考虑需求响应和有功损耗的含可再生能源配网节点边际电价

文章提出了一种考虑需求响应和有功损耗的节点边际电价模型;考虑有功损耗的确定性和节点边际电价模型,提出了网损灵敏度因数。针对可再生能源接入配网对节点边际的影响进行了分析和建模,并建立了需求响应模型;建立了以有功损耗最小为目标的节点边际电价模型,并计及约束条件;针对概率场景,采用2m点估计法对模型进行求解。最后采用IEEE33节点模型对算例进行了仿真分析,验证了所建模型的有效性。     

基于中值步长法的光伏阵列最大功率点跟踪

提出了光伏阵列最大功率点跟踪的新型变步长扰动观察法,即中值步长法。在外界环境发生变化时,利用斜率判断扰动方向,利用启动步长快速跟踪光伏阵列最大功率点的大概位置;然后每扰动一次就将扰动步长取中值,使系统在稳态时最大限度地逼近最大功率点,减小最大功率点附近的震荡,提高跟踪精度;利用功率判断避免在扰动过程中出现算法错误。通过Matlab进行仿真,证明了中值步长法能达到理想的跟踪效果。     

Investigation on multi-objective performance optimization algorithm application of fan based on response surface method and entropy method

A multi-objective performance optimization method is proposed,and the problem that single structural parameters of small fan balance the optimization between the static characteristics and the aerodynamic noise is solved.In this method,three structural parameters are selected as the optimization variables.Besides,the static pressure efficiency and the aerodynamic noise of the fan are regarded as the multi-objective performance.Furthermore,the response surface method and the entropy method are used to establish the optimization function between the optimization variables and the multi-objective performances.Finally,the optimized model is found when the optimization function reaches its maximum value.Experimental data shows that the optimized model not only enhances the static characteristics of the fan but also obviously reduces the noise.The results of the study will provide some reference for the optimization of multi-objective performance of other types of rotating machinery.     

跨区域全时空基于深度学习的分布式潮流态势预估方法

基于人工智能中的深度学习方法,文章提出了分布式经济调度及其潮流态势预估方法。在传统集中式经济调度方法的基础上,给出了分布式经济调度模型及其求解方法;在该模型中,建立了负荷分布关联的特性及其模型;使用深度学习方法对负荷分布关联模型及分布式经济调度潮流进行学习训练,预估未来电网潮流运行态势。以实际电网对所提方法进行验证,结果显示具有较高的准确度。     

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

The new generation of artificial intelligence (AI), called AI 2.0, has recently become a research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and electric power system (Smart EEPS). In AI 2.0, machine learning (ML) forms a typical representative algorithm category used to achieve predictions and judgments by analyzing and learning from massive amounts of historical and synthetic data to help people make optimal decisions. ML has preliminarily been applied to the Smart Grid (SG) and Energy Internet (EI) fields, which are important Smart EEPS representatives. AI 2.0, especially ML, is undergoing a critical period of rapid development worldwide and will play an essential role in Smart EEPS. In this context, this study, combined with the emerging SG and EI technologies, takes the typical representative of AI 2.0—ML—as the research objective and reviews its research status in the operation, optimization, control, dispatching, and management of SG and EI. The paper focuses on introducing and summarizing the mainstream uses of seven representative ML methods, including reinforcement learning, deep learning, transfer learning, parallel learning, hybrid learning, adversarial learning, and ensemble learning, in the SG and EI fields. In this survey, we begin with an introduction to these seven types of ML methods and then systematically review their applications in Smart EEPS. Finally, we discuss ML development under the big data thinking and offer a prospect for the future development of AI 2.0 and ML in Smart EEPS. We conduct this survey intended to arouse the interest and excitement of experts and scholars in the EEPS industry and to look ahead to efforts that jointly promote the rapid development of AI 2.0 in the Smart EEPS field.     

A stochastic method for battery sizing with uninterruptible-power and demand shift capabilities in PV (photovoltaic) systems

This paper presents a stochastic simulation using Monte Carlo technique to size a battery to meet dual objectives of demand shift at peak electricity cost times and outage protection in BIPV (building integrated photovoltaic) systems. Both functions require battery storage and the sizing of battery using numerical optimization is popularly used. However, the weather conditions, outage events and demand peaks are not deterministic in nature. Therefore, the sizing of battery storage capacity should also be based on a probabilistic approach. The Monte Carlo simulation is a rigorous method to sizing BIPV system as it takes into account a real building load profiles, the weather information and the local historical outage distribution. The simulation is split into seasonal basis for the analysis of demand shifting and outage events in order to match the seasonal weather conditions and load profiles. Five configurations of PV (photovoltaic) are assessed that cover different areas and orientations. The simulation output includes the predicted PV energy yield, the amount of energy required for demand management and outage event. Therefore, consumers can base sizing decisions on the historical data and local risk of outage statistics and the success rate of meeting the demand shift required. Finally, the economic evaluations together with the sensitivity analysis and the assessment of customers’ outage cost are discussed.     

A novel forecasting method based on the economic and demand response for FC/WT/PV unit and a 3 in 1 TES energy storage

Due to the lack of distribution resources and increasing demand in the daily market, the use of renewable resources is increasing. But renewable sources and market prices are uncertain behavior and cause economic problems. This paper introduces a novel market participation model include wind turbine, photovoltaic, fuel cell integrated with a novel hybrid TES energy storage system (3 in 1 concept) to minimize cost and improve load demand reliability. Also, to solve he mentioned problem a novel forecasting method are proposed. This model is a new multi artificial neural network based on the complete ensemble empirical mode decomposition which is coupled with Tanh function and using RMSE, MAPE and NMAE method the error rate of the proposed method is calculated. By using this method, the forecasting accuracy is improved and also with a novel energy storage the economic issue and market reliability are improved. Also, using the stochastic model the uncertainty system's behavior are modeled to obtain an accurate results of market participation and increase demand supply. Finally, a testing system includes wind turbine/photovoltaic/fuel cell/storage system and demand response are used to prove the superiority of the proposed model in comparison to other models.     

A robust decision making approach for hydrogen power plant site selection utilizing (R,S)-Norm Pythagorean Fuzzy information measures based on VIKOR and TOPSIS method

In recent years, there is a rapid development in the direction of hydrogen energy industry having primary involvement in automobile transportation fuel. Hydrogen is the most abundant element and serves as a perfect energy carrier. In general, choosing an appropriate hydrogen power plant site is a complex selection multi-criteria decision making (MCDM) problem which involves proper assessment of a location based on various essential criteria, decision maker's expert opinion and other qualitative/quantitative factors. In the present communication, we first incorporate (R,S)-Norm Pythagorean fuzzy entropy and respective discriminant measure in the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) MCDM techniques and proposed the modified MCDM algorithms in two different stages. Further, the hydrogen power plant site selection problem has been dealt with proper matching of the laid down essential criteria under a wider sense of Pythagorean fuzzy information measures. Such measures have not been utilized in the study of site selection problems of the hydrogen energy resources. In view of the existing literature on the MCDM problems, comparative remarks including the sensitivity analysis and the advantages have been presented for providing the novelty of the proposed methodologies. The presented work proves to be an effective tool for handling the various similar types of selection problems with a desired consistency.     

A maximum power tracking algorithm based on Impp = f(Pmax) function for matching passive and active loads to a photovoltaic generator

In this paper, a method that forces a photovoltaic generator (PVG) to operate at its maximum power point under variable load and insolation conditions is developed. The method is based on closed loop current control, in which the reference current is determined from the fitted function of I_mpp versus P_max, points of a particular PVG. A simplified computer model of the PVG is given and computer simulations for demonstrating the effectiveness of the proposed algorithm are presented. The method has also been applied using a PC with IO interface card in the laboratory. From the results of the simulations and experimental studies, it is concluded that the proposed approach can be used as a robust and fast acting maximum power point tracker.