Stochastic Model-Based Analysis of Energy Consumption in a Rail Road Switch Heating System

Stochastic Model-Based Analysis of Energy Consumptionin a Rail Road Switch Heating System

Davide Basile Silvano Chiaradonna Felicita Di GiandomenicoStefania Gnesi Franco Mazzanti

Istituto di Scienza e Tecnologia dell'Informazione �A. Faedo�,

Consiglio Nazionale delle Ricerche, ISTI-CNR, Pisa, Italy

SERENE 2015, 08/09/2015

D.B. et al. (ISTI CNR) Stochastich Model Based Analysis SERENE 2015 1 / 18

Overline of the presentation

1 Motivations and Objectives

2 The System Under Analysis

3 Stochastic Model-Based Analysis

4 Description of The Model

5 Analysis Results

6 Conclusion


Motivations and Objectives

Motivations and Objectives

Nowadays there is a great attention towards cautious usage of energysources, to save both in �nancial terms and in environmental impact;

While optmizing the energy consumption, other non functionalproperties must be guaranteed: reliability, availability, safety;

A study devoted to the energy saving in the context of a railwaystation is presented;

Stochastic-model based analysis exploited to measure the energysaving and the reliability of the system under analysis.


The System Under Analysis

The System Under Analysis (1/2)

Rail road switches enabling trains to be guided from one track toanother:

Reliability of the railway system depends on them.

Snow and ice can block them. Heaters are used to kept thetemperature above freezing.

Reliability and energy consumption of rail road switch heaters:

an overhead of energy consumption can lead to a blackout,an parsimonious policy of energy saving can cause a failure.

Minimum and maximum temperature thresholds policy, to guarantee asatisfactory trade-o� between energy consumption and reliability.

Stochastic model-based approach, modular, parametric and extensible.

Stochastic Activity Network (SAN) to model the system,Möbius tool to perform experiments.



The System Under Analysis (2/2)

Tubular �at heaters along the rail road track, induction heating;

Sensors to read the temperatures of the air and of the rail road;

simulated by a physic engine to perform experiments.

The policy to activate/deactivate the heating on two thresholdtemperatures:

warning threshold: if lower, the heating system needs to be activated;working threshold: if higher, the heating system can be turned o�.

Location and the weather conditions in�uence the actual temperature;

Network of heaters, Powerline, Central unit to manage the maximumamount of power that can be delivered to the system and priorities.


Stochastic Model-Based Analysis

Stochastic Model-Based Analysis

Useful to support development in all phase of software life cycle, forvalidation, analysis of dependability, performance;

SAN: a generalization of Stochastic Petri Nets;

composed of places,activities,arcs,input gates,output gates;activities are instantaneous or timed (stochastic temporal distributionof time), with associated probabilistic cases;di�erent policies of activation/reactivation of an activity;activities can use C++ code.

Möbius: a tool for modelling complex systems in a modular way, withdi�erent analytical and simulative solvers;

used for evaluate the energy consumption and the probability of failure.



Description of The Model (1/3)

Five atomic models: stochastic selector of weather pro�le and locality,selector of unique ID, main model, queue shared among the heaters;

Parameters: temperature thresholds, maximum amount of heatersthat can be turned on at the same time, table with averagetemperatures and localities.


Description of The Model

Description of The Model (2/3) Heater Module

mc ∂T∂t = −uA(T − Tenv ) + Q̇


Description of The Model

Description of The Model (3/3) Composed Model


Analysis Results

Analysis Results: Measure of Interests (1/8)

CE (t, l): the mean energy consumed by a heater in the interval[t, t + l ] (6:00 pm to 6:00 am);

PFAIL(t, l): the mean probability that a switch fails at time t + l ,given that at time t is not failed;

Simulations with Möbius tool with 10000 batches, we analyse 4 pairsof thresholds more and more wider and distant from the temperatureof failure;

We have analysed the measure of interests trend at the varying of theavailable energy and the gap between the temperature thresholds.


Analysis Results

Analysis Results: Probability of Failure (2/8)

1 - 4 4 - 5 3 - 7 4 - 50

0

0.2

0.4

0.60.27

4.89

·10−

2

0.23

0.61

2.4·10−

2

0 2.5·10−

5

0.37

1.35

·10−

3

0 0

0.16

(warning threshold-working threshold)

PFA

IL(t,l)

NHMax = 1 NHMax = 2 NHMax = 3


Analysis Results

Analysis Results: Energy consumption (3/8)

1 - 4 4 - 5 3 - 7 4 - 50

0

2

4

6

8

101.25

2.57

2.19 2.65

1.68

3.15

3.36

5.18

1.7

3.18 3.61

7.6

1.7

3.18 3.73

9.91

(warning threshold-working threshold)

CE(t,l)

NHMax = 1 NHMax = 2 NHMax = 3 NHMax = 4


Analysis Results

Analysis Results (4/8)


Analysis Results



Analysis Results



Analysis Results



Analysis Results



Conclusion

Conclusion

Model-based analysis for a rail road switch heating system;

SAN and Möbius tool to evaluate energy consumption and probabilityof failure at varying of temperature thresholds and available energy;

Physical model for calculating the temperature of the rail road track,weather data taken from http://www.meteoam.it/;

Results: small gap between thresholds that are reasonably distantfrom freezing temperature are better;

Future works: FIFO queue with priority to implement a smart logicfor activating/deactivating the heaters, apply our technique to anexisting railway station.


http://www.meteoam.it/

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Stochastic Model-Based Analysis of Energy Consumption in a Rail Road Switch Heating System