The theme of this study is spatial mismatch, a concept that gave rise to an ever-expanding body of research concerned with how and why residential and employment distributions have shifted within cities and across metropolitan areas. The concept grew out of John F. Kain's research on how racial discrimination and segregation affects the spatial patterns of people/subgroups and jobs in the postwar American urban environment. Specifically, "Housing Segregation" posits that housing-market discrimination is at the root of increased unemployment among inner-city, nonwhite workers; concurrently, the pace and volume of decentralization (of residents and employment) from central-cities reinforces low-income, overwhelmingly African-American isolation and immobility. This study contributes to the New Orleans literature by providing a pre- and post-Katrina snapshot of spatial mismatch. The analysis addresses research questions aimed at gauging the extent to which mismatch and job-isolation have changed for poor workers in the New Orleans metro area since Hurricane Katrina.

Temporal and spatial distribution of incoming vehicular charging demand is a significant challenge for the future planning of power systems. In this thesis the vehicular loading is-sue is categorized into two classes of stationary and mobile; they are then addressed in two phases. The mobile vehicular load is investigated first; a location-based forecasting algorithm for the charging demand of plug-in electric vehicles at potential off-home charging stations is proposed and implemented for real-world case-studies. The result of this part of the re-search is essential to realize the scale of fortification required for a power grid to handle vehicular charging demand at public charging stations. In the second phase of the thesis, a novel decentralized control strategy for scheduling vehicular charging demand at residential distribution networks is developed. The per-formance of the proposed algorithm is then evaluated on a sample test feeder employing real-world driving data. The proposed charging scheduling algorithm will significantly postpone the necessity for upgrading the assets of the network while effectively fulfilling customers’ transportation requirements and preferences. / October 2014

The recent increase of electric cars adoption will inuence the electricity demand in the distributionnetworks which risks to be higher than the maximum power available in the grid, if not well planned. Forthis reason, it is on the DSOs and TSOs's interest to plan carefully coordinated charging of a bulk of EVsas well as assess the possibility of EVs acting as energy storages with the Vehicle-to-Grid (V2G) or Vehicleto-Building (V2B) capability. When parked and plugged into the electric grid, EVs will absorb energy andstore it, being also able to deliver electricity back to the grid/building (V2G/B system).This can be anoptimized process, performed by an aggregator, gathering multiple EVs that discharge the battery into thegrid at peak time and charge when there is low demand i.e. overnight and o-peak hours.Numerous studies have investigated the possibility of aggregating multiple EVs and optimizing theircharging and discharging schedules for peak load reduction or energy arbitrage with participation in theelectricity market. However, no study was found for optimizing a shared eet of EVs with daily reservationsfor dierent users trying to perform V2B. In this study an optimization modelling algorithm (mixed integerlinear problem - MILP) that manages the possible reservations of the shared eet of EVs, coordinates thecharging and discharging schedules, and provides V2B (Vehicle-to-Building), with the objective of minimizingenergy costs and accounting with battery ageing has been developed. A case study with real data for abuilding is carried out modelling dierent number of EVs for two dierent days in year 2017, one in Marchand other in June.Results show that the prots are higher for all cases when introducing V2B as compared to a no optimizationscenario: V2B with battery degradation (50 ore/kWh) has decreased daily variable electricity costsbetween 54 and 59% in March and 60 and 63% for June when compared without smart charging. Integrationof battery degradation cost in V2B applications is necessary and inuences signicantly the chargingand discharging strategies adopted by EV and nally the total daily costs: The total daily cost increaseby maximal 10% for the day in March and 13% for the day in June when comparing the scenario that hasstationary battery and uses only-charging model for EVs with the scenario applying V2B mode consideringa degradation cost of 80 ore/kWh. / Ö kningen av antalet elbilar kommer att påverka lasten i elnätet som riskerar att bli högre än kapacitetom det inte är väl planerat. Därför är det i elnätsföretags intresse att samordna laddningen av de flesta elbilarna samt att utvärdera möjligheterna att använda elbilar som energilager gentemot elnätet (Vehicleto-Grid,V2G) eller byggnader (Vehicle-to-Building, V2B). Vid parkering och anslutning till elnätet kommer elbilar att ladda energi och lagra den, samtidigt de kan leverera el tillbaka till elnätet eller byggnaden (V2G/V2B). Detta kan vara en optimerad process som utförs av en aggregator genom att ladda flera elbilar i låglasttimmar och ladda ur dem under höglasttimmar.Många studier har undersökt möjligheten att aggregera flera elbilar och optimera laddningsoch urladdningsplaner för topplastreduktion eller energiarbitrage på elmarknaden. Ingen studie har dock hittats för att optimera en gemensam flotta av elbilar med dagliga reservationer för olika användare som försöker utföra V2B. Denna studie har utvecklat en optimeringsmodell (blandad heltalsprogrammering MILP) som hanterar möjliga reservationer av en flotta av elbilar, koordinerar laddning och urladdning planering, och utför V2B för att minimera energikostnader med hänsyn till batteriets åldrande. En fallstudie för en byggnad genomfördes modellering av olika antal elbilar för två dagar 2017, en i mars och andra i juni.Resultaten visar att vinsten är högre i samtliga fall då man introducerar V2B jämfört med scenario utan optimering: V2B med batteriladdningskostnad 50 öre/kWh minskade dagliga rörliga elkostnader mellan 54% och 59% i mars och mellan 60% och 63% i juni jämfört med utan smart laddning. Att inkludera batteriladdningskostnaden i V2B-applikationer är nödvändigt och har en signifikant inverkan på laddningsstrategierna och de totala kostnaderna: De totala dagliga kostnaderna ökar med upp till 10% i mars och upp till 13% i juni då man jämför scenariot att bara ladda elbilar och ha stationärt batteri med scenariot V2B med hänsyntill batteriladdningskostnad 80 öre/kWh.