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This paper reviews the Open Charge Point Protocol (OCPP) and implements a Charging Station Management System (CSMS) targeting heavy-duty trucks. The new technique proposed in this paper is designed to maximize Electric vehicle (EV) owner benefits by charging at a low cost, and also the electric utility benefits (operating the system within the acceptable limits) by proper choice of electricity tariff structure. EV owners can be motivated to charge at off-peak hours which have low electricity prices and stop charging at peak hours which have high electricity prices. / Detta dokument granskar Open Charge Point Protocol (OCPP) och implementerar en Charging Station Management System (CSMS) inriktat på tunga lastbilar. Den nya tekniken som föreslås i detta dokument är utformad för att maximera fördelarna för ägare av elfordon (EV) genom att ladda till en låg kostnad, och även fördelarna med elnätet (drift av systemet inom acceptabla gränser) genom korrekt val av elprisstruktur. Elbilsägare kan motiveras att ladda under lågtrafik som har låga elpriser och sluta ladda under rusningstid som har höga elpriser.

The rapid escalation in plug-in electric vehicles (PEVs) and their uncoordinated charging patterns pose several challenges in distribution system operation. Some of the undesirable effects include overloading of transformers, rapid voltage fluctuations, and over/under voltages. While this compromises the consumer power quality, it also puts on extra stress on the local voltage control devices. These challenges demand a well-coordinated and power network-aware charging approach for PEVs in a community. This paper formulates a realtime electric vehicle charging scheduling problem as a mixed-integer linear program (MILP). The problem is to be solved by an aggregator that provides charging services in a residential community. The proposed formulation maximizes the profit of the aggregator, enhancing the utilization of available infrastructure. With prior knowledge of load demand and hourly electricity prices, the algorithm uses a moving time horizon optimization approach, allowing an unknown number of arriving vehicles. In this realistic setting, the proposed framework ensures that power system constraints are satisfied and guarantees the desired PEV charging level within the stipulated time. Numerical tests on an IEEE 13-node feeder system demonstrate the computational and performance superiority of the proposed MILP technique. / M.S. / There is an enhanced rate of global warming due to emissions and increased usage of fossil fuels in the transportation sector. As a feasible solution, electrification of transportation has become a necessary step towards an environment-friendly future. The escalation in plug-in electric vehicles (PEVs) has increased the impact on loading and voltage fluctuations in the distribution grid due to uncoordinated charging. This puts on extra stress on the grid system and compromises the system performance. As a measure to control the vehicle charging in a residential setup, a real-time optimal charging scheduling algorithm is developed which is implemented at the neighborhood level. To increase the charging performance with the limited available resources, an aggregator is introduced. The charging profit is maximized as the PEV charging problem is solved optimally by the aggregator. This facilitates the reduction in night-time grid congestion and maximization of number of PEVs getting charged with limited dependency on communication to avoid long delays in charging control. The proposed technique guarantees the complete charging of the selected PEVs in the stipulated time while considering the power grid operational constraints. It also reduces the impact of peak load demand by flattening the base load demand curve. To demonstrate the efficiency of the proposed mixed integer linear programming optimization algorithm, numerical tests for an IEEE 13 node feeder are performed. The results are discussed to give an outlook on the balance between system and user requirements by meeting the demand of the PEV users.

Recent years have seen an unprecedented uptake in electric vehicles, driven by the global push to reduce carbon emissions. At the same time, intermittent renewables are being deployed increasingly. These developments are putting flexibility measures such as dynamic load management in the spotlight of the energy transition. Flexibility measures must consider EV charging, as it has the ability to introduce grid constraints: In Germany, the cumulative power of all EV onboard chargers amounts to ca. 120 GW, while the German peak load only amounts to 80 GW. Commercial operations have strong incentives to optimize charging and flatten peak loads in real-time, given that the highest quarter-hour can determine the power-related energy bill, and that a blown fuse due to overloading can halt operations. Increasing research efforts have therefore gone into real-time-capable optimization methods. Reinforcement Learning (RL) has particularly gained attention due to its versatility, performance and realtime capabilities. This thesis implements such an approach and introduces FleetRL as a realistic RL environment for EV charging, with a focus on commercial vehicle fleets. Through its implementation, it was found that RL saved up to 83% compared to static benchmarks, and that grid overloading was entirely avoided in some scenariosby sacrificing small portions of SOC, or by delaying the charging process. Linear optimization with one year of perfect knowledge outperformed RL, but reached its practical limits in one use-case, where a feasible solution could not be found by thesolver. Overall, this thesis makes a strong case for RL-based EV charging. It further provides a foundation which can be built upon: a modular, open-source software framework that integrates an MDP model, schedule generation, and non-linear battery degradation. / Elektrifieringen av transportsektorn är en nödvändig men utmanande uppgift. I kombination med ökande solcellsproduktion och förnybara energikällor skapar det ett dilemma för elnätet som kräver omfattande flexibilitetsåtgärder. Dessa åtgärder måste inkludera laddning av elbilar, ett fenomen som har lett till aldrig tidigare skådade belastningstoppar. Ur ett kommersiellt perspektiv är incitamentet att optimera laddningsprocessen och säkerställa drifttid. Forskningen har fokuserat på realtidsoptimeringsmetoder som Deep Reinforcement Learning (DRL). Denna avhandling introducerar FleetRL som en ny RL-miljö för EV-laddning av kommersiella flottor. Genom att tillämpa ramverket visade det sig att RL sparade upp till 83% jämfört med statiska riktmärken, och att överbelastning av nätet helt kunde undvikas i de flesta scenarier. Linjär optimering överträffade RL men nådde sina gränser i snävt begränsade användningsfall. Efter att ha funnit ett positivt business case förvarje kommersiellt användningsområde, ger denna avhandling ett starkt argument för RL-baserad laddning och en grund för framtida arbete via praktiska insikter och ett modulärt mjukvaruramverk med öppen källkod.