Battery Bank Research Articles

Design and fabrication of eco - friendly solar scooter from electric scooter

Solar energy, a renewable source of energy, is an imminent form that, if properly used, can give rise to incredible energy that can be further used in different forms. Research is still in progress on applications like solar-powered automobiles, solar-powered steam turbines, etc. The concept of the design and fabrication of solar-powered scooters was mainly intended for the reduction of non-renewable energy consumption and environmental protection. In this connection, it is essential to design and develop an eco-friendly solar-powered vehicle. This is an electric vehicle powered by solar energy obtained from solar panels on the surface of the vehicle, and it works on the principle of the photoelectric effect. Solar vehicles aren't practical day-to-day transportation vehicles at present, but they are used as primary demonstration vehicles. In our work, a Li-ion battery is an energy storage device that can store charge from a flexible monocrystalline solar panel. A brushless DC hub motor is the prime mover, and it drives the vehicle with the support of two wheels. A solar charge controller is used to manage the charging of a battery bank and control the ratio of current and voltage. This solar electric scooter has the capability to carry weights up to 150 kg and can travel at speeds of 40 km/h. The developed solar scooter can provide an incessant journey of 7 km, or it can travel up to 3 hours continuously. It is designed to be user-friendly, free from pollution, and entirely different from conventional vehicles. This solar powered vehicle solves various problems related to the environment, and it is the most efficient pollution-free technique.

Study of electrification system based on solar photovoltaic generation to supply riverside buildings

This work focuses on the design and computational simulation of solar-based electrification systems aimed at supplying buildings not connected to the conventional electrical grid in the Urubuéua Cabeceira location, within the municipality of Abaetetuba, in the state of Pará, Brazil. The systems designed for these buildings have the following specifications: a 5.28 kWp photovoltaic generator, three bidirectional inverters, each with a 5 kW capacity, and a 53.32 kWh battery bank. Following the sizing, the systems are simulated using the PV*SOL computer program. In these simulations, three operational scenarios are considered. The first scenario involves a 50% increase in household consumption on weekends; the second deals with a 15% rise in workshop consumption during October; the third scenario explores the non-operation of the workshop system, resulting in a system without energy generation. Regarding the results achieved, the system simulations have been particularly noteworthy, delivering excellent outcomes that align well with the intended objectives. The flexibility of the interconnected systems enables efficient energy exchange among sources.

Strategy for Hydrogen Generation from a Photovoltaic System Using a Boost Converter

This research presents two schemes for hydrogen generation by alkaline water electrolysis using a photovoltaic system and a boost converter as a power source. The first scheme aims to extract the maximum power from the photovoltaic system using the P&O algorithm and use this energy to generate hydrogen. Thus, obtaining the maximum efficiency of the photovoltaic-electrolytic cell system. The second scheme aims to generate hydrogen on demand, eliminating the need for storage and reducing the associated costs and risks. Nevertheless, this approach does not fully maximize the power available in the PV system, resulting in lower overall efficiency. Finally, it concludes with the proposal of an approach that combines on-demand hydrogen generation and maximum energy extraction from solar panels. Any unused energy from the electrolytic reactor is stored in a battery bank through a bidirectional converter. However, the simulation and implementation of this third scheme are proposed as future work.

Introducing AI applications in engineering education (PBL): An implementation of power generation at minimum wind velocity and turbine faults classification using AI

AbstractThis article explores the integration of artificial intelligence (AI) applications into project‐based learning (PBL) education as a means to enhance students' education. Specifically, the implementation of AI in the context of power generation is addressed, focusing on achieving power generation at minimum wind velocity and classifying turbine faults using AI techniques. The researchers have proposed a novel generating unit which is going to generate 1 KW of electric power at a specific flow rate of air and the generated power will be stored in the battery bank through the charge controller and then the load is driven from the battery through an inverter. Iron or core losses (Hysteresis, Eddy Current losses) can be acknowledged as one of the major reasons for the inefficiency of conventional generators, therefore anovel coreless model generator was proposed which also improved efficiency and reduces drag. Wind Turbine prototype was fabricated and deployed for the testing and validation of the proposed novel design. The design produced outstanding power ratings and electrical generation characteristics compared with other existing strategies at minimal air flow. Results proved that the proposed coreless axial flux generator has the capability to produce a better power rating compared with the existing wind turbine generators. Proposed Axial flux achieved 10.73 watt power at wind velocity at around 80 rpm. At a wind velocity of 10 m/s and around 800 rpm 313–330 kwh was produced by the proposed generator while the conventional generator produced around 300 kwh. The proposed generator design performed 35% better in terms of production efficiency under load and no load conditions. Moreover, faults in turbines are very common due to the various temperatures, therefore the faults have been classified using state‐of‐the‐art AI‐based classifiers. A comparison of space vector modulation (SVM) and Naive Bayes classifiers was performed in the study to classify wind turbine faults. It was found that both classifiers performed well in achieving high accuracy. SVM achieved a slightly higher accuracy of 0.9861 compared with Naive Bayes, which achieved an accuracy of 0.967. Based on the results, it can be inferred that SVM may be a more suitable classifier for wind turbine fault classification. The case study results demonstrated the potential of AI applications in PBL education, offering students a multidisciplinary learning experience that enhances their technical knowledge, problem‐solving skills, and teamwork abilities.

Performance assessment and resilience of solar mini-grids for sustainable energy access in Ghana

Mini-grids are increasingly adopted as a solution to extending electricity to remote communities in Africa. However, the system's resilience to meet increasing demand amidst degrading performance over time continues to be challenging. This study assessed mini-grid performance and explored factors that should be considered in scaling up mini-grids for sustainability in five island communities in Ghana's Volta Lake. Power generation and load data, visual inspection and thermal imaging data, and electrical systems performance characteristics data were collected and analysed using data loggers installed on site, the IEA PVPS Task 13 guidelines, and the IEC-TS 61724-3: 2016 guidelines, respectively. The study's findings indicate that the solar power conversion efficiency of the mini-grids was generally low and had a 25% - 45% performance ratio. This low efficiency was due to inefficiencies in the battery bank, distribution losses, and unused losses. The overall system efficiency of the mini-grids ranged from 4% to 7%. This is consistent with the literature, which reports that PV power systems in tropical climates have an efficiency of 5% – 15%. The research emphasises the importance of system maintenance, load management, and considering climate change's impact on mini-grid reliability and sustainability.

Probabilistic sizing and scheduling co-optimisation of hybrid battery/super-capacitor energy storage systems in micro-grids

While established deterministic capacity planning models for single-component energy storage systems exist, little attention has been given to probabilistic sizing of hybrid energy storage systems (ESSs) using swarm-based meta-heuristic algorithms. This highlights two key research opportunities, namely: (1) studying the impact of preserving model-inherent characteristics and optimising daily system dispatch on narrowing reality gaps in hybrid ESS designs, and (2) the optimal integration of hybrid ESSs into grid-connected micro-grids based on their applications, with potentially significant financial implications for model designs. In response, this paper introduces a novel probabilistic hybrid ESS capacity planning optimisation model based on a state-of-the-art meta-heuristic algorithm. To demonstrate the effectiveness of the model within a community micro-grid scheme, a case study of an eco-village in Aotearoa New Zealand is presented. The simulation results indicate a ∼4 % and ∼36 % premium above the deterministic results respectively in the most likely case and worst-case probabilistic scenarios. On the other hand, the best-case stochastic estimate of the life-cycle cost of the hybrid ESS is found to be ∼39 % lower than that of the deterministic modelling. Additionally, the economics of temporal energy arbitrage using the battery bank is investigated, indicating that at the current capital cost of stationary LiFePO4 batteries and the present fixed feed-in-tariff (NZ$0.08/kWh), it is not economically viable to cycle the storage for arbitrage reasons alone. In conclusion, this paper highlights the critical need to incorporate probabilistic optimisation techniques and emphasises the importance of sizing and scheduling co-optimisation when designing hybrid ESSs for integration into grid-connected micro-grids.

Development of control strategy for community battery energy storage system in grid-connected microgrid of high photovoltaic penetration level

The focus of this paper is to develop a control strategy for a community battery bank in a grid-connected microgrid in which a significant level of photovoltaic generation is embedded. In order to minimize the capacity of the community battery, the power transfer capacity of the interconnection link between the microgrid and the external grid system is utilized to safe maximum levels. Through Empirical Mode Decomposition analysis of the net power flows of the microgrid, the daily and seasonal modes of the net power oscillations are identified as the two dominant low-frequency components. Whence a rule-based operational strategy is developed to control the power flows of the community battery via a novel dynamic referencing scheme for the state-of-charge of the battery bank. The battery control scheme counteracts the dominant daily and seasonal modes of oscillations of the net power. The numerical calculations performed for a case study shows that the proposed scheme leads to an approximately 16% decrease in the required battery capacity for particular growth rate of solid-electrolyte interphase film in the battery bank. The scheme does not require the forecasting of the net power, and thus, it has an increased degree of robustness when the community battery undertakes the power buffering task.

Design of an autonomous photovoltaic power supply system for Nyabikenke Hospital (Burundi) using the ETAP tool

Our energy future must be based on non-polluting energies with significant resources. Renewable energies are the best candidates, but the intermittency of their production requires finding efficient and environmentally friendly storage means. Solar electricity must be stored to be used later when needed. There are many ways to store electricity, and they all involve converting it to another form of energy that is easier to contain. A study on the design of an autonomous electrical system was carried out at Nyabikenke Hospital to solve the major problem of the lack of hydroelectric power at Nyabikenke Hospital. We therefore first sought to estimate the energy needs of the user and then proceeded to an optimal dimensioning of our system using the ETAP software. In normal operation, the photovoltaic field and the battery bank will operate, in the event of a cut in the two sources, the emergency Diesel generator intervenes. The design tool simulates the operation of an autonomous electrical system for a load of 131.5kW necessary for the hospital to be supplied. He can design any system from the experimental results of the components. The results of the simulation make it possible to characterize the operation of the system in an autonomous application, determine its performance, and evaluate the influence of the various losses.

SoC-Adaptive Power Sharing Strategy Applied to DC Microgrids Supplied by Multiple Energy Storage Systems

A non-linear <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$SoC$</tex-math></inline-formula> -adaptive management and control scheme is applied to DC microgrids (MGs) supplied by energy storage systems (ESSs) and connected to the grid. The management and control strategy are performed via decentralized methodologies, allowing power sharing between the battery banks with or without a low-speed communication link, while their limits of operation for charging and discharging are not exceeded. Additionally, the aforementioned technique is based on the sigmoidal function where the solution shows two degrees of freedom as the DC-link voltage error and the state-of-charge <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(SoC)$</tex-math></inline-formula> in the outer loop (DC-link voltage control), and classical proportional-integral <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(PI)$</tex-math></inline-formula> controllers regulating the current flow through the DC/DC power converters in the inner loop. To analyze the system stability, the DC MG model was calculated to evaluate the poles movement, frequency and step responses at different scenarios. Finally, the authors rely on a set of results in the Typhoon <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\text{\text{®}}}$</tex-math></inline-formula> 602+ hardware to demonstrate the effectiveness of the proposed solution as well as, a comparison between it and the classical droop controller.

Measured and estimated wind speed of a stand-alone energy system with controlled bi-directional DC-DC battery bank

Simple control of a stand-alone variable speed wind turbine with a permanent magnet synchronous generator (PMSG) is presented in this paper. PMSG fed different types of loads by means of a converter where switched mode rectifier is connected to the PMSG, boost DC-DC converter, and voltage source inverter (VSI). The wind turbine is operated at measured wind speed and it is tested at estimated wind speed which is based on the power calculation of turbine power, generating electric power, losses power, and the dynamic power during the variation of wind speed. The estimation of wind speed would provide a scope of what a control method is applicable in practice and this would increase the performance by scheduling the connected loads. The bidirectional battery charger is controlled and used during the shortage of wind speed. The converter of the battery charger is controlled and implemented on an embedded system by using an STM32-based microcontroller. During the shortage or fluctuation of wind speed, the load voltage is controlled within the acceptable limit, where the voltage has been compensated. An extensive simulation has been conducted by MATLAB/Simulink, and the system shows a good dynamic response to the variation and decrease in wind speed.

Techno-economic comparison of dispatch strategies for stand-alone industrial demand integrated with fossil and renewable energy resources

The industrial sector is the world’s largest energy consumer and carbon emitter. In Iran, the importance of applying new energy resources is even greater due to industrial growth, the capacity shortage of conventional power plants, and the low reliability of power supply during peak times. However, detecting the optimal dispatch strategy for managing the application of nonrenewable sources along with renewable technologies is a major challenge. In this study, we performed the design and optimization of a hybrid energy system, including photovoltaic, wind turbines, a diesel generator, a natural gas generator, and a storage bank, for a small-scale industrial case in a southern industrial park of Iran. The HOMER Pro software was utilized to optimize the hybrid renewable energy system and supply an average demand of about 820 kWh/day through five different dispatch strategies. The results showed that predictive dispatch, with the foresight of renewable production and demand in the next 48 h, outperforms combined dispatch, cycle charging, load following, and generator order dispatch strategies. The optimum scenario showed an 84% renewable fraction, a cost of energy of 0.105–0.120 $/kWh, and about 24.8% excess power, which improves local grid reliability and the potential for renewable energy utilization by the industrial unit. The technical results showed that the optimum use of battery bank capacity based on upcoming predicted power consumption/generation potential effectively reduces the need for installation capacity of power equipment, which is critical for remote industrial demands.

An effective sizing and sensitivity analysis of a hybrid renewable energy system for household, multi-media and rural healthcare centres power supply: A case study of Kaele, Cameroon

The vast majority of rural Africans lack access to clean, reliable, and sustainable electrical energy. This has deprived rural inhabitants of the opportunity to receive advanced healthcare delivery and modern education. In this study, an off-grid hybrid system composed of solar panels, wind turbines, battery banks and diesel-powered generators has been designed to fulfil the electrical loads requirements of a household, a multi-media and healthcare centres situated at Kaele, Cameroon. Initially, the wind and solar potential of Kaele were investigated and four meta-heuristics algorithms, namely, the colliding bodies’ optimization, charged system search, teaching-learning-based optimization (TLBO) and the water evaporation optimization algorithms are applied to the hybrid system and the results are compared with regards to the system net present cost (NPC). Thereafter, three off-grid hybrid systems: PV/Wind/Battery/Diesel, PV/Battery/Diesel, and Wind/Battery/Diesel were compared based on a techno-economic and environmental analysis. The results showed that, the mean yearly wind speed and solar radiation of Kaele are 6.7 m/s and 7.20 kWh/m2/day, respectively. Comparing the search power of the aforementioned algorithms on the sizing problem, it has been found that the TLBO algorithm produces better results than the other algorithms. The results also showed that, the PV/Wind/Battery/Diesel configuration is the cost-effective option for supplying the household, multi-media and healthcare centres. The obtained optimal combination and the NPC of the PV/Wind/Battery/Diesel system are as follows: For household, the most appropriate configuration combines 9 solar panels, 2 wind turbines, 33 battery banks, 1 diesel generator and the obtained NPC corresponds to 26111.2$; for the multi-media centre, 52 solar panels, 2 wind turbines, 96 battery banks and 1 diesel generator which resulted to a NPC of 55511.2$; and for the healthcare centre, the ideal configuration consists of 13 solar panels, 1 wind turbine, 13 battery banks and 1 diesel generator with a NPC of 19008.7$. The cost of energy of the optimal system was found to be 0.2419$/kWh, 0.2195$/kWh and 0.2158$/kWh for household, multi-media and healthcare establishments, respectively. The results also revealed that for household and healthcare centre the obtained break-even grid extension distance was 0.443 km and 1.23 km, respectively for the PV/Wind/Battery/Diesel system. Since this separation is smaller than the nearest grid extension distance of 5 km, it is concluded that the proposed hybrid energy system is suitable for powering the household, multi-media and healthcare centres in Kaele, Cameroon. Finally, a sensitivity analysis of each relevant component cost, resources availability and microeconomic variables of the PV/Wind/Battery/Diesel system is carried out to acquire a more suitable configuration.

Enhancing Performance of Hybrid Electric Vehicle using Optimized Energy Management Methodology

The fuel consumption and the fuel management strategy (PMS) of the hybrid electric vehicle are closely linked (HEV). In this study, a hybrid power management technique and an adaptive neuro-fuzzy inference (ANFIS) method are established. Artificial intelligence represents a huge improvement in electricity management across different energy sources (AI). The main energy source of the hybrid power supply is a proton exchange membrane fuel cell (PEMFC), while its electrical storage devices are a battery bank and an ultracapacitor. The hybrid electric vehicle's power management strategy (PMS) and fuel consumption are closely related (HEV). In this paper, an adaptive neuro-fuzzy inference and hybrid power management strategy (ANFIS) approach is developed. A significant advance in electricity management across multiple energy sources is artificial intelligence (AI). The proton exchange membrane fuel cell (PEMFC) serves as the primary energy source of the hybrid power supply, and the ultracapacitor and battery bank serve as its electrical storage components.

Bi-objective optimal design of desalination plants considering the uncertainty of renewable energy sources

Desalination systems are commonly used in isolated areas to supply drinking water. However, they usually rely on fossil fuels, which emit greenhouse gases. Renewable energy sources can be used to power desalination systems, but they are intermittent. To address this, the power generation or the capacity of energy and/or water storage can be increased, but this increases the cost. In this paper, we present a bi-objective optimization methodology for designing a desalination system that is powered by renewable energy sources. The methodology aims to maximize reliability while minimizing the levelized cost of water (LCOW) by including the inherent uncertainty in renewable energy generation. We applied this methodology in the Guajira Peninsula, a region of Colombia with serious problems of drinking water scarcity, to evaluate photovoltaic (PV), wind, and PV/wind hybrid systems, determining the optimal combination of wind turbines, photovoltaic (PV) modules, desalination unit characteristics, water storage capacity, and batteries. We also introduced a new indicator, the Reliability - LCOW ratio (RLR), to assist in selecting the optimal configuration. Our results indicate that the renewable systems are feasible, achieving reliability levels between 92% and 95% and LCOW values between 0.82-0.84 USD/m3, when compared to fossil fuel configurations. Despite this, the PV-only system exhibited a significantly higher installed capacity when compared to wind and hybrid systems, while the latter two showed relatively similar characteristics. An analysis about the use of the water tank as a natural battery compared with electricity storage proved that the battery bank was more effective for the PV-only system, while the water tank was more suitable in systems where the use of wind power is predominant. Furthermore, an analysis of the direct CO2 emissions of the fossil fuel configurations shows that the renewable solutions reduce emissions by over 150 tons-eq/y.

Residual energy extraction from near end-of-life lithium-ion batteries using a self-adaptive pulse discharge method

A modified self-adaptive pulse discharge (SAPD) method is adopted by this study to examine the feasibility of extracting residual energy from near end-of-life non-reusable lithium-ion batteries before disassembled. The SAPD model is used to determine the optimal frequency and duty cycle in the process of energy recovery, so the highest pulse discharge current can be obtained. A prototype including an improved active cell-balancing circuit, a modified SAPD control circuit, and a supercapacitor module switching circuit is developed to extract a battery bank that consists of at least one and at most 12 cells in parallel to reduce the discharge current as well as prevent damaged circuit components. The results of testing Sanyo UR14500P batteries show that the energy recovered from six to 12 cells ranges from 2630 to 2955 joules, and the corresponding recovery efficiency is from 64.97 to 66.19 %. Besides, with a randomly selected eight-cell pack, the SAPD method yields higher recovery efficiency than the short circuit discharge and pulse discharge methods, and the recovery efficiency increases by 11.74 % and 9.39 %, respectively. Although the results presented are far from being satisfactory from the perspective of efficiency, this study serves as a starting point for reusing and recycling resources.

A novel strategy for simultaneous active/reactive power design and management using artificial intelligence techniques

Adequate capacity and strategy of an energy management system improve technical and economic characteristics, promote flexibility, and encourage higher penetration of distributed generation. This paper presents a new strategy for simultaneously carry out the optimal equipment design and the management of active and reactive power in smart electrical installations. The proposed optimization algorithm defines the capacity of the photovoltaic generation and the battery banks, the capacitor banks and substation configuration, in addition to selecting the optimal parameters of the battery energy and capacitor bank management strategy making the methodology adaptable to any consumption profile and energy billing format. The purpose of the algorithm is to reduce the global cost of energy over a 20-year time horizon using net present value as the metric, considering energy bill, investment, maintenance, and replacement costs. The energy tariff is composed of time-of-use scheme, demand cost, fines associated with low grid power factor, and feed-in remuneration. The approach also considers transformer losses and 3 typologies defined by the k-means technique used to represent the power consumption profile and meteorological data to reduce the computational effort. The Genetic Algorithm has been applied in a real case study whose project’s net present value was reduced by 28.15%, with an internal rate of return of 21.20% and a computation time of 15 s. The results show high algorithm performance with a reduction of 78.16% in energy and 16.43% in demand costs, virtually eliminating costs caused by low power factor and reducing the energy injected into the grid.

Preliminary design of an OWC wave energy converter battery charger

This paper introduces a low-power off-grid oscillating water column wave energy converter with an internal battery bank. The research aims at the preliminary design and devising of the control strategy of a power electronics interface between the turbo generator and the battery bank. The converter comprises a spar buoy, a biradial turbine, a permanent magnet generator, a full-wave bridge rectifier, a braking chopper, a DC-to-DC step-down converter, and a lead-acid battery bank. The power-take-off system was modelled in Simulink/MATLAB, and its performance was assessed with steady-state simulations, considering a wave climate characteristic of Leixões, Portugal. The chamber pressure, the turbine, generator and rectifier performance were taken from experimental data sets. A simple battery model was derived from the manufacturer's datasheet. An ideal step-down DC-to-DC converter operating in discontinuous conduction mode regulates the battery charging current. This converter, in parallel with the braking chopper, adjusts the generator counter torque by regulating the current through the rectifier. Twelve system variables were recorded for selected pairs of input pressure and step-down converter design coefficient. The power at the rectifier's output terminals was mapped for the rotational speed and input pressure. The results show a system rating of 1.4 kW with 400 W of electrical power at 200 rad/s for the most frequent sea states. The range of the duty cycle, the inductance and the braking resistance were derived. Two closed-loop controllers were proposed for managing the step-down converter and the braking chopper. Their set points and saturation limits were derived from the simulation results.

Optimization of switching losses of the Weinberg converter

DC-DC converters are widely used in hybrid power supply systems. A buffer source of electrical energy (most often a battery bank) and its charging and discharging devices are integral parts of such systems. In systems where the battery bank voltage is less than the DC-bus voltage and galvanic isolation is not required, the Weinberg converter is widely used as a discharge device, for example, in spacecraft power supply systems. The current trend in the development of hybrid power supply systems is to increase the power density of the system. Increasing the operating frequency is the simplest and most effective way to increase the power density of converter. A differential characteristic of this converter is the zero-current switching (ZCS) of transistors, which occurs due to the influence of the leakage inductance of coupled-inductors and the leakage inductance of the transformer. The paper presents an analysis of the influence of leakage inductances on the operation of the converter and a method of determining a level of turn on switching losses of transistors, which allows to ensure the necessary level of efficiency of the converter. The results of simulation modeling of the converter confirm the effectiveness of this method.

Quantitative techno-economic comparison of a photovoltaic/wind hybrid power system with different energy storage technologies for electrification of three remote areas in Cameroon using Cuckoo search algorithm

This research work presents a techno-economic comparisons and optimal design of a photovoltaic/wind hybrid systems with different energy storage technologies for rural electrification of three different locations in Cameroon. The determination of the optimal, cost-effective, and reliable configuration is performed for the locations of Fotokol, Figuil and Idabato which describe the weather conditions diversity in Cameroon with different solar and wind energy potentials. The sizing-optimisation problem is founded on real data of wind speed and solar radiation, with the principal goal of minimizing the hybrid system net present cost (NPC) while taking into consideration the load deficit probability (LDP) as the reliability constraint. The cuckoo search algorithm with a high convergence rate is utilised to find the optimal combination of the hybrid system with the lowest cost of energy (COE) and NPC which satisfies the LDP of the considered locations. At each location, six different configurations namely photovoltaic/wind/battery, photovoltaic/battery, wind/battery, photovoltaic/wind/fuel-cell, photovoltaic/fuel-cell and wind/fuel-cell are optimised and compared in view of satisfying the heavy, medium and small activities electrical loads demand of non-domestic subscribers. The optimal configuration is obtained for each activity and location in connection with the minimum cost and best reliability. The results revealed that the optimal, cost-effective, and reliable configuration for all activities and locations is a battery-based photovoltaic/wind hybrid system. The results showed that integration of 20 photovoltaic arrays, 1 wind turbine and 27 batteries with LDP value of 0.0486 is the most appropriate configuration which leads to the lowest NPC and COE of 23,024$ and 0.1570 $/kWh, respectively for satisfying the heavy activity load demand at Fotokol. The best hybrid system configuration needed for covering loads demand for medium and small activities at Fotokol were found to be 8 photovoltaic arrays, 1 wind turbine and 19 batteries with LDP, NPC and COE values of 0.0497, 16,640$ and 0.1942 $/kWh, respectively for medium activity; and 5 photovoltaic arrays, 1 wind turbine and 10 batteries with NPC, COE and LDP values of 12,860$, 0.2120 $/kWh and 0.0494, respectively for small activity. The optimisations studies performed at the site of Figuil considering the heavy activity load demand revealed that the cost-effective configuration which leads to the minimum NPC and COE of 28,400$ and 0.1937 $/kWh, respectively consists of 26 photovoltaic panels, 1 wind turbine and 38 batteries with the LDP equal to 0.0499. The obtained results also indicated that substituting the battery banks with a chemical storage which consist of a combination of an electrolyser, hydrogen reservoir and fuel cell is possible, though the cost increases because of the important investment cost of the system constituents. Integrating photovoltaic/wind/fuel-cell at the site of Idabato gives the lowest NPC and COE values of 48,864$ and 0.8056 $/kWh, respectively with a LDP of 0.0491. The optimum sizing of this configuration has 9 photovoltaic arrays, 1 wind turbine and 15 hydrogen tanks.

Power Quality Improvement of a Hybrid Renewable Energy Sources Based Standalone System Using Neuro-Fuzzy Controllers

Wind and Photovoltaic (PV) based hybrid standalone systems for supplying electricity to remote consumers are becoming increasingly popular around the world. In general, power generation from both PV and wind is always variable due to irradiance and speed fluctuations. Furthermore, the load is unpredictably variable and changes at random. As a result, one of the main challenges in such standalone micro grids is maintaining power quality. To maintain power quality in such standalone systems, a novel controller should be developed. Furthermore, due to the unpredictable random changes in wind speed, solar irradiance, and load, a battery bank must be integrated into the standalone system via a bidirectional converter. Furthermore, the interconnection of multiple wind turbines and PV panels can create a powerful hybrid system. This paper examines a 1 MW stand-alone power generation system using numerous PV and wind systems. Maximum power points tracking devices with perturbed and observe algorithms are used to optimize the utilization of individual PV modules and wind turbines. Because of its superior priority over conventional PI controllers, a Neuro-Fuzzy integrated controller-based novel control scheme for both inverter and bidirectional converter is proposed to achieve precise response while maintaining power quality. This paper discusses extensive results obtained by establishing hardware-in-the-loop on the OPAL-RT platform to evaluate the responses of proposed controllers. The experimental results also included to provide strength to the paper.