Method for controlling a collective electrical energy storage system

Abstract

The invention relates to the control of a battery, comprising steps consisting of: • determining the value of a set of parameters and variables relating to the battery, the electrical network, a battery charging device and a battery discharging device; • solving a system of equations corresponding to a set of constraints that must be respected by parameters and variables relating to the battery, the electrical network, the battery charging and discharging device, each solution comprising a set of variables which respect said set of constraints; • selecting, from the set of solutions, a single solution comprising a set of variables which respect the set of constraints; and • issuing a selective activation instruction of the battery management system (BMS) for the single selected solution, for frequency support (FCR) of the network, charging or discharging of the battery.

Description

Title of the invention: Method for controlling a collective electrical energy storage system

[0001] The present invention relates to the field of electrical energy management.

[0002] Electrical energy is produced by different means. It is transported in particular by a network. An electrical network is a system that must be in balance to function: energy production must be equal to energy consumption. If there is overconsumption, there is a risk of cascading outages; and if there is underproduction then the network must call on a "reserve" and / or load shedding system.

[0003] The present invention is advantageously implemented in the field of automatic power reserve, or frequency stabilization reserve, called FCR (Frequency Containment Reserve in English) or even “primary reserve”, which is a reserve used to adjust the production distributed on a network when consumption is disturbed.

[0004] In this area, local electricity producers are asked to declare their FCR capacity to the network manager. If the primary FCR reserve does not allow the nominal frequency value to be restored, then a secondary reserve (aFRR: for automatic frequency restoration reserve) is requested, according to a similar principle.

[0005] The electricity transmission system operator, RTE in France, which is responsible for the public high-voltage electricity transmission system in metropolitan France, sends a signal to all the players in the aFRR secondary reserve. For the FCR primary reserve, the activation signal is directly deduced from the frequency measurement and the calculation of the frequency variation in real time.

[0006] For a day J of supply of FCR, this declaration must be made, for example in France today, at the latest on D-1 (24 hours in advance), in 4-hour slots.

[0007] Beyond the electricity grid, electrical energy can be produced locally, for example by renewable energy production devices (REP), in particular photovoltaic panels. Electrical energy is consumed locally by various consumption devices (homes, industry, etc.).

[0008] In self-consumption, if consumption and production are identical at the same time, then the electricity is produced and consumed locally, otherwise the difference is returned to the network, stored, or is lost.

[0009] In addition to the network, a collective electrical energy storage system, or battery, can be implemented, which makes it possible to supply or store electricity and which is connected to both the electricity grid, renewable energy production devices and consumption devices.

[0010] There is therefore a need to optimize battery management, i.e. to control the charging and discharging of such batteries.

[0011] The present invention aims to enable participation in the primary FCR reserve of a network, with electricity produced using a renewable energy source (RE) associated with a storage system. By RE, we mean at least any one of the energy sources among: solar photovoltaic, wind, hydroelectric (waterfalls, tides) allowing electricity to be produced.

[0012] In this context, the invention relates, according to a first of its objects, to a method for controlling a collective electrical energy storage system, or battery,

[0013] The battery being connected to: • an electrical network selectively allowing charging or discharging of said battery; • a device for charging said battery, separate from the electricity network, and for example using renewable energy; • a device for discharging said battery, or device for self-consumption of electrical energy, separate from the electrical network,

[0014] the battery comprising a battery management system (BMS), configured to selectively activate: • a discharge of the battery on the electrical network, for frequency support (FCR) of the latter; or • a discharge of the battery on the self-consumption electrical energy device; or • charging the battery by the charging device of said battery or charging by the network.

[0015] it is essentially characterized in that the method comprises steps consisting of: • determine the value of a set of parameters relating to the battery, the electrical network, the battery charging device and the battery discharging device; • determine the value of a set of variables relating to the battery, the electrical network, the battery charging device and the battery discharging device; • solve a system of mathematical equations, said system of equations corresponding to a set of constraints that must be respected by at least some of said parameters and at least some of said variables, in order to

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[0026] find a set of solutions, each solution comprising a set of variables that satisfy said set of constraints; • select, from the set of solutions, a single solution comprising a set of variables which respect the set of constraints; and • issue a selective activation instruction of the battery management system (BMS) for said set of variable values ​​of the single selected solution, said set of variables comprising binary variables, including: • a binary variable corresponds to a battery discharge instruction on the electricity network, for frequency support (FCR) of the latter; • a binary variable corresponds to a battery discharge instruction on the electrical energy self-consumption device; • a binary variable corresponds to an instruction to charge the battery by the battery charging device. It can be expected that the step of selecting a single solution comprises a step of minimizing the value of an objective function, the objective function comprising a relationship between a predetermined set of parameters and variables, and corresponding to a predetermined optimization of the charging or discharging of the battery. We can predict that the minimization of the objective function is determined by the following relation: minFO- min (fl-ajl) With I PsoutRj' injetTr " ^FCRj Battery »FCR) ] « / / P supported *dt \\ N a^f2 = 04* 1- —— * £. Pconsoj*dt \ \ * etmsoj. - / j with : * ^soutR i “ Ppvi 4" Pinjcti 4" Pchj ” Poéehj 4" ^conso, i • a, a predetermined coefficient whose value can be dynamic, and which represents, depending on its value, one allocation or another, for example in favor of self-consumption in order to encourage it; Or : T(s,i) is a parameter relating to the purchase (withdrawal) tariff on the network in time slot i; T_(r,i) is a parameter relating to the resale rate (injection) of surplus to the network in time slot i;

[0027] R_(FCR,i) is a parameter relating to the FCR income on the time slot i for 1 MW sold;

[0028] P_(Battery) is a parameter relating to the maximum power sold for the FCR;

[0029] P_(conso,i) is a parameter relating to consumption (power) over time slot i;

[0030] P_(soutR,i) is a variable relating to the power drawn from the network over time slot i;

[0031] P_(injct,i) is a variable relating to the power injected into the network over time slot i;

[0032] FCR_i is a binary variable relating to the selection or not of time slot i for FCR

[0033] It can be provided that the values ​​of the parameters and variables are determined over a time slot i of 30 minutes and that the objective function is determined over a time slot of 48 hours.

[0034] It can be provided that the constraints include at least one equation making it possible to define time slots of 4 consecutive hours for possible frequency support (FCR) of the electrical network.

[0035] It can be provided that the values ​​of the variables or parameters relating to electricity consumption and production are determined by extrapolation of historical values, by correlation or by prediction, including meteorological.

[0036] It can be provided that the step of issuing an instruction for selective activation of the battery management system comprises the following instructions: • If, for a time slot, an instruction to discharge the battery onto the electrical network is issued for frequency support (FCR) of the latter, then the instructions to charge or discharge the battery are inhibited on this time slot; • If, for a time slot, an instruction to discharge the battery onto the electrical network is not issued for frequency support (FCR) of the latter, then an instruction to charge or discharge the battery can be issued on this time slot, depending on the value of the constraints on this slot.

[0037] It can be provided that for a time slot, an instruction to discharge the battery on the electrical network is not issued for frequency support (FCR) thereof, the method further comprising, for said time slot, steps consisting of:

[0038] • Divide said time slot into a set of at least one sub-slot of duration predetermined, and for each sub-slot:

[0039] o Determine the electrical production,

[0040] o Determine the electrical consumption, and

[0041] if the determined electrical production is greater than or equal to the determined electrical consumption, then the battery management system (BMS) issues a battery discharge instruction in the discharge device or a battery charge instruction; otherwise

[0042] if the determined electrical production is lower than the determined electrical consumption, then the battery management system (BMS) issues an inhibition instruction.

[0043] We can provide a step consisting of solving the following equation:

[0044] / । j] ( 1 I■ j max( 1- \eco„s<3 )) ^conso + \ \ EpV / / ^PV

[0045] With

[0046] Esout the energy drawn (consumed) from the network over the optimization period, in this case a sub-slot;

[0047] Econso the total energy consumed over the optimization period;

[0048] EPV the total energy produced over the optimization period by the charging device;

[0049] Emy the total energy injected into the network over the optimization period

[0050] and

[0051] Ex^^fdt

[0052] The charging device is a renewable energy production device (RE), in this case photovoltaic (PV) panels.

[0053] It can be provided that the step of solving the system of equations is implemented by a mathematical solver to calculate a set of solutions meeting all of the constraints.

[0054] According to another of its objects, the invention relates to a computer program comprising program code instructions for executing the steps of the method according to the invention, when said program is executed on a computer.

[0055] Other characteristics and advantages of the present invention will appear more clearly on reading the following description given by way of illustrative and non-limiting example and made with reference to the appended figures.

[0056] The figures are not to scale. Some details may have been omitted and others enlarged, to facilitate understanding.

[0057] DESCRIPTION OF THE DRAWINGS

[0058] [Fig. 1] illustrates the principle of electrical connections of a battery within the meaning of the present invention,

[0059] [Fig.2] illustrates a variation of the frequency around 50Hz. DETAILED DESCRIPTION

[0060] As explained at the beginning of the description, the invention makes it possible to determine which predetermined time ranges should be selected to enable FCR support by a producer of electricity produced in renewable energy. Typically in France, the predetermined time ranges are 4 consecutive and predetermined hours: 0h-4h; 4h-8h; 8h-12h; 12h-16h; 16h-20h and 20h-24h for the FCR primary reserve. For all other markets, these time ranges vary, for example, either 1h or 30 minutes.

[0061] Each electricity producer must declare the day before the predetermined time slot(s) over which it is positioned to support the FCR.

[0062] It is therefore essential to control the destination of the electricity produced in renewable energy, therefore stored in a battery: either in self-consumption or in FCR / aFRR.

[0063] There is therefore a need for prediction to be able to anticipate the decision of self-consumption or support for FCR / FRR.

[0064] This involves battery management (BMS for Battery Management System; or EMS for Energy Management System by anglicism), which makes it possible to optimize the battery charge / discharge instructions in order to improve its aging.

[0065] Preferably, it is planned to charge the battery from the network only during a self-consumption slot, but the battery can be charged and discharged on the network when it is in FCR service.

[0066] It is planned to model the electrical energy exchange system between the network, the battery, the self-consumption device (therefore battery discharge) and the self-production device (ENR, therefore battery charging) according to a system of mathematical equations.

[0067] This system of equations must be solved, in this case by a solver. For example, the solver is the GLPK solver for GNU Linear Programming Kit. It comprises a set of quantities which must satisfy a set of predefined constraints, for example the constraint that the power balance is zero (see below).

[0068] The quantities of the system of equations are a set of variables and a set of parameters.

[0069] The parameters are values ​​considered constant over a given time range, in this case 30 minutes.

[0070] Table 1 below gives an example of parameters. Table! Parameter Parameter description Solar energy production (power) P . Consumption (power) Purchase tariff [withdrawal] on the network on the slot i FCR income on the slot for 1 MW sold Storage system efficiency, for example of value -0.95 Resale tariff (injection} of surplus to the network Battery capacities (kwh) Maximum power sold for the FCR Minimum admissible SOC for the battery, for example of value 0.05 P Maximum power of the customer meter

[0071] The value of each parameter is known. Preferably, it is constant over each time step. It can be measured or estimated, for example by a history, in particular from the previous year, possibly coupled with forecasts, in particular meteorological forecasts.

[0072] Table 2 below gives an example of variables, as well as the domain of each variable.

[0073] In this example there are three binary variables and 5 normalized variables. Table 2 Variable Deso'îpUoB of the variable Domain Power drawn from the network on time slot s bw Power charged in U battery on time slot i la ri Power discharged from the battery on time slot 1 Battery charge status on time slot s >ari Power injected into the network on time slot i FŒ If the slot is chosen for FCR on time slot i 0 or i If the slot is chosen for self-consumption charging on time slot i Ü OR 1 If the slot is chosen for self-consumption discharging on time slot 1 ü or 1

[0074] The relationships between variables and parameters are a set of constraints which are modeled by a system of equations, one embodiment of which is illustrated in Table 3 below. Table 3 br Description Constraint 1 Power balance 2 Only from charging 4 0 2bs.s Only from discharging 4 ~ $ 3 Charge Limit in If î multiple of 4 Casmrm * U ~ 4 ~ Ü ~ ~ *5 >0 If not C»snw K (U “ 5oC5-} ~ (1 — ~ ^c»4 * * 7 Û 4 Discharge Limit in AC If î multiple of 4 * H>3 * 4 ™ SeC^)) - > 0 If not ?" . «' / .j» x * dt kggÈs-^ï-iÿ, * j ................................ b 4 5 Resale Lnergi® of gNR ]PV) only If > 0 pfn.“ Pn»,r 4 $i not PjRjÿ.çf.j - 0 6 gvôMîo” of the share SI i - first “réneaa of ?hudîm; If not: * $7¾ ™ ( * SOCU + -2^---+ . di i J» Q 7 energy priority of W If if 0 rOuwiS: ® If not £ 8 s Sale of FCR on If not divisible by 4 O g Return to Soc For U last half hour i: Pt * US» Csfrsrs* * OO '10 Chutx of the strategy -F - '1

[0075] In this non-limiting example, the constraints can be described as follows.

[0076] Constraint 1: Balance of active powers on the network

[0077] Constraints 2 and 2a: sets the value of the binary variable of the charge (discharge) according to the value of the charge (discharge) power, allows the setpoint powers (charge and discharge) to be forced to be zero if the binary variable is zero

[0078] Constraint 3: This constraint limits the battery charge. If i (hour) is a multiple of 4, we are at the beginning of a 4-hour slot. We must check whether (1-SOC), which corresponds to the space available in the battery, is greater than 30%, to perform the FCR. If FCR(i) is zero, there is no constraint on the maximum SOC charge. If not, we apply the classic exchange constraint: the SOC must not exceed 1.

[0079] Constraint 4: This constraint represents the discharge limit, according to the same principle as constraint 3. This constraint makes it possible to limit the SOC to zero. In the case of the start of a 4-hour slot, we check with the FCR requirements in addition (SOC min of 30%), we also add a SOCmin to limit deep exchange of the battery in self-consumption.

[0080] Constraint 5: We can inject into the network only if production is greater than consumption. The quantity injected into the network must only be the surplus.

[0081] Constraint 6: The variation of the SOC is the sum of the exchanges which take place on the battery. The charged power is divided by the efficiency, we draw more energy than what is actually stored, loss of the converter at the charge. The discharged power is multiplied by the efficiency, it is the losses of the converter at the discharge.

[0082] Constraint 7: Use of PV / RE energy as a priority when PV production covers consumption, no discharge or withdrawal during these time steps. If production does not cover consumption, we cannot withdraw more than what is necessary for direct consumption. This limits purchase / resale on the network between peak and off-peak hours.

[0083] Constraint 8: The binary variable FCR of i must have the same value over the 4-hour slots.

[0084] Constraint 9:

[0085] To limit the discharge, it is desirable to limit the value of SOC on the last slot, otherwise a strong discharge can be observed. To this end, the energy exchanges can be limited in this case by adding and subtracting a limit e from the initial SOC. This limit e can be equal to 0. In this case it is a value fixed at 0.02, which allows constraint 9 to be relaxed.

[0086] Constraint 10: This constraint limits the number of possible actions in the same slot; the battery can either be charging for self-consumption, or discharging for self-consumption, or in service for FCR.

[0087] For each time step i, in this case 30 minutes, the solver solves all the equations of the system and thus determines the values ​​of all the variables which meet all the constraints, in this case P_(soutR,i); P_(Ch,i); P_(Dch,i); SOC_(,i); P_(injct,i); FCR_i; AC_(charge,i) and AC_(decharge,i).

[0088] According to the invention, the problem to be solved is of the MILP (Mixed interger linear programming) type. Optimization makes it possible to determine the ranges, or slots, most conducive to self-consumption. This optimization makes it possible to choose the best slots, i.e. those which optimize the balance between FCR and self-consumption while promoting self-production.

[0089] For each time step i, each variable has only one possible value. These values ​​are stored in a memory.

[0090] Thus, over a predetermined time range, a set of variable values ​​is obtained. In this case, for a time step i of 30 minutes over a 4-hour range, a set of 8 values ​​per variable is obtained, or 48 values ​​per variable over a 24-hour range. Objective function

[0091] To select a value per variable from the set of values ​​per variable, we plan to minimize an objective function.

[0092] The objective function corresponds to the desired optimization of the collective electrical energy storage system.

[0093] The objective function therefore depends on what one wishes to optimize. Typically, the objective function aims to find the set of variables that makes it possible to maximize or minimize a function defined as a linear combination (MILP type problem) of the parameters and variables, for example to maximize the power sold for the FCR, to maximize solar production, etc. and typically, to optimize at least one of: the power drawn from the network, the power charged into the battery, the power discharged from the battery, the state of charge of the battery, and the power injected into the network.

[0094] The objective function can be defined as a kind of constraint, but which is resolved by the solver after the resolution of the system of mathematical equations.

[0095] It is a mathematical function comprising a relationship between some of the parameters and / or variables described above.

[0096] In this case, in a non-limiting example, the objective function FO is determined as follows:

[0097] minFO= min / fl-cq / l)

[0098] With fy 'soutRi^dt " P injet Tr j dt-Rp(jRj ^Battery«FCR,} ] [OiOO] / H a}*f2 = a}*EnergieAC N 1- * Lf ^coiiso, / 'dt \ \ L, ] I 1

[0101] with: * PsaatR, i “ " ^Pvi "1“ P in jeti "f Pchi - Poéchi + ^conso, i ' el • a, a predetermined coefficient whose value can be dynamic, and which represents, depending on its value, one allocation or another, for example in favor of self-consumption in order to encourage it.

[0102] In this case, the objective function is calculated over N predetermined time steps of 30 minutes (N = 48). All data, variables, instructions and parameters are over a 30-minute step.

[0103] The optimization determines the optimal slots, in this case 4 hours, over a determined time horizon, in this case 48 hours. That is to say that the objective function is calculated over a time horizon of 48 hours, and not over the 30-minute time step because this is too short for fine management of the charging / discharging of the battery.

[0104] We choose a two-day optimization because it is difficult to store energy over long periods. Indeed, the FCR requires a SOC of 50% because the frequency variations are not predictable. Energy storage can be done mainly in the same self-consumption slot.

[0105] Thanks to the present invention it is thus possible for example to control the energy management in a battery, so that it can serve both as an energy source for self-consumption and as an energy source for support of the network frequency (FCR).

[0106] According to the invention, this energy management is hierarchical: if a 4-hour slot is determined to operate the battery in support of the FCR, then this slot is blocked and the battery is only operated for this purpose. If a 4-hour slot is not determined to operate the battery in support of the FCR, then the battery can be controlled for local charging or discharging (self-consumption), depending on its state of charge, the energy demand and its recharging capacity via renewable energy sources.

[0107] For example, the battery can be in FCR support over a time slot from 8 a.m. to 12 p.m., then in the following slot: charging over a time slot from 12 p.m. to 2 p.m., then discharging from 2 p.m. to 3 p.m., then charging from 3 p.m. to 4 p.m., then in the following slot again in FCR support from 4 p.m. to 8 p.m., etc. Charge or discharge outside FCR.

[0108] Once the slots have been selected for FCR support, battery management can be provided for the other slots, called self-consumption slots, typically in order to maximize self-production and self-consumption.

[0109] This is a second optimization loop, downstream of the previous optimization, and which makes it possible to calculate the charge and discharge instructions as close as possible to real time.

[0110] Indeed, in this case, it is possible to charge the battery using the charging device (ENR) or to discharge the battery in the discharging device, depending on the power available by the charging device (ENR) and the power required by the discharging device.

[0111] In this case, the battery management system selectively emits: - either a battery charging instruction by the charging device; - either a battery discharge instruction in the discharge device; - either an inhibition instruction, which amounts to not issuing any instruction charging or discharging the battery.

[0112] Advantageously, the 4-hour slots can be subdivided into a set of sub-slots, each sub-slot being, for example, of a time range between 1 a.m. and 4 a.m., and each sub-slot can be assigned a respective instruction.

[0113] Thus, a sub-slot can be of the same duration as a (FCR) slot, i.e. 4 hours. However, as the climatic conditions can vary over such a 4-hour slot, it may be clever to subdivide the 4-hour slots into a set of sub-slots and thus optimize the management of the energy produced by photovoltaic panels for example and consumed for self-consumption.

[0114] In this sense, a sub-slot is an optimization period.

[0115] In this case, each sub-slot is 1 hour each, the instructions being issued according to values ​​determined according to a 30-minute step preceding the sub-slot. Note that as two successive 4-hour slots can be self-consumption slots, we can have a succession of sub-slots which spread over a time span of 8 hours.

[0116] According to the present invention, each instruction allows the charging or discharging of the battery depending on: - electrical production by charging device, - electricity consumption per discharge device, and - the state of charge (SOC) of the battery,

[0117] according to an optimization described below, and similar to the objective function seen previously for the selection of slots in support of the FCR.

[0118] In this case, the electrical production is determined by estimation or measurement, the electrical consumption is determined by estimation or measurement and the SOC is measured.

[0119] Typically, for a given sub-slot: - if the determined electrical production is greater than or equal to the determined electrical consumption, then the battery management system (BMS) can issue a battery discharge instruction in the discharge device and / or a battery charge instruction, for example a surplus charge instruction; - if the determined electrical production is lower than the determined electrical consumption, then the battery management system (BMS) issues a setpoint which depends on the value of the battery SOC: • if the battery SOC value is higher than a predetermined threshold value, then the battery management system (BMS) can issue a battery discharge instruction; • if the battery SOC value is lower than the said predetermined threshold value, then the battery management system (BMS) can issue an inhibition instruction.

[0120] As the sub-slots have a low hourly amplitude (Ih), it is estimated that the values ​​are constant and reliable in each sub-slot.

[0121] Similar to the determination of time slots to enable FCR support, the aim here is to determine an instruction for each sub-slot, using a second objective function. In this case, the aim is to maximize the second objective function in the following manner:

[0122] m„„ fi / jj *17, / i / ^££1 j

[0123] With

[0124] Ego# = Energy drawn (total consumption) from the network over the optimization period (kWh);

[0125] Econso = Total energy consumed over the optimization period (kWh);

[0126] EPV = Total energy produced over the optimization period (kWh) by the device charge ;

[0127] Einjc = Total energy injected into the network (resold) over the optimization period (kWh); and 101281

[0129] with N the number of time steps of the given self-consumption slot and the index x representing the type of energy: sout, conso, PV or injet, respectively for withdrawn, consumed, photovoltaic and injected.

[0130] For example the time step is 30 minutes.

[0131] It is possible to plan to simulate the entire operation of the battery, and to calculate the energy exchanges requested by the FCR and the aFRR based on frequency signal histories (FCR) or the N signal given by RTE (aFRR).

[0132] Advantageously, the variables are the same as those in table 2 above.

[0133] On the other hand, the parameters of the following table 4 are provided: Table 4 Parameter Description of the parameter Solar energy production Consumption Purchase tariff (withdrawal] on the network on the time slot i FCR income on the time slot for 1 MW sold Storage system efficiency £ aesm-i s? Battery capacity kWh ^msrn'ris Max. throughput sold for FCR Minimum admissible SOC for the battery Upper Soc terminal for the $ constraint £ bs«'*»' fs / sr&w^ Lower Soc terminal for the S constraint $ * Max. meter power

[0134] And we predict the constraints of the following table 5: Table 5 Ns Constraint Power balance ^ssî / y- Cros “ *Vs / «t3 “ ^O*î-?s,s ~ $ Only from the charge y$ÿfr * & + > Ô 2bss Only from the discharge ^^Xs^si^a * ™ $ 3 Charge limit in * 0< > "' ( A "' »^xd< $ ?: > Û Discharge limit in AC * « - 5<ïiss) -- > $ 5 Resale Energy from the PV only $ Evolution of the SOC If i ~ first slot of the horhem: If wrs: « 50C; - * 5O.q„s T. dî | * p} ss: û y Corse ers energy priority of the Pv ai ^$>sr.î ~ -~ d ^amsu ~ ^£i«Sm " $ If not, + Cry.rb Choosing your strategy Limit energy exchange 9.9J ' Cscf.rfst'js —■ SeÀ's'Ssü.gs -- kx ' Ceajjs-'jg

[0135] In this non-limiting example, the constraints can be described as follows.

[0136] Constraint 1: Balance of active power on the network.

[0137] Constraints 2 and 2a: sets the value of the binary variable of the charge (discharge) according to the value of the charge (discharge) power, allows the setpoint powers (charge and discharge) to be forced to be zero if the binary variable is zero.

[0138] Constraint 3: This constraint limits the battery charge. If i (hour) is a multiple of 4, we are at the beginning of a 4-hour slot. We must check whether (1-SOC), which corresponds to the space available in the battery, is greater than 30%, to perform the FCR. If FCR(i) is zero, there is no constraint on the maximum SOC charge. If not, we apply the classic exchange constraint: the SOC must not exceed 1.

[0139] Constraint 4: It represents the discharge limit, same principle as constraint 3. This constraint makes it possible to limit the soc to zero. In the case of the start of a 4-hour slot, we check with the FCR requirements in addition (min SOC of 30%), we also add a SOCmin to limit the deep exchange of the battery in self-consumption.

[0140] Constraint 5: We can inject into the network only if production is greater than consumption. The quantity injected into the network must only be the surplus.

[0141] Constraint 6: The variation of the SOC is the sum of the exchanges which take place on the battery. The charged power is divided by the efficiency, we draw more energy than what is actually stored, loss of the converter at the charge. The discharged power is multiplied by the efficiency, it is the losses of the converter at the discharge.

[0142] Constraint 7: Use of renewable energy (PV for photovoltaic) as a priority when PV production covers consumption, no discharge or withdrawal during these time steps. If production does not cover consumption, we cannot withdraw more than what is necessary for direct consumption. This limits purchase / resale on the network between peak and off-peak hours.

[0143] Constraint 8: The SOC value must be limited on the last slot, otherwise a strong discharge is seen to maximize gains. In reality, there is no end of data series, so this constraint is only necessary for simulations. A power ramp limit can be added to limit the end-of-simulation effects.

[0144] Constraint 9: In this constraint, we limit the total amount of energy exchanged during the self-consumption slot. The total amount of energy exchanged during a self-consumption slot is the sum of the charged energy and the discharged energy or P*dt. Depending on the state of the SOC at the start of the self-consumption slot, we define different upper and lower bounds. For example, if the battery has a SOC lower than the SOC defined as the lower bound, the total energy exchange during the slot must be positive but must not exceed 20% of the battery storage capacity.

Claims

Claims

1. Method for controlling a collective electrical energy storage system, or battery, the battery being connected to: • an electrical network selectively allowing charging or discharging of said battery; • a device for charging said battery, separate from the electricity network, and for example using renewable energy; • a device for discharging said battery, or a device for self-consumption of electrical energy, separate from the electrical network, the battery comprising a battery management system (BMS), configured to selectively activate: • a discharge of the battery on the electrical network, for frequency support (FCR) of the latter; or • a discharge of the battery on the self-consumption electrical energy device; or • charging the battery by the charging device of said battery or charging by the network; characterized in that the method comprises steps consisting of: • determine the value of a set of parameters relating to the battery, the electrical network, the battery charging device and the battery discharging device; • determine the value of a set of variables relating to the battery, the electrical network, the battery charging device and the battery discharging device; • solving a system of mathematical equations, said system of equations corresponding to a set of constraints that must be respected by at least some of said parameters and at least some of said variables, to find a set of solutions, each solution comprising a set of variables that respect said set of constraints; • select, from the set of solutions, a single solution comprising a set of variables which respect the set of constraints; and • issue a selective activation instruction of the battery management system (BMS) for said set of variable values ​​of the single selected solution, said set of variables comprising binary variables, including: • a binary variable corresponds to an instruction to discharge the battery onto the electrical network, for frequency support (FCR) of the latter; • a binary variable corresponds to an instruction to discharge the battery on the electrical energy self-consumption device; • a binary variable corresponds to an instruction to charge the battery by the battery charging device.

2. The method of claim 1, wherein the step of selecting a single solution comprises a step of minimizing the value of an objective function, the objective function comprising a relationship between a predetermined set of parameters and variables, and corresponding to a predetermined optimization of the charging or discharging of the battery.

3. Method according to claim 2, wherein the minimization of the objective function is determined by the following relation: minFO = min( / l-tZ| / 2) with f. = E*[ TsfPsoutRfdt - PuyetfTr *dt - Rfc^P Battery *FCR^ ] and l / \ \ V— «* / 2 = «,* 1- PconsQ;*dt 1 «z il IYP conso, i \ \ i 1 consû / U l / / with : * PsoutR i — Ppvj P in jetj 3" ^cfù " ^Déchi cotisa, i , • oq a predetermined coefficient whose value can be dynamic, and which represents, depending on its value, one allocation or another, for example in favor of self-consumption in order to encourage it; Or : T(s,i) is a parameter relating to the purchase tariff (withdrawal) on the network on time slot i; T_(r,i) is a parameter relating to the resale tariff (injection) of surplus to the network on time slot i; R_(FCR,i) is a parameter relating to the FCR income on time slot i for 1 MW sold; P_(Battery) is a parameter relating to the maximum power sold for the FCR; P_(conso,i) is a parameter relating to the consumption (power) on time slot i; P_(soutR,i) is a variable relating to the power withdrawn from the network on time slot i; P_(injct,i) is a variable relating to the power injected into the network on time slot i; FCR_i is a binary variable relating to the selection or not of time slot i for FCR

4. Method according to any one of the preceding claims, in which the values ​​of the parameters and variables are determined over a time slot i of 30 minutes and the objective function is determined over a time slot of 48 hours.

5. Method according to any one of the preceding claims, in which the constraints comprise at least one equation making it possible to define time slots of 4 consecutive hours for possible frequency support (FCR) of the electrical network.

6. Method according to any one of the preceding claims, in which the values ​​of the variables or parameters relating to electricity consumption and production are determined by extrapolation of historical values, by correlation or by prediction, including meteorological.

7. Method according to any one of the preceding claims, in which the step of issuing an instruction for selective activation of the battery management system comprises the following instructions: • If, for a time slot, an instruction to discharge the battery onto the electrical network is issued for frequency support (FCR) thereof, then the instructions for battery charging or discharging are inhibited during this time slot; • If, for a time slot, an instruction to discharge the battery onto the electrical network is not issued for frequency support (FCR) of the latter, then an instruction to charge or discharge the battery can be issued on this time slot, depending on the value of the constraints on this slot.

8. A method according to claim 7, wherein for a time slot, an instruction to discharge the battery on the electrical network is not issued for frequency support (FCR) thereof, the method further comprising, for said time slot, steps consisting of: Divide said time slot into a set of at least one sub-slot of predetermined duration, called optimization period, and for each sub-slot: • Determine the electrical production, • Determine the electricity consumption, and • if the determined electrical production is greater than or equal to the determined electrical consumption, then the battery management system (BMS) issues a battery discharge instruction in the discharge device and / or a battery charge instruction; otherwise • if the determined electrical production is lower than the determined electrical consumption, then the battery management system (BMS) issues a setpoint which depends on the value of the state of charge (SOC) of the battery: • if the battery state of charge (SOC) value is above a predetermined threshold value, then the battery management system (BMS) issues a battery discharge instruction; • if the battery state of charge (SOC) value is lower than the predetermined threshold value, then the battery management system (BMS) issues an inhibition instruction.

9. A method according to claim 8, comprising a step of solving the following equation: Z 1 Z \ \ , Zi Z | iy? + fepv With Esoul the energy drawn (consumed) from the network over the optimization period; EConso the total energy consumed over the optimization period; EPV the total energy produced over the optimization period by the charging device; the total energy injected into the network over the optimization period

10. A computer program comprising program code instructions for carrying out the steps of the method according to any one of the preceding claims, when said program is executed on a computer.

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