Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F30/00—Computer-aided design [CAD]
  • G06F30/20—Design optimisation, verification or simulation
• • H—ELECTRICITY
  • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
  • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
  • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
  • H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
  • H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
• • 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
  • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
  • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
  • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
  • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
• • 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

Definitions

• the technical field of the invention is that of energy management.
• the present invention relates to a method for determining the parameters of a physical model and in particular to a method for determining the parameters of a simplified model of an energy storage system.
• the storage is, in general, provided by an energy storage system comprising a storage device in charge of storing the energy itself and a conversion device which will ensure the charging or discharging of the device. storing and converting the stored energy into a form suitable for domestic or industrial use, for example by converting a direct current to alternating current.
• a storage system often includes auxiliaries that may be associated with the storage device or the conversion device. In the following text, when we speak of storage system, these auxiliary systems are considered included in said storage system.
• the invention offers a solution to the problems mentioned above, by making it possible to obtain a simplified model whose simulation time is limited.
• the simplified model obtained using a method according to the invention makes it possible to perform a simulation of a system for storing energy with a time step of the same order of magnitude as the time step of piloting said system, while guaranteeing a precision simulated results similar to that obtained with a simulation having a time step much smaller than the time step of the pilot itself.
• a first aspect of the invention relates to a method for determining the parameters of a simplified model of an energy storage system, said system comprising an energy storage device and a conversion device, said system being able to be modeled using a complex model including a model of the energy storage device and a model of the conversion device; said complex model receiving as input a nominal power Pac sp and a charge state SOC P , and outputting the state of charge SOC of the storage device and the power P ac at the output of the storage device; said method being characterized in that it comprises:
• ⁇ a table of the time variation of the load state of the system based on set power Pac_ sp and the state of charge SOC;
• the simplified model obtained makes it possible to allocate, as a function of a reference power Pac sp and a state of charge SOC P supplied as input, and from the tables determined during the second stage, a power P ac and a state SOC system load.
• the power Pac during charging is considered negative and the power P ac during the discharge is considered positive.
• the invention it is no longer necessary to choose between a simulation time step of the same order of magnitude as the pilot time step (ie the time separating two updates of the piloting instructions) leading to a unsatisfactory accuracy, and no more simulation time low to obtain a simulation certainly accurate, but greedy computing resources and memory.
• the simplified model obtained using a method according to a first aspect of the invention makes it possible to adopt a simulation time step of the same order of magnitude as the pilot time step while maintaining a precision sufficient for said piloting.
• each value in the table of the change in the system state of charge is obtained with a simulation of a duration iess made for a state of charge SOC (j) and a desired power Pac_ sp (i) given and belonging to a first subset of the plurality of simulations, said value being:
• ⁇ interp ⁇ [x0, xl], [y 0, y], x] is the function that determines the value of y corresponding to the value of x by interpolation from the values of
• Mean that equal power P AC is equal to the desired power Pac_ sp plus or minus 5% or plus or minus 2%, preferably roughly 1%.
• each value of the table of the variation of the state of charge of the system is obtained by means of a simulation of a duration ÎESS carried out for a state of charge SOC (j) and a nominal power Pac_ sp (i) given and belonging to a first subset of the plurality of simulations, said value being equal to the value of the variation of ASOC charge state obtained in said simulation time iess.
• each value of the table of the variation of the state of charge of the system is obtained by means of a simulation of a duration SESS performed for a state of charge SOC (j) and a nominal power Pac_ sp (i) given and belonging to a first subset of the plurality of simulations, said value being:
• DSOC TM 'II or dS0C (TM> D is
• Pac_ s (m) being the power the closest to the target power set Pac_ sp (i) for which the average power ⁇ P ac> during said simulation is equal to the desired power Pac_s P (m).
• each simulation the first sub-set of simulations is performed to a state of charge SOC (j) and a desired power Pac_ sp (i) data and in that each state of charge SOC (j) is separated from the previous SOC (j-1) and / or the next SOC (j + 1) by a step of adaptive state of charge and / or each desired power Pac_ sp (i) is separated from the previous P ac _s (i-1) and / or the following Pac_ s (i + 1) a step of adaptive target power.
• the simulation step is repeated for a plurality of ISES durations.
• the simulation step is repeated for a plurality of ISES durations.
• the plurality of simulations comprises a simulation carried out with an initial state of charge SOCini equal to the maximum state of charge SOCmax, a duration equal to the time necessary for the complete unloading of the storage system tDch and a reference power P ac _ s infinite positive, and the calculation of the maximum power table provided as a function of SOC state of charge comprises:
• the plurality of pairs (SOCk, ⁇ P ac > k) constitute the maximum power table provided as a function of the charge state SOC.
• the term power sp positive infinity Pac_ set a desired power Pac_ sp much higher in absolute value to the power that the system can provide.
• a positive value of the power P ac is associated with the discharge of the storage system.
• the plurality of simulations includes a simulation carried out with an initial load state of Socini equal to the condition of minimum load SOCmin, a duration equal to the duration necessary to complete loading of tch storage system and a target power Pac_ sp infinite negative, and the calculation of the minimum power table supplied as a function of the state of charge SOC comprises:
• the plurality of pairs (SOCk ', ⁇ P ac > k) constitutes the minimum power table supplied as a function of the state of charge SOC.
• a negative value of the power P ac is associated with the load of the storage system.
• a second aspect of the invention relates to a method of controlling an energy storage system calculating operating instructions of said system from a model of said system, said model being obtained using a method according to a first aspect of the invention.
• a third aspect of the invention relates to a device for controlling an energy storage system comprising means for sending operating instructions to the energy storage system, means for receiving data concerning the operation of the energy storage system, energy storage system and means for implementing a control method according to a second aspect of the invention.
• a fourth aspect of the invention relates to a computer program product comprising instructions which, when the program is executed by a computer, lead it to implement the steps of the method according to a first aspect of the invention.
• a fifth aspect of the invention relates to a computer program product comprising instructions that drive the driver according to a third aspect of the invention to perform the steps of the method according to a second aspect of the invention.
• a sixth aspect of the invention relates to a computer readable medium on which the computer program according to a fourth or fifth aspect of the invention is stored.
• FIG. 1 shows a flow chart of an embodiment of a method according to a first aspect of the invention.
• FIG. 2 shows a schematic representation of an energy storage system.
• FIGS. 4A and 4B show a simulation involved in a method according to a first aspect of the invention.
• FIGS. 5A and 5B show a simulation involved in a method according to a first aspect of the invention.
• FIGS. 6A and 6B show a 3D illustration of a table of the temporal variation of the state of charge of the system according to a first aspect of the invention.
• FIGS. 7A and 7B show a simulation involved in a method according to a first aspect of the invention.
• FIGS. 8A and 8B show a simulation involved in a method according to a first aspect of the invention.
• FIG. 9 shows a graph illustrating the time and precision performances of a model obtained using a method according to a first aspect of the invention.
• a first embodiment of a method according to a first aspect of the invention illustrated in FIG. 1 concerns a method 100 for determining the parameters of a simplified model MS of an energy storage system ESS.
• the ESS energy storage system illustrated in FIG. 2 comprises an energy storage device DSE, for example a battery, and a DC conversion device, and can be modeled by means of a complex model MC illustrated in FIG. Figure 3 including a model of the MCS energy storage device and a model of the MCC converter device.
• the complex model MC receives as input a reference power Pac sp and a state of charge SOC P , and outputs the state of charge SOC of the storage device and the power P ac at the output of the storage system of the energy.
• the method 100 comprises a first step 101 of implementing a plurality of simulations of the storage system SSE energy using the complex model MC, each simulation being performed for example with a time step ⁇ .
• the state of charge SOC P supplied at the input of the complex model is the initial state of charge of the system for the first iteration then, for the following iterations, the state of charge calculated during the previous iteration .
• the method according to a first aspect of the invention also comprises a second calculation step 102 based on the results obtained during the first step 101:
• the power P ac during charging is considered negative because it is absorbed and the power P ac during the discharge is considered positive because it is supplied outside the considered system.
• the minimum power is the minimum Pac_min power (negative) that can be absorbed system for said state of charge and the maximum power output is the maximum P ac _max power (positive) that can provide the system for said state of charge.
• the simplified model obtained makes it possible to allocate as a function of a reference power ac sp and a charge state SOC P provided as input, and from the tables SOCV_TC, PAC_MAX_TC, PAC_MIN_TC determined during the second step 102, a Pac power and a state of charge of the SOC system.
• a target power Pac_ sp SOCP and a state of charge provided as input to the model, it is possible to determine the minimum power with the minimum power PACJVIIN table TC and maximum power using the maximum power table PAC_MAX_TC. If the target power Pac_s P is in this range, then the power delivered by the system is equal to said Pac_ sp set, otherwise it is equal to the limit value closest to said target.
• the saturated Pac sp once saturated power and SOC P charge state input it is also possible, from the saturated Pac sp once saturated power and SOC P charge state input, to determine the temporal variation of the state of charge using the SOCV TC table.
• the temporal variation of the state of charge of the ESS energy storage system may for example correspond to a state of charge of a previous iteration when the simplified model is used to perform a simulation.
• the model obtained makes it possible to model an ESS energy storage system in a rapid manner without calling into question the accuracy of the simulations making it possible to model said system.
• each value of the SOCV table TC of the temporal variation of the state of charge of the system is obtained by means of a simulation performed for a state of charge SOC (j) chosen as state of charge. Socini initial and a target power Pac_ sp (i) given and belonging to a first subset of the plurality of simulations, said simulation being performed on a iess duration. This simulation will make it possible to associate with each reference power ac sp (i) and with each state of charge SOC (j) a temporal variation of the state of charge ⁇ (ij ' ).
• the time step At used for the simulations using the complex model is chosen such that 1 0 2 At ⁇ tEss, preferably 1 0 3 At ⁇ tEss.
• FIGS. 4A-4B and 5A-5B illustrate two simulations of the first subset of the plurality of simulations performed for two different SOC load states and for the same ISE simulation time.
• FIGS. 4A and 5A reproduce the power P ac at the output of the energy management system as a function of time
• FIGS. 4B and 5B reproduce the state of charge SOC of the energy management system as a function of time.
• the simulation illustrated in Figures 4A and 4B describes a simulation in which the power P ac output of the energy storage system on the whole iess simulation time is equal to the power setpoint Pac_ sp.
• Equals means that the power P ac at the output of the system ESS is equal to the reference power Pac_p sp at plus or minus 5%, or more or less 2%, preferably plus or minus 1%.
• the state of charge SOC decreases steadily so that it is easy, from this simulation, to extract a value of the temporal variation of the state of charge FIGS. 5A and 5B describe for their part, a simulation in which the power Pac at the output of the energy storage system over the duration ISESS of the simulation is not constant, but varies during the simulation. This variation can for example be explained by the fact that the storage device DS of the energy storage system ESS is almost fully loaded (or discharged).
• each value of the correspondence table SOCV TC is:
• interp ⁇ [x 0 , xl], [y 0 , yi], x] is the function that determines the value of y corresponding to the value of x by interpolation from the values of
• an interpolation is performed using simulations previously performed.
• a first table in which the columns represent different states of SOC fillers, rows represent different target powers Pac_ sp and whose cells contain the average power ⁇ P ac> during said simulation (see table 1) and a second table wherein the columns represent different states of SOC fillers, lines represent target powers Pac_ s different and which the boxes contain the total variation of the ASOC load state during the simulation (see Table 2).
• a simulation is performed for a nominal power P ac _ s zero then the following simulations are performed for powers instructions ac P _ s growing up.
• the temporal variation of the state of charge dSOC (il, j)
• 6A and 6B illustrate 3D table the change in TC SOCV load state of the system (along the z axis) as a function of the desired power Pac_ sp and the state of charge SOC.
• These two figures clearly show two zones separated by two black lines which correspond to the limits imposed by the minimum power and the maximum power, these limit values varying according to SOC state of charge as will be described later.
• This representation allows to reveal an important aspect on the table of the change in SOCV TC charge state of the system: it can assign a value to the time variation of the state of charge for setpoint powers Pac_ sp higher (in absolute value) than the limit values which are the maximum power and the minimum power for a given state of charge.
• each value of the SOCV TC match table is equal to A50 (t ,;) , with ASOC (i, j) the tESS
• each value of the table of the variation of the state of charge SOCV TC of the system is equal to:
• the plurality of simulations comprises a simulation carried out with a state of charge.
• initial SOCini equal to the state of maximum load SOCmax
• a duration equal to the time necessary for the complete unloading of the storage system tDch (that is to say until the state of charge reaches the value of the minimum charge state SOCmin)
• a power Pac_ sp positive infinite set a power sp positive infinity Pac_ set a desired power Pac_ sp much greater than the power that the system can provide.
• the average power ⁇ Pac> k over a given interval can be considered as the maximum power for the state of charge SOCk corresponding to said interval.
• the calculation of the table PAC_MAX_TC of maximum power as a function of the state of charge SOC comprises:
• the plurality of pairs (SOCk, ⁇ P ac > k) then constitutes the table PAC_MAX_TC of maximum power as a function of the state of charge SOC so that for each state of charge SOCk given, it is possible to attribute a power maximum (equal to ⁇ P ac > k).
• PAC_MAX_TC of maximum power as a function of the state of charge SOC so that for each state of charge SOCk given, it is possible to attribute a power maximum (equal to ⁇ P ac > k).
• the plurality of simulations comprises a simulation carried out with an initial state of charge SOCini equal to the minimum state of charge SOCmin, a duration equal to the time necessary for the complete charging of the storage system tcn (that is to say until the state of charge reaches the value of the maximum state of charge SOCmax) and a negative power of infinite sp Pac_ set.
• Shall mean a set of ac power P _ s infinite negative a nominal power P ac _ s much greater than the power that the system can accept when charging.
• the average power ⁇ P ac > k over a given interval can be considered as the minimum power for the state of charge SOCk 'corresponding to said interval.
• the calculation of the table PACJVIIN TC of minimum power according to the state SOC charge includes:
• the plurality of pairs (SOCk, ⁇ P ac > k) then constitutes the table PAC_MIN_TC of minimum power as a function of the state of charge SOC so that for each state of charge SOCk 'given, it is possible to assign a minimum power (equal to ⁇ Pac> k).
• each simulation of a first subset of simulations is performed for a charge state SOC (j) and a given ac power sp (i) and each state of charge SOC (j) is separated.
• SOC of the previous (j-1) and / or the next SOC (j + 1) by a step of adaptive state of charge and / or each desired power Pac_ sp (i) is separated from the previous Pac_ sp (i- 1) and / or the next power Pac_s P (i + 1) of an adaptive nominal power step.
• the simulation step is repeated for a plurality of ISES durations.
• a correspondence table simulation duration ÎESS the correspondence table used by the control system being chosen according to the required accuracy.
• FIG. 9 makes it possible to highlight the advantages of the model obtained by means of a method according to a first aspect of the present invention.
• the first HMC histogram corresponds to the simulation time when the simulation is performed using a complex system and with a time step ⁇ equal to 1 second.
• the graph also comprises five groups H1, H2, H3, H4, H5 of four histograms, each group corresponding to a simulation identical to that performed with the complex model, but carried out using a simplified model obtained by a method according to a first aspect of the invention and for a given simulation time.
• each grouping corresponds to a simulation performed with a simplified model and with a simulation time step equal to the simulation time ÎESS used during the method for determining the parameters of said simplified model.
• the first histogram concerns the simulation time and the second, third and fourth histograms relate to:
• the grouping H1 relates to a case where the time ISESS is equal to 926 seconds.
• the precision obtained with the methods "with interpolation” and “without interpolation” is substantially identical while the precision obtained with the method “with the previous value” is lower.
• the simulation time using a model obtained by a method according to the invention is significantly less than the simulation time using a complex model according to the state of the prior art.
• the group H2 relates to a case where the time ISESS is equal to 463 seconds. It can be seen that the accuracy of the "interpolation” and “non-interpolation” methods is significantly improved while the precision with the "with the previous value” method has deteriorated significantly.
• a complex model comprising a model relating to a conversion device and a model relating to a storage device. It is important to note that the method according to a first aspect of the invention does not depend on the type of complex model used and the following complex model is given for purely illustrative purposes. It will make it possible to show the advantage in terms of simplification of the modeling (and therefore of the piloting) of an energy storage system that the model obtained by using a method according to a first aspect of the invention.
• the input values are initialized during the first iteration and then updated during the following iterations.
• the output values are calculated according to the input values as well as the model parameters.
• the model parameters are usually provided by the manufacturer of the conversion device or storage device, but can also be determined from experimental tests and measurements.
• X the value of the magnitude X at the iteration t.
• Y the function related to a correspondence table Y will be noted fv. It is also assumed that when a value is not directly available in a correspondence table, the latter is obtained by interpolation, for example linear interpolation, from the values available in said correspondence table (it is a standard method of using a lookup table).
• the use of the model relating to a conversion device will now be detailed.
• the inputs, parameters and outputs relating to the model will be introduced as and when.
• the model allows firstly to calculate the ⁇ phase angle between the active part Pac_ sp and sp Qac_ the reactive part of the target power.
• the active part Pac_ sp and the reactive part Qac_s P of the power set points are inputs of the model.
• the angle of departure is simply obtained using the following relation:
• the reactive power setpoint Qac_ sp is null the entire simulation and thus the value of the phase angle ⁇ is independent of iteration.
• the model makes it possible to calculate the apparent maximum power Smax_LUT by means of a correspondence table Smax_LUT providing the maximum apparent power Smax as a function of the phase shift angle ⁇ so that:
• the model then makes it possible to calculate the active powers Pac sat and reactive Qac_stat maximum that the conversion system can absorb. This calculation is carried out using a saturation function fsat which models the saturation applied to the active and reactive power setpoints. In other words :
• the model calculates the DC power Pdc corresponding to the target power Pac_ sp using the correspondence table giving Pac_LUT alternative ac power P as a function of the DC power Pdc, reactive power Ckc, tension Udc received at the input and the voltage U ac ; in other words :
• Pac fp ac _LUT (Pdc> Qao U do U ac)
• the model also makes it possible to calculate the active power P ac using the correspondence table P ac _LUT introduced previously so that:
• the set reactive power Q ac _ sp is considered to be zero throughout the simulation and therefore the same is true of Q ac . It is also important to note that the output voltage U ac is imposed by the system so that it does not have a fluctuating voltage and its value is given by the nominal AC voltage of the Unom conversion system, the latter being a parameter of the model.
• the model also makes it possible to take into account the fact that the current Idc 'is limited by a maximum current IchMax during the charging of the storage system and a maximum current i DchMax during the discharge of said system.
• the maximum current I DchMax during the discharge is obtained using correspondence table l DchMax_LUT providing the maximum current I DchMax during the discharge according to the state of charge SOC, the state of health SOH and temperature T:
• the current Idc can be determined using the following relation: ax
• the model also makes it possible to calculate the variation of the state of charge of the storage device.
• This variation can be obtained using a SOCs P eed_LUT correspondence table providing the temporal variation of the state of charge as a function of SOC state of charge, SOH state of health, Idc current. and the temperature T so that:
• the model is able to calculate the states of charges corresponding to the following iterations using the following relations: dSOC *
• ⁇ is the time step between two successive iterations.
• the model also makes it possible to calculate the state of health of the storage means.
• This state of health is estimated through aging that corresponds to a change in health status. This aging has two components (which are negative):
• calendar ⁇ 50 ⁇ ⁇ 15 ⁇ ;
• the model makes it possible to determine aging due to time ASOHj alSpeed by means of a mapping table ASOHcais P eed_LUT providing aging over time as a function of state of charge SOC, state of health and the temperature so that:
• the model makes it possible to determine the aging due to the cyclization ASOHcycspee d using a correspondence table ASOHcycs P eed_LUT providing the aging due to the cycling as a function of the state of charge SOC, the state of health SOH , the current Idc and the temperature T so that:
• the state of health is then calculated using the following relationship
• RCH 1 fR ch _LUT (SOH T , T T , SOC T , I ⁇ 1 )
• the model allows to determine the system resistance Rûch during the discharge using a correspondence table RDch_LUT providing the resistance Rûch of the system during the discharge according to the current Idc during the discharge, the state of charge SOC, state of health SOH and temperature T so that:
• the model makes it possible to know the open circuit voltage OCVch during charging by means of a correspondence table OCVch_LUT providing the open circuit voltage OCVch during charging as a function of the charge state SOC, of the state of health SOH and current Idc so that:
• the model makes it possible to know the open circuit voltage OCVuch during the discharge by means of a correspondence table OCVDch_LUT supplying the open circuit voltage OCVuch during the discharge as a function of the state of charge SOC, of the state of health SOH and current Idc so that:
• the model makes it possible to determine the DC voltage Udc at the terminals of the storage system using Ohm's law by means of the following relation:
• U dc OCVc h / Dch - l dc x Rch / och where I is negative when charging and positive when discharging.
• U ⁇ axch an indicator for exceeding the maximum voltage Umax_limit_status is updated.
• This maximum voltage can be calculated using the following relation:
• the model then makes it possible to calculate the power supplied by the battery Pdc by means of the following relation:
• the conversion device model is combined with the storage device model to obtain a complex model capable of modeling an energy management device.
• the energy storage device can be simulated.
• the complex model comprises a large number of parameters and variables to be calculated and is difficult to use for efficient control of such an installation, especially since the pilot's time step of such a system is of a few minutes, even a few hours, whereas the step of simulation time in the case of a complex model must be of the order of one second or even less than the second so as not to lose precision in the results obtained.
• PAC_MAX_TC a maximum power table according to the state of charge SOC
• PAC_MIN_TC minimum power table
• PACM X fpAC_MAx_Tc (SOC T )
• the Pac power is equal to the power setpoint Pac_ sp. Otherwise, the Pac power is equal to the power limit PACMin / PACmax closest to the target power Pac_ sp.
• time variation of the state of charge can be calculated using the table of the variation of the state of charge (SOCV TC) of the system according to the target power and a Pac_ sp state of charge SOC using the following relation: dSOC * _ tt
• the time step between two iterations is not equal to ⁇ like, but the term used when iess implementation of the method according to a first aspect of the invention.
• the model's correspondence tables were calculated from simulations performed with a time step ⁇ , the precision of the results obtained with the simplified model remains very close (see Figure 9) of those obtained with a complex model, without however, require as much computing resource.
• the simplified model obtained using a method according to a first aspect of the invention makes it possible to go from a simulation time step equal to ⁇ to a simulation step equal to ÎESS without significant loss in accuracy. results obtained.
• an embodiment of a second aspect of the invention relates to a method for controlling an energy storage system ESS calculating operating instructions of said ESS system from a model of said system characterized by said model is obtained using a method 100 according to one of the preceding claims.
• the method may comprise a first initialization phase during which a method 100 according to a first aspect of the invention is implemented.
• the method comprises a second phase during which instructions to the energy storage system are generated at regular intervals using the model obtained during the initialization phase of the system. to drive said ESS system.
• said setpoints take the form of an optimal flight path whose calculation is performed for a given time horizon, typically of the order of several hours, for example 12h.
• This trajectory is updated regularly according to a period of PEI update, usually several times per hour, for example every 15 minutes. For each update (every 15 min for example), a simulation covering the horizon of the trajectory (12h for example) must be carried out within the control system.
• This update takes into account the evolution of the state of charge of the storage system and, possibly, information concerning parameters likely to influence the evolution of the system (such as weather forecast information if the system of storage is connected to a renewable energy source for example).
• This update is performed using a simulation whose time step is equal to the duration ISESS, for example 1 minute, said simulation being carried out using a simplified model obtained by a method according to a first aspect of the invention. It is interesting to note that with a complex model according to the prior art, a simulation time step of one minute would not make it possible to obtain sufficient accuracy to update the flight path. It would therefore be necessary to reduce the simulation time step and thus increase the calculation time and the resources required for this calculation.
• the simplified model obtained by means of a method according to a first aspect of the invention makes it possible to solve this technical problem by allowing precisely to adopt a much higher time step. than that of the prior art without this entailing significant losses in the precision of the flight path.
• an embodiment of a third aspect of the invention relates to a control device of an energy storage system ESS comprising means for sending operating instructions to the system of storage. storing the ESS energy and means for receiving data relating to the operation of the ESS energy storage system.
• the control device and the ESS energy storage system communicate via an Ethernet network and the control device comprises an Ethernet type network card.
• the control device also comprises means for implementing a control method 100 according to the preceding claim.
• the control device may in particular comprise data acquisition means relating to the complex model relating to the energy storage device DSE and / or the conversion device DC, such as a keyboard associated with a screen or even a touch screen.
• control device comprises means of connection to a network, for example the Internet, and the data relating to the complex model associated with the energy storage system ESS are retrieved from a server, for example the server of the manufacturer of the DC conversion and energy storage devices DE that comprises said ESS system.
• the control device also comprises calculation means, for example a processor or an ASIC card, said calculation means making it possible to carry out the steps of the method according to a first aspect of the invention in order to obtain a simplified model of the storage system. energy, but also to generate operating instructions from said simplified model.

Landscapes

• Engineering & Computer Science (AREA)
• Physics & Mathematics (AREA)
• Evolutionary Computation (AREA)
• General Physics & Mathematics (AREA)
• Theoretical Computer Science (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Medical Informatics (AREA)
• Software Systems (AREA)
• Artificial Intelligence (AREA)
• Health & Medical Sciences (AREA)
• Automation & Control Theory (AREA)
• Power Engineering (AREA)
• Computer Hardware Design (AREA)
• Geometry (AREA)
• General Engineering & Computer Science (AREA)
• Supply And Distribution Of Alternating Current (AREA)

Applications Claiming Priority (2)

Publications (1)

Family

ID=61258330

Family Applications (1)

Country Status (4)

Families Citing this family (2)

Family Cites Families (4)

• 2017
  • 2017-10-20 FR FR1759892A patent/FR3072794B1/fr active Active
• 2018
  • 2018-10-12 EP EP18783502.0A patent/EP3698261A1/de active Pending
  • 2018-10-12 US US16/757,257 patent/US11784504B2/en active Active
  • 2018-10-12 WO PCT/EP2018/077960 patent/WO2019076776A1/fr unknown

Also Published As

Similar Documents

Legal Events