Robust Server Consolidation: Coping with Peak Demand Underestimation

2016 ◽  
Author(s):  
Diarmuid Grimes ◽  
Deepak Mehta ◽  
Barry O'Sullivan ◽  
Robert Birke ◽  
Lydia Chen ◽  
...  
Keyword(s):  
Peak Demand ◽  
Server Consolidation

An Evaluation of Control Parameters for Load Fluctuation Compensation by Battery Control Aimed at Reducing Peak Demand for a Building

2015 ◽  
Vol 135 (1) ◽  
pp. 71-77
Author(s):  
Takayuki Sugimoto ◽  
Eisuke Shimoda ◽  
Toshihiro Yamane ◽  
Shigeo Numata
Keyword(s):  
Control Parameters ◽  
Peak Demand ◽  
Load Fluctuation

Stochastic framework for peak demand reduction opportunities with solar energy for manufacturing facilities

2021 ◽  
pp. 127891
Author(s):  
Miguel A. Peinado-Guerrero ◽  
Jesus R. Villalobos ◽  
Patrick E. Phelan ◽  
Nicolas A. Campbell
Keyword(s):  
Solar Energy ◽  
Peak Demand ◽  
Demand Reduction ◽  
Stochastic Framework ◽  
Manufacturing Facilities

scholarly journals Optimal Control Strategies for Switchable Transparent Insulation Systems Applied to Smart Windows for US Residential Buildings

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2917
Author(s):  
Mohammad Dabbagh ◽  
Moncef Krarti
Keyword(s):  
Energy Use ◽  
Control Strategies ◽  
Residential Buildings ◽  
Residential Building ◽  
Optimization Approach ◽  
Optimal Controls ◽  
Peak Demand ◽  
Smart Windows ◽  
Insulation Systems ◽  
Rule Set

This paper evaluates the potential energy use and peak demand savings associated with optimal controls of switchable transparent insulation systems (STIS) applied to smart windows for US residential buildings. The optimal controls are developed based on Genetic Algorithm (GA) to identify the automatic settings of the dynamic shades. First, switchable insulation systems and their operation mechanisms are briefly described when combined with smart windows. Then, the GA-based optimization approach is outlined to operate switchable insulation systems applied to windows for a prototypical US residential building. The optimized controls are implemented to reduce heating and cooling energy end-uses for a house located four US locations, during three representative days of swing, summer, and winter seasons. The performance of optimal controller is compared to that obtained using simplified rule-based control sets to operate the dynamic insulation systems. The analysis results indicate that optimized controls of STISs can save up to 81.8% in daily thermal loads compared to the simplified rule-set especially when dwellings are located in hot climates such as that of Phoenix, AZ. Moreover, optimally controlled STISs can reduce electrical peak demand by up to 49.8% compared to the simplified rule-set, indicating significant energy efficiency and demand response potentials of the SIS technology when applied to US residential buildings.

scholarly journals Model Predictive Control Strategies to Activate the Energy Flexibility for Zones with Hydronic Radiant Systems

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1195
Author(s):  
Ali Saberi Derakhtenjani ◽  
Andreas K. Athienitis
Keyword(s):  
Control Strategies ◽  
High Price ◽  
Electricity Prices ◽  
Peak Demand ◽  
Heating Systems ◽  
Electrical Grid ◽  
Demand Reduction ◽  
The Cost ◽  
Floor System ◽  
Radiant Floor Heating

This paper presents control strategies to activate energy flexibility for zones with radiant heating systems in response to changes in electricity prices. The focus is on zones with radiant floor heating systems for which the hydronic pipes are located deep in the concrete and, therefore, there is a significant thermal lag. A perimeter zone test-room equipped with a hydronic radiant floor system in an environmental chamber is used as a case study. A low order thermal network model for the perimeter zone, validated with experimental measurements, is utilized to study various control strategies in response to changes in the electrical grid price signal, including short term (nearly reactive) changes of the order of 10–15 min notice. An index is utilized to quantify the building energy flexibility with the focus on peak demand reduction for specific periods of time when the electricity prices are higher than usual. It is shown that the developed control strategies can aid greatly in enhancing the zone energy flexibility and minimizing the cost of electricity and up to 100% reduction in peak power demand and energy consumption is attained during the high-price and peak-demand periods, while maintaining acceptable comfort conditions.

scholarly journals Bottom-Up Generation of Peak Demand Scenarios in Water Distribution Networks

2020 ◽  
Vol 13 (1) ◽  
pp. 31
Author(s):  
Enrico Creaco ◽  
Giacomo Galuppini ◽  
Alberto Campisano ◽  
Marco Franchini
Keyword(s):  
Water Distribution ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Peak Demand ◽  
Long Time ◽  
Cross Correlations ◽  
User Demand ◽  
One Year ◽  
The One ◽  
Best Fit

This paper presents a two-step methodology for the stochastic generation of snapshot peak demand scenarios in water distribution networks (WDNs), each of which is based on a single combination of demand values at WDN nodes. The methodology describes the hourly demand at both nodal and WDN scales through a beta probabilistic model, which is flexible enough to suit both small and large demand aggregations in terms of mean, standard deviation, and skewness. The first step of the methodology enables generating separately the peak demand samples at WDN nodes. Then, in the second step, the nodal demand samples are consistently reordered to build snapshot demand scenarios for the WDN, while respecting the rank cross-correlations at lag 0. The applications concerned the one-year long dataset of about 1000 user demand values from the district of Soccavo, Naples (Italy). Best-fit scaling equations were constructed to express the main statistics of peak demand as a function of the average demand value on a long-time horizon, i.e., one year. The results of applications to four case studies proved the methodology effective and robust for various numbers and sizes of users.

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