Solar Power Research Articles

A Convolutional Neural Network–Long Short-Term Memory–Attention Solar Photovoltaic Power Prediction–Correction Model Based on the Division of Twenty-Four Solar Terms

The prevalence of extreme weather events gives rise to a significant degree of prediction bias in the forecasting of photovoltaic (PV) power. In order to enhance the precision of forecasting outcomes, this study examines the interrelationships between China’s 24 conventional solar terms and extreme meteorological events. Additionally, it proposes a methodology for estimating the short-term generation of PV power based on the division of solar term time series. Firstly, given that the meteorological data from the same festival is more representative of the climate state at the current prediction moment, the sample data are grouped according to the 24 festival time nodes. Secondly, a convolutional neural network–long short-term memory (CNN-LSTM) PV power prediction model based on an Attention mechanism is proposed. This model extracts temporal change information from nonlinear sample data through LSTM, and a CNN link is added at the front end of LSTM to address the issue of LSTM being unable to obtain the spatial linkage of multiple features. Additionally, an Attention mechanism is incorporated at the back end of the CNN to obtain the feature information of crucial time steps, further reducing the multi-step prediction error. Concurrently, a PV power error prediction model is constructed to rectify the outcomes of the aforementioned prediction model. The examination of the measured data from PV power stations and the comparison and analysis with other prediction models demonstrate that the model presented in this paper can effectively enhance the accuracy of PV power predictions.

Profit enhancement and imbalance cost reduction with solar PV-fuel cell-EV hybrid system in a wind-integrated day-ahead power market

Abstract In a wind-integrated deregulated network, the wind farm must intimate the power-generating capacity bids to the market controller at least one day before it begins operations. The wind farm's bid submissions are based on the estimated wind speed (EWS); nevertheless, slight differences between the real wind speed (RWS) and the EWS will result in penalties or incentives imposed by the Independent System Operator (ISO). This occurrence is known as the power market imbalance cost, and it has a direct influence on the system's profitability. To mitigate this effect, solar PV and fuel cell storage technologies are used with the wind farm to enhance system profit by offsetting the negative effects of the imbalance cost. Here, solar PV and fuel cells are used to function in the required time i.e. operate in the charging mode when the RWS is greater than the EWS and in discharging mode when the EWS is greater than the RWS to balance the power supply in the grid as to fulfill the power bidding conditions. Furthermore, the study focuses on minimizing potential system risks, which have been assessed using several risk assessment tools such as Value-at-Risk (VaR) and Cumulative Value-at-Risk (CVaR). The work was conducted using an IEEE 14 bus test system. Initially, the solar PV-fuel cell system supplies power to meet local needs, and the remaining energy is sent to the grid to maximize the system's profit. Electric vehicles (EVs) have also been incorporated to maximize the system economy in more quantities and to reduce the system risk further as compared to the solar PV-fuel cell operation. Three different optimization methods, i.e. AGTO (Artificial Gorilla Troops Optimizer Algorithm), ABC (Artificial Bee Colony Algorithm), and SQP (Sequential Quadratic Programming), were used in a comparative analysis to assess the effectiveness of the proposed approach.

Evidence for Magnetically‐Driven Accretion in the Distal Solar System

AbstractPaleomagnetic measurements of meteorites indicate that magnetic fields existed in the inner solar nebula capable of driving accretion at rates similar to those observed for young stellar objects with protoplanetary disks. However, the field strength in the solar system beyond ∼7 astronomical units (AU) and its role in accretion remain poorly constrained. Returned samples from asteroid (162173) Ryugu offer the possibility of determining the nebular field intensity in this distal region. Here, we report paleomagnetic studies of three Ryugu particles which reveal that alteration occurred in the presence of a null or relatively weak (<15.8 μT) field within 3 million years (Ma) after solar system formation. This resolves previously contrasting reports that Ryugu's parent body experienced alteration in the presence of a strong (>80 μT) magnetic field and weak or null field (<3 μT). In addition, we re‐examine previous paleomagnetic and Mn‐Cr chronometry studies of three other distally‐sourced meteorites, Tagish Lake, Tarda, and Wisconsin Range 91600, which measured paleointensities of <0.9, <1.7 and 5.1 ± 4.5 μT respectively. While it was previously unclear whether these records were acquired while the nebula was present, our re‐analysis suggests that their records are sufficiently old (i.e., <3.5 Ma after solar system formation) to be nebular in origin. Collectively, these data demonstrate that the distal solar system nebular field, while faint, was likely still strong enough to drive accretion at rates like those observed in the inner solar system.

Structural Decomposition of the Passivity-Based Control System of Wind–Solar Power Generating and Hybrid Battery-Supercapacitor Energy Storage Complex

Wind–solar power generating and hybrid battery-supercapacitor energy storage complex is used for autonomous power supply of consumers in remote areas. This work uses passivity-based control (PBC) for this complex in accordance with the accepted energy management strategy (EMS). Structural and parametric synthesis of the overall PBC system was carried out, which was accompanied by a significant amount of research. In order to simplify this synthesis, a structural decomposition of the overall dynamic system of the object presented in the form of a port-Hamiltonian system, which was described by a system of differential equations of the seventh order, into three subsystems was applied. These subsystems are a wind turbine, a PV plant, and a hybrid battery-supercapacitor system. For each of the subsystems, it is quite simple to synthesize the control influence formers according to the interconnections and damping assignment (IDA) method of PBC, which locally performs the tasks set by the EMS. The results obtained by computer simulation of the overall and decomposed systems demonstrate the effectiveness of this approach in simplifying synthesis and debugging procedures of complex multi-physical systems.

Simulation and Optimization of a Hybrid Photovoltaic/Li-Ion Battery System

The coupling of solar cells and Li-ion batteries is an efficient method of energy storage, but solar power suffers from the disadvantages of randomness, intermittency and fluctuation, which cause the low conversion efficiency from solar energy into electric energy. In this paper, a circuit model for the coupling system with PV cells and a charge controller for a Li-ion battery is presented in the MATLAB/Simulink environment. A new three-stage charging strategy is proposed to explore the changing performance of the Li-ion battery, comprising constant-current charging, maximum power point tracker (MPPT) charging and constant-voltage charging stages, among which the MPPT charging stage can achieve the fastest maximum power point (MPP) capture and, therefore, improve battery charging efficiency. Furthermore, the charge controller can improve the lifetime of the battery through the constant-current and constant-voltage charging scheme. The simulation results indicate that the three-stage charging strategy can achieve an improvement in the maximum power tracking efficiency of 99.9%, and the average charge controller efficiency can reach 96.25%, which is higher than that of commercial chargers. This work efficiently matches PV cells and Li-ion batteries to enhance solar energy storages, and provides a new optimization idea for hybrid PV/Li-ion systems.

Renewable Solar Energy Facilities in South America—The Road to a Low-Carbon Sustainable Energy Matrix: A Systematic Review

South America is a place on the planet that stands out with enormous potential linked to renewable energies. Countries in this region have developed private investment projects to carry out an energy transition from fossil energies to clean energies and contribute to climate change mitigation. The sun resource is one of the more abundant sources of renewable energies that stands out in South America, especially in the Atacama Desert. In this context, South American countries are developing sustainable actions/strategies linked to implementing solar photovoltaic (PV) and concentrated solar power (CSP) facilities and achieving carbon neutrality for the year 2050. As a result, this systematic review presents the progress, new trends, and the road to a sustainable paradigm with disruptive innovations like artificial intelligence, robots, and unmanned aerial vehicles (UAVs) for solar energy facilities in the region. According to the findings, solar energy infrastructure was applied in South America during the global climate change crisis era. Different levels of implementation in solar photovoltaic (PV) facilities have been reached in each country, with the region being a worldwide research and development (R&D) hotspot. Also, high potential exists for concentrated solar power (CSP) facilities considering the technology evolution, and for the implementation of the hybridization of solar photovoltaic (PV) facilities with onshore wind farm infrastructures, decreasing the capital/operation costs of the projects. Finally, synergy between solar energy infrastructures with emerging technologies linked with low-carbon economies like battery energy storage systems (BESSs) and the use of floating solar PV plants looks like a promising sustainable solution.

Trends and opportunities in renewable energy investment in Saudi Arabia: Insights for achieving vision 2030 and enhancing environmental sustainability

Renewable energy has emerged as a critical alternative to traditional energy sources due to growing environmental concerns and the need for sustainable energy development. Saudi Arabia, driven by its Vision 2030, has strongly committed to shifting from fossil fuels to renewable energy sources such as solar, wind, and hydrogen. Despite significant progress, existing investment methods often focus narrowly on financial returns and do not adequately address the risks and uncertainties associated with renewable energy projects, such as policy changes, technological challenges, and fluctuating energy prices. This paper aims to highlight and compare the trends in renewable energy investments and incorporate a risk analysis matrix to address the economic, environmental, and social aspects of Saudi Arabia’s environment. The details used in the study are obtained from Kaggle, while the adopted analysis technique involves multi-criteria decision-making (MCDM). It provides a fresh qualitative approach that allows an all-sided assessment of the investment prospects by focusing on appropriate weight for different factors. The analysis results show that using the proposed framework can help effectively prioritize investment opportunities identifying major trends and risks in developing renewable energy sources in Saudi Arabia. Current investment trends have been reviewed systematically; experts have been interviewed; MCDM techniques have been employed, as the methodology effectiveness improves the comprehension of investment tendencies and contributes to strategic management in the renewable energy market stakeholder’s activity. The MCDM analysis results show that the solar energy system has the highest investment index in the Saudi Arabian cities; the first place belongs to Abha with an investment index of 0.93, the second place belongs to Jeddah with an investment index of 0.85, and the third place belongs to Dammam with an investment index of 0.75. From these outcomes, we can easily conclude that Abha is the best place to place a solar energy project, probably because of environmental and legal factors. The analysis underscores geographic factors in deploying renewable energy, directing investors and policymakers to cities with favourable solar power generation about sustainability and economic objectives. The results are expected to enhance investment awareness among local and international stakeholders, support the strategic goals of Vision 2030, and contribute to a more sustainable and diversified energy future for Saudi Arabia. This study provides a strategic framework for optimizing renewable energy investments, promoting sustainable development, and achieving long-term economic growth. Data AvailabilityData sharing is not applicable to this article as no data sets were generated during the current study.

Working fluid selection, exergy, energy and exergoeconomic assessment of a novel combined Brayton cycle and regenerative-recuperative organic Rankine cycle for concentrated solar power application

Working fluid selection, exergy, energy and exergoeconomic assessment of a novel combined Brayton cycle and regenerative-recuperative organic Rankine cycle for concentrated solar power application

Enhancing the performance of solar-powered EV charging stations using the TOSSI-based CTF technique

In this paper, the design and analysis of a novel solar-powered EV-charging system employing a third-order sinusoidal signal integrator (TOSSI) based-CTF (character of triangular function) is proposed. The TOSSI-based CTF is used to extract fundamental active components by eliminating harmonic distortions from the load currents. This control structure has the unique capability of active current separation, employing simple mathematical operations. Moreover, the response is further refined with the help of optimized gain parameters. The designed system is capable of handling various power quality issues, including harmonic mitigation, current balancing, and power factor improvement. The EV battery is primarily charged by solar-PV power employing a bidirectional DC-DC converter. Alternatively, the EV battery may be charged by the grid supply during the unavailability of sunlight by taking the power quality issues into due consideration. The suggested control topology is used to enhance the dynamic operation of solar-powered EV charging stations experiencing solar power intermittency and variation of load. Using MATLAB, the efficacy of the proposed control topology is tested for different operating scenarios. The suggested control topology is also verified and validated using prototype hardware developed in the laboratory, where the suggested controller has proven its utility over and above existing state-of-the-art controllers in the domain.

An Approach to Multivariable Regression Analysis of a Rooftop PV System in Households’ Sector in Tirana. A Novel Approach using Energy Modeling Tool

Solar power prediction plays an essential role in functioning, mapping, and obtaining energy and climate goals in 2030 and beyond and contributing to real-time balancing of the power system. On the other side, electricity consumption is influenced by Heating Degree Days (HDD), Cooling Degree Days (CDD), average monthly temperature, energy management, human behaviour, architecture, orientation, and many other factors. The simulations are performed based on three consecutive energy consumption data for a typical dwelling in Tirana city. Once the base case scenario is designed, the model is validated based on the monthly electricity bills. In the base case scenario are included some energy efficiency measures (EEM) afterward the optimization of the supply side using PV and Solar Water Heating is part of our simulation, too. The focus of our work is to design a correlation between the relationship between consumption and generation of electricity given as a dependent variable (Y), which is regressed with weather parameters given as independent variables (Xi) under Durbin Watson statistics. The output of the study can help designers to compile a reliable power system, better utilization of energy resources, and forecasting accuracy analysis from both sides of the energy system (demand and supply side). The tested household and EEM applied in the proposed scenario may lead to an electricity reduction level of 8655 kWh per year and 76.6 % of solar fraction is used to meet the hot water demand.

Improving quality of electricity generated by grid-tied inverters in solar power plants in low generated power mode using frequency adaptive PWM

Abstract The article presents the results of a study of the dependence of the quality indicators of electricity generated by solar power plants in three-phase electrical networks on the level of generated power. In the course of the study, it was established that when the generated power is reduced, the grid voltage inverters that are part of the solar power plants cause an increase in the share of higher harmonic currents to the electric grid. An analysis of international standards regarding the quality requirements of the generated current was performed, which showed that when the level of generated power decreases, the quality of electric energy decreases and may not meet the requirements of the standard. The algorithm of frequency-adaptive pulse-width modulation is proposed, which allows to improve the quality indicators of the electricity generated by the grid inverter of the voltage of the solar power plant to the electrical grid.

Thermal stress in externally irradiated tubes of solar central receivers with a gas–particle fluidized dense suspension

One of the objectives of the new generation of concentrating solar power plants is to increase the actual temperature limit (565 °C) up to or near 1000 °C. An alternative heat transfer fluid that could help to reach this objective is the use of a fluidized dense gas–particle suspension ascending through a vertical tube. Since the predominant cause of rupture in solar receivers is the creep-fatigue damage process, which is mostly influenced by the maximum temperatures and stress experienced along the tube, the main objective of this work is to numerically study the thermomechanical behavior of a non-uniform externally irradiated tube where particles are fluidized and moves upwards. The heat transfer problem in the two media present (i.e. dense suspension of particles and tube wall) was solved separately: a Computational Particles Fluid Dynamic model solves the heat transfer in the particles, whereas a three dimensional Finite Volume model simulates the heat conduction through the tube wall to obtain the temperature profile, which serves as input to calculate the thermal stress of the tube with an analytical method. Higher thermal stresses were obtained for an absorbed power of 250 kW/m2 (σVM,max=219MPa) compared to that for an absorbed power of 500 kW/m2 (σVM,max=72MPa) due to the lower temperature difference between the front and rear sides of the tube caused by the more pronounced increase of the radiative losses in the front side of the tube compared to that at the rear side. The thermomechanical behavior of a particle receiver was compared to that of a molten salt receiver. For an incident solar flux of 500 kW/m2, the particle receiver reduced the maximum thermal stress by 73% compared to the molten salt receiver. However, the tube’s maximum temperature exceeded the working limit for stainless steel tubes. In order for the receiver to survive heat fluxes characteristic of solar power plants, research into technological solutions that allow to improve the effectiveness of the heat transfer rate from the tube to the particle flow is necessary.

DOWN DRAFT GASIFIER TO PRODUCE SYNGAS BY BIO WASTE

Biowaste is a valuable resource for sustainable energy production. Through combustion, it generates thermal energy, while gasification a thermo chemical process which transforms solid fuel into gaseous fuel. This process, specifically through a downdraft gasifier, converts biowaste into a combustible gas known as producer gas. Utilizing biowaste as a fuel source plays a crucial role in reducing dependency on fossil fuels, providing a renewable, low-carbon energy alternative. Combining biowaste with other renewable sources like wind and solar power is an effective solution for small-scale, decentralized power generation. This approach not only meets local electricity needs but also supplies heat for rural development. Optimizing operating conditions and methodologies for maximum syngas production in biomass gasification is essential. The gasification system designed here includes a downdraft biomass thermo chemical conversion gasifier, a gas transport line with tar removal, and a fully enclosed combustion chamber—together, forming a robust setup for efficient, clean energy production. KEY WORD: Downdraft gasifier, biowaste, gasification, pyrolysis, syngas

Two‐stage stochastic energy and reserve market clearing model considering real‐time offering strategy of renewable energy

AbstractWith the introduction of decarbonization policies worldwide, renewable energy, especially wind and solar power, is gradually taking a significant share in the power system. The two‐stage stochastic optimization methods are generally adopted to address the uncertainty issues and the two‐stage stochastic market clearing models are established with the participation of renewable energy. However, most existing two‐stage stochastic market clearing models usually intuitively presume the offering quantities of renewable energy are equal to their forecast values, neglecting the potential impacts of the strategic offerings on the market results and thus affecting the secure operation of power systems. To fill the gaps, this paper incorporates the strategic offering models of renewable energy into the two‐stage stochastic energy and reserve market clearing model. Within the two‐stage framework, the first day‐ahead stage serves for energy and reserve market clearing, while the second real‐time stage considers the adjustments on offering quantities of renewable energy according to the fluctuations of renewable generations. The two‐stage stochastic market clearing model is formulated as a mixed‐integer non‐linear programming problem, and an iterative algorithm is devised to obtain market clearing results after proper linearizations. The effectiveness of the proposed method is validated using the extended IEEE‐118 test system.

Harnessing Deep Learning for Enhanced MPPT in Solar PV Systems: An LSTM Approach Using Real-World Data

Maximum Power Point Tracking (MPPT) is essential for maximizing the efficiency of solar photovoltaic (PV) systems. While numerous MPPT methods exist, practical implementations often lean towards conventional techniques due to their simplicity. However, these traditional methods can struggle with rapid fluctuations in solar irradiance and temperature. This paper introduces a novel deep learning-based MPPT algorithm that leverages a Long Short-Term Memory (LSTM) deep neural network (DNN) to effectively track maximum power from solar PV panels, utilizing real-world data. The simulations of three algorithms—Perturb and Observe (P&O), Artificial Neural Network (ANN), and the proposed LSTM-based MPPT—were conducted using MATLAB (2021b) and RT_LAB (24.3.3) with an OPAL-RT simulator for real-time analysis. The data used for this study were sourced from NASA/POWER’s Native Resolution Daily Data of solar irradiation and temperature specific to Imphal, Manipur, India. The obtained results demonstrate that the LSTM-based MPPT system achieves a superior power tracking accuracy under changing solar conditions, producing an average output of 74 W. In comparison, the ANN and P&O methods yield average outputs of 57 W and 62 W, respectively. This significant improvement, i.e., 20–30%, underscores the effectiveness of the LSTM technique in enhancing the power output of solar PV systems. By incorporating real-world data, valuable insights into solar power generation specific to the selected location are provided. Furthermore, the outputs of the model were verified through real-time simulations using the OPAL-RT simulator OP4510, showcasing the practical applicability of this approach in real-world scenarios.

Nonlinear Control of Photovoltaic (PV) Solar-Powered Centrifugal Pump for Irrigation System: A Case Study of Fadak Farm in Karbala

Abstract In both the agricultural and industrial sectors, pumping water is essential. Due to its arid climate, our Saharan area has a substantial solar energy resource, making constructing a PV solar irrigation system possible. Given solar resources' unpredictable and intermittent nature, it is imperative to set up a system that permits optimal usage. This study aims to design and simulate a solar pumping system's effective nonlinear direct torque command. A solar generator provides an asynchronous three-phase machine coupled to a centrifugal pump as part of the system. MPPT monitors the boost converter via duty cycle. The solar generator's functioning at full power will be ensured. The first uses PSO in the MPPT system to supply the motor pump with three phases of power. The second one employs direct torque command (DTC) to regulate the operation of a centrifugal pump coupled with an induction machine. More advancements will be made to the DTC scenario. The proposed mathematical model of the solar irrigation system was simulated using a MATLAB simulation environment, adopting accurate data provided by Fadak Farm. The maximum power extracted from PV tends to peak at 280 Wp with the assistance of particle swarm optimization that energizes the use of the centrifugal pump for the proposed irrigation system. It is evident from numerical simulation results based on accurate input data that the power extracted from PV solar is positively affected by solar radiation and surface temperature variations. It is clear from simulation results that the speed of three–phase I.M motor converges to 22.5 rad/sec, and water flow pumping by centrifugal is decreased and increased, converging to 7.5 *10-8 m/s with the variation of solar irradiance.

Effect of Solar Irradiation Inter-Annual Variability on PV and CSP Power Plants Production Capacity: Portugal Case-Study

The sizing of solar energy power plants is usually made using typical meteorological years, which disregards the inter-annual variability of the solar resource. Nevertheless, such variability is crucial for the bankability of these projects because it impacts on the production goals set at the time of the supply agreement. For that reason, this study aims to fill the gap in the existing literature and analyse the impact that solar resource variability has on solar power plant production as applied to the case of Portugal (southern Europe). To that end, 17 years (2003–2019) of meteorological data from a network of 90 ground stations hosted by the Portuguese Meteorological Service is examined. Annual capacity factor regarding photovoltaic (PV) and concentrating solar power (CSP) plants is computed using the System Advisor Model, used here for solar power performance simulations. In terms of results, while a long-term trend for increase in annual irradiation is found for Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), 0.4148 and 3.2711 kWh/m2/year, respectively, consistent with a solar brightening period, no corresponding trend is found for PV or CSP production. The latter is attributed to the long-term upward trend of 0.0231 °C/year in annual average ambient temperature, which contributes to PV and CSP efficiency reduction. Spatial analysis of inter-annual relative variability for GHI and DNI shows a reduction in variability from the north to the south of the country, as well as for the respective power plant productions. Particularly, for PV, inter-annual variability ranges between 2.45% and 12.07% in Faro and Santarém, respectively, while higher values are generally found for CSP, 3.71% in Faro and 16.04% in São Pedro de Moel. These results are a contribution to future instalments of PV and CSP systems in southern Portugal, a region with very favourable conditions for solar energy harvesting, due to the combination of high production capacity and low inter-annual variability.

Assessment of Insulation Coordination and Overvoltage for Utility Girds Integrated with Solar Farms

Due to the economic and environmental concerns associated with fossil fuels, many government and private organizations are progressively shifting towards the integration of solar farms with Utility Grids. However, these systems are facing insulation failure issues due to internal and external transient overvoltage’s, in which their shape, magnitude, and duration are unpredictable, and consequently, the insulation stress also becomes unpredictable. To ensure the safety and integrity of the system against any transient overvoltage event, it is important to carry out an insulation coordination analysis. The primary goal of this research work is to achieve this optimization in an economically viable manner, ensuring both operational stability and cost-effectiveness in the design of electrical equipment like surge Arresters. The research work presented in the literature does not fully evaluate all International Electrotechnical Commission (IEC) overvoltage classes as specified in the insulation coordination standards for Utility Grids integrated with solar farms. Therefore, this research paper investigates the impact of various transient and switching overvoltage conditions, as defined in the IEC 60071.4 Insulation Coordination Standard at the Solar and Utility Grid Electrical power system using PSCAD 4.6/EMTP Software. Five distinct simulation scenarios were developed to assess the systems’ resilience against insulation stress events. The proposed system was also examined with and without the application of a lightning surge arrester.

Capacity Optimization of Wind–Solar–Storage Multi-Power Microgrid Based on Two-Layer Model and an Improved Snake Optimization Algorithm

A two-layer optimization model and an improved snake optimization algorithm (ISOA) are proposed to solve the capacity optimization problem of wind–solar–storage multi-power microgrids in the whole life cycle. In the upper optimization model, the wind–solar–storage capacity optimization model is established. It takes wind–solar power supply and storage capacity as decision variables and the construction cost of the whole life cycle as the objective function. At the lower level, the optimal scheduling model is established, considering the output characteristics of various types of power supplies and energy storage, microgrid sales, and purchases of power as constraints. At the same time, the model considers constraints, such as the power balance, the operating state of the energy storage system, the power sales and purchases, and the network fluctuations, to ensure the system operates efficiently. Taking a microgrid in South China as an application scenario, the model is solved and the optimal capacity allocation scheme of the microgrid is obtained. Meanwhile, the demand response mechanism and the influence of planning years are introduced to further optimize the configuration scheme, and the impact of different rigid–flexible load ratios and various planning horizons on microgrid capacity optimization is analyzed, respectively, by a numerical example. The comparison shows that the ISOA has better optimization performance in solving the proposed two-layer model.

Simultaneous realization of efficient solar evaporation and electricity collection through dual regulation of chemical composition and pore channels

The solar-driven interfacial evaporation is a recently developed zero-energy technology for seawater desalination. However, the low evaporation rate poses a significant obstacle to its industrial application, and the collection of streaming potential formed during the evaporation process remains challenging. In this paper, we constructed a solar evaporator capable of simultaneously achieving efficient solar evaporation and electricity collection. Specifically, a double network structure hydrogel comprising polyvinyl alcohol (PVA) and TEMPO-oxidized cellulose nanoparticles nanofibers (TOCNFs) was fabricated using ice templating, with the addition of sodium lignosulfonate (SLS) to further modulate water distribution within the network. The incorporation of carbon nanotubes (CNTs) enabled the achievement of a PVA/TOCNFs/SLS (PTFS) gel-based solar evaporator. The strong electronegativity carried by the TOCNFs enhances the electrostatic repulsion in the PVA-TOCNFs network, thus improving the mechanical properties of PTFS evaporator. The introduction of SLS, which contains abundant sulfonate groups, provides certain antibacterial properties to PTFS evaporator. Due to the presence of numerous hydrophilic groups, both TOCNFs and SLS can form weak hydrogen bonds with water. Through the synergistic effect of TOCNFs and SLS, the latent heat of water in PTFS evaporator is reduced to 924 kJ kg−1, enabling an evaporation rate of 3.70 kg m−2h−1 under 1 sun irradiation. Owing to the smaller pore size relative to the Debye radius and the presence of electronegative functional groups within the pores of PTFS evaporators, an overlapping bilayer is formed when water flows through, resulting in an open-circuit voltage increase of up to 270 mV. The integration of high-efficiency solar evaporation with streaming potential collection presents a novel avenue for enhancing the utilization of solar energy.