Sustainable distribution network planning considering multi-energy systems and plug-in electric vehicles parking lots

Entre todos os recursos associados à evolução das redes elétricas para o conceito de smart grid, os sistemas de multi-energia e os veículos eléctricos do tipo plug-in (PEV) são dois dos principais tópicos de investigação hoje em dia. Embora estes recursos possam acarretar uma maior incerteza para o sistema de energia, as suas capacidades de demanda/armazenamento flexível de energia podem melhorar a operacionalidade do sistema como um todo. Quando o conceito de sistemas de multi-energia e os parques de estacionamento com estações de carregamento para os PEVs são combinados no sistema de distribuição, a demanda pode variar significativamente. Sendo a demanda de energia uma importante informação no processo de planeamento, é essencial estimar de precisa essa demanda. Deste modo, três níveis padrão de carga podem ser extraídos tendo em conta a substituição da procura entre carriers de energia, a demanda associada ao carregamento dos PEVs, e presença de parques de estacionamento com estações de carregamento no sistema. A presença de PEVs num sistema multi-energia obriga a outros requisitos (por exemplo, um sistema de alimentação) que devem ser fornecidos pelo sistema, incluindo as estações de carregamento. A componente elétrica dos PEVs dificulta a tarefa ao operador do sistema na tentativa de encontrar a melhor solução para fornecer os serviços necessários e utilizar o potencial dos PEVs num sistema multi-energia. Contudo, o comportamento sociotécnico dos utilizadores de PEVs torna difícil ao operador do sistema a potencial gestão das fontes de energia associada às baterias. Desta forma, este estudo visa providenciar uma solução para os novos problemas que irão ocorrer no planeamento do sistema. Nesta tese, vários aspetos da integração de PEVs num sistema multi-energia são estudados. Primeiro, um programa de resposta à demanda é proposto para o sistema multi-energia com tecnologias do lado da procura que possibilitem alternar entre fornecedores de serviços. Em seguida, é realizado um estudo abrangente sobre as questões relativas à modelação dos PEVs no sistema, incluindo a modelação das incertezas, as preferências dos proprietários dos veículos, o nível de carregamento dos PEV e a sua interação com a rede. Posteriormente é proposta a melhor estratégia para a participação no mercado de energia e reserva. A alocação na rede e os possíveis efeitos subjacentes são também estudados nesta tese, incluindo o modelo dos PEVs e dos parques de estacionamento com estações de carregamento nesse sistema de multi-energia.Among all resources introduced by the evolution of smart grid, multi-energy systems and plugin electric vehicles are the two main challenges in research topics. Although, these resources bring new levels of uncertainties to the system, their capabilities as flexible demand or stochastic generation can enhance the operability of system. When the concept of multienergy systems and plug-in electric vehicles (PEV) parking lots are merged in a distribution system, the demand estimation may vary significantly. As the main feed of planning process, it is critical to estimate the most accurate amount of required demand. Therefore, three stages of load pattern should be extracted taking into account the demand substitution between energy carriers, demand affected by home-charging PEVs, and parking lot presence in system. The presence of PEVs in a multi-energy system oblige other requirements (i.e. fueling system) that should be provided in the system, including charging stations. However, the electric base of PEVs adds to the responsibilities of the system operator to think about the best solution to provide the required services for PEVs and utilize their potentials in a multi-energy concept. However, the socio-technical behavior of PEV users makes it difficult for the system operator to be able to manage the potential sources of PEV batteries. As a result, this study tries to raise the solution to new problems that will occur for the system planners and operators by the future components of the system. In this thesis, various aspects of integrating PEVs in a multi-energy system is studied.Firstly, a carrier-based demand response program is proposed for the multi-energy system with the technologies on the demand side to switch between the carriers for providing their services. Then, a comprehensive study on the issues regarding the modeling of the PEVs in the system are conducted including modeling their uncertain traffic behavior, modeling the preferences of vehicle owners on the required charging, modeling the PEV parking lot behavior and its interactions with the network. After that the best strategy and framework for participating the PEVs energy in the energy and reserve market is proposed. The allocation of the parking lot in the network and the possible effects it will have on the network constraints is studied. Finally, the derived model of the PEVs and the parking lot is added to the multi-energy system model with multi-energy demand

Optimization and Integration of Electric Vehicle Charging System in Coupled Transportation and Distribution Networks

With the development of the EV market, the demand for charging facilities is growing rapidly. The rapid increase in Electric Vehicle and different market factors bring challenges to the prediction of the penetration rate of EV number. The estimates of the uptake rate of EVs for light passenger use vary widely with some scenarios gradual and others aggressive. And there have been many effects on EV penetration rate from incentives, tax breaks, and market price. Given this background, this research is devoted to addressing a stochastic joint planning framework for both EV charging system and distribution network where the EV behaviours in both transportation network and electrical system are considered. And the planning issue is formulated as a multi-objective model with both the capital investment cost and service convenience optimized. The optimal planning of EV charging system in the urban area is the target geographical planning area in this work where the service radius and driving distance is relatively limited. The mathematical modelling of EV driving and charging behaviour in the urban area is developed

Models and Solution Approaches for Development and Installation of PEV Infrastructure

This dissertation formulates and develops models and solution approaches for plug-in electric vehicle (PEV) charging station installation. The models are formulated in the form of bilevel programming and stochastic programming problems, while a meta-heuristic method, genetic algorithm, and Monte Carlo bounding techniques are used to solve the problems. Demand for PEVs is increasing with the growing concerns about environment pollution, energy resources, and the economy. However, battery capacity in PEVs is still limited and represents one of the key barriers to a more widespread adoption of PEVs. It is expected that drivers who have long-distance commutes hesitate to replace their internal combustion engine vehicles with PEVs due to range anxiety. To address this concern, PEV infrastructure can be developed to provide re-fully status when they are needed. This dissertation is primarily focused on the development of mathematical models that can be used to support decisions regarding a charging station location and installation problem. The major parts of developing the models included identification of the problem, development of mathematical models in the form of bilevel and stochastic programming problems, and development of a solution approach using a meta-heuristic method. PEV parking building problem was formulated as a bilevel programming problem in order to consider interaction between transportation flow and a manager decisions, while the charging station installation problem was formulated as a stochastic programming problem in order to consider uncertainty in parameters. In order to find the best-quality solution, a genetic algorithm method was used because the formulation problems are NP-hard. In addition, the Monte Carlo bounding method was used to solve the stochastic program with continuous distributions. Managerial implications and recommendations for PEV parking building developers and managers were suggested in terms of sensitivity analysis. First, in the planning stage, the developer of the PEV parking building should consider long-term changes in future traffic flow and locate a PEV parking building closer to the node with the highest destination trip rate. Second, to attract more parking users, the operator needs to consider the walkability of walking links

Research on economic planning and operation of electric vehicle charging stations

Appropriately planning and scheduling strategies can improve the enthusiasm of Electric vehicles (EVs), reduce charging losses, and support the power grid system. Thus, this dissertation studies the planning and operating of the EV charging station. First, an EV charging station planning strategy considering the overall social cost is proposed. Then, to reduce the charging cost and guarantee the charging demand, an optimal charging scheduling method is proposed. Additionally, by considering the uncertainty of charging demand, a data-driven intelligent EV charging scheduling algorithm is proposed. Finally, a collaborative optimal routing and scheduling method is proposed

How many fast-charging stations do we need along European highways?

For a successful market take-up of plug-in electric vehicles, fast-charging stations along the highway network play a significant role. This paper provides results from a first study on estimating the minimum number of fast-charging stations along the European highway network of selected countries (i.e., France, Germany, the Benelux countries, Switzerland, Austria, Denmark, the Czech Republic, and Poland) and gives an estimate on their future profitability. The combination of a comprehensive dataset of passenger car trips in Europe and an efficient arc-coverpath-cover flow-refueling location model allows generating results for such a comprehensive transnational highway network for the first time. Besides the minimum number of required fastcharging stations which results from the applied flow-refueling location model (FRLM), an estimation of their profitability as well as some country-specific results are also identified. According to these results the operation of fast-charging stations along the highway will be attractive in 2030 because the number of customers per day and their willingness to pay for a charge is high compared to inner-city charging stations. Their location-specific workloads as well as revenues differ significantly and a careful selection of locations is decisive for their economic operation

Smart charging strategies for electric vehicle charging stations

Although the concept of transportation electrification holds enormous prospects in addressing the global environmental pollution problem, consumer concerns over the limited availability of charging stations and long charging/waiting times are major contributors to the slow uptake of plug-in electric vehicles (PEVs) in many countries. To address the consumer concerns, many countries have undertaken projects to deploy a network of both fast and slow charging stations, commonly known as electric vehicle charging networks. While a large electric vehicle charging network will certainly be helpful in addressing PEV owners\u27 concerns, the full potential of this network cannot be realised without the implementation of smart charging strategies. For example, the charging load distribution in an EV charging network would be expected to be skewed towards stations located in hotspot areas, instigating longer queues and waiting times in these areas, particularly during afternoon peak traffic hours. This can also lead to a major challenge for the utilities in the form of an extended PEV charging load period, which could overlap with residential evening peak load hours, increasing peak demand and causing serious issues including network instability and power outages. This thesis presents a smart charging strategy for EV charging networks. The proposed smart charging strategy finds the optimum charging station for a PEV owner to ensure minimum charging time, travel time and charging cost. The problem is modelled as a multi-objective optimisation problem. A metaheuristic solution in the form of ant colony optimisation (ACO) is applied to solve the problem. Considering the influence of pricing on PEV owners\u27 behaviour, the smart charging strategy is then extended to address the charging load imbalance problem in the EV network. A coordinated dynamic pricing model is presented to reduce the load imbalance, which contributes to a reduction in overlaps between residential and charging loads. A constraint optimization problem is formulated and a heuristic solution is introduced to minimize the overlap between the PEV and residential peak load periods. In the last part of this thesis, a smart management strategy for portable charging stations (PCSs) is introduced. It is shown that when smartly managed, PCSs can play an important role in the reduction of waiting times in an EV charging network. A new strategy is proposed for dispatching/allocating PCSs during various hours of the day to reduce waiting times at public charging stations. This also helps to decrease the overlap between the total PEV demand and peak residential load

Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid

PEV Charging Infrastructure Integration into Smart Grid

Plug-in electric vehicles (PEVs) represent a huge step forward in a green transportation system, contribute to the reduction of greenhouse gas emission, and reduce the dependence on fossil fuel. With the increasing popularity of PEVs, public electric-vehicle charging infrastructure (EVCI) becomes indispensable to meet the PEV user requirements. EVCI can consist of various types of charging technologies, offering multiple charging services for PEV users. Proper integration of the charging infrastructure into smart grid is key to promote widespread adoption of PEVs. Planning and operation of EVCI are technically challenging, since PEVs are characterized by their limited driving range, long charging duration, and high charging power, in addition to the randomness in driving patterns and charging decisions of PEV users. EVCI planning involves both the siting and capacity planning of charging facilities. Charging facility siting must ensure not only a satisfactory charging service for PEV users but also a high utilization and profitability for the chosen facility locations. Thus, the various types of charging facilities should be located based on an accurate location estimation of the potential PEV charging demand. Capacity planning of charging facilities must ensure a satisfactory charging service for PEV users in addition to a reliable operation of the power grid. During the operation of EVCI, price-based coordination mechanisms can be leveraged to dynamically preserve the quality-of-service (QoS) requirements of charging facilities and ensure the profitability of the charging service. This research is to investigate and develop solutions for integrating the EVCI into the smart grid. It consists of three research topics: First, we investigate PEV charging infrastructure siting. We propose a spatial-temporal flow capturing location model. This model determines the locations of various types of charging facilities based on the spatial-temporal distribution of traffic flows. In the proposed model, we consider transportation network dynamics and congestion, in addition to different characteristics and usage patterns of each charging facility type. Second, we propose a QoS aware capacity planning of EVCI. The proposed framework accounts for the link between the charging QoS and the power distribution network (PDN) capability. Towards this end, we firstly optimize charging facility sizes to achieve a targeted QoS level. Then, we minimize the integration cost for the PDN by attaining the most cost-effective allocation of the energy storage systems and/or upgrading the PDN substation and feeders. Additionally, we capture the correlation between the occupation levels of neighboring charging facilities and the blocked PEV user behaviors. Lastly, we investigate the coordination of PEV charging demands. We develop a differentiated pricing mechanism for a multiservice EVCI using deep reinforcement learning (RL). The proposed framework enhances the performance of charging facilities by motivating PEV users to avoid over-usage of particular service classes. Since customer-side information is stochastic, non-stationary, and expensive to collect at scale, the proposed pricing mechanism utilizes the model-free deep RL approach. In the proposed RL approach, deep neural networks are trained to determine a pricing policy while interacting with the dynamically changing environment. The neural networks take the current EVCI state as input and generate pricing signals that coordinate the anticipated PEV charging demand

City of Vancouver EV Infrastructure Strategy Report

The role of the local government in supporting the growth and maintenance of a strong plug‐in electric vehicle market in Vancouver is evaluated in this report. This report identifies areas of action in which a local government, such as Vancouver, can impact their region based on a thorough understanding of the current plug‐in vehicle market, international demonstration projects, and research efforts. Specifically, workplace and public charging is needed to reinforce and fulfill the gaps from home‐based charging in dense urban regions. Local government can encourage investments in workplace and public charging by providing clear regional guidelines for installers and customers, providing appropriate incentives to businesses, allowing for an innovative marketplace in the vehicle charging industry, and collaborating with the regional utility to identify specific opportunities for optimization and encouragement of utility rates and vehicle‐grid interactions