[SOLVED] matlab The four most usual types of energy storage techniques are battery, super capacitor, pumped-storage power stations and compressed air energy storage.

The four most usual types of energy storage techniques are battery, super capacitor, pumped-storage power stations and compressed air energy storage.
Batteries are efficient when they are used to supply low and steady loads., and it can be used to provide backup power for about 10-15 minutes. However, batteries possess several disadvantages like low power density, limited charging and discharging cycles etc. Also pulsed or transient power cannot be extracted from the battery as it greatly reduce its run time and lifespan.
A super capacitor can store thousand times more energy than typical capacitors, while it has an energy density about 20% of a battery and can deliver power only for 5-20 seconds. So it is better to combine the battery and SCs for a good performance for control the stability of the grid.
Pumped-storage power stations may be a highly effective method of storing energy for locations with suitable terrains while an obvious limitation of the system is that it requires a suitable difference in elevation, which does not exist in most plains and sea environments.
Compressed air energy storage is suitable for large-scale wind field, because the mechanical work generated by wind energy can directly drive the compressor to rotate, reducing the intermediate conversion into electricity, thus improving efficiency. Its major drawback is the low efficiency because that the temperature rises when the air is compressed, and the temperature decreases during the release of the air.
2. Models
2.1 Supercapacitor Model
Figure 2.1 Supercapacitor
2.2 An integrated system Model of wind turbine, battery bank and
super-capacitor

Figure 2.2.1 Design of the whole system
Figure 2.2.2 Simulation of the whole system
The main power supply is the wind turbine and it generates a three-phase voltage. A AC-DC converter connected to it and convert it to be a single-phase voltage. For simplify the simulation process, we suppose that the output line power is the data in grid watch wind data.csv. Two DC-DC converters are attached to battery and super capacitor respectively in case the input voltage exceeds the rated voltage of them. We also set the rated power of load to be 500kW at the beginning.
2.3 Controller Model
Figure 2.3 Design of the controller

The feedback input of the controller is voltage and current on super capacitor and battery while the output are input parameters of two DC-DC converters. Besides, the controlling parameter of DC-DC boost converter come from MPPT PnO. The PID controller has a very large integral part (5000) to make sure that the error at steady state is as tiny as possible. Besides, a relative large proportional part (1.5) helps the system reaches the steady state more quickly.
3. Simulation
The total data in grid watch wind data.csv of sampled wind data is compressed into 1.44s so that the period of each simulation wont be too long.
1. Rated power of load is 500W and no disturbance:
(1) Wind turbine output:
(2) The following lines represent Batterys Output Power, Super capacitors Output Power and Loads Output Power respectively.
For battery and super capacitor, the negative power value represents the charge period of battery while the positive value represents the discharge period. The load shows excellent stability for the whole period in the no-disturbance condition.

2. Rated power of load is 500W with disturbance:
The disturbance is attached to the DC-Link controller PID. We want to add a disturbance of 50%. To achieve that, we add a step signal with the value 5, which is about half the output of PID.
(1) Wind turbine output:
(2) The following lines represent Batterys Output Power, Super capacitors Output Power and Loads Output Power respectively.
The disturbance seems to have no large influence on battery, which provides good stability to the system. The load power is very stable even though 50% disturbance is added through the sharp jumping at the beginning is a little larger, which illustrates the anti-interference performance of the system.

3. Rated power of load is 800W and no disturbance:
The initial load was set at 500kW at the beginning, then a sudden load increment to 800kW was applied at the 15th second and lasted for about 15 seconds simulation time. To show the change clearly, we adjust the simulation time again, the power data change interval was lengthened to 0.5 seconds. The total simulation time is then compressed into 144s. To achieve the sudden change, we add a power load model to supply the sudden change of load power.
(1) Super capacitors Output Power and Batterys Output Power
The rated output of wind turbine we set is 500W and the rated load is 800W. Apparently, the power supplied by wind turbine is not enough, so the battery keeps discharging during the simulation. The output power of super capacitor is still fluctuating around zero as the performance is as expected, however, one potential risk is that at the beginning there is a sharp jumping( about 50% higher than 500W).
(2) Loads Output Power:

The power of load still shows stability at steady state. The system can keep the stability even there is a sudden load increment (500kW to 800kW).
4. Conclusion
The simulation of this Battery/Super-capacitor Model powered by Wind Turbine shows great superiority than the traditional electric power system and the traditional electric power system powered by wind turbine. Firstly, it takes good use of renewable resource and is environment-friendly. Secondly, the spare power generated by wind turbine can be stored. Thirdly, it can provide great stability for grid even there are large disturbance or sudden changes in load.
However, there is a sharp jumping in super capacitor and load output power at the beginning, which cannot be eliminated by adjusting the parameter. That may have bad effects on the actual devices. Maybe more trials are needed to make super capacitor and battery match up better.
5. Reference
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2. MATLAB, Supercapacitor Parameter Identification, accessed online on 02/10/2018
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pp. 199-205.
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