Forecasting Tariff Rates and Enhancing Power Quality in Microgrids: The Synergistic Role of LSTM and UPQC

Authors

  • Satyabrata Sahoo School of Electrical Engineering, KIIT Deemed to be University, India
  • Sarat Chandra Swain School of Electrical Engineering, KIIT Deemed to be University, India
  • Ritesh Dash School of EEE, REVA University, India
  • Padarbinda Samal School of Electrical Engineering, KIIT Deemed to be University, India
Volume: 14 | Issue: 1 | Pages: 12506-12511 | February 2024 | https://doi.org/10.48084/etasr.6481

Abstract

The current paper presents an original approach into the microgrid control framework by incorporating LSTM-based optimization with specific emphasis on refining the gain parameters of a Proportional-Integral-Derivative (PID) controller. This integration represents a significant advancement in improving the overall efficiency of microgrid control systems. By creatively applying LSTM optimization, the paper achieves dynamic adjustments of the PID controller's parameters, resulting in more precise regulation of output power quality. Through the utilization of the Unified Power Quality Conditioner (UPQC) in conjunction with LSTM-based optimization, the paper establishes a compelling link between improved power quality and the resultant tariff rates. This highlights their combined influence on enhancing power quality and calibrating tariff rates, providing a fresh perspective on optimizing microgrid operations.

Keywords:

formatting, microgrid, forecasting, LSTM, power quality, UPQC

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References

S. A. Alavi, K. Mehran, V. Vahidinasab, and J. P. S. Catalão, "Forecast-Based Consensus Control for DC Microgrids Using Distributed Long Short-Term Memory Deep Learning Models," IEEE Transactions on Smart Grid, vol. 12, no. 5, pp. 3718–3730, Sep. 2021.

D. Kumar, H. D. Mathur, S. Bhanot, and R. C. Bansal, "Forecasting of solar and wind power using LSTM RNN for load frequency control in isolated microgrid," International Journal of Modelling and Simulation, vol. 41, no. 4, pp. 311–323, Jul. 2021.

A. A. Muzumdar, C. N. Modi, M. G. M, and C. Vyjayanthi, "Designing a Robust and Accurate Model for Consumer-Centric Short-Term Load Forecasting in Microgrid Environment," IEEE Systems Journal, vol. 16, no. 2, pp. 2448–2459, Jun. 2022.

W. Kong, Z. Y. Dong, Y. Jia, D. J. Hill, Y. Xu, and Y. Zhang, "Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network," IEEE Transactions on Smart Grid, vol. 10, no. 1, pp. 841–851, Jan. 2019.

J. Li et al., "A Novel Hybrid Short-Term Load Forecasting Method of Smart Grid Using MLR and LSTM Neural Network," IEEE Transactions on Industrial Informatics, vol. 17, no. 4, pp. 2443–2452, Apr. 2021.

T. Wang, D. O’Neill, and H. Kamath, "Dynamic Control and Optimization of Distributed Energy Resources in a Microgrid," IEEE Transactions on Smart Grid, vol. 6, no. 6, pp. 2884–2894, Aug. 2015.

X. Cao, S. Dong, Z. Wu, and Y. Jing, "A Data-Driven Hybrid Optimization Model for Short-Term Residential Load Forecasting," in 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, Liverpool, UK, Jul. 2015, pp. 283–287.

T. Ergen and S. S. Kozat, "Online Training of LSTM Networks in Distributed Systems for Variable Length Data Sequences," IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, pp. 5159–5165, Jul. 2018.

J. Xie, R. Yang, H. B. Gooi, and H. D. Nguyen, "PID-based CNN-LSTM for accuracy-boosted virtual sensor in battery thermal management system," Applied Energy, vol. 331, Feb. 2023, Art. no. 120424.

H. Jahangir, H. Tayarani, S. S. Gougheri, M. A. Golkar, A. Ahmadian, and A. Elkamel, "Deep Learning-Based Forecasting Approach in Smart Grids With Microclustering and Bidirectional LSTM Network," IEEE Transactions on Industrial Electronics, vol. 68, no. 9, pp. 8298–8309, Sep. 2021.

D. El Bourakadi, A. Yahyaouy, and J. Boumhidi, "Intelligent energy management for micro-grid based on deep learning LSTM prediction model and fuzzy decision-making," Sustainable Computing: Informatics and Systems, vol. 35, Sep. 2022, Art. no. 100709.

M. Mohamed, F. E. Mahmood, M. A. Abd, A. Chandra, and B. Singh, "Dynamic Forecasting of Solar Energy Microgrid Systems Using Feature Engineering," IEEE Transactions on Industry Applications, vol. 58, no. 6, pp. 7857–7869, Aug. 2022.

X. Lin, R. Zamora, C. A. Baguley, and A. K. Srivastava, "A Hybrid Short-Term Load Forecasting Approach for Individual Residential Customer," IEEE Transactions on Power Delivery, vol. 38, no. 1, pp. 26–37, Oct. 2023.

S. Sahoo, S. Swain, R. Dash, P. Sanjeevikumar, K. Jyotheeswara Reddy, and V. Subburaj, "Novel Gaussian flower pollination algorithm with IoT for unit price prediction in peer-to-peer energy trading market," Energy Reports, vol. 7, pp. 8265–8276, Nov. 2021.

S. Sahoo, S. Chandra Swain, and R. Dash, "A Novel Flower Pollination Method for Unit Price Estimation in a Microgrid," in 2022 3rd International Conference for Emerging Technology (INCET), Belgaum, India, Feb. 2022.

V. P. Rajderkar and V. K. Chandrakar, "Design Coordination of a Fuzzy-based Unified Power Flow Controller with Hybrid Energy Storage for Enriching Power System Dynamics," Engineering, Technology & Applied Science Research, vol. 13, no. 1, pp. 10027–10032, Feb. 2023.

V. H. Nguyen, H. Nguyen, M. T. Cao, and K. H. Le, "Performance Comparison between PSO and GA in Improving Dynamic Voltage Stability in ANFIS Controllers for STATCOM," Engineering, Technology & Applied Science Research, vol. 9, no. 6, pp. 4863–4869, Dec. 2019.

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How to Cite

[1]
Sahoo, S., Swain, S.C., Dash, R. and Samal, P. 2024. Forecasting Tariff Rates and Enhancing Power Quality in Microgrids: The Synergistic Role of LSTM and UPQC. Engineering, Technology & Applied Science Research. 14, 1 (Feb. 2024), 12506–12511. DOI:https://doi.org/10.48084/etasr.6481.

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