Moving Aviation towards Environmental Sustainability: An Examination of Operational Optimization

Huang Lian^*
*Correspondence: Huang Lian, Department of Environmental Science, Harbin Institute of Technology, Harbin 150001, China, Email:

Keywords

Emission • Harmonizing • Grid • Energy • Storage • Framework

Introduction

The power sector plays a critical role in modern economies, providing the necessary electricity for residential, commercial, and industrial activities. However, it also significantly contributes to atmospheric pollution through the emission of Greenhouse Gases (GHGs) and other pollutants such as Sulfur Dioxide (SO₂), Nitrogen Oxides (NO_x), and particulate matter. The Economic Dispatch (ED) of power systems, which involves determining the optimal output of multiple power-generating units to meet the demand at the lowest possible cost, must increasingly consider environmental impacts. This review examines the integration of atmospheric pollutant emissions into the economic dispatch of power systems, exploring methods, challenges, and potential solutions. Economic dispatch is a fundamental optimization problem in the operation of power systems. The primary objective is to minimize the total operational cost while satisfying the demand for electricity and adhering to operational constraints. Traditionally, the cost function in ED models includes fuel costs and operational expenses. However, with growing environmental concerns, there is a push to include the costs associated with emissions of pollutants. Multi-objective optimization techniques are used to handle the trade-offs between minimizing costs and reducing emissions. This approach treats cost and emissions as separate objectives and seeks to find a balance between them. Environmental/Economic Dispatch (EED) models aim to minimize both fuel costs and emissions simultaneously. This involves formulating a composite objective function that includes both economic and environmental costs [1].

Techniques such as genetic algorithms, particle swarm optimization, and fuzzy logic can handle the non-linear, multi-objective nature of the problem more effectively than traditional linear programming methods. In the United States, the Clean Air Act has driven the integration of emissions considerations into economic dispatch. The use of emissions trading schemes and state-level initiatives, such as the Regional Greenhouse Gas Initiative (RGGI), provides practical examples of how regulatory frameworks can influence ED practices.

Literature Review

Multi-objective optimization methods are essential in addressing the conflicting goals of cost minimization and emission reduction. Techniques such as Pareto front analysis help identify optimal trade-offs between competing objectives. The Pareto front represents a set of solutions where any improvement in one objective would lead to a deterioration in another. This allows decision-makers to choose solutions that best balance economic and environmental goals. A case study from the US Midwest Independent System Operator (MISO) region demonstrates the application of Pareto optimization. By incorporating emissions into the dispatch model, MISO could identify operational strategies that significantly reduced emissions with minimal cost increases. This approach provided valuable insights into how different pricing scenarios for carbon could influence dispatch decisions. Incorporating emission cost functions into economic dispatch models involves quantifying the environmental impact of emissions and translating this into a cost metric. The use of carbon pricing is particularly effective. Carbon pricing mechanisms, such as carbon taxes or cap-and-trade systems, create financial incentives for power producers to reduce their carbon footprint [2].

Discussion

The European Union's Emissions Trading System (EU ETS) serves as a prime example of using carbon pricing to influence economic dispatch. The EU ETS sets a cap on total emissions and allows power producers to buy and sell emission allowances. This market-based approach incentivizes lowcarbon technologies and operational efficiency. Analysis of the EU ETS has shown that it has led to significant reductions in CO₂ emissions across the power sector, demonstrating the effectiveness of integrating emission costs into economic dispatch [3-5].

Environmental/Economic Dispatch (EED) models extend traditional ED by including both fuel costs and environmental costs in the objective function. This integrated approach requires sophisticated algorithms to solve, as it introduces non-linearity and increases the complexity of the optimization problem. China's power sector has implemented EED models to address severe air pollution issues. By integrating SO₂ and NO_x emission costs into dispatch models, Chinese grid operators can better manage the environmental impact of power generation. Studies have shown that EED implementation in China has led to reduced emissions of major pollutants, contributing to improved air quality and public health. A study in India demonstrated the application of GA-based EED for a thermal power plant. By considering both fuel costs and emissions, the GA approach identified dispatch strategies that significantly reduced CO₂ and NO_x emissions while keeping costs within acceptable limits [6].

Conclusion

Technological advancements play a pivotal role in reducing emissions from the power sector. The development and deployment of renewable energy sources, energy storage systems, and carbon capture technologies are essential components of a sustainable power system. The integration of renewable energy sources such as wind, solar, and hydro into the power grid can significantly reduce emissions. However, the intermittent nature of renewables poses challenges for economic dispatch, requiring advanced forecasting and grid management techniques. Germany's Energiewende (energy transition) initiative aims to increase the share of renewables in the power mix. The integration of wind and solar power has led to significant reductions in CO₂.

Acknowledgement

None.

Conflict of Interest

None.

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