Large-scale Wind Power Integration Research Articles

Research on Multi-Objective Reactive Power Optimization of Power Grid With High Proportion of New Energy

The integration of large-scale wind power and photovoltaics into the power system will aggravate the voltage fluctuation of grid nodes, while when the reactive power of new energy units participates in the optimization of reactive power and voltage of the system, it will promote the consumption of new energy while effectively improving the stable operation level of the system. In this work, the grey wolf algorithm (GWO) of entropy weight is introduced to solve the optimal compromise solution of multi-objective reactive power optimization of transmission network, and the network loss and voltage deviation and static voltage stability margin are taken as three objective functions to evaluate and make use of the reactive power regulation performance of new energy units. Specifically, the cooperative optimization of reactive power regulation performance of wind turbine and photovoltaic generating set and reactive power compensation of original power system is adopted, the efficient GWO) is incorporated to solve the multi-objective reactive power optimization model, the entropy method is introduced to obtain the optimal compromise solution of reactive power optimization, and a numerical example is analyzed by using IEEE39 transmission network system. Simulation results confirmed that the proposed multi-objective reactive power and voltage optimization model and grey wolf optimization algorithm can effectively improve the system node voltage quality and improve the stable operation level of the system.

Research on wind farm participating in AGC based on wind power variogram characteristics.

The increasing integration of large-scale wind power aggravates the difficulty of maintaining system frequency deviations in a certain range. The frequency regulation pressure of conventional generators increases, which requires wind farms to participate in system frequency regulation. In this paper, a multi-area interconnected power system frequency response model with wind power is established. Based on the frequency response model, the state space model of regional interconnected power system is presented. Then, the wind power variogram characteristics are introduced for estimating wind power variations in different time-scales. By predicting the wind power variations in AGC time-scale, a strategy of wind farm participating in AGC system is proposed and performed based on model predictive control (MPC). The control strategy makes the conventional units and wind farms to participate in AGC system coordinately. Simulation results are provided which verifies the feasibility and validity of the proposed strategy.

Developing a long-lasting offshore wind business case towards a Dutch decarbonised energy system by 2050

The integration of large-scale offshore wind power in the energy system will have a major effect on electricity markets. It may lead to large market price volatility due to the inherent variability of wind energy in terms of power fluctuations, forecast errors and insufficient flexibility on the demand side. This study models future business cases for offshore wind farms in two reference national scenarios for the Netherlands, namely NECP2030 and TRANSFORM. Results show that the value of offshore wind in 2030, according to NECP scenario is 39.9 €/MWh, and that electricity price in the TRANSFORM scenario, which considers aggressive development towards a sustainable economy, is a high 104 €/MWh by 2050. To consider ways of improving the return on investment for offshore wind farms in future markets, an optimisation problem is defined, where the wind farm has the choice to either sell the generated electricity to the spot market or to an electrolyser. Results show a potential advantage in using an electrolyser to produce green hydrogen for offshore wind farms, with net profit of 41.4 M€, compared to when offshore wind farms sell electricity solely to the spot market, where the profits are 36 M€.

A combined model for short-term wind power forecasting based on the analysis of numerical weather prediction data

Due to the fluctuation and intermittency of wind power resources, large-scale wind power integration brings serious challenges to power systems. Among the existing short-term forecasting methods, the accuracy and forecast period length can hardly meet the demand of power system economic dispatching and day-ahead power purchase markets. To further enhance the accuracy and increase the time scale, a short-term wind power forecasting (WPF) combined model based on numerical weather prediction (NWP) analysis is presented in this paper. First, according to the criterion of the minimum redundancy maximum relevance (mRMR) algorithm, several factors are sifted from the NWP multivariate data. Second, different characteristics of the factors are extracted, and weather patterns are divided into different types on the basis of these characteristics. Third, two deep learning models, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), are applied for short-term WPF respectively under different weather types. Furthermore, the forecast results of the two models are combined using the Induced Ordered Weighted Average (IOWA) operator. The actual data collected from a wind farm in Northwest China are used to verify the conclusions. Results show that the proposed method can forecast wind power under different weather circumstances and outperform existing Radial Basis Function (RBF), Extreme Learning Machine (ELM) and Support Vector Machine (SVM)-based methods with respect to forecasting accuracy.

Probabilistic Voltage Profiles Analysis of Power System with Large Scale Wind Power Integration

Probabilistic Voltage Profiles Analysis of Power System with Large Scale Wind Power Integration

A Flexible Demand Response Dispatch Strategy Considering Multiple Response Modes and Wind Power Uncertainty

The growth of energy consumption has led to the depletion of fossil energy and the increasing greenhouse effect. In this case, low carbonization has become an important trend in the world’s energy development, in which clean energy occupies an important position. The uncertainties brought by the large-scale integration of wind power, photovoltaic and other renewable energy sources into the grid pose a serious challenge to system dispatch. The participation of demand response (DR) resources can flexibly cooperate with renewable energy, optimizing system dispatch and promoting renewable energy consumption. Thus, we propose a flexible DR scheduling strategy based on multiple response modes in this paper. We first present a DR resource operation model based on multivariate response modes. Then, the uncertainties are considered and dealt with by scenario generation and reduction technology. Finally, a day-head dispatch strategy considering flexible DR operation and wind power uncertainties is established. The simulation results show that the proposed strategy promotes wind power consumption and reduces system operation costs.

Intelligent Frequency Control Strategy Based on Reinforcement Learning of Multi-Objective Collaborative Reward Function

Large scale wind power integration into the power grid will pose a serious threat to the frequency control of power system. If only Control Performance Standard (CPS) index is used as the evaluation standard of frequency quality, it will easily lead to short-term centralized frequency crossing, which will affect the effect of intelligent Automatic Generation Control (AGC) on frequency quality. In order to solve this problem, a multi-objective collaborative reward function is constructed by introducing a collaborative evaluation mechanism with multiple evaluation indexes. In addition, Negotiated W-Learning strategy is proposed to globally optimize the solution of the objective function from multi dimensions, it avoids the poor learning efficiency of the traditional Greedy strategy. The AGC control model simulation of standard two area interconnected power grid shows that the proposed intelligent strategy can effectively improve the frequency control performance and improve the frequency quality of the system in the whole-time scale.

Optimization of loop-network reconfiguration strategies to eliminate transmission line overloads in power system restoration process with wind power integration

With large-scale wind power integration, the uncertainty of the wind power outputs will aggravate the transmission line overloads during the black-start restoration process. By constructing regional loop-networks, this paper proposes an optimization method of network reconfiguration strategies to eliminate the line overloads. Firstly, a multi-scenario set of the wind power outputs is constructed based on the extreme scenario method. Secondly, a loop-network reconfiguration optimization model is established to meet the requirements of the multi-scenario set simultaneously, where the line overloads are not permitted. Then, a two-layer solving framework is proposed to solve the optimization model. The upper-level layer is a multi-scenario mixed integer linear programming (MILP) model with the DC power flow constraints, whose objective is to minimize the operation complexity index. The lower-level layer serves to check the standing phase angle (SPA) constraint and the AC power flow constraints of the optimal schemes obtained from the upper-level layer. Therefore, the loop-network reconfiguration optimization model is alternatively solved by iterative computation. The effectiveness of the proposed method is verified by the modified IEEE 39-bus system and the modified IEEE 118-bus system, and applicability of this method to practical systems is verified by the southwestern Yunnan power system in China.

Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization

The intermittent and uncertain characteristics of wind generation have brought new challenges for the hosting capacity and the integration of large-scale wind power into the power system. Consequently, reasonable forecasting wind power installed capacity (WPIC) is the most effective and applicable solution to meet this challenge. However, the single parameter optimization of the conventional grey model has some limitations in improving its modeling ability. To this end, a novel grey prediction model with parameters combination optimization is proposed in this paper. Firstly, considering the modeling mechanism and process, the order of accumulation generation of the grey prediction model is optimized by Particle Swarm Optimization (PSO) Algorithm. Secondly, as different orders of accumulation generation correspond to different parameter matrixes, the background value coefficient of the grey prediction model is optimized based on the optimal accumulation order. Finally, the novel model of combinational optimization is employed to simulate and forecast Chinese WPIC, and the comprehensive error of the novel model is only 1.34%, which is superior to the other three grey prediction models (2.82%, 1.68%, and 2.60%, respectively). The forecast shows that China’s WPIC will keep growing in the next five years, and some reasonable suggestions are put forward from the standpoint of the practitioners and governments.

Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges

The integration of large-scale wind power introduces issues in modern power systems operations due to its strong randomness and volatility. These issues can be resolved via wind power forecasting that can provide comprehensive future information about wind power uncertainties. This paper presents a timely and comprehensive review of meta-heuristic algorithms in the framework of wind power forecasting. The framework is based on the auxiliary layer, forecasting base layer, and core layer. The auxiliary layer, such as the data-decomposition layer, decomposes the wind power time series into many relatively stationary subseries, and uses prediction models, including artificial neural networks (ANNs) and machine learning (ML). The core layer is based on meta-heuristic algorithms, which include evolutionary-based algorithms, physics-based algorithms, human-based algorithms, swarm-based algorithms, hybrid algorithms, and multi-objective optimization algorithms. These algorithms aim to search for the optimal solutions under constraints, which is highly significant for optimizing the key parameters of the prediction models. Besides, multiple error evaluation metrics, e.g., deterministic, uncertainty, and testing methods used in the field of wind power prediction are described. A quantitative analysis focusing on their advantages, disadvantages, forecasting accuracy, and computational costs are also provided. Finally, a few open research issues and trends related to the topic are discussed, which can contribute to improving the understanding of each wind power forecasting method. In general, this review paper provides valuable insights to wind power engineers.

Short-Term Wind Speed Prediction of Wind Farms Based on Particle Swarm Optimization Support Vector Machine

With the large-scale integration of wind power into the grid, grid-side instability caused by fluctuations in wind speed is becoming more and more significant, and the topic of wind speed prediction for wind farms is urgent. Based on this, in order to solve the problem of low prediction accuracy of a single support vector machine, this paper proposes a wind speed prediction method that uses particle swarm optimization to optimize the support vector machine. Use particle swarm algorithm to optimize the selection of penalty factors and kernel function parameters of support vector machines, then conduct model training on the optimized parameters, and then apply the established prediction model to wind speed prediction of wind farms, and finally analyze the prediction results. The prediction results show that the support vector machine algorithm optimized by the particle swarm optimization algorithm has better accuracy in predicting the wind speed of wind farms, which is significantly higher than the prediction accuracy of a single support vector machine.

Copula Correlation Modeling of Wind Farms Generation and Its Application in Power Dispatching

With the large-scale integration of wind power into the grid, a model that can accurately describe the randomness of wind farm output and the correlation between them is of great significance to be constructed. A joint distribution function of multiple wind plants output model based on Copula theory is constructed. By introducing correlation and fitting coefficient, the attribute recognition theory is put forward to select the optimal model based on entropy weight method. Finally, the validity of Copula modeling is verified by using the synchronized historical data of California coastal wind farms as a sample. The results show that t-copula can not only describe the correlation between the original variables well, but also fit the empirical distribution function of the original samples accurately.

Analysis of Offshore Wind Power Integration

In recent years, global energy and environmental problems have become increasingly prominent, and wind power has entered a period of rapid development. At the same time, offshore wind power integration is also facing many challenges and problems. Based on the analysis and research of offshore wind power integration, this paper introduces the technologies of offshore wind power integration such as HVAC, LCC-HVDC and VSC-HVDC, and discusses the impact of large-scale offshore wind power on power grid, interaction with UHVDC, voltage ride through and harmonic vibration of offshore wind power, and the impact of large-scale wind power integration on power grid and power quality problems are studied. The research and breakthrough of these problems will greatly benefit the development of offshore wind power industry.

Nash equilibria in electricity pool markets with large-scale wind power integration

This work investigates the interaction between power producers with conventional and wind generation portfolios participating in a network-constrained pool-based market. A stochastic bi-level problem is introduced to model the strategic behavior of each single producer. The upper-level problem maximizes the producers’ expected profits and the lower-level problem optimizes the jointly cleared energy and balancing market under economic dispatch. Market participants’ offers are modeled using linear stepwise curves, and the stochastic wind power generation is realized through a set of plausible wind scenarios. The bi-level problem is recast into a single-level mathematical problem with equilibrium constraints with primal-dual formulation using the Karush-Kuhn-Tacker first order optimality conditions and the strong duality theorem. The joint solution of all strategic producers’ problems constitutes an equilibrium problem with equilibrium constraints. The latter is reduced into an equivalent mixed integer linear program by using disjunctive constraints. Different objective functions are applied to the final program to define the range of market equilibria, and a single-iterate diagonalization process is used to justify those equilibria that are meaningful. The model addresses several cases considering different types of market competition, transmission line congestions, and different levels of wind power penetration and volatility.

Big-M based MILP method for SCUC considering allowable wind power output interval and its adjustable conservativeness

In contrast to most existing works on robust unit commitment (UC), this study proposes a novel big-M-based mixed-integer linear programming (MILP) method to solve security-constrained UC problems considering the allowable wind power output interval and its adjustable conservativeness. The wind power accommodation capability is usually limited by spinning reserve requirements and transmission line capacity in power systems with large-scale wind power integration. Therefore, by employing the big-M method and adding auxiliary 0-1 binary variables to describe the allowable wind power output interval, a bilinear programming problem meeting the security constraints of system operation is presented. Furthermore, an adjustable confidence level was introduced into the proposed robust optimization model to decrease the level of conservatism of the robust solutions. This can establish a trade-off between economy and security. To develop an MILP problem that can be solved by commercial solvers such as CPLEX, the big-M method is utilized again to represent the bilinear formulation as a series of linear inequality constraints and approximately address the nonlinear formulation caused by the adjustable conservativeness. Simulation studies on a modified IEEE 26-generator reliability test system connected to wind farms were performed to confirm the effectiveness and advantages of the proposed method.

An Optimized Method of Wind Speed Prediction with Support Vector Machine and Genetic Algorithm

Due to the randomness of wind speed and direction, the output power of wind turbine also has randomness. After large-scale wind power integration, it will bring a lot of adverse effects on the power quality of the power system, and also bring difficulties to the formulation of power system dispatching plan. In order to improve the prediction accuracy, an optimized method of wind speed prediction with support vector machine and genetic algorithm is put forward. Compared with other optimization methods, the simulation results show that the optimized genetic algorithm not only has good convergence speed, but also can find more suitable parameters for data samples. When the data is updated according to time series, the optimization range of vaccine and parameters is adaptively adjusted and updated. Therefore, as a new optimization method, the optimization method has certain theoretical significance and practical application value, and can be applied to other time series prediction models.

Large-scale optimal integration of wind and solar photovoltaic power in water-energy systems on islands

This paper presents a new method based on the Smart Energy System concept to link the water infrastructure and the energy system of an island. The principal aim of this study is to determine whether this new method can increase the contribution of renewables (wind power and photovoltaic) to the primary energy supply of the island. The method considers water production and treatment systems as flexible loads and explores a wide range of possible water supply infrastructures and PV/wind power combinations in the search for an optimal energy-water configuration. The final optimal solution is based on a balance between energy fuel needs and energy excesses, CO2 emissions, oil consumption, minimization of total annual costs and maximization of the renewable contribution. The proposed method increased the contribution of renewables from 5.14% to 24.6%. This corresponds to, on average, over 35% of the hourly electricity demand throughout 2018 being covered by renewables, against the current 6.6%. The study reveals that wind technology integration is of fundamental importance for renewable exploitation in insular water-energy systems, with wind energy contributing more than 70% of the renewable participation in this case study.

Research on Reactive Power Compensation Configuration of Wind Farm Integration

The large-scale wind power integration into power system brings great impact on the grid voltage stability. In order to reduce the influences of grid-connected wind power on the power system and improve the safety of wind power integration, it is necessary to conduct research on reactive power compensation configuration of wind farm integration. Firstly, based on the actual situation of wind power project, the principle of reactive power compensation configuration is studied. Secondly, a theoretical calculation method of reactive power compensation configuration is proposed. On this basis, combined with the practical 300MW wind farm project, the reactive power compensation configuration example is analyzed. Finally, through the modeling and simulation by power system analysis software package (PSASP), the accuracy and effectiveness of the calculation method proposed by this paper are verified. This paper put forward a reactive power compensation configuration method which is applicable to all wind farms connected to the power system. This provides important support for voltage stability in the wind power integration project.

Projected changes in wind power potential over China and India in high resolution climate models

As more countries commit to emissions reductions by midcentury to curb anthropogenic climate change, decarbonization of the electricity sector becomes a first-order task in reaching this goal. Renewables, particularly wind and solar power, will be predominant components of this transition. How availability of the wind and solar resource will change in the future in response to regional climate changes is an important and underdiscussed topic of the decarbonization process. Here, we study changes in potential for wind power in China and India, evaluating prospectively until the year 2060. To do this, we study a downscaled, high-resolution multimodel ensemble of CMIP5 models under high and low emissions scenarios. While there is some intermodel variability, we find that spatial changes are generally consistent across models, with decreases of up to 965 (a 1% change) and 186 TWh (a 2% change) in annual electricity generation potential for China and India, respectively. Compensating for the declining resource are weakened seasonal and diurnal variabilities, allowing for easier large-scale wind power integration. We conclude that while the ensemble indicates available wind resource over China and India will decline slightly in the future, there remains enormous potential for significant wind power expansion, which must play a major role in carbon neutral aspirations.

Instantaneous Power Control Strategy for Voltage Improvement in Power Network Equipped by Wind Generator

The uncertainty of wind power brings great challenges to large-scale wind power integration. The conventional integration of wind power is difficult to adapt the demand of power grid planning and operation. This paper proposes an instantaneous power control strategy for voltage improvement in power networks using wind turbine improving the dynamical response of power systems performances (voltage and transient stability) after fault. In which the proposed control algorithm based on a new advanced control strategy to control the injected wind power into power system. The efficiency of developed control strategy has been tested using IEEE 9 Bus. Simulation results have showed that the proposed method perform better to preserve optimal performances over wide range of disturbances for both considered scenarios studied short circuit and variable loads.