Power system real-time thermal rating estimation

This Thesis describes the development and testing of a real-time rating estimation algorithm developed at Durham University within the framework of the partially Government-funded research and development project “Active network management based on component thermal properties”, involving Durham University, ScottishPower EnergyNetworks, AREVA-T&D, PB Power and Imass. The concept of real time ratings is based on the observation that power system component current carrying capacity is strongly influenced by variable environmental parameters such as air temperature or wind speed. On the contrary, the current operating practice consists of using static component ratings based on conservative assumptions. Therefore, the adoption of real-time ratings would allow latent network capacity to be unlocked with positive outcomes in a number of aspects of distribution network operation. This research is mainly focused on facilitating renewable energy connection to the distribution level, since thermal overloads are the main cause of constraints for connections at the medium and high voltage levels. Additionally its application is expected to facilitate network operation in case of thermal problems created by load growth, delaying and optimizing network reinforcements. The work aims at providing a solution to part of the problems inherent in the development of a real-time rating system, such as reducing measurements points, data uncertainty and communication failure. An extensive validation allowed a quantification of the performance of the algorithm developed, building the necessary confidence for a practical application of the system developed

An aggregator for distributed energy storage units under multiple constraints in the nice grid demonstrator

International audienceThis paper presents the description of an algorithm for the management of a portfolio of distributed energy storage systems able to provide flexibility services to the distribution system operator. The algorithm will be referred to as Network Battery Aggregator (NBA). The work described in this paper is realized within the framework of the NiceGrid, a demonstration of the Grid4EU project. The project aims at developing a smart solar neighbourhood in an urban area near the city of Nice, France, and to combine controllable distributed electricity and thermal storage devices with forecasts of solar power production and load in a local energy management system. The local network energy manager (NEM) developed in this project will provide voltage control at the distribution level and congestion control at the transmission level

Electricity Demand Forecasting through Natural Language Processing with Long Short-Term Memory Networks

Electricity demand forecasting is a well established research field. Usually this task is performed considering historical loads, weather forecasts, calendar information and known major events. Recently attention has been given on the possible use of new sources of information from textual news in order to improve the performance of these predictions. This paper proposes a Long and Short-Term Memory (LSTM) network incorporating textual news features that successfully predicts the deterministic and probabilistic tasks of the UK national electricity demand. The study finds that public sentiment and word vector representations related to transport and geopolitics have time-continuity effects on electricity demand. The experimental results show that the LSTM with textual features improves by more than 3% compared to the pure LSTM benchmark and by close to 10% over the official benchmark. Furthermore, the proposed model effectively reduces forecasting uncertainty by narrowing the confidence interval and bringing the forecast distribution closer to the truth.Comment: 5 pages, 3 figures, 2023 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe

Impact of PV forecasts uncertainty on batteries management in microgrids

International audienceThis paper is motivated by the question of the impact that uncertainty in PV forecasts has in forecast-based battery schedule optimisation in microgrids in presence of network constraints. We examine a specific case where forecast accuracy can be impacted by the lack of enough data history to finetune the forecasting models. This situation can be expected to be frequent with new PV installations. A probabilistic PV production forecast algorithm is used in combination with a battery schedule optimisation algorithm. The size of the learning dataset of the forecast algorithm is modified in order to simulate the application of the system to new plants and the impact on the performance in the management of the battery is analyse

The impact of available data history on the performance of photovoltaïc generation forecasting models

International audienceThe continuous growth of solar power capacity raises challenges to distribution system operators regarding power quality and security of supply. Network management systems must be enhanced with short-term forecasting functionalities able to predict the solar plants production in the next hours or days. The provision of individual forecasts for each solar plant on the network is often required. To that purpose, historical measurements are needed for tuning the forecasting models. The situation is challenging for new plants for which long history of measurements is not yet available. In that case, models able to provide accurate production forecasts based on few historical production data, are required. In this paper, we investigate the performance of state-of-the-art short-term PV forecasting models as a function of the historical data available for tuning. We compare the results with those obtained by a reference model whose utilization does not require more than one day of past production data. Our analysis relies on production data from a 200 kWc solar plant located in the south-east of France. It shows that satisfactory performances can be expected from state-of-the-art models, when calibrated with no more than one or two weeks of training data

Coordinated control of dispersed battery energy storage systems for services to network operators

International audienceDistributed Battery Energy Storage Systems, controlled either per resource or in coordination, can provide many services to the network operators. The present study emphasizes the contributions of dedicated scheduling applications in order to enhance the reliability, performance and life expectation of the storage assets, based on the Nice Grid case, a Smart Grid pilot project

The value of schedule update frequency on distributed energy storage performance in renewable energy integration

International audienceThis paper describes preliminary findings of research on the use of Distributed Energy Storage devices for Renewable Energy integration. The primary objective is to describe the effect of different storage scheduling strategies, and namely the benefits from intraday intraday scheduling on the storage performance in renewable energy integration. Optimal schedules of Distributed Energy Storage devices are based on forecasts of Renewable Energy production, local consumption and prices, along with other criteria. These forecasts tend to have a higher uncertainty for higher time horizons, resulting in losses due to errors and to the underutilization of the assets. The use of frequent schedules updates can reduce part of these drawbacks and this paper aims at quantifying this reduction. The importance of the quantification of the benefits arising from different rescheduling frequencies lies in its influence on the ICT infrastructure necessary to implement it and its cost

Increasing the Energy Yield of Generation from New and Renewable Energy Resources

Non

A local energy management system for solar integration and improved security of supply: The Nice Grid project

International audienceThis paper describes Nice Grid, a demonstration project part of the European initiative Grid4EU. The project aims at developing a smart solar neighbourhood in the urban area of the city of Nice, France. The four year project started in November 2011 and will test the suitability of recent developments in distribution networks management for facilitating the connection of distributed renewable generators, improving the security of supply and let customers and other actors to provide network services. The idea behind Nice Grid is to combine controllable distributed electricity and thermal storage devices with forecasts of solar power production and load in a local energy management system. The paper, which represents a useful reference for the project, presents also a detailed overview of relevant European demonstration projects on Smart Grid

Modélisation et prise de décision pour le système électrique

Ce mémoire présente les travaux scientifiques menés ou cordonnés dans le domaine de la modélisation et prise de décisions pour le système électrique. Dans la plupart des cas ils se réfèrent aux périodes des études doctorales à l’Université de Durham et surtout à mon rôle au Centre Procédés, Energies Renouvelables et Systèmes Energétiques (PESREE) de MINES ParisTech, rejoint en 2011 en tant que Tenure Track et ensuite intégré comme chercheur permanent. Apres une description de ces travaux, le document présente des perspectives pour le futur.Cette recherche a été influencée et inspirée par trois macro-trends à caractère éminemment économique et technologique qui se résument dans la diffusion : 1) Des sources d’énergies renouvelables, 2) Du stockage électrochimique pour le transport et le réseau et 3) Des technologies TIC, y compris dans le secteur électrique. L’idée est celle de travailler à la frontière des domaines classiques de l’ingénierie mécanique et électrique, en utilisant les meilleures méthodes disponibles et proposer les solutions plus adéquates. L’ambition est double : faire maturer des nouvelles approches et technologies jusqu’a des applications industrielles et contribuer au partage d’informations entre domaines de recherche qui, à cause de la croissante spécialisation, tendent à être de plus en plus isolés.Dans ce panorama, la recherche s’est orientée sur trois axes liées par le fil commun du support à la prise de décisions, avec l’ambition de se concentrer sur des problèmes à la fois originaux et stratégiques qui peuvent être résumés comme ici. Météo et réseau électrique. Qui se décline en : 1) Estimation du gisement et avantages. 2) Estimation de l’ampacité à partir de la météo. 3) Application dans le transport et pour la fiabilité. Stockage distribué. Avec des travaux sur 1) Gestion du stockage basée sur les prévisions. 2) Dimensionnement du stockage. 3) Gestion du stockage distribué. Prévisions pour la prise de décisions. 1) Prévision de l’ampacité des composants du réseau. 2) Prévision pour les services système par les renouvelables. 3) Amélioration des quantiles bas. Dans les prochaines années la recherche continuera sur la thématique du support aux décisions pour le système électrique et pour l’intégration des renouvelables et sur les axes de recherche précédemment exposés. Notamment : 1) des travaux sur l’ampacité dynamique des composant à propos de la prévision, la planification, et les capteurs. 2) la gestion active du réseau en considérant la prévision des prix de l’électricité e des services système, des solutions pour l’autoconsommation locale et la gestion de la flexibilité décentralisée et des stratégies compétitives de participation aux marchés locaux de flexibilité et énergie. Enfin 3) la nécessité d’intégrer nouvelles sources de donnes dans le cycle décisionnel et d’intégrer une modélisation plus fine du comportement thermochimique des batteries. Ou possibles, les mesures mise en place pour mener ces recherches, comme la mise en place de collaborations ou l’ouverture de thèses, ont été présentées