With a growing number of electric vehicles (EVs) on the road and charging infrastructure investments lagging, occupation of installed charging stations is growing and available charging points for EV drivers are becoming scarce. Installing more charging infrastructure is problematic from both a public(tax payers money, parking availability) and private (business case) perspective. Increasing the utilization of available charging stations is one of the solutions to satisfy the growing charging need of EV drivers and managing other stakeholders interests. Currently, in the Netherlands only 15-25% of the time connected to a public charging station is actually used for charging. The longest 4% of all sessions account for over 20% of all time connected while barely using this time for actually charging. The behaviour in which EV users stay connected to a charging station longer than necessary to charge their car is called “charging station hogging”. Using a large dataset (1.3 million sessions) on publiccharging infrastructure usage, this paper analyses the inefficient use of charging stations along three axes: where the hogging takes place (spatial), by whom (the characteristics of the user) and during which time frames (day, week and year). Using the results potential solutions are evaluated and assessed including their potential and pitfalls.
This paper aims to answer the question: “Which factors influence the success of public charging stations?”. For the empirical analyses we used data provided by the public charging stations of the city The Hague. In the second half of 2015 more than 91.795 charge sessions were logged of more than 6.693 unique charge cards.---Analyse van de verschillen in het gebruik van de zogeheten ‘demand-driven’ en strategische laadpunten. Onderzoek naar de uitrol en het gebruik van E-Laad oplaadpunten in Nederland (EN).
At gas stations, tetrahydrothiophene (THT) is added to odorless biogas (and natural gas) for quick leak detection through its distinctive smell. However, for low bio and natural gas velocities, evaporation is not complete and the odorization process is compromised, causing odor fluctuations and undesired liquid accumulation on the pipeline. Inefficient odorization not only endangers the safety and well-being of gas users, but also increases gas distribution companies OPEX. To enhance THT evaporation during low bio and natural gas flow, an alternative approach involves improve the currently used atomization process. Electrohydrodynamic Atomization (EHDA), also known as Electrospray (ES), is a technology that uses strong electric fields to create nano and micro droplets with a narrow size distribution. This relatively new atomization technology can improve the odorization process as it can manipulate droplet sizes according to the natural and bio gas flow. BiomEHD aims to develop, manufacture, and test an EHDA odorization system for applying THT in biogas odorization.
In the coming decades, a substantial number of electric vehicle (EV) chargers need to be installed. The Dutch Climate Accord, accordingly, urges for preparation of regional-scale spatial programs with focus on transport infrastructure for three major metropolitan regions among them Amsterdam Metropolitan Area (AMA). Spatial allocation of EV chargers could be approached at two different spatial scales. At the metropolitan scale, given the inter-regional flow of cars, the EV chargers of one neighbourhood could serve visitors from other neighbourhoods during days. At the neighbourhood scale, EV chargers need to be allocated as close as possible to electricity substations, and within a walkable distance from the final destination of EV drivers during days and nights, i.e. amenities, jobs, and dwellings. This study aims to bridge the gap in the previous studies, that is dealing with only of the two scales, by conducting a two-phase study on EV infrastructure. At the first phase of the study, the necessary number of new EV chargers in 353 4-digit postcodes of AMA will be calculated. On the basis of the findings of the Phase 1, as a case study, EV chargers will be allocated at the candidate street parking locations in the Amsterdam West borough. The methods of the study are Mixed-integer nonlinear programming, accessibility and street pattern analysis. The study will be conducted on the basis of data of regional scale travel behaviour survey and the location of dwellings, existing chargers, jobs, amenities, and electricity substations.
Fijnstof in de pluimveehouderij Aanleiding Fijnstof in de pluimveehouderij is een actueel onderwerp. Het is onduidelijk wat de fijnstof rondom pluimveestallen doet omdat er nog geen goed meetsysteem bestaat om rondom de stal continu te meten wat fijnstof doet en wat de invloed van elementen buiten de stal (omgeving, weer) doet. Het innovatieve pluimveebedrijf Kipster wil samen met het burgerinitiatief Behoud de Parel meer inzicht krijgen in de fijnstofconcentraties. GreenTechLab wilt een systeem ontwikkelen waarbij door combinatie van meerdere fijnstofmeetstations beter inzicht ontstaat van de fijnstofconcentraties rondom het bedrijf en er tevens data wordt verzameld van het buitenklimaat. Doel GreenTechLab en partners gaan gezamenlijk een proof of concept ontwikkelen van een realtime 24/7 fijnstofmeetsysteem voor de (pluim)veehouderij om op macroniveau (rondom stallen) fijnstofconcentraties te meten en dit middels te ontwikkelen slimme software te gaan combineren met andere data (weerstation) en bedrijfsactiviteiten (voeren, verlichting, aan- afvoer, instellingen luchtwassers en klimaatsystemen enz) en zodoende te kunnen experimenteren met bedrijfsactiviteiten die leiden tot minder fijnstof emissies. Beoogde resultaten Het project levert een proof of concept op van een fijnstofmeetsysteem, waarmee we realtime 24/7 fijnstofconcentraties voor de (pluim)veehouderij op macroniveau (rondom stallen) kunnen meten en dit middels slimme software combineren met andere data (weerstation) en bedrijfsactiviteiten (voeren, verlichting, aan- afvoer, instellingen luchtwassers en klimaatsystemen enz) om zodoende te kunnen experimenteren met bedrijfsactiviteiten die leiden tot minder fijnstof emissies. Op basis van deep learning technieken en met behulp van big-data is gemeten wat bepaalde aanpassingen aan parameters (actuaties) voor gevolg hebben op de uitstoot van fijnstof.
