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This study introduces a novel method to assess solar-wind hybrid energy systems, utilizing data analysis for key metrics (Performance Yields Y R , Capacity Factor CF , and Performance Ratio PR), focused on their uncertainties estimation. Employing a bootstrapped-based K-means algorithm on features from a local weather station in Universidad de La Salle, Bogotá, the approach evaluates the technical feasibility of a photovoltaic system on the campus. It discusses the clustering algorithm advantages, emphasizing its suitability for modeling continuous processes in renewable energy systems. The hypothesis assumes independence and identically distributed centroid coordinates, aligning with large-scale energy system characteristics. Methods include data normalization, feature selection, statistical calculations, bootstrapping, and Probability Density Functions (PDF) estimation through a Maximum Likelihood Estimator (MLE) to assess uncertainties. Results suggest the unfeasibility of a wind energy system due to technological constraints. The study evaluates system flexibility, and design considerations, providing valuable decision-making insights. The conclusion highlights the potential use for accurate estimations, enhancing the system reliability. [ABSTRACT FROM AUTHOR]

We propose a self-sustaining power supply system consisting of a "Hybrid Energy Storage System (HESS)" and renewable energy sources to ensure a stable supply of high-quality power in remote islands. The configuration of the self-sustaining power supply system that can utilize renewable energy sources effectively on remote islands where the installation area is limited is investigated. It is found that it is important to select renewable energy sources whose output power curve is close to the load curve to improve the efficiency of the system. The operation methods that can increase the cost-effectiveness of the self-sustaining power supply system are also investigated. It is clarified that it is important for increasing the cost effectiveness of the self-sustaining power supply system to operate the HESS with a smaller capacity of its components by setting upper limits on the output power of the renewable energy sources and cutting the infrequent generated power. • Self-sustaining power supply system with renewable energy source and HESS is proposed. • Proposed power system enables stable supply of high-quality power to remote islands. • Selecting renewable energy source according to power consumption trend is important. • We get optimal ratios of capacity and output suppression of renewable energy sources. • These optimal ratios are important for efficient and stable long-term power supply. [ABSTRACT FROM AUTHOR]

This study examines the drivers and development strategies of PV and wind energy development in China based on the logarithmic mean Divisia index (LMDI) model and the elasticity analysis model. The study revealed a significant increase in the value added of photovoltaic (PV) and wind power generation in China, with growth rates of 400.54 % and 102.27 %, respectively, from the 12th Five-Year Plan to the 13th Five-Year Plan. The primary driver of PV power generation in each region is the substitution effect between PV power and traditional energy sources. The key drivers of China's wind power generation are the substitution factor and economic considerations between wind power and traditional energy sources, demonstrating that China's wind power generation is influenced by both production and consumption factors. Model predictions indicate an upward trend in PV and wind power generation across all provinces in China. Western China continues to be the primary region for future PV power generation, while wind power generation in certain regions may experience a decline. The strategic analysis results suggest that a capital-led strategy continues to be the preferred approach for the future advancement of PV power generation in Chinese provinces, while the development of wind energy should be tailored to specific circumstances. [Display omitted] • A feasible analytical framework for regional energy development strategies is proposed, utilizing the LMDI model. • We offer a comprehensive classification of the factors influencing wind and PV power generation in China. • China's PV development is driven by the production sector, and wind power generation is driven by a combination of production and consumption. • China's wind energy development lacks market mechanisms. • Different development strategies should be adopted for future PV and wind energy development in China. [ABSTRACT FROM AUTHOR]

As one of the most efficient and advantageous sources of renewable energy, wind energy is being developed and utilized at an expansive scale. The increase in installed capacity and the trend toward high-power wind turbines highlight the impacts of common-mode voltage (CMV), because CMV induces high-frequency electromagnetic interference (EMI) noise and destructive bearing currents through paths consisting of parasitic and coupling parameters. This review briefly introduces how CMV causes damages to wind power generation system, and then introduces CMV suppression strategies, including hardware-based and software-based methods. Three typical hardware-based methods are reviewed, including enhancing the components or structure of the generator, adding common-mode filters to the electric circuits, and changing the power converter topologies. These methods can effectively reduce CMV but introduce challenges such as demanding routine maintenance, and hardware redesigns. Software-based methods reviewed are mainly the modified pulse width modulation (PWM) schemes for the both the conventional three-phase two-level converters and the multilevel or multiphase converters. They actively suppress the source of CMV and have good cost-effectiveness. For example, a modified carrier-based PWM method can reduce the CMV root mean square value by more than 90 %. Pros and cons of all the reviewed CMV suppression methods are presented, which helps the readers to identify the most suitable solutions for practical applications. In general, it is recommended to adopt electrically insulated bearings, hybrid filters, multilevel converters, modified carrier-based PWM methods to compose the best wind power generation system in terms of the CMV performance. • Illustrates how common-mode voltage causes electromagnetic interference and bearing current in wind power systems. • Hardware-based solutions include improving the generator, adding EMI filters, and changing the power converter topology. • Software-based methods for three-phase two-level converters and multilevel or multiphase converters are reviewed. • Discuss pros and cons of common-mode voltage suppression methods to choose the best solutions for specific applications. [ABSTRACT FROM AUTHOR]

In this paper, two variants of the magnetless dual rotor wound field excited flux switching generator (DRWFEFSG) are comparatively analyzed for single rotor wind turbine (termed as machine I (MI)) and counter rotating wind turbine applications (termed as machine II (MII)) for household direct current (DC) microgrid system. First, design concept and integration to wind turbine system are discussed. Secondly, comprehensively performance analysis i.e., static analysis, dynamic load/speed analysis and electromagnetic performance analysis are executed. Finite element analysis (FEA) based static analysis evident that MI offers 7.6% higher torque in inner rotor whereas MII exhibits 18% higher torque in outer rotor. Dynamic respond unveils that in comparison to MII, MI suffers from the issue of voltage regulation. Additionally, electromagnetic analysis reveals that both MI and MII exhibits excellent response under variable field Magneto-motive force (MMF). Comparative analysis unveils that MII requires lower field MMF to achieve same response to counterpart MI. Quantitative analysis divulge efficiency of 88.49% and power factor of 0.93 in MI whereas MII at rated operating point demonstrates 85.9% efficiency and 0.96 power factor. Finally, effectiveness of the study and validity of DRWFEFSG for wind turbine applications are tested with hardware prototypes of both MI and MII that fairly matched with FEA results. • Generator design and comparative analysis for household DC microgrid system. • Generator design for co-rotating and counter rotating wind turbine application. • Generator for rooftop wind turbine integrated with wind turbine. • Improve power wind turbines generators. • Analysis and comparative study of generator design for wind power generation. [ABSTRACT FROM AUTHOR]

• A novel wind power prediction model based on polynomial chaotic expansion method. • Global sensitivity analysis for quantitative description of wind speed and direction. • Uncertainty of output power is described through mean and standard deviation. • Distribution of output power is analyzed based on the probability density function. Wind farm power prediction plays an important role in power system dispatching, stable operation and maintenance. And the uncertainty of medium and long-term meteorological environment brings uncertainty to the prediction of wind farm power. A statistical method for predicting the medium and long-term power of the wind farm is proposed in this paper, which takes into account the wake effect and the wind speed differences in the vertical wind profile. The uncertainty model of wind farm power is developed by combining numerical weather prediction and generalized Polynomial Chaos Expansion (PCE) method with wind direction and wind speed as uncertainties. And the PCE coefficients are solved using the stochastic response surface method. The mean, standard deviation, and global sensitivity of the wind farm power can be easily calculated and analyzed by using the coefficients. The proposed method is validated from two scales of single moment and continuous time, and the validation shows that the proposed method performs better compared to Monte Carlo simulation, especially in terms of running time. In addition, according to the global sensitivity, the effect of wind speed on output active power is significantly greater than that of wind direction. [ABSTRACT FROM AUTHOR]

This paper describes the design and implementation of a H ∞ -based robust controller for a commercial 2MW variable-speed variable-pitch wind turbine. The single controller is able to deliver specification performance for the entire full load region. Various aspects of modeling and design procedures including reduced order model validation, torsional mode damping of drivetrain and uncertainty estimation are explained in detail. To make the design more reliable and industrially attractive, simple routines for the derivation of required weighting functions are introduced. To investigate the benefits of the suggested design, its closed-loop performance is compared with the wind turbine's baseline controller and an alternative state–space controller proposed in the literature. This is first evaluated using a FAST-based simulator under IEC-61400 compatible scenarios. It is then verified by a real-life hardware in the loop simulator using an industrial PLC. • Reduced-order model is developed using Kane's method and validated with FAST code. • Controller scheduling is obviated with proper active drivetrain damping strategy. • A time-based identification method is introduced for reliable uncertainty estimation. • Performance weighting functions are obtained using simple routines. • Proposed controller delivers better performance than the benchmark ones. [ABSTRACT FROM AUTHOR]

Accurate energy forecasting is of great significance for the energy sector to formulate short-term plans and long-term development strategies for meeting energy needs. This study develops a stacked hierarchy of reservoirs (DeepESN) for forecasting energy consumption and wind power generation by introducing the deep learning framework into the basic echo state network. DeepESN combines the powerful nonlinear time series modeling ability of echo state network and the efficient learning ability of the deep learning framework. Two comparative examples and an extended application are analyzed to validate the accuracy and reliability of DeepESN. These comparative examples reveal that DeepESN outperforms the existing popular models, persistence model, back-propagation neural network, and echo state network. Moreover, compared with echo state network, DeepESN shows 51.56%, 51.53%, and 35.43% improvements in terms of mean absolute error, root mean square error, and mean absolute percentage error in the extended application, respectively. Therefore, DeepESN is an appropriate tool for forecasting energy consumption and wind power generation on account of its effective and stable forecasting performance. • Develop DeepESN by introducing deep learning idea into echo state network (ESN). • DeepESN is firstly applied to energy forecasting. • DeepESN outperforms the existing popular models for two comparative examples. • Compared with ESN, DeepESN yields improvement of 35.43% on MAPE in example 3. [ABSTRACT FROM AUTHOR]

A battery energy storage system (BESS) can smooth the fluctuation of output power for micro-grid by eliminating negative characteristics of uncertainty and intermittent for renewable energy for power generation, especially for wind power. By integrated with lithium battery storage system the utilization and overall energy efficiency can be improved. However, this target could be obtained only if the BESS is optimal matched. For this issue, the degradation of battery capacity has a significant impact on the operating costs of Wind-ESS system. The research focus on the optimal method for components sizing of BESS in Wind-ESS system with independent system operators. We present an operating cost model for the hybrid energy storage system considering capacity fading of lithium battery in the cycle life. For the optimal objective of component sizing, the global optimization method of dynamic programming (DP) is adopted by setting operating costs and capacity degradation as optimal objectives under the constrains of performance for lithium battery and requirement for grid operation. Based on the DP algorithm and capacity degradation of battery model, the optimal output of the wind power is obtained. The rule based method and genetic algorithm are also be used for simulation. The simulation results show that compared with other two optimal approaches, capacity degradation and operation cost of energy storage for wind power generation system are significantly reduced. • Construction of a system operating cost model. • Use of dynamic programming to reduce operating cost. • Capacity sizing method for wind power-energy storage system. [ABSTRACT FROM AUTHOR]

The energy sector is highly dependent on climate variability for electricity generation, maintenance activities and demand. In recent years, a few climate services have appeared that provide tailored information for the energy sector. In particular, seasonal climate predictions of wind speed have proven useful to the wind power industry. However, most of the service users are ultimately interested in forecasts of electricity generation instead of wind. Although power generation depends on many factors other than wind conditions, the capacity factor is a suitable indicator to quantify the impact of wind variability on production. In this paper a methodology to produce seasonal predictions of capacity factor for a range of turbine classes is proposed for the first time. The strengths and weaknesses of the method are discussed and the forecast quality is evaluated for an application example over Europe. • A method to produce seasonal forecasts of renewable generation is presented. • A unified approach that fits the specific nature of any wind farm is employed. • Some limitations of seasonal prediction systems are identified and addressed. • The generation forecasts perform better than climatology in some European regions. [ABSTRACT FROM AUTHOR]

• Daily operation of hydro based systems avoids wind curtailment for energy security. • This paper models wind generation as decision variable of optimization problem. • Present/future costs optimized with unit commitment and network representation. • Curtailing wind is economic without increasing hydro based systems level of risk. • Pumped-storage hydroelectric plants reduces both cost and wind curtailment. This paper assesses some important economic issues that arise with increased wind power penetration in hydrothermal systems. It is shown that it may become necessary to restrict the injection of wind power into the grid to reduce the total operation cost, even in predominantly hydro systems which are very suitable to offset the variability in wind power. In the approach applied in this paper for the Daily Operation Scheduling (DOS) of hydrothermal systems, although wind power plants are designed to be dispatched with priority as compared to other sources, their power injection into the grid is not mandatory. Thus, the level of generation of each wind power plant becomes one of the decision variables of the optimal system generation dispatch, and therefore wind can be curtailed not only due to feasibility requirements, but also to obtain a more economic operation. The DOS problem is solved via Mixed-Integer Programming, considering the unit commitment constraints of thermal generating units and many operational constraints of hydroelectric power plants, in addition to the representation of the electric grid. A system based on the IEEE Reliability Test System (RTS) was used, with real data from wind, thermal and hydroelectric power plants of the Brazilian power system. The results show that restricting the injection of mandatory wind generation into the grid can reduce the total operation cost, even when the hydro share in the generation mix is high. In addition, the economic benefits of storing energy in pumped-storage hydroelectric plants to consume the surplus wind energy in this hydro-dominated context are also assessed. [ABSTRACT FROM AUTHOR]

As the energy system becomes increasingly dependent on intermittent meteorological sources, there is a need to understand better the possible consequences of a changing climate on weather variability and how it will affect wind power generation in the future. Most previous validation studies of regional climate datasets focus on the distributional properties or, in fewer cases, the variability at lower frequencies, such as interannual and seasonal. However, accurate wind variability at a high-enough resolution (e.g., hourly) is crucial for many aspects of power and energy system analyses. Using European country-level wind generation data and metrics relevant to power and energy system applications, we validate the wind speed output from seven regional climate models from the EURO-CORDEX project. We show that the EURO-CORDEX models adjusted with the Global Wind Atlas (GWA2) can simulate temporal dependencies and generation distributions with accuracy similar to or better than the ERA5 reanalysis. The spatial correlations, however, are overestimated compared to observations by most analysed models. Assuming the GWA2 scaling is also valid for the future, the projections under the RCP8.5 scenario show a slight negative trend (2026–2065) in capacity factors for most analysed European countries. • Three-hourly climate data validated for power and energy system applications. • Only wind speed time series at heights compatible with modern wind turbines selected. • Focus on the correlations of wind power generation at country level in Europe. • Accuracy of EURO-CORDEX simulations adjusted by microscale details. [ABSTRACT FROM AUTHOR]

[Display omitted] • A research of biomimetic array membranes for water harvesting and power generation. • The mechanism to induce high piezoelectric self-assembly PVDF was revealed. • An optimal water collection efficiency of 0.63 LMH was achieved by the membrane. • The membrane was able to produce a notable power density of 99.2 mW/m2 from wind. Due to geographical restrictions, water and electricity shortages have always been crucial problems in remote areas. Among the numerous potential solutions, the fog harvesting technique using a film has been recognized as one of the effective methods for water collection in dry regions. In this study, researchers drew inspiration from desert beetles in nature to fabricate a biomimetic chitosan hydrophilic array coated on a hydrophobic PVDF/graphene (PG) film that is capable of efficient fog water harvesting, reaching an efficiency of up to 0.63 LMH. Graphene and an ionic liquid (IL) were used to synergistically induce the self-assembly of PVDF crystalline phases into a piezoelectric β-phase, and the resulting optimized PG/IL 1.5 wt% film could have a voltage output of up to 13 V (±6.5 V). Upon conducting an integrated assessment of water and electricity extraction from the film, and the results indicated a water collection efficiency of 0.74 LMH and a power generation of 99.2 mW/m2 at a fog wind speed of 4 m/s. Based on the aforementioned research findings, we have demonstrated that a single film utilizing fog and wind as driving forces can achieve simultaneous water and electricity production, providing new research directions for the development of next-generation functional films in the future. [ABSTRACT FROM AUTHOR]

This work proposes a new robust coordination procedure for the coordination of overcurrent relays that takes into account the specific requirements and conditions that arise in wind power generation plants. It is discussed here that, in those systems, coordination schemes that guarantee that protection coordination is maintained in the face of the simultaneous loss of several lines in the network can be of great importance, although this characteristic is not considered relevant in the usual transmission and distribution systems. Several new features have been incorporated in the proposed procedure, for achieving the computational efficiency that is necessary for performing the proposed task. A new mathematical model is presented, considering the characteristic curves of relays as design variables and including new constraints/penalties related to the maximum desirable relay actuation time. The proposed tool integrates a Differential Evolution algorithm with Linear Programming and a new Discrete Feasibility Search operator to minimize the sum of relay actuation times in scenarios for multiple fault conditions and to maximize robustness of the solution under scenarios with changes in network topology. The proposed robust coordination procedure with those features constitutes the first coordination algorithm designed for dealing with the reconfigured topology after the simultaneous loss of multiple lines in a network. A set of Pareto-optimal solutions is delivered, allowing the designer to perform a trade-off analysis between smaller actuation times and smaller disconnected areas after the occurrence of a fault. In the specific case of wind power generation plants, those robust solutions allow a more efficient use of resources allocated to network maintenance. A case study of the application of the proposed methodology to the protection of a real wind farm is presented, showing that the proposed procedure allows a reduction of 44.9% of the non-delivered energy in the case of the simultaneous occurrence of two faults, and of 51.9% of the non-delivered energy in the case of three simultaneous faults. • Methodology for overcurrent relay coordination problem in wind farms. • Resulting coordination becomes robust against the simultaneous loss of several lines. • Allows the network reconfiguration after faults, performing the recovery of lost generation. • The mainteance after faults may be scheduled, rather than always being emergency. • Case study: electric network protection in a real wind farm. [ABSTRACT FROM AUTHOR]

Wind power efficiency is an essential factor affecting wind power development, and efficient wind power control methods are the key to improving wind power efficiency. Previous wind power control methods require high internal system expertise in internal systems and struggled to balance the accuracy of the maximum power control and the output stability under high wind speed. Therefore, this paper proposes a reward-adaptive control method for wind power tracking based on Deep Deterministic Policy Gradient (DDPG). The method can use one controller to simultaneously control the generator torque and pitch angle in various operating conditions following the system state and the designed flag of operating conditions, thus enabling efficient tracking of the maximum power generation, and stabilizing the output power under high wind speed. Moreover, this paper proposes an internal and external reward algorithm that integrates the Intrinsic Curiosity Module (ICM) and the Actor–Critic (AC) architecture, which can adaptively calculate the reward of DDPG, thereby effectively resolving the sparse reward problem, accelerating the convergence of neural network, and improving the learning effect. From the simulation, the control method proposed in this paper can effectively improve the power generation efficiency under turbulent wind speed, reduce the pitch angle variation by about 30%, and improve the power tracking accuracy by more than 70%. • A reward-adaptive wind power control method based on DDPG is studied. • The method uses one controller to control the power in different operation. • A reward algorithm integrates the ICM and the Actor–Critic is proposed. • The results show that the method improves the power tracking performance. [ABSTRACT FROM AUTHOR]

• The dominant turbulence frequency components strongly correlated with the optimal control parameters of maximum power point tracking in turbulent wind are found. • The extraction and index description of dominant turbulence frequency components are realized based on the equivalent sinusoidal wind. • By utilizing the equivalent sine frequency, the sample size required to construct the optimal parameter model that describes the relationship between turbulence characteristics and optimal control parameters is significantly reduced. • The control parameters estimated by the optimal parameter model based on equivalent sine frequency are closer to the actual optimal value. • A method for calculating and applying the equivalent sine frequency in maximum power point tracking control of wind turbine is proposed. The optimal parameter model method for maximum power point tracking (MPPT) control of wind turbines has good wind energy capture performance. However, there is a precondition that the quantitative relationship between optimal control parameters and turbulence characteristics is accurately described. The resolution of the existing turbulence frequency characterization indicators are insufficient. Therefore, a large number of sample data need to be obtained to clearly show the statistical relationship between them and the optimal control parameters, which is extremely time-consuming, and has a negative impact on the optimization results of control parameters. This paper equates the turbulent wind to the sinusoidal wind with the objective of the results of both acting on wind turbines are the same, so as to realize the extraction and index description of the dominant turbulent frequency components strongly related to the optimal control parameters. On this basis, a MPPT control method based on equivalent sinusoidal wind is proposed. This method greatly reduces the required sample size, and makes the setting of control parameters closer to the optimal value, which can further improve the efficiency of wind energy capture. Simulation analysis and experimental results based on wind turbine simulator verify the effectiveness of the proposed method. [Display omitted] [ABSTRACT FROM AUTHOR]

• A two-stage method is proposed for electricity price forecasting. • The method diversifies the data resources improve the forecasting accuracy. • The method is tested using the real-world data of Texas electricity market. • Causal relationship combined with physical models guarantee better results. • Deep neural networks show a great performance in electricity price forecasting. Over the years, the growing penetration of renewable energy into the electricity market has resulted in a significant change in the electricity market price. This change makes the existing forecasting method prone to error, decreasing the economic benefits. Hence, more precise forecasting methods need to be developed. This paper starts with a survey and benchmark of existing machine learning approaches for forecasting the real-time market (RTM) price. While these methods provide sufficient modeling capability via supervised learning, their accuracy is still limited due to the single data source, e.g., historical price information only. In this paper, a novel two-stage supervised learning approach is proposed by diversifying the data sources such as highly correlated power data. This idea is inspired by the recent load forecasting methods that have shown extremely well performances. Specifically, the proposed two-stage method, namely the rerouted method, learns two types of mapping rules. The first one is the mapping between the historical wind power and the historical price. The second is the forecasting rule for wind generation. Based on the two rules, we forecast the price via the forecasted generation and the first learned mapping between power and price. Additionally, we observed that it is not the more training data the better, leading to our validation steps to quantify the best training intervals for different datasets. We conduct comparisons of numerical results between existing methods and the proposed methods based on datasets from the Electric Reliability Council of Texas (ERCOT). For each machine learning step, we examine different learning methods, such as polynomial regression, support vector regression, neural network, and deep neural network. The results show that the proposed method is significantly better than existing approaches when renewables are involved. [ABSTRACT FROM AUTHOR]

Highlights • A new approach has been proposed to enhance the power system voltage stability. • A two-stage stochastic method is used to model the uncertainties of the system. • To achieve the optimal performance, an energy hub integrated system is presented. • The voltage stability remains in a secure range during different critical conditions. Abstract Voltage instability is one of the main problems of power systems operation. Hence, voltage stability margin is kept within a sufficient range by several costly procedures such as load shedding. Coupling the electrical and natural gas systems by energy hub is a useful way to keep system voltage within a proper range following contingencies in the system. This paper proposes a new approach for assessment and enhancement of the power system voltage stability in the presence of wind power generation, as well as electrical and heat demands uncertainty. A stochastic programming method is adopted to handle the uncertainties of the wind power generation and demands. The proposed methodology shows that coupled operation of the electrical and gas systems in the presence of renewable resources can provide flexibility for the system along with a decrease in the operational cost in both regular cases and post-contingency conditions. In comparison with previous methods to enhance the power system voltage stability, in the proposed model the natural gas system could help power system to supply electrical demand without any voltage stability problem, where there is no load curtailment in some contingencies while in other cases, load curtailment is reduced dramatically which results in undeniable reduction of the total operation cost. Load margin is considered as voltage stability index, and the proposed framework is implemented on the IEEE 14-bus electrical system and 17-node natural gas system to indicate its applicability and appropriateness. [ABSTRACT FROM AUTHOR]

Abstract With increasing demand for a renewable source of energy and the rapid development of smart cities and cluster computing, wind energy is gaining widespread significance on a global basis. There has been the unprecedented increase in annual installation of wind turbines reaching 60 GW for the first time in history from 51.7 GW of wind energy installation in 2014. Most of the high-power installations utilized double-fed induction generators (DFIG) due to low power losses, which reduced the cost of power converters required in the circuit. Nevertheless, natural factors, such as varying wind speed, influence the output of generators which results in the unsteady generation of power thus affecting the stability of the system. A neural network-based DFIG integrated with super capacitor energy storage system (SCESS) is proposed in this paper. Modeling and simulation have been carried out in MATLAB/Simulink and the observations indicate the significant improvement in the stability of the system. [ABSTRACT FROM AUTHOR]

Abstract The stochastic character of wind power generation suggests limitations on the increased shares of wind energy in electricity systems and challenges market integration of wind power, mainly due to the fact that nowadays, new wind parks are set to cope with more dynamic pricing mechanisms. In this new environment, where advanced bidding strategies need to be adopted from wind power actors, the introduction of novel elements to support wind power generation and address the inherent impact of variability is thought to be a prerequisite. To this end, the current study expands the work of previous studies by examining different methods of prediction with regards to wind power forecasting. More specifically, both Artificial Neural Networks (ANNs) and Support Vector Regression (SVR) models are trained and tested on the basis of different prediction horizons, using as case study real wind speed and wind power generation measurements from a wind park operating in the Greek territory. Models are trained using an in-house forecasting tool, with results obtained reflecting the better fit of the SVR method overall, especially for time horizons longer than 6 hours ahead. At the same time, an effort is made in order to optimize prediction of wind power generation through the combination of both prediction approaches via clustering of prediction areas. This novel approach results in an improvement of the predictions obtained, despite the fact that the SVR method already performs sufficiently even for 24 hours ahead. [ABSTRACT FROM AUTHOR]

Abstract In this paper, the optimal distribution model of hydropower load is proposed and the operation constraints of wind power and hydropower are taken into consideration. In the process of sending power to the provincial network, the harmonic search algorithm is introduced to optimize the maximum power generation target, and the validity of the model is verified in the case of central China regional power grid. [ABSTRACT FROM AUTHOR]

Abstract A fully integrated renewable energy atlas is presented which provides the wind and solar photo-voltaic (PV) power generation potential as well as cooling demand for Pakistan at a temporal resolution of 1-hr and spatial resolution of 14 × 14 km 2 . The proposed atlas uses weather based modelling for calculating renewable power generation time-series and the power-demand modelling is performed using real hourly electrical-load demand, conventional power generation and power consumption data for the year 2016. It has been found that Pakistan has much higher potential for the wind power generation than solar (PV) power generation and very good potential for the concentrated solar power. Furthermore, the optimum wind/solar power mix suggests that 95% of wind power generation and 5% of solar (PV) power generation leads to the least amount of power-shortfall. It is envisioned that the integration of renewable energy with cooling sector can be instrumental in overcoming Pakistan's electrical power-crisis. The current power-shortfall of 38.36 TW h can be resolved by installing rated wind and solar (PV) power generation capacity of 10.4 GW and 882 MW, respectively. Highlights • A fast and flexible high-resolution renewable energy atlas for Pakistan is presented. • Power-losses between the current power generation and consumption are almost 8%. • Power-shortfall of 38.36 TW h can be resolved with optimum wind/solar power mix of 95/5. • Geographical wind, solar PV power potential and cooling demand timeseries are calculated. • The significance of using weather-based modelling in energy system is shown. [ABSTRACT FROM AUTHOR]

A non-communication-based coordinated control strategy for a hybrid wind farm with doubly fed induction generator (DFIG)-based and direct-driven permanent magnet synchronous generator (PMSG)-based wind farms under severe asymmetrical grid faults is proposed in this paper. Firstly, the in-depth research of the severe asymmetrical fault and its impact on the operation characteristics of the DFIG and PMSG systems are investigated. Secondly, based on the operation characteristics analysis, the control objectives and priorities of the hybrid DFIG and PMSG systems are described first time during severe asymmetrical fault, respectively. In addition, the current allocation principles of each control unit in the DFIG and PMSG systems are investigated in detail according to the converter capacity and the system operation conditions. Furthermore, a coordinated control strategy for the hybrid wind farm is proposed. This strategy make full use of each wind farm's current capability, both the operation performance of the entire hybrid wind farm and the voltage quality of the power grid was greatly improved collectively. Finally, the correctness of the theoretical analysis and the effectiveness of the proposed control strategy for the hybrid wind farm with DFIG and PMSG are validated by the simulation and experimental results. [ABSTRACT FROM AUTHOR]

• A new idea of utilizing fault transient energy of DFWT to provide emergency power support for the power system is proposed. • The dynamic model of rotor speed of DFWT in fault process is established, and the calculation method of fault transient energy of DFWT is proposed. • The calculation method of control duration time and reference value is proposed, and the transient energy emergency control method is proposed. The anti-interference ability of the power system continues to decline with the increase of wind power penetration. Due to the limitation of the reduction of the synchronous generators proportion and the response speed, the emergency power controllability of the power system with large-scale wind power is insufficient, and the stability problems of the power system continue to highlight under the grid fault. At the same time, wind turbines can hardly provide emergency support for the power system due to the maximum power point tracking mode. Therefore, the transient energy of doubly fed induction generator based wind turbine (DFWT) during the grid fault is depicted, which is accumulated by the imbalance between the input mechanical power and output electromagnetic power. And a new idea of utilizing the fault transient energy of DFWT to provide emergency power support for the power system is proposed in this paper. Firstly, the expression of stator short-circuit current of DFWT in the crowbar input and reactive power support stage is derived. The dynamic model of the rotor speed of DFWT is established further, and the calculation method of fault transient energy of DFWT is proposed. Next, in order to maximize the utilization of the transient energy, the calculation method of control duration time and control reference value is proposed, and the transient energy emergency control (TEEC) method of DFWT is proposed. The case studies and experimental results show that the TEEC method can not only provide stable power support for the power system, but also alleviate the short-term power impulse of DFWT after the grid fault removal, and accelerate the recovery of the rotor speed of DFWT, which is conducive to the safe and stable operation of DFWT and the power system. [ABSTRACT FROM AUTHOR]

With the increasing penetration of wind power in the modern power system, the economic model predictive control (EMPC) has been widely employed in the control of the offshore wind power generation system for promoting the dynamic economy. A robust EMPC strategy, aiming to minimizing damage to the turbine while maximizing the electric power output, is developed in this paper to enhance the dynamic economic performance under uncertain wind speed variation. A linear model is utilized to approximate the original nonlinear behavior of the wind power generation system, while the consequent linearization error is taken as the external disturbance of the control system. A linear feedback controller with a robust control invariant set is designed to restrict the deviation between the nominal linear system and the actual nonlinear wind power generation system. Plant simulations, as well as NREL FAST code testing, are given to demonstrate the proposed controller's effectiveness throughout the overall operating region. • Develop a robust economic model predictive control for wind power generation system. • Take the uncertainty of wind speed into theoretical account. • Tackle the model mismatch through the tube-based disturbance rejection scheme. • Design the linear feedback controller to ensure the robust control invariant property. • Validate the controller effectiveness utilizing the NREL FAST and MATLAB simulations. [ABSTRACT FROM AUTHOR]

• Real case studies of Tazouta and Fez rural area in Morocco. • Hybrid biomass, wind, and solar electricity generation in areas those are rich in renewable energies. • Potential of bioenergy for producing power in rural areas of Fez-Meknes region in Morocco. High dependence of Morocco's energy sector on imported fossil fuels and subsequent associated expensive import bills, as well as global agreements with greenhouse gas emission reduction, has motivated Morocco to utilize renewable energy sources such as hydro, wind, and solar for energy generation. However, in recent years, the use of wind and solar energies in Morocco has accelerated more than hydropower usage because of the reduction in water levels as a result of rainfall drop, global warming, and natural droughts. Morocco has a great potential for electricity generation from solar and wind energies because of many sunny days, vast desert areas, long coastlines, and appropriate wind blow. However, accommodations of wind and solar generating units in electric power systems are difficult because of wind speed variations, solar irradiation uncertainty, and costly energy storage systems. Regarding the huge amounts of biowaste in Morocco, power generation from biomass can resolve this problem and compensate for the decrease in wind and solar energy generation during cloudy, rainy, and/or non-windy moments. Therefore, in the present paper, the capability of hybrid electric energy generation, using biomass, wind, and solar in a Moroccan village of Tazouta and the rural area of Fez city is evaluated. The proposed mixed-integer linear programming model was implemented in A Mathematical Programming Language (AMPL) using a linear solver of CPLEX. The biomass, solar, and wind power plants are biogas units fed by cow manure, 1-kW photovoltaic (PV) modules, and 5.1-kW wind turbines, respectively. The evaluation of the results indicates that hybrid utilization of wind, PV, and biomass energy resources is a more appropriate way for electricity generation in Tazouta and Fez rural areas compared to the individual operation of wind turbines and other combinations of mentioned renewable energy sources. [ABSTRACT FROM AUTHOR]

The ability to accurately predict future power demands, power available from renewable resources and the environmental impact of power generation is vital to the energy sector for the purposes of planning, scheduling and policy making. Machine learning techniques, neural networks in particular, have proven to be very effective methods for addressing these challenging forecasting problems. This research utilizes the powerful evolutionary optimisation algorithm, covariance matrix adaptation evolutionary strategy, as a means of training neural networks to predict short term power demand, wind power generation and carbon dioxide intensity levels in Ireland over a two month period. The network is trained over one month and then tested over the following month. A neural network trained with covariance matrix adaptation evolutionary strategy performs very competitively when compared to other state of the art prediction methods when forecasting Ireland's energy needs, providing fast convergence, more accurate predictions and robust performance. The covariance matrix adaptation evolutionary strategy trained network also gives accurate predictions when predicting multiple time steps into the future. [ABSTRACT FROM AUTHOR]

This paper addresses the stochastic security constrained unit commitment (SSCUC) problem with flexibility resources for managing the uncertainty of wind power generation (WPG). Departing from the traditional flexibility resources such as the thermal units with fast up/down spinning reserves and transmission switching (TS), this paper explores also the use of demand response (DR) and energy storage (ES) systems in an innovative integrated scheme. The proposed scheme utilizes a stochastic optimization framework to coordinate the flexibility resources dealing with the uncertainty of WPGs and equipment failures. The stochastic optimization model is formulated as a mixed-integer linear programming (MIP), and this problem is large and computationally complex even for medium sized systems. Accordingly, we present a novel accelerating decomposition technique aimed at solving this problem and reducing the number of iterations and CPU time. Numerical simulation results on the modified 6-bus system and on large-scale power systems, i.e. IEEE 118 and 300-bus systems, clearly demonstrate the benefits of applying flexibility resources for uncertainty management and the efficacy of the proposed solution strategy for large-scale systems. [ABSTRACT FROM AUTHOR]

For a cost efficient design of a future renewable European electricity system, the placement of renewable generation capacity will seek to exploit locations with good resource quality, that is for instance onshore wind in countries bordering the North Sea and solar PV in South European countries. Regions with less favorable renewable generation conditions benefit from this remote capacity by importing the respective electricity as power flows through the transmission grid. The resulting intricate pattern of imports and exports represents a challenge for the analysis of system costs on the level of individual countries. Using a tracing technique, we introduce flow-based nodal levelized costs of electricity (LCOE) which allow to incorporate capital and operational costs associated with the usage of generation capacity located outside the respective country under consideration. This concept and a complementary allocation of transmission infrastructure costs is applied to a simplified model of an interconnected highly renewable European electricity system. We observe that cooperation between the European countries in a heterogeneous system layout does not only reduce the system-wide LCOE, but also the flow-based nodal LCOEs for every country individually. [ABSTRACT FROM AUTHOR]

A method is presented for dynamic determination of the operating reserve requirement in power systems with high wind penetration, by considering the non-normal nature of wind power uncertainty and variability as well as their stochastic dependence on the wind power forecast. Distinct from previous methods, a generalised approach is proposed to characterise the probability density of wind power uncertainty and variability at distinct wind power forecast levels. As an illustration, the approach is applied in Ireland to determine the required operating reserve at different time scales within hourly scheduling periods to meet a certain reliability level. Statistical analysis indicates that the results are sensitive to normality and independence assumptions generally adopted in previous studies, and such assumptions may lead to under/over-estimation of the required operating reserve. Thus, the proposed method, which is not based on these limiting assumptions, emerges as a more realistic solution to define operating reserve targets dynamically. It further provides insight for planning studies and the operation of power systems with high wind power penetration. [ABSTRACT FROM AUTHOR]

The rotor structure varies with different types of wind power induction generator (WPIG), which leads to their different dynamic behaviors during power system disturbances. This paper proposes a generic implementation framework of explicit damping torque analysis to investigate the damping mechanisms of power system integrated with induction generator based wind power generation, so that the essential difference and inner connection between two main types of WPIG (i.e., DFIG and FSIG) in damping power system oscillation can be revealed. The linearized models which can represent DFIG and FSIG as well as three transitional wound rotor generators are established to facilitate the analytical comparison analysis. Phillips-Heffron system linearized model is employed to derive an explicit expression of damping torque contribution from main dynamic components of WPIGs. In the paper, 16-machine 5-area NYPS-NETS example system is used for the demonstration of proposed framework and comparison analysis. Both damping effectiveness and robustness of different WPIGs are extensively examined under multiple operating status, in order to provide useful guidance to system planner for the real-time operation of induction generator based wind generation. [ABSTRACT FROM AUTHOR]

This paper examines changes in Frequency Control Ancillary Services (FCAS) generator output due to several large wind farm power variations occurring in the Australian National Electricity Market (NEM) during 2012 and 2013. Using data from actual FCAS generator output fluctuations during selected storm events and reconstructed heat rate curves for each FCAS generator, the authors address the research question of what effect such variations have on net carbon emissions from electricity generation, given balancing fossil-fueled FCAS generators will be pushed back into less efficient operating ranges. The study finds that, for all storm events evaluated, net carbon emissions decreased with each large increase in wind power, even though FCAS generator carbon intensity increased in two of the three events. This finding is consistent with the conclusions of other studies that while FCAS generators do move into less efficient operating ranges, the generation of zero carbon emission wind power displaces other fossil fuel generators and more than compensates for this efficiency reduction, creating an overall positive carbon effect. This is a significant finding as it shows that the inclusion of wind farms in the generation mix serving the Australian NEM can lower carbon emissions in both short and long time frames. [ABSTRACT FROM AUTHOR]

An approach for multiple-feedback control of generator power and nacelle fore-aft speed employing blade pitch and generator torque manipulations was developed for a spar-type floating offshore wind turbine-generator system. The development of this approach was carried out through numerical analysis using the aeroelastic simulation model (FAST), measured high wind speed data, and simulated irregular sea waves. The novelty of this multiple-feedback control approach is the increase of the generator torque in response to the platform pitching motion to the leeward side and the control of the generator power instead of the rotational speed. First, theoretical analysis of the open loop transfer function revealed that the multiple-feedback control approach improves the stability of the generator power control in the floating offshore wind turbine-generator system. Then, the optimal parameter settings of the multiple-feedback control were determined and the effectiveness of employing a first-order lag filter for the nacelle fore-aft speed was shown through a sensitivity analysis. A comparison of system performances demonstrated that the multiple-feedback control approach reduces the power output fluctuation, the platform pitching motion, and the damage equivalent fatigue load of the fore-aft bending moment at the tower-base part by 52%, 22%, and 16%, respectively, relative to the gain-scheduled feedback control of the rotational speed. [ABSTRACT FROM AUTHOR]

The growing number of doubly-fed induction generator (DFIG) based wind farms has significantly increased the model complexity and simulation burden for power system stability analysis. In this paper, a novel method to assess the modeling adequacy of DFIGs for small-signal stability analysis is introduced. By evaluating the damping torque contribution to stability margin from different DFIG dynamic model components, the proposed method provides a quantitative index to show the participation level of each DFIG model component in affecting power system damping performance. In addition, five DFIG model reduction schemes are established, and a novel strategy to reduce individual DFIG model complexity based on the participation level is proposed. The effectiveness of the proposed strategy has been demonstrated in the New England test system. It can be concluded that the proposed DFIG model reduction for dynamic studies is undoubtedly beneficial to system planner and operator, in the way of improving computational efficiency when analyzing large-scale power systems with the increasing penetration of wind energy. [ABSTRACT FROM AUTHOR]

Pyrolysis is an effective method for recycling waste wind turbine blades. However, there have been few studies on the pyrolysis mechanism of the basic components of blade composites. In this study, isothermal pyrolysis experiments on four basic components (epoxy resin, thermoplastic polyurethane, carbon fiber and glass fiber) of blades were carried out in a tube furnace at 600 ℃. The product distribution and properties of each basic component were then analyzed after pyrolysis. The results showed that the pyrolysis reactions of the matrix materials were intense, and the mass loss rates reached 90%. A certain amount of gas products, mainly CO 2 and CO, were released during the pyrolysis process; CO 2 was eliminated from the ester and carboxyl groups of the aromatic compounds and CO was formed from the decomposition of the carbonyl groups. A large number of liquid products were also produced, including phenol and p-isopropenylphenol, and these could be reused as industrial raw materials. The resins were decomposed from blocks into fragments after pyrolysis. The pore structures of resins became more abundant and developed mesopores and micropores, and the microcrystalline structures were disordered. The fiber materials barely reacted, and the mass loss of each was only approximately 1%. The surface morphologies and microcrystalline structures of the fiber materials did not change significantly, but the pore diameter distributions showed diametrically opposite trends relative to those of the resins. These results indicated that the resin matrix could be removed from the composites via pyrolysis and that the structures of the fiber materials could be retained. This research on the pyrolytic characteristics of the basic components of blade composites shows the thermochemical characteristics of each component and provides basic data and reference information for pyrolytic recycling of scrap blades. • Resins exhibit high gas and liquid yields, while fibers undergo limited pyrolysis. • CO 2 , CO and CH 4 are the main pyrolytic gas products of each basic component. • Further purification of pyrolytic tar provides high value-added products. • The surface morphologies and microstructures of resins changed significantly. • Fibers retain their original properties after pyrolysis and can thus be recycled. [ABSTRACT FROM AUTHOR]

• A BERT model predicts a vulnerability's severity based on its textual description analysis. • The improved MTTC, combined with a Bayesian network, leading to more accurate reliability analysis. • A Markov-based interaction model illustrates the consequences of cyber attacks on the CPPS's operation state. This paper presents a reliability assessment technique for cyber-physical power systems (CPPSs) that incorporates cybersecurity issues by considering non-normal random variables with non-linear dependencies. Our model uses bidirectional encoder representations from transformers (BERTs) to predict the severity of cyber vulnerabilities by analyzing their textual description. This practical model allows the engineer to manually enter text that describes threats to predict the probability of cyber vulnerability. We use a Bayesian attack graph to simulate the attack paths and a Markov model to demonstrate the consequences of cyber attacks on the CPPS. Furthermore, a hybrid approximate-analytical approach is employed to assess the overall reliability of the CPPS when considering the load and wind generation uncertainties, given their non-linear dependencies. The proposed method is implemented on the modified IEEE Reliability Test System. The results demonstrate that the reliability indices for a simultaneous cyber attack on corporate networks and wind farm control centers are more significant than those obtained for other attack scenarios. The proposed prediction method is highly accurate, and the predicted vulnerability scores align with those provided by the National Vulnerability Database. The results are compared with sequential Monte Carlo simulation, subset simulation, and cross-entropy-based importance sampling methods. The comparison shows that the proposed method outperforms other methods in terms of convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

• The equivalent impedance of the wind farm integrated VSC-HVDC is deduced, where effect of the cable line is considered. • The occurrence mechanism of high frequency oscillation is analyzed, which indicates that the control delay of HVDC and the capacitive characteristic of the cable line both contribute to the instability. • A vector modified resonator control (VMRC) is proposed, the design thought, procedure and superiority are detailly presented. Its validity is certified by experimental tests. When a wind farm connects to the grid via a high voltage direct current transmission based on voltage source converter (VSC-HVDC), high frequency oscillation (HFO) is a critical issue that threatens the system stability, this issue has been reported in many HDVC projects all over the world. The current research proved the negative resistance brought by the effect of control delay in VSC-HVDC is a primary cause of instability. This paper ulteriorly analyzes the influence on system stability caused by the long ac cable between the wind farm and the VSC-HVDC, and indicates that the effect of the cable transmission line can contribute to the system instability as well, while the existing damping methods did not consider this effect. Then the control failure of the exiting damming methods when considering the cable transmission line is analyzed and verified by the impedance analysis theory. Thereby, this paper proposed a vector modified resonant controller (VMRC) which can independently and flexibly regulate the magnitude and phase of HVDC impedance, to solve the HFO issue caused by the combined effect of the control delay and the cable transmission line. Also, the design procedure of the proposed control strategy is detailly presented. Finally, experimental results are provided to verify the correctness of the mechanism analysis and the validity of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The development of AI has enabled the fault detection of industrial components to be achieved through the combination with deep learning. A detection method combined with deep learning has also emerged for the fault detection of fan blades, such as models based on neural networks using the appearance or sound of the blades. However, the detection model obtained from a single data type often has limitations, such as low accuracy and overfitting. This is also the problem with fan blade detection. In contrast, multimodal data fusion detection models are often more stable. The modality diversity of blade diagnosis is strong, and it can be achieved from multiple modalities such as image, sound, and vibration. To improve the accuracy of fault diagnosis of fan blades, this article proposes a multimodal double-layer detection system (MTDS) based on decision-level and feature-level fusion. The system includes a wind turbine simulation platform and a multimodal detection system. It mainly obtains different modal data of the simulated wind turbine from the image, sound, and vibration signals, including blade images through unmanned aerial vehicle photography, blade vibration signals through electronic vibrators, and blade sound signals through microphones. The highly correlated sound and vibration modal data are fused at the feature level, and a detection model based on the sound and vibration mixed mode is implemented using a sound-vibration-CNN (SV-CNN) proposed in this case. Then, a detection model of the image mode is trained based on the blade image using a Convolution Block Attention Module ResNet (CBAM-ResNet) network. Finally, the detection input of the two modal models is fed into a perceptron to obtain the final prediction result, and the decision-level fusion is implemented to achieve fan blade detection based on multimodal, namely the implementation of MTDS. [ABSTRACT FROM AUTHOR]

The resource quality and the temporal generation pattern of variable renewable energy sources vary significantly across Europe. In this paper spatial distributions of renewable assets are explored which exploit this heterogeneity to lower the total system costs for a high level of renewable electricity in Europe. Several intuitive heuristic algorithms, optimal portfolio theory and a local search algorithm are used to find optimal distributions of renewable generation capacities that minimise the total costs of backup, transmission and renewable capacity simultaneously. Using current cost projections, an optimal heterogeneous distribution favours onshore wind, particularly in countries bordering the North Sea, which results in average electricity costs that are up to 11% lower than for a homogeneous reference distribution of renewables proportional to each country's mean load. The reduction becomes even larger, namely 18%, once the transmission capacities are put to zero in the homogeneous reference distribution. Heuristic algorithms to distribute renewable capacity based on each country's wind and solar capacity factors are shown to provide a satisfactory approximation to fully optimised renewable distributions, while maintaining the benefits of transparency and comprehensibility. The sensitivities of the results to changing costs of solar generation and gas supply as well as to the possible cross-sectoral usage of unavoidable curtailment energy are also examined. [ABSTRACT FROM AUTHOR]

The growing share of electricity production from solar and mainly wind resources constantly increases the stochastic nature of the power system. Modelling the high share of renewable energy sources – and in particular wind power – crucially depends on the adequate representation of the intermittency and characteristics of the wind resource which is related to the accuracy of the approach in converting wind speed data into power values. One of the main factors contributing to the uncertainty in these conversion methods is the selection of the spatial resolution. Although numerical weather prediction models can simulate wind speeds at higher spatial resolution (up to 1 × 1 km) than a reanalysis (generally, ranging from about 25 km to 70 km), they require high computational resources and massive storage systems: therefore, the most common alternative is to use the reanalysis data. However, local wind features could not be captured by the use of a reanalysis technique and could be translated into misinterpretations of the wind power peaks, ramping capacities, the behaviour of power prices, as well as bidding strategies for the electricity market. This study contributes to the understanding what is captured by different wind speeds spatial resolution datasets, the importance of using high resolution data for the conversion into power and the implications in power system analyses. It is proposed a methodology to increase the spatial resolution from a reanalysis. This study presents an open access renewable generation time series dataset for the EU-28 and neighbouring countries at hourly intervals and at different geographical aggregation levels (country, bidding zone and administrative territorial unit), for a 30 year period taking into account the wind generating fleet at the end of 2015. [ABSTRACT FROM AUTHOR]

This paper addresses passive and active preventive maintenance scheduling in an islanded microgrid with storage and renewable energy sources. At first, under a centralized framework, a single-level cost-minimization formulation for passive maintenance scheduling is developed and used as a benchmark in the operation. An independent microgrid operator is responsible for the operation in this framework. Then, through a bi-level formulation, the active maintenance scheduling and operation is carried out with profit-maximization objective. These two developed frameworks provide the houses with opportunity to earn profit and the regulator and the operator to analyze the performance of the system. The bi-level formulation is transformed into a single-level problem through Karush–Kuhn–Tucker conditions. Furthermore, the proposed model provides the capability of incorporating condition monitoring data into the operation. The model is validated through a test system and the outcomes demonstrate the advantages, applicability and challenges of utilizing the proposed model. [ABSTRACT FROM AUTHOR]

Under the need to reduce CO 2 emissions, renewable energy sources for electricity (RES-E) and more efficient processes are needed to decrease GHG emission rates. Carbon dioxide utilization (CDU) for the production of fuels, chemicals and materials is becoming complementary to captured CO 2 and a promising source of competitive advantage for the European industry. CDU processes are at conceptual design, up to small demonstration readiness level. Since the CDU process has to compete with well-established conventional synthesis processes, they may not be profitable at current market conditions. Moreover, to have lower carbon footprint than the benchmark process, it has been demonstrated that they need electricity from renewable sources when CO 2 is combined with H 2 as raw material [1] . This work aims at evaluating the connection of a methanol CDU plant with a wind power portfolio, to elucidate under which conditions this relationship is advantageous for both: (i) the CDU plant as a source of low-cost and zero emission energy, and (ii) the wind producer for the maximization of its profit. The business model proposed here lies in the methodology developed by [2] , aiming at reducing the wind power forecasting uncertainty, between the closure of the day-ahead market and the first intraday bidding session in the Spanish power market. We have implemented an ad-hoc linear programming optimization based in GAMS that maximizes the revenue from the wind producer's perspective. The model considers the options of a wind producer of selling its RES-E to the day-ahead power market or of using its RES-E to synthesize and sell methanol. The intraday market is used as balancing market for the deviations of the wind power forecasting. [ABSTRACT FROM AUTHOR]

This paper investigates the coordinated control strategy for a hybrid wind farm with doubly fed induction generator (DFIG)-based and direct-driven permanent-magnet synchronous generator (PMSG)-based wind farms during network unbalance. The negative-sequence current output capabilities of DFIG and PMSG systems under unbalanced grid voltage condition are described. Furthermore, by considering both the negative-sequence current capabilities and requirements of different control targets for each control unit, the controllable operating regions of DFIG and PMSG systems are investigated. According to the controllable operating regions, a targets selection scheme for each control unit is proposed to improve the stability of the hybrid wind farms containing both DFIG-based and PMSG-based wind farms during network unbalance, especially to avoid DFIG-based wind farm tripping from connected power grid under severe grid voltage unbalance conditions. Finally, the proposed coordinated control strategy is validated by the simulation results of a 30-MW-DFIG-based wind farm and a 30-MW-PMSG-based wind farm under different operation conditions and experimental results on a laboratory-scale experimental rig under severe grid voltage unbalance conditions. [ABSTRACT FROM AUTHOR]

This paper presents the experimental results of the operation of the system interconnection inverter control side for wind power generation system (WPGS) using PMSG. The diode bridge rectifier converter methods are utilized as converters for the WPGS. The experimental results are obtained by the test bench using the wind turbine emulator. The wind turbine emulator are reproduced the behaviours of windmill by the servo motor drives. The servo motor is calculated from windmill wing profile, the wind velocity and windmill rotational speed. The WPGS using PMSG employs the vector control system to control the torque and speed of PMSG and the active power and reactive power are controlled and generated power is sent to the grid-connected power generation while controlling the DC link voltage in the inverter side. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid economic development, wind energy becomes more and more fierce competition in the market. Based on the basic concept of wind power generation, this article will analyze wind power generation in the significance and the role of sustainable development in our country. In the risk analysis, the first based on the analysis of wind power projects characteristics, the various stages of the wind power project risk factors identified by phasing of construction of wind power feasibility study and design phase, investment and financing stage, building construction phase and operations and maintenance phases of the four stages of risk into account, and then build a relatively complete risk assessment system. In this model, the evaluation is treated atter-element determination, and then it will select the appropriate classical field and section domain, by using AHP to 17 sub-indexes were four stages weight distribution, and finally a combination of elements and AHP model, got the project Risk level. [ABSTRACT FROM AUTHOR]

Installed wind power capacity is increasing rapidly in many power systems around the world, with challenging penetration targets set at national, and/or regional level. Wind power, particularly at higher penetration levels, introduces various grid issues, with frequency and voltage stability being particularly critical concerns. Voltage dip induced frequency stability following a network fault in such systems is one potential risk that has so far received limited attention by the research community. With state of the art modelling, the potential impact of a delayed active power recovery from wind generation following a network fault induced voltage dip is investigated. The subsequent voltage oscillations introduced by wind turbines, exacerbating frequency stability, are also examined. Analysis is carried out for a wide range of wind penetration levels and system scenarios, with the results demonstrated on the New England benchmark system. [ABSTRACT FROM AUTHOR]

Due to the development of renewable energy sources (RESs), maximization of hosting capacity (HC) of RESs has gained significant interest in the existing and future power systems. HC maximization should be performed considering various technical constraints like power flow equations, limits on the distribution feeders' voltages and currents, as well as economic constraints such as the cost of energy procurement from the upstream network and power generation by RESs. RESs are volatile and uncertain in nature. Thus, it is necessary to handle their inherent uncertainties in the HC maximization problem. Wind power is now the fastest growing RESs around the world. Hence, in this paper a stochastic multi-objective optimization model is proposed to maximize the distribution network's HC for wind power and minimize the energy procurement costs in a wind integrated power system. The following objective functions are considered: 1) Cost of the purchased energy from upstream network (to be minimized) and 2) Operation and maintenance cost of wind farms. The proposed model is examined on a standard radial 69 bus distribution feeder and a practical 152 bus distribution system. The numerical results substantiate that the proposed model is an effective tool for distribution network operators (DNOs) to consider both technical and economic aspects of distribution network's HC for RESs. [ABSTRACT FROM AUTHOR]

This paper presents, for the first time, the verification of the classic model for the origin of interharmonic emission from a frequency converter, for a Type-IV wind turbine by using long-term measurements. Interharmonic variations in magnitude and in frequency are due to the difference between the generator-side and grid-side frequency of the full-power converter. The model verification consists of three parts: correlation between frequencies; relation between magnitude of interharmonics and active-power production, and relation between magnitudes of different interharmonics. The measurements are in agreement with the model predictions. The paper also introduces a novel graphical correlation method to extract information on interharmonics from long-term measurement series. [ABSTRACT FROM AUTHOR]

This paper describes a method of voltage regulation, maximum power extraction and power transfer operation in a variable speed wind-driven Induction Generator (IG) feeding DC micro grid (DCM). The IG is self-excited in a hybrid manner with fixed capacitor and a current controlled, voltage source converter (CCVSC). The control principle of CCVSC which intends to provide the VAR requirement of IG based on the power curves along with voltage regulation and maximum power tracking (MPT) with PI (proportional-integral) based controllers claims the merit of the setup. The sinusoidal reference current templates of CCVSC are generated with voltage loop delivering the peak value and MPT loop delivering phase value. The frequency synchronization is effected with a 1Ø Phase Locked Loop (PLL). The wind power is utilized by meeting the load demand and supplying the excess power to DCM through PWM rectification of CCVSC without dissipating it through any dump load. During conditions of machine stall, the system amenably transfers power to AC loads by PWM inversion of CCVSC with fixed modulation index and frequency. When there is any fall in DCM voltage, DC voltage regulation is effected by the PWM rectification operation of CCVSC. To execute the reliable power transfer scheme, a supervisory control is employed for the automatic smooth change over in the modes of operation depending on load and wind availability. The successful operation of the system for various load/speed conditions is proved through extensive MATLAB based simulation and dSPACE 1104 controller based experimentation on a 500 W induction machine. [ABSTRACT FROM AUTHOR]

• An adaptive soft sensor model construction strategy is established; • A k-means localization method based on a spatiotemporal criterion is proposed; • Improved semi-supervised learning to realize the utilization of unlabeled samples; • Particle swarm optimization algorithm is used to optimize parameters. • The LS-EL-LSSVR strategy increases the prediction accuracy of wind power prediction. Soft sensors have been widely employed to estimate the power prediction in wind power generation that are difficult to measure in real-time. However, strongly nonlinear, dynamic time-varying, and multi-rate data characteristics are major causes of poor soft sensor performance. To address this problem, a strategy of building a soft sensor model based on local semi-supervised ensemble learning of least squares support vector regression (LS-EL-LSSVR) is proposed in this paper. Firstly, to reduce the influence of nonlinearity, a localization method based on the combination of time and space criteria is proposed, which can overcome the drawback of the clustering method based on a spatial criterion. Subsequently, the self-training semi-supervised classification method is used to achieve semi-supervised clustering of unlabeled samples to improve the generalization performance of the soft sensor model. Afterward, based on the local semi-supervised clustering sample dataset, the soft sensor model is established according to the least squares support vector machine method. Moreover, a selective ensemble learning method based on the prediction accuracy of local models and an adaptive combination weight calculation method of sub-models is proposed to realize selective ensemble learning and improve the prediction accuracy. The parameters of the proposed method are finalized automatically by the particle swarm optimization technique. The proposed method is simulated by using the actual spatial dynamic wind power prediction dataset, the results demonstrate the effectiveness of the proposed method in dealing with nonlinear, dynamic time-varying, and multi-rate data regression problems in wind power generation process. [ABSTRACT FROM AUTHOR]