Abstract
Recently, renewable power generation and electric vehicles (EVs) have been attracting more and more attention in smart grid. This paper presents a grid-connected solar-wind hybrid system to supply the electrical load demand of a small shopping complex located in a university campus in India. Further, an EV charging station is incorporated in the system. Economic analysis is performed for the proposed setup to satisfy the charging demand of EVs as well as the electrical load demand of the shopping complex. The proposed system is designed by considering the cost of the purchased energy, which is sold to the utility grid, while the power exchange is ensured between the utility grid and other components of the system. The sizing of the component is performed to obtain the least levelized cost of electricity (LCOE) while minimizing the loss of power supply probability (LPSP) by using recent optimization techniques. The results demonstrate that the LCOE and LPSP for the proposed system are measured at 0.038 $/kWh and 0.19% with a renewable fraction of 0.87, respectively. It is determined that a cost-effective and reliable system can be designed by the proper management of renewable power generation and load demands. The proposed system may be helpful in reducing the reliance on the over-burdened grid, particularly in developing countries.
THE availability of limited fossil fuel reserves, climatic effects, and greenhouse gas emissions has compelled the focus toward renewable power generation in the past years. According to the Ministry of New and Renewable Energy, in India, renewable energy generation accounts for approximately 20% of the total power generation. The major contributions are from solar and wind power rather than hydro, thermal, ocean, and biomass energies [
In recent years, extensive research has been conducted to design a solar-wind hybrid system. Reference [
Reference [
In recent years, a significant growth of EVs and plug-in hybrid EVs has been observed in the existing power system. The integration of EVs in terms of economic analysis and power management in the renewable energy based system is an interesting and challenging research area. Reference [
Reference [
A variety of optimization methods such as GA [
Researchers primarily concentrate on the control and power management of the EVs in the microgrid or grid-connected systems. However, an economic analysis considering the power exchange with the grid is one of the major parameters that must be addressed. The rapid inclusion of EVs presents both challenges and opportunities to the existing power system. The major parameters in designing a renewable energy based system include a smooth power flow between the components, reliability, and ACS of the system. In this paper, a small grid-connected solar-wind hybrid system with EVs is proposed for a small shopping complex located in a university campus in the state of Punjab in India. The primary focus is to formulate a mathematical model of a solar-wind hybrid system incorporating EVs with a grid as the backup. Further, this paper aims to minimize the power exchange with the grid. The optimal configuration of the proposed system while minimizing the ACS is performed using the ABC and PSO algorithms. A detailed comparison of the results with both algorithms is performed. A parameter sensitivity analysis is performed to analyze the impact of the algorithm parameter on the solution quality of both algorithms. Finally, a sensitivity analysis is performed to analyze the impact of the maximum grid sales and the purchase capacities on LCOE.
The remaining paper is organized into five sections. Section Ⅱ explains the mathematical model along with the working methodology of the proposed solar-wind hybrid system. Section Ⅲ considers the objective function and constraints of the system. Section Ⅳ presents a description of the algorithms. Section Ⅴ presents the outcomes and discussions. The conclusion of the work is presented in Section Ⅵ.
A schematic diagram of the proposed grid-connected solar-wind hybrid system is illustrated in

Fig. 1 Schematic diagram of proposed system.
The power produced by a wind turbine depends on the area through which the wind passes and the velocity of the wind. The power obtained from a wind turbine can be calculated as:
(1) |
where , , , , are the wind speeds at the required height, cut-in speed, cut-off speed, rated power of the wind turbine, and rated wind speed, respectively [
Wind speed at different hours of the year is represented in the form of a curve called probability density function (PDF). If a curve is plotted by considering the variable wind speeds, the area under the curve between any two wind speeds is equal to the probability of which the wind is between the two speeds. It can be expressed as:
(2) |
(3) |
where is the wind speed PDF, i.e., density; and and are any two wind speeds. In wind speed statistics, the most important PDF is Weibull probability function. The Weibull probability function is defined as the starting point for characterizing the statistics of wind speed, and its expression is given as:
(4) |
where , , and are the shape parameter, scale parameter, and wind speed, respectively.
Wind speed is different over different surfaces of the earth, e.g., over a calm sea and a forest, there are different wind speeds. The variation is based on the height at which the wind speed is measured. The speed of wind at a particular height is calculated as:
(5) |
where , , and are the wind speed at a required height , the wind speed at reference height , and the friction coefficient, respectively. The friction coefficient depends on the terrain over which the wind blows. Generally, an approximated value of is considered to be 1/7.
The total power generated from wind turbines is calculated as:
(6) |
where is the number of wind turbines.
An SPV panel is used to generate the power by harnessing solar energy. Only a small amount of solar radiation falling on the SPV panels is converted into electricity, and the rest is converted to heat. Therefore, the effect of solar radiation along with temperature on output power is considered. The output of an individual SPV panel is given as:
(7) |
where is the solar radiation.
The cell temperature and variation of power with respect to the changes in temperature is given by:
(8) |
where , , and NOCT are the percentage drop in power corresponding to the temperature of the cell at which power is to be calculated, ambient temperature, and cell temperature when ambient temperature is ℃ and solar radiation is 0.8 , respectively.
The rated maximum power output is calculated as:
(9) |
where and are the maximum voltage and current ratings, respectively. Further, the total power generated from SPV panels is calculated as:
(10) |
where is the number of SPV panels.
The charging station is comprised of a dual converter, charging bays, and EVs. To control the power flow, the charging station is connected to a microgrid controller, which helps control the direction of the power flow at a particular time. EVs can be charged according to their state of charge (SOC), which is defined as the ratio of the available capacity to its maximum capacity when a battery is completely charged. Thus, it describes the remaining charging percentage of the battery.
Mathematically, the practical constraints imposed on the charging of EVs are described as:
(11) |
(12) |
where , , and are the minimum, maximum, and current SOC values for a particular EV at a particular time t, respectively; and and are the present charging rate and maximum allowed charging rate of EVs, respectively. At any instant t, the power of the EVs at the charging station is calculated as:
(13) |
where is the maximum energy capacity of the vehicle; and is the time interval considered as one hour. The difference in charging requirements of EVs is determined by comparing their with , which is the critical . In this paper, a level 2 charger with advantages of easy and user-friendly charging is considered to charge 20 EVs at a charging station.
PV panels and wind turbines feed DC power to EVs at the charging station. However, to satisfy load demand or supply power to the grid or vice versa, converters are required. The size of the converter is chosen according to the maximum grid sales and purchase capacity . The rated power of the inverter and that of the rectifier are calculated as:
(14) |
(15) |
where and are the efficiency values of the inverter and rectifier, respectively.
To ensure that the solar-wind hybrid system satisfies the load power demand, as shown in
A system is considered reliable if it has sufficient power to satisfy the load demand, which depends on LPSP. Two types of load demands are required to be satisfied: the first is the load demand of EVs and the second is the AC load demand . at time interval is calculated as:
(16) |
In this paper, it is considered that the vehicles having SOC less than critical SOC are charged before 11:00 a.m.. After 11:00 a.m., the priority is given to all EVs owing to sufficient solar power availability. Further, it is assumed that all EVs arrive at 08:00 a.m. and park throughout the day. The second load demand is . These demands are satisfied by two sources, i.e., SPV panels and the wind turbine. The difference between the power generated and the power demand is calculated as:
(17) |
If the sources are unable to satisfy the load demand, the power is purchased from the grid to fulfill the requirements. Moreover, if more power is available from the sources after fulfilling the demand, the power is sold to the grid . However, there are limits on purchasing and selling power to the grid, which are defined as the maximum grid purchase capacity and maximum grid selling capacity . The power cannot be purchased from the grid or sold to the grid beyond these limits. Therefore, depending on , the cases are formed as follows.
1) If , the total power output obtained from SPV panels and wind turbines is sufficient to charge the EVs and fulfill the requirement of the electric load. Further, the available extra power is sold to the grid, which is computed as:
(18) |
2) If and , the extra power is supplied to the dump load. Dump load power is calculated as:
(19) |
3) If , the power generated from SPV panels and wind turbines is not able to satisfy the power demand of EVs and the electric load. Therefore, the required power is purchased from the grid, which is given as:
(20) |
4) If , there is no exchange of power from the grid, and the demand of EVs and electric load is satisfied by the power generated from SPV panels and wind turbines.
When , both sources and grid are unable to satisfy the load demand. Consequently, power deficiency occurs, which is calculated as:
(21) |
must be zero to ensure that the total load demand is served reliably when minimizing the LPSP. Mathematically, LPSP can be calculated as:
(22) |
To solve the optimal sizing problem, the LPSP can be maintained within a specific tolerance band. In this paper, the maximum limit of LPSP is considered to be 1%. The energy management algorithm is demonstrated through a simplified flowchart to calculate the power provided to the dump load, power purchased from the grid, and power sold to the grid, as shown in Figs.

Fig. 2 Flowchart of charging methodology for EVs at charging station.

Fig. 3 Flowchart for operation strategy of proposed system.
The main objective of this paper is to achieve power exchange between various components of the system and minimize LCOE of the overall proposed system. The decision variables are and required to maintain minimum LPSP and ACS. The ACS includes the costs of installing SPV panels and a wind turbine, costs of energy purchased and sold back to the grid, and costs of the converters.
(23) |
where and are the costs of SPV panels and wind turbines, respectively; and are the costs of energy purchased and sold to the grid, respectively; and are the amounts of energy purchased and sold to the grid, respectively; and is the cost of the converters. Further, and are calculated as:
(24) |
(25) |
where is the annual capital cost; is the cost of replacing the components; is the cost of operation and maintrnance; and is the salvage value; and the superscripts PV and WT denote the SPV and wind turbine, respectively.
In the annual capital cost, the installation and purchasing costs of the elements are also included. The annual capital cost of SPV panels and wind turbines are calculated by considering capital recovery factor (CRF) as:
(26) |
(27) |
(28) |
where and are the initial capital costs of SPV panels and wind turbines, respectively; and n and i are the project life time and the annual interest rate, respectively.
Annual replacement cost includes the cost of replacing SPV panels and wind turbines if their life time is less than that of the project. The total annual cost of replacing the SPV panels and wind turbine is calculated as:
(29) |
(30) |
where and are the costs of replacing the SPV panel and wind turbine, respectively; and is the life time of the panel and wind turbine in years.
The maintenance cost constitutes the labor cost, cleaning cost, and cost of repairing in case of any temporary damage. The maintenance costs of SPV panels and wind turbines are calculated as:
(31) |
(32) |
where and are the maintenance costs of SPV panel and wind turbine, respectively.
The cost of the remaining life for the component after the project ends is known as salvage value. The salvage values of a wind turbine and SPV panel are calculated as:
(33) |
(34) |
where and are the replacement costs of a single wind turbine and SPV panel, respectively; and is the remaining life.
The total amount of electricity purchased and sold back to the grid can be calculated as:
(35) |
(36) |
The cost of electricity purchased and sold can be calculated as:
(37) |
(38) |
where and are the unit costs of electricity purchased and sold back to grid, respectively.
Moreover, the cost potency of a system can be determined by the objective function LCOE, which is the average cost of energy obtained from the system. It can be calculated as:
(39) |
where is the total energy served.
The constraints of the objective functions are:
(40) |
(41) |
(42) |
(43) |
where and are the maximum numbers of SPV panels and wind turbines, respectively; and and are the maximum grid purchase and sale capacities, respectively. LPSP is maintained within limits while minimizing the objective function using the exterior penalty method.
The proposed hybrid renewable energy system consists of more than one energy source. Therefore, this problem has multiple decision variables resulting in complex optimization problems. This problem requires the identification of energy sources for uninterrupted power supply to the EV charging station and AC load. Hence, the optimization problem incorporates economic objectives. Moreover, it requires the computation of long-term system performance to achieve the best compromise between LPSP and LCOE. LCOE is minimized using ABC and PSO algorithms as they dynamically search for an optimum system configuration while maintaining LPSP within desired limits.
This algorithm explores the idea of the ABC algorithm. There are three types of bees in the ABC algorithm: employed, scout, and onlooker bees. In ABC algorithm, there are three steps in each cycle: ① employed bees search for food source or nectar amount and its location is stored in the memory; ② onlooker bees collect the information from employed bees and make the decision for selecting the best food source by doing the quality search, i.e., measuring the nectar amount of food source; ③ scout bees search for new food sources. Half of the population of bees or swarms are employed bees and the rest half are onlooker bees. For each employed bee, there is only one food source. When the food source is abandoned by the employed bees, the new food source location is randomly determined by the scout bees, and it is replaced with the abandoned food source [
The steps for the implementation of ABC algorithm can be summed up as presented in

Fig. 4 Flowchart for proposed ABC algorithm.
PSO contains a swarm of particles. Swarm indicates that the population and particles are candidate solutions. By optimizing the objective function, the fitness of each solution is calculated iteratively.
These candidate solutions move around the search space and their movement is directed by the swarm. And the best position in the search space becomes the upgraded position, which directs the swarm. This procedure is repeated until the best solution is obtained. At the iteration, the particle position is denoted as , which moves in the search space with velocity . Therefore, the upgraded position of the particle is calculated as:
(44) |
The upgraded velocity is given by:
(45) |
where and are the acceleration parameters; is the uniformly distributed random numbers; and are the p-best and g-best, respectively; and is an inertial weight factor, which is used for managing the capability of the search [
The system configuration is optimized by employing both the algorithms to determine the optimal configuration that minimizes LCOE. For each system configuration observed in the search process, LPSP is determined. Therefore, for the desired LPSP, the optimal configuration can be selected by obtaining the minimum LCOE while satisfying the maximum LPSP target of 1%.

Fig. 5 Availability. (a) Solar insolation throughout the year. (b) Wind speed throughout the year. (c) Histogram representing wind speed frequency at site.

Fig. 6 Load profile. (a) During winter and summer (weekdays and weekends). (b) Throughout year after 10% variation.
Table Ⅰ lists different costs and specifications associated with the components. The system is proposed for a life time of 20 years. For time value of money, an interest rate of 6% is considered. The life time of SPV panels, wind turbines, and the inverter is considered to be equal to the project life time. Therefore, no replacement is required. The simulation is conducted in MATLAB 2017a, considering the complete data of one year at 1-hour interval to calculate the power exchange and costs of the hybrid system. Moreover, the numbers of SPV panels and wind turbines are optimized by using ABC and PSO algorithms. The different parameters used in optimization technique are listed in Table Ⅱ.
Table Ⅲ lists the optimized results of the solar-wind hybrid system using both ABC and PSO algorithms. It is inferred from the results that by fixing the grid sales and purchase capacity to 10 kW, the ACS using ABC and PSO algorithms are 2618.3 and 2953.2 , respectively. Moreover, the LCOE using ABC and PSO algorithms are 0.038 and 0.043 $/kWh, respectively. It can be deduced from the table that ABC algorithm provides an acceptable solution as its LCOE is comparatively less.

Fig. 7 Comparison of convergence characteristics of ABC and PSO algorithms.
Table Ⅳ lists the energy production and consumption by all components of the proposed system for the configurations using ABC and PSO algorithms. In the case of energy consumption, the EV demand accounts for 25.2% of the total consumed energy, while the shopping complex accounts for 35.7% and grid sales accounts for 39%. The excess electricity is equal to 27976 kWh/year using ABC algorithm and 23488 kWh/year using PSO algorithm, respectively. The excess electricity is greater by using ABC algorithm than PSO algorithm, which is provided to the dump load. Further, LPSP from both algorithms is maintained at 0.19%.
It is inferred from the results that the ABC algorithm provides better results. Therefore, the configuration obtained from the ABC algorithm is selected. Table Ⅴ lists the annualized cost analysis using the ABC algorithm. The total ACS is equal to 2618.3 , which is obtained by adding the annualized capital cost of SPV panels, wind turbines, and inverters while subtracting the cost of annual grid sales. The annualized cost of components is calculated by using CRF. Further, it can be deduced from

Fig. 8 Monthly power generation and consumption for whole year.
In certain months such as April, August, and September, better solar and wind power generation is achieved, resulting in more grid sales and less grid purchases.
For better understanding of energy management in the system, two days are selected, i.e., one in winter and the other in summer.

Fig. 9 One day power balance in proposed system. (a) Winter. (b) Summer.
In a grid-connected system, the grid power exchange capacities, i.e., maximum sales and purchase capacities, are two major parameters. To analyze the operation of the proposed system, the effect of grid capacity verses LCOE is evaluated. Firstly, by maintaining the maximum grid purchased capacity at 10 kW, the grid sales capacity varies and LCOE is observed.

Fig. 10 Variation of levelized cost of energy with maximum grid sales and purchase capacities.
It can be observed that LCOE decreases significantly as the maximum grid sales increases. Further, by maintaining the maximum grid sales capacity, i.e., 10 kW, the maximum grid purchase capacity varies. Moreover, the results are plotted in
The above case study is proposed considering a time limit in the arrival and departure of EVs at the charging station within office hours. To prove the efficiency of the proposed model, another case study is analyzed and presented in this subsection. A more generalized and flexible, charging station is considered. Further, it is assumed that the arrival and departure time of the EVs is uncertain. No charging preference is given to any of the EVs, and it is assumed that the charging occur as per the user requests. The maximum grid sales and purchase capacities are maintained similar to the previous case study.

Fig. 11 One-day power balance. (a) Winter. (b) Summer.
The power balance shows that the EV load is distributed throughout the day and night compared to the previous case study. The second case is more generic and can be applied to any charging station powered by renewable sources, and the grid can be treated as a backup. LCOE is competitive in both case studies. However, the system will be more grid dependent rather than depending on renewable power sources in the second case.
The performance of metaheuristic algorithms is dependent on the control parameter. Therefore, to verify the effectiveness of the considered algorithms, a comparison of the results of the PSO and ABC algorithms is performed with respect to variations in different control parameters. In both algorithms, the values of the common parameters used such as population size and total evaluation number are chosen to be the same, i.e., 20 and 100, respectively. The other specific parameters considered for sensitivity analysis in the case of PSO are cognitive and social components, which are represented in this paper as and , respectively. In the experiments, the cognitive and social components are both set to be 2.0. In the case of the ABC algorithm, excluding the common parameters, only one control parameter is considered as . The aforementioned parameters vary by of the standard parameter values proposed in the literature [
Hybrid renewable energy systems have the potential to provide electricity to grid and off-grid locations economically and reliably. The efficiency of the system is enhanced if the renewable energy systems are integrated with the grid. In this paper, a detailed mathematical model and operation strategy are presented to deduce the component sizing of solar-wind hybrid system that incorporates an EV charging station. The optimal configuration consists of solar capacity of 36 kW and wind capacity of 20 kW along with grid sales and purchase capacities of 10 kW. The EV demand of 28 MWh/year and the shopping arcade demand of 40 MWh/year are completely managed by the renewable energy sources and the grid. Excess electricity of 27 MWh/year is given to the dump load. The energy sold to the grid is 43 MWh/year, which is much higher than the energy purchased from the grid, i.e., 8 MWh/year. LCOEs obtained from ABC and PSO algorithms are measured to be 0.038 and 0.043 $/kWh, respectively. LCOEs using both algorithms are highly competitive with the cost of energy purchased from the grid, while maintaining the LPSP to be 0.19%.
Moreover, a more generic case study is considered by relaxing the time constraints, and the system is satisfactorily economical. However, the grid dependency increases in this case. Further, a sensitivity impact analysis on the maximum grid sales and purchase capacities on the LCOE is performed. In a grid-connected system, the maximum grid sales and purchase capacities demonstrate a significant impact on LCOE and ACS of the system. Finally, a parameter sensitivity analysis is performed to analyze the impact of the algorithm parameters on the solution quality of both algorithms. The obtained solution shows minor variations around the mean value, and the relative deviation from the minimum ACS is less than 1%. The proposed system will be helpful in promoting renewable energy sources in smart grid and can reduce the dependence of a small community on the grid.
References
Ministry of New and Renewable Energy Annual Report. (2019, Dec.) [Online]. Available: https://mnre.gov.in/knowledge-center/publication [百度学术]
M. Nehrir, C. Wang, K. Strunz et al., “A review of hybrid renewable/alternative energy systems for electric power generation: configurations, control, and applications,” IEEE Transactions on Sustainable Energy, vol. 2, no. 4, pp. 392-403, May 2011. [百度学术]
X. Chen, M. B. Mcelroy, Q. Wu et al., “Transition towards higher penetration of renewables: an overview of interlinked technical, environmental and socio-economic challenges,” Journal of Modern Power Systems and Clean Energy, vol. 7, no. 1, pp. 1-8, Aug. 2019. [百度学术]
W. Zhou, C. Lou, Z. Li et al., “Current status of research on optimum sizing of stand-alone hybrid solar-wind power generation systems,” Applied Energy, vol. 87, no. 2, pp. 380-389, Feb. 2010. [百度学术]
A. Kanase-Patil, R. Saini, and M. Sharma, “Integrated renewable energy systems for off grid rural electrification of remote area,” Renewable Energy, vol. 35, no. 6, pp. 1342-1349, Jun. 2010. [百度学术]
V. J. Babrekar, S. D. Bandawar, and A. R. Behade, “Review paper on hybrid solar-wind power generator,” International Journal of Computer Applications, vol. 165, no. 5, pp. 36-40, May 2017. [百度学术]
S. Bhattacharjee and S. Acharya, “PV-wind hybrid power option for a low wind topography,” Energy Conversion and Management, vol. 89, pp. 942-954, Jan. 2015. [百度学术]
G. Tina, S. Gagliano, and S. Raiti, “Hybrid solar/wind power system probabilistic modelling for long-term performance assessment,” Solar Energy, vol. 80, no. 5, pp. 578-588, May 2006. [百度学术]
H. Yang, W. Zhou, L. Lu et al., “Optimal sizing method for stand-alone hybrid solar-wind system with LPSP technology by using genetic algorithm,” Solar Energy, vol. 82, no. 4, pp. 354-367, Apr. 2008. [百度学术]
A. Kaabeche, M. Belhamel, and R. Ibtiouen, “Sizing optimization of grid-independent hybrid photovoltaic/wind power generation system,” Energy, vol. 36, no. 2, pp. 1214-1222, Feb. 2011. [百度学术]
S. Diaf, M. Belhamel, M. Haddadi et al., “Technical and economic assessment of hybrid photovoltaic/wind system with battery storage in corsica island,” Energy Policy, vol. 36, no. 2, pp. 743-754, Feb. 2008. [百度学术]
B. Ye, J. Jiang, L. Miao et al., “Feasibility study of a solar-powered electric vehicle charging station model,” Energies, vol. 8, no. 11, pp. 13265-13283, Nov. 2015. [百度学术]
A. Koochaki, M. Divandari, E. Amiri et al., “Optimal design of solar-wind hybrid system using teaching-learning based optimization applied in charging station for electric vehicles,” in Proceedings of IEEE Transportation Electrification Conference and Expo, Long beach, USA, Jun. 2018, pp. 1-6. [百度学术]
R. Kaur, V. Krishnasamy, and N. K. Kandasamy, “Optimal sizing ofwind-PV-based DC microgrid for telecom power supply in remote areas,” IET Renewable Power Generation, vol. 12, no. 7, pp. 859-866, May 2018. [百度学术]
M. Nizam and F. R. Wicaksono, “Design and optimization of solar,wind, and distributed energy resource hybrid power plant for electric vehicle charging station in rural area,” in Proceedings of 5th International Conference on Electric Vehicular Technology, Surakarta, Indonesia, Oct. 2018, pp. 41-45. [百度学术]
M. Habib, S. Said, M. El-Hadidy et al., “Optimization procedure of a hybrid photovoltaic wind energy system,” Energy, vol. 24, no. 11, pp. 919-929, Nov. 1999. [百度学术]
J. Li, W. Wei, and J. Xiang, “A simple sizing algorithm for stand-alone [百度学术]
PV/wind/battery hybrid microgrids,” Energies, vol. 5, no. 12, pp. 5307- [百度学术]
5323, Dec. 2012. [百度学术]
H. Yang, L. Lu, and W. Zhou, “A novel optimization sizing model for hybrid solar-wind power generation system,” Solar Energy, vol. 81, no. 1, pp. 76-84, Jan. 2007. [百度学术]
G. Bekele and G. Boneya, “Design of a photovoltaic-wind hybrid power generation system for ethiopian remote area,” Energy Procedia, vol. 14, pp. 1760-1765, 2012. [百度学术]
F. Mwasilu, J. J. Justo, E.-K. Kim et al., “Electric vehicles and smart grid interaction: a review on vehicle to grid and renewable energy sources integration,” Renewable and Sustainable Energy Reviews, vol. 34, pp. 501-516, Jun. 2014. [百度学术]
S. Singh, S. Jagota, and M. Singh, “Energy management and voltage stabilization in an islanded microgrid through an electric vehicle charging station,” Sustainable Cities and Society, vol. 41, pp. 679-694, Jun. 2018. [百度学术]
H. C. Yu and C. G. Lu, “Recent development of electric vehicles,” Applied Mechanics and Materials, vol. 490, pp. 968-971, Jan. 2014. [百度学术]
C. Chellaswamy, V. Nagaraju, and R. Muthammal, “Solar and wind energy based charging station for electric vehicles,” International Journal of Advanced Research in Electrical Electronics and Instrumentation Engineering, vol. 7, no. 1, pp. 313-324, Jan. 2018. [百度学术]
S. Khan, A. Ahmad, F. Ahmad et al., “A comprehensive review on solar powered electric vehicle charging system,” Smart Science, vol. 6,no. 1, pp. 54-79, Dec. 2017. [百度学术]
H. Li, H. Liu, A. Ji et al., “Design of a hybrid solar-wind powered charging station for electric vehicles,” in Proceedings of International Conference on Materials for Renewable Energy and Environment, Chengdu, China, Aug. 2014, pp. 977-981. [百度学术]
M. Nashed and I. Edwar, “Wind/PV hybrid of DC electric vehicle charging station with bi-directional converter,” in Proceedings of World Engineering Conference and Convention, Kyoto, Japan, Dec. 2015, pp. 1-6. [百度学术]
O. Hafez and K. Bhattacharya, “Optimal design of electric vehicle charging stations considering various energy resources,” Renewable Energy, vol. 107, pp. 576-589, Jul. 2017. [百度学术]
G. C. Mouli, P. Bauer, and M. Zeman, “System design for a solar powered electric vehicle charging station for workplaces,” Applied Energy, vol. 168, pp. 434-443, Apr. 2016. [百度学术]
O. Sadeghian, M. Nazari-Heris, M. Abapour et al., “Improving reliability of distribution networks using plug-in electric vehicles and demand response,” Journal of Modern Power Systems and Clean Energy, vol. 7, no. 5, pp. 1189-1199, May 2019. [百度学术]
A. E. S. A. Nafeh, “Optimal economical sizing of a PV-wind hybrid energy system using genetic algorithm,” International Journal of Green Energy, vol. 8, no. 1, pp. 25-43, Feb. 2011. [百度学术]
M. R. Javadi, A. Jalilvand, R. Noroozian et al., “Optimal design and economic assessment of battery based stand-alone wind/PV generating system using ABC,” in Proceedings of the 3rd Conference on Thermal Power Plants, Tehran, Iran, Jan. 2011, pp. 1-7. [百度学术]
L. Wang and C. Singh, “Multicriteria design of hybrid power generation systems based on a modified particle swarm optimization algorithm,” IEEE Transactions on Energy Conversion, vol. 24, no. 1, pp. 163-172, Jan. 2009. [百度学术]
M. Kharrich, M. Akherraz, and Y. Sayouti, “Optimal sizing and cost of a microgrid based in PV, Wind and BESS for a school of engineering,” in Proceedings of International Conference on Wireless Technologies, Embedded and Intelligent Systems, Fez, Morocco, Apr. 2017, pp. 1-5. [百度学术]
M. Nazari-Heris, B. Mohammadi-Ivatloo, S. Asadi et al., “Large-scale combined heat and power economic dispatch using a novel multi-player harmony search method,” Applied Thermal Engineering, vol. 154, pp. 493-504, May 2019. [百度学术]
O. Hoseynpour, B. Mohammadi-Ivatloo, M. Nazari-Heris et al., “Application of dynamic non-linear programming technique to nonconvex short-term hydrothermal scheduling problem,” Energies, vol. 10, no. 9, p. 1440, Sept. 2017. [百度学术]
R. Kumar, R. Gupta, and A. K. Bansal, “Economic analysis and power management of a stand-alone wind/photovoltaic hybrid energy system using biogeography based optimization algorithm,” Swarm and Evolutionary Computation, vol. 8, pp. 33-43, Feb. 2013. [百度学术]
M. Nazari-Heris, A. F. Babaei, B. Mohammadi-Ivatloo et al., “Improved harmony search algorithm for the solution of non-linear nonconvex short-term hydrothermal scheduling,” Energy, vol. 151, pp. 226-237, May 2018. [百度学术]
O. Ekren and B. Y. Ekren, “Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing,” Applied Energy, vol. 87, no. 2, pp. 592-598, Feb. 2010. [百度学术]
D. Karaboga and B. Akay, “A comparative study of artificial bee colony algorithm,” Applied Mathematics and Computation, vol. 214, no. 1, pp. 108-132, Aug. 2009. [百度学术]
D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony algorithm,” Journal of Global Optimization, vol. 39, no. 3, pp. 459-471, Nov. 2007. [百度学术]
J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95-International Conference on Neural Networks, Perth, Australia, Mar. 1995, pp. 1942-1948. [百度学术]
Surface meteorology and solar energy. (2019, Dec.). [Online]. http://eosweb.larc.nasa.gov/sse/RETScreen, NASA [百度学术]
HOMER. (2019, Dec.). [Online]. https://www.homerenergy.com/products [百度学术]
S. Singh and S. C. Kaushik, “Optimal sizing of grid integrated hybrid PV-biomass energy system using artificial bee colony algorithm,” IET Renewable Power Generation, vol. 10, no. 5, pp. 642-650, Apr. 2016. [百度学术]
N. J. Singh, J. S. Dhillon, and D. P. Kothari, “Synergic predatorprey optimization for economic thermal power dispatch problem,” Applied Soft Computing, vol. 43, pp. 298-311, Jun. 2016. [百度学术]