Water Supply Network Control Using Safety Fill Level Costs
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Solution Overview
Problem
Current water supply network control methods prioritize security of supply over energy and cost efficiency, limiting the scope for savings due to conservatively selected minimum filling levels, which can lead to suboptimal operational decisions.
Innovation Solution
A method that determines a safety level exceeding the minimum fill level for water-storing nodes, incorporating virtual costs to optimize the control plan for pumps and valves, balancing energy consumption, costs, and security of supply through a forward-looking model analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conservatively selected minimum fill levels are maintained to guarantee security of supply, then security of supply is improved, but energy efficiency and cost efficiency deteriorate due to restricted scope for optimization
Solution Approach 1:
The system performs forward-looking model analysis to predict future water demands and network states, enabling preliminary scheduling of pump operations and valve adjustments. This allows the system to prepare optimal control plans in advance that balance security requirements with energy efficiency, rather than reacting conservatively to current fill levels alone.
Solution Approach 2:
The system dynamically adjusts operational parameters (pump schedules, valve positions, fill level targets) based on predicted future conditions rather than maintaining fixed conservative minimum fill levels. This allows optimization of energy consumption while still guaranteeing security of supply through model-based prediction of future states.
2Reliability
If conservatively selected minimum fill levels are maintained to guarantee security of supply, then security of supply is improved, but cost efficiency deteriorates due to restricted scope for optimization
Solution Approach 1:
The system performs forward-looking model analysis to predict future water demands and network states, enabling preliminary scheduling of pump operations and valve adjustments. This allows the system to prepare optimal control plans in advance that balance security requirements with cost efficiency, rather than reacting conservatively to current fill levels alone.
Solution Approach 2:
The system dynamically adjusts operational parameters (pump schedules, valve positions, fill level targets) based on predicted future conditions rather than maintaining fixed conservative minimum fill levels. This allows optimization of operational costs while still guaranteeing security of supply through model-based prediction of future states.
3Use of energy by moving object
If forward-looking model analysis is used to determine energy-optimal schedules, then energy efficiency is improved, but the scope for savings is limited by conservatively selected minimum levels
Solution Approach 1:
The system performs forward-looking model analysis to predict future water demands and network states, enabling preliminary scheduling of pump operations and valve adjustments. This allows the system to prepare optimal control plans in advance that balance security requirements with energy optimization, rather than reacting conservatively to current fill levels alone.
Solution Approach 2:
The system dynamically adjusts operational parameters (pump schedules, valve positions, fill level targets) based on predicted future conditions rather than maintaining fixed conservative minimum fill levels. This allows optimization of energy consumption while still guaranteeing security of supply through model-based prediction of future states.
Data Source
Figure 1
Figure 2~3
AI summary
In a water supply network (1) which comprises node components (2; 4, 5, 6, 7) and edge components (3; 8, 9, 10, 11), with water being fed to, removed from or stored at the node points and the water being transported between the node components via the edge components, edge components (9, 10, 11) that are controllable in respect of the flow behaviour of the water are controlled on the basis of a control plan (15). The control plan is determined on the basis of boundary conditions and is optimised on the basis of a target function, in which the number of actuations for each controllable edge component is multiplied by a cost rate for an energy consumption and/or state of wear of the edge component, and then the sum across all controllable edge components is formed. According to the invention, a safety fill level (hsec) exceeding a minimum fill level (hmin) is defined for each of the water-storing node points (7), and the target function for each water-storing node point (7) is supplemented by a further summand in the form of a further cost rate dependent on the fill level, which cost rate is minimal at a fill level corresponding to the safety fill level (hsec) and is maximal at a fill level corresponding to the minimum fill level (hmin).