A Cyber-Physical Residential Energy Management System via Virtualized Packets

Tomé, Mauricio de Castro; Nardelli, Pedro H. J.; Hussain, Hafiz Majid; Wahid, Sohail; Narayanan, Arun

doi:10.3390/en13030699

Cited by 12 publications

(11 citation statements)

References 42 publications

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“…To achieve QoS, the authors emphasized the role of massive machinetype communication (MTC) with ultra-reliable low latency. Nardelli and his group further extended their work in [147] and proposed PEM for flexible loads in the residential sector. Their work considers a cyber-physical system in which three types of loads send requests as virtual energy packets to the energy server through a residential energy router.…”

Section: Packetized Energy Managementmentioning

confidence: 99%

What is Energy Internet? Concepts, Technologies, and Future Directions

et al. 2020

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The climate change crisis, exacerbated by the global dependency of fossil fuels, has brought significant challenges. In the medium to long term, extensive renewable-energy-based electrification is considered to be one of the most promising development paths to address these challenges. However, this is tangible only if the energy infrastructure can accommodate renewable energy sources and distributed energy resources, such as batteries and heat pumps, without adversely affecting power grid operations. To realize renewable-energy-based electrification goals, a new concept-the Energy Internet (EI)-has been proposed, inspired by the most recent advances in information and telecommunication network technologies. Recently, many measures have also been taken to practically implement the EI. Although these EI models share many ideas, a definitive universal definition of the EI is yet to be agreed. Additionally, some studies have proposed protocols and architectures, but a generalized technological overview is still missing. An understanding of the technologies that underpin and encompass the current and future EI is very important to push toward a standardized version of the EI that will eventually make it easier to implement it across the world. In this paper, we first examine and analyze the typical popular definitions of the EI in scientific literature. Based on definitions, assumptions, scope, and application areas, the scientific literature is then classified into four different groups representing the way in which the papers have approached the EI. Then, we synthesize these definitions and concepts, and keeping in mind the future smart grid, we propose a new universal definition of the EI. We also identify the underlying key technologies for managing, coordinating, and controlling the multiple (distributed or not) subsystems with their own particular challenges. The survey concludes by highlighting the main challenges facing a future EI-based energy system and indicating core requirements in terms of system complexity, security, standardization, energy trading and business models and social acceptance.

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Section: Packetized Energy Managementmentioning

confidence: 99%

What is Energy Internet? Concepts, Technologies, and Future Directions

et al. 2020

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“…BitTorrent uses the "tit-for-tat" rule to support cooperative behavior amongst its users. PEM follows a similar premise: energy consumption becomes quantized into chunks of certain watt-hours with a predetermined duration (length) of certain minutes [43]. Therefore, multiplexed energy demand becomes possible, opening the possibility of managing energy loads in a decentralized manner based on service requests.…”

Section: Diverging Trajectories Of Technological Developmentmentioning

confidence: 99%

From private to public governance: The case for reconfiguring energy systems as a commons

Giotitsas

Nardelli

Kostakis

et al. 2020

Energy Research & Social Science

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“…Recently, numerous EMTs have been exploited to determine economical and optimal energy allocation plans considering energy sources (like PV and grid energy) [1]- [4] and DRS with dynamic pricings [5]- [7] subject to the various household loads, quality of service [8], [9] and energy trading [10]- [12] constraints. For instance, the authors in [5], [6] studied energy scheduling of residential users to reduce the peak to average ratio (PAR) and minimize energy cost.…”

Section: Introductionmentioning

confidence: 99%

“…Ahmed et al [7] examined consumer behavior patterns for the prediction of future aggregated load and analyzed different user reference models, comfort, and control parameters of appliances in the context of activation delay. Some authors [8], [9] proposed packetized energy management (PEM) approach to address the demand of thermostatically controlled loads (TCL) and validate the quality of service (QoS). In contrast, the authors in [1]- [4], [10]- [12] focused on incorporating RERs together with battery storage system and management techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Some of the former works [5]- [7] have addressed consumercentric problems such as load scheduling and energy cost minimization, but they ignored the integration of RERs and bilateral energy trading. Others [8], [9] considered interesting PEM approaches, but they did not discus the role of energy retailers (i.e., utilities) that coexist with consumers and provide pricing mechanisms. In subsequent works [1]- [4], [10]- [12], RERs with battery storage systems were incorporated in the system model, but the impacts of user inconvenience and PEM approaches have not been explicitly studied.…”

Section: Introductionmentioning

confidence: 99%

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Packetized Energy Management Controller for Residential Consumers

Hussain¹,

Ahmad²,

Narayanan³

et al. 2021

Preprint

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In this paper, we investigate the management of energy storage control and load scheduling in scenarios considering a grid-connected photovoltaic (PV) system using packetized energy management. The aim is to reduce an average aggregated system cost through the proposed packetized energy management controller considering household energy consumption, procurement price, load scheduling delays, PV self-sufficiency via generated renewable energy and battery degradation. The proposed approach solves the joint optimization problem using established heuristics, namely genetic algorithm (GA), binary particle swarm optimization (BPSO), and differential evolution (DE). Additionally, the performances of heuristic algorithms are also compared in terms of the effectiveness of load scheduling with delay constraints, packetized energy transactions, and battery degradation cost. Case studies have been provided to demonstrate and extensively evaluate the algorithms. The numerical results show that the proposed packetized energy management controller can considerably reduce the aggregated average system cost up to 4.7%, 5.14%, and 1.35% by GA, BPSO, and DE, respectively, while meeting the packetized energy demand and scheduling delays requirements.

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A Cyber-Physical Residential Energy Management System via Virtualized Packets

Cited by 12 publications

References 42 publications

What is Energy Internet? Concepts, Technologies, and Future Directions

What is Energy Internet? Concepts, Technologies, and Future Directions

From private to public governance: The case for reconfiguring energy systems as a commons

Packetized Energy Management Controller for Residential Consumers

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