Investigating the Impacts of Distributed Energy Resource Expansion on Flexible and Overall Reliability

Indices of Hybrid AC-DC Microgrid Payam Teimourzadeh Baboli

Faculty of Engineering & Technology University of Mazandaran (UMZ)

Babolsar, Iran p.teimourzadeh@umz.ac.ir

Abstract— In this paper, different impacts of distributed energy resources (DERs) technologies on the flexible reliability (FR) and overall reliability (OR) have been investigated in hybrid AC-DC microgrid. A procedure for calculating the FR and OR is introduced. Then, a sensitivity analysis is carried out to reveal the impacts of each DER’s capacity expansion on the OR and FR indices. It has been shown that some DERs may improve the OR indices, but impose some negative impacts on FR indices or vice versa. Indeed, OR and FR indices are highly dependent to the type and capacity of each DER technology and the topology of the microgrid. The results of this study show that different DER technologies have different impacts on OR and FR indices and increasing the capacity of each DER technology reveals a specific trend in changing the OR and FR indices.

Index Terms— Demand response, flexible reliability, renewable energy resource, hybrid AC-DC microgrid.

I. INTRODUCTION

Customers’ request from electricity network is changing and they ask more efficiency, reliability, security, and quality of service from power service providers. Obviously, upgrading the current power system to meet completely the customer desires is so costly. Moreover, finding the optimal upgrading level has constantly been one of the main issues in operation and planning studies [1]. Customers have different desires depending on their type, location, and time. Thus, inaccurate estimation in average customers’ desire will lead to over/under investment in power system expansion planning.

If an optimal fixed reliability level is calculated and employed in a part of the grid, there may be some free-ride customers that do not require such level of reliability but possibly will enjoy from the advantages of higher reliability level [2]. Alternatively, some power sensitive customers that demand more reliability level may suffer from uniform level of reliability. Although in smart grid environment distinct nodal reliability can be served in distribution network and the resolution of the distinct nodal reliability levels may become smaller [3], always there are free-ride customers.

The new concept of serving Flexible Reliability (FR) is introduced in distribution network level. FR is defined as the ability of a grid to continue serving the high priority customers in contingency states [4]. Customers’ priority is recognized based on their tendency for continuous supply that represents their value of service reliability. FR can be enabled in hybrid AC-DC microgrid (HMG) [4, 5] that consists of AC and DC loads, and distributed energy resources (DERs); e.g. renewable

energy resources, controllable DGs, demand response (DR) resources and energy storage systems; which are connected through separate AC and DC links [6].

The operator of the HMG estimates the customers’ priority based on the customers willingness-to-pay to avoid and willingness-to-accept to compensate the interruption in a specified period. Naturally, customers demand different reliability levels in different operation periods. For example, the desired reliability level for two neighbors may be different in different periods. Since the customers priority ranking is carried out based on the comparison between their priorities, it should not be constant in different times and locations. Indeed, the FR depends more on customer desires unlike the overall reliability (OR) of the system that depends on the network characteristics. The overall reliability of a grid might be low, satisfy though most customers, but its FR may be high enough to serve the high priority customers in contingency states. Thus, increasing the FR of the system will decrease the free ride customers and the total investment of the grid could be planned in a minimum acceptable level.

Although finding the optimal level of OR is a vital issue [7], finding the optimal levels of both OR and FR may result in more economical solution. Regarding this matter, the distributed energy resource expansion planning study in HMG has to meet both optimal levels of OR and FR. In this paper, a procedure for calculating the FR and OR is introduced and some indices are defined to show the OR and FR levels of the HMG. Then, a sensitivity analysis is carried out to reveal the impacts of the capacity of each DER technology on the OR and FR indices of the microgrid. It has been shown that some DER technologies may improve the OR indices, but simultaneously impose some negative impacts on FR indices. Indeed, OR and FR indices are highly dependent to the type and capacity of each DER in the microgrid. The results of the study will show:

1) Different DER technologies have different impacts on OR and FR indices.

2) Increasing the capacity of different DER technologies reveals a specific trend in improving the OR and FR indices.

The rest of this paper is organized as follows. In section II, the optimal power flow (OPF) problem in distribution network using graph theory is formulated. In section III, the procedure of calculating flexible reliability of the HMG is presented. In Section IV, simulation results are addressed, and the conclusion is drawn in Section V.

II. DISTRIBUTION NETWORK OPTIMAL POWER FLOW USING

GRAPH THEORY

Generally, the first step of reliability analysis is generating the historical data of failures and repairs of the grid's components. In distribution networks, for serving the consumers in different load points the connectivity of the distribution network to the upstream network, the availability of the controllable generators, and the operating point of the intermittent renewable generators such as wind turbine and PV array has to be considered. Accordingly, loss of supply or line failure can cause loss of load at a number of load points. On this basis, every state change in the reliability analysis study of all system components requires an evaluation of supplying the demanded load. Thus, finding the available capacity of generators and lines in contingency states is a crucial issue for calculating the amount of unsupplied loads. This section presents a technique for computing the minimum amount of unsupplied load in contingency state using the graph theory.

A. Distribution network's representative directed flow graph

Electrical networks can be modeled by their corresponding graphs [8, 9]. Edges denote generators, distribution lines, or loads. A power flow capacity is associated with every edge of generator and distribution line. In the same way, each load edge is associated with demand capacity. Each vertex represents an interconnection point between two lines, generators, etc. In order to solve the model, source and destination vertices are added to the graph. Fig. 1(a) shows a single-line diagram of a distribution feeder with two load points (LP1 and LP2), a distributed generator (DG), and two buses (B1 and B2). The feeder has two sections (S1 and S2) and is supplied from the distribution substation. Fig 1(b) represents the corresponding directed flow graph of the above distribution network. The source vertices contain only outgoing edges. Similarly, destination vertices contain only incoming edges. On this basis, each vertex can be reached from the source vertex and the destination vertex can be reached from any other vertex. The network flow is the sum of all edges’ flow leaving the source and is equal to sum of flow into the destination vertex.

B. Max-flow Min-cut theorem

Using the network's representative directed flow graph, the optimum power flow can be represented as follows. By finding the maximum possible flow between the source and destination vertices of the network's graph, the maximum possible flow of each edge can be calculated. Indeed, if the maximum possible flow of load edges is equal to its capacity then it can be concluded that distribution network has the ability to completely supply the loads.

Total flow through the network must pass through the bottleneck at once. Therefore, to find the maximum possible flow, the narrowest bottleneck has to be found, which is named as the capacity of the minimum cut. In other words, the Max-Flow Min-Cut Theorem states that "the cut of minimum capacity vertex cut of a network is equal to the maximum flow that could travel along that network" or "for any network, the value of the maximum flow is equal to the minimum-capacity cut" [10]. A flow network is defined as a directed graph where each edge (u,v), between vertexes u and v, has a capacity c(u,v) equal or bigger than zero, and there is source vertex s and a

destination vertex d. A flow f(u,v) has to satisfy the following constraints:

1) Capacity constraint: ( ) ( ) ( )0 , , , for all ,f u v c u v u v≤ ≤ (1)

2) Flow conservation: ( ) ( ) ( ), , , for all ,f u v f v u u v= − (2)

3) Skew symmetry: ( ), 0, for all expect and

v V

f u v u s d∈

= (3)

where vertex v is a member of set of graphs vertices V. The Max-flow is formulated as the maximum flow F from s to d: { }

,Max-Flow max

u v VF

∈= (4)

( ) ( ), ,v V v V

F f s v f v d∈ ∈

= = . (5)

The Ford–Fulkerson method [8, 9, 11], is used in this paper to find the Max-flow of the representative directed flow graph. The residual capacity cf(u,v) is defined as follows: ( ) ( ) ( ), , ,fc u v c u v f u v= − . (6)

The residual graph Gf is defined as a graph with edge capacity equal to residual capacity. The residual edges Ef are the edges with positive residual capacities, as shown in (7) and edges with zero capacity are omitted from the residual graph.

( ) ( ){ }, : , 0f fE u v V V c u v= ∈ × > . (7)

The augmenting path is defined as each path from s to d in the residual graph. The Ford–Fulkerson method can be summarized in the following steps [11]:

1) Initialize flow f to 0; 2) Find an augmenting path P from s to d in Gf ; 3) Update the cf and find the minimum cf in the P, which

is indicated by minfc ;

4) For each edge (u,v) in P, update the flow of the graph as follows:

( ) ( )( ) ( )

min, ,

, ,

ff u v f u v c

f v u f u v

← +

←. (8)

5) Return to step 2 if another augmenting path exists; 6) Calculate F as the Max-flow of the graph.

Since, the Max-flow of the represented graph of the microgrid has to be solved for each reliability state and the

(a)

(b)

Figure 1. (a) Single-line diagram and (b) representative directed flow graph of a two-bus distribution feeder.

Distribution Substation

DG

LP1 LP2

S1 S2B1 B2

Source

B1S1

B2DG

DestinationLP1

S2LP2

capacity of each edge has to be updated based on the availability of the grid components or the operating point of the intermittent generators, the minimum free capacity of every edge can be found in each reliability state. Meanwhile, the maximum possible flow of the load edges are calculated, too. If the maximum possible flow of a load edge is lower than the amount of the demanded load at that load point it can be concluded that some part of the demanded load cannot be supplied and some load may be shed. In other words, the proposed method can calculate the minimum amount of unsupplied/shed load, which is an appropriate idea for solving the OPF problem. In other words, solving the Max-Flow of the representative graph of the microgrid using Ford-Fulkerson method represents the OPF solution of the microgrid.

III. FLEXIBLE RELIABILITY ANALYSIS USING MAX-FLOW

ALGORITHM

The concept of FR was introduced in [4]. One of the most important advantages of enhancing the FR indices of the system in comparison with OR indices in the planning studies is the smart enhancement of the reliability levels of the network based on the priority of the customers. Indeed, instead of increasing the OR of the network, which may boost the free riding problem, the FR of the network can be increased by making different reliability levels for different customers based of their priority level. A high priority customer may be located in a system with low OR level, but with high level of FR. In this paper, two topologies are considered for the studied microgrids, i.e. AC and hybrid AC-DC microgrids. Figs 2(a) and 2(b) show the topologies and components of studied microgrids. Following assumptions are made for underlining the reliability-wised impacts of microgrids topologies:

1) The capacity of the AC and DC resources are similar in both microgrids.

2) The amount of load demand in both microgrids are the same. Equation (9) describes this assumption.

Hybrid Hybrid HybridACTotal AC DC FlexD D D D= + + (9)

where ACTotalD is Total load in AC microgrid. Hybrid

ACD , HybridDCD and Hybrid

FlexD are the AC, DC and Flex load of

the hybrid AC-DC microgrid, respectively. 3) The components' reliability data in both microgrids are

the same.

Since the Total load represents the aggregated loads of all customers, the proposed AC microgrid of Fig. 2(a) can serve single reliability level for all of them. This reliability level represents the OR of the microgrid. But, in hybrid AC-DC microgrid, the Total load of the system is divided in 3 clusters, i.e. AC, DC and Flex. Load. Some customers are considered as the high priority customers and request higher reliability level. Indeed, they should be equipped with an inverter, which makes them flexible in choosing the primary source of electrical energy between AC and DC sub-systems. In addition to Flex. Load, AC and DC share of the customers' load are aggregated and addressed with AC Load and DC Load, respectively. The desired reliability level of AC Load and DC Load are assumed to be less than Flex. Load's reliability level.

The OFP of both microgrids can be obtained by solving the Max-flow of their representative directed flow graph in different states of the system. The expected load not supplied

(ELNS) is used as the reliability index, which is appropriate for assessing the variation of the reliability level in different hours of the study year. Since the amount of ELNS depends on the amount of its load, the absolute value of ELNS is not appropriate for comparing the reliability level of different customers. However, the normalized value of ELNS, which is shown by NELNSl,t, is a suitable index for comparison of reliability level, as indicated in (10) [12]:

,,

,

l tl t

l t

ELNSNELNS

D= . (10)

where Dl,t is the demand of load-point l of the grid at time t. Fig. 3 shows the proposed flowchart for calculating the

flexible reliability. The input data is consisted of two clusters; as follows:

1) Hourly power generation for uncontrollable resources, i.e. wind turbine and PV array;

2) Reliability data of microgrids components including AC&DC resources, transformers, inverters and battery.

State enumeration simulation method is used for running the microgrid reliability analysis. In this paper, the reliability analysis is carried out for all 8760 hours of the year individually and the nodal reliability indices are calculated. Regarding this matter and as it can be seen in Fig. 3 the proposed flowchart has two main loops. The outer loop enumerates the 8760 hours of the year and the inner loop enumerates all possible contingency states of the system. Firstly, the probability of the state is calculated based on the probability of all microgrids components using the input reliability data (cluster 2 of the mentioned input data). For reducing the number of states, the states with probability (Pt,i) less than 10-6 are ignored. For each contingency state i, the edge capacities of both microgrids representative directed flow graph will be updated as follows:

(a)

(b)

Figure 2. Single-line diagram of (a) AC microgrid, and (b) hybrid AC-DC microgrid.

Considering the failure states: if one of the system component in a specific state is in the failure mode, the corresponding edge capacity of that component will be considered as zero.

Considering the output power of the resources: since the generated power of the wind turbine and PV arrays changes over the 24-hours of a day, the edge capacity of the corresponding renewable resources are limited up to that amount. The edge capacity of a load is considered equal to the corresponding load at hour t. Moreover, the edge capacity of the controllable resources, i.e. the output power AC generator and discharging power of the battery, always consider equal to its rated values, regardless to its scheduled generation. Because these are controllable resources and can inject power up to their nominal value in the contingency state in order to minimized the unsupplied load.

Then using the Ford–Fulkerson method the Max-flow of the graph has been calculated. As it is shown in (11), the free capacity of the load edges represents the amount of unsupplied load at i-th contingency state, LNSl,t,i. , , , , , ,l t i l t i l t iLNS C F= − (11)

where Cl,t,i and Fl,t,i are the capacity and the flow of the edge l at hour t for contingency i, respectively. As it is indicated in (1), Fl,t,i is smaller than Cl,t,i, so LNSl,t,i is a positive value. After enumerating all contingency states, ELNS for each customer l at hour t will be equal to the sum of the unsupplied demand over all contingency states weighted by probability of each state Pt,i:

, , , ,1

CN

l t l t i t ii

ELNS LNS P=

= × . (12)

where NC is the total number of contingencies. Equation (12) calculates the ELNS of customer l in hour t. The outer loop determines this reliability index for all 8760 hours of the year. Regarding this matter, the nodal reliability indices of the microgrid considering the output power of intermittent resources, i.e. wind turbine and PV array, will be calculated.

The OR index depicts about the total unsupplied loads of the system in a same zone. Thus, ORt which represent the OR of the microgrid in hour t is defined as: , , ,t AC t DC t Flex tOR ELNS ELNS ELNS= + + . (13)

where ELNSAC,t, ELNSDC,t, and ELNSFlex,t are the expected load not supplied of AC, DC and Flex. load points in hour t.

For investigating the effects of DER expansion on the OR of the microgrid, the proposed index should defined over the whole lead time of the study period (8760 hours in this study) to consider the seasonal variation of the loads and resources. Equation (14) addresses the averaged OR index ( OR ).

8760

1

1

8760 tt

OR OR=

= (14)

In the same way, the hourly average of the ELNS in AC, DC, and Flex load point over the study time can be presented as follows:

8760

,1

1

8760AC AC tt

ELNS ELNS=

= (15)

8760

,1

1

8760DC DC tt

ELNS ELNS=

= (16)

8760

,1

1

8760Flex Flex tt

ELNS ELNS=

= . (17)

One of the most important parameter in analyzing the FR index is the averaged share of Flex. load from the total load of the microgrid, which is presented in (18).

8760

,

, , ,1

1

8760Flex t

FlexAC t DC t Flex tt

D

D D Dα

==

+ + (18)

where αFlex is the averaged share of the Flex load from the total load of the system and DFlex,t is the amount of Flex load in hour t. Using (14), (17) and (18) the averaged FR index of the

microgrid ( FR ) is introduced as:

( )Flex Flex

Flex

OR ELNSFR

OR

α

α

× −=

× (19)

In [4], we defined the FR index of the grid based on the expected energy retrieved by the available alternative resources. But, in this paper, we propose the FR index of the grid based on the ability to make different reliability levels for different customers. Accordingly, if an equal reliability level is

Start

Hour t

Pt,i >10-6No

Are allstates enumerated?

No

Yesi =

i+1

Yes

End

Contingency state i

Calculate the Prob. of the state i (Pt,i)

Update the edge capacity of the microgrid’s corresponding

directed flow graph

Calculate the Max-Flow of the microgrid’s corresponding

directed flow graph

Calculate the free capacity of the load edges, LNSl,t,i

Update the ELNS of customers indices using (5)

Are allhours enumerated?

t = t+

1

No

Save the ELNS of all load points for all t

Input hourly data of wind turbine,

PV array and load

Input reliability data of system’s

components

Stat

e E

num

erat

ion

Rel

iabi

lity

Ana

lysi

s us

ing

Max

-Flo

w A

lgor

ithm

Yes

Figure 3. The proposed flowchart of calculating the customer-wised reliability indices using Max-Flow algorithm.

served for all customers, it is considered that the FR of that system is equal with zero. In another words, if an equal reliability level is served for all load pints in the microgrid then it can be concluded that the FlexELNS will be equal with

( )Flex ORα × . Otherwise, FlexELNS will be lower than

( )Flex ORα × . Therefore, FR is bounded between zero and one ( 0 1FR≤ ≤ ). Smaller FR values represent lower FR level and bigger FR values represent the high ability of the microgrid in supplying Flex. load in contingency states.

IV. NUMERICAL STUDY

The proposed AC and hybrid AC-DC microgrids of Fig. 2 is considered as the study case to evaluate the ability of microgrids in offering different reliability levels under the flexible reliability context. For better comparison, these two microgrids are considered with a same size of resources and as it is addressed in (9), both AC and hybrid AC-DC microgrids serve the same amount of loads. Equations (20)-(22) describe the share of AC, DC and Flex. load in this study.

0.35Hybrid ACTotalACD D= × (20)

0.15Hybrid ACTotalDCD D= × (21)

0.50Hybrid ACTotalFlexD D= × (22)

The electricity resources and storage system of the case study model are considered as follows: 40 kW gas engine generator (DG unit), 25 kW wind turbine, 25 kW PV array and a storage device (battery) by maximum charging/discharging power of 2.191 and 4.929 kW and total capacity of 10 kWh. Moreover, these microgrids are connected to external grid to balance their shortage/extra power by a transformer with rated power of 20 kW for serving the load with maximum 63 kW demand. For more accuracy, the actual data of wind turbine and PV array output power as well as the actual demand are considered and all of them are obtained from hourly historical data for 8760 hours of year 2010 in a same geographical location, i.e. Sotavento in Spain, [13]. Since the output power of wind turbine and PV array and the demand was not in our desired range those are rescaled to meet the considered capacities of the system resources and load. Table I addresses the considered forced outage rate (FOR) of both microgrids' components [14-16].

The ELNSl,t indices of both AC and hybrid AC-DC microgrids are calculated for all 8760 hours of the study year. For better demonstration of ELNSl,t over the study period, they are sorted in descending order. Fig. 4 indicates these values over the time while, the horizontal axis represented the percent of time (instead of absolute hours of the study period). As it is shown in Fig. 4, one index is calculated for AC microgrid because, all customers are integrated in one load point and this microgrid can offer uniform level of reliability for all of them. However, in hybrid AC-DC microgrid three different levels of reliability can be served for the AC, DC and Flex. load points.

Moreover, the averaged of ELNS indices are calculated over the study period based on (15)-(17) for better comparison between two mentioned microgrids topology. Table II shows the averaged ELNS for each load point of both microgrids. Since both microgrids are equipped with same resources, it can be predicted that the reliability of both microgrids are the same.

However, in this paper the impacts of microgrid architecture on the reliability analysis has been investigated and as it can be seen in the last column of the Table II, the total amount of unsupplied load in the hybrid microgrid is reduced about 6.45% compare with AC microgrid. Loads in hybrid microgrid are shed less frequently, which can be interpreted as the OR of hybrid microgrid is higher than AC microgrid with same conditions. Indeed, the reliability level is increased but the OR index is reduced.

Figs 5-7 show the different effects of expanding DERs on OR and FR of the proposed hybrid microgrid. The horizontal axis of Fig.s 5-7 is the changes of DER capacities regarding to current value. In the proposed sensitivity study, when the capacity of one resource is changed the capacity of the other resources remains constant. Thus, zero represent the current capacity. For example, the capacity of the AC generator was considered 40 kW, but in this sensitivity analysis its capacity varies from 20 (20 kW less than current value) to 60 kW (20 kW more than current value). Moreover, for better comparison between the proposed reliability indices and investigating the

Figure 4. The absolute value of the load points' ELNS in descending

order over the study period (8760 hours).

TABLE I. RELIABILITY DATA OF MICROGRIDS' COMPONENTS

Forced Outage Rate (%) AC Microgrid Hybrid AC-DC Microgrid

External Grid 1 1 Grid Transformer 2 2 AC Generator 4 4 DG Transformer 2 2 Wind Turbine 4 4 Wind Inverter 10 −PV Array 4 4 PV Inverter 10 −Battery 4 4 Battery Inverter 10 −Intergrid Inverter − 10

TABLE II. AVERAGED ELNS FOR EACH LOAD POINT OF AC & HYBRID MICROGRIDS

Individual (kW) Total (kW)

ACELNS 0.58065 0.58065

HybridACELNS 0.42988

0.54322 HybridDCELNS 0.09765

HybridFlexELNS 0.01568

0

0.5

1

1.5

2

0 20 40 60 80 100

Exp

ecte

d L

oad

Not

Ser

ved

(kW

)

Percent of time (%)

ELNS of AC MicrogridELNS_AC of Hybrid MicrogridELNS_DC of Hybrid MicrogridELNS_Flex of Hybrid Microgrid

sensitivity of DER capacities, the vertical axis addresses the per unit value of the indices based on the initial value.

Fig. 5 shows the per-unit averaged OR index of the hybrid microgrid versus the capacity variation of DERs. Actually, this kind of curves, which investigates the sensitivity of the OR indices versus DER expansion, can be find in the literature a lot and this decreasing trend is familiar for reliability experts [17]. More slope of the curves represents more impacts of the resource capacity on OR level of the microgrid. As a result, the most effective DER resources are battery, employing DR programs, the AC generator, wind turbine and PV arrays, respectively. DR program in this paper is considered a general type of emergency demand response program (EDRP), which in the case of emergency situations it can be triggered and lead to load reduction in the selected load points.

Fig. 6 shows the per-unit averaged ELNS index of the Flex. load versus the capacity variation of DERs. The following interesting points can be extracted from these results:

1) Ineffectiveness of implementing DR program on the ELNS of the Flex. load. Although DR expansion impose positive impacts on OR of the microgrid, it is not suitable for improving the reliability of Flex. loads.

2) Negligible impacts of AC generator expansion compare with other resources. This point underlines an aspect of renewable resources that shed light on adva-ntages of renewable resources versus AC generators.

3) The salient impacts of battery on this index. As it can be seen in fig 6 the ELNS of Flex. loads are so sensitive regarding the size of the battery. Thus, the optimal size of the battery can be calculated from the required level of the reliability of the Flex load.

Fig. 7 demonstrates the per-unit averaged FR index of the hybrid microgrid versus the capacity variation of DERs. This figure presents the different impacts of the capacity of the DERs on the FR level of the microgrid. As it is shown in fig 7, some resources have positive impacts, while other resources have negative effects. Bigger value of the index represents more level of FR in the microgrid. The following points can be extracted from fig 7:

1) Reducing the capacity of the AC generator improves the FR of the microgrid. Because, by reducing the capacity of AC generator the OR of the grid may be decreased and the difference between the reliability levels of the high priority customers and the other customers can be more. Regarding this matter, FR of the microgrid may improve. But, by increasing the capacity of AC generator the OR reliability of the microgrid as well as the reliability level of all customers will be increased, so it has no impact on FR index.

2) Changing the capacity of the renewable energy resources have a tiny impacts on FR of the microgrid.

3) Increasing the capacity of the battery has a great impact on improving the FR of the microgrid. Due to le location of the battery in the microgrid (in the DC sub-system) not only it can directly inject power to the Flex. load but also it is completely controllable. Thus increasing the capacity of the battery impose a dominant impact on the FR indices. Similarly, reducing its capacity reduces the FR of the microgrid.

4) Unlike the battery which impose a positive impact on FR indices, increasing DR programs show negative consequences on FR of the microgrid. DR programs are considered to be implemented just in the AC and DC load points. Regarding this matter, in a contingency state, which some parts of AC or DC load is shed, if an emergency DR program can be implemented, then those part of the shed load is considered as the decreased load and will not considered as the shed load. Thus, by increasing the DR programs the reliability level of the

Figure 5. Per-unit averaged OR index of the hybrid microgrid versus the capacity variation of DERs.

Figure 6. Per-unit averaged ELNS index of the Flex. load versus the capacity variation of DERs.

Figure 7. Per-unit averaged FR index of the hybrid microgrid versus the capacity variation of DERs.

AC and DC load points will be improved, but the reliability level of the Flex. load remains constant. In other words, although the OR of the microgrid will be improved by increasing the DR programs, the FR indices will be decreased.

V. CONCLUSION

By results of this study it has been shown that the expansion of each DER technology impose different impacts on flexible and overall reliability (FR and OR) of the hybrid microgrid. The following outcomes can be concluded for the results of current FR and OR analysis on the proposed hybrid microgrid of the case study. Decreasing the capacity of the AC generator increases the FR index. Variation of renewable energy resources capacity doesn’t have any salient impact on FR index. Increasing the capacity of the battery increase the FR index. But, increasing the amount of employing demand response programs decrease the FR index. Although the mentioned results may be different in other hybrid microgrids, this paper aims to reveal an applicable viewpoint on traditional reliability analysis. Indeed, FR analysis add a new dimension to the reliability analysis context.

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