• Energy Research

Abstract With an increasing penetration of wind power in the modern electrical grid, the increasing replacement of large conventional synchronous generators by wind power plants will potentially result in deteriorated frequency regulation performance due to the reduced system inertia and primary frequency response. A series of challenging issues arise from the aspects of power system planning, operation, control and protection. Therefore, it is valuable to develop variable speed wind turbines (VSWTs) equipped with frequency regulation capabilities that allow them to effectively participate in addressing severe frequency contingencies. This paper provides a comprehensive survey on frequency regulation methods for VSWTs. It fully describes the concepts, principles and control strategies of prevailing frequency controls of VSWTs, including future development trends. It concludes with a performance comparison of frequency regulation by the four main types of wind power plants.

This article proposes a multienergy trading market model based on price matching, aiming to foster multienergy collaboration and enhance energy utilization through individual participation. With the ongoing advancements in energy distribution and marketization, the energy Internet necessitates improved applicability and efficiency for personalized energy responses. To address these requirements, a multienergy trading market model is proposed, which enables the avoidance of user information disclosure and guarantees user trading autonomy. In addition, a joint trading mechanism is designed that accounts for multiple time scales and energy types, consequently reducing trading failures caused by overlooking energy transmission processes. By performing the proposed trading mechanism, the market operator can match various energy types using conversion devices, thereby augmenting matching efficiency. An income mechanism is also established to deter the operator from purposefully evading potential trading opportunities for personal gain. To address the proposed model, an improved hierarchical reinforcement learning algorithm is employed, which effectively overcomes challenges associated with large state action spaces and sparse rewards. Numerical examples are provided to confirm the efficacy of the proposed approach.

With the growing trend of plug‐in electric vehicles (PEVs), the charging load of PEVs can have a huge impact on current transmission‐level system. In the meantime, there also comes a great potential in the optimisation aspect for both power generation economic and power transmission safety concerns. Here, a dual‐optimisation algorithm is developed to minimise the total production cost of power sources including renewable energy resources (RER) and PEV cluster. In the meantime, the overloading in transmission lines and transformers is avoided. Linear programming and DC optimal power flow (DCOPF) are separately applied to step one and step two optimisation. The simulation result from a modified IEEE 9‐bus system reveals that dual‐optimisation method successfully reduced the production cost based on a hybrid model of power grid in step one optimisation. In step two optimisation, the overloaded transmission line is detected and held at capacity limit to prevent overheating. In conclusion, in dual‐optimisation algorithm, PEVs can serve as both the energy storage device and the energy supply device. Consequently, the total production cost can be reduced, and the optimal power flow can be obtained.

As the percentage of renewable energy in the Energy Internet (EI) gradually increases, how to deal with the uncertainty of renewable energy in the economic dispatch problem (EDP) becomes an important issue. This paper proposes a digital twin (DT) assisted economic dispatch strategy for EI with information entropy. First, we leverage the storage capacity of the DT and an extensive historical data set to provide a theoretical framework for quantifying uncertainty of renewable energy. Second, a renewable energy cost function based on the maximum entropy principle, confidence interval, and penalty factor is proposed to model the renewable energy resources considering the uncertainty. Further, we design a fully distributed Newton-surplus-based optimization algorithm. This algorithm achieves fast second-order convergence to ensure the real-time performance of the DT-assisted economic dispatch framework and overcome the asymmetry caused by the directed communication network. In addition, we give theoretical proof that the Newton-surplus-based algorithm can converge to the global optimal point. Finally, simulations validate the effectiveness of the proposed algorithm. Note to Practitioners—The essence of EDP is to minimize the total costs through optimal resource allocation while ensuring compliance with all operational constraints. With the increasing penetration of renewable energy resources, their strong stochasticity and uncertainty pose challenges to achieve reliable dispatch strategy. To address this issue, this paper presents the DT-assisted economic dispatch framework, model, and method to quantify the uncertainty of renewable energy resources and achieve distributed economic dispatch with fast convergence speed for EI. Our research is beneficial for practitioners to understand how to use the DT and information entropy to deal with the uncertain of renewable energy resources. The theory and simulation results demonstrate the correctness and effectiveness of the proposed method.

Wind energy is increasingly vital globally, requiring precise output forecasting for stable, efficient power systems. However, this becomes particularly challenging for newly built wind farms that lack historical data. Statistical models are unsuitable in this context due to their reliance on historical data. While both physical models and data-driven transfer learning methods offer some solutions, they exhibit limitations when applied to newly built wind farms. Physical models require complex parameter tuning and high computational costs, and transfer learning generally necessitate a certain amount of historical data for model transfer. More critically, existing methods fall short in capturing the causal relationships between wind power and meteorological variables, impacting both the accuracy and robustness of the models in this specialized scenario characterized by distribution shifts. To address these challenges, this study introduces an integrated wind power forecasting model named CPTCFS, comprising two core components: Causal Feature Selection (CFS) and CausalPatchTST. The CFS identifies key features with direct causal relationships to wind power output through causal inference, surpassing traditional feature selection methods like PCA and correlation coefficient analysis. CausalPatchTST, integrating a sample weighting mechanism with the advanced Transformer variant model PatchTST, effectively addresses distribution shift issues caused by the lack of historical data in newly built wind farms, ensuring prediction accuracy and robustness in data-scarce environments. In 24-hour prediction tests using hourly data from two Australian wind farm clusters, the CausalPatchTST model with the sample weighting mechanism achieved a significant 13.29% reduction in Root Mean Square Error (RMSE) compared to the PatchTST model without this mechanism. Furthermore, the entire CPTCFS model outperforms existing models on other key accuracy indicators, demonstrating its broad applicability in the wind power forecasting domain and immense potential in other renewable energy prediction areas.

The wide-area measurement system (WAMS) provides the synchronized dynamic measurement for power systems; however, the effective monitoring of subsynchronous oscillations (SSO) with WAMS has not been achieved. To achieve SSO monitoring based on the synchrophasors provided by WAMS, a synchrophasors-based SSO identification technique was proposed in this paper. On the basis of existed synchrophasors, this technique can obtain the frequencies and amplitudes of both the fundamental and subsynchronous components of currents and voltages in SSOs effectively. In this technique, the impacts of both the fast Fourier transform synchrophasor algorithm and the sampling rate of synchrophasors on the spectrum of the subsynchronous component were taken into account. On one hand, the SSO frequency was obtained according to the spectrum of magnitudes of synchrophasors. On the other hand, a correction method for the spectrum of magnitudes of synchrophasors, in contrast to a constructed standard waveform that proportionally matches with the measured SSO, was applied to obtain the actual amplitudes of both the fundamental and subsynchronous components. Both the numerical simulation and the illustrations of two different actual SSO incidents were presented with comparisons to verify the correctness and feasibility of the proposed technique. This technique is of important practical value for mitigations of SSO in actual power systems.