IEEE Paper

The 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications 15-17 September 2011, Prague, Czech Republic

A System Concept of an Energy Information System in Flats Using Wireless Technologies and Smart Metering Devices Ingo Kunold, Markus Kuller, Joerg Bauer, Nursi Karaoglan Institute of Communications Technology, Dortmund University of Applied Sciences and Arts, Sonnenstrasse 96-100 44139 Dortmund Germany, [email protected], http://www.ikt.fh-dortmund.de Abstract—The solid supply with electrical power in Europe needs the development of smart grids [1], [2]. To solve the control problems of these grids, smart metering devices and energy information systems (SCADA) are needed on the carrier level [3]. Considering private households and small enterprises, it is therefore required to replace plain old energy meters by modern smart metering components. Smart meters offer a lot of new features, for example handling of different dynamic tariffs and in addition to their carrier interface a data access capability for in-house applications. Using these capabilities an embedded in-house energy information system with a smart energy controller (SEC) will be proposed, which allows displaying real-time data information and analysis of power consumption as well as power generation. The concept is also useful to combine smart home applications with smart grid functions, if data exchange with a multi utility controller (MUC) with SML [4], and smart meter devices, e.g. eHZ [4] is used. In that case, in connection with actors, demand side management functions may be executed by the controller. This was one of the intentions of the e-energy projects EENEAS, e-energy@home and E-DeMa1.

On the one hand, especially for energy efficiency in smart grids with renewable energy components, it is helpful to control the load in smart grids in order to respond to variation of regenerative power feeding. In this case, control energy can be saved in a smart grid. On the other hand, rational use of energy means the consumption of energy in the case of an (temporary) “oversupply” in the grid. So it is useful for the grid control to be able to control the load. There are two possibilities to achieve this. First, tariff changes depending on the available power. Then, a local controller can switch the suitable loads according to predefined conditions. Second, the grid control is allowed to switch these suitable loads by a control message. The SCADA system, being the global information system of the smart grid, is able to provide internet access to historical smart metering data. However, the delay of data transfer from a smart meter via the data base of a SCADA system to an internet user is too large for real time visualization. In this case, a local smart energy controller is useful to carry out these functions (Fig. 1).

Keywords— smart grid; smart metering; demand side managemen; e-energy; wireless communication; user interfaces; mobile devices; multi utility controller; in-house power management

I.

INTRODUCTION

Most of today´s power grids are organized in a hierarchical order. Power plants insert electrical power at an entry point and the consumers extract the energy at different places in the grid. In the future power grids will migrate to smart grids and the consumer will migrate to a so called prosumer [3], i.e. he will not only consume power, but he will produce it e.g. by photovoltaic or wind energy plants. To control, manage, maintain and account a distributed system, which contains these generation units, it is needed to develop energy information systems as developed in the with several data interfaces [5] for the different roles in an energy supply environment.

Figure 1.

A smart energy controller as an embedded system with low power consumption is useful for smart metering data

1

Funded by the Federal Government of Germany, the Federal State Government of North-Rhine-Westfalia, RWE AG and DEW21 GmbH

978-1-4577-1425-2/11/$26.00 ©2011 IEEE

System overview of the communication processes in an e-energy flat environment

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processing, in-house data communication and load control. II.

Data communication and handling between the controller and metering as well as switchable devices will be done by the OSGI framework [12]. From the application view, the controller software is arranged in 4 sections,

RELATED WORK

In 2006 the European Commission started community research on the smart grid model. Several European projects are currently working [2] on different aspects in this field. In Germany six research projects in model regions are currently funded by the national government. They are in progress since January 2009. These projects shall develop energy information systems and applications for smart grids. Goals are the lowering of emissions, the reduction of power consumption, the integration of renewable energy components as well as the development of an e-energy market place of the future. III.

E_ASM, for management and control of the whole multithreading system.

2.

ESA, i.e. energy storage archive, with E_M and E_DB for rapid data access of real time data (E_M), system conditions, parameters and historical data (E_DB). Time critical data are held in system memory by the E_M-module and periodical saved by reducing the sampling rate to E_DB for long term storage.

3.

EDU, i.e. energy device unit. Any data access for data acquisition control or visualization is carried out by the EDU , which allows data access with different data formats e.g. SML, Mbus protocol and different physical interfaces, e.g. ZigBee, Mbus, KNX or standard interfaces like LAN, WLAN for in-house communications and mobile and remote access.

4.

ESU, i.e. energy services unit. The component E_UI realizes object oriented data access for the JavaFX rich internet application for browser or desktop use. The component E_DSM accomplishes the schedule management for switchable loads as well as tariff dependent and conditioned control functions, e.g. for charge control. (Condition data are held in the E_M module for fast data access and real time use.) E_NTM organizes tariff dependent time shift of loads and generates device specific switching conditions.

METHODOLOGY

Based on the metering and control model depicted in Fig. 2, a hardware structure was configured [6] based on smart metering components for gas, water and electric power. Standard interfaces and bus systems as LAN WLAN, wireless Mbus [7], [8], KNX [9] were used to communicate with the different devices. This basic structure was completed with additional sensors and actors, e.g. using ZigBee [10], [11] or KNX components.

Figure 2.

1.

Hardware structure of a smart metering and control environment

The generic device driver module handles the different devices and converts all data to a system standard format and reads/writes data from/to the database as any other module of the controller. The energy service unit has to handle the interfaces. For data transmission between controller and a thin client in a flat environment an encrypted LAN or WLAN data exchange is intended using VPN and/or SSL/TLS.

An embedded system, running Linux or embedded WinXP with the mentioned interfaces, has been defined for the smart energy controller. The system software is implemented on a Java virtual machine. For easy expandability for other interfaces, a modular software concept as depicted in Fig. 3 has been developed.

IV.

Figure 3.

SENSOR AND ACTOR COMMUNICATIONS

Smart metering data deliver the sum of power consumption of a flat. In order to detect detailed power consumption and for power switching of different devices, to limit the sum, it is useful to gather more information about the behavior of the relevant in-house devices. Additional metering data from these devices, e.g. heating or cooling systems, dryer, washer and others are needed. In this case, it is useful to get KNX or ZigBee sensor and actor systems.

Software structure of the smart energy controller

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send to the smart energy controller. When this message is received, the service thread starts data exchange until ready state is detected. Sensor actor communications poses installation problems based on the ZigBee technology. Primarily in multiple dwelling units, where various ZigBee network systems could be installed in the future, i.e. one for each apartment, installation problems, for instance by adding new actors / sensors, have forced to be reckoned with. The following considerations are based on a ZigBee star communication system with two types of nodes: FullFunctionDevices (FFD) and ReducedFunctionDevices (RFD). As implied by the wording, FFDs have full standard functions. As a rule, they serve administrational functions within the communication network. Therefore, they cannot be operated by batteries, such as coordinators. RFDs are sensors such as temperature sensors or multiple sensors or actors. They have reduced standard functions and remain in a low-current mode most of the time. In a star network topology one FFD per network assumes the superior role of a PAN (Personal Area Network) coordinator. It asses the PAN identifier that separates the network from other IEEE-802.15.4 networks within radio range, while, in Slotted Mode, it synchronises all nodes. A network can consist of a total of 254 nodes. One problem is to solve in this case: As soon as there are various PAN coordinators for one end device only which allows admission, login at a specified coordinator is no longer guaranteed as can be seen in Fig. 6. A solution for this problem is: The ZigBee API command NJ (configuration of the permit joining property) enables the coordinator to reduce the access of other devices. Access can be allowed by default, thus posing the above mentioned problems, or a time frame is set, where access is granted. With the parameter "Permit Joining" < 0xFF (NJ=0xFF, coordinator allows access at any time), devices such as routers or end devices can register within a set time frame (max. 254 seconds).

For continuous measurements and switching, a ZigBee sensor actor communication with heartbeat is helpful. Fig. 4 shows an example of a communication diagram which is implemented for the smart energy controller using ZigBee.

Figure 4.

ZigBee commnication diagram using “heartbeat” loops2

If no continuous data acquisition is needed, it is helpful to use threshold functions of the sensor actor devices (Fig. 5).

Figure 5.

ZigBee commnication diagram using “on demand” loops

In order to detect the “switched on” state of a device, this predefined threshold level initializes that a message is 2

MV: power consumption of the device; measured by the sensor; ML: configurable threshold; Sensor: power measurement; Actor: on-off switch; SEC: smart energy controller; Dev.: device; DevInfo: device information (e.g. actor status, power consumption); POW: power

Figure 6.

Interacting PAN coordinators in multiple Zigbee networks

In order to allow a successful installation or expansion of a ZigBee network, the smart energy controller offers a

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configuration facility to the prosumer via the user interface. The Permit Joining can be parameterized at the coordinator by using a software switch. The access function for the network is automatically disabled when the time slot has elapsed. Therefore, the permanent disablement of the joining function for ZigBee coordinators in operation mode should be considered. So access can be allowed only by the user for a defined time frame. V.

USER INTERFACE

The user interface for non mobile devices was designed as a thin client browser application. The user interface shows a solution of the visualization of a time variable tariff for one day in Fig. 7. The different tariff zones are colored with green for the cheapest tariff, yellow for the normal tariff and red for max. tariff. The indicator shows the actual time on a 24 hours watch.

Figure 7.

Figure 8.

Visualization of actual power consumption at a UI measured by a smart meter

Real time metering data are stored during a window in data memory of the controller (E_M module). After that, power consumption data are stored with lower data rate in the system database for a period up to one year. This is done in order to generate long term reference data. For even longer terms, data have to be saved externally, using an advisory rich client application for further data analysis. Fig. 9 shows an example for data analysis of the power consumption at the UI for the last day. This shows the possibilities to visualize the short term specific power consumption behavior and to find a basic daily criterion to save energy costs on one hand and to shift energy load in a smart grid in a daily period on the other hand.

User interface example of the visualization of time varying tariffs (3 tariffs: low, middle, high)

Fig. 8 shows the actual power consumption for real time view at the UI. This needs a low delay of the data transmission channel. The user interface is refreshed every 2 seconds (transmission frequency). The packet length amounts 73/99 byte for request/response (TCP-Payload). The SEC provides the data via a TCP/IP connection for the user interface (fig. 3, ESA/E_M for rapid data access of real time data). Asynchronously, the SEC and the electronic domestic supply meter communicate via another TCP/IP connection. Using the application protocol SML3 the SEC (source) records data of the smart meter sampled per second (transmission frequency) in a request/response process at a packet length of 165/295 bytes. The packet length corresponds to the TCP-payload with SSL4. The RTT5 is recoded at 389.53 msec6 for an SML request.

Figure 9.

Visualization of Power consumption history (last day) depending on time variing tariffs

The user interface for mobile devices was designed as a thin client browser application, as well. Data communication over the internet is protected, using VPN or a peer-to-peer (PTP) modem channel. Data transfer was implemented, using web services with SOAP (Simple Object Access Protocol) and Web Service Description Language (WSDL) - both based on XML (Extensible Markup Language). Mobile devices based on Android, Windows mobile and Apple iOS will be supported.

3

Smart Message Language Secure Sockets Layer 5 Round Trip Time 6 Average value for 100 SML requests (100 Mbit ethernet without additional significant net load). 4

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VI.

VII. RESUME

LOAD CONTROL EXAMPLE

The DSM module can be used to control shiftable loads by condition estimation in an automatic way. Clearly, the better the system behavior is known, the better is the estimation of the real behavior. In a simple case, we define “shiftable loads”, which can be switched on or off, and which will stop running, if their duty cycle is done. During their duty cycle, they may have a nearly constant (LTI) behavior or a set of adjustable constant functions.

In this paper, a concept for an in-house energy information system is shown which allows real-time data acquisition, visualization, analysis and switching. Interfaces for different wireless and wired bus systems allow the integration of various sensors. A common database synchronizes measurement and visualization. It also collects smart metering and additional sensor data and allows displaying of data by LAN and WLAN based services. It supports consumers in energy and cost saving by graphical illustrated tariff information as well as realtime and previous metering data. With time varying tariffs, the controller is basically able to control the energy consumption of the energy devices of flats - depending on a given consumption roadmap (maximum start tariff). ACKNOWLEDGMENT

Figure 10.

This work has been realized in the projects EENEAS, e-energy@home and E-DeMa. The project EENEAS aims to the guidance for optimum energy consumption behavior. The project e-energy@home aims at the development of in-house controller systems for optimization of power consumption, as well as load shifting in flats. E-DeMa aims at the development of a future internet energy market place. The authors would like to thank the partners of these projects for their contribution.

Index card with parameters for switchable load devices

A device, in this case for example a washer, has some parameters which are able to describe some basic characteristics (Fig. 10). By measuring and by monitoring the actual power consumption and, additionally, some other relevant parameters, the states “stop” (or “smart start”), “running”, “waiting” and “operating time” can be detected or estimated. In this case, we can create a time table as shown in Fig. 11.

Figure 11.

REFERENCES Community research: New ERA for electricity in Europe, European Commission, Brussels, 2003, ISBN 92-894-6262-0 [2] Community research: European Technology Plattform SmartGrids, European Commission, Brussels, 2006, ISBN 92-7901414-5 [3] Community research: European Technology Plattform SmartGrids, European Commission, Brussels, April 2010 [4] FNN Forum Netztechnik / Netzbetrieb im VDE, 2011, http://www.vde.com/fnn (in German) [5] C. Wietfeld et al., “ICT - Reference Architecture: Requirements and Design,“ in: Innovative, ICT Oriented Concepts for the Energy Sector of the Future, Proceedings of Smart Energy 2010, Dortmund, ISBN 978-3-940317-79-7 (in German) [6] I. Kunold, M. Kuller et. al., “Model of an E-Energy Controller for Dynamic Control of Power Consumption in One Family Dwellings and Small Enterprises ,“ in: Innovative, ICT Oriented Concepts for the Energy Sector of the Future, Proceedings of Smart Energy 2010, Dortmund, ISBN 978-3-940317-79-7 (in German) [7] Communication systems for remote reading of meters, physical and data link layer, EN 13757-2, Feb.2005 [8] Communication systems for remote reading of meters, application layer, EN 13757-3, May 2007 [9] Home electronic system (HES) architecture, ISO/IEC 14543-3, 2006, http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detai l.htm?csnumber=43315 [10] IEEE Standard 802.15.4, Dez. 2004 http://www.ieee802.org/15/pub/TG4.html [11] ZigBee Specifications, Zigee Alliance, 2011, http://www.zigbee.org/Specifications.aspx [12] OSGI-Service plattform, OSGI Alliance Rev 4.2, Sept 2009, http://www.osgi.org/Main/HomePage [1]

Visualization of a timetable for “shiftable loads”

Using this time table and interrelating this with a quotation oriented tariff scheme, we have developed an algorithm that decreases energy costs, moves power consumption to the tariff interval with the lowest possible price and shifts it to times, where the energy quotation in a smart grid is high. So the energy controller can be part of a distributed demand side management system in an m2m network.

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