An Extensible Simulator for Dynamic Control of Residential Area: Case Study on Heating Control

Basso, Gillian; Ferrez, Pierre W.; Gabioud, Dominique; Roduit, Pierre

doi:10.1109/dsd.2015.76

Cited by 7 publications

(2 citation statements)

References 7 publications

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“…The problem of controlling heterogeneous ADS units or BESS can be modeled with the help of agents interacting in some environment (e.g. Steinbrink et al (2018); Schütte and Sonnenschein (2012); Basso et al (2015)). A major feature of such frameworks is the handling and coordination of communication among the agents.…”

Section: Related Researchmentioning

confidence: 99%

Scaling: managing a large number of distributed battery energy storage systems

Abgottspon

Schumann

Epiney³

et al. 2018

Energy Inform

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This paper analyzes the management of a large number of distributed battery energy storage systems (BESSs) by a energy utility in order to provide some market services. A heuristic algorithm based on two parts is proposed for this task. The first part, the aggregation, combines the abilities and behavior of the fleet of BESS into a virtual power plant (VPP) by a concise but flexible model. This VPP can be used by the utility as they are used to with traditional power plants. The second part, the disaggregation, distributes VPP control schedules back to the individual BESS by a greedy first-fit decreasing heuristic. The management of a fleet of BESS can also be modeled as a mathematical linear optimization program. The proposed heuristic is compared to and evaluated against this global optimization regarding computational performance and quality of results. It is shown, that the heuristic provides a remarkable speedup when applied to larger number of units. With it, it is possible to handle a group of at least 100,000 individual BESS. Further, the quality of the results are shown. First, the solution of the heuristic is compared to the optimal one of the mathematical program. Second, the methods are both applied and compared in a realistic case study.

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Section: Related Researchmentioning

confidence: 99%

Scaling: managing a large number of distributed battery energy storage systems

Abgottspon

Schumann

Epiney³

et al. 2018

Energy Inform

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“…However, heating and cooling system of all buildings consumed approx. 65% of electric [8]. Smart homes contain intelligent home management system to provide the highest level of comfort to the inhabitant.…”

Section: Introductionmentioning

confidence: 99%

Presence Aware Power Saving Mode (PA-PSM) Enhancement for IoT Devices for Energy Conservation

Saleem¹,

Jazani²

2019

OJEE

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The Internet of Things has brought a vision to turn the digital object into smart devices by adding an intelligence system and thereafter connecting them to the internet world. These smart devices accumulate environmental information with the help of sensors and act consequently without human intervention. The Internet of Thing is a rapidly growing industry with expected 50-200 billion smart devices to connect to the internet. Multi-billions of smart devices will produce a substantial amount of data to provide services to human society, although, it will lead to increase energy consumption at the highest level and drive to high energy bills. Moreover, the flood of IoT devices may also lead to energy scarcity. IoT is nowadays mainly focused on the IT industry and researchers believe the next wave of IoT may connect 1 trillion sensors by 2025. Even if these sensors would have 10 years of battery life, it will still require 275 million batteries to be replaced every day. Therefore, it is a necessity to reduce energy consumption in smart devices. "Presence Aware Power Saving Mode (PA-PSM) Enhancement for IoT Devices for Energy Conservation", a proposed novel approach in this research paper by the help of a proposed algorithm in this research paper to reduce power consumption by individual devices within smart homes. In the proposed approach, a centralized automation controller keeps the less priority smart devices into deep sleep mode to save energy and experiments suggest the proposed system may help to reduce 25.81% of the energy consumed by smart devices within the smart home.

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