In spite of all advantages of solar energy, its deployment will significantly change the typical electric load profile, thus necessitating a change in traditional distribution grid management practices. In particular, the net load ramping, created as a result of simultaneous solar generation drop and load increase at early evening hours, is one of the major operational issues that needs to be c...

Two new computing models, namely a fuzzy expert system and a hybrid neural network-fuzzy expert system for time series forecasting of electric load, are presented in this paper. The fuzzy-logic-based expert system utilizes the historical relationship between load and dry-bulb temperature, and predicts electric loads fairly accurately, 1-24 h ahead. In the case of the hybrid neural network-fuzzy...

This paper presents a comparative analysis of electric vehicles (EV) charging devices impact on microgrid. Increased load variation may create a need for upgrades in grid infrastructure. For this purpose a set of typical load profiles of EV charging modes is studied and presented. A software implementation and a 24h case study of low voltage distribution network with EV charging devices is pres...

In this paper, we propose a neural network approach to forecast AM/PM Jordan electric power load curves based on several parameters (temperature, date and the status of the day). The proposed method has an advantage of dealing with not only the nonlinear part of load curve but also with rapid temperature change of forecasted day, weekend and special day features. The proposed neural network is ...

The short-term load forecasting is an important method for security dispatching and economical operation in electric power system, and its prediction accuracy directly affects the operating reliability of the electric system. So the global optimization ability of particle swarm optimization (PSO) algorithm and classification prediction ability of support vector machine (SVM) are combined in ord...

In recent years, Secondary Substations (SSs) are being provided with equipment that allows their full management. This is particularly useful not only for monitoring and planning purposes but also for detecting erroneous measurements, which could negatively affect the performance of the SS. On the other hand, load forecasting is extremely important since they help electricity companies to make ...

In this paper, the impact of charging a large number of electric vehicles (EV) on the power system voltage stability is investigated on an example of a real power transmission system. First, the maximum load factors for different states in a selected part of the power system are determined using the continuation power flow (CPF) calculations and PV curves. The approach provides information abou...

Application of support vector regression (SVR) with chaotic sequence and evolutionary algorithms not only could improve forecasting accuracy performance, but also could effectively avoid converging prematurely (i.e., trapping into a local optimum). However, the tendency of electric load sometimes reveals cyclic changes (such as hourly peak in a working day, weekly peak in a business week, and m...

Electric load forecasting plays an important role in electricity markets and power systems. Because electric load time series are complicated and nonlinear, it is very difficult to achieve a satisfactory forecasting accuracy. In this paper, a hybrid model, Wavelet Denoising-Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EWKM), which combines k-Nearest Neighbor (KNN) and Ex...