Wind Power Output Characteristics Research Articles

Method for analysing of wind power wave nature based on kernel density estimation

It is beneficial for improving the accuracy of wind power output prediction by analysing and mastering the inherent laws of the fluctuation characteristics of wind power output, guiding the power grid dispatching department to reasonably arrange power generation plans, and improving the economic efficiency of system operation. In order to characterize the probability density distribution of wind power output fluctuation, two adaptive bandwidth kernel density estimation models are established by correcting the fixed bandwidth obtained from the empirical method and unbiased cross-validation method respectively, and then the two models are combined and optimized, and ultimately, the probability density distribution model of wind power output fluctuation based on Hybrid Adaptive Kernel Density Estimation (HAKDE) is established. A variety of probability density distribution models are used to fit the wind power output fluctuations at different spatial and temporal scales in a province in North China, and the example results show that the hybrid adaptive kernel density estimation model has the best fitting effect, thus verifying the effectiveness of the hybrid adaptive kernel density estimation model.

Long-term cost planning of data-driven wind-storage hybrid systems

—To address the long-term cost planning problem accurately, typically a significant number of formulas are required. However, this increases computational complexity and limits the generalizability of the approach to practical problems. To overcome these challenges, this study adopts a data-driven approach that considers uncertainties to evaluate the long-term cost planning problem accurately for wind power generation with hybrid energy storage. A method for predicting wind power output is proposed using temporal convolutional networks to handle long-term uncertainties. Additionally, an instantaneous phase-synchronous wind power output feature extraction method based on Hilbert transform theory is introduced to capture time-series fluctuation characteristics and address short-term uncertainties. Furthermore, the long-term operational costs and planning of high-percentage new energy wind farms with hybrid energy storage are discussed by combining the wind power output characteristics with the operating characteristics and lifespan of hybrid energy storage devices. The experimental results demonstrate the significant advantages of the deep learning probabilistic prediction model combined with wind power feature extraction. The algorithm validates the effectiveness of considering multiple uncertainties in the planning of microgrids with hybrid energy storage, particularly in considering the uncertainty of the decreasing trend in the investment cost of battery energy storage.

Analysis of wind power output characteristics and output prediction

A fuzzy clustering method and a prediction correction method based on the Copula function are used to establish an analytical model of wind power generation output characteristics. It also predicts the expected and corrected values of its power generation to alleviate the pressure on the grid caused by the high proportion of random new energy sources connected to the grid. Based on the natural properties of wind, a new characteristic index based on the traditional load characteristic index and the new energy output curve is proposed to quantify and evaluate the characteristics of new energy power generation. The results show that the method proposed in this paper can effectively reduce the influence of climate change on output prediction; in comparison to the traditional index, the new index is more refined and instructive.

Research on energy storage allocation strategy considering smoothing the fluctuation of renewable energy

Energy storage technology can effectively solve the problems caused by large-scale grid connection of renewable energy with volatility and uncertainty. Due to the high cost of the energy storage system, the research on capacity allocation of energy storage system has important theoretical and application value. In this paper, an optimization method for determining the capacity of energy storage system for smoothing the power output of renewable energy is proposed. First, based on the actual data of Ulanqab, the output characteristics of wind power and photovoltaic power generation are studied, and the K-means algorithm is used to select typical days. Then, the energy storage configuration model is built according to the objective function and constraints. Finally, genetic algorithm is used to solve the optimization model, obtain the corresponding parameters, and complete the configuration of energy storage capacity. Based on the results of renewable energy spectrum analysis, the minimum capacity of the energy storage system that meets the constraint of target power output volatility after compensation by the energy storage system can be optimized. The simulation results show that at 1 and 10 min, the flattened volatility is about 2% and 5%, while the actual penetration volatility is about 20% and 30%. The volatility of the optimized model is greatly reduced, which proves the effectiveness of the proposed strategy.

RAC-GAN-Based Scenario Generation for Newly Built Wind Farm

Due to the lack of historical output data of new wind farms, there are difficulties in the scheduling and planning of power grid and wind power output scenario generation. The randomness and uncertainty of meteorological factors lead to the results of traditional scenario generation methods not having the ability to accurately reflect their uncertainty. This article proposes a RAC-GAN-based scenario generation method for a new wind farm output. First, the Pearson coefficient is adopted in this method to screen the meteorological factors and obtain the ones that have larger impact on wind power output; Second, based on the obtained meteorological factors, the Grey Relation Analysis (GRA) is used to analyze the meteorological correlation between multiple wind farms with sufficient output data and new wind farms (target power stations), so that the wind farm with high meteorological correlation is selected as the source power station. Then, the K-means method is adopted to cluster the meteorological data of the source power station, thus generating the target power station scenario in which the cluster information serves as the label of the robust auxiliary classifier generative adversarial network (RAC-GAN) model and the output data of the source power station is considered as the basis. Finally, the actual wind farm output and meteorological data of a region in northeast China are employed for arithmetic analysis to verify the effectiveness of the proposed method. It is proved that the proposed method can effectively reflect the characteristics of wind power output and solve the problem of insufficient historical data of new wind farm output.

Research on the Economic Dispatch of Power Systems with a High Proportion of Renewable Energy based on Improved PSO

The penetration rate of renewable energy sources represented by wind power and photovoltaics in the power grid is increasing, adding complexity to unit dispatch. In this paper, a multi-objective optimization model is established for power systems containing wind power and photovoltaic economic dispatch. Firstly, the power generation cost of traditional units, the emission cost of traditional units, the power generation costs of wind turbines and photovoltaics, and the output characteristics of wind power and photovoltaics are analyzed. Secondly, a multi-objective economic dispatch model with the lowest power generation cost, the smallest emission cost, and the smallest power generation cost of new energy units are established, and the power flow balance constraint, supply and demand balance constraint, unit output constraint, and node voltage constraint are considered. Then the improved particle swarm algorithm is proposed to solve the multi-objective function to obtain better convergence and optimal solution. Finally, the simulation is carried out in the IEEE-30 node system and the HRP-38 system, which compares and illustrates the similarities and differences of the solution results of various algorithms under multi-objective and verifies the effectiveness of the algorithm.

Short period Wind Power Forecast Method Based on Maximum Correntropy Criterion

Wind power forecast is an essential measure to increase the level of wind velocity consumption and also the basis of wind power dispatching operations. In the cause of increasing the precision of short-period wind velocity forecast, this srticle adopts a new evaluation criterion(MCC) in order to direct optimization of the parameters of the wind power model. The method first filters and normalizes the measured historical data and determines the optimal input variable dimension through a set of fixed parameters. Then, grid optimization and Particle Swarm Optimization (PSO) are used as evaluation criteria for parameter optimization. Finally, the obtained optimal parameters are used to forecast the short-term wind power, and four evaluation indexes are used to evaluate the forecasting results. The results show that the parameter optimization process using MCC as the evaluation criterion is more suitable for wind power output characteristics, and comparing with traditional optimisic methodthe the forecast accuracy is increased by 5 -10%.

Analysis and Effect Evaluation of Offshore Wind Power Output Characteristics Based on Gaussian Mixed Clustering

China's offshore wind power (OWP) has become an important part of China's energy strategy and marine strategy, so studying the output characteristic curve of offshore wind power is of great significance for the development of offshore wind power. In this paper, a Gaussian Mixture Model (GMM) based on QAIC (Quasi-AIC, Akaike Information Criterion) was proposed. The original output curve of offshore wind power was classified and the characteristic curve was extracted. The characteristic curve of offshore wind power which can reflect the characteristics of different wind areas is obtained. At last, taking the actual wind power in Coastal Jiangsu Province as the research object, the results of this clustering model and the Gaussian Mixture Model (GMM) based on BIC (Bayesian Information Criterion) are compared via the CH score (Calinski Harabasz Score) to evaluate the effect of this clustering model for the intention of verifying the effectiveness of the method in this paper.

A Review of Wind Energy Output Simulation for New Power System Planning

The uncertain and volatile nature of wind energy have brought huge challenges to power system planning and operation. Therefore, it is necessary to model the wind power output. In this paper simulation models of wind energy output for new power system planning are reviewed. We begin by discussing the characteristics of wind power output, and then introduce the wind power output simulation model based on different application scenarios that are based on probability and time series. Finally, the directions for further research in the future are anticipated.

Optimal scheduling strategy of a regional integrated energy system considering renewable energy uncertainty and heat network transmission characteristics

In order to overcome the curtailment of wind energy caused by “power determined by heat” of combined heat and power units, this study proposes an integrated demand response strategy and optimization method for heat-electric energy dispatching in a regionally integrated energy system. Firstly, a scenario analysis algorithm based on Latin Hypercube Sampling (LHS) and Backward Reduction (BR) is applied to characterize the probability characteristics of wind power output. In addition, a novel integrated demand response scheduling model with the consideration of transmission characteristics of the heating network, aiming at minimizing the operation cost, is proposed for a regional integrated energy system. To solve this model, the thermos-electric coupling relationship is evaluated and the proportion of thermoelectric demand response is also weighted. The user satisfaction and economy will finally reach a more optimal level, and thereby is solved via CPLEX solver in MATLAB. The simulation results on a actual integrated energy system reveal that the proposed method is capable of optimizing energy consumption and lowering operational costs while maintaining user satisfaction with the uncertain wind generation by leveraging thermal inertia of the integrated energy system.

Probability Density Analysis of Wind Storage Output Based on Monte Carlo Simulation

Due to the relatively large fluctuation amplitude of the output power of wind farms, when using energy storage to stabilize the output characteristics of wind power, the impact of the addition of energy storage on the probability density of wind power output needs to be further analyzed. This paper uses Monte Carlo simulation algorithm to analyze the probability density of active power output in consideration of the operating state of wind power and compares the change of the output probability density curve after adding energy storage. According to the output probability density curve obtained by the algorithm, the abscissa of the curve is shifted to the right by one energy storage capacity unit after the addition of energy storage, so that the output probability density curve of the wind storage system can reach the required value by adding energy storage.

The Bi-Level Optimization Model Research for Energy-Intensive Load and Energy Storage System Considering Congested Wind Power Consumption

Due to the uncertainty of wind power output, the congestion of wind power has become prominent. Exactly how to improve the capacity of wind power consumption has become a problem that needs to be studied urgently. In this paper, an energy storage system and energy-extensive load with adjustable characteristics are used as an important means of consuming wind power. Firstly, we analyze the reasons for the congestion according to the characteristics of wind power output, and establish a model of the grid’s ability to integrate wind power based on the concept of a wind power admissible interval. Secondly, we analyze the energy-extensive load regulation characteristics and establish an energy-extensive load dispatch model. Thirdly, on the basis of considering the energy-extensive load and energy storage system adjustment constraints, a bi-level optimization model is established. The upper level determines the configured capacity of the energy storage system with the goal of minimizing the total economic investment of the energy storage system, and the lower level coordinates the dispatching with the goal of maximizing wind power consumption and minimizing system operating costs. Finally, a certain region is taken as an example to verify the validity of the proposed method.

Study on Renewable Energy Accommodation Capacity Assessment Method Based on Time Series Production Simulation

In order to solve the problem of low accommodation rate of renewable energy in a single region, considering the output characteristics of wind power and photovoltaic, grid constraints and cross regional power exchange, an optimization model of renewable energy accommodation capacity in a cross regional power grid year is established. This paper analyzes the factors restricting the accommodation capacity of renewable energy, and determines the access scale of renewable energy units according to the characteristics of different power grid scale, load and power supply structure. And then taking the annual maximum accommodation capacity of renewable energy as the goal, the time series production simulation model is established, considering the constraints such as regional load balancing, power transmission and output of the network cable, unit output, unit optimization power ramp rate and renewable energy output. To improve the renewable energy accommodation capacity, the three factors of power system interconnection level, power regulation performance and load characteristics can be considered.

Research on frequency Control Strategy of micro grid including wind power and hybrid energy storage system

In view of the frequency fluctuation of the micro-grid system containing wind power, hybrid energy storage system composed of batteries and supercapacitors is adopted to coordinate the output, so as to maintain the frequency stability of the micro-grid and provide the possibility for the micro-grid to further absorb wind power and other clean energy. Firstly, by analyzing the characteristics of wind power output, the power deviation signal of the system is decomposed into low-frequency and high-frequency parts through wavelet packet decomposition, so as to maximize the consumption of wind power output power on the premise of meeting the stability of the system. Then, the high frequency part of the power gap is further divided to determine the initial FM power distribution scheme of the energy storage system. Finally, the energy storage output is modified by the Logistic regression function to take into account both the energy storage adaptive frequency modulation and the energy storage charged state self-recovery. Example analysis shows that the proposed control strategy can realize the optimal distribution of wind power, ensure the full utilization of wind farm output, improve wind power grid connection capacity, and increase efficiency and economy.

A Short-Term Optimal Dispatch Model Considering Uncertain Wind Power Output

Aiming at the security and economic problems caused by wind power connected to the grid, a short-term optimal dispatch model for power system considering the uncertain wind power output is proposed. Although wind power is an important clean energy source, wind power output is characterized by volatility and randomness, which damages the system power balance and brings difficulties to dispatching. In this paper, wind power output characteristics are described by uncertain sets of wind power output. Considering the positive and negative spinning reserve constraints, a bi-level optimization model is constructed. According to the dual principle, the bi-level optimization model can be transformed into a solvable robust optimization scheduling model. Finally, the numerical simulation shows that the proposed method can realize the utilization of wind power, rationally arrange the commitment operation and output of thermal power units, which has good robustness to wind power output fluctuations to ensure the safe and economic operation of the system.

Optimal model for deviation penalty of electricity in joint operation of wind power retailer and heat storage electric boiler

Because of the randomness and fluctuation characteristics of wind power output, there is a deviation between the bidding output and the actual output of the wind power retailer, and it faces with a lot of penalty fees for deviated power. In order to promote clean heating in the northern heating area, the heat storage electric boiler (this article abbreviates as HSEB) have been gradually promoted. Based on the thermal storage capacity of the HSEB, the deviation of wind power output can be reduced to a certain extent. Considering the comfort range of the indoor human body, the indoor heat supply can fluctuate within a certain range, further increasing the regulation capacity of the HSEB. In this study, taking reducing the cost of deviation penalty as the objective function, and a joint operation model of wind power retailer and HSEB is established. By an example, the feasibility of using HSEB to reduce the cost of deviation penalty is verified.

Multi-Power Joint Peak-Shaving Optimization for Power System Considering Coordinated Dispatching of Nuclear Power and Wind Power

The matching characteristics of wind power output and nuclear power peak shaving are studied and the influence of coordinated dispatching of wind power and nuclear power on peak shaving flexibility and cost of the power grid is analyzed. Taking the lowest total operation cost of the system as the objective and considering the operation constraints, peak shaving cost of nuclear power units and the abandoned wind cost, a multi-power dispatching model for power system is proposed. In addition, in order to accurately optimize the output of nuclear power, a method of subdividing the peak shaving depth of nuclear power to linearize the constraint of nuclear power peak shaving is proposed. The analysis of example shows that the multi-power dispatching model considering the coordinated operation of wind and nuclear power has a high operation economy and can significantly reduce the amount of wind abandonment. The daily load tracking mode can be used for the participation of nuclear power units in daily peak shaving and the feasibility and necessity of coordinated wind-nuclear dispatching are verified.

Power System Stochastic Unit Commitment Model for Large-scare Wind Power Integration Considering Demand Side Resources

As the uncertainty output characteristics of wind power, the large-scale wind power integrated to the grid, has brought new challenges to power system operation. Based on the uncertainty characteristics of wind power, this paper establishes the wind power output model, then focuses on the positive effects of price-based demand response (PDR) and incentive-based demand response (IDR), which are allowed to participate in the power balance, on the unit commitment (UC) with high wind power penetration, and establishes the PDR and IDR models. Based on the chance constrained programming, this paper proposes a stochastic UC model with large-scale wind power integration considering demand side resources(DSR). Stochastic simulation combined with particle swarm optimization (PSO) algorithm was presented to solve this model. At last, the model is proved feasibly and effectively by testing the IEEE 10 machines system.

마이크로그리드에서 신재생에너지의 최적 용량 및 위치 선정

Currently, with the increasing interest in renewable energy sources (RES), their penetration in the power system is expected to increase considerably. South Korea plans to increase the proportion of renewable energy to 20% by 2029 based on the 7th basic plan for long-term electricity supply and demand. On the other hand, expanding the penetration of renewable energy sources is a new challenge for conventional power systems. Most renewable energy sources, such as photovoltaic and wind power, are intermittent, and thus difficult to control without a loss of power. In addition, the intermittent characteristics leads to an imbalance of power and load. This can be solved by combining renewable energies because the output characteristics of wind power and solar are different. A reliability assessment is an appropriate method for achieving the optimal combination of renewable energy sources. Therefore, this paper proposes the optimal sizing and location of renewable energy sources in a microgrid based on a reliability assessment. In addition, a stochastic method for LOLE (Loss of Load Expectation) is also proposed to evaluate the reliability. In this case study, optimal sizing and locations are determined by the minimum LOLE and power loss in an IEEE 34-bus system.

Analysis of Output Characteristics of Typical Distributed Wind Power

With the development of smart power distribution technology in the future, a large range of power supply load (such as distributed wind power generation) will appear on the power receiving end. When distributed wind power is connected to the power grid on a large scale, it will have a certain impact on the safe and stable operation of the power grid. However, if the wind power output characteristics can be analyzed and the wind power output is properly regulated, the one-way flow of power from the distribution network to the user side will be broken, so that the future "network-load" has dual interaction characteristics based on response and substantial power exchange.