Households’ hourly electricity consumption and peak demand in Denmark

Andersen, Frits Møller; Baldini, Mattia; Hansen, Lars Gårn; Jensen, Carsten Lynge

doi:10.1016/j.apenergy.2017.09.094

Cited by 63 publications

(28 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Peak electricity demand is a global policy concern which creates transmission constraints and congestion, and raises the cost of electricity for all end-users [5]. In addition, a considerable investment is required to upgrade electricity distribution and transmission infrastructure, and build generation plants to provide power during peak-demand periods [6].…”

mentioning

confidence: 99%

Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV

et al. 2018

View full text Add to dashboard Cite

Cooling in the industry sector contributes significantly to the peak demand placed on an electrical utility grid. New electricity tariff structures include high charges for electricity consumption in peak hours which leads to elevated annual electricity costs for high-demanding consumers. Demand side management (DSM) is a promising solution to increase the energy efficiency among customers by reducing their electricity peak demand and consumption. In recent years, researchers have shown an increased interest in utilizing DSM techniques with thermal energy storage (TES) and solar PV technologies for peak demand reduction in industrial and commercial sectors. The main objective of the present study is to address the potential for applying optimization-based time-of-use DSM in the industry sector by using cold thermal energy storage and off-grid solar PV to decrease and shift peak electricity demands and to reduce the annual electricity consumption costs. The results show that when cold thermal energy storage and solar PV are coupled together higher annual electricity cost savings can be achieved compared to using these two technologies independently. Additionally, considerable reductions can be seen in electricity power demands in different tariff periods by coupling thermal energy storage with off-grid solar PV.

show abstract

mentioning

confidence: 99%

Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The load profiles outputs are presented as high-resolution data. Existing energy studies were generating 60-min output data [6,21,22,[24][25][26][27][28][29] and one-minute resolution data [14,[16][17][18][19][20]30]. Some works [14,15] have provided a more detailed output in one-minute resolution at once generated 60-min report data.…”

Section: Load Profile Modelling Methodsmentioning

confidence: 99%

“…A range of statistical analysis methods have also been applied in order to model residential electricity load profiles [6,28,[37][38][39], including determination of the key drivers of residential peak electricity demand. Some studies provided panel datasets including data from smart-meters [24,26,40]. A model was developed using Australian data for the greater Sydney region to analyse and model residential peak demand by providing both daily and seasonal patterns [37].…”

Section: Load Profile Modelling Methodsmentioning

confidence: 99%

Synthesising Residential Electricity Load Profiles at the City Level Using a Weighted Proportion (Wepro) Model

2020

View full text Add to dashboard Cite

It is important to understand residential energy use as it is a large energy consumption sector and the potential for change is of great importance for global energy sustainability. A large energy-saving potential and emission reduction potential can be achieved, among others, by understanding energy consumption patterns in more detail. However, existing studies show that it requires many input parameters or disaggregated individual end-uses input data to generate the load profiles. Therefore, we have developed a simplified approach, called weighted proportion (Wepro) model, to synthesise the residential electricity load profile by proportionally matching the city’s main characteristics: Age group, labour force and gender structure with the representative households profiles provided in the load profile generator. The findings indicate that the synthetic load profiles can represent the local electricity consumption characteristics in the case city of Amsterdam based on time variation analyses. The approach is in particular advantageous to tackle the drawbacks of the existing studies and the standard load model used by the utilities. Furthermore, the model is found to be more efficient in the computational process of the residential sector’s load profiles, given the number of households in the city that is represented in the local profile.

show abstract

“…In comparison to the residential sector, there are fewer studies focused on the industrial, service and transport sector. The hourly electricity demand in residential households has been modelled for an entire year in Japan [16], United Kingdom [17], Finland [18], Brazil [19], Sweden [20], Australia [21], United States [22],Germany [23], and Denmark [24] . These models typically involve using behavioural, economic, social, technical and weather data to model the electricity demand of representative households and its members.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling and forecasting hourly electricity demand in West African countries

Adeoye

Spataru

2019

Applied Energy

View full text Add to dashboard Cite

The Economic Community of West African States aims to achieve 100% electrification rates by 2030 in all member countries. To achieve this ambitious target, electricity generation capacities need to be increased significantly. Forecasting hourly electricity demand is imperative for capacity planners in optimizing investment options and ensuring reliable electricity supply. However, modelling hourly electricity demand in developing countries can be a challenge due to paucity of historical demand data and methodological frameworks that adequately capture technology transitions and urban-rural communities. In this study, we address this gap by developing an hourly electricity demand model for 14 West African countries in the year 2016 and 2030.The model takes into accounts electrification rates, available household appliances, occupancy patterns of household members, type of day, available daylight hours and hourly weather conditions. Annual electricity demand in nonresidential sectors and electricity access rates in urban and rural households are forecasted using multiple regression analysis. We validated the developed model using actual 2016 monthly and annual electricity demand data. The results show the seasonal variations of electricity demand, with hourly electricity demand in dry seasons relatively higher than demand in wet seasons. The results also indicate that in 2030, electricity demand in the West African region is estimated to be five times its 2016 level. The methodology presented in this study can be applicable for modelling hourly electricity demand in developing countries that have scarce historical hourly demand data, a significant electricity supply-demand gap, and varying electricity access rates in urban and rural areas.

show abstract

Households’ hourly electricity consumption and peak demand in Denmark

Cited by 63 publications

References 34 publications

Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV

Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV

Synthesising Residential Electricity Load Profiles at the City Level Using a Weighted Proportion (Wepro) Model

Modelling and forecasting hourly electricity demand in West African countries

Contact Info

Product

Resources

About