Two-Stage Optimization Model for Smart House Daily Scheduling Considering User Perceived Benefits

DOI

10.2991/mmsa-18.2018.15How to use a DOI?

Keywords

two-stage optimization model; smart house daily scheduling; consumption perceived benefits

Abstract

Smart house scheduling is the grid terminal demand response based on the reference signal, the electricity market price. In this paper, the user consumption perceived benefits and the system running cost were considered. In the intelligent house system with energy storage device, the load control model and the energy storage control model were established, and the two-stage optimization scheduling was carried out for the smart house appliances to get rid of the disturbance of load uncertainty. The first stage took the flexible load as the control object, and the genetic algorithm was proposed to provide a schedule for smart home appliances. The second stage considered the energy storage device as the control object and a particle swarm algorithm was used to generate a charge/discharge rates schedule for the battery. The optimal solution of the first stage optimization control participated in the second stage optimization control in the form of the load curve. The fitness value of the optimal solution of the load control stage was taken as the minimum objective function constraint of the energy storage control stage, thus further reducing the electricity cost of the terminal-user. The simulation example in MATLAB verifies the effectiveness of models.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Changhong Deng
AU  - Sijie Zhang
AU  - Wei Yang
AU  - Weiwei Yao
AU  - Jin Tan
AU  - Zhengyi Liu
PY  - 2018/03
DA  - 2018/03
TI  - Two-Stage Optimization Model for Smart House Daily Scheduling Considering User Perceived Benefits
BT  - Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018)
PB  - Atlantis Press
SP  - 64
EP  - 68
SN  - 1951-6851
UR  - https://doi.org/10.2991/mmsa-18.2018.15
DO  - 10.2991/mmsa-18.2018.15
ID  - Deng2018/03
ER  -