Availability Of Electric Vehicles Research Articles

Improved Golden Jackal Optimization for Optimal Allocation and Scheduling of Wind Turbine and Electric Vehicles Parking Lots in Electrical Distribution Network Using Rosenbrock’s Direct Rotation Strategy

In this paper, a multi-objective allocation and scheduling of wind turbines and electric vehicle parking lots are performed in an IEEE 33-bus radial distribution network to reach the minimum annual costs of power loss, purchased grid energy, wind energy, PHEV energy, battery degradation cost, and network voltage deviations. Decision variables, such as the site and size of wind turbines and electric parking lots in the distribution system, are found using an improved golden jackal optimization (IGJO) algorithm based on Rosenbrock’s direct rotational (RDR) strategy. The results showed that the IGJO finds the optimal solution with a lower convergence tolerance and a better (lower) objective function value compared to conventional GJO, the artificial electric field algorithm (AEFA), particle swarm optimization (PSO), and manta ray foraging optimization (MRFO) methods. The results showed that using the proposed method based on the IGJO, the energy loss cost, grid energy cost, and network voltage deviations were reduced by 29.76%, 65.86%, and 18.63%, respectively, compared to the base network. Moreover, the statistical analysis results proved their superiority compared to the conventional GJO, AEFA, PSO, and MRFO algorithms. Moreover, considering vehicles battery degradation costs, the losses cost, grid energy cost, and network voltage deviations have been reduced by 3.28%, 1.07%, and 4.32%, respectively, compared to the case without battery degradation costs. In addition, the results showed that the decrease in electric vehicle availability causes increasing losses for grid energy costs and weakens the network voltage profile, and vice versa.

Evaluation of user experience in the operation of electric vehicles in the Arctic regions

The availability of electric vehicle (EV) technology for users in various regions of the planet has increased significantly in recent years due to the reduction of their cost and the development of service infrastructure. Electric vehicles have traditionally focused on operation in temperate and warm climates, but gradually they are also beginning to be used in the northern and Arctic regions of the planet with a cold climate. One of the main keys to the success of electric vehicle technology in the northern and Arctic regions is an understanding of the user experience (UE) that arises in drivers when using EVs in the special climatic conditions of such regions. This article presents the results of an experimental assessment of UE users of battery electric vehicles in the northern and Arctic regions on the example of one of the largest Arctic regions of the planet – the Republic of Sakha (Yakutia).

Electric Vehicle Aggregator as an Automatic Reserves Provider Under Uncertain Balancing Energy Procurement

Shift of the power system generation from the fossil to the variable renewables prompted the system operators to search for new sources of flexibility, i.e., new reserve providers. With the introduction of electric vehicles, smart charging emerged as one of the promising solutions. However, electric vehicle aggregators face the uncertainty both on the reserve activation and the electric vehicle availability. These uncertainties can have a detrimental effect on both the aggregators’ profitability and users’ comfort. State-of-the art literature mostly neglects the reserve activation or it’s uncertainty. On top of that, they rarely model European markets which are different that those commonly addressed in the literature. This paper introduces a new method for modeling the reserve activation uncertainty, also termed as balancing energy procurement in the European context, based on the real historic data from the European power system. Three electric vehicle scheduling models were designed and tested: the deterministic, the stochastic and the robust. The results demonstrate that the current deterministic approaches inaccurately represent the activation uncertainty and that the proposed models that consider uncertainty, both the stochastic and the robust, substantially improve the results. Additionally, the sensitivity analysis for the robust model was performed and it demonstrates how a decision-maker can choose its level of conservativeness, portraying its risk-awareness.

Optimal planning of solar photovoltaic and battery storage for electric vehicle owner households with time‐of‐use tariff

This paper presents a practical optimal planning of solar photovoltaic (SPV) and battery storage system (BSS) for electric vehicle (EV) owner households with time of use (TOU) electricity pricing. The main aim of the optimisation problem is to minimize the Cost of Electricity (COE) while satisfying the design constraints over 20-year project lifespan. Novel rule-based home energy management systems are created to investigate the optimal sizing of two system configurations: (1) SPV-EV, and (2) SPV-BSS-EV. The arrival and departure times of the EV's availability, as well as its initial state-of-charge are incorporated into the energy management system via stochastic functions. Sensitivity analyses of the feed in tariff, grid constraint, electricity demand, and available rooftop area are provided to investigate the variations of cost of electricity and capacities of SPV and BSS. From the study results, practical guidelines for grid-connected households, based on TOU energy pricing, are provided to select the optimal capacity of SPV and BSS to accommodate the existing EV. The optimisation method is general and applicable to any household owning EV; however, in this study realistic data of solar insolation and temperature as well as household electricity demand and electricity prices are implemented for South Australia. Uncertainty analysis is investigated based on 10-year real and stochastic data to prove the optimal results. It is found that the optimal configuration for a typical SA household utilising TOU pricing with an EV requires a 10-kW SPV with a 10-kWh capacity of BSS.

Robust self-scheduling of parking lot microgrids leveraging responsive electric vehicles

The penetration of plug-in electric vehicles is increasing in power systems all over the world. Due to imperfect data about the availability of electric vehicles, the uncertainty of the demand-side increases considerably. To overcome the problem, the power storage capacity of electric vehicles is addressed to provide demand flexibility for the supply-side. This paper proposes a novel structure for parking lot microgrids to provide day-ahead peak-shaving and valley-filling for power systems with distinct peak hours in the daily operation. The microgrid is comprised of electrical demands, e.g. heat ventilation and air conditioning, lighting, escalators, as well as self-generation facilities, including roof-top photovoltaic sites and gas-fired engines. Besides, the parking lot has smart charging stations to charge/discharge the electric vehicles based on the flexibility requirements of power systems and/or owners’ preferences. The microgrid is supplied by the wholesale electricity market and bilateral contracts. To leverage the flexibility potentials of electric vehicles, a data-driven approach is suggested that classifies the electric vehicles based on distinct characteristics, e.g. dwell time and preferred state of charge. The robust optimization approach is adopted to determine the optimum procurement strategies for the microgrid in the worst-case realization of the wholesale market price uncertainty. Finally, the robust-mixed integer linear programming is examined in Iran Power Grid not only to provide flexibility for the power network but also to make a profit for the microgrid.

Vehicle grid integration for demand response with mixture user model and decentralized optimization

With the rapidly growing electric vehicle adoption rate and increasing number of public electric vehicle charging stations in recent years, electric vehicle becomes more and more critical in the demand response programs. Development in Vehicle to Grid technology has converted electric vehicle to distributed energy resources. Using the Electric Vehicle Smart Charging Infrastructures on UCLA campus and city of Santa Monica as testbeds, we have collected real-world datasets of electric vehicle usage, based on which, we proposed optimal bi-directional charging control strategies to integrate electric vehicle in commercial and public parking facilities into the power grid as distributed energy resources for demand response programs by two-stage distributed optimization and water-filling algorithm. Driver behavioral uncertainties have been considered in our approach. Specifically, electric vehicle users are clustered by their behavioral patterns using a modified Latent Semantic Analysis. The first-stage optimization is performed to minimize energy cost using day-ahead wholesale energy price with predictions on energy demand and electric vehicle availability which generated by a mixture user model. Decentralized optimization (second-stage) is carried out on the next day in real-time to control individual electric vehicle so that the aggregated load can follow the first-stage optimal profile. As an alternative, a fast converging water filling algorithm is proposed and compared with two-stage optimization. Extensive simulation results show that proposed charging controls can utilize electric vehicle as distributed energy resource to accommodate demand response program while satisfying electric vehicle energy demands and providing significant energy cost savings.

Integrating renewable energy sources by electric vehicle fleets under uncertainty

Abstract Electric vehicles are one of the concepts towards green and sustainable transportation systems. However, several uncertainties with respect to electricity demand and availability of electric vehicles as well as electricity supply by renewable energy sources influence an optimal scheduling through smart charging strategies. This paper investigates the possibilities to integrate additionally loads of uncertain renewable energy sources by smart charging strategies of three different electric vehicle fleets – namely, commercial customers, commuters, and opportunity parkers. Therefore, data from an empiric field test with a public charging infrastructure in a parking garage with a photovoltaic system is taken. Various strategies are analyzed, considering the changing individual electricity demand, restrictions and parking times of electric vehicle fleets by combining a Monte Carlo simulation approach with different methodologies like a heuristic algorithm, an optimization model and stochastic programming. The numerical results indicate that the domestic photovoltaic generation of the car park can be fully used by the electric vehicle fleets for charging and the utilization of photovoltaic can be doubled when comparing uncontrolled and optimized charging strategies. The commuter fleet has the highest CO2 emission reduction potential of all three electric vehicle fleets. Moreover, load management decreases costs, even when uncertainties are considered.

Regime-switching based vehicle-to-building operation against electricity price spikes

Electricity price may present very large spikes due to imbalance between generation and demand, especially during heavily loaded periods. Such peak price may incur significant cost to building operation. With the vehicle-to-building (V2B) technology, electric vehicle battery can be used as temporal energy source for the building load for a short period, which leads to a possible solution for reducing the energy cost during peak-price periods. In this paper, the problem of reducing the energy cost due to the peak price is approached from the prospective of risk management. A regime-switching based risk management scheme is proposed for the V2B operation based on the availability of electric vehicles (EV) plugged in the parking lots attached to the building. In the low risk regime, the objective is to minimize the EV charging cost. While in the high risk regime, the objective is to reduce the potentially high energy cost due the peak price via the power stored in EV batteries. Based on Markov regime-switching model, the operation minimizes the conditional value at risk involved. Simulation results show that the proposed framework can greatly reduce the energy cost against the electricity peak prices.

A multi-objective optimization problem for allocating parking lots in a distribution network

This paper presents a multi-objective approach to determine optimal site and size of parking lots, which provide vehicle to grid (V2G) power in distribution system as new type of distributed generations (DGs). In this approach, the reliability of distribution system and power losses along with investment cost are considered in optimization problem. This optimization problem is solved using genetic algorithm (GA) method. Simulation study is carried out on a nine bus test system. The results of simulations show that the economic issue of parking lots installation depends on many factors such as availability of electric vehicles (EVs) as well as the electricity price. Also, it is shown that by taking enough incentive for EVs owners, optimal sitting and sizing of parking lots has economical benefit for distribution system companies. Also, optimal allocation of parking lots can improve the distribution system voltage profile.