A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1315&context=electricalengineeringfacpub

A Survey on Smart Grid CommunicationInfrastructures: Motivations, Requirements andChallenges

Energy is vital for sustainable development of any nation – be it social, economic or environment. In the past decade energy consumption has increased exponentially globally. Energy management is crucial for the future economic prosperity and environmental security. Energy is linked to industrial production, agricultural output, health, access to water, population, education, quality of life, etc. Energy demand management is required for proper allocation of the available resources. During the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs. In this paper an attempt is made to review the various energy demand forecasting models. Traditional methods such as time series, regression, econometric, ARIMA as well as soft computing techniques such as fuzzy logic, genetic algorithm, and neural networks are being extensively used for demand side management. Support vector regression, ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting. Bottom up models such as MARKAL and LEAP are also being used at the national and regional level for energy demand management.

Energy models for demand forecasting—A review

Thank you for reading voltage stability of electric power systems. Maybe you have knowledge that, people have search hundreds times for their chosen books like this voltage stability of electric power systems, but end up in infectious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some harmful bugs inside their desktop computer. voltage stability of electric power systems is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the voltage stability of electric power systems is universally compatible with any devices to read.

Voltage Stability Of Electric Power Systems

Electric load forecasting by support vector model

Energy planning models (EPMs) play an indispensable role in policy formulation and energy sector development. The forecasting of energy demand and supply is at the heart of an EPM. Different forecasting methods, from statistical to machine learning have been applied in the past. The selection of a forecasting method is mostly based on data availability and the objectives of the tool and planning exercise. We present a systematic and critical review of forecasting methods used in 483 EPMs. The methods were analyzed for forecasting accuracy; applicability for temporal and spatial predictions; and relevance to planning and policy objectives. Fifty different forecasting methods have been identified. Artificial neural network (ANN) is the most widely used method, which is applied in 40% of the reviewed EPMs. The other popular methods, in descending order, are: support vector machine (SVM), autoregressive integrated moving average (ARIMA), fuzzy logic (FL), linear regression (LR), genetic algorithm (GA), particle swarm optimization (PSO), grey prediction (GM) and autoregressive moving average (ARMA). In terms of accuracy, computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data. However, hybrid methods yield better accuracy than that of the stand-alone ones. Statistical methods are useful for only short and medium range, while CI methods are preferable for all temporal forecasting ranges (short, medium and long). Based on objective, most EPMs focused on energy demand and load forecasting. In terms geographical coverage, the highest number of EPMs were developed on China. However, collectively, more models were established for the developed countries than the developing ones. Findings would benefit researchers and professionals in gaining an appreciation of the forecasting methods, and enable them to select appropriate method(s) to meet their needs.

/pdf/forecasting-methods-in-energy-planning-models-443zr1kmvi.pdf

Forecasting methods in energy planning models

The use of renewable energy sources has increased year-on-year. Thus, there is an increasing rate of small generating units connected directly to distribution networks and micro-grids close to consumers. At the same time, these micro-sources must provide stability and reliability of electrical energy to the power network to which they are connected. In the technical literature, several studies have been done to ensure power systems with traditional generating sources to operate in a stable and reliable way, but there is an issue regarding generation uncertainty when a distribution system has many micro-sources. This is because of the uncertainty of primary sources, for example, wind and radiation intensity, and could result in intermittent generation. In this study, stability and reliability of voltage in a power system with distributed generation is analysed using simulation techniques. In the proposed method in this study voltage security analysis is jointly considered with probability laws. Moreover reliability theory is also considered in the proposed voltage collapse analysis methodology. The responsibility of generator in the voltage collapse process, the probabilistic risk of voltage collapse of each operating point and the probability of enlarging the system load as a function of different operating points are the outcome of the methodology, and it is validated by using the IEEE34 test feeder.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rpg.2012.0265

Probabilistic voltage stability assessment considering renewable sources with the help of the PV and QV curves

Long-term load forecasting via a hierarchical neural model with time integrators

This study deals with the problem of voltage security in microgrids. In general, voltage security is an issue for power systems, where the lack of reactive power is a concern. For microgrids a complexity is imposed, since frequency and voltage level deviations may take place by the operation of droop-controlled inverters. This study incorporates the load margin calculation into a microgrid structure. For this purpose, a special power flow program is developed in order to consider the effects of the droop-controlled inverters. Simulation results are obtained with the help of an islanded distribution system with only generators coupled by droop-controlled inverters, so the proposed methodology may be tested and discussed.

/pdf/voltage-security-in-ac-microgrids-a-power-flow-based-1dvwjmp0ar.pdf

Voltage security in AC microgrids: a power flow-based approach considering droop-controlled inverters

Microgrids Design and Implementation

The paper proposes the use of the multilayer perceptron model to the problem of detecting ham and spam e-mail patterns. It also proposes an intensive use of data pre-processing and feature selection methods to simplify the task of the multilayer perceptron in classifying ham and spam e-mails. The multilayer perceptron is trained and assessed on patterns extracted from the SpamAssassin Public Corpus. It is required to classify novel types of ham and spam patterns. The results are presented and evaluated in the paper.

Antonio Carlos Zambroni de Souza

Papers

Probabilistic voltage stability assessment considering renewable sources with the help of the PV and QV curves

Long-term load forecasting via a hierarchical neural model with time integrators

Voltage security in AC microgrids: a power flow-based approach considering droop-controlled inverters

Microgrids Design and Implementation

A neural model in anti-spam systems