Criteria of morphometric analysis of a daily load profile

Analysis of electrical loads is crucial for a proper operation and control of electrical energy sources and planning and design of electrical power systems in terms of optimum capacity of the electricity generation. A typical daily load profile significantly varies over the 24‐hour day and requires levelling actions which can be advised from the detailed analysis of the profile. This paper discusses the principles and implementation of morphometric analysis for a daily load profile evaluation using three criteria: roundness, compactness, and elongation. In order to conduct the morphometric analysis, the daily load profile represented as a time series has to be converted into a polygon of a particular form in a radar chart. The criteria for the profile analysis are based on geometrical interpretation of the shape of the polygon. The criterion roundness assesses the maximum and minimum loads of the profile and are related to the ratio of the inner circle of the polygon to the outer circle. The criterion compactness is based on the polygon perimeter and its inner area. The criterion elongation is defined as a relationship between the length of perpendicular to main axis of the polygon and the length of the main axis. The examples of the load profiles represented as a regular polygon and the case study have been used to demonstrate implementation of the analysis using the roundness, compactness, and elongation. It has been shown that the analysis using the morphometric criteria can be effectively applied for the detailed assessment of the load profiles

Reinforced Demand Side Management for Educational Institution with incorporation of User’s Comfort

Soaring energy demand and the establishment of various trends in the energy market have paved the way for developing demand-side management (DSM) from the consumer side. This paper proposes a reinforced DSM (RDSM) approach that uses an enhanced binary gray wolf optimization algorithm (EBGWO) that benefits the consumer premises with load scheduling, and peak demand reduction. To date, DSM research has been carried out for residential, commercial and industrial loads, whereas DSM approaches for educational loads have been less studied. The institution load also consumes much utility energy during peak hours, making institutional consumers pay a high amount of cost for energy consumption during peak hours. The proposed objective is to reduce the total electricity cost and to improve the operating efficiency of the entire load profile at an educational institution. The proposed architecture integrates the solar PV (SPV) generation that supplies the user-comfort loads during peak operating hours. User comfort is determined with a metric termed the user comfort index (UCI). The novelty of the proposed work is highlighted by modeling a separate class of loads for temperature-controlled air conditioners (AC), supplying the user comfort loads from SPV generation and determining user comfort with percentage UCI. The improved transfer function used in the proposed EBGWO algorithm performs faster in optimizing nonlinear objective problems. The electricity price in the peak hours is high compared to the offpeak hours. The proposed EBGWO algorithm shift and schedules the loads from the peak hours to off-peak hours, and incorporating SPV in satisfying the user comfort loads aids in reducing the power consumption from the utility during peak hours. Thus, the proposed EBGWO algorithm greatly helps the consumer side decrease the peak-to-average ratio (PAR), improve user comfort significantly, reduce the peak demand, and save the institution’s electricity cost by USD 653.046.publishedVersio

Two approaches for synthesizing scalable residential energy consumption data

© 2019 Elsevier B.V. Many fields require scalable and detailed energy consumption data for different study purposes. However, due to privacy issues, it is often difficult to obtain sufficiently large datasets. This paper proposes two different methods for synthesizing fine-grained energy consumption data for residential households, namely a regression-based method and a probability-based method. They each use a supervised machine learning method, which trains models with a relatively small real-world dataset and then generates large-scale time series based on the models. This paper describes the two methods in details, including data generation process, optimization techniques, and parallel data generation. This paper evaluates the performance of the two methods, which compare the resulting consumption profiles with real-world data, including patterns, statistics, and parallel data generation in the cluster. The results demonstrate the effectiveness of the proposed methods and their efficiency in generating large-scale datasets

Optimum operation of low voltage variable‐frequency drives to improve the performance of heating, ventilation, and air conditioning chiller system

A tremendous increase in the industrial and commercial applications of low voltage variable frequency drives (VFD) has been observed in the last decade. VFD are mainly used for energy saving and reliable speed control of industrial motors in an electrical system. However, power quality issues are becoming more apparent due to the installation of VFD. In this paper, a nonintrusive technique has been employed to determine the optimum operation of VFD system for improving the performance of heating, ventilation, and air conditioning centrifugal chiller system. The effect of load and speed ratios of VFD systems on total harmonic distortion (THD), power factor, distortion loss ratio, temperature, and efficiency has been investigated. The measurement of these parameters has been carried out by performing a series of experiments under variable operating conditions. The binomial relations have been developed between the various performance parameters and the operating conditions. Based on experimental results, optimum operating conditions have been proposed and implemented for the VFD system. Moreover, the results have been compared with the existing operating conditions and indicate a considerable improvement in THD, power factor, distortion loss ratio, efficiency, and the annual cost of energy consumed

Demand Dispatch Control for Balancing Load with Generation

There are different methods to implement demand management. In this thesis, a Demand Side Frequency Droop is proposed to calculate the require power reduction. Moreover, Demand Dispatch (DD) can provide ancillary service to the grid and maintains the power system frequency. Besides, to improve the operation of DD, the renewable resources and the storage devices are integrated to the DD. The proposed methods in this thesis have been validated through PSCAD software simulation and MATLAB