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#### The Transactions of the Korean Institute of Electrical Engineers

##### ISO Journal TitleTrans. Korean. Inst. Elect. Eng.

1. (Electric Power System Research Team, Korea Institute of Energy Research, Korea.)

Stand-alone Microgrid, Energy Storage System, Hosing Capacity, Bi-directional Power Flow

## 1. Introduction

In small islands or mountainous areas where it is difficult to connect commercial power grids, electricity is supplied using diesel generator(DG). However, stable power management is difficult due to fluctuations in power demand during the day and at night. In particular, due to eco-friendly policies, electric vehicles(EVs) have been widely used in place of vehicles using fossil fuels in most small islands. There are many difficulties for the DG to respond to all the power supplied to these loads. Therefore, a lot of research is being done on the use of a MG composed of a DG, renewable energy and an ESS (1-3).

For renewable energies like photovoltaic(PV) and wind turbines which is generally used as a power source in stand-alone MGs, there are not contribute to reducing the capacity of the DG that supplies power to the load due to the intermittent output characteristics (4).

In the case of ESS, it is used for various purposes such as peak cut, frequency and voltage control (5,6). Recently, research utilizing the advantages of ESS in MG has been actively conducted. The authors of (7,8) proposed the high penetration method of PV using ESS in MG. and in (9) the authors proposed the voltage control for MG with high renewable energy penetration. The author of (10) also addressed the design framework considering economic efficiency in MG. However, in the existing researches of stand-alone MG, the size of the load is determined according to the capacity of the DG itself because reducing the capacity of the DG makes it difficult to control the voltage and frequency in MG. Therefore, research is needed to increase the capacity of the load and renewable energy while minimizing the output of the DG using existing facilities.

In order to improve the hosting capacity of load and renewable energy without increasing the capacity of the line and DG, this paper proposes a method to minimize the capacity of a DG in an stand-alone MG including renewable energy and ESS. For this, ESS control algorithm is presented to use the bi-directional power flow to be determined by the charge/discharge mode, operation time and operating capacity based on the data measured from the bus to which each load is connected. Specifically, in case the capacity of renewable energy and consumers are exceeded upper than DG and the line capacity, an ESS control algorithm performs that voltage is maintained within the allowable range(220±6%) and the line capacity is also controlled to be not deviated from the rating through the charge/discharge operation of the ESS located at the end of the feeder. Also, Describing the composition of MG, it is consist of DG, PV, load, dumpload and ESS, and the ESS is located in the end of the line in consideration of PV and loads including electric vehicle(EV). Also, This paper propose an EMS for the ESS operation to keep the balance of the power and voltage in the MG considered the state of charge(SOC) of the ESS by including the power balance equation on each bus.

From the simulation results as proposed method using Matlab/ Simulink, it is confirmed that the use of DG can be properly controlled, the voltage of the line is maintained within the allowable range. And also, it is clear that the hosting capacity of the load and renewable energy can be greatly improved.

## 2. Concept for ESS control to ensure hosting capacity of MG

### 2.1 Bi-directional power flow using ESS

The power flow of the existing stand-alone MG is unidirectional from the DG to the load, and the overall size of the customer is designed to be less than the capacity of the DG. In this case, when the ESS is connected to the end of the line and power is operated in both directions, it is possible to reduce the voltage drop at the end of the line and to operate a load that exceeds the existing line capacity (11,12).

Fig. 1 and Fig. 2 show how to stably maintain the voltage rise and voltage drop caused by renewable energy and loads using ESS (13). That is, if the voltage exceeds the upper limit due to the reverse power flow caused by renewable energy, the ESS maintains the voltage within the allowable value through charging operation. And if the system voltage due to the increase in load exceeds the lower limit, the ESS maintains the voltage within the allowable value through discharge operation.

Fig. 1. BUS voltage during ESS charging

Fig. 2. BUS voltage during ESS discharging

### 2.2 characteristic of Hostiong capacity at stand-alone MG

The stand-alone MG requires efficient operation method to prevent congestion and to ensure hosting capacity. The traditional approach to increasing grid capacity is reinforcing the system with additional network components and existing feeder or cables to solve power or voltage constraints. However, reinforcement for existing MG, it has the disadvantage of spending a lot of cost and time.

Recently, to solve above problems, it presents operation method by ESS introduction at power line as of non-feeder without reinforcing the power line. Therefore, to keep within the existing hosting capacity of stand-alone MG, this paper presents bi-directional power flow Method by ESS operation without reinforcing the power line. Instead of reinforcing or building additional cable. First, the ESS as supply side operation has charging function for power that cannot be transmitted due to MG congestion when renewable energy is generating power. Second, as demand-side operation has discharging function for power, ESS is used to meet demand during periods when there is insufficient hosting capacity of MG as shown in Fig. 3 Where, ESS charged during previous periods of off-peak load and generation of renewable energy.

Fig. 3. Concept for ESS operation to improve hosting capacity

As mentioned earlier, this paper proposes a method of using a bi-directional power flow to accommodate more loads and renewable energy between the ESS and the grid. That is, the existing system is a one-way system that supplies power from the grid to the load and sends power from renewable energy to the grid. If only one-way power flow between the existing grid and renewable energy is used, only the capacity of the line can be used. On the other hand, if a bi-directional power flow is used as shown in Fig. 4, the grid and the ESS can supply or absorb the power supplied to the load and the power generated from renewable energy. Therefore, it is possible to stably operate the load and renewable energy beyond the capacity limit of the existing line (14).

Fig. 4. Bi-directional power flow between grid and ESS

## 3. ESS control strategy to improve the hosting capacity of MG

### 3.1 EMS of the MG

EMS uses simplified control-oriented model necessary for the effective operation of a stand-alone MG. The dynamics of the load and DG are very fast compared to the characteristic sampling time, so we have to ignore them and consider the SOC of the ESS by including the power balance equation on each bus of the MG.

ESS composed of battery and power conversion system(PCS) is a device that stores and discharges electric energy when necessary and the SOC dynamic model of ESS and upper and lower limit conditions are as follows (15).

##### (1)
$SOC(t+1)=\begin{cases} SOC(t)-\dfrac{\eta_{ch}T_{s}}{C_{\max}}P_{ess}(t),\: & P_{ess}(t)<0\\ SOC(t)-\dfrac{T_{s}}{\eta_{dis}C_{\max}}P_{ess}(t),\: & P_{ess}(t)>0 \end{cases}$

##### (2)
SOC_{\min}<SOC(t)<SOC_{\max},\: P_{bat,\:\min}<P_{bat}(t)<P_{\begin{aligned}bat,\:\max \\ \end{aligned}}

Where, $T_{s}$ is the sampling time ($T_{s}= 1 s$), $C_{\max}$ is the rated capacity of the battery, $\eta_{ch}$ and $\eta_{dis}$ are charging and discharging efficiency, and $P_{esst}(t)$ denotes the ESS power to be charged and discharged.

On the other hand, DG is mainly used as the main power source to supply power to the load in island areas and isolated areas. In this paper, DG is base power generation, and output is rated in all modes regardless of the increase or decrease of the load.

If the load that consumes power during power generation is small as the DG is the base, the ESS operates in the charging mode. If the SOC of the ESS reaches the set value, the dumpload is operated as shown in Equation (3) to adjust the balance of the total power of the MG.

##### (3)
$P_{dp,\:i}(t)=\delta_{i}(t)P_{dp,\:nom}i=1,\:2$

where $P_{dp,\:i}(t)$ is the nominal output of the dump load, $\delta_{1}(t)$ is 1 when the SOC reaches the set value for operating the dump load, and $\delta_{2}(t)$ becomes 0 when the SOC reaches the set value for stopping the dump load.

On the other hand, when the EV is parked for a certain period of time, the EMS calculates the optimal charging interval during the parking period in consideration of the SOC of the ESS and charges the EV with a constant power. When the voltage of the line is out of the set range due to PV power generation, load and EV charging consumption in stand-alone MG, EMS controls the voltage drop of the line by reducing the EV charging power.

##### (4)
$P_{ev,\:i}(t)=\epsilon_{i}(t)\left(P_{ev,\:ch}-P_{ev,\:curr}\right),\: i=1,\:2,\:3$

Where, $\epsilon_{i}(t)$ is 1 when the EV is being charged, and $P_{ev,\:ch}$ and $P_{ev,\:curr}$ denote the EV rated charging output and the power reduced by the EMS for charging the EV, respectively.

The EMS of the stand-alone MG operates based on the data collected at each point. Charging/discharging operation by bi-directional power flow of ESS stabilizes all bus voltages within the allowable value and minimizes each feeder current to improve the acceptability of load and renewable energy. In addition, when the SOC of the ESS reaches the upper/lower limit specified by the EMS, the stability of the stand-alone MG is secured by adjusting the controllable load or operating a dump load. The charge/discharge power of the ESS to increase the hosting capacity is determined according to Equation (5).

##### (5)
$P_{ess}(t)=\sum_{t=1}^{n}P_{load}(t)-\sum_{t=1}^{n}P_{pv}(t)-P_{dg}$

where, $P_{ess}(t)$ is the charging/discharging power of ESS, $P_{load}(t)$ is the load demand, $P_{pv}(t)$ is the PV generation and $P_{dg}$ is DG output.

In this case, if the ESS is connected to the end of the feeder and many PVs are generated at one time, the grid voltage will deviate from the allowable range. EMS must maintain the grid voltage to satisfy the following condition.

##### (6)
$v_{MG,\:\min}\le v_{MG}(t)\le v_{MG,\:\max}$

where $v_{MG,\:\min}$ and $v_{MG,\:\max}$ denote the lower limit and the upper limit of the voltage, respectively.

### 3.2 Operation algorithm of MG

In the ESS control algorithm to use the bi-directional power flow, the charge/discharge mode, operation time, and operating capacity are determined based on the data measured from the bus to which each load is connected, and the ESS is operated stably within the set SOC range. For this, three alarm signals are included in the EMS for each mode, and the operation procedure is as follows.

[Step 1] Determination of charge/discharge of ESS

In order to determine the mode of the ESS, the direction of the power flow at the measurement point must first be determined. In MG, If the sum of the active power measured in each bus in MG is positive, it means that the consumed power is greater than the generated power. At this time, as in Equation (7), a becomes 1, and when the sum of active power is negative, b becomes 0. That is, when $\alpha(t)$ is activated, the grid voltage may drop below the lower limit, so the ESS operates in the discharge mode. Conversely, if $\alpha(t)$ is deactivated, the grid voltage may deviate from the upper limit, so the ESS operates in the charging mode.

##### (7)
\alpha(t)=\begin{cases} 1,\: & \begin{aligned}\sum_{i=1}^{n}P_{i}(t)\ge 0\\ \end{aligned}\\ 0,\: & \sum_{i=1}^{n}P_{i}(t)<0 \end{cases}

where, $P_{i}(t)$ is the sum of the measurement active power in each bus.

[Step 2] Determining the operation mode of dump load

When the ESS is charging, the dump load is operated when the SOC reaches the set value for the safety of the ESS and charging operation can no longer be performed. In this paper, as shown in Equation (8), $\beta(t)$ is activated according to the SOC of the ESS, and the dump load operates at the nominal output until the SOC falls to a certain extent. When the SOC of the ESS is below the set value, $\beta(t)$ is deactivated and the dump load stops operating.

##### (8)
\beta(t)=\begin{cases} 1,\: & \begin{aligned}SOC(t)\ge SOC_{set,\:\max}\\ \end{aligned}\\ 0,\: & SOC(t)<SOC_{set,\:\min} \end{cases}

[Step 3] Determining the operation mode of the controllable load

The controllable load operates when the ESS is discharged, and the SOC reaches the set value for the safety of the ESS and the discharge operation can no longer be performed. In this paper, as shown in Equation (9), $\gamma(t)$ is activated according to the SOC of the ESS, and the controllable load reduces power consumption until the SOC increases to a certain extent. When the SOC of the ESS exceeds the set value, $\gamma(t)$ is deactivated and the controllable load restores power consumption to its original state.

##### (9)
\gamma(t)=\begin{cases} 1,\: & \begin{aligned}SOC(t)\ge SOC_{set,\:\max}\\ \end{aligned}\\ 0,\: & SOC(t)<SOC_{set,\:\min} \end{cases}

Table 1. Classification of ESS operation signal

 Classification $\alpha$ $\beta$ $\gamma$ ESS charging Operation 1 - - ESS discharging Operation 0 - - SOC ≥ 70%, 1 1 - SOC < 68% 1 0 - SOC ≤ 20% 0 - 1 SOC > 23% 0 - 0

Fig. 5 shows the ESS control algorithm of the stand-alone MG based on the above-mentioned procedures. In a situation where the power generated by controllable load such as EV and PV generation exceeds the capacity that can be accommodated by DG, ESS maximizes the hosting capacity through charging and discharging operation.

Fig. 5. MG ESS Operation Algorithm

Table 2. Model parameter of MG

 Bus number [sending-outgoing] length[km] impedance [ohm/km] DG (kVA/V) Load+EV Dump load [kW] PV [kWp] ESS [kW/kWh] PF [PU] DG-1 0.01 Z=0.730+j0.085 20kVA/230V - - - 1 1-2 0.1 Z=0.730+j0.085 - 12kW 16kWp - 1 2-3 0.1 Z=0.730+j0.085 - 18kW 16kWp - 1 3-4 0.1 Z=0.730+j0.085 - 18kW 16kWp - 1 4-5 0.1 Z=0.730+j0.085 - 8kW 30kW/100kWh 1

## 4. Case Studies

### 4.1 Simulation model

In order to verify proposed algorithm of the stand-alone MG, the system with an ESS, PV, dumpload and DG are modeled using Matlab/Simulink. Fig. 6 shows the simulation model of a stand-alone MG consisting of DG, feeder, 3 loads consisting of the customer and EV, dumpload, 3 PVs and ESS. The load and PV are connected to each bus in the feeder, and ESS is connected to the end of the feeder for grid stabilization and hosting capacity. The feeder model applies a pi circuit model that can apply the line impedance and length.

Fig. 6. Simulation modeling of MG

(2) Simulation condition

To carry out simulation, facility of stand-alone MG is composed of four buses of total customer, customer including EV charger, ESS , PV system and dump load as shown in Fig. 7. Where, dump load is assumed that is installed in same location as ESS at end bus. Also the hosting capacity was analyzed as average value per 10 minute from 00:00 to 24:00 (all time). And also, Table 2 shows the model parameter of the stand-alone MG for the simulation.

Fig. 7. Configuration of MG

To verify proposed operation algorithm, test conditions for the load pattern and output pattern of PV system are adapted as real output pattern as shown in Fig. 8. Where, it is assumed that the PV output capacity of individual customers is 6 kW. Therefore, the output of the PV system range has a variable characteristic from 0 kW to 48 kW. In case of the loads, there are 16 loads having the 3 kW capacity in the MG and the total consume power of the loads ranges from 0 kW to 48 kW.

Fig. 8. Pattern of PV and load

### 4.2 Power Characteristic of Stand-alone MG

Fig. 9 shows the simulation results in which EMS matches power demand and response through control of ESS charging and discharging. In order to effectively operate MG including DG and dump load for the 24 hours, the ESS is charged when the amount of power generated by the DG is greater than the consumption. And also, the ESS is discharged when the amount of output by the DG is less than the load consumption. From the simulation results, it is shows that the power balance control is smoothly performed according to the algorithm.

Fig. 9. Power of each component in MG

Fig. 10 shows the detailed simulation results in stand-alone MG using given load and PV patterns in the ESS connection state. In the simulation, the initial SOC of ESS is 58 % and the rated power of DG is 20 kW. Since the power consumption of the load is less than DG power, the ESS is charged up to T1 hours. After time T1, the dumpload is operated at T1 when the SOC of the ESS is 70%. Where, the capacity of the dump load is 10kW, and it operates until the SOC of the ESS drops to 68%.

At T2, the ESS discharges because the load consumption exceeds the DG capacity. From T4, the ESS operates in charging mode again due to solar power generation, and at the point where the SOC of the ESS reaches 70%, the dump load operates again at T5. Since the SOC of the ESS becomes 68% at T6, the dump load stops operating and operates in the discharge mode. After T7, when the power consumption of the load decreases, it operates in the charging mode.

Fig. 10. Simulation result of MG

### 4.3 Characteristic analysis of hosting capacity according ESS operation

As simulation result by proposed operation algorithm, Fig. 11(a) shows the measured total power in MG according the introduction of ESS. Also, Fig. 11 (b) and Fig. 11 (c) mean the measured power on the each bus of MG when is interconnected without ESS and with ESS. Specifically, From the simulation result of Fig. 11 (a), (b) and (c), output power of DG is over than 50 kW without ESS and the total output power of DG is 20 kW when ESS is introduced at end bus in MG. Comparing the power characteristics according to the introduction of ESS, It was confirmed that the output of DG in MG can be reduced to a maximum of 20kW with the introduction of ESS.

Fig. 11. (a) measured total power in MG

Fig. 11. (b) Measured power on the each bus of MG without ESS

Fig. 11. (c) Measured power on the each bus of MG with ESS

On the other hands, Table 3 shows the result on the reduced DG output by up to 24 kW when ESS was introduced to improve the hosting capacity of MG. In other words, if ESS is not operated in MG as function to improve hosting capacity, it is shows that generator with a larger capacity can be replaced. Therefore, from the simulation results when ESS is introduced in MG, it is confirmed that hosting capacity of the MG can be improved without replacing existing facilities.

Table 3. Compared results for cable and DG output

 DG(kVA/V) existing(without ESS) 50kVA/230V proposed(with ESS) 24kVA/230V

On the other hands, in order to ensure the hosting capacity of MG with PV, customer and EV facility, result of ESS operation characteristic during the 24 hour is expressed by Fig. 12. Meanwhile, for the time that real-time power range violates the capacity of MG, it was clear that the ESS properly compensates power to ensure hosting capacity by charging and discharging according to the reverse and forward power flow. And also, dump load shows that activation/deactivation operates smoothly according to the SOC value set of ESS in EMS. Based the control characteristic of ESS, it was clear that proposed algorithm of ESS to ensure hosting capacity was useful tool.

Fig. 12. SOC of ESS and dumpload power

### 4.4 Characteristic analysis of voltage according ESS operation

Also, Fig. 13 shows the voltage characteristics at each bus in stand-alone MG when ESS and dumpload are introduced by proposed algorithm as to improve hosting capacity. Based on the test result by Fig. 13 and Table 4, voltages of all bus during the 24 hour could be perfectly kept within the allowable range. Therefore, it is confirmed that better voltage conditions can be maintained through the operation algorithm of ESS in order to be kept within the hosting capacity.

Fig. 13. Voltage characteristic of each bus in MG

Table 4. Voltage analysis of each bus in MG

 Bus number voltage characteristic voltage range [V] Time 1 Max. upper limit 221 09:55 Min. lower limit 219 22:53 2 Max. upper limit 219 09:55 Min. lower limit 217 22:53 3 Max. upper limit 217 09:55 Min. lower limit 215 22:53 4 Max. upper limit 217 17:57 Min. lower limit 213 12:25 5 Max. upper limit 219 17:57 Min. lower limit 210 12:43

## 5. Conclusion

In order to improve the hosting capacity of the load and renewable energy, this paper propose a method to minimize the capacity of a DG in an independent MG including renewable energy and ESS to improve the acceptability of load and renewable energy without increasing the capacity of the line. The main results are summarized as follows;

(1) From the proposed algorithm, the ESS is charged when the amount of power generated by the DG is greater than the consumption. And also, the ESS is discharged when the amount of output by the DG is less than the load consumption.

(2) For the time that real-time power range violates the capacity of MG, it was clear that the ESS properly compensates power to ensure hosting capacity by charging and discharging according to the reverse and forward power flow. And also, the dumpload shows that activation/deactivation operates smoothly according to the SOC value set of ESS in EMS. Based the control characteristic of ESS, it was clear that proposed algorithm of ESS to ensure hosting capacity was useful tool.

(3) Based on the test result, voltages of all bus during the 24 hour could be perfectly kept within the allowable range (220V±6%). Therefore, it is confirmed that better voltage conditions can be maintained through the operation algorithm of ESS in order to be kept within the hosting capacity.

### Acknowledgements

This research was funded by the Ministry of Trade, Industry and Energy, and supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) (No. 20193010025790, No. 20203030020200).

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## 저자소개

##### 유경상(Kyung-Sang Ryu)

He received Ph.D. in electrical engineering from Jeju national university.

His research interests are ESS, distribution system and microgrid.

##### 김찬수(Chan-Soo Kim)

He received Ph.D. in chemical engineering from Hiroshima University.

His research interests are ESS, ESS safety and smart grid.

##### 남양현(Yang-Hyun Nam)

He received M.S. degree in Electrical Engineering from Korea University of Technology and Education.

His research interests are distribution system and renewable energy system.

##### 김대진(Dae-Jin Kim)

He is a PhD candidate degree in electrical Engineering from Seoul National University of Technology.

His research interests are MPC control, control system for EV Charging Station.

##### 김병기(Byungki Kim)

He is a PhD candidate degree in electrical Engineering from Seoul National University of Technology.

His research interests are MPC control, control system for EV Charging Station.