With the integration of renewable energy and the transformation of power system forms, research on the collaborative operation of the battery energy storage system (BESS) with renewable energy grid-connected generation has attracted much attention. In our work, we explore the operational effects and evaluation methods for BESS integration into high-penetration renewable energy generation systems. Based on domestic and international research advances, we analyze grid-connected BESS on the renewable energy side from two aspects: operation modes and operation evaluation. We establish a five-dimensional BESS operation indicator system covering technicality, safety, reliability, economy, and social benefits. We then summarize the applicability and effectiveness of existing comprehensive evaluation methods for BESS operation evaluation, analyze problems in BESS operation indicators and evaluation methods, and provide suggestions and prospects.
1. Operation Mode Analysis of BESS on Renewable Energy Side
With the increasing penetration of renewable energy, the imbalance between supply and demand has become prominent. The battery energy storage system is a flexible resource that can effectively address this issue. In our analysis, we classify BESS operation modes into off-grid, grid-connected autonomous operation, and grid-connected active support. Each mode addresses different technical and economic requirements.
1.1 Off-grid BESS Operation Mode
Off-grid BESS is mainly applied in small-scale renewable energy stations to improve local energy consumption without grid support. We consider reliability, economy, and technicality as key factors for capacity configuration. The optimization strategy focuses on minimizing net present cost and power deficiency probability.
1.2 Grid-connected Autonomous BESS Operation Mode
In this mode, BESS aims to maximize economic benefits, primarily through peak shaving and electricity market participation. We analyze BESS control strategies and its role in power spot markets. The optimal bidding strategy accounts for day-ahead and intra-day deviations, with direct revenue from electricity markets and indirect revenue from carbon trading.
1.3 Grid-connected Active Support BESS Operation Mode
BESS provides inertia support, primary frequency regulation, and voltage support to enhance grid stability. We highlight that controlling the state of charge (SOC) between 30% and 50% can extend battery lifetime. Frequency decomposition strategies allow BESS to handle high-frequency signals, improving overall frequency regulation performance.
1.4 Direct and Indirect Values of BESS
The battery energy storage system generates both direct and indirect values. Direct values include peak shaving, frequency regulation, and reactive power support. Indirect values include alleviating line congestion, improving supply reliability, delaying grid upgrades, enhancing renewable energy accommodation, and providing social benefits. These values are summarized in the following table:
| Operation Mode | Direct Value | Indirect Value |
|---|---|---|
| Off-grid | Peak shaving, renewable energy shifting, load tracking, compensating prediction errors | Renewable energy accommodation, social benefits |
| Grid-connected autonomous | Peak shaving, stable output, power quality, reserve capacity, reactive support | Alleviating line congestion, supply reliability, grid upgrade deferral, renewable energy accommodation, social benefits |
| Grid-connected active support | Inertia support, frequency regulation, compensating prediction errors | Supply reliability, renewable energy accommodation, social benefits |
2. BESS Operation Indicators and Improvement Strategies
We establish a comprehensive indicator system covering technicality, safety, reliability, economy, and social benefits. These indicators are essential for evaluating the performance of the battery energy storage system in different scenarios.
2.1 Technical Indicators
Technical indicators include charge/discharge capability and energy efficiency. The maximum dischargeable energy $E_A$ reflects the state of health (SOH) of the BESS. The charge/discharge capability is given by:
$$P_A = \max(P_{CDE,1}, P_{CDE,2}, \ldots, P_{CDE,m})$$
$$E_A = \sum_{k=1}^{N_{\text{num}}} E_{A,k}$$
Energy efficiency $\eta_{\text{BESS}}$ is defined as:
$$\eta_{\text{BESS}} = \frac{E_{\text{on}}}{E_{\text{off}}} \times 100\%$$
2.2 Safety Indicators
Safety indicators include state of balance (SOB) and safety margins. Voltage, temperature, internal resistance, SOH, and SOC consistency are monitored. Battery safety margins cover overcharge, overdischarge, and over-temperature thresholds.
2.3 Reliability Indicators
Reliability indicators measure the ability of BESS to respond to dispatch commands. The response success rate $\theta_{\text{SUC}}$ is given by:
$$\theta_{\text{SUC}} = \frac{N_{\text{SUC}}}{N_T} \times 100\%$$
Other indicators include utilization factor, unplanned outage factor, and SOH-based reliability index:
$$\theta_B = P(h_s \geq \alpha) = \sum_{h_s \geq \alpha} P_s$$
2.4 Economic Indicators
Economic indicators include levelized cost of energy (LCOE) and net present value (NPV). LCOE is calculated as:
$$\text{LCOE} = \frac{C_{\text{total}}}{E_D}$$
NPV over the life cycle $T_{\text{lcc}}$ is:
$$S_{\text{NPV}} = \sum_{n=1}^{T_{\text{lcc}}} (S_n – C_n)(1 + \gamma)^{-n}$$
2.5 Environmental Benefit Indicators
The carbon emission reduction $\psi_{\text{CO}_2}$ and effective emission reduction coefficient $\chi_{\text{CO}_2}$ are defined as:
$$\psi_{\text{CO}_2} = \sum_{n=1}^{T_{\text{lcc}}} e_{\text{CO}_2} E_{\text{Dres},n}$$
$$\chi_{\text{CO}_2} = \frac{E_{\text{Dres}}}{E_D} \times 100\%$$
2.6 Improvement Strategies for Operation Indicators
We summarize improvement methods for the battery energy storage system across key targets:
| Indicator Category | Key Target | Model/Method |
|---|---|---|
| Technical | Energy efficiency improvement | Intermittent pulse charging/discharging, load-rate-based energy consumption model |
| Safety | State of balance estimation | Voltage, temperature, SOC consistency; active-passive cooperative equalization |
| Reliability | SOH prediction and remaining life | Machine learning models, joint SOC-SOH estimation |
| Economic | Cost minimization and market revenue | Bi-level optimization, day-ahead and real-time market bidding |
| Environmental | Carbon reduction | Carbon trading market integration, stepped carbon pricing |
3. Comprehensive Evaluation of BESS Operation
We review existing comprehensive evaluation methods for battery energy storage system operation. The evaluation model is represented as:
$$Y_{\text{BESS}} = \sum_{i=1}^{N} \mu_i \omega_i F_i$$
where $\mu_i$ indicates whether indicator $i$ is included, $\omega_i$ is the weight, and $F_i$ is the score. Common weighting methods include Analytic Hierarchy Process (AHP), entropy weight method, and fuzzy AHP. We adopt a combined AHP-entropy method to overcome subjectivity and objectivity issues. The combined weight is:
$$\omega_i = \omega_{i,\text{AHP}} H_i + \omega_{i,\text{EWM}} (1 – H_i)$$
We summarize typical studies that apply different evaluation methods:
| Literature | Indicators Considered | Evaluation Method |
|---|---|---|
| Ref. [2] | Technical, safety, reliability, economy, social | Interval type-2 FAHP and entropy weight |
| Ref. [4] | Technical, safety, reliability, economy | AHP and fuzzy comprehensive |
| Ref. [17] | Technical, reliability, economy, social | FAHP and improved TOPSIS |
| Ref. [75] | Technical, safety, reliability, other | Combined weighting and TOPSIS |
4. Current Problems and Prospects
4.1 Current Problems
Despite progress, several challenges remain in the evaluation of battery energy storage system:
- Economic viability of off-grid BESS: High initial and operational costs limit economic benefits. More precise models are needed, integrating uncertainty constraints.
- Optimal capacity configuration: Most studies focus solely on economic optimization, neglecting safety and reliability indicators that are hard to monetize. Multi-objective optimization is required.
- Comprehensive indicator system: Single indicators are insufficient. A holistic system covering technicality, safety, reliability, economy, and environment is necessary.
- Internal interactions affecting safety: Cell consistency and thermal propagation significantly impact large-scale BESS reliability. Advanced thermal management and real-time monitoring are critical.
- Practical engineering limitations: Grid dispatch and BESS operation are not yet precisely optimized due to dynamic uncertainties. Traditional dispatch modes still dominate.
- Low utilization of BESS: In joint operation, BESS is often treated as auxiliary, leading to low equivalent utilization and standby losses.
4.2 Prospects
We propose the following directions for future research on battery energy storage system operation evaluation:
- Refined modeling and strategy optimization: Develop detailed models that capture chemical differences of various battery types for off-grid and grid-connected scenarios.
- Comprehensive evaluation system: Design a multi-dimensional system including technical, safety, reliability, economic, and social indicators, adaptable to different operation modes.
- Application of new information technologies: Leverage IoT, blockchain, and AI for real-time monitoring, predictive maintenance, and data-driven evaluation.
- Advanced evaluation methods: Develop multi-criteria decision-making tools that account for regional policies and cost-benefit trade-offs.
- Practical engineering solutions: Implement machine learning algorithms (e.g., unsupervised clustering) to identify weak cells, optimize dispatch, and reduce idle time through improved energy management systems.
5. Conclusion
In this paper, we have reviewed the operation modes and evaluation indicator systems for battery energy storage system on the renewable energy side. We identified key challenges such as economic constraints, incomplete indicator systems, and practical implementation gaps. We provided a comprehensive evaluation framework and suggested future research directions including refined models, new technologies, and holistic evaluation methods. Our work aims to support the sustainable development and efficient utilization of BESS in high-penetration renewable energy systems.