Long-term load forecasting and economical operation of wind farms for Egyptian electrical network

Several traditional methods have been presented for long-term load forecasting of electrical power systems without sufficient accuracy of the relevant results. In this paper, in order to improve the results accuracy, the artificial neural network (ANN) technique for long-term peak load forecasting is modified and proposed as an alternative technique in long-term load forecasting. The modified technique is applied on the Egyptian electrical network dependent on its historical data to predict the electrical peak load demand forecasting up to year 2017. This technique is compared with extrapolation of trend curves as a traditional method. Installed power generation capacities of Egyptian electrical network up to year 2017 are estimated dependent on the peak load forecasting of this network. Also, a proposed methodology to assess the economical operation of the wind farms (WFs) beside the conventional power system (CPS) is introduced. This methodology includes a mathematical model to develop the economical operation of wind farms on the whole power generation capacity through a considered period.     

Risk quantification for ANN based short‐term load forecasting

A new risk assessment method for short‐term load forecasting is proposed. The proposed method makes use of an artificial neural network (ANN) to forecast one‐step‐ahead daily maximum loads and evaluate uncertainty of load forecasting. With ANN as the model, the radial basis function (RBF) network is employed to forecast loads due to its good performance. Sufficient realistic pseudo‐scenarios are required to carry out quantitative risk analysis. The multivariate normal distribution with the correlation between input variables is used to give more realistic results to ANN. In addition, the method of moment matching is used to improve the accuracy of the multivariate normal distribution. The peak over threshold (POT) approach is used to evaluate risk that exceeds the upper bounds of generation capacity. The proposed method is successfully applied to real data of daily maximum load forecasting. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 166(2): 54– 62, 2009; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20464     

The application of artificial neural networks to substation load forecasting

The substation loading is highly correlated with the customers served. The substations in a distribution system can be categorized as residential, commercial and industrial. Each type has a different power consumption pattern. The substation loading will be varied according to the combination of the above three types of customers. In this paper, a supervisory functional artificial neural network (ANN) technique is applied to solve the load forecasting of three Taipower substations which serve the different customer types. The load forecasting accuracy is enhanced by considering the temperature effect on the substation load demand. With the converged ANN models derived by a training procedure, the temperature sensitivity of the substation load demand is easily obtained by the recall process. It is suggested that the substation load forecasting can be performed efficiently by the proposed method to support distribution operation effectively.     

Long-Term Load Forecasting Using Grey System Theory

Supply and demand in power system planning and operation is required to be balanced. An operational reserve for protection against faults or accidental demands also is required. Therefore load forecasting is one of the most important fields and various load forecasting methods have been applied. In this paper the grey system theory which mats uncertain information is applied to the long-term load forecasting from three aspects: the point prediction; the interval prediction; and the topological forecasting. In the point prediction, the annual total demand is predicted, in the interval prediction, the annual peak demand is predicted, and in the topological forecasting, the date where a yearly maximum peak demand would occur is predicted. The grey dynamic model (abbreviated as GM model) is adopted as the predicted model. The GM model is a differential equation model which is different from most forecasting models. The GM model is quite powerful when combined with the preliminary transformation called the accumulated generating operation (AGO). This paper proposes a new method for the long-term load-forecasting problems involving uncertainty. The predicted results have been found to be very satisfactory. The grey system theory is a new tool which is very efficient for load forecasting.     

计及需求响应的主动配电网短期负荷预测

随着分布式电源、电动汽车及储能等广义需求响应资源的接入,用户在电力市场各种激励影响下进行需求响应,将改变负荷特性并影响负荷预测。根据需求响应计划信号的可预知性及季节性基础负荷的独立性,利用小波分解等方法对主动配电网负荷在不同层面上进行了分解,形成季节性基础负荷和需求响应信号及各种气象因素作用的负荷部分,利用时间序列模型对季节性基础负荷进行预测,利用支持向量回归模型对需求响应信号及气象因素影响的负荷部分进行预测,形成组合预测模型,两部分预测负荷叠加得到总负荷。利用线性时变模型仿真的主动配电网负荷数据算例,进行了预测测试与分析,通过与其他方法相比较,证明了所提方法预测计及需求响应的主动配电网负荷的有效性及精确度。     

神经网络在电力系统短期负荷预测中的应用综述

神经网络是目前研究最多的短期负荷预测方法。详细综述了BP网络、RBF网络以及小波神经网络在电力负荷预测领域的研究和应用现状。简单介绍了神经网络中常用的BP算法以及改进的BP算法。综述了神经网络在组合预测中的应用，并指出目前神经网络还存在的一些问题。     

A proposed methodology for medium-range maximum demand anticipation and application

One to three years' anticipation of monthly and weekly peak demand is required to prepare maintenance schedules, develop power pooling agreements, select peaking capacity and provide data required by certain reliability coordinating centers. A total monthly forecast of the maximum demand is deduced and computed for the three years up to April 1981. This is accomplished for an important electrical network in Egypt. The anticipated maximum demand is executed for El-Mehalla El-Kubra city network. This network has an industrial and residential daily load characteristic. Direct monthly maximum demand forecasting is executed by separate treatment of weather-independent and weather-induced demand. The required forecast is derived by two methodologies: the probabilistic extrapolation-correlation, and that suggested by the authors. Daily and monthly data have been collected for more reliable determination of weather load models. Complete analysis, discussion and comments on the results are presented, and the results compared. This comparison reveals that an acceptable and reasonable percentage error is obtained on applying the proposed methodology.     

区域电力负荷中长期预测复杂性研究

为了解决我国各个地区电力需求区域性、结构性波动的问题,保持国民经济和区域经济的持续健康与又快又好发展,创新性地将区域电力负荷中长期预测作为一个系统研究,并从四个方面对其复杂性进行分析.首先描述区域电力需求的系统构成,然后分析区域中长期电力负荷预测的影响因素和复杂性特征,最后提出区域电力负荷中长期预测复杂性测度模型.结论...     

A DSM Approach for Distribution Systems with High Wind Power Penetration

Abstract

The basic objectives of demand side management (DSM) are shifting load from peak hours to off-peak hours and reducing consumption during peak hours. The DSM operation is cleared when deregulated electricity market is considered where the retailer purchases electricity from the electricity market to cover the end users requirements of energy. In this paper, DSM techniques (load shifting and peak clipping) are used to maximize the profit for retailer company by reducing total demand at peak hours and achieve an optimal daily load schedule using linear programing (LP) and genetic algorithm (GA). These techniques are implemented on the 33-bus radial network included wind generation penetration. A short term artificial neural network technique (ANN) is used to get forecasted wind speed and forecasted users load for date 25-March-2018. The ANN uses an actual hourly load data and an actual hourly wind speed data. Then the forecasted data is used in the optimization to get optimal daily load schedule to maximize the profit for retailer company. Finally, comparison is carried out between profit using LP and GA. The optimized DSM succeeded to increase the profits of the company by around 4.5 times its old profit using LP and around 2.5 times using GA.     

基于扩展索洛长期均衡模型的饱和电力需求预测

饱和电力需求是电网规划中确定电网健康发展最终规模的关键性指标,能为电网规划提供重要信息,指导电网的建设和输电线路的合理布局。鉴于索洛模型在经济学的规模报酬及劳动产出问题中的成功应用,借鉴索洛模型的思路研究饱和电力需求规模问题,通过引入以产业结构及用电结构为表征的结构效应因素,同时结合多种规模效应及技术进步因素对索洛模型进行扩展,在此基础上建立了长期均衡预测模型。以我国南方某大型都市为例,应用本文所提出的预测模型,对该地区的未来饱和用电规模进行预测,结果表明该模型能够结合社会经济、电网现状及未来政策,对未来饱和电力需求进行预测,是一项有益的探索。     

基于模糊粗糙集和神经网络的短期负荷预测方法

针对采用神经网络进行电力系统短期负荷预测时其网络输入变量的选择是影响预测效果的关键问题，该文提出使用模糊粗糙集理论解决这一问题：对采集到的信息进行特征提取、形成决策表；利用模糊粗糙集理论进行属性约简、去除冗余信息；用得到的属性作为BP网络的输入进行训练预测。该方法既全面考虑了影响负荷预测的历史时间序列、气象等各种因素，为合理地选择神经网络的输入变量提供了一种新的方法，又避免了由于输入变量过多而导致神经网络拓扑结构复杂、训练时间长等不足。计算实例表明，文中提出的方法是有效且可行的。     

人工鱼群神经网络在电力系统短期负荷预测中的应用

短期负荷预测结果对电力系统的经济效益具有重要影响.人工鱼群算法是最新提出的新型寻优策略,具有良好的克服局部极值、获得全局极值的能力.文章建立了一种新的人工鱼群神经网络预测模型,利用人工鱼群算法训练神经网络的权值,再将该神经网络用于短期负荷预测.对某电力系统进行的负荷预测结果表明,该方法与传统的BP神经网络预测方法相比具有较强的自适应能力和较好的预测效果.     

Electric load forecasting using an artificial neural network

An artificial neural network (ANN) approach is presented for electric load forecasting. The ANN is used to learn the relationship among past, current and future temperatures and loads. In order to provide the forecasted load, the ANN interpolates among the load and temperature data in a training data set. The average absolute errors of the 1 h and 24 h-ahead forecasts in tests on actual utility data are shown to be 1.40% and 2.06%, respectively. This compares with an average error of 4.22% for 24 h ahead forecasts with a currently used forecasting technique applied to the same data     

Long-term load forecasting using improved recurrent neural network

In general, electric power companies must prepare power supply capability for maximum electric load demand because it is very difficult at present to store electric power. It takes several years and requires a great amount of money to construct power generation and transmission facilities. Therefore, it is necessary to forecast long-term load demand exactly in order to plan or operate power systems efficiently. Several methods have been investigated so far for the long-term load forecasting. However, because the electric loads consist of many complex factors, good forecasting has been very difficult. This paper proposes a long-term load forecasting method using a recurrent neural network (RNN). This is a mutually connected network that has the ability of learning patterns and past records. In general, when interpolation is used for unlearned data sets, the neural network provides reasonably good outputs. However, when extrapolation is used, such as in long-term load forecasting, some kind of tunings have been necessary to obtain good results. Therefore, to solve the problem, a method is proposed in which growth rates are used as input and output data. Using the proposed method, successful results have been obtained and comparisons have been made with the conventional methods.     

基于灰色神经网络优化组合的风力发电量预测研究

文中提出一种新型灰色神经网络优化组合的风力发电量预测研究,将人工神经网络预测模型和灰色预测模型有效结合,不仅考虑了风力、风向和温度等影响因素,而且将往年风力发电量的历史数据综合考虑,结合两种预测优点,从而提高了预测的准确度并降低预测误差。算例结果证明,这种新型的灰色神经网络优化组合预测值误差低于单一的灰色预测或神经网络预测。     

Short-term load forecasting using an artificial neural network

An artificial neural network (ANN) method is applied to forecast the short-term load for a large power system. The load has two distinct patterns: weekday and weekend-day patterns. The weekend-day pattern includes Saturday, Sunday, and Monday loads. A nonlinear load model is proposed and several structures of an ANN for short-term load forecasting were tested. Inputs to the ANN are past loads and the output of the ANN is the load forecast for a given day. The network with one or two hidden layers was tested with various combinations of neurons, and results are compared in terms of forecasting error. The neural network, when grouped into different load patterns, gives a good load forecast     

增量配电背景下考虑不确定性修正的规划负荷预测方法

针对新一轮电改推进背景下的城市及园区级增量配电网市场,在分析该背景下电网规划中负荷预测问题与需求的基础上,对面向增量配网规划的负荷预测结论描述方式进行了改进,并进一步提出了考虑分布式能源、入住率等不确定性因素的负荷预测模型,根据预测需求设定不同的置信区间,对不同发展状态下的增量配电园区进行负荷预测修正。实际算例表明,该方法可为园区增量配电网规划提供考虑不确定性因素的量化思路。     

Neural network based short term load forecasting

The artificial neural network (ANN) technique for short-term load forecasting (STLF) has been proposed previously. In order to evaluate ANNs as a viable technique for STLF, one has to evaluate the performance of ANN methodology for practical considerations of STLF problems. The authors make an attempt to address these issues. The results of a study to investigate whether the ANN model is system dependent, and/or case dependent, are presented. Data from two utilities are used in modeling and forecasting. In addition, the effectiveness of a next 24 h ANN model in predicting 24 h load profile at one time was compared with the traditional next 1 h ANN model     

Microgrid dynamic responses enhancement using artificial neural network‐genetic algorithm for photovoltaic system and fuzzy controller for high wind speeds

The microgrid (MG) is described as an electrical network of small modular distributed generation, energy storage devices and controllable loads. In order to maximize the output of solar arrays, maximum power point tracking (MPPT) technique is used by artificial neural network (ANN), and also, control of turbine output power in high wind speeds is proposed using pitch angle control technic by fuzzy logic. To track the maximum power point (MPP) in the photovoltaic (PV), the proposed ANN is trained by the genetic algorithm (GA). In other word, the data are optimized by GA, and then these optimum values are used in ANN. The simulation results show that the ANN‐GA in comparison with the conventional algorithms with high accuracy can track the peak power point under different insolation conditions and meet the load demand with less fluctuation around the MPP; also it can increase convergence speed to achieve MPP. Moreover, pitch angle controller based on fuzzy logic with wind speed and active power as inputs that have faster responses which leads to have flatter power curves enhances the dynamic responses of wind turbine. The models are developed and applied in Matlab/Simulink. Copyright © 2015 John Wiley & Sons, Ltd.     

考虑综合能源站柔性调控作用的城市配电网多阶段规划方法

随着城市经济发展与终端用能电气化的普及,以制冷用电为首的电负荷需求持续增加且峰谷差情况愈发严重化,导致现有配电网运行负担逐渐加重。在此背景下,将综合能源站(IES)作为多能流耦合节点,在配电网中引入多能协调互补及蓄能移峰功能,计及多个阶段负荷发展及多类型负荷特征,提出在IES柔性调控作用下的配电网多阶段规划方法。基于IES能流结构及其运行机制,分别在不同气候类型地区负荷模拟场景下,以总规划期内投资与运行经济性最优为目标,建立由线路、变电站和IES构成决策对象的配电网双层多阶段规划模型,规划与运行层模型通过决策变量关联达成一体化协同求解。通过修改后的IEEE 33节点配电系统对所提方法的有效性进行校验,并进一步将所提方法应用于某地实际152节点配电系统,分析不同负荷特征下配电网规划运行效益、负荷峰谷差调节效果及IES运行状况,得出计及IES的城市配电网规划方法的实用性结论。