Congestion avoidance for recharging electric vehicles using smoothed particle hydrodynamics

In this paper, a novel approach for recharging electric vehicles (EVs) is proposed based on managing multiple discrete units of electric power flow, named energy demand particles (EDPs). Key similarities between EDPs and fluid particles (FPs) are established that allow the use of a smoothed particle hydrodynamics (SPH) method for scheduling the recharging times of EVs. It is shown, via simulation, that the scheduling procedure not only minimizes the variance of voltage drops in the secondary circuits, but it also can be used to implement a dynamic demand response and frequency control mechanism. The performance of the proposed scheduling procedure is also compared with alternative approaches recently published in the literature

Optimal Decentralized Protocols for Electric Vehicle Charging

We propose decentralized algorithms for optimally scheduling electric vehicle charging. The algorithms exploit the elasticity and controllability of electric vehicle related loads in order to fill the valleys in electric demand profile. We formulate a global optimization problem whose objective is to impose a generalized notion of valley-filling, study properties of the optimal charging profiles, and give decentralized offline and online algorithms to solve the problem. In each iteration of the proposed algorithms, electric vehicles choose their own charging profiles for the rest horizon according to the price profile broadcast by the utility, and the utility updates the price profile to guide their behavior. The offline algorithms are guaranteed to converge to optimal charging profiles irrespective of the specifications (e.g., maximum charging rate and deadline) of electric vehicles at the expense of a restrictive assumption that all electric vehicles are available for negotiation at the beginning of the planning horizon. The online algorithms relax this assumption by using a scalar prediction of future total charging demand at each time instance and yield near optimal charging profiles. The proposed algorithms need no coordination among the electric vehicles, hence their implementation requires low communication and computation capability. Simulation results are provided to support these results

Electromobility in Public Transport: Scheduling of Electric Vehicles and Location Planning of the Charging Infrastructure

In recent years, considerable efforts have been made to make public transport more environmentally friendly. This should primarily be achieved by reducing greenhouse gas emissions. Electromobility is considered to be a key technology as electric vehicles create a variety of benefits. However, the use of electric vehicles involves a number of challenges. Modern battery electric vehicles have only a fractional part of the ranges of combustion engine vehicles. Thus, a major challenge is charging the vehicles at specific charging stations to compensate for this disadvantage. Technological aspects of electric vehicles are also of importance and have to be considered. Planning tasks of public transport companies are affected by these challanges, especially vehicle scheduling. Vehicle scheduling is a well-studied optimization problem. The objective is to cover a given set of timetabled service trips by a set of vehicles at minimum costs. An issue strongly related to vehicle scheduling is location planning of the charging infrastructure. For an effcient use of electric vehicles, charging stations must be located at suitable locations in order to minimize operational costs. Location planning of charging stations is a long-term planning task whereas vehicle scheduling is a more short-term planning task in public transport. This thesis examines optimization methods for scheduling electric vehicles in public transport and location planning of the charging infrastructure. Electric vehicles' technological aspects are particularly considered. Case studies based on real-world data are used for evaluation of the artifacts developed. An exact optimization method addresses scheduling of mixed vehicles fleets consisting of electric vehicles and vehicles without range limitations. It is examined whether traditional solution methods for vehicle scheduling are able to cope with the challenges imposed by electric vehicles. The results show, that solution methods for vehicle scheduling are able to deal with the additional challenges to a certain degree. However, novel methods are required to fully deal with the requirements of electric vehicles. A heuristic solution method for scheduling electric vehicles and models for the charging process of batteries are developed. The impact of the detail level of electric vehicles' technological aspects on resulting solutions is analyzed. A computational study reveales major discrepancies between model assumptions and real charging behaviours. A metaheuristic solution method for the simultaneous optimization of location planning of charging stations and scheduling electric vehicles is designed to connect the optimization problems and to open up synergy effects. In comparison to a sequential planning, the simultaneous problem solving is necessary because a sequential planning generally leads to either infeasible solutions or to significant increases in costs.In den letzten Jahren wurden erhebliche Anstrengungen unternommen, um den öffentlichen Personennahverkehr (ÖPNV) umweltfreundlicher zu gestalten. Dabei sollen insbesondere Treibhausgasemissionen reduziert werden. Elektromobilität wird dabei auf Grund der zahlreichen Vorteile von Elektrofahrzeugen als Schlüsseltechnologie angesehen. Der Einsatz von Elektrofahrzeugen ist jedoch mit Herausforderungen verbunden, da diese über weitaus geringere Reichweiten im Vergleich zu Fahrzeugen mit Verbrennungsmotoren verfügen, weshalb ein Nachladen der Fahrzeugbatterien während des Betriebs notwendig ist. Zudem müssen technische Aspekte von Elektrofahrzeugen, wie beispielsweise Batteriealterungsprozesse, berücksichtigt werden. Die Fahrzeugeinsatzplanung als Teil des Planungsprozesses von Verkehrsunternehmen im ÖPNV ist besonders von diesen Herausforderungen betroffen. Diese legt den Fahrzeugeinsatz für die Bedienung der angebotenen Fahrplanfahrten bei Minimierung der Gesamtkosten fest. Die Standortplanung der Ladeinfrastruktur ist eng mit dieser Aufgabe verbunden, da für einen effizienten Einsatz der Fahrzeuge Ladestationen an geeigneten Orten errichtet werden müssen, um Betriebskosten zu minimieren. Die Planung der Ladeinfrastruktur ist ein langfristiges Planungsproblem, wohingegen die Fahrzeugeinsatzplanung eine eher kurzfristige Planungsaufgabe darstellt. Diese Dissertation befasst sich mit Optimierungsmethoden für die Fahrzeugeinsatzplanung mit Elektrofahrzeugen und mit der Standortplanung der Ladeinfrastruktur. Technische Aspekte von Elektrofahrzeugen werden dabei berücksichtigt. Die entwickelten Artefakte werden mit Hilfe von realen Datensätzen evaluiert. Durch eine exakte Optimierungsmethode für die Fahrzeugeinsatzplanung mit gemischten Fahrzeugflotten bestehend aus Fahrzeugen mit und ohne Reichweiterestriktionen wird die Anwendbarkeit von Optimierungsmethoden ohne Berücksichtigung von Reichweitebeschränkungen auf die Herausforderungen von Elektrofahrzeugen untersucht. Die Ergebnisse zeigen, dass herkömmliche Optimierungsmethoden für die neuen Herausforderungen bis zu einem gewissen Grad geeignet sind, es jedoch neuartige Lösungsmethoden erfordert, um den Anforderungen von Elektrofahrzeugen vollständig gerecht zu werden. Mit Hilfe einer heuristischen Lösungsmethode für die Fahrzeugeinsatzplanung mit Elektrofahrzeugen und Modellen für den Ladeprozess von Batterien wird untersucht, inwiefern sich der Detailgrad bei der Abbildung von Ladeprozessen auf resultierende Lösungen auswirkt. Erhebliche Unterschiede zwischen Modellannahmen und realen Gegebenheiten von Ladeprozessen werden herausgearbeitet. Durch ein metaheuristisches Lösungsverfahren für die simultane Optimierung der Standortplanung der Ladeinfrastruktur und der Fahrzeugeinsatzplanung werden beide Problemstellungen miteinander verbunden, um Synergieeffekte offenzulegen. Im Vergleich zu einer sequentiellen Planung ist ein simultanes Lösen notwendig, da ein sequentielles Lösen entweder zu unzulässigen Ergebnissen oder zu erheblichen Kostensteigerungen führt

Challenges of Primary Frequency Control and Benefits of Primary Frequency Response Support from Electric Vehicles

As the integration of wind generation displaces conventional plants, system inertia provided by rotating mass declines, causing concerns over system frequency stability. This paper implements an advanced stochastic scheduling model with inertia-dependent fast frequency response requirements to investigate the challenges on the primary frequency control in the future Great Britain electricity system. The results suggest that the required volume and the associated cost of primary frequency response increase significantly along with the increased capacity of wind plants. Alternative measures (e.g. electric vehicles) have been proposed to alleviate these concerns. Therefore, this paper also analyses the benefits of primary frequency response support from electric vehicles in reducing system operation cost, wind curtailment and carbon emissions

A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles

Conventional unit commitment is a mixed integer optimization problem and has long been a key issue for power system operators. The complexity of this problem has increased in recent years given the emergence of new participants such as large penetration of plug-in electric vehicles. In this paper, a new model is established for simultaneously considering the day-ahead hourly based power system scheduling and a significant number of plug-in electric vehicles charging and discharging behaviours. For solving the problem, a novel hybrid mixed coding meta-heuristic algorithm is proposed, where V-shape symmetric transfer functions based binary particle swarm optimization are employed. The impact of transfer functions utilised in binary optimization on solving unit commitment and plug-in electric vehicle integration are investigated in a 10 unit power system with 50,000 plug-in electric vehicles. In addition, two unidirectional modes including grid to vehicle and vehicle to grid, as well as a bi-directional mode combining plug-in electric vehicle charging and discharging are comparatively examined. The numerical results show that the novel symmetric transfer function based optimization algorithm demonstrates competitive performance in reducing the fossil fuel cost and increasing the scheduling flexibility of plug-in electric vehicles in three intelligent scheduling modes

Model Design on Emergency Power Supply of Electric Vehicle

According to the mobile storage characteristic of electric vehicles, an emergency power supply model about the electric vehicles is presented through analyzing its storage characteristic. The model can ensure important consumer loss minimization during power failure or emergency and can make electric vehicles cost minimization about running, scheduling, and vindicating. In view of the random dispersion feature in one area, an emergency power supply scheme using the electric vehicles is designed based on the K-means algorithm. The purpose is to improve the electric vehicles initiative gathering ability and reduce the electric vehicles gathering time. The study can reduce the number of other emergency power supply equipment and improve the urban electricity reliability

Solution Approaches for Vehicle and Crew Scheduling with Electric Buses

The use of electric buses is expected to rise due to its environmental benefits. However, electric vehicles are less exible than conventional diesel buses due to their limited driving range and longer recharging times. Therefore, scheduling electric vehicles adds further operational dificulties. Additionally, various labor regulations challenge public transport companies to find a cost-effcient crew schedule. Vehicle and crew scheduling problems essentially define the cost of operations. In practice, these two problems are often solved sequentially. In this paper, we introduce the integrated electric vehicle and crew scheduling problem (E-VCSP). Given a set of timetabled trips and recharging stations, the E-VCSP is concerned with finding vehicle and crew schedules that cover the timetabled trips and satisfy operational constraints, such as limited driving range of electric vehicles and labor regulations for the crew while minimizing total operational cost. An adaptive large neighborhood search that utilizes branch-and-price heuristics is proposed to tackle the E-VCSP. The proposed method is tested on real-life instances from public transport companies in Denmark and Sweden that contain up to 1,109 timetabled trips. The heuristic approach provides evidence of improving efficiency of transport systems when the electric vehicle and crew scheduling aspects are considered simultaneously. By comparing to the traditional sequential approach, the heuristic finds improvements in the range of 1.17-4.37% on average. A sensitivity analysis of the electric bus technology is carried out to indicate its implications for the crew schedule and the total operational cost. The analysis shows that the operational cost decreases with increasing driving range (120 to 250 kilometers) of electric vehicles

Energy resources management in three distinct time horizons considering a large variation in wind power

The intensive use of distributed generation based on renewable resources increases the complexity of power systems management, particularly the short-term scheduling. Demand response, storage units and electric and plug-in hybrid vehicles also pose new challenges to the short-term scheduling. However, these distributed energy resources can contribute significantly to turn the shortterm scheduling more efficient and effective improving the power system reliability. This paper proposes a short-term scheduling methodology based on two distinct time horizons: hour-ahead scheduling, and real-time scheduling considering the point of view of one aggregator agent. In each scheduling process, it is necessary to update the generation and consumption operation, and the storage and electric vehicles status. Besides the new operation condition, more accurate forecast values of wind generation and consumption are available, for the resulting of short-term and very short-term methods. In this paper, the aggregator has the main goal of maximizing his profits while, fulfilling the established contracts with the aggregated and external players