Stochastic planning of electric vehicle charging station integrated with photovoltaic and battery systems

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166260/1/gtd2bf00020.pd

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

Planning of Regional Urban Bus Charging Facility:A Case Study of Fengxian, Shanghai

The electrification of public transport is of great significance to alleviating environmental pollution and energy problems. The construction of charging stations for electric buses (EBs) is the key step for the electrification of public transport and receives more and more attention. This paper proposes a new urban electric bus charging station planning algorithm which consists of two parts, park-maintaining (PM) charging station planning and midway supply (MS) charging station planning. Firstly, bus routes are classified based on charging demands. Accordingly, the PM charging station planning model is divided into full slow charging (FSC) model, Bus Rapid Transit (BRT) model and Hybrid model. Secondly, the improved grid AP algorithm is applied to plan MS charging stations to enhance the EB operation reliability. Then by multi-terminal charging pile optimization model, the economics of charging facilities construction is enhanced. Finally, via an ordered control charging algorithm, the economic profits of overall planning schemes are enhanced. The bus system in Fengxian, Shanghai is taken as an example to demonstrate the proposed method. Results prove that the proposed method can effectively meet the charging demands of EBs and improve the operating reliability of the EB system. </p

Stochastic planning for active distribution networks hosting fast charging stations

With the advent of electric vehicles (EVs), charging infrastructure needs to become more available and electricity providers must build additional power generation capacity to support the grid. In siting and sizing of fast charging stations (FCSs), both the distribution network constraints, as well as the traffic network limitations, must be considered because FCSs exist on both levels. Moreover, the siting and sizing of wind-powered distributed generation (WPDG) is a solution to gradually decarbonizing the grid; therefore, reducing our carbon footprint. In addition to providing capacity, they also have other benefits in the distribution network such as reducing transmission losses. In this thesis, a new framework is proposed which successfully implements a novel scoring technique to rate the attractiveness of FCS candidate locations thus, determining the expected FCS demand in each candidate location and uses WPDGs to support that load. A study has been conducted to compare the suitability of industrial-scale turbines versus micro-wind turbines in an urban area. A method for selecting candidate locations for the later has been developed. A stochastic program is proposed to account for the non-deterministic elements of the problem including generic loads, residential electric vehicle loads, FCS loads, and wind speed where they are accounted for collectively using a method called convolution. This comes hand-in-hand with a mixed-integer non-linear programming model that sites and sizes both FCSs and WPDGs with an objective of maximizing profits to incentivize investments. A list of novel constraints has been introduced that connect the traffic network to the power network. The problem is modeled from the perspective of electric utilities but also considers the perspectives of the urban planners and potential investors. A case study was implemented showing how the scoring technique works and the results show that the math model considered all the parameters and respected all the constraints delivering a holistic set of decisions to site and size both FCSs and micro WPDGs in an urban area

Towards electric bus system: planning, operating and evaluating

The green transformation of public transportation is an indispensable way to achieve carbon neutrality. Governments and authorities are vigorously implementing electric bus procurement and charging infrastructure deployment programs. At this primary but urgent stage, how to reasonably plan the procurement of electric buses, how to arrange the operation of the heterogeneous fleet, and how to locate and scale the infrastructure are urgent issues to be solved. For a smooth transition to full electrification, this thesis aims to propose systematic guidance for the fleet and charging facilities, to ensure life-cycle efficiency and energy conservation from the planning to the operational phase.One of the most important issues in the operational phase is the charge scheduling for electric buses, a new issue that is not present in the conventional transit system. How to take into account the charging location and time duration in bus scheduling and not cause additional load peaks to the grid is the first issue being addressed. A charging schedule optimization model is constructed for opportunity charging with battery wear and charging costs as optimization objectives. Besides, the uncertainty in energy consumption poses new challenges to daily operations. This thesis further specifies the daily charging schedules with the consideration of energy consumption uncertainty while safeguarding the punctuality of bus services.In the context of e-mobility systems, battery sizing, charging station deployment, and bus scheduling emerge as crucial factors. Traditionally these elements have been approached and organized separately with battery sizing and charging facility deployment termed planning phase problems and bus scheduling belonging to operational phase issues. However, the integrated optimization of the three problems has advantages in terms of life-cycle costs and emissions. Therefore, a consolidated optimization model is proposed to collaboratively optimize the three problems and a life-cycle costs analysis framework is developed to examine the performance of the system from both economic and environmental aspects. To improve the attractiveness and utilization of electric public transportation resources, two new solutions have been proposed in terms of charging strategy (vehicle-to-vehicle charging) and operational efficiency (mixed-flow transport). Vehicle-to-vehicle charging allows energy to be continuously transmitted along the road, reducing reliance on the accessibility and deployment of charging facilities. Mixed flow transport mode balances the directional travel demands and facilities the parcel delivery while ensuring the punctuality and safety of passenger transport

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

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