## 1 Introduction  
With the proposal of global low-carbon goals and the extensive application of user-side energy storage (ES), multi-microgrid collaborative scheduling in industrial parks has developed rapidly [1]. However, the uncertainty and volatility of renewable energy and loads pose substantial challenges to the safety and stability of multi-microgrid operation in industrial parks [2, 3]. ES can alleviate the impact of photovoltaic (PV) and load uncertainty on the park to a certain extent but the high cost of ES is still a limiting factor for its widespread implementation [4]. Consequently, it becomes crucial to investigate the integration of PV generation and load management within industrial park smart microgrids [5, 6], especially for the optimization control of smart microgrids [7]. In fact, an industrial park comprises various types of topological structures, each equipped with a transformer for connection to the main grid [8]. To address challenges such as low renewable energy penetration and increasing peak-to-valley load differentials, an interconnected multi-microgrid approach is adopted to mitigate uncertainty and balance the peak-to-valley differentials of the net load [9]. By establishing decentralized and coordinated dispatch among the microgrids, it has been demonstrated that multi-microgrid systems can reduce the risks associated with renewable energy integration and operating costs [10]. Research on the energy management framework of multi-microgrids confirms that collaborative scheduling among these microgrids yields greater economic benefits compared to individual microgrid operations [11]. Furthermore, a hierarchical power outage scheme for multi-microgrids is proposed to enhance system resilience against disasters and improve overall system security [12]. To flexibly coordinate distributed energy resources and improve the system's economic performance, a resource-sharing multi-microgrid model predictive control framework is proposed [13]. However, it is worth noting that the accuracy of PV and load forecasts is prone to errors which can significantly impact the collaborative dispatch of multi-microgrids [14; 15]. The presence of PV and load forecast errors introduces uncertainties into the system's operation; robust optimization and stochastic optimization are two commonly used methods to address such uncertainties [16; 17]. For instance, a scenario-based approach using the Monte Carlo method is employed to generate scenarios and corresponding probabilities considering multiple uncertainties such as PV, wind turbine output, and load power. The resulting stochastic optimization model is then solved using mixed-integer linear programming techniques [18]. Another approach proposes a two-stage adaptive robust trading model for interconnecting multi-microgrids. This model minimizes the operating costs under worst-case scenarios of uncertain PV outputs [19]. However, both robust and stochastic optimizations have their limitations [20]. On the one hand, robust optimization entails considering the worst case of the system to obtain the optimality of the system whereby the uncertainty parameters always belong to the uncertainty set. Although this optimization method can ensure the robustness of the system, it loses some economy. Without considering the preferences of decision makers, in the actual operation process it will be subject to cost constraints and cannot obtain the optimal solution. On the other hand, stochastic optimization needs to know or assume the probability distribution function of uncertainty so as to establish the expected value function of the system. This not only requires a large amount of original data to fit the probability distribution function or rely on the uncertainty probability distribution but also increases the difficulty of solving according to the number of selected probabilities. To overcome these limitations, this paper presents a stochastic optimal scheduling model for industrial parks based on the information gap decision theory (IGDT).