A Unified Control and Power Management Schemefor PV-Battery-Based Hybrid Microgrids for BothGrid-Connected and Islanded Modes

ABSTRACT:  

Battery storage is usually employed in Photovoltaic (PV) system to mitigate the power fluctuations due to the characteristics of PV panels and solar irradiance. Control schemes for PV-battery systems must be able to stabilize the bus voltages as well as to control the power flows flexibly. This paper proposes a comprehensive control and power management system (CAPMS) for PV-battery-based hybrid microgrids with both AC and DC buses, for both grid-connected and islanded modes. The proposed CAPMS is successful in regulating the DC and AC bus voltages and frequency stably, controlling the voltage and power of each unit flexibly, and balancing the power flows in the systems automatically under different operating circumstances, regardless of disturbances from switching operating modes, fluctuations of irradiance and temperature, and change of loads. Both simulation and experimental case studies are carried out to verify the performance of the proposed method.

KEYWORDS:

  1. Solar PV System
  2. Battery
  3. Control and Power Management System
  4. Distributed Energy Resource
  5. Microgrid
  6. Power Electronics
  7. dSPACE

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1. The proposed control and power management system (CAPMS) for PV-battery-based hybrid microgrids.

 EXPECTED SIMULATION RESULTS:

Fig.. 2.. (Gb)rid-connected mode Case A-1: (a) power flows and (b) voltage

values of the PV-battery system.

Fig. 3. Grid-connected mode Case A-2: power flows of the PV-battery system.

Fig. 4. Grid-connected mode Case A-3-1: PV array in power-reference mode.

Fig. 5. Grid-connected mode Case A-3-2: DC bus and PV array voltages

during transitions between MPPT and power-reference modes.

Fig. 6. Grid-connected mode Case A-4: the PV-battery system is receiving

power from the grid after 2.2 s.

Fig. 7. Grid-connected mode Case A-5: Reactive power control of the

inverter.

Fig. 8. Grid-connected mode Case A-6: transition from grid-connected to

islanded mode.

Fig. 9. Islanded mode Case B-1: power flows of the PV-battery system with

changing loads.

Fig. 10. Islanded mode Case B-2: battery power changes with PV generation.

Fig. 11. Islanded mode Case B-3: bus voltage control of the PV-battery

system.

Fig. 12. Islanded mode Case B-4: (a) unsynchronized and (b) synchronized

AC bus voltages (displaying phase-a) when closing the breaker at the PCC.

CONCLUSION:

 This paper proposes a control and power management system (CAPMS) for hybrid PV-battery systems with both DC and AC buses and loads, in both grid-connected and islanded modes. The presented CAPMS is able to manage the power flows in the converters of all units flexibly and effectively, and ultimately to realize the power balance between the hybrid microgrid system and the grid. Furthermore, CAPMS ensures a reliable power supply to the system when PV power fluctuates due to unstable irradiance or when the PV array is shut down due to faults. DC and AC buses are under full control by the CAPMS in both grid-connected and islanded modes, providing a stable voltage environment for electrical loads even during transitions between these two modes. This also allows additional loads to access the system without extra converters, reducing operation and control costs. Numerous simulation and experimental case studies are carried out in Section IV that verifies the satisfactory performance of the proposed CAPMS.

REFERENCES:

[1] T. A. Nguyen, X. Qiu, J. D. G. II, M. L. Crow, and A. C. Elmore, “Performance characterization for photovoltaic-vanadium redox battery microgrid systems,” IEEE Trans. Sustain. Energy, vol. 5, no. 4, pp. 1379–1388, Oct 2014.

[2] S. Kolesnik and A. Kuperman, “On the equivalence of major variable step- size MPPT algorithms,” IEEE J. Photovolt., vol. 6, no. 2, pp. 590– 594, March 2016.

[3] H. A. Sher, A. F. Murtaza, A. Noman, K. E. Addoweesh, K. Al-Haddad, and M. Chiaberge, “A new sensorless hybrid MPPT algorithm based on fractional short-circuit current measurement and P&O MPPT,” IEEE Trans. Sustain. Energy, vol. 6, no. 4, pp. 1426–1434, Oct 2015.

[4] Y. Riffonneau, S. Bacha, F. Barruel, and S. Ploix, “Optimal power flow management for grid connected PV systems wi0th batteries,” IEEE Trans. Sustain. Energy, vol. 2, no. 3, pp. 309–320, July 2011.

[5] H. Kim, B. Parkhideh, T. D. Bongers, and H. Gao, “Reconfigurable solar converter: A single-stage power conversion PV-battery system,” IEEE Trans. Power Electron., vol. 28, no. 8, pp. 3788–3797, Aug 2013.

An Autonomous Wind Energy Conversion System with Permanent Magnet Synchronous Generator

ABSTRACT:

This paper manages a lasting magnet synchronous generator (PMSG) based variable speed self-governing breeze vitality transformation framework (AWECS). Back associated voltage source converter (VSC) and a voltage source inverter (VSI) with a battery vitality stockpiling framework (BESS) at the middle dc connect are utilized to understand the voltage and recurrence controller (VFC). The BESS is utilized for load leveling and to guarantee the unwavering quality of the supply to customers associated at load transport under change in wind speed. The generator-side converter worked in vector control mode for accomplishing most extreme power point following (MPPT) and to accomplish solidarity control factor activity at PMSG terminals. The heap side converter is worked to manage plentifulness of the heap voltage and recurrence under change in load conditions. The three-stage four wire buyer loads are nourished with a non-separated star-delta transformer associated at the heap transport to give stable nonpartisan terminal. The proposed AWECS is displayed, plan and mimicked utilizing MATLAB R2007b simulink with its sim control framework tool kit and discrete advance solver.

 

BLOCK DIAGRAM:

 

Fig. 1 Proposed control scheme of VFC for PMSG based AWECS

 EXPECTED SIMULATION RESULTS:

 

 Fig. 2 Performance of Controller during fall in wind speed

Fig. 3 Performance of Controller during rise in wind speed

Fig. 4 Performance of Controller at fixed wind speed and balanced/unbalanced non-linear loads

CONCLUSION:

Another arrangement of voltage and recurrence controller for a perpetual magnet synchronous generator based variable speed self-governing breeze vitality transformation framework has been planned demonstrated and its execution is reenacted. The VFC has utilized two back-back associated VSC’s and BESS at halfway dc connect. The GSC has been controlled in vector controlled to accomplish MPPT, solidarity control factor activity of PMSG. The LSI has been controlled to keep up abundancy of load voltage and its recurrence. The VFC has played out the capacity of a heap leveler, a heap balancer, and a consonant eliminator.

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.