Typical Wind Farm Research Articles

Wind Farm Energy Storage System Based on Cat Swarm Optimization–Backpropagation Neural Network Wind Power Prediction

To solve the instability problem of wind turbine power output, the wind power was predicted, and a wind power prediction algorithm optimized by the backpropagation neural network based on the CSO (cat swarm optimization) algorithm was studied, and a wind farm energy storage system model was built on this basis. By collecting the wind power plant’s historical wind speed, power, and other parameters, the short-term wind farm output power was predicted, and the operation of the wind farm energy storage system was controlled to suppress the output power of the wind farm when the wind farm was connected to the grid so as to improve the stability of the output power of the wind farm. At the same time, typical wind farm data were taken as an example to verify the feasibility of the proposed method.

Hedging Wind Power Risk Exposure through Weather Derivatives

We introduce the industrial portfolio of a wind farm of a hypothetical company and its valuation consistent with the financial market. Next, we propose a static risk management policy originating from hedging against volumetric risk due to drops in wind intensity and we discuss the consequences. The hedging effectiveness firstly requires adequate modeling calibration and an extensive knowledge of these atypical financial (commodity) markets. In this hedging experiment, we find significant benefits for weather-sensitive companies, which can lead to new business opportunities. We provide a new financial econometrics approach to derive weather risk exposure in a typical wind farm. Our results show how accurate risk management can have a real benefit on corporate revenues. Specifically, we apply the spot market price simulation (SMaPS) model for the spot price of electricity. The parameters are calibrated using the prices of the French day-ahead market, and the historical series of the total hourly load is used as the final consumption. Next, we analyze wind speed and its relationship with electricity spot prices. As our main contribution, we demonstrate the effects of a hypothetical hedging strategy with collar options implemented against volumetric risk to satisfy demand at a specific time. Regarding the hedged portfolio, we observe that the “worst value” increases considerably while the earnings-at-risk (EaR) decreases. We consider only volumetric risk management, thus neglecting the market risk associated with electricity price volatility, allowing us to conclude that the hedging operation of our industrial portfolio provides substantial benefits in terms of the worst-case scenario.

Development of Improved Suppression Measures Against Reignition Overvoltages Caused by Vacuum Circuit Breakers in Offshore Wind Farms

In offshore wind farms, reignition overvoltages are usually caused by switching-off operations of vacuum circuit breakers, which degrade the insulation of power equipment, especially transformers. This paper proposes a new quantitative evaluation method of overvoltage suppression measures comprehensively considering three main indicators, which include the amplitude, steepness and reignition number. Based on the quantitative evaluation results, an improved suppression measure against high-steepness reignition overvoltages is developed. Firstly, high-frequency transient models of main components of a typical wind farm are developed in PSCAD/EMTDC. Then the stochasticity of opening instants is considered, and three main indicators are extracted for comprehensively analyzing the transient characteristics of reignition overvoltages. Subsequently, a quantitative evaluation is performed to analyze the effectiveness of different suppression measures, which involves surge arresters, RC snubbers and smart chokes with different properties and installation positions. Results show that a new combination of a surge arrester and a smart choke is capable of providing sufficient protection against a high-amplitude, high-steepness and severe-reignitions overvoltage. This research can provide a new overvoltage suppression solution for the insulation coordination of medium-voltage systems in offshore wind farms.

Wind Turbine Clustering and Equivalent Parameter Identification in Multitime Scales Based on the Deep Migration of Multiview Features

To improve the precision of wind farm multi-machine equivalence and multi-scene generalization, this paper proposes a method for wind turbine clustering and equivalent parameter identification in multi-time scales based on the deep migration of multi-view features. The proposed technique carries out multi-machine equivalence by leveraging the multi-view information from each turbine in a wind farm. Specifically, a deep spatio-temporal Improved Auto-Encoder is designed, jointly trained with the target clustering layer. IAE is used for mining multi-view latent characteristics of wind turbines orienting to grouping turbines to improve the model’s adaptability to multiple scenarios and divide turbines in an unsupervised manner. This method generates a visual heat map to represent the attended area of characteristics based on transfer learning and Class Activation Map to enable interpretability. In the next phase, this technique constructs a multi-objective optimization model by synthesizing the equivalent deviation of voltage, current, active power, and reactive power to further improve accuracy. It can identify the equivalent parameters of collector lines, the mechanical structure, and the control system at different time scales simultaneously via the black-box paralleled optimization method based on Bayes and Multi-arm Bandit. The proposed approach is evaluated on a typical double-fed wind farm with grid-side faults under various conditions of disturbing winds. Also, an ablation study is conducted to make analysis according to the two phases, i.e., turbine division and parameter identification. The results validate the accuracy and robustness of this method.

Towards Better Wind Resource Modeling in Complex Terrain: A k-Nearest Neighbors Approach

Wind turbines are often placed in complex terrains, where benefits from orography-related speed up can be capitalized. However, accurately modeling the wind resource over the extended areas covered by a typical wind farm is still challenging over a flat terrain, and over a complex terrain, the challenge can be even be greater. Here, a novel approach for wind resource modeling is proposed, where a linearized flow model is combined with a machine learning approach based on the k-nearest neighbor (k-NN) method. Model predictors include combinations of distance, vertical shear exponent, a measure of the terrain complexity and speedup. The method was tested by performing cross-validations on a complex site using the measurements of five tall meteorological towers. All versions of the k-NN approach yield significant improvements over the predictions obtained using the linearized model alone; they also outperform the predictions of non-linear flow models. The new method improves the capabilities of current wind resource modeling approaches, and it is easily implemented.

Analysis for action degree and influence factors of harmonic interaction in multi-wind farms

With the increasing proportion of wind power in the power system, a significant threat is posed to the safe and stable operation of regional power grids and the consumption of the new energy by the harmonic interaction effects among wind farms. Aimed at the problem of mechanism for the harmonic interaction between wind farms and the degree of influence, an aggregation equivalence method of wind farms in harmonic power loss is proposed, where the complexity of wind farm equivalence calculation is reduced. Based on this, a harmonic impedance model of multiple wind farms grid-connected system is established, and the mechanism of harmonic interaction influence among the wind farms is revealed. The definition and calculation method of harmonic current interaction factor is presented, where the relationship between the harmonic current interaction and the potential factors, i.e., the coupling impedance, the dynamic reactive power compensation devices, and the number of wind turbines connected to the grid among multiple wind farms, is quantitatively analyzed. Finally, a typical wind farm of new energy base in Northwest China is used as a toy example to verify the effectiveness of the harmonic current interaction factor in the characterization of the interaction strength within multiple wind farms.

Switching transients caused by vacuum circuit breakers in collection grids of offshore wind farms

AbstractIn this study, transients that can occur in the collection grids of offshore wind farms were investigated. On the basis of the multiple prestrike and reignition model that can reflect the actual operation of a vacuum circuit breaker (VCB), a switching transient simulation model of a typical offshore wind farm was built. The developed model included a VCB, a transformer, a cable, and an arrester. Issues concerning the closing time, length of the feeder, and topology of the collection grid were discussed. Results showed that the relationship between overvoltage amplitude and closing time was approximately sinusoidal and the maximum value was obtained when the closing time was near the peak of the power source. In addition, the overvoltage at the end of a feeder terminal was the largest among all of the transformer overvoltages of the same feeder. The overvoltage of the main transformer and that of the transformer located at the end of a feeder decreased slowly with the increase in feeder length during the closing of VCB. However, these overvoltages were positively related to feeder length during the opening of VCB. The star topology was superior to the other topologies in terms of coping with the overvoltage caused by switching transients. The effectiveness of a suppression measure, namely, installation of a resistance‐capacitor filter, was also verified.

Isothermal compressed wind energy storage using abandoned oil/gas wells or coal mines

Wind energy has rapidly increased and is expected to continue to do so over the next few decades. This will exacerbate the issue whereby its intermittent energy production does not generally coincide with energy demand. This can be addressed by integrating cost-effective energy storage with wind farms. The present study develops a concept that leverages the capacity of underground reservoirs of abandoned oil or gas wells to avoid the costs of expensive storage vessels and employs isothermal processes for the compressed air energy storage to improve round-trip efficiency. By levelizing the production using compressed air energy storage, the electrical generator size (and associated) cost may be reduced while maintaining the same average power production. These generator cost savings are projected to offset the cost of the storage system, so increased dispachtability is obtained with little to no added cost. This allows the predicted Cost of Valued Energy to be lowered by more than 10% for a typical wind farm, used as a case study. In addition, the simulated dispatchability ratio can reach 86.7%, which is far better than the 55.7% of a wind farm without storage. Importantly, the siting of wind farms near abandoned wells and mines also has the potential for significant new infrastructure investment and jobs in areas that might be economically-depressed. However, experimental verification with a pilot facility combined with ramifications of operational costs and geological factors are needed to demonstrate and quantify the benefits of this concept.

Energization of windfarm infrastructure as part of microgrid based restoration

Studies on windfarms have focussed on their integration under normal and short duration weak grid conditions. With their increasing penetration at the same time that there is increasing disaster related blackouts, there is need to investigate their role during restorative conditions assuming a bottom-up approach in which local generation is used to re-energize the network. This is commonly referred to as microgrid based restoration. Wind Energy Conversion Systems (WECSs) are not blackstart units thus they require cranking from designated blackstart units in order to play a role in re-energizing the network. Prior to the WECSs being cranked up, the windfarm infrastructure, which includes underground cables, reactors (applicable to long HV cables), WECS transformers and yaw-pitch motor system, needs to be re-energized. This paper investigates the energization of different components within the windfarm considering availability of a single medium sized (5 MVA) blackstart synchronous source. Firstly, diesel based generation (DBG) will be used to study the different challenges associated with the energization of the different components considering different re-energization sequences after which thermal (TBG) and hydro (HBG) based generation will be used for comparison. High frequency modelling has been considered for static network components such as transformers, cables and reactors while dynamic models have been considered for rotating network components such as generators and motors. Excitation limiters have been included in the generation systems in order to obtain a near accurate response on energization of the different components. Discussion is provided on the different factors that influence the success of energization. Analysis is also provided on the effect of size of the excitation source and influence of the synchronous condenser which has been considered as part of the windfarm. Resonance, using a simple empirical formula, is briefly discussed as a factor in influencing and determining the energization sequence. A typical windfarm layout has been considered in this study with MATLAB/Simulink being used as the simulation platform due its flexibility in modelling.

Intelligent Parameter Design-Based Impedance Optimization of STATCOM to Mitigate Resonance in Wind Farms

In certain regions and countries, the static synchronous compensator (STATCOM) is required in large-scale wind farms to provide reactive power support. Due to the limitations and complexity in distributed wind power systems, the STATCOM is considered as an effective and cheap alternative for impedance shaping to mitigate resonance. However, there is still a lack of quantitative stability analysis for practical large-scale wind farms and an optimization approach for the STATCOM impedance to achieve so. Therefore, based on the sequence impedance matrix models of the Type-IV wind turbine generator (WTG) and STATCOM for the network modeling, this article presents a sequence impedance network-based stability analysis for practical wind farms to accurately reveal the stability of wind farms and quantitatively evaluate the damping provided by the STATCOM. A typical wind farm in Western China is exemplified to demonstrate the proposed stability analysis. Then, an intelligent parameter design-based optimization approach for the STATCOM impedance is proposed, which uses the heuristic intelligence algorithm to solve the STATCOM controller parameters to obtain the optimal stability margin of the wind farm under the constraints of desired performance. Finally, a comparison with the understanding-of-models-based optimization method confirms the efficacy of the proposed impedance optimization approach.

Influences of DC bus voltage dynamics in modulation algorithm on power oscillations in PMSG-based wind farms

In the research of power oscillations caused by interaction between permanent magnetic synchronous generator (PMSG)-based wind farms and AC systems, influences of the machine side are usually ignored for simplification. This paper finds out that the indispensable condition for ignoring the machine side is the inclusion of DC bus voltage dynamics in modulation algorithm. Ignoring this condition could lead to inaccurate stability analysis results. Firstly, this paper constructs the small signal model and impedance model of the system when the modulation algorithm excludes and includes the DC bus voltage dynamics, which are both well-developed methods. Then the different influences of the machine side on the system stability with the two modulation algorithms are analyzed and explained theoretically. Thereby, the condition of ignoring the machine side in stability analysis is obtained. Eigenvalue analysis and time-domain simulation in a typical PMSG-based wind farm system demonstrate the theoretical analysis.

Combined Floating Offshore Wind and Solar PV

To mitigate the effects of wind variability on power output, hybrid systems that combine offshore wind with other renewables are a promising option. In this work we explore the potential of combining offshore wind and solar power through a case study in Asturias (Spain)—a region where floating solutions are the only option for marine renewables due to the lack of shallow water areas, which renders bottom-fixed wind turbines inviable. Offshore wind and solar power resources and production are assessed based on high-resolution data and the technical specifications of commercial wind turbines and solar photovoltaic (PV) panels. Relative to a typical offshore wind farm, a combined offshore wind–solar farm is found to increase the capacity and the energy production per unit surface area by factors of ten and seven, respectively. In this manner, the utilization of the marine space is optimized. Moreover, the power output is significantly smoother. To quantify this benefit, a novel Power Smoothing (PS) index is introduced in this work. The PS index achieved by combining floating offshore wind and solar PV is found to be of up to 63%. Beyond the interest of hybrid systems in the case study, the advantages of combining floating wind and solar PV are extensible to other regions where marine renewable energies are being considered.

Design and optimization of a novel system for trigeneration

Subcooled compressed air energy storage (SCAES) is a system cogenerating heat, cooling, and power at a high coefficient of performance. In this study, hybridization of a SCAES system with a large-scale solar-powered absorption chiller (SPAC) is proposed. The hybrid system sustainably provides cooling and power at high efficiency. The combined SPAC-SCAES system is appropriate for locations with large cooling demand and grid-connected renewable power plants. Employing this system, the renewable power plant may efficiently operate in the power market, maximizing the financial benefits by storing its surplus power and reclaiming the stored energy for balancing the demand and the production. In addition, a large amount of cold is produced, increasing the profitability of the system. This combined system is designed and simulated for a typical wind farm plus an absorption chiller of a hospital. Non-linear programming (NLP) is used to optimize the operation strategy of the SCAES and based on the given results; the components of the system are sized. The results show that by the combined system a massive amount of balancing power can be produced for the grid, a reliable integration between the cold and electricity sectors is made, and the levelized cost of energy (LCOE) decreases remarkably.

Extreme wind power forecast error analysis considering its application in day‐ahead reserve capacity planning

Accurate wind power forecasting is crucial for setting reserve dimensions of a power system. From the perspective of power grid dispatcher, one of the most critical issues of dealing with the uncertainty brought by wind power forecast error (WPFE) is to analyse its extreme value to decide the new reserve requirements induced by variable wind power. This study targets at predicting the daily maximum of day-ahead WPFE via extreme value analysis approach considering its application of operating reserve planning. Non-stationary block maxima method (NS-BMM) is applied to model the probabilistic characteristic of daily maxima WPFE via fitting the non-linear relationship between distribution parameters and the covariates. To better shape the non-stationarity, a conditional NS-BMM (CNS-BMM) via partition according to wind conditions is proposed. Moreover, indicators to evaluate the quality of applying NS-BMMs in the day-ahead reserve plan are also discussed. Finally, a case study based on the operational data from a typical wind farm in northern China is carried out. Results show that CNS-BMM offers a better model of daily maximum WPFE compared to not only traditional quantile regression but also non-CNS-BMM, and applying the estimate of daily maximum WPFE on reserve plan demonstrates an advantage over conventional three-sigma method.

Remaining Useful Life Estimation of Wind Turbine Blades under Variable Wind Speed Conditions Using Particle Filters

Rotor blades are the most complex structural components in a wind turbine and are subjected to continuous cyclic loads of wind and self-weight variation. The structural maintenance operations in wind farms are moving towards condition based maintenance (CBM) to avoid premature failures. For this, damage prognosis with remaining useful life (RUL) estimation in wind turbine blades is necessary. Wind speed variation plays an important role influencing the loading and consequently the RUL of the structural components. This study investigates the effect of variable wind speed between the cutin and cut-out speeds of a typical wind farm on the RUL of a damage detected wind turbine blade as opposed to average wind speed assumption. RUL of wind turbine blades are estimated for different initial crack sizes using particle filtering method which forecasts the evolution of fatigue crack addressing the non-linearity and uncertainty in crack propagation. The stresses on a numerically simulated life size onshore wind turbine blade subjected to the above wind speed loading cases are used in computing the crack propagation observation data for particle filters. The effects of variable wind speed on the damage propagation rates and RUL in comparison to those at an average wind speed condition are studied and discussed.

Study of wind farm control potential based on SCADA data

In this work, a control-oriented wind farm model is validated against SCADA data from a typical on-shore wind farm, without additional instrumentation available. The comparison of model-predicted and measured power deficits due to wake impingement shows good agreement. Furthermore, the model is used to compute optimum yaw misalignments for yaw-induced wake steering, leading to an estimated 1.7% increase in annual energy production by mitigation of wake losses. Results show that wake steering based on standard SCADA data, which is usually available in operational wind farms, has promising potential for open-loop model-based wind farm control.

Investigation on Overvoltage caused by Connecting Shunt Reactors with Vacuum Circuit Breakers in Offshore Wind Farms

Nowadays, the transient overvoltage occurs in offshore wind farms has aroused widespread concern. To improve the existing offshore wind farm model’s accuracy in high frequency conditions, this paper proposes a high frequency model of the vacuum circuit breaker as well as a shunt reactor model considering the influence of stray capacitance. After that, a model of a typical offshore wind farm is constructed for the purpose to analyze the overvoltage occurring at bus bars and wind turbine transformers in the process of connecting a shunt reactor. Simulations show that connecting a shunt reactor in offshore wind farms cause overvoltage of high peak and high steepness compared than with those in onshore wind farms.

Parametric CAPEX, OPEX, and LCOE expressions for offshore wind farms based on global deployment parameters

ABSTRACTInstalled wind energy capacity has been rapidly increasing over the last decade, with deployments in deeper waters and further offshore, with higher turbine ratings within new farms. Understanding the impact of different deployment factors on the overall cost of wind farms is pertinent toward benchmarking the potential of different investment decision alternatives. In this article, a set of parametric expressions for capital expenditure, operational expenditure, and levelized cost of energy are developed as a function of wind turbine capacity (), water depth (WD), distance from port (D), and wind farm capacity (). These expressions have been developed through a series of simulations based on a fully integrated, tested cost model which are then generalized through the application of appropriate nonlinear regression equations for a typical offshore wind farm investment and taking into account most current published cost figures. The effectiveness of the models are countersigned through a series of cases, estimating the predicted values with a maximum error of 3.3%. These expressions will be particularly useful for the preliminary assessment of available deployment sites, offering cost estimates based on global decision variables.

The life-cycle energy and environmental emissions of a typical offshore wind farm in China

Abstract China's vast untapped offshore wind energy and the concentration of electricity consumers in coastal regions make offshore wind power a promising solution to the country's cleaner power transition. However, the potential energy and environmental emissions of offshore wind farms have not been thoroughly investigated. Thus, this study employed a process-based life cycle inventory (LCI) model to calculate the life-cycle energy and emissions of offshore wind power in China based on the country's first offshore wind energy project. Results showed that the life-cycle energy of the studied wind farm was 2.28E+09 MJ, or 0.39 MJ/kwh, with emission intensities of 25.5 g CO2-eq/kWh for GHG, 0.02 g/kWh for PM2.5, 0.06 g/kWh for SO2 and 0.09 g/kWh for NOx. The life-cycle footprints are dominated by the manufacture of wind turbines and foundation materials production. Compared to onshore wind farms, offshore wind power plants usually have greater life-cycle electricity yields, but their levelized energy and environmental footprints are less favorable. The green manufacturing of China's steel sector, and scientific operation and maintenance programs of wind facilities contribute to greening offshore wind power. Results of this study facilitate robust policy making of government authorities and contribute to the green deployment of offshore wind technologies in China.

Calculation of Admittance Capacity of Wind Farms under Harmonic Constraints

The harmonic voltage at the wind farm interconnection point does not exceed the harmonic distortion limits of all voltage levels stipulated by the national standard. The allowable value of the harmonic current injected into the power system shall be allocated in proportion to the total capacity and installed capacity of the harmonic source equipment. However, judging from the status quo of the implementation of harmonic national standard, there is still much controversy over this standard. One of the focal points of controversy is determining the limits of whether different types of harmonic sources should be treated differently. Based on the theoretical research, this paper selects the typical wind farm interconnection points and calculates the admittance capacity limits under different harmonic constraints of the wind farm based on the historical data and the level of harmonic planning, and analyzes the rationality of the calculation results.