Maximum Solar Power Research Articles

Implementation of Solar PV Protection System in Indonesia: A Review

Conventional energy sources will run out if used continuously and damage the environment. Therefore, it is necessary to develop other energy sources that are safe, environmentally friendly, and inexhaustible known as renewable energy sources to meet energy needs in Indonesia. This article presents a review that discusses the protection of solar panels and PLTS. Solar panels are vulnerable to lightning strikes and other electrical disturbances, so an effective protection system is needed. This study uses a qualitative method through a literature study, reviewing various protection methods such as conventional and electrostatic lightning rods, good grounding systems, and overcurrent detection devices such as Solar Charge Controller (SCC) and Maximum Power Point Tracking (MPPT). In addition, battery protection with Automatic Transfer Switch (ATS) and Low Voltage Disconnected (LVD) is also discussed. Using Arduino Uno, ESP 32, and PLC, automation approaches offer real-time monitoring and control solutions for solar power plant performance. Innovations in the lightning protection zone concept and minimal quantity measurement schemes enable more cost-effective design of protection systems without reducing the level of protection. The results show that these various protection methods can provide effective and efficient protection for solar power plants, enabling optimization of system function and safety. In conclusion, the protection systems reviewed in this study offer a sustainable solution to Indonesia's energy challenges by utilizing the maximum potential of solar energy. The potential for further research is presented in this article to develop more efficient and effective protection methods.

Powering sustainability: A techno-economic analysis of photovoltaic systems in dairy farming

ABSTRACT Dairy farming plays an indispensable role in the socio-economic growth of rural India. But, the modernization and sustainable growth of dairy farming relies critically upon the availability of reliable and affordable renewable electric energy. However, lack of access to electricity is a major challenge that needs to be addressed to encourage sustainable dairy farming. In this direction, the deployment of solar photovoltaic (PV) systems in dairy farms has enormous potential to combat the existing critical challenges of fossil fuels and rural energy crises. This investigation assesses the techno-economic viability of PV systems through detailed mathematical modeling and electrical load estimation using the MATLAB simulation environment. The study determines the availability of maximum of solar radiations at each instant, and optimizes the inclined surface to extract maximum solar power. The outcomes of the study reveal that deploying an off-grid PV system consisting of 20 PV modules of 335 Wp, 24 batteries of each 150 Ah, and a 10 kVA inverter meets the average electric load of 9.5 MWh/year for twenty-five years with one-day autonomy. The economic analysis comprised the life-cycle cost (LCC), annualized life-cycle cost (ALCC), and costs of electricity (COE) of the study under reference as US$13050, US$323.1, and US$ 0.02596/kWh, respectively. The study results in saving of 0.285 MT CO2 emissions for the total life span. The resulted stand-alone PV system is both economically and technically viable. The present results reinforce the dispersal of green energy measures for their implementation in rural dairy farms having similar climate conditions in the Indian context.

Geothermal Power, and a Battery as Well

One of geothermal energy’s key advantages is that it’s always on. But making money selling power increasingly requires being able to react to shifts in electric prices by adjusting production up and down. The fact this is a concern now is an indication of the ambition of a new generation of companies that have shown it is possible to use horizontal drilling and fracturing to build subsurface heat exchangers to harvest large amounts of geothermal energy. The geologic potential is huge—there are a lot of places with hot rock at a drillable depth. But monetizing that potential will require grabbing a sizeable share of the market in big states such as California and Texas where the price of electricity can sink toward zero when sunny conditions allow maximum solar power production, and then rebound as the sun sets. This is a pressing concern for Houston-based Fervo Energy because it is one of the first firms in a small pack of innovators to reach the point where it is building a large scale power plant using this emerging technology. Unlike wind and solar, it can deliver power on demand, similar to coal, gas, and nuclear-powered plants that supply baseload power to the electric market. Fervo’s founders play up its ability to deliver power on demand, but they resist the label baseload power provider because, as Jack Norbeck, chief technology officer and cofounder of Fervo, said, “In the future, baseload power will be a thing of the past.” Baseload power plants are not yet an endangered species. There are still large, mostly old fossil-fueled plants providing baseload power that are still around because they were paid for long ago. However, their numbers are declining as they wear out and are hit with demands for costly emission reductions. The initial sales by Fervo’s Cape Station project in Utah will provide baseload power to southern California power suppliers, complying with a unique California rule that requires them to source low-carbon power supplies that are available 24/7. But for new geothermal companies to sell enough power to become significant players in US power markets, they will need to provide what the electricity business describes as “more flexible, dispatchable power.” Big baseload plants ruled the power universe back when regulators set rates high enough to maintain a sizable surplus of generating capacity—ensuring the lights stayed on. Now, in markets such as Texas and California, power pricing rises and falls based on supply and demand, with selling at all times sometimes resulting in very low payments during peak production periods for solar and wind. This is particularly true during seasons where days tend to be sunny and windy with moderate temperatures. “If you are consuming electricity, particularly in the spring and fall, it is an extremely clean grid you are pulling from,” said Christian Gradl, vice president of operations for Fervo during a panel at this year’s Offshore Technology Conference.

Reactive Passivation of Wide-Bandgap Organic-Inorganic Perovskites with Benzylamine.

While amines are widely used as additives in metal-halide perovskites, our understanding of the way amines in perovskite precursor solutions impact the resultant perovskite film is still limited. In this paper, we explore the multiple effects of benzylamine (BnAm), also referred to as phenylmethylamine, used to passivate both FA0.75Cs0.25Pb(I0.8Br0.2)3 and FA0.8Cs0.2PbI3 perovskite compositions. We show that, unlike benzylammonium (BnA+) halide salts, BnAm reacts rapidly with the formamidinium (FA+) cation, forming new chemical products in solution and these products passivate the perovskite crystal domains when processed into a thin film. In addition, when BnAm is used as a bulk additive, the average perovskite solar cell maximum power point tracked efficiency (for 30 s) increased to 19.3% compared to the control devices 16.8% for a 1.68 eV perovskite. Under combined full spectrum simulated sunlight and 65 °C temperature, the devices maintained a better T80 stability of close to 2500 h while the control devices have T80 stabilities of <100 h. We obtained similar results when presynthesizing the product BnFAI and adding it directly into the perovskite precursor solution. These findings highlight the mechanistic differences between amine and ammonium salt passivation, enabling the rational design of molecular strategies to improve the material quality and device performance of metal-halide perovskites.

A comprehensive analysis of the optimal GWO based FOPID MPPT controller for grid-tied photovoltaics system under atmospheric uncertainty

Recent years have seen an increase in the deployment of renewable energy sources on power system networks as a result of the gradual development of renewable energy sources. In order to utilize these intermittent sources, grid integration is crucial, since weather conditions are hard to predict accurately and do not match the pattern of generation. As a result, controlling energy becomes an unavoidable challenge when the alternative energy source is connected to the grid in a distributed manner. A number of factors contribute to the problem, including sporadic sources, daytime expenses, and limitations concerning the specifications of solar panels. Therefore, a more robust control system is needed to enhance the reliability and efficiency of the renewable energy integrated power network. Thus, this manuscript presents the design, implementation, and performance of an improved fractional order PID (FOPID) controller for proposed grid-connected photovoltaic systems. To maximize the power from the photovoltaic source, the controller is designed to extract as much energy as possible. In addition to having the adaptive nature of a PID controller, the proposed FOPID controller is capable of optimizing its gain parameter according to the generator and grid side parameters being considered. The proposed study utilized grey wolf optimization (GWO) to tune the FOPID controller for tracking the quadrature axis model, DC link voltage and current regulation, and maximizing the maximum power point. Further, FOPID is used to perform the system's current control functions, and each time the error is measured, the regulating parameters are updated accordingly. This paper contrasts fuzzy logic controllers (FLC), flying squirrel search optimization (FSSO), and PSO-tuned FOPID controllers with the proposed work. The simulation's results are displayed and examined in the section on results and outcomes. The obtained results demonstrate maximum solar power output under erratic weather conditions, validating the effectiveness of the proposed controller.

The improvement of field-oriented control performance for double stator IPMSM based on NFC powered by dual photovoltaic solar panels

This paper presents a proposed method Neuro Fuzzy Control (NFC)-Field Oriente Control (FOC)-Space Vector Modulation (SVM) using IP controller for a five-level NPC inverter-Double Stator Interior Permanent Magnet Synchronous Motor (DSIPMSM) supplied by two photovoltaic solar panels Maximum Power Point Tracking (MPPT) Fuzzy Sliding Mode Control (FSMC). The torque generated and the magnetic flux of the DSIPMSM may be independently controlled thanks to FOC, while IP controllers are used to minimize dq axis current errors to zero. A boost converter is made up of an inductor, a capacitor, and a separate input-output connection. A MPPT approach based on the FSMC algorithm is used to extract the maximum amount of energy from the solar panel while controlling the inverter duty cycle. The NFC tool is an extremely potent instrument that bridges the gaps created by the fuzzy logic controller. The proposed method’s worth was demonstrated by the simulation results in terms of durability, decoupling between the d-q axis, and dynamic performance.

Application of circle search algorithm for solar PV maximum power point tracking under complex partial shading conditions

Solar photovoltaic (SPV) energy exhibits a potential role in the world for generating electricity and avoiding the block out in the power system. However, the hardest task in the PV system is to track the global maxima power (GPP) instead of local peak power (LPP) under partial shading conditions (PSC). To track the GPP, a new circle search algorithm (CSA) is implemented in this paper and is executed in MATLAB/SIMULINK environment. This method is examined at various irradiation conditions and compared their performance with other studied methods in terms of tracking time, settling time and efficiency. The simulation outcome of the CSA method efficiencies 99.96 % in case 1, 99.74 % in case 2, and 99.94 % in case 3. For checking the robustness and stability of the system statistical analysis was done on the proposed methods. It is found that the suggested method exhibits higher performance than the other methods.

COMPARATIVE ANALYSIS OF CLOT-ADJUSTED AHI-8/9 AND FY-4A SOLAR RADIATION DATA FOR SOLAR PV POWER POTENTIAL ASSESSMENT IN THE PHILIPPINES

Abstract. The theoretical solar photovoltaic (PV) power (PPV) potential in the Philippines has been estimated to range from 1.87 to 3.19 kWh/kWp daily and 681.80 to 1,162.58 kWh/kWp yearly using the shortwave radiation (SWR) product derived from the Advanced Himawari Imager-8 and 9 (AHI-8/9) satellite data. However, solar radiation measurements, especially for satellite-derived data, must be accurate when used to estimate the maximum solar energy available for solar PV power production. The objective of this study is to evaluate the satellite-derived measurements of AHI-8/9 SWR and Fengyun 4A surface solar irradiance (SSI) data products in the estimation of theoretical PPV output in the Philippines. Moreover, selected actual solar PV installation sites located across the Philippines, such as in the cities of Baguio, Cebu, and Tagum, are included in the analysis PV power production data. Both SWR and SSI products were adjusted using the cloud optical thickness (CLOT) product for cloud effects and consequently, obtain the maximum solar PV power potential. In this study, SWR and SSI products were analyzed to determine which factors may have contributed to the differences in the estimated values. The in-situ PV power production data was compared to the two satellite-based solar radiation data to assess the degree of correlation between the two. Diurnal trends for representative areas in Luzon, Visayas and Mindanao show that for all sites, the SWR data agrees with the actual PV production data better than the SSI data. Results show that the range of CLOT-adjusted solar irradiance values are higher when using SSI than SWR. It was also observed that CLOT reduces the solar radiation by an average of 19.36%, 16.08% and 10.54% during the cool dry season (December to January), rainy season (June to November) and hot dry season (March to May), respectively. In future studies, the adjusted solar radiation (R') data may be used instead of the raw AHI8 SWR or FY-4A SSI to estimate the theoretical maximum solar PV power potential (PPV). Geomorphological and meteorological factors can also be considered in the estimation of effective PPV.

Optimizing solar panel maximum power point tracking and parasitic parameter extraction in partial shading with Enhanced Slime Mold optimization

Solar energy offers several environmental, economic, and energy security advantages. Parasitic parameters and shading on solar panels can reduce efficiency. This paper presents a bio-inspired Enhanced Slime Mold (ESM) algorithm search strategy to find near-optimal power points by simulating the behaviour of slime molds in a virtual environment. In a solar panel, proposed ESM provides not only for parameter extraction but also serves as Maximum Power Point Tracking (MPPT) during Partial Shading Conditions (PSC). The proposed ESM dynamic behaviour is examined under solar irradiation and various temperature conditions. The effectiveness of proposed technique has been validated by extracting parameters from conventional polycrystalline and monocrystalline modules in the form of a 5 S–5P arrangement. In the instance of MPPT operation, the proposed ESM algorithm is compared with Ant Bee Colony and Perturb& Observe (ABC-PO) to determine its efficacy. Moreover, during extraction of unknown parameters of solar cell ESM is compared with existing optimization algorithms such as Artificial Bee Swarm Optimization (ABSO), Genetic Algorithm (GA), Covariant Matrix (CM), Ant Bee Colony (ABC), and Advanced Particle Swarm Optimization (APSO). In this connection, proposed ESM algorithm is superior to the above-mentioned algorithms due to high accuracy, a smaller number of computations, and minimum computational time.

Complementary operation based sizing and scheduling strategy for hybrid hydro-PV-wind generation systems connected to long-distance transmission lines

Wind and solar power are expected to play important roles in many countries to achieve carbon neutrality; however, their inherent instabilities pose significant threats to existing power grids. Because hydropower has been recognized as a viable compensatory resource for solar and wind energy uncertainties, many studies have sought to determine optimal scheduling strategies for hydro-PV, hydro-wind, and hydro-PV-wind systems. However, few studies have simultaneously considered the sizing and scheduling of large-scale hydro-PV-wind hybrid systems connected to long-distance transmission lines. Therefore, this paper develops a mathematical metric to measure the wind and solar output complementarity and incorporates it into a multi-objective sizing and scheduling model for a hybrid hydro-PV-wind system, in which resource complementarity, profit generation, system reliability, and power curtailment trade-offs are concurrently considered. The ɛ-constraint method is employed and decision makers’ attitude parameters are introduced to transform the proposed model into its equivalent single objective form. A case study in China reveals that the maximum wind and solar power output complementarity rate can be at least 0.19 for the studied hybrid hydro-PV-wind system. The incorporation of wind enables the hybrid hydro-PV-wind system to provide 4.11% more load than a hydro-PV system. In addition, if 2% of power curtailment is permitted, 1.53% more load can be served and 14.9% higher profits can be earned. To better explore the application of hybrid systems, management recommendations are provided for system operators, the power industry, and management administrations.

Microwave wireless power transfer efficiency analysis framework for a thin film space solar power satellite

Space-based solar power systems (SSPS) envision the usage of focused microwave beams to transfer power from space to ground. To supply a large amount of power and to be able to focus a microwave beam efficiently, a large infrastructure must be assembled in orbit, thus, making the implementation of SSPS an arduous task. In the history of SSPS designs, aside from economical obstacles, technical obstacles such as large ohmic losses due to long DC lines in the structure, large rotary joints to decouple beam pointing and solar power collection, heating problems from power lines converging into a single point, and complex control mechanisms, manufacture, and assembly are present. A simpler implementation using multiple foldable thin film membrane satellite modules is proposed as a feasible design, born from the advancement of thin film electronics. Its economical feasibility is achieved through the reduced weight, large surface area, and membrane’s ability to be stowed. The thin film SSPS combines advantageous properties of past designs such as a simple structure, simple control mechanism, and a distributed power collection and transfer architecture avoiding the ohmic loss and heating problems. However, due to the thin film membrane hosting both the antenna and the solar cells, competition occurs between maximum solar power collection and high efficiency power transfer capabilities. In this paper, we propose a framework to analyze the performance of the thin film SSPS solar cell-antenna integration design in terms of the wireless power transfer efficiency and fuel consumption, from which we identified the trade-offs on designing and operating the station. We used this framework to analyze three different antenna element designs in combination with three different attitude control goals, and found out that the key aspects of the antenna design are beam width, radiation efficiency, and efficient solar cell integration.

Разработка эффективного контроллера отслеживания точки максимальной мощности для фотоэлектрической системы

A method is presented to improve the performance of off-grid photovoltaic systems using a battery charge controller equipped with solar panel maximum power point tracking (MPPT) technology. Today, the MPPT controller is an effective technology that ensures the ideal functioning of solar panels by searching and selecting the optimal power point. The purpose of the study is to develop and evaluate the performance of a battery charge controller for solar panels based on the maximum power point tracking method. The proposed method uses a perturbation and observation algorithm and tracks the maximum power point based on the current and voltage output characteristics. A DC/DC buck converter was used in the study. The model of the proposed method was implemented in the Matlab Simulink software environment, and the efficiency of the operating algorithm was assessed. In addition, based on this model, an MPPT controller was developed, which was tested in real conditions. The reliability of the proposed method was also assessed. As a result of the study, the effectiveness of the developed system was revealed through a comparative analysis with a commercial battery charge controller

Adaptive Hybrid NFS-MSOGIQ Control Technique for Improving Power Quality in Solar PV Distributed Generation System

A two-stage circuit configuration with 3-phase utility grid assisted solar power generation system is designed. In order to track the solar PV arrays maximum peak power (MPP), a DC boost converter with Composite Incremental Conductance (InC) MPP technique is employed in first stage. In Second stage, a grid-tied four-leg DC-AC converter, is employed to provide the solar power that was extracted into the utility grid and to enhancing power quality. An adaptive hybrid Multi-Second Order Generalized Integrator-Quadrature (MSOGI-Q) control algorithm, in conjunction with Neuro Fuzzy system (NFS) is proposed for controlling this DC-AC converter. MSOGI-Q reference current generation strategy is designed to mitigate current harmonics by retrieving the fundamental constituents from non-linear load. The Neuro Fuzzy system along with PID controller, by adjusting the voltage from dc link keeps consistent power in between AC and DC sides for varying isolation condition. To get the peak power out of a PV panel even in different environmental conditions, the composite incremental conductance algorithm is used. To upgrade the dynamic performance of voltage variation at Point of Common Coupling (PCC) a feedforward descriptor for Photovoltaic contributions has been implemented. In addition, a comparison between the proposed NFS-MSOGIQ control technique and the existing adaptive control technique is done to assess the effectiveness of the proposed adaptive control technique. The proposed adaptive control technique for the distributed generation system is designed and stimulated in MATLAB/SIMULINK and the simulation outcomes verify the effectiveness of the proposed control technique under various transition circumstances. A laboratory prototype model of the proposed system is developed for theoretical analysis and its results are found to be compliant with IEEE standards. Furthermore, THD values of voltage and current are found to be within standard limits.

Efisiensi Daya Sel Surya Menggunakan Konverter DC-DC Mensuplai Beban Listrik

The maximum solar cell power capacity without load parameter Pm(no-load) = 0.4 watts is used to supply an external electrical load with a resistor R in ohms. Irradiation of the solar cell surface area during the day in bright sunlight supplies an electrical load with a resistor R = 100 Ω which absorbs solar cell power with parameters Psel(R) = 0.09 watts. The power efficiency of a solar cell loaded with a resistor R is expressed as the parameter eff.sel(R) which is the result of a comparison between the power Psel(R) = 0.09 watts and the power Pm(no-load) resulting in a value of eff.sel(R) = 22.5% . Achieving solar cell to more effectively supply a resistor load of R = 100 Ω is efficiency done by adding a parallel FC-75 type dc-dc buck converter device to the solar cell output. Test results: The input power of the FC-75 dc-dc buck converter is Pi(conv.) = 0.178 watts. The resistor load R absorbs the output power Po[conv.(R)] = 0.078 watts. The power efficiency of solar cells using a dc-dc buck converter is expressed as the eff.sel(conv.) parameter. The results of the comparison between the power Po[conv.(R)] and the power Pi(conv.) reach 43.82%. The achieved power efficiency results for solar cells supplying resistor load R using a dc-dc buck converter are eff.sel(conv.) = 43.82%, which is greater than eff.sel(R) = 22.5%.

IoT based battery energy monitoring and management for electric vehicles with improved converter efficiency.

Given the recent trends in the MPPT converters in PV systems, which have been researched extensively to improve design, modified closed-loop converter technology based on SoC is presented here. This paper aims to provide detailed information on the modern-day solar Maximum Power Point Tracking (MPPT) controller and Battery Management System (BMS). Most MPPT controller examination researched in the past is suitable only for fixed-rated battery capacity, which limits the converter capability and applications. The proposed paper uses the distributed energy management control technique to dispatch multi-battery charging based on the State of Charge (SoC). The converter construction is modified here as per the prerequisite of the model. The system hardware is developed and tested using Atmega2560 low power RISC based high-performance microcontroller. The batteries' SoC level and State of Health (SoH) are calculated using embedded sensors and communication platforms through the IoT platform and Global System Monitoring (GSM) technology. The GSM and IoT technology ensure that the different batteries are monitored periodically, and any irregularities are immediately addressed through the distributed energy management control technique. This ensures a safe, reliable, and effective charging of multiple batteries with increased accuracy, thereby maximizing battery life and reducing operational costs.

Parametric Observer Controlled Motor Invariant Electronic Commutation of Photovoltaic Powered BLDCM Without Position Sensor

The development of parameter independent position sensor free control of permanent magnet brushless DC (PMBLDC) motor is one of the most challenging tasks. A motor parameter independent parametric observer based electronic commutation control is developed here without using rotor position sensor. The effect of motor parameter variation along with the change of motor, on electronic commutation control without using the rotor position sensor is considered as the disruption parameter for the developed control algorithm to mitigate the external disruption. Mitigation of control loop disturbances is done using the parametric observer based feedback control. A hybrid maximum power extraction algorithm using traverse and incremental conductance is implemented here for effective solar maximum power extraction. The implemented parametric observer motor invariant (POMI) based position sensor free control algorithm is verified using experimental process. Performance of the implemented POMI control is observed better in performance in comparison with the conventional position sensor free control as parametric variation is not considered in conventional control. The solution proves to be the best fit for any type of BLDC motor based water pumping solution irrespective of the pump configuration.

Coordinated Control and Load Shifting-Based Demand Management of a Smart Microgrid Adopting Energy Internet

High renewable energy penetration worsens systems instability. Balancing consumption energy and generation output energy reduces this instability. This paper introduces coordination control to coordinate the flow of electricity between MG buses and to stabilize the system under variable load, generation conditions. The adopted MG regulates the bidirectional DC/AC main converter using digital proportional resonant controllers in a synchronous reference frame. A maximum power point tracker-based boost DC/DC converter enables the wind turbine and solar photovoltaic to harvest maximum power. Traditional methods such as perturb and observe and incremental conductance maximum power trackers cannot solve nonlinearity and inaccurate responses. This work provides a hybrid maximum power tracker strategy to modify the responses of standard maximum power point techniques based on particle swarm optimization-trained adaptive neuro-fuzzy inference system (ANFIS-PSO) to achieve quick and maximum solar power with minimal oscillation tracking. Concerning the management system, this paper adopts a recent meta-heuristic algorithms-based DSM program to modify consumers’ electricity use by shifting the load appliances to off-peak demand periods. The adopted algorithms for DSM are sparrow search algorithm (SSA), binary orientation search algorithm (BSOA), and cockroach algorithm (CA). Finally, based on energy Internet technology, ThingSpeak cloud-based MATLAB is adopted to gather and display real-time data streams and generate graphical analyses. The simulation results reveal that the recommended coordinating control produces quick grid frequency responsiveness and zero steady-state errors. The optimal demand management program minimizes peak energy consumption from 5.2 kWh to 4.6 kWh. All DSM methods cost 439.1 $ per month, compared to 484.4 $ for the nonscheduling load profile.

Experimental results for a MW-scale fluidized particle-in-tube solar receiver in its first test campaign

To advance the development of particle-driven technologies, this study presents experimental results of a megawatt-scale solar receiver installed atop the Themis Solar Tower (France). The whole experimental loop and the solar flux distribution at the receiver aperture are described with the associated instrumentation. The solar receiver is composed of forty 4-meter long metallic tubes, and the irradiated part size is 3 m high and 2.6 m wide. The facility was operated at partial load with a maximum solar power of approximately 800 kW with a particle mass flow rate ranging from 2148 kg/h to 12456 kg/h. The tube wall temperature distribution is presented along with a particle temperature increase that reached 400 °C. The dynamic behavior of the solar loop is detailed, in particular the starting phase. The thermal efficiency of the solar receiver ranges from 35 to 63%. An assessment of the lessons learned during this experimental campaign is offered.

Adaptive Control Approach-Based Isolated Microgrid System with Alleviating Power Quality Problems

In this article, a dual third-order generalized integrator-based phase-locked loop (DTOGI-PLL) and an additional degree of freedom-based frequency locked loop (ADF-FLL) control approaches are utilized on a voltage source converter (VSC) to control an isolated microgrid system under different operating conditions. The proposed adaptive approaches provide a fast dynamic response with a precise estimation of frequency, phase angle, and fundamental load current component compared to that of the TOGI-PLL and least mean square (LMS) algorithms. An ADF-FLL allows closed-loop poles to perform more optimally, resulting in faster dynamic performance without compromising filtering capability. Moreover, the proposed adaptive approaches with VSC provide power support, load leveling, harmonic eliminations, and enhance the isolated microgrid’s overall power quality during dynamics in load and energy sources. A solar PV (SPV) array with perturb & observe (P&O) MPPT control is interfaced at the DC link of a bidirectional controller (BDC) to draw maximum solar power. Through dc-link voltage control, the BDC with energy storage system (ESS) ensures the appropriate power flow and power equilibrium between energy sources and load. The Simulation and test results show the dynamic performance of the isolated microgrid is examined under different operational scenarios.

Development and assessment of integrated hydrogen and renewable energy systems for a sustainable city

Advances in renewable energies, hydrogen, electrification, and improvements in efficiency are projected to be the main avenues for carbon emission reductions. Therefore, a combination of renewable energy sources integrated with hydrogen production is needed to meet the climate targets. The novel system combinations studied in this paper promote renewable energies, hydrogen production, and electrification to reduce and/or eliminate the dependency on fossil fuels for sustainability. The communities chosen for this study were in Canada [A], South Africa [B], the Netherlands [C] and Denmark [D]. The proposed systems consisted of wind plants (including offshore), solar photovoltaics, and biomass-based integrated systems for power generation, heat recovery, fresh water, bioethanol and hydrogen production. The hydrogen production is considered using a proton exchange membrane (PEM) electrolyzer and excess electricity generated from renewables. Sustainable fuel sources for transportation proposed in this system are hydrogen, electrical charging stations and bioethanol. All locations had good potential for renewable power generation and hydrogen production. The maximum capacities for the wind and solar farms in the selected locations are identified as 187.5 MW and 32.7 MW respectively. The actual power generated from the plants varies based on the climate conditions of different locations. At the maximum wind and solar power generated (without accounting for community electrical demand), the maximum annual potential capacities obtained for hydrogen generation are 307 tons for location [A], 232 tons for location [B], 399 tons for location [C] and 487 tons for location [D].