Frequency Control Approach and Load Forecasting Assessment for Wind Systems

Frequency deviation has to be controlled in power generation units when there arefluctuations in system frequency.With several renewable energy sources,wind energy forecasting is majorly focused in this work which is a tough task due to its variations and uncontrollable nature.Whenever there is a mismatch between generation and demand,the frequency deviation may arise from the actual frequency 50 Hz(in India).To mitigate the frequency deviation issue,it is necessary to develop an effective technique for better frequency control in wind energy systems.In this work,heuristic Fuzzy Logic Based Controller(FLC)is developed for providing an effective frequency control support by modeling the complex behavior of the system to enhance the load forecasting in wind based hybrid power systems.Frequency control is applied to reduce the frequency deviation due tofluctuations and load prediction information using ANN(Artificial Neural Network)and SVM(Support Vector Machine)learning models.The performance analysis of the proposed method is done with different machine learning based approaches.The forecasting assessment is done over various climates with the aim to decrease the prediction errors and to demote the forecasting accuracy.Simulation results show that the Mean Absolute Percentage Error(MAPE),Root Mean Square Error(RMSE)and Normalized Mean Absolute Error(NMAE)values are scaled down by 41.1%,9.9%and 23.1%respectively in the proposed method while comparing with existing wavelet and BPN based approach.

Day-Ahead Probabilistic Load Flow Analysis Considering Wind Power Forecast Error Correlation

Short-term power flow analysis has a significant influence on day-ahead generation schedule. This paper proposes a time series model and prediction error distribution model of wind power output. With the consideration of wind speed and wind power output forecast error’s correlation, the probabilistic distributions of transmission line flows during tomorrow’s 96 time intervals are obtained using cumulants combined Gram-Charlier expansion method. The probability density function and cumulative distribution function of transmission lines on each time interval could provide scheduling planners with more accurate and comprehensive information. Simulation in IEEE 39-bus system demonstrates effectiveness of the proposed model and algorithm.

Grid Power Optimization Based on Adapting Load Forecasting and Weather Forecasting for System Which Involves Wind Power Systems

This paper describes the performance, generated power flow distribution and redistribution for each power plant on the grid based on adapting load and weather forecasting data. Both load forecasting and weather forecasting are used for collecting predicting data which are required for optimizing the performance of the grid. The stability of each power systems on the grid highly affected by load varying, and with the presence of the wind power systems on the grid, the grid will be more exposed to lowering its performance and increase the instability to other power systems on the gird. This is because of the intermittence behavior of the generated power from wind turbines as they depend on the wind speed which is varying all the time. However, with a good prediction of the wind speed, a close to the actual power of the wind can be determined. Furthermore, with knowing the load characteristics in advance, the new load curve can be determined after being subtracted from the wind power. Thus, with having the knowledge of the new load curve, and data that collected from SACADA system of the status of all power plants, the power optimization, load distribution and redistribution of the power flows between power plants can be successfully achieved. That is, the improvement of performance, more reliable, and more stable power grid.

A comprehensive review for wind,solar,and electrical load forecasting methods

Wind power,solar power,and electrical load forecasting are essential works to ensure the safe and stable operation of the electric power system.With the increasing permeability of new energy and the rising demand response load,the uncertainty on the production and load sides are both increased,bringing new challenges to the forecasting work and putting forward higher requirements to the forecasting accuracy.Most review/survey papers focus on one specific forecasting object(wind,solar,or load),a few involve the above two or three objects,but the forecasting objects are surveyed separately.Some papers predict at least two kinds of objects simultaneously to cope with the increasing uncertainty at both production and load sides.However,there is no corresponding review at present.Hence,our study provides a comprehensive review of wind,solar,and electrical load forecasting methods.Furthermore,the survey of Numerical Weather Prediction wind speed/irradiance correction methods is also included in this manuscript.Challenges and future research directions are discussed at last.

基于误差幅空特性分析的空间负荷预测误差评价方法

对空间负荷预测误差进行有效评价是客观认识预测结果,指导预测结果合理应用的前提。然而,现有空间负荷预测误差评价的研究存在对误差的空间分布不考虑或考虑不充分导致评价不准确的问题。为此,提出一种基于误差幅空特性分析的空间负荷预测误差评价方法。首先,从空间负荷预测误差幅值大小和空间分布对电网规划影响的角度出发,对误差的幅空特性进行详细分析;其次,利用运输问题的数学模型来表征正负误差的幅空抵消特性,使用各空间邻近度–幅值误差值曲线与x轴围成面积之和来表征剩余未抵消误差的幅空叠加特性;然后,分别通过伏格尔法和各梯形面积累加公式来计算正负误差的幅空抵消影响值和剩余未抵消误差的幅空叠加影响值,并在此基础上构建空间负荷预测误差评价指标;最后,基于误差对电网规划的实际影响给出对误差评价指标性能的检验方法。算例分析表明,与传统方法相比,该文所提误差评价方法从幅值和空间两个维度实现了对空间负荷预测误差更为全面的评估,与误差对电网规划影响的实际情况贴近度更高。

计及误差信息的自适应超短期风速预测模型

为提升超短期风速预测精度,提出一种计及误差信息的自适应混合预测模型。应用自适应噪声的完备集合经验模态分解模型与鲸鱼优化的变分模态分解模型分别对风速样本数据与预测误差进行分解,同时计算各子序列的模糊熵以判断序列复杂程度。在此基础上,应用鲸鱼优化的长短期网络预测复杂程度较高的序列,差分自回归移动平均模型预测复杂程度较低的序列。最后,将初始风速预测结果和风速误差预测值相加得到基于误差修正的超短期风速预测值。结果表明,修正预测误差与考虑分解策略可有效提升点预测的性能,与基准模型相比,所提模型在多场景下均具备优良的预测精度。

Spatial dispersion of wind speeds and its influence on the forecasting error of wind power in a wind farm

Big wind farms must be integrated to power system.Wind power from big wind farms,with randomness,volatility and intermittent,will bring adverse impacts on the connected power system.High precision wind power forecasting is helpful to reduce above adverse impacts.There are two kinds of wind power forecasting.One is to forecast wind power only based on its time series data.The other is to forecast wind power based on wind speeds from weather forecast.For a big wind farm,due to its spatial scale and dynamics of wind,wind speeds at different wind turbines are obviously different,that is called wind speed spatial dispersion.Spatial dispersion of wind speeds and its influence on the wind power forecasting errors have been studied in this paper.An error evaluation framework has been established to account for the errors caused by wind speed spatial dispersion.A case study of several wind farms has demonstrated that even ifthe forecasting average wind speed is accurate,the error caused by wind speed spatial dispersion cannot be ignored for the wind power forecasting of a wind farm.

基于校正条件生成对抗网络的风电场群绿氢储能系统容量配置

以条件生成对抗网络(CGAN)为代表的半监督学习可计及风电波动并生成出力场景集合,生成的数据可输入氢气储能容量配置模型以支撑优化求解。为此,该文首先设计一种校正条件生成对抗网络(CCGAN),并基于风电预测误差构建条件校正器,对预测失准事件和风电爬坡事件下输入生成器的标签信息进行识别和校正;然后,以储能定容的综合成本和各风场弃风成本为目标函数,构建绿氢储能容量配置的多目标优化模型,并引入基于切比雪夫距离的膝区域数学概念,以指导多目标优化算法设计;最后,以新英格兰39节点系统为例进行算例分析,结果表明未经校正的条件信息将导致定容决策偏离实际,而CCGAN能生成计及风电不确定性的高置信出力场景,使得容量配置结果兼顾鲁棒性和经济性。

基于混合特征双重衍生和误差修正的风电功率超短期预测

随着风电渗透率的不断提高,对风电功率进行精准、可靠的预测是提升风电消纳水平的有效措施。针对功率预测时风电数据种类不足和特征数量稀缺的问题,提出基于混合特征双重衍生和误差修正的风电功率超短期预测模型。首先,在原始功率特征中施加混沌噪声,构造出多条混沌扰动特征,改善原始功率特征分布过于单一的状况。其次,提出基于免疫算法的特征衍生算法,挖掘风电功率数据的潜在信息,增加优质特征数量,进而构建误差预测模型,通过预测风电功率预测误差修正风电功率预测结果,进一步提升预测准确率。最后,基于比利时风电场实际运行数据进行算例分析。所提模型预测效果较好,且相较其他传统预测模型精确度更高,验证了所提模型的有效性。

基于VMD和改进BiLSTM的短期风电功率预测

精准的短期风电功率预测对电力系统稳定运行至关重要。为提高短期预测精确度,提出一种基于变分模态分解(VMD)-样本熵(SE)和利用注意力(attention)机制改进双向长短期记忆网络(BiLSTM)以及误差修正的组合预测模型。首先,采用VMD将原始功率数据分解为若干个相对平稳的子序列,重构样本熵值相似分量以降低复杂性;然后,引入Attention对BiLSTM的隐含层状态输出分配相应的权重以突出重要影响的输入特征,同时采用极限梯度提升(XGBoost)对误差进行修正,从而进一步提高预测精确度;最后,将初步预测值和修正预测值相加得到最终结果。采用风电场实际数据进行验证,结果表明,所提组合模型的平均绝对误差(MAE)下降至1.6565,与其他模型相比精度提升25.8%~56.5%,具有较好的预测效果。

物理与数据双驱动水风光短期功率联合预报研究

短期功率预报是保障可再生能源电站稳定运行的重要举措。针对多能互补系统功率预报难以考虑不同能源间时空相关性和预报误差互补特性的问题,提出了物理与数据双驱动水风光短期功率联合预报模型。采用WRF-新安江模型驱动气象水文要素预报,通过互信息方法识别功率预报的关键因子。基于长短期记忆网络构建功率联合预报模型,并将该方法应用于雅砻江流域官地水风光互补系统。结果表明:①相较于独立预报模型,物理与数据双驱动的短期联合预报模型总功率预报平均绝对误差和均方根误差分别降低了9.62%和8.31%;②由于预报误差存在互补特性,系统总功率预报误差小于各电站功率预报误差之和;③相对于单独预报模型,水风光预报误差互补率提高了10.96%。所提方法可为水风光系统协同运行提供技术支撑。

基于ICEEMDAN-DCN-Transformer的短期电力负荷预测

针对传统负荷预测方法易受复杂环境因素影响的问题,提出了基于ICEEMDAN-DCN-Transformer的短期电力负荷组合预测模型,该模型将电力负荷数据通过ICEEMDAN方法分解为若干个IMF和一个Res函数,考虑复杂环境因素的影响,将分解后各分量与环境特征并行输入到DCN-Transformer中进行预测,并将各组预测数据线性相加得到完整的预测结果。以泉州市电力负荷历史数据为基础进行实验,建立4种单一预测模型和3种组合预测模型作为对比模型,对该地10 d、240 h的电力负荷序列加以预测。结果表明,相较于传统算法,所提算法可以显著提高负荷预测的精度并有效降低误差评价指标值,为电力系统的安全运行和规划制定提供理论依据。

Short-Term Wind Power Prediction Method Based on Combination of Meteorological Features and CatBoost

As one of the hot topics in the field of new energy,short-term wind power prediction research should pay attention to the impact of meteorological characteristics on wind power while improving the prediction accuracy.Therefore,a short-term wind power prediction method based on the combination of meteorological features and Cat Boost is presented.Firstly,morgan-stone algebras and sure independence screening(MS-SIS)method is designed to filter the meteorological features,and the influence of the meteorological features on the wind power is explored.Then,a sort enhancement algorithm is designed to increase the accuracy and calculation efficiency of the method and reduce the prediction risk of a single element.Finally,a prediction method based on Cat Boost network is constructed to further realize short-term wind power prediction.The National Renewable Energy Laboratory(NREL)dataset is used for experimental analysis.The results show that the short-term wind power prediction method based on the combination of meteorological features and Cat Boost not only improve the prediction accuracy of short-term wind power,but also have higher calculation efficiency.

考虑误差修正的MC-BP短期电力负荷预测方法

准确的短期负荷预测是电网日常调度的重要依据。针对目前短期电力负荷预测精度问题,提出了一种考虑误差滚动修正的MC-BP短期电力负荷预测方法。首先,建立了基于逐步试错法的BP负荷预测模型,分析了预测误差的概率密度分布,构建了基于蒙特卡洛(Monte Carlo,MC)的日负荷误差滚动修正策略;其次,选用了某地区2015-2019年的负荷数据进行预测,比较了CNN-BiLSTM、LSTM和BP模型的预测结果,3种预测模型的测试集NRMSE分别为5.97%、6.49%和5.5%;最后,对比了BP和LSTM预测方法修正误差、线性回归方法修正误差和误差滚动修正方法的误差修正策略的修正效果,对后一天的误差修正NRMSE的相对变化率分别为-26.68%、-28.81%、-43.90%、-88.64%。预测结果表明:所提出的考虑误差滚动修正的MC-BP短期电力负荷预测方法具有良好的预测效果。

Modelling of wind power forecasting errors based on kernel recursive least-squares method

Forecasting error amending is a universal solution to improve short-term wind power forecasting accuracy no matter what specific forecasting algorithms are applied. The error correction model should be presented considering not only the nonlinear and non-stationary characteristics of forecasting errors but also the field application adaptability problems. The kernel recursive least-squares(KRLS) model is introduced to meet the requirements of online error correction. An iterative error modification approach is designed in this paper to yield the potential benefits of statistical models, including a set of error forecasting models. The teleconnection in forecasting errors from aggregated wind farms serves as the physical background to choose the hybrid regression variables. A case study based on field data is found to validate the properties of the proposed approach. The results show that our approach could effectively extend the modifying horizon of statistical models and has a better performance than the traditional linear method for amending short-term forecasts.

Wind power forecasting error-based dispatch method for wind farm cluster

With the technical development of wind power forecasting,making wind power generation schedule in power systems become an inevitable tendency.This paper proposes a new dispatch method for wind farm(WF)cluster by considering wind power forecasting errors.A probability distribution model of wind power forecasting errors and a mathematic expectation of the power shortage caused by forecasting errors are established.Then,the total mathematic expectation of power shortage from all WFs is minimized.Case study with respect to power dispatch in a WF cluster is conducted using forecasting and actual wind power data within 30 days from sites located at Gansu Province.Compared with the variable proportion method,the power shortage of the WF cluster caused by wind power forecasting errors is reduced.Along with the increment of wind power integrated into power systems,the method positively influences future wind power operation.

Mixed Aleatory-epistemic Uncertainty Modeling of Wind Power Forecast Errors in Operation Reliability Evaluation of Power Systems

As the share of wind power in power systems continues to increase, the limited predictability of wind power generation brings serious potential risks to power system reliability. Previous research works have generally described the uncertainty of wind power forecast errors(WPFEs) based on normal distribution or other standard distribution models, which only characterize the aleatory uncertainty. In fact, epistemic uncertainty in WPFE modeling due to limited data and knowledge should also be addressed. This paper proposes a multi-source information fusion method(MSIFM) to quantify WPFEs when considering both aleatory and epistemic uncertainties. An extended focal element(EFE) selection method based on the adequacy of historical data is developed to consider the characteristics of WPFEs. Two supplementary expert information sources are modeled to improve the accuracy in the case of insufficient historical data. An operation reliability evaluation technique is also developed considering the proposed WPFE model. Finally,a double-layer Monte Carlo simulation method is introduced to generate a time-series output of the wind power. The effectiveness and accuracy of the proposed MSIFM are demonstrated through simulation results.

考虑预测误差分析的混合储能补偿优化策略

为提高风电场预测功率精度,对于风电场和混合储能构成的风储混合系统,提出基于预测信息搭建的混合储能补偿方案。首先,依据风电预测误差进行概率统计分析,提出风电功率预测误差的分层补偿策略,制定针对补偿预测误差的允许误差域及置信区间补偿域。其次,利用储能对不同误差层的预测误差给出相应的储能补偿方案,基于小波包分解将超出区域外待补偿预测误差分解,通过不平衡功率DFT分频法确定临界点作为功率型储能和能量型储能的目标分配值,对于荷电状态越限问题通过自适应模糊控制进行二次修正。最后,以新疆某风电场为例进行仿真分析,结果表明所提策略可有效减少风电功率预测误差。

考虑风电功率预测误差分时补偿的电热联合系统多时间尺度调度

由于风电具有不确定性,风电功率预测误差难以避免。热力系统具备储放热能力,通过调整热功率平移风电功率,可以降低风电功率预测误差的影响。但在风电并入电热联合系统时,风电变化频繁,热力系统具有较大惯性和延迟,难以与风电功率同步;同时,热力系统不同区域对电力系统指令的响应速度也不同。因此,为了解决利用热力系统补偿风电功率预测误差时响应时间不同步的问题,提出了一种考虑风电功率预测误差分时补偿的电热联合系统多时间尺度调度策略。首先,分析概率区间误差评估和实时预测误差评估的评估周期。然后,研究热力系统中供热区域和管网的时间特性。最后,在日前阶段利用响应速度较慢的供热区域补偿评估周期较长的概率区间误差,在实时阶段利用响应速度较快的管网补偿评估周期较短的实时预测误差,建立了多时间尺度调度模型。算例分析表明,所提策略实现了不同调度周期、不同预测误差评估周期、不同热力系统区域响应速度在时间上的匹配,减少了风电功率预测误差的影响,提高了系统的风电消纳能力。

基于鲸鱼优化算法的新型电力系统中抽水蓄能机组容量最优规划策略

在新能源高占比的新型电力系统中,抽水蓄能可弥补新能源出力的波动性和随机性带来的不足.根据负荷预测数据,结合风电机组、光伏机组和火电机组的出力特性与抽水蓄能自身调峰特性,建立最大消纳新能源及减少碳排放的目标函数,通过鲸鱼优化算法模拟鲸鱼觅食的3个阶段来规划地区不同程度新能源渗透率下的抽水蓄能装机容量,以达到清洁能源消纳,节约成本的目的.