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The increasing integration of renewable energy sources (RESs) into the power system reduces system inertia, which presents substantial challenges for maintaining frequency stability. Existing studies have mainly focused on using the load frequency control method (LFC) to stabilize area frequency and tie-line power by optimizing the parameters of supplementary control for synchronous generators (SGs). However, these works primarily used simplified first-order transfer functions for wind turbines (WT), high-voltage direct current (HVDC), and battery energy storage systems (BESS). They neglected dynamic models and, importantly, the impact of control parameters in supplementary frequency control strategies on the stability of the studied system. Therefore, this study proposes a novel load frequency control strategy that coordinates the operation of WT, HVDC, and BESS in conjunction with traditional SGs to participate in frequency regulation. The proposed strategy develops a comprehensive state-space model that incorporates accurate models of WT, HVDC, BESS, and SGs, along with their supplementary control strategies. Then, the developed state-space model was utilized to investigate the impact of control parameters in the supplementary frequency control strategies of BESS, HVDC, and WT on system stability. As a result, this work yields a specific range of coefficients for supplementary strategies to improve stability in the studied system. Lastly, a two-area interconnected benchmark system is adopted to validate the effectiveness of the proposed strategy. Based on the suggested parameters, the results demonstrate that the proposed strategy ensures frequency stability with a high penetration of RESs into the power grid. Furthermore, the results indicate that incorporating diverse sources, such as HVDC, WT, and BESS, to support SGs in frequency regulation reduces the impacts of low system inertia. This approach leads to an improved frequency response not only in areas where disturbances occur but also in other regions, provided that tie-line power interchanges are maintained in balance.

In modern power systems, the increasing penetration of renewable energy resources has reduced frequency stability. Consequently, wind turbines (WTs) are expected to participate in frequency regulation. By integrating the electrostatic energy stored in the DC capacitor with the kinetic energy stored in the rotating mass of the WT, an advanced control method is implemented to facilitate frequency regulation. However, previous studies have mainly concentrated on frequency control using WTs but ignored the rotor speed recovery of WT. Therefore, this work proposes a novel control strategy that separates the operating mode of a WT into two distinct states: the frequency support and the recovery of rotation speed stages. The proposed method improves the recovery process for rotor speeds after a WT participates in frequency support, returning the WT quickly to its normal operation to reduce wind energy loss. Furthermore, a new adaptive DC virtual inertia (DVIC) coefficient that utilizes real-time DC voltage to adjust the DVIC’s output smoothly is proposed. The proposed strategy is compared with other mature control strategies and implemented in PSCAD/EMTDC to verify their effectiveness and robustness. The simulation results demonstrate that the proposed method outperforms other mature methods because it reduces wind energy loss by at least 15 % when WTs participate in the frequency support process.

As the utilization of wind power systems continues to increase, reducing overall system inertia, there is a consequential negative impact on the power system’s ability to regulate frequency. Consequently, this study focuses on examining the fast-frequency regulation in high penetration of wind power, especially doubly fed induction generators—the most commonly installed wind turbine type, and an energy storage system installed in the wind farm. This study proposes a coordinated control of wind turbine generators and battery energy storage systems that provides fast-frequency regulation to the system while simultaneously ensuring the safety of the battery. Firstly, the fast-frequency regulation capability of the wind turbine will be studied. Secondly, primary frequency control of the battery energy storage system considering adaptive droop control based on state of charge is proposed to prevent both over-charging and over-discharging of the battery. Finally, this study proposed a coordinated fast-frequency regulation approach for the hybrid wind-storage system, which is evaluated under various wind speed scenarios. This approach involves a detailed analysis of the operational characteristics of the wind turbine generators to ensure optimal performance. The proposed method is validated through simulation using a MATLAB model of the wind-storage system, and comparative results with alternative control methods confirm the effectiveness of the proposed approach in raising the frequency nadir and avoiding the secondary frequency dip.

This study introduced a novel control approach based on fuzzy logic control (FLC) to enhance the frequency regulation capacity of variable-speed wind turbines (VSWTs). The proposed method integrates FLC within droop and inertia control loops. Real-time measurements of the system frequency and the rate of change of frequency (ROCOF) serve as inputs to the FLC, enabling the method to improve the frequency response by VSWTs. In addition, the method employs FLC for pitch angle frequency control, optimizing reserve power for frequency regulation under varying wind speed levels. The innovative aspect of this study lies in the simultaneous application of FLC to pitch angle frequency control and droop/inertia control, leading to the enhanced frequency regulation capability of VSWTs and smoother operation across a range of wind speeds. Compared with traditional methods, the proposed approach provides a comprehensive and effective solution to the challenges associated with frequency regulation in VSWTs. Through simulations across different wind speed scenarios, the proposed control method demonstrated the best performance among various mature methods, highlighting the efficacy of the proposed method on the frequency regulation of VSWTs under different wind speeds. This study’s findings highlight the potential of the proposed FLC-based method to optimize frequency regulation and contribute to more reliable and efficient wind energy systems.

Power systems are generally designed to be reliable when faced with low-impact, high-probability, and expected power outages. By contrast, the probability of extreme event (extreme weather or natural disasters) occurrence is low, but may seriously affect the power system, from long outage times to damage to major equipment such as substations, transmission lines, and power plants. As, in the short term, it is extremely difficult to completely avoid the damage caused by extreme events, it is important to enhance the resilience of power systems. This study has provided a comprehensive review of power system resilience by discussing its concepts, assessment, and enhancement measures. This article summarized possible impacts and quantitative indicators of various types of disasters on power grids, presented the concept of power system resilience, and analyzed the main characteristics that a resilient system should possess. Moreover, this article further distinguished the differences between the resilience, flexibility, and survivability of a power system. More importantly, this paper has proposed a novel framework and the corresponding metric for assessing resilience, which makes the evaluation of system resilience more accurate. Finally, this paper discussed various measures to enhance power system resilience and outlined potential challenges for future research.

Climate change poses a significant threat to humanity. Achieving net-zero emissions is a key goal in many countries. Among various energy resources, solar power generation is one of the prominent renewable energy sources. Previous studies have demonstrated that post-processing techniques such as bias correction can enhance the accuracy of solar power forecasting based on numerical weather prediction (NWP) models. To improve the post-processing technique, this study proposes a new day-ahead forecasting framework that integrates weather research and forecasting solar (WRF-Solar) irradiances and the total solar power generation measurements for five cities in northern, central, and southern Taiwan. The WRF-Solar irradiances generated by the Taiwan Central Weather Bureau (CWB) were first subjected to bias correction using the decaying average (DA) method. Then, the effectiveness of this correction method was verified, which led to an improvement of 22% in the forecasting capability from the WRF-Solar model. Subsequently, the WRF-Solar irradiances after bias correction using the DA method were utilized as inputs into the transformer model to predict the day-ahead total solar power generation. The experimental results demonstrate that the application of bias-corrected WRF-Solar irradiances enhances the accuracy of day-ahead solar power forecasts by 15% compared with experiments conducted without bias correction. These findings highlight the necessity of correcting numerical weather predictions to improve the accuracy of solar power forecasts.

Abstract Peer‐to‐peer (P2P) energy trading is expected to be emerged as a new energy management paradigm that allows the transaction of energy from one prosumer to another prosumer without any dependency on a central controller. In Malaysia, first pilot test of P2P trading has been conducted recently, which exclusively participated by industrial prosumers and commercial consumers only. In order to investigate the impact of P2P energy trading to Malaysia market, this paper proposes an auction‐based two‐stage P2P trading market‐clearing strategy, for multi‐cities and intra‐city in Malaysia. A motivational game theory‐based price scheme is presented to ensure an unbiased market operation, to maximize the saving and payoff for each prosumer, at the same time benefits the power utility company. The proposed two‐stage market clearing model is solved by Linear Programming optimization approach. A realistic representation of P2P trading in Malaysia is constructed and tested out under multi‐cities and intra‐city energy trading test cases. Comparison between the proposed P2P energy trading with existing solar generation net metering scheme is also presented. The numerical results indicate the viability and potential of motivational game theory based P2P trading in future Malaysia transactive energy market.

Wind power generation is one of the mainstream renewable energy resources. Voltage stability is as important as the frequency stability of a power system with a high penetration of wind power generation. The advantages of high-voltage direct current (HVDC) transmission systems become more significant with the increase of both installed capacity and transmission distance in offshore wind farms. Therefore, this study discusses various voltage control methods for wind turbines and HVDC transmission systems. First, various voltage control methods of a wind farm were introduced, and they include QV control and voltage droop control. The reactive power of a wind turbine varies with active power, while the active power from each wind turbine may be different owing to wake effects. Thus, QV and voltage droop control with varying gain values are also discussed in this paper. Next, the voltage control methods for an HVDC transmission system, such as power factor control, voltage control, and Vac-Q control, are also summarized and tested in this study. When a three-phase short circuit fault occurs or a sudden reactive power load increases, the system voltage would drop immediately. Thus, various voltage control methods for wind turbines or HVDC can make the system’s transient response more stable. Therefore, this study implemented the simulation scenarios, including a three-phase short circuit fault at the point of common coupling (PCC) or a sudden increase of reactive power load, and adopted various voltage control methods, which aim to verify whether additional voltage control methods are effective to improve the performance of transient voltage. The voltage control method has been implemented in PSCAD/EMTDC, and the simulation results show that the QV control performs better than the droop control. In addition, when applying the voltage control technique during a three-phase fault, transient voltage nadir can be improved through either an HVDC transmission system or an AC transmission system.

Nowadays, wind power generation has become vital thanks to its advantages in cost, ecological friendliness, enormousness, and sustainability. However, the erratic and intermittent nature of this energy poses significant operational and management difficulties for power systems. Currently, the methods of wind power forecasting (WPF) are various and numerous. An accurate forecasting method of WPF can help system dispatchers plan unit commitment and reduce the risk of the unreliability of electricity supply. In order to improve the accuracy of short-term prediction for wind power and address the multi-step ahead forecasting, this research presents a Stacked Temporal Convolutional Network (S-TCN) model. By using dilated causal convolutions and residual connections, the suggested solution addresses the issue of long-term dependencies and performance degradation of deep convolutional models in sequence prediction. The simulation outcomes demonstrate that the S-TCN model’s training procedure is extremely stable and has a powerful capacity for generalization. Besides, the performance of the proposed model shows a higher forecasting accuracy compared to other existing neural networks like the Vanilla Long Short-Term Memory model or the Bidirectional Long Short-Term Memory model.

In modern power systems, the increasing penetration of renewable energy resources has reduced the overall system inertia. However, the intermittent nature of wind power generation reduces frequency stability, which is a crucial issue. Consequently, modern power systems require that renewable energy sources, such as offshore wind farms, support the frequency regulation. High voltage direct current (HVDC) systems based on voltage source converters (VSCs) are currently being used to connect wind farms with the main grid. By adjusting the DC-link voltage of the VSC-HVDC, a capacitor can absorb or release energy to provide frequency support. In addition, the VSC-HVDC system can coordinate wind farms to support frequency regulation and thereby achieve better performance. Thus, this paper reviews and compares various frequency-control strategies for a VSC-HVDC-connected wind farm. This work implements in PSCAD/EMTDC a simulation of typical frequency-control techniques to verify their effectiveness and robustness. Furthermore, this paper shows the advantages and drawbacks of each frequency regulation method. Other auxiliary services for supporting frequency regulation, such as by energy storage systems, are also discussed. Finally, this paper provides complete recommendations for frequency regulation techniques for VSC-HVDC-integrated wind farms.

By connecting many wind farms and grids via multi-terminal high-voltage direct current transmission (MT-HVDC), the reserve power of each grid can be shared to balance the power mismatch. Resulting from a shortage of generated power, the frequency nadir and the lowest DC voltage may violate the constraints of continuous operation. Using a trade-off, this paper proposes a new control strategy based on adaptive droop control to make a compromise between DC voltage and grid frequency. By applying the proposed method, the demands from both sides are efficiently met. The proposed method relies on a decentralized approach, avoiding dependence on the communication link. In addition, wind farms are allowable to operate at the optimal power point without using a de-loading strategy. Time-domain simulations are conducted in PSCAD. The improvement in system reliability is proved by comparing with existing methods.

One of the major issues about the operation of power systems is the prediction of load demand. Moreover, load forecasting is of prime concern to system operators. Recently, the integration of power system elements, such as renewable energy sources, energy storages and electricity vehicle, brings more challenges, particularly when there are large fluctuations in forecasting cycle. This study concentrates on short-term load demand forecasting and proposes a hybrid method that combines Singular Spectrum Analysis (SSA) with deep-learning Neural Network (NN) techniques. In the beginning, the SSA technique is applied as an initial filter to remove noises. Next, a hybrid neural network, including Backpropagation Neural Network (BPNN) and Long Short-Term Memory (LSTM), is developed and trained. Then, the trained network is used as the core forecasting algorithm. Each SSA has different forms to combine with neural networks. The performance of the proposed forecasting algorithm is demonstrated using the power demand data recorded in Taiwan. Furthermore, this study compares the forecasting results by five models, including SSA, SSA-BPNN, ANN, SSA-LSTM, Wavelet Neural Network (WNN) and LSTM. The forecasting results reveal that the proposed forecasting model using Singular Spectrum Analysis provides the best performance on load forecasts.

The proportion of renewable power generation in the world has been increasing in recent years. However, the fluctuations and uncertainties of renewable power generation bring a considerable challenge to future unit scheduling. Therefore, the generation flexibility in power systems becomes more critical as a large amount of renewable generation is integrated into power systems. The use of flexible generators with energy storage systems is one of the most efficient methods of improving power system flexibility. The primary purpose of this study is to explore the effect of generation flexibility on the cost of unit scheduling. A flexibility index is used to evaluate the generation flexibility in the Taiwan power system, and a multi-scenario analysis for renewable power integration is considered. This study also considers various system constraints, such as the unit commitment of actual hydro and thermal units, the scheduling of flexible internal combustion engines (ICEs) and energy storage systems, and possible curtailments of renewable power generation. According to the seasonable characteristics of renewable power generation, this study provides a suitable capacity for flexible ICE units and energy storage systems. Furthermore, this study demonstrates that the cost of unit scheduling is effectively reduced by increasing flexible ICE units and energy storage systems. The results of this study can be used as a reference for power systems in preparing flexible generating units and energy storage systems under the integration of a large amount of renewable power generation in the future.

Solar power has rapidly become an increasingly important energy source in many countries over recent years; however, the intermittent nature of photovoltaic (PV) power generation has a significant impact on existing power systems. To reduce this uncertainty and maintain system security, precise solar power forecasting methods are required. This study summarizes and compares various PV power forecasting approaches, including time-series statistical methods, physical methods, ensemble methods, and machine and deep learning methods, the last of which there is a particular focus. In addition, various optimization algorithms for model parameters are summarized, the crucial factors that influence PV power forecasts are investigated, and input selection for PV power generation forecasting models are discussed. Probabilistic forecasting is expected to play a key role in the PV power forecasting required to meet the challenges faced by modern grid systems, and so this study provides a comparative analysis of existing deterministic and probabilistic forecasting models. Additionally, the importance of data processing techniques that enhance forecasting performance are highlighted. In comparison with the extant literature, this paper addresses more of the issues concerning the application of deep and machine learning to PV power forecasting. Based on the survey results, a complete and comprehensive solar power forecasting process must include data processing and feature extraction capabilities, a powerful deep learning structure for training, and a method to evaluate the uncertainty in its predictions.

In recent years, the penetration of photovoltaic (PV) power generation in Taiwan has increased significantly. However, most photovoltaic facilities, especially for small-scale sites, do not include relevant monitoring and real-time measurement devices. The invisible power generation from these PV sites would cause a huge challenge on power system scheduling. Therefore, appropriate methods to estimate invisible PV power generation are needed. The main purpose of this paper is to propose an improved fuzzy model for estimating the PV power generation, which includes the clustering processing for PV sites, selection of representative PV sites, and the improvement of the conventional fuzzy model. First, this research uses the K-nearest neighbor (KNN) algorithm to fill in some of the missing data; then, two clustering algorithms are applied to cluster all the photovoltaic sites. Next, the relationship between the power generation of a single PV site and the total generation of all sites at the same cluster is further analyzed to select the representative PV sites. Finally, an improved fuzzy model is implemented to estimate the PV power generation. This research used actual data that were measured from PV sites in Taiwan for the estimation, verification, and comparison study. The numerical results demonstrate that the proposed method can obtain an average estimation error about 7% by using limit measurements from PV sites, highlighting the high efficiency and practicability of the proposed method.

Maintaining the frequency within an allowable range is the necessity to ensure the stable operation of the grid. When a large-scale frequency disturbance occurs in the grid, the synchronous generators (SGs) will temporarily release the kinetic energy to make the generation and demand balanced. This is well known as the inertial response of the SG. However, the traditional variable-speed wind turbines (VSWTs) only generate a negligible inertial response when a frequency disturbance happens. The traditional VSWT is designed to maximise the wind energy injected into the grid. Thus, when a frequency disturbance is detected in the grid, the traditional VSWT cannot change the active power output to support the grid. To solve this problem, the concepts of the inertial control and inertial support capability for VSWT were proposed. The inertial control measures the frequency of the grid and adjusts the active power output of VSWT. Thus, during the frequency disturbance, the VSWT has the inertial support capability. The purpose of this study is to provide a comprehensive review of the inertial control for the wind power plant (WPP) consisting of VSWTs. With the inertial control, the integration of the WPPs can enhance the frequency stability of the grid.

This work develops an underfrequency preventive control strategy for an islanded power system with a high penetration of wind power generation. First, the preventive control strategy uses the frequency nadir forecasting module to analyze the frequency stability under largest diesel generator tripping (N-1) contingency events. If predicted frequency nadir is too low, four frequency support methods are then analyzed and used for preventing potential frequency stability problem. They include generator rescheduling (GR), the use of battery energy storage system (BESS), direct load control (DLC) and emergency demand response program (EDRP). In terms of the GR method, the optimal diesel generator dispatch is obtained, with sufficient frequency stability and minimal fuel cost and start-up cost. In the BESS method, the optimal instantaneous power output from BESS is obtained based on its frequency support capability. With the DLC or EDRP method, the optimal contract-based load-shedding or the load-reduction to provide frequency support is obtained, respectively. Then, the operating costs of each method to support frequency are analyzed. The research methods and simulation results are very useful to the low-frequency protection of actual power systems with high renewable power generation. This work proposed a complete defense strategy in a microgrid system. It combines GR, BESS, DLC and EDRP. Therefore, the system operators have many options to implement their defense strategies, based on the operating costs of various methods. In other words, the proposed defense strategy provides a more flexible solution for the protection of micro grids with a high renewable power penetration. Therefore, the solution considers the system safety and economical aspects.

Offshore wind farms will have larger capacities in the future than they do today. Thus, the costs that are associated with the installation of wind turbines and the connection of power grids will be much higher, thus the location of wind turbines and the design of internal cable connections will be even more important. A large wind farm comprises of hundreds of wind turbines. Modeling each using a complex model leads to long simulation times—especially in transient response analyses. Therefore, in the future, simulations of power systems with a high wind power penetration must apply the equivalent wind-farm model to reduce the burden of calculation. This investigation examines significant issues around the optimal design of a modern offshore wind farm layout and its equivalent model. According to a review of the literature, the wake effect and its modeling, layout optimization technologies, cable connection design, and wind farm reliability, are significant issues in offshore wind farm design. This investigation will summarize these important issues and present a list of factors that strongly influence the design of an offshore wind farm.

The increasing use of solar power as a source of electricity has led to increased interest in forecasting its power output over short-time horizons. Short-term forecasts are needed for operational planning, switching sources, programming backup, reserve usage, and peak load matching. However, the output of a photovoltaic (PV) system is influenced by irradiation, cloud cover, and other weather conditions. These factors make it difficult to conduct short-term PV output forecasting. In this paper, an experimental database of solar power output, solar irradiance, air, and module temperature data has been utilized. It includes data from the Green Energy Office Building in Malaysia, the Taichung Thermal Plant of Taipower, and National Penghu University. Based on the historical PV power and weather data provided in the experiment, all factors that influence photovoltaic-generated energy are discussed. Moreover, five types of forecasting modules were developed and utilized to predict the one-hour-ahead PV output. They include the ARIMA, SVM, ANN, ANFIS, and the combination models using GA algorithm. Forecasting results show the high precision and efficiency of this combination model. Therefore, the proposed model is suitable for ensuring the stable operation of a photovoltaic generation system.

The abundant wind resources in the Penghu area, where the capacity factor of wind turbines can reach 45%, have inspired authorities to build more wind turbines in a diesel-based power system. However, because the wind turbine output is unstable, high wind power penetration in the system may relatively affect the power quality (voltage and frequency) and reliability of the system. Previous studies have suggested that the optimal penetration level for wind power generation under the present dispatching criteria in Penghu is 26.3%. The present study suggests that the criteria for current unit scheduling should be modified; in particular, the spinning reserve (SR) capacity of the diesel engines should be increased without affecting the reliability and power quality of the power system. Such an increase could help avoid the construction of high-cost energy storage systems.