Residential Power Scheduling Based on Cost Efficiency for Demand Response in Smart Grid

Residential power scheduling for demand response in a smart grid is a complex task. The traditional methods aim to minimize consumption costs and maximize consumption payoffs. In this paper, a power scheduling algorithm based on cost efficiency for smart homes is proposed to improve consumers’ consumption efficiency and satisfaction. In this proposed method, a definition of cost efficiency for residential power scheduling is introduced. Consumers’ consumption costs are modelled based on electricity payments and users’ discomfort. A pair of parameters for a trade-off between users’ discomfort and their electricity payment is designed to model cost efficiency based on the consumer’s preference. A power scheduling algorithm based on cost efficiency is developed by adopting a fractional programming approach. Four consumption models are analysed and discussed. The results show that this proposed method can effectively improve consumers’ consumption efficiency and satisfaction while saving costs. It is shown that discomfort and additional payment can impact consumers’ consumption behaviour and smooth their consumption profile curves.

[1]  Haiyang Lin,et al.  Management of household electricity consumption under price-based demand response scheme , 2018, Journal of Cleaner Production.

[2]  Jiming Chen,et al.  A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches , 2015, IEEE Transactions on Industrial Informatics.

[3]  Martin Kumar Patel,et al.  DSM interactions: What is the impact of appliance energy efficiency measures on the demand response (peak load management)? , 2020 .

[4]  Amjad Anvari-Moghaddam,et al.  Stochastic Operation of a Solar-Powered Smart Home: Capturing Thermal Load Uncertainties , 2020, Sustainability.

[5]  Lingyang Song,et al.  Residential Load Scheduling in Smart Grid: A Cost Efficiency Perspective , 2016, IEEE Transactions on Smart Grid.

[6]  Jiming Chen,et al.  Distributed Real-Time Demand Response in Multiseller–Multibuyer Smart Distribution Grid , 2015, IEEE Transactions on Power Systems.

[7]  Gm. Shafiullah,et al.  Additional Controls to Enhance the Active Power Management within Islanded Microgrids , 2019 .

[8]  Lei Wu,et al.  A Residential Load Scheduling Based on Cost Efficiency and Consumer's Preference for Demand Response in Smart Grid , 2020 .

[9]  R. Faranda,et al.  Load Shedding: A New Proposal , 2007, IEEE Transactions on Power Systems.

[10]  A. Rahimi-Kian,et al.  Cost-effective and comfort-aware residential energy management under different pricing schemes and weather conditions , 2015 .

[11]  Long He,et al.  Stochastic Control for Smart Grid Users With Flexible Demand , 2013, IEEE Transactions on Smart Grid.

[12]  Manfred Morari,et al.  Communication limitations in iterative real time pricing for power systems , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[13]  Xinping Guan,et al.  Residential power scheduling for demand response in smart grid , 2016 .

[14]  Ahmad Faruqui,et al.  The Power of Dynamic Pricing , 2009 .

[15]  Muhammad Naeem,et al.  Joint Energy Management and Energy Trading in Residential Microgrid System , 2020, IEEE Access.

[16]  Carsten Maple,et al.  Towards Energy Efficient Smart Grids Using Bio-Inspired Scheduling Techniques , 2020, IEEE Access.

[17]  Bernardete Ribeiro,et al.  A Survey on Home Energy Management , 2020, IEEE Access.

[18]  Michael Negnevitsky,et al.  Pool-Based Demand Response Exchange—Concept and Modeling , 2011, IEEE Transactions on Power Systems.

[19]  Amjad Anvari-Moghaddam,et al.  A multi-agent based energy management solution for integrated buildings and microgrid system , 2017 .

[20]  Leonardo Santiago,et al.  On the trade-off between real-time pricing and the social acceptability costs of demand response , 2018 .

[21]  Azam Khalili,et al.  A distributed game-theoretic demand response with multi-class appliance control in smart grid , 2019, Electric Power Systems Research.

[22]  Yu-Wen Su,et al.  Residential electricity demand in Taiwan: Consumption behavior and rebound effect , 2019, Energy Policy.

[23]  Leandros Tassiulas,et al.  Optimal Control Policies for Power Demand Scheduling in the Smart Grid , 2012, IEEE Journal on Selected Areas in Communications.

[24]  Zechun Hu,et al.  A review on price-driven residential demand response , 2018, Renewable and Sustainable Energy Reviews.

[25]  Wolfgang Ketter,et al.  Demand side management—A simulation of household behavior under variable prices , 2011 .

[26]  J. Douglas Barrett,et al.  Taguchi's Quality Engineering Handbook , 2007, Technometrics.

[27]  Abdulkerim Karabiber,et al.  A user-mode distributed energy management architecture for smart grid applications , 2012 .

[28]  Amjad Anvari-Moghaddam,et al.  Optimal Smart Home Energy Management Considering Energy Saving and a Comfortable Lifestyle , 2016, IEEE Transactions on Smart Grid.

[29]  Qie Sun,et al.  Outline of principles for building scenarios – Transition toward more sustainable energy systems , 2017 .

[30]  Amir Hossein Sharifi,et al.  Energy management of smart homes equipped with energy storage systems considering the PAR index based on real-time pricing , 2019, Sustainable Cities and Society.

[31]  Hailong Li,et al.  A review of the pricing mechanisms for district heating systems , 2015 .

[32]  Christos V. Verikoukis,et al.  A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms , 2015, IEEE Communications Surveys & Tutorials.

[33]  Meysam Doostizadeh,et al.  A day-ahead electricity pricing model based on smart metering and demand-side management , 2012 .