Real-Time Energy Management in Microgrids

Shi, Wenbo; Li, Na; Chu, Chi-Cheng; Gadh, Rajit

doi:10.1109/tsg.2015.2462294

Cited by 286 publications

(194 citation statements)

References 34 publications

Supporting

Mentioning

193

Contrasting

Unclassified

Order By: Relevance

“…It shows that the proposed counts offer optimality to both essentialness provider and customers. Demand Side Management [5] can smooth best to-ordinary Proportion (PAR) of vitality use in the grid, which in this manner diminishes the abuse of fuel and the release of ozone hurting substance. It first centre to constrain PAR with a united arrangement.…”

Section: IImentioning

confidence: 99%

A Survey on Smart Energy Monitoring System

Lavanya¹

2018

IJRASET

View full text Add to dashboard Cite

The motivation to manage energy usage at residential home in India is influenced by economics environment condition and technical reasons. Economically, it offers reduction of government subsidies and electricity bill. The environment condition aspect enables reduction of CO 2 level. We can limit the power supply to the home by pre-default setting the value to be consumed, so that energy can be managed by limit. The power management system is consist of ZigBee Digital Power meters installed in every consumer unit and an Electricity e-Billing system at the energy provider side. The ZigBee Digital Power Meter (ZPM) is a single phase digital kWh power meter with embedded ZigBee modem which utilize the Wireless Sensor Network to send its power usage reading using information back to the energy provider wirelessly. At the power provider side, they have the control to change priority of the devices when power distributed in low range. Keywords : DSM (Demand Side Management), CTPT (Current Transformer Power Transformer), Potentiometer. I.INTRODUCTION The main objective of Energy Tracking System is to reduce the Energy Consumption at multifamily in a residential house. By using a smart meter and power line communication, so that we can provide the detailed information of energy consumption. The hardware components that we used in this. The potentiometer can operate the high voltage and low voltage. Raspberry pi can be connected with the virtual terminal, motor, lamp and voltage. If the power value gets above 20 is high voltage and it gets below 20 is low voltage. At finally, plug the GSM with the above raspberry pi. We can easily get the meter reading day by day and its shows the threshold unit how much we used in the whole day. This will reduce the consumption of energy. By using GSM technology, our daily usage of current units can be easily gets through message. Meter reading is taken automatically by using the GSM technology. The Meter reading is taken manually. The Errors are occurred due to less concentration. The Meter reading is not accurate. Delayed works are due to external conditions. The Power can be cut manually due to lack of payment. The Meter reading is taken automatically using the GSM technology. A Power cut is achieved from EB office through wireless. A Normal work happens at any time and external conditions. A High Accurate meter reading is done. The Power management can be capable.

show abstract

Section: IImentioning

confidence: 99%

A Survey on Smart Energy Monitoring System

Lavanya¹

2018

IJRASET

View full text Add to dashboard Cite

show abstract

“…In 2011, Sanseverino et al [63] presented an execution monitoring and replanning approach to solve the optimal generation dispatch problem in a smart grid through the minimization of carbon emissions and production costs as well as improve the quality. In 2015, Shi et al [64] presented an online energy management strategy (EMS) for real-time operation of microgrids. In 2013, Dall' Anese et al [65] presented the numerical tests.…”

Section: 2mentioning

confidence: 99%

Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

Khan

Singh

2017

Journal of Engineering

View full text Add to dashboard Cite

The exhaustive knowledge of optimal power flow (OPF) methods is critical for proper system operation and planning, since OPF methods are utilized for finding the optimal state of any system under system constraint conditions, such as loss minimization, reactive power limits, thermal limits of transmission lines, and reactive power optimization. Incorporating renewable energy sources optimized the power flow of system under different constraints. This work presents a comprehensive study of optimal power flows methods with conventional and renewable energy constraints. Additionally, this work presents a progress of optimal power flow solution from its beginning to its present form. Authors classify the optimal power flow methods under different constraints condition of conventional and renewable energy sources. The current and future applications of optimal power flow programs in smart system planning, operations, sensitivity calculation, and control are presented. This study will help the engineers and researchers to optimize power flow with conventional and renewable energy sources.

show abstract

“…increasingly provide practical recommendations or references to similar households [1], and offer more useful premium services [2] in order to appeal new customers as well as retaining existing ones. Private households then get better control over their own electricity usage [3], energy consumption [4], [5] and caused carbon footprint using consumption feedback. Generally, household characteristic information is not easily accessed, or needs to be manually collected by conducting conveys.…”

Section: Introductionmentioning

confidence: 99%

Joint Household Characteristic Prediction via Smart Meter Data

Sun

Cong

Hou

et al. 2019

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

Abstract-Predicting specific household characteristics (e.g., age of person, household income, cooking style, etc) from their everyday electricity consumption (i.e., smart meter data) enables energy provider to develop many intelligent business applications or help consumers to reduce their energy consumption. However, most existing works intend to predict single household characteristic via smart meter data independently, and ignore the joint analysis of different characteristics. In this paper, we consider each characteristic as an independent task and intend to predict multiple household characteristics simultaneously by designing a new multi-task learning formulation: Discriminative MultiTask Relationship Learning (DisMTRL). Specifically, two main challenges need to be handled: 1) task relationship, that is the embedded structure of relationships among different characteristics; 2) feature learning, there exist redundant features in original training data. To achieve these, our DisMTRL model aims to obtain a simple but robust weight matrix through capturing the intrinsic relatedness among different characteristics by task covariance matrix (MTRL) and incorporating the discriminative features via feature covariance matrix (Dis). For model optimization, we employ an alternating minimization strategy to learn the optimal weight matrix as well as the relationship between tasks by converting feature learning regularization as trace minimization problem. For evaluation, we adopt a smart meter dataset collected from 4232 households in Ireland at a 30min granularity over an interval of 1.5 years. The experimental results justify the effectiveness of our proposed model.

show abstract

Real-Time Energy Management in Microgrids

Cited by 286 publications

References 34 publications

A Survey on Smart Energy Monitoring System

A Survey on Smart Energy Monitoring System

Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

Joint Household Characteristic Prediction via Smart Meter Data

Contact Info

Product

Resources

About