# Control Strategy of Multiple Battery Energy Storage Stations for Power Grid Peak Shaving
## 1 Introduction  
Renewable energy has emerged as a critical pathway for achieving the goals of carbon neutrality [1]. The intermittent and stochastic nature of renewable energy, combined with its rising grid share, has increased peak-shaving pressure on power systems [2]. As for an urban power grid, the predominance of industrial loads has resulted in a progressive widening of the diurnal peak-to-valley load differential [3]. This context emphasizes the fundamental importance of peak-shaving and valley-filling strategies as core mechanisms within load management for optimizing overall system operation. The conventional approaches employed, such as capacity expansion of generation units and transmission infrastructure, achieve their objectives at the expense of grid operational efficiency, particularly concerning economic efficiency and resource utilization. Consequently, traditional peak-shaving measures are proving increasingly inadequate to effectively mitigate the exacerbating load peak-valley disparity and the concomitant decline in power supply reliability. To alleviate the mounting pressure on the power system supply-demand equilibrium, theimplementation of more sophisticated, efficient, and reliable demand-side management strategies is imperative. Under the circumstance, battery energy storage stations (BESSs) offer a new solution to peak regulation pressure by leveraging their flexible \"low storage and high generation\" capabilities and rapid response [4]. Compared to traditional thermal power peak load regulation, BESSs feature millisecond-level response speeds. By leveraging their bidirectional energy flow characteristics, they can regulate load peak-to-valley differences, thereby smoothing out load fluctuations [5]. From a technical and economic perspective, the cost of lithium-ion battery energy storage has decreased by over 60% in the past five years, making it more widely applicable in daily peak-shaving scenarios [6]. More importantly, BESSs can be deployed in a distributed manner at load centers, directly alleviating transmission and power-grid congestion and reducing network losses, which further demonstrate the advantages [7]. Previous studies on BESS participation in peak-valley regulation have been conducted, primarily focusing on two different strategies: one involves BESSs directly participating in grid peak-valley regulation, whereas the other involves BESSs indirectly participating in grid peak-valley regulation [8]. In the first strategy, the energy storage station directly participates in peak-valley regulation, with peak-valley regulation and valley filling as the core optimization objectives [9], aiming to establish an optimal charging and discharging strategy. Such studies directly address the effectiveness of peak regulation and typically incorporate actual operational constraints. For example, models may consider optimizing peak-valley regulation while reducing grid losses [10] or accounting for limitations on charge-discharge cycle depth [11]. In complex systems, models must also integrate the peak-valley periods of the main grid and system power balance to formulate energy storage scheduling strategies [12]. Some studies employ simplified constant-power charging and discharging models to achieve peak-valley regulation objectives or conduct economic evaluations [13]. The algorithms for addressing such direct peak-shaving strategies are more diverse. Due to their ability to handle complex nonlinear problems [14], intelligent algorithms such as genetic algorithms and particle swarm algorithms are widely used [15]. In addition, active control strategies combining short-term load forecasting have been proposed for grid energy storage optimization [16]. The second strategy involves BESSs indirectly participating in peak-valley regulation. Although peak-valley regulation is not directly set as an optimization objective, it can be indirectly achieved by constructing a refined economic model to guide the charging and discharging behavior of the energy storage station.