A Honey Badger Optimization for Minimizing the Pollutant Environmental Emissions-Based Economic Dispatch Model Integrating Combined Heat and Power Units

In plain language

Managing combined heat and power systems requires balancing fuel expenses with environmental impacts. A meta-heuristic approach called the honey badger optimisation algorithm addresses this challenge by mimicking the digging and honing foraging behaviours of honey badgers to balance exploration and exploitation phases during search tasks. Applied to an economic dispatch model on a seven-unit test system, the algorithm was tested against alternative modern algorithms, including the African vultures, dwarf mongoose, coot, and beluga whale optimisation techniques. The evaluation considered multiple loading levels, variations in power and heat demand, and scenarios with and without power losses. Across the test conditions, the honey badger algorithm achieved substantial cuts in pollutant emissions, outperforming competing meta-heuristic methods in lowering fuel expenditures while significantly driving down emissions compared to baseline operations.

Key takeaways

The honey badger optimisation algorithm was applied to combined heat and power economic dispatch models to cut pollutant emissions and fuel costs.
The algorithm splits honey badger foraging behaviours into distinct exploration and exploitation phases to improve computational search.
On a seven-unit test system with power losses considered, the algorithm reduced emissions by up to 92.595% compared to baseline conditions across different load levels.
The proposed method outperformed alternative algorithms, including African vultures, dwarf mongoose, coot, and beluga whale optimisers, in reducing fuel expenses.

Why it matters

Power generation facilities that provide both electricity and district heat face increasing regulatory pressure to lower harmful environmental emissions. Utilising bio-inspired computational algorithms helps operators find optimal dispatch schedules that lower operating fuel costs while dramatically reducing airborne pollutants, enabling cleaner and more economical energy management across varied network demands.

Commercialisation angle

This research provides an algorithmic tool for utility operators and power dispatch software developers seeking to improve economic dispatch in combined heat and power facilities. Because testing was conducted on a simulated seven-unit system across differing power losses and load demands, the methodology represents early-stage to applied computational research that requires integration into commercial energy management software before achieving real-world deployment.

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Abstract

Traditionally, the Economic Dispatch Model (EDM) integrating Combined Heat and Power (CHP) units aims to reduce fuel costs by managing power-only, CHP, and heat-only units. Today, reducing pollutant emissions to the environment is of paramount concern. This research presents a novel honey badger optimization algorithm (HBOA) for EDM-integrated CHP units. HBOA is a novel meta-heuristic search strategy inspired by the honey badger’s sophisticated hunting behavior. In HBOA, the dynamic searching activity of the honey badger, which includes digging and honing, is separated into exploration and exploitation phases. In addition, several modern meta-heuristic optimization algorithms are employed, which are the African Vultures Algorithm (AVO), Dwarf Mongoose Optimization Algorithm (DMOA), Coot Optimization Algorithm (COA), and Beluga Whale Optimization Algorithm (BWOA). These algorithms are applied in a comparative manner considering the seven-unit test system. Various loading levels are considered with different power and heat loading. Four cases are investigated for each loading level, which differ based on the objective task and the consideration of power losses. Moreover, considering the pollutant emissions minimization objective, the proposed HBOA achieves reductions, without loss considerations, of 75.32%, 26.053%, and 87.233% for the three loading levels, respectively, compared to the initial case. Moreover, considering minimizing pollutant emissions, the suggested HBOA achieves decreases of 75.32%, 26.053%, and 87.233%, relative to the baseline scenario, for the three loading levels, respectively. Similarly, it performs reductions of 73.841%, 26.155%, and 92.595%, respectively, for the three loading levels compared to the baseline situation when power losses are considered. Consequently, the recommended HBOA surpasses the AVO, DMOA, COA, and BWOA when the purpose is to minimize fuel expenditures. In addition, the proposed HBOA significantly reduces pollutant emissions compared to the baseline scenario.

Research topics

Electric Power System Optimization
Optimal Power Flow Distribution
Energy Load and Power Forecasting

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