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Due to global environmental impacts, the electric vehicle (EV) adoption rate is increasing. However, unlike conventional petrol vehicles, EVs take a considerable time to charge. EVs on the road with different battery charging statuses and driving demographics may cause uncertain peak time arrivals at charging stations. Battery-swappable charging stations are a quick and easier way to replace uncharged batteries with charged ones. However, charging due to uncertain EV arrival causes higher charging profiles posing load to the grid, management of charged and discharged batteries, and peak time charging tariffs. These challenges hinder the wide operation of battery-swappable charging stations. Nevertheless, a pre-assessment of peak hours using EV demographics can reduce congestion. In recent literature surveys for battery-swappable charging stations, spot congestion has not been given much attention, which has a direct influence on the sizing and operation of battery-swappable charging stations. This research study is focused on estimating peak time events using a novel integrated techno-economic assessment framework. A fuzzy-based parametric assessment tool is developed that identifies the factors that influence higher congestion events. Based on the peak event assessment, grid, and solar PV-based generation is optimized using mixed integer linear programming. In the final step, an environment analysis of a swappable charging station is performed. Furthermore, the results achieved using the proposed framework for battery-swappable charging stations (BSCSs) were compared with fast-charging (FC) stations. FC can economically perform well if integrated with solar PV systems; however, the capital cost is 80% greater than the BSCSs designed under the proposed framework. The operational cost of BSCSs is 39% higher than FC stations as they use 29% higher grid units than FC stations due to night operations under congestion. [ABSTRACT FROM AUTHOR]

A novel solution for optimized energy management comprising a microgrid system for industries in Pakistan is proposed. The proposed study considered microgrids based on photovoltaics, wind turbines, power storage systems, and dual-fuel (DF) generators as backup. A heuristic methodology with a cuckoo search algorithm (CSA) is presented for efficient power trading by scheduling machines. The study was conducted to prove that CSA is adaptable and flexible for self-governing choices for the efficient management and scheduling of machines and power trade between the microgrid and commercial grid. A mixed integer linear programming algorithm is introduced to optimize the system design problems that control decision making for the ideal operation management. A real-time pricing scheme is utilized for electricity price figures. The simulation results show the efficient performance of the proposed scheme to maximize profitability, reduction in electricity cost, and peak to average ratio. Furthermore, the proposed optimization technique was compared with a highly in-use strawberry algorithm to prove the supremacy of the proposed technique. The proposed efficient and robust energy management system was implemented in Shafi Dyeing Industry, Faisalabad, to validate the simulated model. [ABSTRACT FROM AUTHOR]

This paper presents the optimal design of a photovoltaic (PV) drip irrigation system. Designing a PV system is based on calculated motor power, solar irradiance level and other meteorological parameters at a certain geographical location. Therefore, a simulation study of the designed PV system were performed by a PVGIS simulation tool. The PVGIS simulation tool analyzes the potential of power generation with optimal PV modules tilt angle and orientation on a monthly and annual basis, and an analysis of the overall shading situation (horizon) as well as the internal shading between the PV module rows. The selection of water pump and motor depends upon the depth of water table and desired discharge and head to operate the irrigation system. Furthermore, a locally developed Solar-Drip Simulation Tool (SoSiT) was used for load and supply optimization. Based on ambient temperature, solar irradiation and water requirements, SoSiT calculates net generation by a PV system and resultant water output of the irrigation system. The particular drip irrigation site has two zones; the maximum water requirement for zone 1 (row crop) is 50,918.40 Liters/day and for zone 2 (orchards) is 56,908.80 L/day. From PVGIS simulation results, the maximum daily energy production of the designed PV system was 6.48 kWh and monthly energy production was 201 kWh in the month of May. SoSiT results showed that the PV system fulfilled the required crop requirement by only using 28% of the potential water supply, and 72% of the potential water supply from a solar-powered pump was not used. This value is high, and it is recommended to grow more or different crops to utilize the fuel-free electricity from the PV system. The unit cost of PV-powered drip irrigation is USD 0.1013/kWh, which is 4.74% and 66.26% lower than the cost of subsidized electricity and diesel, respectively. [ABSTRACT FROM AUTHOR]

Renewable-energy-resource-based microgrids can overcome excessive carbon footprints and increase the overall economic profile of a country. However, the intermittent nature of renewables and load variation may cause various control problems which highly affect the power quality (frequency and voltages) of the overall system. This study aims to develop an adaptive technique for the optimization of renewable energy resources (RERs). The proposed grid-tied microgrid has been designed using a wind-turbine (WT) based distributed generation, a photovoltaic (PV) system, a diesel generator as an emergency backup, and battery energy storage system (BESS). The flexible (residential) and non-flexible (industrial) loads are connected with the proposed grid. Matlab/Simulink has been used to evaluate the performance of the proposed optimization technique. Comparison with different in-use techniques shows that the proposed technique is more reliable and efficient than the state of the art optimization techniques currently in use. Moreover, this proposed system provides robust optimization of parameters of concern such as frequency and voltages, makes efficient use of the maximum power point tracking while regulating voltages, reduces the overall system cost, and increases economic profitability. [ABSTRACT FROM AUTHOR]

This paper discusses the effectiveness of simultaneous use of CuO nanofluid and air as a dual-fluid coolant for the thermal management of a photovoltaic/thermal (PV/T) system. Outdoor experimental studies were performed to calculate the discrepancies between indoor and outdoor test findings. The thermal efficiency and the electrical characteristics of the dual-fluid PV/T system were investigated under steady-state test conditions following ISO standards. It was found that the divergence in electrical efficiency between indoor and outdoor-based PVT testing was significantly higher, while the difference in thermal efficiencies was marginal. It was observed that nanofluid/air, even at the lowest flow rates, outclassed the water/air coolant at higher flow rates in terms of PV/T energy output, which also ultimately helps in reducing the energy requirement for pumping. Unlike conventional solar air heaters, the proposed dual-fluid PV/T system produces a high air temperature when operated with only air at stagnant nanofluid. The maximum PV/T efficiency of approximately 85% was recorded when the nanofluid and air flows were kept at 0.02 kg/s and 0.04 kg/s, respectively. It is concluded that outdoor steady state testing provides comprehensive performance characterization of the nanofluid powered dual-fluid coolant for the PV/T system. [ABSTRACT FROM AUTHOR]