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Future nuclear power outages in a changing climate - A case study on two contrasted French power plants

ENErGY 2025年 320卷

作者： Collet, L. Gailhard, J. Guenand, Y. Monteil, C. Oueslati, B. Peton, P. -y. Martinet, C. Bono, C. EDF R&D OSIRIS Optimizat SImulat Risks & Stat Dept F-91120 Palaiseau France EDF DTG Water & Environm Dept F-38950 St Martin Le Vinoux France EDF R&D LNHE Natl Lab Hydraul & Environm F-78400 Chatou France EDF R&D PRISME Performance Ind Risk Monitoring Maintenance & Oper F-78400 Chatou France Elect Syst & Energy Markets Dept EDF R&D Syst F-91120 Palaiseau France

Climate change is expected to cause significant differences in the quality and quantity of the water in rivers and lakes. These changes can become a critical issue for water-dependent energy assets, such as nuclear power plants. The goal of this study is to assess the impacts of climate change on the availability of two different French nuclear power plants located on riversides, the Meuse and the Garonne rivers. A modelling framework that was evaluated in a previous study was used to assess future plant outages in these case studies. This framework is based on bias- corrected projections for one emission scenario and 10 CMIP5 EUrO-COrdEX climate models that feed a hydrological, water temperature, and cooling system modelling chain. The model projects a decrease in the flow rate of the Garonne river, and an increase in the mean temperature of the water in both rivers. As a result, the availability of both of these reactors is projected to decrease but remains under 2 % of the annual electricity production by 2050.

关键词： Climate change impact decarbonized energy CMIP5 EUrO-COrdEX Hydrological modelling Nuclear power plant modelling Water temperature modelling

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Lack of robustness of hydrological models: a large-sample diagnosis and an attempt to identify hydrological and climatic drivers

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HYdrOLOGY ANd EArTH SYSTEM SCIENCES 2025年第3期29卷 683-700页

作者： Santos, Leonard Andreassian, Vazken Sonnenborg, Torben O. Lindstroem, Goeran de Lavenne, Alban Perrin, Charles Collet, Lila Thirel, Guillaume Univ Paris Saclay INRAE UR HYCAR Antony France GEUS Copenhagen Denmark SMHI Norrkoping Sweden EDF R&D OSIRIS Dept 7 Blvd Gaspard Monge F-91120 Palaiseau France

The transferability of hydrological models over contrasting climate conditions, also identified as model robustness, has been the subject of much research in recent decades. The occasional lack of robustness identified in such models is not only an operational challenge - since it affects the confidence that can be placed in projections of climate change impact - it also hints at possible deficiencies in the structures of these models. This paper presents a large-scale application of the robustness assessment test (rAT) for three hydrological models with different levels of complexity: Gr6J, HYPE and MIKE SHE. The dataset comprises 352 catchments located in denmark, France and Sweden. Our aim is to evaluate how robustness varies over the dataset and between models and whether the lack of robustness can be linked to some hydrological and/or climate characteristics of the catchments (thus providing a clue as to where to focus model improvement efforts). We show that, although the tested models are very different, they encounter similar robustness issues over the dataset. However, models do not necessarily lack robustness in the same catchments and are not sensitive to the same hydrological characteristics. This work highlights the applicability of the rAT regardless of model type and its ability to provide a detailed diagnostic evaluation of model robustness issues.

关键词： Climate models

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Quantifying the Uncertainty of Electric Vehicle Charging with Probabilistic Load Forecasting

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WOrLd ELECTrIC VEHICLE JOUrNAL 2025年第2期16卷 88-88页

作者： Amara-Ouali, Yvenn Hamrouche, Bachir Principato, Guillaume Goude, Yannig EDF R&D OSIRIS F-91120 Palaiseau France Univ Paris Saclay LMO F-91400 Orsay France INRIA CELESTE F-91400 Orsay France

The transition to electric vehicles (EVs) presents challenges and opportunities for the management of electrical networks. This paper focuses on developing and evaluating probabilistic forecasting algorithms to understand and predict EV charging behaviours, crucial for optimising grid operations and ensuring a balance between electricity demand and generation. Several forecasting approaches tailored to different time horizons are proposed across diverse model classes, including direct, bottom-up, and adaptive approaches. In all approaches, the target variable can be the load curve quantiles from 0.1 to 0.9 with 0.1 increments or prediction sets with a target coverage of 80%. direct approaches learn from past load curves using GAMLSS or QGAM methods. Bottom-up approaches predict individual charging session characteristics (arrival time, charging duration, and energy demand) with mixture models before reconstructing the load curve. Adaptive approaches correct in real-time the prediction sets issued by direct or bottom-up approaches with conformal predictions. The experiments, conducted on real-world charging session data from Palo Alto, demonstrate the effectiveness of the proposed methods with regard to different metrics, including pinball loss, empirical coverage, and rPS. Overall, the results highlight the importance of quantifying uncertainty in load forecasts and the potential of probabilistic forecasting for EV load management.

关键词： time series Gaussian Mixture Models Monte-Carlo Simulation conformal prediction smart charging

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Optimizing financial and physical assets with chance-constrained programming in the electrical industry

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OPTIMIZATION ANd ENGINEErING 2011年第1-2期12卷 237-255页

作者： Zorgati, riadh van Ackooij, Wim EDF R&D OSIRIS 1 F-92141 Clamart France

This paper deals with a Chance-Constrained Programming formulation and approximate resolution of an offer-demand equilibrium problem in the context of electricity markets. First, we state the probabilistic model. Computing the coefficients of the problem matrix is easy for financial assets, but a challenging task for physical assets. By introducing maximal production capacities, the computation becomes tractable for thermal plants but still leads to a combinatorial problem for hydraulic production. The obtained problem matrix is sparse, large scale and with random coefficients describing underlying uncertainty factors affecting the available power of assets. Second, we suggest some ways for approximately solving the obtained combinatorial chance-constrained program, which is in fact a stochastic multi-knapsack problem. A formal link between joint and individual chance constraints is exhibited and may lead to a simplified processing of the problem. Finally we illustrate our approximate algorithm on a stylized example.

关键词： Unit commitment Hedging Chance-constrained programming

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A discussion of Probability Functions and Constraints from a Variational Perspective

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SET-VALUEd ANd VArIATIONAL ANALYSIS 2020年第4期28卷 585-609页

作者： van Ackooij, Wim EDF R&D OSIRIS 7 Blvd Gaspard Monge F-91120 Palaiseau France

Probability constraints are a popular modelling mechanism in applications. They help to model feasible decisions when the latter are taken prior to observing uncertainty and both decisions and uncertainty are involved in a constraint structure of an optimization problem. The popularity of this paradigm is attested by a vast literature using probability constraints. In this work we try to provide, with variational analysis in mind, an introduction to the topic. We wish to highlight questions regarding the understanding of theoretical properties, such as continuity, (generalized) differentiability, convexity, but also regarding algorithms. We try to highlight open research avenues whenever possible.

关键词： Probability constraints Probability functions Set-Valued analysis Stochastic optimization

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Nonatomic aggregative games with infinitely many types

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EUrOPEAN JOUrNAL OF OPErATIONAL rESEArCH 2022年第3期301卷 1149-1165页

作者： Jacquot, Paulin Wan, Cheng INRIA EDF R&D OSIRIS Palaiseau France Ecole Polytech CNRS Palaiseau France EDF R&D Palaiseau France FIME Res Ctr Paris France

We define and analyze the notion of variational Wardrop equilibrium for nonatomic aggregative games with an infinity of player types. These equilibria are characterized through an infinite-dimensional variational inequality. We show, under monotonicity conditions, a convergence theorem which enables to compute such an equilibrium with arbitrary precision. To this end, we introduce a sequence of nonatomic games with a finite number of player types, which approximates the initial game. We show the existence of a symmetric Wardrop equilibrium in each of these games. We prove that those symmetric equilibria converge to an equilibrium of the infinite game, and that they can be computed as solutions of finite dimensional variational inequalities. The model is illustrated through an example from smart grids: the description of a large population of electricity consumers by a parametric distribution gives a nonatomic game with an infinity of different player types, with actions subject to coupling constraints.(c) 2021 Elsevier B.V. All rights reserved.

关键词： Game theory Nonatomic aggregative game Coupling aggregative constraints Generalized variational inequality Monotone game Variational equilibrium

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Joint chance constrained programming for hydro reservoir management

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OPTIMIZATION ANd ENGINEErING 2014年第2期15卷 509-531页

作者： van Ackooij, Wim Henrion, rene Moeller, Andris Zorgati, riadh EDF R&D OSIRIS F-92141 Clamart France Weierstrass Inst Berlin D-10117 Berlin Germany

In this paper, we deal with a cascaded reservoir optimization problem with uncertainty on inflows in a joint chance constrained programming setting. In particular, we will consider inflows with a persistency effect, following a causal time series model with Gaussian innovations. We present an iterative algorithm for solving similarly structured joint chance constrained programming problems that requires a Slater point and the computation of gradients. Several alternatives to the joint chance constraint problem are presented. In particular, we present an individual chance constraint problem and a robust model. We illustrate the interest of joint chance constrained programming by comparing results obtained on a realistic hydro valley with those obtained from the alternative models. despite the fact that the alternative models often require less hypothesis on the law of the inflows, we show that they yield conservative and costly solutions. The simpler models, such as the individual chance constraint one, are shown to yield insufficient robustness and are therefore not useful. We therefore conclude that Joint Chance Constrained programming appears as an approach offering a good trade-off between cost and robustness and can be tractable for complex realistic models.

关键词： Chance constrained programming Hydro reservoir management Joint chance constraints Stochastic inflows

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Peer-to-peer electricity market analysis: From variational to Generalized Nash Equilibrium

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EUrOPEAN JOUrNAL OF OPErATIONAL rESEArCH 2020年第2期282卷 753-771页

作者： Le Cadre, Helene Jacquot, Paulin Wan, Cheng Alasseur, Clemence VITO EnergyVille Res Ctr Thor Sci Pk Genk Belgium CNRS CMAP Ecole Polytech OSIRIS EDF R&DInria Paris France OSIRIS EDF R&D Roussillon Isere France Energy Markets Res Ctr EDF R&D Paris France Energy Markets Res Ctr FIME Paris France

We consider a network of prosumers involved in peer-to-peer energy exchanges, with differentiation price preferences on the trades with their neighbors, and we analyze two market designs: (i) a centralized market, used as a benchmark, where a global market operator optimizes the flows (trades) between the nodes, local demand and flexibility activation to maximize the system overall social welfare;(ii) a distributed peer-to-peer market design where prosumers in local energy communities optimize selfishly their trades, demand, and flexibility activation. We first characterize the solution of the peer-to-peer market as a Variational Equilibrium and prove that the set of Variational Equilibria coincides with the set of social welfare optimal solutions of market design (i). We give several results that help understanding the structure of the trades at an equilibrium or at the optimum. We characterize the impact of preferences on the network line congestion and renewable energy surplus under both designs. We provide a reduced example for which we give the set of all possible generalized equilibria, which enables to give an approximation of the price of anarchy. We provide a more realistic example which relies on the IEEE 14-bus network, for which we can simulate the trades under different preference prices. Our analysis shows in particular that the preferences have a large impact on the structure of the trades, but that one equilibrium (variational) is optimal. Finally, the learning mechanism needed to reach an equilibrium state in the peer-to-peer market design is discussed together with privacy issues. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Or in energy Peer-to-peer energy trading Preferences Variational Equilibrium Generalized Nash Equilibrium

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A comparison of four approaches from stochastic programming for large-scale unit-commitment

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EUrO JOUrNAL ON COMPUTATIONAL OPTIMIZATION 2017年第1-2期5卷 119-147页

作者： van Ackooij, Wim OSIRIS EDF R&D 1 Ave Gen Gaulle F-92141 Clamart France

In energy management, the unit-commitment problem deals with computing the most cost-efficient production schedule that meets customer load, while satisfying the operational constraints of the units. When the problem is large scale and/or much modelling detail is required, decomposition approaches are vital for solving this problem. The recent strong increase in intermittent, relative unforeseeable production has brought forth the need of examining methods from stochastic programming. In this paper we investigate and compare four such methods: probabilistically constrained programming, robust optimization and 2-stage stochastic and robust programming, on several large-scale instances from practice. The results show that the robust optimization approach is computationally the least costly but difficult to parameterize and has the highest recourse cost. The probabilistically constrained approach is second as computational cost is concerned and improves significantly the recourse cost functions with respect to the robust optimization approach. The 2-stage optimization approaches do poorly in terms of robustness, because the recourse decisions can compensate for this. Their total computational cost is highest. This leads to the insight that 2-stage flexibility and robustness can be (practically) orthogonal concepts.

关键词： Probabilistically constrained programming robust optimization Stochastic programming Unit-commitment

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On conditional cuts for stochastic dual dynamic programming

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EUrO JOUrNAL ON COMPUTATIONAL OPTIMIZATION 2020年第2期8卷 173-199页

作者： van Ackooij, W. Warin, X. EDF R&D OSIRIS 7 Blvd Gaspard Monge F-91120 Palaiseau France

Multistage stochastic programs arise in many applications from engineering whenever a set of inventories or stocks has to be valued. Such is the case in seasonal storage valuation of a set of cascaded reservoir chains in hydro management. A popular method is stochastic dual dynamic programming (SddP), especially when the dimensionality of the problem is large and dynamic programming is no longer an option. The usual assumption of SddP is that uncertainty is stage-wise independent, which is highly restrictive from a practical viewpoint. When possible, the usual remedy is to increase the state-space to account for some degree of dependency. In applications, this may not be possible or it may increase the state-space by too much. In this paper, we present an alternative based on keeping a functional dependency in the SddP-cuts related to the conditional expectations in the dynamic programming equations. Our method is based on popular methodology in mathematical finance, where it has progressively replaced scenario trees due to superior numerical performance. We demonstrate the interest of combining this way of handling dependency in uncertainty and SddP on a set of numerical examples. Our method is readily available in the open-source software package StOpt.

关键词： Stochastic programming SddP algorithm Nonsmooth optimization Conditional expectations

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