PI control method based storage to control the microgrid ...€¦ · In the research a tow-stag...

Vol. 3(8) Jan. 2017, pp. 522-534

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Article History: JKBEI DOI: 649123/10.112671 Received Date: 18 Aug. 2016 Accepted Date: 23 Nov. 2016 Available Online: 04 Jan. 2017

PI control method based storage to control the microgrid in a state

separated from the main network Hosein Shayeghi, Alireza Nouruzi, Mehdi Mirzaei, Mohammad Yazdani

Electrical Engineering Department, Faculty of Engineering, University of Mohaghegh Ardabili

Ardabil, Iran

Department of Electrical Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran *Corresponding Author's E-mail: [email protected]

Abstract Microgrid is a localized grouping of electricity generation energy storage and loads that normally connected to a main network micro grid generation resources can in clade some distributed generation utilities, wind turbines, photovoltaic systems (PV) diesel generator and

fuel cell which are the electricity supplies and heat provider. One of the most important problems of micro grid. Is voltage control and frequency, in other words, the balance between generated power and consumed load, in the micro grid. In the network–connected modes the main network or macro grid establishes the power equilibrium. Since the unreproducible resources used in microgrid such as fuel cells and diesel generator act un satisfactory and in other hand, they can't use in the power balance, so in micro grids the rechargeable and energy storage resources like battery and flywheel more often used. These resources have high velocity and arable to create power equilibrium as soon as possible. In the research a tow-stag control strategy based on battery energy storage system is introduced for voltage control and micro grid frequency in the isolated mode. In the first stage energy storage system provide primary voltage and frequency control, then controllable resources act the role of .power equilibrium in the micro grid. The aim of this processes that the energy saving system can fall down the output voltage and continuously the maximum capacity of energy saving would be available to establish power equilibrium in the micro grid. The simulation results on micro grids prove the efficiency of controller.

Keywords: micro grid, micro resources, energy saving system, voltage and frequency.

1. Introduction A microgrid is a distribution system which is consisted of different distributed generation sources

(photovoltaic, wind, fuel cells, micro turbines, etc.) and electric energy storage systems which feed a collection of the loads including residential, commercial, industrial or combination of these loads. The distributed generation sources in the microgrid are small units which typically are less than 100 kW and can include energy storage systems. One of the inherent advantages of this system is that it can provide a better scheme for providing the loads with the local generations and each microgrid can have an owner who can enter the market privately and participate in the energy supply by its different distributed generation sources [1]. In other words, the microgrid is part of the power system including one or several different DGs and it’s expected to operate even after the isolation from the main grid. The phenomenon of isolation from the main grid which leads to the creation of microgrid can be because of either planned or unplanned switching events [2]. Ref.[3] proposes a control architecture for a low-voltage AC microgrid with distributed battery energy storage. Ref. [4] proposes an adaptive sliding-mode controller (ASMC) to enhance disturbance-rejection performance

A

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Hosein Shayeghi et al. / Vol. 3(8) Jan. 2017, pp. 522-534 JKBEI DOI: 649123/10.112671

523

Journal of Knowledge-Based Engineering and Innovation (JKBEI) Universal Scientific Organization, http://www.aeuso.org/jkbei

ISSN: 2413-6794 (Online)

of control system of islanded parallel inverters. In [5], the VBD control is extended for storage applications, taking into account the state of charge. As the VBD control automatically fixes the priority of power changes of the microgrid elements, the storage elements are included in this priority list without inter-unit communication. Authors in [6] have introduced a control architecture for a low-voltage AC microgrid with distributed battery energy storage working in isolated mode.

Ref.[7] proposes a robust control strategy for a grid-connected multi-bus microgrid containing several inverter-based Distributed Generation (DG) units. Ref.[8] presents an optimal power control strategy, for an inverter based DG unit, in an autonomous microgrid operation based on real-time self-tuning method. Ref.[9] proposes a new robust voltage control strategy for an isolated Microgrid (MG). The MG consists of several DG units and local loads, which should be capable to operate in both connected and disconnected modes Ref.[10] introduces the concept of a smart transformer (ST) at the point of common coupling (PCC). This unit controls the active power exchange between a microgrid and the utility grid dependent on the state of both networks and other information communicated to the ST. In [11], a power control strategy is proposed for a low-voltage microgrid, where the mainly resistive line impedance, the unequal impedance among DG units, and the microgrid load locations make the conventional frequency and voltage droop method unpractical. In [12], a nonlinear disturbance observer (NDO) based dc-bus voltage control is proposed, which does not need the remote measurement and enables the important “plug-and-play” feature. Based on this observer, a novel DC-bus voltage control scheme is developed to suppress the transient fluctuations of dc-bus voltage and improve the power quality in such a microgrid system.

In this paper, the microgrid voltage and frequency is controlled by battery energy storage system. This context consists of seven sections. Concept of voltage and frequency control in microgrid has been presented in section 2. Solar sell and fuel cell as well as diesel generator have been modeled in third to fifth sections, respectively. Simulation results are visible in Section 6. This work has been concluded in seventh section.

2. Microgrid voltage and frequency control The balance between the supply and the load is one of the key requirements of the microgrid

management [13]. In case of connection to the main grid, the microgrid can exchange power with the grid to keep the balance. But in case of disconnection, the microgrid keeps the balance by change of power supply or load shedding. In the connection case, the microgrid voltage and frequency is determined by the main grid and is in the allowed range. But in the disconnection case, the balance cannot be reached fast. Therefore, the microgrid voltage and frequency will oscillate and in case of the lack of a power balancing process, the microgrid will collapse. The existing DGs in the microgrid including diesel generator, microturbine and the fuel cell, because of slow response time cannot have a proper dynamic performance for the load tracking in the islanding mode. Besides that, the microgrid frequency can vary fast because of its low inertia. Therefore, the frequency control is one of the key problems in the grid performance in the islanding mode. To overcome this issue, the ESS is introduced to solve the problem. ESS has a very fast response time and it can be connected to the grid through the power electronic converters. A properly designed ESS can stabilize the system by absorbing or injecting the power. In other words, ESS in the microgrid is like the spinning reserve in the conventional grid which guarantees the balance of the supply and the load. ESS can be connected to grid through an inverter separately or it can be installed into the DC link of microgrids to compensate the slow output power of the energy supply. With installation of the ESS, the microgrid can stably operate in the case of the disconnection from the main grid even if the load varies instantaneously.

3. Photovoltaic systems There are two ways to model a PV module:

Electrical model: in this way, an electrical model of a cell is used for each cell in the module. This model doesn’t provide an accurate solution and its simulation is cumbersome. This model is not

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popular in practice. Mathematical model: this model is invented in 1994 by Lorenzo. The advantage of this model is that it can be created by the manufacturer’s data. In this model, the module current (IM) can be calculated by equation (1) [14].

.. 1 exp( )M M M M

M M OC SSC M

t

V V I RI IV

− += −

(1)

Where RSM is the series resistance of the module, IM is the module current, VM is the module voltage, is the module open circuit voltage and is the module thermal voltage. The following is the list of the assumptions used in this model:

• All the solar cell arrays are the same and they have similar ambient temperature and solar irradiation.

• The voltage drop of the conductors connecting the solar cells is ignored.

• The short circuit current depends only on the temperature and the solar irradiation.

• The solar cell open circuit voltage depends only on the cell temperature.

• The cell temperature depends on solar irradiation and the ambient temperature.

• The series resistance and the diode quality coefficient in the solar cell is considered constant.

The steps in the algorithm of calculating module current is as follows: i. The necessary data is collected from the manufacturer’s catalogue. ii. Using the module data in the standard working condition, the cell data in the standard working condition can be calculated.

,0 ,0 ,0,0 ,0 ,0

.. , ,

M MC OC SCC C C

t OC SCSM PM

k T V IV m V I

e N N= = =

(2) The series resistance of the module is determined in this level.

,max. max. max.max. max. max. ,

,

, ,max.

, , ,

,

,

, , ,.

ln( 0.72),

1

1 ,

CM M MOC oC C Co o o

o o o OC o CSM PM SM PM t o

COC o OC o M Co SM

o S SC COC o SC o OC o PM

COC oC

S S S Co SC o

VP V IP V I vN N N N V

v vP NFF FF R RV I v N

VFFr R rFF I

= = = =

− + = = ⇒ =+ = − =

(3) iii. In this step, module parameters are calculated in the working condition (Ga, Ta, VM). The cell temperature (Tc) depends on the solar irradiation (Ga) and ambient temperature (Ta).

(4) 20.

800C a aNOCTT T G −

= +

Then the module short circuit current is calculated by equation (5) suing Ga and Tc. (5)

.

, .( 25)SC

a o

M MaSC SC o I c

GI I TG

µ = + −

The cell open circuit voltage is calculated by equation (6): (6)

, ,.( )oc

M MOC OC o V C C oV V T Tµ= + −

and then for the thermal voltage:



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.( 273).M ct SM

K TV N me+

=

(7)

iv. In this step, based on the results of the previous two steps, the module current can be calculated. (8)

.. 1 exp( )M M M M

M M OC SSC M

t

V V I RI IV

− += −

Where, IMmax,0، VM

max and PMmax are the module current, voltage and maximum power in the standard

working condition respectively. IMSC,0 is the module short circuit current in the standard working

condition, NSM and NPM are the number of series and parallel cells respectively, NOCT is a cell temperature in the normal working condition, Ga,0 is the solar irradiation in the standard working condition and TC

o is the PV cell temperature in the standard working condition.

4. Fuel cell The main electrochemical reactions in SOFC are as follows:

(9) 2 2 2

12

H O H O+ →

It’s assumed that there is just H2 in anode and O2 in cathode. The potential difference between anode and cathode, based the Ohm’s law and Nernst equation follows the equation (10) [15].

(10) 2 2

2

1/2. ..[ ln ] .2

H Hr rfc o o fc

H O

p pR TV N E r IF p

= + −

Where, E0 is the voltage created by the energy freed from the reaction, PO2, PH2, PH2O are the partial pressure, N0 is the number of cells, Ir

fc is the output current, R the universal constant of gas, T is the canal temperature and F is the Faraday constant. According to the ideal gases law, the partial pressure of the gases follows the equation (11).

(11) . . .i ch iP V n R T=

where, Vch is the canal volume and ni is the number of moles. Therefore, it gives: (12)

. .i i

ch

dp dnR Tdt V dt

=

(13)

( )ch in rii i i

dn q q qdt

= = −

Where, qiin is the fuel entered into the fuel cell and qir is the fuel reacted in the fuel cell. For H2, qir can be obtained by equation (14).

(14)

2

.2. .

2.

ro fcr r

rH fc

N Iq K I

F= =

qr for the other elements can be obtained by equations (15-16). (15)

2 2

1 .2

r r rrO H fcq q K I= =

(16)

2 22. .r r r

rH O H fcq q K I= =



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If the coefficient of the fuel utilization (Uf) is defined as the proportion of the reacted fuel to the input fuel, then it gives:

(17)

2

2

rH

f inH

qU

q=

Basically this coefficient is between 80-90 %. For particular value of H2, the desired input current for the fuel cell is in the range of equation (18).

(18) 2 2

0.8 0.92 2

in inH Hin

fcr r

q qI

K K< <

Since it’s possible to measure the output current of the fuel cell. With controlling the input fuel, Uf can be controlled in 85% value. It’s worth mentioning that the optimal utilization coefficient is calculated to be 85%.

(19)

2

2. .0.85

rr fcin

H

K Iq =

The pressure difference between the gas passed from the anode and the cathode should be kept less than 4 kPa in the steady state and less than 8 kPa in that transient state for the safety reasons. Based on the simulations in [16], for this purpose, the proportion of the input Oxygen to the input Hydrogen should be kept at 1.145.

(20)

2 2.

1.145

in inO H O H

H O

q r q

r−

−

=

=

All the reactions in the fuel cell has a delay time. This dynamic response function is model with the first order transfer function with the time constant of t=5s. (Tf). The electrical time response in the fuel cell is fast. This dynamic response function is modeled with the first order transfer function with the time constant of t=0.8 s. (Te). The dynamic response function of the current is also modeled with the first order transfer function with tH2, tO2 and tH2O as a time constant of Hydrogen, Oxygen and water respectively. According to the details mentioned, the proposed model for SOFC is as used in [17].

5. Diesel generator Different methods are proposed in the literature to model the diesel generator so far. Among

others, [18] can be mentioned. In the following, the model presented in [18] will be discussed in detail. In this model, the diesel generator is consisted of two parts of diesel motor and the generator unit. From the control system point of view, the diesel generator can be considered as a speed feedback system. After setting the governor speed by the operator, the motor governor acts likes a sensor and based on the difference between the motor speed and the desired speed, it will correct the speed by changing the input fuel. The fuel activator system basically can be modeled as first order phase-lag system with the gain of Ka and the time constant of ta. Equation (21) shows the transfer function of the fuel activator system.

(21)

( ) ( )(1 )

a

a

Ks I ss

φτ

=−

Φ(s) is the fuel flux and I(s) is the input flux. According to equation (21), the fuel flux is a function of the input flux. The fuel flux will convert to the torque, T(s) after the time constant of t and the constant torque of Kb. This relation is presented with the transfer function of the equation (22)..



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( ) ( ). sbT s s K e τφ −= (22)

Flywheel is the representation of the complicated dynamic effect of the motor inertia. Angular speed, ωw, the viscosity fraction constant is ρ. The model of the flywheel includes an integrator with the accelerator constant of γ and filters most of the disturbances and the noises. As shown, one integrator is added between the reference signal, r and the motor actuator to remove the speed droop in the steady state. It will increase the order of the system.

6. Case study

Figure (1) shows the test case constructed in the Korea’s electric power industry research center. This 380V low voltage microgrid is connect to 22.9 kV grid through a 100 kVA transformer. It includes photovoltaic, the combined system of wind turbine and photovoltaic, two diesel generator, battery energy storage and three loads.

Figure I. STRUCTURE OF TEST SYSTEM

The generated PV is 10 kW, diesel generator: 70 kW (diesel generator 1: 50 kW and diesel generator 2: 20 kW) and hybrid wind-photovoltaic: 20 kW (wind turbine: 10 kW and solar cell: 10 kW). Battery energy storage: energy storage system 20 kW and 10 kW. Loads specifications: Load 1: 8 kW, Load 2: 80kW+j 32kVAr and Load 12kW+j12kW. Line and transformer specifications: three-phase transformer: 22.9/0.38 kV, 100kVA, leakage impedance: %Z=%6, line impedance: R=0.01878 and 0.09680. For testing the system, several scenarios are designed including the isolation of from the main grid and varying the load in the islanding mode. The simulation results verifies the performance of the controller. For model the diesel generator, the combustion motor model and the synchronous generator are used. These models are available in the library of PSCAD software. Parameters used in the simulation are: P-bess and Q-bess, active and reactive power of the energy storage system, Pg and Qg, active and reactive power of the upstream network, P-PV and Q-PV, active and reactive power of PV, P-PW and Q-PW, active and reactive power of the combined system of PV and wind turbine.

A. First scenario: microgrid in connected to system In this scenario, the microgrid is connected to the main grid and in second 5, the active power of all the loads are increased from 70 kW to 90kW. In case of connection to the main grid, it is the main grid which is responsible to keep the balance of power and consequently control the voltage and frequency. In this case, the energy storage unit operates in PQ mode. Since the maximum storage capacity is kept for the unplanned isolation, the output power is considered zero. Figures 3-5 show the power, frequency and voltage curve in this scenario respectively. The upstream network prevents the voltage and frequency from deviation with providing the required power.



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Figure II. SOURCE ACTIVE POWER OF MICROGRID AND BATTERY IN FIRST SCENARIO

Figure III. FREQUENCY OF MICROGRID AND BATTERY IN FIRST SCENARIO

Figure IV. VOLTAGE OF MICROGRID AND BATTERY IN FIRST SCENARIO

B. Second scenario: Independent function of microgrid without secondary control In this scenario, the microgrid disconnects from the main grid in second 5 and then the active

power of all the loads is increased from 70 kW to 80 kW in second 15. In the islanding mode operation, the control strategy will change from PQ to PV. Figure (6) shows the power graph in this scenario. According to the graph, the total increase of 15kW due to the islanding mode in second 5 is supplied through energy storage source. The output power of the DG sources stays unchanged. The energy storage system is capable of providing maximum 20 kW. In second 15, due to the lack of enough capacity to compensate 10kW load increase, only about 5kW of the load is compensated and the rest is supplied through the diesel generators. Because of the low inertia of the diesel generators, the network frequency, as shown in figure (7), deviated out of the allowed range. Figures (8-9) show the active power and the voltage at PCC point in this scenario.



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Figure V. SOURCE ACTIVE POWER OF MICROGRID AND BATTERY IN SECOND SCENARIO

Figure VI. FREQUENCY OF MICROGRID AND BATTERY IN SECOND SCENARIO

Figure VII. SOURCE REACTIVE POWER OF MICROGRID AND BATTERY IN SECOND SCENARIO

Figure VIII. EFFECTIVE VOLTAGE OF MICROGRID AND BATTERY IN FIRST SCENARIO

C. Third scenario: Independent function of microgrid with secondary control In this scenario, similar to the previous scenario, the microgrid is disconnected from the main grid

in second 5 and then the active power of all the loads is increased from 70 kW to 80 kW. The difference is that the output power of the energy storage system, after balancing 15kW load increase due to the loss of the main network, gradually is decrease to zero by secondary control system. In this case, it can compensate the 10 kW load increase in second 15. Figures (10-13) show the output



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active power of DG and energy storage system, microgrid frequency, output reactive power of DG and the energy storage system and the voltage in this scenario respectively. According to these graphs, with the presence of the secondary control system, the power balance can control the microgrid voltage and frequency.

Figure IX. OUTPUT ACTIVE POWER OF DG IN THIRD SCENARIO

Figure X. FREQUENCY OF MICROGRID IN THIRD SCENARIO

Figure XI. OUTPUT ACTIVE POWER OF DG IN THIRD SCENARIO

Figure XII. VOLTAGE OF MICROGRID AND BATTERY IN FIRST SCENARIO



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D. The fourth scenario: the independent operation of the microgrid with the presence of the secondary control system and constant variation of the load

In this scenario, the performance controller will check with the changing load. Load function has been illustrated in Figure (14). Figures (15-18) have shown active and reactive powers, voltage and frequency in this scenario, respectively.

Figure XIII. CHANGEOF TOTALLOAD IN MICROGRID

Figure XIV. ACTIVE POWERS IN THIRD SCENARIO

Figure XV. REACTIVE POWERS IN THIRD SCENARIO

Figure XVI. FREQUENCY IN THIRD SCENARIO



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Figure XVII. VOLTAGE IN THIRD SCENARIO

E. Fifth scenario: three-phase to earth fault In this scenario, one three phase to ground fault has been occurred in the upstream network in

second 5. (look at figures (19-21)) and it’s assumed that it takes 0.1 second for the circuit breaker to disconnect the microgrid from the main grid. In this scenario, Voltage and frequency are deviated due to the fault, but with supplying the required power by battery, they settled back to their nominal value.

Figure XVIII. ACTIVE POWER IN FIFTH SCENARIO

Figure XIX. FREQUENCY IN FIFTH SCENARIO

Figure XX. MICROGRID VOLTAGE IN FIFTH SCENARIO

Conclusion In this paper, the microgrid voltage and frequency is controlled by battery energy storage system.

For this purpose, a two-stage control strategy is used as follows. For the verification of the correct operation of the energy storage system, several different scenarios, including the disconnection of the microgrid from the main grid and the constant variation of the load in the islanding mode, are designed. The simulations gives the following results: In case of the lack of energy storage system in the microgrid, at the moment of the disconnection from the main grid, since the DG sources (which lack the energy storage system) have a time varying output, they can’t supply the required balancing power. On the other hand, if the diesel generators try to supply the balancing power, since they have a slow response time, the microgrid voltage and frequency will deviate out of the allowed range and the microgrid cannot operate anymore.



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In case that the microgrid is equipped with the energy storage system and lack the secondary control system, at the moment of islanding, it can compensate the balancing power and prevent the voltage and frequency from the deviation. After the transient state, the microgrid should continue its operation in the isolated mode. In the isolated mode, part of the battery capacity is occupied at the beginning of the islanding for balancing the power and just only the rest of the capacity is available. If the load increase exceeds the available capacity of the battery energy storage system, it cannot supply any more balancing power and the diesel generators should step in to supply the extra power. If so, because of the slow response time of the diesel generators, similar to the previous case, the microgrid might collapse. If the energy storage system is equipped with the secondary control system, after supplying the balancing power at the moments of the disconnection of the microgrid from the main grid, the control strategy functions in a way that the output power of the energy storage system decline to zero and simultaneously the output power of DGs rises. With this arrangement, the maximum capacity of the energy storage system is available all the time and with the variation of the load in the isolated model, the energy storage system is capable of balancing the power to let the microgrid continue to operate in the isolated mode.

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