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.     

Effectiveness of smart charging of electric vehicles under power limitations

This article investigates charging strategies for plug‐in hybrid electric vehicles (PHEV) as part of the energy system. The objective was to increase the combined all‐electric mileage (total distance driven using only the traction batteries in each PHEV) when the total charging power at each workplace is subject to severe limitations imposed by the energy system. In order to allocate this power optimally, different input variables, such as state‐of‐charge, battery size, travel distance, and parking time, were considered. The required vehicle mobility was generated using a novel agent‐based model that describes the spatiotemporal movement of individual PHEVs. The results show that, in the case of Helsinki (Finland), smart control strategies could lead to an increase of over 5% in the all‐electric mileage compared to a no‐control strategy. With a high prediction error, or with a particularly small or large battery, the benefits of smart charging fade off. Smart PHEV charging strategies, when applied to the optimal allocation of limited charging power between the cars of a vehicle fleet, seem counterintuitively to provide only a modest increase in the all‐electric mileage. A simple charging strategy based on allocating power to PHEVs equally could thus perform sufficiently well. This finding may be important for the future planning of smart grids as limiting the charging power of larger PHEV fleets will sometimes be necessary as a result of grid restrictions. Copyright © 2013 John Wiley & Sons, Ltd.     

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 novel real‐time energy management strategy for plug‐in hybrid electric vehicles based on equivalence factor dynamic optimization method

The universal adaptive equivalent consumption minimization strategy (A‐ECMS) has the potential of being implemented in real‐time for plug‐in hybrid electric vehicles (PHEVs). However, the imprecise prediction of a long‐term future driving cycle and biggish computation burdens remain the barriers for further real vehicle application. Thus, it is of great significance to develop a real‐time optimal energy management strategy for PHEVs by weakening the influence of future driving cycle to the control accuracy and improving its computation efficiency. In this paper, a novel real‐time energy management strategy for PHEVs based on equivalence factor (EF) dynamic optimization method is proposed. Firstly, a novel proportional plus integral adaption law for calculating the dynamic optimal EF is established for A‐ECMS using only instantaneous information of current vehicle speed and battery state of charge. Second, three key coefficients are obtained and converted into a three‐dimensional look up tables, so as to determine the dynamic optimal EF. Finally, the method of fast searching the optimal engine torque is proposed, which significantly enhances the computational efficiency. Compared with A‐ECMS, the computational time of A‐ECMS2 is decreased near 94.8% and the deviation of fuel consumption is controlled within 4.4%. Both the numerical results and hardware‐in‐loop results prove that the proposed novel energy management strategy A‐ECMS2 has better real‐time performance and less computing burden than the general A‐ECMS.     

A comprehensive optimized model for on‐board solar photovoltaic system for plug‐in electric vehicles: energy and economic impacts

Environmental concerns along with high energy demand in transportation are leading to major development in sustainable transportation technologies, not the least of which is the utilization of clean energy sources. Solar energy as an auxiliary power source of on‐board fuel has not been extensively investigated. This study focuses on the energy and economic aspects of optimizing and hybridizing, the conventional energy path of plug‐in electric vehicles (EVs) using solar energy by means of on‐board photovoltaic (PV) system as an auxiliary fuel source. This study is novel in that the authors (i) modeled the comprehensive on‐board PV system for plug‐in EV; (ii) optimized various design parameters for optimum well‐to‐tank efficiency (solar energy to battery bank); (iii) estimated hybrid solar plug‐in EVs energy generation and consumption, as well as pure solar PV daily range extender; and (iv) estimated the economic return of investment (ROI) value of adding on‐board PVs for plug‐in EVs under different cost scenarios, driving locations, and vehicle specifications. For this study, two months in two US cities were selected, which represent the extremities in terms of available solar energy; June in Phoenix, Arizona and December in Boston, Massachusetts to represent the driving conditions in all the US states at any time followed by assessment of the results worldwide. The results show that, by adding on‐board PVs to cover less than 50% (around 3.2 m²) of the projected horizontal surface area of a typical passenger EV, the daily driving range could be extended from 3.0 miles to 62.5 miles by solar energy based on vehicle specifications, locations, season, and total time the EV remains at Sun. In addition, the ROI of adding PVs on‐board with EV over its lifetime shows only small negative values (larger than ?45%) when the price of electricity remains below Environmental concerns along with high energy demand in transportation are leading to major development in sustainable transportation technologies, not the least of which is the utilization of clean energy sources. Solar energy as an auxiliary power source of on‐board fuel has not been extensively investigated. This study focuses on the energy and economic aspects of optimizing and hybridizing, the conventional energy path of plug‐in electric vehicles (EVs) using solar energy by means of on‐board photovoltaic (PV) system as an auxiliary fuel source. This study is novel in that the authors (i) modeled the comprehensive on‐board PV system for plug‐in EV; (ii) optimized various design parameters for optimum well‐to‐tank efficiency (solar energy to battery bank); (iii) estimated hybrid solar plug‐in EVs energy generation and consumption, as well as pure solar PV daily range extender; and (iv) estimated the economic return of investment (ROI) value of adding on‐board PVs for plug‐in EVs under different cost scenarios, driving locations, and vehicle specifications. For this study, two months in two US cities were selected, which represent the extremities in terms of available solar energy; June in Phoenix, Arizona and December in Boston, Massachusetts to represent the driving conditions in all the US states at any time followed by assessment of the results worldwide. The results show that, by adding on‐board PVs to cover less than 50% (around 3.2 m²) of the projected horizontal surface area of a typical passenger EV, the daily driving range could be extended from 3.0 miles to 62.5 miles by solar energy based on vehicle specifications, locations, season, and total time the EV remains at Sun. In addition, the ROI of adding PVs on‐board with EV over its lifetime shows only small negative values (larger than ?45%) when the price of electricity remains below $0.18/kWh and the vehicle is driven in low‐solar energy area (e.g. Massachusetts in the US and majority of Europe countries). The ROI is more than 148% if the vehicle is driven in high‐solar energy area (e.g. Arizona in the US, most Africa countries, Middle East, and Mumbai in India), even if the electricity price remains low. For high electricity price regions ($0.35/kWh), the ROI is positive and high under all driving scenarios (above 560%). Also, the reported system has the potential to reduce electricity consumption from grid by around 4.5 to 21.0 MWh per EV lifetime. A sensitivity analysis has been carried out, in order to study the impacts of the car parked in the shade on the results. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

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.     

Regulatory framework and business models for charging plug-in electric vehicles: Infrastructure,agents, and commercial relationships

Electric vehicles (EVs) present efficiency and environmental advantages over conventional transportation. It is expected that in the next decade this technology will progressively penetrate the market. The integration of plug-in electric vehicles in electric power systems poses new challenges in terms of regulation and business models. This paper proposes a conceptual regulatory framework for charging EVs. Two new electricity market agents, the EV charging manager and the EV aggregator, in charge of developing charging infrastructure and providing charging services are introduced. According to that, several charging modes such as EV home charging, public charging on streets, and dedicated charging stations are formulated. Involved market agents and their commercial relationships are analysed in detail. The paper elaborates the opportunities to formulate more sophisticated business models for vehicle-to-grid applications under which the storage capability of EV batteries is used for providing peak power or frequency regulation to support the power system operation. Finally penetration phase dependent policy and regulatory recommendations are given concerning time-of-use pricing, smart meter deployment, stable and simple regulation for reselling energy on private property, roll-out of public charging infrastructure as well as reviewing of grid codes and operational system procedures for interactions between network operators and vehicle aggregators.