Large-scale Wind Power Research Articles

A novel EMD and causal convolutional network integrated with Transformer for ultra short-term wind power forecasting

Accurate wind power forecasting can enhance the safety, stability, economy and controllability of the power system. Traditional physical methods and statistical methods are easily affected by data quality and extraction methods in wind power forecasting. The commonly used recursive neural network method may have memory decline phenomenon in wind power forecasting and does not support parallel calculation, thus limiting the forecasting accuracy. To solve the above problems, in this paper, a wind power forecasting method based on EMD-CCTransformer is proposed. The network model is based on an encoder–decoder structure, where the encoder is used to parse historical wind power sequences, the decoder generates future wind power, and the encoder and decoder are connected using an attention mechanism. In this method, the EMD algorithm is used to decompose the wind power series and obtain the changes of power signals in different time scales, which improves the ability of Transformer to maintain long-term information. Meanwhile, in view of the partial unknowability of the Transformer model, the convolutional attention mechanism is introduced to replace the dot product attention mechanism to form the CCTransformer model, which further improves the forecasting accuracy. We use large-scale wind power data (annual data of the year of 2020 ) to train and test the proposed model. Experimental results show that compared with commonly used wind power forecasting methods, the forecasting error of the proposed EMD-CCTransformer forecasting method in this paper is lower and its model training time is further shortened.

Security-constrained Transmission Expansion Planning with N-k Security Criterion and Transient Stability

The integration of large-scale wind power into the power system is a global trend in response to climate change, however, the intermittent nature of wind power presents new challenges for planners. Conventional transmission expansion planning (TEP) methods, which only consider N-1 static security, may not ensure the security of multiple contingencies and transient stability in a large-scale wind power integration scenario. Incorporating N-k-based static constraints and dynamic constraints, such as differential-algebraic-equations-(DAEs)-based transient stability constraints, into TEP can result in a heavy computational burden and render the TEP model intractable. This paper proposes a tractable mathematical model (S&DSTEP) for joint consideration of static and dynamic security in TEP with wind power. The uncertain wind power generation is captured through selected scenarios and the model is divided into a TEP master problem and multiple sub-problems for evaluating static security and transient stability. Static security is determined by the use of fault chain theory (FCT), avoiding the need for enumerating all N-k contingencies, thus reducing the computational burden. Transient stability is analyzed through the use of stabilizing cuts generated from the extended equal-area criteria (EEAC) rather than dynamic constraints expressed as DAEs. The proposed model is verified on the modified New England 39-bus system and IEEE 118-bus system, demonstrating its ability to consider transient stability while being more computationally efficient compared to the robust method that directly incorporates all contingency scenarios.

Wind Power Group Prediction Model Based on Multi-Task Learning

Large-scale wind power grid connection increases the uncertainty of the power system, which reduces the economy and security of power system operations. Wind power prediction technology provides the wind power sequence for a period of time in the future, which provides key technical support for the reasonable development of the power generation plan and the arrangement of spare capacity. For large-scale wind farm groups, we propose a cluster model of wind power prediction based on multi-task learning, which can directly output the power prediction results of multiple wind farms. Firstly, the spatial and temporal feature matrix is constructed based on the meteorological forecast data provided by eight wind farms, and the dimensionality of each attribute is reduced by the principal component analysis algorithm to form the spatial fusion feature set. Then, a network structure with bidirectional gated cycle units is constructed, and a multi-output network structure is designed based on the Multi-gate Mixture-of-Experts (MMoE) framework to design the wind power group prediction model. Finally, the data provided by eight wind farms in Jilin, China, was used for experimental analysis, and the predicted average normalized root mean square error is 0.1754, meaning the prediction precision meets the scheduling requirement, which verifies the validity of the wind power prediction model.

Integrated Reactive Power Optimisation for Power Grids Containing Large-Scale Wind Power Based on Improved HHO Algorithm

Large-scale wind power grid integration will greatly change the system current distribution, making it difficult for the reactive power regulator to adjust to the optimal state. In this paper, an integrated reactive power optimisation method based on the improved Harris Hawk (HHO) algorithm is proposed. Firstly, a reactive power regulation model is constructed to solve the reactive power regulation interval of wind turbines, and the reactive power margin of wind turbines is used to participate in the system’s reactive power optimisation. Finally, a reactive power compensation capacity allocation optimisation model considering nodal voltage deviation, line loss and equipment investment cost, is established, and a reactive power optimisation scheme is obtained using the Harris Hawk optimisation algorithm on the basis of considering the constraints of the wind turbine reactive power output interval. The improved HHO algorithm is used to solve the reactive power optimisation scheme considering the constraints of tidal power, machine end voltage, a conventional generator and wind farm reactive power. In the simulation, the effects of the improved Harris Hawk optimisation algorithm and the particle swarm optimisation algorithm are compared, and the experimental results prove that compared to the particle swarm algorithm, the optimisation result of the improved Harris Hawk optimisation algorithm reduces the average loss of the system by 42.6% and reduces the average voltage deviation by 30.3%, which confirms that the improved Harris Hawk intelligent optimisation algorithm is effective in proving its superiority and solving the multi-objective model for reactive power optimisation.

Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model

In order to solve the security threat brought by the volatility and randomness of large-scale distributed wind power, this paper proposed a wind power prediction model which integrates two-layer decomposition and deep learning, effectively realizing the accurate prediction of wind power series with non-stationary characteristics. Initially, pearson correlation coefficient (PCC) is employed to identify primary meteorological variables as input series. Second, the wind power series are smoothed by implementing complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and then all subseries are decomposed and obtained by utilizing empirical wavelet transform (EWT) for the components with the highest complexity. Subsequently, hidden information related to wind speed, wind direction, and wind power series are extracted through the bidirectional temporal convolutional network (BiTCN), and the obtained information is fed into a bidirectional long short-term memory network (BiLSTM) optimized by attention mechanism for prediction. Finally, the predicted values of all components are summed to derive the final prediction results. In addition, the significant advantages of the prediction model in this paper are verified by five comparison experiments. The mean absolute error (MAE) and root mean square error (RMSE) of the model's one-step prediction in the January dataset are 2.1647 and 2.8456, respectively.

Large-signal stability analysis for offshore wind power fractional frequency transmission system with modular multilevel matrix converter

Aiming to achieve zero carbon emission, the rapid development of offshore wind power transmission system faces stability challenge. The fractional frequency transmission system (FFTS) with modular multilevel matrix converter (M3C) holds considerable promise among the solutions for large-scale and long-distance offshore wind power transmission. Currently, numerous demonstration projects involving FFTS are being conducted worldwide, necessitating urgent attention to system stability analysis for practical engineering applications. Notably, conventional approaches focusing on small-signal stability are inadequate when confronted with large-signal disturbances. In this study, the Takagi–Sugeno fuzzy model of FFTS is proposed considering the complex control strategy of M3C and the system strong nonlinearity. Subsequently, the Lyapunov function and the large-signal stability domain of attraction (LS-DOA) are constructed expeditiously based on the proposed fuzzy model. When analyzing the system larger-signal stability, the novel stability strength and optimal optimization factor are further introduced to develop a quantitative and efficient methodology for parameter optimization guidance compared with the conventional method. Thus, the FFTS different-frequency interaction features are elucidated from normal and different characteristics parameters mathematically and comprehensively. The effectiveness of proposed methods is verified by the simulation-based study.

Dynamic Economic Dispatch Problem in Hybrid Wind Based Power Systems Using Quasi Oppositional Based Chaotic Honey Badger Algorithm

This article proposes a dynamic economic load dispatch (DELD) including wind power involvement process, which is capable to produce least possible generation cost of electrical systems by integrating large-scale wind power penetration. The primary component of hybridized power system comprises of conventional thermal generators and wind generators. To expedite the convergence flexibility and escape the clarifications from the confined optimal purpose, quasi-oppositional based learning (Q-OBL) is combined with HBA, which results in quasi oppositional chaotic honey badger algorithm (QOHBA). Moreover, chaotic strategy is integrated with QOHBA to boost the convergence speed as well as enhance the performance of the basic honey badger algorithm (HBA). The proposed QOCHBA is suggested to explain wind-based shared DELD problem to reduce thermal-wind generation energy cost. The superiority and expediency of the prospective technique is proved through its operation in two benchmark investigation systems. We use probability density functions (PDF) for wind energy (WE) forecasting to assessment dynamic working reserve necessities, based on the readjust of uncertainty in the prediction. To establish the efficacy of the recommended QOCHBA method in explaining non-linear, non-convex, and constrained DELD problem, the suggested structures are implemented on benchmark of 10-unit in addition to 15-unit test methods.

Cooperative control strategy for frequency regulation of wind-pumped storage system considering wind speed partition

In order to solve the problem of system inertia reduction and frequency response degradation caused by large-scale wind power interconnection, a strategy is proposed. This strategy incorporates a joint system model composed of doubly-fed wind turbine (DFWT) and variable speed pumped storage (VSPS), utilizing a fuzzy controller to dynamically determine the virtual inertia coefficient of the wind-pumped storage system based on the frequency state of the system, and adjusts the frequency-regulation participation factor of the DFWT in real time according to the operating conditions of it. The simulation results show that the proposed strategy can not only significantly improve the frequency nadir, speed up the system frequency and each generating unit in the system to restore to steady state, but also avoid the second frequency drop (SFD) caused by the wind farm out of primary frequency regulation (PFR), and improve the stability of the system frequency.

A Review of Modern Wind Power Generation Forecasting Technologies

The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

Nonlinear rotor kinetic energy control strategy of DFIG-based wind turbine participating in grid frequency regulation

Large-scale wind power integration reduces the level of inertia in power systems, significantly deteriorating their frequency stability. To address this issue, a nonlinear rotor kinetic release control strategy is designed for wind power grid-connected systems based on an extended state observer (ESO) and nonlinear feedforward state feedback transformation (NFSFT) theories. First, the affine nonlinear system of a DFIG-based wind turbine participating in grid frequency regulation is established. The system is transformed into a basic equivalent linear system based on the NFSFT theory, and a nonlinear control law is obtained. Second, the ESO is used to observe the generalized disturbance composed of the complex parameter operation required by the nonlinear control law, which circumvents the huge computational burden of the nonlinear control law and the unknown uncertainty of the system. Finally, a limiting link and speed control link are added to prevent excessive release of the kinetic energy of the wind turbine rotor and complete the wind turbine speed recovery. A wind power grid-connected system is built using MATLAB / SIMULINK for simulation verification. The results show that the proposed strategy exhibits a better frequency regulation effect than other strategies.

The use of innovative tools in the medium voltage grid development, a case study of series voltage regulator

The rapid technological improvements concerning the renewable energy sources and the energy policy of the European Union, and the National Energy Strategy are leading to a rapid increase in the number of intermittent renewable power plants. Thus, new challenges emerge in the operation of the distribution system. To operate the system efficiently, the use of innovative technologies must be considered, because conventional network development strategies cannot always provide an optimal solution for the problem. This paper analyses the effects of largescale wind power generation on a medium voltage system and a solution of the problems faced through a case study of a serial voltage regulator. The difference between the profiles of generation and loads causes the residential transformers at the end of the line to encounter large, more than 8% voltage fluctuation. To assess the site’s voltage profile, time series symmetrical load flow calculations were performed. After the thorough analysis of the circumstances a serial voltage regulator device was implemented at 3 different nodes of the system and a placement analysis was carried out with statistical tools. The results showed a 3% decrease in the voltage fluctuation at the end of the line even when the device was far from these nodes; and with an optimal placement, the device could halve the largest voltage fluctuation on the line.

Evaluating offshore wind power potential in the context of climate change and technological advancement: Insights from Republic of Korea

Offshore wind power is constantly being invested and expanded around the world. National plans for wind power generation must be evaluated in light of climate change and technological advancement since wind speed changes at sea level due to climate change along with continuous technological development significantly affect the wind power generation potential. Republic of Korea has set ambitious plans to develop and expand large-scale offshore wind power generation and with the aim of accurately predicting domestic wind power generation potential, the current study analyzed the patterns and consequences of changes in wind speed due to climate change. Furthermore, the contribution of developing a 15 MW large-scale wind power generator to the national target for offshore wind power generation was evaluated. Changes in wind speed under RCP 4.5 and RCP 8.5 scenarios were examined for 10-year interval changes and for seasonal variabilities from 2010 to 2100. Results indicate that the wind speed would continuously decrease in the RCP 8.5 scenario, suggesting that failing to reduce greenhouse gas emission would eventually lead to loss of wind resources and resultant decline in wind power generation. Analyses also revealed that installing 3 MW wind turbines (utilization rate of 22%) or 15 MW wind turbines (utilization rate of 29%) can respectively achieve 40% or 55% of the national electricity production target for 2030. Assuming an aggressive deployment in high wind speed areas, it was found feasible to achieve up to 64% of the 2030 target, and even 100% but with a high utilization rate of 42%.

A Grid Status Analysis Method with Large-Scale Wind Power Access Using Big Data

Targeting the problem of the power grid facing greater risks with the connection of large-scale wind power, a method for power grid state analysis using big data is proposed. First, based on the big data, the wind power matrix and the branch power matrix are each constructed. Second, for the wind energy matrix, the eigenvalue index in the complex domain and the spectral density index in the real domain are constructed based on the circular law and the M-P law, respectively, to describe the variation of wind energy. Then, based on the concept of entropy and the M-P law, the index for describing the variation of the branch power is constructed. Finally, in order to analyze the real-time status of the grid connected to large-scale wind power, the proposed index is combined with the sliding time window. The simulation results based on the enhanced IEEE-33 bus system show that the proposed method can perform real-time analysis on the grid state of large-scale wind power connection from different perspectives, and its sensitivity is good.

Intentional Controlled Islanding Strategy for Wind Power Plant Integrated Systems

The concept of intentional controlled islanding (ICI) is introduced as a proactive measure to safeguard the power system against blackouts in the event of significant disturbances. It involves strategically partitioning the system into self-healing islands, thereby mitigating the impact of such disturbances. This study introduces a new framework for creating stable, controlled islands in power systems with large-scale wind power plants. The proposed islanding strategy takes into account the impact of wind power plants on the coherency grouping of generators as a constraint in the ICI problem. The proposed algorithm employs the Virtual Synchronous Motion Equation (VSME) model of asynchronous generators to replace wind power plants in power systems and groups all generators, including synchronous generators and wind turbine generators, based on their dynamic coupling. Support Vector Clustering is employed in the ICI problem to determine the coherent generator clusters as the cores of the formed islands. The algorithm can identify the optimal number of islands without prior information about the number of clusters. In this study, a Mixed Integer Linear Programming (MILP) model is formulated to address the ICI problem with the objective of minimising the power imbalance in each island after splitting while ensuring the transient stability and dynamic frequency stability of ICI. Simulation results on modified 39-bus and 118-bus test power systems demonstrate that integrating a Virtual Inertia Controller into the wind-integrated power systems results in a high-inertia power system that behaves similarly to a conventional power system with only synchronous generators during the islanding procedure.

Optimal scheduling of electro-thermal system considering refined demand response and source-load-storage cooperative hydrogen production

How to further increase the level of wind power consumption is a hot research topic in constructing new power systems around the world. It's a consensus that the electrolytic hydrogen production's participation in the power system can further improve the consumption of new energy. This paper proposes a new optimal scheduling strategy for the electro-thermal system, considering refined demand response and source-load-storage cooperative hydrogen production to increase the large-scale wind power consumption. Firstly, to alleviate the impact of wind power fluctuations on hydrogen production, a “wind-storage” combined hydrogen production module is proposed based on the characteristics of hydrogen energy to ensure the safe operation. Secondly, a refined demand response model is built to optimize the electric load curve through price and incentive guidance, to improve the flexibility of “source-load” coordination deeply. Then, to address the issue of wind power competition caused by benefits conflicts of system operation and hydrogen production, a Bi-level scheduling model of source-load-storage cooperative operation is established to achieve the maximum tracking of wind power, while ensure the interests of both sides. Simulation results show that the proposed strategy can balance the system operation cost and hydrogen production benefits, improve hydrogen revenue by 24%. And even if the capacity of hydrogen production device is low, 100% consumption of wind power can be achieved. This study is heuristic for the large-scale wind power consumption in the multi-energy coupled environment, and provides an idea for operation of new energy power system.

Optimization Design of Voltage Level of Flexible DC Transmission with Offshore Wind Power Based on Genetic Algorithm

Voltage level selection is the foundation of planning and designing for flexible DC transmission with offshore wind power. Traditional voltage level selection method is largely dependent on the manufacturing level of DC cable and converter with having standardization or project experience. For flexible DC transmission with large-scale offshore wind power, the voltage level using the traditional selection method may be not the best. By studying calculation methods of main devices and system cost, under the condition of system capacity and length of DC cable known, system cost function with DC voltage and sub-module capacitor voltage as variables was built. Taking the lowest cost as a target, aiming at the low stability of the optimization results of the traditional genetic algorithm, an optimization design method of DC voltage level based on subsection searching genetic algorithm was proposed. The simulation model of flexible DC transmission with offshore wind power was built based on PSCAD/EMTDC. The AC fault ride-through of the converter station and the safety of the main equipment under the optimal DC voltage level was verified.

Coordination Control of Wind Farm Integrated Cascaded Hybrid HVDC System in Weak Grids

Combing the benefits of line-commutated converter (LCC) and modular multilevel converter (MMC), the hybrid high-voltage direct-current (HVDC) system is a viable option for the long-distance transmission of large-scale wind power. However, as renewable energy becomes more prevalent, the inertia of power networks will decrease, possibly leading to instability problems. In this scenario, the conventional grid following (GFL) control scheme is no longer applicable, and suitable coordination control strategies are required. This paper proposes two coordinated grid forming (GFM) control strategies for the cascaded hybrid system and evaluates the control performance of different control types. Firstly, the conventional control scheme of the cascaded hybrid HVDC system and problems under weak AC power systems integration are introduced. Then, coordinated grid forming control strategies are proposed, and the control performance of the GFL and GFM control schemes are compared. Finally, the cascaded hybrid HVDC system's electromagnetic transient model is constructed in PSCAD/EMTDC to confirm the efficacy of the proposed coordinated control strategies.

Cooperative control of time-delay wind power grid-connected unit under actuator saturation based on Hamiltonian method

In large-scale wind power generation, wide area control is often employed to overcome power oscillation, but this can result in the appearance of time-delayed voltage, which is transmitted to the wind power grid-connected system as an input signal. Additionally, ensuring the security of multi-grid parallel connection requires limiting the amplitude of the input signal. To address these challenges, this paper investigates the cooperative control problem for time-delayed wind power grid-connected unit under actuator saturation constraints. A novel control strategy that combines passive control with synchronous control is proposed. The system is transformed into a Hamiltonian structure, and a passive controller is designed using the principle of interconnection and damping assignment passivity-based (IDA-PB) to ensure stability. Based on this, an output feedback synchronous controller is proposed to account for saturation when multiple grids are connected in parallel. Stability criteria are provided to ensure the asymptotic stability of each closed-loop system and synchronous stabilization of all systems. Finally, simulation results verify the effectiveness of the proposed control strategy.

Steady-state deduction methods of a power system based on the prediction of large-scale wind power clusters

The integration of a high proportion of wind power has brought disorderly impacts on the stability of the power system. Accurate wind power forecasting technology is the foundation for achieving wind power dispatchability. To improve the stability of the power system after the high proportion of wind power integration, this paper proposes a steady-state deduction method for the power system based on large-scale wind power cluster power forecasting. First, a wind power cluster reorganization method based on an improved DBSCAN algorithm is designed to fully use the spatial correlation of wind resources in small-scale wind power groups. Second, to extract the temporal evolution characteristics of wind power data, the traditional GRU network is improved based on the Huber loss function, and a wind power cluster power prediction model based on the improved GRU network is constructed to output ultra-short-term power prediction results for each wind sub-cluster. Finally, the wind power integration stability index is defined to evaluate the reliability of the prediction results and further realize the steady-state deduction of the power system after wind power integration. Experimental analysis is conducted on 18 wind power farms in a province of China, and the simulation results show that the RMSE of the proposed method is only 0.0869 and the probability of extreme error events is low, which has an important reference value for the stability evaluation of large-scale wind power cluster integration.

A novel modeling and emergency control method of transient energy of DFIG-based wind turbine under grid fault

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.