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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.
Solar energy and bioenergy are two leading renewable forms of energy in the move toward a near-zero-emission electric power industry. Concentrated solar power units coupled with thermal storage and biomass power plant offer dispatchable electricity, raising their ever-growing role in future renewable-dominated networks. This paper proposes a day-ahead and intraday dispatch model for maximizing the profit of an Integrated Biomass-Concentrated Solar (IBCS) system considering the synergies arising from their coupled operation. To sensibly capture uncertainty and decision sequence of real-life electricity markets, a two-stage stochastic structure is proposed, while the solar-related uncertainty is involved using Information Gap Decision Theory (IGDT). The model is complemented with a novel multi-objective architecture based on the compound of IGDT and Conditional Value-at-Risk (CVaR), which allows handling risk exposure to both stochastic and IGDT inputs. The Pareto strategies in the multi-objective model are extracted through an expanded form of the -constraint method, whereas a posteriori approach based upon the out-of-sample assessment is applied to derive the optimal dispatch pattern among the generated Pareto strategies. The simulation results demonstrate that: 1) the proposed integrated dispatch model achieves substantial profitability, and 2) the performance of the suggested CVaR-IGDT model is superior to conventional approaches.
Hooman Khaloie; Francois Vallee; Chun Sing Lai; Jean-Francois Toubeau; Nikos D. Hatziargyriou. Day-ahead and Intraday Dispatch of an Integrated Biomass-Concentrated Solar System: A Multi-Objective Risk-Controlling Approach. IEEE Transactions on Power Systems 2021, PP, 1 -1.
AMA StyleHooman Khaloie, Francois Vallee, Chun Sing Lai, Jean-Francois Toubeau, Nikos D. Hatziargyriou. Day-ahead and Intraday Dispatch of an Integrated Biomass-Concentrated Solar System: A Multi-Objective Risk-Controlling Approach. IEEE Transactions on Power Systems. 2021; PP (99):1-1.
Chicago/Turabian StyleHooman Khaloie; Francois Vallee; Chun Sing Lai; Jean-Francois Toubeau; Nikos D. Hatziargyriou. 2021. "Day-ahead and Intraday Dispatch of an Integrated Biomass-Concentrated Solar System: A Multi-Objective Risk-Controlling Approach." IEEE Transactions on Power Systems PP, no. 99: 1-1.
High penetration of renewable energy such as wind power and photovoltaic (PV) requires large amounts of flexibility to balance their inherent variability. Making an accurate prediction of the future power system imbalance is an efficient approach to reduce these balancing costs. However, the imbalance is affected not only by renewables but also by complex market dynamics and technology constraints, for which the dependence structure is unknown. Therefore, this paper introduces a new architecture of sequence-to-sequence recurrent neural networks to efficiently process time-based information in an interpretable fashion. To that end, the selection of relevant variables is internalized into the model, which provides insights on the relative importance of individual inputs, while bypassing the cumbersome need for data-preprocessing. Then, the model is further enriched with an attention mechanism that is tailored to focus on the relevant contextual information, which is useful to better understand the underlying dynamics such as seasonal patterns. Outcomes show that adding modules to generate explainable forecasts makes the model more efficient and robust, thus leading to enhanced performance.
Jean-Francois Toubeau; Jeremie Bottieau; Yi Wang; Francois Vallee. Interpretable Probabilistic Forecasting of Imbalances in Renewable-Dominated Electricity Systems. IEEE Transactions on Sustainable Energy 2021, PP, 1 -1.
AMA StyleJean-Francois Toubeau, Jeremie Bottieau, Yi Wang, Francois Vallee. Interpretable Probabilistic Forecasting of Imbalances in Renewable-Dominated Electricity Systems. IEEE Transactions on Sustainable Energy. 2021; PP (99):1-1.
Chicago/Turabian StyleJean-Francois Toubeau; Jeremie Bottieau; Yi Wang; Francois Vallee. 2021. "Interpretable Probabilistic Forecasting of Imbalances in Renewable-Dominated Electricity Systems." IEEE Transactions on Sustainable Energy PP, no. 99: 1-1.
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.
This paper presents a new spatio-temporal framework for the day-ahead probabilistic forecasting of Distribution Locational Marginal Prices (DLMPs). The approach relies on a recurrent neural network, whose architecture is enriched by introducing a deep bidirectional variant designed to capture the complex time dynamics in multi-step forecasts. In order to account for nodal price differentiation (arising from grid constraints) within a procedure that is scalable to large distribution systems, nodal DLMPs are predicted individually by a single model guided by a generic representation of the grid. This strategy offers the additional benefit to enable cold-start forecasting for new nodes with no history. Indeed, in case of topological changes, e.g. building of a new home or installation of photovoltaic panels, the forecaster intrinsically leverages the statistical information learned from neighbouring nodes to predict the new DLMP, without needing any modification of the tool. The approach is evaluated, along with several other methods, on a radial low voltage network. Outcomes highlight that relying on a compact model is a key component to boost its generalization capabilities in high-dimensionality, while indicating that the proposed tool is effective for both temporal and spatial learning.
Jean-Francois Toubeau; Thomas Morstyn; Jeremie Bottieau; Kedi Zheng; Dimitra Apostolopoulou; Zacharie De Greve; Yi Wang; Fran¸c’Ois Vallée. Capturing Spatio-Temporal Dependencies in the Probabilistic Forecasting of Distribution Locational Marginal Prices. IEEE Transactions on Smart Grid 2020, 12, 2663 -2674.
AMA StyleJean-Francois Toubeau, Thomas Morstyn, Jeremie Bottieau, Kedi Zheng, Dimitra Apostolopoulou, Zacharie De Greve, Yi Wang, Fran¸c’Ois Vallée. Capturing Spatio-Temporal Dependencies in the Probabilistic Forecasting of Distribution Locational Marginal Prices. IEEE Transactions on Smart Grid. 2020; 12 (3):2663-2674.
Chicago/Turabian StyleJean-Francois Toubeau; Thomas Morstyn; Jeremie Bottieau; Kedi Zheng; Dimitra Apostolopoulou; Zacharie De Greve; Yi Wang; Fran¸c’Ois Vallée. 2020. "Capturing Spatio-Temporal Dependencies in the Probabilistic Forecasting of Distribution Locational Marginal Prices." IEEE Transactions on Smart Grid 12, no. 3: 2663-2674.
Energy storage systems (ESS) may provide the required flexibility to cost-effectively integrate weather-dependent renewable generation, in particular by offering operating reserves. However, since the real-time deployment of these services is uncertain, ensuring their availability requires merchant ESS to fully reserve the associated energy capacity in their day-ahead schedule. To improve such conservative policies, we propose a data-driven probabilistic characterization of the real-time balancing stage to inform the day-ahead scheduling problem of an ESS owner. This distributional information is used to enforce a tailored probabilistic guarantee on the availability of the scheduled reserve capacity via chance constrained programming, which allows a profit-maximizing participation in energy, reserve and balancing markets. The merit order-based competition with rival resources in reserve capacity and balancing markets is captured via a bi-level model, which is reformulated as a computationally efficient mixed-integer linear problem. Results show that a merchant ESS owner may leverage the competition effect to avoid violations of its energy capacity limits, and that the proposed risk-aware method allows sourcing more reserve capacity, and thus more value, from storage, without jeopardizing the real-time reliability of the power system.
Jean-Francois Toubeau; Jeremie Bottieau; Zacharie De Greeve; Francois Vallee; Kenneth Bruninx. Data-Driven Scheduling of Energy Storage in Day-Ahead Energy and Reserve Markets With Probabilistic Guarantees on Real-Time Delivery. IEEE Transactions on Power Systems 2020, 36, 2815 -2828.
AMA StyleJean-Francois Toubeau, Jeremie Bottieau, Zacharie De Greeve, Francois Vallee, Kenneth Bruninx. Data-Driven Scheduling of Energy Storage in Day-Ahead Energy and Reserve Markets With Probabilistic Guarantees on Real-Time Delivery. IEEE Transactions on Power Systems. 2020; 36 (4):2815-2828.
Chicago/Turabian StyleJean-Francois Toubeau; Jeremie Bottieau; Zacharie De Greeve; Francois Vallee; Kenneth Bruninx. 2020. "Data-Driven Scheduling of Energy Storage in Day-Ahead Energy and Reserve Markets With Probabilistic Guarantees on Real-Time Delivery." IEEE Transactions on Power Systems 36, no. 4: 2815-2828.
This paper presents an original collaborative framework for power exchanges inside a low voltage community. The community seeks to minimize its total costs by scheduling on a daily basis the resources of its members. In this respect, their flexibility such as excess storage capacity, unused local generation or shiftable load are exploited. Total costs include not only the energy commodity, but also grid fees associated to the community operation, through the integration of power flow constraints. In order to share the community costs in a fair manner, two different cost distributions are proposed. The first one adopts a distribution key based on the Shapley value, while the other relies on a natural consensus defined by a Nash equilibrium. Outcomes show that both collaboration schemes lead to important savings for all individual members. In particular, it is observed that the Shapley-based solution gives more value to mobilized flexible resources, whereas the Nash equilibrium rewards the potential flexibility consent of end-users.
Martin Hupez; Jean-Francois Toubeau; Zacharie De Greve; Francois Vallee. A New Cooperative Framework for a Fair and Cost-Optimal Allocation of Resources Within a Low Voltage Electricity Community. IEEE Transactions on Smart Grid 2020, 12, 2201 -2211.
AMA StyleMartin Hupez, Jean-Francois Toubeau, Zacharie De Greve, Francois Vallee. A New Cooperative Framework for a Fair and Cost-Optimal Allocation of Resources Within a Low Voltage Electricity Community. IEEE Transactions on Smart Grid. 2020; 12 (3):2201-2211.
Chicago/Turabian StyleMartin Hupez; Jean-Francois Toubeau; Zacharie De Greve; Francois Vallee. 2020. "A New Cooperative Framework for a Fair and Cost-Optimal Allocation of Resources Within a Low Voltage Electricity Community." IEEE Transactions on Smart Grid 12, no. 3: 2201-2211.
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 is focused on the day-ahead prediction of the onshore wind generation. This information is indeed published each day, ahead of the market clearing, by European Transmission System Operators (TSOs) to help market actors in their scheduling strategy. In that regard, our first objective is to improve the forecast performance by efficiently capturing the complex temporal dynamics of the wind power using recurrent neural networks. Practically, advanced architectures of Long Short Term Memory (LSTM) networks are implemented and compared. Secondly, in order to continuously refine the prediction tool, different techniques for recalibrating the model during its practical utilization are analyzed. This procedure consists in adjusting the parameters of the neural networks by taking advantage of the new information revealed over time, without the (time-consuming) need to retrain the model from scratch using the whole available dataset. Finally, the financial savings from the improvement of the forecast accuracy are estimated. Outcomes from the Belgian case study show that an optimal model recalibration can significantly improve forecast reliability, thereby decreasing the balancing costs of the system.
Jean-François Toubeau; Pierre-David Dapoz; Jérémie Bottieau; Aurélien Wautier; Zacharie De Grève; François Vallée. Recalibration of recurrent neural networks for short-term wind power forecasting. Electric Power Systems Research 2020, 190, 106639 .
AMA StyleJean-François Toubeau, Pierre-David Dapoz, Jérémie Bottieau, Aurélien Wautier, Zacharie De Grève, François Vallée. Recalibration of recurrent neural networks for short-term wind power forecasting. Electric Power Systems Research. 2020; 190 ():106639.
Chicago/Turabian StyleJean-François Toubeau; Pierre-David Dapoz; Jérémie Bottieau; Aurélien Wautier; Zacharie De Grève; François Vallée. 2020. "Recalibration of recurrent neural networks for short-term wind power forecasting." Electric Power Systems Research 190, no. : 106639.
This paper investigates the techno‐economic feasibility of the innovative concept of gravity energy storage, where heavy weights are raised and lowered in a water environment. Such eco‐friendly systems can be implemented in existing flooded pits or quarries, by leveraging the important depth of these cavities. Moreover, in addition to their long lifetime, they have no visual impact on the landscape, and offer a lot of flexibility to the power system. In this work, we firstly present an analytical study of the storage solution, which allows deriving tractable mathematical constraints describing its operation, such as its nonlinear speed‐power curves in both charge and discharge modes. These constraints are then integrated into the investment strategy of a merchant unit that seeks to maximize its profit by jointly participating in the energy and secondary reserve markets. The model is formulated within a stochastic framework to ensure robustness of sizing decisions in view of future market uncertainties. Results from a practical case study (on a natural cavity of 200 m) show that underwater gravity storage is a cost‐efficient technology that offers payback periods of less than 10 years, mainly due to its intrinsic low capital costs estimated at around 100 €/kWh.
Jean‐François Toubeau; Chloé Ponsart; Christophe Stevens; Zacharie De Grève; François Vallée. Sizing of underwater gravity storage with solid weights participating in electricity markets. International Transactions on Electrical Energy Systems 2020, 30, 1 .
AMA StyleJean‐François Toubeau, Chloé Ponsart, Christophe Stevens, Zacharie De Grève, François Vallée. Sizing of underwater gravity storage with solid weights participating in electricity markets. International Transactions on Electrical Energy Systems. 2020; 30 (10):1.
Chicago/Turabian StyleJean‐François Toubeau; Chloé Ponsart; Christophe Stevens; Zacharie De Grève; François Vallée. 2020. "Sizing of underwater gravity storage with solid weights participating in electricity markets." International Transactions on Electrical Energy Systems 30, no. 10: 1.
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.
As a result of the increased penetration of stochastic renewable generation, power systems have a growing need of flexibility for compensating real-time mismatches between production and consumption of electricity. This flexibility can be efficiently provided by underground pumped hydro energy storage (UPHES), a new solution where end-of-life quarries or mines are rehabilitated as natural reservoirs. However, the operation of UPHES is significantly different from existing facilities, and is characterised by multiple non-linear effects with fast dynamics mainly arising from the complex geometry of the unit, and water exchanges between the porous reservoirs and their surrounding aquifers. This study aims thus at integrating these complex effects within the co-optimisation of a UPHES system in the European day-ahead energy and reserve markets. To that end, the authors leverage a hybrid iterative approach combining an optimisation tool with an advanced simulation model. The results from a real-world case study demonstrate that accurately considering these non-linear effects is a key component to fully extract the economic potential of merchant UPHES, and suggest that the proposed tool offers an effective solution for the scheduling of UPHES owners.
Jean‐François Toubeau; Sergei Iassinovski; Emmanuel Jean; Jean‐Yves Parfait; Jérémie Bottieau; Zacharie De Grève; François Vallée. Non‐linear hybrid approach for the scheduling of merchant underground pumped hydro energy storage. IET Generation, Transmission & Distribution 2019, 13, 4798 -4808.
AMA StyleJean‐François Toubeau, Sergei Iassinovski, Emmanuel Jean, Jean‐Yves Parfait, Jérémie Bottieau, Zacharie De Grève, François Vallée. Non‐linear hybrid approach for the scheduling of merchant underground pumped hydro energy storage. IET Generation, Transmission & Distribution. 2019; 13 (21):4798-4808.
Chicago/Turabian StyleJean‐François Toubeau; Sergei Iassinovski; Emmanuel Jean; Jean‐Yves Parfait; Jérémie Bottieau; Zacharie De Grève; François Vallée. 2019. "Non‐linear hybrid approach for the scheduling of merchant underground pumped hydro energy storage." IET Generation, Transmission & Distribution 13, no. 21: 4798-4808.
The single imbalance pricing is an emerging mechanism in European electricity markets where all positive and negative imbalances are settled at a unique price. This real-time scheme thereby stimulates market participants to deviate from their schedule to restore the power system balance. However, exploiting this market opportunity is very risky due to the extreme volatility of the real-time power system conditions. In order to address this issue, we implement a new tailored deep-learning model, named encoder-decoder, to generate improved probabilistic forecasts of the imbalance signal, by efficiently capturing its complex spatio-temporal dynamics. The predicted distributions are then used to quantify and optimize the risk associated with the real-time participation of market players, acting as price-makers, in the imbalance settlement. This leads to an integrated forecast-driven strategy, modeled as a robust bilevel optimization. Results show that our probabilistic forecaster achieves better performance than other state of the art tools, and that the subsequent risk-aware robust dispatch tool allows finding a tradeoff between conservative and risk-seeking policies, leading to improved economic benefits. Moreover, we show that the model is computationally efficient and can thus be incorporated in the very-short-term dispatch of market players with flexible resources.
Jeremie Bottieau; Louis Hubert; Zacharie De Greve; Francois Vallee; Jean-Francois Toubeau. Very-Short-Term Probabilistic Forecasting for a Risk-Aware Participation in the Single Price Imbalance Settlement. IEEE Transactions on Power Systems 2019, 35, 1218 -1230.
AMA StyleJeremie Bottieau, Louis Hubert, Zacharie De Greve, Francois Vallee, Jean-Francois Toubeau. Very-Short-Term Probabilistic Forecasting for a Risk-Aware Participation in the Single Price Imbalance Settlement. IEEE Transactions on Power Systems. 2019; 35 (2):1218-1230.
Chicago/Turabian StyleJeremie Bottieau; Louis Hubert; Zacharie De Greve; Francois Vallee; Jean-Francois Toubeau. 2019. "Very-Short-Term Probabilistic Forecasting for a Risk-Aware Participation in the Single Price Imbalance Settlement." IEEE Transactions on Power Systems 35, no. 2: 1218-1230.
The uncertainty induced by high penetration of stochastic generation in power systems requires to be properly taken into account within Optimal Power Flow (OPF) problems to make informed day-ahead decisions that minimize the social cost in view of potential balancing actions. This ends up in a two-stage OPF problem that is usually solved using two-stage stochastic programming or adaptive robust optimization. Another alternative is the use of chance-constrained programming that allows to control the conservativeness of the decisions. In this paper, we aim at defining a fair basis for assessing the performance of these three techniques, using an extensive out-of-sample evaluation. Considering a common wind power database, each technique leads to optimal day-ahead decisions that are a posteriori assessed through the real-time stage on unseen realizations of the uncertainty. Our main conclusion is that undertaking conservative decisions results in lower standard deviations of the cost, but at the expense of higher expected cost.
Adriano Arrigo; Christos Ordoudis; Jalal Kazempour; Zacharie De Greve; Jean-François Toubeau; Francois Vallee. Optimal Power Flow Under Uncertainty: An Extensive Out-of-Sample Analysis. 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) 2019, 1 -5.
AMA StyleAdriano Arrigo, Christos Ordoudis, Jalal Kazempour, Zacharie De Greve, Jean-François Toubeau, Francois Vallee. Optimal Power Flow Under Uncertainty: An Extensive Out-of-Sample Analysis. 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). 2019; ():1-5.
Chicago/Turabian StyleAdriano Arrigo; Christos Ordoudis; Jalal Kazempour; Zacharie De Greve; Jean-François Toubeau; Francois Vallee. 2019. "Optimal Power Flow Under Uncertainty: An Extensive Out-of-Sample Analysis." 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) , no. : 1-5.
This study addresses the voltage control problem of the medium-voltage distribution systems under uncertainty of the network model. A robust voltage control algorithm (RVCA) is developed in order to manage the voltage constraints considering uncertainties associated with the parameters of load, line, and transformer models. The RVCA determines a corrective solution that remains immunised against any realisation of uncertainty associated with the parameters of the network model. To this end, prior to formulating the voltage control problem, Monte Carlo (MC) simulations are used to characterise uncertain parameters of the network component models and load flow (LF) calculations are carried out to evaluate their impacts. The voltage constraints management under the uncertain environment is then formulated as a robust optimisation (RO) problem. The latter is constructed based on the results obtained through the MC simulations and LF calculations. Once the RO is solved, in order to check the robustness of the solution, system voltages are evaluated using the LF calculations considering the new set-points of control variables and uncertainty of network parameters. The simulation results reveal that neglecting model uncertainty in the voltage control problem can lead to infeasible solutions while the proposed RVCA, at an extra cost, determines a corrective solution which remains protected against the studied uncertainties.
Bashir Bakhshideh Zad; Jean‐François Toubeau; Jacques Lobry; François Vallée. Robust voltage control algorithm incorporating model uncertainty impacts. IET Generation, Transmission & Distribution 2019, 13, 3921 -3931.
AMA StyleBashir Bakhshideh Zad, Jean‐François Toubeau, Jacques Lobry, François Vallée. Robust voltage control algorithm incorporating model uncertainty impacts. IET Generation, Transmission & Distribution. 2019; 13 (17):3921-3931.
Chicago/Turabian StyleBashir Bakhshideh Zad; Jean‐François Toubeau; Jacques Lobry; François Vallée. 2019. "Robust voltage control algorithm incorporating model uncertainty impacts." IET Generation, Transmission & Distribution 13, no. 17: 3921-3931.
Abandoned underground quarries or mines may be rehabilitated as natural reservoirs for underground pumped-hydro energy storage (UPHES). In addition to the inherent modeling inaccuracies of traditional PHES that arise from, e.g., approximating the nonlinear pump/turbine head-dependent performance curves, the optimal operation of these underground plants is also affected by endogenous model uncertainties. The latter typically arise from a limited knowledge of the physical characteristics of the system such as the geometry and hydraulic properties of the underground cavity. In this paper, chance-constrained programming is leveraged to immunize the day-ahead scheduling of an UPHES owner against both these model uncertainties and the modeling approximations. The proposed method is tested on a fictitious UPHES system using an existing underground quarry as lower reservoir. Results demonstrate that the methodology allows finding a compromise between conservativeness and economic performance, while being computationally efficient. This model may thus be integrated in the daily scheduling routine of UPHES owners, or may help regulators and system operators to better estimate the available flexibility of such resources.
Jean-Francois Toubeau; Zacharie De Greve; Pascal Goderniaux; Francois Vallee; Kenneth Bruninx. Chance-Constrained Scheduling of Underground Pumped Hydro Energy Storage in Presence of Model Uncertainties. IEEE Transactions on Sustainable Energy 2019, 11, 1516 -1527.
AMA StyleJean-Francois Toubeau, Zacharie De Greve, Pascal Goderniaux, Francois Vallee, Kenneth Bruninx. Chance-Constrained Scheduling of Underground Pumped Hydro Energy Storage in Presence of Model Uncertainties. IEEE Transactions on Sustainable Energy. 2019; 11 (3):1516-1527.
Chicago/Turabian StyleJean-Francois Toubeau; Zacharie De Greve; Pascal Goderniaux; Francois Vallee; Kenneth Bruninx. 2019. "Chance-Constrained Scheduling of Underground Pumped Hydro Energy Storage in Presence of Model Uncertainties." IEEE Transactions on Sustainable Energy 11, no. 3: 1516-1527.
In the current competition framework governing the electricity sector, complex dependencies exist between electrical and market data, which complicates the decision-making procedure of energy actors. These must indeed operate within a complex, uncertain environment, and consequently need to rely on accurate multivariate, multi-step ahead probabilistic predictions. This paper aims to take advantage of recent breakthroughs in deep learning, while exploiting the structure of the problem to design prediction tools with tailored architectural alterations that improve their performance. The method can provide prediction intervals and densities, but is here extended with the objective to generate predictive scenarios. It is achieved by sampling the predicted multivariate distribution with a copula-based strategy so as to embody both temporal information and cross-variable dependencies. The effectiveness of the proposed methodology is emphasized and compared with several other architectures in terms of both statistical performance and impact on the quality of decisions optimized within a dedicated stochastic optimization tool of an electricity retailer participating in short-term electricity markets.
Jean-Francois Toubeau; Jeremie Bottieau; Francois Vallee; Zacharie De Greve. Deep Learning-Based Multivariate Probabilistic Forecasting for Short-Term Scheduling in Power Markets. IEEE Transactions on Power Systems 2018, 34, 1203 -1215.
AMA StyleJean-Francois Toubeau, Jeremie Bottieau, Francois Vallee, Zacharie De Greve. Deep Learning-Based Multivariate Probabilistic Forecasting for Short-Term Scheduling in Power Markets. IEEE Transactions on Power Systems. 2018; 34 (2):1203-1215.
Chicago/Turabian StyleJean-Francois Toubeau; Jeremie Bottieau; Francois Vallee; Zacharie De Greve. 2018. "Deep Learning-Based Multivariate Probabilistic Forecasting for Short-Term Scheduling in Power Markets." IEEE Transactions on Power Systems 34, no. 2: 1203-1215.
The growing penetration of volatile renewable generation is substantially impacting the operation of power systems, which results in increased balancing needs. Since wind turbines are equipped with power-electronic converters, they can be used to provide such frequency regulation reserves. However, in a competitive environment in which reservation of ancillary services capacity has to be carried in day-ahead, their provision is hampered by the limited knowledge on the energy that will be available with sufficient reliability. In this context, this paper aims at estimating the impact of prediction accuracy on the ability of wind turbines to increase their profitability by delivering power reserves. To that end, an advanced recurrent neural network architecture known as Long Short Term Memory is used to provide high-quality predictions. Simulations demonstrate that forecast accuracy is a key element to increase the economic value of wind farms, ultimately fostering their large-scale deployment by lowering integration costs.
Jeremie Bottieau; Francois Vallee; Zacharie De Greve; Jean-François Toubeau. Leveraging provision of frequency regulation services from wind generation by improving day-ahead predictions using LSTM neural networks. 2018 IEEE International Energy Conference (ENERGYCON) 2018, 1 -6.
AMA StyleJeremie Bottieau, Francois Vallee, Zacharie De Greve, Jean-François Toubeau. Leveraging provision of frequency regulation services from wind generation by improving day-ahead predictions using LSTM neural networks. 2018 IEEE International Energy Conference (ENERGYCON). 2018; ():1-6.
Chicago/Turabian StyleJeremie Bottieau; Francois Vallee; Zacharie De Greve; Jean-François Toubeau. 2018. "Leveraging provision of frequency regulation services from wind generation by improving day-ahead predictions using LSTM neural networks." 2018 IEEE International Energy Conference (ENERGYCON) , no. : 1-6.
The increased contribution of renewable generation is substantially impacting the operation of power systems. In this context, it is essential to better characterize uncertainties by improving predictions so as to prevent the need to rely on costly oversized regulation reserves for alleviating system imbalances. This paper aims at developing a generic tool dedicated to the day-ahead forecasting of main sources of uncertainties in power grids, namely load as well as wind and photovoltaic generation. The objective is to overcome issues faced by current forecasting tools by using a recurrent neural network based on Gated- Feedback Long Short-Term Memory, an advanced architecture designed to process complicated time series with multi-scale characteristics. The results demonstrate the benefits of this method on forecasting complex and highly volatile variables. However, the architectural complexity of the neural network is more likely to lead to overfitting for variables with a strong deterministic component such as the load.
Jean-Francois Toubeau; Jeremie Bottieau; Francois Vallee; Zacharie De Greve. Improved day-ahead predictions of load and renewable generation by optimally exploiting multi-scale dependencies. 2017 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia) 2017, 1 -5.
AMA StyleJean-Francois Toubeau, Jeremie Bottieau, Francois Vallee, Zacharie De Greve. Improved day-ahead predictions of load and renewable generation by optimally exploiting multi-scale dependencies. 2017 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia). 2017; ():1-5.
Chicago/Turabian StyleJean-Francois Toubeau; Jeremie Bottieau; Francois Vallee; Zacharie De Greve. 2017. "Improved day-ahead predictions of load and renewable generation by optimally exploiting multi-scale dependencies." 2017 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia) , no. : 1-5.
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Jean-François Toubeau; Zacharie De Greve; François Vallee. Technical impacts on distribution systems of medium-sized storage plants participating in energy and power reserve markets. CIRED - Open Access Proceedings Journal 2017, 2017, 2918 -2921.
AMA StyleJean-François Toubeau, Zacharie De Greve, François Vallee. Technical impacts on distribution systems of medium-sized storage plants participating in energy and power reserve markets. CIRED - Open Access Proceedings Journal. 2017; 2017 (1):2918-2921.
Chicago/Turabian StyleJean-François Toubeau; Zacharie De Greve; François Vallee. 2017. "Technical impacts on distribution systems of medium-sized storage plants participating in energy and power reserve markets." CIRED - Open Access Proceedings Journal 2017, no. 1: 2918-2921.