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

This paper presents a methodology to determine the energy and reserve schedules with consideration of power system uncertainties. Unlike contingency analysis, the requirements of operating reserves based on the stochastic optimal power flow are not predefined. Instead, the reserve requirement is generated to meet both the system reliability and economic efficiency under system uncertainties. The stochastic optimal power flow with reserve determination is proposed and studied with different types of uncertainties, including variation of system demand, outages of transmission lines and generation units, and intermittent generation. This study emphasizes the impact of the intermittent generation on the energy schedule and reserve determination, and so provides a new methodology for addressing emerging system issues with the emergence of renewable energy. Exploring a variety of cases reveals the differences between contingency analysis and risk-based motivations for reserve.

Reserve Determination with Stochastic Optimal Power Flow

This work proposes a practical two-stage architecture for online voltage stability margin estimation using statistical models and classification techniques. The approach models the relationship between reactive power reserves and voltage stability margin using multi-linear regression models. In order to handle uncertainty related to variable loading conditions and network topology, a few regression models are required. A classification tool is then developed in order to discern which regression model should be employed at any given operating condition. Both the regression models and the classification tool are developed offline form a database generated through a comprehensive VSA. The methodology is implemented on a reduced case of the U.S. eastern interconnection, which contains around 21k buses. The studied area represents a large part of the state of Iowa and small portions of neighboring states. NERC category B, C and D contingencies have been considered in the study. Several load increase directions are used in order to account for uncertainty in load variation. Results have shown that the methodology can successfully estimate voltage stability margin in the presence of uncertainty related to variable loading condition and network topology.

A practical two-stage online voltage stability margin estimation method for utility-scale systems

This paper proposes a new method of computing the probability distribution functions of line flows using combined Cumulants and Gram-Charlier expansion method. This new method significantly reduces the computation burden so that it can be used as a quick screening tool to determine the major investment on reinforcing transmission networks. This allows system planners to perform probabilistic analysis of large-scale power systems within reasonable time frame.

A new computation method for probabilistic load flow study

The power industry has undergone the significant restructuring throughout the world since the 1990s. In particular, its traditional, vertically monopolistic structure has been reformed into competitive markets in pursuit of increased efficiency in the electricity production and utilization. Along with the introduction of competitive and deregulated electricity markets, some power system problems have become difficult to analyse with traditional methods, especially when power system stability, reliability, and planning problems are involved. Traditionally, the power system analysis was based on deterministic frameworks; but they only consider the specific configurations, which ignore the stochastic or probabilistic nature of real power systems. Moreover, many exterior constraints as well as growing system uncertainties now need to be taken into consideration. All these have made existing challenges even more complex. One consequence is that more effective and efficient power system analysis methods are required in the deregulated, market-oriented environment. The mature theory background has facilitated effective employment of probabilistic based analysis methods. The study of probabilistic approaches based power system analysis has become highly important.

Probabilistic vs Deterministic Power System Stability and Reliability Assessment

The efficient restoration after large-scale electric interruption is an important subject for ensuring the secure operation of power systems. This paper applies the concept of "generic restoration milestones (GRMs)" to develop two different restoration strategies of Chongqing power system. One adopts build-outward approach that utilizes internal hydropower as black-start units. Another adopts build-inward approach that utilizes external power source as black-start units. Comparison study results show that the build-inward strategy uses less time than the build-outward strategy, thereby promotes the process of restoration. It can assist system operators in selecting an optimal restoration plan.

Pei Zhang

Papers

Reserve Determination with Stochastic Optimal Power Flow

A practical two-stage online voltage stability margin estimation method for utility-scale systems

A new computation method for probabilistic load flow study

Probabilistic vs Deterministic Power System Stability and Reliability Assessment

Comparison study on restoration strategies of chongqing power grid