Key Takeaways & Executive Findings
- •• • Electricity cost reduction of 32.60% without flexible load and 37.73% with flexible load compared to grid-only supply, demonstrating significant economic benefits for port operators. • • Clean energy utilization ratio increased by 55.46% (without flexible load) and 58.54% (with flexible load), indicating substantial decarbonization potential and reduced reliance on fossil-based grid electricity. • • Flexible load scheduling enables an additional 5.13 percentage point cost reduction and 3.08 percentage point clean energy increase over the non-flexible scenario, highlighting the value of demand-side management in port energy systems. • • The PSO-based optimization achieves peak shaving and valley filling, improving grid stability and reducing the port's carbon footprint, with validated performance on a typical spring day using real measured data.
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Abstract
This study addresses the high operational costs and low renewable penetration in large ports by proposing a multi-energy integrated supply system that coordinates wind, solar, hydrogen, battery storage, and grid electricity. A mathematical model is formulated to minimize electricity cost, and particle swarm optimization (PSO) is employed to schedule energy resources and flexible loads. Using measured data from a typical spring day at a port, two scenarios are evaluated: one without flexible load consideration and one with flexible load participation. Results show that compared to grid-only supply, the optimized system reduces electricity cost by 32.60% and 37.73% for the two scenarios, respectively, while increasing the clean energy utilization ratio by 55.46% and 58.54%. The integration of flexible loads further enhances peak shaving and valley filling, improves dynamic response, and optimizes the power consumption structure. The findings validate the feasibility and practicality of the proposed multi-energy integrated supply system for large ports, offering a viable pathway for decarbonizing port operations and achieving dual-carbon goals.
1. Introduction
Maritime shipping handles nearly 90% of global trade logistics, yet its heavy reliance on fossil fuels generates substantial particulate matter and greenhouse gas emissions. Large ports, as critical nodes in the supply chain, face low clean energy penetration, inadequate coordination among energy sources, high operational costs, and pronounced peak-valley load disparities. Existing multi-energy systems have been deployed in various regions, but few studies address the specific operational constraints and load characteristics of large ports. This gap leaves port authorities without tailored optimization frameworks to integrate wind, solar, hydrogen, and storage assets effectively.
This research develops a multi-energy integrated supply system model that dynamically balances source, storage, and load within a port environment. By applying particle swarm optimization to schedule energy outputs and flexible loads, the study evaluates two scenarios—with and without flexible load participation—using measured data from a typical spring day. The protocol specifically targets the bottleneck of suboptimal energy dispatch by incorporating flexible loads as dispatchable resources, thereby enhancing peak shaving, reducing electricity costs, and increasing clean energy utilization. The results provide a replicable methodology for port decarbonization and operational cost reduction.
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LIU Shuming, SHI Hongda, CAO Feifei, FEI Huaping (2026). Key Optimization Technologies for Multi-Energy Integrated Supply Systems in Large Ports. Acta Energiae Solaris Sinica. https://doi.org/10.19912/j.0254-0096.tynxb.202608_9689
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Frequently Asked Questions
What are the measured cost and clean energy improvements compared to grid-only supply?
The optimized system reduces electricity cost by 32.60% without flexible load and by 37.73% with flexible load. Clean energy utilization ratio increases by 55.46% and 58.54%, respectively, based on a typical spring day at a large port.
How does flexible load scheduling enhance system performance?
Flexible load scheduling adds 5.13 percentage points of additional cost reduction and 3.08 percentage points of clean energy increase over the non-flexible scenario. It enables demand response, improves peak shaving and valley filling, and optimizes the power consumption structure.
What optimization algorithm is used and why?
Particle swarm optimization (PSO) is employed due to its effectiveness in solving nonlinear, multi-constraint energy dispatch problems. It optimizes the output of wind, solar, hydrogen, storage, and grid electricity to minimize cost while satisfying power balance and battery constraints.
What are the key constraints in the model?
The model enforces power balance each period, battery charge/discharge power limits (≤ Pb,max), and state-of-charge bounds (Qmin ≤ Qt ≤ Qmax). These ensure safe and stable operation of the storage system and reliable supply to port loads.
What is the practical significance for port decarbonization?
The system achieves significant cost savings and clean energy penetration, validating its feasibility for large ports. It supports dual-carbon goals by reducing reliance on grid electricity and integrating local renewables, with flexible loads further enhancing economic and environmental benefits.
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