Uncontrolled Charging Research Articles

Behavior of Electric Vehicles Charging and Discharging and Impact on the Grid

With the promotion of our country policy about electric vehicles, the development conditions of electric vehicles are improving day by day. However, the application of large-scale electric vehicles will have a direct impact on the power grid. Guilin is taken as an example to analysis the impact of models including uncontrolled charging, controlled charging and controlled charging/discharging model in different scales on the power grid using the Monte Carlo algorithm. The simulation results show that the proper number of electric vehicles involved in the controlled charging/discharging model can effectively achieve the target of peak clipping and valley filling. However, after more than a certain number, it will bring new pressure to the power grid.

Tuning the surface Fermi level on p-type gallium nitride nanowires for efficient overall water splitting.

Solar water splitting is one of the key steps in artificial photosynthesis for future carbon-neutral, storable and sustainable source of energy. Here we show that one of the major obstacles for achieving efficient and stable overall water splitting over the emerging nanostructured photocatalyst is directly related to the uncontrolled surface charge properties. By tuning the Fermi level on the nonpolar surfaces of gallium nitride nanowire arrays, we demonstrate that the quantum efficiency can be enhanced by more than two orders of magnitude. The internal quantum efficiency and activity on p-type gallium nitride nanowires can reach ~51% and ~4.0 mol hydrogen h(-1) g(-1), respectively. The nanowires remain virtually unchanged after over 50,000 μmol gas (hydrogen and oxygen) is produced, which is more than 10,000 times the amount of photocatalyst itself (~4.6 μmol). The essential role of Fermi-level tuning in balancing redox reactions and in enhancing the efficiency and stability is also elucidated.

Day-Ahead Energy Planning with 100% Electric Vehicle Penetration in the Nordic Region by 2050

This paper presents the day-ahead energy planning of passenger cars with 100% electric vehicle (EV) penetration in the Nordic region by 2050. EVs will play an important role in the future energy systems which can both reduce the greenhouse gas (GHG) emissions from the transport sector and provide the demand side flexibility required by smart grids. On the other hand, the EVs will increase the electricity consumption. In order to quantify the electricity consumption increase due to the 100% EV penetration in the Nordic region to facilitate the power system planning studies, the day-ahead energy planning of EVs has been investigated with different EV charging scenarios. Five EV charging scenarios have been considered in the energy planning analysis which are: uncontrolled charging all day, uncontrolled charging at home, timed charging, spot price based charging all day and spot price based charging at home. The demand profiles of the five charging analysis show that timed charging is the least favorable charging option and the spot priced based EV charging might induce high peak demands. The EV charging demand will have a considerable share of the energy consumption in the future Nordic power system.

Load shift potential of electric vehicles in Europe

Many governments highly encourage electric mobility today, aiming at a high market penetration. This development would bring forth an impact on the energy system, which strongly depends on the driving and charging behavior of the users. While an uncontrolled immediate charging might strain the local grid and/or higher peak loads, there are benefits to be gained by a controlled charging. We examine six European mobility studies in order to display the effects of controlled and uncontrolled unidirectional charging. Taking into account country-specific driving patterns, we generate for each country a charging load curve corresponding to uncontrolled charging and consider the corresponding parking time at charging facilities in order to identify load shift potentials. The main results are that besides the charging power of the vehicles, the possibility to charge at the work place has a significant influence on the uncontrolled charging curve. Neither national nor regional differences are as significant. When charging is only possible at home, the vehicle availability at charging facilities during the day for all countries is at least 24%. With the additional possibility to charge at work, at least 45% are constantly available. Accordingly, we identified a big potential for load shifting through controlled charging.

A Comprehensive Study of the Impacts of PHEVs on Residential Distribution Networks

This paper presents the development of a probabilistic benchmark for assessing the impacts of the uncontrolled charging of plug-in hybrid electric vehicles (PHEVs) on residential distribution networks. Unlike the previous research, which adopted several assumptions and approximations, this paper analyzes the available load and transportation data to extract probability distribution functions describing different uncertainties characterizing the charging process. Monte Carlo simulation is utilized to handle these uncertainties and to predict the anticipated impacts of PHEVs on a representative test network. Finally, conclusions are drawn to assist utilities in integrating PHEVs into their networks.

Models for optimal charging scheduling for electric vehicles in smart grid

Electric vehicles have experienced high demand in the market, which will introduce a substantial new load to the current electricity grid. Furthermore, uncontrolled electric vehicle charging from residential users will exacerbate the existing peak load during evening hours. In this paper, we propose optimisation models to alleviate the impact of extra load from electric vehicles on the power grid. Particularly, two centralised models allow the controller to minimise the total electricity costs for all users. Consequently, load levelling of the entire power grid is achieved. On the other hand, a decentralised model allows each user to minimise his/her own electricity cost and studies the resulting system-wide load levelling. Our numerical results indicate significant improvements in the total cost and peak-to-average ratio by both the centralised and decentralised optimal charging scheduling models, when compared to the uncontrolled charging scenarios.

Analysis of electric vehicle charge scheduling and effects on electricity demand costs

Due to the recent inflow of electric vehicles (EVs) to the automobile market, new concerns have risen with respect to the additional electrical load and the resultant effects on an overloaded electric grid. It takes approximately three to four hours to fully charge the battery of some common EVs depending on battery capacities. Either for convenience purposes or possibly necessity due to limited electric range on EVs, some EV owners may desire to charge their EV while at work in addition to charging at home. These forward-thinking daytime charging providers are typically Commercial and Industrial (C&I) electric ratepayers, or other large electric consumers which constitute the majority of businesses, shopping centers, academic campuses and manufacturing facilities. C&I consumers generally pay for electricity consumption based not only on total energy consumed, but also on peak electric demand measurements because certain portions of the working day are traditionally associated with C&I peak load periods. Electricity consumption during these periods results in higher electricity costs due to differences in the billing structures. Therefore, uncontrolled EV charging in large numbers potentially creates significant peak demand increases resulting in higher electric costs for the C&I ratepayer and exacerbating grid issues relating to electric generation and grid stability. Proper scheduling of EV charging is a necessity to avoid such increases in peak electric demand. Scheduling can be implemented in a number of ways including traditional process control strategies such as model predictive control or machine scheduling algorithms. When viewed as a parallel machine scheduling problem, charging stations can be considered as identical machines and the EV charging transactions required can be seen as jobs with varying processing times and due dates. The goal of such an algorithm is to schedule EV charging so that total electricity cost is minimized but off-peak charging activity is maximized. Due to the nature of electricity billing structures and cost calculations for C&I ratepayers, the machine scheduling algorithm must use time index based operations. This paper examines the EV charge scheduling problem and casts it as a parallel machine scheduling problem with availability constraints. Particularly, this study develops several heuristic scheduling algorithms such as Greedy Local Search and Simulated Annealing. These algorithms are evaluated using MATLAB (Mathworks MATLAB R2012b, 2012) to compare performance. In addition, the general-purpose optimization solver CPLEX (Ibm, ILOG CPLEX, 2012) is used to determine exact solutions for small size problems, which is then used to benchmark the performance of the other algorithms studied.

Analysis of the Exploitation of EV Fast Charging to Prevent Extensive Grid Investments in Suburban Areas

AbstractFast charging of electric vehicles (EV) is assumed to affect the operation of electricity grids. In order to decrease the need for conventional grid reinforcement, energy legislators are increasingly motivated to design load‐management incentives. Using the example of Germany, we investigate the extent to which voltage‐control strategies decrease critical voltage levels in suburban low‐voltage grids compared to uncontrolled charging. Voltage violations are identified by load flow simulation. Results showed that, for uncontrolled charging of 22 kW, each scenario except for EVs at the beginning of the line violated the voltage operation limit. Under these circumstances, the grid was operated within the voltage limits for 99.2 % of the year. Violations no longer occurred if active power limitation was performed. With reactive power control, full prevention of voltage‐limit violation could not be achieved but the frequency of violations was reduced. A combination of both active power‐limitation and reactive power reduced the volume and frequency of power limitation. In most cases, a power limitation of 2 kW satisfied the voltage boundaries. Thus, voltage control decreases the need for conventional grid reinforcement if grid operators gain more real‐time information about the state of their distribution grids.

Demonstrating Demand Management: How Intelligent EV Charging Can Benefit Everyone

This paper establishes the case for Electric Vehicle (EV) charging demand management through in-field demonstration, electricity network modelling and financial assessment. As part of the Victorian Government Electric Vehicle Trial, DiUS Computing demonstrated EV charging demand management using United Energy’s Smart Grid. Modelling of the United Energy network by the University of Melbourne found that uncontrolled charging would require network augmentation once EVs are adopted by 10% of households. In contrast, managed charging would allow the network to support in excess of 50% uptake using existing capacity and infrastructure. Furthermore, the end-to-end EV charging demand management solution demonstrated by DiUS could be implemented for one tenth the cost of the network augmentation. Although success factors were identified during the demonstration that may serve as an input for demand management program design, electricity market arrangements may be the strongest determinant of adoption generally.

Optimal scheduling of electric vehicles in an intelligent parking lot considering vehicle-to-grid concept and battery condition

The anticipation of a large penetration of EVs (electric vehicles) into the market brings up many technical issues. The power system may put at risk the security and reliability of operation due to uncontrolled EV charging and discharging. It is necessary to carry out intelligent scheduling for charging and discharging of EVs. In this paper, a smart management and scheduling model is proposed for large number of EVs parked in an urban parking lot. The proposed model considered practical constraints such as desired charging electricity price, remaining battery capacity, remaining charging time and age of the battery. The results show that the proposed parking lot energy management system satisfies both financial and technical goals. Moreover, EV owners could earn profit from discharging their vehicles as well as having desired SOC (state of charge) in the departure time.

Integrated V2G, G2V, and Renewable Energy Sources Coordination Over a Converged Fiber-Wireless Broadband Access Network

In this paper, an integrated vehicle-to-grid, grid-to-vehicle, and renewable energy sources (IntVGR) coordination algorithm is proposed. The focus of this work is to provide a multidisciplinary study on implementing the proposed IntVGR scheme over a broadband fiber-wireless communications infrastructure by co-simulating both power and communications perspectives. For the power systems perspective, results show that the scheme is able to achieve a 21% reduction in peak demand compared to uncontrolled charging, and a better performance in flattening the overall demand profile and maintaining network constraints in comparison to a benchmark scenario. The scheme has also been demonstrated to successfully coordinate PEVs to take maximum utilization of local renewable energy. For the communications perspective, the measured upstream traffic for executing the proposed IntVGR scheme on a residential area of 342 households is found to be 1-2 Mbps with an end-to-end latency level of 1 ms. The scheme has also been validated from both perspectives in a sensitivity analysis with a higher PEV adoption rate.

Enhanced AIMD-based decentralized residential charging of EVs

Moving from combustion engine to electric vehicle (EV)-based transport is recognized as having a major role to play in reducing pollution, combating climate change and improving energy security. However, the introduction of EVs poses major challenges for power system operation. With increasing penetration of EVs, uncontrolled coincident charging may overload the grid and substantially increase peak power requirements. Developing smart grid technologies and appropriate charging strategies to support the role out of EVs is therefore a high priority. In this paper, we investigate the effectiveness of distributed additive increase and multiplicative decrease (AIMD) charging algorithms, as proposed by Stüdli et al. in 2012, at mitigating the impact of domestic charging of EVs on low-voltage distribution networks. In particular, a number of enhancements to the basic AIMD implementation are introduced to enable local power system infrastructure and voltage level constraints to be taken into account and to reduce peak power requirements. The enhanced AIMD EV charging strategies are evaluated using power system simulations for a typical low-voltage residential feeder network in Ireland. Results show that by using the proposed AIMD-based smart charging algorithms, 50% EV penetration can be accommodated, compared with only 10% with uncontrolled charging, without exceeding network infrastructure constraints.

Integrated analysis of high-penetration PV and PHEV with energy storage and demand response

The increasing utilization of Plug-in Hybrid Electric Vehicles (PHEVs) in future will have significant impact on utility companies and customers depending on their charging characteristics. Uncontrolled or unscheduled vehicle charging may increase the residential peak and the risk of electric distribution network failure. In order to reduce the peak load utility companies encourage customers who have such high electricity consuming devices to participate in various demand response (DR) programs. Additionally, considering increasing Photovoltaic (PV) generation and storage facility installation on customer side, the integration of PHEVs needs more attention as system’s performance becomes highly fluctuating and unpredictable. This paper presents a simulation based optimization framework for integrated analysis of demand response programs, with high penetration of PHEV and PV and storage systems from residential customer’s perspective as well as utility company’s perspective. An agent-based simulation model is built in AnyLogic to mimic the household load, PHEV charging, PV generation and storage together with various demand response programs such as time-of-use (TOU), Real-time Pricing (RTP), and curtailment. In the considered scenarios, customers schedule their PHEV charging time and duration to minimize the electricity bill considering the price at the time of use, while the utility company aims to minimize the peak due to the PHEV charging by changing prices. The constructed simulation helps the utility company identify the best policies, and helps customers make better response under different DR programs. Three demand response programs are simulated with and without PV and storage. Results show that the simulation model successfully represents the cases and appropriate scheduling has benefits for both customers and utility companies.

Ant-Based Swarm Algorithm for Charging Coordination of Electric Vehicles

Uncontrolled charging of large-scale electric vehicles (EVs) can affect the safe and economic operation of power systems, especially at the distribution level. The centralized EVs charging optimization methods require complete information of physical appliances and using habits, which will cause problems of high dimensionality and communication block. Given this, an ant-based swarm algorithm (ASA) is proposed to realize the EVs charging coordination at the transformer level, which can overcome the drawbacks of centralized control method. First, the EV charging load model is developed, and the charging management structure based on swarm intelligence is presented. Second, basic data of the EV using habit is sampled by the Monte Carlo method, and the ASA is applied to realize the load valley filling. The load fluctuation and the transformer capacity are also considered in the algorithm. Finally, the charging coordination of 500 EVs under a 12.47 KV transformer is simulated to demonstrate the validity of the proposed method.

A Multi-Agent System for Controlled Charging of a Large Population of Electric Vehicles

The share of electric vehicles (EVs) is expected to increase significantly in the vehicle market sales in the forthcoming years. Uncontrolled charging of EVs can affect adversely the normal operation of the power system, especially at the distribution level. This paper proposes a distributed, multi-agent EV charging control method based on the Nash Certainty Equivalence Principle that considers network impacts. Convergence of the method when the EVs control agents are uncoupled and weakly-coupled is discussed. The efficiency of the proposed management system is evaluated through simulation of a realistic, urban distribution network.

Economic and environmental impacts of a PV powered workplace parking garage charging station

Plug-in hybrid electric vehicles (PHEVs) and electric vehicles (EVs) have high potential for reducing fuel consumption and emissions, and for providing a way to utilize renewable energy sources for the transportation sector. On the other hand, charging millions of PEVs could overload the power grid, increase emissions and significantly alter economic characteristics. A day-time photovoltaic (PV) based, plug-in electric vehicle charging station located in a workplace parking garage is considered in this research. The results show the impact of PV based workplace charging on the economics and emissions from the power grid. An optimal charge scheduling strategy is compared with an uncontrolled charging case to perform the economics and emissions analysis. Two locations (Columbus, OH and Los Angeles, CA) are selected such that the analysis includes different scenarios of yearly variation of solar radiation and finance structure. A high fidelity hourly simulation model for energy economic analysis is developed considering different types of vehicles, statistical data for driving distances, parking time, installation cost, tax rebates and incentives. An incremental parking rate for accessing the charging facility is considered for economic analysis for the garage owner and the vehicle owner. The analysis is extended to consider the impact of carbon tax implementation on the driver economics and shows the feasibility of such PV based charging stations. Parametric analysis for different parking rates and installed capacities show (i) the feasibility of a PV based workplace charging facility, (ii) benefits to the vehicle owner and the garage owner, and (iii) the need for an optimal charging controller.

Day-Ahead Resource Scheduling Including Demand Response for Electric Vehicles

The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and V2G. The main focus is the comparison of different EV management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs in the V2G approach. Three different DR programs are designed and tested (trip reduce, shifting reduce and reduce+shifting). Other important contribution of the paper is the comparison between deterministic and computational intelligence techniques to reduce the execution time. The proposed scheduling is solved with a modified particle swarm optimization. Mixed integer non-linear programming is also used for comparison purposes. Full ac power flow calculation is included to allow taking into account the network constraints. A case study with a 33-bus distribution network and 2000 V2G resources is used to illustrate the performance of the proposed method.

Heuristic methods for optimal electric vehicle charging scheduling in smart grid

The increasing popularity of electric vehicles (EV) will pose great challenge to the nation’s existing power grid by adding extra load during evening peak hours. This paper develops a centralised optimal charging scheduling (OCS) model with a mixed integer non-linear programme to mitigate the negative impact of extra load from EVs on the power grid. The objective of the OCS model is to minimise the energy cost of the entire system, which essentially levels the load of the entire power grid throughout a day under the dynamic pricing environment. Furthermore, a rolling horizon heuristic algorithm is proposed as an alternative solution that addresses large-scale OCS instances. Finally, when centralised scheduling is impractical, this paper proposes a decentralised optimal charging heuristic using the concepts of game theory and coordinate search. Numerical results show that the optimal charging scheduling model can significantly lower the total energy cost and the peak-to-average ratio (PAR) for a power system. When compared to uncontrolled charging, the decentralised charging heuristic yields considerable energy savings as well, although not as efficient as the centralised optimal charging solutions.

Integration of intermittent renewable power supply using grid-connected vehicles – A 2030 case study for California and Germany

This paper describes a method to characterize the fluctuating electricity generation of renewable energy sources (RESs) in a power system and compares the different parameters for California and Germany. Based on this method describing the fluctuation and residual load, the potential contribution of grid-connected vehicles to balancing generation from renewable energy sources is analyzed for a 2030 scenario using the agent-based simulation model PowerACE. The analysis reveals that integrating fluctuating RES is possible with less effort in California because of a higher correlation between RES generation and the load curve here. In addition, RES capacity factors are higher for California and therefore the ratio of installed capacity to peak load is lower. Germany, on the other hand, faces extreme residual load changes between periods with and without supply from RES. In both power system scenarios, grid-connected vehicles play an important role in reducing residual load fluctuation if smart charging is used. Uncontrolled charging or static time-of-use tariffs do not significantly improve the grid integration of RES.

Comparison of intelligent charging algorithms for electric vehicles to reduce peak load and demand variability in a distribution grid

A potential breakthrough of the electrification of the vehicle fleet will incur a steep rise in the load on the electrical power grid. To avoid huge grid investments, coordinated charging of those vehicles is a must. In this paper, we assess algorithms to schedule charging of plug-in (hybrid) electric vehicles as to minimize the additional peak load they might cause. We first introduce two approaches, one based on a classical optimization approach using quadratic programming, and a second one, market based coordination, which is a multi-agent system that uses bidding on a virtual market to reach an equilibrium price that matches demand and supply. We benchmark these two methods against each other, as well as to a baseline scenario of uncontrolled charging. Our simulation results covering a residential area with 63 households show that controlled charging reduces peak load, load variability, and deviations from the nominal grid voltage.