A model predictive control strategy for load shifting in a water pumping scheme with maximum demand charges

► A model predictive control strategy for pumping system scheduling is introduced. ► Time-of-use tariff and maximum demand charges are included in the model. ► A comprehensive case study is presented to illustrate the proposed control model. This paper defines and simulates a closed-loop optimal con...

Full description

Saved in:
Bibliographic Details
Published in:Applied energy 2011-12, Vol.88 (12), p.4785-4794
Main Authors: van Staden, Adam Jacobus, Zhang, Jiangfeng, Xia, Xiaohua
Format: Article
Language:English
Subjects:
Applied sciences
Computer simulation
Demand
Demand side management
electricity
Energy
Exact sciences and technology
Marketing
Mathematical models
Maximum demand
Model predictive control
model uncertainty
Optimal control
Power plants
Predictive control
Strategy
system optimization
Water pumping
yields
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:► A model predictive control strategy for pumping system scheduling is introduced. ► Time-of-use tariff and maximum demand charges are included in the model. ► A comprehensive case study is presented to illustrate the proposed control model. This paper defines and simulates a closed-loop optimal control strategy for load shifting in a plant that is charged for electricity on both time-of-use (TOU) and maximum demand (MD). A model predictive control approach is used to implement the closed-loop optimal control model, and the optimization problem is solved with integer programming. The simulated control model yields near optimal switching times which reduce the TOU and MD costs. The results show a saving of 5.8% for the overall plant, and the largest portion of the saving is due to a reduction in MD. The effect of disturbances, model uncertainty and plant failure is also simulated to demonstrate the benefits of a model predictive control model.
ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2011.06.054