Incentive-Based Integrated Demand Response for Multiple Energy Carriers Considering Behavioral Coupling Effect of Consumers

Incentive-based demand response (DR) has been recognized as a powerful tool to mitigate supply-demand imbalance in electricity market. However, it cannot be directly applied into integrated demand response (IDR) for multi-energy systems where there are energy substitution effect among different types of energy carriers and behavioral coupling effect of consumers. In this paper, an incentive-based IDR model for multiple energy carriers considering behavioral coupling effect of consumers is proposed. The proposed model not only effectively deals with the above two effects but also enhances applicability of incentive-based DR into the scenario where curtailment DR and absorbing DR are planned simultaneously. Furtherly, the proposed IDR model is also extended to multi-period coupling IDR considering the effect of energy storage unit. The solution methodologies for the proposed model and extended model are also presented. The existence and uniqueness of optimal solution are identified. Finally, simulation results verify merits of the proposed model in cutting down total cost of multi-energy aggregator (MEA), reducing dissatisfaction of consumers, improving accuracy of optimal solution, and improving the utilization of energy storage unit.

[1]  Moein Moeini-Aghtaie,et al.  A Decentralized Energy Management Framework for Energy Hubs in Dynamic Pricing Markets , 2018, IEEE Transactions on Smart Grid.

[2]  H. Vincent Poor,et al.  Multiobjective Optimization for Demand Side Management Program in Smart Grid , 2018, IEEE Transactions on Industrial Informatics.

[3]  Shahab Bahrami,et al.  From Demand Response in Smart Grid Toward Integrated Demand Response in Smart Energy Hub , 2016, IEEE Transactions on Smart Grid.

[4]  Brian Vad Mathiesen,et al.  Smart Energy Systems for coherent 100% renewable energy and transport solutions , 2015 .

[5]  Seung Ho Hong,et al.  Incentive-based demand response for smart grid with reinforcement learning and deep neural network , 2019, Applied Energy.

[6]  Yi Ding,et al.  A Framework for Incorporating Demand Response of Smart Buildings Into the Integrated Heat and Electricity Energy System , 2019, IEEE Transactions on Industrial Electronics.

[7]  Pierluigi Mancarella,et al.  Integrated electrical and gas network flexibility assessment in low-carbon multi-energy systems , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[8]  A. Rezaee Jordehi,et al.  Optimisation of demand response in electric power systems, a review , 2019, Renewable and Sustainable Energy Reviews.

[9]  Mengmeng Yu,et al.  Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach , 2017 .

[10]  Mehdi Abapour,et al.  MINLP Probabilistic Scheduling Model for Demand Response Programs Integrated Energy Hubs , 2018, IEEE Transactions on Industrial Informatics.

[11]  Fangxing Li,et al.  A Framework of Residential Demand Aggregation With Financial Incentives , 2018, IEEE Transactions on Smart Grid.

[12]  Tariq Samad,et al.  Automated Demand Response for Smart Buildings and Microgrids: The State of the Practice and Research Challenges , 2016, Proceedings of the IEEE.

[13]  Suzhi Bi,et al.  Demand Response Management for Profit Maximizing Energy Loads in Real-Time Electricity Market , 2018, IEEE Transactions on Power Systems.

[14]  Seung Ho Hong,et al.  An Incentive-Based Demand Response (DR) Model Considering Composited DR Resources , 2019, IEEE Transactions on Industrial Electronics.

[15]  G. Andersson,et al.  Energy hubs for the future , 2007, IEEE Power and Energy Magazine.

[16]  H. Madsen,et al.  Benefits and challenges of electrical demand response: A critical review , 2014 .

[17]  Zhilong Wang Analysis on Economic Operation of Multi-energy Flow System , 2017 .

[18]  Ali Reza Seifi,et al.  Simultaneous integrated optimal energy flow of electricity, gas, and heat , 2015 .

[19]  Ram Rajagopal,et al.  Efficient Customer Selection Process for Various DR Objectives , 2019, IEEE Transactions on Smart Grid.

[20]  Carlos Henggeler Antunes,et al.  Integrated Management of Energy Resources in Residential Buildings—A Markovian Approach , 2018, IEEE Transactions on Smart Grid.

[21]  Qian Ai,et al.  Extended multi-energy demand response scheme for industrial integrated energy system , 2018 .

[22]  Chongqing Kang,et al.  Standardized Matrix Modeling of Multiple Energy Systems , 2019, IEEE Transactions on Smart Grid.

[23]  Pan Li,et al.  A Distributed Online Pricing Strategy for Demand Response Programs , 2017, IEEE Transactions on Smart Grid.

[24]  Joao P. S. Catalao,et al.  An overview of Demand Response: Key-elements and international experience , 2017 .

[25]  Joongheon Kim,et al.  Residential Demand Response for Renewable Energy Resources in Smart Grid Systems , 2017, IEEE Transactions on Industrial Informatics.

[26]  Zhe Chen,et al.  Steady-state analysis of the integrated natural gas and electric power system with bi-directional energy conversion , 2016 .

[27]  Zita Vale,et al.  A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers , 2015 .

[28]  Dan Wang,et al.  Review of key problems related to integrated energy distribution systems , 2018, CSEE Journal of Power and Energy Systems.

[29]  Seung Ho Hong,et al.  A Real-Time Demand-Response Algorithm for Smart Grids: A Stackelberg Game Approach , 2016, IEEE Transactions on Smart Grid.

[30]  Javier Contreras,et al.  Strategic Behavior of Multi-Energy Players in Electricity Markets as Aggregators of Demand Side Resources Using a Bi-Level Approach , 2018, IEEE Transactions on Power Systems.

[31]  Ying Li,et al.  Automated Residential Demand Response: Algorithmic Implications of Pricing Models , 2012, IEEE Transactions on Smart Grid.

[32]  Farrokh Rahimi,et al.  Demand Response as a Market Resource Under the Smart Grid Paradigm , 2010, IEEE Transactions on Smart Grid.

[33]  Rui Bo,et al.  A Two-Stage Game Model for Combined Heat and Power Trading Market , 2019, IEEE Transactions on Power Systems.

[34]  Pierluigi Mancarella,et al.  Automated Demand Response From Home Energy Management System Under Dynamic Pricing and Power and Comfort Constraints , 2015, IEEE Transactions on Smart Grid.

[35]  Xinghuo Yu,et al.  Multiparty Energy Management for Grid-Connected Microgrids With Heat- and Electricity-Coupled Demand Response , 2018, IEEE Transactions on Industrial Informatics.