Renewable Energy Based Power Generation Research Articles

Optimal sizing of an integrated renewable energy system and effective utilization of surplus energy in electric vehicle charging

Renewable energy based power generation has proven to be a viable standalone option in areas where extending the grid is challenging. This study addresses this issue by assessing the possibility of an integrated renewable energy system (IRES) to electrify twelve villages in the Uttarakhand state of India. Four battery energy storage (BES) devices, namely Lead-Acid (LA), Sodium-Sulfur (NAS), Lithium-Ion (Li-Ion), and Nickel-Iron (Ni-Fe) are considered for storage in this study. Using the Chimp optimization algorithm (ChOA) on the MATLAB© platform, eight different configurations consisting of solar photovoltaic (SPV) array, a micro-hydropower (MHP) plant, and a biogas generator (BGG) are modeled and optimized. The study reveals that the optimal IRES configuration with the lowest cost and highest performance comprises 676 SPV panels (260 kWp), one MHP plant (25 kW), one BGG (40 kW), and 648 NAS batteries (778 kWh). This configuration has a total system life cycle cost (LCC) of INR 68.77 million and cost of energy (COE) of 16.77 INR/kWh at 0 % loss of power supply probability. The optimization problem was run 1–50 times and found that the proposed ChOA algorithm is more robust compared to others, displaying the lowest Best, Worst, and Mean values of LCC (across all eight configurations), convergence rapidity (26th iteration), and least computational time (3481 sec). However, GWO (seven configurations), MFO (34th iteration), and GA (4154 sec) stand very close to ChOA performance in terms of providing minimum LCCs, convergence rapidity, and computational time, respectively. Furthermore, surplus energy (SE) is effectively utilized by incorporating electric vehicles (EVs) as dump load in the system. The proposed integrated charging (IC) strategy outperforms other charging strategies by energizing 134 EVs, utilizing 99.59 % of SE, and reducing the total COE to 10.57 INR/kWh. Finally, the proposed IC strategy results in a net saving of 94,479.39 tons of greenhouse gas emissions. These findings support the feasibility of implementing a standalone IRES to electrify the study area and provide electricity to EVs.

Controlling the output current of a SEPIC H-bridge inverter

The rapid depletion of fossil fuels and humanity's increasing dependence on electricity drive us to adopt renewable energy-based power generation. However, using renewable energy requires a system to maximize the results of converting renewable energy into electrical energy. An inverter is a DC-to-AC power conversion device. Inverters have two types: buck and boost. In closed-loop control, it can be controlled using current or voltage control. Current-controlled inverters sent to the grid have constraints, such as the inverter input voltage value must exceed the voltage on the grid. This study suggests using an H-bridge inverter and an AC-AC SEPIC to generate a voltage value more significant than the input voltage value. The proposed converter uses a single DC source and bases grid current injection on the regulated output current approach. The proposed converter satisfies the IEEE Std 519-2014 requirement of less than 5% and has a Total Harmonic Distortion (THD) of 4.6%.

Assessment of photovoltaic powered flywheel energy storage system for power generation and conditioning

Energy storage and power conditioning are the two major issues related to renewable energy-based power generation and utilisation. This work discusses an energy storage option for a short-term power requirement, which also acts as a power conditioner. The flywheel, an old invention, is included in the electrical power generation arrangement to achieve energy storage and power conditioning requirements. A Photovoltaic solar system is used as a primary source from which the required DC voltage is obtained through an SPWM-based DC-DC converter. This power is fed to an arrangement consisting of a BLDC machine as prime mover, which drives a mass of flywheel. An alternator connected with the motor and flywheel to supply a 1000 W load. The flywheel is designed to take care of the dynamic stability assisted by a suitably designed controller unit and supporting power supply units t ensure the system's reliability. Hence a conditioned and reliable power supply is provided to the load. This research aims to decide on the dimensions and material of the flywheel to be used, which was achieved using ANSYS. Two different materials were selected, and stress analysis was done to ensure their suitability as an energy storage medium in the power generation system. Similarly, the converters and controllers were simulated using MATLAB to fit them to the renewable power generation scheme based on which a working model is fabricated, and tested through proper commissioning, to get the aimed objectives. The outcome of simulation and experimentation were compared, and suitable illustrations were given to prove the successful implementation of a flywheel-based energy storage system.

Design Analysis of Permanent Magnet Synchronous Generator (Pmsg) 18 Slot 8 Pole to Seek Efficiency in Series and Parallel Circuits

Generator is an important component for converting renewable energy into electrical energy. This study aims to analyze the efficiency of designing a Permanent Magnet Synchronous Generator (PMSG) with a configuration of 18 slots 8 poles in series and parallel circuits. The research was conducted by simulating using Infolytica MagNet software and data analysis using Microsoft Excel. The test results show that the design efficiency of the PMSG generator is higher in a series circuit up to 84% at a speed of 2000 RPM and a load of 20 W compared to a parallel circuit which is only 13% at a speed of 3000 RPM and a load of 10 W.. PMSG generator design, series configuration is more efficient than parallel configuration. The results of the study show that the performance and efficiency of the 18 slot 8 pole PMSG generator can be used for renewable energy based power generation systems.

Battery Energy Storage Capacity Estimation for Microgrids Using Digital Twin Concept

Globally, renewable energy-based power generation is experiencing exponential growth due to concerns over the environmental impacts of traditional power generation methods. Microgrids (MGs) are commonly employed to integrate renewable sources due to their distributed nature, with batteries often used to compensate for power fluctuations caused by the intermittency of renewable energy sources. However, sudden fluctuations in the power supply can negatively impact battery performance, making it challenging to select an appropriate battery energy storage system (BESS) at the design stage of an MG. The cycle count of a battery in relation to battery stress is a useful measure for determining the general health of a battery and can aid in BESS selection. An accurate digital replica of an MG is required to determine the required cycle count and stress levels of a BESS. The Digital Twin (DT) concept can be used to replicate the dynamics of the MG in a virtual environment, allowing for the estimation of required cycle numbers and applied stress levels to a BESS. This paper presents a Microgrid Digital Twin (MGDT) model that can estimate the required cycle count and stress levels of a BESS without considering any unique battery type. Based on the results, designers can select an appropriate BESS for the MG, and the MGDT can also be used to roughly estimate the health of the currently operating BESS, allowing for cost-effective predictive maintenance scheduling for MGs.

Thermo-economic and environmental assessment of a combined cycle fueled by MSW and geothermal hybrid energies

The hybrid renewable energy-based power generation systems offer a reliable solution to mitigate the drawbacks of individual energy source. In this respect the biomass and geothermal energies have some well-known advantages (such as continuous provision) over the solar and wind energies. In the present paper, two integration modes are proposed for hybridization of geothermal heat with a biomass-driven gas turbine based combined power cycle. The Mode 1 configuration employs geothermal heat to increase the temperature of feedwater before the deaerator, whereas the Mode 2 structure applies the geothermal energy to generate more steam for the low pressure steam turbine. Detailed exergetic, environmental and economic appraisals are carried out to examine the hybrid systems performances. In order to represent an accurate comparison of the suggested hybridization modes, the systems are optimized to achieve minimum levelized electricity cost. The results have indicated better performance for Mode 2 hybridization scenario over the Mode 1. The former system yields 10.0% greater exergy efficiency with 20.1% less electricity cost compared to Mode 1, while it can generate 22.5% more electricity. Also, it brings about 22.5% lower CO2 emission. However, the Mode 2 configuration possess one drawback compared to Mode 1, which is its higher overall system cost rate by 17.9%. The results show that, the higher cost rate of Mode 2 configuration is mainly due to larger pressure ratio of the air compressor which causes more costs for the compressor and gas turbine components.

Mathematical Modeling and Planning of Energy Production using a Neural Network

This paper examines the investigation and optimization of existing approaches for the efficient deployment of renewable energy-based power generation facilities and a genetic algorithm for predicting the operating mode with the help of efficient deployment of production facilities. The developed genetic algorithm model is based on the use of a radial basic neural network. As a result of these neural networks, it becomes possible to minimize the cost of data processing time and use them in solving technical and economic problems that require high-speed processing. The proposed approach allows for obtaining the most accurate and justified option for the deployment of renewable energy sources to solve the problem of active power reserves and allows for forecasting with an error of no more than 20%.

Microgrid Hybrid Solar/Wind/Diesel and Battery Energy Storage Power Generation System: Application to Koh Samui, Southern Thailand

This paper presents the optimization of a 10 MW solar/wind/diesel power generation system with a battery energy storage system (BESS) for one feeder of the distribution system in Koh Samui, an island in southern Thailand. The main objectives are to maximize the deployment of renewable energy-based power generation and to minimize the levelized cost of energy (LCOE). A hybrid renewable energy-based power generation system, consisting of solar PV, wind turbine generators, diesel generator (DiG), bi-directional grid-tied charging inverter (CONV) and BESS, was simulated using HOMER Pro®. This study accessed the database of the National Aeronautics and Space Administration (NASA) for the Surface meteorology and Solar Energy (SSE) for the global solar radiation and temperature, along with the Modern-Era Retrospective analysis for Research and Applications (MERRA-2) wind database. The simulations show that Scenario 1 (PV/Wind/DiG/BESS/CONV) and Scenario 3 (PV/DiG/BESS/CONV) are the optimal configurations regarding the economic indicators (i.e. minimum net present costs (NPC) of 438 M$ and LCOE of 0.20 $/kWh) and the environmental indicators (i.e. lowest greenhouse gases (GHG) emission avoidances of 6,339 tonnes/year and highest renewable fraction (RF) of 89.4%). Furthermore, the sensitivity analysis illustrates that Scenario 3 offers the optimal system type with the largest annual energy production (AEP). Besides contributing to the body of knowledge of optimization methodologies for microgrid hybrid power systems, the outcome of this work will assist the regional energy practitioners and policy makers regarding optimal configurations of microgrid hybrid systems in the development of a Green Island concept for Koh Samui.

Improving Net Energy Metering (NEM) Actual Load Prediction Accuracy using an Adaptive Learning Rate LSTM Model for Residential Use Case

As an effort to promote renewable energy-based power generation, one of Malaysia’s initiatives is the net-energy metering (NEM) scheme. One of the shortcomings of residential Photovoltaic (PV) systems under the NEM scheme is that it operates with smart meters only whereby the actual load profiles by the residential consumers remain unknown. Accurate load prediction for NEM consumers is crucial for optimizing energy consumption and effectively managing net metering credits. This study proposes a new model that incorporates an adaptive learning rate and Long Short-Term Memory (LSTM) to predict the solar output power that subsequently predicts the actual load used by the NEM residential consumers. The proposed model is trained and tested using historical time series data of projected PV power and weather conditions, considering the GPS location of the PV system. The outcome of the proposed model is then compared with other state-of-the-art models like ARIMA and regression methods. It is shown that the proposed model outperforms the traditional forecasting models with a Root Mean Square Error (RMSE) value of 0.1942.

Cascade feedforward neural network and deep neural network controller on photovoltaic system with cascaded multilevel inverters: Comparison on standalone and grid integrated system

The introduction of a micro-grid-based power generation network will help to meet the demands of consumers while reducing environmental impact. Several industrialized and emerging countries allocate considerable resources to renewable energy-based power generation and invest significant sums of money in this area. This study examines the challenges involved with electricity generation through photovoltaic (PV) systems and the integration of the same with the grid to mitigate power quality issues and improve the power factor for various loading conditions. An innovative multilayer inverter for grid-connected PV systems has been developed to enhance the voltage profile and resulted in a drop in total harmonic distortion (THD). A cascade multilevel inverter (associated with a grid-integrated PV system and managed using multiple innovative artificial intelligence controllers) was developed in this research project. Various advanced intelligent controllers, such as cascade feedforward neural networks (CFFNN) and deep neural networks (DNN), have been analyzed under various operating situations and observed that the THD of voltage, current at the grid, and the load is less than 3 % as per the IEEE 519 standards along with this power factor is maintained nearly unity for the grid-connected system. The quality of power in terms of voltage, frequency, total harmonics distortion, and power factor are improved by using a novel deep neural network algorithm in a cascaded multilevel inverter and verified according to IEEE 1547 and IEEE 519 standards to determine the efficacy of the proposed system.

Techno-economic analysis of off-grid PV-Diesel power generation system for rural electrification: A case study of Chilubi district in Zambia

The study explores the techno-economic feasibility and viability of a Photovoltaic-Diesel Hybrid system for rural electrification in sub-Sahara Africa with a case study of Chilubi island, a remote district without access to electricity in the Northern Province of Zambia. Using HOMER (Hybrid Optimization of Multiple Electric Renewables) Pro software, the best and most feasible technical solutions through different hybrid system configurations, combinations and the district's rate of access to electricity were considered based on the least Levelized Cost of Energy (LCoE) and life cycle costs of the project. The results show that operating diesel generators as stand-alone is not economically sustainable and has a high LCoE. Influencing factors include variability in diesel pump prices, high fuel transportation costs, high cost of operation and maintenance, among other factors of concern. 100% photovoltaic (PV) with a battery system gave the lowest LCoE. However, the initial capital cost of solar energy projects in Zambia is relatively high compared to the equivalent diesel-based plants, as shown herein. It explains why diesel power plants are favoured for off-grid settlements. On the hand, the low operational cost and LCoE of PV power plants favour rural districts as they offset the high initial capital costs. Additionally, the continued downward trend in the cost of PV installations per kWp has opened discussions among policymakers and energy planners in Zambia to favour rural electrification with renewable energy-based power generation. This study contributes to this discussion.

Efficient interleaved high step‐up converter with wide load range soft switching operation

The output voltage of the renewable energy resources such as photovoltaic and fuel cell systems is relatively low. Therefore, in the renewable energy-based power generation systems, high step-up (HSU) DC-DC converters are demanded between the renewable energy resource and the grid. The required characteristics of such HSU converters are low size, cost, number of elements, current ripple, and high efficiency. In response, in this paper, a new non-isolated HSU converter with interleaved structure and resonant operation is presented. The proposed converter achieves high voltage gain and full soft switching performance over a wide range of load variations while utilizing low number of components. In this converter, the voltage gain is adjusted by tuning the coupled inductors turns ratio and the resonant frequency. The semiconductors voltage stress is low in comparison to the other counterpart converters with resonant operation. Also, the converter uses interleaving technique to reduce the input current ripple. Comprehensive theoretical analysis of the converter is presented and its performance is compared with the recent counterparts. In order to validate the converter effectiveness, a laboratory prototype is implemented and the experimental results are provided.

GNLMP Control for Solar PV-Battery Based Microgrid With Ms-EPLL Synchronization

Solar photovoltaic (SPV)-based microgrid systems have established themselves as the novel clean and green solution to climate change. Thus, research is oriented toward a viable and universal solution to renewable energy based power generation. Though much progress is made in this frontier, there are still specific problems in the conventional control approach. Set-point controllers, an easy choice for control architecture, fail to satisfy the intermittent natured SPV source, compelling to look for a dynamic gain tuning technique to overcome stability margin issues. In this article, a graph signal processing least mean <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> th (GNLMP) adaptive current control method is introduced, which is computationally fast, robust, and improves the power quality (PQ) of the injected utility current. A microgrid, by definition, must possess the facility to work in an islanded mode to supply local commodities whenever there is a utility outage or utility faults and resynchronizes to the utility when the grid codes are in match according to the IEEE-519 std. Synchronization is demonstrated here using a more stable enhanced phase-locked loop (Ms-EPLL) technique, a modified version of EPLL. The PLL technique is more stable than EPLL by adding nonlinear term in each of phase and amplitude line. The GNLMP current control and Ms-EPLL synchronization technique are verified for acceptable steady-state and dynamic behavior via tests performed on an experimental setup. Seamless transfer synchronization and meeting PQ as the IEEE-519 std., i.e., total harmonic distortion <5%, are the significant outcome of this article.

Advances in Fault Condition Monitoring for Solar Photovoltaic and Wind Turbine Energy Generation: A Review

Renewable energy-based power generation technologies are becoming more and more popular since they represent alternative solutions to the recent economic and environmental problems that modern society is facing. In this sense, the most widely spread applications for renewable energy generation are the solar photovoltaic and wind generation. Once installed, typically outside, the wind generators and photovoltaic panels suffer the environmental effects due to the weather conditions in the geographical location where they are placed. This situation, along with the normal operation of the systems, cause failures in their components, and on some occasions such problems could be difficult to identify and hence to fix. Thus, there are generated energy production stops bringing as consequence economical losses for investors. Therefore, it is important to develop strategies, schemes, and techniques that allow to perform a proper identification of faults in systems that introduce renewable generation, keeping energy production. In this work, an analysis of the most common faults that appear in wind and photovoltaic generation systems is presented. Moreover, the main techniques and strategies developed for the identification of such faults are discussed in order to address the advantages, drawbacks, and trends in the field of detection and classification of specific and combined faults. Due to the role played by wind and photovoltaic generation, this work aims to serve as a guide to properly select a monitoring strategy for a more reliable and efficient power grid. Additionally, this work will propose some prospective with views toward the existing areas of opportunity, e.g., system improvements, lacks in the fault detection, and tendency techniques that could be useful in solving them.

Decentralized Model-Predictive Control of a Coupled Wind Turbine and Diesel Engine Generator System

It is highly critical that renewable energy-based power generation units provide continuous and high-quality electricity. This requirement is even more pronounced in standalone wind–diesel systems where the wind power is not always constant or available. Moreover, it is desired that the extracted power be maximized in such a way that less fuel is consumed from the diesel engine. This paper proposes a novel method to design decentralized model-predictive controllers to control the frequency and power of a single standalone generation system, which consists of a wind turbine subsystem mechanically coupled with a diesel engine generator subsystem. Two decentralized model-predictive controllers are designed to regulate the frequency and active power, while the mechanical coupling between the two subsystems is considered, and no communication links exist between the two controllers. Simulation results show that the proposed decentralized controllers outperform the benchmark decentralized linear-quadratic Gaussian (LQG) controllers in terms of eliminating the disturbances from the wind and load power changes. Furthermore, it is demonstrated that the proposed control strategy has an acceptable robust performance against the concurrent variations in all parameters of the system as compared to the LQG controllers.

Dynamic Performance Evaluation of Grid-Connected Hybrid Renewable Energy-Based Power Generation for Stability and Power Quality Enhancement in Smart Grid

Today’s stochastic grid system is experiencing huge voltage fluctuations, which is responsible for power quality issues in the smart microgrid network due to its intermittent nature as well as penetration of hybrid renewable resources. Thus, the dynamic performance evaluation and their control are essential to sustaining the stability of the grid network. A d-q controller mechanism is suggested to maintain the balance of the distributed generation network and grid side network. A dynamic control mechanism of voltage source converter (VSC) is presented in the MPPT-based wind power generating station, where an induction generator generates the power by the optimal control of the wind energy-based subsystem. The distributed hybrid generation (solar PV and wind) subsystem’s output terminal is linked to the DC bus bar’s common link via the VSC. A VSI is utilized to convert the desired DC power to alternating current power. To regulate and improve the performance of the proposed hybrid power generating systems (HPGS), a supercapacitor (SC) is used to smooth out the ripple on the distribution side in the power grid. Furthermore, the dynamic stability of grid-connected solar PV and wind power generation systems is investigated. This article also proposed an effective control scheme for the SC in HPGS under the influence of weak grid conditions. This article aimed to validate the efficiency of the VSI topology; a PI controller stability enhancement approach is used in a proposed grid system under various disturbance conditions. Finally, the simulation results and FFT-based power quality response analysis are validated through the effective utilization of an SC.

Optimized CUK Converter Based 1Φ Grid Tied Photovoltaic System

Renewable energy-based power generation, particularly photovoltaic (PV)-connected grid systems, has gained popularity in recent years due to its widespread adoption for residential and commercial customers of all sizes, from kilowatt (KW) to megawatt (MW). The purpose of this work is to demonstrate how an efficient CUK-integrated boost converter with continuous current flow may be used to maximise the output of solar arrays. The constant voltage at the converter output is maintained with increased dynamic performance using a Proportional Integral (PI) controller based on a hybrid optimization technique GWO-PSO (Grey Wolf Optimization-Particle Swarm Optimization). This hybrid solution permits accurate and speedy monitoring of the PI controller, which reduces voltage ripples successfully. This system is connected to the 1 grid via a voltage source inverter (VSI). To ensure grid synchronisation, the inverter’s command pulses are generated by a PI controller depending on the grid’s actual power. The Total Harmonic Distribution (THD) of grid current is used to characterize the overall system. The THD is determined to be 1.8 percent with the suggested PI controller and 3.41 percent with the PI controller, and the tracking performance of the GWO-PSO algorithm is 99.8 percent.

Emission Mitigation and Energy Security Trade-Off: Role of Natural Gas in the Indian Power Sector

India’s Nationally Determined Contributions (NDC) aim to increase the share of non-fossil fuel, especially renewables, in power generation. But at the same time, it mentions that coal is likely to dominate the power generation in the short and medium term to meet the increase in demand and support the intermittency of renewable energy-based power generation. Thus, additional efforts to transform the thermal power generation to a more efficient and less emitting one in the near term by increasing the use of natural gas (a fossil fuel with a lower emission factor than coal) may be planned towards achieving India’s additional mitigation commitments. The paper presents the implications of a proposed increase in the share of natural gas in thermal power generation of India by looking into the trade-off between emission mitigation and energy security. Along with a Reference Scenario, three alternative emission scenarios are proposed to understand the likely impacts of increased penetration of natural gas in power generation on India’s projected emission profile up to 2050. Results suggest that higher mitigation potential can be achieved through fuel-switch in thermal generation and technological up-gradation to enhance energy use efficiency. The energy security due to the increased share of natural gas imports can be dealt with by diversifying import sources. Shifts can achieve this in the emphasis on long-term contracts.

Decarbonizing the electricity system in Sumatra region using nuclear and renewable energy based power generation

The Indonesian national energy policy mandates that the new and renewable energy in 2025 reaches a share of 23% and in 2050 rises a share of 31% in the primary energy mix. The energy mix by types is further detailed in the national energy plan (RUEN). Since Indonesia is committed to participating in efforts to mitigate global climate change, the energy sector is expected to contribute to the greenhouse gas (GHG) reduction target of up to 29% (unconditional) and 41% (conditional) under Business as Usual (BAU) by 2030. The use of nuclear power and renewable energy-based power generations can reduce GHG emissions in the electricity sector. Sumatra region is a potential region for the development of renewable energy. In this study, various options were made to reduce GHG emissions by utilizing nuclear power and renewable energy-based power generations. Assuming that electricity planning in the Sumatra region, as stated in the Electricity Supply Business Plan (RUPTL) from PLN as a Business as Usual (BaU) scenario, then an alternative scenario can be made by replacing coal power generation using low-carbon power generation. Hydropower, geothermal, and nuclear power generations are prospective options as they have large capacity per unit. At the same time, the utilization of other renewable energy sources still face some challenges due to the intermittent generation. The east coast regions of Sumatra that do not have the potential hydropower and geothermal sources, suit for nuclear power generation.

Hybrid energy of Photovoltaic and Palm Oil Diesel for alternative electricity supply of Base Transceiver Station (BTS) on rural Area-South Sumatera

The lack of electricity supply from PLN (The Indonesia State Electricity Enterprise) due to the remote and far geographical location, Some of the BTS rely on diesel generators as the main power plant. So, the cost of electricity generation in the BTS is very high because of the high cost of transportation of these fuels. Therefore, to make efficient use of diesel fuel requires a renewable energy-based power generation model by utilizing the natural potential around the area. This study to analyse the system models hybridization of Photovoltaic System with Palm oil Diesel (POD) to get power plants that have high reliability, low energy costs and low emissions for powering BTS in remote area located at a Sungai Ketupak Village of Ogan Komering Ilir-South Sumatera (Latitude 3o19.9’S and Longitude 105o38.9’E). The Hybrid Optimization Model for Electric Renewable Energy (HOMER) has been carried out to obtain an optimal design based on energy cost/kWh, annual operating costs, the daily loads demand, the NPV cost. The combination PV system and palm oil diesel will provide benefits for telecommunications companies in the savings for fuel generators. This system could also reduce gas emissions from hydrocarbon fuels. Therefore, the join of PV and palm oil diesel systems shows a good prospect of alternative energy for BTS tower energy sources.