Wind Integrated Power System Research Articles

Evaluation of reliability and resilience in wind integrated power systems using 80 meter mast measurements

Renewable energy sources, especially wind turbine generators, are increasingly considered viable alternatives to conventional electric power generation due to their sustainability and positive environmental impact. The rising penetration of wind power highlights the importance of developing widely applicable methods to evaluate the concrete benefits of incorporating wind turbines into traditional power systems. This work proposes an approach for assessing the reliability and resilience of power systems incorporating wind power. Evaluating the impact of wind energy generation on system reliability involves analyzing metrics such as Loss of Load Expectation (LOLE) and Expected Energy Not Supplied (EENS). Additionally, a conceptual framework is developed which is aimed at enhancing the evaluation of power system resilience, when subjected to severe wind events. To evaluate the impact of wind storms on network resilience, a Monte Carlo Optimal Power Flow approach is utilized. The Average Energy Not Supplied (AENS) is used as the resilience metric and is computed for different rated speeds of wind turbines. The proposed methods for evaluating the reliability and resilience of the power systems are tested using the IEEE-RTS 24 bus system. Real-time wind resource data measured at an 80-meters mast at Paradeep location in the Odisha state on the east coast of India are used to evaluate the indices.

The Calibrated Safety Constraints Optimal Power Flow for the Operation of Wind-Integrated Power Systems

As the penetration of renewable energy sources (RESs), particularly wind power, continues to rise, the uncertainty in power systems increases. This challenges traditional optimal power flow (OPF) methods. This paper proposes a Calibrated Safety Constraints Optimal Power Flow (CSCOPF) model that uses the Improved Acceleration Coefficient-Based Bee Swarm algorithm (IACBS) in combination with the equivalent current injection (ECI) model. The proposed method addresses key challenges in wind-integrated power systems by ensuring preventive safety scheduling and enabling effective power incident safety analysis (PISA). This improves system reliability and stability. This method incorporates mixed-integer programming, with continuous and discrete variables representing power outputs and control mechanisms. Detailed numerical simulations were conducted on the IEEE 30-bus test system, and the feasibility of the proposed method was further validated on the IEEE 118-bus test system. The results show that the IACBS algorithm outperforms the existing methods in both computational efficiency and robustness. It achieves lower generation costs and faster convergence times. Additionally, the CSCOPF model effectively prevents power grid disruptions during critical incidents, ensuring that wind farms remain operational within predefined safety limits, even in fault scenarios. These findings suggest that the CSCOPF model provides a reliable solution for optimizing power flow in renewable energy-integrated systems, significantly contributing to grid stability and operational safety.

Application of cascaded neural network for prediction of voltage stability margin in a solar and wind integrated power system

Voltage stability is a paramount concern in the management of renewable-rich power systems. As the diffusion of renewable energy sources continues to increase, accurately estimating voltage stability margins becomes essential. This paper addresses the critical need for effective voltage stability margin estimation and presents a novel approach to predict the same on the solar and wind integrated power system. A new machine learning methodology is developed based on the cascaded neural network (CNN). The proposed CNN methodology effectively estimates the loadability margin in a renewable-rich power system under normal operating conditions, varied loading directions, and various contingency situations. The results of CNN are compared with different machine learning techniques and tested in the IEEE 30 bus system and IEEE 118 bus system under various operational circumstances. The results illustrate the efficiency of the suggested method for online assessment of a power system's Voltage Stability Margin. The proposed approach gives real-time insights into voltage stability margins by leveraging the power of data and powerful algorithms, allowing grid operators to proactively manage and optimize renewable energy integration while ensuring grid dependability and stability.

Operation of coupled power-gas networks with hydrogen refueling stations, responsive demands and storage systems: A novel stochastic framework

Because of factors such as low natural gas prices, low greenhouse gas emission and high efficiency has increased the penetration of natural gas-fired generators in electric power systems. Large gas consumption of gas-fired generators has caused intense interdependence of electricity and gas systems. Most often, the operational planning of power and gas systems is conducted through a co-optimisation model in which the total cost of electricity and gas systems is minimised and the constraints of both systems are considered. On the other hand, due to environmental concerns, the usage of fuel cell vehicles (FCVs) is increasing. Hydrogen refueling stations (HRSs) are needed for refueling FCVs. This paper puts forward a stochastic model for co-optimisation of wind-integrated power and gas systems, hosting HRSs, electric storage systems (ESS's) and responsive electricity and gas demands. The effect of ESS's, responsive demands, contingencies, wind uncertainties and demand response on operation of power-gas nexus is scrutinized. An incentive-based demand response program has been used for both electricity and gas demands. The case study is the Belgian gas network connected to the a 24-bus power system. The findings indicate that responsive electric demands reduce electric system cost by 9.4 %, while responsive gas demands reduce the gas network cost and total operation cost by 1 %. The contingency analysis on the power-gas nexus shows that single line outage contingencies in power system does not result in any demand shed and does not increase the operation cost of either electricity network or gas network.

Estimating the region of attraction of wind integrated power systems based on improved expanding interior algorithm

AbstractThis paper proposes an improved expanding interior algorithm (EIA) to estimate the region of attraction (ROA) of power systems with wind power generation based on sum of squares (SOS) programming. An ordinary differential equation (ODE) model is derived for the doubly‐fed induction generator‐based wind turbine (DFIGWT), which is named as an enhanced synchronous‐generator‐mimicking (ESGM) model. The ESGM model bridges the gap between the requirement of an ODE model in ROA estimation and the conventional differential‐algebraic equation (DAE) model of the DFIGWT system. The ESGM model is able to accurately reflect the low frequency dynamics of the DFIGWT. Moreover, an improved EIA is designed to estimate the ROA based on SOS programming, which has higher efficiency than the existing ROA estimation algorithms based on SOS programming. It is able to adaptively search for the Lyapunov function and obtain an optimal estimation of the ROA in an iterative process. The accuracy and efficiency of this algorithm are verified in three test systems composed of DFIGWTs and synchronous generators (SGs). The morphological changes in the ROA of the test systems caused by the penetration of DFIGWT are examined.

Joint non-fragile automatic generation control and multi-event driven mechanism co-design for wind integrated power system under denial of services attacks

Denial of services (DoS) attacks exist in wind integrated power system. DoS attacks can cause network-induced delay and packages loss in information transmission. Meanwhile, considering the parameter perturbation of controller and system model uncertainty in wind integrated power system, these may cause the system dynamic performances degradation or even instability. Based on the above considerations, the joint non-fragile automatic generation robust control of wind integrated power system under DoS attacks is studied in this paper. In order to ensure the expected system performance and more effectively utilize the limited network communication resources under DoS attacks, a novel dynamic multi-event driven mechanism based joint non-fragile H∞ automatic generation control method is proposed. By constructing a suitable Lyapunov-Krasovskii functional and utilizing the Shur complement lemma to handle nonlinear matrix inequality, the sufficient conditions are derived to guarantee the asymptotic stability of wind integrated power system under DoS attacks. Furthermore, the performance of the proposed non-fragile regulator is demonstrated through a four-area wind integrated power system to show the feasibility and applicability. The analysis result indicates that the proposed scheme provides stronger robustness, higher wind energy utilization efficiency and more efficient communication mechanism.

Identification method of all-operating-point admittance model for wind farms considering frequency-coupling characteristics

Admittance identification is an effective tool to solve the issue of black-box admittance modeling in wind farms. However, due to the large operating range of wind farms, it is difficult to accurately identify the admittance model of wind farms under all operating conditions, which limits the research on wideband oscillation caused by the interaction between wind farms and the power grid. This paper analyses the characteristics of the frequency-coupling admittance for wind farms. It is found that the power-sharing ratio of wind turbine generators (WTGs) affects the admittance characteristic of the wind farm, which leads to a huge number of operating points for the wind farm. The all-operating-point (AOP) admittance model of wind farms considering frequency-coupling characteristics is hard to directly identify. Aiming for this problem, this paper proposes to first identify the AOP-admittance model for WTGs. Then, based on the operating points of each WTG and the network structure, the AOP-admittance model of the wind farm is obtained via the admittance aggregation and model reduction method. This proposed method solves the problem of black-box admittance modeling and addresses the problem of dimension disaster in admittance modeling for wind farms. The identified model of the wind farm can be used to accurately assess the stability of the wind power integration system under various operating conditions. Finally, the effectiveness of the proposed method is verified through the admittance measurement and stability analysis of the wind farm.

A Novel Stochastic Unit Commitment Characterized by Closed-Loop Forecast-and-Decision for Wind Integrated Power Systems

A Novel Stochastic Unit Commitment Characterized by Closed-Loop Forecast-and-Decision for Wind Integrated Power Systems

A Comprehensive Review on Voltage Stability in Wind-Integrated Power Systems

The fast growth of the world’s energy demand in the modernized world has stirred many countries around the globe to focus on power generation by abundantly available renewable energy resources. Among them, wind energy has attained significant attention owing to its environment-friendly nature along with other fabulous advantages. However, wind-integrated power systems experience numerous voltage instability complexities due to the sporadic nature of wind. This paper comprehensively reviews the problems of voltage instability in wind-integrated power systems, its causes, consequences, improvement techniques, and implication of grid codes to keep the operation of the network secure. Thorough understanding of the underlying issues related to voltage instability is necessary for the development of effective mitigation techniques in order to facilitate wind integration into power systems. Therefore, this review delves into the origin and consequences of voltage instability, emphasizing its adverse impacts on the performance and reliability of power systems. Moreover, it sheds light on the challenges of integrating wind energy with existing grids. This manuscript provides a comprehensive overview of the essential features required for critical analysis through a detailed examination of Voltage Stability Indices (VSIs). To address voltage stability issues in wind-integrated power systems, this review examines diverse techniques proposed by researchers, encompassing the tools utilized for assessment and mitigation. Therefore, in the field of power system operation and renewable energy integration, this manuscript serves as a valuable resource for researchers by comprehensively addressing the complexities and challenges associated with voltage instability in wind-integrated power systems.

Application of optimal control for wind integrated power system

<div align="center"><span>This paper presents the modeling and application of an optimal controller for frequency and tie-line power stability for a two-area interconnected hydro-thermal power plant having tandem compound non-reheat turbines integrated with wind power generation having doubly fed induction generator (DFIG) wind turbines in each area. The power system is interconnected using AC tie-lines. The designed optimal controller was implemented and the system dynamic responses for three power system model states were obtained considering a 1% load fluctuation in one of the areas. The optimal control strategy presented in this paper depends on formulating an error value and finding the feedback gains corresponding to each state of the system, which are easily attainable as outputs. The analysis was undertaken and verified by calculating the performance index value, the closed ring real and imaginary values, finding the feedback gains, and through graphical simulations of the three system models under investigation. The output of the optimal controller was enhanced when the DFIG-based wind turbines were installed in each area combined with a superconducting magnetic energy storage (SMES) unit and a thyristor control phase shifter (TCPS).</span></div>

Multi-Time Scale Optimal Dispatch for the Wind Power Integrated System With Demand Response of Data Centers Based on Neural Network-Based Model Predictive Control

Multi-Time Scale Optimal Dispatch for the Wind Power Integrated System With Demand Response of Data Centers Based on Neural Network-Based Model Predictive Control

Harmonic filtering and fault ride‐through of diode rectifier unit and modular multilevel converter based offshore wind power integration

AbstractThe diode rectifier unit (DRU) is promising in offshore wind power integration due to its reliability and economy. Offshore AC voltage control is the key technology of the DRU‐based scheme. In this paper, a low‐cost offshore wind power integration system based on the DRU and the modular multilevel converter (MMC) in parallel on the rectifier side is proposed. The low‐capacity MMC rectifier is controlled to establish the offshore AC voltage amplitude and frequency, maintain all offshore wind active power transmitted by the DRU, and provide reactive power compensation and AC harmonic filtering for the DRU. Besides, the fault ride‐through strategies of both the offshore fault and the onshore fault are proposed. Moreover, the calculation method of the rated capacity of the MMC rectifier is derived. The time domain simulations of the hybrid system under typical operating conditions are investigated in PSCAD/EMTDC, and the feasibility of the proposed scheme is verified.

A Grey Wolf Optimization Algorithm-Based Optimal Reactive Power Dispatch with Wind-Integrated Power Systems

Keeping the bus voltage within acceptable limits depends on dispatching reactive power. Power quality improves as a result of creating an effective power flow system, which also helps to reduce power loss. Therefore, optimal reactive power dispatch (ORPD) studies aim at designing appropriate system configurations to enable a reliable operation of power systems. Establishment of such a configuration is handled through control variables in power systems. Various control variables, such as adjusting generator bus voltages, transformer tap locations, and switchable shunt capacitor sizes, are utilized to achieve this objective. Additionally, the integration of wind power can greatly impact power quality and mitigate power loss. In this study, the Grey Wolf Optimization (GWO) approach was applied to the ORPD issue for the first time to discover the best placement of newly installed wind power in the power system while taking into account tap changer settings, shunt capacitor sizes, and generated power levels. The main objective was to determine optimal wind placement to minimize power loss and voltage deviation, while maintaining control variables within specified limits. On the basis of IEEE 30-bus and IEEE 118-bus systems, the performance of the proposed method was investigated. The results demonstrated the superiority of GWO in multiple scenarios. In IEEE-30, GWO outperformed the PSO, GA, ABC, OGSA, HBMO, and HFA methods, reducing total loss by 10.36%, 18.03%, 9.19%, 7.13%, 5.23%, and 7.73%, respectively, and voltage deviation by 68.00%, 1.59%, 36.34%, 41.97%, 46.29%, and 71.08%, respectively. In wind integration scenarios, GWO achieved the simultaneous reduction of power loss and voltage deviation. In IEEE-118, GWO outperformed the ABC, PSO, GSA, and CFA methods, reducing power loss by approximately 19.91%, 16.83%, 14.09%, and 4.36%, respectively, and voltage deviation by 8.50%, 14.15%, 16.19%, and 7.17%, respectively. These promising results highlighted the potential of the GWO algorithm to facilitate the integration of renewable energy sources, and its role in promoting sustainable energy solutions. In addition, this study conducted an analysis to investigate site-specific wind placement by using the Weibull distribution function and commercial wind turbines.

Intentional Controlled Islanding Strategy for Wind Power Plant Integrated Systems

The concept of intentional controlled islanding (ICI) is introduced as a proactive measure to safeguard the power system against blackouts in the event of significant disturbances. It involves strategically partitioning the system into self-healing islands, thereby mitigating the impact of such disturbances. This study introduces a new framework for creating stable, controlled islands in power systems with large-scale wind power plants. The proposed islanding strategy takes into account the impact of wind power plants on the coherency grouping of generators as a constraint in the ICI problem. The proposed algorithm employs the Virtual Synchronous Motion Equation (VSME) model of asynchronous generators to replace wind power plants in power systems and groups all generators, including synchronous generators and wind turbine generators, based on their dynamic coupling. Support Vector Clustering is employed in the ICI problem to determine the coherent generator clusters as the cores of the formed islands. The algorithm can identify the optimal number of islands without prior information about the number of clusters. In this study, a Mixed Integer Linear Programming (MILP) model is formulated to address the ICI problem with the objective of minimising the power imbalance in each island after splitting while ensuring the transient stability and dynamic frequency stability of ICI. Simulation results on modified 39-bus and 118-bus test power systems demonstrate that integrating a Virtual Inertia Controller into the wind-integrated power systems results in a high-inertia power system that behaves similarly to a conventional power system with only synchronous generators during the islanding procedure.

A Review of Load Frequency Control Schemes Deployed for Wind-Integrated Power Systems

Load frequency control (LFC) has recently gained importance due to the increasing integration of wind energy in contemporary power systems. Hence, several power system models, control techniques, and controllers have been developed to improve the efficiency, resilience, flexibility, and economic feasibility of LFC. Critical factors, such as energy systems, resources, optimization approaches, resilience, and transient stability have been studied to demonstrate the uniqueness of the proposed design. This paper examines the most recent advances in LFC techniques for wind-based power systems. Moreover, the use of classical, artificial intelligence, model predictive control, sliding mode control, cascade controllers, and other newly designed and adopted controllers in the LFC area is thoroughly examined. Statistical analysis and a comparison table are used to evaluate the advantages, disadvantages, and applications of various controllers. Finally, this paper presents a comprehensive overview of contemporary and other widely used soft computing tools for the LFC issue. This detailed literature review will assist researchers in overcoming the gap between current progress, application, limitations, and future developments of wind energy in LFC.

Tight power balance multi-time scale disposal strategy for wind integrated system considering electric vehicle charging station

High wind power penetration and peak load pose significant challenges to the power system in maintaining the power supply–demand balance. Once the power supply–demand balance is disrupted, system load shedding and wind spillage become inevitable. In this study, the aforementioned problem is defined as the tight power balance (TPB) problem. The accuracy of wind power and load forecasting increases with a decrease in the forecasting timescale. Based on the aforementioned characteristics, a TPB multi-timescale disposal strategy for wind-integrated power systems considering electric vehicle (EV) charging stations is established to address the TPB problem at different timescales. First, the operation model of an electric vehicle charging station offering flexible ramping capacity is established. Second, a multi-timescale disposal strategy, which includes an intra-day 4-h plan, an intra-day 1-h plan, and a real-time 15-min plan, is presented by quantifying the flexible demand at different timescales. Finally, the proposed strategy is verified using a modified IEEE 118-bus system. The analysis results show that the proposed TPB multi-timescale disposal strategy effectively promotes the disposal level of the TPB problem.

Multi-objective two-stage stochastic unit commitment model for wind-integrated power systems: A compromise programming approach

In this paper, we introduce a compromise programming (CP) framework for solving a multi-objective two-stage stochastic unit commitment problem characterized by high penetration of wind power. The proposed framework aims at finding best-compromise Pareto efficient on/off schedules, accounting for wind and power demand uncertainties: such solutions must trade off the three objectives of operating cost, CO2 emissions, and wind power curtailment in accordance to the decision maker preferences. To achieve this, we introduce a practical procedure to compute the ideal and Nadir points associated to the multi-objective two-stage stochastic unit commitment problem and propose a linearized ℓ1 norm-based compromise program to design best-compromise on/off schedules that correspondingly minimize and maximize their weighted distances to the ideal point and to the Nadir point, considering the preference weights assigned by the decision maker to each of the three objective functions. The proposed CP framework is applied to a case study related to the New England IEEE-39 bus test system. The results show that, compared to the schedule obtained through the traditional minimization of operating cost, the designed best-compromised schedules considerably improve CO2 emissions reduction and wind power curtailment performance by conservatively sacrificing operating cost performance.

A CVaR-constrained optimal power flow model for wind integrated power systems considering Transmission-side flexibility

The integration of renewable power can pose uncertainty to power system operation, causing operational risk like power imbalance or line congestion. To improve the operational security and economy of the system under uncertainties, this paper considers two potential directions. The first is to enhance the system capability for safely accommodating uncertainties using various network resources. Such capability is often termed as the operational flexibility. The second direction is to employ appropriate risk management methods. With these two directions, a conditional value-at-risk (CVaR)-constrained optimal power flow (OPF) model is developed in this paper for wind-integrated power systems. In this model, dynamic line rating (DLR) and flexible AC transmission system (FACTS) technologies are exploited to unlock transfer capacities of transmission lines and enable line reactance control. As a result, power system has capability to allow more flexible and cost-efficient power flow distributions. That is, additional operational flexibility is provided by the transmission-side DLR and FACTS technologies. Such flexibility is termed as the transmission-side flexibility. Meanwhile, CVaR is employed as the risk management tool to control the security level of each operational constraint. To address the non-linearity of CVaR and solve the proposed model, a novel solution method is developed by combining Gaussian mixture model for uncertainty modelling with cutting-planes for CVaR reformulation. Numerical experiments in MATLAB reveal the economic benefit of the proposed model: the inclusion of transmission-side flexibility in formula can reduce the operational cost by 6.10% on a 14-bus system. The simulation results also show that the proposed solution method overcomes the poor system security associated with the traditional Gaussian method and is more economical than the previous robust method (3.79% reduction in operational cost on a 14-bus system).

Distributed resilient multi-event cooperative triggered mechanism based discrete sliding-mode control for wind-integrated power systems under denial of service attacks

In wind-integrated power systems (WIPSs), denial of service (DoS) attacks bring about time delays and packages dropout in system data transmission, it may even deteriorate system performance. In order to maintain the reliability and safety of WIPSs, under the dynamical event triggered framework, a novel distributed resilient multi-event cooperative triggered scheme is derived to decrease the occupation of network channel. Furthermore, considering the random load disturbance of WIPSs and wind speed fluctuation, the resilient triggered scheme based H∞ load frequency sliding-mode control strategy is proposed. Based on Lyapunov stability theory, the controller is obtained by solving linear matrix inequality. Compared with the traditional event triggered mechanism, the proposed distributed resilient multi-event cooperative triggered mechanism based sliding-mode controller provides stronger robustness, higher wind energy capture efficiency for WIPSs under DoS attacks. Meanwhile, the resilient triggered scheme improves the utilization efficiency of network resources. A four-area WIPSs is investigated and the numerical analysis shows the proposed strategy is feasibility and effectiveness.

An Optimized Algorithm for Renewable Energy Forecasting Based on Machine Learning

The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids and energy management on the load side. Microgrid can effectively solve this problem by using its regulation and flexibility, and is considered to be an ideal platform. The traditional method of computing total transfer capability is difficult due to the central integration of wind farms. As a result, the differential evolution extreme learning machine is offered as a data mining approach for extracting operating rules for the total transfer capability of tie-lines in wind-integrated power systems. K-medoids clustering under the two-dimensional “wind power-load consumption” feature space is used to define representative operational scenarios initially. Then, using stochastic sampling and repetitive power flow, a knowledge base for total transfer capability operating rule mining is created. Then, a novel method is used to filter redundant characteristics and find features that are closely associated to the total transfer capability in order to decrease the ultra-high dimensionality of operational features. Finally, by feeding the training data into the proposed algorithm, the total transfer capability operation rules are derived from the knowledge base. It can be seen that, the proposed algorithm can optimize the system performance with good accuracy and generality, according to numerical data.