In recent years, the global shift toward renewable energy sources has accelerated, with solar energy storage playing a pivotal role in enabling a sustainable and low-carbon future. As a researcher focused on energy systems, I have observed that solar energy storage, particularly through battery energy storage systems (BESS), is essential for mitigating the intermittency of solar power generation. Solar energy storage not only stabilizes grid operations but also enhances the efficiency of solar power integration by storing excess energy during peak production and releasing it during demand surges. However, the widespread adoption of solar energy storage faces challenges related to battery performance, such as state inconsistencies and degradation. In this paper, I explore the intricacies of solar energy storage systems, emphasizing lithium-ion batteries, and propose strategies to improve their reliability and longevity. The integration of solar energy storage into microgrids and larger power systems requires advanced management techniques to address issues like state-of-charge (SOC) and state-of-health (SOH) estimation, battery inconsistency, and power allocation. By delving into these aspects, I aim to contribute to the optimization of solar energy storage, ensuring it meets the growing demands of modern energy networks.
Solar energy storage relies heavily on battery technologies, with lithium-ion batteries being a preferred choice due to their high energy density and efficiency. A critical aspect of managing solar energy storage systems is accurately estimating the SOC and SOH of batteries. SOC represents the remaining capacity of a battery, while SOH indicates its degradation over time. For solar energy storage applications, precise SOC and SOH estimations are vital for determining the available energy and predicting battery lifespan. I have employed the Thevenin equivalent circuit model to simulate battery behavior, as it effectively captures the dynamic characteristics of lithium-ion batteries used in solar energy storage. The model includes components such as internal resistance and polarization elements, which are described by the following equations:
$$ \begin{bmatrix} \dot{U_1} \ \dot{U_2} \end{bmatrix} = \begin{bmatrix} -\frac{1}{R_1 C_1} & 0 \ 0 & -\frac{1}{R_2 C_2} \end{bmatrix} \begin{bmatrix} U_1 \ U_2 \end{bmatrix} + \begin{bmatrix} \frac{1}{C_1} \ \frac{1}{C_2} \end{bmatrix} I(t) $$
and
$$ U_t = U_{oc} – I(t) R_0 – U_1 – U_2 $$
where ( U_t ) is the terminal voltage, ( U_{oc} ) is the open-circuit voltage, ( I(t) ) is the current, and ( R_0 ), ( R_1 ), ( R_2 ), ( C_1 ), and ( C_2 ) are model parameters. For SOH estimation in solar energy storage, I utilize a discharge-weighted cumulative method combined with rainflow counting to account for varying depth-of-discharge (DOD) cycles. This approach calculates SOH as:
$$ \text{SOH} = \frac{C_{\text{total-max}} – \sum C_{\text{dis-A}} K_A}{C_{\text{total-max}}} $$
where ( C_{\text{total-max}} ) is the maximum total discharge capacity, ( C_{\text{dis-A}} ) is the discharge capacity at DOD ( D_A ), and ( K_A ) is a weighting factor. For SOC estimation, I combine the ampere-hour integral method with open-circuit voltage measurements to enhance accuracy, especially in the mid-SOC range where voltage plateaus occur. This hybrid method is expressed as:
$$ S(t) = S(t_0) + \frac{\eta \int_{t_0}^{t} I(t) \, dt}{E_N} $$
where ( S(t) ) is SOC at time ( t ), ( \eta ) is efficiency, and ( E_N ) is the rated capacity. These estimation techniques are foundational for optimizing solar energy storage systems, as they provide real-time insights into battery status, enabling better control and scheduling.
In solar energy storage systems, battery inconsistency is a major concern that can reduce the effective capacity and safety of BESS. Inconsistencies arise from manufacturing variations and operational conditions, such as temperature fluctuations and unequal SOC distributions. For instance, in a solar energy storage setup, batteries connected in series or parallel may exhibit diverging SOC and SOH values over time, leading to reduced overall performance. I have identified three primary types of inconsistencies in solar energy storage: peak imbalance, valley imbalance, and peak-valley imbalance, which limit the charge and discharge capabilities of battery units. To address this, I propose an active balancing technique based on an auxiliary power architecture, which transfers energy between cells to equalize SOC levels. The balancing circuit employs a switch array and a bidirectional flyback converter, controlled by a master battery management unit (MBMU) that selects cells for balancing based on SOC deviations. The balancing strategy involves a small-period control approach, where the most imbalanced cell is prioritized every cycle period ( T ), as defined by:
$$ r = \text{SOC}{\text{max}} – \text{SOC}{\text{min}} $$
where ( r ) is the SOC range, and balancing is triggered if ( r > \alpha ) (a threshold). The SOC deviation for each cell is calculated as:
$$ \Delta \text{SOC}_i = \text{SOC}_i – \overline{\text{SOC}} $$
with balancing continuing until ( |\Delta \text{SOC}_i| \leq \beta ). To evaluate consistency, I use the standard deviation of SOC:
$$ \epsilon = \sqrt{\frac{\sum_{i=1}^{n} (\text{SOC}_i – \overline{\text{SOC}})^2}{n-1}} $$
This method has been simulated in MATLAB/Simulink for a 6-cell battery pack under various operating conditions, demonstrating faster convergence and improved consistency compared to traditional strategies. For solar energy storage, such balancing techniques are crucial for maintaining high efficiency and prolonging system life, as they prevent overcharging or deep discharging of individual cells.
Another critical aspect of solar energy storage is the power allocation among multiple battery units in a BESS. Irregular charging and discharging patterns in solar energy storage can exacerbate SOH and SOC inconsistencies, leading to accelerated degradation. I have analyzed the coupling relationship between SOH differences and SOC inconsistencies in solar energy storage units, where lower SOH batteries tend to experience wider SOC swings, further deteriorating their health. To mitigate this, I developed a power allocation strategy that incorporates charging and discharge priority sorting with an adaptive mutation particle swarm optimization (AMPSO) algorithm. This strategy aims to minimize energy losses and enhance the lifespan of solar energy storage systems. The priority function ( F_1 ) is defined as:
$$ F_1 = b f_1 + c f_2 $$
where ( f_1 ) and ( f_2 ) are sub-functions based on SOC and SOH parameters, respectively. For example, ( f_1 ) is derived from an inverse hyperbolic tangent function to prioritize cells with SOC near the midpoint, while ( f_2 ) considers SOH variations and SOC imbalances. The power allocation optimization uses the AMPSO algorithm to minimize an objective function ( F_2 ), which accounts for SOC inconsistencies after power dispatch:
$$ F_2 = \sum_{i=1}^{n} d_i \left[ 0.15 \ln \left( \frac{1 + \Delta \text{SOC}{P,i}(t)}{1 – \Delta \text{SOC}{P,i}(t)} \right) \right] $$
where ( d_i ) is a binary indicator for unit participation, and ( \Delta \text{SOC}_{P,i}(t) ) is the SOC difference induced by power ( P ). The AMPSO algorithm enhances global search capabilities by introducing mutation operations, preventing premature convergence. In simulations, this approach reduced the average SOC inconsistency by over 26% compared to traditional methods, while improving the overall efficiency of solar energy storage systems. The table below summarizes key parameters used in the power allocation strategy for a solar energy storage scenario with four battery units.
| Battery Unit | Weakest Cell SOH | Best Cell SOH | SOC Min | SOC Max | Initial SOC |
|---|---|---|---|---|---|
| Unit 1 | 95% | 97% | 0.2 | 0.8 | 0.5 |
| Unit 2 | 93% | 96% | 0.2 | 0.8 | 0.5 |
| Unit 3 | 89% | 95% | 0.2 | 0.8 | 0.5 |
| Unit 4 | 84% | 94% | 0.2 | 0.8 | 0.5 |
To validate the proposed strategies for solar energy storage, I conducted simulations using real-world data from a DC-bus microgrid system integrating solar power, wind generation, electric vehicles, and conventional loads. The solar energy storage system was configured with a capacity of 64 kWh and a power rating of 16 kW, divided into four units. The power smoothing objective was to limit grid connection point fluctuations to within 7% of the renewable capacity, achieved through a sliding average method. The BESS power requirement ( P_B(t) ) was derived from the imbalance power ( P(t) ), and the target grid power ( P^*(t) ) was computed as:
$$ P^*(t) = P(t) – P_B(t) $$
Using the AMPSO-based allocation, the solar energy storage system demonstrated superior performance, with an average charge-discharge efficiency of 84.44%, compared to 73.98% for traditional methods. The table below compares the cycle ranges of battery units under different strategies, highlighting how the proposed approach maintains SOC levels closer to the optimal midpoint, thereby reducing degradation in solar energy storage systems.
| Strategy | Unit 1 Cycle Range | Unit 2 Cycle Range | Unit 3 Cycle Range | Unit 4 Cycle Range |
|---|---|---|---|---|
| Proposed | 37.11%–69.71% | 38.21%–69.66% | 42.09%–65.37% | 46.28%–58.96% |
| Proportional | 38.70%–63.84% | 38.49%–64.02% | 38.07%–64.40% | 37.53%–64.89% |
| Max Power | 41.15%–65.31% | 38.29%–63.66% | 40.90%–65.40% | 41.47%–66.45% |
Furthermore, the reduction in SOC inconsistency, measured as the average ( \Delta \text{SOC}_B ), was 32.59% lower than proportional allocation methods. This underscores the effectiveness of the proposed strategy in enhancing the reliability and efficiency of solar energy storage. The integration of solar energy storage into such microgrids not only stabilizes power output but also maximizes the utilization of solar resources, contributing to a resilient energy infrastructure.
In conclusion, the optimization of solar energy storage systems requires a holistic approach that addresses battery state estimation, inconsistency management, and intelligent power allocation. My research demonstrates that advanced techniques, such as SOC-based balancing and AMPSO-driven power distribution, can significantly improve the performance and longevity of solar energy storage. By implementing these strategies, solar energy storage can better support the integration of renewable sources, reduce energy losses, and extend system lifespan. Future work could focus on refining these models to include real-time environmental factors and expanding them to larger-scale solar energy storage applications. Ultimately, the continued advancement of solar energy storage technologies will play a crucial role in achieving global sustainability goals and fostering a cleaner energy landscape.