A Superconducting Magnetic Energy Storage- Emulator/Battery Supported Dynamic Voltage Restorer

IEEE Transactions on Energy Conversion, 2016

ABSTRACT: This study examines the use of superconducting magnetic and battery hybrid energy storage to compensate grid voltage fluctuations. The superconducting magnetic energy storage system (SMES) has been emulated by a high current inductor to investigate a system employing both SMES and battery energy storage experimentally. The design of the laboratory prototype is described in detail, which consists of a series-connected three phase voltage source inverter used to regulate AC voltage, and two bidirectional DC/DC converters used to control energy storage system charge and discharge. ‘DC bus level signaling’ and ‘voltage droop control’ have been used to automatically control power from the magnetic energy storage system during short-duration, high power voltage sags, while the battery is used to provide power during longer-term, low power under-voltages. Energy storage system hybridisation is shown to be advantageous by reducing battery peak power demand compared with a battery-only system, and by improving long term voltage support capability compared with a SMES-only system. Consequently, the SMES/battery hybrid DVR can support both short term high-power voltage sags and long term under voltages with significantly reduced superconducting material cost compared with a SMES-based system.

KEYWORDS:

  1. Dynamic Voltage Restorer (DVR)
  2. Energy Storage Integration
  3. Sag
  4. Superconducting Magnetic Energy Storage
  5. Battery

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Figure 1. Hybrid energy storage DVR system configuration.

EXPECTED SIMULATION RESULTS:

Figure 2. Simulated PLL Algorithm results: (a) Simulated voltage sag with phase jump (b) Phase jump angle (c) Blue trace: supply phase angle. Red trace: PLL output: ‘Pre-sag compensation’ with controller gains: kp = 0.5, ki = 5, (d) Blue trace: supply phase angle. Red trace: PLL output: ‘In phase compensation’ with controller gains kp = 200, ki = 50.

Figure 3. Hybrid System Experimental results: 0.1s Three phase sag to 35% of nominal voltage. (a) Supply voltages (b) Load voltages (c) DC Link Voltage (d) Battery Current (e) SMES-inductor current.

Figure 4. Battery System Experimental results: 0.1s Three phase sag to 35% of nominal voltage. (a) Supply voltages (b) Load voltages (c) DC Link Voltage (d) Battery Current.

 

Figure 5. Hybrid System Experimental results: Long-term three phase under voltage (a) RMS supply phase-voltage. (b) RMS load phase-voltage (c) DC Bus Voltage (d) Battery Current (e) SMES-inductor current.

 CONCLUSION:

The performance a novel hybrid DVR system topology has been assessed experimentally and shown to effectively provide voltage compensation for short-term sags and long-term under-voltages. A prototype system has been developed which demonstrates an effective method of interfacing SMES and battery energy storage systems to support a three phase load. The system has been shown to autonomously prioritise the use of the short-term energy storage system to support the load during deep, short-term voltage sags and a battery for lower depth, long-term under-voltages. This can have benefits in terms of improved voltage support capability and reduced costs compared with a SMES-based system. Additional benefits include reduced battery power rating requirement and an expected improvement in battery life compared with a battery-only system due to reduced battery power cycling and peak discharge power.

REFERENCES:

[1] P.K. Ray, S.R. Mohanty, N. Kishor, and J.P.S. Catalao, “Optimal Feature and Decision Tree-Based Classification of Power Quality Disturbances in Distributed Generation Systems,” Sustainable Energy, IEEE Trans., vol. 5, Sept. 2014, pp. 200-208.

[2] D. Novosel, G. Bartok, G. Henneberg, P. Mysore, D. Tziouvaras, and S. Ward, “IEEE PSRC Report on Performance of Relaying During Wide-Area Stressed Conditions,” Power Delivery, IEEE Trans., vol. 25, Jan. 2010, pp. 3-16.

[3] “IEEE Recommended Practice for Monitoring Electric Power Quality,” in IEEE Std 1159-1995, ed. New York, NY: IEEE Standards Board, 1995, p. i.

[4] S. Jothibasu and M.K. Mishra, “A Control Scheme for Storageless DVR Based on Characterization of Voltage Sags,” Power Delivery, IEEE Trans., vol. 29, July 2014, pp. 2261-2269.

 

A Synchronous Generator Based Diesel-PV Hybrid Micro-grid with Power Quality Controller

 

ABSTRACT:

This paper presents an isolated microgrid, with synchronous generator(SG) based diesel generation (DG) system in combination with solar photo-voltaic(PV). The DG supplies power to the load directly, and a battery supported voltage source converter (VSC) is connected in shunt at point of common coupling (PCC). The PV array is connected at DC-link of the VSC through a boost converter. A high order optimization based adaptive filter control scheme is used for maintaining the quality of PCC voltages and source currents. This controller makes the waveform free of distortion, removes errors due to unbalances, corrects the power factor and makes the source current smooth sinusoidal, irrespective of the nature of load. MATLAB/Simulink based simulation results demonstrate satisfactory performance of the given system.

KEYWORDS:

  1. Battery
  2. Diesel generator
  3. LMF
  4. Power quality
  5. PV

SOFTWARE: MATLAB/SIMULINK

CIRCUIT DIAGRAM:

 

 

Fig. 1 System model

 EXPECTED SIMULATION RESULTS:

 

 Fig. 2 Steady State Response of DG-PV micro-grid

Fig. 3 Dynamic Response of DG-PV micro-grid

CONCLUSION:

An isolated SG based DG and PV hybrid micro-grid has been presented here, with a battery suppported VSC connected at PCC. Three-phase adaptive control is used for power quality improvement through VSC. The given system and control have been simulated in MATLAB/Simulink environment and results demonstrate their satisfactory performance in both steady state and dynamic conditions.

REFERENCES:

[1] G. Shafiullah et al., “Meeting energy demand and global warming by integrating renewable energy into the grid,” in 22nd Australasian Universities Power Engg. Conf. (AUPEC), pp. 1–7, Bali, 2012.

[2] M. Milligan et al., “Alternatives No More: Wind and Solar Power Are Mainstays of a Clean, Reliable, Affordable Grid,” IEEE Power & Energy Mag., vol. 13, no. 6, pp. 78–87, Nov.-Dec. 2015.

[3] L. Partain and L. Fraas, “Displacing California’s coal and nuclear generation with solar PV and wind by 2022 using vehicle-to-grid energy storage,” IEEE Photovoltaic Specialist Conf., pp. 1–6, LA, 2015.

[4] Daniel E. Olivares et al., “Trends in Microgrid Control,” in 2015 IEEE Trans. Smart Grid, vol. 5, no.4, pp. 1905–1919, July, 2014.

[5] Z. Zavody, “The grid challenges for renewable energy An overview and some priorities,” IET Seminar on Integrating Renewable Energy to the Grid, pp. 1–24, London 2014.

A Superconducting Magnetic Energy Storage- Emulator/Battery Supported Dynamic Voltage Restorer

 

ABSTRACT:

This study examines the use of superconducting magnetic and battery hybrid energy storage to compensate grid voltage fluctuations. The superconducting magnetic energy storage system (SMES) has been emulated by a high current inductor to investigate a system employing both SMES and battery energy storage experimentally. The design of the laboratory prototype is described in detail, which consists of a series-connected three phase voltage source inverter used to regulate AC voltage, and two bidirectional DC/DC converters used to control energy storage system charge and discharge. ‘DC bus level signaling’ and ‘voltage droop control’ have been used to automatically control power from the magnetic energy storage system during short-duration, high power voltage sags, while the battery is used to provide power during longer-term, low power under-voltages.

Energy storage system hybridisation is shown to be advantageous by reducing battery peak power demand compared with a battery-only system, and by improving long term voltage support capability compared with a SMES-only system. Consequently, the SMES/battery hybrid DVR can support both short term high-power voltage sags and long term undervoltages with significantly reduced superconducting material cost compared with a SMES-based system.

KEYWORDS:

  1. Dynamic Voltage Restorer (DVR)
  2. Energy Storage Integration
  3. Sag
  4. Superconducting Magnetic Energy Storage
  5. Battery

 SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

 

Figure 1. Hybrid energy storage DVR system configuration.

EXPECTED SIMULATION RESULTS:

 

 Figure 2. Simulated PLL Algorithm results: (a) Simulated voltage sag with phase jump (b) Phase jump angle (c) Blue trace: supply phase angle. Red trace: PLL output: ‘Pre-sag compensation’ with controller gains: kp = 0.5, ki = 5, (d) Blue trace: supply phase angle. Red trace: PLL output: ‘In phase compensation’ with controller gains kp = 200, ki = 50.

Figure 3. Hybrid System Experimental results: 0.1s Three phase sag to 35% of nominal voltage. (a) Supply voltages (b) Load voltages (c) DC Link Voltage (d) Battery Current (e) SMES-inductor current.

Figure 4. Battery System Experimental results: 0.1s Three phase sag to 35% of nominal voltage. (a) Supply voltages (b) Load voltages (c) DC Link Voltage (d) Battery Current.

Figure 5. Hybrid System Experimental results: Long-term three phase under voltage (a) RMS supply phase-voltage. (b) RMS load phase-voltage (c) DC Bus Voltage (d) Battery Current (e) SMES-inductor current.

CONCLUSION:

The performance a novel hybrid DVR system topology has been assessed experimentally and shown to effectively provide voltage compensation for short-term sags and long-term under-voltages. A prototype system has been developed which demonstrates an effective method of interfacing SMES and battery energy storage systems to support a three phase load. The system has been shown to autonomously prioritise the use of the short-term energy storage system to support the load during deep, short-term voltage sags and a battery for lower depth, long-term under-voltages. This can have benefits in terms of improved voltage support capability and reduced costs compared with a SMES-based system. Additional benefits include reduced battery power rating requirement and an expected improvement in battery life compared with a battery-only system due to reduced battery power cycling and peak discharge power.

REFERENCES:

[1] P.K. Ray, S.R. Mohanty, N. Kishor, and J.P.S. Catalao, “Optimal Feature and Decision Tree-Based Classification of Power Quality Disturbances in Distributed Generation Systems,” Sustainable Energy, IEEE Trans., vol. 5, Sept. 2014, pp. 200-208.

[2] D. Novosel, G. Bartok, G. Henneberg, P. Mysore, D. Tziouvaras, and S. Ward, “IEEE PSRC Report on Performance of Relaying During Wide-Area Stressed Conditions,” Power Delivery, IEEE Trans., vol. 25, Jan. 2010, pp. 3-16.

[3] “IEEE Recommended Practice for Monitoring Electric Power Quality,” in IEEE Std 1159-1995, ed. New York, NY: IEEE Standards Board, 1995, p. i.

[4] S. Jothibasu and M.K. Mishra, “A Control Scheme for Storageless DVR Based on Characterization of Voltage Sags,” Power Delivery, IEEE Trans., vol. 29, July 2014, pp. 2261-2269.

[5] B. Otomega and T. Van Cutsem, “Undervoltage Load Shedding Using Distributed Controllers,” Power Systems, IEEE Trans., vol. 22, Nov. 2007, pp. 1898-1907.

The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer

ABSTRACT:

With the development of electric drive vehicles (EDVs), the state-of-charge (SOC) estimation for lithium-ion (Li-ion) batteries has become increasingly more important. Based on the analysis of some of the most popular model-based SOC estimation methods, the proportional-integral (PI) observer is proposed to estimate the SOC of lithium-ion batteries in EDVs. The structure of the proposed PI observer is analyzed, and the convergence of the estimation method with model errors is verified. To demonstrate the superiority and compensation properties of the proposed PI observer, the simple-structure RC battery model is utilized to model the Li-ion battery. To validate the results of the proposed PI-based SOC estimation method, the experimental battery test bench is established. In the validation, the urban dynamometer driving schedule (UDDS) drive cycle is utilized, and the PI-based SOC estimation results are found to agree with the reference SOC, generally within the 2% error band for both the known and unknown initial SOC cases.

KEYWORDS:

  1. Battery
  2. Electric vehicle
  3. Lithium-ion (Li-ion) battery
  4. Proportional-integral (PI) observer
  5. Sliding-mode observer
  6. State of charge (SOC)

 SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

Fig. 1. Block diagram of different observer-based SOC estimation methods for Li-ion batteries. (a) Block diagram of the common structure. (b) Block diagram of a PI observer.

 EXPECTED SIMULATION RESULTS:

 Fig. 2. Identification results.

Fig. 3. UDDS current profile.

Fig. 4. SOC estimation results when the initial SOC is given.

Fig. 5. SOC estimation results when the initial SOC is unknown.

 CONCLUSION:

A battery SOC estimation algorithm based on a PI observer has been proposed for Li-ion batteries. Acceptable accuracy has been verified by experiments on battery bench testing for both known and unknown initial SOC. The PI-based SOC estimation has a simple structure and is easy to implement. The compensation properties of the PI observer demonstrate that a simple RC model can be utilized to model the Li-ion battery. The estimated SOC with the PI observer converges to the reference SOC quickly, and the SOC estimation errors are maintained in a small band. Most of the errors of the PI-based SOC estimation method are confined to 2% when compared with the reference SOC that is based on Coulomb counting with known initial SOC.

REFERENCES:

[1] B. Pattipati, C. Sankavaram, and K. Pattipati, “System identification and estimation framework for pivotal automotive battery management system characteristics,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 41, no. 6, pp. 869–884, Nov. 2011.

[2] K. Kutluay, Y. Cadirci, Y. S. Ozkazanc, and I. Cadirci, “A new online state-of-charge estimation and monitoring system for sealed lead-acid batteries in Telecommunication power supplies,” IEEE Trans. Ind. Electron., vol. 52, no. 5, pp. 1315–1327, Oct. 2005.

[3] M. Charkhgard and M. Farrokhi, “State-of-charge estimation for Lithiumion batteries using neural networks and EKF,” IEEE Trans. Ind. Electron., vol. 57, no. 12, pp. 4178–4187, Dec. 2010.

[4] L. Xu, J.Wang, and Q. Chen, “Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model,” Energy Convers. Manag., vol. 53, no. 1, pp. 33–39, Jan. 2012.

[5] X. Hu, F. Sun, and Y. Zou, “Estimation of state of charge of a Lithium-ion battery pack for electric vehicles using an adaptive Luenberger observer,” Energies, vol. 3, no. 9, pp. 1586–1603, 2010.