Energy flexibility assessment of a multi agent-based smart home energy system

Power systems worldwide are complex and challenging environments. The increasing necessity for an adequate integration of renewable energy sources is resulting in a rising complexity in power systems operation. Multi-agent based simulation platforms have proven to be a good option to study the several issues related to these systems. In a smaller scale, a home energy management system would be effective for the both sides of the network. It can reduce the electricity costs of the demand side, and it can assist to relieve the grid congestion in peak times. This paper represents a domestic energy management system as part of a multi-agent system that models the smart home energy system. Our proposed system consists of energy management and predictor systems. This way, homes are able to transact with the local electricity market according to the energy flexibility that is provided by the electric vehicle, and it can manage produced electrical energy of the photovoltaic system inside of the home.

[1]  Omid Abrishambaf,et al.  Real-time simulation of renewable energy transactions in microgrid context using real hardware resources , 2016, 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D).

[2]  Witold Pedrycz,et al.  Notions and Concepts of Fuzzy Sets , 2007, Multicriteria Decision‐Making under Conditions of Uncertainty.

[3]  Zita Vale,et al.  Distribution system operation supported by contextual energy resource management based on intelligent SCADA , 2013 .

[4]  Zita A. Vale,et al.  Economic Evaluation of Predictive Dispatch Model in MAS-Based Smart Home , 2017, PAAMS.

[5]  Silvia Ferrari,et al.  A Constrained Backpropagation Approach for the Adaptive Solution of Partial Differential Equations , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[6]  Z. Vale,et al.  Demand response in electrical energy supply: An optimal real time pricing approach , 2011 .

[7]  Isabel Praça,et al.  Energy consumption forecasting based on Hybrid Neural Fuzzy Inference System , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

[8]  Nikola K. Kasabov,et al.  HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems , 1999, Neural Networks.

[9]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[10]  Xia Chen,et al.  Distributed Control of Multiple Electric Springs for Voltage Control in Microgrid , 2017, IEEE Transactions on Smart Grid.

[11]  E. D. Spooner,et al.  Improved energy services provision through the intelligent control of distributed energy resources , 2009, 2009 IEEE Bucharest PowerTech.

[12]  Juan M. Corchado,et al.  Application of a Home Energy Management System for Incentive-Based Demand Response Program Implementation , 2016, 2016 27th International Workshop on Database and Expert Systems Applications (DEXA).

[13]  Paulo Leitão,et al.  Agent-based distributed manufacturing control: A state-of-the-art survey , 2009, Eng. Appl. Artif. Intell..

[14]  Zita Vale,et al.  MASCEM: Optimizing the performance of a multi-agent system , 2016 .

[15]  Juan M. Corchado,et al.  Organization-based Multi-Agent structure of the Smart Home Electricity System , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[16]  Juan M. Corchado,et al.  Residential energy management using a novel interval optimization method , 2017, 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT).

[17]  Yinliang Xu,et al.  Distributed control for energy management in a microgrid , 2016, 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D).

[18]  Trudie Wang,et al.  Dynamic Control and Optimization of Distributed Energy Resources in a Microgrid , 2014, IEEE Transactions on Smart Grid.

[19]  Reza Abrishambaf,et al.  Structural modeling of industrial wireless sensor and actuator networks for reconfigurable mechatronic systems , 2013 .

[20]  Osama A. Mohammed,et al.  Multiagent-Based Optimal Microgrid Control Using Fully Distributed Diffusion Strategy , 2017, IEEE Transactions on Smart Grid.