Optimizing Real and Reactive Power Dispatch Using a Multi-Objective Approach Combining the ϵ-Constraint Method and Fuzzy Satisfaction

Electrical power systems are the lifeblood of modern civilization, providing the essential energy infrastructure that powers our homes, industries, and technologies. As our world increasingly relies on electricity, and modern power systems incorporate renewable energy sources, the challenges have become more complex, necessitating advanced forecasting and optimization to ensure effective operation and sustainability. This review paper provides a comprehensive overview of electrical power systems and delves into the crucial roles that forecasting and optimization play in ensuring future sustainability. The paper examines various forecasting methodologies from traditional statistical approaches to advanced machine learning techniques, and it explores the challenges and importance of renewable energy forecasting. Additionally, the paper offers an in-depth look at various optimization problems in power systems including economic dispatch, unit commitment, optimal power flow, and network reconfiguration. Classical optimization methods and newer approaches such as meta-heuristic algorithms and artificial intelligence-based techniques are discussed. Furthermore, the review paper examines the integration of forecasting and optimization, demonstrating how accurate forecasts can enhance the effectiveness of optimization algorithms. This review serves as a reference for electrical engineers developing sophisticated forecasting and optimization techniques, leading to changing consumer behaviors, addressing environmental concerns, and ensuring a reliable, efficient, and sustainable energy future.

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Optimal Reactive Power Dispatch and Demand Response in Electricity Market Using Multi-Objective Grasshopper Optimization Algorithm

Das,

Dawn,

Gope

et al. 2024

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Optimal Reactive Power Dispatch (ORPD) is a power system optimization tool that modifies system control variables such as bus voltage and transformer tap settings, and it compensates devices’ Volt Ampere Reactive (VAR) output. It is used to decrease real power loss, enhance the voltage profile, and promote stability. Furthermore, several issues have been faced in electricity markets, such as price volatility, transmission line congestion, and an increase in the cost of electricity during peak hours. Programs such as demand response (DR) provide system operators with more control over how small customers participate in lowering peak-hour energy prices and demand. This paper presents an extensive study on ORPD methodologies and DR programs for lowering voltage deviation, limiting cost, and minimizing power losses to create effective and economical operations systems. The main objectives of this work are to minimize costs and losses in the system and reduce voltage variation. The Grasshopper Optimization Algorithm (GOA) and Dragonfly Algorithm (DA) have been implemented successfully to solve this problem. The proposed technique has been evaluated by using the IEEE-30 bus system. The results obtained by the implementation of demand response systems show a considerable reduction in costs and load demands that benefit consumers through DR considerations. The results obtained from the GOA and DA are compared with those generated by other researchers and published in the literature to ascertain the algorithm’s efficiency.

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Optimizing Real and Reactive Power Dispatch Using a Multi-Objective Approach Combining the ϵ-Constraint Method and Fuzzy Satisfaction

Cited by 3 publications

References 27 publications

Flamingo Search Algorithm for solving the cost-effective multi-objective optimal reactive power dispatch problem

Flamingo Search Algorithm for solving the cost-effective multi-objective optimal reactive power dispatch problem

Investigating Intelligent Forecasting and Optimization in Electrical Power Systems: A Comprehensive Review of Techniques and Applications

Optimal Reactive Power Dispatch and Demand Response in Electricity Market Using Multi-Objective Grasshopper Optimization Algorithm

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