Characteristics Of Load Curves Research Articles

An effective quantum artificial rabbits optimizer for energy management in microgrid considering demand response

Solving the energy management (EM) problem in microgrids with the incorporation of demand response programs helps in achieving technical and economic advantages and enhancing the load curve characteristics. The EM problem, with its large number of constraints, is considered as a nonlinear optimization problem. Artificial rabbits optimization has an exceptional performance, however there is no single algorithm can solve all engineering problem. So, this paper proposes a modified version of artificial rabbits optimization algorithm, called QARO, by quantum mechanics based on Monte Carlo method to determine the optimal scheduling for MG resources effectively. The main objective is minimization of the daily operating cost with the maximization of MG operator (MGO) benefit. The operating cost includes the conventional diesel generator operating cost and the cost of power transactions with the grid. The performance of the proposed algorithm is assessed using different standard benchmark test functions. A ranking order for the test function based on the average value and Tied rank technique, Wilcoxon's rank test based on median value, and Anova Kruskal–Wallis test showed that QARO achieved best results on the most functions and outperforms all other compared technique. The obtained results of the proposed QARO are compared with those obtained by employing well-known and newly-developed algorithms. Moreover, the proposed QARO is used to solve two case studies of day-ahead EM problem in MG, then the obtained results are also compared with other well-known optimization techniques, the results demonstrate the effectiveness of QARO in reducing the operating cost and maximization the MGO benefit.

CEEMDAN-CatBoost-SATCN-based short-term load forecasting model considering time series decomposition and feature selection

The rapidly increasing randomness and volatility of electrical power loads urge computationally efficient and accurate short-term load forecasting methods for ensuring the operational efficiency and reliability of the power system. Focusing on the non-stationary and non-linear characteristics of load curves that could easily compromise the forecasting accuracy, this paper proposes a complete ensemble empirical mode decomposition with adaptive noise–CatBoost–self-attention mechanism-integrated temporal convolutional network (CEEMDAN-CatBoost-SATCN)-based short-term load forecasting method, integrating time series decomposition and feature selection. CEEMDAN decomposes the original load into some periodically fluctuating components with different frequencies. With their fluctuation patterns being evaluated with permutation entropy, these components with close fluctuation patterns are further merged to improve computational efficiency. Thereafter, a CatBoost-based recursive feature elimination algorithm is applied to obtain the optimal feature subsets to the merged components based on feature importance, which can effectively reduce the dimension of input variables. On this basis, SATCN which consists of a convolutional neural network and self-attention mechanism is proposed. The case study shows that time series decomposition and feature selection have a positive effect on improving forecasting accuracy. Compared with other forecasting methods and evaluated with a mean absolute percentage error and root mean square error, the proposed method outperforms in forecasting accuracy.

Defect Data Mining of Power Consumption Law Based on Improved K-Means Algorithm Clustering

With the further construction and development of the smart grid, in the process of power development, production, and use, as well as the process of power distribution and use, each link will produce some high-dimensional data on the power grid with huge volume, complex structure, and complex correlation among them. The distribution of high-dimensional data in space is different from that in low-dimensional space, and the computational cost increases dramatically, which increases the complexity of visualization of high-dimensional power consumption data. Clustering analysis is a way to cluster a large number of users and summarize the typical load characteristics of different types of users. How to determine the prior information conditions of data and how to select the clustering criteria become the key to clustering. Aiming at the problems of traditional clustering algorithms in the current feature clustering analysis, this paper first deals with the load through t-SNE dimensional reduction technology, then combines the GSA elbow criterion and dichotomous K-means algorithm to cluster the load, and finally summarizes three typical load features according to the clustering results. Effective data mining technology is used to cluster and divide the massive load characteristics efficiently, which will dynamically respond to and manage the demand side. The error of classification results is less than 4.28% through the example of load characteristics. The classification accuracy of the test is 12.2% higher than that of the traditional method. According to the experimental results, the characteristics of typical load patterns and the corresponding load curve characteristics are analyzed. It overcomes the dependence of the traditional K-means algorithm on the initial centroid, avoids the algorithm falling into local optimum, and plays an important role in the defect data mining of power consumption law in power enterprises.

Method for Clustering Daily Load Curve Based on SVD-KICIC

Clustering electric load curves is an important part of the load data mining process. In this paper, we propose a clustering algorithm by combining singular value decomposition and KICIC clustering algorithm (SVD-KICIC) for analyzing the characteristics of daily load curves to mitigate some of the traditional clustering algorithm problems, such as only considering intra-class distance and low computational efficiency when dealing with massive load data. Our method identifies effective daily load curve characteristics using the singular value decomposition technique to improve dimensionality reduction, which improves low computational efficiency by reducing the number of dimensions inherent in big data. Additionally, the method performs SVD on the load data to obtain singular values for determination of weight of the KICIC algorithm, which leverages intra-class and inter-class distances of the load data and further improves the computational efficiency of the algorithm. Finally, we perform a series of simulations of actual load curves from a certain city to validate that the algorithm proposed in this paper has a short operation time, high clustering quality, and solid robustness that improves the clustering performance of the load curves.

Wireless Power Transfer for Electric Vehicles Application using the Principle of ICPT System

Wireless Power Transfer is method of transferring the power from the power source to the electrical load without the physical contact, by using the principle of magnetic resonance. Due to the increase in the cost of the petroleum product and environmental issues led to development of the Electrical Vehicles (EV’s) globally for transportation. The charging of electric vehicles using wire causes losses therefore, it decrease in efficiency of the system. The preliminary review of the WPT analysis tells that the Inductively Coupled Power Transfer system ICPT is one of the promising and convenient methods for charging the EV’s. The paper is about designing the circuit using the MATLAB Simulink Software .The results for different values of load versus Efficiency of the system of ideal case is been plotted were the maximum efficiency is around 98% assuming the alignment errors as zero. The system parameters and performance equations are coded using MATLAB for Model based design for charging the EV application. Also, efficiency and load curve characteristics are demonstrated. The paper demonstrates the experimental setup of the same for the distance of about 15 cm. Each part of the setup and the components used along with their features has been discussed briefly. The same setup for higher power rating can be implemented in future with design modifications.

Experiment and simulation for the crushing of tailor rolled tubes with various geometric parameters

By properly controlling the parameters of the rolling mill, multiple types of tailor rolled blanks (TRBs) are obtained, which in turn are used to form the tailor rolled tubes (TRTs) with axially varied thickness. Axial-crushing tests have been performed, and the results show that the reaction forces of TRTs show a rising trend with the integrative effect of crushing and repetitive load fluctuations after reaching the relatively low initial loads. Furthermore, investigations of different thickness and lengths are introduced into the TRT structures. It can be concluded that more energy can be absorbed for TRTs when choosing more edge numbers of the polygonal cross-sections. By contrast, the biggest difference in energy absorption is between the triangle and circular section shape, which is about 43.4%. In addition, the energy absorption capacities of TRTs with longer tube or side lengths are improved, but the energy absorption efficiencies are decreased by 21.76% and 8.7%, respectively. Meanwhile, the various distributions of transition zone and layout mode not only alter the characteristics of load curves but also affect the energy absorption capacity and initial resistance. Furthermore, multi-tube structures including TRTs, tailor welded tubes (TWTs) and traditional uniform tubes (UTs) are introduced to carry out the contrast crushing experiments. The initial peak loads of TRTs are the lowest, which are in the range of 43.05–58.28 kN. For the energy absorption, the UT structures can absorb more energy before the moving head reaches about 2/3 of the overall collapse stroke. However, the ability of TRTs to absorb energy is improved significantly in the later stage, making the overall energy absorption greater than in UTs. The reasons for the improved energy absorption efficiency of TRTs are discussed in detail. The corresponding FE models considering the variation of thickness and material properties of TRTs are established and verified with the experiment results. In order to conveniently predict the energy absorption performance of crushing TRTs, mathematical models are adopted.

Determining Optimal Buses for Implementing Demand Response as an Effective Congestion Management Method

Demand response (DR) can be counted as an effective congestion management method in power systems. However, appointing the optimal buses for the demand response programs (DRPs) implementation is one of the main challenges for the power system operators. In this paper, a new procedure has been developed by which the optimal locations and times of DRPs implementation are determined. Optimal buses are identified based on the power transfer distribution factors (PTDFs), available transfer capability, and dynamic dc optimal power flow problem. Applying the developed method results in reducing lines' congestion, increasing customers, and independent system operator's benefits, improving load curve's characteristics, preventing line outages and black outs, and consequently increasing the network reliability. The proposed method has been applied on the IEEE 39-bus New England test system. Results indicate the effectiveness and practical benefits of the proposed method.

Filter‐Based Short‐Term Electric Load Prediction Considering Characteristics of Load Curve

SUMMARYThis paper deals with filter‐based short‐term electric load prediction taking into consideration the characteristics of the load curve. We propose a predictive method to forecast the future electric load demand for 36 h from 12:00 PM, and evaluate the peak and bottom of the load curves on the next day. We propose a load model, estimate the unknown parameters of the model by means of an filter using the data separated for nonworking days and weekdays, with the same pattern of the previous data chosen and assigned to the model parameters. The simulation results show the effectiveness of the proposed prediction methodology.

Influence of residents on energy use in 57 Swedish houses measured during four winter days

The aim of this paper is to contribute to the knowledge about energy use in residential buildings. Field measurements were carried out in 57 electrically heated Swedish single-family houses, where the total energy use (i.e. electricity use for space heating, household electricity and electricity use for hot water production), indoor temperatures and water use were recorded during four winter days. Some of the findings were that differences for households’ total energy use of similar houses were large. The characteristics of load curves clearly differ between weekdays and weekend days. The water use differs greatly for the households. This paper is part of a larger project where time use diaries were used in order to further link the energy use to residents’ every day energy related behaviours. To get a better understanding of the energy use in our homes is a prerequisite to be able to both recognise the problems as well as the potentials of reaching energy goals of our society, leading to a decreased impact on the environment from the building sector.