Photovoltaic Generation Research Articles

Maximizing hydrogen production through photovoltaic generator by using improved incremental conductance algorithm with proportional integral PI controller

Effectively storing energy for prolonged periods poses a primary challenge for renewable and innovative energy sources. This research focuses on two key objectives: first, converting photovoltaic (PV) voltage to the necessary level for electrolysis through a buck converter, and second, utilizing a maximum power point tracking (MPPT) method to optimize the solar generator's efficiency. The simulation of the solar-driven buck converter for the electrolysis load was carried out using MATLAB/Simulink, integrating an Incremental Conductance (INC) MPPT algorithm with a PI controller for system optimization. The simulation results reveal the stabilization of both the generated power from the PV system and the load voltage. Significantly, the proposed system achieves an efficiency surpassing 90% under high irradiance levels.

Solar Irradiance Prediction Method for PV Power Supply System of Mobile Sprinkler Machine Using WOA-XGBoost Model

Solar energy can mitigate the power supply shortage in remote regions for portable irrigation systems. The accurate prediction of solar irradiance is crucial for determining the power capacity of photovoltaic power generation (PVPG) systems for mobile sprinkler machines. In this study, a prediction method is proposed to estimate the solar irradiance of typical irrigation areas. The relation between meteorological parameters and solar irradiance is studied, and four different parameter combinations are formed and considered as inputs to the prediction model. Based on meteorological data provided by ten typical radiation stations uniformly distributed nationwide, an Extreme Gradient Boosting (XGBoost) model optimized using the Whale Optimization Algorithm (WOA) is developed to predict solar radiation. The prediction accuracy and stability of the proposed method are then evaluated for different input parameters through training and testing. The differences between the prediction performances of models trained based on single-station data and mixed data from multiple stations are also compared. The obtained results show that the proposed model achieves the highest prediction accuracy when the maximum temperature, minimum temperature, sunshine hours ratio, relative humidity, wind speed, and extraterrestrial radiation are used as input parameters. In the model testing, the RMSE and MAE of WOA-XGBoost are 2.142 MJ·m−2·d−1 and 1.531 MJ·m−2·d−1, respectively, while those of XGBoost are 2.298 MJ·m−2·d−1 and 1.598 MJ·m−2·d−1. The prediction effectiveness is also verified based on measured data. The WOA-XGBoost model has higher prediction accuracy than the XGBoost model. The model developed in this study can be applied to forecast solar irradiance in different regions. By inputting the meteorological parameter data specific to a given area, this model can effectively produce accurate solar irradiance predictions for that region. This study provides a foundation for the optimization of the configuration of PVPG systems for mobile sprinkler machines.

Event-Triggered Transmission of Sensor Measurements Using Twin Hybrid Filters for Renewable Energy Resource Management Systems

Recently, solar and wind power generation have gained attention as pathways to achieving carbon neutrality, and Renewable Energy Resource Management System (RERMS) technology has been developed to monitor and control small-scale, distributed renewable energy resources. In this work, we present an Event-Triggered Transmission (ETT) algorithm for RERMS, which transmits sensor measurements to the base station only when necessary. The ETT algorithm helps prevent congestion in the communication channel between RERMS and the base station, avoiding time delays or packet loss caused by the excessive transmission of sensor measurements. We design a hybrid state estimation algorithm that combines Kalman and Finite Impulse Response (FIR) filters to enhance the estimation performance, and we propose a new ETT algorithm based on this design. We evaluate the performance of the proposed algorithm through experiments that transmit actual sensor measurements from a photovoltaic power generation system to the base station, demonstrating that it outperforms existing algorithms.

Cascaded Integral Minus Tilt Integral Derivative With Filter for Frequency Stabilization of V2G/G2V Enabled Hybrid Microgrid

ABSTRACTRenewable generation plays an important part in today's power system. With the inclusion of the inverter interfaced renewable energy sources (RESs) into a microgrid, the total system inertia decreases and it leads to increased frequency deviation in presence of a disturbance. This paper proposes a cascaded Integral Minus Tilt Integral Derivative with Filter (I–TDN)‐Proportional‐Integral (PI), [(I‐TDN)‐PI] controller for frequency stabilization of a hybrid microgrid in presence of electric vehicles (EV). The microgrid model includes reheated thermal power plant with high degree of non‐linear system such as inverter based RESs like photovoltaic and wind generation systems. A diesel engine generator is incorporated for load frequency control during perturbation in the system frequency. Virtual inertia controller (VIC) with inverter based energy storage system (ESS) is commonly used to improve system inertia and frequency stability of the microgrid. In addition to the ESS, this paper proposes inclusion of the EVs in this VIC. The optimal gains of the proposed cascaded I‐TDN‐PI controller are determined using Mountain Gazelle Optimizer (MGO), a modern metaheuristic optimization algorithm. Sensitivity of the proposed controller is investigated in presence of system nonlinearities, load perturbations, time delay, system parameter variation and RES power fluctuations. The simulation results justify the robustness of the proposed control structure for frequency stabilization of the microgrid.

Load frequency and virtual inertia control for power system using fuzzy self-tuned PID controller with high penetration of renewable energy

AbstractReliance on renewable energy is increasing, and generating units are being added to the network. Since renewable power fluctuates greatly, the frequency deviation of the grid becomes a crucial problem with access to renewable power generation. The fluctuation of the renewable power output of the system puts forward a higher demand for load frequency control of the power grid to increase the penetration of renewable power in the system. PID controller has proven its effectiveness for the LFC due to its simple structure and clear concept. In this article, the virtual synchronous generator is introduced and a fuzzy self-tuned PID controller is proposed for inertia control. The proposed controller is implemented in light of the significant integration of renewable energy and virtual inertia. The efficacy of the suggested controller is evaluated against the traditional PID controller for the Egyptian Power System as a case study under various load disturbance scenarios. The control technique is employed for variable loads with photovoltaic and wind turbine generation systems. Three instances of load changes are studied and the controller design is performed based on grey wolf optimizer in each case. The overshot and integral time absolute error are considered as comparison measures. The new contribution is applied to the proposed controller for the grid and virtual inertia. In the case of many load variations imposed, the disturbances of residential and industrial loads varied from 0.05, 0.01, 0.15, and 0.02 pu. The maximum overshoot is 0.005 for the proposed controller and 0.0078 for the classic PID controller. The integral time absolute error is 0.06429 for the proposed controller and 0.11481 for the classic PID controller. The results demonstrate the efficacy of the proposed controller for inertia control with high penetration of renewable energy. The results show that the proposed fuzzy self-tuned PID controller has an overshot less than the classical PID controller by 25% and integral time absolute error by 45%. These results show that the use of the proposed fuzzy self-tune controller for the grid and inertia gave a better performance in terms of the overshot value and the integral time absolute error.

Developing a photovoltaic energy generation forecast system using neural networks

Developing a photovoltaic energy generation forecast system using neural networks

Modeling and Analysis of Micro-Turbine Integrating PV Generation Systems

Abstract. As a matter of fact, in the emerging field of distributed generation, micro turbines (MT) are seen as having significant potential in power generation especially in recent years. In reality, it can provide flexibility as well as robustness for PV generation system with great performances. Therefore, with this in mind, this paper proposes a hybrid power system integrating MT into PV generation system. To be specific, this study investigates as well as discusses the operating principles of MT. On this basis, a coordinated PQ control strategy for the hybrid PVMT power generation system is proposed. At the same time, the inertia enhancement brought by the MT is analyzed validating the potential and feasibility of the proposed strategy. According to the analysis, the current limitations as well as future improvements are discussed in the meantime. Overall, these results shed light on guiding further exploration of MT integrating PV generation systems and pave a novel path for designs.

Fuzzy logic controller of a step-up boost converter applied to a photovoltaic system

the constantly increasing global energy need and the polluting nature of fossil energies have increased the interest in the development of renewable energies. Among them, photovoltaic energy. The function of a photovoltaic (PV) system is to ensure the conversion of solar energy into electrical energy and the conditioning of this energy in order to adapt it to various needs. The performance of a PV system depends essentially on the quality of the conditioning of the energy, which allows the optimal transfer of the energy of the PV generator, and the management of the energy produced to meet energy needs by incorporating of converters. In this article, we present an intelligent approach to improve and optimize the performance of a photovoltaic system, by the fuzzy MPPT control. Our system consists of a photovoltaic panel, a Boost-type DC-DC converter con-sidered as an adaptation stage between the PV and the load. The IC technique was compared with FLC to evaluate the best and most accurate controller for MPPT. The results suggest that the use of FLC could improve the MPPT and increase the sTableility of the PV system. System modeling and simulation performed with Matlab/Simulink.

Effects of Single‐Axis and Fixed‐Tilt Photovoltaic Array Construction on the Soil Seed Bank Characteristics in Semi‐Arid Grasslands

ABSTRACTWith the rapid global development of photovoltaic power generation, research on its impact on land and ecosystems has become increasingly significant. However, its impact on soil seed bank characteristics has yet to be better assessed. In this study, monitoring plots were established in a semi‐arid grassland undergoing solar energy development. This setup allowed us, for the first time, to investigate how soil seed bank characteristics respond to the construction of two typical photovoltaic array systems: single‐axis and fixed‐tilt systems. This study demonstrated that in both single‐axis and fixed‐tilt systems, the establishment of photovoltaic arrays resulted in a significant increase in soil seed density, with seed counts rising by approximately 47.5% compared with control sites without arrays. The aggregation effect of soil seed density under the photovoltaic array primarily occurred in the 0–10 cm soil layer. The soil seed density under the single‐axis arrays was higher than that under the fixed‐tilt arrays. The construction of photovoltaic arrays resets local soil and directly changes the micro‐environment—including reductions in solar radiation, decreases in average temperature by 0.1°C, and wind speed decreases by 1.5 m/s—which negatively affected the richness and diversity of the soil seed bank, resulting in a 21.1% decrease in species richness and a 10.1% reduction in seed diversity. Furthermore, this study highlights that seed germination in semi‐arid grasslands is under pressure due to environmental changes associated with photovoltaic construction areas. Specifically, soil moisture and organic matter were the key factors affecting the vegetation restoration potential of the entire construction area. We recommend selecting the single‐axis system of photovoltaic components. This selection is crucial, which considers both energy production efficiency and supports the facilitation of future vegetation ecosystem succession. Altogether, this study provides information for future land‐use planning in photovoltaic construction areas and sustainable development of photovoltaic power generation.

Data-Driven Day-Ahead Dispatch Method for Grid-Tied Distributed Batteries Considering Conflict Between Service Interests

The rapid advancement of battery technology has drawn attention to the effective dispatch of distributed battery storage systems. Batteries offer significant benefits in flexible energy supply and grid support, but maximising their cost-effectiveness remains a challenge. A key issue is balancing conflicts between intentional network services, such as energy arbitrage to reduce the overall electricity costs, and unintentional services, like fault-induced unintentional islanding. This paper presents a novel dispatch methodology that addresses these conflicts by considering both energy arbitrage and unintentional islanding services. First, demand profiles are clustered to reduce uncertainty, and uncertainty sets for photovoltaic (PV) generation and demand are derived. The dispatch strategy is originally formulated as a robust optimal power flow problem, accounting for both economic benefits and risks from unresponsive islanding requests, alongside energy loss reduction to prevent a battery-induced artificial peak. Last, this paper updates the objective function for adapting possible long-run competition changes. The IEEE 33-bus system is utilised to validate the methodology. Case studies show that, by considering the reserve for possible islanding requests, a battery with limited capacity will start to discharge after a demand drop from the peak, leading to the profit dropping from USD 185/day (without reserving capacity) to USD 21/day. It also finds that low-resolution dynamic pricing would be more appropriate for accommodating battery systems. This finding offers valuable guidance for pricing strategies.

Research on Energy Management Technology of Photovoltaic-FESS-EV Load Microgrid System

This study focuses on the development and implementation of coordinated control and energy management strategies for a photovoltaic–flywheel energy storage system (PV-FESS)-electric vehicle (EV) load microgrid with direct current (DC). A comprehensive PV-FESS microgrid system is constructed, comprising PV power generation, a flywheel energy storage array, and electric vehicle loads. The research delves into the control strategies for each subsystem within the microgrid, investigating both steady-state operations and transitions between different states. A novel energy management strategy, centered on event-driven mode switching, is proposed to ensure the coordinated control and stable operation of the entire system. Based on the simulation results, the PV system cannot cope with the load demand power when it is increased to a maximum of 2800 W, the effectiveness of the individual control strategies, the coordinated control of the subsystems, and the overall energy management approach are confirmed. The main contribution of this research is the development of a coordinated control mechanism that integrates PV generation with FESS and EV loads, ensuring synchronized operation and enhanced stability of the microgrid. This work provides significant insights into optimizing energy distribution and minimizing losses within microgrid systems, thereby advancing the field of energy management in DC microgrids.

Operation study of the photovoltaic thermal heat pump integrated energy system under trigeneration condition and harsh conditions

In recent years, the photovoltaic thermal heat pump has gained significant attention as a near zero-carbon energy technology, due to the comprehensive ability to provide heating in winter, cooling in summer, photovoltaic power generation and domestic hot water throughout the year. However, owing to the incomplete research on the system operating characteristics in trigeneration condition and harsh conditions, the production performance and operational stability of the photovoltaic thermal heat pump were not widely recognized. In view of the above problems, the following studies were conducted in this paper: First, the integrated energy system based on the modular photovoltaic thermal heat pump was designed and constructed. Then, the system operation in severe hot and cold season was carried out and the operational stability of the system under harsh operating conditions was demonstrated sufficiently. Afterwards, the operation of the system under the continuous trigeneration operation were analyzed. During the 7-day trigeneration operation, the average hot water output was 12.12 tons and the average photovoltaic power generation was 88.5 kWh in the daytime heating operation. Besides, the average coefficient of cooling performance was 2.32 in the nighttime cooling operation and the effective cooling-supply time of the system was approximately 4 h.

Distributed photovoltaic reactive power control strategy based on improved multiobjective particle swarm algorithm

AbstractDistributed power supply access to the distribution network, although it can effectively support the band voltage, will also cause problems such as voltage overruns at the point of grid connection and large network losses, so this paper establishes a reactive power optimization model containing three objectives: network loss, voltage fluctuation rate, and static reactive power generator (SVG) installation capacity in distributed photovoltaic power generation scenarios by taking advantage of the characteristics of SVG that both absorb and send out reactive power. A multiobjective particle swarm algorithm with an adaptive grid and roulette mechanism is introduced to ensure the uniformity and diversity of the Pareto boundaries under the constraint that the output of each device does not exceed the constraints, and to obtain the optimal set of solutions capable of coping with the stochastic fluctuations of distributed power sources. When the algorithm is compared with three other algorithms, such as nondominated sorting genetic algorithm‐II, the results show that it reduces the network loss by about 25% and significantly improves the voltage fluctuation rate.

Multi‐timescale optimal scheduling of microgrids for generating new energy output scenarios based on correction error sampling intervals

AbstractMicrogrid can realize energy saving and emission reduction and multi‐energy complementation, but the fluctuation of renewable energy output and the error of day‐ahead scheduling will threaten the stability of microgrid operation. For this reason, this article proposes a microgrid multi‐timescale optimal scheduling method based on new energy output scenario generation. First, the microgrid framework of this article is introduced, and an energy cycle emission reduction model taking into account electricity‐to‐gas conversion is designed; second, a new energy prediction error model is established based on the prediction box and Gaussian hybrid model, and the scenario of wind and photovoltaic power generation is generated by correcting the sampling intervals for error sampling; and then, based on the day‐ahead scheduling plan, an intraday cooling, heating and electricity two‐layer rolling optimization model is established to correct the day‐ahead scenario. Finally, the example analysis shows that the cyclic emission reduction model can realize the recycling of resources, reduce the fuel cost and carbon emission, and the generation of scenarios for wind power can smooth out the fluctuation of new energy power, which, together with the intra‐day two‐layer rolling optimization, can reduce the day‐ahead scheduling error and improve the stability of microgrid operation.

A Design and Safety Analysis of the “Electricity-Hydrogen-Ammonia” Energy Storage System: A Case Study of Haiyang Nuclear Power Plant

With the development of modernization, traditional fossil energy reserves are decreasing, and the power industry, as one of the main energy consumption forces, has begun to pay attention to increasing the proportion of clean energy generation. With the deepening of electrification, the peak-valley difference of residential electricity consumption increases, but photovoltaic and wind power generation have fluctuations and are manifested as reverse peak regulation. Thermal power plants as the main force of peak regulation gradually reduce the market share, making nuclear power plants bear the heavy responsibility of participating in peak regulation. The traditional method of adjusting operating power by inserting and removing control rods has great safety risks and wastes resources. Therefore, this paper proposes a new energy storage system that can keep the nuclear power plant running at full power and produce hydrogen to synthesize ammonia from excess power. A comprehensive evaluation model of energy storage based on z-score data standardization and objective parameter assignment AHP (analytic hierarchy process) analysis method was established to evaluate energy storage systems according to a multi-index system. With an AP1000 daily load tracking curve as the input model, the simulation model built by Aspen Plus V14 was used to calculate the operating conditions of the system. In order to provide a construction basis for practical engineering use, Haiyang Nuclear Power Plant in Shandong Province is taken as an example. The system layout scheme is proposed according to the local environmental conditions. The accident tree analysis method is combined with ALOHA 5.4.1.2 (Areal Locations of Hazardous Atmospheres) hazardous chemical analysis software and MARPLOT 5.1.1 geographic information technology. A qualitative and quantitative assessment of risk factors and the consequences of leakage, fire, and explosion accidents caused by hydrogen and ammonia storage processes is carried out to provide guidance for accident prevention and emergency rescue. The design of an “Electric-Hydrogen-Ammonia” energy storage system proposed in this paper provides a new idea for zero-carbon energy storage for the peak shaving of nuclear power plants and has a certain role in promoting the development of clean energy.

Current Compensation for Faulted Grid-Connected PV Arrays Using a Modified Voltage-Fed Quasi-Z-Source Inverter

Large-scale photovoltaic (PV) systems are being widely deployed to meet global environmental goals and renewable energy targets. Advances in PV technology have driven investment in the electric sector. However, as the size of PV arrays grows, more obstacles and challenges emerge. The primary obstacles are the occurrence of direct current (DC) faults and shading in a large array of PV panels, where any malfunction in a single panel can have a detrimental impact on the overall output power of the entire series-connected PV string and therefore the PV array. Due to the abrupt and frequent fluctuations in power, beside the low-PV systems’ moment of inertia, various technical problems may arise at the point of common coupling (PCC) of grid-connected PV generations, such as frequency and voltage stability, power efficiency, voltage sag, harmonic distortion, and other power quality factors. The majority of the suggested solutions were deficient in several crucial transient operating features and cost feasibility; therefore, this paper introduces a novel power electronic DC–DC converter that seeks to mitigate these effects by compensating for the decrease in current on the DC side of the system. The suggested solution was derived from the dual-source voltage-fed quasi-Z-source inverter (VF-qZSI), where the PV generation power can be supported by an energy storage element. This paper also presents the system architecture and the corresponding power switching control. The feasibility of the proposed method is investigated with real field data and the PSCAD simulation platform during all possible weather conditions and array faults. The results demonstrate the feasibility and capability of the proposed scheme, which contributes in suppressing the peak of the transient power-to-time variation (dP/dt) by 72% and reducing its normalized root-mean-square error by about 38%, with an AC current total harmonic distortion (THD) of only 1.04%.

99.6% efficiency DC-DC coupling for green hydrogen production using PEM electrolyzer, photovoltaic generation and battery storage operating in an off-grid area

This paper presents a novel connection and control strategy for a hydrogen generation system using a proton exchange membrane electrolyzer powered by solar energy in an off-grid area without network backup. Given that, the proposed architecture is based on the indirect control of the Photovoltaic plant (achieved by removing a power-converter), the presented solution is more efficient and more reliable than the traditional scheme. Furthermore, with this innovation one can reach the same hydrogen production with smaller electrolyzers.The methodology includes detailed models of the photovoltaic panel and the electrolyzer, along with a control strategy that considers the degradation mechanisms of the electrolyzer to ensure reliable and prolonged operation. The results show that the proposed strategy keeps the operating power of the electrolyzer constant, even under variations in irradiance, thanks to energy storage in batteries. It is demonstrated that the proposed system offers efficiency above 99.6% during the analyzed period, with a 100% utilization rate of the electrolyzer, avoiding periods of inactivity and high current peaks. The study also includes simulations and experimental tests that confirm the feasibility and effectiveness of the presented solution, highlighting its advantages in terms of efficiency and investment costs compared to direct connections and other existing methods.

Hybrid Photovoltaic and Gravity Energy Storage Integration for Smart Homes with Grid-Connected Management

This paper introduces a dynamic Smart Home Energy Management System (SHEMS) integrating a hybrid photovoltaic (PV) and gravity energy storage (GES) system aimed at minimizing environmental impacts and household energy consumption. The novel SHEMS features a one-week dynamic forecasting model that adapts to variable electricity prices, smart appliance schedules, solar output, and energy storage states. These results demonstrate that the system not only reduces household energy usage but also cuts electricity bills significantly, supplying power autonomously for up to 8.5 hours daily. By leveraging real-time data from the Dark Sky API on cloud cover and temperature, this model accurately predicts solar radiation and PV generation, aligning it with both grid and residential demands. The forecasting accuracy was assessed using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), which improved to 12.55% and 4.91% respectively, from initial values of 22.46% for RMSE and 11.78% for MAPE. These advancements enhance grid stability and optimize energy storage during peak periods, reducing dependence on fossil fuels. The integration of innovative renewable energy technologies and sophisticated forecast modeling significantly boosts the system's efficiency, promoting the sustainable use of energy resources in line with environmental and economic goals.

Observer-based fuzzy T–S control with an estimation error guarantee for MPPT of a photovoltaic battery charger in partial shade conditions

To improve the efficiency and performance of a photovoltaic system (PV) an observer-based fuzzy controller design methodology is provided in the study. The desired controller is achieved by employing a combination of linear matrix inequalities (LMIs). The system consists of a photovoltaic generator (PVG) connected to a boost converter. A battery is linked to the boost converter to stock additional energy for further use. A fuzzy controller based on a T–S fuzzy type observer that guarantees a predefined L2 performance is suggested to achieve maximum power point tracking (MPPT) even under changing weather conditions. An optimal trajectory should be tracked to ensure maximum power operation. For this aim, a specific reference fuzzy model is proposed to create the aimed trajectories. Using this method, the system dynamics are precisely reproduced over a large range of operations. The whole T–S fuzzy methodology, suggested in this paper, aims to ensure the most efficient energy recovery to recharge a battery under partially shaded conditions, resulting in high system efficiency. The proposed method is simulated with MATLAB/SIMULINK and the simulation results, with realistic reference trajectories, are driven while taking into account climate variations. The analysis of these simulations, along with a comparison with two other commonly used approaches, led to the conclusion that the suggested strategy succeeded in reducing the tracking time, as well as eliminating the oscillation that often occurs around maximum power point (MPP).

A portable balloon integrated photovoltaic system deployed at low altitude

This paper proposed a portable balloon-integrated photovoltaic system (BIPVS) deployed at low altitude. The inflatable and deflatable design enhances the proposed system flexibility and mobility, enabling it have a wider range of application scenarios. Case studies were conducted based on cities' data of Vasteras, Vancouver, New York, Shanghai and Hong Kong to evaluate 10,000 BIPVS's annual power generation potential. Mid-to-high latitudes are not suitable for photovoltaic power generation in winter due to snow and ice coverage. Excluding the unsuitable winter months, simulation results show that the average monthly power generation of the BIPVSs amounts to 3.921 GWh, 4.238 GWh, 4.275 GWh, 3.337 GWh, and 3.379 GWh, respectively, during the effective working months within a year, which shows the superior performance of mid-to-high latitudes over their low latitudes. Over the life cycle, the BIPVSs exhibit a cumulative power generation capacity, amounting to 479.492 GWh, 592.18 GWh, 672.105 GWh, 641.155 GWh, and 708.334 GWh, respectively, and their total profits are 79.614 million USD, 37.007 million USD, 75.146 million USD, 12.946 million USD, 107.369 million USD, accompanied by the return on investment of 218.6 %, 101.6 %, 206.3 %, 35.5 %, 294.8 %, respectively. These findings illustrate the significant energy and economic advantages and potential of BIPVS.