Énergies renouvelables en Écosse

right|thumb|Le vent, les vagues et les marées représentent plus de 80 % du potentiel d'énergies renouvelables en Écosse. La production d'énergies renouvelables en Écosse est une question qui s'est fait connaître d'un point de vue technique, économique et politique au début du . La base de ressources naturelles utilisées pour les énergies renouvelables est extraordinaire pour l'Europe. Les sources potentielles les plus importantes étant le vent, les vagues et les marées. Explication À la fin du troisième trimestre en 2014, il y avait de capacité d'électricité renouvelable installée en Écosse, une augmentation de 10,5 % (ou ) par rapport à la fin du deuxième trimestre de 2013. En 2013, la production d'électricité renouvelable était de en Écosse. Soit une hausse de 16,4 % par rapport à 2012. La génération d'électricité renouvelable en Écosse était en 2013, en hausse de 16,4 %. En 2012, 40,3 % de l'électricité produite en Écosse en 2012 a été apportée par des énergies re

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

Spatio-Temporal Estimation of Renewable Energy Potential in Built Environments using Big Data

Alina Ursula Rosa Walch

To achieve the goals of the Paris Agreement on climate change, governments around the world must shift to renewable energy (RE) to drastically reduce their CO2 emissions within the next 10-30 years. The development of effective strategies for RE integration into national energy systems requires assessing the spatial and temporal availability of these resources at country scale. This spatio-temporal availability is described by the technical RE potential, defined as the maximum electrical or thermal energy that can be obtained from a given technology. While most previous technical potential assessments have used empirical models and geospatial tools to estimate RE potential, the increasing opportunities provided by Big Data and Machine Learning approaches, which extract additional information from large building and environmental datasets, are largely under-exploited. Integrating such data-driven methods with existing models enables national-scale RE resource assessments with higher precision and at higher spatial and temporal resolution than ever before.This work proposes Big Data approaches to estimate renewable energy potentials at national scale, with application to Switzerland. Focusing on the built environment, the technical potentials of Rooftop Solar Photovoltaics (RPV) and shallow Ground-Source Heat Pumps (GSHPs) are assessed at the spatial resolution of individual buildings and at hourly (RPV) or monthly (GSHP) temporal resolution. The proposed approaches combine large-scale data processing and Machine Learning with novel computational methods and state-of-the-art models of RE technologies. Furthermore, the quantification and propagation of uncertainties is addressed for RPV. The results are publicly available datasets of RE potentials, which enable spatio-temporal assessments of energy systems with a high share of renewables. At national level, the estimated RPV potential for Switzerland is 25+/-9 TWh, which may increase up to 39 TWh for more optimistic scenarios for solar PV panel installation, or beyond 60 TWh if rooftop-integrated PV is used. The shallow GSHP potential is estimated at up to 97 TWh, of which 20% is located in urban areas. I show that this potential may increase by over 50% through seasonal regeneration from space cooling. Spatial mismatches between supply and demand may be reduced by district heating networks (DHN).These findings have several implications for the Swiss Energy Transition for 2050: (i) To achieve the national target of 34 TWh from PV systems, 50-55% of all nearly flat (< 20° tilt) or south-facing surfaces must be exploited, whereby roofs with a high annual yield should be prioritised; (ii) GSHPs may deliver 30-70% of the heat demand of the targeted 1.5 million heat pumps and contribute significantly to the expansion of DHN in urban areas; (iii) these fractions may be increased through seasonal regeneration, for example from space cooling or excess solar thermal generation. The proposed approaches are transferable to RE potential assessments in other regions or countries, in particular by adapting them to open and cross-country input data, as well as to related RE technologies. The proposed data-driven methods and the publicly available datasets can be used by researchers, practitioners and policy makers to assess future decarbonisation pathways. I hope that this work will contribute to achieving national CO2 emission targets, moving one step closer to the goals of the Paris Agreement.

EPFL2022

Locating multi energy systems for a neighborhood in Geneva using K-means clustering

Dasaraden Mauree, Amarasinghage Tharindu Dasun Perera, Jean-Louis Scartezzini

To determine the optimum location for non dispatchable renewable energy systems, this study comes up with an integrated tool to place energy systems considering distributed energy demand and renewable energy potential. The Citysim urban energy planning software is used to compute the hourly heating demand of 371 buildings in Jonction, a neighborhood in Geneva. Hourly solar irradiation on the roof tops of each building is computed using the Citysim model. The electricity demand profile for each building is generated using the hourly profile for Geneva using the databases of Swissgrid and Swiss building database. Subsequently, k-means algorithm is used to cluster the buildings based on spatial distribution. An energy economic model is used to evaluate the losses in the thermal and electrical distribution networks and initial investment. Sensitivity of cluster size is evaluated using the energy economic model to obtain an optimum number of clusters and the locations for the energy systems.

Elsevier2017

Spatio-Temporal Estimation of Renewable Energy Potential in Built Environments using Big Data

Alina Ursula Rosa Walch

EPFL2022