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Targeted demand response for flexible energy communities using clustering techniques (2303.00186v3)

Published 1 Mar 2023 in cs.LG and cs.AI

Abstract: The present study proposes clustering techniques for designing demand response (DR) programs for commercial and residential prosumers. The goal is to alter the consumption behavior of the prosumers within a distributed energy community in Italy. This aggregation aims to: a) minimize the reverse power flow at the primary substation, occuring when generation from solar panels in the local grid exceeds consumption, and b) shift the system wide peak demand, that typically occurs during late afternoon. Regarding the clustering stage, we consider daily prosumer load profiles and divide them across the extracted clusters. Three popular machine learning algorithms are employed, namely k-means, k-medoids and agglomerative clustering. We evaluate the methods using multiple metrics including a novel metric proposed within this study, namely peak performance score (PPS). The k-means algorithm with dynamic time warping distance considering 14 clusters exhibits the highest performance with a PPS of 0.689. Subsequently, we analyze each extracted cluster with respect to load shape, entropy, and load types. These characteristics are used to distinguish the clusters that have the potential to serve the optimization objectives by matching them to proper DR schemes including time of use, critical peak pricing, and real-time pricing. Our results confirm the effectiveness of the proposed clustering algorithm in generating meaningful flexibility clusters, while the derived DR pricing policy encourages consumption during off-peak hours. The developed methodology is robust to the low availability and quality of training datasets and can be used by aggregator companies for segmenting energy communities and developing personalized DR policies.

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References (61)
  1. Residential Load Forecasting for Flexibility Prediction Using Machine Learning-Based Regression Model, in: 2019 IEEE International Conference on Environment and Electrical Engineering, Institute of Electrical and Electronics Engineers Inc. doi:10.1109/EEEIC.2019.8783634.
  2. Beginning MLOps with MLFlow. Apress Berkeley, CA. doi:10.1007/978-1-4842-6549-9.
  3. Real time pricing and electricity markets. Citeseer URL: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=484174e34d4ff1b52373fe057e324595d102d315.
  4. Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. doi:10.1016/j.rser.2020.109899.
  5. K-means++: The Advantages of Careful Seeding, in: SODA 2007: The eighteenth annual ACM-SIAM symposium on Discrete algorithms, pp. 1027–1035.
  6. Deep reinforcement learning for direct load control in distribution networks. IEEE Power and Energy Society General Meeting 2020-August. doi:10.1109/PESGM41954.2020.9281703.
  7. Deep Reinforcement Learning for Demand Response in Distribution Networks. IEEE Transactions on Smart Grid 12, 1496–1506. doi:10.1109/TSG.2020.3037066.
  8. Identification of Suitable Consumer Groups forParticipation in Demand Response Programs. IEEE Power & Energy Society .
  9. Innovative Tools for Demand Response Strategies: A Real-Life Experience. Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019 doi:10.1109/EEEIC.2019.8783584.
  10. Are domestic load profiles stable over time? An attempt to identify target households for demand side management campaigns. IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society .
  11. Fast Constrained Dynamic Time Warping for Similarity Measure of Time Series Data. IEEE Access 8, 222841–222858. doi:10.1109/ACCESS.2020.3043839.
  12. An embedded deep-clustering-based load profiling framework. Sustainable Cities and Society 78, 103618. doi:10.1016/J.SCS.2021.103618.
  13. Demand response spinning reserve demonstration doi:10.1109/HICSS.2012.39.
  14. Delivering the internal electricity market and making the most of public intervention. Brussels 5, 16.
  15. Bargaining-based cooperative energy trading for distribution company and demand response. Applied Energy 226, 469–482. doi:10.1016/J.APENERGY.2018.05.095.
  16. Household response to dynamic pricing of electricity: A survey of 15 experiments. Journal of Regulatory Economics 38, 193–225. doi:10.1007/S11149-010-9127-Y/METRICS.
  17. A power load clustering method based on limited DTW algorithm. Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019 , 253–256doi:10.1109/ITNEC.2019.8729249.
  18. GDPR, 2016. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). URL: https://eur-lex.europa.eu/eli/reg/2016/679/oj.
  19. Distributed generation monitoring: a cost-effective Raspberry Pi-based device. 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2022 doi:10.1109/IRASET52964.2022.9737768.
  20. Reinforcement learning based pricing for demand response, in: 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc.. pp. 1–6. doi:10.1109/ICCW.2018.8403783.
  21. Demand side management-A simulation of household behavior under variable prices. Energy Policy 39, 8163–8174. doi:10.1016/j.enpol.2011.10.016.
  22. Pacific Northwest GridWise Testbed demonstration projects. Part I. Technical Report. Olympic Peninsula Project.
  23. Residential implementation of critical-peak pricing of electricity. Energy Policy 35, 2121–2130. doi:10.1016/j.enpol.2006.06.019.
  24. Analysis of similarity measures in times series clustering for the discovery of building energy patterns. Energies 6, 579–597. doi:10.3390/en6020579.
  25. Energy peak shaving with local storage. Sustainable Computing: Informatics and Systems 1, 177–188. doi:10.1016/J.SUSCOM.2011.05.001.
  26. Characterizing the energy flexibility of buildings and districts. Applied Energy 225, 175–182. doi:10.1016/J.APENERGY.2018.05.037.
  27. An end-to-end approach for scalable real time Anomaly detection in smart buildings. 13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022 doi:10.1109/IISA56318.2022.9904410.
  28. THE I-NERGY REFERENCE ARCHITECTURE FOR THE PROVISION OF NEXT GENERATION ENERGY SERVICES THROUGH ARTIFICIAL INTELLIGENCE, in: ES 2023 : 21st International Conference e-Society 2023.
  29. ARTIFICIAL INTELLIGENCE FOR NEXT GENERATION ENERGY SERVICES ACROSS EUROPE - THE I-NERGY PROJECT, in: ES 2022: 20th International Conference e-Society 2022, Lisbon. pp. 61–68. URL: https://cordis.europa.eu/project/id/101016508.
  30. Keycloak, 2023. Keycloak. URL: https://www.keycloak.org/.
  31. DR: Controlling Small Loads over the Internet, in: ACEEE Summer Study on Energy Efficiency Buildings.
  32. Dynamic pricing and energy consumption scheduling with reinforcement learning. IEEE Transactions on Smart Grid 7, 2187–2198. doi:10.1109/TSG.2015.2495145.
  33. Household energy consumption segmentation using hourly data. IEEE Transactions on Smart Grid 5, 420–430. doi:10.1109/TSG.2013.2278477.
  34. Lifestyle Segmentation Based on Energy Consumption Data. IEEE Transactions on Smart Grid 9, 2409–2418. doi:10.1109/TSG.2016.2611600.
  35. Opportunities of power-to-gas technology in different energy systems architectures. Applied Energy 228, 57–67. doi:10.1016/J.APENERGY.2018.06.001.
  36. Clustering load profiles for demand response applications. IEEE Transactions on Smart Grid 10, 1599–1607. doi:10.1109/TSG.2017.2773573.
  37. A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach. Applied Energy 220, 220–230. doi:10.1016/j.apenergy.2018.03.072.
  38. A Perspective on Reinforcement Learning in Price-Based Demand Response for Smart Grid, in: Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017, Institute of Electrical and Electronics Engineers Inc.. pp. 1822–1823. doi:10.1109/CSCI.2017.327.
  39. Behavioral Assumptions Underlying California Residential Sector Energy Efficiency Programs.
  40. Commercial building loads providing ancillary services in PJM, in: 2014 ACEEE Summer Study on Energy Efficiency in Buildings.
  41. A clustering approach to domestic electricity load profile characterisation using smart metering data. Applied Energy 141, 190–199. doi:10.1016/j.apenergy.2014.12.039.
  42. Multi-block ADMM Approach for Decentralized Demand Response of Energy Communities with Flexible Loads and Shared Energy Storage System. 2022 30th Mediterranean Conference on Control and Automation, MED 2022 , 67–72doi:10.1109/MED54222.2022.9837173.
  43. The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review. Energy Policy 38, 3289–3296. doi:10.1016/J.ENPOL.2010.01.027.
  44. Consumer demand for time of use electricity tariffs: A systematized review of the empirical evidence. doi:10.1016/j.rser.2018.08.040.
  45. DeepTSF: Codeless machine learning operations for time series forecasting. arXiv URL: https://arxiv.org/abs/2308.00709.
  46. In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance, in: 2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA), IEEE. pp. 1–8. doi:10.1109/IISA56318.2022.9904363.
  47. A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers. arXiv:2302.12168 doi:10.48550/arxiv.2302.12168.
  48. Load peak shaving and power smoothing of a distribution grid with high renewable energy penetration. Renewable Energy 86, 1372–1379. doi:10.1016/J.RENENE.2015.09.050.
  49. Market Power and Demand Responsiveness: Letting Customers Protect Themselves. The Electricity Journal 16, 11–23. doi:10.1016/S1040-6190(03)00048-4.
  50. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20, 53–65. doi:10.1016/0377-0427(87)90125-7.
  51. Dynamic Programming Algorithm Optimization for Spoken Word Recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing 26, 43–49. doi:10.1109/TASSP.1978.1163055.
  52. Finding Groups in Data: An Introduction to Cluster Analysis. Journal of the American Statistical Association 86, 830. doi:10.2307/2290430.
  53. Impact of TOU rates on distribution load shapes in a smart grid with PHEV penetration, in: IEEE/PES Transmission and Distribution Conference, IEEE. doi:10.1109/TDC.2010.5484336.
  54. Electric Load Clustering in Smart Grid: Methodologies, Applications, and Future Trends. Journal of Modern Power Systems and Clean Energy 9, 237–252. doi:10.35833/MPCE.2020.000472.
  55. A Simple Way to Use Interval Data to Segment Residential Customers for Energy Efficiency and Demand Response Program Targeting Use of Segmentation Schemes within the Utility Industry: A Review, in: 2012 ACEEE Summer Study on Energy Efficiency in Buildings, Stanford.
  56. A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms. IEEE Communications Surveys and Tutorials 17, 152–178. doi:10.1109/COMST.2014.2341586.
  57. Power to liquid and power to gas: An option for the German Energiewende. Renewable and Sustainable Energy Reviews 45, 207–218. doi:10.1016/J.RSER.2015.01.049.
  58. Predicting Visitor Distribution for Large Events in Smart Cities, in: 2019 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 1–8. doi:10.1109/BIGCOMP.2019.8679181.
  59. A review on price-driven residential demand response. Renewable and Sustainable Energy Reviews 96, 411–419. doi:10.1016/j.rser.2018.08.003.
  60. Quantifying flexibility of commercial and residential loads for demand response using setpoint changes. Applied Energy 177, 149–164. doi:10.1016/j.apenergy.2016.05.090.
  61. Residential Demand Response Targeting Using Machine Learning with Observational Data. 2016 IEEE 55th Conference on Decision and Control, CDC 2016 , 6663–6668doi:10.48550/arxiv.1607.00595.
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