1. Introduction to Battery Energy Storage Systems (BESS)
The rapid growth of renewable energy sources like wind and solar has highlighted the critical role of battery energy storage systems (BESS) in stabilizing power grids. These systems mitigate the inherent intermittency and volatility of renewables, enabling higher penetration into grids. However, the effective operation of battery energy storage systems (BESS)relies on sophisticated control strategies that address multi-layered challenges, including power allocation, state-of-charge (SOC) management, and hardware limitations. This article explores a hierarchical control framework designed to optimize the performance of battery energy storage systems, ensuring grid stability, prolonging battery life, and minimizing operational risks.
2. Hierarchical Control Structure of Battery Energy Storage Systems (BESS)
The hierarchical control architecture of a battery energy storage system is divided into four layers, each addressing distinct operational objectives:
| Layer | Control Tasks | Key Objectives |
|---|---|---|
| Grid Layer | Power dispatch, voltage/frequency regulation, and local mode switching. | Ensure grid stability and respond to调度指令 (dispatch commands). |
| Energy Layer | SOC management, energy redistribution among units, and lifecycle optimization. | Balance energy across units and extend battery lifespan. |
| Power Layer | Active/reactive power control, mode transitions, and hardware limitations. | Achieve smooth power output and avoid overcurrent/overvoltage. |
| Current Layer | Current tracking, harmonic suppression, and zero-sequence circulating current mitigation. | Ensure high-quality grid injection and parallel converter safety. |
This structure enables the battery energy storage system to operate efficiently across different time scales and physical layers.
3. Grid Layer Control Strategy
The grid layer acts as the interface between the battery energy storage systems (BESS) and the power grid, handling both remote dispatch and local control modes.
3.1 Power Demand Calculation
- Remote Dispatch: Directly follows grid调度指令 for active (PAGCPAGC) and reactive (QAVCQAVC) power.
- Local Control: Computes power demands based on local measurements (e.g., PPCCPPCC, VPCCVPCC):PLOCAL=PPCC−Pfilter,where Pfilter=PPCC1τs+1PLOCAL=PPCC−Pfilter,where Pfilter=PPCCτs+11Here, τ=1/(2πfc)τ=1/(2πfc) adjusts the cutoff frequency for smoothing renewable fluctuations.
3.2 State-Based Power Allocation
Storage units are classified into six states (S1S1-S6S6) based on SOC and fault conditions (Table 1).
Table 1: State Classification for Active Power Control
| State | SOC Range | Power Capability | Dispatch Priority |
|---|---|---|---|
| S1 | [0, SOC₁] | Limited charging | Priority during charging |
| S2 | [SOC₁, SOC₂] | High charge, low discharge | Secondary for charging |
| S3 | [SOC₂, SOC₃] | Balanced charge/discharge | Last priority |
| S4 | [SOC₃, SOC₄] | High discharge, low charge | Priority during discharging |
| S5 | [SOC₄, 1] | Limited discharging | Priority during discharging |
| S6 | Fault | No output | Excluded |
Power allocation dynamically prioritizes units based on their state. For example, during charging, S1 units are activated first, followed by S2-S4.
3.3 Simulation Results
- Scenario: A 10-unit battery energy storage systems (BESS) with SOCs distributed between 10%–90%.
- Outcome: The proposed strategy reduced active power tracking errors by 34% compared to average allocation methods. Reactive power utilization improved by 25%, minimizing the need for external compensators.
4. Energy Layer Control Strategy
The energy layer ensures that the battery energy storage system maintains optimal SOC levels while minimizing grid impact.
4.1 SOC Feedback with Variable Filtering
A variable low-pass filter adjusts the SOC correction power (PbatPbat) based on SOC zones (Figure 1):Pbat=PSOC⋅τsτs+1,where τ=f(SOC)Pbat=PSOC⋅τs+1τs,where τ=f(SOC)
- Filter Time Constant (ττ): Increases near normal SOC (0.4–0.6) to reduce grid disturbance.
Figure 1: Variable Filter Time Constant vs. SOC
(Imagine a curve where ττ peaks at SOC = 0.5 and decreases toward SOC extremes.)
4.2 Performance Evaluation
Table 2: Comparison of SOC Control Strategies
| Metric | Power Limitation | Direct Superposition | Proposed Method |
|---|---|---|---|
| FHC (Mid-band, %) | 4.84 | 4.26 | 3.19 |
| Max DOD | 0.30 | 0.24 | 0.12 |
| Equivalent Cycle Life | 0.64 | 0.66 | 0.59 |
The proposed method reduced mid-band fluctuations by 34% and extended battery life by 11.9% compared to traditional approaches.
5. Power Layer Control Strategy
The power layer ensures seamless transitions between charging, discharging, and reactive compensation modes.
5.1 Flexible Active Power Control
A triple-loop structure (DC voltage → DC current → d-axis current) enables unified control across modes. Key equations include:
- DC Current Reference:idc∗=PrefVdcidc∗=VdcPref
- d-axis Current Limitation:id,max∗=(2MmaxUdc3)2−(ωLPrefUg)2⋅1ωLid,max∗=(32MmaxUdc)2−(ωLUgPref)2⋅ωL1
5.2 Reactive Power Limitation
Reactive power is constrained by the converter’s modulation index and active power output:Qmax=UgωL(2MmaxUdc3)2−(ωLPrefUg)2−Ug2Qmax=ωLUg(32MmaxUdc)2−(ωLUgPref)2−Ug2
5.3 Simulation of Power Limits
Figure 2: Active Power Tracking Under Current Limits
(Imagine waveforms showing smooth transitions between 50 kW charging/discharging with <2% ripple.)
6. Current Layer Control Strategy
Parallel converters in a battery energy storage system face zero-sequence circulating currents (ZSCC), which increase losses and harmonics.
6.1 ZSCC Suppression Strategy
- Dual-Carrier Modulation: Eliminates high-frequency ZSCC by phase-shifting carriers.
- Proportional Resonant (PR) Control: Targets low-frequency ZSCC at 3rd, 9th, 15th harmonics.
Table 3: ZSCC Suppression Methods Comparison
| Method | ZSCC Reduction | Complexity | Cost |
|---|---|---|---|
| Increased Impedance | 30% | Low | High (hardware) |
| Single Inverter Control | 50% | High | Moderate |
| Proposed (Dual+PR) | 85% | Moderate | Low |
7. Experimental Validation
7.1 Laboratory Tests
- Single-Unit battery energy storage systems (BESS): Achieved 98% current tracking accuracy with THD < 2%.
- Parallel Units: ZSCC reduced from 12A to 2A using dual-carrier modulation and PR control.
7.2 Field Tests with Wind-Storage System
- Scenario: 1.5 MW wind farm + 400 kW battery energy storage systems (BESS).
- Outcome: The battery energy storage systems (BESS) reduced 1-minute power fluctuations by 70% and maintained grid voltage within ±2%.
8. Conclusion and Future Work
The hierarchical control strategy significantly enhances the performance of battery energy storage systems in renewable integration. Future work will focus on AI-driven SOC prediction and multi-objective optimization for larger-scale grids.