Wind Power Injection Research Articles

Dynamic impact of hybrid wind-solar photovoltaic power injection on small signal stability of Nigerian 11kV power system using Self Organizing Map neural network

Power industry reorganization, legislation on greenhouse gas emissions and inadequate conventional generation capacity encourage the idea of incorporating Distributed Generation (DG) into the distribution networks as unconventional sources of power generation. High penetration of DG has further increased the complexity of the analysis of small signal stability because the variability of renewable energy sources affects the stability of power system. Therefore, this research work investigates the effect of wind and solar photovoltaic power injection on small signal stability of Nigerian 11 kV power network. Wind and solar photovoltaic were modelled using probabilistic methods and output power at twenty-one different locations within Nigeria was computed. Using the real and reactive power data of the network, Self-Organizing Map (SOM) neural network was trained and tested for the assessment of the small signal stability of the distribution network before and after the injection of renewable power in python virtual environment. The integration of hybrid wind-solar PV distributed generation at 30 %, 60 %, 90 % and 100 % into the studied 11 kV power distribution network improved its small signal stability by 1.4 %, 2.1 %, 2.5 % and 3.2 % respectively. Moreover, increase in the injection of power gotten from wind and solar sources reduces the oscillation frequency of the system from 1.39 Hz to 1.01 Hz and increases the damping from 4.6 % to 13.2 %. Reactive power increment in the range of 5 %-20 % improved the small signal stability of the system by the range 2.4 %-7 %. This improvement will translate to better power transfer capability of the network.

A Decision Tree Based Ultra-high-speed Protection Scheme for Meshed MMC-MTDC Grids with Hybrid Lines

The reliable operation of modular multi-level converter based multi-terminal high voltage direct current (MMC-MTDC) grids requires high-speed and selective DC line protection. A single-ended protection technique is proposed for MTDC grids with offshore wind farms and hybrid lines that involve overhead lines and submarine cables in series. Positive and negative poles’ currents at one end of each line section are analyzed using discrete wavelet analysis (DWA). To classify fault type and identify fault zone, a fine decision tree is supplied with an energy index and the envelope slop of detail 1 coefficient (D1) obtained from DWA. Only 0.2 ms following the fault inception are needed for calculating the energy index and the envelope slope to perform relay functions. The approach was tested on a three-terminal two-poles ±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document} 400 kV MMC-MTDC model. The simulation results validate the effectiveness of the suggested protection technique under various fault scenarios, even with up to 200 Ω fault resistance. The proposed technique is not only able to detect the faulty line, but also it identifies overhead line faults and submarine cable faults for hybrid type lines. Moreover, the proposed technique is not affected by wind power injection changes, AC faults, or data noise. The simulated model was produced in the MATLAB/Simulink platform.

Short-term wind power forecasting by bidirectional attention mechanism LSTM and its probability interval prediction by sliding-window KDE

Deterministic point wind power forecasting (DP-WPF) and its probability interval prediction (PIP) are indispensable to short-term peak alleviation and frequency regulation in power systems with large-scale wind power injection. To improve short-term DP-WPF by long short-term memory (LSTM), a horizontal/vertical bidirectional feature attention (BFA) based LSTM model is proposed. More specifically, the BFA-LSTM model has three parts: first, multivariate time series are fed into LSTM to extract long-short-term temporal features; second, the LSTM outputs are processed horizontally as well as vertically for retrieving step-wise/multistep-wise temporal features, respectively, namely, in the bidirectional attention sense; third, both the horizontal and vertical attention weights are adaptively adjusted according to the feature importance in DP-WPF. Cases comparison shows that the suggested modeling is stably superior to most common counterparts. To address PIP by kernel density estimation (KDE), sliding-window KDE is leveraged for probability analysis. More precisely, probability density functions (PDF) and probability intervals are estimated with sliding-window samples, which are non-parametric operations and involve finitely many local samples. Superior performances of PIP by sliding-window KDE at different confidence levels indicate that the sliding-window PDF approach is highly effective in contrast to those with all samples.

Characteristics and Dynamics of the Interannual Eddy Kinetic Energy Variation in the Central Pacific Sector of the Southern Ocean

AbstractMesoscale eddies play a crucial role in the dynamical balance of the Southern Ocean (SO) circulation. Yet, it remains unclear why the SO transient eddy activity has significant variations on interannual time scales, and to what extent these low‐frequency variations are attributed to wind forcing changes. Here we use a functional analysis tool, namely, the multiscale window transform (MWT), the MWT‐based theory of canonical transfer and a time‐dependent energetics framework to investigate these issues. Our focus is on the central Pacific sector of the SO in which there is a significant time‐mean and interannual variability of the eddy kinetic energy (EKE). It is found that wind stress does not directly contribute to the interannual EKE variability through wind power injection to the eddies; instead, the influence is fulfilled indirectly through two internal pathways. First, the baroclinic pathway in which the wind‐generated mean kinetic energy (MKE) is converted to the mean available potential energy (APE), and is further released to eddy APE and finally to EKE through baroclinic instability, is the dominant one. Second, the barotropic pathway, in which MKE directly fuels EKE through barotropic instability, is faster but secondary, and its influence is only concentrated along the axis of the Antarctic Circumpolar Current. Our results highlight the dynamical connections between the internal and external processes in the SO energy system, and provide insights into the predictability of the SO eddies on interannual time scales.

Consider Carbon Emission Flow Analysis of Power System with Regards to Power Loss and Wind Power Uncertainty

A method for analyzing the impact of wind power uncertainty and active power loss on carbon emission flow in power systems is proposed. Based on the stochastic power flow calculation of wind power injection, the correlation function between wind power output and the total carbon injection rate into the system is determined. Meanwhile, the reverse power flow method is used to analyze the contribution of each unit to the active power flow in different parts of the system and calculate the corresponding carbon emissions. Under the assumption that wind power fluctuations are borne by balancing units, this method combines a conventional unit and a system node, branch, load, and active network loss correlation matrix calculation model to solve the average impact factor of wind power injection on other nodes and branches, thereby determining the uncertain characteristics of carbon emission flow in power systems under stochastic and intermittent wind power injection. The correctness and effectiveness of the method are proved by analyzing the IEEE 14-node standard example, providing new theoretical support and ideas for quantitatively evaluating the low-carbon contribution of wind power generation to the system.

Multi-Terminal DC Grid with Wind Power Injection

With the development of offshore wind generation, the interest in cross-country connections is also increasing, which requires models to study their complex static and dynamic behaviors. This paper presents the mathematical modeling of an offshore wind farm integrated into a cross-country HVDC network forming a multi-terminal high-voltage DC (MTDC) network. The voltage source converter models were added with the control of active power, reactive power, frequency, and DC link voltages at appropriate nodes in the MTDC, resembling a typical cross-country multi-terminal type of HVDC scenario. The mathematical model for the network together with the controllers were simulated in MATLABTM and experimentally verified using a real-time digital simulator hardware setup. The resulting static and dynamic responses from the hardware setup agreed well with those from simulations of the developed models.

Voltage Profile Analysis on a Grid with Power Injection from a Wind Farm

Wind power penetration into the grid is increasing throughout the world due to centralized power generation constraints such as shortage of fossil fuel, need to reduce gas emissions, long transmission losses and need for more supply of electrical power. Connection of wind power into the grid results in power quality issues such as voltage profile changes and harmonics. This necessitates coming up with correction measures in order to meet power quality standards. This paper deals with the analysis of the effects of injecting wind power to the grid on voltage profile. Branch participation factors are used to analyze the sections of the grid where effects on voltage profile are highest due to wind power penetration. Reactive power-voltage sensitivity analysis is used to determine the buses that are more sensitive to the changes brought by the wind power injection. Two cases were considered for the injection of the wind power: IEEE 14 test bus system and IEEE 39 test bus system.

A Voltage Stability-Based Approach to Determining the Maximum Size of Wind Farms in Power Systems

Current methods to determine the wind farms maximum size do not consider the effect of new wind generation on the Voltage Stability Margins (VSMs). Installing wind power in one area may affect VSMs in other areas of the power system. Buses with high VSMs before wind power injection may be converted into weak buses after wind power injections in other parts of power systems, which may lead to limited future wind farms expansion in other areas. In this paper, two methods are proposed to determine two new wind farms maximum size in order to maximize wind power penetration level. In both methods, the size of any new wind farm is determined using an iterative process which is increased by a constant value. Proposed methods were used in the IEEE 14-bus power system. The results of applying these new methods indicate that the second method results in higher maximum sizes than the first method.

Voltage Profile and Sensitivity Analysis for a Grid Connected Solar, Wind and Small Hydro Hybrid System

Due to increase in integration of renewable energy into the grid and power quality issues arising from it, there is need for analysis and power improvement of such networks. This paper presents voltage profile, Q-V sensitivity analysis and Q-V curves analysis for a grid that is highly penetrated by renewable energy sources; solar PV, wind power and small hydro systems. Analysis is done on IEEE 39 bus test system with Wind power injection alone, PV power injection alone, with PV and wind power injection and with PV, wind and micro hydro power injection to the grid. The analysis is used to determine the buses where voltage stability improvement is needed. From the results, it was concluded that injection of the modeled wind power alone helped in stabilizing the voltage levels as determined from voltage profiles and reactive power margins. Replacing some of the conventional sources with PV power led to reduction of voltages for weak buses below the required standards. Injection of power from more than one renewable energy source helped in slightly improving the voltage levels. Distribution Static compensators (D-STATCOMs) were used to improve the voltage levels of the buses that were below the required standards.

Power System Voltage Stability analysis with Renewable power Integration

The purpose of this research is to find the loading limit of a power system before hitting voltage instability and to assess the margin to voltage instability of a system consisting of a wind farm. An index called Bus Apparent Power Difference Criterion (BSDC) is used to find maximum loadable point. The measure depends on the way that in the region of the voltage collapse no extra apparent power can be delivered to the affected bus. The analysis is performed combination of wind power injection at different wind speeds and line outages in the network. In the feasibility and siting studies of wind farms the steady state analysis with network contingencies give the utility or the developer a sense of network condition upon the injection of power in the network. However, the extent of voltage stability impacted due to load growth in the system is not assessed. The research paper makes way to assess the impact on voltage stability margin with obtaining the maximum loadable point of the system and assessing the best suited bus to integrate a wind farm into the system.

Insecurity Early Warning for Large Scale Hybrid AC/DC Grids Based on Decision Tree and Semi-Supervised Deep Learning

Fast insecurity early warning is the key technique to resist the dynamic insecurity risk, which becomes intractable due to the strong nonlinearity of hybrid AC/DC grids and the high uncertainty of wind generation. Considering dynamic security constraints and wind power uncertainty, this paper presents an insecurity early warning method based on decision tree (DT) and semi-supervised deep learning. First, semi-supervised deep learning is deployed to estimate the dynamic security limit of the critical interface of hybrid AC/DC grids. The system security is assessed by comparing the actual power transfer of the critical interface with the security limit. Then, operating conditions (OCs) are ranked into different insecure levels according to the type of preventive control actions that is needed to ensure the system security. Finally, oblique DT is utilized to identify insecurity classification boundaries in the wind power injection space. Insecure OC sets are constructed based on these classification boundaries. Simulation results of the real-life Jiangsu-Shanghai interconnected grid in east China demonstrate that the proposed method can fast construct the insecure OC sets corresponding to different insecure levels.

Dynamic regulation reliability of a pumped-storage power generating system: Effects of wind power injection

Multi-energy integrated sources are increasingly being used as the bundling-sale for electric systems. Estimating the power supply reliability and regulation performance of the fixed-speed Pumped-Storage Generation Systems (PSGSs) in suppressing power fluctuations of intermittent energy is essential to the operational safety and reliability of a system. However, the regulation process relates to the coupling fluctuations of hydraulic-mechanical-electrical factors, leading to a multiple time-scale effect to the whole hybrid power system. This makes the PSGS’s power response lagging behind the wind power fluctuation and further impacts the power supply reliability of the hybrid power system. To enable the analysis, a numerical model which describes operating states of the presented integrated system, including wind energy and PSGS, is developed in MATLAB/Simulink. Then, ten wind scenarios are selected to study complementation and regulation ability of the PSGS based on a framework of evaluation indicators. The results show that longer power response delay and larger guide vane distance opening result in weaker complementary response of PSGS to wind disturbances. It is also shown that the PSGS provides good complementary capability at lower random wind standard deviation (0.5 vs 1.5), larger wind speed mean value (15 m/s vs 13 m/s) and lower wind speed deviation (5 m/s vs 12 m/s). Furthermore, an unexpected regulation behavior is demonstrated: fast settling and peak times coincide with large variation in overshoot, undershoot and peak value of the power response. This presents a challenge in assessing performance of the integrated generation system. The proposed evaluation method and presented results are important steps to increasing the national power grid capability to accept high integration of multiple energy sources.

C-Vine pair copula based wind power correlation modelling in probabilistic small signal stability analysis

The increasing integration of wind power generation brings more uncertainty into the power system. Since the correlation may have a notable influence on the power system, the output powers of wind farms are generally considered as correlated random variables in uncertainty analysis. In this paper, the C-vine pair copula theory is introduced to describe the complicated dependence of multidimensional wind power injection, and samples obeying this dependence structure are generated. Monte Carlo simulation is performed to analyze the small signal stability of a test system. The probabilistic stability under different correlation models and different operating conditions scenarios is investigated. The results indicate that the probabilistic small signal stability analysis adopting pair copula model is more accurate and stable than other dependence models under different conditions.

Optimal offshore wind farms connection considering the wind capacity maximisation – a Benders decomposition approach

This study presents a new methodology to determine the optimal Offshore Wind Farms connection point in a power system, with the purpose of maximising the wind power penetration, considering a Benders decomposition approach to represent the security constraints related to the ‘’ contingency criterion. The proposed algorithm uses a mixed-integer non-linear programming approach associated with a non-linear programming, and it is evaluated in a tutorial system (IEEE 14-Bus) and two networks from the literature: a small case (IEEE RTS-79), and a medium and real case (Brazilian Birds System). In each case, three points geographically close to each other are selected as candidates in the onshore grid for the wind power injection. The experiments conducted clearly show that the connection decision from the proposed approach led to the maximum safe wind power penetration among the candidate options, which proves its applicability and effectiveness, even for more complex networks, and considering all single transmission line contingencies for safe operation.

Intelligently Controlled Flywheel Storage for Enhanced Dynamic Performance

This paper investigates the development and application of a nonlinear adaptive intelligent controller with superior disturbance-rejection capability for a doubly-fed-induction-machine driven flywheel energy storage system (FESS), to mitigate the intermittency in wind power injection, as well as enhance the transient stability of the connected multimachine power system thereby isolating the grid power from fluctuations. Intelligent supervisors for the rotor-side-converter of the FESS have been constructed using a fuzzy rule set. The consequent part in every rule includes a wavelet function for function learning. The novel normalized gradient descent algorithm with adaptive learning rate has been used to derive the update laws for unknown controller parameters. The control law has been derived by minimizing the predictive performance index. Convergence of the developed algorithm is guaranteed as proven via the Lyapunov stability method. Realistic wind speed profile has been used to test the efficacy of the developed control scheme. The sizing strategy has been devised for optimal sizing of FESS for its efficient utilization. Modified WSCC nine-bus test system has been used for the nonlinear time-domain simulations and novel indices have been used for evaluation of controller performance against its predecessors. $\boldsymbol{OP}{\text{5600}}$ Real-Time-Digital-Simulator has been used to demonstrate the real-time implementation of the suggested scheme.

Heterogeneities in electricity grids strongly enhance non-Gaussian features of frequency fluctuations under stochastic power input.

Stochastic feed-in of fluctuating renewable energies is steadily increasing in modern electricity grids, and this becomes an important risk factor for maintaining power grid stability. Here, we study the impact of wind power feed-in on the short-term frequency fluctuations in power grids based on an Institute of Electrical and Electronics Engineers test grid structure, the swing equation for the dynamics of voltage phase angles, and a series of measured wind speed data. External control measures are accounted for by adjusting the grid state to the average power feed-in on a time scale of 1 min. The wind power is injected at a single node by replacing one of the conventional generator nodes in the test grid by a wind farm. We determine histograms of local frequencies for a large number of 1-min wind speed sequences taken from the measured data and for different injection nodes. These histograms exhibit a common type of shape, which can be described by a Gaussian distribution for small frequencies and a nearly exponentially decaying tail part. Non-Gaussian features become particularly pronounced for wind power injection at locations, which are weakly connected to the main grid structure. This effect is only present when taking into account the heterogeneities in transmission line and node properties of the grid, while it disappears upon homogenizing of these features. The standard deviation of the frequency fluctuations increases linearly with the average injected wind power.

Small signal stability analysis of power systems integrated with wind power using sensitivity-based index

To know the inherent property of the power system connected with a wind farm along with its associated controllers, a small signal stability study is required. For this, a linearized system model has to be developed. The small signal model of the system with wind farm along with its controllers is discussed here. The eigenvalues of system matrix are computed from the linearized model of the power system. The absolute value of the real part of eigenvalues indicates distance from instability. The sensitivity of this minimum real part of eigenvalues with respect to small changes of wind power injection is used as an index to assess the small signal stability under various operating conditions. The optimum value of the wind power can also be determined with the help of the proposed eigenvalue sensitivity index. The systems used for the study are WSCC 3-machine 9-bus system and IEEE 16-machine 68-bus system.

Who gets my flex? An evolutionary game theory analysis of flexibility market dynamics

Maintaining a real time balance between energy consumption and production is challenging when faced with increasing penetration of Renewable Energy Sources (RES) because of the increased variability in generation output. Demand-Side Management (DSM) techniques address this issue by steering consumers’ energy off-take, thereby enabling further penetration of RES. Present paper addresses the problem of overproduction from distribution grid connected wind generation. We present and analyze two business cases in the Belgian-European energy landscape for using upward consumption flexibility to deal with excessive wind power injection. We focus on the perspective of the flexibility providers and the strategic choice they face in choosing the business partner that maximizes their expected financial compensation. Evolutionary game theory is used to model this strategic choice and to provide a framework for quantifying realistic financial compensation bounds based on real world market and wind production data for multiple locations in Belgium. Results show that in a competitive market setting compensation payments for flexible power consumption are higher when dealing with higher wind forecast error levels. These results validate the economic benefits of having accurate wind production forecasts.

Resilience of electricity grids against transmission line overloads under wind power injection at different nodes

A steadily increasing fraction of renewable energy sources for electricity production requires a better understanding of how stochastic power generation affects the stability of electricity grids. Here, we assess the resilience of an IEEE test grid against single transmission line overloads under wind power injection based on the dc power flow equations and a quasi-static grid response to wind fluctuations. Thereby we focus on the mutual influence of wind power generation at different nodes. We find that overload probabilities vary strongly between different pairs of nodes and become highly affected by spatial correlations of wind fluctuations. An unexpected behaviour is uncovered: for a large number of node pairs, increasing wind power injection at one node can increase the power threshold at the other node with respect to line overloads in the grid. We find that this seemingly paradoxical behaviour is related to the topological distance of the overloaded line from the shortest path connecting the wind nodes. In the considered test grid, it occurs for all node pairs, where the overloaded line belongs to the shortest path.

Practical Robust Optimization Method for Unit Commitment of a System with Integrated Wind Resource

Unit commitment, one of the significant tasks in power system operations, faces new challenges as the system uncertainty increases dramatically due to the integration of time-varying resources, such as wind. To address these challenges, we propose the formulation and solution of a generalized unit commitment problem for a system with integrated wind resources. Given the prespecified interval information acquired from real central wind forecasting system for uncertainty representation of nodal wind injections with their correlation information, the proposed unit commitment problem solution is computationally tractable and robust against all uncertain wind power injection realizations. We provide a solution approach to tackle this problem with complex mathematical basics and illustrate the capabilities of the proposed mixed integer solution approach on the large-scale power system of the Northwest China Grid. The numerical results demonstrate that the approach is realistic and not overly conservative in terms of the resulting dispatch cost outcomes.