scholarly journals Integrated model predictive control of water resource recovery facilities and sewer systems in a smart grid: example of full-scale implementation in Kolding

2020 ◽  
Vol 81 (8) ◽  
pp. 1766-1777 ◽  
Author(s):  
P. A. Stentoft ◽  
L. Vezzaro ◽  
P. S. Mikkelsen ◽  
M. Grum ◽  
T. Munk-Nielsen ◽  
...  
Keyword(s):  
Smart Grid ◽  
Model Predictive Control ◽  
Power Consumption ◽  
Water Resource ◽  
Predictive Control ◽  
Storage Capacity ◽  
Resource Recovery ◽  
Integrated Model ◽  
Full Scale ◽  
Sewer System

Abstract An integrated model predictive control (MPC) strategy to control the power consumption and the effluent quality of a water resource recovery facility (WRRF) by utilizing the storage capacity from the sewer system was implemented and put into operation for a 7-day trial period. This price-based MPC reacted to electricity prices and forecasted pollutant loads 24 hours ahead. The large storage capacity available in the sewer system directly upstream from the plant was used to control the incoming loads and, indirectly, the power consumption of the WRRF during dry weather operations. The MPC balances electricity costs and treatment quality based on linear dynamical models and predictions of storage capacity and effluent concentrations. This article first shows the modelling results involved in the design of this MPC. Secondly, results from full-scale MPC operation of the WRRF are shown. The monetary savings of the MPC strategy for the specific plant were quantified around approximately 200 DKK per day when fully exploiting the allowed storage capacity. The developed MPC strategy provides a new option for linking WRRFs to smart grid electricity systems.

scholarly journals Towards model predictive control: online predictions of ammonium and nitrate removal by using a stochastic ASM

2018 ◽  
Vol 79 (1) ◽  
pp. 51-62 ◽  
Author(s):  
Peter Alexander Stentoft ◽  
Thomas Munk-Nielsen ◽  
Luca Vezzaro ◽  
Henrik Madsen ◽  
Peter Steen Mikkelsen ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Water Resource ◽  
Predictive Control ◽  
Nitrate Removal ◽  
Resource Recovery ◽  
Residual Error ◽  
Online Data ◽  
Modelling Framework ◽  
Nitrate Concentrations ◽  
Aeration Process

Abstract Online model predictive control (MPC) of water resource recovery facilities (WRRFs) requires simple and fast models to improve the operation of energy-demanding processes, such as aeration for nitrogen removal. Selected elements of the activated sludge model number 1 modelling framework for ammonium and nitrate removal were included in discretely observed stochastic differential equations in which online data are assimilated to update the model states. This allows us to produce model-based predictions including uncertainty in real time while it also reduces the number of parameters compared to many detailed models. It introduces only a small residual error when used to predict ammonium and nitrate concentrations in a small recirculating WRRF facility. The error when predicting 2 min ahead corresponds to the uncertainty from the sensors. When predicting 24 hours ahead the mean relative residual error increases to ∼10% and ∼20% for ammonium and nitrate concentrations respectively. Consequently this is considered a first step towards stochastic MPC of the aeration process. Ultimately this can reduce electricity demand and cost for water resource recovery, allowing the prioritization of aeration during periods of cheaper electricity.

scholarly journals From wastewater treatment to water resource recovery: Environmental and economic impacts of full-scale implementation

2021 ◽  
pp. 117554
Author(s):  
Maria Faragò ◽  
Anders Damgaard ◽  
Jeanette Agertved Madsen ◽  
Jacob Kragh Andersen ◽  
Dines Thornberg ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Water Resource ◽  
Economic Impacts ◽  
Resource Recovery ◽  
Full Scale

scholarly journals Model predictive control based on artificial intelligence and EPA-SWMM model to reduce CSOs impacts in sewer systems

2021 ◽  
Author(s):  
Khalid El Ghazouli ◽  
Jamal El Khatabi ◽  
Aziz Soulhi ◽  
Isam Shahrour
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategies ◽  
Dynamic Control ◽  
Combined Sewer Overflows ◽  
Sewer System ◽  
Sewer Systems ◽  
Proposed Model ◽  
The Impact ◽  
The City

Abstract Urbanization and an increase in precipitation intensities due to climate change, in addition to limited urban drainage systems (UDS) capacity, are the main causes of combined sewer overflows (CSOs) that cause serious water pollution problems in many cities around the world. Model predictive control (MPC) systems offer a new approach to mitigate the impact of CSOs by generating optimal temporally and spatially varied dynamic control strategies of sewer system actuators. This paper presents a novel MPC based on neural networks for predicting flows, a stormwater management model (SWMM) for flow conveyance, and a genetic algorithm for optimizing the operation of sewer systems and defining the best control strategies. The proposed model was tested on the sewer system of the city of Casablanca in Morocco. The results have shown the efficiency of the developed MPC to reduce CSOs while considering short optimization time thanks to parallel computing.

scholarly journals Model Predictive Control with Binary Quadratic Programming for the Scheduled Operation of Domestic Refrigerators

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4649 ◽  
Author(s):  
Mohammad Reza Zavvar Sabegh ◽  
Chris Bingham
Keyword(s):  
Model Predictive Control ◽  
Power Consumption ◽  
Quadratic Programming ◽  
Predictive Control ◽  
Peak Power ◽  
Peak Load ◽  
Temperature Hysteresis ◽  
Binary Quadratic Programming ◽  
Peak Power Consumption ◽  
Experimental Trials

The rapid proliferation of the ‘Internet of Things’ (IoT) now affords the opportunity to schedule the operation of widely distributed domestic refrigerator and freezers to collectively improve energy efficiency and reduce peak power consumption on the electrical grid. To accomplish this, the paper proposes the real-time estimation of the thermal mass of each refrigerator in a network using on-line parameter identification, and the co-ordinated (ON-OFF) scheduling of the refrigerator compressors to maintain their respective temperatures within specified hysteresis bands commensurate with accommodating food safety standards. A custom model predictive control (MPC) scheme is devised using binary quadratic programming to realize the scheduling methodology which is implemented through IoT hardware (based on a NodeMCU). Benefits afforded by the proposed scheme are investigated through experimental trials which show that the co-ordinated operation of domestic refrigerators can i) reduce the peak power consumption as seen from the perspective of the electrical power grid (i.e., peak load levelling), ii) can adaptively control the temperature hysteresis band of individual refrigerators to increase operational efficiency, and iii) contribute to a widely distributed aggregated load shed for demand side response purposes in order to aid grid stability. Importantly, the number of compressor starts per hour for each refrigerator is also bounded as an inherent design feature of the algorithm so as not to operationally overstress the compressors and reduce their lifetime. Experimental trials show that such co-ordinated operation of refrigerators can reduce energy consumption by ~30% whilst also providing peak load levelling, thereby affording benefits to both individual consumers as well as electrical network suppliers.

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