Ziegler-Nichols based Controller Parameters Tuning for Load Frequency Control in a Microgrid

ABSTRACT

This paper deals with the load frequency control of a small scale microgrid consisting of wind, solar, diesel generator and fuel cell as power generating sources and battery, flywheel and aqua electrolyzer as energy storage elements. To improve the load frequency control, the controllers are properly tuned so as to reduce the mismatch between the real power generation and the load demand leading to minimum power and frequency deviations. A systematic approach to obtain frequency bias parameter followed by tuning the gains of Proportional, Integral and Derivative controller (PID) using Integral Square Time Error evaluation criterion (ITSE)and Ziegler Nichols method respectively is proposed. The simulation studies are carried out for different cases and it is found that the dynamic responses of the frequency and power of the microgrid is quite acceptable.

INDEX TERMS—Automatic generation control, frequency and power deviations ,proportional, integral and derivative Control (PID), integral square time error evaluation criterion (ITSE), simulation analysis, Ziegler-Nichols method.

BLOCK DIAGRAM:

Fig. 1. The block diagram of the microgrid with primary sources : solar, wind energy system and secondary sources: diesel generators, fuel cell, aqua electrolyzer, battery, flywheel and power system.

SIMULATION RESULTS:

Fig. 2. Simulation results of Case 1: (a) Supply power PS (b) Power supply form diesel generator Pdg (c) Fuel cell Pf c (d) Aqua electrolyzer Pae (e) Battery Pbat (f) Flywheel Pfw (g) Error in power supply ∆P (h) Frequency deviation of power systems ∆f

Fig. 3. Simulation results of Case 2: (a) Supply power PS (b) Power supply form diesel generator Pdg (c) Fuel cell Pf c (d) Aqua electrolyzer Pae (e) Battery Pbat (f) Flywheel Pfw (g) Error in power supply ∆P (h) Frequency deviation of power systems ∆f

Fig. 4. Simulation results of Case 3: (a) Supply power PS (b) Power supply form diesel generator Pdg (c) Fuel cell Pf c (d) Aqua electrolyzer Pae (e) Battery Pbat (f) Flywheel Pfw (g) Error in power supply ∆P (h) Frequency deviation of power systems ∆f.

CONCLUSION

In this paper a systematic approach for tuning of PID controllers in microgrid and calculation of optimal frequency bias are presented. The frequency bias calculation is an important aspect in the power system dynamics and plays a key role in controller gains. This factor directly effects the individual components and subsequently the overall performance of the microgrid. So the selection of frequency bias is very crucial and is addressed in this paper. The tuning of the PID controller through Zeigler Nichols approach is quite robust to tackle different types of disturbances. The simulation analysis of microgrid with PID controller shows acceptable dynamic performance with zero steady state error. It is also found that when the load is less than the power generated by the primary sources the excess power goes into the battery and flywheel. Similarly when load is more than the power generated by the primary sources, the excess power requirement is mitigated by diesel generator and fuel cell. Thus, the controllers work in coordination with the demand from load to obtain a proper energy management scenario.

REFERENCES

[1] T. Senjyo, T. Nakaji, K. Uezato, and T. Funabashi, “A hybrid power system using alternative energy facilities in isolated island,” IEEE Trans. Energy Convers., vol. 20, no. 2, pp. 406-414, Jun. 2005.

[2] A. Keyhani and Jin-Woo Jung,“Distributed energy systems,” Journal of Iranian Association of Electrical and Electronics Engineers, vol. 1, no. 2, pp. 33-40,Summer and Fall 2004.

 [3] D. J. Hall and R. G. Colclaser,“Transient modeling and simulation of a tubular solid oxide fuel cell,” IEEE Trans. Energy Convers., vol. 14, no. 3, pp. 749-753, Sep. 1999.

[4] M. D. Lukas, K. Y. Lee, and H. Ghezel-Ayagh, “Development of a stack simulation model for control study on direct reforming molten carbonate fuel cell power plant,” IEEE Trans. Energy Convers., vol. 14, no. 4, pp.1651-1657, Dec. 1999.

[5] P. S. Dokopoulos, A. C. Saramourtsis, and A. G. Bakirtzis, “Prediction and evaluation of the performance of wind-diesel energy systems,” IEEETrans. Energy Convers., vol. 11, no. 2, pp. 385-393, Jun. 1996.

Tuning of Microgrid Controllers using Cuckoo Search Algorithm

ABSTRACT

Microgrid at islanding mode is operated with renewable energy sources like Solar, Wind and non-renewable energy sources like Diesel generator and Battery which supply load to the system efficiently. With the change in load there is frequency deviation and controllers are required. There is a requirement to tune controllers to have optimal utilization of electrical energy and to maintain frequency at desired level. Cuckoo Search Algorithm (CSA) has been implemented to tune the controllers of microgrid. CSA gives optimal solutions in MATLAB using Integral Time Square Error principle (ITSE). The proposed results using CSA in comparison to the trial and error method is improving the steady state response of the considered microgrid, maintaining the system frequency constant. We have proposed a method for tuning the controller to have the frequency of the system at desired level.

KEYWORDS: Microgrid, Tuning, Diesel generator, Battery, Cuckoo Search Algorithm.

BLOCK DIAGRAM:

Fig. 1. The block diagram of microgrid with renewable energy sources and controllers.

SIMULATION RESULTS:

Fig. 2. Comparison of frequency deviation using PID, PI, and P controller for conventional method.

Fig. 3. The change in frequency (∆f) response of microgrid using PID controller by conventional and CSA method

Fig. 4. Response of the microgrid with battery using CSA method.

Fig. 5. The change in frequency (∆f) response of Microgrid with and without battery using PID controller.

CONCLUSION

In this paper the optimal values of the controller gains in the microgrid were calculated using the conventional methods and CSA. In this work renewable and non renewable energy sources are used to meet the load. The principle behind the new stratagem adopted is to somehow or other steady the system at the output end and independent of variations in the system due to load fluctuations on all counts. Thus the Cuckoo Search Algorithm has been implemented in the tuning of controller parameters MATLAB atmosphere. By trial and error method, the results achieved were comprehensively compared with Cuckoo Search Algorithm. The resultant conclusion proved that the latter is more efficient and better choice among other optimization techniques.

REFERENCES

[1] S. C. S. Chowdhury and P. Crossley, “Microgrids and active distribution networks,” Institution of Engineering and Technology, 2009.

[2] T. Senjyu, T. Nakaji, K. Uezato, and T. Funabashi, “A hybrid power system using alternative energy facilities in isolated island,” IEEE Transactions on Energy Conversion, vol. 20, no. 2, pp. 406–414, June 2005.

[3] N. Hatziargyriou, H. Asano, R. Iravani, and C. Marnay, “Microgrids,” IEEE Power and Energy Magazine, vol. 5, no. 4, pp. 78–94, July 2007.

[4] P. Basak, S. Chowdhury, S. P. Chowdhury, and S. H. nee Dey, “Simulation of microgrid in the perspective of integration of distributed energy resources,” in 2011 International Conference on Energy, Automation and Signal, Dec 2011, pp. 1–6.

[5] M. Bhoye, S. N. Purohit, I. N. Trivedi, M. H. Pandya, P. Jangir, and N. Jangir, “Energy management of renewable energy sources in a microgrid using cuckoo search algorithm,” in 2016 IEEE Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), Mar 2016, pp. 1–6.

Maiden Application of Ziegler-Nichols Method to AGC of Distributed Generation System

 Abstract

 This paper deals with the load frequency control of Distributed Generation Systems (DGS) consisting of Wind, Solar and Diesel Generator. The Diesel Generator is controlled either by P or PI or PID controller to inject regulated amount of real power to the power system based on its rating. As a result it regulates the mismatch between the real power generation and the load which will lead to a minimum power and frequency deviations. A systematic way of deciding frequency bias parameter along with tuning the gains of the Proportional, Integral and Derivative controller (PID) based on Ziegler-Nichols method and ITSE performance criterion is proposed. The simulation studies are carried out for different types of controllers, and disturbances and it is found that it regulates the frequency with less number of oscillations, minimum peak over shoot, and settling time in the case of PID controller.

Index Terms—Distributed Generation Systems (DGS), Proportional, Integral and Derivative Control (PID), ZieglerNichols method, Optimization methods, Tuning, Frequency Control, Diesel Generators, Wind and Solar, Simulation Analysis.

BLOCK DIAGRAM

Fig. 1. The Block diagram of the Distribution Generation System with Diesel Generator, Wind, Solar power supply and Power System.

SIMULATION RESULTS

Fig. 2. Simulation results of case 1 when wind (0.6 pu), solar (0.3 pu) constant and change in load (0.9 to 0.95 pu) at 100sec : (a) Power Demand and Power Supply in pu (b) Power generated by diesel generator in pu (c) Frequency deviation in Hz.

 

Fig. 3. Simulation results of case 2 when Load (0.9 pu), solar (0.3 pu) constant and change in wind power (0.6 to 0.4 pu) at 100sec : (a) Power Demand and Power Supply in pu (b) Power generated by diesel generator in pu (c) Frequency deviation in Hz.

Fig. 4. Simulation results of case 3 when Load (0.9 pu), wind (0.6 pu) constant and change in solar power (0.3 to 0.2 pu) at 250sec : (a) Power Demand and Power Supply in pu (b) Power generated by diesel generator in pu (c) Frequency deviation in Hz.

CONCLUSION

In this paper a systematic approach for tuning of PID controllers in DGS and calculation of optimal frequency bias are presented. The robustness of the proposed controller is checked with different case studies. The simulation studies of DGS with PID controller shows a better performance in terms of time domain specifications: rise time, peak over shoot, peak time, settling time, and steady state error, than P and PI controllers. When the load or power generation changes occur in the DGS, the PID controller acts such that the Diesel Generator will compensate for the required power. This resulted in the minimum oscillations in the frequency and power. Finally the PID controllers stabilize the system quickly with zero steady state error in less settling time. The frequency bias calculation is very important in the power system dynamics and played a key role in controller gains. This factor directly effects the individual components like Diesel Generators and finally overall performance of the DGS. So the selection of frequency bias is very crucial and is addressed in this paper.

REFERENCES

[1] D.Lee and Li Wang, ”Small Signal Stability analysis of an Autonomous Hybrid Renewable Energy Power Generation/Energy Storage system time domain simulations”, IEEE Trans. Energy Convers., vol.23,no.1, March.2008.

[2] A. Keyhani and Jin-Woo Jung, “Distributed energy systems,” Journal of Iranian Association of Electrical and Electronics Engineers, vol. 1, no. 2, pp. 33-40,Summer and Fall 2004

[3] 3] D. J. Hall and R. G. Colclaser, “Transient modeling and simulation of a tubular solid oxide fuel cell,” IEEE Trans. Energy Convers., vol. 14, no. 3, pp. 749–753, Sep. 1999.

[4] M. D. Lukas, K. Y. Lee, and H. Ghezel-Ayagh, “Development of a stack simulation model for control study on direct reforming molten carbonate fuel cell power plant,” IEEE Trans. Energy Convers., vol. 14, no. 4, pp. 1651–1657, Dec. 1999.

[5] P. S. Dokopoulos, A. C. Saramourtsis, and A. G. Bakirtzis, “Prediction and evaluation of the performance of wind-diesel energy systems,” IEEETrans. Energy Convers., vol. 11, no. 2, pp. 385–393, Jun. 1996.

Automatic Generation Control of Microgrid using Artificial Intelligence Techniques

ABSTRACT

 Microgrid is a small scale independent power system consisting of renewable energy sources: solar and wind power generation and backup by controllable sources: diesel generator, fuel cell, aqua electrolyzer and battery. In the microgrid, the ramp rate limit in power change in controllable sources has been implemented by means of generation rate constraint (GRC) and power frequency (P-f) droop characteristics (R) is also included for the parallel operation of generating sources participating in automatic generation control (AGC). These GRC and P-f droop make the system non linear and we have used artificial intelligence techniques (AI) like bacterial foraging optimization (BFO), particle swarm optimization (PSO), genetic algorithm (GA) to tune the important parameters simultaneously in AGC of microgrid. Simulation results show the superiority of BFO for optimal calculation of multiple parameters in microgrid over PSO, GA and classical methods.

INDEX TERMS

Automatic generation control (AGC), bacterial foraging optimization (BFO), generation rate constraint (GRC), genetic algorithm (GA), microgrid, power frequency (P-f) droop, particle swarm optimization (PSO), simulation analysis, tuning of parameters.

BLOCK DIAGRAM:

Fig. 1. The block diagram of the microgrid with diesel generator, fuel cell, aqua electrolyzer, battery wind, solar power supply and power system.

SIMULATION RESULTS:

Fig. 2. Response of the microgrid with and without GRC for a increase in load by 10%

Fig. 3. Simulation results of Case 1: PG : power generation , PL : load demand, Pw: wind power, Ps: Solar power, H2s: hydrogen stored, Pb : battery bower, Qb : battery state of the charge and Δf : frequency deviation of microgrid.

Fig. 4. Frequency deviation Δf in microgrid for 5 % increase in wind power for different methods.

Fig. 5. Simulation results of Case 2: PG : power generation , PL : load demand, Pw: wind power, Ps: Solar power, Pdg : diesel generator power, Pfc : fuel cell power, H2s: hydrogen stored, Pb : battery bower, Qb : battery state of the charge and Δf : frequency deviation of microgrid.

Fig. 6. Frequency deviation Δf in microgrid for increase in 10 % load demand for different methods.

Fig. 7. Simulation results of Case 3: PG : power generation , PL : load demand, Pw: wind power, Ps: Solar power, H2s: hydrogen stored, Pb : battery bower, Qb : battery state of the charge and Δf : frequency deviation of microgrid.

CONCLUSIONS

For the first time a systematic approach for tuning of controller gains, frequency bias, droop in the presence of GRC in microgrid is presented in the paper. The difficulty in tuning large number of parameters in complex systems can be achieved through the artificial intelligence techniques. From the simulation results it is observed that for optimal tuning of multiple parameters in non linear microgrid BFO is better than PSO, GA and classical methods. It is also observed that when the load is less than the power generated by the renewable sources the excess power goes into the energy storage devices. Similarly when load is more than the power generated by the renewable sources, the excess power requirement is mitigated by diesel generator, fuel cell and battery. Thus, the controllers work in coordination with the demand from load to obtain a proper energy management.

REFERENCES

[1] A. Keyhani and Jin-Woo Jung, “Distributed energy systems,” Journal of Iranian Association of Electrical and Electronics Engineers, vol. 1, no. 2, pp. 33-40, 2004.

 [2] The renewable energy in India, Available: http: //mnes. gov.in.

 [3] S. P. Chowdhury, P. Crossley, S. Chowdhury, and E. Clarke, Microgrids and Active Distribution Networks. London: Institution of Engineering and Technology, 2009.

 [4] P. Kundur, Power System Stability and Control, Tata Mc Graw Hill, Inc., New York,1994. [5] Ngo, M.-L.D., King, R.L., and Luck, R."Implications of frequency bias settings on AGC," IEEE Proceedings of the Twenty-Seventh Southeastern Symposium on system theory, Mar 1995, pp.83 – 86