Modeling, Simulation, and Performance Analysis of Power Management Strategies for an Islanded Microg

International Journal of Energy Science (IJES) Volume 3 Issue 6, December 2013 doi: 10.14355/ijes.2013.0306.02

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Modeling, Simulation, and Performance Analysis of Power Management Strategies for an Islanded Microgrid Faruk A. Bhuiyan*1, Amirnaser Yazdani2, Serguei L. Primak3 Electrical and Computer Engineering Dept., Western University, London, Ontario, Canada Electrical and Computer Engineering Dept., Ryerson University, Toronto, Ontario, Canada

*1,3 2

*1

fbhuiyan@uwo.ca; 2yazdani@ryerson.ca; 3slprimak@uwo.ca

Abstract Considering both real and reactive power, this paper presents the modeling, simulation, and performances analysis of various power management strategies (PMSs) for the sizing of an enhanced renewable energy penetrated islanded microgrid (IMG), which consists of an integrated photovoltaic‐wind‐diesel‐battery system. The models of the PMSs are illustrated by flowcharts which can be utilized to determine optimal sizes of the IMG components. When the IMG contains both a battery energy storage system (BESS) and a diesel generator system (DGS), there are several ways to meet the load demand. The various options of fulfilling the load demand make the PMSs complex. The complexities in PMSs have significant impact on the incurred costs through the fuel usages rate of DGS and the deterioration level of BESS. The PMSs are presented by considering unknown component sizes of the IMG. The paper considers the effect of reactive power on BESS charging. The effectiveness of the modified PMSs is demonstrated through simulation studies in the MATLAB/Simulink environment.

Then the performances of the PMSs are compared and the cost‐varying areas are identified meticulously by utilizing the simulation results.

Keywords Battery; Dispatch; Microgrid; Photovoltaic; Power Management; Wind

Introduction Electrification of remote communities remains a challenge

in Canada and elsewhere due to the economical and technical barriers. Most of the Canadian remote communities have been supplied by diesel generator systems (DGSs), while the rest have used low to medium penetration wind/pv diesel systems (Arriaga et al. 2012; Weis et al. 2008). Thus far, many studies (Bernal−Agustin et al. 2009) of islanded microgrid (IMG) have been performed and many systems are installed with various configuration of components.

Among the installed systems, many projects have failed due to improper design. The systems design can be improved if optimization methods are applied (Zhou et al. 2010). The sizing optimization of an IMG can be performed by either analytical techniques or chronological time‐series/probabilistic simulations.

Analytical techniques are difficult to implement due to the large number of variables, non‐linearity in the models, and complexity of the configurations. The timeseries/probabilistic simulation techniques require well‐defined power management strategies (PMSs) and are computationally intensive. Power management in an IMG can be performed by either controlling the energy resources or/and load management (Lujano−Rojas et al. 2012); the accommodation of load management strategies during the design phase may not be a good idea.

Barley et al. (1996) proposes a number of control strategies for the operation and simulation of a wind‐photovoltaic‐diesel‐battery system. Utilizing load setpoint to start and stop the diesel engine, and state of energy (SOE) setpoint to charge the battery energy storage system (BESS), an optimization method for the control strategies is proposed in Ashari et al.

(1999). Bagen et al. (2005) presents a simulation technique for the operating strategies of stand‐alone power systems by giving the priorities to the operation of nonconventional generating units, conventional generating units, and BESS in sequence. Dufo−López et al. (2005) combines two of the operating strategies of Barley et al. (1996) in a photovoltaic (PV) system with energy storage and identified a critical load. Katiraei et al. (2007) proposes an energy‐flow model for an autonomous wind‐diesel system for the investigation of the the daily and monthly performances. Various PMSs for standalone hybrid power system integrated with hydrogen energy storage are proposed in Ipsakis

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et al. (2009). Zhou et al. (2011) proposes the ‘source‐following’ and ‘grid‐following’ dynamic control approaches in order to enable exchange power amongst the sources, and to manage energy of the grid‐connected system. Considering real power only, Vrettos et al. (2011) proposes three operating modes for a small isolated high‐penetration renewable energy system integrated with a BESS. Ohsawa et al. (1993) has applied artificial neural network to the control strategies of PV‐diesel power system, although few researchers (Lin et al. 2011; Yasin et al. 2011) have proposed intelligence‐based approaches for the dynamic control of autonomous hybrid power systems.

Various software tools (HOMER ; HYBRID2; iHOGA; INSEL ; RETScreen ; TRANSYS ) are used for simulation, optimization, and performance evaluation of IMGs. Amongst them, HOMER (Hybrid Optimization Model for Electric Renewable) has been widely used for optimization studies, HYBRID2 is popular for simulations, and HOGA (Hybrid Optimization by Genetic Algorithms) is primarily used for multi‐objective optimization.

is referred to as the discharged power and denoted by Pb. The real and reactive powers of the power‐electronic converter of the BESS are denoted as Pcon and Qcon, respectively; the typical high efficiency of the power‐electronic converter implies that Pb and Pcon are almost equal and are therefore used interchangeably in this paper. The real and reactive power components of the primary load are represented by Pl and Ql, respectively. The dump load is assumed to be a resistive load and represented as Pdl. At any instant, stable operation of the IMG corresponds to following power‐balance equations (1): Pw(t) + Ppv(t) + Pdi(t) + Pcon(t) − Pl(t) − Pdl(t) = 0 Qdi(t) + Qpv(t) + Qcon(t) − Ql(t) = 0 (1)

However, the aforementioned software tools and papers have not modeled PMSs for the objective of determining the component sizes considering both real and reactive powers of the load. This paper models the PMSs in detail by utilizing flowcharts, where both real and reactive powers of the load are considered.

Study System Fig. 1 shows a schematic diagram of an IMG whose main components are DGS, a wind power system (WPS), a photovoltaic power system (PVS), an energy storage system, a dump load, a primary load, and power‐electronic converters. In this configuration, the energy storage system is assumed to be a battery energy storage system (BESS) connected to the power system through a bi‐directional power‐electronic converter. In turn, the PV power system comprises multiple PV modules interface with the point of common coupling (PCC) through a DC/AC converter.

Also, the BESS is composed of a bank of series‐/ parallelconnected batteries and a power‐electronic converter. The WPS delivers power at unity power factor to the PCC, represented as Pw. The delivered real and reactive powers of the PVS are denoted respectively by Ppv and Qpv. The real and reactive powers delivered by the DGS are denoted by Pdi and Qdi, respectively. The power delivered by the batteries

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FIG. 1 SCHEMATIC DIAGRAM OF AN ISLANDED MICROGRID

Reactive Power and Charging/Discharging of BESS Figs. 2 (a) and (b) show that the primary load is decoupled into real power, Pl, and reactive power, Ql. Depending on the control strategy, the load reactive power, Ql, can be supplied by the converters of the PVS, BESS or DGS. To deliver the rated power of the pv PVS into the PCC, the PVS converter rating, Scon , pv . must be the same as the rated power of the PVS, Prat

The power delivered by the PVS usually falls below pv Prat in a year. Therefore, the PVS converter can be utilized to supply reactive power of the load. If the instantaneous real power of the PVS is Ppv(t), then the PVS can deliver a reactive power of

pv 2 pv 2 Q pv  ( Scon )  ( P pv )2 where ( Scon )  ( P pv )2

(2)

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than the aggregated value of

con di Srat and S rat .

Consequently, the effective real power of the BESS con di ) and the DGS ( Pmax converter ( Pava ) decrease as con con 2 Pava  ( Srat )  (Q con ) 2 di con 2 Pmax  ( Srat )  (Q di ) 2

(5)

b The rate of charge of BESS, ( Pava ) , depends on state of

energy (SOE), Eb(t), and the rated charging power, b Pmax , is

(a)

 b E b  E b (t )  b  min  Pmax Pava , max(0, max  (6) Δt   con b If Pava in (6) is larger than Pava , then the charging of con the BESS needs to be lowered to Pava ; otherwise, the b BESS can be charged by Pava . Fig. 2 (a) shows surplus

(b) FIG. 2 REAL AND REACTIVE POWER PHASORS AT (a) EXCESS RENEWABLE GENERATION, AND (b) SHORTAGE OF RENEWABLE GENERATION

Figs. 2(a) and (b) indicate that a part of the load reactive power is supplied from PVS, as Qpv, while the remaining load reactive power can be supplied from the BESS converter and/or DGS. Fig. 2 further illustrates that the real power supplying capacity of the BESS converter decreases when the load reactive power needs to be compensated. Thus, the BESS con converter real power, Pava , is formulated as, con Pava



con 2 ( Srat )





2

 (max 0,(Q  Q )  l Qrem pv

l

(3)

con where Srat is the rating of the BESS converter, and l Qrem is the remaining load reactive power demand;

con l Srat needs to be higher than Qrem when DGS does not

run. However, if DGS runs and delivers real power, the load reactive power can be shared by the converters and DGS, Q con 

Q di 

con S rat (max 0,(Q l  Q pv ) ) con di S rat  Srat   l Qrem (4)





di Srat (max 0,(Q l  Q pv ) ) di  Srat   l Qrem

con Srat





power, Psur, at higher renewable power generation. The actual charging of the BESS, equation (7), is the lowest con b , Pava and Psur. of the three values Pava con b Pb   min( Psur , Pava , Pava ) (7)

If the surplus power Psur is higher than the absolute value of Pb, then the remaining power must be dumped. Fig. 2 (b) illustrates the real and reactive powers of DGS and BESS converter. When DGS and BESS converter run, the load reactive power is shared

by DGS and BESS as described by (4). During the BESS discharge, the available discharging power from BESS can be formulated as b  b  E b (t )  Emin b  min  Pmax Pava , max(0, )  (8) Δt  

Since the BESS discharges through its converter, the maximum discharge power of the BESS is determined by the real power that flows through the converter. If the net load, Pde f, needs to be delivered by the BESS, then the BESS discharge power is con b P b  min( Pava , Pava , Pdef ) (9)

Depending on the PMS, Pde f may need to be supplied by both the BESS and the DGS. Otherwise, power shortage will occur. In Fig. 2 (b), the negative Psur means positive Pde f .

The SOE in the BESS can be expressed as E b (t  Δt )  E b (t )(1   )   b Pb (t )Δt (10)

where Qcon and Qdi are the reactive‐power output of the di BESS converter and DGS, respectively. In (4) S rat is the

where  is the self discharge coefficient,  b is the

l should not be higher rated power of DGS, and, Qrem

efficiency during charging and discharging, and Δt is

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the time step.

employed to compensate the demand (at ( P2b  Pdef ));

Power Management Strategies and Their Flowcharts

otherwise, the DGS shall start. The “power shortage” in Fig. 3 indicates that the resource sizes do not meet the load demand. The BESS enters into charging mode if the aggregated delivered power from the renewable resources exceeds the primary load demand (Psur > 0).

The actual charging power (Pb) depends on the capacity and power rating of the batteries, and the real power handling capability of the BESS converter. If the surplus power cannot be absorbed by the BESS (i.e.

Psur  P1b ), the rest is burnt in the dump load. The

Two distinct types of control in an IMG are (i) dynamic control, which deals with the frequency and magnitude of the voltage, and (ii) PMS, which controls the energy resources of an IMG. This paper deals with four different PMSs, which are described in the following subsections.

Power Management Strategy‐A (PMS‐A) Fig. 3 illustrates the algorithm of the PMS‐A, utilizing the renewable resources first for the real load power demand. The load reactive‐power demand is compensated from converters (PVS and BESS) and con DGS. The real power limit of the BESS converter ( Pava ) di and that of the DGS ( Pmax ) are then calculated

(explained earlier). If the renewable resources cannot meet the net real load i.e., Pde f > 0, then the BESS is

PMS‐A does not allow DGS to charge the BESS. The exception is when the DGS operates at a low load with di a non‐zero minimum setpoint ( Pmin ) setting. When the

net load demand falls below the minimum setpoint of the DGS, the DGS then runs at its minimum operating point. The extra generation from the DGS is then used to charge the BESS. Otherwise it can be dumped. The PMS‐A is a modified form of load following strategy.

FIG. 3 FLOWCHART OF PMS‐A

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Power Management Strategy‐B (PMS‐B) The PMS‐B allows the DGS for charging the BESS. Fig.4 illustrates a flowchart for the PMS‐B. First the aggregate renewable power is compared with load real power. The real powers from the converter of BESS and the DGS are then calculated upon supplying load reactive power (described earlier). If the surplus generation, i.e. Psur > 0 is higher than Pb, then the BESS enter the charging mode at its maximum rate and the rest is burnt to the dump load; otherwise at Psur < Pb, the DGS runs for a while to charge the BESS at a b maximum rate. At E b (t )  Emin of the BESS and/or at

‘on’ status (k = 1) of DGS, if aggregate surplus generation from the renewable resources is lower than Pb, then the DGS starts/continues operation for charging the BESS. The DGS is commended to stop b when SOE in the BESS reaches at Esoc . If Pde f > 0, and

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real load (Pde f) is supplied by the BESS; if fails, the DGS starts to compensate the remaining net load. When DGS is already in ‘on’ status (k = 1) and SOE of the b BESS stays above minimum ( E b (t )  Emin ) and below b Esoc , the DGS continues its operation for charging

BESS at its maximum rate. The energy at BESS may fall b at/below minimum level ( E b (t )  Emin ) with DGS ‘off’

status. In such a situation, the DGS goes into operation mode for charging BESS. As soon as the SOE of the b BESS reaches at Esoc , the DGS stops for charging the b BESS. The Esoc is considered a lavel which is lower than b Emax . The Fig. 4 represents all possible scenarios under

unknown sizes. The “power shortage” of Fig. 4 is true if the reserve and renewable resources cannot meet the primary load demand. The PMS‐B is a modified form of SOC setpoint dispatch strategy.

b E b (t )  Esoc in BESS, and the DGS is off (k = 0), the net

FIG. 4 FLOWCHART OF PMS‐B AND PMS‐C

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FIG. 5 FLOWCHART OF PMS‐D

Power Management Strategy‐C (PMS‐C)

Power Management Strategy‐D (PMS‐D)

et al. 1996), the calculation steps of critical load Pd and cycle charge load Pc are shown in the end of this subsection. The Pd is the intersecting point of direct diesel cost and BESS deterioration cost. It is more economical to use the batteries when net load stays below Pd; otherwise the operation of DGS is more economical. Fig. 5 illustrates the PMS‐D which utilizes the critical load, Pd, upon fulfilling load reactive power demand. The PMS‐D is the modified form of the frugal discharge strategy. Fig. 5 illustrates that when the net load Pdef is above Pd, the DGS provides power to the load at its fullest capacity; otherwise, the BESS delivers the remaining load power. Alternatively, if the net load, Pdef, falls below Pd, the BESS discharges power at its extent. Power failure may occurs if both the DGS and the BESS cannot meet the net load demand, which may occur due to inappropriate component sizes.

Subsection‐1 shows the procedure for deriving Pd and Pc.

Utilizing the per unit energy cost curve of DGS (Barley

1) Determination of Critical and Cycle Charge Load: The

When the DGS is utilized for charging the BESS at b b instead of Esoc , then the PMS‐B becomes PMS‐C. It Emax is a modified form of the cycle charge strategy. The strategy may need the DGS to continue at its operation mode for a longer period than that of PMS‐B. According to PMS‐B and PMS‐C, the DGS runs either at full power or at a rate not exceeding the aggregate of BESS charging power and net load demand. The flowchart for the PMS‐C is given in Fig. 4 where both real and reactive power balances are depicted. The PMS‐C also indicates that the dumping of power, generated from the DGS, is required. Fig. 4 illustrates that “power shortage” may occur under various conditions, especially with improper sizes of components in IMG. Fig. 4 illustrates the DGS ‘on’/‘off’ status by a flag variable ‘k’.

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per unit costs of energy for the DGS includes capital, operation, maintenance, and replenishment. Capital costs depend on the rating of the DGS. The running costs depend on operation and maintenance costs. The operation cost is mainly the fuel costs, which including fuel, transportation, and inventory holding cost. The fuel consumption of the DGS is not linear. The fuel consumption can be assumed as a quadratic function with a cost at no‐load operation. Thus, the fuel consumption (L/h) of DGS for Pdi, can be expressed as (Zhu 2009). di Fcdi  g1 ( P di )2  g 2 P di  g3 Prat (11)

Where g1 , g 2 , and g3 are fuel consumption coefficients in L/kW2h, L/kWh, and L/kWh, respectively. di di Here S rat is the same as Prat taken at unity power factor. The fuel consumption cost in $/kWh for DGS can thus be expressed as

C dif  ( g1P di  g 2  g3

di Prat )C f (12) P di

where Cf is in $/L and includes fuel, transportation and inventory holding cost.

Maintenance cost of the DGS varies at the level of produced power and number of start‐stop. The per di , is given as kWh hourly maintenance cost, Cmh di Cmh 

Cmdi (13) di Prat

Where

Cmdi

is the maintenance cost

di for Prat

capacity of

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respectively. Number of cycle to failure (Ncf) for the BESS can be expressed as (Drouilhet et al. 1997).

N cf 

N ce e

u1 ( DOD 1)

DOD

(16)

u0

where Nce, u1, and u0 are parameters for the equation.

The DOD is the depth of discharge for the BESS. Based on battery manufacturer life cycle data, (16) can be best fitted for a specific BESS. As an example, manufacturer’s life cycle data for NiCd cell is best fitted, which provides the parameter values u0=1.67, u1=‐0.52, and Nce = 2055. Once the best fitted values are fixed, then the equivalent depth of discharge can be calculated as

DODeqc 

1 M

M

( N cf ) n ( DOD) n (17)  n1

where M is the total number of observation point to figure out the values of Ncf for different DOD utilizing (16). This DODeqc is required for (15). For charging the BESS, DGS can be used to run in excess to the net‐load and thus requires extra generating costs. If the round trip efficiency of the BESS and the charger is  R , then the cost of cycle charge can be written as Ccdi 

Fi di c f

R

(18)

The sum of battery wear costs and cycle charge costs can be written as Cce  Ccdi  Cwb  Cwb 

DGS. The hourly running cost of the DGS for per kWh diesel generated energy is the sum of the fuel consumption cost (operation cost) and the hourly maintenance cost expressed as di Com  ( g1 P di  g 2  g 3

where

di Com

is

di Prat di )C  Cmh (14) Pdi f

the

hourly

operation

and

maintenance/running cost. The capital cost of the BESS depends on size. The BESS wear cost is treated as the cost of delivered energy from BESS and the maintenance cost. The BESS wear cost is expressed as Cwb 

Ccb b (15)  Cmh b DODeqc Erat b w

b c

where C , C , DODeqc, E

b rat

, and C

b mh

are wear cost,

capital cost, equivalent depth of discharge, kWh rating of BESS, and hourly maintenance cost of BESS

Fi di c f

R

(19)

where Fi di is the extra fuel required to charge the BESS. The DGS optimum stopping set‐point occurs when the BESS wear costs and DGS running costs becomes equal.

Thus, the equation (14) and (15) can be equalized to calculate the roots of Pdi, which is expressed as Pd 

di Cwb  g 2c f  Cmh

2 g1c f



di di ( g2c f  Cmh  Cwb )2  4 g1 g3c 2f Prat

2 g1c f

(20)

From (14) and (19) the cycle charge load is expressed as Pc and can be calculated by (21) Pc 

di Cce  g 2c f  Cmh

2 g1c f



di di ( g 2c f  Cmh  Cce )2  4 g1 g3c 2f Prat

2 g1c f

(21)

Simulation Results To demonstrate the performances of the PMSs, several case studies are conducted in the MATLAB/Simulink environment by utilizing the values of Table I. The

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values of Table II are used on (20) to determine the critical load, Pd, and thus to simulate PMS‐D. The Cwb of (20) is determined using (15) and (17). The hourly average time series wind speed data, for a Canadian site, is obtained from Weather Canada; and hourly average time series solar irradiation data, based on latitude and longitude, is produced by using HOMER package. The wind power and PV power are produced by employing (Bhuiyan et al. 2010) and (Lee et al. 2008), respectively. To generate Fig. 6, probabilistic treatment is included on the time series data at 15 minutes interval. For other figures, year around hourly average time‐series data is used. In Fig. 1, the Pb is positive for delivered power of the BESS; however, in all figures except Fig. 6, the BESS power is represented by −Pb. The real and reactive load power components are calculated utilizing IEEE RTS load model (IEEE RTS 1979) and a time series of load power factor (Saha et al.

2010).

TABLE I SIMULATION PARAMETERS

390

Simulation Parameters

Values

WPS rating

600 kW

PV power rating

150 kW

Diesel generator rating

320 kVA

Inverter rating

640 kVA

BESS capacity rating

7.2 MWh

BESS power rating

450 kW

Base power

300 kW

Base power factor

0.9

Base BESS discharge time

1 hr

Base BESS capacity

300 kWh

Efficiency of PV system (  pv )

15%

Cut‐in wind speed

3.5 m/s

Rated wind speed

12 m/s

Cut‐out wind speed

23 m/s

Period under observation

8760 hrs

BESS minimum level Emin

40%

BESS SOC level Esoc

60%

Initial BESS SOC

80% of rated

Self discharge of battery

0.2% per hour

Efficiency of inverter (discharging)

95%

Efficiency of rectifier (charging)

95%

TABLE II CRITICAL LOAD, Pd, DETERMINATION PARAMETERS FOR PMS‐D Parameter

Value

Comments

g1

0.00012 L/kW2h

equation (11)

g2

‐0.011 L/kWh

equation (11)

g3

0.16 L/kWh

equation (11)

300 kW

equation (11)

1 $/L

equation (12)

0.11 $/h for 1 kWh

equation (13)

400 $/kWh

equation (15)

0.05 $/h for 1 kWh

equation (15)

1100 cycles

equation (17)

di Prat

Fuel price, c f di

Cmh b cc

/

b Erat

b Cmh

DODeqc

Impact of Converter Reactive Power on BESS Charging This case demonstrates the impact of converter reactive power on the BESS charging for the PMS‐A of the IMG which is considered composed of without PVS. The rating of the BESS converter is taken 0.5 p.u..

As Fig.6 (a) shows, Pw remains above 1 p.u., which is more than the primary load demand. Therefore, DGS does not require to run, and thus Pdi and Qdi in Fig. 6 (a) are zero from 1520th hour to 1525th hour. Fig. 6 (b) indicates that the SOE of BESS has not reached to the maximum level until 1525th hour. Thus, the excess wind power is stored in the BESS. At 1520.25th hour, the reactive power demand is 0.5 p.u., which is supplied by the BESS converter, as shown in Fig. 6 (a).

Although there is excess wind power, and BESS has the capacity remaining for charging, the actual BESS charging power (Pb) is zero due to the lower size converter.

FIG. 6 IMPACT OF CONVERTER REACTIVE POWER ON BESS CHARGING

Simulation Results for PMS‐A Fig. 7 illustrates the effectiveness of the PMS‐A by the real power, reactive power and SOE of the BESS. From 2171th hour to 2192th hour, the aggregate renewable

International Journal of Energy Science (IJES) Volume 3 Issue 6, December 2013

power is sufficient for primary load as shown in Fig. 7(a); thus, the BESS is in charging mode. As Fig. 7(a) shows, the aggregate renewable power is low for primary load from 2238th hour to 2285th hour. Thus, the shortage is compensated by the BESS; if fails, the DGS runs e.g. 2286th hour to 2378th hour. The operation from 2114th hour to 2170th hour in Fig. 7(b) indicates that the SOE of BESS stays at minimum. Fig. 7(a) further demonstrates that short‐term start/stop of DGS (e.g. 2325th hour) may occur depending on wind and PV resource output. The Pdl of Fig. 7(a) indicates that the excess renewable power is dumped when BESS and converter cannot accommodate. As Fig. 7(c) shows, the load reactive power is mostly delivered by the PVS converter and then the rest is supplied by the BESS converter and/or DGS.

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BESS delivers the remaining reactive power and at the end the DGS helps.

FIG. 8 POWER AND ENERGY OF IMG COMPONENTS FOR PMS‐B

Simulation Results for PMS‐C

FIG. 7 POWER AND ENERGY OF IMG COMPONENTS FOR PMS‐A

Fig. 9 illustrates the productiveness of the PMS‐C by the real power, reactive power and SOE of the BESS. From 3395th hour to 3410th hour in Fig. 9 (b) indicates that the DGS operates for charging the BESS up to the maximum level. As Fig. 9(a) shows, the DGS operates almost at full load for charging the BESS and for fulfilling the net load demand. By observing Figs. 8 and 9, it can be stated that the operating periods of DGS in this strategy are longer than those of PMS‐B. As the SOE of BESS goes at maximum level by the DGS operation, Fig. 9(a) illustrates that the dumping power has increased (e.g. from 3350 th hour to 3367th hour). Thus, the renewable energy penetration for the primary load is expected to decrease.

Simulation Results for PMS‐B Fig. 8 demonstrates the capability of PMS‐B by the SOE of the BESS, the real and reactive powers of the IMG components, presented from 3300th hour to 3475th hour. Fig. 8 (a) shows that the delivered wind power is low for many hours, between 3300th hour and 3475th hour. The BESS does not have enough SOE for discharging as shown in Fig. 8 (b). Therefore, the DGS operates for load real power and for charging the BESS (e.g. 3305th hour to 3316th hour). Once the SOE of b BESS reaches at Esoc , then the DGS stops. The BESS can deliver the part of net load until the SOE of BESS stays b above Emin , and thus Fig. 8(b) illustrates that the DGS b starts again when the SOE of BESS touches at Emin .

Thus, the DGS mostly runs with heavy load, subsequently its short‐period start/stop decreases. Fig. 8(c) demonstrates further that the reactive power is supplied mostly by the PVS converter; after that the

FIG. 9 POWER AND ENERGY OF IMG COMPONENTS FOR PMS‐C

Simulation Results for PMS‐D Fig. 10 demonstrates the performance of PMS‐D by the simulation results, presented from 2105th hour to 2110th hour. As Fig. 10(a) shows, the DGS operates

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when net load exceeds Pd; otherwise the BESS delivers the net load, e.g. 2111th hour to 2117th hour. Thus, the operation of the DGS and the BESS is complementary for the net load demand unless any constraints work.

Therefore, the DGS operation contains frequent start/stop, and it can be inferred that this causes higher maintenance costs for DGS. Fig. 10(b) demonstrates that the SOE decrease rate in BESS is lower compared to that in other strategies. The slow decreasing rate of SOE in the BESS may enhance the life cycles of the batteries. Fig. 10 indicates that the charging rate of the BESS is much higher than that of the discharging rate.

(a) and (c) show that the SOEs and self discharges of BESS are similar both in PMS‐A and in PMS‐D.

FIG. 11 SOE OF BESS FOR (a) PMS-A, (b) PMS-C, AND (c) PMS-D

Reactive Power Management in IMG without PVS

FIG. 10 POWER AND ENERGY OF IMG COMPONENTS FOR PMS‐D

SOEs of BESS for the PMSs Figs. 11 (a), (b) and (c) respectively illustrate the SOEs of the BESS for the PMS‐A, PMS‐C, and PMS‐D. As Fig. 11(a) shows, the SOE of BESS for PMS‐A remains low from 2900th hour to 5500th hour due to seasonal variations of wind speed. The magnified plot in Fig. 11(a) demonstrates that the SOE of BESS has decreased significantly below minimum level due to self‐discharge of the BESS. The situation becomes worse when the BESS reaches at minimum level along with low renewable power generation (e.g. 1376th hour to 1475th hour). Thus, the PMS‐B and PMS‐C utilize the DGS for charging BESS; Fig. 11(b) shows that the SOE of BESS does not reach below minimum level. Fig. 11(b) further shows the SOE of BESS reaches frequently at maximum level. The SOE of the BESS for PMS‐B is excluded as it is very similar to PMS‐C. It can be inferred that the SOE of BESS in PMS‐B would not frequently reach the maximum level like PMS‐C. The SOE of BESS for PMS‐D, (shown in Fig. 11(c)), illustrates that the rate of discharge from BESS is lower, as the BESS only discharges for low net load. Figs. 11

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The reactive powers of the IMG, which is constituted without PVS for the PMS‐A, PMS‐B, PMS‐C and PMS‐D are shown in Figs. 12 (a), (b), (c) and (d), respectively. All of the sub‐figures of Fig. 12 illustrate that the Qpv is zero. Thus, the BESS converter mostly delivers the reactive power (Qcon), when the DGS does not require to share. Fig. 12 (a) demonstrates that the reactive power demand at around 2121th hour is 0.45 p.u., which is shared by both the BESS converter and the DGS; where the Fig. 12 (b) (c) and (d) indicate that the reactive powers are delivered only from the BESSs.

FIG. 12 REACTIVE POWER MANAGEMENT IN IMG OF NO PVS

Comparison of PMSs All of the aforementioned studies are represented for few hours of total simulation which is for the clarity of the figures. Table III compares the performances of the strategies based on a year around (8760 hrs) simulation.

As Table III shows, the SOE of BESS stays at minimum level for 3898 hours, 156 hours, 72 hours, and 1143 hours, respectively, for PMS‐A, PMS‐B, PMS‐C, and

International Journal of Energy Science (IJES) Volume 3 Issue 6, December 2013

PMS‐D. The number of hours, the SOE of the BESS at minimum level, is significantly lower for PMS‐B and PMS‐C compare to others. The number of hours, the SOE stays above the 97% of its maximum level, is the highest for the PMS‐C compare to the others’.

Consequently, the renewable energy penetration is the lowest and thus the dumping power is the highest for the PMS‐C. Although the DGS is used for charging the BESS in PMS‐B and PMS‐C, the number of DGS operating hours are lower in the strategies due to near full load operation. Alternately, the DGS operates longer hours with variable load in PMS‐A and PMS‐D. Moreover, the self discharges in PMS‐A and PMS‐D are higher (presented earlier). The low operating hours of the DGS indicate low maintenance and operating costs needed for the DGS. TABLE III PERFORMANCES COMPARISON OF PMSs

www.ijesci.org

flowcharts of the PMSs. ACKNOWLEDGEMENT The first author would like to thank Ministry of Training, Colleges, and Universities, Ontario, and Natural Sciences and Engineering Research Council (NSERC), Canada, for awarding Ontario Graduate Scholarship (OGS) and Canada Graduate Scholarship (Doctoral), respectively.

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