Archivo para enero, 2016


Evolution of electricity metering

Electricity meters are used to measure the quantity of electricity supplied to customers as well as to calculate energy and transportation charges for electricity retailers and network operators. The most common type of meter is an accumulation meter, which records energy consumption over time. Accumulation meters in consumer premises are read manually to assess how much energy has been used within a billing period. In recent years, industrial and commercial consumers with large loads have increasingly been using more advanced meters, for example, interval meters which record energy use over short intervals, typically every half hour. This allows the energy suppliers to design tariffs and charging structures that reflect wholesale prices and helps the customers understand and manage their pattern of electricity demand. Smart meters are even more sophisticated as they have two-way communications and provide a real-time display of energy use and pricing information, dynamic tariffs and facilitate the automatic control of electrical appliances

Source:
SMART GRID
TECHNOLOGY AND APPLICATIONS
Janaka Ekanayake
Cardiff University, UK
Kithsiri Liyanage
University of Peradeniya, Sri Lanka
Jianzhong Wu
Cardiff University, UK
Akihiko Yokoyama
University of Tokyo, Japan
Nick Jenkins
Cardiff University, UK
A John Wiley & Sons, Ltd., Publication

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Possible communication infrastructure for the Smart Grid

The communication infrastructure of a power system typically consists of SCADA systems with dedicated communication channels to and from the System Control Centre and a Wide Area Network (WAN). Some long-established power utilities may have private telephone networks and other legacy communication systems. The SCADA systems connect all the major power system operational facilities, that is, the central generating stations, the transmission grid substations and the primary distribution substations to the System Control Centre. The WAN is used for corporate business and market operations. These form the core communication networks of the traditional power system. However, in the Smart Grid,it is expected that these two elements of communication infrastructure will merge into a Utility WAN.
An essential development of the Smart Grid (see figure ) is to extend communication throughout the distribution system and to establish two-way communications with customers through Neighbourhood Area Networks (NANs) covering the areas served by distribution substations. Customers’ premises will have Home Area Networks (HANs). The interface of the Home and Neighbourhood Area Networks will be through a smart meter or smart interfacing device.

Source:
SMART GRID
TECHNOLOGY AND APPLICATIONS
Janaka Ekanayake
Cardiff University, UK
Kithsiri Liyanage
University of Peradeniya, Sri Lanka
Jianzhong Wu
Cardiff University, UK
Akihiko Yokoyama
University of Tokyo, Japan
Nick Jenkins
Cardiff University, UK
A John Wiley & Sons, Ltd., Publication


Architecture of a DMSC

 

The figure shows the DMSC controller building blocks that assess operating conditions and find the control settings for devices connected to the network. The key functions of the DMSC are state estimation, bad data detection and the calculation of optimal control settings. The DMSC receives a limited number of real-time measurements at set intervals from the network nodes. The measurements are normally voltage, load injections and power flow measurements from the primary substation and other secondary substations. These measurements are used to calculate the network operating conditions. In addition to these real-time measurements, the DMSC uses load models to forecast load injections at each node on the network for a given period that coincides with the real-time measurements. The network topology and impedances are also supplied to the DMSC.
The state estimator uses this data to assess the network conditions in terms of node voltage magnitudes, line power flows and network injections. Bad measurements coming to the system will be filtered using bad data detection and identification methods.

Source:
SMART GRID
TECHNOLOGY AND APPLICATIONS
Janaka Ekanayake
Cardiff University, UK
Kithsiri Liyanage
University of Peradeniya, Sri Lanka
Jianzhong Wu
Cardiff University, UK
Akihiko Yokoyama
University of Tokyo, Japan
Nick Jenkins
Cardiff University, UK
A John Wiley & Sons, Ltd., Publication


Distribution network active management scheme

The Figure is a schematic of a simple distribution network with distributed generation (DG).There are many characteristics of this network that differ from a typical passive distribution network. First, the power flow is not unidirectional. The direction of power flows and the voltage magnitudes on the network depend on both the demand and the injected generation. Second, the distributed generators give rise to a wide range of fault currents and hence complex protection and coordination settings are required to protect the network. Third, the reactive power flow on the network can be independent of the active power flows. Fourth, many types of DGs are interfaced through power electronics and may inject harmonics into the network. The Figure also shows a control scheme suitable for achieving the functions of active control. In this scheme a Distribution Management System Controller (DMSC) assesses the network conditions and takes action to control the network voltages and flows. The DMSC obtains measurements from the network and sends signals to the devices under its control. Control actions may be a transformer tap operation, altering the DG output and injection/absorption
of reactive power.

Source:
SMART GRID
TECHNOLOGY AND APPLICATIONS
Janaka Ekanayake
Cardiff University, UK
Kithsiri Liyanage
University of Peradeniya, Sri Lanka
Jianzhong Wu
Cardiff University, UK
Akihiko Yokoyama
University of Tokyo, Japan
Nick Jenkins
Cardiff University, UK
A John Wiley & Sons, Ltd., Publication

 


Curves_of_Wind_Turbine_100_kW_made_for_Jorge_MIREZ

This is the results of the mathematic modeling and numerical simulation of a wind turbine of 100 kW capacity nominal. The attack angle is optimized for best catch of energy contain in the wind, with it, power coefficient is modified. The speed range of this simulation is from 0 to 25 m/s, with start speed in 4 m/s.


 

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