A novel Q/P droop control of standalone micro grid having multiple sources with adaptive controller

Abstract

In this paper a novel Q/P droop control strategy for regulating the voltage and frequency in Standalone micro grid with multiple renewable sources like solar and wind is presented. In contemporary to frequency control by synchronous machine a battery storage system is used for frequency control of Micro Grid. During the case of high discharge of the battery system a low rated synchronous generator is used to maintain the state of charge of the battery during frequency control. Since the output of the wind and solar varies continuously, a novel reactive (Q/P) droop control instead of conventional (P/F) and (Q/V) for voltage control. Adaptive Neuro Fuzzy logic Interface system (ANFIS) controller is used for frequency and voltage control for Renewable generation system. The induced voltage fluctuations are reduced to get nominal output power. The proposed model is tested on different cases and results show that the proposed method is capable of compensating voltage and frequency variations occurring in the micro grid with minimal rated synchronous generator.

Keywords

Adaptive neuro-fuzzy interface system (ANFIS)battery energy storage system (BESS)state of charge (SOC)frequency control Q/P droop control standalone micro grid voltage damping effect voltage control

1. Introduction

Micro-grids operating in standalone mode are more commonly exposed to variation in voltage and frequency. The grid becomes weaker than a conventional power system due to an isolated system. In recent years, dependence on renewable energy sources like solar and wind generation system [1, 2] has increased the instability of grid due its varying input nature. In case of isolated networks diesel based synchronous machines are used for voltage and nominal frequency control. the pattern recognition and decision making is done by mapping input points to the output using fuzzy logic interface. The SIMULINK software system can access the Fuzzy logic test system in a block diagram. It describes all membership functions, logical operators and If-Then rules. This control strategy is applied specially to penetrate the intermittent Renewable power generation for voltage and frequency control in stable region of the system [3]. Several methods are being examined to support frequency control. The methods for controlling the power dispatch from the wind generator are similar to conventional power generating schemes. There are multiple implementation options for energy storage. Some authors propose an energy storage independent from generators, as in [4, 5, 6]. Another option is to integrate storage and generation in a single system. Parallel integrated systems with a common dc bus are proposed in [7, 8, 9] wind is a varying source and hence the active power control of wind generator is proportional to variations in wind speed [10]. Advancement of power electronics has led to new generator configuration called DFIG, this configuration has more wind power generation capability over large range of wind speed with good controllability [11]. The control approach of DFIG is to set a point of active power at fixed pitch angle [6]. With the proper reference frame, $I_{\rm dr}$ will come up with the electromagnetic torque ( $T_{\rm em}$ ) against turbines mechanical torque ( $T_{\text{mech}}$ ) at some rotor speed. Consequently, the torque difference between $T_{\text{mech}}$ and $T_{\rm em}$ can make rotor to accelerate/decelerate.

$\displaystyle J\frac{{\rm d}\omega_{\rm r}}{{\rm d}t}=T_{\text{mech}}-T_{\rm em}$ (1) $\displaystyle\frac{{\rm d}\omega_{\rm r}}{{\rm d}t}=\frac{1}{2H}(T_{\text{mech% }}-T_{\rm e})$ (2)

where, $w_{\rm r}$ -Rotor speed, J-Inertia of motion, H-Inertia constant (s).

A variable droop strategy for de loaded wind turbines for frequency support is developed in [12]. Frequency disturbance is the main problem in micro-grids with more wind generators, this is because the variations in wind speed leads to varying generator active power output/f droop control is applied to the pv inverter for frequency control [13]. Even though the fuel cost is free but its cost of installation is high. Several MPPT techniques are being developed to extract maximum power from the solar. However, increase in PV generation has enhanced frequency regulation. This can also be achieved by synchronous machines but reduction in inertia may lead to severe frequency variations during major disturbance.

If the PVs don’t have any frequency regulating capability the load changes may also lead to frequency disturbance in the grid. In order to overcome this the PVs are designed with virtual governor that is the inverter control to achieve droop characteristics that are similar to behavior of synchronous generator. These frequency control strategies in presence of renewable generation is not economical because of losses and high converter ratings. In case of voltage control droop there are many methods proposed and discuss in the literature [14, 15].

Decentralized voltage control is another method which uses local data to control voltage issues. But this device operates autonomously and no interference between substation and loads occur. The effect and the reliability must be maintained for these methods. The linear quadratic tracking method is one of the voltage control method used to obtain desired results. The voltage is considered at each node then the controller increases/decreases voltage to minimize the error. The monitoring of controller is based on entire system conditions. Decentralized control may be adopted in this case but it needs more in-depth study due its complexity. analyzing and modeling of power distribution would become more complex and time taking.

Many control strategies are developed on the basis of optimization algorithms to achieve the control objective like loss minimization, improved voltage profile, mitigating voltage fluctuation, and good voltage regulation [16]. As these algorithms depend on forecasting of load demand and changing input characteristics, which makes them less reliable. The voltage compensator, shunt capacitors, LQT methods which leads to increase in extra costs. Triggering the compensation mechanism by sensing the voltage deviation is the widely used Q/V droop control strategy. The proposed Q-P droop method is explained in Section 2. The control scheme implemented for voltage and frequency control is discussed in Section 3. The analysis of the results obtained in different cases is done in Section 4. The advantages of the proposed method are present in conclusion section.

2. Proposed methodology

In a remote power system (P/F) and (Q/V) droop control are implemented to maintain system voltage and frequency at a nominal value used to generate nominal system frequency, voltage and some voltage compensation devices are used for control strategy. The increase in slope of the droop curve decreases the response ability of the generating system to frequency variations.as said in the literature frequency control for distributed generation is not beneficial, because they are not able to make the most of their ability to utilize free energy.so the frequency of the micro grid can be maintained at a nominal value using BESS [17]. Improved control of RES generators, BESS evolved as better alternative for mitigating frequency variations. Triggering the compensation mechanism by sensing the voltage deviation is the widely used Q/V droop control strategy. The recommended strategies include

BESS is employed to produce desired system frequency instead of using diesel generators as they are not depending on the machine inertia which is present in case of synchronous generator.

Figure 1.

Block diagram of the test system.

SOC of the battery system is used by the diesel generator at a value such that the reference limit of the SOC is maintained at a value such that power output of generators are within a permissible range.

Active power fluctuations leading to deviation in voltage are compensated by adding Q/P droop power is implemented for the renewable generation system.

Adaptive Neuro fuzzy logic controller reduces the frequency and voltage variations and improves the system performance.

3. Control strategies

To maintain frequency and voltage control there are many strategies to conventional power plant. A droop strategy for frequency controls implemented to PV generation. But these control strategies are not economically beneficial, since they cannot, maximize their usage of free energy. So, by adopting BESS (Battery energy storage system) the control range for supporting system frequency is enhanced during the case of frequency deviation from its nominal value [18]. The implementation of p/f and q/v droop along with voltage compensation devices to the isolated power system during different cases is analyzed. For mitigating the voltage fluctuations, q/v droop is most commonly used and voltage fluctuations are compensated by sensing the voltage deviation.

3.1 Test system configuration

The proposed control strategy with ANFIS (Adaptive Neuro-Fuzzy Interface system) is tested on the below test system as shown in Fig. 1 location of loads and power generation system are also indicated. The distance. The ratings of the power generation using different sources considered in this analysis are given in Appendix.

The nominal system frequency and voltage considered in this study are 50 HZ and 11 KV respectively and the change in load during the day and night are shown in the Table 1. The inverters are modeled as two level and the gate signals to the inverter switches are generated using conventional sinusoidal pulse width modulation.

Table 1
System load demand

Bus number	Day (MW)	Night (MW)
2	3	1.5
3	2	1
5	1	0.5
6	1	0.5
8	2	1
9	0.2	0.1
10	0.2	0.1
11	0.1	0.05
12	0.5	0.25
Total	10	5

Figure 2.

Implementation of the grid-side imverter control scheme.

Figure 3.

Proposed SOC based scheme for control of diesel generator.

3.2 Frequency control strategy

Battery system is used to deliver power at nominal frequency instead of using synchronous machine. As the frequency of the system is depending on the generator speed and inertia BESS rather than diesel generator is adopted to overcome this weakness to control the system frequency [19]. The BESS controls the nominal system frequency by adopting relevant switching mechanism based on the control scheme designed. Chargeable characteristic of BESS is used to the execute frequency control strategy which enables Battery system to take twice the amount of change in load value than any other devices with same rate of power. The rapidly changing charging and discharging abilities of BESS make it to respond quickly to the fluctuations in output power of renewable generation system [20]. However, SOC and implementation of control scheme using frequency droop in Fig. 2 is not achieved by the BESS alone.

Figure 4.

Grid-side inverter control of the wind/solar system.

BESS should work in synchronization with Diesel Generator where the frequency, voltage and phase are to be matched. To generate nominal system frequency, the diesel generator should be controlled [21]. During normal operation of diesel generator in Fig. 3, the switch is moved to node A to control the power output diesel generator reducing the discharge from BESS and hence maintaining SOC at the desired value SOC ${}_{\text{ref}}$ . SOC load control is same as the conventional load frequency control and SOC ${}_{\text{ref}}$ is chosen as 0.5 PU. The charge and discharge cycle of the BESS is controlled by the operator depending on the requirement.

The diesel generator output active power of is maintained within specified value ranging from 0 p.u to 1 p.u by adding the anti-windup function and output limiter value at the output of the PI-controller. The frequency of the system is mostly depending upon the effective implementation of BESS control strategy. To overcome the reliability problem that may arise due to tripping action of BESS, the switch is connected to node B, when the BESS is isolated from the network due to reduction in SOC value. During the node B connection of switch, the diesel generator is controlled same as conventional one. The $P_{\rm di,ref}$ is obtained from the output of the PI controller, it adjusts the valve actuator of diesel engine there by increasing the mechanical input to the synchronous generator, where. $T_{\rm v}$ is the valve actuator time constant 0.05 s and $T_{\rm d}$ diesel engine time constant 0.5 s. $P_{{\rm d}i}$ is the output active power of the diesel generator. The diesel generators acting as auxiliary source for SOC control which resembles the frequency control and its priority is changed to control SOC of the BESS rather than frequency control.

3.3 Voltage control strategy

The excitation of the diesel generator helps in maintaining its nominal voltage with in the specified limits. In contradictory to frequency control which can be done in central control, the voltage control should be done locally. the Control algorithm for grid side inverter control of the wind generator is shown in Fig. 4. To solve this voltage variation caused by the renewable system, a new Q/P droop strategy for voltage control is applied to the distributed generators [22].

$\displaystyle K_{\text{QV}}=\frac{\frac{Q}{P_{\text{rate}}}}{\frac{\Delta V_{% \text{bus}}}{V_{\text{base}}}}$ (3)

where $v_{\rm id}$ are the $d$ -axis component of inverter voltage and $v_{\rm iq}$ $q$ -axis component of inverter voltage. $i_{\rm id}$ and $i_{\rm iq}$ are the $d$ - and $q$ -components of the inverter currents, $w_{\rm s}$ represents the angular frequency of the system voltage, $L_{\rm f}$ represents the inductance of the filter, $K_{\text{QP}}$ and $K_{\text{QV}}$ are the droop coefficients of Q/P and Q/V, operating points of active and reactive power are represented by $P_{\rm o}$ and $Q_{\rm o}$ , $V_{\text{bus}}$ is the voltage at generation bus.

When voltage droop is not activated the generating system is made to operate at unity power factor when $Q_{\rm o}$ $K_{\text{QV}}$ is varied from 0 to 25 depending on the ability of power generation. MPPT control is applied by giving Wind speed or solar irradiance as inputs to generate reference active power value ( $P_{\text{ref}}$ ). $P_{\text{ref}}$ , P measured value are given to PI controller converting it to $i_{\rm id,ref}$ . $i_{\rm d}$ which is parks transformation value. The error obtained by comparing $i_{\rm id,ref}$ and $i_{\rm id}$ is fed to PI controller generating $V_{\rm d}^{*}$ . It can be written as,

$\displaystyle V_{\rm id,ref}=V_{\rm sd}+V_{\rm d}^{*}-\bm{\omega}_{\rm s}L_{% \rm f}i_{\rm iq}$ (4)

Q/P droop control is the comparison of active power whereas Q/V droop control is the comparison of voltage control. $K_{\text{QP}}$ , $K_{\text{QV}}$ are the Q/P and Q/V droop coefficients respectively. $K_{\text{QP}}$ and $K_{\text{QV}}$ convert the error to reactive power component.

$\displaystyle Q_{\text{ref}}=Q_{\rm o}+Q_{\rm P}+Q_{\rm V}$ (5)

where $Q_{\rm o}$ is the operating point of reactive power and is set to zero. Again $Q_{\text{ref}}$ and Q are given to PI controller and generates $i_{\rm iq,ref}$ . $i_{\rm iq,ref}$ are compared with $i_{\rm q}$ in the PI controller generating an error $V_{\rm q}^{*}$ .

$\displaystyle V_{\rm iq,ref}=V_{\rm q}^{*}+V_{\rm sq}+\bm{\omega}_{\rm s}L_{% \rm f}i_{\rm id}$ (6)

Therefore, $V_{\rm id,ref}$ and $V_{\rm iq,ref}$ are fed as input to the dq-abc transformation and is converted into $V_{\rm iabc,ref}$ using inverse parks transformation for sinusoidal pulse width modulation which generates six pulses for the inverter. The speed of the generator is taken as 1.2 P.U, pitch angle is 0 and the speed of the wind is 11 m/s. Instead of using PI controller in the control schemes, Adaptive Neuro fuzzy logic controllers are used to improve stability and performance of the system. Solar irradiance is about 660 W/m ${}^{2}$ . By adding MPPT control scheme and Boost converters to maximize the utilization of free energy and to maintain constant output power.

Figure 5.

(a) MATLAB simulation diagram of adaptive NEURO fuzzy logic control strategy for standalone micro grid system with multiple renewable sources, (b) Flow chart for proposed methodology.

3.4 Adaptive neuro fuzzy interface system (ANFIS)

The Effective technique called ANFIS (Adaptive Neuro-Fuzzy Interface system) which was developed by Dr. Roger Jang. Apart from various optimizing methodologies in soft computing, the fuzzy logic and Neuro computing has visibility, which leads to Neuro-fuzzy systems. The combination of Artificial Neural Network (ANN) and Fuzzy Interface systems (FIS) has attracted the interest of researchers in various applications. Fuzzy logic interface system is a mapping point to map an input space to output space from starting point to the ending for all. Fuzzy logic is an intriguing area of research because it has a premium quality of trading off among significance and precision. Neuro adaptive learning methods similar to methods used for training neural networks is used for tuning parameters of fuzzy membership functions. This methodology is called as adaptive neuro-fuzzy inference system (ANFIS). The backpropagation (BP) algorithm is used to trine the adaptive Neural network and $7\times 7=49$ rule based fuzzy Logic command-line functions are used for training SUGENO-type fuzzy inference systems using given input/output training data [23].

Figure 6.

Case I (frequency control): Active power. (a) wind and PV, (b) BESS, (c) diesel generator, (d) SOC, (e) frequency.

Figure 7.

Case I (voltage control): Reactive power. (a) wind generator, (b) PV system, bus voltage, (c) wind generator, (d) PV system.

4. Results and analysis

To establish the effectiveness of the proposed control strategies, simulation results are observed during the day time in the standalone micro-grid with high penetration of renewable generation system. Voltage waveforms of PV, wind power, BESS and diesel generator are clearly presented in MATLAB simulation. The MATLAB simulation diagram for Adaptive Neuro Fuzzy Control Strategy for Standalone Micro Grid System with Multiple Renewable Sources show in Fig. 5a and proposed control methodology is presented in Fig. 5b.

Scenario I: during day period

During the day period, the speed of the wind is considered to be varying from 10.5 to 11.5 m/s and set to an average of 11 m/s, the solar irradiance ranges at 660 W/m ${}^{2}$ . The active and reactive power of wind, solar are set at 0.413 and 0.495 respectively. The BESS come into action during voltage variations whereas the duty of the diesel generator is to support BESS by maintaining SOC. There are some oscillations of SOC at diesel generator due to inertia of the machine leading to slow system Dynamics and output variations in active power of renewable generation systems. Without droop control, filter and inverter switching losses may lead to slight reduction in SOC Using droop control method SOC fluctuations are reduced with the support of diesel generation, even though the fluctuations in SOC is maintained at desired value. The frequency also fluctuates from its desired value without droop but the deviations are reduced with proposed droop method.

Figure 8.

Comparison of active power with adaptive neuro fuzzy logic contoller and PI-conroller during day time.

Figure 9.

Frequency control results for case II. (a) active power of wind and PV, (b) active power of BESS, (c) active power of diesel generator, (d) SOC, (e) frequency.

Figure 6 shows the output active power of PV and wind, and active power flow characteristics of diesel generator BESS. the results show that the diesel generator takes full response in the absence of droop control, for the output fluctuation of the renewable generation system with droop control, BESS supports diesel generator to meet the power demand with P/F droop control method. Figure 7 shows the variation of reactive power of wind and solar power respectively without implementation of droop control, the renewable generation system has same power factor, but by applying Q/V droop control, the reactive power is controlled by compensating voltage deviation. By implementing the proposed method, the reactive powers of solar and wind are controlled, also mitigates the voltage fluctuation.

Figure 10.

Case II (voltage control): Reactive power. (a) wind generator, (b) PV system, bus voltage, (c) wind power, (d) PV system.

Figure 11.

Comparison of Active power wih Adaptive Neuro Fuzzy logic contoller and PI-contoller during Night time.

The bus voltage of PV and wind are kept near to nominal value using Q/V droop control. Even though, the fluctuations are not effectively prevented. There is a considerable reduction in voltage fluctuations when Q/P droop control is implemented. The Fig. 8 shows simulation results for Adaptive Neuro Fuzzy interface system (ANFIS) and PI-controller. ANFIS response rate is faster than PI controller and the simulation time to get output is less and easy to access and also the participation of DG id reduced to some extent giving optimal solution.

Figure 12.

Case I (frequency contro): Active power. (a) wind and PV, (b) BESS, (c) diesel generator, (d) SOC, (e) frequency.

Figure 13.

Case III (voltage control): Reactive power. (a) wind generator, (b) PV system, bus voltage, (c) wind generator, (d) PV system.

Case II: Night time

At night time, the solar irradiance is 0 W/m ${}^{2}$ and speed of the wind varies from 7.1 to 10.2 m/s and set to an average of 8.5 m/s. The reactive power value $K_{\text{QP}}$ are set as 0.473 for wind and 0.514 for PV power. Figure 10 shows the power output from PV and wind, also the output active power of diesel generator as well as Battery system. The output power response is like that of day time but the diesel generator output power is reduced and it is observed from the simulation results, accordingly SOC ${}_{\text{ref}}$ is to be increased. The diesel generator is made to generate active power within allowable range. It is also observed that the frequency deviation during the night is more compared to day time, but frequency is maintained at 50 Hz during the proposed method.

Figure 14.

Comparison of active power with adaptive neuro fuzzy logic controller and PI-controller during worst case.

Figure 15.

Case IV. (a) active power of BESS and diesel generator, (b) SOC, (c) frequency.

Figure 10 shows that during voltage control mode the output power fluctuation of wind generator is greater than that in day case. The more compensation of reactive power has led to more wind power output power fluctuation of during the day. Since there is no solar irradiance, the voltage fluctuations are prevented. The change in voltage of the PV during the droop and proposed method is less. In the Fig. 11, the violet color indicates the ANFIS controller output whereas the blue color denotes the PI controller. More over the bus voltages are maintained at a constant rated value even though the solar PV is not able to produce active power. The active participation of DG in compensating load power requirement makes the system to operate at constant frequency. The Fig. 11 shows that, the system operation with fuzzy control gives fast response in case of network variations.

Case III: Worst case (When there is no solar irradiance and wind speed)

To study the robustness of the proposed method the absence of both solar and wind is considered as worst case. In this case solar irradiance has to be varied, and the load demand is same as day time. Hence the $K_{\text{QP}}$ is set as 0.413 for wind and 0.495 PV based on day time data. Figure 12 shows active power response of the of wind and PV power generation systems. However, frequency remains unchanged.

Figure 16.

Effect of load changes on active power with ANFIS and PI.

Figure 17.

BESS tipping simulation results for case IV. (a) active power of BESS and diesel generator, (b) frequency.

Figure 18.

Active power variation with ANFIS and PI during BESS tripping.

Figure 19.

Simulation results for case V. (a) reactive power of PV power, (b) bus voltage of PV power.

Figure 13 represents the voltage control simulation results. As a result, there are some deviations around some points but the proposed method performs better than others. It is observed that the participation of BESS and DG is more in this case and the reactive power requirement of the load, Wind generator are compensated by the solar inverter setup. The bus voltages are also maintained at constant rated value even in the worst case operation. The simulation results of Fig. 14 show the comparison of both PI and Adaptive Neuro-fuzzy logic controllers even in worst case has better performance.

Case IV: Effect of load change and BESS tripping on the system performance

We consider two cases (i) Load change (ii) Tripping of BESS for the frequency control strategy. Figure 15 shows the load change in day time. There is a load decrement at 0.5 MW at 3 sec of time. SOC and frequency are maintained same as previous cases.

Figure 16 shows the load change simulation of Adaptive fuzzy controller scheme and PI controller. Taking Time (sec) on $X$ -axis and Active power (MW) on $Y$ -axis. Figure 17 represents the results for the case of while battery system is tripped. The BESS is tripped out of the system due to fault maintenance etc. The diesel generator operates by changing its switch position to ‘b’ when BESS trips out of the system. Figure 18 shows the Tripping of BESS simulation results of Adaptive fuzzy controller and PI controller scheme.

Case V: Considering only solar power generation

In this case, the output of wind power system is kept constant and active power fluctuation of the PV system is considered and the effect of voltage control strategy on PV power system bus are observed. The PV power system shows results in Fig. 19 and the reactive power is limited.

Case VI: Adjusting charge/discharge of BESS

BESS should be controllable for the energy efficiency perspective. By varying the slope of the ramp of SOC, BESS is controlled to output the desired level of active power. Figure 20 shows charging of SOC at 1 MW and active power of Battery storage system and diesel generator.

Figure 20.

Simulation results for case VI. (a) active power of BESS and diesel generator during charging, (b) SOC during charging.

BESS is consequently adjusted such a way to discharge if the power output of the diesel generator varies at $t$ ramp rate and the effect is shown in Fig. 21. The BESS is controlled in this manner, to discharge active power required by adjusting ramp rate of SOC which includes the controller of diesel generator.

Figure 21.

Simulation results for case VI. (a) active power of BESS and diesel generator during discharging, (b) SOC during discharging.

5. Conclusion

To mitigate the problems of diminishing voltage and frequency fluctuations, Adaptive Neuro Fuzzy Interface system is used, which has quick response rate compared to PI-Controller. The implementation of BESS leads to stable operation of the system maintaining the frequency at nominal value of 50 Hz without any deviation. For this reliable voltage control a novel Q/P droop is introduces into the control scheme for controlling the reactive power flow in test system with multiple Renewable generators. The active power fluctuations are effectively prevented by damping voltage fluctuations in the renewable generation. The output Active power of PI and Adaptive Neuro fuzzy controllers are compared and simulation results are observed on the graph during different cases. Simulation results are observed in MATLAB software by using these control strategies. The ANFIS controller improves system stability without any interruptions and produces effective performance.

Footnotes

Appendix

Table A1

Power ratings of different sources considered for analysis

s.No	Name of source	Rating (MW)
1	Diesel generator	14
2	Wind generator	9.1
3	PV system	1
4	BESS	15

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A novel Q/P droop control of standalone micro grid having multiple sources with adaptive controller

Abstract

Keywords

1. Introduction

3.1 Test system configuration

Table 1 System load demand

Scenario I: during day period

Case II: Night time

Case III: Worst case (When there is no solar irradiance and wind speed)

Case IV: Effect of load change and BESS tripping on the system performance

Case V: Considering only solar power generation

Case VI: Adjusting charge/discharge of BESS

Footnotes

Appendix

References

Table 1
System load demand