred when it comes to long-term planning evaluation and adapted time series models are developed to characterize wind generation on a given site. The consideration of the geographical correlation between those models has been recently investigated in some references. This paper proposes to complete those contributions by evaluating the impact of wind geographical correlation on classical reliability indices such as the Loss of Load Expectation (LOLE) or the Expected Energy not Served (EENS). Copyright © 2014 Praise Worthy Prize - All rights reserved.
François Vallée; Jean-François Toubeau; Z. De Grève; J. Lobry. Consideration of Extreme Wind Geographical Correlation Scenarios in Reliability Assessment Studies Using Sequential Monte Carlo Simulations. International Review of Electrical Engineering (IREE) 2014, 9, 1148 .
AMA StyleFrançois Vallée, Jean-François Toubeau, Z. De Grève, J. Lobry. Consideration of Extreme Wind Geographical Correlation Scenarios in Reliability Assessment Studies Using Sequential Monte Carlo Simulations. International Review of Electrical Engineering (IREE). 2014; 9 (6):1148.
Chicago/Turabian StyleFrançois Vallée; Jean-François Toubeau; Z. De Grève; J. Lobry. 2014. "Consideration of Extreme Wind Geographical Correlation Scenarios in Reliability Assessment Studies Using Sequential Monte Carlo Simulations." International Review of Electrical Engineering (IREE) 9, no. 6: 1148.
An original nonsequential Monte Carlo simulation tool is developed. It permits to compute the optimal dispatch of classical thermal generation in order to minimize pollutants emissions in presence of wind power and under operating constraints.
François Vallée; Cristophe Versèle; F. Moiny; Jacques Lobry. Impact of Wind Power on the Gaseous Pollutants Emissions in Electrical Systems Operating under Constraints. ISRN Renewable Energy 2012, 2012, 1 -8.
AMA StyleFrançois Vallée, Cristophe Versèle, F. Moiny, Jacques Lobry. Impact of Wind Power on the Gaseous Pollutants Emissions in Electrical Systems Operating under Constraints. ISRN Renewable Energy. 2012; 2012 ():1-8.
Chicago/Turabian StyleFrançois Vallée; Cristophe Versèle; F. Moiny; Jacques Lobry. 2012. "Impact of Wind Power on the Gaseous Pollutants Emissions in Electrical Systems Operating under Constraints." ISRN Renewable Energy 2012, no. : 1-8.
François Vallée; Jacques Lobry; Olivier Deblecker. Wind generation modelling to help the managerial process of modern transmission systems. Renewable Energy 2011, 36, 1632 -1638.
AMA StyleFrançois Vallée, Jacques Lobry, Olivier Deblecker. Wind generation modelling to help the managerial process of modern transmission systems. Renewable Energy. 2011; 36 (5):1632-1638.
Chicago/Turabian StyleFrançois Vallée; Jacques Lobry; Olivier Deblecker. 2011. "Wind generation modelling to help the managerial process of modern transmission systems." Renewable Energy 36, no. 5: 1632-1638.
François Vallée; Jacques Lobry; Olivier Deblecker. Impact of real case transmission systems constraints on wind power operation. European Transactions on Electrical Power 2010, 21, 2142 -2159.
AMA StyleFrançois Vallée, Jacques Lobry, Olivier Deblecker. Impact of real case transmission systems constraints on wind power operation. European Transactions on Electrical Power. 2010; 21 (8):2142-2159.
Chicago/Turabian StyleFrançois Vallée; Jacques Lobry; Olivier Deblecker. 2010. "Impact of real case transmission systems constraints on wind power operation." European Transactions on Electrical Power 21, no. 8: 2142-2159.
François Vallée; Olivier Deblecker; Jacques Lobry. Impact of Real Case Transmission Systems Constraints on Wind Power Operation. Wind Power 2010, 1 .
AMA StyleFrançois Vallée, Olivier Deblecker, Jacques Lobry. Impact of Real Case Transmission Systems Constraints on Wind Power Operation. Wind Power. 2010; ():1.
Chicago/Turabian StyleFrançois Vallée; Olivier Deblecker; Jacques Lobry. 2010. "Impact of Real Case Transmission Systems Constraints on Wind Power Operation." Wind Power , no. : 1.
Adéquation du réseau électrique en présence de production décentralisée de type éolien. Étude de niveau hiérarchique HL-I pour le réseau belge Electrical network adequacy in presence of decentralized wind generation
François Vallée; Olivier Deblecker; Jacques Lobry. Adéquation du réseau électrique en présence de production décentralisée de type éolien. Étude de niveau hiérarchique HL-I pour le réseau belge. Revue internationale de génie électrique 2009, 12, 9 -31.
AMA StyleFrançois Vallée, Olivier Deblecker, Jacques Lobry. Adéquation du réseau électrique en présence de production décentralisée de type éolien. Étude de niveau hiérarchique HL-I pour le réseau belge. Revue internationale de génie électrique. 2009; 12 (1):9-31.
Chicago/Turabian StyleFrançois Vallée; Olivier Deblecker; Jacques Lobry. 2009. "Adéquation du réseau électrique en présence de production décentralisée de type éolien. Étude de niveau hiérarchique HL-I pour le réseau belge." Revue internationale de génie électrique 12, no. 1: 9-31.
In modern railways coaches, the electrical separation between the high voltage side and the auxiliary equipments on the consumer side is realized by means of heavy and bulky 50-Hz transformers. In order to reduce the weight and size of the devices, today new power supply systems are proposed that consist in soft-switched isolated DC-DC converters with a lightweight medium frequency transformer and diverse output modules supplied by a common 600-V dc intermediate circuit. This paper aims to investigate in detail two such solutions of isolated DC-DC converters for auxiliary railway supply where zero-current transitions are achieved for the primary inverter switches. A comparison based on several criteria (overall power rating, losses in power semiconductor devices, operation in the whole range of load, etc.) is presented.
Olivier Deblecker; Adriano Moretti; François Vallee. Comparative Study of Soft-Switched Isolated DC-DC Converters for Auxiliary Railway Supply. IEEE Transactions on Power Electronics 2008, 23, 2218 -2229.
AMA StyleOlivier Deblecker, Adriano Moretti, François Vallee. Comparative Study of Soft-Switched Isolated DC-DC Converters for Auxiliary Railway Supply. IEEE Transactions on Power Electronics. 2008; 23 (5):2218-2229.
Chicago/Turabian StyleOlivier Deblecker; Adriano Moretti; François Vallee. 2008. "Comparative Study of Soft-Switched Isolated DC-DC Converters for Auxiliary Railway Supply." IEEE Transactions on Power Electronics 23, no. 5: 2218-2229.
Following several governmental policies trying to reduce CO emissions, renewable energies have been largely promoted during the last decade. Among the green energies that have been developed, wind can actually be seen as one of the most promising solution if we refer to its past and predicted evolutions. However and, even though wind can be considered as an interesting alternative to fossil fuels, it is imperious to emphasize on problematic situations that could be due to the intermittent behavior of wind generation. Indeed, actually, most of the electric utilities do not consider wind power in the classical units management; consequently involving, in the case of increased wind power penetration, untimely stopping of big thermal (or nuclear) units. It is, therefore, necessary to calculate wind equivalent capacities in order to introduce a coherent evaluation of wind production in the management of the centralized production park. In this paper, wind equivalent capacities are calculated using a nonsequential Monte Carlo Simulation. Moreover, in order to evaluate the importance of the wind correlation level between parks located in the same geographical region, two cases are investigated, respectively considering entirely correlated wind parks and, on the opposite, totally independent wind sites. Finally, simulation results show that durations of simultaneous zero wind power production for each considered wind parks stay, in both cases, very low involving quasi-identical calculated equivalent capacities; what tends to demonstrate the minor importance of the wind parks correlation level for wind power estimation in reliability studies. Note that, here, the proposed conclusions are obtained using the Belgian production park data and that the objective is not to dynamically estimate wind production on the short-term (by the use of an autocorrelation function) but rather to give a simplified way to introduce an hourly evaluation of wind production in the day by day management of the classical thermal (nuclear) units.
François Vallee; Jacques Lobry; Olivier Deblecker. Impact of the Wind Geographical Correlation Level for Reliability Studies. IEEE Transactions on Power Systems 2007, 22, 2232 -2239.
AMA StyleFrançois Vallee, Jacques Lobry, Olivier Deblecker. Impact of the Wind Geographical Correlation Level for Reliability Studies. IEEE Transactions on Power Systems. 2007; 22 (4):2232-2239.
Chicago/Turabian StyleFrançois Vallee; Jacques Lobry; Olivier Deblecker. 2007. "Impact of the Wind Geographical Correlation Level for Reliability Studies." IEEE Transactions on Power Systems 22, no. 4: 2232-2239.