Smart management of distinct plug‐in hybrid electric vehicle charging stations considering mobility pattern and site characteristics

Management of plug‐in hybrid electric vehicles (PHEVs) is an important alternative energy solution to accord the prevailing environmental depletion. However, adding PHEVs to the existing distribution network may stimulate issues such as increase in peak load, power loss, and voltage deviation. Addressing the aforementioned issues by incorporating distinct mobility patterns together will develop an attractive energy management. In this paper, suitable location of the charging station is presented for a novel 2‐area distribution system following distinct mobility patterns. A comprehensive study by considering the optimal, midst, and unfit site for placing the charging station is incorporated. For managing the charging sequence of PHEVs, a meta‐heuristic solving tool is developed. The main contribution of this programming model is its ability to schedule the vehicles simultaneously in both the areas. The efficiency of the proposed energy management framework is evaluated on the IEEE 33‐bus and IEEE 69‐bus distribution systems. The test system is subjected to different scenarios for demonstrating the superior performance of the proposed solving tool in satisfying the convenience of vehicle owner along with reducing the peak demand. The results show that charging at low electricity price period and discharging at high electricity price period enables the minimum operational cost.     

A realistic framework to a greener supply chain for electric vehicles

The quantity of electric vehicles in the transport sector has steadily risen over the last 10 years. Most developed countries and China have laid out ambitious plans for electric vehicles penetration. However, there are several challenges that must be addressed on the supply‐chain side of the problem for a successful transition toward an alternative and less environmentally harmful transport system. This study proposes a methodology for the optimal plan and decision making of primary energy sources, electricity generation, electricity distribution to vehicles' charging stations, carbon capture and sequestration, and electric vehicles' charging stations network to satisfy the electricity demand of the overall economy including electric vehicles at a regional/countrywide level under operation and green constraints. The optimization problem was modeled as a mixed integer program in general algebraic modeling system (GAMS). The formulation was employed to propose the upcoming electricity supply chain for electric vehicles in the most populous German state (North Rhine‐Westphalia) in 2025. The optimization show that fossil‐based power still controls the generation in 2025, while carbon capture and sequestration along with higher renewable penetration help meeting the state's greenhouse gases (GHG) emission target. The charging stations network expansion consists of 12 820 charging points mainly alternating current (AC) chargers (22‐kW capacity).     

Role of hydrogen in future North European power system in 2060

The Balmorel model has been used to calculate the economic optimal energy system configuration for the Scandinavian countries and Germany in 2060 assuming a nearly 100% coverage of the energy demands in the power, heat and transport sector with renewable energy sources. Different assumptions about the future success of fuel cell technologies have been investigated as well as different electricity and heat demand assumptions. The variability of wind power production was handled by varying the hydropower production and the production on CHP plants using biomass, by power transmission, by varying the heat production in heat pumps and electric heat boilers, and by varying the production of hydrogen in electrolysis plants in combination with hydrogen storage. Investment in hydrogen storage capacity corresponded to 1.2% of annual wind power production in the scenarios without a hydrogen demand from the transport sector, and approximately 4% in the scenarios with a hydrogen demand from the transport sector. Even the scenarios without a demand for hydrogen from the transport sector saw investments in hydrogen storage due to the need for flexibility provided by the ability to store hydrogen. The storage capacities of the electricity storages provided by plug-in hybrid electric vehicles were too small to make hydrogen storage superfluous.     

Fuel reduction and electricity consumption impact of different charging scenarios for plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEVs) consume both gasoline and grid electricity. The corresponding temporal energy consumption and emission trends are valuable to investigate in order to fully understand the environmental benefits. The 24-h energy consumption and emission profile depends on different vehicle designs, driving, and charging scenarios. This study assesses the potential energy impact of PHEVs by considering different charging scenarios defined by different charging power levels, locations, and charging time. The region selected for the study is the South Coast Air Basin of California. Driving behaviors are derived from the National Household Travel Survey 2009 (NHTS 2009) and vehicle parameters are based on realistic assumptions consistent with projected vehicle deployments. Results show that the reduction in petroleum consumption is significant compared to standard gasoline vehicles and the ability to operate on electricity alone is crucial to cold start emission reduction. The benefit of higher power charging on petroleum consumption is small. Delayed and average charging are better than immediate charging for home, and non-home charging increases peak grid loads.     

The effects of vehicle‐to‐grid systems on wind power integration

Renewable energy portfolio standards have created a large increase in the amount of renewable electricity production, and one technology that has benefited greatly from these standards is wind power. The uncertainty inherent in wind electricity production dictates that additional amounts of conventional generation resources be kept in reserve, should wind electricity output suddenly dip. The introduction of plug‐in hybrid electric vehicles into the transportation fleet presents an possible solution to this problem through the concept of vehicle‐to‐grid power. The ability of vehicle‐to‐grid power systems to help solve the variability and uncertainty issuess in systems with large amounts of wind power capacity is examined through a multiparadigm simulation model. The problem is examined from the perspectives of three different stakeholders: policy makers, the electricity system operator and plug‐in hybrid electric vehicle owners. Additionally, a preliminary economic analysis of the technology is performed, and a comparison made with generation technologies that perform similar functions. Copyright © 2011 John Wiley & Sons, Ltd.     

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%.     

Optimising residential electric vehicle charging under renewable energy: Multi‐agent learning in software simulation and hardware‐in‐the‐loop evaluation

The integration of intermittent renewable energy sources coupled with the increasing demand of electric vehicles (EVs) poses new challenges to the electrical grid. To address this, many solutions based on demand response have been presented. These solutions are typically tested only in software‐based simulations. In this paper, we present the application in hardware‐in‐the‐loop (HIL) of a recently proposed algorithm for decentralised EV charging, prediction‐based multi‐agent reinforcement learning (P‐MARL), to the problem of optimal EV residential charging under intermittent wind power and variable household baseload demands. P‐MARL is an approach that can address EV charging objectives in a demand response aware manner, to avoid peak power usage while maximising the exploitation of renewable energy sources. We first train and test our algorithm in a residential neighbourhood scenario using GridLAB‐D, a software power network simulator. Once agents learn optimal behaviour for EV charging while avoiding peak power demand in the software simulator, we port our solution to HIL while emulating the same scenario, in order to decrease the effects of agent learning on power networks. Experimental results carried out in a laboratory microgrid show that our approach makes full use of the available wind power, and smooths grid demand while charging EVs for their next day's trip, achieving a peak‐to‐average ration of 1.67, down from 2.24 in the baseline case. We also provide an analysis of the additional demand response effects observed in HIL, such as voltage drops and transients, which can impact the grid and are not observable in the GridLAB‐D software simulation.     

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...     

Smart utilization of renewable energy sources in a microgrid system integrated with plug‐in hybrid electric vehicles

Mass roll‐out of plug‐in hybrid electric vehicles (PHEVs) and significant penetration of renewable energy sources in distribution system play a major role in delivering low carbon environment. However, placing and utilizing these units randomly result in overloading, increased power loss, and reduced voltage profile. This paper responds to these technical challenges by using a strategic placement method for locating the distributed generation (DG) and the charging station (CS) of PHEVs in a multi‐zone distribution system. For simultaneously scheduling of these units in each zone, the smart energy management framework is proposed in this paper. Apart from usual energy management constraints, this paper also incorporates the real‐time constraints involving the capacity of PHEV batteries, the mobility pattern, and the power level of the charging infrastructure. The simulation studies are carried out for each hour of a day. To cope with this time constraint execution, particle swarm optimization algorithm‐based approach is used. The proposed framework is tested in IEEE 33 and IEEE 69 bus radial distribution system. The obtained results imply that the presented energy management framework provides maximum profits for the vehicle owner, and meanwhile it fulfills preferences of the user in each zone simultaneously.     

Model‐based approach for planning renewable energy transition in a resource‐constrained electricity system—A case study from India

Globally, electricity systems are going through transitions. The contributions from renewable energy‐based power generation, both in installed capacity and electricity generation, are moving from marginal to the mainstream. India is not an exception; it is aggressively pursuing this transition by fixing steep targets for renewable capacity additions. While the cost of renewable energy sources is expected to fast reach grid parity, the policy interventions play a critical role in ramping up the efforts to support the proposed investments in renewable capacity and renewable electricity generation. In this respect, this research attempts to analyze the effectiveness of renewable energy policies such as Renewable Purchase Obligation (RPO) and Renewable Energy Certificate mechanisms in tapping the renewable energy potential in India. We propose a mixed‐integer linear programming model‐based approach to evaluate the effectiveness of the above interventions in the Indian context. The model is developed and validated as a low carbon electricity planning tool to optimally meet the dynamic electricity demand and RPO targets as well as to manage the unmet total electricity demand and RPO targets. The Karnataka state electricity system (a state in south India) is chosen as a case study. The results suggest that Karnataka Electricity System is moving toward a sustainable renewable energy future even without any support from nonsolar Renewable Energy Certificate policy. However, policy interventions are critical for optimally utilizing the solar generation capacity.     

Stochastic security-constrained operation of wind and hydrogen energy storage systems integrated with price-based demand response

Because of highly increasing energy consumption, environmental issues and lack of common energy sources, the use of renewable energy sources especially wind power generation technology is increasing with significant growth in the world. But due to the variable nature of these sources, new challenges have been created in the balance between production and consumption of power system. The hydrogen energy storage (HES) system by storing excess wind power through the technology of power to hydrogen (P2H) and delivering it to the electricity network through hydrogen-based gas turbine at the required hours reduces not only wind alternation but can play an important role in balancing power production and consumption. On the other hand, power consumers by participating in demand response (DR) programs can reduce their consumption at peak load or wind power shortage hours, and increase their consumption at low-load or excess wind power hours to reduce wind power spillage and system energy cost. This paper proposes a stochastic security constrained unit commitment (SCUC) with wind energy considering coordinated operation of price-based DR and HES system. Price-based DR has been formulated as a price responsive shiftable demand bidding mechanism. The proposed model has been tested on modified 6-bus and 24-bus systems. The numerical results show the effect of simultaneous consideration of HES system and price-based DR integrated with wind energy on hourly generation scheduling of thermal units. As a result there is some reduction in wind generation power spillage and daily operation cost.     

Environmental impact assessment of green energy systems for power supply of electric vehicle charging station

The Electric Vehicle (EV) as a clean alternative to Classic Vehicle that use fossil fuels is promoted as an immediate solution to improve the quality parameters of the environment related to the transport sector. The transition to clean electrified mobility must be considered from the sustainability spectrum, and the planning of a strategy related to the implementation of electric vehicles implies, from the beginning, providing clean energy conditions to go toward a green-to-green paradigm. It should be noted that the successful implementation of the “green electro mobility” concept depends heavily on the green energy supply solutions of green electric vehicle, so Electric Vehicle Charging Stations (EV-CS) should be powered by electricity generation systems based on green resources. This research article has as main objective the environmental impact assessment from the perspective of CO₂ emissions embedded in green stand-alone energy systems and the estimation of the environmental benefits of their implementation in the power supply of EV-CS from the perspective of avoided CO₂ emissions compared to the classic electricity supply grid. The results indicate that the green energy systems represent feasible solutions for the independent energy support of electric vehicle charging stations, being able to supply electricity based on on-site available 100% alternative energy sources. Related to 1 kWh of electricity, the CO₂ emissions embedded in these systems represent on average 11.40% of the CO₂ emissions of the electricity supplied through the grid at European level and on average 7.10% of the CO₂ emissions of the electricity supplied through the grid worldwide. Results also show that the average price of 1kWh of electricity generated by the analyzed systems is 4.3 times higher than the average unit price of the European Union grid energy, but this indicator must be correlated with the kgCO₂/kWh cost savings compared to the electricity production from classic power plants.     

Optimal asset allocation of wind energy units in conjunction with demand response for a large‐scale electric grid

Demand response is considered to be a realistic and comparatively inexpensive solution aimed at increasing the penetration of renewable generations into the bulk electricity systems. The work in this paper highlights the demand response in conjunction with the optimal capacity of installed wind energy resources allocation. Authors proposed a total annual system cost model to minimize the cost of allocating wind power generating assets. This model contains capacity expansion, production, uncertainty, wind variability, emissions, and elasticity in demand to find out cost per hour to deliver electricity. A large‐scale electric grid (25 GW) is used to apply this model. Authors discovered that demand response based on interhourly system is not as much helpful as demand response grounded on intrahourly system. According to results, 32% wind generation share will provide the least cost. It is also worth noting that optimal amount of wind generation is much sensitive to installation cost as well as carbon tax.     

Comparative impacts of wind and photovoltaic generation on energy storage for small islanded electricity systems

This paper addresses the annual energy storage requirements of small islanded electricity systems with wind and photovoltaic (PV) generation, using hourly demand and resource data for a range of locations in New Zealand. Normalised storage capacities with respect to annual demand for six locations with winter-peaking demand profiles were lower for wind generation than for PV generation, with an average PV:wind storage ratio of 1.768:1. For two summer-peaking demand profiles, normalised storage capacities were lower for PV generation, with storage ratios of 0.613:1 and 0.455:1. When the sensitivity of storage was modelled for winter-peaking demand profiles, average storage ratios were reduced. Hybrid wind/PV systems had lower storage capacity requirements than for wind generation alone for two locations. Peak power for storage charging was generally greater with PV generation than with wind generation, and peak charging power increased for the hybrid systems. The results are compared with those for country-scale electricity systems, and measures for minimising storage capacity are discussed. It is proposed that modelling of storage capacity requirements should be included in the design process at the earliest possible stage, and that new policy settings may be required to facilitate a transition to energy storage in fully renewable electricity systems.     

Energy management system for smart grid: An overview and key issues

Energy crisis and the global impetus to “go green” have encouraged the integration of renewable energy resources, plug-in electric vehicles, and energy storage systems to the grid. The presence of more than one energy source in the grid necessitates the need for an efficient energy management system to guide the flow of energy. Moreover, the variability and volatile nature of renewable energy sources, uncertainties associated with plug-in electric vehicles, the electricity price, and the time-varying load bring new challenges to the power engineers to achieve demand-supply balance for stable operation of the power system. The energy management system can effectively coordinate the energy sharing/trading among all available energy resources, and supply loads economically in all the conditions for the reliable, secure, and efficient operation of the power system. This paper reviews the framework, objectives, architecture, benefits, and challenges of the energy management system with a comprehensive analysis of different stakeholders and participants involved in it. The review paper gives a critical analysis of the distributed energy resources behavior and different programs such as demand response, demand-side management, and power quality management implemented in the energy management system. Different uncertainty quantification methods are also summarized. This review paper also presents a comparative and critical analysis of the main optimization techniques used to achieve different energy management system objectives while satisfying multiple constraints. Thus, the review offers numerous recommendations for research and development of the cutting-edge optimized energy management system applicable for homes, buildings, industries, electric vehicles, and the whole community.     

Life cycle cost analysis: A case study of hydrogen energy application on the Orkney Islands

Hydrogen can compensate for the intermittent nature of some renewable energy sources and encompass the options of supplying renewables to offset the use of fossil fuels. The integrating of hydrogen application into the energy system will change the current energy market. Therefore, this paper deploys the life cycle cost analysis of hydrogen production by polymer electrolyte membrane (PEM) electrolysis and applications for electricity and mobility purposes. The hydrogen production process includes electricity generated from wind turbines, PEM electrolyser, hydrogen compression, storage, and distribution by H₂ truck and tube trailer. The hydrogen application process includes PEM fuel cell stacks generating electricity, a H₂ refuelling station supplying hydrogen, and range extender fuel cell electric vehicles (RE-FCEVs). The cost analysis is conducted from a demonstration project of green hydrogen on a remote archipelago. The methodology of life cycle cost is employed to conduct the cost of hydrogen production and application. Five scenarios are developed to compare the cost of hydrogen applications with the conventional energy sources considering CO₂ emission cost. The comparisons show the cost of using hydrogen for energy purposes is still higher than the cost of using fossil fuels. The largest contributor of the cost is the electricity consumption. In the sensitivity analysis, policy supports such as feed-in tariff (FITs) could bring completive of hydrogen with fossil fuels in current energy market.     

配电网对电动汽车可接纳能力分析

随着新能源汽车保有量持续上涨,大规模电动汽车接入对城市配电网的安全稳定运行带来了一定影响,研究配电网对电动汽车可接纳能力十分迫切。该研究基于不同类型电动汽车性能参数和驾驶者行为特性建立了电动汽车充电功率需求模型,分析了一定规模电动汽车接入对配电网日负荷曲线的影响。设置了含电动汽车V2G和DG并网发电4种不同场景,从电压偏差和静态电压稳定极限两个方面定量分析了配电网对电动汽车的接纳能力,以IEEE33节点配电系统为例,对比了4种不同场景下配电网对电动汽车的接纳能力。     

Future security of power supply in Germany—The role of stochastic power plant outages and intermittent generation

Many power plants in Germany and Europe are approaching the end of their technical lifetime. Moreover, the increasing wind and solar power generation reduces the operation times of thermal power plants, making future investments in new generation capacity uncertain under current market conditions. Consequently, the future development of security of power supply is unclear. In this paper, we assess the impact of stochastic fluctuations in power plant availability, renewable generation, and grid load on the future security of supply in Germany. We model variations in power plant availability by application of a combined Mean‐reversion Jump‐diffusion approach. On the basis of that and using Monte‐Carlo methods, we simulate 300 different time series of availability. These profiles are fed into the fundamental power system model REMix, applied to evaluate the appearance of supply shortfalls in hourly resolution. We assess 6 scenarios for the year 2025, differing in renewable generation and demand profiles, as well as grid infrastructure. Geographical focus of the analysis is Germany, but the electricity exchange with its European neighbours is modelled as well. Our results show that the choice of the power plant availability profile can change the loss of load expectation and loss of load hours by up to 50%. However, the influence of load and renewable generation profiles is found to be significantly higher. Assuming that no new conventional power plants are built and existing plants are decommissioned at the end of their empirical lifetime, we identify supply gaps of up to 2.7 GW in Germany.     

高铁客运站分布式多能源系统供能协同优化策略研究

为解决传统高铁客运站供能系统中能源利用率较低的问题,以日运行购气费用和购电费用最优为优化目标,以系统运行过程中实时能量平衡为约束条件,以可再生能源出力和吸收式制冷占比为优化变量,建立多能源协同供能的分布式能源系统,并将该模型应用于北方某高铁客运站,分析可再生能源的利用率、制冷系统中可再生能源电出力的电制冷占比以及电网出力的节电率。仿真计算结果表明,分布式能源系统的使用提高了可再生能源的利用率,其中风电机组出力占其出力极限的96.5%,光伏机组出力94.7%;相比于参比系统,分布式能源系统的成本节约率为12.5%;电制冷占比为13%;电网的节电率为53.9%。     

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.