Balbir Mahinder Singh

Integrated PV based solar insolation measurement and performance monitoring system

The focus of this project is to develop a PV monitoring system that requires the development of mathematical model to estimate the solar insolation based on PV output power. The sustainability and reliability of PV based electricity generating system depends on the solar insolation, and optimization is important to meet the demand of the load. It is important to measure solar insolation in order to obtain an optimum PV sizing and also the system must be attached to a monitoring unit. The monitoring unit will be able to give feedback, so that the performance of the system is kept at optimum level. The relationship between solar insolation and output voltage of PV module was determined by using suitable experimental set up, and the modeling equation was used to determine the level of solar insolation. Monitoring software was developed using Visualbasic.Net together with hardware package, which can transfer the voltage signal from the PV panel to the computer. The integrated system can be used by multiple users to remotely monitor the performance of the PV system via internet.

Intelligent energy allocation strategy for PHEV charging station using gravitational search algorithm

Recent researches towards the use of green technologies to reduce pollution and increase penetration of renewable energy sources in the transportation sector are gaining popularity. The development of the smart grid environment focusing on PHEVs may also heal some of the prevailing grid problems by enabling the implementation of Vehicle-to-Grid (V2G) concept. Intelligent energy management is an important issue which has already drawn much attention to researchers. Most of these works require formulation of mathematical models which extensively use computational intelligence-based optimization techniques to solve many technical problems. Higher penetration of PHEVs require adequate charging infrastructure as well as smart charging strategies. We used Gravitational Search Algorithm (GSA) to intelligently allocate energy to the PHEVs considering constraints such as energy price, remaining battery capacity, and remaining charging time.

Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles

Recent researches towards the use of green technologies to reduce pollution and higher penetration of renewable energy sources in the transportation sector have been gaining popularity. In this wake, extensive participation of plug-in hybrid electric vehicles (PHEVs) requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. Daytime charging stations will be needed for daily usage of PHEVs due to the limited all-electric range. Intelligent energy management is an important issue which has already drawn much attention of researchers. Most of these works require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. In this paper, gravitational search algorithm (GSA) has been applied and compared with another member of swarm family, particle swarm optimization (PSO), considering constraints such as energy price, remaining battery capacity, and remaining charging time. Simulation results obtained for maximizing the highly nonlinear objective function evaluate the performance of both techniques in terms of best fitness.

Optimisation of PHEV/EV charging infrastructures: a review

Plug-in hybrid electric vehicle (PHEV) or electric vehicle (EV) has the potential to facilitate the energy and environmental aspects of personal transportation, but face a hurdle of access to charging system. The charging infrastructure has its own complexities when it is compared with petrol stations because of the involvement of the different charging alternatives. As a result, the topic related to optimisation of PHEV/EV charging infrastructure has attracted the attention of researchers from different communities in the past few years. Recently introduced smart grid technology has brought new challenges and opportunities for the development of electric vehicle infrastructure facilities. This paper is a review of different computational approaches and techniques used for the optimisation of charging infrastructure for electric vehicles.

Data compression technique for modeling of global solar radiation

This paper studies the use of wavelet transform and curve fitting methods for solar radiation compression and prediction-based modeling. Symlet 6 and Meyer wavelets will be utilized for analyzing the transient nature of solar radiation. The measured solar radiation data will be decomposed up to level 4. After the data has been filtered, curve fitting method will be used to derive the modeling equation to estimate the horizontal global solar radiation. The instantaneous global solar radiation received on the surface of earth in Malaysia on a clear sky day around noon time can be up to 980 W/m2. The amount of solar energy received is highly transient in nature, due to the meteorological conditions. Therefore data captured using electronic data acquisition system is quite dense, and there is a need to filter the data, in order to reduce the amount of dataset. The data is highly useful for the designing of solar energy related electricity generation systems. The accuracy of the predicted output from a photovoltaic (PV) based electricity generation system depends on the accuracy of the intercepted solar radiation. The sizing of the electricity generating system cannot be based on a single power density value. Therefore compressed time based solar radiation values serve as an important input for the designing of a PV based system during the availability of sunshine.

Sizing and designing a stand-alone photovoltaic electricity generation system using a customized simulation program

The electricity consumption in Malaysia has been rising steadily over the last decade and electricity demand has been projected to increase by 4 – 5 % per annum for the next five years. The increase in electricity demand in the years to come only means that more fuel is needed to generate the amount of electricity needed to meet demand. However, a disturbing fact is that electricity generation in our nation is still largely dependent of the combustion of fossil fuels. Concerns and issues tied to these fuels are raising interest in the utilization of alternative energy sources such as solar energy for electricity generation. The stand-alone photovoltaic electricity generation system (PVEGS) needs to be sized and designed properly prior to system set up. Since setting up a PVEGS is rather extensive and complicated, a simulation program that performed system sizing and designing prior to implementation was necessary. A simulation program was developed in MATLAB and is customized for the Malaysian context. In this paper, the sizing and designing of a stand-alone PVEGS for a small household load performed using the locally acclimatized simulation program is discussed. The site selected for this study is Ipoh. The life-cycle cost (LCC) analysis of the system is also reviewed to estimate the cost of the proposed PVEGS. The thorough sizing, designing and costing method discussed in this paper encourages the use of PVEGSs to meet our electricity needs especially for rural electrification.

Novel metaheuristic optimization strategies for plug-in hybrid electric vehicles: A holistic review

Hybrid Vehicles have experienced major modifications since the last decade. Smart grid success with combination of renewable energy exclusively depends upon the large-scale penetration of Plug-in Hybrid Electric Vehicles (PHEVs) for a sustainable and carbon-free transportation. Recent technical studies regarding various optimization strategies related to PHEV integrated smart grid; such as control and battery charging, vehicle-to-grid (V2G), unit commitment, charging infrastructures, integration of solar and wind energy and demand management prove that electrification of transportation as a rapidly growing field of research. This work presents a holistic review of all substantial research applying metaheuritics optimization for plug-in- hybrid electric vehicles. A summary on future perspective of metaheuristic algorithms is also provided, covering Cuckoo Search (CS), Harmony Search (HS), Artificial Bee Colony (ABC), etc. with a comprehensive reviews on previously applied methods and their performance for solving different real-world problems in the domain of PHEVs. Moreover, significant shifts towards hybrid and hyper metaheuristics are also highlighted.

Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

Abstract Green technologies gain popularity to reduce the pollution and give higher penetration of renewable energy source in the transportation. This research induce that the extensive involvement of plug-in hybrid electric vehicles (PHEVs) requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. It is also noticed that daytime charging station are necessary for daily usage of PHEVs due to the limited all-electric-range. Most of the researches in the past have been stated that only proper charging control and infrastructure management can assure the larger participation of PHEVs. Therefore, researchers are trying to develop efficient control mechanism for charging infrastructure in order to facilitate upcoming PHEVs penetration in highway. Nevertheless, most of the past researcher already aware with the issue related to intelligent energy management. Yet, these studies could not fill the gap of the problem associated with intelligent energy management and require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. The outcome of this research study provides four optimization techniques that include Hybrid method within swarm intelligence group for the State-of-Charge (SoC) optimization of PHEVs. The finding of this research simulation results obtained for maximizing the highly nonlinear objective function evaluate the comparative performance of all four techniques in terms of best fitness, convergence speed, and computation time. Finally, the hybridization method (PSOGSA) presented in this dissertation uses the advantages of both PSO and GSA optimization and thus produce higher best fitness values. This study evaluates the performance of standard PSO, then Accelerated version of PSO (APSO), GSA algorithm and then Hybrid of PSO and GSA. The hybridization method (PSOGSA) uses the advantages of both PSO and GSA optimization and thus produce higher best fitness values. However, PSOGSA method takes much longer computational time than single methods because of incorporating two single methods in one algorithm. This research study suggests that PSOGSA method is a great promise for SoC optimization but it takes much longer computational time.

Two diode model for parameters extraction of PV module

This paper introduces an improved two-diode model of photovoltaic (PV) module. The main influence of this work is the combination of the accuracy of the two diode model and the short computational time of the single diode model. The proposed model is validated against various types of PV modules (mono-crystalline, multi-crystalline, and thin-film) from a variety of manufacturers. The model performance is evaluated against the famous single diode model and the two diode model. The model proves its superiority when exposed to variation in temperature, as it matches accurately for all important points of the I-V curve.

NOVEL CONFIGURATIONS OF SOLAR DISTILLATION SYSTEM FOR POTABLE WATER PRODUCTION

More and more surface water are polluted with toxic chemicals. Alternatively brackish and saline water are used as feed water to water treatment plants. Expensive desalination process via reverse osmosis or distillation is used in the plants. Thus, this conventional desalination is not suitable for low and medium income countries. A cheaper method is by solar distillation. However the rate of water production by this method is generally considered low. This research attempts to enhance water production of solar distillation by optimizing solar capture, evaporation and condensation processes. Solar radiation data was captured in several days in Perak, Malaysia. Three kinds of experiments were done by fabricating triangular solar distillation systems. First type was conventional solar still, second type was combined with 50 Watt solar photovoltaic panel and 40 Watt Dc heater, while third type was integrated with 12 Volt Solar battery and 40 Watt Dc heater. The present investigation showed that the productivity of second and third systems were 150% and 480% of the conventional still type, respectively. The finding of this research can be expected to have wide application in water supply particularly in areas where fresh surface water is limited.