Wind Power Penetration Rate Research Articles

Critical lines dynamic identification for grid-connected wind power system under N-k contingency based on Oscillating equations

Abstract As the penetration rate of wind power and other renewable energy sources increases, power systems are more prone to experiencing multiple component failures, known as N-k contingency Identifying and then closely monitoring and protecting critical components during N-k contingency can effectively prevent widespread cascading failures. The primary task in identifying critical components is to model the power system; A refined model better captures the time-varying characteristics of electrical components. Therefore, a dynamic model of line power flow decay and oscillation based on swing equations is established; it introduces time-varying saturated cut-set and transfer margin indices to identify critical lines. Simulations on the IEEE-118 nodes system, along with MATCASC cascading failure simulations, demonstrate that targeting identified critical lines as deliberate attack points in the grid results in a normalized remaining load demand decrease to 50% of the original.

A graph attention network with spatio-temporal wind propagation graph for wind power ramp events prediction

The increasing penetration rate of wind power underscores the necessity for accurate forecasting and alerting of wind power ramp events (WPREs), given the unpredictable nature of wind. This article presents a novel approach to predicting WPREs, emphasizing the complexities of wind propagation across multiple locations, in contrast to traditional single-site analyses. By integrating a wind propagation graph into a Graph Attention Network (GAT), the prediction of ramp events is significantly enhanced. The efficacy of this approach is validated through comprehensive case studies utilizing the Spatial Dynamic Wind Power Forecasting (SDWPF) dataset from the Baidu KDD Cup 2022 and the WIND toolkit dataset from NREL, demonstrating superior results at both the wind turbine and wind farm scales.

Optimal planning of integrated electricity-natural gas distribution systems with hydrogen enriched compressed natural gas operation

In future integrated electricity-natural gas distribution systems (IEGDS), hydrogen and methane converted from excessive renewable generation will be mixed with natural gas to form hydrogen enriched compressed natural gas (HCNG) for fewer carbon emissions. Consequently, optimal planning of the HCNG-integrated IEGDS plays an essential role in increasing the economy and technical benefits. However, integrating HCNG operation into the planning of IEGDS still has many challenges, such as unrealistic modeling, nonlinear constraints, and the potential risk of voltage violations. The purpose of this paper is to develop a tractable optimization model for planning and operation of the HCNG-integrated IEGDS, aiming to minimize the investment and operating cost, reduce the voltage violation, and improve the renewable power penetration rate. To achieve this, a bilevel optimization model is developed for optimal planning of wind turbines and soft open points (SOPs) in the IEGDS considering comprehensive HCNG operation, in which both hydrogen and methane injection are considered and optimized for different system operation scenarios. Furthermore, the proposed nonlinear model is reformulated to a tractable bilevel mixed-integer second-order cone model using several reformulation techniques and solved by the reformulation and decomposition algorithm. Case studies, performed using the publicly available datasets, are conducted to demonstrate the economic and technical improvement by implementing the proposed planning model. The annual planning results show that incorporating the coordination between SOPs and methane injection results in a 6.77% reduction in investment cost, a 22.64% reduction in operating cost, a 62.69% decrease in voltage violation, and a 23.58% increase in the wind power penetration rate. Moreover, SOPs are more applicable to the safe operation of the IEGDS under high energy load conditions.

Active power control of wind turbine based on fuzzy pitch control

With the continuous increase in wind power penetration rate, wind turbine generators (referred to as WTGs or wind turbines) need to have the capability of active power control (APC) to respond to grid power commands. To reduce the burden of pitch control on wind turbines, existing studies have adopted a technological approach that uses variable-speed operation of the wind rotor instead of pitch control. Typically, pitch control is only initiated at the speed boundaries to limit variable-speed operation within the speed range. However, due to the influence of factors such as wind speed, rotor speed and control parameters, there is a noticeable saturation phenomenon in pitch control at the speed boundaries, which can affect the variable-speed process of the wind rotor and APC performance within the speed range. Therefore, this paper determines whether blade pitch adjustment needs to be performed in advance when the wind turbine operates within the speed range based on the rotor speed state. This avoids the pitch action at the speed boundaries and the associated pitch saturation problem. The results show that the proposed method in this paper reduces the frequency of the rotor speed reaching the speed boundaries by performing pitch actions within the speed range. This alleviates the influence of pitch saturation on the variable-speed process of the wind rotor, as well as the occurrence of overspeed and electromagnetic power drop, thereby improving the APC performance of the wind turbine.

Capacity Demand Analysis of Rural Biogas Power Generation System with Independent Operation Considering Source-Load Uncertainty

With the greatly increased penetration rate of wind power, photovoltaic, and other new energy sources in the power system, the proportion of controllable units gradually decreased, resulting in increased system uncertainty. The biogas power generation system can effectively alleviate the pressure caused by source-load uncertainty in such high-permeability systems of new energy sources such as wind power and photovoltaic. Hence, from the perspective of the power system, this paper introduces a capacity demand analysis method for a rural biogas power generation system capable of independent operation amidst source-load uncertainty. To enhance the depiction of pure load demand uncertainty, a scene set generation method is proposed, leveraging quantile regression analysis and Gaussian mixture model clustering. Each scene’s data and probability of occurrence elucidate the uncertainty of pure load demand. An integrated optimal operation model for new energy and biogas-generating units, free from energy storage capacity constraints, is established based on the generated scenario set. Addressing considerations such as biogas utilization rate and system operation cost, a biogas storage correction model, utilizing the gas storage deviation degree index and the cost growth rate index, is developed to determine biogas demand and capacity. The example results demonstrate the significant reduction in gas storage construction costs and charging and discharging imbalances achieved by the proposed model while ensuring systemic operational cost effectiveness.

Coordinated control of wind-storage combined with primary frequency regulation and variable coefficient based on wind speed and SOC

The increase of wind power penetration rate will cause the power system to face the problems of lower inertia level and insufficient primary frequency regulation capability, which will seriously affect the system frequency security. Wind turbine generally operate in MPPT mode, and the primary frequency regulation capability is realized through additional control, but when the wind turbine uses the kinetic energy of the rotor to participate in the primary frequency regulation of the system, it will cause a secondary drop in the system frequency. Participating in the primary frequency regulation of the system with the energy storage auxiliary wind turbine can further reduce the depth of the system frequency drop and improve the secondary drop of the system frequency. However, after the energy storage participates in the system frequency regulation, the State of Charge (SOC) will decrease, which will affect the frequency regulation capability of the subsequent energy storage. In view of the above problems, a control strategy of wind and storage participating in the primary frequency regulation of the power system is proposed considering the energy storage recovery strategy. During the primary frequency regulation, the joint output of the wind turbine using virtual inertia control and the Energy storage battery using droop control can effectively suppress the system frequency drop; During frequency regulation, the Energy storage battery is charged using a recovery strategy to ensure that the SOC of the Energy storage battery is in the state of maximum frequency regulation capacity. The three-machine and nine-node model of the wind and storage system is built through RTLAB. The real-time simulation verifies that the joint output of the wind and storage system under the step disturbance increases the minimum value of the power grid frequency drop and increases the minimum value of the frequency secondary drop. It can also be used under continuous disturbances. Realize the recovery of energy storage SOC.

Optimal scheduling of integrated energy systems considering wind power uncertainty

Abstract To reduce the impact of high wind power penetration rate on integrated energy systems, a wind power scenario generation method based on polynomial regression is proposed and an optimization scheduling model is constructed accordingly. First, a probability distribution model for wind power is constructed. Then, scenario generation is performed through Latin hypercube sampling and typical scenarios are obtained using the synchronous back propagation elimination method. Finally, the optimal output of each piece of equipment in the system is obtained with the lowest overall cost as the optimization objective. The simulation results validate the effectiveness and low-carbon nature of this method, while reducing costs.

Wind Speed Modeling for Wind Farms Based on Deterministic Broad Learning System

As the penetration rate of wind power in the grid continues to increase, wind speed forecasting plays a crucial role in wind power generation systems. Wind speed prediction helps optimize the operation and management of wind power generation, enhancing efficiency and reliability. However, wind speed is a nonlinear and nonstationary system, and traditional statistical methods and classical intelligent algorithms struggle to cope with dynamically updating operating conditions based on sampled data. Therefore, from the perspective of optimizing intelligent algorithms, a wind speed prediction model for wind farms was researched. In this study, we propose the Deterministic Broad Learning System (DBLS) algorithm for wind farm wind speed prediction. It effectively addresses the issues of data saturation and local minima that often occur in continuous-time system modeling. To adapt to the continuous updating of sample data, we improve the sample input of the Broad Learning System (BLS) by using a fixed-width input. When new samples are added, an equivalent number of old samples is removed to maintain the same input width, ensuring the feature capture capability of the model. Additionally, we construct a dataset of wind speed samples from 10 wind farms in Gansu Province, China. Based on this dataset, we conducted comparative experiments between the DBLS and other algorithms such as Random Forest (RF), Support Vector Regression (SVR), Extreme Learning Machines (ELM), and BLS. The comparison analysis of different algorithms was conducted using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Among them, the DBLS algorithm exhibited the best performance. The RMSE of the DBLS ranged from 0.762 m/s to 0.776 m/s, and the MAPE of the DBLS ranged from 0.138 to 0.149.

A Bi-Level Optimal Operation Model for Small-Scale Active Distribution Networks Considering the Coupling Fluctuation of Spot Electricity Prices and Renewable Energy Sources

As the penetration rate of variable renewable energy such as wind power increases in the power system, the composition and balance of the system also change gradually. The intermittency of renewable energy poses great stability challenges to the traditional centralized generation and load-oriented transmission and distribution methods. Therefore, the Active Distribution Network Operator (ADNO) with distributed installation at the local level has a good application prospect in the new scenario. However, ADNO needs to improve its operational efficiency based on the types of local generation and storage devices and the nature of the market environment. To address this issue, this paper proposes a forecasting method that considers the coupling fluctuations of spot electricity prices and renewable energy, and a bi-level optimization operation method based on the Stackelberg game for optimizing the operation of small-scale ADNO under high wind power penetration rate. Simulation results show that the proposed methods achieve greater positive impact on the operational efficiency of ADNO than conventional methods. In addition, the proposed methods ensure the long-term profitability of ADNO, even with fluctuations in external factors.

Source-Load Coordinated Low-Carbon Economic Dispatch of Electric-Gas Integrated Energy System Based on Carbon Emission Flow Theory

The development of emerging technologies has enhanced the demand response (DR) capability of conventional loads. To study the effect of DR on the reduction in carbon emissions in an integrated energy system (IES), a two-stage low-carbon economic dispatch model based on the carbon emission flow (CEF) theory was proposed in this study. In the first stage, the energy supply cost was taken as the objective function for economic dispatch, and the actual carbon emissions of each energy hub (EH) were calculated based on the CEF theory. In the second stage, a low-carbon DR optimization was performed with the objective function of the load-side carbon trading cost. Then, based on the modified IEEE 39-bus power system/Belgian 20-node natural gas system, MATLAB/Gurobi was used for the simulation analysis in three scenarios. The results showed that the proposed model could effectively promote the system to reduce the load peak-to-valley difference, enhance the ability to consume wind power, and reduce the carbon emissions and carbon trading cost. Furthermore, as the wind power penetration rate increased from 20% to 80%, the carbon reduction effect basically remained stable. Therefore, with the growth of renewable energy, the proposed model can still effectively reduce carbon emissions.

Virtual Inertial Control Strategy Based on Fuzzy Logic Algorithm for PMSG Wind Turbines to Enhance Frequency Stability

With the increase of the penetration rate of wind power in the power grid, the high proportion of renewable energy and the high proportion of power electronic equipment in the power system will continuously reduce the inertia of the grid, and the frequency stability of the system will be seriously affected. The inertia of the system is an important parameter for system frequency regulation and stability calculation. For this reason, a virtual inertial control technology based on fuzzy logic control is proposed in this paper, which is used for wind turbines to participate in grid frequency regulation. In this method, based on power tracking, a fuzzy logic controller is designed to adjust the frequency adjustment coefficient adaptively, and fuzzy logic rules are used to optimize the power tracking curve online. Finally, by building a hardware-in-the-loop real-time simulation platform, the effectiveness of this method in providing system frequency support and improving the frequency response of the power grid is verified.

Wind Power Forecasting Method Based on Bidirectional Long Short-Term Memory Neural Network and Error Correction

With the improvement of penetration rate of wind power in the power system, its volatility and intermittence bring new problems to the power grid. The accurate forecasting of wind power is an effective way to alleviate the impact on the power grid. In this paper, a novel wind power forecasting method is proposed. Firstly, a wind power forecasting model based on the bidirectional long short-term memory (BiLSTM) neural network is established to forecast the wind power according to the wind speed of numerical weather forecasting (NWF). Secondly, a wind power forecasting error time series model based on empirical mode decomposition (EMD) is established to decrease the forecasting error. Finally, this paper uses the real data to simulate and verify the proposed method. Evaluating by the root mean square error (RMSE), symmetric mean absolute percentage error (SMAPE), and Theil inequality coefficient (TIC), the simulation results show that the forecasting accuracy of the BiLSTM neural network model are 10.25%, 6.71% and 12.18% higher than LSTM model respectively. After correcting wind power forecasting error using the proposed time series model based on EMD, the accuracy of wind power forecasting is further improved.

Review of Grid Codes and Implementation for Wind Farm Frequency Regulation

With the increase of wind power and the construction of UHVDC transmission, the wind turbines must actively participate in the system frequency regulation through inertia response and primary frequency regulation to ensure the safe and stable operation of the power system. However, the wind power penetration rate and the grid structure of each regional power grid are different, so that the corresponding power grid codes are also slightly different. Firstly, this paper introduces the specific requirements of wind power grid guidelines in typical countries and regions, and with reference to the experience of international frequency regulation codes, it puts forward the proposed implementation mode of active support new energy frequency regulation suitable for domestic power grid, and the recommended parameter for wind farm frequency regulation. Secondly, this paper summarizes the frequency modulation implementation schemes of the main wind turbine manufacturers in the world, and demonstrates the potential frequency modulation capability of the wind turbine. Finally, the challenges and realizable solutions of virtual inertia and primary frequency regulation of wind farms are prospected, and some suggestions are given.

Feasibility Analysis of Converting Hydropower Stations into Power Regulation Centers-Taking the Three Gorges Project for Example

In order to effectively respond to the global climate change and energy crisis, the structural transformation of energy has become an irreversible trend in the future development of China’s energy system. As a new kind of clean and renewable energy source, wind power technology has made great progress in recent years and has become relatively mature. The penetration rate of wind power in China’s power system increases continuously, playing an important role in promoting energy saving and emission reduction. However, because of their instability in time and space, wind power and solar power may pose great challenges to the safety of the power grid. To ensure power supply, thermal power generating units, combined with pumped-storage power stations, are still mainly used for peak load regulation nowadays, which is inconsistent with the long-term goal of carbon neutrality. This essay mainly studies the feasibility of turning hydropower stations into power regulation centers, taking the Three Gorges Project as the analysis object.

Study of offshore wind power penetration rate in gas turbine generator platform power grid

Because of the energy supply of the offshore oil platforms mainly relies on the supply of gas turbine, the development of renewable energy will become a rich energy supply for offshore oil platforms and a reliable way to improve the stability of the grid of the platforms. This paper developed the simulation model of offshore oil gas turbine platform power grid, simulated the hybrid power grid structure, the energy storage of frequency regulation, high wind power penetration rate, wind power and gas turbine power frequency stability and fault treatment. The method of increasing renewable energy penetration rate was proposed, and the hybrid energy coordinated control strategy of offshore oil platform was also carried out. Based on the weakly connected distributed offshore oil platform power grid project, this paper studied the influence of wind power and turbine power penetration rate on the power grid, in order to explore, determine and verify the limit of high wind power penetration rate under the condition of “large generation machine and small grid”. The simulation experiment of high wind power penetration rate under the condition of different load conditions and different wind speed limits were discussed, on the basis of research the influence degree of the stability of power grid frequency of low wind power swings, as well as to the wind power is given priority to with gas turbine mainly in both cases the advantages and disadvantages of the grid system, and attempt to put forward the corresponding switch control strategy.

Expansion Planning Method of Offshore Multiplatform Power System With Wind Power Considering Cable Size Selection

With the expansion of the scale of subsea oil and gas resource exploitation, the offshore multiplatform interconnected power systems have become the main form of offshore platform group energy supply. The offshore multiplatform interconnected power system with wind power can effectively reduce the system fuel consumption and carbon emission, which has become an important direction for the development of future offshore multiplatform interconnected power systems. In this paper, the scenario method is adopted to portray the wind power output. System evaluation indicators considering the economy, reliability and environmental benefits of the system are proposed. Considering the nonnegligible impact of the cable size selection problem on the system economic cost, this paper introduces cable size selection variables in the expansion planning model of the offshore multiplatform interconnected power system with wind power to achieve accurate optimization of the system planning scheme but also greatly increases the difficulty of solving the model. A model linearization method is applied to linearize the nonlinear part of the model, so that the optimized model is a mixed-integer linear programming model under the determined system topology, and thus can be solved using commercial planning software. In addition, an improved genetic algorithm is proposed to optimize the system topology, which improves the efficiency of the model solution. The cases of different scale verify the validity of the model and the solution algorithm and show the impact of the maximum penetration rate of wind power on the total system cost.

Optimal configuration of energy storage for remotely delivering wind power by ultra-high voltage lines

Abstract Power generated by large-scale wind farms in northwest China needs to be remotely delivered by ultra-high voltage lines (UHVs) before consumption. However, fluctuation and intermittency of wind power output results in high costs and low efficiency of transmission. This study proposes a novel optimal model and practical suggestions to design an energy storage involved system for remotely delivering of wind power. Based on a concept model of wind-thermal-storage-transmission (WTST) system, an optimization model is established to determine optimal configurations of the system. As validated by a case study, the model is capable of determining installed capacities of wind power plants, thermal power plants, pumped hydro storage stations, and maximum transmission capability of UHVs; and it can be fast solved by mixed integer programming. According to the results of case simulation, the best configuration can significantly reduce wind curtailment rate and improve operational efficiencies of UHVs by load shifting before and after power transmission. Specifically, improving energy storage capacity and remolding thermal power plants to be flexible ones are feasible ways to realize the objectives, which are vital to increase the penetration rate of wind power in China.

Research on the Low Voltage Ride Through Capability of Hybrid Modular Multilevel Converters

With the increase of the penetration rate of wind power in the power system, the latest grid-connection rules require that the inverter of Wind Turbine should have the capability of low voltage ride through (LVRT). This paper aims at a Wind Turbine based on hybrid modular multilevel converter (MMC). Firstly, the mathematical model of the system is established in the stationary coordinate system. In order to ensure that the Wind Turbine can operate without network disconnection during grid fault, this paper adopts a corresponding hierarchical control strategy, the strategy applies to hybrid modular multilevel converter and provide control flexibility, and realize the LVRT of a Wind Turbine, an electromagnetic transient model of the hybrid MMC based Wind Turbine is simulated in PSCAD/EMTDC and the results verify the feasibility and effectiveness of the proposed scheme and control strategy.

Dynamic dependence modelling of wind power uncertainty considering heteroscedastic effect

The inherent stochastic nature of wind power poses significant challenges to the safe and economic operation of power systems, and an accurate model of wind power forecast error can give operators great help in dealing with the negative impact of wind power uncertainty on the power system. To obtain accurate forecast error intervals, a wind power forecast error (WPFE) model based on dynamic Copula theory is proposed in this paper. By combining Autoregressive integrated moving average (ARIMA) method and generalized autoregressive conditional heteroscedasticity (GARCH) model, the marginal distribution of WPFE model with the ability to describe its time-varying feature is achieved. In this proposed model, 4 dynamic Copula functions are estimated and evaluated by the combination method of Inference Functions for Margins (IFM) and Akaike Information Criterion (AIC), and the best-fitted one is selected to be applied in a set of synchronous data of wind power and its forecast. The result shows that compared with the conventional method, the proposed method can provide more accurate forecast error intervals. This advantage can benefit the scheduling of the power system with high wind power penetration rate, which is also verified by a stochastic unit commitment case in modified IEEE 118-Bus system.

Optimization of controlling parameters of DFIG and battery energy storage combined frequency modulation based on PSO

Wind power has randomicity and intermittence. As the wind power penetration rate increases, it poses a threat to the frequency of the power grid. In the power systems with high-permeability of wind power, relying solely on conventional crew tuning is no longer sufficient to ensure that the frequency is within safe limits. The battery energy storage has a fast response speed, but it is expensive. The frequency regulation provided by the wind farm itself has a short inertial control support time, the pitch control response is slow, and both frequency modulation methods are subject to wind speed. In order to improve the frequency adjustment capability of the wind farm, the battery energy storage and the wind farm’s own frequency modulation means are combined to adjust the system frequency. However, how to set the battery energy storage output to make the system frequency characteristics optimal hasn’t been studied. In this paper, the particle swarm optimization algorithm is used to select the optimal parameters, so that the frequency characteristics are optimal. The example shows that compared with the unoptimized energy storage frequency modulation coefficient, the optimal frequency modulation coefficient found by the PSO algorithm can greatly reduce the maximum frequency drop of the power system and improve the frequency stability of the power system.