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Description du sujetTitle:  "Seamless forecasting of local energy production and demand using multiple heterogeneous data sources"Context and background:Short-term energy forecasting for the next minutes to days ahead, is a prerequisite for the economic and safe operation of modern power systems and electricity markets especially under high renewable energy sources (RES) penetration. The different contexts of application make that end-users require models that have a broad number of properties especially when they are applied operationally. They should cover multiple time frames (from minutes to days ahead) and multiple RES technologies (i.e. wind, solar, hydro) as well as their aggregations (i.e. in the form of virtual power plants – VPP). They should use as input the very large amount of data available, while dealing efficiently with dimensionality. The data sources may be measurements from the power plants, various types of satellite images, sky camera images, various feeds of numerical weather prediction and others. They should be generic enough to be easily replicable to different sites or demand forecasting. They should also be resilient against imperfect or corrupted data streams; be interpretable enough; and be able to deal with structural changes in the physical system (e.g. addition of assets to a VPP or equipment in a smart home). So far separate models are developed for each of these aspects. The thesis is realized in the frame of the PEPR TASE project Fine4Cast coordinated by the supervisors of this thesis. PERSEE has an international visibility in the field of energy forecasting thanks to a long track of national and European projects, PhDs and publications in the area.Scientific objectives:This thesis will develop a seamless forecasting approach for net-load and joint load and renewable production that meets the above requirements, while being at least as accurate as the currently used partial models. It will also preserve privacy of the different data sources. The modelling approach should be probabilistic giving the possibility to estimate the uncertainty in the forecasts. Combination methods of probabilistic forecasts will be assessed. Methodology and expected results:  A seamless method has been proposed by PERSEE that optimally combines the available data sources to derive a probabilistic forecast of RES production at multiple temporal scales and aggregation levels. Adapting this seamless concept to local demand or net-load has not yet been proposed in the literature. The methodology will start by identification of adequate explanatory variables from multiple data sources (multiple weather predictions and simulations, local measurements, multiple types of satellite-based images, etc.). The second step will ensure the scalability of the forecasting approach to large dimensions and the adaptivity to structural change in the production and demand assets. Validation will be done using available real-world data sets. Emphasis will be given on assessing the contribution of each available data source in a cost-benefit analysis context.Nature du financementAutre financement publicPrécisions sur le financementProject PEPR TASE "Fine4Cast": "Next Generation Energy Demand and Renewable Production Forecasting Tools for Fine Geographical and Temporal Scales"Présentation établissement et labo d'accueilMines Paris - PSL, Centre PERSEEThe PERSEE Center is one of the 18 research centers of MINES Paris. Its field of expertise concerns New Energy Technologies and Renewable Energy Sources (RES). Its research strategy is based on a "micro/macro" approach ranging from (nano)materials to energy systems. It is built around three structuring themes: i) materials and components for energy, ii) sustainable energy conversion and storage processes and technologies, and iii) renewable energies and smart energy systems.This late is developped by one of the three groups of the Center, ERSEI, which stands for “Renewable Energies and Smart Energy Systems”. The ERSEI group develops methods and tools allowing the optimal integration of decentralized sources, including RES, storage devices, electric vehicles, active demand and other technologies, in energy systems and electricity markets. The research activity of the group is divided into three main themes. The first is based on the development of advanced short-term forecasting methods for different applications in power systems (i.e. forecasting of RES production, demand, dynamic line rating, market quantities, etc.). The second concerns the control and predictive management of energy systems. The aim is to design innovative approaches to optimise the operation (from real-time to days ahead) of different types of systems (smart-homes, microgrids, virtual power plants, energy communities, hybrid RES/storage plants, distribution grids multi-energy systems a.o.) considering uncertainties. The third axis concern planning and prospective studies that aim to optimise the design of future energy systems, generate furture scenarios, optimise investements etc.  The PERSEE Center is located within the scientific parc of Sophia-Antipolis, near the cities of Nice, Cannes and Antibes in the south of France. Its workforce is around 50 people.Site web :http://www.minesparis.psl.euIntitulé du doctoratDoctorat en Énergétique et Procédés Pays d'obtention du doctoratFranceEtablissement délivrant le doctoratMines Paris - PSL (Ecole Nationale Supérieure des Mines de Paris)Ecole doctoraleIngénierie des Systèmes, Matériaux, Mécanique, EnergétiqueProfil du candidatPROFILE:Engineer and / or Master of Science degree (candidates may apply prior to obtaining their master's degree. The PhD will start though after the degree is succesfully obtained).Good level of general and scientific culture. Good analytical, synthesis, innovation and communication skills. Qualities of adaptability and creativity. Motivation for research activity. Coherent professional project. Skills in programming (eg R, Python, Julia,…).  A succesful candidate will have a solid background in three or more of the following competencies: applied mathematics, statistics and probabilitiesdata science, machine learning, artificial intelligenceenergy forecastingpower system management, integration of renewablesoptimization Expected level in french : Good level  Expected level in english : Proficiency Desired starting date as soon as possible in 2024. Duration 36 months. Full-time position. For more information please contact Prof. Georges Kariniotakis and Dr Simon Camal.   Date limite de candidature  20/07/2024

Description du sujetTitle:  "Optimization of flexibility services under multiple local uncertainties in the context of smart grids"Context and challenges: In the context of the energy transition, power grids integrate massive amounts of renewable generation (mostly wind and solar) whose volatility and uncertainty bring unprecedented challenges to the grid operation. Flexible generation and demand, as well as storage or storage-like resources, are key for the efficient and reliable management of future power systems. The quest for flexibility is paramount at different temporal but also spatial scales (at the transmission level, at the perimeter of an aggregator or, more locally, at distribution networks) and has expanded to multi-energy systems, e.g., the coupling between electrical and gas networks. Existing methodologies that propose flexibility indicators at a national level need to be revisited at the local level, by considering local characteristics and uncertainties in production and demand at a given territory (district, region), and accounting for events that deviate from normal operating conditions (e.g., peaks due to electric vehicle charging, low renewable availability during long periods, etc.). In a given territory, flexibility valorization raises a multitude of territory-specific questions, e.g.: Should a local flexibility market be deployed? What is the potential of local energy communities? How do local conditions affect territory-level decisions for the flexibility provision and use? Main objective of the thesis:  The overarching objective of this research project is to develop an approach for the optimal provision and use of flexibility at the level of a territory, which accounts for the uncertainties associated with local renewable production and local energy consumption of the potential flexible consumers (residential, commercial, industrial). Methodology and expected results: The first step of this research project is to define flexibility provision indicators, based on production/consumption adequacy and contextual assessment at the level of a territory, relying on predictive methodologies to quantify the local flexibility potential. These indicators will be used as inputs to produce a risk-aware analytical decision-aid methodology of flexibility valorization in multi-energy systems (second step), employingforecasting models and optimization. The third step is to simplify the arguably complex modelling chain by integrating forecasting and optimization via end-to-end learning of flexibility decisions based on AI, thus predicting directly flexibility decisions that are optimal as a function of the predicted local weather conditions (e.g. uncertain mixed-cloudy day) and the local socio-economic context (e.g. high commercial activity expected due to fair/sales etc.).Nature du financementAutre financement publicPrécisions sur le financementProject PEPR TASE "FLEX TASE"Présentation établissement et labo d'accueilMines Paris - PSL, Centre PERSEEThe PERSEE Center is on e of the 18 research centers of MINES Paris. Its field of expertise concerns New Energy Technologies and Renewable Energy Sources (RES). Its research strategy is based on a "micro/macro" approach ranging from (nano)materials to energy systems. It is built around three structuring themes: i) materials and components for energy, ii) sustainable energy conversion and storage processes and technologies, and iii) renewable energies and smart energy systems.This late is developped by one of the three groups of the Center, ERSEI, which stands for “Renewable Energies and Smart Energy Systems”. The ERSEI group develops methods and tools allowing the optimal integration of decentralized sources, including RES, storage devices, electric vehicles, active demand and other technologies, in energy systems and electricity markets. The research activity of the group is divided into three main themes. The first is based on the development of advanced short-term forecasting methods for different applications in power systems (i.e. forecasting of RES production, demand, dynamic line rating, market quantities, etc.). The second concerns the control and predictive management of energy systems. The aim is to design innovative approaches to optimise the operation (from real-time to days ahead) of different types of systems (smart-homes, microgrids, virtual power plants, energy communities, hybrid RES/storage plants, distribution grids multi-energy systems a.o.) considering uncertainties. The third axis concern planning and prospective studies that aim to optimise the design of future energy systems, generate furture scenarios, optimise investements etc.  The PERSEE Center is located within the scientific parc of Sophia-Antipolis, near the cities of Nice, Cannes and Antibes in the south of France. Its workforce is around 55 people.Site web :http://www.minesparis.psl.euIntitulé du doctoratDoctorat en Énergétique et Procédés Pays d'obtention du doctoratFranceEtablissement délivrant le doctoratMines Paris - PSL (Ecole Nationale Supérieure des Mines de Paris)Ecole doctoraleIngénierie des Systèmes, Matériaux, Mécanique, EnergétiqueProfil du candidatProfile:  Engineer and / or Master of Science degree (candidates may apply prior to obtaining their master's degree. The PhD will start though after the degree is succesfully obtained).Good level of general and scientific culture. Good analytical, synthesis, innovation and communication skills. Qualities of adaptability and creativity. Motivation for research activity. Coherent professional project. Skills in programming (eg R, Python, Julia,…).  A succesful candidate will have a solid background in three or more of the following competencies: applied mathematics, statistics and probabilitiesdata science, artificial intelligenceoptimisationenergy forecastingpower system management, integration of renewablesExpected level in french : bon niveau souhaitableExpected level in english : excellentDate limite de candidature  20/07/2024

Description du sujetTitle:  "High-dimensional optimization of distributed assets in smart grids" Context and background:In the vertically integrated electrical energy systems of the past, power system management was carried out centrally by the transmission and distribution system operators (TSOs, DSOs). In the frame of the energy transition, emerging new actors (aggregators, microgrid operators, energy community managers, self-consumption etc.) and the proliferation of assets connected to the grid, such as renewable energy (RES) plants, storage devices, electric vehicles (EVs), smart-homes/prosumers with IoT devices, electric heating/cooling systems, etc., urge for a paradigm shift towards decentralization. New business models are likely to be based on physical or virtual groupings of assets (“cells”), instantiated as virtual power plants (VPPs), energy communities, microgrids, and others. In the power systems of the future, all these “cell” variants will probably coexist, and their operation should be optimized accounting for the specific interests of the involved actors. For example, a VPP operator aggregates hundreds to thousands of assets to achieve a critical mass of flexibility and valorize it in electricity markets. Optimization functions (“distributed intelligence”) are necessary at these lower levels down to the grid edge (cell, feeders, assets/devices…) and also need to be aligned with the grid operation.Scientific objectives:The overarching objective of this research project is to develop distributed optimization methods for grids with a very large number (tens/hundreds of thousands to millions) of connected devices. The aim is to account for the involved uncertainties, the classification of assets/devices into different typologies of virtual/physical cells, their computational/communication capabilities/limitations, environmental disturbances, QoS/grid constraints, and privacy concerns. Large scale distributed optimization requires the design of appropriate grid-aware signals that affect the local optimization processes for a multitude of devices, so that their aggregation provides a predictable response (even though it is the result of the response of different assets), while ensuring an optimal use of grid infrastructure.Methodology and expected results:The first step of the research project is a bibliographic research and familiarization with the methods and tools developed at our Group. The initial use case of focus will be the predictive management (scheduling) of the assets (for time frames in the order of a few minutes to a few days ahead). The developed approaches should be scalable to a very high number of assets, with inherent uncertainties in their production/consumption profiles, to cover use cases such as distribution grids and/or VPPs that integrate EVs, RES plants, storage devices, prosumers and the like. The research project will integrate predictive models that reduce the complexity associated to multiple uncertainties, employ machine learning and/or statistical methods for high dimensional problems (e.g., sparse models, functional data analysis, edge ML), and explore distributed optimization strategies to cope with the very large problem sizes. Optimization strategies will involve decomposition methods, blended with machine learning developments (e.g., optimal decision trees able to adapt dynamically to the vast amount of incoming information), and produce signals that account for the local grid conditions (i.e., congestions/overloads) to which the different cells of assets respond based on their capabilities and individual objectives.Nature du financementAutre financement publicPrécisions sur le financementProject PEPR TASE "AI.NRGY - Distributed AI-based architecture of future energy systems integrating very large amounts of distributed sources"Présentation établissement et labo d'accueilMines Paris - PSL, Centre PERSEEThe PERSEE Center is one of the 18 research centers of MINES Paris. Its field of expertise concerns New Energy Technologies and Renewable Energy Sources (RES). Its research strategy is based on a "micro/macro" approach ranging from (nano)materials to energy systems. It is built around three structuring themes: i) materials and components for energy, ii) sustainable energy conversion and storage processes and technologies, and iii) renewable energies and smart energy systems.This late is developped by one of the three groups of the Center, ERSEI, which stands for “Renewable Energies and Smart Energy Systems”. The ERSEI group develops methods and tools allowing the optimal integration of decentralized sources, including RES, storage devices, electric vehicles, active demand and other technologies, in energy systems and electricity markets. The research activity of the group is divided into three main themes. The first is based on the development of advanced short-term forecasting methods for different applications in power systems (i.e. forecasting of RES production, demand, dynamic line rating, market quantities, etc.). The second concerns the control and predictive management of energy systems. The aim is to design innovative approaches to optimise the operation (from real-time to days ahead) of different types of systems (smart-homes, microgrids, virtual power plants, energy communities, hybrid RES/storage plants, distribution grids multi-energy systems a.o.) considering uncertainties. The third axis concern planning and prospective studies that aim to optimise the design of future energy systems, generate furture scenarios, optimise investements etc.  The PERSEE Center is located within the scientific parc of Sophia-Antipolis, near the cities of Nice, Cannes and Antibes in the south of France. Its workforce is around 55 people.Site web :http://www.minesparis.psl.euIntitulé du doctoratDoctorat en Énergétique et Procédés Pays d'obtention du doctoratFranceEtablissement délivrant le doctoratMines Paris - PSL (Ecole Nationale Supérieure des Mines de Paris)Ecole doctoraleIngénierie des Systèmes, Matériaux, Mécanique, EnergétiqueProfil du candidatProfile:   Engineer and / or Master of Science degree (candidates may apply prior to obtaining their master's degree. The PhD will start though after the degree is succesfully obtained).Good level of general and scientific culture. Good analytical, synthesis, innovation and communication skills. Qualities of adaptability and creativity. Motivation for research activity. Coherent professional project. Skills in programming (eg R, Python, Julia,…).  A succesful candidate will have a solid background in three or more of the following competencies:applied mathematics, statistics and probabilitiesmachine learning, data scienceoptimizationenergy forecastingpower systems, renewable integrationExpected level in french : Good levelExpected level in english : Proficiency  Desired starting date: as soon as possible in 2024. Duration 36 months. Full-time paid position. For more information please contact Prof. Georges Kariniotakis and Dr. Panagiotis Andrianesis Date limite de candidature  20/07/2024

Description du sujetTitle:  "Development of prescriptive analytics for energy systems" Context and background:Operational management of energy systems in time scales of a few minutes to days ahead involves decision making that results from two major steps: (1) leveraging contextual information to forecast uncertain input quantities like electricity demand, weather-dependent renewable production (wind, solar, hydro…), electricity market quantities (prices, imbalances…), and (2) optimization, where the forecasts are used as input to optimization tools for congestion management, economic dispatch, unit commitment, electricity trading, reserves estimation and other applications. The classical “Forecast then Optimise” approach may involve a complex model chain of multiple models that one has to tune and maintain. For example, when trading the production of a virtual power plant (VPP) in day ahead and ancillary service markets, one may need as many as 11 models (energy and market quantities forecasting and stochastic optimization). Further, forecast accuracy might not align with performance optimality. In the last years the new paradigm of “Prescriptive Analytics” emerged, where data-driven approaches integrated the two steps. For example, end-to-end machine learning (ML) models can be trained to minimize the downstream decisions regret or even directly learn a mapping from data to decisions. First works that focus on the energy trading model chain have shown that equivalent results can be obtained with the analytical approach.  This is a new and very promising field that needs to be further explored in different use cases and contexts.Scientific objectives:The overarching objective of this thesis is to develop and validate the prescriptive analytics approach in different use cases of the energy sector. The aim is to develop methods based on data-driven optimization and ML that improve decision quality and simplify complex model chains. The considered use cases can be the classical ones of predictive management of power systems but also applications that can be related to edge computing or even industrial processes. The application of prescriptive analytics at the “edge”, and especially at the consumers level (EVs, smart homes, smart buildings…), is promising since it permits to simplify the model chain at that level and thus to robustify and automatize the “intelligence” layer of applications at that level. In this context, it is also important to consider aspects such as the intepretability and explainability of decisions given the context, as well as to ensure that the algorithmic design permits implementation in micro-computers.Methodology and expected results:The methodological focus is on the edge of machine learning and mathematical programming. The first step will be to carry out a bibliographic research and familiarize with previous developments in this area in our Centre. These developments concern existing probabilistic forecasting methods for energy forecasting (demand, EVs demand, prices, renewable generation), as well as optimization algorithms for trading of VPPs, optimal power flow, congestion management, microgrids scheduling and other applications. PERSEE has developed first approaches for prescriptive analytics based on artificial neural networks and prescriptive decision trees focusing on the energy trading application. The aim is to propose generic solutions for a broad number of typical use cases and deal with requirements for adaptability/replicability, interpretability, feasibility of decisions, as well as privacy/confidentiality preservation when information/data is shared, automatization of the process, resilience on disruptive events, etc. An experimental implementation for such a typical use-case (i.e. energy community with smart homes or microgrid management) may be envisaged to demonstrate AI-based prescriptive solutions on the edge.  Nature du financementAutre financement publicPrécisions sur le financementProject PEPR TASE "AI.NRGY - Distributed AI-based architecture of future energy systems integrating very large amounts of distributed sources"Présentation établissement et labo d'accueilMines Paris - PSL, Centre PERSEEThe PERSEE Center is one of the 18 research centers of MINES Paris. Its field of expertise concerns New Energy Technologies and Renewable Energy Sources (RES). Its research strategy is based on a "micro/macro" approach ranging from (nano)materials to energy systems. It is built around three structuring themes: i) materials and components for energy, ii) sustainable energy conversion and storage processes and technologies, and iii) renewable energies and smart energy systems.This late is developped by one of the three groups of the Center, ERSEI, which stands for “Renewable Energies and Smart Energy Systems”. The ERSEI group develops methods and tools allowing the optimal integration of decentralized sources, including RES, storage devices, electric vehicles, active demand and other technologies, in energy systems and electricity markets. The research activity of the group is divided into three main themes. The first is based on the development of advanced short-term forecasting methods for different applications in power systems (i.e. forecasting of RES production, demand, dynamic line rating, market quantities, etc.). The second concerns the control and predictive management of energy systems. The aim is to design innovative approaches to optimise the operation (from real-time to days ahead) of different types of systems (smart-homes, microgrids, virtual power plants, energy communities, hybrid RES/storage plants, distribution grids multi-energy systems a.o.) considering uncertainties. The third axis concern planning and prospective studies that aim to optimise the design of future energy systems, generate furture scenarios, optimise investements etc.  The PERSEE Center is located within the scientific parc of Sophia-Antipolis, near the cities of Nice, Cannes and Antibes in the south of France. Its workforce is around 55 people.Site web :http://www.minesparis.psl.euIntitulé du doctoratDoctorat en Énergétique et Procédés Pays d'obtention du doctoratFranceEtablissement délivrant le doctoratMines Paris - PSL (Ecole Nationale Supérieure des Mines de Paris)Ecole doctoraleIngénierie des Systèmes, Matériaux, Mécanique, EnergétiqueProfil du candidatPROFILE:Engineer and / or Master of Science degree (candidates may apply prior to obtaining their master's degree. The PhD will start though after the degree is succesfully obtained).Good level of general and scientific culture. Good analytical, synthesis, innovation and communication skills. Qualities of adaptability and creativity. Motivation for research activity. Coherent professional project. Skills in programming (eg,Python, R, Julia,…) and knowledge of optimization tools (e.g, Gurobi, CPLEX).  A succesful candidate will have a solid background in two or more of the following competencies: data science, machine learning, artificial intelligenceapplied mathematics, statistics and probabilitiesoptimisationenergy forecastingpower system management, integration of renewablesExpected level in french : Good levelExpected level in english : Proficiency Desired starting date is as soon as possiblein 2024. Duration 36 months. Full-time paid position. For more information and applications please contact Prof. Georges Kariniotakis and Dr. Simon Camal Date limite de candidature  20/07/2024

Description du sujetTitle:  "Optimization of multi-actor energy systems enhanced with AI and privacy-preserving data sharing".     Context and background:The energy transition over the next decades is characterized by the development of clean energy sources like renewables (RES), which drive the transition from the centralized energy system towards a decentralized one, where new paradigms for the organization of actors emerge. As an example, we can mention the paradigm of industrial basins with industries that access common resources (i.e. electricity and gas networks), energy communities, microgrids, territories with sector coupling (i.e. gas, e-mobility, district heating). In these paradigms, it is expected that collaborative behavior of the actors, based on the assumption that exchange of information may take place, may permit to reach easier a common objective, like the decarbonization of the industry or the transition to a net-zero territory. From a simple example in the literature we know that, when forecasting the power output of a wind farm, accuracy in the next 6 hours can be improved by up to 20% if data from neighbor wind farms are used as input. However, in practice, data sharing may be hindered due to regulation or commercial/industrial security constraints. Data often comes from smart meters and is therefore subject to confidentiality constraints, or it comes from industrial or renewable energy installations and is therefore commercially sensitive and the corresponding players are reluctant to share it. The obligation to publish information as open data in different sectors does not solve the problem; often, open data is of little value for operational purposes because it is published in aggregate form to mask confidentiality. These constraints limit the value we can extract from available data. Without data sharing, optimization of the energy systems mentioned above leads to sub-optimal solutions. Scientific objectives and Methodology: The development of distributed optimization methods, automated transactive peer-to-peer and privacy preserving algorithms offer technical solutions in the field of artificial intelligence that can resolve the above-mentioned bottlenecks and promote collaborative strategies towards common goals like the reduction of the global carbon footprint in highly integrated basins.The overarching objective of this thesis is double fold:to develop methods that permit data sharing, while respecting confidentiality constraints and for different types of data.to develop AI-based agents that can support decision-making in multi-actor and collaborative energy schemes and are compatible with privacy-preserving data sharing. Priority will be given to the use case of an industrial basin. Examples of energy intensive industrial basins in Europe exist, where a single basin represents up to 20% of the country's CO2 emissions. In the process of decarbonization of the involved industries, data sharing can be critical. Key themes and questions proposed in this topic are along the following lines:How can automated negotiation technology provided by peer to peer exchange design can support coordination between industrial agents in a basin?Can decomposition methods be used to coordinate heterogeneous optimization problems to reach a common goal without sharing of the underlying data and models.How can privacy-preserving learning provide a collaborative modelling framework for the actors of an industrial basin to improve the global modelling accuracy with no or limited data sharing?Expected results: Although the applicative field in this PhD project is energy systems, the techniques developed have a replication potential to other fields like health or other. The expected results contain:Algorithms for data sharing, while preserving confidentiality constraints. Distributed optimization methods that implement data sharing and AI techniques. Evaluation on real data with focus to use cases of industrial basins.Recommendations to regulatory authorities and policy makers.International collaboration: The candidate will pass a period of at least one 6 months at Imperial College in London in the team of the PhD co-director Prof. Pierre PINSON. This will permit, among others, to apply for a European Doctorate Label. Additional stays are possible at laboratories with related activity to the topic like INESC TEC in Porto. Non-academic stays are foreseen at the R&D center of Air-Liquide in Paris area.Prise de fonction : 01/10/2024Nature du financementAutre financement privéPrécisions sur le financementAir-LiquidePrésentation établissement et labo d'accueilMines Paris - PSL, Centre PERSEEThe PERSEE Center is one of the 18 research centers of MINES Paris. Its field of expertise concerns New Energy Technologies and Renewable Energy Sources (RES). Its research strategy is based on a "micro/macro" approach ranging from (nano)materials to energy systems. It is built around three structuring themes: i) materials and components for energy, ii) sustainable energy conversion and storage processes and technologies, and iii) renewable energies and smart energy systems.This late is developped by one of the three groups of the Center, ERSEI, which stands for “Renewable Energies and Smart Energy Systems”. The ERSEI group develops methods and tools allowing the optimal integration of decentralized sources, including RES, storage devices, electric vehicles, active demand and other technologies, in energy systems and electricity markets. The research activity of the group is divided into three main themes. The first is based on the development of advanced short-term forecasting methods for different applications in power systems (i.e. forecasting of RES production, demand, dynamic line rating, market quantities, etc.). The second concerns the control and predictive management of energy systems. The aim is to design innovative approaches to optimise the operation (from real-time to days ahead) of different types of systems (smart-homes, microgrids, virtual power plants, energy communities, hybrid RES/storage plants, distribution grids multi-energy systems a.o.) considering uncertainties. The third axis concern planning and prospective studies that aim to optimise the design of future energy systems, generate furture scenarios, optimise investements etc.  PERSEE participates in different projects involving AI and coordinated the H2020 Smart4RES on RES forecasting and applications that involves data sharing techniques. PERSE is also involved in the PEPR TASE projects AI.NRGY with complementary problematics on privacy-preserving data sharing.The PERSEE Center is located within the scientific parc of Sophia-Antipolis, near the cities of Nice, Cannes and Antibes in the south of France. Its workforce is around 55 persons.Site web :http://www.minesparis.psl.euIntitulé du doctoratDoctorat en Énergétique et Procédés Pays d'obtention du doctoratFranceEtablissement délivrant le doctoratMines Paris - PSL (Ecole Nationale Supérieure des Mines de Paris)Ecole doctoraleIngénierie des Systèmes, Matériaux, Mécanique, EnergétiqueProfil du candidatREQUESTED PROFILE:  Engineer and / or Master of Science degree (candidates may apply prior to obtaining their master's degree. The PhD will start though after the degree is succesfully obtained). Good level of general and scientific culture. Good analytical, synthesis, innovation and communication skills. Qualities of adaptability and creativity. Motivation for research activity. Coherent professional project. Specific skills for this thesis: A successful candidate will have a strong background in applied mathematics (i.e. optimization) and/or AI and data science.  Skills in computer programming (i.e. Python, R, Julia) are required together with knowledge of optimization tools (e.g, Gurobi, CPLEX).   Expected level in French: Good levelExpected level in English: Proficiency

Description du sujetTitle:  "AI-supported stochastic optimization distillation for decision-makers in the energy transition" Context and background: The stochastic optimization framework has been extensively studied in energy systems, mainly driven to handle the variability and uncertainty of renewable energy sources (RES). Despite the advances in tractability and computational performance of these methods, they are not widely adopted by the industry due to a complex understanding from the decision-maker, which leads to algorithmic aversion. Recently, data-driven prescriptive models (e.g., based on decision trees or reinforcement learning) have been proposed to remove model chain complexity and offer explainability in terms of input features importance, yet they may remain as a “black-box” model to decision-makers and may be negatively affected by data quality. Moreover, in cases where uncertainty is represented by a large number of input scenarios, the interpretability of mathematical optimization models or data-driven techniques is significantly diminished and makes ex-ante and ex-post analysis from humans very complex. An alternative interesting approach in the literature proposes solution sets from the stochastic optimization problem and lets the operators chose the solution according to their priorities and in the region close to the optimum rather than proposing them a hard solution.Scientific objectives & Methodology: This Ph.D. topic is inspired by the concept of knowledge distillation (i.e., the process of transferring knowledge from a large model to a smaller one), and the goal is to build reduced or data-driven models that can explain the decisions suggested by stochastic optimization tools. For instance, these models can be trained with data generated by solving multiple instances of the stochastic optimization problem and with several objectives, such as a) explaining how the decision-maker preferences and risk profile affect the optimization output or b) deriving a set of rules that relate input data (e.g., scenarios) and decisions, or c) support the decision-maker in choosing between deterministic and stochastic optimization for a particular context.Expected results:The results of this thesis are expected to contribute in increase the acceptability of AI solutions by the different actors of the energy systems. The AI-solutions developed will enhance human capabilities (rather than replacement of human intelligence) in control and trading centers. It will bring to the industry alternative paradigm for accounting for uncertainties in power system management. International collaboration:  This PhD project, which is supported by the ITN institute or Mines Paris (Institut des Transformations Numériques), is proposed in collaboration with Dr. Ricardo Bessa, Senior Scientist at INESC TEC in Porto who will be PhD co-director. The candidate will pass a period of at least 6 months at INESC TEC. This will permit, among others, to apply for a European Doctorate Label. Additional stays are envisaged to laboratories with related activity to the topic.INESC TEC is part of the European network HumanE-AI-Net, which aims to develop a new scientific approach to make AI more humanised, consistent with European and universal values and closer to citizens. This network, composed of top European laboratories and researchers in AI, was one of 5 recently approved in the ICT-48 call, and it aims to take an important step in the European AI strategy promoted by the EU, thus consolidating scientific connections in Europe. INESC TEC is also coordinator of the TRUST-AI (“Transparent, Reliable and Unbiased Smart Tool for AI”) project that employs explainable-by-design symbolic models and learning algorithms and adopt a human-centric, guided empirical learning process that integrates cognition. Ricardo Bessa is also coordinator of the EU project AI4REALNET ("AI for real-world network operation).  Prise de fonction : 01/10/2024Nature du financementContrat doctoralPrécisions sur le financementInstitut des Transformations Numériques (ITN) of Mines ParisPrésentation établissement et labo d'accueilMines Paris - PSL, Centre PERSEEThe PERSEE Center is one of the 18 research centers of MINES Paris. Its field of expertise concerns New Energy Technologies and Renewable Energy Sources (RES). Its research strategy is based on a "micro/macro" approach ranging from (nano)materials to energy systems. It is built around three structuring themes: i) materials and components for energy, ii) sustainable energy conversion and storage processes and technologies, and iii) renewable energies and smart energy systems.This late is developped by one of the three groups of the Center, ERSEI, which stands for “Renewable Energies and Smart Energy Systems”. The ERSEI group develops methods and tools allowing the optimal integration of decentralized sources, including RES, storage devices, electric vehicles, active demand and other technologies, in energy systems and electricity markets. The research activity of the group is divided into three main themes. The first is based on the development of advanced short-term forecasting methods for different applications in power systems (i.e. forecasting of RES production, demand, dynamic line rating, market quantities, etc.). The second concerns the control and predictive management of energy systems. The aim is to design innovative approaches to optimise the operation (from real-time to days ahead) of different types of systems (smart-homes, microgrids, virtual power plants, energy communities, hybrid RES/storage plants, distribution grids multi-energy systems a.o.) considering uncertainties. The third axis concern planning and prospective studies that aim to optimise the design of future energy systems, generate furture scenarios, optimise investements etc.  PERSEE participates in different projects involving AI and coordinated the H2020 Smart4RES on RES forecasting and applications that involves data science techniques. PERSE is also involved in the PEPR TASE projects AI.NRGY with complementary problematics to the proposed project as well as to Fine4CAST, where it develops energy forecasting methods based on AI.The PERSEE Center is located within the scientific parc of Sophia-Antipolis, near the cities of Nice, Cannes and Antibes in the south of France. Its workforce is around 55 people.Site web :http://www.minesparis.psl.euIntitulé du doctoratDoctorat en Énergétique et Procédés Pays d'obtention du doctoratFranceEtablissement délivrant le doctoratMines Paris - PSL (Ecole Nationale Supérieure des Mines de Paris)Ecole doctoraleIngénierie des Systèmes, Matériaux, Mécanique, EnergétiqueProfil du candidatPROFILE:Engineer and / or Master of Science degree (candidates may apply prior to obtaining their master's degree. The PhD will start though after the degree is succesfully obtained).Good level of general and scientific culture. Good analytical, synthesis, innovation and communication skills. Qualities of adaptability and creativity. Motivation for research activity. Coherent professional project. Skills in programming (eg,Python, R, Julia,…) and knowledge of optimization tools (e.g, Gurobi, CPLEX).  A succesful candidate will have a solid background in two or more of the following competencies: optimisationapplied mathematics, statistics and probabilitiesdata science, machine learning, artificial intelligencepower system management, integration of renewablesExpected level in french : Good levelExpected level in english : Proficiency Desired starting date : Fall 2024. Duration 36 months. Full-time paid position. For more information and applications please contact Prof. Georges Kariniotakis and Dr. Simon Camal Date limite de candidature  31/07/2024

Description du sujetTitle:  "Flexibility-aware forecasting of local energy demand" Context and background:Short-term forecasts of energy demand (electricity, heating/cooling, gas) at local level, ranging from a single household up to a group of buildings, a district, a node of the grid or a microgrid, become more and more necessary in the context of smart grids. Several new business models emerge, where the involved actors require such forecasts (together with information on the associated uncertainty) for a few minutes to days ahead in order to manage the corresponding energy systems. The objective may be auto-consumption (when generation and/or storage capabilities are available), provision of flexibility services to the grid, energy exchanges with other members of an energy community a.o. Although the literature on electricity forecasting at a national level is broad and the accuracy of existing models is very high, this is not the case for demand at local level. The existing models are not adapted to the ongoing transformation and digitalization of energy networks and the electrification of new usages that results in increasing demand by consumers (i.e. electric vehicles charging). Furthermore, the integration of high shares of renewables (wind, solar) is a challenge for grid operators. It becomes more and more necessary to adopt solutions that permit to adapt consumption to the variable renewable generation. For that, they deploy technologies that enable more flexibility of the consumption (e.g. load shifting, EV charging in zones with lower grid operational constraints, etc.). The activation of such flexibility options becomes more frequent and concerns more and more consumers. This induces spatio-temporal modifications in energy consumption patterns. As digital information has become central in the organization and dynamic behavior of both rural and urban territories, an efficient treatment of contextual information regarding the expected use of energy at the local scale is needed.Scientific objectives:The overall objective of the thesis is to develop a forecasting approach for local energy demand that is flexibility-aware, i.e. that can adapt to flexibility activations from energy networks (electricity, heat/cold, gas). The forecasting approach will be able to integrate contextual information relative to local situations, e.g. traffic, environmental conditions, weather conditions, news and social media.  Methodology and expected results:  The first step of this thesis will be to characterize and define typical patterns of energy demand at local level as a function of the aggregation of consumers (from single households up to tens or hundreds of consumers). Then existing load forecasting methods will be applied to estimate the reference performance that can be achieved. Several sources of input data, like smart meters data, weather forecasts, EV charging information etc. will be considered. The sensitivity of the existing methods on missing data will be studied together with the contribution of spatiotemporal information (i.e. measurements from neighbor smart homes with similar consumption profiles). It will be studied what are the requirements of the considered and future applications (use cases) for forecasting models that have to be taken into account in the design of such models. I.e. if they have to operate autonomously (i.e. at the “edge” or at automats with minimal maintenance and tuning requirements), if they have to be adaptive to structural changes of the consumption (i.e. addition of new usages), their robustness on missing data, their sensitivity on personal information a.o. In a next step, the contribution of NLP (natural language processing methods) methods will be assessed. Existing models developed at PERSEE for prediction at regional and national level will be considered. These methods will extract explanatory variables from text and audiovisual sources that are potentially informative for local energy consumption. Language data generated by AI will be integrated into the NLP methodology, which requires potentially AI detection and ethics analysis. Representative case studies will be provided by ongoing projects and will prioritize open-source datasets. A second step will be dedicated to the dynamic adaptation of the forecasting approach to massive penetration of flexibility in energy networks, e.g. from the electric distribution and transmission systems. This means to develop a prediction of the evolution of local demand before, during and after flexibility activations of different characteristics, durations and amplitudes. Nature du financementAutre financement publicPrécisions sur le financementProject PEPR TASE "Fine4Cast": "Next Generation Energy Demand and Renewable Production Forecasting Tools for Fine Geographical and Temporal Scales"Présentation établissement et labo d'accueilMines Paris - PSL, Centre PERSEEThe PERSEE Center is one of the 18 research centers of MINES Paris. Its field of expertise concerns New Energy Technologies and Renewable Energy Sources (RES). Its research strategy is based on a "micro/macro" approach ranging from (nano)materials to energy systems. It is built around three structuring themes: i) materials and components for energy, ii) sustainable energy conversion and storage processes and technologies, and iii) renewable energies and smart energy systems.This late is developped by one of the three groups of the Center, ERSEI, which stands for “Renewable Energies and Smart Energy Systems”. The ERSEI group develops methods and tools allowing the optimal integration of decentralized sources, including RES, storage devices, electric vehicles, active demand and other technologies, in energy systems and electricity markets. The research activity of the group is divided into three main themes. The first is based on the development of advanced short-term forecasting methods for different applications in power systems (i.e. forecasting of RES production, demand, dynamic line rating, market quantities, etc.). The second concerns the control and predictive management of energy systems. The aim is to design innovative approaches to optimise the operation (from real-time to days ahead) of different types of systems (smart-homes, microgrids, virtual power plants, energy communities, hybrid RES/storage plants, distribution grids multi-energy systems a.o.) considering uncertainties. The third axis concern planning and prospective studies that aim to optimise the design of future energy systems, generate furture scenarios, optimise investements etc.  The PERSEE Center is located within the scientific parc of Sophia-Antipolis, near the cities of Nice, Cannes and Antibes in the south of France. Its workforce is around 55 people.Site web :http://www.minesparis.psl.euIntitulé du doctoratDoctorat en Énergétique et Procédés Pays d'obtention du doctoratFranceEtablissement délivrant le doctoratMines Paris - PSL (Ecole Nationale Supérieure des Mines de Paris)Ecole doctoraleIngénierie des Systèmes, Matériaux, Mécanique, EnergétiqueProfil du candidatPROFILE:Engineer and / or Master of Science degree (candidates may apply prior to obtaining their master's degree. The PhD will start though after the degree is succesfully obtained).Good level of general and scientific culture. Good analytical, synthesis, innovation and communication skills. Qualities of adaptability and creativity. Motivation for research activity. Coherent professional project. Skills in programming (eg R, Python, Julia,…).  A succesful candidate will have a solid background in three or more of the following competencies: applied mathematics, statistics and probabilitiesdata science, machine learning, artificial intelligenceenergy forecastingpower system management, integration of renewablesoptimization Expected level in french : Good level  Expected level in english : Proficiency Desired starting date: as soon as possible in 2024. Duration 36 months. Full-time paid position. For more information please contact Prof. Georges Kariniotakis and Dr Simon Camal (emails below)  Date limite de candidature  20/07/2024