QoS Promotion in Energy-Efficient Datacenters Through Peak Load Scheduling

2018 ◽  
pp. 1-1 ◽  
Author(s):  
Cheng Hu ◽  
Yuhui Deng ◽  
Geyong Min ◽  
Ping Huang ◽  
Xiao Qin
Keyword(s):  
Energy Efficient ◽  
Peak Load ◽  
Load Scheduling

Adaptive Peak Load Scheduling For Domestic Loads In The Future Electricity Market

2021 ◽  
Author(s):  
N. Priyadharshini ◽  
S. Gomathy ◽  
K. Dhivya ◽  
M.Dhivya Priya ◽  
S.S. Gopala Krishnan ◽  
...  
Keyword(s):  
Electricity Market ◽  
Peak Load ◽  
Load Scheduling ◽  
The Future ◽  
Adaptive Peak

Measurements and Simulations of Electrical Demand From Residential Buildings for Peak Load Reduction

2011 ◽  
Author(s):  
Suresh B. Sadineni ◽  
Fady Atallah ◽  
Robert F. Boehm
Keyword(s):  
Energy Efficient ◽  
Las Vegas ◽  
Residential Buildings ◽  
Peak Load ◽  
Electricity Demand ◽  
Load Reduction ◽  
Peak Reduction ◽  
Reduction Strategies ◽  
Code Standard ◽  
The U.S

Due to extreme summers in the Desert Southwest region of the U.S., there are substantial peaks in electricity demand. Through a grant from the U.S. Department of Energy, a consortium has been formed between the University of Nevada Las Vegas, Pulte Homes, and NV Energy (formerly known as Nevada Power) to address this issue. The team has been developing a series of approximately 200 homes in Las Vegas to study substation level peak electric load reduction strategies. The targeted goal of the project is a peak reduction of more than 65%, between 1:00 PM and 7:00 PM, compared to code standard housing developments. Energy performances of the homes have been monitored and the results were stored for further analysis. A computer model has been developed for one of the homes in the new development using building energy simulation code, ENERGY 10. Influence of different peak reduction strategies on the electricity demand from the home has been analyzed using the developed model. The simulations predict that the annual electrical energy demand from the energy efficient home compared to a code standard home of the same size decreases by 38%. The simulations have also shown that the energy efficient measures reduce the electricity demand from the home during the peak periods. Simulations on the photovoltaic (PV) orientation show that a south oriented PV system is best suited for a home enrolled to flat electricity pricing schedule and a 220°(40° west of due south) orientation is economically optimal for homes enrolled in the time-of-use pricing. The energy efficiency methods in the building coupled with a 220° oriented PV and two degrees thermostat setback for three hours (from 3:00–6:00 PM) can reduce the peak demand by 62% compared to a code standard building of the same size.

scholarly journals Optimal design of adaptive power scheduling using modified ant colony optimization algorithm

2020 ◽  
Vol 10 (1) ◽  
pp. 738
Author(s):  
Vijo M Joy ◽  
S. Krishnakumar
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Peak Load ◽  
Back Propagation ◽  
Minimum Cost ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Load Scheduling

For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.

scholarly journals Dispatch Strategies for the Utilisation of Battery Storage Systems in Smart Grid Optimised Buildings

Buildings ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 433
Author(s):  
Andreas D. Georgakarakos ◽  
Behrang Vand ◽  
Elizabeth Abigail Hathway ◽  
Martin Mayfield
Keyword(s):  
Smart Grid ◽  
Real Time ◽  
Energy Efficient ◽  
Storage Systems ◽  
Peak Load ◽  
Building Design ◽  
Battery Storage ◽  
Peak Loads ◽  
The Impact ◽  
Energy Efficient Building

This study investigates Smart Grid Optimised Buildings (SGOBs) which can respond to real-time electricity prices by utilising battery storage systems (BSS). Different building design characteristics are assessed to evaluate the impact on energy use, the interaction with the battery, and potential for peak load shifting. Two extreme cases based on minimum and maximum annual energy consumption were selected for further investigation to assess their capability of utilising BSS to perform arbitrage, under real-time pricing. Three operational dispatch strategies were modelled to allow buildings to provide such services. The most energy-efficient building was capable of shifting a higher percentage of its peak loads and export more electricity, when this is allowed. When using the biggest battery (220 kWh) to only meet the building loads, the energy-efficient building was able to shift 39.68% of its original peak loads in comparison to the 33.95% of the least efficient building. With exports allowed, the shifting percentages went down to 31.76% and 29.46%, respectively, while exports of 18.08 and 16.34 kWh/m2 took place. The formation of a regulatory framework is vital in order to establish proper motives for buildings to undertake an active role in the smart grid.

Performance Evaluation of AFOD Protocol for Energy Efficient Routing

2011 ◽  
Author(s):  
B. Smitha Shekar ◽  
M. Sudhakar Pillai ◽  
G. Narendra Kumar
Keyword(s):  
Performance Evaluation ◽  
Energy Efficient ◽  
Energy Efficient Routing

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

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