ABSTRACT:
Smart loads associated with electric springs (ES)
have been used for fast demand-side management for smart grid. While simplified
dynamic ES models have been used for power system simulation, these models do
not include the dynamics of the power electronic circuits and control of the
ES. This paper presents a dynamic and modular ES model that can incorporate
controller design and the dynamics of the power electronic circuits. Based on
experimental measurements, the order of this dynamic model has been reduced so
that the model suits both circuit and system simulations. The model is
demonstrated with the radial chordal decomposition controller for both voltage
and frequency regulation. The modular approach allows the circuit and
controller of the ES model and the load module to be combined in the d-q frame.
Experimental results based on single and multiple smart loads setup are
provided to verify the results obtained from the model simulation. Then the ES
model is incorporated into power system simulations including an IEEE 13 node
power system and a three-phase balanced microgrid system.
KEYWORDS:
1. Electric spring
2. Parameter
estimation
3. Radial-chordal
decomposition
4. Smart loads
5. Microgrids
SOFTWARE: MATLAB/SIMULINK
SCHEMATIC DIAGRAM:
Fig.
1 System setup in Phase III.
(a)Full
results of experiment and the theoretical model.
(b)Zoom
in results of experiment and the theoretical model.
(c)
Full results of experiment and the estimated model.
(d) Zoom in results of experiment and the estimated model.
Fig.
2 Experimental and simulation (theoretical and estimated models) results of ES
output voltage.
(a)
PCC Voltage (Vg).
(b)
Voltage output of ES (Ves).
(c)
Current of the Smart load (Isl).
(d) P-Q power of the smart load.
Fig.
3 Experimental and simulation results on Phase II setup with a ZIP load.
(a) PCC Voltage (Vg).
(b) Voltage output of ES (Ves).
411
(c) Current of the Smart load (Isl).
(d) P-Q power of the smart load.
Fig.
4 Simulation results on Phase II setup with a thermostatic load.
(a) PCC voltage (Vg1/2/3).
(b)Voltage output of ES 1 (Ves1).
(c) Voltage output of ES 2 (Ves2).
(d) Voltage output of ES 3 (Ves3).
(e) P-Q power of smart load 1.
(f) P-Q power of smart load 2.
(g) P-Q power of smart load 3.
Fig.
5 Experimental and simulation results on Phase III setup.
(a) Power delivered by the renewable energy source.
(b)Phase A voltage of node 634 (Vs).
(c) Power absorbed in phase A of node 634.
(d)Sum power absorbed by smart load 1,2 and 3.
(e) Power absorbed by smart load 4.
(f) Power absorbed by smart load 5.
Fig.
6 Simulation results on Phase IV setup.
(a) Utility frequency
(b) PCC voltage (Vg)
Fig.
7 Simulation results on Phase V setup.
CONCLUSION:
In
this paper, the dynamic model of an ES is firstly analyzed as a theoretical
model in state space. An order-reduced model is derived by estimation based on
experimental measurements. A theoretical model of the order of 6 with 4 inputs
has been simplified into a 2nd-order model with 2 inputs. The RCD control is
adopted as the outer-controller module in the smart load. Two models of
noncritical loads, namely ZIP and thermostatic load models, are analyzed to
cooperate with the ES. The estimated ES model (the inner model), outer
controller and the load model can be modelled separated as modules and then
combined to form the smart load model. The modular approach offers the
flexibility of the proposed model in outer-controller design and the
noncritical load selection. The results obtained from the proposed model are
compared with experimental measurements in different setups for model
verification. The proposed model has been tested for voltage and frequency
regulation. This simplified modular modeling method could pave the way for
future work on modeling widely-distributed ESs in distribution networks so that
various control strategies can be studied.
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