IET Renewable Power Generation最新文献

Wind energy is crucial for reducing fossil fuel dependence and promoting sustainability. Offshore wind farms (OWFs) benefit from higher, stable wind speeds but pose challenges such as harmonic distortion and voltage fluctuations when integrated into power grids. This study develops an advanced model for accurate harmonic forecasting in OWFs using permanent magnet synchronous generators (PMSG). Real meteorological data from Zonguldak and Sinop in the Black Sea region of Turkey were used to simulate power output, voltage, and current waveforms. Harmonic components, including total harmonic distortion for voltage (THDV) and current (THDI), were extracted and predicted. Various machine learning (ML) and deep learning (DL) algorithms were applied, including Linear Regression, Decision Tree, Random Forest, Gradient Boosting, XGBoost, KNeighbors, LSTM, GRU, and CNN. Additionally, hybrid ML-DL models were explored to enhance forecasting accuracy. A comparative analysis of these models demonstrated their effectiveness in improving harmonic prediction. Results indicate that hybrid models, particularly LSTM+GB and GRU+GB, improve harmonic forecasting accuracy by reducing RMSE by approximately 15% compared to traditional ML methods. This enhancement contributes to better power quality management and grid stability, making offshore wind farms more viable for large-scale renewable energy integration. The findings of this research provide a fundamental basis for future investigations into offshore wind harmonic forecasting.

The transition from traditional bulk generation to renewable energy is reshaping power systems, introducing security challenges due to decreased system inertia. Natural gas (NG)-fired units, known for their quick response, are often scheduled for operational flexibility, increasing interdependency between electrical and NG networks. However, the decentralized management of these networks by separate operators complicates coordinated system management. This study presents an optimization model that co-optimizes unit commitment (UC) and virtual inertia (VI) from wind farms (WF) while addressing frequency constraints (FC) and NG network limitations. The model's effectiveness is validated through two case studies: a smaller network with seven gas nodes and an IEEE 5-bus system, and a larger system with 20 gas nodes and an IEEE 118-bus system. The results show that incorporating VI constraints reduced costs by 0.8% in Case 1 and 25.8% in Case 2. However, applying FC in Case 2 increased costs by 7.1% while improving frequency stability. These findings underscore the importance of VI in reducing operational costs and enhancing grid stability, especially under variable gas supply conditions. The proposed model demonstrates economic efficiency and grid resilience, making it a useful tool for planning integrated energy systems with increasing renewable energy penetration.This study addresses the scheduling problem in power systems transitioning from traditional to renewable generation, which increases frequency security issues due to reduced system inertia. An optimization model co-optimizes UC and VI provision from WF, considering FC and operational limitations of the NG system (NGS). Case studies demonstrate the model's effectiveness and computational efficiency.

With the rapid growth of electric vehicle (EV) ownership and the lower cost of photovoltaic (PV) modules, photovoltaic-energy storage charging station (PV-ES CS) will gradually become the main configuration method for EV charging station in the future due to its economic and environmental characteristics. However, uncertainty of EV charging behavior has led to the increasing pressure of power grid, so it is necessary to study and establish a new pricing mechanism to guide EV's charging behavior. The paper proposed a new pricing strategy used in three PV-ES CSs based on metamodel optimization algorithm. First, aiming at the uncertainty problem of PV output, a clustering method based on expected cost minimization is utilized to obtain typical PV output curves. Second, a Stackelberg game model between the PV-ES CSs and EV in the parks is established, and the metamodel optimization algorithm is used to solve the Stackelberg game model to simplify the computational complexity. Finally, the validity and practicality of the proposed method are verified through the simulation of a specific example.

In this article, we propose an adaptive robust nonlinear optimal sliding mode control (SMC) using the optimal homotopy asymptotic method (RNOSC-OHAM) for maximizing wind power capture. Because of the unstable nature of the wind and the presence of uncertainties and disturbances in the structure of the wind turbine, the optimal controller cannot provide robustness against uncertainties. Hence, we integrated this approach with a proportional–integral–derivative SMC (PID-SMC) scheme to ensure the robustness of the system. The presented hybrid control input signal involves two components: a nonlinear optimal control law that provides the optimal performance of the nominal system and an adaptive PID-SMC law. The adaptive PID-SMC law employs a PID sliding surface along with adaptive mechanisms to create the robustness of the nonlinear wind turbine system. The nonlinear optimal control policy is designed by addressing the partial differential Hamilton–Jacobi–Bellman (HJB) equation. This equation is approximated using the OHAM. This combination strategy reduces the final time of the optimal control problems, provides desired responses and improves performance. Additionally, the RNOSC-OHAM controller facilitates the safe performance of the wind turbine under uncertainties and maximizes the wind power captured. To evaluate the performance of RNOSC-OHAM, the results of the presented algorithm are compared with some existing control schemes. The results indicate that the designed RNOSC-OHAM controller is very rapid and needs few iterations and computational costs. Indeed, the present control scheme exhibits the best characteristics and has the fastest transient response. The RNOSC-OHAM controller offers a proper balance between enhancing aerodynamic power capture and minimizing low-speed shaft fluctuations using a small control input.

This article presents an optimization-based multi-criteria decision-making framework in order to determine the optimal amount of photovoltaic (PV) hosting capacity in the distribution network, taking into account technical and economic aspects. As an effective tool for facilitating PV integration, the demand response (DR) is also integrated into the model using the price-elasticity concept. In this way, a comprehensive set of DR programs, including price-based, incentive-based and combinational programs, are modelled. In order to provide a guideline for distribution network operator to implement the most effective DR program, different programs are prioritized considering the defined economic and technical features using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The proposed model is tested on a 14-bus IEEE test system, and the simulation results show that critical peak pricing programs increase the ability of the distribution network to integrate PV resources more than 18.4 kW compared to the general case study. Moreover, it reduces the daily energy losses by 227.7 kWh.

This research explores stability challenges in power systems from integrating offshore wind farms (OWFs) with voltage source converter (VSC)-based multi-terminal direct current (MTDC) networks. A novel two-level integrated control (TLIC) framework is proposed to enhance frequency regulation at grid-side VSC (GSVSC) stations. The first level features adaptive inertial control (AIC) and adaptive droop control (ADC). By dynamically adjusting AIC and ADC parameters, wind units (WUs) in maximum power point tracking (MPPT) mode effectively mitigate secondary frequency fall (SFF). WUs are clustered by rotor speeds, enabling staged frequency support for improved responsiveness. The second level uses a communication-independent allocation (CIA) strategy, relying on local frequency measurements in the onshore power system (OPS) to balance power distribution among GSVSC stations. This bolsters OPS frequency stability and minimises SFF during MPPT operations. A robust H∞ controller, designed via loop-shaping, is applied at the wind farm-side VSC (WSVSC), employing multi-criteria decision-making (MCDM) for voltage optimisation. The MTDC DC voltage employs a Master-Slave (MS) configuration to suppress variations under disturbances. MATLAB simulations across scenarios validate the strategy's robustness in damping oscillations from uncertainties.

Ultra-short-term wind power forecasting plays a crucial role in real-time dispatching, frequency regulation, and intraday electricity market transactions. Forecasting accuracy heavily depends on data quality and feature informativeness. However, most existing studies conduct data cleaning offline, with limited attention to real-time data quality during forecasting. Moreover, they often use historical power and NWP data uniformly, neglecting the time-varying importance of input features. To address these issues, this paper proposes an ultra-short-term wind power forecasting method based on dynamic cleaning of streaming data anomalies and adaptive processing of input feature contributions. Firstly, similar samples of the current wind process are retrieved online via time series similarity matching, enabling real-time anomaly detection in streaming data. Secondly, anomalous power sequences are reconstructed using a theoretical restoration model based on wind speed fluctuation identification. Finally, a forecasting architecture with personalised encoding and dynamically fused decoding is designed to enhance prediction accuracy. The proposed method has been successfully applied to a wind-solar-storage power station in Inner Mongolia, supporting both grid dispatching operations and daily maintenance. Compared to baseline methods, it achieves average reductions in forecasting errors of 0.59–9.99 percentage points for RMSE and 0.62–8.49 percentage points for MAE.

The integration of renewable energy sources, particularly wind farms, into modern power systems requires advanced transmission technologies. High voltage direct current (HVDC) systems, especially in multi-terminal configurations (MTDC), are effective for transferring high power to the grid. However, there are concerns about the interaction of HVDC controllers with other devices of the system, which can lead to instability in the power system. Additionally, the complexity of new systems, due to the integration of power electronics and control systems, increases the potential for interaction with the torsional modes of the wind turbine. This paper conducts a nonlinear modal analysis (NLMS) of hybrid MTDC systems connected to wind farms, examining component interactions and their stability on impact. (NLMS is employed as the primary analytical method. The results obtained from this method are compared with those from linear modal analysis and the fourth-order Runge-Kutta (RK4) method.By using the NLMS technique, it reveals insights into complex interactions under various conditions and quantifies how controller parameters affect stability. This research enhances the understanding of dynamics in hybrid HVDC systems and lays the groundwork for future studies and practical applications in resilient power network design and operation.

The microgrid exhibits low inertia levels and small X/R ratios. Consequently, load changes can adversely affect microgrid stability. However, frequency and voltage oscillations can be mitigated through the use of a load frequency control (LFC) system and an automatic voltage regulator (AVR), which serve as secondary control mechanisms. Additionally, the integration of photovoltaic (PV) and wind turbine (WT) in microgrids complicates the performance of LFC and voltage control due to the uncertainties associated with these renewable sources. Therefore, it is essential to employ a suitable controller with optimal parameters. To address this, this paper introduces a novel control technique known as the tilt-proportional-integral-derivative second-order derivative controller (TPIDD²) to concurrently manage the voltage and frequency of microgrids. It also incorporates an intelligent optimisation algorithm integrated with quantum computing, referred to as quantum teaching-learning-based optimisation (QTLBO), to achieve optimal control parameters. The test system consists of a two-area interconnected microgrid, where each area includes various sources such as PV, WT, fuel cell (FC), diesel generator, and battery energy storage system (BESS). The integral of time multiplied by the squared error (ITSE) is utilised as the objective function. To demonstrate the effectiveness of the proposed controller, it is compared with the proportional-integral-derivative (PID) controller. From the ITSE perspective, the proposed controller is 71.96% more effective than the PID controller. Furthermore, the results obtained using QTLBO are contrasted with those from teaching-learning based optimization (TLBO), differential evolution (DE), and RCGA.

In recent years, compound dry-hot events have significantly impacted human society, particularly affecting the source and load sides of the power system. With the increasing penetration of renewable energy, these events pose growing challenges to power supply-demand balances. Therefore, this paper proposes the concept of ‘power drought’ for the first time to quantify the severity of supply-demand imbalances and identify their spatial-temporal evolution under compound dry-hot events. The analysis begins by examining the coupling between meteorological parameters, renewable energy output and load demand under compound dry-hot events. Specifically, the concept of power drought is defined, followed by the formulation of relevant evaluation metrics. Then, a spatial-temporal clustering algorithm and a centroid migration model are applied to analyse the evolution characteristics of power drought events. Finally, the validity and practicality of the proposed method are demonstrated using practical data from a certain region to analyse the evolution of power drought over the past decade. Case studies reveal a south-westward migration of power drought centroids, with 66.49% of grids showing positive correlation between the standardised compound event index and the power drought index.