experiences from a pilot implementation of a gas metering system

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Rynek Energii

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EXPERIENCES FROM A PILOT IMPLEMENTATION OF A GAS METERING SYSTEM Krzysztof Billewicz Key words: AMR system, gas stations, gas meter, gas metering systems, smart meter Summary. In the paper, the author describes his experience that he has gained in the design of a gas metering system. The AGMR (automated gas meter reading) system collected measurement data from gas meters which were installed in high pressure gas regulating stations. The system offered complete data processing services. Then the processed data was sent to the system of forecasting demand. Designers of the system have encountered several challenges which forced them to think. The publication presents the encountered problems, both at the design stage of the system, as well as during its implementation and operation.

1. INTRODUCTION The amount of transferred gas depends on gas temperature and pressure. Therefore, in order to calculate an amount of transferred gas, a measured gas volume in measurement conditions is converted to volume under standard conditions. Quantities of natural gas are measured in normal cubic meters (corresponding to 0 °C at 1.01325 ·105 Pa). Some publications describe a pilot implementation of measurement data systems from residential gas meters. In UK, government has declared its aspiration to see smart meters deployed in Great Britain within 10 years. These are gas and electricity smart meters with In-Home Display units which would be the main gas company (e.g. supplier) /customer interface. The IHD will be a key in enabling the information revolution. But… costs and benefits are not aligned across the value chain e.g. the supplier business case is negative [1]. In Poland, 3% of customers consume 69% of the volume of gas. In this group, there is practically no problem of metering systems. In Poland, approximately 6.4 million gas customers are households. A multimillion audience of tariff group W-1 is characterized by low demand for natural gas with annual consumption below 300 cubic meters (m3) [4].

systems. A pilot project was carried out in one natural gas company in Poland. The people involved in the project were not allowed to access the reduction stations, only they had access to the measurement data.

2. ABOUT HIGH GAS PRESSURE REDUCING-METERING STATIONS High gas pressure regulating stations were designed for gas pressure reducing from high to lower value determined by gas utilities. High gas pressure reducing-metering stations were designed for both gas pressure reducing and gas flow metering. In the gas pressure reducing station, there are installed overpressure and underpressure protection devices. Reducing stations can be divided according to the level of gas pressure:  high pressure gas reduction stations,  medium-pressure gas reduction stations.

3. DESCRIPTION OF THE SYSTEM The proposed system was designed to collect data from high gas pressure reducing-metering stations. This system prepared the data for gas demand forecasting system for each station.

An implementation of smart gas meters for all customers and increase in the frequency of the meters reading (currently twice a year) appear to be economically unjustified because many clients consume small amounts of gas [4]. The article describes key lessons learned from the implementation of gas meters data acquisition

Fig. 1. Architecture of the gas metering system

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The gas metering system consisted of the following elements:  gas-volume conversion devices MacMAT;  an acquisition server (the ability to read data from text files);  an database server with Unix and Oracle 9i relational database;  workstations with Windows XP and customer applications of the AGMR system. A gas volume meter is used to measure gas volume directly. A Gas-Volume Conversion Device MacMAT is a measuring instrument intended for gas under operating conditions volume conversion to volume under standard conditions. The information about flow-through gas volume is scanned via impulse gas meter outputs, while the temperature and gas pressure are measured by digital transducers.

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Therefore, the settlement of gas between the Poland and Russia requires use of gas day (from 10:00 pm day-1 to 10:00 pm current day). This complicates the automatic calculation of data, as well as a simple query in SQL database. In this case, the AGMR system which was used to measure and process data for electricity was implemented in the gas company. A customization was necessary – customized AGMR system to suit the needs of a gas company. It was necessary to rework the conversion function, reporting, viewing data for a particular day or period of time.

5. PROBLEMS WITH METERING SYSTEMS U–2 IN REDUCTION STATIONS Standard configurations of metering systems were fitted in stations.

The measuring system was reading the following measurement data from conversion devices (or it was reading data from files which come from devices) [3]:  gas volume measured at process conditions in cubic meters [m3];  gas temperature [degrees C];   

gas pressure [kPa]; gas volume converted to standard conditions in cubic meters [m3]; ambient temperature (it was not always measured).

Among the measured values only the volume of gas was measured at standard conditions to be used for forecasting demand. A designed and implemented remote meter reading system collected data from MacMAT Gas-Volume Conversion Devices made by PLUM. The software acquisition system read the data values of gas flows, which were saved in text files. The measurement data from some reduction stations were stored in the system TELWIN. The software acquisition imported the date from the TELWIN system. The implemented system is designed to prepare the gas demand forecasts for each gas reduction station, for each hour and for the whole week. It used the ARIMA forecasting model.

4. PROBLEM WITH TIME A problem with a timestamp: If a gas flow is measured from 1:00 to 2:00 what time stamp should be adopted? Poland imports natural gas from Russia. Russia is in a different time zone than Poland.

Fig. 2. Metering system U – 1 2

1

1

GM Fig. 3. Simplified metering system U – 1

Fig.4. Metering system U – 2

Description of diagrams: 1. Ball valve of the meter system 2. Turbine gas meter 3. Ball valve of the by-pass of the metering system 4. Vent valve

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5. Hole plug – eyepiece 6. Plug-in socket for control thermometer 7. Temperature transducer 8. Absolute pressure transducer 9. Volume corrector 10. By-pass 11. Flux straightener 1

2

1

2

1

GM1 3 1 1

GM2

Fig. 5. Simplified metering system U – 2

In the metering system U – 2 there are two different gas meters. The measuring devices are different from each other by method of gas measurement. Typically, first gas meter measures the higher flow of gas and it is used in winter. The second gas meter is designed to measure small flows. The metering system U – 2 is intended for the following work: 1) a gas meter GM1 is the basic meter and the gas meter GM2 does not register any gas flow; 2) a gas meter GM2 is the basic meter and the gas meter GM1 does not register any gas flow; 3) check the correctness of operation – a bypass ball valve no 3 is opened. The gas flows through the gas meter GM1 and then through the gas meter GM2. Both the gas meters should measure similar amount of transferred gas. Changing the operating configuration of the meter system U – 2 and also change of a gas meter through which the gas will flow, takes place during the visit of the engineer, who turns and loosens the set of valves. The employee must first unscrew the new ball valves and he opens a new way of a gas flow, then he turns off other ball valves and he closes current way of a gas flow. Because two gas meters work they correctly measure the amount of transferred gas. If the employee first turns off ball valves, for a short amount time, the gas would not flow to the customers. Typically, the engineer should record the change of working configuration of the metering system U – 2 in a corresponding document. The following algorithms have been proposed:  a sum of the value of gas flow recorded by the two gas meters. This solution was interesting and simple. However it does not take into account that the meter system U – 2 allows the connection in such a way that the gas flows through the first gas

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meter then followed by the second gas meter. This solution is used for control purposes – to check whether the gas meters properly measure the gas flow.  you take the value from the first gas meter, and if it is zero, you take data from the second gas meter. A similar algorithm could be as follows: take the data from the gas meter, in which the recorded data values are higher. Algorithms worked well, however, the worker could accidentally leave loosened two valves and the gas would flow through the two gas meters at the same time. In addition, if the station would be set correctly a similar amount of transferred gas was flow through both the first and the second gas meter. A bypass ball valve is not a measurement device. It does not have remote monitoring or control interface which could be used for remote reading of a ball valve status. In order to solve this problem, it was necessary to define and use a separate variable for each reduction stations which has meter system U – 2. The variable specifying which of the gas meters measured the gas flow at any given time. In this case, it is necessary to:  identify how to write and modify such a variable;  determine how to manage the memory of the variable in the system (e.g. data aggregation period);  identify data verification methods in case when a manual entry of such a variable takes place and record to logs, when and who made the change the variable;  automatically calculate the data in accordance with algorithms in the case when a variable will be changed by user. 10:00

11:00

12:00

13:00

14:00

15:00

16:00

GM1 GM2

a moment of configuration change U-2

gas flow from 13:00 to 14:00 =

time +

Fig. 6. A theoretical method for determining the amount of transferred gas from 13:00 to 14:00?

How do you determine the amount of transferred gas which flowed from 13:00 to 14:00? The gas flow = GM1 + GM2. But... if a bypass ball valve no 3 is opened before or after the moment of a configuration

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change then the algorithm is not true. In such a situation you need to calculate: GM1 (or GM2) * (1 hour – number of minutes to change the configuration) / 1 hour. 10:00

11:00

12:00

13:00

14:00

15:00

16:00

GM1

a duration of the reconfiguration process U-2  t>0

gas flow from 13:00 to 14:00 =

perform the daily gas demand forecasts for each reduction stations. Different Gas-Volume Conversion Devices used various periods of data aggregation:  hour,  half an hour,  

GM2

time +

Fig. 7. How do you determine the amount of transferred gas from 13:00 to 14:00?

The AGMR system was designed to prepare the reduction station data for the demand forecasting system. It was a demand forecast for two reduction stations of the city, through which the gas flowed for customers located within the city. A demand forecast had to be made for each pressure reducing station. If one of cities was supplied with several stations, it was clear that these are alternative routes to deliver the gas. The problem was the case of so-called station groups e.g. two stations located in different cities which deliver gas supply for two city areas. Then it was not obvious that the station named one of the cities delivered a gas supply to another city, where also a reducing station existed.

6. PROBLEMS WITH DEVICES Since many years, Gas-Volume Conversion Devices used in the system were: 

older devices which were installed in the summer had a daylight saving time,



older devices which were installed in the winter had a winter time,



newer devices automatically changed summer/winter time in the day time change.

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twelve minutes, three minutes.

There was no standardized data aggregation period for all devices, the same as it is for measurements in electricity. A quarter an hour has been adopted. Consequently, balancing of gas demand and supply for areas in short periods of time was difficult. Designers of AGMR system took 1 hour as a standard period of aggregation. In addition, in the course of time, operators changed the data aggregation period in some devices e.g. hourly cycle on to 12 minutes. This was problematic, because metering and accounting systems did not provide options for making changes of standard aggregation period for any device in operation.

7. DATA SUBSTITUTION AND VERIFICATION The six steps of the master data management process: 1) data collection (read data from text files or import data from the TELWIN system); 2) aggregation of data to hourly aggregates; 3) standardization of data (a gas flows through GM1, GM2, or GM1 and GM2); 4) verification of data – the rate of change between consecutive data; 5) substitution of data – supplementation of missing values; 6) export data to gas demand forecasting system.

to

The problem was that the clock/time of devices was out of sync. These aspects have caused ambiguity timestamp of measurement data from different devices. Additionally, it complicated data preparation for forecasting system in the days of time change. Therefore, it was impossible to reliably balance areas – the amount of gas delivered and the amount of gas consumed or exported out of areas. Out of sync clocks of the devices would not be a big issue if the measurement data was used only to

Fig. 8. The data management process

Please note that the data used for financial settlements between the parties (gas company vs. customer) must be accurate and unambiguous. In case of the data absence, calculation of the missing values

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(substitution or approximation data) must be done by well-known and clearly defined algorithms [2]. If the data is used for the purposes of balancing, forecasts, determining losses, analysis and profiling, there is no need for strict accuracy of the data. Most of the data in AMR (automated meter reading) systems is used for financial settlements (this is the purpose of the AMR system implementation) as well as for other purposes. This is an added value of the system. In the case of electricity in a very short period of time there may occur a sudden, significant change in demand and the power system is able to handle it. In case of a natural gas, the sudden change in demand is not possible. Therefore, the algorithms have been used for determining whether a change in gas flow rate in the successive periods of aggregation is not too high – physically impossible to achieve. The AGMR system used an appropriate method of data substitution, in the absence of data or lack of reading measurement data from a specific period of time.

8. PERFORMANCE PROBLEMS The measuring system used the Oracle relational database. However, due to the amount of data and the

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need for data conversion, it turned out that it is necessary to delete data older than three months, resulting in inaccuracy in forecasting demand. The demand forecast algorithms which were used accurately estimated the expected demand for gas when the algorithms had got access to the data from the period of two years.

9. CONCLUSIONS In the paper, the AGMR (automated gas meter reading) system and its design components have been discussed. Implementation of AGMR system which reads the gas meters data on the reduction stations is a significant challenge. The AGMR system has been developed and tested successfully. The main problems were encountered with the lack of clock synchronization of gas conversion devices. Also it was troublesome to process the data from the gas meters which were working in the metering system U – 2. It is necessary to develop a standard that would describe standard aggregation periods (e.g. 1 hour and twelve-minutes) for any device working in gas metering systems.

REFERENCES [1] Allison P., Smart Meter Deployment in Great Britain -where we are, where we're going. British Gas Smart Metering, 2010. [2] Ardali E.K., Heybatian E.: Energy regeneration in natural gas pressure reduction stations by use of turbo expanders; evaluation of available potential in Iran. In: Proceedings of the 24th world gas conference; 2009 Oct; Buenos Aires, Argentina. [3] Billewicz K.: Smart metering. Inteligentny system pomiarowy. Wydawnictwo Naukowe PWN, Warszawa 2012. [4] Dzirba D.: Inteligentne opomiarowanie w gazownictwie – korzyści i uwarunkowania, Zaawansowane systemy pomiarowe – smart metering w elektroenergetyce i gazownictwie, Konferencja PTPiREE Zaawansowane systemy pomiarowe – smart metering w elektroenergetyce i gazownictwie. Warszawa 23-24 March 2010. [5] Rezaiea N. Z., Saffar-Avvalb M.,: Feasibility Study of Turbo expander Installation in City Gate Station, Proceedings of ECOS 2012 – the 25th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems. June 26-29, 2012, Perugia, Italy DOŚWIADCZENIA Z PILOTAŻOWEGO WDROŻENIA SYSTEMU AMR W PRZEDSIĘBIORSTWIE GAZOWYM Słowa kluczowe: system AMR, stacje gazowe, gazomierz, system pomiarowy dla gazu, licznik inteligentny Streszczenie. Artykuł opisuje doświadczenia zdobyte podczas projektowania systemów pomiarowych na potrzeby zebrania danych pomiarowych z gazomierzy w stacjach redukcyjnych pierwszego stopnia, przetworzenia tych danych i przygotowania do systemu prognostycznego. W publikacji tej przedstawiono wyzwania i problemy, przed którymi stanęli projektanci systemu zdalnie odczytującego dane z gazomierzy AGMR dla przepływu gazu z różnych punktów granicznych, zarówno na etapie opracowywania systemu, jak również podczas jego wdrażania i eksploatacji. Krzysztof Billewicz, Ph.D. – Institute of Electrical Power Engineering in Wroclaw University of Technology, Poland.