Consumers as innovators in the electricity sector? Consumer perceptions on smart grid services

2016 ◽  
Vol 40 (6) ◽  
pp. 665-674 ◽  
Author(s):  
Eva Heiskanen ◽  
Kaisa Matschoss
Keyword(s):  
Smart Grid ◽  
Consumer Perceptions ◽  
Electricity Sector ◽  
Grid Services

Global Trends in the Political Economy of Smart Grids

2017 ◽  
Author(s):  
Cherrelle Eid ◽  
Rudi Hakvoort ◽  
Martin de Jong
Keyword(s):  
Smart Grid ◽  
Smart Grids ◽  
The United States ◽  
Industry Structure ◽  
Electricity Sector ◽  
Distributed Energy ◽  
Global Trends ◽  
Political Economic ◽  
Regulatory Models ◽  
Global Transition

The global transition towards sustainable, secure, and affordable electricity supply is driving changes in the consumption, production, and transportation of electricity. This chapter provides an overview of three main causes of political–economic tensions with smart grids in the United States, Europe, and China, namely industry structure, regulatory models, and the impact of energy policy. In all cases, the developments are motivated by the possible improvements in reliability and affordability yielded by smart grids, while sustainability of the electricity sector is not a central motivation. A holistic smart grid vision would open up possibilities for better integration of distributed energy resources. The authors recommend that smart grid investments should remain outside of the regulatory framework for utilities and distribution service operators in order to allow for such developments.

scholarly journals A Comparative Analysis of Enhanced Machine Learning Algorithms for Smart Grid Stability Prediction

2021 ◽  
Author(s):  
ANKIT GHOSH ◽  
ALOK KOLE
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Smart Grid ◽  
Smart Grids ◽  
Machine Learning Algorithms ◽  
Electricity Sector ◽  
Stochastic Gradient Descent ◽  
Energy Output ◽  
Gradient Boosting ◽  
Support Vector

<p>Smart grid is an essential concept in the transformation of the electricity sector into an intelligent digitalized energy network that can deliver optimal energy from the source to the consumers. Smart grids being self-sufficient systems are constructed through the integration of information, telecommunication, and advanced power technologies with the existing electricity systems. Artificial Intelligence (AI) is an important technology driver in smart grids. The application of AI techniques in smart grid is becoming more apparent because the traditional modelling optimization and control techniques have their own limitations. Machine Learning (ML) being a sub-set of AI enables intelligent decision-making and response to sudden changes in the customer energy demands, unexpected disruption of power supply, sudden variations in renewable energy output or any other catastrophic events in a smart grid. This paper presents the comparison among some of the state-of-the-art ML algorithms for predicting smart grid stability. The dataset that has been selected contains results from simulations of smart grid stability. Enhanced ML algorithms such as Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (KNN), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Stochastic Gradient Descent (SGD) classifier, XGBoost and Gradient Boosting classifiers have been implemented to forecast smart grid stability. A comparative analysis among the different ML models has been performed based on the following evaluation metrics such as accuracy, precision, recall, F1-score, AUC-ROC, and AUC-PR curves. The test results that have been obtained have been quite promising with the XGBoost classifier outperforming all the other models with an accuracy of 97.5%, recall of 98.4%, precision of 97.6%, F1-score of 97.9%, AUC-ROC of 99.8% and AUC-PR of 99.9%. </p>

scholarly journals 5G‐Enhanced Smart Grid Services

2019 ◽  
pp. 28-102
Author(s):  
Muhammad Ismail ◽  
Islam Safak Bayram ◽  
Khalid Qaraqe ◽  
Erchin Serpedin
Keyword(s):  
Smart Grid ◽  
Grid Services
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