Mix énergétique

Résumé

Le mix énergétique, ou bouquet énergétique, est la répartition des différentes sources d'énergies primaires consommées dans une zone géographique donnée. La part des énergies primaires dans la consommation mondiale, d'un pays, d'une collectivité, d'une industrie est généralement exprimée en pourcentages. Toutes les sources d'énergies primaires sont comptabilisées, notamment celles consommées pour les transports, le chauffage des bâtiments, etc. Le mix électrique, avec lequel il ne doit pas être confondu, ne prend en compte que les sources d'énergie contribuant à la production d'électricité ; or l'électricité ne représente que 18,5 % de la consommation finale d'énergie au niveau mondial. Définitions Le mix énergétique d'une zone géographique (pays, regroupement de pays, subdivision d'un pays) est la répartition de sa consommation intérieure brute d'énergie primaire entre les sources primaires de son approvisionnement ; on trouve aussi des calculs de mix énergétique d'un

Source officielle

https://fr.wikipedia.org/wiki/Mix_%C3%A9nerg%C3%A9tique

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (20)

Publications associées

Chargement

Personnes associées

Chargement

Unités associées

Chargement

Concepts associés (8)

Concepts associés

Chargement

Cours associés (3)

Cours associés

Chargement

Séances de cours associées (4)

Séances de cours associées

Chargement

Purpose In light of the sustainable diet debate, we conducted a large-scale regionalised LCA to answer the following questions: (i) does the climate advantage hypothesis of plant-based fat spreads and creams over dairy butter and cream hold regardless of the variabilities of product recipes, geographies and the influence of land use change (LUC)? (ii) Considering the climate-water-land nexus, is there a risk of shifting impacts from climate to water scarcity and land occupation, and what are the key opportunities for impact mitigation? Methods A framework for conducting a large-scale regionalised LCA was developed and applied to compare the environmental impacts of 212 plant-based fat spreads, 16 plant-based creams and 40 dairy alternatives sold in 21 countries per 1 kg of product. Data was compiled for different product recipes, key ingredient sourcing countries, production factory locations, energy mixes, packaging designs, transportation and end-of-life scenarios. Spatially (archetype) differentiated agricultural life cycle inventory data were generated, as well as LUC emissions for agricultural ingredients. A total of 18 environmental indicators were assessed. Results and discussion All plant-based spreads had a significantly lower climate impact than butter, with and without LUC inclusion. The regionalised analysis highlighted large variabilities across products, ranging from 0.98 to 6.93 (mean 3.3) kg CO2-eq for 212 plant-based spreads and 8.08 to 16.93 (mean 12.1) kg CO2-eq for 21 dairy butter with 95th confidence interval. The main drivers of GHG emissions for plant-based products are oilseed farming and the associated LUC emissions, which can vary significantly depending on type of oilseeds, quantity and sourcing country; in the worst-case scenario, the climate advantage is no longer valid due to LUC. Thus, the inclusion of LUC is essential for a robust assessment and hotspot identification. Overall, the risk of shifting impact was small, as most of the plant-based spreads also had lower impacts for water scarcity footprints and land occupation; 8 of the 212 products were not lower, due to oilseed ingredients with high embodied impacts. Conclusions This study confirmed that plant-based spreads had lower climate, water and land impacts than butter, despite variability of product recipes, geographies and influence of LUC. This research offers a framework for performing regionalised agricultural LCA for a large portfolio of products thereby enabling identification of inter-product variabilities and hotspots for the development of mitigation strategies. Key mitigation opportunities include reducing oilseed ingredients' embodied impacts by optimising product recipe design and adapting supply chain sourcing and agricultural practice.

2020

Chargement

Decomposition Strategy for Districts as Renewable Energy Hubs

François Maréchal, Luise Middelhauve, Cédric Terrier

In light of the energy transition, it becomes a widespread solution to decentralize and to decarbonize energy systems. However, limited transformer capacities are a hurdle for large-scale integration of solar energy in the electricity grid. The aim of this paper is to define a novel concept of renewable energy hubs and to optimize its design strategy at the district scale in an appropriate computational time. To overcome runtime issues, the Dantzig-Wolfe decomposition method is applied to a mixed-integer linear programming framework of the renewable energy hub. Distributed energy units as well as centralized district units are considered. In addition, a method to perform multi-objective optimization as well as respecting district grid constraints in the decomposition algorithm is presented. The decomposed formulation leads to a convergence below 20 min for 31 buildings and a mip gap lower than 0.2%. The centralized design enhances the photovoltaic penetration in the energy mix and reduces the global warming potential and necessary curtailment in order to respect transformer capacity constraints.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Chargement

Model based forecasting for demand response strategies

Lorenzo Nespoli

The incremental deployment of decentralized renewable energy sources in the distribution grid is triggering a paradigm change for the power sector. This shift from a centralized structure with big power plants to a decentralized scenario of distributed energy resources, such as solar and wind, calls for a more active management of the distribution grid. Conventional distribution grids were passive systems, in which the power was flowing unidirectionally from upstream to downstream. Nowadays, and increasingly in the future, the penetration of distributed generation (DG), with its stochastic nature and lack of controllability, represents a major challenge for the stability of the network, especially at the distribution level. In particular, the power flow reversals produced by DG cause voltage excursions, which must be compensated. This poses an obstacle to the energy transition towards a more sustainable energy mix, which can however be mitigated by using a more active approach towards the control of the distribution networks. Demand side management (DSM) offers a possible solution to the problem, allowing to actively control the balance between generation, consumption and storage, close to the point of generation. An active energy management implies not only the capability to react promptly in case of disturbances, but also to ability to anticipate future events and take control actions accordingly. This is usually achieved through model predictive control (MPC), which requires a prediction of the future disturbances acting on the system. This thesis treat challenges of distributed DSM, with a particular focus on the case of a high penetration of PV power plants. The first subject of the thesis is the evaluation of the performance of models for forecasting and control with low computational requirements, of distributed electrical batteries. The proposed methods are compared by means of closed loop deterministic and stochastic MPC performance. The second subject of the thesis is the development of model based forecasting for PV power plants, and methods to estimate these models without the use of dedicated sensors. The third subject of the thesis concerns strategies for increasing forecasting accuracy when dealing with multiple signals linked by hierarchical relations. Hierarchical forecasting methods are introduced and a distributed algorithm for reconciling base forecasters is presented. At the same time, a new methodology for generating aggregate consistent probabilistic forecasts is proposed. This method can be applied to distributed stochastic DSM, in the presence of high penetration of rooftop installed PV systems. In this case, the forecasts' errors become mutually dependent, raising difficulties in the control problem due to the nontrivial summation of dependent random variables. The benefits of considering dependent forecasting errors over considering them as independent and uncorrelated, are investigated. The last part of the thesis concerns models for distributed energy markets, relying on hierarchical aggregators. To be effective, DSM requires a considerable amount of flexible load and storage to be controllable. This generates the need to be able to pool and coordinate several units, in order to reach a critical mass. In a real case scenario, flexible units will have different owners, who will have different and possibly conflicting interests. In order to recruit as much flexibility as possible, it is therefore important

EPFL2019

Chargement

Model based forecasting for demand response strategies

Lorenzo Nespoli

EPFL2019

2020