The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system. We also propose possible future directions in each system. colorred{Specifically, for the smart infrastructure system, we explore the smart energy subsystem, the smart information subsystem, and the smart communication subsystem.} For the smart management system, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection system, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.

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Smart Grid — The New and Improved Power Grid: A Survey

Smart Grid - The New and Improved Power Grid:

As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households.

Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

Trends in extreme learning machines

Simulation and the monte carlo method

As electricity industry reforms continue, utility companies will face more challenges. Meanwhile, the advent of Energy Internet Era brings new opportunities to utility companies. This paper first analyzes the impact of Energy Internet on electricity industry. Secondly, this paper investigates business models under Energy Internet era and proposes categorizing them into four types: distributed generation suppliers, customized service providers, energy platform operators and integrated energy service providers. Each business model is expounded by analyzing business cases. Finally, suggestions on how to choose the right business model are given based on the characteristics of different companies.

Investigations of Business Models Under Energy Internet Era

This paper proposes a new method for transient stability analysis in terms of measuring critical clearing time using grid-computing technology. Grid computing is a highly developed system for jointly solving collaborative complex problems; those are required to solve in limited time and therefore they need high performance computing. Probabilistic transient stability analysis is one such application, which requires a lot of computation for accurate results. This paper presents the grid computing based approach for this purpose, which is able to measure the critical clearing time through time domain simulation by using this method. A prototype system is developed in our research lab in order to deploy this technique. Experiments are performed on the prototype grid for probabilistic transient stability analysis on New England 39 bus test system. Results show that this method has capability of providing accurate results with better performance.

Probabilistic Transient Stability Analysis using Grid Computing Technology

High GDP growth coupled with extensive power consumption of users lead to the rapid growth of peak load, causing the increase of the gap between peak and valley load and the dropping of the power utilization. In order to reduce peak-valley load difference and improve load rate effectively, this paper proposed a new idea for peak load shifting management faced with smart grid. The paper uses big data technology for electricity users' pattern recognition, and applies it to peak load shifting management. This kind of peak load shifting potential analysis of users based on pattern recognition can provide the basis for the management of power supply companies, and cover the shortage of extensive management before, at the same time it supports peak load shifting management as a long-term work to implement.

Electricity consumption pattern recognition based on the big data technology to support the peak shifting potential analysis

Big data has emerged as an attractive concept in various industry domains. For power engineering, enormous amounts of data from different devices, different geographical locations of the po...

Pei Zhang

Papers

Investigations of Business Models Under Energy Internet Era

Probabilistic Transient Stability Analysis using Grid Computing Technology

Electricity consumption pattern recognition based on the big data technology to support the peak shifting potential analysis

Power Big Data: New Assets of Electric Power Utilities

Contingency set partition-based impact transfer approach for the reliability assessment of composite generation and transmission systems