Solar Photovoltaic Sources Research Articles

Multipurpose battery‐assisted solar water pumping system for off‐grid applications: design and development

Providing basic human needs like water and household electricity is a challenging task at remote locations. To support both needs, this study presents the development of a multipurpose battery-assisted solar water pumping system (SWPS). The system consists of only two power electronics converter, viz., bidirectional DC–DC converter, and three-phase voltage source inverter (VSI). The detailed control system is presented, where the DC–DC converter controls the battery power and also ensures the maximum utilisation of stand-alone solar photovoltaic source. In pumping operation, the VSI is operated as a variable frequency motor drive and rotates an induction motor (IM) at the rated power to deliver rated water discharge. In power supply operation, the household electrical load is connected between the mid-point of DC-bus and the star point of an IM winding. The VSI, along with motor inductance, realises the single-phase inverter, which is controlled with synchronous sine pulse-width modulation to deliver single-phase output. The motor does not rotate during this operation and the motor winding acts as a current filter. This multipurpose SWPS assures rated water discharge during the irrigation period and availability of electrical power in the remaining period. A prototype is developed and extensive experiments are performed to investigate system performances.

Optimal Operation of Electrified Railways with Renewable Sources and Storage

This paper proposes an approach for the optimal operation of electrified railways by balancing energy flows among energy exchange with the traditional electrical grid, energy consumption by accelerating trains, energy production from decelerating trains, energy from renewable energy resources (RERs) such as wind and solar photovoltaic (PV) energy systems, and energy storage systems. The objective function considered in this work is the minimization of total operating cost of electrified railway system consisting of cost of power generation from the external power system, cost of power obtained from RERs such as wind and solar PV sources, cost of power from storage systems such as battery storage and supercapacitors, and the income obtained by selling excess power back to the main electrical grid. This problem is formulated as an AC optimal power flow problem subjected to various equality and inequality constraints. In this work, the probability distribution functions (PDFs) are used to the uncertainties related to wind and solar PV powers. The proposed optimization problem is solved by using CONOPT solver of generalized algebraic modeling system (GAMS) software, which is a powerful and efficient optimization tool. The simulation results obtained with GAMS/CONOPT solver are also compared with meta-heuristic based differential evolution algorithm (DEA).

Machine learning models for ecological footprint prediction based on energy parameters

The ecological footprint is an excellent tool to better understand the consequences of the human behavior on the environment. The growing need for natural resources emphasizes the necessity of their accurate observation, calculation, and prediction. This paper develops and compares four hybrid machine learning models for predicting the total ecological footprint of consumption based on a set of hyper-parameters predefined by the Bayesian optimization algorithm. In particular, K-nearest neighbor regression (KNNReg), random forest regression (RFR) with 93 trees, and two artificial neural networks (ANNs) with two hidden layers were developed and later compared in terms of their performance. As energy inputs, the primary energy consumption from (1) natural gas sources, (2) coal sources, (3) oil sources, (4) wind sources, (5) solar photovoltaic sources, (6) hydropower sources, (7) nuclear sources, and (8) other renewable sources was used. Additionally, population number has also been used as an input. The models were developed using a set of data that include 1804 instances. The ANNs were modeled using two different activation functions in the hidden layers: ReLU and SPOCU. The performance was evaluated using the mean absolute percentage error (MAPE), mean absolute scaled error (MASE), normalized root-mean-squared error (NRMSE), and symmetric mean absolute percentage error (SMAPE). The results show that KNNReg performs the best with MASE of 0.029, followed by the RFR (0.032), ReLU ANN (0.064), and SPOCU ANN (0.089). Moreover, SMOGN was utilized to produce a synthetic test set which was used to additionally test the best performed model. The performance on the SMOGN set demonstrates good performance (MASE=0.022). Lastly, the best performed model was implemented into a GUI that calculates the ecological footprint based on user inputs.

Renewable Energy-Based Economic Load Dispatch Using Two-Step Biogeography-Based Optimization and Butterfly Optimization Algorithm

This article introduces a unified approach to solve economic load dispatch (ELD) problem of an integrated power system that comprise of traditional thermal power units and renewable sources of energy such as wind energy (WE) and solar photovoltaic (PV) sources; employing a two-step optimization method consisting of Biogeography Based Optimization (BBO) and Butterfly Optimization Algorithm (BOA). BOA imitates the mating and food search process of butterflies for solving the problems associated with global optimization. Nonlinear characteristics of thermal generators is considered in the problem. Weibull distribution is used for determining the uncertainness in availability of wind power and Lognormal PDF is employed for calculating the availability of solar power. The efficacy, robustness and supremacy of the two-step BBO-BOA (hBBO-BOA) technique, compared to various other approaches in literature, are demonstrated by the simulation results. The outcome is quite inspiring, indicates that proposed hBBO-BOA is an efficient approach in order to solve different ELD problems.

Energy, exergy and sensitivity analyses of a novel hybrid structure for generation of Bio-Liquefied natural Gas, desalinated water and power using solar photovoltaic and geothermal source

Nowadays, with the increased energy demand worldwide, the utilization of various energies with the approach of maximizing the energy systems performance is an inevitable issue. The produced biogas from various types of biomasses as a renewable source of energy can satisfy a portion of the human needs due to its vast resources. In the present study, an integrated structure is developed and analyzed for the generation of bio-liquefied natural gas, desalinated water and power using the upgrading biogas system, multi-effect thermal desalination, and organic Rankine cycle. The photovoltaic system and geothermal source are employed to supply the power and heating for the integrated structure, respectively. This hybrid system generates 5.295 kg/s bio-LNG, 2.773 kg/s desalinated water and 840 kW power. The mixed fluid cascade cycle is used for methane liquefaction that its specific power consumption is reduced to 0.1888 kWh/kg LNG by using the absorption refrigeration system. The overall thermal and exergy efficiencies of the integrated system are 73.22% and 76.84%, respectively. Exergy analysis illustrates that the solar photovoltaic with 70.05% and the towers with 8.05% of exergy destruction have the most exergy destruction to the total equipment. The investigation on the effect of variation of methane percentage in the existing biogas on the system performance shows that with the increase of methane percentage to 75 mol%, the amount of produced bio-LNG and desalinated water increase up to 7.969 kg/s and 2.779 kg/s, respectively. Also, the total thermal efficiency would increase by up to 73.30%.

A New Nonisolated High-Step-Up DC–DC Converter Topology with High Voltage Gain Using Voltage Multiplier Cell Circuit for Solar Photovoltaic System Applications

The solar photovoltaic (PV) source is preferred to be functioned at low voltages. The practical systems such as grid-tied systems require a high output voltage causing a reduction in conversion efficiency. To handle this problem, this paper presents a new nonisolated boost DC–DC converter with a single switch suitable for solar PV applications. This converter comprises dual voltage multiplier (VM) cells, leakage energy recovery scheme, and coupled inductor (CI) techniques to get the desired output voltage from the converter. The double-clamp capacitors are connected to the primary side of the CI. The clamp capacitors can share the current through CI, and it ensures that the leakage inductance energy of CI can be recovered, leading to an expansion in the converter voltage gain and conversion efficiency. In addition, the clamp circuit clamps the voltage stress of the MOSFET switch, and the clamp capacitors will discharge at a specific time to increase the converter gain. A high duty cycle is not necessary for getting a high voltage gain, which avoids the diode reverse recovery issues. The converter can be operated in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM). A 500-W experimental prototype is developed for experimental validation, and the supporting simulation results are presented. The maximum efficiency of the converter is 93.94%, and, at full load, the efficiency is 92.55%.

Drying performance and economic analysis of novel hybrid passive-mode and active-mode solar dryers for drying fruits in East Africa

In East Africa, post-harvest loss of fresh perishable fruits is enormous, causing recurrent seasonal deficits. In response, a modified passive-mode hybrid solar dryer hereafter called; ‘an Improved Solar Dryer (ISD)’, was developed as a potential low-cost technology alternative for drying fruits in Uganda. An improved PV-assisted hot air dryer using active-mode hybrid solar dryer hereafter called; ‘Solar Photovoltaic and Electric (SPE)’ was also fabricated. The ISD dryer utilises freely-available solar energy to dry the produce. In contrast, the SPE dryer uses both solar photovoltaic and electricity sources. In this study, the drying performance of the ISD and SPE dryers were performed and compared to that of the traditional open sun drying (OSD) method. Relative to the OSD method, the economic performance of the ISD and SPE dryers was analysed. The drying performance results show that the mean drying air temperatures achieved by the ISD and SPE dryers were 31.9 and 41.1 °C respectively; relative to the 27.6 °C for the OSD method. On average, the thermal energy attained by the ISD and SPE dryers were 3551 and 5757 Watts (W) respectively, as compared to 2952 (W) obtained for the OSD method. It took the ISD and SPE dryers 10 and 18 h to effectively dry the fruit (pineapple) products, respectively as opposed to the 30 h taken by the OSD method. The results confirm the superior performance of the ISD and SPE dryers than the OSD method. Results for the economic performance analysis show more reduced costs of drying the products using the ISD than the SPE dryer. Similarly, the pay-back period for the ISD was 2.4 times shorter than that of the SPE dryer. Joint results for economic analysis and drying performance confirm the ISD as a better drying technology than the SPE dryer in the context of the resource-constrained farmers/agro-processors without electricity in Uganda and East Africa.

An improved teaching learning–based optimization algorithm for congestion management with the integration of solar photovoltaic system

In restructured power systems, transmission congestion is an imperative issue. Establishment of solar photovoltaic system at appropriate areas is likely to relieve congestion in transmission lines in the restructured power systems. Congestion management technique by utilizing solar photovoltaic sources, using an improved teaching learning–based optimization, is investigated in this article. Bus sensitivity factors which have the direct influence on the congested lines are utilized to locate the solar photovoltaic sources at appropriate areas. Congestion management is figured as an optimization problem with a goal of limiting the congestion management price utilizing the improved teaching learning–based optimization approach, which espouses the self-driven learning principle. IEEE-30 bus test system is simulated and tested in MATLAB environment so as to demonstrate the viability of the suggested methodology than different methodologies.

Non-Isolated Fourth-Order Boost DC-DC Converter for Power Management in Low Voltage Low Power DC Grids: Design and Interaction Analysis

In this paper, a minimum-phase response fourth-order boost dc-dc converter (FBDC) exhibiting continuous input and output current is proposed. A voltage-mode controller is adopted to this converter to perform bus voltage regulation in a low voltage low power dc distribution system (LVPDS). FBDC supports additional load demand by interconnecting a second power source/battery. A systematic steady-state analysis for FBDC is established and the ripple content and other L-C design expressions are derived. The LVPDS is an integration of solar photovoltaic (PV) source using a conventional dc-dc boost converter (CBDC), and constant power load using a conventional dc-dc buck converter (CBuC). In this LVPDS, the FBDC primarily ensures dc bus voltage regulation, CBDC ensures the maximum power point tracking (MPPT) while CBuC regulates the load voltage. Various transfer function models, formulated through small-signal analysis, are used to address the controller design aspects and interconnected LVPDS stability issues. A generalized small-signal model of LVPDS is also developed to analyze the sub-system interactions arising during the coherent operation of BRC in this multi-converter system. The impact of connecting FBDC, as BRC, with other converters in the LVPDS is also analyzed. The laboratory prototype of a 48 V LVPDS is developed for experimental validation of bus voltage regulation and sub-system interactions. The theoretical and experimental results are found to be in close correlation with each other.

Online Estimation of System Inertia in a Power Network Utilizing Synchrophasor Measurements

System inertia plays a vital role in controlling the angular stability of the system during a disturbance. Due to increased penetration of power electronic interfaced sources, such as Solar Photovoltaic (SPV) source, the overall system inertia reduces and varies depending on their operating conditions. In this paper, an approach for online inertia estimation in the power system network with SPV sources is proposed, using the synchronized measurements from Phasor Measurement Units (PMUs). An equivalent swing equation is used to emulate the network dynamics. A relationship between the inertia constant and the roots of this equation is determined. In order to numerically obtain the roots, the Estimation of Signal Parameter via Rotational Invariance Techniques (ESPRIT) method is first used to find the modes present in the frequency signal. A new formulation is proposed to extract an equivalent mode from all the obtained modes. Also, to avoid phase step error, Rate Of Change Of Frequency (ROCOF) is estimated from the equivalent mode of the frequency signal. Results obtained for the 39 bus New England system for various test cases, using Real-Time Digital Simulator (RTDS), prove the efficacy and superiority of the proposed approach over the existing approaches in the literature.

Enhancing inertia of solar photovoltaic‐based microgrid through notch filter‐based PLL in SRF control

Synchronous reference frame (SRF) control strategy for solar photovoltaic (SPV) sources is widely used to deliver maximum power to the grid. However, poor inertia support just after a disturbance and improper phase angle tracking in presence of the harmonics and system unbalance are noticed when the conventional phase-locked loop (PLL) based SRF control structure is used. In this study, an inertia enhancement method for inverter interfaced SPV sources is proposed, which adjusts only the PLL parameters with a notch filter (NF) in the SRF controller. NF in PLL is used due to its disturbance rejection potential and accurate phase angle tracking, even during system unbalance. The dynamic equation of pseudo induced voltage (PIV) vector with respect to the point of common coupling is derived. The kinematic equation of the PIV angle vector correlates inertia contribution and inverter terminal voltage. The impact of the change in PLL parameters on inertia enhancement is analyzed by validating the proposed technique on a test system in the real-time digital simulator. The frequency response by the proposed method has been compared with two state-of-art methods to prove the superiority of the proposed approach in enhancing the inertia of the SPV source in a microgrid.

Demand Response Model f or Duck Curve o n Pv Dominated System u sing Support Vector Machines Based Multistage Modelling

Solar Photovoltaic (PV) generation systems have a less Levelized cost of electricity (LCoE). As such, when solar energy is available, the demand response is scheduled in such a way that maximum utilization of solar energy is practised.But the power generation from a solar PV system is highly uncertain and unpredictable due to irregular solar irradiation. Also, the power generation is limited to a time fraction of a day.The impact of these negative traits in a power system is studied with the help of an analytical curve called “Duck curve”. “Solar Duck curve” is a graphical representation of time scaled imbalances between a SPV generation to peak demand. A steep or rugged part in a duck curve indicates sudden shortcoming of SPV generation with respect to the peak demand. Hence, during this period, the loads are shifted between solar PV sources and the main grid with respect to the insufficiency of solar power from peak demand. The proposed system is a machine learning-based multistage demand response system for meeting demand response of a SPV dominant duck curve. The model has four layers/stages.The primary layer is used to analyse the behaviour of the duck curve with the help of a Support Vector regression algorithm and the second layer is used for determining theoperating parameters based on the economic constraints imposed. The third layer is a demand response model based on the previous layer, and the fourth layer is aadaptive signal-processing model used to improve the stability of the system.The obtained demand response model is updated continuously in an adaptive manner so as to improve the stability of the system.A hardware experimental setup is made with eighteen numbers of 24V/2kW interconnected solar PV real-time system which is used for validating and analysing the method.

Solar PV and Grid Based Isolated Converter for Plug‐in Electric Vehicles

In this research work, a multipurpose power electronic interface (PEI) competent of utilising dual sources during charging process has been proposed for plug-in electric vehicles. Based on the requirement, the battery can either be charged from solar photovoltaic (SPV) or from the grid. Moreover, when charged from an SPV source, converter can extract highest available power with maximum power point tracking (MPPT). The intrinsic novelty of the proposed PEI is that no additional components/switches are employed to achieve dual sources in charging or for MPPT. The proposed PEI consists of a converter derived from a conventional isolated secondary ended primary inductance converter (SEPIC). It is designed to operate effectively in all vehicular modes (charging, propulsion (PP) and regenerative braking (RB)). During battery charging from the grid and SPV, it operates as isolated SEPIC. While, in PP and RB, it operates as a flyback converter. Therefore, the proposed converter keeps isolation in each mode, which results in better safety for battery as well as vehicle users. Further, all vehicular modes are achieved through a single converter. As a result, compactness of the charger increases and making it best solution for on-board battery charging application.

Optimal Voltage Control Strategy for Voltage Regulators in Active Unbalanced Distribution Systems Using Multi-Agents

The rapid increase in the installation of renewable energy sources, particularly solar photovoltaic (PV) sources associated with unbalanced features of distribution systems (DS), disturbs the classic control strategy of voltage regulation devices and causes voltage violation problems. This paper proposes an effective control strategy for voltage regulators in the DS based on the voltage sensitivity using a multi-agent system (MAS) architecture. The features of the unbalanced distribution system (UDS) with the PV and different types and configurations of voltage regulators are considered in the proposed strategy. The novelty of the proposed method lies in realizing both the control optimality of minimizing voltage violations and the flexibility to accommodate changes in the DS topology using an MAS scheme. An advantageous feature of using the MAS scheme is the robust control performance in normal operation and against system failure. Simulation studies have been conducted using IEEE 34-node and 123-node distribution test feeders considering high PV penetration and different sun profiles. The results show that the proposed voltage control strategy can optimally and effectively manage the voltage regulators in the UDS, which decrease their operation stresses and minimize the overall voltage deviation.

Coordinated Control of Distributed Energy Resources Using Features of Voltage Disturbances

Distributed energy resources (DERs) often rely on renewable sources whose random power fluctuations bring about voltage disturbances in distribution networks. Under such circumstances, voltage regulation through centralized control of DERs requires reliable detection and coordination mechanisms. This article proposes a new data-driven approach for event-triggered and coordinated control of DERs based on features of voltage disturbances. Synchrophasor datasets are processed to construct disturbance matrices that quantify spatio-temporal features of voltage disturbances. The estimated features are employed in clustering and control of DERs to suppress incipient events before exceeding a critical time. The proposed approach is tested in the IEEE 123-bus network, which has 15 solar photovoltaic sources with battery energy storage systems. The simulation results validate reliability and efficiency of the proposed method, and confirm that feature extraction combined with coordination of DERs can improve reliable and economic operation of distribution grids with renewables.

Solar Energy a Promising Source for E-Vehicles

The demand and availability of energy is growing day by day in the whole world. Renewable energy resources will play an important role in the future as the conventional energy sources like coal and petroleum are limited and declining. In the present situation, energy crisis is an important very critical problem hence necessary to address. Basically, it is to verify the availability to increase the generation or to install additional generators. Alternatively, load management known as demand side management (DSM) strategy must be adopted. In the current scenario, the uses of fossil fuel for the domestic transport are rapidly increasing and indicate the threat in the near future. The adverse effects of the practice of such vehicles and means leads to the environment pollution and health issues related to the living habitats. In line with this the upcoming mode of transport is E-vehicles keeping concerned about the environment pollution. The drawback of this mode of transport is the use of electric energy for the propulsion and going to create additional burden on the existing power system setup and infrastructure. But the use of power electronics and controls in Electric vehicles (EVs) along with latest technologies is fostering and supporting the green transportation systems.E-Vehicle charger is the role player in reducing the dependence on fossil fuels and is the natural evolution of our energy infrastructure significantly. Consumer incentives are also important to make the purchase of an electric vehicle and home fast-charging station more affordable and attractive. To address this issue most of the developed countries have taken up an initiative to extract solar photovoltaic conversion systems to store and transfer to the grid.The present rate of use of E-vehicles is 70% compared to the earlier decade. From the perspective of solar energy basically, it is extracted as either heat or light energy. But the storage system is the bottle neck issue in this regard due to technical, economic barriers and challenges. The power electronics interfaces male E-vehicle battery to get charged from the grid if there is no source solar energy known as grid to vehicle (G2V). The other mode is to transfer the stored energy from the storage battery to grid (V2G) as and when grid finds the deficit of energy to meet its loads. Then the solar PV systems installed on the roof top of the vehicle generate the electricity and is used for their operation by the battery support.Therefore, the E-vehicles fitted with solar PV panels can even transfer electricity to the grid (V2G). Hence, the suitable design and implementation can prove the solar photovoltaic source as a promising source for such E vehicle application in the very near future. This creates an opportunity to the engineers and enterprises.

Interleaved high gain DC-DC converter for integrating solar PV source to DC bus

In this paper, a novel non-isolated DC-DC converter topology is proposed for solar photovoltaic (PV) application. The proposed converter is constructed from an interleaved boost converter (IBC) to reduce the input current ripple. Voltage gain is extended by (i) using voltage lift technique, (ii) replacing the conventional inductors of the IBC by coupled inductors (CIs) with appropriate turns ratio and (iii) connecting a voltage multiplier cell (VMC) across the secondary windings of the CIs. As the voltage gain is extended mainly at the secondary side of the CIs, the switches are subjected to low voltage stress which is only 12.63% of the output voltage. The converter yields a high voltage conversion ratio of 15.83. Experimental results obtained from a 24 V/380 V, 225 W prototype converter operating at 91.6% efficiency serves as a proof of the presented concept which has been employed to achieve high voltage conversion ratio (15.83) with low input current ripple (20%).

Solar photovoltaic source based magnetic launcher simulation design with thermal requirements consideration

Solar photovoltaic source based military defense systems, especially magnetic launchers, have significant advantages in terms of source independence, mobility, and efficiency. In this study, a solar photovoltaic sourced magnetic launcher system is modeled, and a critical component of the system’s (launcher coil) thermal requirements are analyzed. The designed system contains four energy conversion steps that are respectively photon, electrical, magnetic, and mechanical energy; also the launcher coil has an energy loss by the form of heat. Solar photovoltaic source based magnetic launcher system is sourced by solar photovoltaic panels and produced electrical energy is filtered, limited, and stored by an electrical converter. Stabilized electrical energy is converted to the magnetic energy by a coil. This magnetic energy is transferred to the projectile as mechanical energy. The microcontroller in the electrical converter unit controls all processes and manages the switching operations. Designed launcher coil is subject to a high amplitude current and employs simple, low-cost materials as an air core and copper wire. For this reason, the insulation breakdown on the launcher coil due to high temperatures are investigated, and a simple cooling configuration is suggested.

Cost Analysis of Proposed Solar Photovoltaic Systems Performance for Engineering Faculty, University of Port Harcourt

In Nigeria, grid connected solar based system are not common as most connections are stand-alone This work analyze costing of proposed solar photovoltaic systems performance for Faculty of Engineering (FOE), University of Port Harcourt. With unit cost of grid power at ₦48.35/kWh based on MYTO sets for a 10-year tariff path with bi-annual minor reviews for Port Harcourt Electricity Distribution Company (PHEDC), it is expected that FOE spends ₦5,894,541.9 annually on electricity consumption when the supply is fully dependent on PHEDC.it is worthy to note that the generation source is fossil fuel which is harmful to the ecosystem. If however solar photovoltaic source as an alternative means of power generation is used at a unit cost of electricity production from proposed PV plant at the rate of ₦75/kWh, simple payback time is 7 years and 4 months and performance ratio is 81.8%. The proposed PV plant is expected to meet power requirement of FOE efficiently and reliably. Within life span of 25 years, 907.666 tons of carbon that would have wreaked havoc to the environment would have been saved. Various losses were also considered and calculated accordingly. Effect of weather condition on the overall performance was also discussed. The proposed PV plant is viable and will address the lingering energy crisis in the faculty.

Intelligent Controller based Solar Photovoltaic with Battery Storage, Fuel Cell Integration for Power Conditioning

Providing clean power and meeting the power demand are the biggest issues in the current power scenario. These issues can be alleviated with the integration of renewable energy sources (RES) such as solar, wind etc. The solar photovoltaic source (SPV) became most prominent in the utility system. The day availability and stochastic nature of solar photovoltaic can be used efficiently with the integration of DC link system. In this work, the DC link integration is divided into two parts. In the first part the DC link is integrated with the solar photovoltaic, Fuel cell and battery storage system. The DC link voltages maintain constant with these hybrid sources. The stochastic nature of the solar photovoltaic source power is extracted using adaptive neuro fuzzy information system (ANFIS) based maximum power point tracking technique (MPPT). The battery storage system is used through the bi-directional converter and it’s operate based on the battery state of the charge. The battery state of the charge are monitored with the bi-directional controller. In the second part, the DC link integration is made with utility grid and load through the single phase voltage source Inverter. The grid synchronization can achieve with the grid synchronization unit even under dynamics in the load and sources. The power conditioning of proposed system through the DC link medium can be achieve by maintaining constant voltage at the DC link of voltage source inverter. The dynamic variation in the load and the utility grid connected hybrid system performance are simulated under different source conditions using MATLAB/Simulink software.