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Digital Object Identifier 10.1109/ TSM. 2024.3442019Authorized licensed use limited to the terms of the applicable license agreement with IEEE. Restrictions apply. Authorized licensed use limited to the terms of the applicable license agreement with IEEE. Restrictions apply. [Extracted from the article]

The present work reports on the handwriting of electronic circuits on paper based on the deposition of an inorganic oxide semiconductor, exploiting the pen‐on‐paper (PoP) approach. The method relies on the use of a parallel metal plate pen, well known from calligraphy applications, which permits controlled deposition of a continuous zinc oxide (ZnO) dual‐phase layer on conventional printing paper. The hand‐drawn ZnO film exhibits a continuous and uniform surface with a thickness of roughly 1.5 µm, allowing the application as active layer for UV sensors and field effect transistors on paper. Photocurrent generated from the UV radiation reaches values up to 3.4 mA cm−2 with switching times on the order of few seconds. The final written paper transistors, where paper plays simultaneously the role of substrate and dielectric, reaches On/Off current ratios (IOn/IOff) of about two orders of magnitude and field effect mobility close to 5 × 10−3 cm2 V−1 s−1. As a final proof‐of‐concept a common source amplifier showing clear inverting characteristics is fabricated using a PoP written driving ZnO‐based transistor and a pencil‐drawn track as the load resistance. [ABSTRACT FROM AUTHOR]

The article announces the 2022 Clean Energy Best Paper Prizes, recognizing outstanding research including microgrid-based PV/battery/EV systems by Aziz Haffaf, a model for PV power losses by S. Ghosh, and a comparison of PV modules in India by Niranjan Singh Baghel.

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Abstract: This article reports for the first time in the literature, a dye sensitized solar cells with 1.21% efficiency (Voc=0.56V, J sc=6.70mA/cm2 and F.F.=0.33) on paper substrates. The current dye sensitized solar cell technology is based on fluorine doped SnO2 (FTO) coated glass substrates. The problem with the glass substrate is its rigidity and heavy weight. Making DSSCs on paper opens the door for both photovoltaic and paper industries. The potential of using mature paper making and coating technologies will greatly reduce the current PV cost. Paper substrate based DSSCs not only offer the advantages of flexibility, portability and lightweight but also provide the opportunities for easy implantation to textile. In this study, a low temperature process is developed to coat uniform nickel on paper substrate as the metal contact to replace the traditional expensive FTO. The Ni paper showed excellent conductivity of 8–10Ω/□. It is found that the control of metal oxide electrode morphology is critical to solar cell performance. The TiO2 film has the tendency to crack on Ni coated paper, which resulted in the shunt of the device and no solar cell efficiency was obtained. ZnO film on the other hand had good morphology tolerance on Ni coated paper and yielded solar cell efficiency of 1.21% (V oc=0.56V, J sc=6.70mA/cm2 and F.F.=0.33) under AM 1.5 (activation area is 0.16cm2). The control sample of ZnO solar cell on FTO glasses has the efficiency of 2.66% (V oc=0.64V, J sc=9.97mA/cm2 and F.F.=0.42). [Copyright &y& Elsevier]

A new paper‐based anti‐reflection coating for solar cells is presented showing a large reduction in the reflection over the entire solar spectrum for a wide range of angles. This process is simple and inexpensive, requiring no high temperature or vacuum‐based processing, and is made from renewable cellulose fibers. [ABSTRACT FROM AUTHOR]

Background: One common renewable energy source for substituting fossil sources is photovoltaic (PV) systems. However, installing PV systems in agricultural areas can lead to competition with other land uses. These projects, therefore, often encounter problems with social acceptance in affected communities. Especially from the perspective of nature conservation targets, conflicts can arise. These potential differences are still under-researched but represent important knowledge for the societally broadly accepted design of such facilities and their contribution to energy transformation. In this paper, we investigate the perspectives of nature conservationists on PV in the region of Brandenburg, Germany. We comparatively analyse attitudes towards ground-mounted photovoltaics (GM-PV) and agrophotovoltaics (APV). APV combines energy supply and agricultural production on the same land and could thus be a possible solution for mitigating land-use conflicts. Results: We investigated the degree of local acceptability and positive and negative influencing factors through a qualitative text analysis of ten interviews with local representatives and position papers by Nature Conservation Associations. Our findings show a growing consensus around basic assumptions of the need for renewable energies, the prioritised support for PV systems on rooftops over the installation on agricultural land (GM-PV and APV), and the necessity for PV systems to be compatible with nature conservation objectives. Regarding specific site decisions on agricultural land, we find diverging attitudes when comparing the content of position papers and the responses of interviewees. The interviewees advocate taking into account local interests and specific regional conditions, the effects of PV systems on the local environment, and the need for distributional justice. Large-scale plants are locally less accepted than smaller ones and there is a more open attitude towards the expansion of APV than of GM-PV on agricultural land. However, a range of concerns regarding consequences for landscape and biodiversity persists, and further research and clarification is required to address these issues. Conclusions: We conclude that basic ecological standards and the demands of local stakeholders and interest groups should be taken into account when planning, implementing and reviewing PV projects in the future. [ABSTRACT FROM AUTHOR]

Information about several papers discussed at PhotoVoltaic Technical Conference 2016 that was held in South France on May 9-11, 2016 is presented. It discusses the uses of PhotoVoltaic (PV) materials and advance processes to innovative applications on transparent glazing PV, solar roads and solar veils. The article also presents the contributions of PV as a sustainable source of energy and the development of suitable transparent conducting materials in fabrication process.

Changes in weather conditions directly impact the output of wind power, photovoltaic systems, and other forms of uncontrollable power generation. During extreme weather events, the output from wind and photovoltaic sources is typically reduced. In light of this, this paper proposes a two-stage operational strategy for energy storage, under scenarios of extreme-heat-with-low-wind-speed, in power systems. Firstly, historical data on wind and solar power, along with weather characteristics, are collected to analyze the power output during multi-day periods of extreme heat and low wind speed. Then, Monte Carlo simulations are employed to generate multi-day load curves with inherent uncertainties, based on regional load characteristics of the power system. Finally, a two-stage operation strategy for energy storage charging and discharging is established. In the first stage, normal operations are conducted to identify periods of power shortage across various types of loads. In the second stage, based on the identified moments of power shortage from the first stage, charging and discharging constraints are applied to the energy storage systems. The feasibility and effectiveness of this two-stage operational strategy are then validated through simulations, using historical data to generate scenarios of multi-day extreme-heat-and-low-wind-speed conditions. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including photovoltaics (PV), silicon-based solar cells and electricity generation.

Due to the uncertainty of weather conditions and the nonlinearity of high-dimensional data, as well as the need for a continuous and stable power supply to the power system, traditional regression analysis and time series forecasting methods are no longer able to meet the high accuracy requirements of today's PV power forecasting. To significantly improve the prediction accuracy of short-term PV output power, this paper proposes a short-term PV power forecasting method based on a hybrid model of temporal convolutional networks and gated recurrent units with an efficient channel attention network (TCN-ECANet-GRU) using the generated data of an Australian PV power station as the research object. First, temporal convolutional networks (TCNs) are used as spatial feature extraction layers, and an efficient channel attention network (ECANet) is embedded to enhance the feature capture capability of the convolutional network. Then, the GRU is used to extract the timing information for the final prediction. Finally, based on the experimental validation, the TCN-ECANet-GRU method generally outperformed the other baseline models in all four seasons of the year according to three performance assessment metrics: the normalized root mean square error (RMSE), normalized mean absolute error (MAE) and coefficient of determination (R2). The best RMSE, MAE and R2 reached 0.0195, 0.0128 and 99.72%, respectively, with maximum improvements of 11.32%, 8.57% and 0.38%, respectively, over those of the suboptimal model. Therefore, the model proposed in this paper is effective at improving prediction accuracy. Using the proposed method, this paper concludes with multistep predictions of 3, 6, and 9 steps, which also indicates that the proposed method significantly outperforms the other models. [ABSTRACT FROM AUTHOR]

At present, the operation and maintenance of photovoltaic power generation systems mainly comprise regular maintenance, breakdown maintenance, and condition-based maintenance, which is very likely to lead to over- or under-repair of equipment. Therefore, a preventive maintenance and replacement strategy for PV power generation systems based on reliability as a constraint is proposed. First, a hybrid failure function with a decreasing service age factor and an increasing failure rate factor is introduced to describe the deterioration of PV power generation equipment, and the equipment is replaced when its reliability drops to the replacement threshold in the last cycle. Then, based on the reliability as a constraint, the average maintenance cost and availability of the equipment are considered, and the non-periodic incomplete maintenance model of the PV power generation system is established to obtain the optimal number of repairs, each maintenance cycle and the replacement cycle of the PV power generation system components. Next, the inverter of a PV power plant is used as a research object. The model in this paper is compared and analyzed with the equal cycle maintenance model without considering reliability and the maintenance model without considering the equipment replacement threshold, Through model comparison, when the optimal maintenance strategy is (0.80, 4), the average maintenance cost of this paper's model are decreased by 20.3% and 5.54% and the availability is increased by 0.2395% and 0.0337%, respectively, compared with the equal-cycle maintenance model without considering the reliability constraint and the maintenance model without considering the equipment replacement threshold. Therefore, this maintenance model can ensure the high reliability of PV plant operation while increasing the equipment availability to improve the system economy. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of photovoltaic (PV) power generation has led to an increased focus on accurate forecasting of PV power output. Interval forecasting, which provides uncertainty measurement information for forecasting results, has become a hot research topic in this field. However, the accuracy of single models or traditional multi-model forecasting methods is often insufficient for meeting the forecasting requirements. In addition, interval forecasting places higher demands on the learning and generalization capabilities of forecasting models. To address these issues, this paper proposes an intelligent multi-model forecasting method based on output features clustering and convolutional neural network–long short term memory (CNN–LSTM) for PV power interval forecasting. First, multiple feature indices are constructed to represent the differences in PV output features for different months. The intelligent clustering method is then employed to achieve the multi-model clustering for forecasting model. Finally, CNN–LSTM is utilized to implement the PV power interval forecasting. The combination of CNN and LSTM effectively improves the modeling accuracy of the intelligent forecasting model. Based on actual data from PV power stations, the method described in this paper narrows down the forecasting interval compared with the single model, reducing prediction interval normalized average width by more than 4%. [ABSTRACT FROM AUTHOR]

The transition process from fossil fuels to environmentally friendly renewable energy sources carries the risk of creating new environmental damages. Photovoltaic technology represents one of the alternatives with the least risk of harmful environmental impact. However, this technology has two important drawbacks: the significant land occupation for the installation of PV systems and the uncontrollability of production. By constructing floating photovoltaic plants on hydroelectric reservoirs, both of these problems can be reduced to an acceptable level. Some artificial reservoirs, originally built for hydroelectric power plants, have acquired a significant secondary function as recreational areas and fish breeding sites. Therefore, there is justified resistance from the local community to change the existing appearance and purpose of such reservoirs. This paper proposes a completely new concept of integrating the interests of the local community into such objects. In addition to preserving existing uses, the concept also offers new features. This can make the entire system environmentally friendly and sustainable. This paper details the technology behind the construction of floating photovoltaic power plants on artificial reservoirs and emphasizes their various advantages. These benefits include the non-utilization of cultivable land, the ease of assembly and construction, integration into existing power grids, and the potential to address electricity storage issues. For instance, Buško Lake, covering an area of 55.8 km2, may host 2.93 km2 of installed floating photovoltaic (FPV) facilities, enabling a total installed capacity of 240 MW. With an average of 5.5 h of daily sunshine, this totals 2007 annual hours, equivalent to a 55 MW thermal power plant. An analysis showed that, with losses of 18.2%, the average annual production stands at 302 G W h , translating to an annual production value of 18 million € at 60 €/MWh. The integration of this production into an existing hydroelectric power plant featuring an artificial reservoir might boost its output by 91%. The available transmission line capacity of 237 MW is shared between the hydroelectric power plant (HPP) and FPV; hence during the FPV maximum power generation time, the HPP halts its production. HPP Orlovac operates a small number of hours annually at full capacity (1489 h); therefore in combination with the FPV, this number can be increased to 2852 h. This integration maintains the lake's functions in tourism and fishing while expanding its capabilities without environmental harm. [ABSTRACT FROM AUTHOR]

A reasonable allocation of production schedules and savings in overall electricity costs are crucial for large manufacturing conglomerates. In this study, we develop an optimization model of off-site industrial production scheduling to address the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's power supply, and the difficulty in promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and variations in electricity prices in different places to optimize the scheduling of industrial production in various locations. The model is embedded with two sub-models, i.e., an electricity price prediction model and a distributed photovoltaic power cost model to complete the model parameters, in which the electricity price prediction model utilizes a Long Short-Term Memory (LSTM) neural network. Then, the particle swarm optimization algorithm is used to solve the optimization model. Finally, the production data of two off-site pharmaceutical factories belonging to the same large group of enterprises are substituted into the model for example analysis, and it is concluded that the optimization model can significantly reduce the electricity consumption costs of the enterprises by about 7.9%. This verifies the effectiveness of the optimization model established in this paper in reducing the cost of electricity consumption on the demand side. [ABSTRACT FROM AUTHOR]

Due to the unpredictable output characteristics of distributed photovoltaics, their integration into the grid can lead to voltage fluctuations within the regional power grid. Therefore, the development of spatial-temporal coordination and optimization control methods for distributed photovoltaics and energy storage systems is of utmost importance in various scenarios. This paper approaches the issue from the perspective of spatiotemporal forecasting of distributed photovoltaic (PV) generation and proposes a Temporal Convolutional-Long Short-Term Memory predictionmodel that combines TemporalConvolutionalNetworks (TCN) and Long Short-TermMemory (LSTM). To begin with, an analysis of the spatiotemporal distribution patterns of PV generation is conducted, and outlier data is handled using the 3s rule. Subsequently, a novel approach that combines temporal convolution and LSTM networks is introduced, with TCN extracting spatial features and LSTM capturing temporal features. Finally, a real spatiotemporal dataset fromGansu, China, is established to compare the performance of the proposed network against other models. The results demonstrate that the model presented in this paper exhibits the highest predictive accuracy, with a single-step Mean Absolute Error (MAE) of 1.782 and an average Root Mean Square Error (RMSE) of 3.72 for multi-step predictions. [ABSTRACT FROM AUTHOR]

In response to the issue of short-term fluctuations in photovoltaic (PV) output due to Cloud movement, this paper proposes a method for forecasting short-term PV output based on a Depthwise Separable Convolution Visual Geometry Group (DSCVGG) and a Deep Gate Recurrent Neural Network (DGN). Initially, a cloud motion prediction model is constructed using a DSCVGG, which achieves edge recognition and motion prediction of clouds by replacing the previous convolution layer of the pooling layer in VGG with a depthwise separable convolution. Subsequently, the output results of the DSCVGG network, along with historical PV output data, are introduced into a Deep Gate Recurrent Unit Network (DGN) to establish a PV output prediction model, thereby achieving precise prediction of PV output. Through experiments on actual data, the Mean Absolute Error (MAE) and Mean Squared Error (MSE) of our model are only 2.18% and 5.32 x 10-5, respectively, which validates the effectiveness, accuracy, and superiority of the proposed method. This provides new insights and methods for improving the stability of PV power generation. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization approach for allocating power quality monitors (PQMs) aiming to monitor all harmonic resonance conditions while taking power system uncertainties into account. The placement approach utilizes the frequency scan response for calculating impedances over a range of frequencies and consequently determining harmonic resonance conditions. Thereby, it is capable of building binary matrices, which include harmonic resonance conditions. Also, by utilizing the union operator at binary matrices, power system uncertainties such as photovoltaic generation and load level can be considered in the allocation method. The placement approach is expressed as a linear problem that determines the best locations of PQMs and their optimal number so that they monitor all harmonic resonance conditions. Besides, by considering the area of harmonic pollution of non‐linear loads in the proposed method, owners of electrical networks can find a solution with fewer PQMs to monitor harmonic resonance orders inside a particular area of the network. The performance of the presented approach is demonstrated using the 15‐node distribution network and a real electrical network, as well as a real large electrical network in Iran. Results show that the proposed method suggests fewer PQMs to monitor harmonic resonance conditions compared to previous methods. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power fluctuates with weather changes, and traditional forecasting methods typically decompose the power itself to study its characteristics, ignoring the impact of multidimensional weather conditions on the power decomposition. Therefore, this paper proposes a short-term PV power generation method based on MVMD (multivariate variational mode decomposition) feature extraction and the Informer model. First, MIC correlation analysis is used to extract weather features most related to PV power. Next, to more comprehensively describe the relationship between PV power and environmental conditions, MVMD is used for time–frequency synchronous analysis of the PV power time series combined with the highest MIC correlation weather data, obtaining frequency-aligned multivariate intrinsic modes. These modes incorporate multidimensional weather factors into the data-decomposition-based forecasting method. Finally, to enhance the model's learning capability, the Informer neural network model is employed in the prediction phase. Based on the input PV IMF time series and associated weather mode components, the Informer prediction model is constructed for training and forecasting. The predicted results of different PV IMF modes are then superimposed to obtain the total PV power generation. Experiments show that this method improves PV power generation accuracy, with an MAPE value of 4.31%, demonstrating good robustness. In terms of computational efficiency, the Informer model's ability to handle long sequences with sparse attention mechanisms reduces training and prediction times by approximately 15%, making it faster than conventional deep learning models. [ABSTRACT FROM AUTHOR]

The use of distributed photovoltaics (PVs) on a large scale often causes voltage over-limit problems in distribution networks. This paper proposes a distributed photovoltaic cluster collaborative optimization voltage control strategy based on an improved community algorithm to address the issue of centralized control being unable to respond quickly to the randomness of distributed photovoltaics and the difficulty of achieving overall coordination with local control. First, by improving the community algorithm, the division of reactive and active clusters, considering the power balance and node coupling degree, is realized. Then, the cluster-coordinated voltage control strategy is proposed by making full use of the power control ability of a photovoltaic inverter. Finally, a voltage regulation ability evaluation index is proposed to assess the node regulation ability within the cluster and select key nodes. This effectively reduces the number of control nodes. The simulation analysis of the improved IEEE 69 distribution network shows that the proposed voltage control strategy can mitigate the issue of voltage over-limit in high-permeability distributed photovoltaic access distribution and enhance the photovoltaic consumption capacity. [ABSTRACT FROM AUTHOR]

Due to the extensive use of fossil fuels, energy conservation and sustainable transportation have become hot topics. Electric vehicles (EVs), renowned for their clean and eco-friendly attributes, have garnered considerable global attention and are progressively being embraced worldwide. However, disorganized EV charging not only reduces charging station efficiency but also threatens power grid stability. In this low-carbon era, photovoltaic storage charging stations offer a solution that accommodates future EV growth. However, due to the significant instability in both the charging load and photovoltaic power generation within charging stations, it is critical to maximize local photovoltaic power consumption and minimize the impact of disorganized EV charging on the power grid. This paper formulates an intelligent scheduling strategy for electric heavy trucks within charging stations based on typical photovoltaic output data. The study focuses on a photovoltaic storage charging station in an industrial zone in Xinjiang. While considering the electricity procurement cost of the charging station, the aim is to minimize fluctuations in the electricity procurement load. A simulation analysis was conducted using MATLAB 2021a software, and the results indicated that, compared to an uncoordinated charging strategy for electric heavy trucks, the proposed strategy reduced electricity procurement costs by CNY 1348.25, decreased load fluctuations by 169.45, and improved the utilization efficiency of photovoltaic energy by 30%. A statistical analysis was also used to support the reduction in electricity procurement costs and load variations. Finally, a sensitivity analysis of the weight factors in the objective function was performed, proving that the proposed strategy effectively reduces electricity procurement costs and improves the utilization efficiency of photovoltaic energy. [ABSTRACT FROM AUTHOR]

With the growing energy crisis and environmental problems, distributed photovoltaic (PV), as a clean and renewable form of energy, is receiving more and more attention. However, the large-scale access to distributed PV brings a series of challenges to the distribution network, such as voltage fluctuation, frequency deviation, protection coordination, and other problems. In order to solve these problems, this paper provides a research overview of distribution network consumption strategies containing distributed PV. Firstly, this paper introduces the characteristics of distributed PV and its impact on the distribution grid. Then, the difficulties and challenges of distributed PV consumption are analyzed from the technical and economic levels. On this basis, a comprehensive consumption strategy is proposed, including the following aspects: firstly, it is mentioned to optimize the access method of distributed PV and to reduce the impact on the distribution grid by reasonably choosing the access point and access capacity of distributed PV. Secondly, it summarizes the ways to improve the regulation capacity of distribution networks and improve the consumption capacity of distribution networks for distributed PV through the introduction of energy storage equipment, flexible loads, and other means. Finally, advanced control strategies are summarized to improve the stability and reliability of the distribution network by implementing measures such as power prediction, voltage control, and protection coordination for distributed PV. Overall, this paper synthesizes the research on distribution network consumption strategies containing distributed PV from various aspects, which provides certain theoretical guidance and practical reference for solving distributed PV consumption problems. [ABSTRACT FROM AUTHOR]

To optimize the energy supply potential and complementary advantages of distributed energy, this paper focuses on the dispatching optimization of cogeneration virtual power plant considering uncertainty. First of all, wind power, photovoltaic, combined heat and power (CHP) units, electric boilers, and controllable loads are integrated into a CHP virtual power plant. Then, carbon capture and electric-to-gas devices are introduced to realize carbon recycling. Furthermore, quantify the risk of real-time dispatching of virtual power plants through uncertainty scenario generation and conditional value at risk (CVaR) theory, and the multi-objective stochastic dispatching optimization model of virtual power plants is built with the aim at minimizing the operation cost, carbon emissions, and operation risk as the objectives, and the CRITIC weighting method is adopted to solve it. Finally, the calculation results show that: 1) the electric boiler can use wind and photovoltaic power to supply heat for the system, reduce the dependence ofthe virtual power plant (VPP) on the CHP unit, and make the electric output of the unit more flexible. 2) The risk quantification method proposed can fully measure the risk situation in real-time dispatching, arrange the wind and photovoltaic power generation plan and backup plan more reasonably, and enable the VPP to get more benefits while avoiding the risks in real-time dispatching. [ABSTRACT FROM AUTHOR]

In response to the volatility of photovoltaic power generation, this paper proposes a short-term photovoltaic power generation prediction model (HWOA-MVMD-TPA-TCN) based on a Hybrid Whale Optimization Algorithm (HWOA), multivariate variational mode decomposition (MVMD), temporal pattern attention mechanism (TPA), and temporal convolutional network (TCN). In order to improve the accuracy of photovoltaic power generation forecasting, HWOA-MVMD is used for data decomposition, the Minimum Mode Overlap Component (MMOC) is used as the objective function, the photovoltaic power generation sequence is decomposed into finite Intrinsic Mode Functions (IMFs) according to the optimal solution, and the training set is formed with key meteorological variable data such as total radiation (unit: W/m2), ambient temperature, and humidity. Then, the TPA-TCN model is used to train the sub-sequences, the final predicted values are obtained after superimposing the reconstruction of the prediction results, and finally the prediction error of the photovoltaic power generation data is studied. The proposed method is applied to real photovoltaic power generation data from a commercial center in Tianjin and is compared with HWOA-MVMD-BiLSTM, GWO-MVMD-TPA-TCN, and TPA-TCN prediction models. The simulation results demonstrate that the MAE value of the forecast method proposed in this paper is 1.95 MW and the RMSE value is 2.55 MW, which can be reduced by up to 33.74% and 38.85%, respectively. The HWOA-MVMD-TPA-TCN-based short-term photovoltaic power generation prediction model presented in this paper achieves higher prediction accuracy and superior performance, serving as a valuable reference for related research. [ABSTRACT FROM AUTHOR]

Smart windows are attractive because of their numerous advantages such as elegance in construction design and energy conservation. Smart windows with variable transmission are especially desirable for obvious reasons. The simplest way to make a variable transmission smart window is to use two patterned polarizers, and control the transmission by changing the relative position of the two polarizers. We have demonstrated a high‐dichroic‐ratio polarizer by photoalignment and proposed a simple one‐step photo‐copy method to realize a continuous axis varying polarizer. The patterned polarizer shows high optical performance, and its thickness is as thin as 100 nm, which is favorable in practical window applications. [ABSTRACT FROM AUTHOR]

The worldwide appeal has increased for the development of new technologies that allow the use of green energy. In this category, photovoltaic energy (PV) stands out, especially with regard to the presentation of forecasting methods of solar irradiance or solar power from photovoltaic generators. The development of battery energy storage systems (BESSs) has been investigated to overcome difficulties in electric grid operation, such as using energy in the peaks of load or economic dispatch. These technologies are often applied in the sense that solar irradiance is used to charge the battery. We present a review of solar forecasting methods used together with a PV-BESS. Despite the hundreds of papers investigating solar irradiation forecasting, only a few present discussions on its use on the PV-BESS set. Therefore, we evaluated 49 papers from scientific databases published over the last six years. We performed a quantitative analysis and reported important aspects found in the papers, such as the error metrics addressed, granularity, and where the data are obtained from. We also describe applications of the BESS, present a critical analysis of the current perspectives, and point out promising future research directions on forecasting approaches in conjunction with PV-BESS. [ABSTRACT FROM AUTHOR]

Wood cellulose is a desirable inexpensive, abundant and biodegradable substrate material for flexible organic electronics. While paper-based conductors are easily achieved, their applicability for diode architectures is challenging. To diminish short circuits, smoothing fillers and coatings are used before diode deposition. In this report, an alternative approach for use of cellulose in organic photovoltaics is demonstrated, using natural, rough unmodified kraft pulp fiber networks as a carrier of a diode architecture. Silver nanowires are adsorbed by the hydroxyl groups of cellulose fiber, forming the semi-transparent anode. The follow-on coatings for the diode are simply deposited as a mantle on these fiber anodes. The potential of this approach is demonstrated by the first production of cellulose fiber-based P3HT:PCBM photovoltaic devices, showing clear diode behaviour in the dark and a significant photoresponse under illumination. Fill factors near 35 % and open-circuit voltages of 0.2 V were achieved without any optimization. Devices show even noticeable photocurrent at low unfavourable light conditions, indicating their advantageous capture of stray light within the paper fiber network. These results highlight that for 'paper' diodes, its natural properties can be turned into advantages, instead of seeking routes to suppress them. [ABSTRACT FROM AUTHOR]

As energy shortages and environmental pollution continue to worsen, household photovoltaics has gradually become an important part of household energy management. However, with the penetration rate of household photovoltaics increasing, the access of the high‐proportion household photovoltaics (HPHP) will seriously endanger the operation of the distribution network. Therefore, it is urgent to stabilize the voltage fluctuations after the HPHP connected to the grid. This paper proposes a high‐proportion household photovoltaic optimal configuration method based on integrated–distributed energy storage system. After analyzing the adverse effects of HPHP connected to the grid, this paper uses modified K‐means clustering algorithm to classify energy storage in an integrated and distributed manner. Then based on the modified particle swarm optimization (MPSO), the integrated and distributed energy storage system is configured to realize the voltage stability of HPHP. The analysis of the example shows that the method proposed in this paper can effectively improve the power quality. At the same time, it can achieve the goal of economic optimization, which can effectively solve the adverse impact of HPHP grid connection. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Distributed photovoltaics (DPVs) are widely distributed and the output is random, which brings challenges to the safe operation of the distribution network, so the construction of photovoltaic aggregations can effectively participate in the flexible regulation of the power system. At present, the extraction of DPV clustering features is not sufficient, only considering the output characteristics of PVs. Certain PVs under some nodes may have a more pronounced regulation effect, but they may be ignored in the clustering process. To address the above problems, this paper proposes a DPV aggregation approach considering the distribution network topology. It combines the voltage sensitivity and the power curve and regards them as clustering features to form the DPV aggregation with the highest average voltage sensitivity participating in voltage regulation. The simulation on the IEEE 33-node system verifies that the proposed aggregation approach can select DPV aggregation more suitable for voltage regulation, and make full use of the aggregation to realize the optimal voltage regulation effect. [ABSTRACT FROM AUTHOR]

In the future, wireless power transmission is expected to cover power levels ranging from milliwatts to megawatts and over distances of a few meters to kilometers for both stationary and moving photovoltaic (PV) targets. Optical wireless power transmission (OWPT) is a promising solution for such multi-scale systems, which use the narrow beam divergence of light. To enhance the efficiency of power generation in PV targets, real-time detection of the target's position and attitude is crucial for OWPT systems. The authors propose detecting the PV using differential absorption images as one such method. This paper investigates algorithms for tracking moving PV targets and evaluates their performance. The first algorithm combines thresholds with an autoregressive (AR) model, while the other two use estimation with Kalman filters. The comparison of tracking algorithms can be undertaken using a score function based on the position estimation error. The evaluation results indicate that the AR model combined with thresholds, on average, outperforms the other models. There was no significant difference between the approach involving use of a Kalman filter to estimate positions based on a uniform motion model and the approach involving use of a Kalman filter to estimate the AR model. The authors' series of studies, from a former high-level requirement study to the current target tracking using differential absorption imaging, has verified a concept for one of the crucial processes in OWPT. These works form a step toward the practical implementation of OWPT systems. [ABSTRACT FROM AUTHOR]

This paper proposes a two‐stage stochastic scheduling model for urban distribution network resilience enhancement against ice storms, which coordinates mobile deicing equipment routing and distributed energy resources dispatching. An improved line ice thickness prediction model and a photovoltaic power generation prediction method in accordance with conditional generative adversarial networks are proposed to provide data boundaries for scheduling strategy. Facing the uncertainty of line failure, a two‐stage scenario‐based distribution network optimization model is established. At first stage, the mobile deicing equipment routing strategy is decided to mitigate the impact caused by ice storms. The Monte‐Carlo simulation method is introduced to describe the uncertainty of line failure due to ice acceleration. For the second stage, based on the results of photovoltaic forecasting and possible distribution line failure scenario generated by Monte‐Carlo simulation method, the optimal distributed energy resources dispatching strategy can be obtained through the mixed integer programming. The proposed model is simplified to a mixed integer linear programming model that can be solved by a commercial solver. The test results on the modified IEEE 33‐node system and modified 69‐node system demonstrate that the proposed method can effectively improve the resilience performance of urban distribution network under ice storms. [ABSTRACT FROM AUTHOR]

To enhance the utilization of clean energy sources, such as wind power and photovoltaic within virtual power plants, and mitigate carbon emissions, this paper proposes a virtual power plant participation in the electricity carbon joint market optimization operation model based on ensemble empirical mode decomposition–improved bat algorithm (IBA)–Markov chain new energy output prediction. First, complementary set empirical mode decomposition is performed on historical data to construct a Markov chain based wind power and photovoltaic prediction model optimized by IBA. Second, this prediction model is used to predict the daily generation power of wind power and photovoltaic power. Finally, with the optimization goals of maximizing the benefits and minimizing the carbon costs of virtual power plants, a virtual power plant system participating in the electricity carbon joint market model based on wind power and photovoltaic output prediction results is constructed. At the same time, demand response factors are introduced and solved using the NSGA-II algorithm. Taking a certain park as an example for simulation analysis, the research results show that the combined effect of carbon market and demand response can achieve 99.82% of new energy consumption in virtual power plants without significantly reducing profits, basically achieving complete new energy consumption, demonstrating the effectiveness of the proposed model in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive exploration of an integrated Buck-Boost converter and Sliding Mode Control (SMC) Maximum Power Point Tracking (MPPT) system for optimizing photovoltaic energy conversion. The study focuses on enhancing solar energy extraction efficiency, regulating output currents, and ensuring effective battery utilization. Through a systematic analysis of converter component sizing and operational modes, the paper delves into the intricacies of the Buck-Boost converter. The unique contribution lies in the innovative integration of SMC with the traditional Perturb and Observe (P&O) algorithm, providing robust and adaptive MPPT under varying environmental conditions. Additionally, the paper introduces a battery management system with three distinct modes, namely, Charging, Direct, and Discharging, offering intelligent control over critical scenarios. Simulation results underscore the robustness of the proposed system under diverse conditions, demonstrating its effectiveness in managing power distribution based on battery charge levels, even in scenarios of insufficient solar power. Overall, this research significantly contributes to advancing the understanding of PV/battery systems and offers a practical, sustainable solution for optimizing energy production, distribution, and storage, marking a substantial stride towards a more efficient and sustainable energy future. [ABSTRACT FROM AUTHOR]

Partial shading conditions (PSCs) caused by uneven illumination become one of the most common problems in photovoltaic (PV) systems, which can make the PV power-voltage (P-V) characteristics curve show multipeaks. Traditional maximum power point tracking (MPPT) methods have shortcomings in tracking to the global maximum power point (GMPP), resulting in a dramatic decrease in output power. In order to solve the above problems, intelligent algorithms are used in MPPT. However, the existing intelligent algorithms have some disadvantages, such as slowconvergence speed and large search oscillation. Therefore, an improvedwhale algorithm (IWOA) combined with the P&O (IWOA-P&O) is proposed for the MPPT of PV power generation in this paper. Firstly, IWOA is used to track the range interval of the GMPP, and then P&O is used to accurately find theMPP in that interval. Compared with other algorithms, simulation results show that this method has an average tracking efficiency of 99.79% and an average tracking time of 0.16 s when tracking GMPP. Finally, experimental verification is conducted, and the results show that the proposed algorithmhas better MPPT performance compared to popular particle swarm optimization (PSO) and PSO-P&O algorithms. [ABSTRACT FROM AUTHOR]

Accurate prediction of photovoltaic power generation is a critical technical problem for utilizing solar energy. Aiming at the problem that the model parameters are difficult to obtain in applying photovoltaic power prediction methods, this paper has used long‐term monitoring data of output power, various meteorological data, and solar irradiation intensity of photovoltaic modules. This paper establishes the functional relationship between the output power of photovoltaic modules and the irradiation intensity through Pearson correlation analysis. By deducing the distribution relationship of irradiation intensity, the prediction model of irradiation intensity based on peak sunshine hours and sunshine duration is constructed and based on 340 sites across the country 64 years peak sunshine hours and sunshine duration query database. In this work, the theoretical value of the prediction model on sunny days is close to the measured value (R2 = 0.918–0.985). The solar radiation intensity on rainy days is weak, and the prediction accuracy is low (R2 = 0.838–0.930). The relative errors between the sunshine duration and the peak sunshine hours in the database are less than 4.55% and 4.79%, respectively, under sunny conditions in each quarter, indicating that the accuracy of the database meets the actual needs. [ABSTRACT FROM AUTHOR]

Based on the scenario of high-penetration distributed photovoltaic connected to an AC/DC distribution network, this paper analyzes the dynamic characteristics of frequency and voltage in a distribution network after the blocking failure of the flexible interconnection channel. In order to enhance the transient stability of the system after the fault, this paper comprehensively considers the active regulation ability of photovoltaic units, and puts forward an emergency coordinated control strategy for a single-ended distribution network with flexible interconnection channel blocking. Firstly, the non-fault channel is overloaded for a short time, then the comprehensive influence of factors such as electrical distance, response time and adjustment cost on the frequency modulation effect of the system is quantitatively evaluated; according to the evaluation results, the photovoltaic and synchronous units are controlled by "control instead of tripping", and finally, the high-frequency tripping is carried out, based on the principle of "photovoltaics first". After the frequency control is completed, the reactive power optimization model of the system is established, and the improved tabu–particle swarm optimization algorithm is used to solve it, so as to optimize the voltage of the distribution network nodes. Finally, an equivalent simulation model is established to verify the coordinated control strategy. [ABSTRACT FROM AUTHOR]

At present, electrified railroads with complex road conditions are facing the problems of the existence of a single power supply method, the deterioration in power quality, and the difficulty in recycling regenerative braking energy. In order to improve the above problems, this paper establishes a traction power supply system containing photovoltaic units, proposes an optimized compensation model for the RPC (railroad power conditioner), and improves the particle swarm algorithm for solving the reference value of RPC compensation power. First, the structure of the RPC-based traction photovoltaic power generation system and the establishment of the integrated energy management strategy of the system are constructed. Then, the back-to-back converter compensation model with power quality index parameter constraints is established, which establishes the optimization function with the objectives of minimizing the negative sequence current, maximizing the power factor, and minimizing the RPC compensation power, as well as establishes constraints on the active converter capacity and three-phase voltage imbalance. Then, the particle swarm algorithm for solving the RPC compensation power reference value is improved, specifically in the original PSO by introducing dynamically changing inertia weights and learning factors. This not only solves the problem of the single power supply and realizes the nearby consumption of photovoltaic units in the traction system, but also realizes the recycling of regenerative braking energy and the coordinated control of the traction photovoltaic power generation system, improves the power quality of the system, and meets the demand of the RPC for real-time control. In order to verify the effectiveness of the optimized compensation model established in this paper and improve the convergence of the particle swarm algorithm for solving the RPC compensation power reference value, a simulation model of the traction PV power generation system is established in MATLAB/Simulink, and a real-time simulation is carried out to verify it based on the preset working condition data. The results show that the RPC optimization compensation model developed in this paper can coordinate the control system energy flow and improve the system power quality (the power factor increases and the negative sequence current decreases). The improved particle swarm algorithm is more convergent. [ABSTRACT FROM AUTHOR]

Regional low carbon will play an important role in the path of achieving the "double carbon" goal, and there are still many key technologies to be broken in its planning research. In this paper, an optimal scheduling model of integrated energy system with multiple energy supply devices is established by using Beetle Antennae Search-Particle Swarm Optimization (BAS-PSO) algorithm. First, in the scheduling model, a carbon trading mechanism is introduced and a stepped carbon trading cost model is constructed to constrain the carbon emissions of the plant. Then, using drosophila algorithm, the premise of whether wind power generation and photovoltaic power generation need to be built is determined by judging the economics of wind power generation and photovoltaic power generation in the construction area, and then the target power consumption curve and new energy power supply output curve are fitted. Then use the life cycle analysis method to analyze the carbon emissions generated by electric energy storage equipment, consider the carbon trading mechanism in the system economic operation model, and solve the model by BAS-PSO algorithm to overcome the problems of local optimum and slow convergence speed. Finally, a typical park integrated energy system is simulated to analyze the economic operation conditions and energy efficiency level of the system before and after participation in demand response. The innovation of this paper lies in considering the carbon trading mechanism and solving with the optimized BAS-PSO algorithm. Considering carbon trading can effectively improve the system wind power consumption capacity, and the optimized BAS-PSO algorithm improves the defects of traditional PSO. The simulation analysis results show that the established optimal scheduling model with multiple energy supply devices can realize the optimal operation of the integrated energy system of the park in the demand response environment; the improved BAS-PSO finds the lowest energy consumption within 100 iterations in 21 out of 50 iterations; the improved BAS-PSO algorithm reduces the energy consumption by 27.1 kW on average. [ABSTRACT FROM AUTHOR]

In a power system with a high proportion of renewable energy, sudden increases in wind power or photovoltaic output can lead to huge challenges, such as difficulties in accommodating excess renewable energy and imbalances between supply and demand on the grid. As an important adjustable resource on the demand side, air conditioning load is a flexible load for realizing output consumption. In this paper, a heterogeneous air conditioning load regulation strategy for renewable energy consumption is proposed. Each air conditioning load regulation quantity is obtained based on the day-ahead dispatching mode. Then, the temperature setting value, rated power, and duty cycle are selected as the indexes. The load regulation sequence is obtained by the entropy weight method. Finally, the load regulation time of each air conditioning load is obtained based on the constraint of the quantity of loads during the possible adjustment time. The simulation analysis shows that the temperature regulation strategy presented in this paper can effectively reduce the power fluctuations of air conditioning loads, while ensuring that users with lower temperature settings are selected in the adjustment process. [ABSTRACT FROM AUTHOR]

The fast development of solar radiation and energy applications, such as photovoltaic and solar thermodynamic systems, has increased the need for solar radiation measurement and monitoring, not only for the global component but also for the diffuse and direct. End users look for the best compromise between getting close to state-of-theart measurements and keeping low capital, maintenance and operating costs. Among the existing commercial options, SPN1 is a relatively low cost solar radiometer that estimates global and diffuse solar irradiances from seven thermopile sensors under a shading mask and without moving parts. This work presents a comprehensive study of SPN1 accuracy and sources of uncertainty, which results from laboratory experiments, numerical modeling and comparison studies between measurements from this sensor and state-of-the art instruments for six diverse sites. Several clues are provided for improving the SPN1 accuracy and agreement with state-of-the art measurements. [ABSTRACT FROM AUTHOR]

The performance of the battery used in the traditional solar photovoltaic power generation system is poor, and the solar energy has a certain volatility, which makes the performance of the solar photovoltaic power generation system decline significantly. In order to improve the performance of the solar photovoltaic power generation system, a solar photovoltaic fuel cell combined power generation system has been developed. However, there are some problems in the process of traditional combined generation system optimal scheduling, such as high investment cost and total cost, and short generation time of optimal scheduling scheme. This paper takes solving the problems of traditional system optimal scheduling as the research goal, a new optimization scheduling method of solar photovoltaic and fuel cell combined power generation system was designed. The composition and topological structure of the solar photovoltaic fuel cell combined power generation system are analyzed. The system is composed of photovoltaic array, fuel cell, electrolytic cell, short-term energy storage unit and energy control unit. It can mainly convert sunlight radiation into electric energy, and convert it into DC or AC used by people through multiple links, so as to ensure the stability and security of power supply in our country. A scheduling model is established according to the photovoltaic cell power generation model, the fuel cell power generation model, the electrolytic hydrogen production model, the battery model, the power conversion model and the combined power generation system model. And the multi-objective cuckoo algorithm is used to solve the model, the optimization scheduling results of solar photovoltaic fuel cell combined power generation system are obtained. The experimental results show that the total investment cost of this method is reduced by 123678.4 yuan and 175858.7 yuan compared with the experimental comparison method, and the total cost is reduced by 301195.5 yuan and 414991.8 yuan compared with the experimental comparison method. It shows that compared with the experimental comparison method, the total investment cost and total cost of this method are lower, and the generation time of the optimal scheduling scheme is between 0.19s and 0.25s, and the practical application effect is good. It fully solves the problems existing in the traditional methods and has certain application significance. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation is booming in rural areas, not only to meet the energy needs of local farmers but also to provide additional power to urban areas. Existing methods for estimating the spatial distribution of PV power generation potential either have low accuracy and rely on manual experience or are too costly to be applied in rural areas. In this paper, we discuss three aspects, namely, geographic potential, physical potential, and technical potential, and propose a large-scale and efficient PV potential estimation system applicable to rural rooftops in China. Combined with high-definition map images, we proposed an improved SegNeXt deep learning network to extract roof images. Using the national standard Design Code for Photovoltaic Power Plants (GB50797-2012) and the Bass model, computational results were derived. The average pixel accuracy of the improved SegNeXt was about 96%, which well solved the original problems of insufficient finely extracted edges, poor adhesion, and poor generalization ability and can cope with different types of buildings. Leizhou City has a geographic potential of 1500 kWh/m2, a physical potential of 25,186,181.7 m2, and a technological potential of 442.4 MW. For this paper, we innovatively used the Bass Demand Diffusion Model to estimate the installed capacity over the next 35 years and combined the Commodity Diffusion Model with the installed capacity, which achieved a good result and conformed to the dual-carbon "3060" plan for the future of China. [ABSTRACT FROM AUTHOR]

Accurate four-hour-ahead PV power prediction is crucial to the utilization of PV power. Conventional methods focus on using historical data directly. This paper addresses this issue from a new perspective of Numerical Weather Prediction (NWP) optimization. This paper refers to the predicted PV power given by NWP minus the actual PV power as PV NWP error, analyzes the temporal statistical characteristics of PV NWP error time series through the Ljung-Box test and autocorrelation function, reveals the significant temporal autocorrelation in PV NWP error and verifies the statistical predictability of PV NWP error for the first time. Meanwhile, this paper introduces transfer entropy to verify that introducing multiple meteorological variables including temperature, solar irradiation, clear-sky solar irradiation, cloud thickness, etc. can help predict future PV NWP error better for the first time. Based on the fusion of multiple meteorological variables and artificial neural networks, this paper proposes multiple meteorological variables-aided PV NWP error correction to realize more accurate four-hour-ahead PV power prediction for the first time. Kernel density estimation is used to obtain the probabilistic forecast results. Through experiments in three-year actual data of Brussels, the superiorities of the proposed method and the significant improvement over conventional methods are verified. In comparison to the original NWP method, the proposed approach demonstrates significant improvements in accuracy. Specifically, it achieves a reduction in mean absolute error ranging from 25.04% to 48.12% for 1–4 step predictions, 14.80% to 21.27% for 5–8 step predictions, 6.40% to 11.10% for 9–12 step predictions, and 2.18% to 4.45% for 13–16 step predictions. [ABSTRACT FROM AUTHOR]

Driven by the demand for carbon emission reduction and environmental protection, battery swapping stations (BSS) with battery energy storage stations (BESS) and distributed generation (DG) have become one of the key technologies to achieve the goal of emission peaking and carbon neutrality. Therefore, this paper proposes a strategy to optimize the operation of BSS with photovoltaics (PV) and BESS supplied by transformer spare capacity. Firstly, it introduces the operation mechanism of BSS and uses the spare capacity of building special transformers and the roof PV to supply power to BSS to avoid the investment of transformers. Secondly, this paper establishes the load model of BSS and proposes the charging rules of battery swapping. Thirdly, a segmented pricing mechanism for the rental price of special transformers is formulated to guide BSS operators to preferentially rent spare capacity during low load rate periods. Aiming at the maximum daily profit of BSS, an optimization model is established to optimize the number of batteries to be charged and the charging status of BESS in each period; on this basis, the demand response model is further proposed. Simulation results show that the proposed strategy can improve the daily profit of BSS through shifting load. And the configuration of BESS can improve the battery swapping capacity and peak‐shaving ability. Moreover, the exponential segmented pricing mechanism can greatly reduce the number of high load periods and reduce the burden on the power supply. [ABSTRACT FROM AUTHOR]

Iraqi people have been without energy for nearly two decades, even though their geographic position provides a high intensity of radiation appropriate for the construction of solar plants capable of producing significant quantities of electricity. Also, the annual sunny hours in Iraq are between 3,600 to 4,300 hours which makes it perfect to use the photovoltaics arrays to generate electricity with very high efficiency compared to many countries, especially in Europe. This paper shows the amount of electric energy generated by the meter square of crystalline silicon in the photovoltaic (PV) array that already installed in 18 states in Iraq for each month of the year. The results of the meter-square of PV array in three tracking positions are presented in this paper. This paper shows that the average electricity generated in North cities (Dohuk, Al-Sulaymaniyah, and Erbil) are less than the southern cities in the winter season (three positions) by about 40-50%. Iraq has a stable PV electrical generation during all the year in all regions except the North cities while the highest cities in electricity generation are (Najaf and Al-Anbar). [ABSTRACT FROM AUTHOR]

With the steady increase in the use of renewable energy sources in the energy sector, new challenges arise, especially the unpredictability of these energy sources. This uncertainty complicates the management, planning, and development of energy systems. An effective solution to these challenges is short-term forecasting of the output of photovoltaic power plants. In this paper, a novel method for short-term production prediction was explored which involves continuous photography of the sky above the photovoltaic power plant. By analyzing a series of sky images, patterns can be identified to help predict future photovoltaic power generation. A hybrid model that integrates both a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) for short-term production forecasting was developed and tested. This model effectively detects spatial and temporal patterns from images and power output data, displaying considerable prediction accuracy. In particular, a 74% correlation was found between the model's predictions and actual future production values, demonstrating the model's efficiency. The results of this paper suggest that the hybrid CNN-LSTM model offers an improvement in prediction accuracy and practicality compared to traditional forecasting methods. This paper highlights the potential of Deep Learning in improving renewable energy practices, particularly in power prediction, contributing to the overall sustainability of power systems. [ABSTRACT FROM AUTHOR]

Hydropower and photovoltaic power are widely used as clean energy sources around the world. Hydro-Photovoltaic complementary is precisely the use of the regulation performance of hydropower stations and the peak regulation performance of PV electric fields to improve the system's power generation efficiency. The Yalong River basin as one of China's clean energy bases, is rich in water and light resources. And now its midstream Yangfanggou hydropower station has just been completed, and the relevant PV electric field is in the construction planning stage. It is worth studying how to effectively utilize its hydropower and PV output resources. Therefore, Yangfanggou hydropower station and its PV electric field are taken as the research objects in this paper. The possibility of hydro-photovoltaic complementarity is analyzed within the year and day respectively. Then, a short-term scheduling model of hydrophotovoltaic complementarity is constructed according to the principle, and its operation mode and effect are optimized using the genetic algorithm NSGA-II. The results indicate that the annual power generation of the system is increased by about 1.4 billion kWh from the original hydropower through the hydrophotovoltaic complementary, and the annual guaranteed output is also increased with a large increase degree at the same time. [ABSTRACT FROM AUTHOR]