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.

/pdf/smart-grid-the-new-and-improved-power-grid-a-survey-4jypbbgv3j.pdf

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

Smart Grid - The New and Improved Power Grid:

The smart grid is conceived of as an electric grid that can deliver electricity in a controlled, smart way from points of generation to active consumers. Demand response (DR), by promoting the interaction and responsiveness of the customers, may offer a broad range of potential benefits on system operation and expansion and on market efficiency. Moreover, by improving the reliability of the power system and, in the long term, lowering peak demand, DR reduces overall plant and capital cost investments and postpones the need for network upgrades. In this paper a survey of DR potentials and benefits in smart grids is presented. Innovative enabling technologies and systems, such as smart meters, energy controllers, communication systems, decisive to facilitate the coordination of efficiency and DR in a smart grid, are described and discussed with reference to real industrial case studies and research projects.

Demand response and smart grids—A survey

Simulation and the monte carlo method

The paper reviews different approaches, technologies, and strategies to manage large-scale schemes of variable renewable electricity such as solar and wind power. We consider both supply and demand side measures. In addition to presenting energy system flexibility measures, their importance to renewable electricity is discussed. The flexibility measures available range from traditional ones such as grid extension or pumped hydro storage to more advanced strategies such as demand side management and demand side linked approaches, e.g. the use of electric vehicles for storing excess electricity, but also providing grid support services. Advanced batteries may offer new solutions in the future, though the high costs associated with batteries may restrict their use to smaller scale applications. Different “P2Y”-type of strategies, where P stands for surplus renewable power and Y for the energy form or energy service to which this excess in converted to, e.g. thermal energy, hydrogen, gas or mobility are receiving much attention as potential flexibility solutions, making use of the energy system as a whole. To “functionalize” or to assess the value of the various energy system flexibility measures, these need often be put into an electricity/energy market or utility service context. Summarizing, the outlook for managing large amounts of RE power in terms of options available seems to be promising.

https://research.aalto.fi/files/6557604/lund_et_al_review.pdf

Review of energy system flexibility measures to enable high levels of variable renewable electricity

“Smart grid” is a relatively new term that refers to the application of information technology to power systems. Due to the complexity of such systems; the possibilities have yet to be clearly defined. Drawing a clear distinction between one grid configuration being “smart” and another failing the criterion is not realistic. Instead, it is much more practical to consider the “smart grid” as a term defining opportunities to improve the operation of the power system. One of the key features of a smart grid is the ability to utilize information to make better operational decisions. This paper proposes that significant improvements can be made to the operations of a smart grid by providing information about the likely behavior of renewable energy - through both online short-term forecasting and longerterm assessments.

Building a smarter smart grid through better renewable energy information

The Western Wind and Solar Integration Study (WWSIS) is one of the world's largest regional integration studies to date. This paper discusses the creation of the wind dataset that will be the basis for assessing the operating impacts and mitigation options due to the variability and uncertainty of wind power on the utility grids. The dataset is based on output from a mesoscale numerical weather prediction (NWP) model, covering over 4 million square kilometers with a spatial resolution of approximately two-kilometers over a period of three years with a temporal resolution of 10 minutes. The mesoscale model dataset includes all the meteorological variables necessary to calculate wind energy production. Individual time series were produced for over 30 thousand locations representing more than 900 GW of potential wind power generation.

Creating the Dataset for the Western Wind and Solar Integration Study (U.S.A.)

Wind power continues its rapid worldwide growth. In some places wind energy penetration is so high that the variation in wind energy production is the dominant driving force behind variation in the generation-load balance. To ensure a reliable supply of power, system operators must be able to schedule sufficient operating reserves. However, it is impractical to always supply 100% back-up for all generation units. Typically the N-1 criterion is applied, establishing the effect of the loss of the single largest generation unit. Wind energy compels different operating requirements, because failure of an entire wind energy project is improbable, but rapid variation in energy output is common. In such an environment, wind energy forecasting has significant value, especially during times of rapid change, and the use of a probabilistic forecast tool can minimize reserve requirements.

Potential benefits of a dedicated probabilistic rapid ramp event forecast tool

This paper provides an overview of research into short-term prediction techniques to assist with the operation of windpower generators. Windpower provides a new challenge to generator operators. Unlike conventional power generation sources, windpower generators supply intermittent power, have no intrinsic ability for power storage and cannot be easily ramped up to meet requirements. However, windpower is presently the fastest growing power generation sector in the world; so these problems must be solved. To be able to operate effectively, accurate short-term forecasts are essential. Knowing the future generation output from wind turbines is useful for generators, schedulers, transmission operators, network managers and energy traders. However, the difficulties of short-term wind prediction are well documented. To solve this problem, this research introduces a novel approach - the application of an adaptive neural fuzzy inference system (ANFIS) to forecasting a wind time series. A persistence model is also created to provide a benchmark of the performance. To illustrate the techniques developed, a case study is presented based on the state of Tasmania, the major island, south of mainland Australia

Innovative Short-Term Wind Generation Prediction Techniques

This paper provides an overview of research into short-term prediction techniques to assist with the operation of windpower generators. Windpower provides a new challenge to generator operators. Unlike conventional power generation sources, windpower generators supply intermittent power, have no intrinsic ability for power storage and cannot be easily ramped up to meet requirements. However, windpower is presently the fastest growing power generation sector in the world; so these problems must be solved. To be able to operate effectively, accurate short-term forecasts are essential. Knowing the future generation output from wind turbines is useful for generators, schedulers, transmission operators, network managers and energy traders. However, the difficulties of short-term wind prediction are well documented. To solve this problem, this research introduces a novel approach - the application of an adaptive neural fuzzy inference system (ANFIS) to forecasting a wind time series. A persistence model is also created to provide a benchmark of the performance. To illustrate the techniques developed, a case study is presented based on the state of Tasmania, the major island, south of mainland Australia.

C Potter

Papers

Building a smarter smart grid through better renewable energy information

Creating the Dataset for the Western Wind and Solar Integration Study (U.S.A.)

Potential benefits of a dedicated probabilistic rapid ramp event forecast tool

Innovative Short-Term Wind Generation Prediction Techniques

Innovative short-term wind generation prediction techniques