Coordinated Control and Energy Management of Distributed Generation Inverters in a Microgrid Readymade Electrical Projects

 

ABSTRACT:

This paper presents a microgrid consisting of different distributed generation (DG) units that are connected to the distribution grid. An energy-management algorithm is implemented to coordinate the operations of the different DG units in the microgrid for grid-connected and islanded operations. The proposed microgrid consists of a photovoltaic (PV) array which functions as the primary generation unit of the microgrid and a proton-exchange membrane fuel cell to supplement the variability in the power generated by the PV array. A lithium-ion storage battery is incorporated into the microgrid to mitigate peak demands during grid-connected operation and to compensate for any shortage in the generated power during islanded operation. The control design for the DG inverters employs a new model predictive control algorithm which enables faster computational time for large power systems by optimizing the steady-state and the transient control problems separately. The design concept is verified through various test scenarios to demonstrate the operational capability of the proposed microgrid, and the obtained results are discussed.

KEYWORDS:

  1. Distributed generation (DG)
  2. Energy management
  3. Microgrid
  4. Model predictive control (MPC)

 SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

Fig. 1. Overall configuration of the proposed microgrid architecture.

EXPECTED SIMULATION RESULTS:

 

 Fig. 2. Per-phase currents drawn by loads 1, 2, and 3.

Fig. 3. Waveform of the SB current during charging.

Fig. 4. SOC of the SB during charging.

Fig. 5. Waveforms of three-phase load current (top), three-phase DG current (middle), and three-phase grid current (bottom).

Fig. 6. Waveforms of grid voltage and grid current for phase a.

Fig. 7. Real (top) and reactive (bottom) power consumed by loads.

Fig. 8. Real (top) and reactive (bottom) power delivered by the main DG unit.

Fig. 9. Real (top) and reactive (bottom) power delivered by the grid.

Fig. 10. Real (top) and reactive (bottom) power delivered by the grid.

Fig. 11. Real power delivered by SB.

Fig. 12. Real (top) and reactive (bottom) power delivered by the grid.

Fig. 13. Real power delivered by SB.

Fig. 14. Real (top) and reactive (bottom) power consumed by loads.

CONCLUSION:

In this paper, a control system that coordinates the operation of multiple DG inverters in a microgrid for grid-connected and islanded operations has been presented. The proposed controller for the DG inverters is based on a newly developed MPC algorithm which decomposes the control problem into steady-state and transient subproblems in order to reduce the overall computation time. The controller also integrates Kalman filters into the control design to extract the harmonic spectra of the load currents and to generate the necessary references for the controller. The DG inverters can compensate for load harmonic currents in a similar way as conventional compensators, such as active and passive filters, and, hence, no additional equipment is required for power-quality improvement. To realize the smart grid concept, various energy-management functions, such as peak shaving and load shedding, have also been demonstrated in the simulation studies. The results have validated that the microgrid is able to handle different operating conditions effectively during grid-connected and islanded operations, thus increasing the overall reliability and stability of the microgrid.

REFERENCES:

[1] S. Braithwait, “Behaviormanagement,” IEEE Power and EnergyMag., vol. 8, no. 3, pp. 36–45, May/Jun. 2010.

[2] N. Jenkins, J. Ekanayake, and G. Strbac, Distributed Generation. London, U.K.: IET, 2009.

[3] M. Y. Zhai, “Transmission characteristics of low-voltage distribution networks in China under the smart grids environment,” IEEE Trans. Power Del., vol. 26, no. 1, pp. 173–180, Jan. 2011.

[4] G. C. Heffner, C. A. Goldman, and M. M. Moezzi, “Innovative approaches to verifying demand response of water heater load control,” IEEE Trans. Power Del., vol. 21, no. 1, pp. 1538–1551, Jan. 2006.

[5] R. Lasseter, J. Eto, B. Schenkman, J. Stevens, H. Vollkommer, D. Klapp, E. Linton, H. Hurtado, and J. Roy, “Certs microgrid laboratory test bed, and smart loads,” IEEE Trans. Power Del., vol. 26, no. 1, pp. 325–332, Jan. 2011.

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