Chargeur (électricité) | EPFL Graph Search

thumb|Chargeurs et différents types et format de batteries. Un chargeur est un appareil permettant de recharger un ou plusieurs accumulateurs électriques, groupés ou non en batterie, en injectant un courant électrique inverse au sens d'utilisation (décharge). Le circuit le plus simple est une source électrique continue et stabilisée en tension, limitée en courant ou l'inverse. thumb|upright=1.2|Chargeur simple de type mural (sortie : / ). thumb|Chargeur pour piles rechargeables en lithium 1.5V, et son câble USB-C. Le chargeur peut être une source de tension constante (en volts), le courant de charge diminuant avec la montée en tension de la batterie au fil de la charge (chargeur linéaire). Le chargeur peut être une source de courant constante (en ampères), la tension augmentant au fur et à mesure de la charge ; ce type de chargeur implique généralement une maîtrise scrupuleuse du temps de charge, sous peine de dégradation des accumulateurs. Voir formule du calcul du temps de charge. Les chargeurs linéaires sont généralement composés d'un circuit de régulation secteur assez classique constitué d'un transformateur suivi d'un redressement à diodes puis d'un lissage à l'aide de condensateurs / selfs et enfin d'un circuit de régulation. Les chargeurs à courant constant sont plus complexes et basés sur des circuits à découpage. La quantité d’énergie contenue dans une batterie ou un accumulateur s’exprime en watts-heures (Wh), en kilowatts-heures (kWh), ou en milliwatts-heures, unités non-SI mais homogènes au joule. En pratique, les fabricants annoncent généralement la charge électrique maximum Q d’une batterie annoncée en ampères-heures (Ah) ou milliampères-heures (mAh), unité homogène au coulomb, ainsi que la tension moyenne délivrée par la batterie en volts (V). Le courant de charge maximal Imax est directement donné en ampères ou parfois en coefficient CA, avec . Le courant de charge nominal I (qui doit être inférieur ou égal au courant de charge Imax) est déterminé par le chargeur. Le temps de charge T peut être estimé par le calcul : .

Vehicle-to-Grid for Reliable Frequency Regulation

Dirk Lauinger

Future low-carbon societies will need to store vast amounts of electricity to stabilize electricity grids and to power electric vehicles. Vehicle-to-grid allows vehicle owners and grid operators to share the costs of electricity storage by making the batteries of electric vehicles available to the grid. In practice, vehicle owners decide when to reserve their cars for driving and when to make them available for grid services. Vehicle aggregators then decide how to commit the vehicles to grid services. For vehicle-to-grid to succeed, both vehicle owners and grid operators must be able to trust aggregators, i.e., vehicles should be available for driving and for grid services when the aggregators promise they will be. In this thesis, we solve a decision-making problem that ensures reliable commitments by vehicle aggregators for a particular grid service known as primary frequency regulation, considered one of the most profitable applications of vehicle-to-grid. Mathematically, we first formulate a robust optimization problem with functional uncertainties that maximizes the expected profit from selling primary frequency regulation to grid operators and guarantees that vehicle owners can meet their market commitments for all frequency deviation trajectories in an uncertainty set that encodes applicable European Union regulations. Functional uncertainties ensure that vehicle owners and grid operators can trust the decisions of aggregators at all times. Faithfully modeling the energy conversion losses during battery charging and discharging renders the optimization problem nonconvex. By exploiting a total unimodularity property of the proposed uncertainty sets and an exact linear decision rule reformulation, we prove that the nonconvex robust optimization problem with functional uncertainties is equivalent to a tractable linear program. Somewhat counterintuitively, the underlying deterministic problem for a known frequency deviation trajectory does not reduce to a linear program but results in a large-scale mixed-integer linear program, even if time is discretized. We believe that we have thus discovered the first practically interesting class of optimization problems that become dramatically easier through robustification. Through extensive numerical experiments using real-world data, we quantify the economic value of vehicle-to-grid and elucidate the financial incentives of vehicle owners, aggregators, equipment manufacturers, and regulators. In particular, we find that the prevailing penalties for non-delivery of promised regulation power are too low to incentivize aggregators to honor their promises toward grid operators. For general electricity storage devices, we then solve a simplified version of the decision-making problem analytically to understand how the optimal frequency regulation commitments depend on the roundtrip efficiency of the storage device, the dispersion of the frequency deviations, and the EU delivery guarantee. We show how the marginal cost of frequency regulation decreases with roundtrip efficiency and increases with frequency deviation dispersion. For energy-constrained storage devices, we find that the profits from frequency regulation are inversely proportional to the length of time for which storage operators commit regulation power. Establishing an intra-day market for frequency regulation would thus make electricity storage devices more competitive with other regulation providers, such as thermal power plants.

EPFL2022

A sensitivity analysis on the optimization of grid usage rule and tariff for network impact mitigation.

Emma Anne Elisabeth Nelissen

This report adds to findings concerning the impact of advanced electricity tariff structures on grid-connected hybrid photovoltaic-battery systems. A representative database of 1'608 buildings in the city of Rolle was clustered into 15 medoids. For these 15 medoids and for the years 2020 and 2040, five tariff designs such as real-time pricings, a capacity-based tariff, and a block-rate tariff were evaluated using a mixed-integer-linear programming optimisation. The capacity-based tariff was concluded to be the most dependent on the grid and resulted in no investment in PV capacity or batteries in the year 2020. With Switzerland's current 2050 energy plans, the block-rate tariff scenario was concluded to be the most adequate mainly due to its independence from the electricity grid. In 2040, the block-rate scenario had an average of 18kWh more battery capacity installation compared to all other tariff designs. Even though, PV curtailment was present in this scenario, it was concluded that curtailment could be seen as an opportunity to encourage storage and the renovation of the current grid infrastructure. These conclusions could provide insights for policy makers.

2019

Comparative Assessment of Electrical Generating Systems for a Greek Island

Lucas Mosca

This master thesis is the last part of the master studies in the department of Energy Management and Sustainability, at the Swiss Federal Institute of Technology (EPFL) in Lausanne. This work has been performed in collaboration with the company Renenig Energy, in Athens. The reader should note that the report is an academical work. Results and conclusion are influenced by hypothesis and should not be taken as general truths. The case study is a fictive project but it is composed of values adapted from another real but confidential project, which makes the analysis plausible. The owner of a private island in Greece wants to build a luxury resort on his island. Therefore, he will also need an electrical generating system that must cover the future electricity demand. Usually, diesel generators are used in remote areas in order to produce electricity. However, this technology is very expensive, and harmful for the environment. This master thesis covers the estimation of the electricity demand and the technico-economical assessment of different energy systems. The objective is to evaluate the potential of a microgrid composed of different renewable energy technologies and compare it to the a baseline system composed of generators only. The hourly electricity demand of the entire island has been modeled using a bottom-up approach with a list of representative types of locals. The consumption of these locals have been divided into three categories : The appliances, the lighting and the heating/cooling. For the energy system which must produce electricity, supply strategies have been defined based on the type of technology. For all strategies, an optimization of the technology installed capacities is performed with the software HOMERPro. It is used as a tool to minimize the net present cost of the energy system and analyze the microgrid energetic behavior. The yearly electricity demand of the island has been estimated to around 11 GWh, with a peak load of 2,5 MW during the summer. The cost of serving this load with generators is

63 million with a levelized cost of electricity of 0.467

/kWh. When integrating solar panels, the system cost decreases to

51.3 million with a LCOE of 0.378

/kWh. Moreover, wind turbines and a battery storage system are also economically viable. The last strategy consists of distributed PV panels on building roofs, wind turbines and a battery storage system. Results show that this energy system can decrease the cost to $ 39.8 million while emitting half of the carbon dioxide compared with the baseline system. However, the battery bank has a large impact on CO2 emissions due to its manufacturing processes. Further work can be done for this case study, especially on the network stability assessment, which was not part of this master thesis. Moreover, the potential of implementing hydrogen in the microgrid could be interesting. This could bring a good storage alternative to batteries.

2019

Comparative Assessment of Electrical Generating Systems for a Greek Island

Lucas Mosca

63 million with a levelized cost of electricity of 0.467

/kWh. When integrating solar panels, the system cost decreases to

51.3 million with a LCOE of 0.378

2019