Hybrid Renewable Power System Research Articles

Analysis of energy management in a hybrid renewable power system using MOA technique

Analysis of energy management in a hybrid renewable power system using MOA technique

Techno-Economic Modeling and Optimal Sizing of Autonomous Hybrid Microgrid Renewable Energy System for Rural Electrification Sustainability using HOMER and Grasshopper Optimization Algorithm.

This research paper focuses on techno-economic modeling and optimal sizing of autonomous hybrid microgrid systems. The optimal configuration of the suggested stand-alone system was performed by developing a size optimization model based on the metaheuristic novel Grasshopper Optimization algorithm (GOA) method to minimize the Total Net Present Cost (TNPC), unmet load, and Cost of Energy (COE) in the Nsukka Community which comprises 88 villages. The GOA and HOMER Pro Software are employed to compare results across four possible configurations of hybrid renewable power systems (HRES). The comparative analysis between GOA and HOMER shows that configuration-4 (biogas/Diesel), emerges as the optimal solution, with a 0% unmet load at the COE of $0.01783 per kWh. The findings indicated that the GOA-based HRES, with a higher saturation of Biogas and photovoltaics (PV), proves to be more affordable in comparison to the HOMER-based solutions. The reduction in the COE and NPC of renewable energy for peak demands highlights the growing importance of biogas generators as an affordable local power supply to meet energy demands. This underscores the potential of the GOA in optimizing hybrid renewable energy systems for remote communities, producing an economically viable and sustainable energy solution.

Load frequency stabilization of distinct hybrid conventional and renewable power systems incorporated with electrical vehicles and capacitive energy storage

Maintaining a power balance between generation and demand is generally acknowledged as being essential to maintaining a system frequency within reasonable bounds. This is especially important for linked renewable-based hybrid power systems (HPS), where disruptions are more likely to occur. This paper suggests a prominent modified “Fractional order-proportional-integral with double derivative (FOPIDD2) controller” as an innovative HPS controller in order to navigate these obstacles. The recommended control approach has been validated in power systems including wind, reheat thermal, solar, and hydro generating, as well as capacitive energy storage and electric vehicle. The improved controller’s performance is evaluated by comparing it to regular FOPID, PID, and PIDD2 controllers. Furthermore, the gains of the newly structured FOPIDD2 controller are optimized using a newly intended algorithm terms as squid game optimizer (SGO). The controller’s performance is compared to benchmarks such as the grey wolf optimizer (GWO) and jellyfish search optimization. By comparing performance characteristics such as maximum frequency undershoot/overshoot, and steadying time, the SGO-FOPIDD2 controller outperforms the other techniques. The suggested SGO optimized FOPIDD2 controller was analyzed and validated for its ability to withstand the influence of power system parameter uncertainties under various loading scenarios and situations. Without any complicated design, the results show that the new controller can work steadily and regulate frequency with an appropriate controller coefficient.

Optimum sizing of hybrid renewable power systems for on-site hydrogen refuelling stations: Case studies from Türkiye and Spain

One of the main barriers to the adoption of fuel cell vehicles (FCEVs) is the limited availability of hydrogen refuelling stations (HRSs). The presence of these stations is crucial in facilitating the provision of fuel for FCEVs, which rely on hydrogen as a source of power generation. Renewable energy sources offer significant advantages for hydrogen production at these stations, as they are environmentally friendly and can reduce costs. In this study, it is provided a techno-economic analysis of an on-site hydrogen refuelling station powered by a hybrid renewable energy generation system using HOMER software in Niğde, Türkiye, and Zaragoza, Spain. Three different power system scenarios were evaluated to refuel 24 vehicles per day for each region throughout the year. The results of the analysis showed that the most optimal system architecture for Niğde was with a solar panel power generation system, with a levelized cost of hydrogen (LCOH) of 6.15 $/kg and the net present cost (NPC) of $6,832,393. The most optimal system architecture for Zaragoza was a wind turbine-photovoltaic panel power generation system, with an LCOH of 5.83 $/kg and NPC of $6,499,723. The annual amount of CO2 emissions avoided by using renewable resources in hydrogen production was calculated as 2,673,453 kg for Niğde and 2,366,573 kg for Zaragoza. The study also found that the cost of hydrogen production increases with decreasing the HRS production capacity. The use of renewable energy generation systems for hydrogen production will enable countries to achieve net-zero emission targets and reduce the need to import fossil fuels to meet energy demands in the transportation sector. The present study makes several contributions towards the achievement of the United Nations Sustainable Development Goals (3, 7, 11, and 13) and has the potential to facilitate the rapid adoption of FCEVs.

Power Quality Gain of Self-Lift Luo Converter used for Hybrid Renewable Power System with Battery Grid Integration

The renewable power sources such as wind as well as photovoltaic (PV) energy are fashionable for residential and commercial applications. For the reason, that eco friendlessness plus expenditure are effective. This paper intends a Self-Lift Luo converter integrated with solar photovoltaic system to the utility grid along with its Adaptive Neuro-Fuzzy Inference System (ANFIS) and Recurrent Neural Network (RNN) control strategies are proposed. By achieve an energy management for the proposed system using Bidirectional Battery converter along with battery system. Power supervision move towards is suggested to control the DC bus voltage by way of Self-Lift Luo converter. The wind power with Doubly Fed Induction Generator (DFIG) based grid integration with a PI controller. Under this work, DSTATCOM has been used to improve the quality of power under different load conditions. The obtained results designate that the proposed approach delivers recovered performance with enhanced efficiency and nominal harmonics. The intact system is validated through a MATLAB simulation.

Analysis on data center power supply system based on multiple renewable power configurations and multi-objective optimization

With rapid development of data center industry, achieving low energy consumption and costs become important. How to provide an optimal configuration on renewable distributed energy systems combining data centers is challenging. This work discussed possible hybrid configurations by combining diesel, photovoltaic, wind, and battery capacity in data center. For each mode, renewable penetration (RP) and the Levelized Cost of Electricity (LCOE) were used to evaluate the performance of system. A multi-objective optimization analysis of the case is performed based on the entropy weighted-TOPSIS method, genetic algorithm, and NSGA-II aiming to obtain both a high RP and a low LCOE. Then, comparative research on optimal power configuration with conventional or renewable energy was conducted. The results show that it is recommended to take a high-rated PV power. When wind power is taken, it is only meaningful when combined with PV power system. For the hybrid renewable power system without an energy storage unit, it's easy to realize a lower LCOE compared to Diesel mode, and its realizable maximum RP is 28.31 %. When battery energy storage is taken in the hybrid system, six optimal configurations are recommended which can achieve the lowest cost of 0.21 $/kWh and the highest RP of 44.5 %.

Investment-based optimisation of energy storage design parameters in a grid-connected hybrid renewable energy system

Grid-connected hybrid renewable power systems with energy storage can reduce the intermittency of renewable power supply. However, emerging energy storage technologies need improvement to compete with lithium-ion batteries and reduce the cost of energy. Identifying and optimising the most valuable improvement path of these technologies is challenging due to the non-linearity of the energy system model when considering parameters as independent variables. To overcome this challenge, a novel investment-based optimisation method is proposed. The method involves linear optimisation of the hybrid renewable energy system and subsequent investment optimisation, accounting for diminishing improvements per investment. The results from applying the investment-based optimisation to thermal energy, pumped thermal energy, molten salt, and adiabatic compressed air energy storage technologies, show that improving the discharge efficiency is the most valuable for all technologies. The second most important parameters are the costs of discharge capacity and energy storage capacity, and the least important parameters are the charge capacity cost and charge efficiency. The study provides detailed improvement pathways for each technology under various operational conditions, assisting developers in resource allocation. Overall, the investment-based optimisation method and findings contribute to enhancing the competitiveness of emerging energy storage technologies and reducing reliance on batteries in renewable energy systems.

Use of Physics-Based Models for Different Applications of Electrochemical Energy Storage and Conversion Devices

This talk will present the application of physics-based models applied to two different electrochemical storage and conversion systems, viz. Li-ion batteries and Proton Exchange Membrane Fuel Cells (PEMFCs). Battery energy storage system (BESS) is becoming a crucial part of standalone renewable hybrid power systems due to the intermittent nature of power generation and for aiding in various operations like frequency regulation, voltage support, and peak shaving. The lithium-ion battery is a promising choice for BESS due to its high energy density, power density, operating voltage, negligible self-discharge, suitable operating temperature range, modularity, and reliability. A standalone renewable hybrid power system framework with lithium-ion battery energy storage is developed to investigate its performance using physics-based battery models. Power management and control strategies are developed for standalone renewable hybrid power systems. These control strategies can track the MPP of the solar cells and wind turbines while avoiding overcharging the battery and guaranteeing 0% dumping power under different ambient and working conditions. The battery is exposed to harsh ambient conditions in the real world, resulting in the dynamic and continuous capacity fade. A thermal management and control strategy is developed to analyze the effect of temperature on lithium-ion batteries' performance and degradation without using external cooling systems. Dynamic battery degradation analysis and life prediction are essential for better techno-economic estimation of renewable hybrid power systems. The impact of BESS size variation on degradation and the cost of energy generation is analyzed. The developed renewable hybrid power system framework is independent of location and system size and can be extended to incorporate other renewable energy generation sources and energy storage systems.The second part of this talk will focus on using physics-based models to understand the water dynamics and degradation mechanisms inside PEMFC, which is necessary for water management, long-life operation, and cost reduction. PEMFCs have emerged as an alternative green energy technology in various applications such as automobiles, portable and stationary applications, and auxiliary power supplies for space exploration missions. The cost and durability targets remain unmet largely, which restrains the commercialization of the PEMFCs. The major difficulty in modeling lies in the wide scale of dimensions from the millimeter level (flow channel) to the micron level (catalyst layer (CL) thickness). The multi-scale physics-based model enables the life cycle analysis of PEMFC under various operating conditions. The mass transport limitation is the major hurdle in performance enhancement for higher current density operations. It arises from the liquid water generated during the electrochemical reactions, which hinders the reaction sites of CL and oxygen transport reaction sites in the gas diffusion layer, resulting in performance deterioration. Many experimental studies reported the two-phase flow visualization techniques in PEMFCs, viz. neutron imaging, optical visualization, and X-ray radiography. However, the difficulties in conducting experiments for flow visualization and related parameters such as liquid volume fraction have encouraged researchers to adopt numerical simulations that are less expensive and computationally efficient. Understanding liquid water movement inside the cell at the micro-scale help to improve the PEMFC performance at the stack level. Here, we develop a comprehensive 3-D, multiphase, non-isothermal, steady-state physics-based model of the PEMFC that can closely approximate the liquid water flooding and membrane drying. The findings from this work can be used to design an efficient flow configuration and to study membrane deformation due to the shrinking-swelling under humidification and thermal cycling. The ultimate goal of the multi-scale model is to enhance the PEMFC stack output with less degradation.

Investigation of concentrating solar-biomass-fired power technologies based on advanced exergy, exergoeconomic and exergoenvironmental analyses

ABSTRACT In this study, hybrid renewable power systems (HRPS) based on biomass-fired (BF) and concentrating solar power (CSP) technologies are investigated. Parabolic trough collector (PTC), linear Fresnel reflector (LFR) and solar tower (ST) as CSP technologies are considered. This study aims to determine and compare exergoeconomic and exergoenvironmental factors, as well as and the relative cost and environmental impact differences of three proposed HRPS options for the district of Faro in the province of Garoua, Cameroon. Also, advanced exergy destruction expressions are used. We found the optimized exergoeconomic and exergoenvironmental factors for the subsystems of HRPS to be between 0.04–0.98 and 0.05–0.98, respectively. They have the highest values for the drying system (DS), and the lowest values for the solar-biomass field (SF+BF). The relative cost and environmental impact differences for the subsystems of HRPS are in the ranges of 0.07–0.79 and 0.28–0.96, respectively. They have the highest values for the DS, and the lowest values for the power block (PB). According to the levelized exergoeconomic/exergoenvironmental performances, PTC – BF presents the worst results before the optimization. ST – BF shows the best exergoeconomic and exergoenvironmental performance in the optimization process. The results of the sensitivity and optimization analyses reveal that it is essential to conduct eco-indicator and advanced exergy analyses to avoid high environmental impact points and specific exergy destructions.

Power quality improvement of a proposed grid-connected hybrid system by load flow analysis using static var compensator

Renewable resources are most effective for sustainable development of society and economically efficient for small-scale power generation. However, grid integration is challenging because of the randomness of the source effects on power system parameters. This work proposes power quality enhancement by incorporating Static VAR Compensator (SVC) in a grid-integrated renewable hybrid power system. SVC is one of the shunt type Flexible AC Transmission Systems (FACTS) devices that is adopted in this system for the compensation of reactive power requirement. The proposed hybrid system for the Rohingya Refugee camp is energized by a wind and solar based sources. The objective is to enhance the overall bus voltage profile by minimizing both real and reactive power losses as well as boost the power transmission capability of the entire system. Different case studies have been considered by changing the source availability and generation supply for load flow analysis using ETAP software. Moreover, critical system parameters such as bus voltage, power transfer capacity, and power losses have been reported during the inactive time of one or both renewable sources. The results obtained without SVC have been compared against the ones with the presence of SVC. Our analysis reveals that, as a result of using SVC, the voltage profile improves by 2.9–3.3%, branch loss reduces by 2.1–2.4%, and power transfer capability enhances by 7.5–9 units.

Feasibility of innovative topography-based hybrid renewable electrical power system: A case study

Recently, climate change has attracted significant intension globally. Accordingly, numerous initiatives are under action for reducing the consumption of fossil fuels and increasing the share of renewable energy sources. This research work proposes an eco-friendly and sustainable electrical power system that could fully or partially fulfill requirements of the local load of the system under concern. The proposed power has the merit of utilizing topography of the site under concern, Al- Jabal Al-Gharbi district, Libya, to exploit the locally available resources such as biomass and sewage plants. The availability of resources was first studied, concluding that 110.87 × 106 m3⁄year are available for generating biogas and electricity. Moreover, biogas is extracted from biomass fermentation plants with a capacity of 244,845 tons/year produced from agricultural, municipality organic, livestock, and sewage residues in the proposed system. The feasibility of the proposed system is validated via a comprehensive economic and environmental comparison of different types of fuels. Furthermore, a mathematical model is developed for the Environmental Damage Cost (EDC) of CO2 emission for three scenarios: 1) disposing wastes, 2) firing fuel plus disposing of wastes, and 3) firing fuel plus biogas. The proposed system would save about 57% and 86% of CO2 from diesel and natural gas-fired power stations. Moreover, the Levelized Cost of Electricity (LCOE) generated from the proposed system is about 3.5 ¢⁄kWh, which is much lower than the actual cost of electricity generation in Libya (12 ¢/kWh). The annual savings of EDC due to CO2 is $6,968,613. This study could act as a pivot for implementing eco-friendly and sustainable energy source projects in the site under concern and/or similar sites.

Multi-area automatic generation control of a renewable energy-based hybrid power systems using JAYA optimized model predictive control

This study uses JAYA optimized model predictive control to address a three-area load frequency regulation problem that actively involves stochastic renewable energy sources. Some physical constraints employed in the system model, such as the generation rate constraint, dead band, and time delay by governor-turbine, negatively impact the control performance of the classical MPC technique. As such, a more robust control scheme is required to get rid of this flaw. The JAYA algorithm is adopted to dynamically tune the parameters of MPC, which is a parameter-driven controller for optimal system performance. The performance indices such as the integral of square error (ISE), integral of time multiplied by square error (ITSE), integral of the absolute value of error (IAE), and integral of time multiplied by the absolute value of error (ITAE) have been used to compare the effectiveness of the proposed controller with the classical MPC with constant tuning parameters. The suggested controller is superior to the conventional MPC, as demonstrated by the results from the MATLAB/Simulink environment.

Energy management based on a fuzzy controller of a photovoltaic/fuel cell/Li-ion battery/supercapacitor for unpredictable, fluctuating, high-dynamic three-phase AC load

Introduction. Nowadays, environmental pollution becomes an urgent issue that undoubtedly influences the health of humans and other creatures living in the world. The growth of hydrogen energy increased 97.3 % and was forecast to remain the world’s largest source of green energy. It can be seen that hydrogen is one of the essential elements in the energy structure as well as has great potential to be widely used in the 21st century. Purpose. This paper aims to propose an energy management strategy based a fuzzy logic control, which includes a hybrid renewable energy sources system dedicated to the power supply of a three-phase AC variable load (unpredictable high dynamic). Photovoltaic (PV), fuel cell (FC), Li-ion battery, and supercapacitor (SC) are the four sources that make up the renewable hybrid power system; all these sources are coupled in the DC-link bus. Unlike usual the SC was connected to the DC-link bus directly in this research work in order to ensure the dominant advantage which is a speedy response during load fast change and loads transient. Novelty. The power sources (PV/FC/Battery/SC) are coordinated based on their dynamics in order to keep the DC voltage around its reference. Among the main goals achieved by the fuzzy control strategy in this work are to reduce hydrogen consumption and increase battery lifetime. Methods. This is done by controlling the FC current and by state of charge (SOC) of the battery and SC. To verify the fuzzy control strategy, the simulation was carried out with the same system and compared with the management flowchart strategy. The results obtained confirmed that the hydrogen consumption decreased to 26.5 g and the SOC for the battery was around 62.2-65 and this proves the desired goal.

Design and Implementation of Node-Red Based Open-Source SCADA Architecture for a Hybrid Power System

At present, hybrid renewable power systems (HRPS) are considered reliable combinations for power generation under various conditions. The challenge facing researchers and engineers today is designing and implementing a reliable, efficient, and applicable SCADA system for adequate monitoring and control of hybrid power systems. In order to analyze, observe, and control the essential parameters of an HRPS, a SCADA system is crucial. As part of this study, a low-cost, low-power, open-source SCADA (Supervisory, Control, and Data Acquisition) system for hybrid renewable energy systems is presented. The system utilizes two remote terminal units (RTUs), an Arduino Mega2560 and a Wio terminal, to communicate with all actuators and measure vital system characteristics such as voltage, current, and power. Using the Firmata protocol, a laptop serves as the main terminal unit (MTU) to communicate with the Arduino. In addition to being the system’s central component, Node-Red is utilized for processing, analyzing, storing, and displaying data. In contrast, a Wio terminal is used to display the values of all sensors in real-time on its LCD screen. As a whole, the proposed SCADA system is designed to keep the HRPS running smoothly and safely by displaying vital parameters, reporting any significant faults, and controlling the generator so that the batteries can be charged and discharged correctly. This article presents a complete description of all algorithms, experimental setups, testing, and results.

Sizing Design for a Hybrid Renewable Power System Using HOMER and iHOGA Simulators

In this study, a comparative sizing design for renewable power systems was developed based on HOMER (Hybrid Optimization of Multiple Energy Resources) and iHOGA (improved Hybrid Optimization by Genetic Algorithms) simulators. A comparative analysis of the solutions obtained with iHOGA and HOMER simulators for the same hybrid renewable power system (HRPS) is presented in detail. The system contained a new hybrid configuration that used fuel cell (FC) as a green energy source, replacing the polluting diesel generator system, as well as other renewable energy sources, namely, photovoltaic panels, wind turbine, a battery stack, power converters, and electric load. The same case study was carried out for the design of the hybrid system using HOMER and iHOGA simulators to perform a comparative analysis of the solutions obtained for potential investment. The analysis showed a higher share of renewable energy for iHOGA (92%) compared to HOMER (81%), so the first design produced 51.61 kg/year less carbon dioxide. Moreover, the operating costs (2134 RON/year for HOMER and 70.56 RON/year for iHOGA) and the cost of electricity were higher by 96% and 2.5%, respectively, for HOMER compared to iHOGA. Taking into account the need for high reliability, safe operation, and lower operation/exploitation costs, the design implemented in iHOGA is clearly more efficient and useful in practice, and this is supported by the three iHOGA case studies.

Multi-Objective Hybrid Optimization for Optimal Sizing of a Hybrid Renewable Power System for Home Applications

An optimal energy mix of various renewable energy sources and storage devices is critical for a profitable and reliable hybrid microgrid system. This work proposes a hybrid optimization method to assess the optimal energy mix of wind, photovoltaic, and battery for a hybrid system development. This study considers the hybridization of a Non-dominant Sorting Genetic Algorithm II (NSGA II) and the Grey Wolf Optimizer (GWO). The objective function was formulated to simultaneously minimize the total energy cost and loss of power supply probability. A comparative study among the proposed hybrid optimization method, Non-dominant Sorting Genetic Algorithm II, and multi-objective Particle Swarm Optimization (PSO) was performed to examine the efficiency of the proposed optimization method. The analysis shows that the applied hybrid optimization method performs better than other multi-objective optimization algorithms alone in terms of convergence speed, reaching global minima, lower mean (for minimization objective), and a higher standard deviation. The analysis also reveals that by relaxing the loss of power supply probability from 0% to 4.7%, an additional cost reduction of approximately 12.12% can be achieved. The proposed method can provide improved flexibility to the stakeholders to select the optimum combination of generation mix from the offered solutions.

Solar-Wind Hybrid Power Generation System Optimization Using Superconducting Magnetic Energy Storage (SMES)

This paper proposes a renewable energy hybrid power system that is based on photovoltaic (PV) and wind power generation and is equipped with Superconducting Magnetic Energy Storage (SMES). Wind and solar power generation are two of the most promising renewable power generation technologies. They are suitable for hybrid systems because they are environmentally friendly. However, like most renewable energy sources, they are characterized by high variability and discontinuity. They generate a fluctuating output voltage that damages the machines that operate on a stable supply. Therefore, the energy storage system SMES with the function to reduce output voltage fluctuation problems is introduced. SMES is found to be the most effective energy storage device as a result of its quick time response, high power density, and high energy conversion efficiency. In this paper, modeling of a hybrid system with SMES is built using MATLAB/Simulink. Blocks such as the wind model, PV model, and energy storage model are built separately before combining into a complete hybrid system with SMES. Varying wind speed and solar irradiance values are taken as the input parameters. The obtained results from the simulation reveal that a system with SMES is more reliable than a system without SMES.

Neural network-based adaptive control and energy management system of a direct methanol fuel cell in a hybrid renewable power system

Due to fluctuations in electricity demand and weather patterns, the solar energy-based system should be integrated with energy storage for efficient energy management. However, using only a battery cannot cope with the long-term power demand. Direct methanol fuel cells (DMFCs) have been considered one of the most promising backup power supplies for household applications. Therefore, the photovoltaic and solar thermal collector system incorporated with battery and DMFC is proposed along with the energy management system. In this study, a neural network-based adaptive control is developed to control the DMFC to meet persistent residential power demand and follow the technical requirements. Simulation results show that the neural network-based control outperforms the proportional-integral-derivative control in set-point change, disturbance, and model mismatch cases. Implementing the controlled DMFC in the hybrid system results in lower power requirements from the grid, which is less than twice that of a hybrid system without the DMFC. Furthermore, when solar power was insufficient to supply the load demand, the battery lifecycle in the system integrated with the DMFC seems more extended than that of a system without the DMFC.

Speed control of hybrid energy sources fed BLDC motor drive with FOPID controllerusing various optimization techniques

This article suggests the control of current and speed approach to reduce the torque ripple in BLDC motor. Initially, the renewable energy hybrid power system (REHPS) is composed of a generation system of PV, fuel cell (FC), and the storage system of battery bank. This REHPS uses solar power as their main source of electricity during the day. It uses the fuel cell as a secondary source for maintenance at night or during periods of shaded conditions. The novelty of the proposed method is to achieve torque ripple minimization and to control the speed of the BLDC motor. The speed and error torque of the BLDC motor is optimized by mayfly optimization algorithm (MOA). The MOA provides gain parameters of the fractional order proportional–integral–derivative (FOPID) controller. The advantage of the proposed method is to improve the level of dependability and provide flexibility in solving the system error. The proposed model is implemented in MATLAB/Simulink and experimental setup. The results of the proposed method are compared with the existing research techniques such as particle swarm optimization (PSO) and moth flame algorithm (MFA).

The impact of diesel operation time constraint on total cost of diesel-based hybrid renewable power system simulation model

This study proposes a simulation model with practical constraints on the use of diesel engine and investigates the impact of adding the constraint of the minimum length of diesel operation time on total cost of hybrid power system, which is designed to supply power for a small village located in the east part of Iran. The results indicated that in the presence of the aforementioned constraint, total cost of hybrid power system increased by shortening diesel generator lifespan and growing diesel fuel consumption. The research did not find any specific uptrend or downtrend in battery lifetime by adding the mentioned constraint into the model. The study outcomes also specified that the surplus power of the hybrid power system inclined in the existence of the minimum length of diesel operation time constraint. The results demonstrated that one hour increment in the minimum length of diesel operation time left a remarkable part of generated renewable power as a surplus power.