Coordination of Smart Home Energy Management Systems in Neighborhood Areas: A Systematic Review

Aliabadi, Farshad Etedadi; Agbossou, Kodjo; Kélouwani, Sousso; Henao, Nilson; Hosseini, Sayed Saeed

doi:10.1109/access.2021.3061995

Cited by 44 publications

(20 citation statements)

References 142 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Buildings are responsible for more than one third the energy consumption and CO 2 emissions in most industrialized countries. In spite of several studies on the economy of energy management in buildings, the environmental aspect has most often been overlooked (Etedadi Aliabadi et al 2021). The most common approach is to formulate the energy management problem as a single economic objective function to be optimized within a defined time horizon (Fiorini and Aiello 2019a).…”

Section: Related Workmentioning

confidence: 99%

Automatic optimal multi-energy management of smart homes

Fiorini

Aiello

2022

Energy Inform

View full text Add to dashboard Cite

Residential and commercial buildings are responsible for approximately 35% of carbon emissions in industrialized countries. Making buildings more efficient and sustainable is, therefore, a fundamental step toward a low-carbon energy society. A key to achieving sustainability is by leveraging on energy storage systems and smart technologies to switch between energy carriers in order to optimize environmental impact. However, the research on energy management in buildings has mostly focused on its economic aspect, overlooking the environmental dimension. Additionally, the concept of energy system flexibility has been mostly proposed as the ability to shift demand over time or, at most, to curtail it, aiming at reducing the system’s operating costs. We propose a multi-energy multi-objective scheduling model to optimally manage the supply, demand, and interchange of multiple energy carriers, based on dynamic price and carbon emission signals. Our holistic and integrated approach is applied to a group of 200 smart homes with varying thermal and electric loads, and equipped with different types of smart technologies. The effectiveness of the approach in reducing the home carbon footprint, while remunerating the users, is evaluated using historical and statistical data of three European countries.

show abstract

Section: Related Workmentioning

confidence: 99%

Automatic optimal multi-energy management of smart homes

Fiorini

Aiello

2022

Energy Inform

View full text Add to dashboard Cite

show abstract

“…However, there is extensive work presented in literature to avoid these challenges. For example, the useful methods to avoid the issues such as delays and secure data transmission are proposed in [39] and [40] respectively. With these available literature, it is possible to realize the proposed control method in real time.…”

Section: B Possibility Of Real-time Application Of Proposed Workmentioning

confidence: 99%

Impact of Optimal Control of Distributed Generation Converters in Smart Transformer Based Meshed Hybrid Distribution Network

Kumar¹,

Manojkumar

Ganguly

et al. 2021

IEEE Access

View full text Add to dashboard Cite

“…Energy management in Smart Neighborhoods has attracted significant attention in recent years [8,9,15], and the approaches proposed in the literature can be classified according to different criteria. A first distinction can be made based on whether the topology of the management architecture is centralized or decentralized [15].…”

Section: Related Work and Contributionsmentioning

confidence: 99%

“…Decentralized topologies can assume different forms depending on the presence of a coordinator and on the way households exchange information [15]. In [19], each household is coordinated by a local control unit that schedules shiftable appliances and by a centralized control unit that coordinates local units and EVs.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Multi-Household Energy Management in a Smart Neighborhood in the Presence of Uncertainties and Electric Vehicles

et al. 2021

View full text Add to dashboard Cite

The pathway toward the reduction of greenhouse gas emissions is dependent upon increasing Renewable Energy Sources (RESs), demand response, and electrification of public and private transportation. Energy management techniques are necessary to coordinate the operation in this complex scenario, and in recent years several works have appeared in the literature on this topic. This paper presents a study on multi-household energy management for Smart Neighborhoods integrating RESs and electric vehicles participating in Vehicle-to-Home (V2H) and Vehicle-to-Neighborhood (V2N) programs. The Smart Neighborhood comprises multiple households, a parking lot with public charging stations, and an aggregator that coordinates energy transactions using a Multi-Household Energy Manager (MH-EM). The MH-EM jointly maximizes the profits of the aggregator and the households by using the augmented ϵ-constraint approach. The generated Pareto optimal solutions allow for different decision policies to balance the aggregator’s and households’ profits, prioritizing one of them or the RES energy usage within the Smart Neighborhood. The experiments have been conducted over an entire year considering uncertainties related to the energy price, electric vehicles usage, energy production of RESs, and energy demand of the households. The results show that the MH-EM optimizes the Smart Neighborhood operation and that the solution that maximizes the RES energy usage provides the greatest benefits also in terms of peak-shaving and valley-filling capability of the energy demand.

show abstract

Coordination of Smart Home Energy Management Systems in Neighborhood Areas: A Systematic Review

Cited by 44 publications

References 142 publications

Automatic optimal multi-energy management of smart homes

Automatic optimal multi-energy management of smart homes

Impact of Optimal Control of Distributed Generation Converters in Smart Transformer Based Meshed Hybrid Distribution Network

Multi-Household Energy Management in a Smart Neighborhood in the Presence of Uncertainties and Electric Vehicles

Contact Info

Product

Resources

About