Title :
Optimal Energy Scheduling for a Smart Entity
Author :
Fakhrazari, Amin ; Vakilzadian, Hamid ; Choobineh, F. Fred
Author_Institution :
Dept. of Electr. Eng., Univ. of Nebraska-Lincoln, Lincoln, NE, USA
Abstract :
Real-time availability of electricity prices via a smart power grid has a potential bilateral benefit to electricity users and suppliers. The users can reduce their costs by consuming energy during low-price hours and balancing their energy usage during other hours. This in turn benefits energy utility companies by reducing their peak power demand. This article describes an optimal shrinking horizon model for electricity-consuming units based on user preferences. The proposed model optimizes the end user´s electricity cost while meeting preferred comfort levels. The user can set preferences in the model using a tristate flexibility parameter for each electric-power-consuming unit. The electricity price model used in the optimization model is general and covers all pricing schemes in the electricity market today. The model derived can be described by a simple mixed integer linear program and solved by most optimization software in a short time. The most distinguishing characteristics of our proposed model are its simplicity, generality, comprehensibility, and ease of implementation. Simulation results are used to verify the model´s performance in reducing consumer electricity costs and satisfying comfort preferences.
Keywords :
linear programming; optimisation; power consumption; power generation scheduling; power markets; smart power grids; electric-power-consuming unit; electricity market; electricity prices; energy usage balancing; low-price hours; mixed integer linear program; optimal energy scheduling; optimal shrinking horizon model; optimization model; peak power demand; real-time availability; smart entity; smart power grid; tristate flexibility parameter; user preferences; Energy consumption; Mathematical model; Optimization; Power system planning; Pricing; Real-time systems; Scheduling; Energy management; optimal scheduling; shrinking horizon scheduling; smart grids;
Journal_Title :
Smart Grid, IEEE Transactions on
DOI :
10.1109/TSG.2014.2319247