Simulation of Visualization Support Approaches and Discussions

Figure 5-12 shows a visualization image of data collection status for smart meters grouping by each same connected transformer. The color of the box also would be changed from white to red corresponding to the number of data collection fault meters. With this visualization approach, an outage under a pole transformer should be recognized easily and the accuracy of outage prediction might increase. The distribution system model using these icons is showed in Figure 5-13.

Table 5-5 Basic Simulation Data No. of data

collection fault meters

No. of Min. data

collection No. of Max. data

collection No. of Ave. data collection

1 1 35 9.0

2 3 40 18.5

3 6 48 25.7

4 13 53 32.7

5 20 58 42.2

6 24 60 50.2

The probability of outage occurrence would increase, if several data collections would be failed, and the number of minimum, maximum and average data collection for the detection of several data collection faults are 3, 40 and 18.5 respectively. This means that an outage should be detected 1.5 ~ 20 (average 10) minutes later from the outage occurrence and it should be an issue that it might take 20 minutes to detect the outage.

Therefore, this approach should not be a method for the first outage detection however it should be an effective method for the status confirmation when a customer would notify an outage occurrence, because data collection for meters which connected to the same failure in transformer level or higher would occur. In addition, because the color of the box in which smart meters are connected to the failed pole transformer would be changed to red corresponding to the number of data collection fault meters, operators can easily find the high probability of outage occurrence area by the monitoring of the box color transition.

c. Failure on HV Distribution Line

The failures on HV distribution line would be simulated and how outage would be recognized over time would be discussed. Figure 5-14 shows data collection status transition in an outage for the distribution system model at 1, 3, 5 and 10 minutes later from the outage occurrence and the area covered by the blue colored line shows outage occurrence. 1 minute later from the outage occurrence, data collections for 2 smart meters were failed. In this stage, a power company s operator can recognize the possibility of outage occurrence in the area for load bus B2. 3 minutes later, the operator can recognize that data collections for all smart meters in the area for load bus B2were failed and for other smart meters were completed normally. If there is no communication error in this area, the operator can judge that outage in the area for load bus B2must occur.

Using metering data, area outages can be predicted in a few minutes later from the

outage occurrence only with every 30 minutes metering data, if data collection (transfer) interval would be reduced considering data traffic leveling. The approach was established only with data visualization ideas. All data should be collected with normal metering

Figure 5-14 Data Collection Status Transition in an Outage Utilization of Data Collection Success Information

(2)

In addition to metering data collection fault information, utilization of data collection success data is discussed.

a. Outline of This Method

In an area outage, data collection for all meters located in the area should be all failed. Therefore, if data collection for one meter in an outage assumed area would be

successful, assumed outage would not occur. Using this information, it is possible to focus outage range with alive monitoring to other smart meters in case that data collection for a certain smart meter would be failed. In the system model, data collection failure for one meter has several possibilities of failure events and these events have conditions in metering data collection described in Table 5-6.

Table 5-6 Possibilities of Outage Range for One Metering Data Collection Fault Failure

Point

Conditions in Data Collection Example for the Measuring in the system model*

Collection Fault

(Outage) Collection Success (No Outage) Smart

Meter, Service Line

Collection fault smart

meter Other 5 smart meters

connected to the same pole transformer of the collection fault smart meter

Fault Smart meter 3 (M3) Success: M4, M23-24, M43-44

Pole

Transformer Low Voltage Line

6 Smart meters connected to the same pole

transformer.

Smart meters connected to all pole transformers except for the transformer

connecting to the data collection fault smart meter.

Fault: M3-4, M23-24, M43-44 Success: All meters except for above.

High Voltage line

All smart meters connected to pole transformers on the failure segment

All smart meters connected to pole transformers on HV distribution lines except for the failure segment.

Success: M229-234, M241-252, M259-360 (B2connected meters) Fault:

All meters except for above.

(B1and B3connected meters)

*It is assumed that data collection fault in smart meter 3. In the system model, Smart meter 3,4,23,24,43 and 44 are connected to transformer 5 connecting to B1

In case that data collection for smart meter 3 (M3) would be failed, it is possible to judge whether it is M3 single service outage or an area outage under the pole transformer by alive monitoring executions for other 5 smart meters connected to the same transformer of M3 (M4, M23-24, M43-44). If there is no response from these 5 smart meters, it should be necessary to assume a larger size outage occurrence. In case that single transformer trouble would occur, the alive monitoring information for smart meters connected to other pole transformers would be effective. Because data collection for some other smart meters should be conducted at the same time of the data collection for M3, it is possible to confirm alive monitoring information for other meters without additional actions. If data collection for other smart meters connected to a pole transformer on the same load bus of M3 was successful, it should be specified that the pole transformer connecting to M3 or same layer problem would occur. On the other hand, all meter data collection would be failed, assumed outage range would be enlarged. Figure

5-15 shows procedures for outage prediction and it is possible to track outage range as this way. Although data traffic including alert messages, recovery requests and their data would increase in outages, this approach mostly use metering data and does not send data packets to communication network except for the first alive monitoring .

Figure 5-15 Procedures for Outage Prediction

b. Challenges and Effectiveness

As challenges of this approach, it is possible that necessary data for outage prediction might not be collected by previous data collections. In the previous case, when data collections for 5 other smart meters connected to the same transformer of smart meter 5 would be failed, operator should look for the data collection status for smart meters connected to other 19 pole transformers. However no data collection might be conducted to these smart meters. In this system model, it is the case that the number of candidate meters is 19 6 = 114 and data collection for these candidate smart meters were not conducted in the same metering timing as smart meter 5. Because it is assumed that data collection timing would be leveled and there are 60 groups in this research, at least one smart meter which is in the same data collection group as smart meter 5 would

exist. However, it is realistic that all smart meters might not be leveled completely considering meter installation and removal corresponding to customer contracts.

Therefore, multiple times of data collection status data should be utilized for data collection certainty.

Failure occurrence on more upper layer, candidate smart meters which can be utilized for the confirmation of outage prediction would increase than previous case.

Therefore, in the system model, outage range should be predicted by the utilization of 30 second later data from a data collection fault. However, the major challenge of this approach should be the time from outage occurrence to data collection fault. With every 30 minutes metering, it might take 30 minutes for single outage occurrence recognition. Also, it takes 5 minutes on average and around 20 minutes at a maximum to recognize an outage under a pole transformer.

It is difficult to solve the problem for initial recognition because it depends on the metering interval in each smart meter, however this approach should be effective to reduce time to recover from the first customer call because such customer call would be one of data collection fault information.

Summary 5.4.

In this chapter, the distribution system monitoring measure and the rapid outage area predicting measure were proposed as technologies and measures for optimal power supply and demand and various simulation results were discussed to evaluate these measures.

With respect to the distribution system monitoring measure, this research proposed a new method of distribution system stabilization considering the voltage change problem by many PV systems installation utilizing measured data from smart meters, and some validation works were executed considering future implementations to actual distribution systems through the development of the prototype system. By the validation works, core functions for the future voltage change problem were confirmed and also some important future challenges were clarified. As a technological aspect of information systems, data preparation and I/O time reduction should be focused, in addition to considering speed-up techniques of calculation algorithms such as power flow calculation and active system models calculation. Also as consumer service provision

violation are needed to consider. It should be necessary to consider compensation measures for these consumers and support functions should be implemented. Also as future works, it is necessary to think about some more constraints required for actual implementation including above mentioned challenges. In addition, optimal distribution network configuration, loss reduction by DGs and PVs should be added to this prototype system to support optimal distribution systems operation.

With respect to the measure for the rapid outage area prediction, this research explored the AMI data utilization for outage management and the approach utilizing smart metering data was proposed with some simulation results. The proposed approach might be useful under some outage occurrence conditions and become the one of support methods for effective outage management. In simulations, by the utilization of metering data, area outages can be predicted in a few minutes later from the outage occurrence only with every 30 minutes metering data, if data collection (transfer) interval would be reduced considering data traffic leveling. The approach was established only with data visualization ideas and most data should be collected with normal metering works and no Therefore, new service installation cost should be small compared with other new system development. Since the installation of AMI requires huge time and investment, it is very difficult to recover the investment only with business efficiency improvement from the manual metering to remote metering.

Therefore, AMI data utilization for outage management might be one of effective new services if outage risks would be reduced. In order to improve this approach, actual outage prediction activities in smart meter already installed countries should be researched and practicability of the proposed approach should be discussed with the comparison of outage detection time from the first customer call and the famous outage management indexes such as SAIDI (system average interruption duration index) and SAIFI (system average interruption frequency index) etc.

As mentioned in the opening paragraph in this chapter, beneficiaries of these two measures might be network operators in a direct way. However, future challenges in the network operators should reflect to network utilization cost, solutions or mitigation measures for these challenges gain profitability of competitive power companies, PPS, service providers and service consumers.

Chapter References

[5-1] Study Group of Low Carbon Power Supply S

Low Carbon Power Supply System, Study Group Report, July 2009 (in Japanese) [5-2] Energy Environment Council, "Action Plan to Stabilize Energy Supply-Demand,

November 2011 (in Japanese)

[5-3] r Plant Modeling Impacts on Distribution Systems

PV Case Study, Rural Electric Power Conference, pp.B5-1-10, April 2012 [5-4]

PV solar system connectivity in a utility distribut

Distribution Conference and Exposition (T&D), pp.1 8 , May 2012

[5-5] -DG

Harmonics and Quality of Power (ICHQP), pp.293 298, June 2012

[5-6] -DG in

Power Distribution Systems using Distributed Energy Storage

IEEE PES Innovative Smart Grid Technologies (ISGT), pp.1-6, February 2013 [5-7]

IEEE Transactions on Power Delivery, vol.23, no.3, pp.1636 1644, July 2008

[5-8] D. T.

Inverter- -8,

July 2011 [5-9]

System with large integration of PV - Evaluation of Frequencies and Economic Benefit , Y12008, CRIEPI Report, March 2013 (in Japanese)

[5-10]

Photovoltaic Generation for Residences on Voltage Management in Power Distribution System, R11026, CRIEPI Report, June 2012 (in Japanese)

[5-11] 2001 [Online]

Available at:

http://grouper.ieee.org/groups/td/dist/da/doc/omsapp.ppt

[5-12]

IEEE PES T&D Conference and Exposition 2008, pp.1 3, April 2008

[5-13] tion of AMR Based on Powerline

eedings of International Conference on Sustainable Power Generation and Supply 2009, pp. 1 4, April 2009

[5-14] n Outage

ES Winter Meeting, vol.2, pp.833 837, January 2002 [5-15]

September 2008 [5-16]

Static Synchronous Compensator micro-September 2012 (in Japanese)