Dynamic residential load scheduling based on an adaptive consumption level pricing scheme

• URL: http://arxiv.org/abs/2007.11932v1
• Date: Thu, 23 Jul 2020 11:14:39 GMT
• Title: Dynamic residential load scheduling based on an adaptive consumption level pricing scheme
• Authors: Haider Tarish Haider, Ong Hang See, W. Elmenreich
• Abstract summary: DRLS is proposed for optimal scheduling of household appliances on the basis of an adaptive consumption level (ACLPS) pricing scheme. The proposed load scheduling system encourages customers to manage their energy consumption within the allowable consumption allowance (CA) of the proposed DR pricing scheme to achieve lower energy bills. For a given case study, the proposed residential load scheduling system based on ACLPS allows customers to reduce their energy bills by up to 53% and to decrease the peak load by up to 35%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Demand response (DR) for smart grids, which intends to balance the required power demand with the available supply resources, has been gaining widespread attention. The growing demand for electricity has presented new opportunities for residential load scheduling systems to improve energy consumption by shifting or curtailing the demand required with respect to price change or emergency cases. In this paper, a dynamic residential load scheduling system (DRLS) is proposed for optimal scheduling of household appliances on the basis of an adaptive consumption level (CL) pricing scheme (ACLPS). The proposed load scheduling system encourages customers to manage their energy consumption within the allowable consumption allowance (CA) of the proposed DR pricing scheme to achieve lower energy bills. Simulation results show that employing the proposed DRLS system benefits the customers by reducing their energy bill and the utility companies by decreasing the peak load of the aggregated load demand. For a given case study, the proposed residential load scheduling system based on ACLPS allows customers to reduce their energy bills by up to 53% and to decrease the peak load by up to 35%.

Related papers

• Online Dynamic Pricing for Electric Vehicle Charging Stations with Reservations [0.3374875022248865]
  The transition to electric vehicles (EVs) will significantly impact the electric grid. Unlike conventional fuel sources, electricity for EVs is constrained by grid capacity, price fluctuations, and long EV charging times. This paper proposes a model for online dynamic pricing of reserved EV charging services.
  arXiv  Detail & Related papers  (2024-10-07T22:36:40Z)
• Optimal Scheduling of Electric Vehicle Charging with Deep Reinforcement Learning considering End Users Flexibility [1.3812010983144802]
  This work aims to identify households' EV cost-reducing charging policy under a Time-of-Use tariff scheme, with the use of Deep Reinforcement Learning, and more specifically Deep Q-Networks (DQN) A novel end users flexibility potential reward is inferred from historical data analysis, where households with solar power generation have been used to train and test the algorithm.
  arXiv  Detail & Related papers  (2023-10-13T12:07:36Z)
• Sustainable AIGC Workload Scheduling of Geo-Distributed Data Centers: A Multi-Agent Reinforcement Learning Approach [48.18355658448509]
  Recent breakthroughs in generative artificial intelligence have triggered a surge in demand for machine learning training, which poses significant cost burdens and environmental challenges due to its substantial energy consumption. Scheduling training jobs among geographically distributed cloud data centers unveils the opportunity to optimize the usage of computing capacity powered by inexpensive and low-carbon energy. We propose an algorithm based on multi-agent reinforcement learning and actor-critic methods to learn the optimal collaborative scheduling strategy through interacting with a cloud system built with real-life workload patterns, energy prices, and carbon intensities.
  arXiv  Detail & Related papers  (2023-04-17T02:12:30Z)
• Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
  This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
  arXiv  Detail & Related papers  (2022-11-29T01:18:58Z)
• Optimal Load Scheduling Using Genetic Algorithm to Improve the Load Profile [0.0]
  Genetic algorithm (GA) is used to schedule the load via real time pricing signal (RTP) We conclude that GA provides optimal solution for scheduling of house hold appliances by curtailing overall utilized energy cost and peak to average ratio hence improving the load profile.
  arXiv  Detail & Related papers  (2021-10-14T04:47:17Z)
• Prescribing net demand for two-stage electricity generation scheduling [0.0]
  We consider a two-stage generation scheduling problem comprising a forward dispatch and a real-time re-dispatch. Standard industry practice deals with the uncertain net demand in the forward stage by replacing it with a good estimate of its conditional expectation. We propose a bilevel program to construct a prescription of the net demand that does account for the power system's cost asymmetry.
  arXiv  Detail & Related papers  (2021-08-02T16:05:53Z)
• The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets [69.68068088508505]
  We investigate the impact of eleven offline and five online learning algorithms to predict the electricity demand profile over the next 24h. We show we can reduce the mean absolute error by 30% using an online algorithm when compared to the best offline algorithm. We also show that large errors in prediction accuracy have a disproportionate error on investments made over a 17-year time frame.
  arXiv  Detail & Related papers  (2021-03-07T11:28:54Z)
• Investigating Underlying Drivers of Variability in Residential Energy Usage Patterns with Daily Load Shape Clustering of Smart Meter Data [53.51471969978107]
  Large-scale deployment of smart meters has motivated increasing studies to explore disaggregated daily load patterns. This paper aims to shed light on the mechanisms by which electricity consumption patterns exhibit variability.
  arXiv  Detail & Related papers  (2021-02-16T16:56:27Z)
• A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids [58.666456917115056]
  This paper presents a Reinforcement Learning based energy market for a prosumer dominated microgrid. The proposed market model facilitates a real-time and demanddependent dynamic pricing environment, which reduces grid costs and improves the economic benefits for prosumers.
  arXiv  Detail & Related papers  (2020-09-23T02:17:51Z)
• Intelligent Residential Energy Management System using Deep Reinforcement Learning [5.532477732693001]
  This paper proposes a Deep Reinforcement Learning (DRL) model for demand response where the virtual agent learns the task like humans do. Our method outperformed the state of the art mixed integer linear programming (MILP) for load peak reduction.
  arXiv  Detail & Related papers  (2020-05-28T19:51:22Z)
• NeurOpt: Neural network based optimization for building energy management and climate control [58.06411999767069]
  We propose a data-driven control algorithm based on neural networks to reduce this cost of model identification. We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy.
  arXiv  Detail & Related papers  (2020-01-22T00:51:03Z)

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.