A novel decision strategy for a bilateral energy contract

Abstract Due to the relatively high transportation cost of energy in remote regions, an effective and efficient energy trading market is required. In this paper, first, we extend the idea of a smart home community architecture comprising a small number of smart homes that suit these sorts of regions. Second, we propose an energy trading market at two levels. At the top-level, global generators and global consumers participate in energy trading. At the second level, smart homes in a smart home community participate in the energy market as local generators and consumers. Third, we develop a bilateral energy trading scheme and a novel decision strategy for choosing a bilateral price for both seller and buyer. The developed strategy and energy trading schemes are more efficient than those proposed in traditional work as they require far fewer rounds for negotiations between buyers and sellers to arrive at a mutually appropriate bilateral price. Fourth, we present two distinct cases for bilateral energy trading; single seller single buyer and single seller multiple buyers and show that the latter case has more benefits for both the seller and the buyer. Lastly, we conduct a series of experiments to illustrate the effectiveness of the proposed work.

[1]  Zita Vale,et al.  Decision Support for Small Players Negotiations Under a Transactive Energy Framework , 2019, IEEE Transactions on Power Systems.

[2]  Christof Weinhardt,et al.  Designing microgrid energy markets , 2018 .

[3]  Yi-Ping Phoebe Chen,et al.  Discriminative binary feature learning and quantization in biometric key generation , 2017, Pattern Recognit..

[4]  Michael Negnevitsky,et al.  Demand-Side Management Evaluation Tool , 2015, IEEE Transactions on Power Systems.

[5]  Shengwei Mei,et al.  A multi-lateral trading model for coupled gas-heat-power energy networks , 2017 .

[6]  Jianwei Huang,et al.  Incentivizing Energy Trading for Interconnected Microgrids , 2016, IEEE Transactions on Smart Grid.

[7]  Mert Topcu,et al.  Further evidence on the trade-energy consumption nexus in OECD countries , 2018, Energy Policy.

[8]  C. Fitzpatrick,et al.  Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing , 2014 .

[9]  Xin Wang,et al.  Real-Time Energy Trading and Future Planning for Fifth Generation Wireless Communications , 2017, IEEE Wireless Communications.

[10]  M. Peck,et al.  Energy trading for fun and profit buy your neighbor's rooftop solar power or sell your own-it'll all be on a blockchain , 2017, IEEE Spectrum.

[11]  Pablo Hernandez-Leal,et al.  Local Energy Markets: Paving the Path Toward Fully Transactive Energy Systems , 2019, IEEE Transactions on Power Systems.

[12]  Chao Yang,et al.  Auction Mechanisms for Energy Trading in Multi-Energy Systems , 2018, IEEE Transactions on Industrial Informatics.

[13]  Kyung-Bin Song,et al.  An Optimal Power Scheduling Method for Demand Response in Home Energy Management System , 2013, IEEE Transactions on Smart Grid.

[14]  José Luz Silveira,et al.  Robust multi-objective optimization of a renewable based hybrid power system , 2018, Applied Energy.

[15]  M. Hadi Amini,et al.  A Decentralized Trading Algorithm for an Electricity Market with Generation Uncertainty , 2017, ArXiv.

[16]  Lin Chen,et al.  Small-Scale Renewable Energy Source Trading: A Contract Theory Approach , 2018, IEEE Transactions on Industrial Informatics.

[17]  Meng Cheng,et al.  Peer-to-Peer energy trading in a Microgrid , 2018, Applied Energy.

[18]  F. Galiana,et al.  Negotiating Bilateral Contracts in Electricity Markets , 2007, IEEE Transactions on Power Systems.

[19]  Yi-Ping Phoebe Chen,et al.  True real time pricing and combined power scheduling of electric appliances in residential energy management system , 2016 .

[20]  Juan M. Corchado,et al.  Decision Support for Negotiations among Microgrids Using a Multiagent Architecture , 2018, Energies.

[21]  Mohsen A. Jafari,et al.  A multi-scale adaptive model of residential energy demand , 2015 .

[22]  Yi-Ping Phoebe Chen,et al.  Designing secure substitution boxes based on permutation of symmetric group , 2019, Neural Computing and Applications.

[23]  Z. X. Jing,et al.  A Stackelberg game approach for multiple energies trading in integrated energy systems , 2017 .

[24]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[25]  Ning Zhang,et al.  Economic justification of concentrating solar power in high renewable energy penetrated power systems , 2018, Applied Energy.

[26]  Chao Du,et al.  A Block-Based Medium-Long Term Energy Transaction Method , 2016, IEEE Transactions on Power Systems.

[27]  Hussein T. Mouftah,et al.  Energy Trading in the Smart Grid: A Distributed Game-Theoretic Approach , 2017, Canadian Journal of Electrical and Computer Engineering.