To address the global climate crisis,achieving energy transitions is imperative.Establishing a new-type power system is a key measure to achieve CO2 emissions peaking and carbon neutrality.The core goal is to transform renewable energy resources into primary power sources.The large-scale integration of high proportions of renewable energy sources and power electronic devices will dramatically change the operational mechanisms and control strategies of power systems.Existing wind and solar converters mostly adopt the grid-following control mode,which leads to significant challenges in system security and stability as it is insufficient to support the frequency and voltage of the grid.On the other hand,gridforming control technology (GFM) can provide voltage and frequency support for the system,and thus becomes an effective measure to improve the inertia and damping characteristics of power systems.This paper illustrates the principles,control strategies,equipment types,application scenarios,and project implementation of grid-forming technology.The simulation and analysis based on a renewable-dominated real new-type power system show that GFM can significantly enhance the frequency and voltage support capacity of the power system,improve renewable energy accommodation capacity and grid transmission capacity under weak grid conditions,and play an important role in enhancing the stability and power supply reliability of renewable-dominated new-type power systems.

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As the proportion of renewable energy infiltrating the power grid increases,suppressing its randomness and volatility,reducing its impact on the safe operation of the power grid,and improving the level of new energy consumption are increasingly important.For these purposes,energy storage stations (ESS) are receiving increasing attention.This article discusses the structure,working principle,and control methods of grid-following and grid-forming energy-storage converters,which are currently commonly used.A simulation analysis was conducted to investigate their dynamic response characteristics.The advantages and disadvantages of two types of energy storage power stations are discussed,and a configuration strategy for hybrid ESS is proposed.This paper presents research on and a simulation analysis of gridforming and grid-following hybrid energy storage systems considering two types of energy storage according to different capacity scenarios.Finally,a comparative analysis between the systems is presented.A simulation model was established using PSD-BPA (Power System Department-Bonneville Power Administration) to analyze the impact of the capacity ratio of grid-following and grid-forming ESS on their dynamic response characteristics in a hybrid ESS.In addition,a development direction for future ESSs is indicated.

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The application of virtual synchronous generator (VSG) control in flywheel energy storage systems (FESS) is an effective solution for addressing the challenges related to reduced inertia and inadequate power supply in microgrids.Considering the significant variations among individual units within a flywheel array and the poor frequency regulation performance under conventional control approaches,this paper proposes an adaptive VSG control strategy for a flywheel energy storage array (FESA).First,by leveraging the FESA model,a variable acceleration factor is integrated into the speed-balance control strategy to effectively achieve better state of charge (SOC) equalization across units.Furthermore,energy control with a dead zone is introduced to prevent SOC of the FESA from exceeding the limit.The dead zone parameter is designed based on the SOC warning intervals of the flywheel array to mitigate its impact on regular operation.In addition,VSG technology is applied for the grid-connected control of the FESA,and the damping characteristic of the VSG is decoupled from the primary frequency regulation through power differential feedback.This ensures optimal dynamic performance while reducing the need for frequent involvement in frequency regulation.Subsequently,a parameter design method is developed through a small-signal stability analysis.Consequently,considering the SOC of the FESA,an adaptive control strategy for the inertia damping and the P/ω droop coefficient of the VSG control is proposed to optimize the grid support services of the FESA.Finally,the effectiveness of the proposed control methods is demonstrated through electromagnetic transient simulations using MATLAB/Simulink.

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The merits of compressed air energy storage (CAES) include large power generation capacity,long service life,and environmental safety.When a CAES plant is switched to the grid-connected mode and participates in grid regulation,using the traditional control mode with low accuracy can result in excess grid-connected impulse current and junction voltage.This occurs because the CAES output voltage does not match the frequency,amplitude,and phase of the power grid voltage.Therefore,an adaptive linear active disturbance-rejection control (A-LADRC) strategy was proposed.Based on the LADRC strategy,which is more accurate than the traditional proportional integral controller,the proposed controller is enhanced to allow adaptive adjustment of bandwidth parameters,resulting in improved accuracy and response speed.The problem of large impulse current when CAES is switched to the grid-connected mode is addressed,and the frequency fluctuation is reduced.Finally,the effectiveness of the proposed strategy in reducing the impact of CAES on the grid connection was verified using a hardware-in-the-loop simulation platform.The influence of the k value in the adaptiveadjustment formula on the A-LADRC was analyzed through simulation.The anti-interference performance of the control was verified by increasing and decreasing the load during the presynchronization process.

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In renewable energy systems,energy storage systems can reduce the power fluctuation of renewable energy sources and compensate for the prediction deviation.However,if the renewable energy prediction deviation is small,the energy storage system may work in an underutilized state.To efficiently utilize a renewable-energy-sided energy storage system (RES),this study proposed an optimization dispatching strategy for an energy storage system considering its unused capacity sharing.First,this study proposed an unused capacity-sharing strategy for the RES to fully utilize the storage’s unused capacity and elevate the storage’s service efficiency.Second,RES was divided into“deviation-compensating energy storage (DES)”and“sharing energy storage (SES)”to clarify the function of RES in the operation process.Third,this study established an optimized dispatching model to achieve the lowest system operating cost wherein the unused capacitysharing strategy could be integrated.Finally,a case study was investigated,and the results indicated that the proposed model and algorithm effectively improved the utilization of renewable-energy-side energy storage systems,thereby reducing the total operation cost and pressure on peak shaving.

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Zero-carbon parks have broad prospects in carbon neutralization.As an energy hub,hydrogen energy storage plays an important role in zero-carbon parks.However,the nonlinear characteristics of hydrogen energy storage systems (HESSs) have a significant impact on the system economy.Therefore,considering the variable working condition characteristics of HESSs,a hybrid operation method is proposed for HESS,to support the efficient and economic operation of zero-carbon parks,By analyzing the operating principle of a zero-carbon park with HESS,the system structure framework and variable condition linearization model of the equipment in HESS are established.Moreover,considering the energy output characteristics of hydrogen energy storage equipment under variable working conditions,a multimodule hybrid operation strategy is proposed for electrolytic and fuel cells,effectively meeting the thermoelectric load demand of zerocarbon parks in different scenarios.Finally,the economy of the proposed hybrid operation strategy was verified in typical scenarios,using a zero-carbon park embedded with a HESS.

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To meet the greenhouse gas reduction targets and address the uncertainty introduced by the surging penetration of stochastic renewable energy sources,energy storage systems are being deployed in microgrids.Relying solely on short-term uncertainty forecasts can result in substantial costs when making dispatch decisions for a storage system over an entire day.To mitigate this challenge,an adaptive robust optimization approach tailored for a hybrid hydrogen battery energy storage system (HBESS) operating within a microgrid is proposed,with a focus on efficient state-of-charge (SoC) planning to minimize microgrid expenses.The SoC ranges of the battery energy storage (BES) are determined in the dayahead stage.Concurrently,the power generated by fuel cells and consumed by electrolysis device are optimized.This is followed by the intraday stage,where BES dispatch decisions are made within a predetermined SoC range to accommodate the uncertainties realized.To address this uncertainty and solve the adaptive optimization problem with integer recourse variables in the intraday stage,we proposed an outer-inner column-and-constraint generation algorithm (outer-inner-CCG).Numerical analyses underscored the high effectiveness and efficiency of the proposed adaptive robust operation model in making decisions for HBESS dispatch.

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A novel model for measuring the economics of hydrogen generation via electrolytic water projects was constructed.The model overcomes the current problem of incomplete and inaccurate assessments of the price of producing hydrogen via water,which are caused by ignoring the indirect carbon costs of different power generation sources in the process of determining the cost of producing hydrogen via water.The model was used to analyze the price of producing hydrogen via water electrolysis and its sensitivity to the electricity costs of hydrogen production and carbon prices in various provinces of China.With the continuing increase in the penetration of novel energy in China’s power system and the gradual decline in electricity prices,the price of producing hydrogen via electrolytic water is expected to be close to or even lower than that of producing hydrogen via coal in the future.Geographical differences also have a significant impact on the price of producing hydrogen,which is typically higher in the southeastern coastal region than in the western region,because of the local price of electricity and the composition of the energy sources.Provinces that have been effective in developing novel energy sources,such as Qinghai,Sichuan,and others,have been effective in the hydrogen energy industry.Sichuan and other provinces with significant new energy development have a clear advantage in the hydrogen industry.Because provinces with low hydrogen production costs can transport hydrogen to provinces with high hydrogen production costs through pipelines,hydrogen pipelines are planned from Shaanxi to Henan and from Xinjiang to Nei Mongol.These study results reveal the relative economic advantages of producing hydrogen via water electrolysis under various energy and electricity price policies and provide new perspectives on China’s energy strategy and the growth of the hydrogen energy sector.

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Hydrogen energy,with its abundant reserves,green and low-carbon characteristic,high energy density,diverse sources,and wide applications,is gradually becoming an important carrier in the global energy transformation and development.In this paper,the off-grid wind power hydrogen production system is considered as the research object,and the operating characteristics of a proton exchange membrane (PEM) electrolysis cell,including underload,overload,variable load,and startstop are analyzed.On this basis,the characteristic extraction of wind power output data after noise reduction is carried out,and then the self-organizing mapping neural network algorithm is used for clustering to extract typical wind power output scenarios and perform weight distribution based on the statistical probability.The trend and fluctuation components are superimposed to generate the typical operating conditions of an off-grid PEM electrolytic hydrogen production system.The historical output data of an actual wind farm are used for the case study,and the results confirm the feasibility of the method proposed in this study for obtaining the typical conditions of off-grid wind power hydrogen production.The results provide a basis for studying the dynamic operation characteristics of PEM electrolytic hydrogen production systems,and the performance degradation mechanism of PEM electrolysis cells under fluctuating inputs.

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This paper presents an evaluation method for the entropy-weighting of wind power clusters that comprehensively evaluates the allocation problems of wind power clusters by considering the correlation between indicators and the dynamic performance of weight changes.A dynamic layered sorting allocation method is also proposed.The proposed evaluation method considers the power-limiting degree of the last cycle,the adjustment margin,and volatility.It uses the theory of weight variation to update the entropy weight coefficients of each indicator in real time,and then performs a fuzzy evaluation based on the membership function to obtain intuitive comprehensive evaluation results.A case study of a large-scale wind power base in Northwest China was conducted.The proposed evaluation method is compared with fixed-weight entropy and principal component analysis methods.The results show that the three scoring trends are the same,and that the proposed evaluation method is closer to the average level of the latter two,demonstrating higher accuracy.The proposed allocation method can reduce the number of adjustments made to wind farms,which is significant for the allocation and evaluation of wind power clusters.

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Improving the accuracy of solar power forecasting is crucial to ensure grid stability,optimize solar power plant operations,and enhance grid dispatch efficiency.Although hybrid neural network models can effectively address the complexities of environmental data and power prediction uncertainties,challenges such as labor-intensive parameter adjustments and complex optimization processes persist.Thus,this study proposed a novel approach for solar power prediction using a hybrid model (CNN-LSTM-attention) that combines a convolutional neural network (CNN),long shortterm memory (LSTM),and attention mechanisms.The model incorporates Bayesian optimization to refine the parameters and enhance the prediction accuracy.To prepare high-quality training data,the solar power data were first preprocessed,including feature selection,data cleaning,imputation,and smoothing.The processed data were then used to train a hybrid model based on the CNN-LSTM-attention architecture,followed by hyperparameter optimization employing Bayesian methods.The experimental results indicated that within acceptable model training times,the CNN-LSTM-attention model outperformed the LSTM,GRU,CNN-LSTM,CNN-LSTM with autoencoders,and parallel CNN-LSTM attention models.Furthermore,following Bayesian optimization,the optimized model demonstrated significantly reduced prediction errors during periods of data volatility compared to the original model,as evidenced by MRE evaluations.This highlights the clear advantage of the optimized model in forecasting fluctuating data.

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Automatic voltage regulators (AVR) are designed to manipulate a synchronous generator’s voltage level automatically.Proportional integral derivative (PID) controllers are typically used in AVR systems to regulate voltage.Although advanced PID tuning methods have been proposed,the actual voltage response differs from the theoretical predictions due to modeling errors and system uncertainties.This requires continuous fine tuning of the PID parameters.However,manual adjustment of these parameters can compromise the stability and robustness of the AVR system.This study focuses on the online self-tuning of PID controllers called indirect design approach-2 (IDA-2) in AVR systems while preserving robustness.In particular,we indirectly tune the PID controller by shifting the frequency response.The new PID parameters depend on the frequency-shifting constant and the previously optimized PID parameters.Adjusting the frequency-shifting constant modifies all the PID parameters simultaneously,thereby improving the control performance and robustness.We evaluate the robustness of the proposed online PID tuning method by comparing the gain margins (GMs) and phase margins (PMs) with previously optimized PID parameters during parameter uncertainties.The proposed method is further evaluated in terms of disturbance rejection,measurement noise,and frequency response analysis during parameter uncertainty calculations against existing methods.Simulations show that the proposed method significantly improves the robustness of the controller in the AVR system.In summary,online self-tuning enables automated PID parameter adjustment in an AVR system,while maintaining stability and robustness.

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