Abstract
With the rapid development of renewable energy, photovoltaic (PV) systems have gained significant attention worldwide due to their clean and sustainable nature. However, the intermittent nature of PV power generation poses challenges in maintaining system stability. This paper presents a comprehensive study on the capacity planning and control strategy of photovoltaic hybrid energy storage system (PV-HESS). The proposed system integrates lead-acid batteries and supercapacitors to enhance the overall system performance. Capacity optimization is performed using a particle swarm optimization (PSO) algorithm, considering economic cost, energy excess rate, and load loss rate. Furthermore, a compound frequency division coordinated control strategy is introduced to improve the dynamic response and stability of the DC bus voltage under power fluctuations. Experimental validation on both off-grid and grid-connected modes is conducted using the RT-Box semi-physical platform and a 5kW power converter system. The results demonstrate the effectiveness of the proposed capacity planning and control strategy in enhancing the overall performance of photovoltaic hybrid energy storage system.
1. Introduction
In recent years, the increasing demand for energy and concerns over climate change have driven the global transition towards sustainable and renewable energy sources. Photovoltaic (PV) systems, as a promising renewable energy technology, have gained widespread attention due to their ability to convert solar energy into electricity directly. However, the intermittent nature of solar radiation poses significant challenges to the stability and reliability of PV systems. To mitigate these challenges, energy storage systems (ESS) have been widely integrated into PV systems to smooth out power fluctuations and ensure continuous power supply.
This paper focuses on the capacity planning and control strategy of a photovoltaic hybrid energy storage system (PV-HESS) that integrates lead-acid batteries and supercapacitors. The hybrid storage approach leverages the complementary strengths of batteries (high energy density) and supercapacitors (high power density) to enhance system performance. The main contributions of this work are:
- Capacity Optimization: A multi-objective function considering economic cost, energy excess rate, and load loss rate is formulated, and optimized using the particle swarm optimization (PSO) algorithm.
- Control Strategy: A compound frequency division coordinated control strategy is proposed to effectively allocate power between the battery and supercapacitor, improving the dynamic response and stability of the DC bus voltage.
- Experimental Validation: The proposed strategies are experimentally validated using the RT-Box semi-physical platform and a 5kW power converter system.
2. System Overview
The proposed photovoltaic hybrid energy storage system consists of a PV array, lead-acid batteries, supercapacitors, bidirectional DC/DC converters, and a DC bus. The system architecture is illustrated.
2.1 Photovoltaic System
The PV array converts solar energy into direct current (DC) electricity. The output power of the PV array depends on solar irradiance and ambient temperature.
2.2 Energy Storage System
The energy storage system comprises lead-acid batteries and supercapacitors. Lead-acid batteries provide high energy density and are suitable for storing large amounts of energy over an extended period. In contrast, supercapacitors offer high power density and can quickly respond to power fluctuations, providing short-term power support.
2.3 Bidirectional DC/DC Converter
Bidirectional DC/DC converters are employed to interface the energy storage devices with the DC bus. They enable energy to flow bidirectionally between the storage devices and the DC bus, facilitating charging and discharging operations.
3. Capacity Planning
Capacity planning of the photovoltaic hybrid energy storage system aims to determine the optimal sizes of the PV array, batteries, and supercapacitors, while considering economic cost, energy excess rate, and load loss rate.
3.1 Objective Function
The objective function is formulated as a weighted sum of three components: economic cost (C), energy excess rate (EROE), and load loss rate (LER):
F=alphacdotC+betacdotLER+gammacdotEROE
where α, β, and γ are weighting factors satisfying α+β+γ=1.
3.2 Constraints
The capacity planning problem is subject to several constraints, including power balance, charging/discharging depth, and device capacity limits.
3.3 Particle Swarm Optimization
The particle swarm optimization (PSO) algorithm is employed to solve the capacity planning problem. The algorithm initializes a swarm of particles, each representing a potential solution (capacities of PV, battery, and supercapacitor). The particles are iteratively updated based on their individual and global best positions, ultimately converging to the optimal solution.
4. Control Strategy
The proposed compound frequency division coordinated control strategy aims to effectively allocate power between the battery and supercapacitor under power fluctuations, enhancing the dynamic response and stability of the DC bus voltage.
4.1 Traditional Control Strategy
The traditional control strategy employs a low-pass filter (LPF) to divide power fluctuations into low-frequency and high-frequency components. The battery responds to low-frequency components, while the supercapacitor handles high-frequency components.
4.2 Compound Frequency Division Coordinated Control
The proposed control strategy builds upon the traditional approach by incorporating an additional compensation mechanism using the supercapacitor to correct errors in the battery’s power response. This approach leverages the supercapacitor’s fast response time to improve the overall system performance.
5. Simulation and Experimental Results
5.1 Simulation Results
Simulations are conducted using MATLAB/Simulink to evaluate the performance of the proposed control strategy in both off-grid and grid-connected modes. The results demonstrate that the proposed strategy effectively reduces the overshoot and recovery time of the DC bus voltage under power fluctuations.
5.2 Experimental Results
Experimental validation is performed using the RT-Box semi-physical platform and a 5kW power converter system. The experimental results align with the simulation findings, confirming the effectiveness of the proposed capacity planning and control strategy.
| Experimental Scenarios | Traditional Control | Proposed Control |
|---|---|---|
| DC Bus Voltage Overshoot | 423V | 418V |
| Recovery Time | 0.92s | 0.88s |
Table 1: Comparison of experimental results under load power fluctuation.
6. Conclusion
This paper presents a comprehensive study on the capacity planning and control strategy of a photovoltaic hybrid energy storage system. A multi-objective function considering economic cost, energy excess rate, and load loss rate is formulated and optimized using the particle swarm optimization algorithm. A compound frequency division coordinated control strategy is proposed to enhance the dynamic response and stability of the DC bus voltage. Experimental validation confirms the effectiveness of the proposed capacity planning and control strategy in improving the overall performance of the photovoltaic hybrid energy storage system.
Future work could extend the study to consider real-time pricing and the integration of additional renewable energy sources to further optimize photovoltaic hybrid energy storage system for various operating conditions and applications.