Shipboard integrated power systems (SIPSs) are usually equipped with multiple pulsed power loads (PPLs). In complex scenarios with strict operational requirements, the performance of the SIPS is greatly affected by the output energy of PPLs, especially in achieving high suppressive capability in a short period. To maximize the short-time output efficiency of PPLs in emergency situations, the aperiodic scheduling method is studied, aiming at optimizing the coordination of multiple PPLs and energy storage. The proposed aperiodic scheduling model aims to maximize the total utility of PPLs in the given period. The model fully considers constraints related to energy storage, system power balance, aperiodic charging and discharging process, time sequence, and charging power. To solve this complex non-linear model, a bi-layer dynamic programming algorithm is proposed. Simulation tests of SIPS performance are carried out to validate the advantage of the proposed method. The results show that the performance of single PPL and multi PPLs with aperiodic scheduling is improved by 42.3% and 25.7% respectively compared with periodic scheduling.

Shipboard integratedpower systems (SIPSs) usually have multifaceted operational objectives in engineering scenarios, and many key tasks are performed by multiple pulsed power loads (PPLs) with a high power density. Owing to the complexity of the ocean environment, the optimization of SIPS comprehensive performance remains challenging. This article proposes a multiobjective energy management strategy of multiple PPLs in SIPSs. A multiobjective energy coordination model is formulated. By identifying typical operation modes of the SIPS based on discrepant priority, the PPL utility and maneuverability are incorporated into the comprehensive regulation objective in the form of a weighted sum. In the case of limited energy supply, the service duration is further considered in the optimization objective to meet the high survivability requirements. Reasonable methods are adopted to quantitatively evaluate different performances. Moreover, to improve SIPS survivability in the face of emergencies, a system-level energy allocation scheme with maneuverability enhancement is formulated to coordinate the power consumption of different functions. A particle swarm optimization algorithm with directional guidance and selective evaluation of the nonconvex constraint is proposed to solve the optimization problem effectively. Case studies on a typical SIPS under various working scenarios are presented to validate the effectiveness of the proposed method.

Renewable energy is an important means of addressing climate change and achieving carbon peaking and carbon neutrality goals. However, the uncertainty and randomness of renewable energy also have a certain impact on the flexibility, reliability, and transient voltage stability of the power system. These effects also pose great challenges to power system planning. In order to address the impact of renewable energy on power system planning, this paper proposes a two-layer optimization model for power sources and network planning which takes into account both reliability and transient voltage stability requirements. The upper-layer grid planning problem is formulated with consideration of the system reliability index, and the transient stability requirements and construction and operation costs are included in the lower-layer problem to determine a construction scheme for power generation and energy storage units. To solve the complex nonlinear problem efficiently, a two-layer iterative algorithm utilizing the adaptive particle swarm optimization (PSO) technique is proposed. The effectiveness of the proposed method is demonstrated via its application to the IEEE 33 test system. The results show that the proposed optimization approach effectively addresses the power system transmission and generation planning problem while improving the efficiency and reliability of the system’s operation. The findings can guide the design and implementation of future power system planning and operation strategies.