Empowering Aggregators with Practical Data-Driven Tools: Harnessing Aggregated and Disaggregated Flexibility for Demand Response

• URL: http://arxiv.org/abs/2401.10726v3
• Date: Fri, 30 Aug 2024 10:04:21 GMT
• Title: Empowering Aggregators with Practical Data-Driven Tools: Harnessing Aggregated and Disaggregated Flexibility for Demand Response
• Authors: Costas Mylonas, Donata Boric, Leila Luttenberger Maric, Alexandros Tsitsanis, Eleftheria Petrianou, Magda Foti,
• Abstract summary: This study explores the interaction between aggregators and building occupants in activating flexibility through Demand Response (DR) programs. It introduces a methodology of optimizing aggregated flexibility provision strategies in environments with limited data.
• Score: 37.69303106863453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study explores the interaction between aggregators and building occupants in activating flexibility through Demand Response (DR) programs, with a focus on reinforcing the resilience of the energy system considering the uncertainties presented by Renewable Energy Sources (RES). Firstly, it introduces a methodology of optimizing aggregated flexibility provision strategies in environments with limited data, utilizing Discrete Fourier Transformation (DFT) and clustering techniques to identify building occupants' activity patterns. Secondly, the study assesses the disaggregated flexibility provision of Heating Ventilation and Air Conditioning (HVAC) systems during DR events, employing machine learning and optimization techniques for precise, device-level analysis. The first approach offers a non-intrusive pathway for aggregators to provide flexibility services in environments of a single smart meter for the whole building's consumption, while the second approach maximizes the amount of flexibility in the case of dedicated metering devices to the HVAC systems by carefully considering building occupants' thermal comfort profiles. Through the application of data-driven techniques and encompassing case studies from both industrial and residential buildings, this paper not only unveils pivotal opportunities for aggregators in the balancing and emerging flexibility markets but also successfully develops and demonstrates end-to-end practical tools for aggregators.

Related papers

• Exploring Capabilities of Time Series Foundation Models in Building Analytics [15.525789412274587]
  Internet of Things (IoT) networks have transformed the management and optimization of building energy consumption. We conduct a comprehensive benchmarking of two publicly available IoT datasets. Our analysis shows that single-modal models demonstrate significant promise in overcoming the complexities of data variability and physical limitations in buildings.
  arXiv  Detail & Related papers  (2024-10-28T02:49:22Z)
• An IoT Framework for Building Energy Optimization Using Machine Learning-based MPC [0.0]
  This study proposes a machine learning-based Model Predictive Control (MPC) approach for controlling Air Handling Unit (AHU) systems by employing an Internet of Things (IoT) framework. The proposed framework utilizes an Artificial Neural Network (ANN) to provide dynamic-linear thermal model parameters considering building information and disturbances in real time.
  arXiv  Detail & Related papers  (2024-08-23T14:38:18Z)
• A Survey on Semantic Modeling for Building Energy Management [0.2301816954855697]
  This survey explores the leading semantic modeling techniques deployed for energy management in buildings. It aims to offer tangible use cases for applying semantic models, shedding light on the pivotal concepts and limitations intrinsic to each model.
  arXiv  Detail & Related papers  (2024-04-17T20:10:43Z)
• EnergAIze: Multi Agent Deep Deterministic Policy Gradient for Vehicle to Grid Energy Management [0.0]
  This paper introduces EnergAIze, a Multi-Agent Reinforcement Learning (MARL) energy management framework. It enables user-centric and multi-objective energy management by allowing each prosumer to select from a range of personal management objectives. The efficacy of EnergAIze was evaluated through case studies employing the CityLearn simulation framework.
  arXiv  Detail & Related papers  (2024-04-02T23:16:17Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices [72.61177465035031]
  We propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy.
  arXiv  Detail & Related papers  (2023-10-21T12:07:04Z)
• Federated Stochastic Gradient Descent Begets Self-Induced Momentum [151.4322255230084]
  Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems. We show that running to the gradient descent (SGD) in such a setting can be viewed as adding a momentum-like term to the global aggregation process.
  arXiv  Detail & Related papers  (2022-02-17T02:01:37Z)
• Intelligent Building Control Systems for Thermal Comfort and Energy-Efficiency: A Systematic Review of Artificial Intelligence-Assisted Techniques [3.2926483061955922]
  Building operations represent a significant percentage of the total primary energy consumed in most countries. Different AI techniques have been deployed to find the sweet spot between energy use in HVAC systems and suitable indoor comfort levels. The application of AI technology in building control is a promising area of research and still an ongoing, i.e., the performance of AI-based control is not yet completely satisfactory.
  arXiv  Detail & Related papers  (2021-04-06T01:04:28Z)
• Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration [130.89746032163106]
  We propose ALOE, a new algorithm for learning conditional and unconditional EBMs for discrete structured data. We show that the energy function and sampler can be trained efficiently via a new variational form of power iteration. We present an energy model guided fuzzer for software testing that achieves comparable performance to well engineered fuzzing engines like libfuzzer.
  arXiv  Detail & Related papers  (2020-11-10T19:31:29Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.