Impact of charging efficiency variations on the effectiveness of variable-rate-based charging strategies for electric vehicles

The huge energy demand coming from the increasing diffusion of plug-in electric vehicles (PEVs) poses a significant challenge to electricity utilities and vehicle manufacturers in developing smart charging systems interacting in real time with distribution grids.These systems will have to implement smart charging strategies for PEV batteries on the basis of negotiation phases between the user and the electric utility regarding information about battery chemistries, tariffs, required energy and time available for completing the charging. Strategies which adapt the charging current to grid load conditions are very attractive. Indeed, they allow full exploitation of the grid capacity, with a consequent greater final state of charge and higher utility financial profits with respect to approaches based on a fixed charging rate.The paper demonstrates that the charging current should be chosen also taking into account the effect that different charging rates may have on the charging efficiency. To this aim, the performances of two smart variable-rate-based charging strategies, taken as examples, are compared by considering possible realistic relationships between the charging efficiency and the charging rate. The analysis gives useful guidelines for the development of smart charging strategies for PEVs as well as for next-generation battery charging and smart grid management systems.     

Grid integration of intermittent renewable energy sources using price-responsive plug-in electric vehicles

Plug-in electric vehicles (PEVs) are expected to balance the fluctuation of renewable energy sources (RES). To investigate the contribution of PEVs, the availability of mobile battery storage and the control mechanism for load management are crucial. This study therefore combined the following: a stochastic model to determine mobility behavior, an optimization model to minimize vehicle charging costs and an agent-based electricity market equilibrium model to estimate variable electricity prices. The variable electricity prices are calculated based on marginal generation costs. Hence, because of the merit order effect, the electricity prices provide incentives to consume electricity when the supply of renewable generation is high. Depending on the price signals and mobility behavior, PEVs calculate a cost minimizing charging schedule and therefore balance the fluctuation of RES. The analysis shows that it is possible to limit the peak load using the applied control mechanism. The contribution of PEVs to improving the integration of intermittent renewable power generation into the grid depends on the characteristic of the RES generation profile. For the German 2030 scenario used here, the negative residual load was reduced by 15–22% and the additional consumption of negative residual load was between 34 and 52%.     

Economic and environmental evaluations of dedicated and residential electric tariffs for plug‐in electric vehicles

Plug‐in electric vehicle (PEV) owners may have multiple different electric tariffs offered by their local utility companies from which to choose. The offered PEV tariffs are designed mainly to shift the electric demand for charging cars to the time when the grid is less strained. This paper investigates both the economic and the environmental impacts of adopting dedicated PEV electric tariffs from the PEV owners' perspective. The overall conclusion is that the dedicated tariffs are well designed for PEVs from the economical perspective but not from the environmental perspective. Case studies of the cost minimization model show that on average the dedicated PEV tariffs will result in approximately half the cost of the electric bill and slightly lower greenhouse gas (GHG) emissions (less than 1%) compared with the standard flat‐rate residential tariffs. Case studies of the emission minimization model show that the GHG emissions can be reduced by 10.47% as compared with the cost minimization model, but this will lead to an increase in the total charging cost that can be as high as 15.44% on average.     

The impact of plug-in vehicles on greenhouse gas and criteria pollutants emissions in an urban air shed using a spatially and temporally resolved dispatch model

With the introduction of plug-in vehicles (PEVs) into the light-duty vehicle fleet, the tail-pipe emissions of GHGs and criteria pollutants will be partly transferred to electricity generating units. To study the impact of PEVs on well-to-wheels emissions, the U.S. Western electrical grid serving the South Coast Air Basin (SoCAB) of California is modeled with both spatial and temporal resolution at the level of individual power plants. Electricity load is calculated and projected for future years, and the temporal electricity generation of each power plant within the SoCAB is modeled based on historical data and knowledge of electricity generation and dispatch.Due to the efficiency and pollutant controls governing the performance of the Western grid, the deployment of PEVs results in a daily reduction of greenhouse gases (GHGs) and tail-pipe emissions, especially in the critical morning and afternoon commute hours. The extent of improvement depends on charging scenarios, future grid mix, and the number and type of plug-in vehicles. In addition, charging PEVs using wind energy that would otherwise be curtailed can result in a substantial emissions reduction. Smart control will be required to manage PEV charging in order to mitigate renewable intermittencies and decrease emissions associated with peaking power production.     

A centralized vehicle‐to‐grid scheme with distributed computing capacity engaging internet of smart charging points: Case study

In order to accommodate additional plug‐in electric vehicle (PEV) charging loads for existing distribution power grids, the vehicle‐to‐grid (V2G) technology has been regarded as a cost‐effective solution. Nevertheless, it can hardly scale up to large PEVs fleet coordination due to the computational complexity issue. In this paper, a centralized V2G scheme with distributed computing capability engaging internet of smart charging points (ISCP) is proposed. Within ISCP, each smart charging point equips a computing unit and does not upload PEV sensitive information to the energy coordinator, to protect PEV users’ privacy. Particularly, the computational complexity can be decreased dramatically by employing distributed computing, viz., by decomposing the overall scheduling problem into many manageable sub‐problems. Moreover, six typical V2G scenarios are analyzed deliberately, and based on that, a load peak‐shaving and valley‐filling scheduling algorithm is built up. The proposed algorithm can be conducted in real‐time to mitigate the uncertainties in arrival time, departure time, and energy demand. Finally, the proposed scheme and its algorithm are verified under the distribution grid of the SUSTech campus (China). Compared with uncoordinated charging, the proposed scheme realizes load peak‐shaving and valley‐filling by 11.98% and 12.68%, respectively. The voltage values are ensured within the limitation range by engaging power flow calculation, in which the minimum voltage values are increasing and the maximum voltage values are decreasing with the expansion of PEV penetration. What is more, the computational complexity of peak‐shaving and valley‐filling strategy is near‐linear, which verifies the proposed scheme can be carried out very efficiently.     

一种新型太阳能冷库系统的模拟及实验研究

针对太阳能光伏发电技术、需求侧管理（DSM）技术和储能技术提出一种新型太阳能冷库系统。探讨利用冷能储存和光伏发电来减少和转移峰值电力需求、降低电力消耗成本的潜力。建立系统的仿真模型并通过实验进一步修正,结果表明系统在5—8月份基本可节约60%以上的电费,能源节约率最高可达48.38%,相应的电费支付最高可节省65.40%。     

Preference and lifestyle heterogeneity among potential plug-in electric vehicle buyers

We characterize heterogeneity in preferences and motivations regarding plug-in electric vehicles (PEVs)—including plug-in hybrids (PHEVs) and electric vehicles (EVs). Using survey data collected from 1754 new vehicle buying households in Canada in 2013, we segment respondents using two approaches that prove to be complementary. Preference-based segments were constructed using latent-class analysis of discrete choice experiment data. Potential PEV buyers were split into a “PEV-enthusiast” segment (8% of the sample) with extremely high valuation of PEVs and a broader “PHEV-oriented” segment (25%) that expressed moderately positive valuation of PHEVs. Preference-based segments also varied by respondents' valuation of specific attributes such as fuel savings. Our second approach constructed lifestyle-based segments using cluster analysis on a subset of potential early PEV buyers (33% of the total sample). The six lifestyle-based clusters varied in engagement in environment- and technology-oriented lifestyles, environmental concern and openness to change. Overall preferences were fairly similar across the clusters, though apparent motivations varied substantially by cluster as indicated by their differing engagement in lifestyles and environmental concern. Taken together, both approaches suggest that PHEVs are the most likely PEV to have broad market appeal and that car buyers have high degrees of heterogeneity in both preferences and motivations.     

Economic-environmental energy supply of mobile base stations in isolated nanogrids with smart plug-in electric vehicles and hydrogen energy storage system

The mobile base stations (MBS) are fundamental communication devices that ensure the constant stream of interconnectivity. However, they are mostly installed in off-grid regions. This study investigates the economic-environmental energy supply of a MBS in an isolated nanogrid (ING) that also includes a hydrogen energy storage system (HES), photovoltaic (PV) system, controllable plug-in electric vehicles (PEV) and a diesel generator (DG). A novel mixed-integer second-order cone programming (MISOCP) formulation is proposed to capture the nonlinearities of the various components through a convex optimization model. The study included different uncertainties including the traffic rate of the MBS, driving schedule of the PEVs, and PV generation via a hybrid stochastic programming (SP) and robust optimization (RO) methods. The influence of the coordinated PEV charging strategy, risk-averse RO and multi-objective optimization was studied through various case studies. The outcomes show that coordinated PEV-charging can have a significant contribution in reducing the risks and curtailing both cost and emission objective functions, while using economic-environmental operation model can cut the emissions by 17.70%.     

Dynamic planning and energy management strategy of integrated charging and hydrogen refueling at highway energy supply stations considering on-site green hydrogen production

The layout of electric vehicles charging stations and hydrogen refueling stations (HRSs) is more and more necessary with the development of electric vehicles (EVs) and progress in hydrogen energy storage technology. Due to the high costs of HRSs and the low demand for hydrogen, it is difficult for independent HRSs to make a profit. This study focuses on the dynamic planning of energy supply stations on highways in the medium and long term, considering the growth of EV charging demand and the change in the proportion of hydrogen fuel cell vehicles (HFCVs). Based on the perspective of renewable energy generators (REGs), this study seeks the dynamic optimal configuration and comprehensive benefits of adding HRS and battery to existing EVCS considering the travel rules of new energy vehicles (NEVs). The results show that (1) It is profitable for REGs to invest in HRSs; (2) The economy of investment in batteries by REGs depends on the source-load matching. It is feasible only when the output of renewable energy is difficult to meet the demand. (3) The business model of REGs producing hydrogen on-site and supplying both electricity and hydrogen is feasible.     

Modeling light-duty plug-in electric vehicles for national energy and transportation planning

This paper sets forth a family of models of light-duty plug-in electric vehicle (PEV) fleets, appropriate for conducting long-term national-level planning studies of the energy and transportation sectors in an integrated manner. Using one of the proposed models, three case studies on the evolution of the U.S. energy and transportation infrastructures are performed, where portfolios of optimum investments over a 40-year horizon are identified, and interdependencies between the two sectors are highlighted. The results indicate that with a gradual but aggressive introduction of PEVs coupled with investments in renewable energy, the total cost from the energy and transportation systems can be reduced by 5%, and that overall emissions from electricity generation and light-duty vehicle (LDV) tailpipes can be reduced by 10% over the 40-year horizon. The annual gasoline consumption from LDVs can be reduced by 66% by the end of the planning horizon, but an additional 800 TWh of annual electricity demand will be introduced. In addition, various scenarios of greenhouse gas (GHG) emissions reductions are investigated. It is found that GHG emissions can be significantly reduced with only a marginal cost increment, by shifting electricity generation from coal to renewable sources.     

基于智能电网的电动汽车充放电分时电价及引导策略

为了以绿色、环保能源满足全球可持续发展的需求,可再生能源和电动汽车在全球范围内受到广泛推崇.在此情形下,高比例可再生能源发电和大规模电动汽车无序分散接入电网必将导致供求曲线的不稳定.为此,借助云存储技术和智能电网,提出了一种基于供求曲线的电动汽车充放电分时电价,并在制定充放电价格时考虑充电站的空闲率.以实现充电站和用户...     

Storage and Demand Side Management as power generator’s strategic instruments to influence demand and prices

Classic storage utilisation is mainly based on charging/discharging strategies enabling a power generation company to generate revenues by buying electricity in low-price periods and selling it at higher prices. Within this paper another feasible way to gain arbitrage in storage utilisation is considered: Strategically increasing demand disregarding existing market prices. This means, charging electricity for storage is not only bought in low price periods, but in all periods where storage charging could influence demand so that market prices increase. This idea is expanded by focussing on another frequently discussed topic which could serve utilities for the same purpose: Automated Demand Side Management (DSM). By using data from Ontario’s electricity market and applying a particular storage and DSM strategy it is analysed to which extent a non-regulated dominant power generation company could influence hourly demand and corresponding prices. It turns out that both strategies analysed derive additional revenues for the dominant power generation company compared to a Business-as-Usual (BAU) case. The results provide an indicator of potential threats for misuse from particular storage or DSM utilisation. Therefore, especially in countries where DSM and/or storage applications are still in its fledgling stages, appropriate market surveillance has to be guaranteed.     

智能电网下多时间尺度家庭能量管理优化策略

建立负荷在功率约束与需求响应约束下的激励需求响应模型以及含分布式电源、储能与电动汽车的家庭用电模型,在预测模型多时间尺度能量管理的基础上,以最小化用户自身用电费用与买电功率波动的两层目标函数实时优化调整策略。通过实时调整储电池、电动汽车的充放电,从而保证用户购电满足需求相应的要求。最后采用改进的粒子群算法对多时间尺度目标函数进行求解,并且与原始的粒子群算法进行对比,结果表明所提算法可显著降低用户的用电费用与功率波动。     

一种电动汽车光伏充电站的分时段有序充电策略

  目的  针对电动汽车光伏充电站的有序充电问题,提出一种分时段有序充电策略。  方法  研究了光伏充电站的结构体系及工作原理,构造了电动汽车充电时间、储能电池荷电状态（SOC）范围等约束条件。负荷高峰时段,在满足车辆充电需求的同时,减少向电网的购电量,降低购电费用,并辅助电网“消峰”。负荷低谷时段,在满足车辆充电需求的同时,增加向电网的购电量,辅助电网“填谷”。  结果  通过仿真事例验证了所建模型的有效性,并与即时充电方案进行比较,说明了所建模型在减小充电站的购电费用,降低电网峰谷差方面的优势。  结论  所提电动汽车光伏充电站的充电策略是正确并有效的,可为实际应用提供参考。     

Integration scenarios of Demand Response into electricity markets: Load shifting,financial savings and policy implications

Demand Response allows for the management of demand side resources in real-time; i.e. shifting electricity demand according to fluctuating supply. When integrated into electricity markets, Demand Response can be used for load shifting and as a replacement for both control reserve and balancing energy. These three usage scenarios are compared based on historic German data from 2011 to determine that load shifting provides the highest benefit: its annual financial savings accumulate to €3.110 M for both households and the service sector. This equals to relative savings of 2.83% compared to a scenario without load shifting. To improve Demand Response integration, the proposed model suggests policy implications: reducing bid sizes, delivery periods and the time-lag between market transactions and delivery dates in electricity markets.     

Greenhouse gas emission intensity factors for marginal electricity generation in Canada

In Canada, each province has its own electric utility system, and each system is responsible for meeting the demand of its customer base. Electricity demand in all provinces is highly variable throughout the day, as well as during the year. In order to achieve a good match between electricity demand and generation, a mix of base, intermediate and peaking load power plants is used, which uses different fuel sources. When a renewable energy technology or an energy efficiency measure that results in electricity savings is implemented on a regional, provincial and national scale, the electricity savings reflect in the peak (marginal) electricity generation. Thus, the greenhouse gas (GHG) emission reduction due to the reduction in electricity generation corresponds to the fuel used to generate the electricity at the margin. In Canada, the fuel used for marginal electricity generation varies from province to province and from hour to hour. To estimate the reduction in GHG emissions due to reducing electricity generation at the margin, it is necessary to have information on the fuel mix used to generate the marginal electricity for each province on a suitable time scale. With such information, it is possible to estimate a marginal GHG emission intensity factor for each province, which would provide the amount of GHG emissions produced as result of producing 1 kWh of electricity on the margin. However, such information is regarded confidential by most electric utilities and is not made public. In this paper, methodologies are presented to estimate the GHG intensity factors (GHGIFs) for marginal electricity generation for each province of Canada based on the information available in the public domain. The GHGIFs developed for each province are also presented, which are expected to be valid within the next 5‐year horizon. Copyright © 2010 John Wiley & Sons, Ltd.     

Evaluation of ground energy storage assisted electric vehicle DC fast charger for demand charge reduction and providing demand response

In 2012 there was approximately 2400 electric vehicle DC Fast Charging stations sold globally. According to Pike Research (Jerram and Gartner, 2012), it is anticipated that by 2020 there will be approximately 460,000 of them installed worldwide. A typical public DC fast charger delivers a maximum power output of 50 kW which allows a typical passenger vehicle to be 80% charged in 10–15 min, compared with 6–8 h for a 6.6 kW AC level 2 charging unit. While DC fast chargers offer users the convenience of being able to rapidly charge their vehicle, the unit's high power demand has the potential to put sudden strain on the electricity network, and incur significant demand charges.Depending on the utility rate structure, a DC fast charger can experience annual demand charges of several thousand dollars. Therefore in these cases there is an opportunity to mitigate or even avoid the demand charges incurred by coupling the unit with an appropriately sized energy storage system and coordinating the way in which it integrates. This paper explores the technical and economical suitability of coupling a ground energy storage system with a DC fast charge unit for mitigation or avoidance of demand charges and lessening the impact on the local electricity network. This paper also discusses the concept of having the system participate in demand response programs in order to provide grid support and to further improve the economic suitability of an energy storage system.     

Assessing green energy growth in Nepal with a hydropower-hydrogen integrated power grid model

The involvement of green hydrogen in energy transformation is getting global attention. This assessment examines the hydrogen production and its utilization potential in one of the hydropower-rich regions, Nepal under various demand growth and technology intervention scenarios by developing a power grid model of 52 nodes and 68 transmission lines operating at an hourly time-step. The model incorporates a grid-connected hydrogen storage system as well as charging stations for electric and hydrogen vehicles. The least-costly pathways for power grid expansion at the nodal and provincial levels are identified through optimization. The results show that 32 GW of installed capacity is required to meet domestic electricity demand and 14 GW more hydropower should be exploited to completely decarbonize the transport sector by 2050. For maintaining 50% shares of hydrogen vehicle in the transport sector and meet government electricity export targets, Nepal requires 5.7 GW, 12 GW and 23 GW of the additional electrolyzer, hydrogen storage tanks and storage-based hydropower capacities respectively. For a given electricity demand, introducing hydrogen systems can reduce the capacity requirements of hydro storage by storing surplus power generated from pondage run-of-the-river and run-of-the-river hydropower during the rainy season and using it in the dry season.     

Impact of electric vehicles on a future renewable energy‐based power system in Europe with a focus on Germany

Electric mobility is expected to play a key role in the decarbonisation of the energy system. Continued development of battery electric vehicles is fundamental to achieving major reductions in the consumption of fossil fuels and of CO₂ emissions in the transport sector. Hydrogen can become an important complementary synthetic fuel providing electric vehicles with longer ranges. However, the environmental benefit of electric vehicles is significant only if their additional electricity consumption is covered by power production from renewable energy sources. Analysing the implications of different scenarios of electric vehicles and renewable power generation considering their spatial and temporal characteristics, we investigate possible effects of electric mobility on the future power system in Germany and Europe. The time horizon of the scenario study is 2050. The approach is based on power system modelling that includes interchange of electricity between European regions, which allows assessing long‐term structural effects in energy systems with over 80% of renewable power generation. The study exhibits strong potential of controlled charging and flexible hydrogen production infrastructure to avoid peak demand increases and to reduce the curtailment of renewable power resulting in reduced system operation, generation, and network expansion costs. A charging strategy that is optimised from a systems perspective avoids in our scenarios 3.5 to 4.5 GW of the residual peak load in Germany and leads to efficiency gains of 10% of the electricity demand of plug‐in electric vehicles compared with uncontrolled loading.     

Evaluation of distributed building thermal energy storage in conjunction with wind and solar electric power generation

Energy storage is often seen as necessary for the electric utility systems with large amounts of solar or wind power generation to compensate for the inability to schedule these facilities to match power demand. This study looks at the potential to use building thermal energy storage as a load shifting technology rather than traditional electric energy storage. Analyses are conducted using hourly electric load, temperature, wind speed, and solar radiation data for a 5-state central U.S. region in conjunction with simple computer simulations and economic models to evaluate the economic benefit of distributed building thermal energy storage (TES). The value of the TES is investigated as wind and solar power generation penetration increases. In addition, building side and smart grid enabled utility side storage management strategies are explored and compared. For a relative point of comparison, batteries are simulated and compared to TES. It is found that cooling TES value remains approximately constant as wind penetration increases, but generally decreases with increasing solar penetration. It is also clearly shown that the storage management strategy is vitally important to the economic value of TES; utility side operating methods perform with at least 75% greater value as compared to building side management strategies. In addition, TES compares fairly well against batteries, obtaining nearly 90% of the battery value in the base case; this result is significant considering TES can only impact building thermal loads, whereas batteries can impact any electrical load. Surprisingly, the value of energy storage does not increase substantially with increased wind and solar penetration and in some cases it decreases. This result is true for both TES and batteries and suggests that the tie between load shifting energy storage and renewable electric power generation may not be nearly as strong as typically thought.