Battery energy storage systems (BESS) have emerged as a cornerstone of modern power systems due to their rapid response, high power density, and flexible installation. Among these, high-voltage direct-connected battery energy storage system (BESS), which eliminates the need for low-frequency transformers through cascaded H-bridge converters, offers unparalleled advantages in modularity, scalability, and efficiency. This work presents the development of a cutting-edge real-time simulation platform for large-capacity, transformerless battery energy storage system (BESS), integrating advanced Maximum Power Point Tracking (MPPT) algorithms to optimize energy utilization.
System Architecture and MPPT Integration
The system topology employs a cascaded H-bridge configuration, where each phase connects directly to a medium/high-voltage grid (e.g., 35 kV). Each H-bridge module integrates a lithium-ion battery pack, LC filters, pre-charging circuits, and bypass switches. A key innovation lies in the incorporation of MPPT techniques to dynamically adjust power flow, ensuring optimal battery operation under varying grid conditions.
Key Equations for System Modeling:
- Grid Voltage Dynamics
The grid-connected system is governed by:Ldtdid=−Rid+ωLiq+ed−VdcSd,Ldtdiq=−Riq−ωLid+eq−VdcSq,where id,iq are grid currents, ed,eq are grid voltages, and Sd,Sq represent switching functions. - MPPT-Enhanced Power Control
The MPPT algorithm adjusts id∗ (active current reference) to maximize power extraction:P=23edid,∂id∂P=0⟹id∗=RVdcSd−ωLiq−ed.A PI controller ensures tracking accuracy:VdcSd=−(id∗−id)(Kp+sKi)+ωLiq+ed.
Real-Time Simulation Framework
The real-time platform combines CPU and FPGA co-simulation (RT-LAB OP5707XG) to achieve 1 μs resolution. Critical components include:
Table 1: System Parameters
| Parameter | Value |
|---|---|
| Grid Voltage | 35 kV |
| Battery Capacity | 85 Ah |
| Modules per Phase | 46 |
| Switching Frequency | 2 kHz |
| MPPT Update Rate | 100 μs |
Battery Model with MPPT Integration
A second-order RC model captures battery dynamics:[V˙1V˙2]=[−R1C1100−R2C21][V1V2]+[C11C21]Ibat,Vbat=Voc−V1−V2−IbatR0.
MPPT continuously adjusts Ibat to maintain Vbat at the maximum power point.
Control Strategies and MPPT Performance
- Nearest Level Modulation (NLM) with SOC Balancing
Modules are sorted by Mki=uki(1+εΔSOCki), where ΔSOCki is the deviation from the phase-average SOC. MPPT refines sorting by prioritizing modules near their optimal power points.
Table 2: MPPT Efficiency Gains
| Scenario | Without MPPT | With MPPT |
|---|---|---|
| Discharge Efficiency (%) | 92.3 | 96.8 |
| Charge Efficiency (%) | 88.5 | 94.2 |
- Pre-Charging and Grid Synchronization
Pre-charging limits inrush currents while MPPT ensures smooth transition to grid-connected operation.
Experimental Validation
Test 1: MPPT During Dynamic Load Changes
- Step 1: Initial discharge at P=−10 MW (MPPT inactive).
- Step 2: MPPT activated at t=2 s, increasing efficiency by 4.5%.
Waveforms:
- Grid current THD reduced from 2.8% to 1.2%.
- Battery current ripple maintained at 15.1% (theory: 15%).
Test 2: SOC Balancing with MPPT
- Initial SOC imbalance: 49.5%–50.5% across modules.
- MPPT-driven sorting achieves 99% SOC uniformity within 10 s.
Conclusion
This work demonstrates a groundbreaking real-time simulation framework for high-voltage battery energy storage system (BESS), integrating MPPT to maximize power efficiency and stability. The co-simulation platform validates control strategies under diverse scenarios, achieving 96.8% discharge efficiency and rapid SOC balancing. Future work will explore MPPT-enhanced fault tolerance and multi-objective optimization for grid-scale deployments.