An Integrated Economic and Emissions Dispatch Problem using the Sparrow Search Algorithm

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 8

Year of Publication : 2023

Authors : V. Mahesh Kumar Reddy, P. Venkatesh, R. Dharmaprakash, S. Sendil Kumar

10.14445/23488379/IJEEE-V10I8P120

How to Cite?

V. Mahesh Kumar Reddy, P. Venkatesh, R. Dharmaprakash, S. Sendil Kumar, "An Integrated Economic and Emissions Dispatch Problem using the Sparrow Search Algorithm," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 8, pp. 206-214, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I8P120

Abstract:

The economic dispatch of the Micro-Grid (MG) in the current scenario considers the scheduling cycle with the lowest cost and effectively manages multiple Distributed Generations (DGs) over an extended duration, making it a more appropriate choice for a functional system. The unpredictability of wind and solar energy poses a challenge in solving the economic dispatch problem. Implementing intelligent algorithms and multi-objective optimum dispatching systems has demonstrated significant cost reduction and enhanced environmental sustainability in microgrid operations. The primary objective of the economic dispatch algorithm in the context of a Micro-Grid (MG) is to minimize expenses throughout the scheduling cycle while efficiently coordinating many distributed generators over an extended duration. This makes it highly suitable for operational systems. Due to their inherent variability, the economic resolution of the dispatch problem associated with wind and solar energy requires significant time. Utilizing smart algorithms and multi-objective functions for optimized dispatching generator capacity can potentially.

Keywords:

Distributed generation, Economic-dispatch, Micro-grid, Linear integer programming, Optimization techniques.

References:

[1] Sanchari Deb et al., “Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem,” IEEE Access, vol. 9, pp. 44322-44338, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Abhishek Srivastava, and Dushmanta Kumar Das, “A New Aggrandized Class Topper Optimization Algorithm to Solve Economic Load Dispatch Problem in a Power System,” IEEE Transactions on Cybernetics, vol. 52, no. 6, pp. 4187-4197, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Xiaoting Wang et al., “A Data-Driven Uncertainty Quantification Method for Stochastic Economic Dispatch,” IEEE Transactions on Power Systems, vol. 37, no. 1, pp. 812-815, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] E. Parimalasundar et al., “Investigation Analysis of Open Circuit and Short Circuit Fault on Cascaded H-Bridged Multilevel Inverter using Artificial Neural Network Approach,” International Journal of Electrical and Electronics Research, vol. 10, no. 2, pp. 320-326, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Xiaoting Wang et al., “A Data-Driven Uncertainty Quantification Method for Stochastic Economic Dispatch,” IEEE Transactions on Power Systems, vol. 37, no. 1, pp. 812-815, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] P. Venkatesh, and N. Visali, “Machine Learning for Hybrid Line Stability Ranking Index in Polynomial Load Modeling under Contingency Conditions,” Intelligent Automation & Soft Computing, vol. 37, no. 1, pp. 1001–1012, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Dewen Li et al., “Virtual-Action-Based Coordinated Reinforcement Learning for Distributed Economic Dispatch,” IEEE Transactions on Power Systems, vol. 36, no. 6, pp. 5143-5152, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] K. Suresh et al., “A Performance Investigations of Modular Multilevel Inverter with Reduced Switch Count,” International Conference on Intelligent Innovations in Engineering and Technology (ICIIET), pp. 83-87, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Yamin Yan et al., “Distributed Consensus-Based Economic Dispatch in Power Grids using the Paillier Cryptosystem,” IEEE Transactions on Smart Grid, vol. 12, no. 4, pp. 3493-3502, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] P. Venkatesh, and N. Visali, “Enhancing Power System Security using Soft Computing and Machine Learning,” Electrical Engineering & Electromechanics, no. 4, pp. 90–94, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] B. Perumal, K. Suresh, and E. Parimalasundar, “Fault Analysis in the 5-Level Multilevel NCA DC–AC Converter,” Automation, vol. 64, no. 3, pp. 606-612, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] K. Suresh, and E. Parimalasundar, “Fault Analysis and Clearance in FL-APC DC–AC Converter,” IEEE Canadian Journal of Electrical and Computer Engineering, vol. 46, no. 1, pp. 1-6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Zhenwei Zhang et al., “Multitime Scale Co-Optimized Dispatch for Integrated Electricity and Natural Gas System Considering Bidirectional Interactions and Renewable Uncertainties,” IEEE Transactions on Industry Applications, vol. 58, no. 4, pp. 5317-5327, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] K. Suresh et al., “Design and Implementation Bidirectional DC–AC Converter for Energy Storage System,” IEEE Canadian Journal of Electrical and Computer Engineering, vol. 46, no. 2, pp. 130-136, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Krishnan Suresh, and Ezhilvannan Parimalasundar, “Newly Designed Single-Stage Dual Leg DC-DC/AC Buck-Boost Converter for Grid Connected Solar System,” International Journal of Circuit Theory and Applications, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Shuai Lu et al., “Economic Dispatch of Integrated Energy Systems with Robust Thermal Comfort Management,” IEEE Transactions on Sustainable Energy, vol. 12, no. 1, pp. 222-233, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Alireza Akbari-Dibavar et al., “Economic-Emission Dispatch Problem in Power Systems with Carbon Capture Power Plants,” IEEE Transactions on Industry Applications, vol. 57, no. 4, pp. 3341-3351, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Krishnan Suresh, and Ezhilvannan Parimalasundar, “Design and Implementation of Dual-Leg Generic Converter for DC/AC Grid Integration,” International Journal of Circuit Theory and Applications, vol. 51, no. 8, pp. 3865-3879, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Krishnan Suresh, and Ezhilvannan Parimalasundar, “Fault Analysis and Compensation in a Five Level Multilevel DC-AC Converter,” El-Cezeri, vol. 10, no. 1, 99-108, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] David Troxell, Miju Ahn, and Harsha Gangammanavar, “A Cardinality Minimization Approach to Security-Constrained Economic Dispatch,” IEEE Transactions on Power Systems, vol. 37, no. 5, pp. 3642-3652, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Stefanos Baros, Yu Christine Chen, and Sairaj V. Dhople, “Examining the Economic Optimality of Automatic Generation Control,” IEEE Transactions on Power Systems, vol. 36, no. 5, pp. 4611-4620, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Kasinath Jena et al., “A Single DC Source Generalized Switched Capacitors Multilevel Inverter with Minimal Component Count,” International Transactions on Electrical Energy Systems, vol. 2023, pp. 1–12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Kavali Janardhan et al., “Performance Investigation of Solar Photovoltaic System for Mobile Communication Tower Power Feeding Application,” International Journal of Electrical and Electronics Research, vol. 10, no. 4, pp. 921–925, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] B. Hemanth Kumar et al., “Control of Modified Switched Reluctance Motor for EV Applications,” Trends in Electrical, Electronics, Computer Engineering Conference (TEECCON), pp. 123-127, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[25] B. Hemanth Kumar et al., “An Enhanced Space Vector PWM Strategies for Three Phase Asymmetric Multilevel Inverter,” International Transactions on Electrical Energy Systems, vol. 2023, pp. 1-29, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Abdulkerim Ali, Getachew Biru, and Belachew Bantyirga, “Design the Potential Application of Fuzzy Logic-Based AGC and AVR for Multi-Area Interconnected Power Systems: A Case Study on Ethiopia,” International Journal of Engineering Trends and Technology, vol. 71, no. 5, pp. 84-97, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[27] E. Parimalasundar et al., “Performance Analysis of DC-DC Converter for Electric Vehicle Charging Applications,” International Conference on Computing Methodologies and Communication (ICCMC), pp. 1543-1546, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Thi-Mai-Phuong Dao, Ngoc-Khoat Nguyen, and Duy-Trung Nguyen, “Different Fuzzy Logic–Based Load-Frequency Controllers for Interconnected Power Systems – A Comparative Study Applied in Vietnam,” International Journal of Engineering Trends and Technology, vol. 71, no. 7, pp. 223-232, 2023.
[CrossRef] [Publisher Link]
[29] Yiqiao Xu et al., “Distributed Optimization for Integrated Frequency Regulation and Economic Dispatch in Microgrids,” IEEE Transactions on Smart Grid, vol. 12, no. 6, pp. 4595-4606, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[30] H.S. Shreenidhi, and Narayana Swamy Ramaiah, “Sensor-Assisted Machine Learning Models Approach to Equipoise Renewable Energy using Microgrids,” SSRG International Journal of Electronics and Communication Engineering, vol. 10, no. 7, pp. 63-73, 2023.
[CrossRef] [Publisher Link]
[31] Kasinath Jena et al., “A Novel Three-Phase Switched-Capacitor Five-Level Multilevel Inverter with Reduced Components and SelfBalancing Ability,” Applied Sciences, vol. 13, no. 3, p. 1713, 2023
[CrossRef] [Google Scholar] [Publisher Link]
[32] Parimalasundar Ezhilvannan et al., “Analysis of the Effectiveness of a Two-Stage Three-Phase Grid Connected Inverter for Photovoltaic Applications,” Journal of Solar Energy Research, vol. 8, no. 2, 1471-1483, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Farnaz Safdarian, Amin Kargarian, and Fouad Hasan, “Multiclass Learning-Aided Temporal Decomposition and Distributed Optimization for Power Systems,” IEEE Transactions on Power Systems, vol. 36, no. 6, pp. 4941-4952, 2021.
[CrossRef] [Google Scholar] [Publisher Link]