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• The similarities and differences of inertia response between SG and DFIG are analyzed and compared. • An analytical model of DFIG with inertia control is developed for studying frequency dynamics considering aerodynamics and rotor side power of wind turbine. • The virtual inertia function is derived accounting for the differences between SG and DFIG, and the key factors affecting virtual inertia are analyzed. • An optimal design approach of virtual inertia is constructed and solved for improving system frequency trajectory. • The effectiveness and advantages of proposed model are verified through a generic two machine power system and modified IEEE-9 bus test system. Modern power systems are gradually evolving into low-inertia systems due to the integration of high penetration of renewable energy and power electronic equipment. For the natural inertia on the rotor, doubly-fed induction generator (DFIG) based wind farms are expected to provide inertia support for systems. The paper proposed an analytical model of DFIG with df / dt inertia control for studying frequency dynamics, and investigates the optimal design of virtual inertia from wind farms, as well as its equivalent inertia contribution to system. The inertia response differences between DFIG and synchronous generator (SG) are discussed and analyzed for the first time, and then to accurately describe the frequency response with low cost, an analytical model of DFIG with inertia control is developed considering wind turbine aerodynamic characteristics. The explicit expression of virtual inertia provided by DFIG is derived according to the kinetic energy equilibrium, and the key factors that dominate the magnitude of virtual inertia are illustrated. Based on the system frequency response model including wind farms, taking the system frequency nadir (SFN) and the time of reaching nadir (ToN) as targets, a multi-objective optimal configuration model of virtual inertia is constructed and solved through MOPSO. The time domain simulations of a generic two machine four bus system and modified IEEE 9-bus test system are both performed in MATLAB/Simulink, the accuracy and efficiency of our proposed DFIG model on system frequency dynamics research are demonstrated, the pareto frontiers of virtual inertia configuration under different working conditions are optimized and analyzed as well as the impacts of different parameters on inertia evaluation. [ABSTRACT FROM AUTHOR]

Multi-step real-time prediction based on the spatial correlation of wind speed is a research hotspot for large-scale wind power grid integration, and this paper proposes a multi-location multi-step wind speed combination prediction method based on the spatial correlation of wind speed. The correlation coefficients were determined by gray relational analysis for each turbine in the wind farm. Based on this, timing-control spatial association optimization is used for optimization and scheduling, obtaining spatial information on the typical turbine and its neighborhood information. This spatial information is reconstructed to improve the efficiency of spatial feature extraction. The reconstructed spatio-temporal information is input into a convolutional neural network with memory cells. Spatial feature extraction and multi-step real-time prediction are carried out, avoiding the problem of missing information affecting prediction accuracy. The method is innovative in terms of both efficiency and accuracy, and the prediction accuracy and generalization ability of the proposed method is verified by predicting wind speed and wind power for different wind farms. [ABSTRACT FROM AUTHOR]

The low and variable inertia due to the integration of large-scale wind generation poses severe challenges to the frequency control of the power grid. In this study, a novel model predictive control (MPC)-based load frequency controller (LFC), which can adapt to the changes in system inertia, is proposed to improve the frequency control effect. First, the inertia estimation scheme based on measurements is designed. Then, the state-space model, which is the key that determines the control effect of MPC, is corrected in time based on the estimated inertia. The proposed method in this study can avoid the problem of MPC-based LFC failure caused by inaccurate model parameters. The performance of the proposed strategy is demonstrated in two scenarios, i.e. wind power fluctuation and load multi-step, using a two-area interconnected power system with time-variant inertia. [ABSTRACT FROM AUTHOR]

• A general model of regulating characteristics for Energy-intensive load is given. • A multi-objective scheduling approach is developed for wind utilization. • The scheduling benefits both power system and Energy-intensive load. • The production demand of load are taken as the most precondition of regulation. • Cases study of multi-scenario and multi-mode in regulating. Energy-intensive load (EIL) has excellent regulating potential in the utilization of wind power: it not only offers high flexibility and large power capacity but also features significant price sensitivity. Without considering the production tasks and the regulating characteristic of energy-intensive load, the current scheduling approach is difficult to implement. A novel multi-objective coordinative scheduling approach is proposed in this paper for wind-power utilization considering the regulating models of energy-intensive load to guarantee the benefits of both power system and energy-intensive load, which can greatly improve the willingness of energy-intensive load to participate in scheduling. Firstly, A general model of regulating characteristics for energy-intensive load is given based on production process. Next, based on this modeling, a coordinative scheduling model is established with three layer. Layer A determines the total tasks of load side and generating side with the goal of minimizing the wind curtailment. Layer B and layer C make decomposition optimization to thermal power units and load control units with the goal of minimizing the generation cost and load-adjustable cost, respectively. Finally, a case study to verify the modeling is presented. The study shows that the proposed method can significantly improve the utilization of wind power at lower cost on the basis of ensuring the production requirements and benefits of energy-intensive load. [ABSTRACT FROM AUTHOR]

The doubly-fed induction generator (DFIG) has been widely applied in wind power generation systems. However, the applications of DFIG are limited by the failure of power system and inefficient fault ride-through (FRT) strategy, which lead to overcurrent in rotor windings. The overcurrent that results from continuous operation of DFIG may cause a serious tripping accident in wind turbine generators. The occurrence of overcurrent at the rotor side of DFIG must be prevented to ensure safe operation of the rotor-side converter. Preventing overcurrent can enhance the control ability of rotor current and the FRT capacity of DFIG. A novel rotor current controller for DFIG is designed to overcome its strong nonlinearity. The controller combines extended state observer (ESO) with backstepping theory. ESO is used to dynamically estimate and eliminate the model error and the uncertain external disturbance of the rotor current of DFIG. The controller is designed on the backstepping method, and then the feedback of ESO is used to eliminate the nonlinear factor. As a result, computation complexity is reduced. The controller has both superior static, dynamic performance, and strong robustness. The effectiveness of the proposed control approach is verified. [ABSTRACT FROM PUBLISHER]

Wind speed forecasting is an indispensable part of wind energy assessment and power system scheduling. In the modeling of wind speed forecasting, there are problems of insufficiency of the high input feature dimension, weak pertinence of the model and a lack of consideration about the redundancy between features. To address these problems, a short-term wind speed forecast method based on low redundancy feature selection is proposed. Firstly, complementary ensemble empirical mode decomposition (CEEMD) is used to pretreat the wind speed data to reduce the randomness and fluctuation of wind speed data. Secondly, conditional mutual information (CMI) is used to analyze the correlation between the input features on different predicted days and wind speed series. The feature order based on conditional mutual information is used to reduce the redundancy between candidate features and establish subsets with candidate features. After that, according to different candidate feature subsets of different predicted days, the outlier-robust extreme learning machine (ORELM) is used to carry out the forward feature selection and obtain optimal feature subsets for different predicted days. Finally, the optimal prediction model is constructed by using the optimal feature subset and the short-term wind speed forecasting is carried out. The validity and advance of the new method are verified by measured data through comparison experiments. [ABSTRACT FROM AUTHOR]

With the increasing proportion of renewable energy (mainly wind power and photovoltaic) connected to the grid, the fluctuation of renewable energy power brings great challenges to the safe and reliable operation of power grid. As a clean, low-carbon secondary energy, hydrogen energy is applied in renewable energy (mainly wind power and photovoltaic) grid-connected power smoothing, which opens up a new way of coupling hydrogen storage energy with renewable energy. This paper focuses on the optimization of capacity of electrolyzers and fuel cells and the analysis of system economy in the process of power output smoothing of wind/photovoltaic coupled hydrogen energy grid-connected system. Based on the complementary characteristics of particle swarm optimization (PSO) and chemical reaction optimization algorithm (CROA), a particle swarm optimization-chemical reaction optimization algorithm (PSO-CROA) are proposed. Aiming at maximizing system profit, the capacity of electrolyzers and fuel cells are constrained by wind power fluctuation, and considering environmental benefits, government subsidies and time value of funds, the objective function and its constraints are established. According to the simulation analysis, by comparing the calculated results with PSO and CROA, it shows that PSO-CROA effectively evaluates the economy of the system, and optimizes the optimal capacity of the electrolyzers and fuel cells. The conclusion of this paper is of great significance for the application of hydrogen energy storage in the evaluation of power smoothness and economy of renewable energy grid connection and the calculation of economic allocation of hydrogen energy storage capacity. • The particle swarm optimization-chemical reaction optimization algorithm (PSO-CROA) is proposed. • Based on PSO-CROA, the techno-economic of coupled renewable energy and hydrogen system is analyzed. • Aiming at maximizing system profit, the capacity of electrolyzers and fuel cells are optimized. • PSO-CROA effectively evaluates the economy of system and the optimal capacity of the electrolyzers and fuel cells. [ABSTRACT FROM AUTHOR]

As the uncertainty in energy supply increases, engaging various flexible resources in power systems has emerged as an effective strategy to address wind curtailment issues. Existing research insufficiently explores how EAFs can participate in reducing wind curtailment or optimizing flexible power system resources to decrease CO 2 emissions from TTPs and enhance wind energy absorption across various timescales. This study introduces a dual timescale, dual-tier scheduling methodology incorporating EAF regulation and wind power modal decomposition. The day-ahead model integrates EAF demand response to decrease wind curtailment, a comprehensive wind power allocation, and a TTP carbon minimization model. The intra-day model employs wind power modal decomposition for optimizing BESSs within WFs and schedules TTPs to minimize CO 2 emissions. Implemented through iterative genetic algorithms and CPLEX solver techniques, simulation results from a real-case scenario indicate that incorporating EAF loads reduces wind curtailment by 40.49 % and cuts CO 2 emissions by 2.5 % in the day-ahead phase. Furthermore, by applying modal decomposition, TTPs and BESSs absorb fluctuating wind power components, ensuring maximal wind utilization and substantial CO 2 reduction at TTPs. This approach offers vast potential to enhance power system flexibility, advance energy-intensive industries' transition, and foster low-carbon initiatives at TTPs. • An energy-intensive model for EAFs is devised based on operational traits. • TPP scheduling achieves minimal CO 2 emissions. • VMD decomposes day-ahead wind curves, integrating thermal and storage for optimal absorption. • A dual timescale, dual-tier optimization scheduling approach is introduced. [ABSTRACT FROM AUTHOR]

The significant expansion of wind power has presented challenging obstacles to power system operation. The inherent stochasticity and variability of wind power have emerged as critical issues that impede the advancement of wind energy, affecting its integration and volatility control. Moreover, with the momentum of carbon peaking and carbon neutrality objectives, reducing CO 2 emissions has become urgent. This study proposes a low-carbon dispatching methodology for power systems to address these challenges. It considers regulating energy-intensive loads of electrically fused magnesium and mitigating wind power fluctuations. The approach involves integrating electrically Fused magnesium loads with tight regulation characteristics on the demand side alongside thermal power plants to optimize the power grid and dispatch collectively. And battery energy storage devices are introduced into various wind power stations on the supply side to smooth out wind power fluctuations. The objective function aims to minimize the sum of the average variance of power fluctuations at all wind power busbars while adhering to constraints of maximum wind power consumption and minimum carbon emissions from thermal power plants. The problem is solved using genetic algorithms. Ultimately, we show how effective our proposed low-carbon dispatching method for power systems is, which deals with regulating energy-intensive loads of electrically fused magnesium and reducing wind power fluctuations. We demonstrate this through a case study of wind curtailment, CO 2 emissions, wind power fluctuations, and other related parameters under different conditions. The approach can enhance renewable energy consumption, reduce carbon emissions, and mitigate the power fluctuations of renewable energy. • A model of regulating energy-intensive load characteristics of electrofused magnesium is proposed. • The fluctuation of wind power is smoothed out by utilizing energy storage devices within wind farms. • A multi-objective scheduling approach is proposed that considers source-load coordination. • A case study on double-scheme regulation for the upper and lower level models. [ABSTRACT FROM AUTHOR]

A renewable energy consumption method based on variational mode decomposition and combined regulation of thermal power and storage system is presented in this paper. By decomposing the equivalent load curve using variational mode decomposition, the equivalent load curve is divided into parts with different center frequencies and energy system constraints and solved separately. We then take the maximum wind power absorption rate as the upper model target and solve it. At the same time, through the operation curves of the thermal power unit and battery storage system, the lower optimization model is established to minimize the system's operating cost with carbon emission costs. The example shows that the regulation method with a storage system improves the consumption rate of wind power and reduces carbon dioxide emission compared with the regulation method without a storage system. Compared with the method without decomposition, the power output curve of the thermal power unit is smoother under the new method, effectively improving the solving speed. It reduces the operating cost of the system and its carbon dioxide emissions. • Variational mode decomposition of equivalent load curve and analysis of its characteristics. • Upper levels model improves wind power accommodation rate. • Lower levels model reduces carbon emissions and operating costs of thermal power units. • Frequency block adjustment makes output curve of thermal power unit smoother. [ABSTRACT FROM AUTHOR]

In Northeast China's electric power auxiliary service market, guiding interruptible load users to participate in bilateral transactions is an effective measure to ease the difficulty of grid peak regulation and improve the utilization rate of wind power. Aiming at the lack of theoretical guidance in the current bilateral transactions, this paper proposes a two-stage pricing (TSP) method. Firstly, the freshwater supply and demand balance model of the desalination plant is constructed. The upper-level optimization model of bilateral transaction for profit maximization of the desalination plant is constructed. Then, considering the profit distribution among transaction parties and constraints on wind power utilization, a low-level calculation model of wind power pricing is constructed. Finally, the TSP method is applied to the numerical analysis of bilateral transaction between a desalination plant and a wind power company. The TSP method jointly optimizes the dispatching scheme between the variable speed pump and the water storage tank of the desalination plant. Under the constraints of reasonable profit distribution and wind power utilization, the transaction price finally converges to a range acceptable to both parties. The TSP method can effectively improve the utilization rate of wind power and optimize the flexibility of energy utilization. • A detailed mixed-integer linear programming (MILP) model is developed for desalination plants. • A model for seawater desalination plants to participate in bilateral trading markets is proposed. • A TSP method for calculating the price of bilateral transactions is proposed. • The TSP method can effectively optimize the flexibility of energy utilization. [ABSTRACT FROM AUTHOR]