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.
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
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