Journal of Energy Management and Technology

Journal of Energy Management and Technology

Dynamic Incentive Pricing for Demand Response Programs Based on in-use Appliances Utilizing Non-Intrusive Load Monitoring

Document Type : Original Article

Authors
Parsa Eslami 1
Amir Abdollahi 2
Masoud Rashidinejad 1
1 Electrical Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran
2 Electrical Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran.
10.22109/jemt.2024.449080.1499
Abstract
Demand response programs (DRPs) have gained significant importance in optimizing power systems by reducing peak demand, enhancing grid stability, promoting energy efficiency, and facilitating the integration of renewable energy sources. This paper introduces a novel approach for DRPs by utilizing dynamic incentive pricing strategies with in-use appliances. The proposed approach aims to incentivize consumers to curtail their energy consumption during peak periods, thereby alleviating strain on the grid. In order to address the challenges faced by existing DRPs in accurately monitoring appliance-level energy usage, this paper adopts non-intrusive load monitoring (NILM) as a powerful tool for monitoring and analyzing energy consumption patterns at the appliance level. The implemented DRP in this study is direct load control (DLC), complemented by the sequence to point (seq2point) algorithm for NILM. The proposed approach exhibits several advantages over traditional DRPs. Firstly, it enhances the accuracy of monitoring by utilizing NILM, allowing for appliance-level energy consumption analysis. Secondly, the dynamic incentive pricing strategy creates a financial incentive for consumers to reduce their energy consumption during peak periods, resulting in reduced strain on the grid and overall energy costs. The effectiveness of the proposed approach is evaluated through comprehensive economical and technical analyses. The results demonstrate its superiority compared to traditional DRPs. Notably, the proposed approach achieves a 15.7% reduction in peak demand and a 4% decrease in overall energy consumption. Furthermore, it significantly improves the load factor and peak-to-valley ratio, indicating enhanced grid stability and better utilization of energy resources.
Keywords
Demand response programs
Dynamic incentive
Non-intrusive load monitoring
In-use appliances
Sequence to point
Subjects
  1.  S. Poorvaezi-Roukerd, A. Abdollahi, and W. Peng, “Flexibility-constraint integrated resource planning framework considering demand and supply side uncertainties with high dimensional dependencies,” International Journal of Electrical Power and Energy Systems, vol. 133, 2021, doi: 10.1016/j.ijepes.2021.107223.
  2. 2022 Assessment of Demand Response and Advanced Metering | Federal Energy Regulatory Commission.” Accessed: Mar. 13, 2024. [Online]. Available: https://www.ferc.gov/media/2022-assessment-demand-response-and-advanced-metering
  3. C. Ibrahim, I. Mougharbel, H. Y. Kanaan, N. A. Daher, S. Georges, and M. Saad, “A review on the deployment of demand response programs with multiple aspects coexistence over smart grid platform,” Renewable and Sustainable Energy Reviews, vol. 162. 2022. doi: 10.1016/j.rser.2022.112446.
  4. H. A. Aalami, M. P. Moghaddam, and G. R. Yousefi, “Demand response modeling considering Interruptible/Curtailable loads and capacity market programs,” Appl Energy, vol. 87, no. 1, 2010, doi: 10.1016/j.apenergy.2009.05.041.
  5. H. A. Aalami, H. Pashaei-Didani, and S. Nojavan, “Deriving nonlinear models for incentive-based demand response programs,” International Journal of Electrical Power and Energy Systems, vol. 106, 2019, doi: 10.1016/j.ijepes.2018.10.003.
  6. T. Cai, M. Dong, H. Liu, and S. Nojavan, “Integration of hydrogen storage system and wind generation in power systems under demand response program: A novel p-robust stochastic programming,” Int J Hydrogen Energy, vol. 47, no. 1, 2022, doi: 10.1016/j.ijhydene.2021.10.027.
  7. X. Sun, H. Xie, Y. Xiao, and Z. Bie, “Incentive Compatible Pricing for Enhancing the Controllability of Price-Based Demand Response,” IEEE Trans Smart Grid, vol. 15, no. 1, 2024, doi: 10.1109/TSG.2023.3279415.
  8.  B. Xu, J. Wang, M. Guo, J. Lu, G. Li, and L. Han, “A hybrid demand response mechanism based on real-time incentive and real-time pricing,” Energy, vol. 231, 2021, doi: 10.1016/j.energy.2021.120940.
  9.  M. A. Mengistu, A. A. Girmay, C. Camarda, A. Acquaviva, and E. Patti, “A Cloud-Based On-Line Disaggregation Algorithm for Home Appliance Loads,” IEEE Trans Smart Grid, vol. 10, no. 3, 2019, doi: 10.1109/TSG.2018.2826844.
  10.  K. He, L. Stankovic, J. Liao, and V. Stankovic, “Non-Intrusive Load Disaggregation Using Graph Signal Processing,” IEEE Trans Smart Grid, vol. 9, no. 3, 2018, doi: 10.1109/TSG.2016.2598872.
  11.  F. Gong, N. Han, Y. Zhou, S. Chen, D. Li, and S. Tian, “A SVM Optimized by Particle Swarm Optimization Approach to Load Disaggregation in Non-Intrusive Load Monitoring in Smart Homes,” in 2019 3rd IEEE Conference on Energy Internet and Energy System Integration: Ubiquitous Energy Network Connecting Everything, EI2 2019, 2019. doi: 10.1109/EI247390.2019.9062124.
  12.  C. C. Yang, C. S. Soh, and V. V. Yap, “A non-intrusive appliance load monitoring for efficient energy consumption based on Naive Bayes classifier,” Sustainable Computing: Informatics and Systems, vol. 14, 2017, doi: 10.1016/j.suscom.2017.03.001.
  13. ] F. Hidiyanto and A. Halim, “KNN Methods with Varied K, Distance and Training Data to Disaggregate NILM with Similar Load Characteristic,” in ACM International Conference Proceeding Series, 2020. doi: 10.1145/3400934.3400953.
  14.  Z. Xiao, W. Gang, J. Yuan, Y. Zhang, and C. Fan, “Cooling load disaggregation using a NILM method based on random forest for smart buildings,” Sustain Cities Soc, vol. 74, 2021, doi: 10.1016/j.scs.2021.103202.
  15.  Z. Chen, J. Chen, X. Xu, S. Peng, J. Xiao, and H. Qiao, “Non-intrusive load monitoring based on feature extraction of change-point and xgboost classifier,” in 2020 IEEE 4th Conference on Energy Internet and Energy System Integration: Connecting the Grids Towards a Low-Carbon High-Efficiency Energy System, EI2 2020, 2020. doi: 10.1109/EI250167.2020.9347014.
  16.  R. C. Lekshmi et al., “Non-intrusive Load Monitoring with ANN-Based Active Power Disaggregation of Electrical Appliances,” 2021. doi: 10.1007/978-981-33-6691-6_41.
  17.  F. Ciancetta, G. Bucci, E. Fiorucci, S. Mari, and A. Fioravanti, “A New Convolutional Neural Network-Based System for NILM Applications,” IEEE Trans Instrum Meas, vol. 70, 2021, doi: 10.1109/TIM.2020.3035193.
  18.  M. Kaselimi, N. Doulamis, A. Voulodimos, E. Protopapadakis, and A. Doulamis, “Context Aware Energy Disaggregation Using Adaptive Bidirectional LSTM Models,” IEEE Trans Smart Grid, vol. 11, no. 4, 2020, doi: 10.1109/TSG.2020.2974347.
  19.  A. Langevin, M. Cheriet, and G. Gagnon, “Efficient deep generative model for short-term household load forecasting using non-intrusive load monitoring,” Sustainable Energy, Grids and Networks, vol. 34, 2023, doi: 10.1016/j.segan.2023.101006.
  20. M. D’Incecco, S. Squartini, and M. Zhong, “Transfer Learning for Non-Intrusive Load Monitoring,” IEEE Trans Smart Grid, vol. 11, no. 2, 2020, doi: 10.1109/TSG.2019.2938068.
  21.  Y. Zhang et al., “FedNILM: Applying Federated Learning to NILM Applications at the Edge,” IEEE Transactions on Green Communications and Networking, vol. 7, no. 2, 2023, doi: 10.1109/TGCN.2022.3167392.
  22.  H. Abbasimehr, M. Shabani, and M. Yousefi, “An optimized model using LSTM network for demand forecasting,” Comput Ind Eng, vol. 143, 2020, doi: 10.1016/j.cie.2020.106435.
  23.  A. Kurani, P. Doshi, A. Vakharia, and M. Shah, “A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting,” Annals of Data Science, vol. 10, no. 1. 2023. doi: 10.1007/s40745-021-00344-x.
  24.  Syaharuddin, Fatmawati, and H. Suprajitno, “Investigations on Impact of Feature Normalization Techniques for Prediction of Hydro-Climatology Data Using Neural Network Backpropagation with Three Layer Hidden,” International Journal of Sustainable Development and Planning, vol. 17, no. 7, 2022, doi: 10.18280/ijsdp.170707.
  25.  C. Zhang, M. Zhong, Z. Wang, N. Goddard, and C. Sutton, “Sequence-to-point learning with neural networks for non-intrusive load monitoring,” in 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, 2018. doi: 10.1609/aaai.v32i1.11873.
  26.  S. Guessoum et al., “The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN),” Sensors, vol. 22, no. 23, 2022, doi: 10.3390/s22239517.
  27.  T. Szandała, “Review and comparison of commonly used activation functions for deep neural networks,” in Studies in Computational Intelligence, vol. 903, 2021. doi: 10.1007/978-981-15-5495-7_11.
  28.  D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 2015.
  29.  M. P. Moghaddam, A. Abdollahi, and M. Rashidinejad, “Flexible demand response programs modeling in competitive electricity markets,” Appl Energy, vol. 88, no. 9, 2011, doi: 10.1016/j.apenergy.2011.02.039.
  30. V. Sharifi, A. Abdollahi, and M. Rashidinejad, “Flexibility-based generation maintenance scheduling in presence of uncertain wind power plants forecasted by deep learning considering demand response programs portfolio,” International Journal of Electrical Power and Energy Systems, vol. 141, 2022, doi: 10.1016/j.ijepes.2022.108225.
Journal of Energy Management and Technology
Volume 9, Issue 4
Autumn 2025
Pages 215-223

  • Receive Date 19 March 2024
  • Revise Date 30 May 2024
  • Accept Date 12 June 2024