EP3752966A1 - Procédé de détermination d'une conception d'un système d'énergie et système d'énergie - Google Patents

Procédé de détermination d'une conception d'un système d'énergie et système d'énergie

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Publication number
EP3752966A1
EP3752966A1 EP19716839.6A EP19716839A EP3752966A1 EP 3752966 A1 EP3752966 A1 EP 3752966A1 EP 19716839 A EP19716839 A EP 19716839A EP 3752966 A1 EP3752966 A1 EP 3752966A1
Authority
EP
European Patent Office
Prior art keywords
energy system
parameter
values
energy
design
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP19716839.6A
Other languages
German (de)
English (en)
Inventor
Simon Ackermann
Martin Kautz
Jochen SCHÄFER
Andrei Szabo
Sebastian THIEM
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Siemens AG
Original Assignee
Siemens AG
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Siemens AG filed Critical Siemens AG
Publication of EP3752966A1 publication Critical patent/EP3752966A1/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G06—COMPUTING; CALCULATING OR 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/06—Power analysis or power optimisation
    • 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
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B10/00—Integration of renewable energy sources in buildings
    • Y02B10/30—Wind power
    • 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
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P80/00—Climate change mitigation technologies for sector-wide applications
    • Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
    • Y02P80/14—District level solutions, i.e. local energy networks
    • 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
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P80/00—Climate change mitigation technologies for sector-wide applications
    • Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
    • Y02P80/15—On-site combined power, heat or cool generation or distribution, e.g. combined heat and power [CHP] 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
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P80/00—Climate change mitigation technologies for sector-wide applications
    • Y02P80/20—Climate change mitigation technologies for sector-wide applications using renewable energy

Definitions

  • the invention relates to a method for determining a design of an energy system by means of a optimization process. Furthermore, the invention relates to an energy system whose components are designed according to a method of the present invention or one of its embodiments.
  • Energy systems for example multimodal energy systems, provide at least one form of energy for an energy consumer, for example a building, an industrial installation or a private installation, the provision being made in particular by conversion of different forms of energy, transport of the various forms of energy and / or energy stored energy forms can be done.
  • the various forms of energy in particular special heat, cold and / or electrical energy by means of the (multimodal) energy system with respect to their generation, ih rer provision and / or their storage coupled.
  • a mathematical model of the energy system is typically used, which enables an optimization of the energy system with respect to a target function by means of a numerical optimization method.
  • a plurality of parameters for example, predictions of load profiles and / or state measurements, are required for the parameterization of the mathematical model.
  • the parameters required for the parameterization for example load time series, energy prices and / or weather data, are determined deterministically. This also optimizes the energy system deterministically based on the deterministic parameter.
  • the method according to the invention for determining a design of an energy system by means of an optimization method comprises at least the following steps:
  • the inventive method for determining the design of the energy system corresponds to a Energysystemdesignver drive or can be referred to as energy system design method.
  • the design of the energy system in particular the determination of its components, determined.
  • the design of the energy system may include a determination and / or determination and / or determination of its components comprising the energy system. Furthermore, the design of the energy system, the dimension and / or capacity of its components, may include the cost of the components, for example energy storage costs, energy flows and / or power flows. In other words, any arbitrary system size of the energy system and / or any variable characterizing the energy system may be taken into account in the optimization, in the sense that an optimum value of the system variable or the characterizing variable is determined as far as possible. The consideration of the system size and / or characterizing quantity can be made by means of its input into the individual objective function or overall objective function.
  • the design of the energy system in particular its structure and / or its structure in terms of ner ner components whose dimensioning and / or its economic analysis referred to.
  • the most optimal design of the energy system is also referred to as optimization problem.
  • this optimum possible design of the energy system that is, for example, its structure, its dimensions, its profitability analysis and / or the like, ermit mined.
  • the energy system can each one or more power generators, cogeneration plants, in particular special combined heat and power plants, gas boiler, diesel generators, heat pumps, Kompressionsuraltemaschinen, Absorptionshimltema machines, pumps, district heating networks, Energytransferleitun gene, wind turbines or wind turbines, Photovoltaikanla gene, biomass plants, biogas plants, waste incineration plants, industrial plants , conventional power plants and / or the same include.
  • a variable is a parameter of the optimization method if its value or its values are considered constant in the optimization.
  • the parameter is fixed but arbitrary.
  • Parameters can also be referred to as an input parameter.
  • a variable is a variable of the optimization method if its value or its values vary in the optimization.
  • constraints - are conditions, properties and / or relations that must meet the parameters and / or variables of the optimization process. These may be given as an equation and / or inequality and / or explicitly describe a set of permissible values of the parameters and / or permissible values of the variables.
  • the optimization method is, for example, a mathematical and / or numerical optimization method, in particular a simplex method, which can be carried out, for example, by means of a computing device, in particular by means of a computer.
  • the overall objective function is extremalized, ie maximized or minimized. It is not necessary to have an exact calculate maximum or minimum. It is suffi cient to average an approximately optimal interpretation (solution), for example by means of an approximation algorithm and / or by establishing a threshold value for an error of the optimization process.
  • At least one parameter is provided for the optimization method.
  • the values of the parameter are determined by means of a drag according to its probability distributions or distribution.
  • the parameter is a random stochastic variable (not to be confused with the variables of the optimization method), which assumes different values according to its probability distribution.
  • the values of the parameter thus represent a sample in terms of the probability distribution and can also be referred to as sample values.
  • a plurality of such stochastic parameters with corresponding probability distributions may be provided.
  • the terminological parameters can be provided.
  • a single target function of the power system For each of the dragged values of the parameter, a single target function of the power system is set. In other words, the single function already models the energy system for the dragged value of the parameter. According to the present invention, a total target function is formed from the individual target functions formed by the dragged values of the parameter. According to the invention, the overall target function is extremalized by means of the optimization method. As a result, a plurality of similar energy systems corresponding to the number of drawn values of the parameter is optimized.
  • the majority of values of the parameter stochastically drawn (random samples) are taken into account in the optimization process and thus in the determination of the interpretation of the energy system.
  • the consideration The different values of the parameter are determined by the total target function, which is optimally optimized by means of the optimization method, that is to say extremalized.
  • the drawn values of the parameter thus flow into the optimization.
  • known methods merely consider the most plausible value of the parameter.
  • a scenario ie a model of the energy system, is provided for each (drawn) value of the parameter.
  • a single target function of the energy system corresponds to each scenario.
  • the parameter is a gas price with a single target function provided for each gas price value. It could be said figuratively that there is an energy system for each value of the parameter, whereby by means of the formation of the total objective function, this plurality of existing energy systems are viewed in parallel by means of the optimization method and at the same time as optimally as possible.
  • a particular advantage of the method according to the invention is that the probability distribution of the parameter over the sample values of the parameter is taken into account when determining the design of the energy system.
  • the optimization method has knowledge of the probability distribution of the parameter.
  • the energy system is not optimally optimized in the context of a parameter variation or sensitivity analysis, as is the case in the prior art, but a plurality of parallel scenarios are considered simultaneously and optimized according to the probability distribution of the parameter.
  • a weighting of the scenarios is typically not required here, since the values of the parameter are already drawn according to its probability distribution and thus weighted accordingly.
  • an at least partial weighting of the various scenarios or individual target functions can be provided.
  • a basic idea of the present invention is the determination of random samples of the stochastic parameter for an energy system design problem and the parallel and simultaneous consideration of this plurality of samples within the optimization method. This advantageously takes place in relation to the parameter stochastic Optimie tion of the energy system.
  • the overall objective function is formed by means of a sum of the individual objective functions.
  • a weighted sum can be provided to form the overall target function from the individual target functions.
  • a weighting according to the present method is not mandatory, since the values of the parameter have already been drawn according to its probability distribution.
  • the parameter is an example of the gas price.
  • this is a random variable which assumes different values according to a probability distribution. This is the case because the future values of the gas price can only be given with a certain probability.
  • the probability can describe, represent and / or model the uncertainty of the gas price for the future.
  • Gi G (P ⁇ , where G is the gas price, Gi the tth value of the gas price G and Pi denotes the probability of the t-th value G j of the gas price G.
  • Pi is the probability that the gas price G has the value G j .
  • a single target function Z j is defined.
  • a weighted sum Z S ⁇ Z ⁇ with weighting factors can be provided. Typically, the weighting factors a value in the closed range from zero to one.
  • the overall objective function may be associated with the constraints of the individual objective functions and / or additional constraints.
  • the total target function is extremalized, ie optimized with regard to its value, taking into account the secondary conditions, whereby the design of the energy system is determined taking into account the values of the gas price G j .
  • At least one secondary condition is provided for each of the individual target functions, the secondary conditions being taken into account when the overall target function is extremalized.
  • typical parameters are normally distributed, that is, their values are calculated according to a normal distribution. shares are. Another advantage is that a normal distribution can be efficiently generated numerically.
  • the values of the parameter are drawn by means of a Monte Carlo method.
  • the Monte Carlo method enables an efficient and fast numerical provision of the values of the parameter according to its probability distribution.
  • a plurality of samples are taken by the Monte Carlo method for the parameter.
  • the Monte Carlo method allows representative representation of the probability distribution of the stochastic parameter.
  • the Monte Carlo method makes it possible to provide a plurality of similar random experiments (scenarios), the random experiments corresponding to the values of the parameter.
  • an electrical, thermal, chemical and / or mechanical load, a price and / or at least one metrological variable are used as parameters.
  • a multiplicity of stochastic parameters in particular prices and / or mechanical loads and / or metrological variables, are therefore taken into account in the optimization process and thus in the determination of the design of the energy system.
  • the provision of dragged values of a plurality of parameters is provided.
  • a plurality of stochastic parameters of the energy system is taken into account in its design, that is to say in the optimization method.
  • the energy system will characterized advantageously stochastisch optimally designed.
  • the total cost of multimo dalen energy system and / or the carbon dioxide emission of the multimodal energy system and / or the primary energy use of the energy system is a single objective function.
  • the overall costs of the energy system which optimizes carbon dioxide emissions and / or the primary energy input of the energy system, are thereby minimized in particular.
  • the total cost of the energy system which typically consists of investment costs, variable investment costs, operating costs and / or maintenance costs and / or costs for maintenance, consumption costs, and energy costs / or energy costs and / or start-up costs, used as individual target functions and minimized.
  • the overall cost of the energy system is minimized.
  • the value of each individual objective function is calculated in accordance with the determined interpretation of the energy system and by means of its associated value of the parameter.
  • each of the individual target functions is evaluated after determining the design of the energy system, that is, after the extremalization of the overall objective function, for the corresponding value of the parameter.
  • This results in said value of the single target function which corresponds, for example, to the total cost of an energy system designed according to the individual target function.
  • a probability distribution of the input and thus a probability distribution of the scenarios corresponding to the values of the parameter are calculated and / or provided.
  • the distribution of the target functions is advantageously derivable. If the individual target functions are given, for example, by the respective total costs (for each value of the parameter) of the energy system, the determination of the probability distribution of the individual target functions results in a probability distribution of the total costs, which is recorded, for example, by means of a histogram. From this, for example, the probability distribution of profit for an operator of the energy system can be represented. Alternatively or additionally, the probability distribution of carbon dioxide emissions and / or the use of primary energy can be determined.
  • the energy system according to the invention comprises at least a plurality of components for providing one or more forms of energy, the energy system according to the invention being characterized in that the components have a design according to a method of the present invention or one of its embodiments.
  • the energy system is particularly preferably designed as a multi-modal energy system.
  • FIG. 1 shows a representation of values of a parameter according to a probability distribution
  • FIG. 2 shows a distribution of the values of the individual target functions corresponding to the values of the parameter and a probability distribution of the individual target functions derived therefrom;
  • FIG. 3 shows a schematic flow diagram of the method according to the invention.
  • FIG. 1 symbolizes the dragging of values (random samples) of at least one parameter, in particular one or more parameters, according to a probability distribution 42 present for the parameter.
  • the probability distribution 42 of the parameter is plotted in a diagram 11 of FIG.
  • the abscissa 100 of the diagram 11 the parameter is plotted.
  • the ordinate 101 of the diagram 11 plots the probability for a specific value of the parameter.
  • Diagram 11 thus shows the probability distribution 42 of the parameter.
  • a plurality of values of the parameter are extracted.
  • the ge drawn values of the parameter are identified by the reference numeral 421.
  • According to the drawn values 421 of the parameter are determined and / or determined individual target functions of the energy system that correspond to different scenarios 41.
  • the various scenarios 41 are taken into account in parallel during the optimization process.
  • the parameter is a gas price.
  • the price of gas and thus with the parameter for a current period for the next five years following the current period is distributed normally.
  • the uncertainty of gas price values is modeled or estimated over the next five years by a normal distribution (Gaussian distribution).
  • the probability distribution 42 is thus a normal distribution.
  • the mean or expected value of the parameter is denoted by the reference numeral 40.
  • samples or samples For example, according to the normal distribution, twenty percent of the gas price is taken (samples or samples). This drawing is symbolized in FIG. 1 by the vertical dashed arrows (in FIG. 1 symbolically only for three random samples 421).
  • the samples may also be referred to as periods in terms of the energy system as scenarios or in terms of the optimization process.
  • the twenty individual target functions of the energy system may be provided or determined with associated constraints.
  • the twenty individual target functions or scenarios are considered together and in parallel within the optimization process.
  • the optimization method or an optimization algorithm of the optimization method does not determine twenty individual solutions, but instead determines a common design of the energy system. This is done by forming an overall objective function by means of the individual objective functions.
  • a design of the energy system which is as optimal as possible for the totality of the twenty values of the gas price is determined. In this case, the total costs, the carbon dioxide emissions and / or a primary energy input of the energy system can each be used as single target functions and thus as an overall target function.
  • the individual target functions can be evaluated for themselves, that is, in at least one or more of the Einzelzielfunk functions their associated value of the parameter, in particular their associated value of the gas price , as well as the determined optimal values of the variables of the optimization.
  • each of the scenarios may not consider an entire optimization time horizon, but only a limited time range, for example fixed days, weeks, months and / or periods.
  • FIG. 2 shows in a diagram 21 the distribution 43 of the values of the individual target functions as a function of the parameter and in a further diagram 22 the probability distributions of the values of the individual target functions or the probability distributions of the individual target functions.
  • the abscissa 100 of the diagram 21 thus plots the parameter, in particular the gas price.
  • the individual target function or the values of the individual target functions are plotted.
  • the totality of the values of the individual target functions can also be called a success factor.
  • the curve 43 thus represents the success factor as a function of the parameter.
  • the mean or expected value of the parameter is identified by the reference numeral 40.
  • the success factor is negative, so from this value of the parameter, in particular the installation or operation of a component, such as a combined heat and power plant, not more worth. In this way, an appraisal of the efficiency of the determined design of the energy system can be made in the probabilistic sense.
  • the probability distribution 44 of the individual target functions can likewise be determined.
  • This probability distribution 44 is shown in a further diagram 22.
  • the value of the individual target function or the success factor is plotted on the abscissa 102 of the further diagram 22.
  • the associated probability is plotted.
  • a maximum in the probability can arise for a specific value of one of the individual target functions. If the individual target functions are formed by the total costs of the energy system, this represents the probability of the occurrence of the total costs of the designed energy system. This can result in an improved determination of the design of the energy system, which is not based on human assumptions, but can be determined theoretically and thus objectively.
  • FIG. 3 shows a flow chart of the method according to the invention.
  • a plurality of values of at least one parameter, in particular for a plurality of parameters, of the optimization method by means of pulling the values of Parameters provided according to a probability distribution of the parameter.
  • the values of the parameter form a sample or sample values, the parameter forming a random variable with the probability distribution mentioned in the probabilistic sense.
  • an associated individual target function of the energy system is determined for each value of the parameter.
  • the single target function for the value of the parameter characterizes the energy system. It is thus considered symbolically a plurality of similar energy systems, which differ in the value of the parameter (scenarios).
  • an overall target function is formed by means of the individual target functions.
  • the overall objective function can be formed by means of the mathematical tables and / or by means of a weighted mathematical sum of the individual objective functions. Further equi-valent embodiments, for example affine and / or by means of a plurality of scalar multiplications, can be provided.
  • the overall target function is extremalized by means of the optimization method.
  • the design of the energy system is determined. This will be the energy system advantageously optimally designed taking into account the stochastic variation of the parameter.
  • the optimization method can be carried out by means of a computing device, in particular by means of a computer.
  • an optimization algorithm is provided in particular.
  • the optimization problem may be linear or non-linear.
  • a simplex method also known as a simplex algorithm
  • the optimization method comprises a simplex method.
  • the present invention enables a particularly advantageous determination of a design of an energy system taking into account the stochastic variation of at least one parameter, in particular of a plurality of parameters.
  • the stochastic behavior of the parameter or the parameters is taken into account in the optimization process.
  • an optimization method is not carried out for each value of the parameter, but an extrapolation of the overall objective function takes place under a holistic consideration of the stochastic variation of the parameter with respect to the values of the parameter taken.
  • Computer-readable memories are, for example, volatile memories such as caches, buffers or RAM as well as non-volatile memories such as removable data carriers or hard disks.
  • the functions or method steps described above may be in the form of at least one instruction set in or on a computer-readable memory.
  • the functions or method steps are not specific to a particular instruction set or to a certain form of instruction sentences or to a particular one Storage medium or bound to a particular processor or to certain execution schemes and can be run by software, firmware, microcode, hardware, processors or inte grated circuits alone or in any com bination.
  • Various processing strategies can be used, for example serial processing by a single processor, multiprocessing, multitasking or parallel processing.
  • the Instruk tions can be stored in local memories, but it is also possible to the instructions on a remote system, in particular a cloud system (English: Cloud), example, MindSphere Siemens AG, store and access it via network.
  • computing device includes processors and processing means in the broadest sense, such as servers, general purpose processors, graphics processors, digital signal processors, application specific integrated circuits (ASICs), programmable logic circuits such as FPGAs, discrete analog or digital circuits, and any combinations thereof including any other processing means known to those skilled in the art or developed in the future.
  • processors can consist of one or more devices. If a processor consists of several devices, these can be configured for parallel or sequential processing of instructions.

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Abstract

L'invention concerne un procédé de détermination d'une conception d'un système d'énergie au moyen d'un procédé d'optimisation, ledit procédé comprenant au moins les étapes suivantes consistant à : fournir une pluralité de valeurs (421) d'au moins un paramètre du procédé d'optimisation au moyen d'un tirage des valeurs (421) selon une répartition de probabilités (42) du paramètre; définir respectivement une fonction cible individuelle du système d'énergie pour chacune des valeurs (421) du paramètre; former une fonction cible globale au moyen des fonctions cibles individuelles; et extrémaliser la fonction cible globale au moyen du procédé d'optimisation. En outre, l'invention concerne un système d'énergie.
EP19716839.6A 2018-04-27 2019-04-01 Procédé de détermination d'une conception d'un système d'énergie et système d'énergie Withdrawn EP3752966A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP18169710.3A EP3561743A1 (fr) 2018-04-27 2018-04-27 Procédé de détermination d'une conception d'un système d'énergétique ainsi qu'un système énergétique
PCT/EP2019/058163 WO2019206574A1 (fr) 2018-04-27 2019-04-01 Procédé de détermination d'une conception d'un système d'énergie et système d'énergie

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EP3752966A1 true EP3752966A1 (fr) 2020-12-23

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EP19716839.6A Withdrawn EP3752966A1 (fr) 2018-04-27 2019-04-01 Procédé de détermination d'une conception d'un système d'énergie et système d'énergie

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