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WO2013026731A1 - Système et procédé pour optimiser le fonctionnement d'un réseau d'eau - Google Patents

Système et procédé pour optimiser le fonctionnement d'un réseau d'eau Download PDF

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Publication number
WO2013026731A1
WO2013026731A1 PCT/EP2012/065802 EP2012065802W WO2013026731A1 WO 2013026731 A1 WO2013026731 A1 WO 2013026731A1 EP 2012065802 W EP2012065802 W EP 2012065802W WO 2013026731 A1 WO2013026731 A1 WO 2013026731A1
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water
unit
scenarios
operational
scenario
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Frederik Blank
Guido Sand
Ralf Lenz
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ABB Technology AG
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ABB Technology AG
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Priority to SE1450161A priority Critical patent/SE1450161A1/sv
Priority to NO20140362A priority patent/NO347239B1/no
Priority to DE112012003487.2T priority patent/DE112012003487T5/de
Publication of WO2013026731A1 publication Critical patent/WO2013026731A1/fr
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Priority to DK201470104A priority patent/DK201470104A/da
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A20/00—Water conservation; Efficient water supply; Efficient water use

Definitions

  • the invention relates to a system and a method to optimize operation of a water transmission and/or distribution network, in the following also called water network.
  • the system comprises at least one data storage unit for storing a hydraulic model as well as at least one operational constraint of the water network, where the hydraulic model represents the dependence of heads and flows in the water network on an operational state of at least one actuating unit of the water network and on an expected water demand, at least one processing unit containing an optimizing unit, where the optimizing unit is adapted to generate at least one operational configuration information for the at least one actuating unit by minimizing an objective function of an optimization problem, where the optimization problem is based on the hydraulic model, and at least one output interface for providing the at least one operational configuration information to the at least one actuating unit.
  • the steps performed by the above named system elements define the corresponding method.
  • a water distribution network delivers water from a water source to various industrial and/or private water consumers
  • a water transmission network transports water over long distances of several hundred kilometers, for example from a seawater desalination plant towards one or several water distribution networks.
  • the operation of these water networks offers great improvement in operational efficiency.
  • Replacing or overhauling energy intensive actuators in the water network such as pumps is one possibility that is most commonly done already today. The other possibility is to review and optimize the interaction of the different actuators and the overall operation strategy of the complete network.
  • the upper diagram in Fig. 1 a shows an operation procedure for a water network as it is done today: water utilities try to adjust the network planning such that the water levels 1 in the water storage facilities, also called water storage heads, are kept in between upper bounds 2 and lower bounds 3.
  • the upper and lower bounds are marked as dotted lines in Fig. 1 a.
  • Changing the water level 1 in between these bounds is controlled in dependence on the water consumption in the network by the configuration of the pump system and by a direct link of the water storage level to the pump control system: if the water level is close to the lower bound, pumps are automatically switched on in order to increase the water storage level. If the water level is high enough, pumps are switched off.
  • the number of pumps which are switched on simultaneously are shown in the lower diagram in Fig. 1 a.
  • the current operation procedure focuses on maintaining high water levels in the water storages to fulfill customer demands at every moment in time, where the customer demands are subject to uncertainty.
  • the water utilities set artificially high lower limits for the storage head bounds, up to 80% of filling capacity, to maintain high water levels in the storage facilities. In this way, enough water is in the storage facilities to cover the demand even in the case of unexpected changes in demand.
  • this approach leads to an increase in operational costs due to the required higher pumping capacity.
  • optimization of water network operation is the determination of operational information to be taken into account when subsequently generating the control signals for at least one of the actuating units of the network, where the actuation units usually are pumps, valves inside the network, as well as actuating units controlling the inflow and/or outflow of water storages and controlling one or multiple source flows of the water network.
  • the operational information for the actuating units is determined so as to minimize the network operation costs, such as energy consumption costs, while satisfying the water demand, the minimum and/or maximum pressure or flow levels in the water pipes of the network as well as other operational constraints of the water network.
  • optimization algorithms are used to generate optimized actuators operating schedules and parameters over a certain time horizon, e.g. 24 hours, taking into account changing energy tariffs, time varying demands and possible storage capabilities over this time horizon. Taking the complete optimization horizon into account enables to take full benefit of storage capabilities in the water network, to shifting pumping or energy extensive operations into times with low energy tariffs and to run pumps at energy efficient speed even if the demand is higher than the amount of pumped water.
  • the left hand diagram in Fig. 1 b shows an example for a predicted water demand trend 5, predicted at OhOO for the next 24 hours.
  • the predicted water demand trend 5 is the darker one of the two solid line curves.
  • the right hand diagram in Fig. 1 b shows an example for a predicted water demand trend 5, predicted at OhOO for the next 24 hours.
  • the predicted water demand trend 5 is the darker one of the two solid line curves.
  • FIG. 1 b shows the resulting optimized water level trend 6 in a water storage facility, in particular a water tank, as it is expected for the predicted water demand 5 when applying optimized actuator schedules calculated at OhOO for the next 24 hours.
  • the optimization of the actuator schedules is, in the example of Fig. 1 b, based on known deterministic optimization algorithms. In case that the real demand varies and differs from the forecasted one - which will almost certainly occur at some point in time, e.g. due to increased outdoor temperatures or due to a water consuming sport event - the lower or upper bound of the water storage tank might be under- or overrun.
  • the lower and upper bound of the water level in the water tank are shown as dotted lines in the right hand diagram of Fig. 1 b.
  • optimized actuator schedules are calculated at OhOO for the predicted demand curve 5 and are applied for the next 24 hours.
  • the brighter one of the two curves in the left hand diagram of Fig. 1 b visualizes the demand as real-world demand realization 6.
  • the real-world demand 6 is higher than the expected demand 5 for about 6 hours, between approximately 6h00 and noon.
  • the real-world water level 8 in the water storage facility undercuts the accepted lower bound considerably. When the undercutting goes too far, the pressure may become too low at least in parts of the water network.
  • a mechanism to overcome the crossing of the lower bound can be integrated in the optimization algorithm but would potentially result in reduced energy savings. In the worst case, it could even lead to higher energy consumption than without any optimization at all.
  • undercutting the lower bound has to be allowed in the optimization algorithm as to feasibly solve the optimization problem.
  • the optimal deterministic solution requires undercutting the lower water level bound while accounting for an arbitrary demand.
  • the at least one processing unit further contains a scenario generating unit for generating a finite set of scenarios in the form of possible realizations over time of the expected water demand and/or of the operational state of the at least one actuating unit and/or of at least one parameter of the hydraulic model, where the finite set of scenarios is based on probabilistic information on the uncertainty of the expected water demand, the operational state or the at least one parameter, respectively, and the optimizing unit is adapted to minimize an objective function of the optimization problem by performing stochastic optimization, where the optimization problem takes into account the objective function for representing at least one undesired side effect of operating the at least one actuating unit, the at least one operational constraint and the finite set of scenarios in the form of scenario based optimization models.
  • the optimization problem is formulated and solved directly taking describable model and operational uncertainties into account, thereby ensuring that defined limits and demands are met.
  • This also allows for an overall better cost-effective solution than when using a deterministic model as a base.
  • the lower level bound can be safely set to a lower level.
  • modeling parameters e.g. pipe roughness coefficients.
  • the scenario generating unit is adapted to apply a successive scenario reduction technique to bundle similar scenarios in order to reduce the computational effort for the optimizing unit during processing the resulting scenario-based optimization models.
  • the scenario generating unit is adapted to generate the finite set of scenarios by constructing an upper scenario and a lower scenario, which define the region of possible realizations over time for at least one of the above named values or parameters which are subject to uncertainty. This is a considerably simple approach for the cases where only few information on the uncertainties and probabilities exist.
  • the scenario generating unit is adapted to represent the uncertainties by a stochastic process, each stochastic process being defined on an underlying continuous probability space over a time horizon with limited duration, thereby generating an explicit representation of the uncertainties.
  • the scenario generating unit is adapted to generate the finite set of scenarios by deriving the uncertainties from measurement data of the water network.
  • uncertainty data could be read from a storage unit or could be input by an operator to the system and thereby to the scenario generating unit.
  • the scenario generating unit could further be adapted to provide the finite set of scenarios in the form of a scenario tree for graphical visualization.
  • the scenario tree could then be further adapted by an operator, before it is used to generate the optimization problem for the optimizing unit.
  • the objective function reflects resulting energy consumption and/or water inflow from a source when operating the at least one actuating unit.
  • the optimizing unit could further be adapted to take into account changing tariffs for energy and for water from the source.
  • the optimization problem is formulated as a finite discretization of a scenario based two stage stochastic programming model.
  • the hydraulic model may be based on a description of the topology of the water network comprising a node set and an edge set, where the node set consists of storage nodes, connection nodes, water demand nodes and source nodes and where the edge set represents water pipes, pumps and valves.
  • the hydraulic model is then formed by state equations representing the dependence of the time dependent state of the water network on the at least one operational configuration information of the at least one actuating unit and on the expected water demand and where the time dependent state consists of heads in the nodes of the node set and flows along the edges of the edge set.
  • the at least one processing unit may further comprise a level adjusting unit which is adapted to estimate and to provide to the optimizing unit as further input to the optimization problem a dependency between a decreasing in the lower level bound and an extent to which the at least one undesired side effect is reduced while simultaneously abiding to the at least one operational constraint.
  • the level configuration unit may be adapted to estimate and to provide to the optimizing unit as further input to the optimization problem a dependency between the reduction in the undesired side effect and a risk for the water storage facility to reach a critical water level, where the critical water level may indicate that the water storage facility is about to run dry or that it reaches a water level which no longer is sufficient to provide enough pressure for transporting the water through the water pipes.
  • the scenario generating unit is adapted to provide the finite set of scenarios to a decision support unit, where the decision support unit is adapted to evaluate for an identified potential cause of a fault in the water network a corresponding impact on the water network, taking into account the finite set of scenarios.
  • FIG. 1 a, b operational diagrams of a water storage facilities as known from the art
  • Fig. 2 a scenario tree for a parameter P
  • Fig. 4 a system for optimizing operation of a water network
  • Fig. 5 a comparison between optimization without and with modeled uncertainties
  • Fig. 6 examples for outputs generated by a level adjusting unit.
  • the main idea of the invention is the following: Instead of formulating only one optimization problem as done with deterministic modeling approaches, the uncertainties in the different parameters of the hydraulic model of the water network are modeled as a finite set of possible realizations of a respective parameter P over time, leading to scenario based optimization models.
  • Each scenario represents a possible parameter realization over time, i.e. where at a certain point in time in the future a certain value for the respective parameter P is assumed with a specific probability ⁇ - ⁇ ... ⁇ ⁇ leading to a so called scenario tree, as is shown in Fig. 2.
  • Each path from the root to one of its leaves accords with one scenario.
  • the nodes correspond to particular points in time t ⁇ 0, T ⁇ , where decisions c(t) are taken.
  • the optimization problem can for example be solved by applying two stage stochastic programming (2SSP) techniques.
  • 2SSP stochastic programming
  • scenario generation is then mostly based on the usage of historical data to employ on estimation, simulation and sampling techniques, or rather to include the knowledge of an experienced network operator.
  • the computational effort for solving scenario-based optimization models strongly depends on the number of modeled scenarios.
  • Applying special scenario reduction techniques helps to reduce calculation time.
  • the idea is therefore to reduce the number of scenario tree nodes by bundling similar scenarios.
  • the finite set of possible parameter realizations i.e. the finite set of scenarios, could for example be reduced to just two scenarios, a lower and an upper scenario, where each of the two scenarios or curves represents the one curve from the lower or upper halve of all curves, respectively, with the highest probability.
  • the operational configuration information for the at least one actuating unit can be one of
  • the first step is to model the water network structure and dynamics.
  • the task of water supply systems is to transport and distribute the required amount of water in the needed quality at the right time to designated consumers, or storage or water treatment facilities.
  • Raw water is fed into the network at sources, such as groundwater or surface water, like rivers or lakes. If required, the raw water gets a chemical treatment.
  • the clean water is either stored in small storage facilities which are directly situated at the sources, or pumped into the network. Pumps convey the water through pipes by boosting the head at particular locations to overcome elevation differences and to compensate head decreases in pipes, which are caused by friction losses.
  • the number of individual pumps being switched on in a pump station is described by a discrete decision variable.
  • Valves are network elements to control flows and heads. They can be throttled to different extents to control the movement of water through pipes. Special attention has to be paid to the storage facilities, because they are the only network elements that provide a buffer between network inflow and outflow. They decouple different water network sections and make the system flexible, thus allowing for different possible operation strategies.
  • the physical, time t dependent state y(t) of the network consists of heads in the nodes and flows along the edges.
  • the state y(t) satisfies a system of differential-algebraic equations (DAEs) that includes conservation laws of Kirchhoff type, nonlinear head-flow relationships of pumps, valves and pipes, and the temporal change rate of water storage levels in the storage facilities due to flows.
  • DAEs differential-algebraic equations
  • the differential-algebraic equations are affected by the control and decision variables c(t), some of which are integral, representing the network operation configuration for the pumps, valves and source flow, i.e. the operational configuration information for the actuating units, in particular the actuator schedules.
  • the water demand ⁇ (t) determines the outflow of the system.
  • the formulation of the optimization problem includes the objective function ⁇ , the state equations F, and operational constraints G, some of which are simple bounds.
  • the objective function ⁇ ( ⁇ ⁇ + ⁇ 5 ⁇ ) which is to be minimized consists of operating costs, such as pump energy costs ⁇ ⁇ and source flow costs ⁇ 5 ⁇ being incurred during the time horizon T.
  • constraints G restrict the state of the system to its practical operating range, such as the requirement that the pumps work in the physical range of positive head increase.
  • the DAE system is discretized on a fixed temporal grid, and the control and decision variables c(t) are chosen to be piecewise constant, which transforms the optimization problem into a dynamic Mixed Integer NonLinear Programming (MINLP) problem.
  • MINLP Mixed Integer NonLinear Programming
  • the mixed integer component is due to the coexistence of continuous and discrete pump variables.
  • the nonlinear part comprises the objective function and the head-flow relationships.
  • smooth functions are strongly desired, especially when applying derivative-based optimization methods.
  • Current practice in water network operation planning is to solve the optimization problem for a fixed demand ⁇ (t), usually the expected demand, which makes the optimization deterministic.
  • stochastic optimization is applied instead which makes it possible to take modelling und demand uncertainties into account.
  • a common practice to correct random disturbances in dynamical processes relies on the application of a so called moving horizon.
  • probabilistic information on the uncertain data is available, stochastic models take a step forward by including explicitly this stochastic information on future events.
  • the decision process becomes predictive, instead of just reacting to the past water demand realizations. Therefore, a stochastic process is required, which describes the random water demand events to appear within their likelihood.
  • the stochastic process for the random water demand ⁇ 3 ⁇ 4 t with te [0,T] is defined on some underlying continuous probability space, which among others is a function of the above described set of state equations F and of a probability distribution P, and where T is the duration of the time horizon.
  • the nodes correspond to particular points in time t ⁇ ⁇ 0, T ⁇ , where decisions c(t) are taken, i.e. where certain actuator actions are performed.
  • the edges delineate the uncertain water demand variables.
  • the scenario tree branches off for every modeled demand realization in each t ⁇ ⁇ 0, T ⁇ .
  • the main goal consists in a suitable representation of the underlying probability distribution of ⁇ , because the optimal value and the optimal solution depend on the chosen scenarios. In many applications, there is only incomplete information about the underlying probability distribution available.
  • the scenario generation is then mostly based on the usage of historical data to employ on estimation, simulation and sampling techniques, or rather to include the knowledge of an experienced network operator.
  • a finite scenario set can be distinguished with respect to the degree of available information: full knowledge of P, known parametric family, sample information and low information level. If only few information exists, that is based on observed data, the construction of upper and lower scenarios are typical samples, as is shown in Fig. 3.
  • the upper scenario 10 and the lower scenario 1 1 then define the region of possible demand realizations and provide bounds for the optimal value of the optimization problem.
  • an expected value scenario denoted by 9, may be generated, which may be the scenario with the currently highest probabibility.
  • control variables z which are recourse actions z, so called second stage decisions, have to be fixed only later in time when part of the demand has already been observed.
  • the energy consumption cost ⁇ ⁇ takes into account, among others, the pump efficiency. Pumps transform electrical energy into mechanical energy of water.
  • the pump efficiency called wire-to-water efficiency, describes the efficiency of this transformation. It increases with the flow rate through the pump up to a certain point, called peak efficiency, and then declines with further rising flow rate.
  • the source flow cost ⁇ 5 ⁇ reflect the costs for the provision of water from the water sources, such as water treatment facilities, desalination facilities or water reservoirs.
  • the term cost is to be understood not only in the monetary sense, but is used to represent in general the level of undesired side effects which are to be minimized.
  • These undesired side effects are in general connected to the operation of the actuating units, since any actuation action may result in at least one of the production of too much noise or carbon dioxide, the usage of too much electrical energy, the increase of the amount of costly water pumped into the network from a reservoir, or the reaching of an undesired frequency and/or amplitude of the actuator operation itself. All these and more undesired side effects may be modelled into the objective function and thereby into the optimization problem and are then avoided as much as possible due to the minimization of this objective function.
  • the equations (5) to (8) Due to the finite discretization of the underlying stochastic process, the equations (5) to (8) have the form of a deterministic optimization model. Hence, it is also called the corresponding deterministic equivalent of the 2SSP problem.
  • the benefit of the deterministic equivalent consists of its better numerical manageability by avoiding probability integrals
  • the water network of Fig. 4 consists of water pipes interconnecting the following operational elements: sources S, from where water flows into the network, consuming units C, where the water leaves the network, pumps 20 for boosting the head at particular locations to overcome elevation differences and to compensate head decreases in the pipes, water storage facilities, such as water reservoirs or tanks 21 , for providing a buffer between network inflow and outflow and for decoupling different water network sections, and various types of valves, as depicted by all the remaining elements in the network 17, for controlling the movement of water through the pipes and thereby controlling flows and heads, by throttling the valves to different extents.
  • VSS stochastic solution
  • the structure of 2SSP-models ensures that 1 st stage decisions are only feasible, if there exists a feasible recourse action for every mapped demand scenario. This means that the 1 st stage variables are calculated in order to make the model feasible in the long term, so that the optimal water storage level trajectories lie in between the predefined bounds. Hence, the water levels are kept under control in 2SSP.
  • Fig. 5 shows the expected water storage level 14 and the resulting real water storage level 15 for the stochastic optimization of the 2SSP-model of above described water network .
  • Both, the expected and the resulting real water storage levels 12 and 13 lie in between the upper and the lower level bounds, depicted as dotted line. Further experiments for arbitrary demand realizations affirmed feasibility of 2SSP, thus enabling the decrease of the lower bounds on the water storage levels in the storage facilities. As a consequence, less pumping is required resulting in additional cost savings.
  • the corresponding model which is based on an expected and not an uncertain demand value, is infeasible, illustrating that an optimal deterministic schedule for an expected demand is not necessarily optimal or even feasible for the real demand.
  • slack variables had to be introduced and in reaction to a sudden increase in water demand, even an undercutting of the lower level bound had to be accepted, as can be seen from the expected water storage level 12 and the resulting real water storage level 13 in the left hand side of Fig. 5.
  • the model size of the 2SSP-models increases linearly with the number of scenarios and the calculation time indicates quadratic growth with respect to the number of scenarios. But even a rough approximation of the underlying probability distribution comprising few scenarios provides good results compared to the deterministic optimization at a moderate increase in computational complexity. Additionally small perturbations of the modelled demand scenarios only lead to marginal difference in the optimal objective values or in the optimal solution of the stochastic programs.
  • Fig. 4 shows an example for a system 16 for the stochastic optimization of operation of a water network.
  • the system 16 contains an optimizing unit OP to perform the above described minimization of the objective function for generating at least one operational configuration information, in particular at least one actuator schedule, which is then output to a control system CS in order to be applied to the intended at least one actuating unit of water network 17 via an output interface 18.
  • the actuating units of water network 17 are the pumps 20 for boosting the head at particular locations inside the network, the pumps and valves at the water storage facilities 21 , and the various types of valves inside the network.
  • System 16 contains further a scenario generating unit SG.
  • the scenario generating unit SG is arranged to generate scenarios oo j for the future water demand from uncertainties for the water demand derived from real-world data of the water network, i.e. from historical measurement data MD which include measurement data of the water demand.
  • the measurement data MD are stored in a storage unit 23 which belongs to system 16.
  • Storage unit 23 can be either a volatile data memory, e.g. a RAM, or a permanent data memory.
  • the scenarios are assumptions for the water demand of the future, where the expected water demand varies for example depending on the time of the day, the week day or the time of the year. Accordingly, day-night variations, higher water demands during hot and sunny months of the year or due to mass events can be taken into account by scenario generating unit SG.
  • the scenario generating unit SG may take into account additional information which may implicitely influence the water demand, such as information on electricity tariffs ET and/or on water source costs WSC, which may be stored in storage unit 23 or which may be input directly to the system by an operator. Further such additional information may be outdoor temperatures, wheather forcasts and information on public mass events.
  • additional information may be outdoor temperatures, wheather forcasts and information on public mass events.
  • the scenario generating unit SG may for example generate scenarios for the variation in the efficiency of the water pumps in the water network.
  • pump efficiency characteristics may be stored in storage unit 23 and may be used by the scenario generation unit together with measurement data on the operation of the water pumps to model the future uncertainty in the water pumps efficiency.
  • the finite set of scenarios for the future water demand, generated by the scenario generating unit SG, is then provided to the optimizing unit OP, where it is input to the optimization problem.
  • the optimization problem further takes into account a hydraulic model F of the water network 17 (see equations (3) and (6)), at least one operational constraint G (see equations (4) and (7)) and at least one objective function ⁇ for representing an undesired side effect U of operating the at least one actuating unit, modeled for example as energy consumption cost ⁇ ⁇ and source flow cost ⁇ 5 ⁇ .
  • the optimization problem may even further take into account variations in parameters from which the at least undesired side effect depends on, such as variations in the electricity tariffs ET which influence the energy consumption cost ⁇ ⁇ and variations in tariffs for water source costs WSC which influence the source flow cost ⁇ 5 ⁇ . Accordingly, optimizing unit OP is adapted to obtain the respective variations of the parameters from storage unit 23.
  • the scenarios may also be presented via a human-machine interface to an operator, which may amend them according to his experience.
  • the scenario generating unit SG is arranged to derive upper and lower scenarios, and - if required - one or more additional expected value scenarios.
  • the scenario generating unit SG may be adapted to provide the water demand scenarios to a graphic display, for visualization for example in form of a tree as in Fig. 2 or in form of a time dependent graph showing the upper and lower scenarios as well as the expected value scenario as in Fig. 3 or in any other suitable graphical form.
  • the system 16 contains further a level adjusting unit LA which is adapted to determine a dependency between a decreasing in a lower level bound of a water storage facility and an extent to which the at least one undesired side effect U is reduced while simultaneously abiding to the at least one operational constraint G and to provide to the optimizing unit OP as one updated operational constraint G to the optimization problem a new value for the lower level bound.
  • a dependency is depicted in Fig. 6, where energy savings are shown over a varying lower bound.
  • the energy savings are in fact an inversed undesired effect U in the sense the undesired effect are the monetary energy costs. The higher the energy costs, the lower the energy savings.
  • the hatched area indicates an area where none of the at least one operational constraints G is violated.
  • the level adjusting unit LA may further be adapted to determine the new value for the lower level bound by further taking into account a dependency between the reduction in the undesired side effect U and a risk for the water storage facility to reach a critical water level.
  • FIG. 7 An example for such a dependency is shown in Fig. 7 as a risk level over energy savings.
  • the above named dependencies may be determined in the form of functional relationships, or tables or rule based relationships.
  • Another element which may contained in system 16 is a decision support unit DS.
  • the decision support unit DS receives from the scenario generating unit SG the finite set of scenarios and uses it as input for evaluating for an identified potential cause of a fault in the water network 17 a corresponding impact on the water network 17.
  • a method performed by the decision support unit may contain the steps of
  • the step of determining an estimated impact for each potential cause includes a risk scenario with initial network conditions and assumed future network conditions of the water network, where the finite set of scenarios is used to represent the future network conditions.
  • the output of the decision support unit DS i.e. the importance indication, may then be used by the control system CS to determine how quickly the notified fault requires a response and/or a priority or order in which a series of notified faults should be reacted to.

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Abstract

L'invention porte sur un système pour optimiser le fonctionnement d'un réseau d'eau, lequel système comprend au moins une unité de stockage de données (23) pour stocker un modèle hydraulique ainsi qu'au moins une contrainte fonctionnelle du réseau d'eau (17), le modèle hydraulique représentant les dépendances de hauteurs de pression et d'écoulements dans le réseau d'eau vis-à-vis d'un état fonctionnel d'au moins une unité d'actionnement (20) du réseau d'eau et d'une demande d'eau prévue, au moins une unité de traitement contenant une unité d'optimisation (OP), l'unité d'optimisation étant conçue pour générer au moins une information de configuration fonctionnelle pour la ou les unités d'actionnement par minimisation d'une fonction objective d'un problème d'optimisation, le problème d'optimisation étant basé sur le modèle hydraulique, au moins une interface de sortie (18) pour fournir la ou les informations de configuration fonctionnelles à la ou aux unités d'actionnement. Pour être apte à utiliser le réseau d'eau avec une limite de niveau inférieur réduite d'une installation de stockage d'eau, la ou les unités de traitement contiennent de plus une unité de génération de scénarios (SG) pour générer un ensemble fini de scénarios sous la forme de réalisations possibles au cours du temps de la demande d'eau prévue et/ou de l'état fonctionnel et/ou de caractéristiques de la ou des unités d'actionnement et/ou d'au moins un paramètre du modèle hydraulique, l'ensemble fini de scénarios étant basé sur une information probabiliste concernant l'incertitude de ces variables. L'unité d'optimisation est conçue pour réduire au minimum la fonction objective du problème d'optimisation par réalisation d'une optimisation stochastique, le problème d'optimisation prenant de plus en compte la fonction objective pour représenter au moins un effet secondaire indésirable de l'activation de la ou des unités d'actionnement, de la ou des contraintes fonctionnelles et de l'ensemble fini de scénarios.
PCT/EP2012/065802 2011-08-22 2012-08-13 Système et procédé pour optimiser le fonctionnement d'un réseau d'eau Ceased WO2013026731A1 (fr)

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SE1450161A SE1450161A1 (sv) 2011-11-16 2012-08-13 System och förfarande för att optimera drift av ett vattennätverk
NO20140362A NO347239B1 (no) 2011-11-16 2012-08-13 System og fremgangsmåte for å optimalisere operasjon av et vann nettverk
DE112012003487.2T DE112012003487T5 (de) 2011-08-22 2012-08-13 System und Verfahren zur Optimierung des Betriebs eines Wassernetzes
DK201470104A DK201470104A (en) 2011-08-22 2014-03-04 System and method to optimize operation of a water network

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CN104732304A (zh) * 2015-04-15 2015-06-24 河南理工大学 基于灰色人工神经网络组合模型导水裂隙带高度预测方法
WO2015121640A1 (fr) * 2014-02-16 2015-08-20 Arscott David Stephen Système d'optimisation de performance dans un réseau d'eau
CN105868841A (zh) * 2016-03-21 2016-08-17 广西电网有限责任公司电力科学研究院 一种基于风电优先上网的风水火联合调度方法
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EP3112959A1 (fr) * 2015-06-29 2017-01-04 SUEZ Groupe Procédé de détection d'anomalies dans un système de distribution d'eau
CN109345010A (zh) * 2018-09-18 2019-02-15 中国水利水电科学研究院 一种梯级泵站的多目标优化调度方法
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CN110136026A (zh) * 2019-04-30 2019-08-16 佛山水业集团高明供水有限公司 一种水厂配水的控制方法及其控制系统
CN110610264A (zh) * 2019-09-05 2019-12-24 大连理工大学 一种针对不确定性情景下供水管网调控的单目标优化算法
WO2020079455A1 (fr) * 2018-10-18 2020-04-23 Inflowmatix Ltd Procédé mis en œuvre par ordinateur pour modéliser un réseau hydraulique
CN111652503A (zh) * 2020-06-01 2020-09-11 中南大学 基于网络流模型的多梯级多线船闸联合调度方法及结构
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EP4428774A1 (fr) * 2023-03-10 2024-09-11 Abb Schweiz Ag Procédé de commande robuste d'un réseau de distribution d'eau
CN120297698A (zh) * 2025-06-11 2025-07-11 福州市城区水系联排联调中心 基于动态数据协同的城区内河水系智能资源调度管理系统及方法

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NO347239B1 (no) 2023-07-24

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