Photovoltaic Power Generation Research Articles

Photovoltaic Power Prediction Based on Irradiation Interval Distribution and Transformer-LSTM

Accurate photovoltaic power prediction is of great significance to the stable operation of the electric power system with renewable energy as the main body. In view of the different influence mechanisms of meteorological factors on photovoltaic power generation in different irradiation intervals and that the data-driven algorithm has the problem of regression to the mean, in this article, a prediction method based on irradiation interval distribution and Transformer-long short-term memory (IID-Transformer-LSTM) is proposed. Firstly, the irradiation interval distribution is calculated based on the boxplot. Secondly, the distributed data of each irradiation interval is input into the Transformer-LSTM model for training. The self-attention mechanism of the Transformer is applied in the coding layer to focus more important information, and LSTM is applied in the decoding layer to further capture the potential change relationship of photovoltaic power generation data. Finally, sunny data, cloudy data, and rainy data are selected as test sets for case analysis. Through experimental verification, the method proposed in this article has a certain improvement in prediction accuracy compared with the traditional methods under different weather conditions. In the case of local extrema and large local fluctuations, the prediction accuracy is clearly improved.

Smart Operation Control of Power and Heat Demands in Active Distribution Grids Leveraging Energy Flexibility

Demand flexibility plays a crucial role in mitigating the intermittency of renewable power sources. This paper focuses on an active distribution grid that incorporates flexible heat and electric demands, specifically heat pumps (HPs) and electric vehicles (EVs). Additionally, it addresses photovoltaic (PV) power generation facilities and electrical batteries to enhance demand flexibility. To exploit demand flexibility from both heat and electric demand, along with the integration of PVs and batteries, Control and Communication Mechanisms (CCMs) are formulated. These CCMs integrate demand flexibility into the distribution grids to obtain economic benefits for private households and, at the same time, facilitate voltage control. Concerning EVs, the paper discusses voltage-based droop control, scheduled charging, priority charging, and up-/down-power regulation to optimize the charging and discharging operations. For heat demands, the on-off operation of the HPs integrated with phase change material (PCM) storage is optimized to unlock heat-to-power flexibility. The HP controllers aim to ensure as much self-consumption as possible and provide voltage support for the distribution grid while ensuring the thermal comfort of residents. Finally, the developed CCMs are implemented on a small and representative community of an active distribution grid with eight houses using Power Factory software and DIgSILENT simulation language (DSL). This scalable size of the active distribution network facilitates the careful study of symbiotic interaction among the flexible load, generation, and different houses thoroughly. The simulation results confirm that the integration of flexible demands into the grid using the designed CCMs results in the grid benefiting from stabilized voltage control, especially during peak demand hours.

Advancing Sustainable Energy Development: A Study of the Factors Influencing Distributed Photovoltaic Industry in Heilongjiang Province

In today’s energy industry, the development of clean energy has become a global concern. As an important clean energy technology, photovoltaic power generation (PV) is widely used in various countries and regions, helping to reduce the dependence on traditional fossil energy sources and to reduce greenhouse gas emissions. Existing studies have some shortcomings in terms of factors affecting distributed PV grid-connected capacity. First, previous studies tend to focus on the influence of a single factor on the grid-connected capacity of distributed PV, ignoring the interaction effects among multiple factors. Second, the sample scope in existing studies is relatively narrow, often limited to specific regions or countries, and lacks comprehensive regional comparative analysis. In addition, previous studies often do not fully consider the moderating effect of carbon emission intensity on the influencing factors. Based on the above deficiencies, this study aims to build a regression model by analyzing panel data of central municipalities and provinces including Heilongjiang, Shandong and Beijing over the past three years, and construct an interaction model based on the interaction term. The empirical results show that effective sunshine hours, average feed-in tariff and annual electricity consumption are the main factors affecting the grid-connected capacity of distributed PV with a positive effect. The regional heterogeneity analysis also finds that carbon emission intensity enhances the impact of each factor on distributed PV grid-connected capacity. The regulation effect analysis confirms that the average feed-in tariff and annual electricity consumption have a regulation effect. With the results of this study, we fill the knowledge gaps left by previous studies and provide a new perspective to explore the influencing factors of distributed PV. A deeper understanding of the impact of these factors on distributed PV capacity helps us to better understand and optimize the potential of PV generation. In addition, our findings provide policy makers with recommendations and guidance on how to promote distributed PV generation, thereby contributing to the sustainable development of the clean energy sector.

Design of Fishing Vessel Lighting System Based on Force-controlled Wind-Solar Complementarity

This paper focuses on the lighting system of fishing vessels, and the emergence of histograms has brought a new vitality to the development of the lighting of fishing vessels. In this paper, after summarizing the knowledge of wind and solar complementary technology, combined with the design needs to use the configuration control of each part of the control function to put forward a series of control schemes. Explains the background of the project development and its importance and introduces the key technical situation of the wind and light photoelectric complementary and the use of solar wind energy in the ship. Master the wind and solar complementary technology overview and summarized the independent wind power generation system and independent photovoltaic power generation system technology as well as the characteristics and working principle of wind and solar complementary power generation. Obtain the specific parameters of each component, including marine lighting parameters, wind turbine parameters, photovoltaic solar panel power generation parameters, in order to prepare for the simulation afterwards. Software base before simulation, simulation according to the requirements of the design, including the design of wind turbine, photovoltaic solar panels, the design of the fishing boat lighting load, and finally the results of the simulation are analyzed as well as the conclusion.

Short-term forecasting of rooftop retrofitted photovoltaic power generation using machine learning

This paper explores short-term forecasting of rooftop retrofitted photovoltaic (PV) power generation using a Neural Networks (NN) model, highlighting its importance for energy management and grid integration. The study used data from the Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) of the Faculty of Electrical & Electronics Engineering Technology (FTKEE), capturing a 570.4 kWp rooftop PV system. The data, collected at 15-min intervals from January 29 to February 4, 2024, included thirty-three input features such as ambient temperature, horizontal irradiation, AC voltages, AC currents, and Maximum Power Point Trackers (MPPTs). The target was the total active power in kilowatts. The methodology involved partitioning the data into a training set covering the first five days and a testing set for the last two days. The NN model was compared with other machine learning approaches, including Long Short-Term Memory Networks (LSTM), Gated Recurrent Units (GRU), Random Forest (RF), and k-Nearest Neighbors (k-NN). Performance metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Maximum Error, and Standard Deviation were used to evaluate the models. The results showed that the NN model outperformed all other models, achieving an RMSE of 0.7098, an MAE of 0.3629, a Maximum Error of 3.901, and a Standard Deviation of 0.7076. These findings suggest that NN effectively capture complex patterns in rooftop PV system data, contributing to enhanced reliability and efficiency in short-term solar power forecasting. The study's implications extend to improved grid management and energy efficiency, underlining the significance of advanced machine learning techniques in renewable energy forecasting.

Synergetic effect evaluation of pollution and carbon emissions in an industrial park: An environmental impact perspective

IntroductionHow to achieve synergistic control of pollution and carbon emissions has become a global concern. This article focuses on an industrial park and aims to evaluate the synergistic effect of pollution and carbon emissions from an environmental impact perspective. We propose a synergistic effect evaluation method based on life cycle assessment (LCA-SE method). The synergistic effect index (Els) indicates that the industrial park exhibits a strong synergistic effect in reducing pollution and carbon emissions. Based on our findings, we draw the following conclusions: First, carbon emissions should be given priority in emissions control. Second, photovoltaic power generation and energy conservation measures can effectively achieve synergistic efficiency in reducing pollution and carbon emissions. Third, when considering synergistic control of pollution and carbon emissions, priority should be given to pollutant reduction measures that can simultaneously reduce energy consumption during the process. This study presents a novel approach, the LCA-SE method, to assessing the synergistic impact of pollution and carbon emissions, offering valuable insights into the coordinated management of pollution and carbon emissions in China.

Interval-based solar photovoltaic energy predictions: A single-parameter approach with direct radiation focus

This study introduces a novel solar photovoltaic (PV) power generation forecasting method for residential installations, focusing on the direct radiation parameter. It has been rigorously compared against four established methods: Linear Regression (Alt1), Gradient Boosting (Alt2), Gradient Boosting with Lags (Alt3), and Long Short-Term Memory (LSTM) Network (Alt4). Alt1 utilizes a simple linear equation, while Alt2 employs ensemble learning with decision trees. Alt3 leverages past 24-h PV generation data, and Alt4 utilizes specialized recurrent neural networks to address challenges of long-term dependencies in time series forecasting. The proposed method showed better performance in 2022, with a Mean Absolute Error (MAE) of 0.1490 kW and a Coverage Probability (CP) of 91.55 %, demonstrating its reliability and consistency in forecasting. Additionally, it obtained an Average Width of Intervals (AWI) of 0.3365 kW. Furthermore, the method significantly boosted solar energy utilization in a residential case, increasing average solar panel generation by 61.33 kWh/year and reducing the average price by 0.0188 €/kWh. These results highlight its effectiveness in enhancing self-consumption and cutting energy costs, presenting a precise and user-friendly forecasting tool for the solar energy sector. Particularly advantageous for residential use, it facilitates optimized solar energy utilization, contributing to the transition towards sustainable energy practices.

Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty

The rising demands for comfort alongside energy conservation underscore the importance of intelligent air conditioning control systems. Model Predictive Control (MPC) stands out as an advanced control strategy capable of addressing these demands. However, accurate prediction of all relevant variables remains a challenge in practical scenarios, complicating MPC’s ability to devise effective control actions amid prediction inaccuracies. To counteract this issue, this paper introduces an enhanced Double-Layer Model Predictive Control (DLMPC) algorithm. This innovative approach adjusts for discrepancies between forecasted and actual values without the need for additional variables and models, thereby reducing the adverse effects of prediction errors. Additionally, we develop precise models for room temperature simulation and for calculating air conditioning (AC) load and energy consumption, grounded in empirical data from residential settings and AC performance tests. Validation of these models demonstrates their efficacy in enabling MPC to formulate efficacious control strategies. When juxtaposed with a baseline model, the DLMPC algorithm significantly improves temperature regulation accuracy by up to 15.12% and achieves a 10.50% reduction in energy consumption over the heating season.

Investigation on a novel hybrid system based on radiative sky cooling and split thermoelectric cooler driven by photovoltaic cell

This paper develops a novel cooling and heating system by integrating a novel split thermoelectric cooler (STEC) with photovoltaic power generation and radiative sky cooling. The system uses photovoltaic driven STEC modules to generate cooling and heating combined with radiative sky cooling to improve cooling capacity. By harnessing the distinctive characteristics of photovoltaic technology and radiative sky cooling, the new system is capable of operating continuously for 24 h without requiring any additional energy input. The system mathematical model is established, and the effects of STEC module number, solar illumination intensity, area ratio and ambient temperature on the performance are studied. Results demonstrate that the cooling efficiency of the system is 3.02 and the heating efficiency is 1.86 when the number of STEC modules is 3. In a year, the monthly average heat production of the system fluctuates due to changes in the solar illumination intensity, while the fluctuation of cooling capacity is small. The system cold production is larger than heat production, which is more suitable for application in the area with high demand for year-round refrigeration. The new system has good energy and economic performance with payback time being shorter than that of the photovoltaic-air conditioning system.

Research on the Economics of Distributed Photovoltaic Power Generation Integrated into the Grid

In order to accelerate the development of distributed energy, the country vigorously promotes distributed power generation technology, and the development of this technology will gradually break the traditional monopoly of electricity, and energy reform will move towards new development. Distributed photovoltaic power generation energy technology has high utilization rate and low pollution, which is a new trend in energy development. This paper studies the economic benefits brought by the integration of distributed photovoltaic power generation into the power grid for people's lives and the resulting economic benefits.

A novel economic and technical dispatch model for household photovoltaic system considering energy storage system in “Duhok” City/Iraq as a case study

Photovoltaic (PV) systems harnessing solar power to generate electricity have gained widespread adoption worldwide due to clean innovations. The geographic position of the Kurdistan region, north of Iraq, is very favourable for the photovoltaic power generation system. In Duhok City, the average daily hours of sunshine per year are 8.5 h; this is a good point, which makes installing photovoltaic systems more interesting because the performance of PV systems depends mainly on solar radiation. This study analyses the design of a PV system that supplies electricity to households in Duhok City during the period when there is no general electricity considering the economic and technical dispatch aspects. The system is to be designed to power the household for 10 h (Grid-connected PV system) since it is assumed that the general electricity supply only lasts for 10 h per day. The system will be an alternative source of electricity to the local generators. The full design of the proposed system is implemented, so that the data on the electrical load and site radiations of a specific household at the site under consideration are collected during the design stages. Further, the technician's and economic evaluations are considered as well during each step of analysis and selection for system components. The cost estimate was based on the following data: the electrical load of the house is 6239.1 Wh/day, the electricity cost is $0.005 per Wh and the total investment amount is $3475. The ultimate outcomes, when juxtaposed with the local expenses of diesel generators and PV solar systems, indicate that within 4.5 years, PV solar systems will recoup their initial costs, providing a clean and noise-free energy source thereafter.

A novel bimodal feature fusion network-based deep learning model with intelligent fusion gate mechanism for short-term photovoltaic power point-interval forecasting

Under the goals of carbon neutrality and peak carbon emissions, photovoltaic (PV) power generation is widely valued for its clean and green characteristics. However, the uncertainty and randomness of PV power pose challenges to energy management. Therefore, this study proposed a novel bimodal feature fusion network-based deep learning model with an intelligent fusion gate mechanism for short-term photovoltaic power point-interval forecasting. First, a threshold-guided iNNE-based outlier detection and repair method is designed for preprocessing PV data. Second, a bimodal feature fusion network was proposed to extract global and local features from PV power sequences, and the environmental factors-based rime optimization algorithm with growth mutation strategy and humidity perception mechanism was devised to optimize model's hyperparameters. Additionally, a photovoltaic power interval prediction model with a volatility segmentation strategy was introduced. Finally, the effectiveness of the proposed model, algorithm, and strategies was validated using measured datasets. The results demonstrated that under various weather conditions, the proposed model achieved point prediction evaluation metrics with an R2 exceeding 98 % and a prediction interval evaluation metric with a Prediction Interval Coverage Probability of 85.07 %. The obtained outcomes contribute to providing a basis for decision-making in the scientific scheduling and management of PV power systems.

Empowering Photovoltaic Panel Anti-Icing: Superhydrophobic Organic Composite Coating with In Situ Photothermal and Transparency.

Solar energy is widely used in photovoltaic power generation as a kind of clean energy. However, the liquid film, frosting, and icing on the photovoltaic module seriously limit the efficiency of photovoltaic power generation. We developed a composite coating (Y6-NanoSH) by combining an in situ photothermal and transparent Y6 organic film with a nanosuperhydrophobic material. The Y6-NanoSH coated glass exhibited excellent optical clarity both indoors and outdoors, indicating that the coating holds great promise in anti-icing applications for photovoltaic panels. The Y6-NanoSH coating absorbs very little visible light but instead absorbs in the near-infrared region, thereby emitting heat. When exposed to sunlight, the Y6-NanoSH coated photovoltaic panel raises its surface temperature, inhibiting the growth and accumulation of ice and frost on its surface. This is achieved through a combination of photothermal emission and superhydrophobic repellency, which promotes the evaporation and rolling away of water droplets. This validates our success in developing a photothermal, transparent, and superhydrophobic coating with excellent anti-icing capabilities, suitable for use on photovoltaic panels, as well as potential applications in car windscreens, transmission lines, curtain walls, and weather radomes.

Efficiency and benchmarks for photovoltaic power generation amid uncertain conditions

Photovoltaic (PV) power generation systems are highly subject to weather and site conditions, thus, the construction of PV power plant projects must consider these uncertainties. This study analyzes the monthly electricity generation of 249 utility-scale PV power plants in Japan to evaluate their electricity generation efficiency. Applying the generic data envelopment analysis, benchmark values were identified for power generation from PV power plants. Furthermore, we implemented a Monte Carlo experiment to evaluate the impact of variability in solar irradiance and temperature on power generation efficiency. For our analysis, we considered three inputs—solar irradiance, temperature, and installed capacity—and electricity generation as the output. The results showed that inter-regional gap in the efficiency score between the west and north regions is 0.03, and this can be covered by a 0.1 increase in the DC/AC ratio. Additionally, variability in weather conditions affect both the efficiency of a power plant and production possibility frontier, in turn causing the benchmark values for a generic decision-making unit to vary. Increasing the generation capacity of power plants and operating them more efficiently is essential to expanding the use of renewable energy resources.

A novel seasonal grey prediction model with time-lag and interactive effects for forecasting the photovoltaic power generation

Electricity is one of the most important energy sources for the progress of human society. Photovoltaic power generation (PPG) has great potential as a renewable energy source to support sustainable development. However, the unpredictability of photovoltaics because of the uncertain weather conditions, time lags and the complex interactions among related factors may result in instabilities in PPG, which limits its applications in the electric power system. Therefore, accurately identifying the related factors and representing the interactions are the key issues in predicting PPG. For this purpose, a novel seasonal grey prediction model with time-lag and interactive effects, denoted as SGMLI(1,n), is developed to predict the capacity of photovoltaic power generation. More specifically, a time-lag driving term, a periodic driving term, and an interaction driving term are proposed to represent the lag relationship, periodicity, and the interactive effects between two factors, respectively. Utilizing the least square method and the Whale Optimization Algorithm (WOA), the optimal solution to the nonlinear programming problem is estimated. For elaboration and verification, six grey forecasting models, two statistical techniques, and two machine learning models, are employed for comparison with the SGMLI(1,n) model. Experimental results reveal that the SGMLI(1,n) outperforms benchmark models in forecasting PPG.

Optimal Capacity and Charging Scheduling of Battery Storage through Forecasting of Photovoltaic Power Production and Electric Vehicle Charging Demand with Deep Learning Models

The transition from internal combustion engine vehicles to electric vehicles (EVs) is gaining momentum due to their significant environmental and economic benefits. This study addresses the challenges of integrating renewable energy sources, particularly solar power, into EV charging infrastructures by using deep learning models to predict photovoltaic (PV) power generation and EV charging demand. The study determines the optimal battery energy storage capacity and charging schedule based on the prediction result and actual data. A dataset of a 15 kWp rooftop PV system and simulated EV charging data are used. The results show that simple RNNs are most effective at predicting PV power due to their adept handling of simple patterns, while bidirectional LSTMs excel at predicting EV charging demand by capturing complex dynamics. The study also identifies an optimal battery storage capacity that will balance the use of the grid and surplus solar power through strategic charging scheduling, thereby improving the sustainability and efficiency of solar energy in EV charging infrastructures. This research highlights the potential for integrating renewable energy sources with advanced energy storage solutions to support the growing electric vehicle infrastructure.

Design and Performance Analysis of Photovoltaic Power Generation Light Emitting Diode Device

This research focuses on an independent photovoltaic power generation system with supercapacitor energy storage as the study subject. The model is simplified, and the photovoltaic power generation light emitting diode (LED) device is designed based on the given parameters. This system selects a Boost-type step-up converter for the supercapacitor charging circuit, capable of achieving Maximum Power Point Tracking (MPPT). The supercapacitor discharge circuit employs an LM2596 series regulator to power the rated load LED (12 V, 1 W), providing good linear regulation capability. In designing other hardware components of the device, the characteristics of a photoresistor are utilized to implement an automatic load switch circuit. An efficient single-chip integrated circuit LM2575 series is used as a regulator to convert the voltage across the supercapacitor terminals to +15 V and +5 V. Considering the need to collect the output voltage of the photovoltaic cell array and the voltage across the supercapacitor terminals, a resistor divider method is used to sample these two voltages. The sampling circuit includes a resistor divider circuit and a linear optocoupler isolation circuit. An HNC-25LTS series Hall current sensor is used for current sampling to measure AC, DC, and pulse signals under electrical isolation conditions. The supercapacitor, solar photovoltaic panel, control unit, and the main circuit for supercapacitor charging and discharging have been assembled in the experiment. Connecting the charging and discharging circuit with the photovoltaic panel and LED, the system provides power to the LED during the night. Under varying light intensity and temperature conditions, the photovoltaic output voltage waveform, PWM waveform, and Boost circuit output voltage waveform remain stable when reaching the MPPT point. The output power with added MPPT control at different times is compared. The results indicate that the designed system has effectively achieved the functionality of MPPT for solar energy.

Simulation test of 50 MW grid-connected “Photovoltaic+Energy storage” system based on pvsyst software

With the implementation of the national “double carbon” strategy, the installed capacity of new energy power generation continues to grow, and stable photovoltaic power generation solutions have received increasing attention. The PV + energy storage system with a capacity of 50 MW represents a certain typicality in terms of scale, which is neither too small to show the characteristics of the system nor too large to simulate and manage. This study builds a 50 MW “PV + energy storage” power generation system based on PVsyst software. A detailed design scheme of the system architecture and energy storage capacity is proposed, which is applied to the design and optimization of the electrochemical energy storage system of photovoltaic power station. Based on the results of PVsyst operation simulation test, the operation performance of 50 MW “PV + energy storage” power generation system is explored. The results show that the 50 MW “PV + energy storage” system can achieve 24-h stable operation even when the sunshine changes significantly or the demand peaks, maintain the balance of power supply of the grid, and save a total of 1121310.388 tons of CO2 emissions during the life cycle of the system. The various parts of the system, including the photovoltaic array, the energy storage unit and the grid interface, demonstrated efficient collaborative performance in the simulation environment of PVsyst.The analysis of power generation shows obvious seasonal changes. The unit power generation is higher from March to September, and the more the power generation is the overall annual power generation shows good consistency and predictability under the regulation of the energy storage system. Through the analysis of different operating scenarios, the key parameters that affect the system performance are further determined, such as lighting conditions, battery storage capacity, power consumption device efficiency. For lighting conditions, the total radiation amount of the incident lighting surface takes the most proportion in the interval of 380–900 W/m2 and 25-60kwh/m2/Bin. The simulation test also reveals the important role of energy storage unit in power grid demand peaking and valley filling, which has an important impact on balancing the instability of photovoltaic power generation and improving the system response ability.

IoT-based smart net energy meter with advanced billing feature for residential buildings including solar PV system

Electricity consumption is rising across all industries. Residential electricity use dominates the sector. Solar photovoltaic (PV) systems on residential roofs are increasing quickly, notably in Dhaka, Bangladesh. PV power generation is high at peak sun irradiance. Due to light loads, residential structures use less electricity. PV system surplus electricity may be transmitted to the national grid. Residential customers may sell power to the government, lowering their electricity expense. Traditional energy meters make it difficult to calculate PV system consumption by load and grid injection. This is possible with net metering. Thus, this study presents an internet of things (IoT)-based smart net energy meter for home users to provide surplus solar PV power and consume grid electricity when needed. With the government's new power tariff rate, the net bill will be calculated automatically. A dedicated mobile application is used to monitor all the activities. The billing statement will be generated automatically, and the payment of that bill will be payable using a redirect link with the same mobile application. The suggested smart net energy meter will inform SMS/mobile app users of gas, smoke, and tempering. The suggested meter's performance and efficacy were evaluated using software simulation and hardware analysis.

A concentration reflector device of a bifacial photovoltaic module

Abstract At present, the development of bifacial photovoltaic modules is rapid, but there is a lack of concentrating light devices in bifacial photovoltaic modules for power generation. This paper analyzes the changing patterns of solar altitude and azimuth angles and the influence of plane mirror reflection concentrating light on the performance of bifacial photovoltaic power generation. Using MATLAB programming, it calculates the pattern of change of the intensity of reflected light with the installation height h of the plane mirror. Then, a double plane mirror concentrating device is designed, which can do real-time dynamic tracking of the sun’s azimuth and altitude angle and realize the power generation capacity of the PV module is multiplied.