IET Renewable Power Generation最新文献

Under the guidance of the ‘dual carbon’ goals, the installed capacity of wind power continues to grow, increasing wind power penetration levels (WPPLs) and posing challenges to system frequency stability. Therefore, it is essential to study the control of wind farms operating in de-loading mode to participate in system frequency regulation (SFR). This paper proposes a power dispatching method for a de-loading operated wind farm that participates in power SFR considering the wake effect. It begins by grouping wind turbines (WTs) considering the wind's incoming angle and wake effects, which simplifies computational needs compared with controlling individual WTs. The method sets a priority for power distribution to maximise the use of WTs’ overspeed de-loading capacity, effectively increasing rotor kinetic energy and reducing pitch angle adjustments. This approach avoids complex optimisations and wind speed measurement for each WT, significantly boosting system robustness. To assess the effectiveness of this method, simulations using the EMTP-RV simulator were conducted under various wind speed angles, disturbance levels and WPPLs. The results indicate that the proposed strategy enhances the WF's ability to regulate system frequency and decreases the need for pitch adjustments.

The increasing reliance on digital infrastructures in power systems, combined with the rising penetration of renewable energy sources (RES), has heightened their vulnerability to sophisticated cyber-physical attacks, particularly false data injection a ttacks (FDIAs). These attacks exploit state estimation processes to disrupt grid operations while remaining undetected. This paper presents a novel multi-layered optimization framework to enhance the resilience of cyber-physical power systems against FDIAs under uncertain attack scenarios. The framework employs a tri-level Stackelberg optimization approach to model the interactions between defenders, attackers, and system operations. The defender's strategy focuses on optimal resource allocation and adaptive decoy placement to misdirect attacker efforts while minimizing operational costs. The middle level simulates attacker strategies using generative adversarial networks (GANs) to generate stealthy and adaptive attack vectors. The lower level incorporates physical and operational constraints of the grid, ensuring realistic scenario modeling. Advanced methodologies, including multi-agent deep reinforcement learning (MADRL), Bayesian inference, and distributionally robust optimization, are integrated to address dynamic uncertainties and evolving attack patterns. The proposed framework is validated on a modified IEEE 123-bus system with synthesized attack scenarios, demonstrating significant improvements in grid resilience. Results indicate an average reduction in attack success rates by 40% and an enhancement in resilience metrics by 35%, achieved through optimized defense budget allocation and adaptive decoy strategies. This research contributes to the field by bridging game theory, robust optimization, and machine learning, offering a comprehensive solution to ensure the security and reliability of modern power systems under extreme cyber-physical threats.

Hybrid wind-wave energy systems, integrating floating offshore wind turbine (FOWT) and wave energy converters (WECs), have received much attention in recent years due to its potential benefits in increasing the power harvesting density and reducing the levelized cost of electricity (LCOE). Recent studies show that advanced model-based control strategies have the great potential to significantly improve their overall control performance. However, the performance of these advanced control strategies relies on the computationally efficient control-oriented models with sufficient fidelity, which are normally difficult to derive due to the complexity of the hydro-, aero-dynamic effects and the couplings. In most available results, the hybrid wind-wave energy system models are established by using the boundary element method (BEM), devoting to understanding the hydrodynamic responses and performance analysis. However, such models are complex and involved in relatively heavy computational burden, which cannot be directly used for the advanced model-based control methods in practice. To overcome this issue, this paper proposes a control-oriented model of the hybrid wind-wave energy system with six degrees of freedom (DOFs). First, the Newton's second law and fluid mechanics are employed to characterize the motion behavior of the hybrid wind-wave energy system with the coupled aero-hydro-mooring dynamics. Then, a novel adaptive parameter estimation algorithm with simple low-pass filter approach is developed to estimate the system unknown coefficients. Different from the conventional parameter estimation methods, such as gradient descent method and recursive least-squares (RLS) method, the estimated parameters can be driven to their true values with guaranteed convergence. Finally, numerical analysis using the AQWA and MATLAB are applied to validate the fidelity of the control-oriented model under different wind and wave conditions. The results indicate that the control-oriented model predicts the motion response accurately in comparison to the BEM-based model. Overall, the results pave the way for designing advanced hybrid wind-wave energy system control method.

Wind power is one of the most widely available renewable energy sources (RES). However, due to the intermittent nature of wind, the output power of wind turbines (WTs) is always variable. In WTs, at speeds lower than the rated wind speed, the goal is to maximise the power extracted from the wind. At higher wind speeds, the goal is to keep the WT's power constant at rated value; that is typically done by the WT's pitch control system. The operation of the pitch system has a delay due to WT's blades and rotor inertia and limited pitch rate, which may lead to output power fluctuations. Superconducting magnetic energy storage (SMES) has fast response and high efficiency. This paper explores the application of SMES to compensate for the pitch system delay in output power smoothing of a permanent magnet synchronous generator (PMSG)-based WT. It is verified that the SMES properly compensates for the pitch lag by absorbing the surplus power and releasing it at power shortage intervals, particularly when pitch control returns the blades to their initial position. In the meantime, the pitch system reduces the SMES coil current and prevents it from saturation, which allows selecting an optimal/practical coil size for the SMES.

Integrating renewable energy sources (RES) into buildings is one of the most important approaches to achieving sustainable energy systems. This paper presents a comprehensive study that evaluates the performance of RES such as photovoltaic (PV), wind, geothermal and biomass in different urban, rural, and coastal scenarios. In this paper, we analyze four types of buildings, including single-family residential, multi-family residential, commercial, and industrial, and evaluate the contribution of energy, supply and demand dynamics, and geographical influences on the performance of renewable energy (RE). Various results such as cost analysis and payback periods for different RESs, technical specifications, RES performance, state of charge (SoC) of the battery system, seasonal performance of RES in various geographic settings, carbon footprint of RES, and fossil fuel-based power generation, supply chain risks, and resilience of RES technologies are obtained and discussed in detail. In addition, PV energy outperforms urban residential buildings due to its high availability on roofs. In coastal areas, wind energy can provide an acceptable amount of energy to industrial buildings. Biomass energy accounts for the lowest energy production in all buildings and locations. In all scenarios, geothermal energy can provide more consistent and sustainable baseload energy and complement the variable outputs of PV and wind. The results show that the interaction between RES provides a more reliable energy supply, reduces dependence on grid energy, and improves sustainability. This study emphasizes the importance of adapting the RE integration methods to the geographical and specific characteristics of the buildings. These results can provide better information for energy and building planners who want to use RE systems and achieve better environmental goals.

This paper presents a comprehensive model for optimally planning electricity and gas distribution networks, integrating energy hubs (EHs) and electric vehicle (EV) charging infrastructure. The goal of the model is to minimize total investment costs and decrease energy purchases from upstream networks. This is achieved by utilizing equipment such as combined heat and power (CHP) units, power-to-gas (P2G) systems, gas-fired (GF) units, wind turbines, and fast charging stations (FCS). The proposed framework models the flow of electricity, gas, and heat energy based on urban transportation traffic and employs the particle swarm optimization (PSO) algorithm for problem-solving. Different scenarios are designed to evaluate the impact of electrical and thermal loads, as well as the presence or absence of fast chargers in the network. Numerical results from a network consisting of 33 power distribution buses and 20 gas nodes indicate that implementing EHs and optimizing the combination of generation resources significantly reduces overall costs, enhances network stability, and decreases dependency on upstream networks. Furthermore, the third scenario, which simultaneously leverages thermal loads, fast chargers, and distributed generation resources, demonstrates superior economic, environmental, and technical performance compared to the other scenarios.

This study investigates South Africa's energy distribution patterns and examines the potential of low-voltage (LV) energy storage to address energy challenges. The research aims to formulate a strategic framework for implementing LV storage technologies by examining diurnal energy fluctuations. Insights drawn from this analysis can inform policymakers and stakeholders in optimising energy management, enhancing grid stability and promoting sustainable development within the South African energy landscape. This paper employs data from the South African National Electricity Utility (Eskom) from 2019 to 2023 to generate the load demand's diurnal, monthly and yearly variations and corresponding irradiance variation. The distribution of irradiance less the load demand is employed to design the optimal energy storage capacity within the LV networks that considers the different load shedding levels presently being experienced in South Africa.

Obtaining maximum power from photovoltaic (PV) systems operating under partial shading conditions (PSC) is quite challenging. Maximum power point tracking (MPPT) algorithms are necessary to extract the maximum power from the PV system in a very short time with minimal error. In this study, a high-efficiency MPPT algorithm with fast tracking speed is proposed for PV systems operating under PSC. The proposed algorithm utilizes the charging behavior of the capacitor between the PV system and the DC-DC converter. The P–V curve of the PV system is obtained either when the system is initially energized or during the capacitor charging phase following discharge. The voltage corresponding to the maximum power point is then determined, and the PV system is operated at this voltage until a change in power is detected. The proposed method was tested on a 2 kW PV system modeled in the MATLAB/Simulink environment. Power outputs and tracking times were evaluated under six different challenging PSC scenarios. As a result of the simulations, the average efficiency across the six PSC cases was 99.678%, while the average tracking time was 0.013 s.

With the evolution of power system structure and the expansion of renewable energy scale, the security and stability challenges brought by extreme events are becoming increasingly prominent. The traditional transient model of power systems is tailored specifically for certain fault scenarios and exhibits nonlinear characteristics. Consequently, its solutions are often characterized by a time-intensive nature and suboptimal generalization performance. Therefore, a security boundary identification method for resilient power system driven by model-data fusion is proposed in this paper. Based on the security constrained unit commitment model of power system, the umbrella constraint identification method is employed to identify the effective constraints. A massive extreme sample set based on the dynamic response model of the CloudPSS platform is established, and support vector machine is leveraged to identify and extract transient safety constraints. The critical security boundary is characterised by the combination of umbrella constraints and transient safety constraints, which can be embedded into the economic dispatch model to facilitate the secure and efficient operation of power system. Case studies based on IEEE-39 systems verified the effectiveness of the proposed method in different fault scenarios.

In order to anticipate photovoltaic (PV) power output in both fixed and tracking solar systems, this study proposes a strong neural network-based framework that models nonlinear dependencies by utilising meteorological factors such as temperature, wind speed, and sun radiation. Strong correlation coefficients (𝑅²) and low mean squared errors (MSE) throughout the training, validation, and testing phases demonstrate the model's high predictive accuracy, which was attained by combining a 10-layer artificial neural network (ANN) architecture optimised with the Adam algorithm and a dynamic learning rate scheduler. To guarantee generalisability, the dataset—which included 8,761 hourly samples over a full year—was carefully divided into three categories: 70% training, 15% validation, and 15% testing. The impact of system design on productivity was highlighted by a comparative analysis that showed a 21% improvement in annual energy yield for tracking systems (231 kWh) versus fixed systems (184 kWh). Regression plots, error histograms, and monthly power generation profiles were among the visual and statistical assessments that showed how well the model captured seasonal and diurnal variations while reducing bias. With error rates lowered to less than 10% and prediction accuracies over 90% in both contexts, the combination of the MATLAB and Python frameworks further confirmed the method's consistency and scalability.