Real-Time Load Consumption Prediction and Demand Response Scheme Using Deep Learning in Smart Grids

2019 ◽  
Author(s):  
Sara Atef ◽  
Amr B. Eltawil
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Demand Response ◽  
Smart Grids ◽  
Consumption Prediction ◽  
Response Scheme

Combining real-time processing streams to enable demand response in smart grids

2017 ◽  
Author(s):  
Ivana Kovacevic ◽  
Aleksandar Erdeljan ◽  
Srdan Vukmirovic ◽  
Nikola Dalcekovic ◽  
Jelena Stankovski
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Smart Grids ◽  
Real Time Processing ◽  
Time Processing

scholarly journals A cheat-proof game theoretic demand response scheme for smart grids

2012 ◽  
Author(s):  
Yan Chen ◽  
W. Sabrina Lin ◽  
Feng Han ◽  
Yu-Han Yang ◽  
Zoltan Safar ◽  
...  
Keyword(s):  
Demand Response ◽  
Smart Grids ◽  
Game Theoretic ◽  
Response Scheme

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

2019 ◽  
Vol 6 (1) ◽  
pp. 1136-1146 ◽  
Author(s):  
Luca Barbierato ◽  
Abouzar Estebsari ◽  
Enrico Pons ◽  
Marco Pau ◽  
Fabio Salassa ◽  
...  
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Smart Grids

A Real Time Price Based Demand-Response Algorithm for Smart Grids

2019 ◽  
Vol 20 (4) ◽  
Author(s):  
Pravat Kumar Ray ◽  
Valiveti Sai Rama Nikhil
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Optimal Strategy ◽  
Smart Grids ◽  
Optimization Problems ◽  
Stackelberg Game ◽  
Stackelberg Equilibrium ◽  
Electricity Trading ◽  
Retail Prices ◽  
Time Price

Abstract Real time Demand-Response algorithm is a real time price (RTP) based demand response (DR) algorithm which helps in obtaining optimal control of load devices in a facility. We form a virtual electricity trading process, where energy management center (EMC) calculates the virtual retail prices and allots to each device, from which devices purchases energy. Here EMC acts as the leader and devices in the facility are followers. So the Stackelberg game between one-leader and M-followers is formulated to extract interactions between them, and optimization problems are formed for each player of the game to assist in finding the optimal strategy. We also demonstrated the unique Stackelberg equilibrium of the Stackelberg game which gives the optimal strategy or optimal energy demands for each device. The results of the simulation showed that Stackelberg game based DR algorithm is successful to get the optimal load control of device at all RTP throughout the day.

scholarly journals Incentive compatible demand response games for distributed load prediction in smart grids

2014 ◽  
Vol 3 ◽  
Author(s):  
Yan Chen ◽  
W. Sabrina Lin ◽  
Feng Han ◽  
Yu-Han Yang ◽  
Zoltan Safar ◽  
...  
Keyword(s):  
Demand Response ◽  
Dynamic Pricing ◽  
Smart Grids ◽  
Demand Curve ◽  
Load Prediction ◽  
Incentive Compatible ◽  
Distributed Load ◽  
Power Substation ◽  
Game Theoretic ◽  
Response Scheme

While demand response has achieved promising results on making the power grid more efficient and reliable, the additional dynamics and flexibility brought by demand response also increase the uncertainty and complexity of the centralized load forecast. In this paper, we propose a game-theoretic demand response scheme that can transform the traditional centralized load prediction structure into a distributed load prediction system by the participation of customers. Moreover, since customers are generally rational and thus naturally selfish, they may cheat if cheating can improve their payoff. Therefore, enforcing truth-telling is crucial. We prove analytically and demonstrate with simulations that the proposed game-theoretic scheme is incentive compatible, i.e., all customers are motivated to report and consume their true optimal demands and any deviation will lead to a utility loss. We also prove theoretically that the proposed demand response scheme can lead to the solution that maximizes social welfare and is proportionally fair in terms of utility function. Moreover, we propose a simple dynamic pricing algorithm for the power substation to control the total demand of all customers to meet the target demand curve. Finally, simulations are shown to demonstrate the efficiency and effectiveness of the proposed game-theoretic algorithm.

scholarly journals IoT-Enabled Real-Time Management of Smart Grids with Demand Response Aggregators

2021 ◽  
pp. 1-1
Author(s):  
Abouzar Estebsari ◽  
Pietro Rando Mazzarino ◽  
Lorenzo Bottaccioli ◽  
Edoardo Patti
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Time Management ◽  
Smart Grids

scholarly journals A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5599
Author(s):  
Zeeshan Aslam ◽  
Nadeem Javaid ◽  
Ashfaq Ahmad ◽  
Abrar Ahmed ◽  
Sardar Muhammad Gulfam
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Ensemble Learning ◽  
Smart Grids ◽  
Power Efficiency ◽  
Short Term Memory ◽  
Support Vector ◽  
Smart Meters ◽  
Adaptive Boosting ◽  
Electricity Theft

Electricity is widely used around 80% of the world. Electricity theft has dangerous effects on utilities in terms of power efficiency and costs billions of dollars per annum. The enhancement of the traditional grids gave rise to smart grids that enable one to resolve the dilemma of electricity theft detection (ETD) using an extensive amount of data formulated by smart meters. This data are used by power utilities to examine the consumption behaviors of consumers and to decide whether the consumer is an electricity thief or benign. However, the traditional data-driven methods for ETD have poor detection performances due to the high-dimensional imbalanced data and their limited ETD capability. In this paper, we present a new class balancing mechanism based on the interquartile minority oversampling technique and a combined ETD model to overcome the shortcomings of conventional approaches. The combined ETD model is composed of long short-term memory (LSTM), UNet and adaptive boosting (Adaboost), and termed LSTM–UNet–Adaboost. In this regard, LSTM–UNet–Adaboost combines the advantages of deep learning (LSTM-UNet) along with ensemble learning (Adaboost) for ETD. Moreover, the performance of the proposed LSTM–UNet–Adaboost scheme was simulated and evaluated over the real-time smart meter dataset given by the State Grid Corporation of China. The simulations were conducted using the most appropriate performance indicators, such as area under the curve, precision, recall and F1 measure. The proposed solution obtained the highest results as compared to the existing benchmark schemes in terms of selected performance measures. More specifically, it achieved the detection rate of 0.92, which was the highest among existing benchmark schemes, such as logistic regression, support vector machine and random under-sampling boosting technique. Therefore, the simulation outcomes validate that the proposed LSTM–UNet–Adaboost model surpasses other traditional methods in terms of ETD and is more acceptable for real-time practices.

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