Dynamic customer demand management: A reinforcement learning model based on real-time pricing and incentives

Stoll, 2014, Including dynamic co2 intensity with demand response, Energy Policy, 65, 490, 10.1016/j.enpol.2013.10.044 Mohsenian-Rad, 2010, Optimal residential load control with price prediction in real-time electricity pricing environments, IEEE Trans. Smart Grid, 1, 120, 10.1109/TSG.2010.2055903 Garg, 2011, Economic and environmental implications of demand-side management options, Energy Policy, 39, 3076, 10.1016/j.enpol.2011.02.009 Yu, 2016, Supply – demand balancing for power management in smart grid: A stackelberg game approach, Appl. Energy, 164, 702, 10.1016/j.apenergy.2015.12.039 Deng, 2015, Fast distributed demand response with spatially and temporally coupled constraints in smart grid, IEEE Trans. Industr. Inf., 11, 1597, 10.1109/TII.2015.2408455 Valdes, 2019, Industry, flexibility, and demand response: Applying german energy transition lessons in chile, Energy Research and Social, Science, 54, 12 Aalami, 2010, Demand response modeling considering interruptible/ curtailable loads and capacity market programs, Appl. Energy, 87, 243, 10.1016/j.apenergy.2009.05.041 Good, 2017, Review and classification of barriers and enablers of demand response in the smart grid, Renew. Sustain. Energy Rev., 72, 57, 10.1016/j.rser.2017.01.043 Ji, 2022, Bidding strategy of two-layer optimization model for electricity market considering renewable energy based on deep reinforcement learning, Electronics, 11, 10.3390/electronics11193107 Singh, 2019, Simplified algorithm for dynamic demand response in smart homes under smart grid environment, IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia), 2019, 259, 10.1109/GTDAsia.2019.8715935 Annala, 2018, Regulation as an enabler of demand response in electricity markets and power systems, J. Clean. Prod., 195, 1139, 10.1016/j.jclepro.2018.05.276 B. Liu, Scheduling strategies of smart community with load aggregator-based demand response, in: 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2), 2018, pp. 1–4. doi:10.1109/EI2.2018.8582656. Aaltonen, 2021, A simulation environment for training a reinforcement learning agent trading a battery storage, Energies, 14, 10.3390/en14175587 Lu, 2021, Data-driven real-time price-based demand response for industrial facilities energy management, Appl. Energy, 283, 116291, 10.1016/j.apenergy.2020.116291 Lu, 2020, Multi-agent deep reinforcement learning based demand response for discrete manufacturing systems energy management, Appl. Energy, 276, 115473, 10.1016/j.apenergy.2020.115473 Lu, 2019, Incentive-based demand response for smart grid with reinforcement learning and deep neural network, Appl. Energy, 236, 937, 10.1016/j.apenergy.2018.12.061 Lu, 2018, A dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach, Appl. Energy, 220, 220, 10.1016/j.apenergy.2018.03.072 Vázquez-Canteli, 2019, Reinforcement learning for demand response: A review of algorithms and modeling techniques, Appl. Energy, 235, 1072, 10.1016/j.apenergy.2018.11.002 Nakabi, 2021, Deep reinforcement learning for energy management in a microgrid with flexible demand, Sustainable Energy, Grids and Networks, 25, 100413, 10.1016/j.segan.2020.100413 Wang, 2016, Renewable energy-aware demand response for distributed data centers in smart grid, 2016 IEEE Green Energy and Systems Conference, IGSEC, 2016 Nandkeolyar, 2018, Management of time-flexible demand to provide power system frequency response, Technol. Smart-City Energy Secur. Power (ICSESP), 2018, 1 Fleschutz, 2021, The effect of price-based demand response on carbon emissions in european electricity markets: The importance of adequate carbon prices, Appl. Energy, 295, 117040, 10.1016/j.apenergy.2021.117040 N. Hajibandeh, M. Ehsan, S. Soleymani, M. Shafie-khah, J.P.S. Catalão, Modeling price- and incentive-based demand response strategies in the renewable-based energy markets, in: 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe, 2017, pp. 1–5. doi:10.1109/EEEIC.2017.7977701. S. Caron, G. Kesidis, Incentive-based energy consumption scheduling algorithms for the smart grid, 2010 First IEEE International Conference on Smart Grid Communications (2010) 391–396. doi:10.1109/smartgrid.2010.5622073. Chai, 2014, Demand response management with multiple utility companies: A two-level game approach, IEEE Trans. Smart Grid, 5, 722, 10.1109/TSG.2013.2295024 Noppakant, 2022, Improving energy management through demand response programs for low-rise university buildings, Sustainability, 14, 10.3390/su142114233 Ye, 2021, Real-time autonomous residential demand response management based on twin delayed deep deterministic policy gradient learning, Energies, 14, 10.3390/en14030531 Charoen, 2022, A demand response implementation with building energy management system, Energies, 15, 10.3390/en15031220 Xu, 2023, Real-time multi-energy demand response for high-renewable buildings, Energy Build., 281, 112764, 10.1016/j.enbuild.2022.112764 Dewangan, 2022, Peak-to-average ratio incentive scheme to tackle the peak-rebound challenge in tou pricing, Electric Power Syst. Res., 210, 108048, 10.1016/j.epsr.2022.108048 Sediqi, 2022, Impact of time-of-use demand response program on optimal operation of afghanistan real power system, Energies, 15, 10.3390/en15010296 Wang, 2022, How social learning affects customer behavior under the implementation of tou in the electricity retailing market, Energy Econ., 106, 105836, 10.1016/j.eneco.2022.105836 Khalid, 2019, Enhanced time-of-use electricity price rate using game theory, Electronics, 8, 10.3390/electronics8010048 Xu, 2021, A hybrid demand response mechanism based on real-time incentive and real-time pricing, Energy, 231, 10.1016/j.energy.2021.120940 Zhang, 2021, Testbed implementation of reinforcement learning-based demand response energy management system, Appl. Energy, 297, 117131, 10.1016/j.apenergy.2021.117131 Kong, 2020, Online pricing of demand response based on long short-term memory and reinforcement learning, Appl. Energy, 271, 114945, 10.1016/j.apenergy.2020.114945 Azuatalam, 2020, Reinforcement learning for whole-building hvac control and demand response, Energy and AI, 2, 10.1016/j.egyai.2020.100020 Peirelinck, 2021, Transfer learning in demand response: A review of algorithms for data-efficient modelling and control, Energy and AI, 100126 Jang, 2023, Deep reinforcement learning with planning guardrails for building energy demand response, Energy and AI, 11, 100204, 10.1016/j.egyai.2022.100204 Zhong, 2021, Deep reinforcement learning framework for dynamic pricing demand response of regenerative electric heating, Appl. Energy, 288, 116623, 10.1016/j.apenergy.2021.116623 Yu, 2017, Incentive-based demand response considering hierarchical electricity market: A stackelberg game approach, Appl. Energy, 203, 267, 10.1016/j.apenergy.2017.06.010 Yang, 2013, A game-theoretic approach for optimal time-of-use electricity pricing, IEEE Trans. Power Syst., 28, 884, 10.1109/TPWRS.2012.2207134 Fan, 2022, A deep reinforcement learning-based method for predictive management of demand response in natural gas pipeline networks, J. Clean. Prod., 335, 130274, 10.1016/j.jclepro.2021.130274 Lee, 2019, Reinforcement learning-based energy management of smart home with rooftop solar photovoltaic system, energy storage system, and home appliances, Sensors, 19, 10.3390/s19183937 Gholizadeh, 2021, Distributed learning applications in power systems: A review of methods, gaps, and challenges, Energies, 14, 10.3390/en14123654 Damjanovic, 2022, Deep reinforcement learning-based approach for autonomous power flow control using only topology changes, Energies, 15, 10.3390/en15196920 Viziteu, 2022, Smart scheduling of electric vehicles based on reinforcement learning, Sensors, 22, 10.3390/s22103718 Kim, 2022, Deep reinforcement learning-based real-time joint optimal power split for battery–ultracapacitor–fuel cell hybrid electric vehicles, Electronics, 11, 10.3390/electronics11121850 Barth, 2022, Distributed reinforcement learning for the management of a smart grid interconnecting independent prosumers, Energies, 15, 10.3390/en15041440 Kazimierski, 2021, Desarrollo fotovoltaico en san juan: un acercamiento al entramado de estrategias públicas para la transición energética, Ciencia, Docencia y Tecnología, 32 Samper, 2021, Grid parity analysis of distributed pv generation considering tariff policies in argentina, Energy Policy, 157, 112519, 10.1016/j.enpol.2021.112519 Naghizadeh, 2020, Condensed silhouette: an optimized filtering process for cluster selection in k-means, Proc. Comput. Sci., 176, 205, 10.1016/j.procs.2020.08.022