Centralized and decentralized approaches to demand response using smart plugs

Siebert, Luciano Cavalcante; Ferreira, Leandro; Yamakawa, Eduardo Kazumi; Custodio, Eliane S.; Aoki, Alexandre Rasi; Fernandes, Thelma Solange Piazza; Cardoso, Kleber Hochwart

doi:10.1109/tdc.2014.6863547

Cited by 22 publications

(20 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, some works present methods for appliance management, protection and control, and scheduling for residential customers aiming to help them to maximize the benefits of DR programs.…”

Section: A Brief Overview Of Price‐based Demand Response Techniquesmentioning

confidence: 99%

Customer targeting optimization system for price-based demand response programs

Siebert

Aoki

Fernandes

et al. 2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary In a demand‐side management (DSM) program, a set of activities are designed to influence electricity consumption in a way that may produce desired changes in the utility's load shape. Detailed analysis when planning DSM programs is crucial because the utility intends to achieve technical and economic benefits but does not wish to lose revenue unnecessarily. To assist utilities in planning for price‐based demand response (DR) programs (an approach to DSM), this paper proposes an optimization system focused on the targeting of residential customers to enroll in a new time‐of‐use tariff. The developed system uses optimal power flow to estimate the reduction needed to achieve the proposed objectives of the DR program in each bus, followed by the selection of the customers that will provide this reduction via binary particle swarm optimization. The optimization system has been tested using real data acquired from a Brazilian utility including customers' load curves, radial distribution feeder data, a time‐of‐use tariff, and estimated elasticity matrices from the literature. The main results show that the system can be of great value supporting power utilities' implementation of price‐based DR programs.

show abstract

“…Finally, some works present methods for appliance management, protection and control, and scheduling for residential customers aiming to help them to maximize the benefits of DR programs.…”

Section: A Brief Overview Of Price‐based Demand Response Techniquesmentioning

confidence: 99%

Customer targeting optimization system for price-based demand response programs

Siebert

Aoki

Fernandes

et al. 2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…[11] defines a decentralized demand response approach that can be used to minimize the amount of residential power consumption, by maximizing the utilization of the generated power from wind energy resources. For a power system consisting of electric vehicles and wind renewable energy generators, the Distributed W-Learning (DWL) algorithm has been utilized.…”

Section: Literature Reviewmentioning

confidence: 99%

Cloud Based Demand Side Management Approach with Microgrid

2018

IJRTER

View full text Add to dashboard Cite

Abstract-To developand increase the expediency of living, Internet of things (IoT) these dayshas become a good research topic. The enhancement in the quality of human living can be increased by using varying home appliances, which can be quick and fast. Different home appliances provide diverse functions and services. Hence, in this research, the IOT based Demand Side Management is asignificant application of the upcoming Smart Grid. DSM programs let consumers to joinand participate in the operation of the electric grid by reducing or shifting their electricity usage during peak periods. This research focuses on cloud-based demand side management to control the residential load of customers equipped with local power generation and storage facilities as auxiliary sources of energy. A power system consisting of multiple regions and equipped with a number of microgrids can be considered. In each region an edge cloud is utilized to find the optimal power consumption schedule for customer appliances in that region. At the edge cloud, the power consumption level of local storage and the amount of power being demanded from both local storage facilities and power grid are scheduled using a bi-level optimization approach. The core cloud then gathers information of the total demand from consumers in different regions and finds the optimal power consumption schedule for each microgrid in the power system. The paper provides a study on the literature research, objectives of the cloud based model, methodology and basic requirements to be considered for the microgrid. The proposed model can reduce power consumption cost for the customers and improves the power grid in terms of peak load and peak-to-average load ratio.

show abstract

“…For instance, an energy consumption schedule for controllable appliances is described in [39]. Moreover, two different approaches for smart plug scheduling in an HEMS for DR programs with a time-of-use tariff are presented in [40]. One of the approaches makes use of centralized information received from several smart plugs that communicate with a home automation controller.…”

Section: Related Workmentioning

confidence: 99%

An Electricity Price-Aware Open-Source Smart Socket for the Internet of Energy

Blanco-Novoa

Fernández‐Caramés

Fraga‐Lamas

et al. 2017

Sensors

View full text Add to dashboard Cite

The Internet of Energy (IoE) represents a novel paradigm where electrical power systems work cooperatively with smart devices to increase the visibility of energy consumption and create safer, cleaner and sustainable energy systems. The implementation of IoE services involves the use of multiple components, like embedded systems, power electronics or sensors, which are an essential part of the infrastructure dedicated to the generation and distribution energy and the one required by the final consumer. This article focuses on the latter and presents a smart socket system that collects the information about energy price and makes use of sensors and actuators to optimize home energy consumption according to the user preferences. Specifically, this article provides three main novel contributions. First, what to our knowledge is the first hardware prototype that manages in a practical real-world scenario the price values obtained from a public electricity operator is presented. The second contribution is related to the definition of a novel wireless sensor network communications protocol based on Wi-Fi that allows for creating an easy-to-deploy smart plug system that self-organizes and auto-configures to collect the sensed data, minimizing user intervention. Third, it is provided a thorough description of the design of one of the few open-source smart plug systems, including its communications architecture, the protocols implemented, the main sensing and actuation components and the most relevant pieces of the software. Moreover, with the aim of illustrating the capabilities of the smart plug system, the results of different experiments performed are shown. Such experiments evaluate in real-world scenarios the system’s ease of use, its communications range and its performance when using HTTPS. Finally, the economic savings are estimated for different appliances, concluding that, in the practical situation proposed, the smart plug system allows certain energy-demanding appliances to save almost €70 per year.

show abstract

Centralized and decentralized approaches to demand response using smart plugs

Cited by 22 publications

References 9 publications

Customer targeting optimization system for price-based demand response programs

Customer targeting optimization system for price-based demand response programs

Cloud Based Demand Side Management Approach with Microgrid

An Electricity Price-Aware Open-Source Smart Socket for the Internet of Energy

Contact Info

Product

Resources

About