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A Multi-Objective Optimization Framework for Economic and Emission Dispatch Using Hybrid Techniques
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Keywords
Multi-objective optimization
Economic dispatch
Emission dispatch
Fuzzy logic
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Abstract
The increasing integration of renewable energy sources and the growing emphasis on environmental sustainability have intensified the complexity of power system operation, particularly in balancing economic efficiency with emission control. Traditional single-objective dispatch approaches, such as Economic Dispatch (ED) or Emission Dispatch (EmD), often fail to simultaneously address these conflicting objectives, resulting in suboptimal operational performance and environmental impact. To address this challenge, this study proposes a hybrid multi-objective optimization framework for economic and emission dispatch (EED), leveraging fuzzy logic-based control and IoT-enabled real-time monitoring to enhance system adaptability, stability, and performance. The framework incorporates a multi-objective evolutionary algorithm to generate Pareto-optimal solutions, enabling operators to evaluate trade-offs between operational cost and pollutant emissions under dynamic load conditions.
Fuzzy logic controllers are implemented to maintain voltage and frequency stability, mitigating deviations caused by sudden load changes or renewable generation fluctuations. Simultaneously, IoT-based sensors provide real-time data acquisition and communication, enabling dynamic adjustment of generation setpoints according to system state. The integration of these hybrid techniques allows the proposed framework to reconcile economic and environmental objectives more effectively than conventional ED and EmD methods. Simulation studies on a five-unit power system, combining thermal and renewable generation, demonstrate that the framework achieves up to 12% reduction in operational cost and 15% reduction in emissions compared to traditional single-objective methods. Voltage deviations were maintained within ±2%, and frequency variations within 0.1 Hz of nominal values, highlighting enhanced stability and reliability. Pareto-front analysis illustrates the flexibility of decision-making, allowing operators to prioritize either economic efficiency or environmental performance without compromising system integrity.
The proposed methodology bridges critical gaps in classical EED literature by introducing real-time, adaptive control mechanisms that account for uncertainties, non-linearities, and dynamic operating conditions. It also establishes a scalable paradigm for integrating IoT and fuzzy logic in power system optimization, facilitating responsive, sustainable, and efficient grid management. While the framework demonstrates significant improvements, limitations include dependency on sensor reliability, communication latency, and computational overhead associated with real-time multi-objective optimization. Future extensions could include the incorporation of machine learning-based load forecasting, decentralized optimization for distributed energy resources, and integration of energy storage systems to further enhance operational flexibility and emission reduction.
Overall, this research provides a comprehensive, hybrid optimization framework that advances both theoretical understanding and practical implementation of economic and emission dispatch in modern power systems, offering actionable insights for utility
operators, policymakers, and researchers focused on sustainable energy management.
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