scholarly journals Identifying the nature of domestic load profile from a single household electricity consumption measurements

Author(s):  
A. Tbal ◽  
H. Rajamani ◽  
R.a. Abd-Alhameed ◽  
M Jalboub
IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 8394-8406 ◽  
Author(s):  
Imran Khan ◽  
Joshua Zhexue Huang ◽  
Md Abdul Masud ◽  
Qingshan Jiang

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 4038 ◽  
Author(s):  
Alejandro Pena-Bello ◽  
Edward Barbour ◽  
Marta C. Gonzalez ◽  
Selin Yilmaz ◽  
Martin K. Patel ◽  
...  

Energy storage is a key solution to supply renewable electricity on demand and in particular batteries are becoming attractive for consumers who install PV panels. In order to minimize their electricity bill and keep the grid stable, batteries can combine applications. The daily match between PV supply and the electricity load profile is often considered as a determinant for the attractiveness of residential PV-coupled battery systems, however, the previous literature has so far mainly focused on the annual energy balance. In this paper, we analyze the techno-economic impact of adding a battery system to a new PV system that would otherwise be installed on its own, for different residential electricity load profiles in Geneva (Switzerland) and Austin (U.S.) using lithium-ion batteries performing various consumer applications, namely PV self-consumption, demand load-shifting, avoidance of PV curtailment, and demand peak shaving, individually and jointly. We employ clustering of the household’s load profile (with 15-minute resolution) for households with low, medium, and high annual electricity consumption in the two locations using a 1:1:1 sizing ratio. Our results show that with this simple sizing rule-of-thumb, the shape of the load profile has a small impact on the net present value of batteries. Overall, our analysis suggests that the effect of the load profile is small and differs across locations, whereas the combination of applications significantly increases profitability while marginally decreasing the share of self-consumption. Moreover, without the combination of applications, batteries are far from being economically viable.


Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6130
Author(s):  
Minseok Jang ◽  
Hyun-Cheol Jeong ◽  
Taegon Kim ◽  
Sung-Kwan Joo

Smart meters and dynamic pricing are key factors in implementing a smart grid. Dynamic pricing is one of the demand-side management methods that can shift demand from on-peak to off-peak. Furthermore, dynamic pricing can help utilities reduce the investment cost of a power system by charging different prices at different times according to system load profile. On the other hand, a dynamic pricing strategy that can satisfy residential customers is required from the customer’s perspective. Residential load profiles can be used to comprehend residential customers’ preferences for electricity tariffs. In this study, in order to analyze the preference for time-of-use (TOU) rates of Korean residential customers through residential electricity consumption data, a representative load profile for each customer can be found by utilizing the hourly consumption of median. In the feature extraction stage, six features that can explain the customer’s daily usage patterns are extracted from the representative load profile. Korean residential load profiles are clustered into four groups using a Gaussian mixture model (GMM) with Bayesian information criterion (BIC), which helps find the optimal number of groups, in the clustering stage. Furthermore, a choice experiment (CE) is performed to identify Korean residential customers’ preferences for TOU with selected attributes. A mixed logit model with a Bayesian approach is used to estimate each group’s customer preference for attributes of a time-of-use (TOU) tariff. Finally, a TOU tariff for each group’s load profile is recommended using the estimated part-worth.


Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2112 ◽  
Author(s):  
Bingtuan Gao ◽  
Xiaofeng Liu ◽  
Zhenyu Zhu

The forecasting of the load profile of the domestic sector is an area of increased concern for the power grid as it appears in many applications, such as grid operations, demand side management, energy trading, and so forth. Accordingly, a bottom-up forecasting framework is presented in this paper based upon bottom level data about the electricity consumption of household appliances. In the proposed framework, a load profile for group households is obtained with a similar day extraction module, household behavior analysis module, and household behavior prediction module. Concretely, similar day extraction module is the core of the prediction and is employed to extract similar historical days by considering the external environmental and household internal influence factors on energy consumption. The household behavior analysis module is used to analyse and formulate the consumption behavior probability of appliances according to the statistical characteristics of appliances’ switch state in historical similar days. Based on the former two modules, household behavior prediction module is responsible for the load profile of group households. Finally, a case study based on the measured data in a practical residential community is performed to illustrate the feasibility and effectiveness of the proposed bottom-up household load forecasting approach.


Author(s):  
Nur Farahin Asa @ Esa ◽  
Md Pauzi Abdullah ◽  
Mohammad Yusri Hassan ◽  
Faridah Hussin

Time of Use tariff is introduced to motivate users to change their electricity usage pattern. Commonly the tariff is high during peak hours and relatively low during off peak hours, to encourage users to reduce consumption during peak hours or shift it to off-peak hours. This tariff scheme provides opportunities for building owners to reduce their electricity bill provided that their electricity usage patterns of various spaces in that building at every hour are known. In practice, the kWh meter installed by the utility can only provide the overall hourly electricity consumption pattern. To know the usage pattern of different spaces or rooms, separate individual meter need to be installed in each space/room, which is costly and impractical.  This paper presented the disaggregated electricity bill method based on user utilization factor and time of use (ToU) tariff. It estimates hourly electricity bill of each appliance at each space/room. Utilization factor is used to represent the electricity usage behavior of the occupants. The proposed method is applied on practical load profile data of a university building.


2019 ◽  
Vol 8 (4) ◽  
pp. 6542-6546

With the high demand in electricity consumption nowadays, it is crucial for regulator and utilities to ensure sufficient energy supply to meet electricity demand. Electricity demand is influenced by several factors such as number of customers, customer behavior, working hours, weather condition and holidays. Integrating renewable energy technology as part of electricity generation for self consumption has indirectly provide an option to customer to reduce his electricity consumption from the grid and help to save his electricity bill. One of the simplest solutions to install renewable energy sources is by installing rooftop solar photovoltaic (PV). In this paper, the economic feasibility of installing solar PV is discussed based on commercial customer load profile. This paper also presents the suitable PV sizing based on the payback analysis based on customer load profile. A commercial customer in Petaling Jaya, Selangor is used as a case study for this analysis. This study indicates that customer will be able to reduce their electricity bill consumption with the integration of solar PV system on the rooftop of their building. Customer is able to save their monthly electricity up to 28% if a total solar PV capacity of 1256kW is installed. The payback from this study indicates the payback period is approximately around 9 years


2019 ◽  
Vol 8 (1) ◽  
pp. 21-29
Author(s):  
M. F. Sulaima ◽  
N. Y. Dahlan ◽  
M. H. Isa ◽  
M. N. Othman ◽  
Z. M. Yasin ◽  
...  

This paper presents load shifting strategy for cost reduction on manufacturing electricity demand side, by which a real test load profile had been used to prove the concept. Superior bio-inspired algorithm, Ant Colony Optimization (ACO) had been implemented to optimize the upright load profile of load shifting strategy in the Malaysia Enhance Time of Use (ETOU) tariff condition. Subsequently, significant simulation results of operation profit gain through 24 hours electricity consumption had been analyzed properly. The proposed method had shown reduction of approximately 6% of the electricity cost at peak and mid peak zones, when 20%, 40%, 60%, 80% and 100% load shifting weightages were applied to the identified 10% controlled loads consequently. It is hoped that the finding of this study can help poise the manufacturers to switch to ETOU tariff as well as support the national Demand Side Management (DSM) program


2014 ◽  
Author(s):  
Ping Dong ◽  
Xun (Irene) Huang ◽  
Chen-Bo Zhong

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