Derecho a la valoración racional de la prueba

After successful characterization of each component in a given system, modeling the behavior of the entire distribution is needed. As we begin to study the behavior of the distribution system it is beneficial to view the system in terms of state spaces. Either the system is in an operational state meaning that no protection devices (sectionalizers, reclosers, or fuses) are tripped, all components are fully functional, any switches are in their initial positions, and loading levels are within source capacity levels; or the system is in any other state, meaning that there has been some modification to the initial settings due to some disturbance in the system usually due to a fault, a malfunction of a

component or even scheduled maintenance. Predicting the outages and interruptions and noting the system’s response to those outages and interruptions is an essential part of

state that is not normal or fully operational and then quantifying the effect it will have on the customer is the key to the reliability assessment.

3.2.3. Radial System Structure

A radial system is defined as a system where each component has a unique path to a source of energy. Each component establishes a well-defined relationship with the components on its adjacent sides, which will be referred to as the parent/child

relationship. The component that is located downstream of a given component is referred to as the child, while the component that is located upstream of a given component is references as the parent. The direction of power in a radial system is always known, it flows away from the source. Therefore power flow calculations are easily preformed.

Navigation through the radial system identifying source of power, protection devices, fault isolations point, affected customers, and switches for customer restoration is of great importance when performing reliability analysis. After a contingency occurs there are a series of events that take place to minimize impact to entire system. These events include but are not limited to isolation of effected area by predetermined switching schemes and possible restoration of power to as many customers as possible. Typically upstream and downstream searches are performed; given a component starting point, a trace of

subsequent parent or child is executed until a pre-determined stopping criterion is reached flagging successive components along the way as shown in Figure 3.3. For example in Figure 3.4, the whole segment or region should be automatically isolated

source

untraced traced node

Figure 3.3 Downstream Search

without performing a search either downstream or upstream. Comparing Figure 3.3 and 3.4, identifying the affected region was achieved in five steps and one step, respectively.

This saves on computation time, and proves to a benefit as the system becomes more complex.

source

Figure 3.4 Segment Identification Scheme

3.3.

Analytical Method Applied to Test System

In order to perform a reliability assessment using the analytical method, faults and the systems’ response to those faults must be simulated. The analytical method includes a sequence of events that generate a set of system states for each contingency. The

generalized sequence of reliability assessment considering the cases with and without DG is listed below:

1. Fault occurs on the system at component i.

2. All areas that are affected by fault is isolated by automatic switching.

Store intermediate results for reliability indices calculation for this case:

without DG and without reconfiguration.

3. Check to see if reconfiguration is possible by running power flow verifying that the source has sufficient power to supply to load

4. Restore power by reconfiguration if possible (DG not considered). Store intermediate results for reliability indices calculation for this case: without DG and with reconfiguration.

5. If restoration by reconfiguration is not possible without DG, the DG will be considered to enable restoration of power. Store intermediate results for reliability indices calculation for this case: with DG and with

reconfiguration.

6. Returns to step 1 until all components in system experiences a fault.

7. Perform reliability calculations for system: without DG and without reconfiguration, without DG and with reconfiguration, and with DG (1MVA and 3MVA) and with reconfiguration.

For example, given the test system in Figure 3.5, if there is a fault on component three immediately the area between component two, which is a circuit breaker, and component nine, a relcoser, is isolated represent with solid double line. Assume there is no second feeder or reconfiguration is not a feasible option. Then, subsequently the area between component 9 and component 35, a normally open switch, must be also isolated with the dotted lines. Again since this is a radial system, hence one source of energy for

R

Figure 3.5 System Response to Faulted Component 3

each component, this area is also isolated leaving the entire feeder to be de-energized. It should be noted that the dotted line in Figure 3.5 represents the segment that is de-energized indirectly by a fault, while the solid double line areas represent the areas that

are directly affected by a fault. Data representing the number of interrupted customers is then stored for reliability indices calculations.

Since the two radial systems are tied together by a normally open switch there is then an attempt to reconfigure the system to allow the energy source from the second system or circuit to supply power to the customers that experiences an interruption;

meaning the fault is not located within their isolation region. Each load point is assigned a constant average load value taken from the load curve that is assigned to it for the Monte Carlo simulation later on. There is then a need to perform a simple power flow analysis to ensure that the line’s capacity need does not exceed the amount available from the source. Data is then stored for reliability indices calculations.

Lastly, there are four distributed generator connected to the test system with a normally open switch. With these four distributed generators, it is more likely for reconfiguration that may not be possible due to line capacity limit in the without-DG case. There are two different sized DG used, 1MVA and 3MVA.

For each of the above four cases each component’s failure rate is multiplied by the number of customer that would experience an interruption if that particular

component was to fail. When we looked at the behavior of the system when component 3 fails, we notice that all 211 customers in the top feeder experience power interruption, therefore 211 is multiplied by the 0.025 failure rate. We then move on to component 4 and then in succession until the last component is reached. Component 67 has the

identical effect on the lower feeder as shown in Figure 3.6 as component 3 has on the first system in Figure 3.5, except the failure rate is 0.079. The summation of these values divided by the total number of effected customers gives an average SAIFI value as:

∑ ∑

Si = number of customers experiencing sustained interruption due to a failure of component i

n = total number of customers.

For instance, the calculation of SAIFI in actual numbers is illustrated as follow:

5

Similarly, when calculating the SAIDI value the number of customers effected is

multiplied by the MTTR and failure rate and divide by the total number of customers that is serviced by the distribution system:

( )

Di = sustained interruption durations for all customers due to a failure of component i

n = total number of customers.

For instance, the calculation of SAIDI in actual numbers is illustrated as follow:

5

Discussions of the system response, reconfiguration and the effect of distributed

generation can be found in the next section that addresses Monte Carlo (MC) simulation.

Since MC simulation represents a more complicate model, many technical details will be addressed in the next sub-section.

R

Figure 3.6 System Response to faulted component 67

3.4.

Monte Carlo Simulation Applied to Test System

The analytical method definitely gives useful results, however it is a constant average

system reliability, it would be beneficial to show the range of possible values through probability distribution. Since the behavior of the distribution system is stochastic, we must rely on predictions in order to demonstrate the behavior. Then, we know the

possibility to have a bad year with unsatisfactory reliability, the possibility to have a good year with a desired reliability, and, most likely, the possibility to have reliability indices close to an average year. Therefore, occasional bad reliability observed in the operation of a distribution company may be justified by the statistical distribution of reliability indices such that this occasional bad performance may be acceptable by regulatory authority. This is an important use to evaluate statistical distribution of distribution of reliability indices.

The Monte Carlo technique offers a way to predict behavioral patterns and to produce a probability distribution. The Monte Carlo technique is divided into two sub-techniques: sequential and non-sequential. The sequential technique models the system as it actually occur through time, while the non-sequential approach uses an arbitrary order [1]. Therefore to make the model more realistic, especially to consider operating

characteristics, time-varying load, and contingence, the sequential approach is employed in this research. The generalized steps in the Monte Carlo simulation are as followed:

1. Start with the first sample year.

2. An artificial, hourly history of faults is generated.

3. Starting at time zero (first hour), identify location of the faults.

4. All areas that are affected by fault are isolated by automatic switching.

Store intermediate results for reliability indices calculation for this case:

without DG and without reconfiguration.

5. Check to see if reconfiguration is possible by running power flow verifying that the source has sufficient power to supply to load

6. Restore power by reconfiguration if possible (DG not considered). Store intermediate results for reliability indices calculation for this case: without DG and with reconfiguration.

7. If restoration by reconfiguration is not possible without DG, the DG will be considered to enable restoration of power. Store intermediate results for reliability indices calculation for this case: with DG and with

reconfiguration.

8. Return to step two until each hour in a year has been analyzed

9. Return to step one until pre-determined stopping criteria is met (typically after thousands of iterations)

10. Perform reliability calculations for system: without DG and without reconfiguration, without DG and with reconfiguration, and with DG (1MVA and 3MVA) and with reconfiguration

11. Aggregate calculated reliability indices to produce probability distribution 12. Repeat Steps 2-11 for the following sample year till reaching a

pre-determined number of sample years.

3.4.1. Artificial Operating History

Apparently, producing the artificial history of faults for each component is a critical requirement when performing a sequential simulation. It is necessary to predict the occurrence of contingencies and this process is driven by the reliability parameters, the failure rate and MTTR. The artificial history is a two-state model, either the

component is energized and in the up state or it is de-energized and in the down state.

The up state is referred to as the time to failure (TTF) and the down state is referred to as the time to repair (TTR) or time-to-switch (TTS). Since here we assume switching is automatic and instantaneous, so only TTF and TTR is considered. The transition between the two states is referred to as the failure process [14]. As previously mentioned this process is random therefore when generating there is a need to use random variables.

Random values are generated between [0,1] following the exponential distribution and used to calculate TTF and TTR for each component.

( )

₈₇₆₀

ln ×

−

=

i i i

TTF U

λ ^{hour s (3.3)}

( )

i i

i U MTTR

TTR =−ln × hours (3.4) where λi =failure rate

MTTRi=mean time to repair

Figure 3.7 shows the typical up down operating history of components.

There is a chance that when a region of the system is down, a fault is predicted to occur there. Of course this is not possible, the system will still be model as

non-operational, however the predicted duration of the new fault is added to the current

TTF

TTR 0

1

Figure 3.7 Component up down operating history

duration time. For instance, if the system is already experiencing a fault and the duration time is predicted to be four hours and then another fault is predicted with a duration time of seven hours when the system has already been down for three hours, then duration time is extended by seven additional hours, instead of becoming operational after one more hour, the system will be down for a total of 8 hours.

3.4.2. System Response

After we have developed an artificial up-down history for each component, the next step is to analyze the entire year hour-by-hour identifying the location of each fault and the systems response to those faults. If we look at each hour it is our hopes and expectation that the system is operating under normal conditions, however there is the possibility that a single or even multiple components are experiencing a malfunction or there is a fault. For example, consider an extremely case that component 3, 28, and 40

might be faulted simultaneously. Figure 3.8 shows the appropriate system response.

Again the dotted line represents the segment that is de-energized indirectly by a fault, and the solid double line areas represent the areas that are directly affected by a fault.

3.4.3. Reconfiguration

As we have seen previously, when the system responds to a contingency there are areas that are de-energized only as a consequence of the radial topology. Therefore it is beneficial and most desirable to restore power to these areas as soon as possible, even before the contingency is resolved. This approach will also improve reliability. Referring back to the test system in Figure 3.1, there is a normally open switch that is tying the two separate circuits together making it possible for the circuits to backfeed. In order for reconfiguration to be possible, first one of the two circuits from the test system must be

R

Figure 3.8 System Response to faulted components 3,28, and 40

fully operational. It then must have the capacity to support the area that is experiencing the outage. The load varies; therefore reconfiguration may be possible at one particular hour while not possible in another. This is the significant difference between the Monte Carlo simulation and analytical approach. The system response to the reconfiguration is regenerated and the data stored for reliability calculations for each hour, which will be discussed in detail later.

3.4.4. Distributed Generation

There is a relatively high probability that reconfiguration is not possible due to capacity issue or even location of isolated area, especially considering the increasing stress in power delivery infrastructure. The indirectly affected area may be sandwiched in between two faulted areas. Therefore DG serves a viable solution to better restore power and improve reliability. As shown in Figure 3.1, there are four DG placed in each area (the areas or regions are located between two disconnect components). The DGs are not normally connected to the circuits; it is only connected if and when there is a need. And it is assumed that they are 100% reliable, therefore the system does not experience any interconnection problems and there are always available when needed. We also assume they are automatically switched on immediately. This model did not take into

consideration what type of distributed generator, whether it was fuel cells or solar

powered. The distributed generators are rated at 1MVA or 3MVA as two scenarios in this study. It should be noted here that the 3MVA scenario essentially means that the

distributed generators are large enough to eliminate the line capacity constraints under the

back feed case. Once DG is inserted, system response is recalculated and respective data is stored for future reliability calculations.

3.4.5. Reliability Assessment

The way that the system responds to contingencies, reconfiguration, and the insertion of DG produces certain parameters that are necessary to perform reliability calculations.

Finding the affected load due to the failure of a component and developing the up-down operating history is one of the more difficult problems when performing a Monte Carlo simulation. For different hour in a year time, the effected load points are different. The number of failures, the durations of those failures, and the duration of the up state at each of the load points are determined for a given year. We can then produce another up down operating history as in Figure 3.7 for each load point. From the load point operating history we can determine the amount of failures (failure rate) and the durations of the failures (MTTR) for each load point. Using the new parameters in Equations 3.1 and 3.2, we are able to calculate SAIFI and SAIDI values, respectively

CHAPTER 4 Results and Discussion

4.1. Analytical Simulation Results

Following the method presented in Chapter 3 Section 3, eight values were generated: four SAIFI values and four SAIDI values. The four values represent the reliability of the system for four cases: Without DG and without reconfiguration, without DG and with reconfiguration, with DG (1MVA) and with reconfiguration, and with DG (3MVA) and with reconfiguration. The MATLAB code for the algorithm can be found in the appendix. The results in Table 4.1 show a significant improvement in reliability, especially when DG is considered.

4.2. Monte Carlo Simulation Results

Following the steps discussed in Chapter 3 Section 4, one of the necessary requirements is generating an artificial operating history for each component. For example, Figure 4.1-4.3 shows the artificial operating history for component 14,51,63, respectively.

Each hour for an entire year is searched for possible contingency and system response recorded allowing for the production of an operating history for each load point in the system. Figure 4.4 shows the operating history for load point 17 without DG and without reconfiguration.

Table 4.1 Results from Analytical Approach

SAIFI SAIDI Without DG

Without reconfiguration

7.82 35.77 Without DG

With reconfiguration

6.32 28.35 With DG (1MVA)

With reconfiguration

5.09 23.58 With DG (3MVA)

With reconfiguration 4.03 18.68

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Component 14

Time (hours)

State

Figure 4.1 Artificial Operating History for Component 14

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Component 51

Time (hours)

State

Figure 4.2 Artificial Operating History for Component 51

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Component 63

Time (hours)

State

Figure 4.3 Artificial Operating History for Component 63

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Time (hours)

State

Load Point 17 Operating History without DG without Reconfiguration

Figure 4.4 Operating History for Load Point 17

When we compare the component operating history to the load point operating history, there appear to be more transitions from state to state in the load point operating history.

This is due to the fact that there is some overlapping in component failures that affect each particular load point. From the load point operating history we are able to determine a failure rate and MTTR for system reliability calculations. This process is preformed for a total of 1000 Monte Carlo sample years producing eight probability distributions: four for SAIFI and four for SAIDI shown in Figures 4.5-4.12.

Figure 4.5 SAIFI Probability Distribution for System without DG without Reconfiguration

Figure 4.6 SAIFI Probability Distribution for System without DG with Reconfiguration

3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5

0 0.05 0.1 0.15 0.2 0.25 0.3

Time (hours)

Probability

SAIFI without DG without Reconfiguration

3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5

0 0.05 0.1 0.15 0.2 0.25 0.3

Time (hours)

Probability

SAIFI without DG with Reconfiguration

Figure 4.7 SAIFI Probability Distribution for System with DG 1MVA

Figure 4.8 SAIFI Probability Distribution for System with DG 3MVA

3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5

0 0.05 0.1 0.15 0.2 0.25 0.3

TIme (hours)

Probability

SAIFI with DG 1MVA

2 3 4 5 6 7 8

0 0.05 0.1 0.15 0.2 0.25 0.3

Probability

Time (hours) SAIFI with DG 3MVA

Figure 4.9 SAIDI Probability Distribution for System without DG without Reconfiguration

Figure 4.10 SAIDI Probability Distribution for System without DG with Reconfiguration

10 15 20 25 30 35 40 45 50 55

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Time (hours)

Probability

SAIDI without DG without Reconfiguration

SAIDI without DG without Reconfiguration

In document EL DERECHO CONSTITUCIONAL A LA PRUEBA Y SU CONFIGURACIÓN EN EL CÓDIGO GENERAL DEL PROCESO COLOMBIANO Luis Bernardo Ruiz Jaramillo (página 144-149)