Load Shedding and Smart-Direct Load Control Using Internet of Things in Smart Grid Demand Response Management

Mortaji, Hamed; Ow, Siew Hock; Moghavvemi, Mahmoud; Almurib, Haider A. F.

doi:10.1109/tia.2017.2740832

Cited by 177 publications

(61 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another simulation is carried out to analyze the computa- According to the depicted results in Fig. 7 we can see that performing the DSM mechanism in a centralized manner (such as [65]) imposes the most computational burden to the system. Note that the MIP technique is based on the B&B methods (i.e., the "Centralized (B&B)" case).…”

Section: Numerical Resultsmentioning

confidence: 99%

A Robust Scalable Demand-Side Management Based on Diffusion-ADMM Strategy for Smart Grid

Latifi

Khalili

Rastegarnia

et al. 2020

IEEE Internet Things J.

View full text Add to dashboard Cite

Demand-side management (DSM) involves a group of programs, initiatives, and technologies designed to encourage consumers to modify their level and pattern of electricity usage. This is performed following methods such as financial incentives and behavioral change through education. While the objective of the DSM is to achieve a balance between energy production and demand, effective and efficient implementation of the program rests within effective use of emerging Internet of things (IoT) concept for online interactions. Here, a novel DSM framework based on diffusion and alternating direction method of multipliers (ADMM) strategies, repeated under a model predictive control (MPC) protocol, is proposed. On the demand side, the customers autonomously and by cooperation with their immediate neighbors estimate the baseline price in real time. Based on the estimated price signal, the customers schedule their energy consumption using the ADMM cost-sharing strategy to minimize their incommodity level. On the supply side, the utility company determines the price parameters based on the customers real-time behavior to make a profit and prevent the infrastructure overload. The proposed mechanism is capable of tracking drifts in the optimal solution resulting from the changes in supply/demand sides. Moreover, it considers all classes of appliances by formulating the DSM problem as a mixed-integer programming (MIP) problem. Numerical examples are provided to show the effectiveness of the proposed framework.

show abstract

Section: Numerical Resultsmentioning

confidence: 99%

A Robust Scalable Demand-Side Management Based on Diffusion-ADMM Strategy for Smart Grid

Latifi

Khalili

Rastegarnia

et al. 2020

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…It should be pre-armed with the corrective action schemes and the organization of urgency actions in distribution. It should take care of the previous assembly of recovery schemes and coordination of corrective actions in the distribution, registration, and reports [15].…”

Section: Advanced Automated Distributionmentioning

confidence: 99%

Effectiveness and Comparison of Digital Substations Over Conventional Substations

Waleed¹,

Virk²,

Riaz³

et al. 2019

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

The entire electrical system is changing at an abrupt speed since its beginning. With the rapid advance of renewable energies (which are constantly subject to fluctuations), the electricity generation is distributed to countless locations. The traditional model of the unidirectional flow of electricity is leaning towards multidirectional flows. This situation needs significant changes in the transport infrastructure of the electrical power system and mode of operation. Technological advancements are making a smarter and more organized management system of electricity supply and demand. This development needs to incorporate modern communication, monitoring, and control capabilities, along with the intelligent transport, generation, and storage value chain. Digital substations integrate thousands of sensors by providing greater comfort, self-reliance, availability, and safety while at the same time reducing costs, risks, and environmental damage. Conventional power system lacks digital communication, remote monitoring & control, and consumer participation. Fiber-optic cabling in digital substations will attain not only real-time data transmission but also remote maintenance. The comparison between conventional and digital substations has also been presented in this work along with latest trends in smart protection. Electronic protection and control devices based on a microprocessor, communicating with cloud servers, will enhance the flexibility to control and monitor the digital substations. A significant amount of data generated by components in the electrical substations will allow greater sophistication of the monitoring, diagnostic, protection and optimization capabilities of the facilities. The goal for the review of said advancements is to have a smart electrical system with greater efficiency and better user experience.

show abstract

“…Besides, natural gas-based CCHP has the advantage of lower fuel prices and lower carbon dioxide emissions compared to existing fossil fuel-based power generations [8]. For the efficient operation of CCHP, accurate short-term load forecasting (STLF) is required [9]. STLF is the basis of the design and implementation of the control strategy of the CCHP system, and the results of the STLF affect the overall energy efficiency of the system directly [10].…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling

et al. 2020

View full text Add to dashboard Cite

Smart grid systems, which have gained much attention due to its ability to reduce operation and management costs of power systems, consist of diverse components including energy storage, renewable energy, and combined cooling, heating and power (CCHP) systems. The CCHP has been investigated to reduce energy costs by using the thermal energy generated during the power generation process. For efficient utilization of CCHP and numerous power generation systems, accurate short-term load forecasting (STLF) is necessary. So far, even though many single algorithm-based STLF models have been proposed, they showed limited success in terms of applicability and coverage. This problem can be alleviated by combining such single algorithm-based models in ways that take advantage of their strengths. In this paper, we propose a novel two-stage STLF scheme; extreme gradient boosting and random forest models are executed in the first stage, and deep neural networks are executed in the second stage to combine them. To show the effectiveness of our proposed scheme, we compare our model with other popular single algorithm-based forecasting models and then show how much electric charges can be saved by operating CCHP based on the schedules made by the economic analysis on the predicted electric loads.

show abstract

Load Shedding and Smart-Direct Load Control Using Internet of Things in Smart Grid Demand Response Management

Cited by 177 publications

References 31 publications

A Robust Scalable Demand-Side Management Based on Diffusion-ADMM Strategy for Smart Grid

A Robust Scalable Demand-Side Management Based on Diffusion-ADMM Strategy for Smart Grid

Effectiveness and Comparison of Digital Substations Over Conventional Substations

A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling

Contact Info

Product

Resources

About