Improve Power Generation Efficiency Research Articles

Potential of Cellulomonas fimi for polysaccharide-fueled microbial fuel cells.

This study examined the feasibility of using Cellulomonas fimi and Shewanella oneidensis as microbial fuel cells fueled by starch, cellulose, chitin, and chitosan. To our knowledge, this is the first report of power generation using C. fimi fueled by these polysaccharides other than cellulose, furthermore the first report of S. oneidensis fueled by chitosan. No differences were observed in the power generation capacities between C. fimi and S. oneidensis when chitin and chitosan were used. However, C. fimi demonstrated effective power generation from starch and cellulose, showing a maximum current density of 17.4mA m-2 for starch and 38.8mA m-2 for cellulose. S. oneidensis could not utilize these fuels to generate electricity. Power generation using C. fimi fueled by starch and cellulose produced acetic acid, lactic acid, and formic acid. However, when chitin and chitosan were used, only acetic acid was produced. These results indicate that electron transfer from C. fimi to the anode may be inefficient. The accumulation of organic acid in the anode solution indicates insufficient electron transfer to the anode. To improve power generation efficiency, it may be necessary to enhance electron transfer from the cells to the anode, for example, by adding a mediator.

Wear fault diagnosis in hydro-turbine via the incorporation of the IWSO algorithm optimized CNN-LSTM neural network

When a hydropower unit operates in a sediment-laden river, the sediment accelerates hydro-turbine wear, leading to efficiency loss or even shutdown. Therefore, wear fault diagnosis is crucial for its safe and stable operation. A hydro-turbine wear fault diagnosis method based on improved WT (wavelet threshold algorithm) preprocessing combined with IWSO (improved white shark optimizer) optimized CNN-LSTM (convolutional neural network-long-short term memory) is proposed. The improved WT algorithm is utilized to denoise the preprocessing of the original signals. Chaotic mapping, bird flock search, and cosine elite variation strategies are introduced to enhance the WSO algorithm's robust performance, and the CNN-LSTM model's hyperparameters are optimized using the IWSO algorithm to improve the diagnostic performance. The experimental results show that the accuracy of the proposed method reaches 96.2%, which is 8.9% higher than that of the IWSO-CNN-LSTM model without denoising. The study also found that the diagnostic accuracy of hydro-turbine wear faults increased with increasing sediment concentration in the water. This study can supplement the existing hydro-turbine condition monitoring and fault diagnosis system. Meanwhile, diagnosing wear faults in hydro-turbines can improve power generation efficiency and quality and minimize resource consumption.

A novel array connection strategy for improving power generation efficiency of photovoltaic systems during localized shading

A novel array connection strategy for improving power generation efficiency of photovoltaic systems during localized shading

Evaluation of Seasonal Prediction of Extreme Wind Resource Potential over China Based on a Dynamic Prediction System SIDRI-ESS V1.0

Wind resources play a pivotal role in building sustainable energy systems, crucial for mitigating and adapting to climate change. With the increasing frequency of extreme events under global warming, effective prediction of extreme wind resource potential can improve the safety of wind farms and other infrastructure, while optimizing resource allocation and emergency response plans. In this study, we evaluate the seasonal prediction skill for summer extreme wind events over China using a 20-year hindcast dataset generated by a dynamical seamless prediction system designed by Shanghai Investigation, Design and Research Institute Co., Ltd. (Shanghai, China) (SIDRI-ESS V1.0). Firstly, the hindcast effectively simulates the spatial distribution of summer extreme wind speed thresholds, even though it tends to overestimate the thresholds in most regions. Secondly, high prediction skills, measured by temporal correlation coefficient (TCC) and normalized root mean square error (nRMSE), are observed in northeast China, central east China, southeast China, and the Tibetan Plateau (TCC is about 0.5 and the nRMSE is below 0.9 in these regions). The highest skills emerge in southeast China with a maximum TCC greater than 0.7, and effective prediction skill can extend up to a 5-month lead time. Ensemble prediction significantly enhances predictive skill and reduces uncertainty, with 24 ensemble members being sufficient to saturate TCC and 12–16 members for nRMSE in most key regions and lead times. Furthermore, we show that the prediction skill for extreme wind counts is strongly related to the prediction skill for summer mean wind speeds, particularly in southeast China. Overall, SIDRI-ESS V1.0 shows promising performance in predicting extreme winds and has great potential to provide services to the wind industry. It can effectively help to optimize wind farm operating strategies and improve power generation efficiency. However, further improvements are needed, particularly in areas where prediction skills for extreme winds are influenced by smaller-scale weather phenomena and areas with complex underlying surfaces and climate characteristics.

Research on Aerodynamic Characteristics of Three Offshore Wind Turbines Based on Large Eddy Simulation and Actuator Line Model

Investigating the aerodynamic performance and wake characteristics of wind farms under different levels of wake effects is crucial for optimizing wind farm layouts and improving power generation efficiency. The Large Eddy Simulation (LES)–actuator line model (ALM) method is widely used to predict the power generation efficiency of wind farms composed of multiple turbines. This study employs the LES-ALM method to numerically investigate the aerodynamic performance and wake characteristics of a single NREL 5 MW horizontal-axis wind turbine and three such turbines under different wake interaction conditions. For the single turbine case, the results obtained using the LES-ALM method were compared with the existing literature, showing good agreement and confirming its reliability for single turbine scenarios. For the three-turbine wake field problem, considering the aerodynamic performance differences under three cases, the results indicate that spacing has a minor impact on the power coefficient and thrust coefficient of the middle turbine but a significant impact on the downstream turbine. For staggered three-turbine arrangements, unilateral turbulent inflow to the downstream turbine causes significant fluctuations in thrust and torque, while bilateral turbulent inflow leads to more stable thrust and torque. The presence of two upstream turbines causes an acceleration effect at the inflow region of the downstream single turbine, significantly increasing its power coefficient. The findings of this study can provide methodological references for reducing wake effects and optimizing the layout of wind farms.

Role of primary drivers leading to emission reduction of major air pollutants and CO2 from global power plants

Electricity production is a significant source of air pollution. Various factors, including electricity demand, generation efficiency, energy mix, and end-of-pipe control measures, are responsible for the emission changes during electricity generation. Although electricity production more than doubled from 1990 to 2017, air pollutant emissions showed a moderate increase or decrease, which was attributed to mitigating drivers such as increased clean energy use, improved power generation efficiency, and widespread installation of end-of-pipe control facilities. The absence of these mitigating drivers would have increased CO2, fine particulate matter (PM2.5), black carbon, SO2, and NOx emissions in 2017 by 165 %, 403 %, 1070 %, 614 %, and 274 % than their actual levels, respectively. The improved electricity generation efficiency reduced potential CO2, PM2.5, SO2, and NOx emissions by 30 %, 295 %, 119 %, and 52 % compared to actual emissions, respectively. Meanwhile, the installation of end-of-pipe facilities reduced potential SO2 and PM2.5 emissions by 34.7 and 4.0 Tg, respectively. Considerable differences in emissions among countries were found to be attributable to their differences in electricity demand and the implementation of local mitigating polices.

Fabrication and optimization of bioelectrochemical system using tetracycline-degrading bacterial strains for antibiotic wastewater treatment

In this study, a microbial fuel cell was constructed using Raoultella sp. XY-1 to efficiently degrade tetracycline (TC) and assess the effectiveness of the electrochemical system. The degradation rate reached 83.2 ± 1.8 % during the 7-day period, in which the system contained 30 mg/L TC, and the degradation pathway and intermediates were identified. Low concentrations of TC enhanced anodic biofilm power production, while high concentrations of TC decreased the electrochemical activity of the biofilm, extracellular polymeric substances, and enzymatic activities associated with electron transfer. Introducing electrogenic bacteria improved power generation efficiency. A three-strain hybrid system was fabricated using Castellaniella sp. A3, Castellaniella sp. A5 and Raoultella sp. XY-1, leading to the enhanced TC degradation rate of 90.4 % and the increased maximum output voltage from 200 to 265 mV. This study presents a strategy utilizing tetracycline-degrading bacteria as bioanodes for TC removal, while incorporating electrogenic bacteria to enhance electricity generation.

Model development and preliminary study of the concentrated-radiative cooling based thermoelectric generator

Photovoltaic (PV) cells, widely adopted in solar energy applications, achieve a maximum efficiency of only 30 % and remain inactive during nighttime. In contrast, solar-thermal power generation has surpassed 50 % efficiency. To address these limitations, we introduce the Solar- Radiative cooling -Thermoelectric system (Solar-RC-TE), integrating solar energy, radiative cooling (RC), and thermoelectric power generation. This system enhances energy density, reduces consumption, lowers costs, and minimizes environmental impact while enabling nighttime power generation. For the optimization of the Solar-RC-TE model to further improve power generation efficiency, we employ the comprehensive three-dimensional methodology of COMSOL, considering material properties, structural configurations, and environmental factors. Research findings reveal that a 710 mm × 710 mm radiative cooling film can generate 386.12 mV of voltage on the thermoelectric generator (TEG). Environmental factors, such as increased solar irradiance, lead to temperature elevation, affecting efficiency. Overheating and higher wind speeds also contribute to decreased TEG efficiency. Regarding materials, improving the thermoelectric figure of merit (ZT) and film emissivity positively impact power generation efficiency. Elevating the film emissivity from 0.7 to 0.89 significantly enhances performance by 1.2 %. These outcomes provide a tangible and practical solution for addressing energy crises and environmental concerns.

Application of Fuzzy Control and Neural Network Control in the Commercial Development of Sustainable Energy System

Sustainable energy systems (SESs) occupy a prominent position in the modern global energy landscape. The purpose of this study is to explore the application of fuzzy control and neural network control in photovoltaic systems to improve the power generation efficiency and stability of the system. By establishing the mathematical model of a photovoltaic system, the nonlinear and uncertain characteristics of photovoltaic system are considered. Fuzzy control and neural network control are used to control the system, and their performance is verified by experiments. The experimental results show that under the conditions of low light and moderate temperature, the fuzzy neural network control achieves a 3.33% improvement in power generation efficiency compared with the single control strategy. Meanwhile, the system can still maintain relatively stable operation under different environmental conditions under this comprehensive control. This shows that fuzzy neural network control has significant advantages in improving power generation efficiency and provides beneficial technical support and guidance for the commercial development of SESs.

Application of optimized photovoltaic grid-connected control system based on modular multilevel converters

Photovoltaic power generation is a promising method for generating electricity with a wide range of applications and development potential. It primarily utilizes solar energy and offers sustainable development, green environmental benefits, and abundant solar energy resources. However, there are many external factors that can affect the output characteristics of Photovoltaic cells and the effectiveness of the grid-connected control system. This study describes the introduction of Modular Multilevel Converter (MMC) technology into photovoltaic power generation systems to improve power generation efficiency. It proposes optimizing and improving the technology by adjusting the temperature and magnitude of lighting and combining traditional algorithms to propose a composite control algorithm. The photovoltaic power generation system employs the modular multi-level converter technology to enhance power generation efficiency alongside optimization and improvement. The temperature and size of light are regulated alongside the traditional algorithm to introduce the composite control algorithm. The improved composite algorithm surpasses the traditional one after experimental comparison of the results. The testing of a model photovoltaic power grid-connected system shows that the combination of modular multi-level converter technology and a photovoltaic grid-connected system, incorporating composite proportional integral control and quasi-proportional resonant control algorithms, yields improved results and feasibility. With rationality and effective control. The simulation results show that at 0.5 s, the light intensity suddenly increases from 750 to 1000 W/m2, and the direct-current voltage suddenly increases for a short time, but then decreases rapidly and finally returns to a stable level close to the rated voltage. From this, it can be seen that when the light intensity continues to change, the voltage value on the direct-current bus side of this MMC grid-tied photovoltaic system can still be maintained close to the rated value, ensuring the operational stability of the entire system. Sensibly and effectively controlled. The implementation of MMC technology in photovoltaic power generation systems enhances power generation efficiency, whilst simultaneously supporting the advancement of photovoltaic power generation and contributing towards environmental protection in the long term.

A novel approach to evaluate dynamic performance for photovoltaic system using software platform

With the growing demand for renewable energy, solar photovoltaic (PV) systems have gained popularity as a reliable source of clean electricity. However, the performance of these systems can be limited by factors such as suboptimal maximum power point tracking (MPPT) algorithms. In order to improve the power generation efficiency of PV systems, it is important to evaluate the performance of dynamic MPPT algorithms that can adapt to varying operating conditions. Traditionally, such evaluations have been time consuming and expensive, often requiring extensive testing and measurement equipment. In this paper, we propose a novel approach to evaluate dynamic MPPT performance very quickly and simply using PSIM software. This approach enables accurate and efficient evaluation of MPPT performance under a wide range of operating conditions, while minimizing the cost and time involved in traditional testing methods. When applying the proposed method to a 3.7 kW inverter using the traditional perturbation and observation (P and O) method, we found that the highest average efficiency was 98.92% at an MPPT control period of 0.1s and a voltage perturbation of 1 V. This evaluation technique provides valuable insights into the design and optimization of more efficient MPPT control algorithms, leading to improved power generation efficiency and increased adoption of solar PV systems.

Heat storage and release characteristics of a prototype CaCO3/CaO thermochemical energy storage system based on a novel fluidized bed solar reactor

CaCO3/CaO thermochemical energy storage (TCES) system has a high heat storage density (1780 kJ/kg) along with high heat storage and release temperature (650–850 °C), which can be applied to concentrated solar power (CSP) technology utilizing CO2 Brayton cycles to improve power generation efficiency. There are several problems to be urgently resolved in current CaCO3/CaO solar calciners, such as poor heat and mass transfer and low thermochemical efficiency. This paper proposed a prototype CaCO3/CaO TCES system based on a novel fluidized bed solar reactor, which has a serrated arc surface in alignment with the direction of the incident solar rays to receive concentrated solar energy. The natural limestone particles were used as the reactive particles. The effects of operating modes (intermittent/continuous), initial mass of reactive particles, and initial carbonation temperature Tcar,initial on the heat storage and release characteristics of the TCES system were experimentally explored. During the carbonation reaction stage, the effective carbonation degrees in the continuous mode were generally higher than in the intermittent mode under the experimental conditions and declined at a slower rate. The reactive particles were recovered from the reactor for microstructural characterization after completing the experiments, high-temperature particle agglomeration and pore structure clogging were directly responsible for the decrease of the effective carbonation degree. In the continuous mode, the average thermochemical and total thermal efficiencies of 24.7 % and 53.5 % were obtained in the 1st calcination reaction, respectively, which represents significant advantages over other CaCO3/CaO solar calciners. The novel fluidized bed reactor demonstrated excellent fluidization in the experiments and did not exhibit mechanical deformation after multiple experiments under non-uniform concentrated radiation provided by an 84 kWe high-flux solar simulator. Overall, this paper provides a successful gas-solid reactor design to be coupled with CSP technology.

Damage identification of wind turbine blades based on deep learning and ultrasonic testing

ABSTRACT Ultrasonic testing (UT) is an effective method for detecting internal damage in wind turbine blades. However, manual interpretation is time-consuming and subjective. To automate UT in the wind turbine industry, an image detection model based on deep learning called UCD-YOLO (Ultrasonic C-scan image Detection You Only Look Once) is proposed to detect internal damage such as stratified and lack of glue in wind turbine blades. In order to enhance the feature extraction ability under complex damage forms, the use of deformable convolutional networks and context augmentation module are proposed. Designing a lightweight cross stage partial spatial pyramid pool fast (LCSP_SPPF) module, which allows the model to learn more information and improve detection accuracy. The introduction of wise intersection over union (WIoU) improves the convergence speed while reducing the regression error. Using UT devices to collect damage images of wind turbine blades and conducting comprehensive evaluation experiments on the detection performance of the proposed method. Results show that compared to the YOLOv5, UCD-YOLO is not only lighter but also achieves a 7.5% increase in mean average precision (mAP50). And compared with other detection methods, further verifying that the proposed method can quickly and effectively detect wind turbine blade internal damage, improving power generation efficiency.

Improving thermoelectric properties of Bernal bilayer graphene by strontium intercalation: A DFT study

To meet the growing energy demand, improving power generation efficiency is required. This study explores the potential of enhancing the thermoelectric properties of Bernal bilayer graphene (BBG) through strontium (Sr) intercalation. Density functional theory and Boltzmann transport equations are employed to conduct ab initio calculations to evaluate its electronic and thermoelectric characteristics. The results reveal that the Sr intercalated AB stacking BG (Sr intercalated in BBG), designated as SrC6, exhibits metallic behavior, resulting in n-type conductivity and a noticeable shift of the Fermi level toward the conduction band. The Seebeck coefficient increases considerably with temperature, shifting from hole carrier to electron carrier. When comparing the intercalated compound with pristine bilayer graphene, a significant improvement in electrical conductivity is observed and remarkable improvement of ZT coefficient at temperature range [300, 1000K]. The alkaline earth metals Sr intercalation in bilayer graphene is therefore a new suitable way to improve the TE properties of bilayer graphene in order to making them a good candidate for TE devices.

Performance prediction of a supercritical CO2 Brayton cycle integrated with wind farm-based molten salt energy storage: Artificial intelligence (AI) approach

Using energy storage technologies in combination with renewable energy sources can improve power generation efficiency and reliability. Here, we predict the performance of a wind farm-based molten salt energy storage system combined with a supercritical CO2 Brayton cycle (s-CO2BC) with the aid of artificial intelligence (AI). For this purpose, the Grasshopper Optimization Algorithm (GOA) and Particle Swarm Optimization (PSO) are applied to train a Multilayer Perceptron Artificial Neural Network (MLP-ANN). The proposed AI approaches model the complex interactions between the wind farm, the molten salt energy storage, and the s-CO2BC components. Integrated system simulations under various operating conditions are used for training the performance prediction model. Power generation, energy storage, and system efficiency can all be accurately predicted by a trained AI model. In addition to providing valuable insight into the optimal operation and control strategies of the integrated system, the proposed approach enables the efficient utilization of renewable energy resources and energy storage to generate sustainable power. This study contributes to the development of AI-based optimization approaches and predicts the performance of integrated renewable energy systems, resulting in improved grid integration and stability. According to the results, the PSO method yields average absolute percentage deviations (AAPDs) between 0.47% and 0.51% across different parameters, while GOA yields AAPDs between 0.05% and 0.27%.

A new dynamic 2D fusion model and output characteristic analysis of floating photovoltaic modules considering motion and environmental factors

As a new application form, floating photovoltaic power generation is a green and sustainable energy source with immense development potential. However, built on marine or riverine surface, the floating photovoltaic system not only faces challenges from environment factors that land-based photovoltaic system meets, but also faces challenges from the motion of the floating bodies which are caused by the continuous external forces of wind and waves. Modelling and analyzing the output characteristics of floating photovoltaic modules is desirable and useful to improve power generation efficiency. This paper presents a new dynamic 2D fusion model and analysis method for analyzing the dynamic characteristics of floating photovoltaic modules under continuous changes in motion, irradiance, and temperature caused by actual wind and waves conditions. To achieve accurate measurement of floating photovoltaic modules, the coupling relationship between the motion state and the environmental factors is established. Then the motion state modeling of floating photovoltaic modules based on multi-sensors information fusion is established to acquire the absolute attitude angle with the continuous superposition of high and low frequency motion in the geographical coordinate system. On this basis, a new dynamic 2D fusion model of floating photovoltaic modules is set up to obtain the dynamic output characteristics, which integrates mechanical motion state information with the electrical parameters. Finally, the proposed modelling and analyzing method is evaluated with experimental data. The experimental and analytical results show that the accuracy of the 2D fusion model is higher than 98.7%, the output efficiency of floating photovoltaic modules is only 94.2% compared to the static state, and power generation efficiency can be increased by 2.5% by using the proposed method. The new modelling method proposed in the paper can accurately obtain the output characteristics of the floating photovoltaic system under actual operating conditions, which can effectively improve the efficiency of power generation.

Rapidly Determine the Maximum Power Point in the Parallel Configuration of the Photovoltaic System.

The maximum power point tracking (MPPT) solutions improve power generation efficiency, quickly stabilizing the output waveform of photovoltaic (PV) systems under variable operating conditions. Along with new algorithms, improved and adjusted methods to exploit energy from PV systems are increasingly being researched and proposed. However, the proposed solutions based on the traditional algorithms and their improvements have poor performance, while the advanced algorithms or hybrid methods bring high performance but need to be simplified, and the response speed is higher. Moreover, a suitable PV configuration makes choosing a simple but highly efficient algorithm, especially in low-power PV system applications such as rooftop solar power, traffic lights, and moving vehicles…where the number of PV panels is insufficient to implement flexible configurations. This paper proposes a modified version of the Perturb and Observe (MPO) algorithm to improve MPPT performance and increase convergence speed in the parallel structure of PV panels. The Short-Circuit Current (Isc) and Open-Circuit Voltage (Voc) are calculated directly at specific operating conditions to quickly determine the potential maximum power point (MPP) that will reduce power interruptions and increase power generation efficiency compared to periodic updates. Therefore, the proposed solution converges faster, with higher efficiency, and the output signal in static and dynamic MPPT situations is more stable. The results show that the highest efficiency in simulation and experiment is 99.99% and 99.93%, respectively, while the convergence speed is 0.01 s and 0.03 s, respectively. They are better than the traditional Perturb and Observe (P&O) algorithm, the Variable Step Size Perturb and Observe (VSSP&O) method, and the Particle Swarm Optimization (PSO) technique under the same operating conditions. In addition, its performance and convergence speed are also compared with the latest introduced algorithms. The results show that it is valuable and reliable for parallel PV configuration.

Laser-texturing of stainless steel as a corrosion mitigation strategy for high-temperature molten salts applications under dynamic conditions

Molten salts are utilized in a wide range of applications including nuclear, fuel cells, Carnot batteries, and Concentrated Solar Power (CSP). In order to enhance the profitability of next-generation CSP plants, increasing their efficiency is imperative. Ternary carbonate salts are capable of elevating the maximum CSP operating temperature, which results in improved power generation efficiency. However, higher temperatures lead to accelerated corrosion and more severe conditions for structural materials. In this study, nanosecond laser micromachining is investigated as a potential corrosion mitigation technique for molten carbonate salts in dynamic conditions. SS310 samples with laser-induced surface textures are subjected to the flow of molten Li2CO3–K2CO3–Na2CO3 at 600 °C for a period of 600 h. Laser treatment significantly impedes corrosion in SS310, as untreated samples display pitting and scaling, while minimal surface changes are noted on treated samples. Cross-sectional analysis of the samples reveals considerable peeling in the untreated samples and a 45% increase in the corrosion rate compared to the treated samples. The concentrations of Cr and Mg in the salt (determined through ICP analysis) are found to be twofold and fourfold higher than those in treated samples. Laser treatment renders Cr and Mg less accessible to the salt ions, particularly Li, which results in improved corrosion resistance.

Experiment study on startup characteristics and operation performance of PV/T solar assisted heat pump water heater system driven by direct current variable frequency compressor

The use of renewable energy can alleviate climate warming and environmental pollution. PV/T (photovoltaic/thermal) system has been widely concerned and rapidly developed owing to its capability of generating electric and thermal energy simultaneously. In the article, a PV/T solar assisted heat pump water heater system driven by direct current variable frequency compressor was proposed to meet the growing needs of residents for domestic hot water. This system was tested in different working conditions in Lanzhou, and the startup characteristics and operation performance were evaluated. The results indicate that at the same ambient temperature, low speed is conducive to the continuity of the refrigerant cycle and improves the system performance. In this system, the initial water temperature of the water tank was 23.1 °C and after 2.5 h it reached 53 °C. The average power generation efficiency is 13.64%, the average thermal efficiency is 26.42%, the overall efficiency is 62.69% and the average COP (coefficient of performance) is 4.87. Compared with traditional PV (photovoltaic) modules, the average temperature reduction is 11.2 ℃. With the increase of solar radiation intensity, the electrical power of the system will increase and the electrical efficiency will decrease. The average daily electrical efficiency of PV/T system is 13.64%, which is 6.25% higher than that of uncooled PV panel. In addition, controlling a proper compressor speed is crucial for enhancing system performance. The PV/T collector can remarkably reduce the temperature of PV panel, improve power generation efficiency and comprehensive utilization efficiency of solar energy.

Jointly improving energy efficiency and smoothing power oscillations of integrated offshore wind and photovoltaic power: a deep reinforcement learning approach

This paper proposes a novel deep reinforcement learning (DRL) control strategy for an integrated offshore wind and photovoltaic (PV) power system for improving power generation efficiency while simultaneously damping oscillations. A variable-speed offshore wind turbine (OWT) with electrical torque control is used in the integrated offshore power system whose dynamic models are detailed. By considering the control system as a partially-observable Markov decision process, an actor-critic architecture model-free DRL algorithm, namely, deep deterministic policy gradient, is adopted and implemented to explore and learn the optimal multi-objective control policy. The potential and effectiveness of the integrated power system are evaluated. The results imply that an OWT can respond quickly to sudden changes of the inflow wind conditions to maximize total power generation. Significant oscillations in the overall power output can also be well suppressed by regulating the generator torque, which further indicates that complementary operation of offshore wind and PV power can be achieved.