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Aiming at the problem of source-load uncertainty caused by the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs) into modern power system, a robust optimal operation scheduling algorithm for regional integrated energy systems (RIESs) with such uncertain situations is urgently needed. Based on this background, aiming at the problem of the irregular charging demand of EV, this paper first proposes an EV charging demand model based on the trip chain theory. Secondly, a multi-RIES optimization operation model including a shared energy storage station (SESS) and integrated demand response (IDR) is established. Aiming at the uncertainty problem of renewable energy, this paper transforms this kind of problem into a dynamic robust optimization with time-varying parameters and proposes an improved robust optimization over time (ROOT) algorithm based on the scenario method and establishes an optimal scheduling mode with the minimum daily operation cost of a multi-regional integrated energy system. Finally, the proposed uncertainty analysis method is verified by an example of multi-RIES. The simulation results show that in the case of the improved ROOT proposed in this paper to solve the robust solution of renewable energy, compared with the traditional charging load demand that regards the EVs as a whole, the EV charging load demand based on the trip chain can reduce the cost of EV charging by 3.5% and the operating cost of the multi-RIES by 11.7%. With the increasing number of EVs, the choice of the starting point of the future EV trip chain is more variable, and the choice of charging methods is more abundant. Therefore, modeling the charging demand of EVs under more complex trip chains is the work that needs to be studied in the future. [ABSTRACT FROM AUTHOR]

The need to reduce pollution and the shortage of fossil fuels has led to the increased development of hybrid and full electric vehicles. There is also increased development and an increased use of renewable energy resources such as photovoltaic, wind, tidal, etc. These two trends pose serious challenges to the existing grids: a lack of supply power when the demand is high, deficient management of excess power, an increased number off grid faults, grid instabilities and others. One way to increase the penetration of electric vehicles (EV) into the market and to keep the existing grid infrastructure is to combine renewable energy resources with the grid and local battery packs to make EV charging stations. This paper focuses on developing such an EV charging station. The main advantages of the proposed EV charger include the fact that it uses only off-the-shelf inverters, and it is intended to be used in households where the maximum installed power is 3.6 kW to enable fast-charging operation modes or to reduce the costs of energy while charging the EV battery; it can reduce the energy demand from the grid during peak power consumption; it has the potential to lower electrical energy costs; it offers the possibility of vehicle-to-home (V2H) implementation; it is modular (if other technologies become available and more affordable, the consumers can easily update the system, adding more power or adding other types of renewable resources). [ABSTRACT FROM AUTHOR]

The number of electric vehicles (EVs) is growing exponentially, which presents the power grid with new challenges to turn their reliance to renewable energy sources (RESs). Coordination between the available generations from RESs and the charging time should be managed to optimally utilize the available generation from RESs. The dispatch scheduling of EVs can significantly reduce the impact of these challenges on power systems. Three different technologies can be used to manage the dispatch of EV batteries which are unregulated charging (UC), unidirectional grid-to-vehicle (G2V), and bidirectional vehicle-to-grid (V2G) technologies. This study aims to address the primary reason for EV owners' disbelief in the accuracy of battery wear models, which is impeding their involvement in V2G technology. This paper introduces a novel accurate EV battery wear model considering the instantaneous change in the operation of the EV battery. Moreover, an effective musical chairs algorithm (MCA) is used to reduce everyday expenses and increase revenue for V2G technologies in a short convergence time with accurate determination of optimal power dispatch scheduling. The results obtained from these three strategies are compared and discussed. The salient result from this comparison is that V2G technology increases wear and reduces the battery lifespan in comparison with the UC and G2V. The yearly expenses of G2V are reduced by 33% compared to the one associated with the UC. Moreover, the use of V2G technology provides each EV owner with USD 3244.4 net yearly profit after covering the charging and wear costs. The superior results extracted from the proposed model showed the supremacy of V2G usage, which is advantageous for both EV owners and the power grid. [ABSTRACT FROM AUTHOR]

Due to the rapid expansion of electric vehicles (EVs), they are expected to be one of the main contributors to transportation. The increasing use of fossil fuels as one of the most available energy sources has led to the emission of greenhouse gases, which will play a vital role in achieving a sustainable transportation system. Developed and developing countries have long-term plans and policies to use EVs instead of internal combustion vehicles and to use renewable energy to generate electricity, which increases the number of charging stations. Recently, to meet the charging demand for EVs, the main focus of researchers has been on smart charging solutions. In addition, maintaining power quality and peak demand for grids has become very difficult due to the widespread deployment of EVs as personal and commercial vehicles. This paper provides information on EV charging control that can be used to improve the design and implementation of charging station infrastructure. An in-depth analysis of EV types, global charging standards, and the architectures of AC-DC and DC-DC converters are covered in this review article. In addition, investigating the role of EV collectors, as well as EV penetration, in electric energy systems to facilitate the integration of electric energy systems with renewable energy sources is one of the main goals of this paper. [ABSTRACT FROM AUTHOR]

In the course of the switch to renewable energy sources, there is a shift from a few large energy sources (power plants) to a large number of small, distributed energy sources (e.g., photovoltaic systems) and energy storage devices (e.g., electric vehicles). This results in the need to know and identify these energy sources and sinks as soon as new devices are installed, in order to ensure grid stability. This paper presents an approach to identify energy sources and energy storage in smart meter data, using photovoltaic systems and electric vehicles as examples. For this purpose, the Pecan Street dataset is used, which has been extended by charging processes from the ACN dataset. The presented approach comprises a combination of a Convolutional Neural Network and a Multilayer Perceptron, which decides separately, on the basis of the smart meter data of a household, whether an electric vehicle and a photovoltaic system are present. It is shown that the combination of both classifiers achieves accuracy of 90.50% in the case of electric vehicle detection and 96.37% in the case of photovoltaic systems. It is also shown that the power levels lower than 0 kW in the case of the photovoltaic system and higher than 5 kW in the case of the electric vehicles have the largest influence on the output of the Multilayer Perceptron branch, which uses the power balance distribution as input. [ABSTRACT FROM AUTHOR]

Global warming, pollution, and the depletion of fossil fuels have compelled human beings to explore alternate sources of energy and cleaner modes of transport. In recent years, renewable energy sources (RES) have been massively introduced to the grid. Furthermore, Electric Vehicles (EVs) are becoming popular as a cleaner mode of transport. However, the introduction of RESs and EVs to the grid has imposed additional challenges on the grid operators because of their random nature. This review aims to focus on the integration of RES and EVs to the grid, thereby presenting the global status of RESs and EVs, the impact of integrating RESs and EVs to the grid, the challenges of integrating RES and EV to the grid, optimization techniques for EV and RES integration to the grid, and mitigation techniques. A total of 153 research papers are meticulously reviewed, and the findings are put forward in this review. Thus, this review will put forward the latest developments in the area of EV and RES integration into the grid and will enlighten the researchers with the unsolved questions in the area that need investigation. [ABSTRACT FROM AUTHOR]

A large reduction in greenhouse gas (GHG) emissions will be needed in the coming decades to keep global warming well below 2 °C. Together, energy and transport sectors are responsible for around ¾ of carbon dioxide (CO2) emissions worldwide. The transition to a low-carbon economy involves an increase in the share of renewable energies in power grid and the electrification of the transports thus demanding policies focused on the joint diffusion of the two technologies This study adopted a hybrid methodology to examine several types of policies and their implications in the use and diffusion of the electric vehicles charged with renewables. We argue that it is not enough to have policies to expand electric mobility with renewable sources. The results reveal that production and technology represent ⅓ of electric vehicles and renewables policies. Broad coverage of policies does not translate into market gains since 20% of the countries that encourage electric vehicles have a low market share. Policies need to be broad, consistent, reach more countries and promote synergies between renewables and electric mobility to provide the conjoint diffusion of both technologies and allow the CO2 emission mitigation targets for 2030 to be achieved. This study contributes to research on sustainable policies and innovations to decarbonizing the energy and transport sectors. [ABSTRACT FROM AUTHOR]

The recent forecasts regarding the penetration of electric vehicles (EVs) in the transport market and their impact on national electricity distribution grids has presented new challenges in the fields of both of application and research. In this context, vehicle-to-grid (V2G) technology presents itself as an extremely valid solution in terms of application of the "demand side flexibility" paradigm. In this context, the aim of the paper is to analyze from a technical and economical point of view the use of EVs as new flexibility resources to provide network flexibility services in an Italian framework. Within this scope, a methodology for evaluating the flexibility service that a single EV or an EV fleet can offer, and therefore for estimating the EV storage system charge and discharge profile and determining its economic benefit, is proposed. Some numerical results and observations are reported to highlight possible incentive mechanisms for motivating EV end-users to offer flexibility services. [ABSTRACT FROM AUTHOR]

Electric vehicles reduce pollution only if a high percentage of the electricity mix comes from renewable sources and if the battery manufacturing takes place at a site far from the vehicle use region. Industries developed due to increased electric vehicle adoption may also cause additional air pollution. The Indian government has committed to solving New Delhi's air pollution issues through an ambitious policy of switching 100% of the light duty consumer vehicles to electric vehicles by 2030. This policy is based on vehicle grid interaction and relies on shared mobility through the electric vehicle fleet. There are several human behavioral changes necessary to achieve 100% adoption of electric vehicles. This paper reviews different steps in the lifecycle of an electric vehicle (EV), their impact on environmental emissions, and recommends policies suitable for different socio-economic group that are relevant to the Indian market. To reduce air pollution through adoption of electric vehicles, the Indian government needs to adopt policies that increase sale of electric vehicles, increase percentage of renewable energy in the electricity mix, and prevent air pollution caused from battery manufacturing. The recommended policies can be customized for any market globally for reducing air pollution through increased adoption of electric vehicles. [ABSTRACT FROM AUTHOR]

Battery energy storage systems (BESSs) can overwhelm some of the environmental challenges of a low-carbon power sector through self-consumption with standalone photovoltaic (PV) systems. This solution can be adapted for different applications such as residential, commercial, and industrial uses. Furthermore, the option to employ second-life batteries derived from electric vehicles represents a promising opportunity for preserving the environment and improving the circular economy (CE) development. Nowadays, the industrial sector is progressively applying CE principles in their business strategies, and focusing on the potential positive consequences of CE eco-innovations on climate change mitigation. With the aim to promote the transition to an open-loop circular economy for automotive batteries, this study assesses and quantifies the potential environmental benefits resulting from the integration of a second-life battery-based BESS (SL-BESS) connected to an industrial machine. For this purpose, various scenarios involving the use of BESS, SL-BESS, and a standalone PV system are compared with a base case, where the machine is entirely powered by electricity from the grid. The examination of life cycle stages follows the life cycle assessment (LCA) cradle-to-grave methodology as outlined in ISO 14040:2006 and ISO 14044:2006/Amd 1:2017. Simapro® 9 is utilized as the software platform. Results demonstrate that the combination of the SL-BESS with a standalone photovoltaic (PV) system represents the optimal solution in terms of global warming potential (GWP) reduction, with a saving of up to −74.8%. However, manufacturing and end-of-life stages of PV and batteries contribute to abiotic depletion and human toxicity, resulting from the use of chemicals and the extraction of resources essential for their manufacture. Indeed, when BESS is made of new batteries, it demonstrates the most significant impacts in terms of AD at 1.22 × 10−1 kg Sb eq and human toxicity (HT) at 3.87 × 103 kg 1,4-DB eq, primarily attributable to the manufacturing stages of both BESS and PV systems. The findings represent a significant breakthrough, highlighting the substantial capacity of incorporating SL-BESS alongside renewable energy sources to mitigate GWP resulting from industrial applications, and the criticality of repurposing decommissioned batteries from the automotive industry for secondary use. [ABSTRACT FROM AUTHOR]

The integration of wind energy sources and plug-in electric vehicles is essential for the efficient planning, reliability, and operation of modern electric power systems. Minimizing the overall operational cost of integrated power systems while dealing with wind energy sources and plug-in electric vehicles in integrated power systems using a chaotic zebra optimization algorithm (CZOA) is described. The proposed system deals with a probabilistic forecasting system for wind power generation and a realistic plug-in electric vehicle charging profile based on travel patterns and infrastructure characteristics. The objective is to identify the optimal scheduling and committed status of the generating unit for thermal and wind power generation while considering the system power demand, charging, and discharging of electric vehicles, as well as power available from wind energy sources. The proposed CZOA adeptly tackles the intricacies of the unit commitment problem by seamlessly integrating scheduling and the unit's committed status, thereby enabling highly effective optimization. The proposed algorithm is tested for 10-, 20-, and 40-generating unit systems. The empirical findings pertaining to the 10-unit system indicate that the amalgamation of a thermal generating unit system with plug-in electric vehicles yields a 0.84% reduction in total generation cost. Furthermore, integrating the same system with a wind energy source results in a substantial 12.71% cost saving. Notably, the integration of the thermal generating system with both plug-in electric vehicles and a wind energy source leads to an even more pronounced overall cost reduction of 13.05%. The outcome of this study reveals competitive test results for 20- and 40-generating unit systems and contributes to the advancement of sustainable and reliable power systems, fostering the transition towards a greener energy future. [ABSTRACT FROM AUTHOR]

The rapid growth of the energy and transport sectors has led to an increase in fuel consumption, resulting in a significant rise in greenhouse gas emissions. Switching to renewable energy sources and replacing internal combustion engines with electric vehicles (EVs) can significantly reduce greenhouse gas emissions. In recent years, the electrification of the transportation sector has become a primary focus of research and development efforts. However, if EVs are charged using conventional energy sources, we are unable to fully capitalize on their potential to reduce emissions. Charging EVs using renewable energy sources is the optimal solution. Otherwise, the increased number of EVs on the roads can significantly impact the stability of existing electric grids. As a result, smart homes with EV charging stations are becoming increasingly popular worldwide. This review focuses on the concept of grid-connected rooftop solar photovoltaic (PV) smart homes integrated with EVs and energy management systems in Australia. Australia can reduce emissions in the building and transport sectors by electrifying a range of vehicles and ultimately powering them with 100% renewable energy sources. The benefits of EV integration alongside rooftop solar systems for smart homes with house-to-vehicle or vehicle-to-house, as well as vehicle-to-grid or grid-to-vehicle (bidirectional EV charging) capabilities are also explored in this article. By adopting these systems, these smart homes can provide energy schemes for commercial use, ultimately contributing to the owner's economic benefit. [ABSTRACT FROM AUTHOR]

In China, the power sector is currently the largest carbon emitter and the transportation sector is the fastest-growing carbon emitter. This paper proposes a model of solar-powered charging stations for electric vehicles to mitigate problems encountered in China's renewable energy utilization processes and to cope with the increasing power demand by electric vehicles for the near future. This study applies the proposed model to Shenzhen City to verify its technical and economic feasibility. Modeling results showed that the total net present value of a photovoltaic power charging station that meets the daily electricity demand of 4500 kWh is $3,579,236 and that the cost of energy of the combined energy system is $0.098/kWh. In addition, the photovoltaic powered electric vehicle model has pollutant reduction potentials of 99.8%, 99.7% and 100% for carbon dioxide, sulfur dioxide, and nitrogen oxides, respectively, compared with a traditional gasoline-fueled car. Sensitivity analysis results indicated that interest rate has a relatively strong influence on COE (Cost of Energy). An increase in the interest rate from 0% to 6% increases COE from $0.027/kWh to $0.097/kWh. This analysis also suggests that carbon pricing promotes renewable energy only when the price of carbon is above $20/t. [ABSTRACT FROM AUTHOR]

Electric vehicles represent an innovation in mobility that can help significantly reduce greenhouse emissions and mitigate climate change. However, replacing internal combustion with electric vehicles is not enough. This replacement needs to be complemented with a change in the energy mix of individual countries towards renewable energy sources and efficient use of electricity generated as a secondary product. Recuperative braking energy from trains can serve as one source of such secondary energy. Following an analysis of recuperative energy generated and analysis of charging requirements of individual electric vehicles, the paper proposes a model of a charging site near train stations. Using this energy to charge electric vehicles helps to reduce energy consumption from the electricity grid and thus reduce carbon emissions. Compared to other articles, the proposed model ensures the efficient use of recuperative braking energy from trains by using the variable charging power function; thereby, the installation of additional battery storage is eliminated. Our model results show that the benefits of a car park with a reservation system near train stations increase the car park efficiency, provide a sufficient number of private charging points, contribute to efficient use of recovered energy, and reduce carbon emissions. [ABSTRACT FROM AUTHOR]

The Energy Internet is an inevitable trend of the development of electric power system in the future. With the development of microgrids and distributed generation (DG), the structure and operation mode of power systems are gradually changing. Energy routers are considered as key technology equipment for the development of the Energy Internet. This paper mainly studies the control of the LAN-level energy router, and discusses the structure and components of the energy router. For better control of the power transmission of an energy router, the energy routing control strategy for an integrated microgrid, including photovoltaic (PV) energy, battery-energy storage and electric vehicles (EVs) is studied. The front stage DC/DC converter of the PV system uses maximum power point tracking (MPPT) control. The constant current control is used by the bidirectional DC/DC converter of the battery-energy storage system and the EV system when they discharge. The DC/AC inverters adopt constant reactive power and constant DC voltage control. Constant current constant voltage control is adopted when an EV is charged. The control strategy model is simulated by Simulink, and the simulation results verify the feasibility and effectiveness of the proposed control strategy. The DG could generate reactive power according to the system instructions and ensure the stable output of the DC voltage of the energy router. [ABSTRACT FROM AUTHOR]

The explosion of the vehicle market in China has caused a series of problems, like energy security, climate change, air pollution, etc. The deployment of electric vehicles (EVs) is considered an effective solution to address these problems. Thus, both the state and local governments in China have launched some policies and incentives to accelerate the development of EVs and the EV industry. Do EVs can effectively solve these problems in short term, viewed from the fleet point? Based on China's most up-to-date deployment plan for EVs, this paper analyzes the energy consumption and greenhouse gas (GHG) emissions caused by China's road transport sector in three different scenarios. The results indicate that, based on current planning, the energy consumption and GHG emissions of the whole fleet will peak in 2025 and 2027, at the level of around 403 mtoe (million tons of oil equivalent) and 1763 mt CO2 eq. (million tons of CO2 equivalent), respectively. The introduction of EVs will significantly reduce the reliance on fossil fuel in the long term, with increasing ownership, while, in the short term, the fuel economy regulation will still play a more important role. Policy makers should continually pay attention to this. Meanwhile, commercial vehicles, especially heavy-duty trucks will account for a bigger and bigger proportion in the energy consumption and GHG emissions of the whole fleet. Thus, to some extent the focus should shift from passenger vehicles to commercial vehicles. More measures could be implemented. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to examine several real house cases as renewable energy resources are installed. It is an empirical study, based on first principles applied to measured data. In the first case presented, a PV solar system has been installed and a hybrid vehicle purchased. Battery storage is being considered. Smart Meter data (provided in Victoria, Australia) measures the electrical energy flowing to and from the grid in each half hour. Missing is the story about what the house is generating and what its energy requirements are through each half hour interval. We apply actual (on site) solar PV data to this study, resolving the unknown energy flows. Analysing energy flow has revealed that there are five fundamental quantities which determine performance, namely energy load, energy import, energy harvesting, energy export and energy storage. As a function of PV size these quantities depend on four parameters, easily derivable from the Smart Meter data, namely the house load, the night-time house load (no PV generation), the rating of the solar PV system and the tariffs charged. This reveals most of the information for providing advice on PV array size and whether to install a battery. An important discovery is that a battery, no matter what size, needs a PV system large enough to charge it during the winter months. The analysis is extended to two more houses located within 5 km for which detailed solar data is unavailable. [ABSTRACT FROM AUTHOR]

Electrification of powertrain systems offers numerous advantages in the global trend in vehicular applications. A wide range of energy sources and zero-emission propulsion in the tank to wheel significantly add to electric vehicles' (EV) attractiveness. This paper presents analyses of the energy balance between micro-photovoltaic (PV) installation and small electric vehicle in real conditions. It is based on monitoring PV panel's energy production and car electricity consumption. The methodology included energy data from real household PV installation (the most common renewable energy source in Poland), electric vehicle energy consumption during real driving conditions, and drivetrain operating parameters, all collected over a period of one year by indirect measuring. A correlation between energy produced by the micro-PV installation and small electric car energy consumption was described. In the Winter, small electric car energy consumption amounted to 14.9 kWh per 100 km and was 14% greater than summer, based on test requirements of real driving conditions. The 4.48 kW PV installation located in Poznań produced 4101 kWh energy in 258 days. The calculation indicated 1406 kWh energy was available for EV charging after household electricity consumption subtraction. The zero-emission daily distance analysis was done by the simplified method. [ABSTRACT FROM AUTHOR]

In the global trend towards decarbonization, peer-to-peer (P2P) energy trading is garnering increasing attention. Furthermore, energy management on the demand side plays a crucial role in decarbonization efforts. The authors have previously developed an automated bidding agent that considers user preferences for renewable energy (RE), assuming users own electric vehicles (EVs). In this study, we expand upon this work by considering users who own not only EVs but also heat pump water heaters, and we develop an automated bidding agent that takes into account their preferences for RE. We propose a method to control the start time and presence of daytime operation shifts for heat pump water heaters, leveraging their daytime operation shift function. Demonstration experiments were conducted to effectively control devices such as EVs and heat pumps using the agent. The results of the experiments revealed that by controlling the daytime operation of heat pumps with our method, the RE utilization rate can be improved compared to scenarios without daytime operation shifts. Furthermore, we developed a simulator to verify the outcomes under different scenarios of demand-side resource ownership rates, demonstrating that higher ownership rates of EVs and heat pumps enable more effective utilization of renewable energy, and that this effect is further enhanced through P2P trading. Based on these findings, we recommend promoting the adoption of demand-side resources such as EVs and heat pumps and encouraging P2P energy trading to maximize the utilization of renewable energy in future energy systems. [ABSTRACT FROM AUTHOR]

The urgent need to reduce greenhouse gas emissions to achieve a decarbonized society has led to the active introduction of electric vehicles worldwide. Renewable energy sources that do not emit greenhouse gases during charging must also be used. However, the uncertainty in the supply of renewable energy is an issue that needs to be considered in practical applications. Therefore, in this study, we predicted the amount of electricity generated by renewable energy using model predictive control, and we considered the operation of a complete island-operated park and ride EV parking station that does not depend on commercial electricity. To perform appropriate model predictive control, we performed comparative simulations for several different forecast interval cases. Based on the obtained results, we determined the forecast horizon and we simulated the economic impact of implementing EV demand response on the electricity demand side. We found that without demand response, large amounts of electricity are recharged and a very high return on investment can be achieved, but with demand response, the return on investment is faster. The results provide a rationale for encouraging infrastructure development in areas that have not yet adopted electric vehicles. [ABSTRACT FROM AUTHOR]

The search for charging electric vehicles using renewable energy sources and ensuring the stability of the electrical system has been growing. This has led to the development of charging stations that integrate photovoltaic systems, energy storage systems in batteries, and charging stations. The storage system allows for applications that increase the reliability of the electrical network. The objective of this work is to study, based on real operational scenarios, the impacts, and criteria of energy quality of a charging solution containing a photovoltaic system, a storage system, and a charging station. In the solution, the batteries are lead-carbon type and photovoltaics have priority in energy supply, using excess generation to charge the batteries. In this aspect, the work presents relevant contributions to the study of energy quality. An improvement in energy quality is pointed out with the use of batteries, proving that even in the most diverse operating situations, such as partial shading of the photovoltaic modules, with 20% shading relative to the nominal power, charging is not impacted and reductions in distortions are achieved, with a 50% reduction in voltage imbalance, when the storage system is a voltage reference and reference to the microgrid. [ABSTRACT FROM AUTHOR]

The adoption of electric vehicles is incentivized by governments around the world to decarbonize the mobility sector. Simultaneously, the continuously increasing amount of renewable energy sources and electric devices such as heat pumps and electric vehicles leads to congested grids. To meet this challenge, several forms of flexibility markets are currently being researched. So far, no analysis has calculated the actual flexibility potential of electric vehicles with different operating strategies, electricity tariffs and charging power levels while taking into account realistic user behavior. Therefore, this paper presents a detailed case study of the flexibility potential of electric vehicles for fixed and dynamic prices, for three charging power levels in consideration of Californian and German user behavior. The model developed uses vehicle and mobility data that is publicly available from field trials in the USA and Germany, cost-optimizes the charging process of the vehicles, and then calculates the flexibility of each electric vehicle for every 15 min. The results show that positive flexibility is mostly available during either the evening or early morning hours. Negative flexibility follows the periodic vehicle availability at home if the user chooses to charge the vehicle as late as possible. Increased charging power levels lead to increased amounts of flexibility. Future research will focus on the integration of stochastic forecasts for vehicle availability and electricity tariffs. [ABSTRACT FROM AUTHOR]

Electric vehicles (EV) have the potential to significantly reduce carbon emissions. Yet, the current electric vehicle charging infrastructure utilizes electricity generated from non-renewable sources. In this study, the rooftop area of structures is analyzed to assess electricity that can be generated through solar- and wind-based technologies. Consequently, planning an electric vehicle charging infrastructure that is powered through 'clean' energy sources is presented. We developed an optimal modeling framework for the consideration of Renewable Energy Technologies (RET) along with EV infrastructure. After examining the level of technology, a MATLAB image segmentation technique was used to assess the available rooftop area. In this study, two competitive objectives including the economic cost of the system and CO2 emissions are considered. Three scenarios are examined to assess the potential of RET to meet the EV demand along with the Abu Dhabi city one while considering the life-cycle emission of RET and EV systems. When meeting only EV demand through Renewable Energy Technologies (RET), about 187 ktonnes CO2 was reduced annually. On the other hand, the best economic option was still to utilize grid-connected electricity, yielding about 2.24 Mt CO2 annually. In the scenario of meeting both 10% EV demand and all Abu Dhabi city electricity demand using RE, wind-based technology is only able to meet around 3%. Analysis carried out by studying EV penetration demonstrated the preference of using level 2 AC home chargers compared to other ones. When the EV penetration exceeds 25%, preference was observed for level 2 (AC public 3ϕ) chargers. [ABSTRACT FROM AUTHOR]

Isolated microgrids, such as islands, rely on fossil fuels for electricity generation and include vehicle fleets, which poses significant environmental challenges. To address this, distributed energy resources based on renewable energy and electric vehicles (EVs) have been deployed in several places. However, they present operational and planning concerns. Hence, the aim of this paper is to propose a two-level microgrid problem. The first problem considers an EV charging strategy that minimizes charging costs and maximizes the renewable energy use. The second level evaluates the impact of this charging strategy on the power generation planning of Santa Cruz Island, Galapagos, Ecuador. This planning model is simulated in HOMER Energy. The results demonstrate the economic and environmental benefits of investing in additional photovoltaic (PV) generation and in the EV charging strategy. Investing in PV and smart charging for EVs could reduce the N P C by 13.58%, but a reduction in the N P C of the EV charging strategy would result in up to 3.12%. [ABSTRACT FROM AUTHOR]

The energy management system is executed in microgrids for optimal integration of distributed energy resources (DERs) into the power distribution grids. To this end, various strategies have been more focused on cost reduction, whereas effectively both economic and technical indices/factors have to be considered simultaneously. Therefore, in this paper, a two-layer optimization model is proposed to minimize the operation costs, voltage fluctuations, and power losses of smart microgrids. In the outer-layer, the size and capacity of DERs including renewable energy sources (RES), electric vehicles (EV) charging stations and energy storage systems (ESS), are obtained simultaneously. The inner-layer corresponds to the scheduled operation of EVs and ESSs using an integrated coordination model (ICM). The ICM is a fuzzy interface that has been adopted to address the multi-objectivity of the cost function developed based on hourly demand response, state of charges of EVs and ESS, and electricity price. Demand response is implemented in the ICM to investigate the effect of time-of-use electricity prices on optimal energy management. To solve the optimization problem and load-flow equations, hybrid genetic algorithm (GA)-particle swarm optimization (PSO) and backward-forward sweep algorithms are deployed, respectively. One-day simulation results confirm that the proposed model can reduce the power loss, voltage fluctuations and electricity supply cost by 51%, 40.77%, and 55.21%, respectively, which can considerably improve power system stability and energy efficiency. [ABSTRACT FROM AUTHOR]

The growing need for electric energy is forcing the construction industry to greater integrate energy production systems based on renewable energy sources. The energy ought to be used not only to support functions of the building but also to charge electric vehicles, whose number has been increasing for the last few years. However, implementation of RES (Renewable Energy Sources) systems in already existing buildings is problematic. Basing on an example of a historical building, the article presents the conversion of a facility into a nearly zero-energy building, where energy surplus may be used to charge EVs (Electric Vehicles). Interdisciplinary research describes energy consumption of the EV in real driving conditions, taking into consideration changing weather conditions and an option of energy being produced by buildings operating in an urban agglomeration: it stipulates the time needed to charge the vehicle, depending on the charging We removed dot, according to email in submitting system, please confirm.method, as well as an energy potential of adapting the solution in selected periods of building operation. The summary presents how electromobility can be supported by the construction industry. [ABSTRACT FROM AUTHOR]

The use of electric vehicles (EVs) is growing in popularity each year, and as a result, considerable demand increase is expected in the distribution network (DN). Additionally, the uncertainty of EV user behavior is high, making it urgent to understand its impact on the network. Thus, this paper proposes an EV user behavior simulator, which operates in conjunction with an innovative smart distribution locational marginal pricing based on operation/reconfiguration, for the purpose of understanding the impact of the dynamic energy pricing on both sides: the grid and the user. The main goal, besides the distribution system operator (DSO) expenditure minimization, is to understand how and to what extent dynamic pricing of energy for EV charging can positively affect the operation of the smart grid and the EV charging cost. A smart city with a 13-bus DN and a high penetration of distributed energy resources is used to demonstrate the application of the proposed models. The results demonstrate that dynamic energy pricing for EV charging is an efficient approach that increases monetary savings considerably for both the DSO and EV users. [ABSTRACT FROM AUTHOR]

Inhabited islands depend primarily on fossil fuels for electricity generation and they also present frequently a vehicle fleet, which result in a significant environmental problem. To address this, several governments are investing in the integration of Renewable Energy Sources (RESs) and Electric Vehicles (EVs), but the combined integration of them creates challenges to the operation of these isolated grid systems. Thus, the aim of this paper is to propose an Electric Vehicle charging strategy considering high penetration of RES. The methodology proposes taxing CO 2 emissions based on high pricing when the electricity is mostly generated by fossil fuels, and low pricing when there is a RES power excess. The Smart charging methodology for EV optimizes the total costs. Nine scenarios with different installed capacity of solar and wind power generation are evaluated and compared to cases of uncoordinated charging. The methodology was simulated in the Galapagos Islands, which is an archipelago of Ecuador, and recognized by the United Nations Educational, Scientific and Cultural Organization (UNESCO) as both a World Heritage site and a biosphere reserve. Simulations results demonstrate that the EV aggregator could reduce costs: 7.9% for a case of 5 MW installed capacity (wind and PV each), and 7% for a case of 10 MW installed (wind and PV each). Moreover, the use of excess of RES power for EV charging will considerably reduce CO 2 emissions. [ABSTRACT FROM AUTHOR]

As many new devices and factors, such as renewable energy sources, energy storage (ESs), electric vehicles (EVs), and demand response (DR), flood into the distribution network, the characteristics of the distribution network are becoming complicated and diversified. In this study, a two-layer collaborative optimized operation model for the multi-character distribution network considering multiple uncertain factors is proposed to achieve optimal dispatching of ES and EV and obtain the optimal grid structure of the distribution network. Based on basic device models of distribution network, the upper layer distribution network reconfiguration (DNR) model is established and solved by the particle swarm optimization (PSO) based on the Pareto optimality and the Prim algorithm. Then, the lower layer optimal dispatching model of ES and EV is established and solved by the binary PSO. The upper layer model and the lower layer model are integrated to form the collaborative optimized operation model for the multi-character distribution network and solved by iterating the upper and lower layers continuously. A case study is conducted on the IEEE 33-bus system. The simulation results show that the total network loss and the voltage deviation are decreased by 15.66% and 15.52%, respectively, after optimal dispatching of ES and EV. The total network loss and the voltage deviation are decreased by 28.39% and 44.46%, respectively, after the DNR with distributed generation (DG) and EV loads with little impact on the average reliability of the power supply. The total network loss and the voltage deviation are decreased by 26.54% and 27.04%, respectively, after the collaborative optimized operation of the multi-character distribution network. The collaborative optimized operation of the distribution network can effectively reduce the total cost by 114.45%, which makes the system change from paying to gaining. [ABSTRACT FROM AUTHOR]

The utilization of electric vehicles (EV) and their used batteries in supporting small-scale energy management systems were studied. Both theoretical study and practical demonstration were performed to measure the feasibility of the developed system. Each five EVs and used EV batteries were used along with 20 kW photovoltaic (PV) panels as a renewable energy source. The main objective of the developed system is performing a peak-load shifting by utilizing EVs, used EV batteries and PV panels. The planning of load leveling was performed 24 h ahead for each 30 min period. The studies showed that the application of EVs and used EV batteries in supporting certain small-scale energy management systems is feasible. In addition, some findings during the demonstration test were listed and analyzed for the purpose of further system development and deployment. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have recently gained momentum as an integral part of the Internet of Vehicles (IoV) when authorities started expanding their low emission zones (LEZs) in an effort to build green cities with low carbon footprints. Energy is one of the key requirements of EVs, not only to support the smooth and sustainable operation of EVs, but also to ensure connectivity between the vehicle and the infrastructure in the critical times such as disaster recovery operation. In this context, renewable energy sources (such as wind energy) have an important role to play in the automobile sector towards designing energy-harvesting electric vehicles (EH-EV) to mitigate energy reliance on the national grid. In this article, a novel approach is presented to harness energy from a small-scale wind turbine due to vehicle mobility to support the communication primitives in electric vehicles which enable plenty of IoV use cases. The harvested power is then processed through a regulation circuitry to consequently achieve the desired power supply for the end load (i.e., battery or super capacitor). The suitable orientation for optimum conversion efficiency is proposed through ANSYS-based aerodynamics analysis. The voltage-induced by the DC generator is 35 V under the no-load condition while it is 25 V at a rated current of 6.9 A at full-load, yielding a supply of 100 W (on constant voltage) at a speed of 90 mph for nominal battery charging. [ABSTRACT FROM AUTHOR]

The rapid spread of renewable energy resources has increased need for demand flexibility as one of the solutions to power system imbalance. However, to properly estimate the demand flexibility, demand characteristics must be analyzed first and the corresponding flexibility measures must be validated. Thus, in this study, a novel approach is proposed to evaluate the demand flexibility provided by Electric Vehicle Chargers (EVC) in the residential sector based upon a new process of electric charging demand characteristic data analysis. The proposed model estimates the frequency, consistency, and operation scores of the flexible demand resource (FDR) during identified ramp-up/down intervals presented in our previous work. The scores are included in the components that calculate the flexibility score referring that the closer it is to 1, the higher utilization as an FDR. A case study was conducted by considering EV user segmentation based on their demand characteristic analysis. The results confirm that flexibility scores of segmented EVC groups are about 0.0273 in ramp-up and ramp-down intervals. Based on the experimental results, the flexibility score can be utilized for multi-dimensional analysis and verification in perspectives of seasonality, participation time interval, customer group classification, and evaluation. Thus, the proposed method can be used as an indicator to determine how a segmented EVC group is adequate to participate as an FDR while suggesting meaningful implications through EVC demand data analysis. [ABSTRACT FROM AUTHOR]

An increase in variable renewable energy sources and soaring electricity demand at peak hours undermines the efficiency and reliability of the power supply. Conventional supply-side solutions, such as additional gas turbine plants and energy storage systems, can help mitigate these problems; however, they are not cost-effective. This study highlights the potential value of electric vehicle demand response programs by analyzing three separate scenarios: electric vehicle charging based on a time-of-use tariff, smart charging controlled by an aggregator through virtual power plant networks, and smart control with vehicle-to-grid capability. The three programs are analyzed based on the stochastic form of a power system optimization model under two hypothetical power system environments in Jeju Island, Korea: one with a low share of variable renewable energy in 2019 and the other with a high share in 2030. The results show that the cost saving realized by the electric vehicle demand response program is higher in 2030 and a smart control with vehicle-to-grid capability provides the largest cost saving. When the costs of implementing an electric vehicle demand response are considered, the difference in cost saving between the scenarios is reduced; however, the benefits are still large enough to attract customers to participate. [ABSTRACT FROM AUTHOR]

In the context of the increased acceptance and usage of electric vehicles (EVs), vehicle-to-building (V2B) has proven to be a new and promising use case. Although this topic is already being discussed in literature, there is still a lack of experience on how such a system, of allowing bidirectional power flows between an EV and building, will work in a residential environment. The challenge is to optimize the interplay of electrical load, photovoltaic (PV) generation, EV, and optionally a home energy storage system (HES). In total, fourteen different scenarios are explored for a German household. A two-step approach is used, which combines a computationally efficient linear optimizer with a detailed modelling of the non-linear effects on the battery. The change in battery degradation, storage system efficiency, and operating expenses (OPEX) as a result of different, unidirectional and bidirectional, EV charging schemes is examined for both an EV battery and a HES. The simulations show that optimizing unidirectional charging can improve the OPEX by 15%. The addition of V2B leads to a further 11% cost reduction, however, this corresponds with a 12% decrease in EV battery lifetime. Techno-economic analysis reveals that the V2B charging solution with no HES leads to strong self-consumption improvements (EUR 1381 savings over ten years), whereas, this charging scheme would not be justified for a residential prosumer with a HES (only EUR 160 savings). [ABSTRACT FROM AUTHOR]

In the integration of electric vehicle (EV) fleets into the smart grid context, charging infrastructure serves as the interlinkage between EV fleets and the power grid and, as such, affects the impacts of EV operation on the smart grid. In this study, the impacts of charging infrastructure on the effectiveness of different EV operational modes were simulated using a multi-component modelling approach, which accounts for both stochastic EV fleet charging behaviors as well as optimal energy vector dispatch operation. Moreover, a campus microgrid case study was presented to demonstrate the various design factors and impacts of charging infrastructure implementation affecting EV fleet adoption and operation. Based on results from the study, it was shown that charging infrastructure should be adopted in excess of the minimum required to satisfy EV charging for driving needs. In addressing uncontrolled charging behaviors, additional charging infrastructure improves EV owner convenience and reduces queuing duration. Meanwhile, controlled charging strategies benefit from increased resilience against uncertain charging behavior and operate more optimally in systems subject to time-of-use (TOU) electricity pricing. Lastly, it was demonstrated that successful vehicle-to-grid (V2G) implementation requires charging infrastructure to emulate the availability and fast response characteristics of stationary energy storage systems, which translates to excess charging port availability, long EV plug-in durations, and bi-directional power flow capabilities well beyond the level 2 charging standard. [ABSTRACT FROM AUTHOR]

Energy markets are based on energy transactions with a central control entity, where the players are companies. In this research work, we propose an IoT (Internet of Things) system for the accounting of energy flows, as well as a blockchain approach to overcome the need for a central control entity. This allows for the creation of local energy markets to handle distributed energy transactions without needing central control. In parallel, the system aggregates users into communities with target goals and creates new markets for players. These two approaches (blockchain and IoT) are brought together using a gamification approach, allowing for the creation and maintenance of a community for electricity market participation based on pre-defined goals. This community approach increases the number of market players and creates the possibility of traditional end users earning money through small coordinated efforts. We apply this approach to the aggregation of batteries from electrical vehicles so that they become a player in the spinning reserve market. It is also possible to apply this approach to local demand flexibility, associated with the demand response (DR) concept. DR is aggregated to allow greater flexibility in the regulation market based on an OpenADR approach that allows the turning on and off of predefined equipment to handle local microgeneration. [ABSTRACT FROM AUTHOR]

With the increasing penetration of new and renewable energy, incorporating variable adjustable power elements on the demand side is of particular interest. The utilization of batteries as flexible loads is a hot research topic. Lithium-ion batteries are key components in electric vehicles (EVs) in terms of capital cost, mass and size. They are retired after around 5 years of service, but still retain up to 80% of their nominal capacity. Disposal of waste batteries will become a significant issue for the automotive industry in the years to come. This work proposes the use of the second life of these batteries as flexible loads to participate in the economic power dispatch. The characteristics of second life batteries (SLBs) are varied and diverse, requiring a new optimization strategy for power dispatch at the system level. In this work, SLBs are characterized and their operating curves are obtained analytically for developing an economic power dispatch model involving wind farms and second life batteries. In addition, a dispatch strategy is developed to reduce the dispatch complex brought by the disperse spatial and time distribution of EVs and decrease the system operating cost by introducing incentive and penalty costs in regulating the EV performance. In theory, SLBs are utilized to reduce the peak-valley difference of power loads and to stabilize the power system. Test results based on a ten-unit power system have verified the effectiveness of the proposed dispatch model and the economic benefit of utilizing SLBs as flexible loads in power systems. This work may provide a viable solution to the disposal of waste batteries from EVs and to the stable operation of fluctuating power systems incorporating stochastic renewable energy. [ABSTRACT FROM AUTHOR]