As global energy transition accelerates under the “dual carbon” targets, the installed capacity of new energy power generation, primarily wind and solar, has been rising year by year. However, due to local climatic influences, the output power of new energy generation often experiences sharp ramping or steep drops, posing challenges to the frequency regulation margin of power systems. The complex output characteristics and grid impedance of large-scale centralized or distributed new energy integration can lead to frequency oscillations, affecting system stability and load safety. The application of battery energy storage systems (BESS) can effectively shave peaks and fill valleys, alleviate pressure on thermal power units, and provide primary frequency regulation to balance load fluctuations, ensuring power system frequency within permissible limits.
Extensive research has been conducted on battery energy storage systems both domestically and internationally. For instance, some studies have introduced the prospects and advantages of battery energy storage systems; others have analyzed the costs and benefits of generation-side and user-side storage; further works have focused on control management systems to enhance stability; and several projects have designed integrated photovoltaic-storage and offshore wind-storage schemes to improve energy utilization and alleviate peak regulation pressure. In this paper, I investigate the application and role of battery energy storage systems in grid-connected new energy power generation systems, and take a specific photovoltaic-storage integrated microgrid project as an example to evaluate the economic performance of the battery energy storage system.
Role of Battery Energy Storage System in Grid-Connected New Energy Power Generation
Peak Shaving and Valley Filling
New energy generation exhibits long-term fluctuations within a day and mismatches with load profiles, i.e., anti-peak regulation characteristics. After grid connection, this increases the requirement for both upward and downward reserve capacity of the power system. During evening peak hours (typically 19:00–22:00), photovoltaic generation produces no output, while wind power may reach full capacity at the lowest load point (e.g., 24:00) in certain periods. This leads to a certain proportion of “curtailed solar” and “curtailed wind” due to insufficient transmission capacity. A battery energy storage system can store the electricity generated by wind at the lowest load point and release it during evening peak hours, effectively shifting the electricity in time to maximize transmission line utilization and match load trends. This reduces the need for thermal power units to provide upward and downward reserve capacity, achieving peak shaving and valley filling.
By configuring a battery energy storage system, the equivalent load (the sum of daily load and new energy output) can be constrained within the maximum and minimum effective power limits for new energy grid injection. This avoids curtailment of new energy and load shedding, improves the system’s ability to absorb renewable power, reduces the demand for reserve capacity, and enhances overall operational efficiency.
Stabilizing the Power System
The short-term rate of change of new energy output must satisfy the stability requirements of the power system. The current active power variation limits for grid-connected new energy systems are shown in the table below.
| Installed Capacity of New Energy System (MW) | Maximum 10-min Active Power Change (MW) | Maximum 1-min Active Power Change (MW) |
|---|---|---|
| < 30 | 10 | 3 |
| 30 – 150 | 10 – 50 | 3 – 15 |
| > 150 | 50 | 15 |
Smoothing the fluctuation of new energy grid connection means that the battery energy storage system controls the storage and release of renewable power to suppress minute-level active power fluctuations. The total active power P = PBES + PNE (where PBES is the BESS output and PNE is the new energy output) must meet the limits in the table above.
Two main control algorithms for the battery energy storage system are the point-by-point limit method and the low-pass filter method.
Point-by-point limit method: The BESS output power at time j, PBES(j), must satisfy:
$$
\max(\Delta P_{10}(j) – P_{y,10}, \ \Delta P_{1}(j) – P_{y,1}) < P_{BES}(j) < \min(\Delta P_{10}(j) – P_{y,10}, \ \Delta P_{1}(j) – P_{y,1})
\tag{1}
$$
where ΔP10(j) is the change in BESS output power over the past 10 minutes; Py,10 is the maximum allowed 10-minute fluctuation; ΔP1(j) is the change over the past 1 minute; and Py,1 is the maximum allowed 1-minute fluctuation.
Low-pass filter method: The BESS output at time j is given by:
$$
P_{BES}(j) = \frac{\tau}{t} \big[ \Sigma P(j) – \Sigma P(j-1) \big]
\tag{2}
$$
where τ is the time constant, t is the control period, and ΣP(j), ΣP(j–1) are the sums of BESS and new energy power at times j and j–1 respectively. The time constant is:
$$
\tau = \frac{1}{2 \pi f_c}
\tag{3}
$$
where fc is the cutoff frequency of the low-pass filter.
Primary Frequency Regulation
Primary frequency regulation responds to short-term rapid load fluctuations. When the power system frequency exceeds preset limits, the BESS autonomously provides or absorbs active power. Different energy sources have different dead bands: for thermal power, the dead band is 50±0.033 Hz; for hydropower, 50±0.05 Hz; for photovoltaics, 50±0.06 Hz; for wind, 50±0.10 Hz.
A battery energy storage system adjusts its output in real time based on frequency deviations, enabling fast response to load changes and maintaining system frequency stability. Compared to thermal units, BESS responds much faster and can undertake primary frequency regulation independently or together with new energy. According to grid regulations, for a 300 MW thermal unit, the primary frequency regulation limit is 8% of rated capacity (24 MW). The regulation amount is 160 MW per Hz, with frequency deviation from 0.033 Hz to 0.183 Hz corresponding to 0–24 MW. Each time the frequency exceeds the dead band, the unit’s output change is ±0.2% of rated power, i.e., ±600 kW. If the BESS solely performs this function, a single charge/discharge lasts only about 10 s, and the probability of up/down frequency excursions is roughly equal. Thus, a 600 kW / 0.5 h BESS is appropriate. With a reasonable state-of-charge (SOC) management strategy, the battery cyclically operates near 50% SOC with shallow depth of charge/discharge, prolonging its lifetime.
To further reduce BESS capacity and keep SOC within a rational range, a dual-boundary improved smoothing control algorithm can be adopted. This algorithm frequently adjusts the BESS power to optimize SOC and minimize the capacity requirement from the power system.
Application Case Study
Project Description and Main Equipment
I analyzed a photovoltaic-storage integrated microgrid project. The system consists of an 800 kW photovoltaic (PV) generation system, a 250 kW / 500 kWh lithium iron phosphate (LFP) battery energy storage system, and user loads. The BESS operates at a maximum voltage level of 10 kV. When PV power exceeds self-consumption, the surplus is stored in the BESS and then released to the grid during peak demand hours. The main equipment list is shown below.
| Equipment | Model/Specification | Quantity |
|---|---|---|
| PV Modules | Monocrystalline silicon, 550 W each | 1455 |
| Inverters | Rated power 33 kW | 22 |
| LFP Battery Cells | 3.2 V / 130 Ah per cell | 1224 |
| Power Conversion System (PCS) | 250 kW | 1 |
| Step-up Transformer (10 kV / 0.4 kV) | 800 kVA | 1 |
Main Operational Functions of the Microgrid
Black Start Capability
The microgrid stores surplus PV power in the LFP battery energy storage system for emergency use. When the utility grid fails, the point of common coupling (PCC) is disconnected, and the BESS together with the PV system supplies loads without any grid support.
Voltage-Current Dual Closed-Loop Control
The BESS output adopts a two-bus configuration. When multiple battery units under one bus operate in voltage-current dual closed-loop mode, the other bus operates under microgrid grid-connected control. This dual-loop control ensures stable charging/discharging, maintains DC bus voltage balance, and improves system efficiency.
Energy Management System (EMS)
The EMS ensures efficient, stable, safe, and reliable operation of the microgrid and optimal utilization of the PV system. It includes data monitoring, equipment management, fault protection, information storage, distribution automation, smart metering, smart consumption, video and environmental monitoring, and comprehensive energy management functions.
Economic Analysis of the Battery Energy Storage System
In this microgrid, the battery energy storage system operates primarily in the peak shaving and valley filling economic mode. During low-load periods, it charges from the grid; during peak hours, it discharges to supply the grid.
Given the local peak-valley electricity price difference of 0.7 CNY/kWh, and a depth of discharge (DoD) of 90% for the LFP battery, the annual revenue from the BESS is calculated as:
Annual Revenue = Price Difference × BESS Capacity × DoD × 365 days = 0.7 CNY/kWh × 500 kWh × 90% × 365 ≈ 115,000 CNY.
In addition to electricity cost savings, the BESS allows the project to use a smaller step-up transformer (e.g., 800 kVA instead of a larger one), reducing the purchase cost of the box-type transformer.
Discussion
Auxiliary services for grid-connected new energy systems constitute a vital application area for battery energy storage systems. The peak shaving, valley filling, and primary frequency regulation functions enable the existing power system to better accommodate new energy, reduce the need for thermal reserve capacity, and mitigate curtailment issues. With improved minute-level forecasting accuracy of new energy output and the deployment of BESS, the dispatchability and predictability of new energy generation are enhanced, thereby increasing grid friendliness and reducing the consumption of fast regulation resources.
However, under current business models, relying solely on these functions often lacks economic viability. Furthermore, the economic benefits of BESS may vary seasonally due to changing electricity price differentials. Therefore, further exploration of additional roles for BESS in grid-connected new energy systems—or improvements in battery performance across all seasons—will be the focus of future research. For instance, integrating BESS with multiple flexibility resources (e.g., hybrid storage) and developing optimization models for capacity allocation across different segments of the power system could yield more balanced and profitable configurations.
Conclusion
In this paper, I have analyzed the application and functions of battery energy storage systems in grid-connected new energy power generation systems. The key roles identified are peak shaving and valley filling, power system stabilization, and primary frequency regulation. Using a photovoltaic-storage integrated microgrid project as a case study, I have evaluated the economic performance of the battery energy storage system. The results show that under the local peak-valley electricity price difference, the BESS operating in economic peak shaving mode can achieve an annual revenue of approximately 115,000 CNY. Moreover, it not only saves electricity costs but also reduces the purchase cost of the box transformer. Consequently, the deployment of a battery energy storage system not only improves the reliability and stability of the power system but also delivers tangible economic benefits.
As the penetration of renewable energy continues to grow, the battery energy storage system will play an increasingly critical role in ensuring grid security and enabling a low-carbon energy future. Future work should focus on optimizing BESS sizing, control strategies, and market mechanisms to maximize both technical and economic performance.