Performance Analysis Of System Research Articles

Correction to: Performance analysis of smart grid distribution system using global EV charging station as smart load based on automatic variac transformer

Correction to: Performance analysis of smart grid distribution system using global EV charging station as smart load based on automatic variac transformer

PAPAYA: A library for performance analysis of SQL-based RDF processing systems

Prescriptive Performance Analysis (PPA) has shown to be more useful than traditional descriptive and diagnostic analyses for making sense of Big Data (BD) frameworks’ performance. In practice, when processing large (RDF) graphs on top of relational BD systems, several design decisions emerge and cannot be decided automatically, e.g., the choice of the schema, the partitioning technique, and the storage formats. PPA, and in particular ranking functions, helps enable actionable insights on performance data, leading practitioners to an easier choice of the best way to deploy BD frameworks, especially for graph processing. However, the amount of experimental work required to implement PPA is still huge. In this paper, we present PAPAYA,11 https://github.com/DataSystemsGroupUT/PAPyA a library for implementing PPA that allows (1) preparing RDF graphs data for a processing pipeline over relational BD systems, (2) enables automatic ranking of the performance in a user-defined solution space of experimental dimensions; (3) allows user-defined flexible extensions in terms of systems to test and ranking methods. We showcase PAPAYA on a set of experiments based on the SparkSQL framework. PAPAYA simplifies the performance analytics of BD systems for processing large (RDF) graphs. We provide PAPAYA as a public open-source library under an MIT license that will be a catalyst for designing new research prescriptive analytical techniques for BD applications.

IoHT Based Tele-Healthcare Support System for Feasibility and Perfor-mance Analysis

With the rapid development in Internet of Things (IoT) it come up with the subbranch Internet of Healthcare Things or the Internet of Medical Things. With the help of portable healthcare sensors and the wearable devices now, ambulance can be converted into critical diagnostics unit on the wheels. Monitoring the home care patient is not the just part of IoT application where doctor can remotely monitor the patient situation by the means of sensors. It requires a complete system may called as telemedicine where multiple components will play similar priority role to monitor the patient status using sensors, let the doctor connect the patient, data analytics calculate the progression, AI engine will detect the emergency and act upon. The proposed system is to design and developing centralized tele-healthcare support system and systematic analytics sharing with multiple level of user with privacy protection. This will allow different authorities to take advantages from patient generated records.

The development of association rules for student performance analysis using FP-Growth algorithm as a guideline for multidisciplinary learning

This study aims to develop association rules for student performance analysis using the FP-Growth algorithm. The data used for developing the association rules comprised 107 student reports. The reports, voluntarily provided by 107 junior high school students, consisted of student achievement results across 8 subject areas: Thai Language, Mathematics, Science, Social Studies, English Language, Computing Science, Visual Arts, and Home Economics. The data was applied to developing association rules using the FP-Growth algorithm towards WEKA, a machine learning software. The research team designed the process consisting of the following 5 stages: data collection, data preparation, model formulation, model evaluation, and model application. After achieving the association rules, the research team applied them to the prototype development of a student performance analysis system for promoting students' academic excellence. The system could be operated by Android mobile phones. According to the research results, the association rules developed by the algorithm provided a confidence level of 92%, and a rule of 7 rules will be generated. The findings indicated the correlations between the subject areas, which shared similar individual students' academic achievements (≥ 80 scores). The association rules could be applied to the multidisciplinary curriculum planning, which benefited students and promoted academic excellence. For example, by applying Rule, it could be assumed that students who earned 80 scores or higher in the English subject would likely earn identical scores from their Thai Language class. Therefore, they could effectively learn to integrate English and Thai languages. To illustrate, students may be asked to translate song lyrics from English to Thai, serve as tourist guides or translators, or even give welcome speeches to foreign guests.

Integration and performance analysis of optimal large-scale hybrid PV and pump hydro storage system based upon floating PV for practical application

The widespread use of green energy sources creates a significant demand for energy storage. Hybrid floating photovoltaic (FPV) and pumped hydro storage (PHS) represent one of the most dependable and cost-effective solutions, which uses the PV system on the water body combined with a pair of lakes with different heights. This study focuses on the load side as well as the PHS capacity factor and aims to lower the cost of energy (COE) by raising the PHS capacity factor. Since building a PHS requires a significant upfront investment, doing so will also lower the COE and enable the acquisition of PHS for large-scale power production, which will guarantee power access in large cities. To simulate the FPV-PHS system, the multi-objective genetic algorithm (MOGA) is employed. Sufficient power management is a prerequisite for achieving system reliability in the best possible hybrid energy system design and implementation. Considering the 60-year system lifespan, the net present cost (NPC) analysis shows that, out of all the communities evaluated, the FPV-PHS system has the lowest NPC and COE. For the optimal configuration (FPV (Block A) 105 MW, PHS 80 MW, FPV (Block B) 357 MW, and the current hydropower plant), the expected NPC and energy costs for implementing the hybrid energy system (HRS) at the chosen location are $44,737,613 and $40/MWh, respectively. The existing FPV system spans 4.65 km2 and lowers the evaporation fraction by 17,279,400 m3. The hybrid FPV-PHS system reduces annual CO2 emissions by 581,830 tons. Our research reveals that a hybrid floating PV and pump storage hydropower system offers more steady clean electricity, implying a great significance for power grid infrastructure.

Aerodynamic Performance and Numerical Analysis of the Coaxial Contra-Rotating Propeller Lift System in eVTOL Vehicles

Electric vertical takeoff and landing (eVTOL) vehicles possess high payload transportation capabilities and compact design features. The traditional method of increasing propeller size to cope with high payload is no longer applicable. Therefore, this study proposes the use of coaxial counter-rotating propellers as the lift system for eVTOL vehicles, consisting of two coaxially mounted, counter-rotating bi-blade propellers. However, if the lift of a single rotating propeller is linearly increased without considering the lift loss caused by the downwash airflow generated by the upper propeller and the torque effect of the lift system, it will significantly impact performance optimization and safety in the eVTOL vehicles design process. To address this issue, this study employed the Moving Reference Frame (MRF) method within Computational Fluid Dynamics (CFD) technology to simulate the lift system, conducting a detailed analysis of the impact of the upper propeller’s downwash flow on the aerodynamic performance of the lower propeller. In addition, the aerodynamic performance indicators of coaxial counter-rotating propellers were quantitatively analyzed under different speed conditions. The results indicated significant lift losses within the coaxial contra-rotating propeller system, which were particularly notable in the lift loss of the lower propeller. Moreover, the total torque decreased by more than 93.8%, and the torque was not completely offset; there was still a small torsional effect in the coaxial counter-rotating propellers. The virtual testing method of this study not only saves a significant amount of time and money but also serves as a vital reference in the design process of eVTOL vehicles.

Performance of a deep in situ pressure-preserving coring controller in a high-temperature and ultrahigh-pressure test system

The pressure-preserving controller is the key component of deep in situ pressure-preserving coring (IPP-Coring). With increasing drilling depth, the environmental temperature and pressure increase accordingly. However, due to the strength and sealing problems of pressure-preserving controllers, the coring pressure is generally lower than 70 MPa. Establishing a high-temperature and ultrahigh-pressure test system is highly important for improving the strength and sealing performance of pressure-preserving controllers. This paper introduces a high-temperature and ultrahigh-pressure test system for deep IPP-Coring controller performance analysis. The device includes six parts: an auxiliary air source system, a pressurization system, a temperature control system, a hydraulic system, a data acquisition and electrical control system, and an ultrahigh-pressure vessel. The test system can reconstruct a 150 °C and 200 MPa in situ environment and simulate and test the movement state of the corer and the stability of the pressure-preserving action trigger of the pressure-preserving controller in the deep IPP-Coring process. To verify the performance of this test system, saddle-shaped pressure-preserving controllers made of four different materials were subjected to pressure tests under normal-temperature and high-temperature conditions. The results showed that the ultimate pressure-bearing capability of the pressure-preserving controller greatly varied between normal-temperature and high-temperature conditions. The pressure-preserving ability and sealing performance of the pressure-preserving controller decreased significantly at high temperature, and the pressure-preserving controller exhibited significantly different sealing failure characteristics due to material differences. This study is important for progressing the extraction and evaluation of deep reservoir resources.

Review of Mechanical, Electrochemical, Electrical, and Hybrid Energy Storage Systems Used for Electric Vehicles

The population rate in the world is increasing rapidly. Depending on the population, the need for transportation increases at the same rate. Traditional vehicles, which provide great convenience in transportation, bring with them some disadvantages. For example, the fossil fuel used in conventional vehicles creates greenhouse gases such as CO2 and N2O. This has a negative impact on global warming. To eliminate these negativities, interest in electric vehicle (EV) and hybrid electric vehicles (HEV) technology studies has increased recently. Some problems have arisen with these technological studies. The range problem in vehicles is the biggest of these problems. Therefore, various solutions are sought for energy storage problems in vehicles. In this article, studies on HEV and energy storage in EVs are examined. According to the data obtained because of this examination, the performance analysis of the Energy Storage Systems (ESS) was made. The performances of the electrochemical batteries used in HEVs and EVs were compared. In addition to these, flywheel energy storage system was also investigated in HEVs and EVs to recover the energy lost because of braking.

Supercritical carbon dioxide cycle thermodynamic and exergoeconomic improvements using a bidirectional coupling strategy

Supercritical carbon dioxide (SCO2) combined cycle power system, one of the potentially effective ways to achieve energy grade utilization of the medium and high-temperature heat sources, is vulnerable to ambient temperatures as the system performance is susceptible to the compressor inlet temperature. So far, most studies have been focused on the system performance analysis and optimization under ideal operation conditions while the mitigation of unfavorable ambient temperature is missed. In this work, a bidirectionally-coupled system is proposed to reduce the compressor inlet temperatures in the original SCO2 cycle power system so that the adverse effects of ambient temperature are alleviated. The proposal utilizes the low-grade waste heat in a SCO2 recompression reheat Brayton cycle (i.e., topping cycle) to drive an absorption refrigeration cycle (i.e., bottoming cycle) which in turn precools the compressor inlet of the topping cycle. The thermodynamic and exergoeconomic analyses have been conducted for the proposed bidirectionally-coupled combined SCO2 RRBC and ARC system, with the attention to the effects of ambient temperatures on the system performance. Our results show that the combined SCO2 RRBC and ARC system increases the energy efficiency relative to the standalone cycle power system by over 5% at the ambient temperatures of 303.15 K or above, while the exergy efficiency improves by 1.124%–3.574%. In terms of the exergoeconomic factor, the combined SCO2 RRBC and ARC system also exhibits superiority against standalone cycle power system. The current work proves that a conventional recuperated SCO2 cycle system can be improved both thermodynamically and economically by coupling it to an absorption refrigeration cycle, despite of the additional upfront costs for the components. The discovery could further promote the commercial application of the SCO2 cycle power systems in new generation plants.

Covariance-analytic performance criteria, Hardy-Schatten norms and Wick-like ordering of cascaded systems

This paper is concerned with robust performance analysis of linear stochastic systems acting as an integral operator on a standard Wiener process at the input and producing a stationary Gaussian random process at the output. We propose a performance criterion which uses the trace of an analytic function of the spectral density of the output process. This class of “covariance-analytic” cost functionals includes the usual mean square and risk-sensitive criteria as particular cases. Due to the “cost-shaping” analytic function, the covariance-analytic performance criterion depends on higher-order Hardy-Schatten norms of the system transfer function. We discuss the links of these norms with the asymptotic behaviour of cumulants of finite-horizon quadratic functionals of the system output and their variational properties pertaining to system robustness to statistically uncertain inputs which differ from the standard Wiener process. In the case of strictly proper finite-dimensional systems, governed in state space by linear stochastic differential equations, we develop a method for recursively computing the Hardy-Schatten norms through a recently proposed technique of rearranging cascaded linear systems, which resembles the Wick ordering of mixed products of noncommuting annihilation and creation operators in quantum mechanics. This computational procedure, which is one of the main results of the paper, involves a recurrence sequence of solutions to algebraic Lyapunov equations and represents the covariance-analytic cost as the squared H2-norm of an auxiliary cascaded system. These results are also compared with an alternative approach, which uses higher-order derivatives of stabilising solutions of parameter-dependent algebraic Riccati equations, and illustrated by a numerical example.

Adaptive stabilization for a class of wave equations with uncertain nonlinear boundary actuator dynamics

This paper investigates the stabilization of a wave equation with uncertain boundary actuator dynamics. The remarkable characters of the system under investigation are revealed by the essential nonlinearities involved in the boundary actuator and the serious parametric unknowns coming from anti-damping coefficient at uncontrolled boundary and those in the actuator, which result into the incapability of the traditional control methods on this topic. For this, a novel adaptive control strategy is proposed in this paper. Specifically, a Riemann variables based state transformation is first introduced to change the original system into a new one whose stabilization implies that of the original system. Then, a state-feedback controller joint with some adaptive laws which are smartly chosen for the compensation of unknown parameters is explicitly designed by a finite-dimensional backstepping procedure. Owing to the compensation of boundary actuator dynamics, the performance analysis of the closed-loop system is more complicated than that of the related literature. For this, a rigorous recursive step is given to show the stability of the resulting closed-loop system. Finally, a numerical example is provided to verify the effectiveness of the proposed theoretical results.

Performance Analysis of Photovoltaic and Wind Turbine Grid-Connected Systems under LVRT Conditions

The integration of grid-connected renewable energy systems has gained significant attention and introduces several challenges and considerations. One of these challenges is ensuring the reliable and stable operation of these systems under various grid conditions. For example, faults at the grid could lead problems such as DC-link over-voltage and AC over-current that may cause disconnection or damage to inverter. This paper presents a comprehensive analysis of the performance of photovoltaic (PV) and doubly-fed induction generator (DFIG) wind turbine grid-connected systems under low voltage ride-through (LVRT) conditions. The study aims to investigate their behavior, and stability during LVRT events and provide insights for enhancing their grid integration capabilities. The PV and DFIG systems are modelled and simulated using MATLAB Simulink under three difference conditions, with and without using reactive current injection and DC chopper circuit. Various performance parameters, including grid voltage, grid current, DC-link voltage, active power, and reactive power, are analyzed to assess the system's behavior and compare their responses. The principal results reveal distinct performance characteristics of the PV and DFIG systems. The PV system shows higher overshoot currents, over-voltage, and significant drops in active power during fault occurrences, while the DFIG system exhibits lower overshoot currents and better stability in the DC-link voltage. Reactive power responses differ between the systems, with the PV system demonstrating a higher capability for support. The implementation of DC chopper shows more effective in the reduction of DC-link voltage and overshoot grid current in the PV system compared to the DFIG system.

Data Information integrated Neural Network (DINN) algorithm for modelling and interpretation performance analysis for energy systems

Developing a well-predictive machine learning model that also offers improved interpretability is a key challenge to widen the application of artificial intelligence in various application domains. In this work, we present a Data Information integrated Neural Network (DINN) algorithm that incorporates the correlation information present in the dataset for the model development. The predictive performance of DINN is also compared with a standard artificial neural network (ANN) model. The DINN algorithm is applied on two case studies of energy systems namely energy efficiency cooling (ENC) & energy efficiency heating (ENH) of the buildings, and power generation from a 365 MW capacity industrial gas turbine. For ENC, DINN presents lower mean RMSE for testing datasets (RMSE_test = 1.23 %) in comparison with the ANN model (RMSE_test = 1.41 %). Similarly, DINN models have presented better predictive performance to model the output variables of the two case studies. The input perturbation analysis following the Gaussian distribution for noise generation reveals the order of significance of the variables, as made by DINN, can be better explained by the domain knowledge of the power generation operation of the gas turbine. This research work demonstrates the potential advantage to integrate the information present in the data for the well-predictive model development complemented with improved interpretation performance thereby opening avenues for industry-wide inclusion and other potential applications of machine learning.

Comparison of 4-dimensional variational and ensemble optimal interpolation data assimilation systems using a Regional Ocean Modeling System (v3.4) configuration of the eddy-dominated East Australian Current system

Abstract. Ocean models must be regularly updated through the assimilation of observations (data assimilation) in order to correctly represent the timing and locations of eddies. Since initial conditions play an important role in the quality of short-term ocean forecasts, an effective data assimilation scheme to produce accurate state estimates is key to improving prediction. Western boundary current regions, such as the East Australia Current system, are highly variable regions, making them particularly challenging to model and predict. This study assesses the performance of two ocean data assimilation systems in the East Australian Current system over a 2-year period. We compare the time-dependent 4-dimensional variational (4D-Var) data assimilation system with the more computationally efficient, time-independent ensemble optimal interpolation (EnOI) system, across a common modelling and observational framework. Both systems assimilate the same observations: satellite-derived sea surface height, sea surface temperature, vertical profiles of temperature and salinity (from Argo floats), and temperature profiles from expendable bathythermographs. We analyse both systems' performance against independent data that are withheld, allowing a thorough analysis of system performance. The 4D-Var system is 25 times more expensive but outperforms the EnOI system against both assimilated and independent observations at the surface and subsurface. For forecast horizons of 5 d, root-mean-squared forecast errors are 20 %–60 % higher for the EnOI system compared to the 4D-Var system. The 4D-Var system, which assimilates observations over 5 d windows, provides a smoother transition from the end of the forecast to the subsequent analysis field. The EnOI system displays elevated low-frequency (>1 d) surface-intensified variability in temperature and elevated kinetic energy at length scales less than 100 km at the beginning of the forecast windows. The 4D-Var system displays elevated energy in the near-inertial range throughout the water column, with the wavenumber kinetic energy spectra remaining unchanged upon assimilation. Overall, this comparison shows quantitatively that the 4D-Var system results in improved predictability as the analysis provides a smoother and more dynamically balanced fit between the observations and the model's time-evolving flow. This advocates the use of advanced, time-dependent data assimilation methods, particularly for highly variable oceanic regions, and motivates future work into further improving data assimilation schemes.

Techno-Economic Performance and Sensitivity Analysis of an Off-Grid Renewable Energy-Based Hybrid System: A Case Study of Kuakata, Bangladesh

Hybrid renewable energy sources (HRES) are increasingly being utilized to meet global energy demands, particularly in rural areas that rely on diesel generators and are disconnected from the utility grid, due to their environmental and human health benefits. This study investigates the performance of an off-grid, hybrid PV/diesel generator/battery system for a decentralized power plant in Kuakata, Bangladesh, meeting a load demand of 3000 kWh/day with a 501.61 kW peak load demand. HOMER Pro (hybrid optimization model for electric renewable) software (version 3.11) was used to simulate and optimize system operations utilizing real-time solar radiation and load profile data from that location. This study also includes a sensitivity analysis of the off-grid HRES system under different electrical load demands, project longevity, and derating variables. The results reveal that CO2 emissions have potentially decreased by more than 30% and over 10 tons per year, respectively, when compared to traditional power plants. The optimized system’s net present cost (NPC) was determined to be around USD 5.19 million, with a cost of energy (COE) of USD 0.367 per kWh per unit with a 100% renewable component. Furthermore, the current study’s findings are compared to previous research that has resulted in an economical hybrid renewable energy system with an affordable COE. The hybrid energy system under consideration might also be applicable to other parts of the world with comparable climate conditions.

Performance analysis of a novel ejector-assisted condenser outlet split dual-evaporator refrigeration system

The performance of an ejector-assisted condenser outlet split dual-evaporator cycle is compared with a conventional dual-evaporator cycle albeit consisting a pressure reducing valve. The cycles do not employ any separator due to its inability to efficiently separate the liquid and the vapor phases. The comparison of both the cycles has been made for the same cooling capacity in low-temperature evaporator and unit flow rate of R134a and R1234yf as refrigerants. The impacts of changing the operating temperatures of evaporator and condenser have been examined in the current investigation. The study reveals that with the increase in temperature of the high-temperature evaporator, the cooling capacity of the high-temperature evaporator yields, while that of the low-temperature evaporator plummets in both the cycles. Further, the compressor work is allayed in the ejector-assisted cycle; thus, the COP is enhanced considerably. The percentage COP improvement over the basic cycle is obtained from 14.7 to 17.53% for the refrigerant R1234yf and from 14.45 to 17.32% for R134a; however, the COP of both the cycles with R12134yf is slightly lower than with R134a. The ejector has been modeled assuming a constant pressure theory. The observed trend indicates that the entrainment ratio is improved with the rise in the temperature of low-temperature evaporator, whereas it is decreased with the rise in the temperature of high-temperature evaporator.

Performance analysis of IRS-aided MISO system under spatial correlation and interference

In this paper, we investigate the performance of an intelligent reflecting surface (IRS) aided multiple-input single-output (MISO) wireless system in the absence of direct link. We focus on the practical scenario where the N reflecting elements of the IRS have a spatial correlation between them, and both the destination and IRS are subject to interferences. Moreover, the IRS design with discrete phase shifting is considered. In order to quantify the impact of spatial correlation, phase error and interference on the system performance, we provide closed-form and asymptotic expressions for the system outage probability. Additionally, approximations for the average channel capacity are also derived. To gain further insight, the achievable diversity gain of both partial and full correlation cases is quantified. The analytical and simulation results reveal that spatial correlation has a dominant effect and reduces the system diversity order, but it is beneficial to the average channel capacity. Phase error results in performance degradation. Interference degrades the system performance only by reducing the coding gain. Compared to the direct path, interference from the reflected path plays a major role in reducing the system performance.

Search for Optimal Parameters in the Control Structure of a Surgical System for Soft Tissue Operations Based on In Vitro Experiments on Cardiovascular Tissue

The surgical robots currently used in cardiac surgery are equipped with a remote center of motion (RCM) mechanism that enables the required spherical workspace. The dynamics model of the surgical robot’s RCM mechanism presented in this work includes a direct current (DC) motor, an optimal proportional–integral–derivative (PID) controller, and a LuGre friction model that takes into account the Stribeck effect and surface deformation. A finite element method (FEM) analysis of transients was carried out using the energy hypothesis of von Mises with an optimal input signal from the mechatronic system with a PID controller obtained using the Runge–Kutta differentiation method in the Dormand–Prince ordinary differential equations variant (ODE45). Five criteria were adopted for the objective function: the safety factor related to the stress function in the time-varying strength problem, the first natural frequency related to stiffness and the resonance phenomenon, the buckling coefficient in the statics problem related to stability, the static factor of safety, and the displacement of the operating tip. The force inputs to the dynamics model were derived from in vitro force measurements on cardiovascular tissue using a force sensor. The normality of the statistical distribution of the experimental data was confirmed using the Kolmogorov–Smirnov statistical test. The problem of multi-criteria optimization was solved using the non-sorter genetic algorithm (NSGA-II), the finite element method, and the von Mises distortion energy hypothesis. Velocity input signals for the transient dynamics model were obtained from a second in vitro experiment on cardiovascular tissue using the minimally robotic invasive surgery (MIRS) technique. An experienced cardiac surgeon conducted the experiment in a modern method using the Robin Heart Vision surgical robot, and a system of four complementary metal–oxide–semiconductor (CMOS) optical sensors and ariel performance analysis system (APAS-XP 2002) software were used to obtain the endoscopic tool trajectory signal. The trajectory signal was accurate to ±2 [mm] in relation to the adopted standard, and it was smoothed using the Savitzky–Golay (SG) polynomial smoothing, whose parameters were optimally selected using the Durbin–Watson (DW) statistical test.

Performance analysis of a two-stage evaporation heat pump drying system for graded cooling/dehumidification

Single-stage evaporation heat pumps are widely used in drying fields, but they suffer from relatively low efficiency. To enhance the performance of conventional heat pump drying systems with single-stage evaporating, a two-stage evaporation heat pump drying (TSE-HPD) system is proposed in this paper for graded cooling/dehumidification. In this innovative system, the quasi dual-stage compression, matching a double temperature level evaporation process, achieves graded cooling and dehumidification of air. Its performance is investigated using energy and exergy analysis methods and compared with that of a single-stage compression heat pump drying (SSC-PHD) system. The results indicate that the TSE-HPD system exhibits 15.5 %–19.3 % improvement in the coefficient of performance (COP) and 16.3 %–18.9 % enhancement in the specific moisture extraction rate (SMER) compared to those of the SSC-PHD system when the condensing temperature varies between 50 °C and 70 °C. The parametric analysis reveals that the highest COP, SMER, and exergy efficiency (ηex) occur when the optimum first-stage evaporating temperatures are 16 °C, 25 °C, and 34 °C, respectively, under three standard operating conditions. At this time, the first-stage evaporator handles 36 %–40 % of the cooling/dehumidification tasks, the refrigerant mass flow ratio at the intermediate-pressure suction port of the compressor ranges from 47 %–58 %, and the optimum first-stage and second-stage compression ratios are 1.49–1.52 and 2.45–2.61, respectively. The research results provide guidance and reference for the determination of matching design parameters and efficient operation control parameters of drying systems.

Techno-economic feasibility assessment and performance analysis of standalone solar photo voltaic-biomass hybrid system with optimized storage: a case study—Grand Bassa, Liberia

Introduction: Liberia has a significant obstacle in terms of restricted power accessibility, as only 26.7% of its populace have access to electrical energy. To tackle this issue, there is a collaborative effort between the government and private sector to undertake energy projects aimed at enhancing the power supply of the grid. The purpose of this study is to evaluate the techno-economic feasibility and analyze the performance of a standalone biomass/solar photovoltaic (PV) hybrid energy system for a rural Liberian community that mostly relies on traditional biomass.Methods: HOMER pro program was used to configure the system, model the community’s load over a year, and generate the resource data of the location.Results and Discussion: Four different configurations were obtained and analyzed both technically and economically to determine the most feasible configuration. This study has shown that a hybrid configuration incorporating Biomass Gasifier, Solar PV, and Battery storage is more feasible with annual output power of 77104 kWh/yr, LCOE ($/kwh) of 0.29 and NPC ($) of 0.3979 million. If implemented with external funding, it will enhance the education, economic and socio-economic status of rural settlements. The results will serve as a valuable resource for informing choices on the implementation of the hybrid energy solution and guaranteeing its sustained efficacy within the community.