Photovoltaic Generation Research Articles

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.

Reliability of Regression Based Hybrid Machine Learning Models for the Prediction of Solar Photovoltaics Power Generation

Solar Photovoltaics (PVs) have become widespread as micro-distributed electric power generators in urban residential and commercial areas due to their affordability and minimal maintenance requirements. Despite these advantages, PV generation is intermittent, necessitating the implementation of robust predictive algorithms to capture power generation trends effectively. Accurate PV generation prediction is crucial for balancing energy demand and supply. Precise predictions help utilities use other grid resources efficiently, enhance market participation, and make informed planning decisions. Therefore, ensuring the precise performance of predictive models is imperative in the power generation industry. However, the performance of the predictive models typically fluctuates over time, necessitating a reliability assessment of their performance. This paper develops a hybrid machine learning model by integrating the support vector machine (SVM), random forest (RF), and gradient boosting machine (GBM) with multivariate adaptive regression spline (MARS) to predict the hourly PV power generation for 3-day and 7-day periods in three urban areas of North Dakota. The performance of the models has been recorded over a year, and a probability distribution model is proposed to demonstrate the hybrid algorithm’s reliability and effectiveness. Results show the proposed prediction model is more reliable for shorter prediction periods. The hybrid-MARS models significantly improve prediction reliability compared to the stand-alone MARS model. Among these models, MARS-SVM exhibits the highest reliability and consistently better accuracy than other hybrid models.

A novel linear programming-based predictive control method for building battery operations with reduced cost and enhanced computational efficiency

Battery energy storage systems can be readily integrated with buildings to enhance renewable energy self-consumptions while leveraging time-variant electricity tariffs for possible operation cost reductions. The extensive variability in building operating conditions presents significant challenges in developing universally applicable methods for optimal controls. To ensure reliable and robust controls, this study integrates predictive control with efficient linear programming to effectively fine-tune battery controls for real-time operations. An adaptive time aggregation scheme has been proposed to streamline the optimization process by accounting for unique changes in energy balances and tariffs. Comprehensive data experiments, based on measurements from 95 unique building operation scenarios, have been conducted to quantify the control performance given different optimization formulations, varying types and levels of prediction uncertainties in building energy demands and PV generations. The results validate the value of the method proposed, leading to 11.75% to 34.63% operation cost reductions on average, while reducing computation steps by 87.75% to 92.60% compared with conventional linear programming approaches. The insights obtained are useful for developing flexible building control strategies with improved computation efficiency and robustness, while providing extensible optimization frameworks for buildings with various energy patterns and storage systems.

A Comparative Study of Floating and Ground-Mounted Photovoltaic Power Generation in Indian Contexts

The escalating global demand for energy and growing environmental concerns have stimulated the development of renewable energy-based power systems. In this context, solar power has gained significant attention, notably in the form of floating photovoltaic systems. These systems, installed on water bodies, not only boost efficiency but also reduce water evaporation from reservoirs. This research explores the power generation capabilities of floating photovoltaic systems in comparison to ground-mounted photovoltaic systems, considering a 250-watt monocrystalline photovoltaic panel. This study utilizes typical meteorological year data to comprehensively analyze four distinct locations in India. By using a single-diode model, this study finds that floating photovoltaic systems provide 6-7% more power output than ground-mounted photovoltaic systems. This efficiency gain is because the floating photovoltaic panels operate at a lower temperature (4-6°C) than their ground-mounted photovoltaic counterparts, positively influencing the overall performance. Furthermore, the degradation and soiling of ground-mounted photovoltaic and floating photovoltaic systems were also compared. The financial analysis reveals that ground-mounted photovoltaic systems typically have a lower levelized cost of electricity and shorter payback periods. Even though the financial indicators of floating photovoltaic systems are not favorable compared to ground-mounted photovoltaic systems, these results show how vital floating photovoltaic technology is for achieving the United Nations’ Sustainable Development Goals and how it could be used as an efficient technique to reduce land requirements for solar photovoltaic solutions in various geographical conditions.

Joint intelligent optimizing economic dispatch and electric vehicles charging in 5G vehicular networks

In recent years, with the rapid development of 5G networks, the road traffic network composed of vehicles with different energy sources has become more and more complex, and the problems of environmental pollution and road congestion have also become increasingly serious. Electric vehicles are favored by people due to their environmental protection and energy-saving characteristics. However, improper charging dispatching will cause excess energy in charging stations, affecting the power grid and road traffic, such as energy shortages and lower traffic throughput. Therefore, how to design a reasonable charging strategy that can maximize the user’s charging satisfaction and consume the energy of the charging station as much as possible becomes a challenge. Meanwhile, this strategy should consider power economic dispatch to reduce power generation costs and polluting gas emissions. With the support of 5G’s high-bandwidth and low-latency characteristics, this paper designs an intelligent charging model which indirectly reflects the charging satisfaction through the time cost, energy consumption cost, charging cost, and the user’s range anxiety, while consuming the remaining energy of the charging station as much as possible. Due to the uncertainty of wind and photovoltaic power generation, this paper proposes a two-stage economic dispatch model to improve the accuracy of power dispatch and reduce power generation costs and carbon emissions. Due to the highly variable traffic environment and energy demand, we employ proximal policy optimization-based deep reinforcement learning algorithms to realize electric vehicle charging dispatching and charging station power dispatching. Numerical results show the efficiency of our proposed strategy for electric vehicle charging in terms of the convergence speed.

A ranking improved teaching-learning-based optimization algorithm for parameters identification of photovoltaic models

Solar energy is an important clean energy source, primarily applied for photovoltaic (PV) power generation. The precise identification of PV system parameters is critical for system control and simulation, posing a challenge due to the models' non-linearity, implicitness, and multiple optimal properties. So, a ranking improved teaching-learning-based optimization (RITLBO) is developed in this work to solve the problem of identifying the parameters of the PV model. RITLBO is a meta-heuristic algorithm based on teaching-learning-based optimization (TLBO) that simulates classroom teacher-student interaction. In RITLBO, learners are classified into inferior and superior groups based on their fitness ranking. During the teacher phase, superior learners emulate the top three agents with the highest fitness for local search, while inferior learners engage in guided mutual learning for global search, effectively utilizing computing resources. In the learner phase, superior learners receive guided information, while inferior learners engage in broader information exchange, balancing exploration and exploitation. RITLBO and fourteen algorithms are used to identify the parameters for five different PV models to confirm that the RITLBO is effective. Statistical results and analysis demonstrate that RITLBO is accurate and reliable in identifying PV model parameters. RITLBO offers promising prospects in optimizing PV system parameters through its unique strategies.

Recyclable polyurethane from castor oil based on dynamic disulfide bonds and multiple hydrogen bonds as adhesive and photothermal conversion materials

As an important polymer material, polyurethane brings convenience to daily life but also causes environmental problems, and the manufacturing of bio-based repairable, re-processable and sturdy materials can effectively reduce environmental pressure. Herein a low temperature recyclable polyurethane (PU) was developed with castor oil (CO) by combining isophorone diisocyanate (IPDI), dynamic disulfide bonds and hydrogen bonds. The castor oil-based PU showed impressive tensile strength (16.1MPa) remarkable elongation at break (1055.8%), and high bonding power (up to ~6MPa) with bonding wood chips. The dynamic disulfide bonds and hydrogen bonds imparted the bio-based PU with outstanding elastic recovery, impressive self-healing capability (up to ~90%), short relaxation time (5~6min at 180°C), favorable shape memory behavior, and multiple recyclability. By mixing different proportions of carbon nanotubes (CNTs), recyclable and stretchable conductive composites are realized. In addition, an integrated system of high-efficiency bio-based solar photovoltaic generator is demonstrated for simulating the ambient sunlight-heat-electricity conversion, which provides some guidance for the efficient use of solar energy.

Data-driven scheduling of a grid-connected university campus battery energy storage system considering variable weather and energy pricing

BESS can provide many crucial grid services including voltage and frequency support, however, capital costs can hinder widespread installation. Thus this work develops a data-driven MILP approach for BESS dispatch to enhance consumer cost recovery through energy arbitrage and mitigate the barrier of high capital costs. The proposed algorithm uses location-relevant weather data, to forecast PV generation and load consumption, as well as market data to create a BESS dispatch schedule. The proposed BESS algorithm is validated on a Raspberry Pi 4b microcontroller using real-time HIL simulation for 72 h on a real-world-based university campus microgrid consisting of a 9MWh/3MW BESS and 1.4MW DC/1.1MW AC PV solar canopy. Economic analyses indicate the proposed algorithms ability to decrease system energy costs when using LMP pricing, translating in a 7.27% decrease in LCOE compared to a 3.12% to 3.20% decrease when using an RB scheduling method. Similarly, LCOE is reduced by 5.32% when using TOU energy pricing. Additionally, a sensitively analysis of PV and load forecasting error with error biases ranging from −10% to 10% indicate a resulting increase in LCOE of 0% to 0.674%. These key findings indicate increased economic viability when implementing the proposed dispatch approach.

Effects of Electric Vehicles on Energy Sharing for Optimal Sizing of Solar PV and Battery Energy Storage

Energy sharing for homes with electric vehicles (EVs) enhances sustainability by optimizing energy usage, reducing peak demand, and integrating renewable energy sources, thereby lowering costs and improving energy resilience. This study investigates the effects of EVs on optimal sizing problem of solar photovoltaic (SPV) and battery energy storage system (BESS) for grid-tied homes which participate in energy sharing schemes. In this paper, it is assumed that the energy is shared between two homes: home-1 as the prosumer which has an EV and intends to buy SPV and BESS, and house-2 which is a consumer. The optimization problem is formulated to achieve the minimum cost of electricity (COE) for home-1 and to reduce the COE for home-2 while taking consideration of the design constraints over the project lifespan. A rule-based energy management system is developed for different sets of configurations to compare the economic and operational results. The optimization is done by incorporating realistic annual data of the irradiance, temperature, load, and uncertainties of EV. The developed optimization technique is general in nature and can be used for any grid tied homes willing to share the electricity. Sensitivity analyses on costs of SPV-BESS, home energy demand, and grid export constraints are provided. Uncertainty analyses investigates the price of energy sharing and solar PV generation. The impact of various EV models with their respective battery capacity is also analyzed. The results show that the proposed energy-sharing methodology reduces the COE for prosumer and consumer by 1.2 ¢/kWh and 3.6 ¢/kWh, respectively.

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.