SWAS: Stealing Work Using Approximate System-Load Information

2017 ◽  
Author(s):  
Stavros Tzilis ◽  
Miquel Pericas ◽  
Pedro Trancoso ◽  
Ioannis Sourdis
Keyword(s):  
System Load ◽  
Approximate System

scholarly journals Coordination of Multiple Electric Vehicle Aggregators for Peak Shaving and Valley Filling in Distribution Feeders

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 352
Author(s):  
Saad Ullah Khan ◽  
Khawaja Khalid Mehmood ◽  
Zunaib Maqsood Haider ◽  
Muhammad Kashif Rafique ◽  
Muhammad Omer Khan ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Power Distribution ◽  
Distribution System ◽  
Energy Demand ◽  
Minimum Energy ◽  
Peak Shaving ◽  
Peak Period ◽  
Actual Distribution ◽  
Load Profile ◽  
System Load

In this paper, a coordination method of multiple electric vehicle (EV) aggregators has been devised to flatten the system load profile. The proposed scheme tends to reduce the peak demand by discharging EVs and fills the valley gap through EV charging in the off-peak period. Upper level fair proportional power distribution to the EV aggregators is exercised by the system operator which provides coordination among the aggregators based on their aggregated energy demand or capacity. The lower level min max objective function is implemented at each aggregator to distribute power to the EVs. Each aggregator ensures that the EV customers’ driving requirements are not relinquished in spite of their employment to support the grid. The scheme has been tested on IEEE 13-node distribution system and an actual distribution system situated in Seoul, Republic of Korea whilst utilizing actual EV mobility data. The results show that the system load profile is smoothed by the coordination of aggregators under peak shaving and valley filling goals. Also, the EVs are fully charged before departure while maintaining a minimum energy for emergency travel.

System load swings

1941 ◽  
Vol 60 (6) ◽  
pp. 735-737
Keyword(s):  
System Load

On the Impact of Treatment Restrictions for the Indigent Suffering from a Chronic Disease: The Case of Compassionate Dialysis

2021 ◽  
Author(s):  
Olga Bountali ◽  
Sila Çetinkaya ◽  
Vishal Ahuja
Keyword(s):  
Chronic Disease ◽  
Performance Measures ◽  
Safety Net ◽  
Unintended Consequences ◽  
Treatment Delays ◽  
System Load ◽  
Network Characteristics ◽  
Screening Assessment ◽  
The Impact ◽  
Treatment Restrictions

We analyze a congested healthcare delivery setting resulting from emergency treatment of a chronic disease on a regular basis. A prominent example of the problem of interest is congestion in the emergency room (ER) at a publicly funded safety net hospital resulting from recurrent arrivals of uninsured end-stage renal disease patients needing dialysis (a.k.a. compassionate dialysis). Unfortunately, this is the only treatment option for un/under-funded patients (e.g., undocumented immigrants) with ESRD, and it is available only when the patient’s clinical condition is deemed as life-threatening after a mandatory protocol, including an initial screening assessment in the ER as dictated and communicated by hospital administration and county policy. After the screening assessment, the so-called treatment restrictions are in place, and a certain percentage of patients are sent back home; the ER, thus, serves as a screening stage. The intention here is to control system load and, hence, overcrowding via restricting service (i.e., dialysis) for recurrent arrivals as a result of the chronic nature of the underlying disease. In order to develop a deeper understanding of potential unintended consequences, we model the problem setting as a stylized queueing network with recurrent arrivals and restricted service subject to the mandatory screening assessment in the ER. We obtain analytical expressions of fundamental quantitative metrics related to network characteristics along with more sophisticated performance measures. The performance measures of interest include both traditional and new problem-specific metrics, such as those that are indicative of deterioration in patient welfare because of rejections and treatment delays. We identify cases for which treatment restrictions alone may alleviate or lead to severe congestion and treatment delays, thereby impacting both the system operation and patient welfare. The fundamental insight we offer is centered around the finding that the impact of mandatory protocol on network characteristics as well as traditional and problem-specific performance measures is nontrivial and counterintuitive. However, impact is analytically and/or numerically quantifiable via our approach. Overall, our quantitative results demonstrate that the thinking behind the mandatory protocol is potentially naive. This is because the approach does not necessarily serve its intended purpose of controlling system-load and overcrowding.

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