Venkateswarlu GUNDU, Sishaj pulikottil SIMON, Kinattingal SUNDARESWARAN, Srinivasa rao nayak PANUGOTHU

Gated recurrent unit based demand response for preventing voltage collapse in a distribution system

This paper presents the application of deep learning algorithms towards demand response management. Demand limit violation and voltage stability are the major problems associated with a secondary distribution system. These problems are solved using demand response models by day ahead scheduling loads at every 15 min interval through linear integer programming and based on short term forecasting of load (kW). A new architecture for short term load forecasting is presented namely gated recurrent unit in which statistical analysis is carried out to get the optimal architecture of the neural network model. Reliability indices such as loss of load probability (LOLP) is evaluated to handle uncertainties that may occur in forecasting due to overestimation and underestimation. Here a novel dynamic power flow is carried out to check limit violation of the voltage. Also, scheduling is performed for two types of loads namely deferrable with interruptible and deferrable with uninterruptable, when either of maximum demand or voltage limit violation occurs. Finally, the suggested model is validated on a modified 12 bus radial distribution system. The result analysis shows that the suggested gated recurrent unit minimizes the forecast error and demand response program schedules household appliances without a demand limit violation and ensures the prevention of voltage collapse.

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[1] Mohsenian-Rad AH, Wong VW, Jatskevich J, Schober R, Leon-Garcia A. Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. Institute of Electrical and Electronics Engineers Transactions on Smart Grid 2010; 1 (3): 320-331.
[2] Albadi MH, El-Saadany EF. A summary of demand response in electricity markets. Electric Power Systems Research 2008; 78 (11): 1989-1996.
[3] Fraser H. The importance of an active demand side in the electricity industry. The Electricity Journal 2001; 14 (9): 52-73.
[4] Chen C, Wang J, Kishore S. A distributed direct load control approach for large-scale residential demand response. Institute of Electrical and Electronics Engineers Transactions on Power Systems 2014; 29 (5): 2219-2228.
[5] Bahrami S, Parniani M, Vafaeimehr A. A modified approach for residential load scheduling using smart meters. In: 2012 3 rd Institute of Electrical and Electronics Engineers Power and Energy Society Innovative Smart Grid Technologies Europe; Berlin, Germany; 2012. pp.1-8.
[6] Nikoukar J. Unit commitment considering the emergency demand response programs and interruptible/curtailable loads. Turkish Journal of Electrical Engineering and Computer Sciences 2018; 26 (2): 1069-1080.
[7] Zehir MA, Batman A, Bagriyanik M. Review and comparison of demand response options for more effective use of renewable energy at consumer level. Renewable and Sustainable Energy Reviews 2016; 56: 631-642.
[8] Ruzbahani HM, Rahimnejad A, Karimipour H. Smart households demand response management with micro grid. In: 2019 Institute of Electrical and Electronics Engineers Power and Energy Society Innovative Smart Grid Technologies; Bucharest, Romania; 2019. pp 1-5.
[9] Jindal A, Kumar N, Singh M. Internet of energy-based demand response management scheme for smart homes and PHEVs using SVM. Future Generation Computer Systems 2020; 108: 1058-1068.
[10] Charytoniuk W, Chen MS. Very short-term load forecasting using artificial neural networks. Institute of Electrical and Electronics Engineers transactions on Power Systems 2000; 15 (1): 263-268.
[11] Taylor JW, Buizza R. Neural network load forecasting with weather ensemble predictions. Institute of Electrical and Electronics Engineers Transactions on Power systems 2002; 17 (3): 626-632.
[12] Eapen RR, Simon SP, Sundareswaran K, Nayak PS. User centric economic demand response management in a secondary distribution system in India. Institution of Engineering and Technology Renewable Power Generation 2019; 13 (6): 834-846.
[13] Peter SE, Raglend IJ, Simon SP. An architectural frame work of ANN based short term electricity price forecast engine for indian energy exchange using similar day approach. International Journal of Research in Engineering and Technology 2014; 2 (4): 111-122.
[14] Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing; Doha, Qatar; 2014. pp. 1724-1734.
[15] Sapankevych NI, Sankar R. Time series prediction using support vector machines: a survey. Institute of Electrical and Electronics Engineers Computational Intelligence Magazine 2009; 4 (2): 24-38.
[16] Hong WC. Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting. Neural Computing and Applications 2012; 21 (3): 583-593.
[17] Arı A, Hanbay D. Deep learning based brain tumor classification and detection system. Turkish Journal of Electrical Engineering and Computer Sciences 2018; 26 (5): 2275-2286.
[18] Hua Y, Zhao Z, Li R, Chen X, Liu Z et al. Deep learning with long short-term memory for time series prediction. Institute of Electrical and Electronics Engineers Communications Magazine 2019; 57 (6): 114-119.
[19] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation 1997; 9 (8): 1735-1780.
[20] Wang J, Tang J, Xu Z, Wang Y, Xue G et al. Spatiotemporal modeling and prediction in cellular networks: a big data enabled deep learning approach. In: Institute of Electrical and Electronics Engineers Conference on Computer Communications; Atlanta, GA, USA; 2017. pp. 1-9.
[21] Alahi A, Goel K, Ramanathan V, Robicquet A, Fei-Fei L et al. Social LSTM: human trajectory prediction in crowded spaces. In: 2016 Proceedings of the Institute of Electrical and Electronics Engineers Conference on Computer Vision and Pattern Recognition; Las Vegas, NV, USA; 2016. pp. 961-971.
[22] Ravanelli M, Brakel P, Omologo M, Bengio Y. Light gated recurrent units for speech recognition. Institute of Electrical and Electronics Engineers Transactions on Emerging Topics in Computational Intelligence 2018; 2 (2): 92-102.
[23] Gers FA, Schmidhuber J, Cummins F. Learning to forget: continual prediction with LSTM. Institution of Engineering and Technology 1999; 2: 850-855.
[24] De Campos LM. Time series prediction with direct and recurrent neural networks. Turkish Journal of Forecasting 2017; 1 (1): 7-15.
[25] Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv 2014; arXiv:1412.3555 [cs.NE].
[26] Das D, Nagi HS, Kothari DP. Novel method for solving radial distribution networks. Institution of Electrical Engineers Proceedings-Generation. Transmission and Distribution 1994; 141 (4): 291-298.
[27] Danish MS, Senjyu T, Danish SM, Sabory NR, Mandal PA. A recap of voltage stability indices in the past three decades. Energies 2019; 12 (8): 1544.
[28] Chakravorty M, Das D. Voltage stability analysis of radial distribution networks. Electrical Power and Energy Systems 2001; 23 (2): 129-135.
[29] Chandrasekaran K, Simon SP, Padhy NP. SCUC problem for solar/thermal power system addressing smart grid issues using FF algorithm. International Journal of Electrical Power and Energy Systems 2014; 62: 450-460.
[30] Amjady N. Short-term hourly load forecasting using time-series modeling with peak load estimation capability. Institute of Electrical and Electronics Engineers Transactions on Power Systems 2001; 16 (3): 498-505.
[31] Arun SL, Selvan MP. Intelligent residential energy management system for dynamic demand response in smart buildings. Institute of Electrical and Electronics Engineers Systems Journal 2018; 12 (2): 1329-1340.
[32] Szultka A, Malkowski R, Czapp S, Szultka S. Impact of R/X ratio of distribution network on selection and control of energy storage units. In: 2017 International Conference on Information and Digital Technologies; Zilina, Slovakia; 2017. pp.359-364.
[33] Zhao T, Wang J, Zhang Y. Day-ahead hierarchical probabilistic load forecasting with linear quantile regression and empirical copulas. Institute of Electrical and Electronics Engineers Access 2019; 7: 80969-80979.
[34] Chen Y, Luh PB, Guan C, Zhao Y, Michel LD et al. Short-term load forecasting: similar day-based wavelet neural networks. Institute of Electrical and Electronics Engineers Transactions on Power Systems 2010; 25 (1): 322-330.
[35] Eapen RR, Simon SP. Performance analysis of combined similar day and day ahead short term electrical load forecasting using sequential hybrid neural networks. Institution of Electronics and Telecommunication Engineers Journal of Research 2019; 65 (2): 216-226.
[36] Murty VV, Kumar A. Optimal placement of DG in radial distribution systems based on new voltage stability index under load growth. International Journal of Electrical Power and Energy Systems 2015; 69: 246-256.
[37] Yuvaraj T, Ravi K, Devabalaji KR. Optimal allocation of DG and DSTATCOM in radial distribution system using cuckoo search optimization algorithm. Modelling and Simulation in Engineering 2017; 1: 1-20.