Existing energy storage systems (ESSs) are mostly deployed at locations that generate the maximum economic benefits of active distribution networks (ADNs). However, mobile energy storage systems (MESSs) hold significant potential in improving the active response capability of ADNs following disruptions due to their flexibility, controllability, and rapid response. To fully tap into the potential of MESSs, a two-stage stochastic mixed-integer programming (SMIP) model is proposed in this article. The first stage involves formulating the planning decisions for the locations and capacities of MESSs, while the second stage evaluates the operating costs of the ADN by considering various operating scenarios such as normal, severe, and extreme scenarios. To demonstrate the feasibility and practicality of the proposed method, we conducted a case study using various test systems. The computational results indicate that by adopting appropriate planning and scheduling strategies, MESSs can not only ensure economic operation under normal weather conditions but also enhance the resilience of the ADN against catastrophic weather events

New energy storage methods based on electrochemistry can not only participate in peak shaving of the power grid but also provide inertia and emergency power support. It is necessary to analyze the planning problem of energy storage from multiple application scenarios, such as peak shaving and emergency frequency regulation. This article proposes an energy storage capacity configuration planning method that considers both peak shaving and emergency frequency regulation scenarios. A frequency response model based on emergency frequency regulation combined with low-frequency load shedding is established, taking into account the frequency safety constraints of the system and the principle of idle time reuse, to establish a bi-level programming model. In the upper-level model, the optimization objective is to minimize the annual operating cost of the system during the planning period, combined with the constraints of power grid operation to plan the energy storage capacity. The lower-level model embeds frequency safety constraints with the optimization objective of minimizing the cost of fault losses. To solve the bi-level optimization problem, the Karush–Kuhn–Tucher (KKT) condition and Big-M method were used to transform the bi-level model into a single-layer linear model. Finally, an improved IEEE RTS-24 system was used for numerical verification. The results show that the method proposed in this article can reasonably plan the capacity of energy storage, improve frequency safety during system operation, and reduce the operating cost of the power grid.

By implementing a proactive reserve scheduling mechanism, distribution networks (DNs) can optimize emergency resource deployment to improve fault recovery resilience. To address the limitations of existing pre-disaster preparation strategies that only consider limited resources, this paper proposes a proactive scheduling strategy that integrates mobile resources and field personnel in a coordinated manner for DNs. By establishing a two-stage stochastic mixed-integer programming (SMIP) model for coordinated control of emergency resources in DNs, the first stage determines the quantity and location of mobile energy storage systems (MESSs), repair crews (RCs), and switching crews (SCs). In the second stage, the emergency resources rapidly reach the affected sites to participate in sequential restoration of the DN. Finally, the model is validated using standard IEEE test systems. The results from the experiments demonstrate that the proposed method reduces load shedding cost by 19.0% and 19.9% on 33-node and 123-node systems. Empirical simulations confirm that the introduced framework enables efficient emergency resource orchestration, thereby enhancing the pre-disaster preventive response capability and post-disaster real-time restoration capability of the DN, significantly mitigating the impact of disruptive events.