SZOA: An Improved Synergistic Zebra Optimization Algorithm for Microgrid Scheduling and Management

To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZ...

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Published in:Biomimetics (Basel, Switzerland) Vol. 10; no. 10; p. 664
Main Authors: Cao, Lihong, Wei, Qi
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01.10.2025
Subjects:
Air quality management
Algorithms
Alternative energy sources
Analysis
Batteries
Carbon
Cost control
economic cost optimization
Efficiency
Emissions
Emissions (Pollution)
Energy consumption
Energy minerals
Energy resources
Foraging behavior
Fossil fuels
Fuel cells
global optimization
Industrial efficiency
innovation management
Linear programming
Mathematical optimization
microgrid scheduling
Operating costs
Optimization algorithms
Optimization techniques
Pollutants
Predation
Renewable resources
Scheduling
Sustainable development
Wind power
Zebra Optimization Algorithm
Zebra Optimization Algorithm
economic cost optimization
innovation management
microgrid scheduling
global optimization
ISSN:2313-7673, 2313-7673
Online Access:Get full text
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Summary:To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with innovative management concepts to enhance the microgrid scheduling process. The SZOA incorporates three core strategies: a multi-population cooperative search mechanism to strengthen global exploration, a vertical crossover–mutation strategy to meet high-dimensional scheduling requirements, and a leader-guided boundary control strategy to ensure variable feasibility. These strategies not only improve algorithmic performance but also provide technical support for innovative management in microgrid scheduling. Extensive experiments on the CEC2017 (d = 30) and CEC2022 (d = 10, 20) benchmark sets demonstrate that the SZOA achieves higher optimization accuracy and stability compared with those of nine state-of-the-art algorithms, including IAGWO and EWOA. Friedman tests further confirm its superiority, with the best average rankings of 1.20 for CEC2017 and 1.08/1.25 for CEC2022 (d = 10, 20). To validate practical applicability, the SZOA is applied to grid-connected microgrid scheduling, where the system model integrates renewable energy sources such as photovoltaic (PV) generation and wind turbines (WT); controllable sources including fuel cells (FC), microturbines (MT), and gas engines (GS); a battery (BT) storage unit; and the main grid. The optimization problem is formulated as a bi-objective model minimizing both economic costs—including fuel, operation, pollutant treatment, main-grid interactions, and imbalance penalties—and carbon emissions, subject to constraints on generation limits and storage state-of-charge safety ranges. Simulation results based on typical daily data from Guangdong, China, show that the optimized microgrid achieves a minimum operating cost of USD 5165.96, an average cost of USD 6853.07, and a standard deviation of only USD 448.53, consistently outperforming all comparison algorithms across economic indicators. Meanwhile, the SZOA dynamically coordinates power outputs: during the daytime, it maximizes PV utilization (with peak output near 35 kW) and WT contribution (30–40 kW), while reducing reliance on fossil-based units such as FC and MT; at night, BT discharges (−20 to −30 kW) to cover load deficits, thereby lowering fossil fuel consumption and pollutant emissions. Overall, the SZOA effectively realizes the synergy of “economic efficiency and low-carbon operation”, offering a reliable and practical technical solution for innovative management and sustainable operation of microgrid scheduling.
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ISSN:2313-7673
2313-7673
DOI:10.3390/biomimetics10100664