Development of Energy Management Strategies for Port Cranes

Abstract

This study focuses on developing an energy management strategy and an optimal load-handling trajectory for port cranes, specifically Ship-to-Shore (STS) cranes. The objective is to minimise load cycle time, optimise the crane’s energy consumption, and reduce its reliance on the grid. This is achieved by applying a battery-supercapacitor hybrid energy storage system (H-ESS), and a combination of filtering and Pontryagin’s Minimum Principle (PMP) energy management strategies (EMSs). The port crane system model is developed using the graphical energetic macroscopic formalism (EMR). The filtering-based EMS manages the power split between the grid and the H-ESS, while the PMP-based EMS manages the power split between the battery and supercapacitor within the H-ESS. Overall, the proposed EMS for this study has a simple analytical cost function, is computationally fast, and is easy to implement while producing optimal and effective results. The EMR-based port crane model is comprised of mathematical equations describing the crane systems, the cranes’ local control system, and a MATLAB Simulink simulation model. The experimental validation of the EMR-based port crane model is conducted by comparing the MATLAB Simulink simulations with real-world crane data. The results show that implementing the EMS reduces the port crane energy consumption from the utility grid by 29% and decreases peak power consumption by 29.6%. During the crane load handling cycle, the supercapacitor and the battery discharge and recharge by 26% and 8%, respectively. Simulations also show that the developed optimal crane load trajectory is 38% faster and more productive than the non-optimal crane load trajectory. Additionally, the results show that the optimal trajectory reduces the cranes' peak power and energy consumption by 36% compared to the non-optimal trajectory. Furthermore, the EMS enhances the performance of the H-ESS by boosting efficiency, improving reliability, and extending its lifespan, making H-ESS more dependable and cost-efficient. Given that STS cranes are the largest port cranes, with power demands of about 2 MW, and share a common drivetrain with many types of port and industry cranes, the EMS developed in this study can be applied to other types of cranes. Lastly, the study presents a generalised port crane H-ESS sizing framework that uses the optimal EMS and particle swarm optimisation (PSO) algorithm to determine the optimal sizes for the battery and supercapacitor packs in the H-ESS.