A Distributed IoT Infrastructure to Test and Deploy Real-Time Demand Response in Smart Grids

Barbierato, Luca; Estebsari, Abouzar; Pons, Enrico; Pau, Massimiliano; Salassa, Fabio; Ghirardi, Marco; Patti, Edoardo

doi:10.1109/jiot.2018.2867511

Cited by 72 publications

(36 citation statements)

References 48 publications

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“…Demand response (DR) is a class of DSM mechanisms that reschedules users' energy pattern usage according to the responses to electricity price or the incentives of the power utility [15]. DR keeps consumer in the loop and makes them aware of energy issues.…”

Section: Demand Responsementioning

confidence: 99%

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Estebsari

Rajabi

2020

Electronics

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The integration of more renewable energy resources into distribution networks makes the operation of these systems more challenging compared to the traditional passive networks. This is mainly due to the intermittent behavior of most renewable resources such as solar and wind generation. There are many different solutions being developed to make systems flexible such as energy storage or demand response. In the context of demand response, a key factor is to estimate the amount of load over time properly to better manage the demand side. There are many different forecasting methods, but the most accurate solutions are mainly found for the prediction of aggregated loads at the substation or building levels. However, more effective demand response from the residential side requires prediction of energy consumption at every single household level. The accuracy of forecasting loads at this level is often lower with the existing methods as the volatility of single residential loads is very high. In this paper, we present a hybrid method based on time series image encoding techniques and a convolutional neural network. The results of the forecasting of a real residential customer using different encoding techniques are compared with some other existing forecasting methods including SVM, ANN, and CNN. Without CNN, the lowest mean absolute percentage of error (MAPE) for a 15 min forecast is above 20%, while with existing CNN, directly applied to time series, an MAPE of around 18% could be achieved. We find the best image encoding technique for time series, which could result in higher accuracy of forecasting using CNN, an MAPE of around 12%.

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Section: Demand Responsementioning

confidence: 99%

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Estebsari

Rajabi

2020

Electronics

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“…This study discusses the EAP software which is providing the services of demand response and demand side management. Further, the study is pointed out the GrAI algorithm which can be embedded into EAP Virtual box and proposed framework [5]. Abrar et al (2018) have proposed a real time load management using advanced infrastructure metering.…”

Section: Review Literaturementioning

confidence: 99%

Energy Efficient Load Optimization Techniques for Smart Grid with Futuristic Ideas

Kumar¹,

Singh²

2019

IJEAT

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The electrical network that is integrated with Internet of Things and synchronize the data in full duplex mode. The electrical grid is consists the generation, transmission, and distribution unit. A distribution unit is supply the energy to domestic and commercial areas. Our appliances need a rated voltage along with specified frequency. But the present scenario we have facing various type of challenges. Sometime our appliances received voltage from source is less than or greater than by its required rating. To overcome such type complications world requirement a next generation grid which fulfill our requirement as per specified at time of modeling and design of equipment/ machines. The smart grid consists the existing grid along with smart sensors, smart equipment, and real time monitoring & centralized controlling capabilities systems. We have performed a review of several research papers and occurred research gaps are discussed in this paper. This research paper is revealing the existing research gap and propose a solution to overcome occurred limitation

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“…Digital Object Identifier XXXXX/XXXXXXX DSM implementation requires real-time information exchange among the electric companies, power equipments and smart meter for each user. Therefore, IoT technology has realized the possibility for implementation of DSM programs in smart grids [2]- [4]. Nevertheless, there are some challenges in using the DSM programs.…”

Section: Introductionmentioning

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.

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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.

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A Distributed IoT Infrastructure to Test and Deploy Real-Time Demand Response in Smart Grids

Cited by 72 publications

References 48 publications

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Energy Efficient Load Optimization Techniques for Smart Grid with Futuristic Ideas

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

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