Robust Optimal Energy Management of a Residential Microgrid Under Uncertainties on Demand and Renewable Power Generation

Hosseini, Seyed Mohsen; Carli, Raffaele; Dotoli, Mariagrazia

doi:10.1109/tase.2020.2986269

Cited by 124 publications

(47 citation statements)

References 57 publications

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“…This IoT system can decrease the cost and energy consumption due to the air conditioner operation. In the future, the proposed approach will involve detailed modeling of IoT and smart energy systems, and it will also be applied in robotics applications towards the development of industry 4.0 [35][36][37][38][39][40].…”

Section: Scenario 3: Disconnecting the Power From The Air Conditionermentioning

confidence: 99%

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings

Elsisi

Tran

Mahmoud

et al. 2021

Sensors

122

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Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that establishes the core of Industry 4.0. The IoT enables the sharing of signals between devices and machines via the internet. Besides, the IoT system enables the utilization of artificial intelligence (AI) techniques to manage and control the signals between different machines based on intelligence decisions. The paper’s innovation is to introduce a deep learning and IoT based approach to control the operation of air conditioners in order to reduce energy consumption. To achieve such an ambitious target, we have proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area. Accordingly, the operation of the air conditioners could be optimally managed in a smart building. Furthermore, the number of persons and the status of the air conditioners are published via the internet to the dashboard of the IoT platform. The proposed system enhances decision making about energy consumption. To affirm the efficacy and effectiveness of the proposed approach, intensive test scenarios are simulated in a specific smart building considering the existence of air conditioners. The simulation results emphasize that the proposed deep learning-based recognition algorithm can accurately detect the number of persons in the specified area, thanks to its ability to model highly non-linear relationships in data. The detection status can also be successfully published on the dashboard of the IoT platform. Another vital application of the proposed promising approach is in the remote management of diverse controllable devices.

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Section: Scenario 3: Disconnecting the Power From The Air Conditionermentioning

confidence: 99%

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings

Elsisi

Tran

Mahmoud

et al. 2021

Sensors

122

View full text Add to dashboard Cite

show abstract

“…Finally, one essential system integration for any virtual power plant is the integration to the system managing the distributed energy resources. Examples of such systems to be integrated include microgrid controllers, battery management systems, and building automation systems [55,56]. The Representational state transfer (REST) Application Programming Interface (API) presented in this article is one possible integration technique; however, the industry standard IEC 104 may be better supported by such systems.…”

Section: Managing Primary Frequency Reserves With a Virtual Power Plantmentioning

confidence: 99%

A Virtual Power Plant Solution for Aggregating Photovoltaic Systems and Other Distributed Energy Resources for Northern European Primary Frequency Reserves

et al. 2021

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Primary frequency reserves in Northern Europe have traditionally been provided with hydro plants and fossil fuel-burning spinning reserves. Recently, smart distributed energy resources have been equipped with functionality needed to participate on frequency reserves. Key categories of such resources include photovoltaic systems, batteries, and smart loads. Most of these resources are small and cannot provide the minimum controllable power required to participate on frequency reserves. Thus, virtual power plants have been used to aggregate the resources and trade them on the frequency reserves markets. The information technology aspects of virtual power plants are proprietary and many of the details have not been made public. The first contribution of this article is to propose a generic data model and application programming interface for a virtual power plant with the above-mentioned capabilities. The second contribution is to use the application programming interface to cope with the unpredictability of the frequency reserve capacity that the photovoltaic systems and other distributed energy resources are able to provide to the frequency reserves markets in the upcoming bidding period. The contributions are demonstrated with an operational virtual power plant installation at a Northern European shopping center, aggregating photovoltaic Primary Frequency Reserves resources.

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“…Giraldo et al [ 26 ] presented an energy management system (EMS) for single-phase or balanced three-phase microgrids via robust convex optimization, represented as a convex mixed-integer second-order cone programming model. Hosseini et al [ 27 ] proposed a novel robust framework for the day-ahead energy scheduling of a residential microgrid comprising interconnected smart users, each with individual renewable energy sources, noncontrollable loads, energy and comfort-based controllable loads, and individual plug-in electric vehicles. These minimize the expected energy cost while satisfying device/comfort/contractual constraints, including feasibility constraints on energy transfer between users and the grid under renewable energy source generation and users’ demand uncertainties.…”

Section: Related Workmentioning

confidence: 99%

Distributed Energy IoT-Based Real-Time Virtual Energy Prosumer Business Model for Distributed Power Resource

Park

Cho

Kim

et al. 2021

Sensors

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Smart energy technologies, services, and business models are being developed to reduce energy consumption and emissions of CO2 and greenhouse gases and to build a sustainable environment. Renewable energy is being actively developed throughout the world, and many intelligent service models related to renewable energy are being proposed. One of the representative service models is the energy prosumer. Through energy trading, the demand for renewable energy and distributed power is efficiently managed, and insufficient energy is covered through energy transaction. Moreover, various incentives can be provided, such as reduced electricity bills. However, despite such a smart service, the energy prosumer model is difficult to expand into a practical business model for application in real life. This is because the production price of renewable energy is higher than that of the actual grid, and it is difficult to accurately set the selling price, restricting the formation of the actual market between sellers and consumers. To solve this problem, this paper proposes a small-scale energy transaction model between a seller and a buyer on a peer-to-peer (P2P) basis. This model employs a virtual prosumer management system that utilizes the existing grid and realizes the power system in real time without using an energy storage system (ESS). Thus, the profits of sellers and consumers of energy transactions are maximized with an improved return on investment (ROI), and an intelligent demand management system can be established.

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Robust Optimal Energy Management of a Residential Microgrid Under Uncertainties on Demand and Renewable Power Generation

Cited by 124 publications

References 57 publications

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings

A Virtual Power Plant Solution for Aggregating Photovoltaic Systems and Other Distributed Energy Resources for Northern European Primary Frequency Reserves

Distributed Energy IoT-Based Real-Time Virtual Energy Prosumer Business Model for Distributed Power Resource

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