scholarly journals Multi-Objective Optimization of Home Appliances and Electric Vehicle Considering Customer’s Benefits and Offsite Shared Photovoltaic Curtailment

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2852
Author(s):  
Yeongenn Kwon ◽  
Taeyoung Kim ◽  
Keon Baek ◽  
Jinho Kim
Keyword(s):  
Energy Consumption ◽  
Energy Cost ◽  
Consumption Pattern ◽  
Consumption Data ◽  
Battery Capacity ◽  
Home Energy Management ◽  
Residential Demand ◽  
Home Energy Management System ◽  
Demand Patterns ◽  
Ev Charging

A Time-of-Use (TOU)-tariff scheme, helps residential customers to adjust their energy consumption voluntarily and reduce energy cost. The TOU tariff provides flexibility in demand, alleviate volatility caused by an increase in renewable energy in the power system. However, the uncertainty in the customer’s behavior, causes difficulty in predicting changes in residential demand patterns through the TOU tariff. In this study, the dissatisfaction model for each time slot is set as the energy consumption data of the customer. Based on the actual customer’s consumption pattern, the user sets up a model of dissatisfaction that enables aggressive energy cost reduction. In the proposed Home Energy Management System (HEMS) model, the efficient use of jointly invested offsite photovoltaic (PV) power generation is also considered. The optimal HEMS scheduling result considering the dissatisfaction, cost, and PV curtailment was obtained. The findings of this study indicate, that incentives are required above a certain EV battery capacity to induce EV charging for minimizing PV curtailment.

scholarly journals Design of Ensemble Forecasting Models for Home Energy Management Systems

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7664
Author(s):  
Karol Bot ◽  
Samira Santos ◽  
Inoussa Laouali ◽  
Antonio Ruano ◽  
Maria da Graça Ruano
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Simple Procedure ◽  
Ensemble Forecasting ◽  
Management Systems ◽  
Energy Forecasting ◽  
Energy Management Systems ◽  
Ensemble Techniques ◽  
Home Energy Management ◽  
Home Energy Management System

The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.

scholarly journals Integration of Electric Vehicles and Energy Storage System in Home Energy Management System with Home to Grid Capability

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8557
Author(s):  
Arshad Mohammad ◽  
Mohd Zuhaib ◽  
Imtiaz Ashraf ◽  
Marwan Alsultan ◽  
Shafiq Ahmad ◽  
...  
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Energy Cost ◽  
Management System ◽  
Economic Benefits ◽  
Binary Particle Swarm Optimization ◽  
Energy Management System ◽  
Multi Objective ◽  
Home Energy Management ◽  
Home Energy Management System

In this paper, we proposed a home energy management system (HEMS) that includes photovoltaic (PV), electric vehicle (EV), and energy storage systems (ESS). The proposed HEMS fully utilizes the PV power in operating domestic appliances and charging EV/ESS. The surplus power is fed back to the grid to achieve economic benefits. A novel charging and discharging scheme of EV/ESS is presented to minimize the energy cost, control the maximum load demand, increase the battery life, and satisfy the user’s-traveling needs. The EV/ESS charges during low pricing periods and discharges in high pricing periods. In the proposed method, a multi-objective problem is formulated, which simultaneously minimizes the energy cost, peak to average ratio (PAR), and customer dissatisfaction. The multi-objective optimization is solved using binary particle swarm optimization (BPSO). The results clearly show that it minimizes the operating cost from 402.89 cents to 191.46 cents, so that a reduction of 52.47% is obtained. Moreover, it reduces the PAR and discomfort index by 15.11% and 16.67%, respectively, in a 24 h time span. Furthermore, the home has home to grid (H2G) capability as it sells the surplus energy, and the total cost is further reduced by 29.41%.

scholarly journals Human Comfort-Based-Home Energy Management for Demand Response Participation

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2463 ◽  
Author(s):  
Herie Park
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Demand Response ◽  
Residential Building ◽  
Limited Participation ◽  
Human Comfort ◽  
Control Approach ◽  
Home Energy Management ◽  
Lighting Systems ◽  
Home Energy Management System

The residential building sector is encouraged to participate in demand response (DR) programs owing to its flexible and effective energy resources during peak hours with the help of a home energy management system (HEMS). Although the HEMS contributes to reducing energy consumption of the building and the participation of occupants in energy saving programs, unwanted interruptions and strict guidance from the system cause inconvenience to the occupants further leading to their limited participation in the DR programs. This paper presents a human comfort-based control approach for home energy management to promote the DR participation of households. Heating and lighting systems were chosen to be controlled by human comfort factors such as thermal comfort and visual comfort. Case studies were conducted to validate the proposed approach. The results showed that the proposed approach could effectively reduce the energy consumption during the DR period and improve the occupants’ comfort.

scholarly journals The ENERTALK dataset, 15 Hz electricity consumption data from 22 houses in Korea

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Changho Shin ◽  
Eunjung Lee ◽  
Jeongyun Han ◽  
Jaeryun Yim ◽  
Wonjong Rhee ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Sampling Rate ◽  
Electricity Consumption ◽  
Consumption Pattern ◽  
Additional Information ◽  
Consumption Data ◽  
Energy Disaggregation ◽  
Residential Electricity ◽  
Residential Electricity Consumption

Abstract AMI has been gradually replacing conventional meters because newer models can acquire more informative energy consumption data. The additional information has enabled significant advances in many fields, including energy disaggregation, energy consumption pattern analysis and prediction, demand response, and user segmentation. However, the quality of AMI data varies significantly across publicly available datasets, and low sampling rates and numbers of houses monitored seriously limit practical analyses. To address these challenges, we herein present the ENERTALK dataset, which contains both aggregate and per-appliance measurements sampled at 15 Hz from 22 houses. Among the publicly available datasets with both aggregate and per-appliance measurements, 15 Hz was the highest sampling rate. The number of houses (22) was the second-largest where the largest one had a sampling rate of 1 Hz. The ENERTALK dataset is also the first Korean open dataset on residential electricity consumption.

scholarly journals Parametric Model Based Approach for Consumer Load Prediction

2019 ◽  
Vol 18 (2) ◽  
pp. 8-15
Author(s):  
Ayodele Isqeel Abdullateef ◽  
Mudathir Folohunso Akorede ◽  
Abubakar Abdulkarim ◽  
Momoh-Jimoh Eyiomika Salami
Keyword(s):  
Energy Consumption ◽  
Moving Average ◽  
Consumption Pattern ◽  
Power Network ◽  
Load Prediction ◽  
Cumulative Energy ◽  
Individual Consumer ◽  
Autoregressive Moving Average Model ◽  
Consumption Data ◽  
Prediction Techniques

Various load prediction techniques have been proposed to predict consumer load which represents the activities of the consumer on the distribution network. Usually, these techniques use cumulative energy consumption data of the consumers connected to the power network to predict consumer load. However, this data fails to reveal and monitor the activities of individual consumers represented by consumer load consumption pattern. A new approach of predicting individual consumer load based on autoregressive moving average model (ARMA) is proposed in this study. Sub- optimal technique of parameter estimation based on Prony method was used to determine the model order of the ARMA models ARMA (10, 8), ARMA (8, 6) and ARMA (6, 4).  ARMA (6, 4) was found to be appropriate for consumer load prediction with an average mean square error of 0.00006986 and 0.0000685 for weekday and weekend loads respectively. The energy consumption data acquired from consumer load prototype for one week, with 288 data points per day used in our previous work, was used and 5-minute step ahead load prediction is achieved. Furthermore, a comparison between autoregressive AR (20) and ARMA (6, 4) was carried out and ARMA (6, 4) was found to be appropriate for consumer load prediction. This facilitates the monitoring of individual consumer activities connected on the power network.

scholarly journals An IoT-Based Home Energy Management System over Dynamic Home Area Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Jongbae Kim ◽  
Jinsung Byun ◽  
Daebeom Jeong ◽  
Myeong-in Choi ◽  
Byeongkwan Kang ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Management System ◽  
Energy Management System ◽  
Total Energy Consumption ◽  
Home Energy Management ◽  
Home Area ◽  
Home Energy Management System ◽  
The Cost ◽  
Home Area Networks

A smart grid (SG) has attracted great attention due to recent environmental problems. SG technologies enable users, such as energy system operators and consumers, to reduce energy consumption and the emission of greenhouse gases, by changing energy infrastructure more efficiently. As a part of the SG, home energy management system (HEMS) has become increasingly important, because energy consumption of a residential sector accounts for a significant amount of total energy consumption. However, a conventional HEMS has some architectural limitations on scalability, reusability, and interoperability. Furthermore, the cost of implementation of a HEMS is very expensive, which leads to the disturbance of the spread of a HEMS. Therefore, this paper proposes an Internet of Things- (IoT-) based HEMS with lightweight photovoltaic (PV) system over dynamic home area networks (DHANs), which enables the construction of a HEMS to be more scalable, reusable, and interoperable. We suggest the techniques for reducing the cost of the HEMS with various perspectives on system, network, and middleware architecture. We designed and implemented the proposed HEMS and conducted a experiment to verify the performance of the proposed system.

scholarly journals Implementation of Advanced Demand Side Management for Microgrid Incorporating Demand Response and Home Energy Management System

2018 ◽  
Vol 3 (4) ◽  
pp. 50 ◽  
Author(s):  
Izaz Zunnurain ◽  
Md. Maruf ◽  
Md. Rahman ◽  
GM Shafiullah
Keyword(s):  
Energy Management ◽  
Demand Response ◽  
Energy Cost ◽  
Energy Savings ◽  
Energy Management System ◽  
Demand Side ◽  
Case Study Analysis ◽  
Home Energy Management ◽  
Home Energy Management System

To facilitate the possible technology and demand changes in a renewable-energy dominated future energy system, an integrated approach that involves Renewable Energy Sources (RES)-based generation, cutting-edge communication strategies, and advanced Demand Side Management (DSM) is essential. A Home Energy Management System (HEMS) with integrated Demand Response (DR) programs is able to perform optimal coordination and scheduling of various smart appliances. This paper develops an advanced DSM framework for microgrids, which encompasses modeling of a microgrid, inclusion of a smart HEMS comprising of smart load monitoring and an intelligent load controller, and finally, incorporation of a DR strategy to reduce peak demand and energy costs. Effectiveness of the proposed framework is assessed through a case study analysis, by investigation of DR opportunities and identification of energy savings for the developed model on a typical summer day in Western Australia. From the case study analysis, it is evident that a maximum amount of 2.95 kWh energy can be shifted to low demand periods, which provides a total daily energy savings of 3%. The total energy cost per day is AU$2.50 and AU$3.49 for a house with and without HEMS, respectively. Finally, maximum possible peak shaving, maximum shiftable energy, and maximum standby power losses and energy cost savings with or without HEMS have been calculated to identify the energy saving opportunities of the proposed strategy for a microgrid of 100 houses with solar, wind, and a back-up diesel generator in the generation side.

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