Method for optimizing a load curve of at least one electrical piece of equipment

Abstract

One aspect of the invention relates to a method (100) for optimizing a load curve for at least one electrical piece of equipment. This method can be implemented by an automated system. The method comprises obtaining (10) various pieces of information, including a forecast of the evolution of carbon dioxide emissions for a given quantity of electricity consumed and a forecast of a load curve for at least one electrical piece of equipment. Thus, from this information, the method enables the identification (60) of a load curve for the at least one electrical piece of equipment that has a cost lower than the maximum permissible cost and minimizes carbon dioxide emissions, the financial cost, or an efficiency index based on the financial cost and carbon dioxide emissions. Figure to be published with the abstract: Figure 2

Description

Title of the invention: Method for optimizing a load curve of at least one electrical device. TECHNICAL FIELD OF THE INVENTION

[0001] The technical field of the invention is that of home automation and in particular home automation ensuring energy management.

[0002] The present invention relates to a method for optimizing the load profiles of one or more electrical devices. The present invention also relates to an automated system for implementing said method. TECHNOLOGICAL BACKGROUND OF THE INVENTION

[0003] Home automation is the set of techniques from electronics, building physics, automation, computer science and telecommunications used in buildings, more or less "interoperable" and allowing control of the different systems and subsystems of the home and the company.

[0004] Home automation provides functions related to comfort, security, and energy management, among other things. Regarding energy management, it is common to control a building's electrical equipment in order to reduce electricity consumption or the associated costs. For example, it is common to program a water heater to turn on at times when electricity prices are lower.

[0005] Electrical equipment consumes electricity from the electrical grid during its operation and / or while charging. Certain electrical equipment, such as water heaters, pool pumps, electric cars, or any other electrically consuming device, are said to be "shiftable," meaning that their consumption period can be shifted over time, thus limiting additional constraints for users. A period of an electrical device can correspond to a period of operation of the electrical device and / or a period of electricity storage by the electrical device (i.e., charging the battery of the electrical device, for example).

[0006] The load curve of one or more electrical devices defines the evolution of energy consumption over a period, for example, a day or an hour, and therefore the electrical power consumed during this period by the electrical device(s). This load curve can be estimated and / or measured at regular intervals called the time step. Thus, optimizing a load curve of one or more electrical devices consists of planning the periods of consumption of the electrical devices with a view to an objective, for example, minimizing the associated electricity consumption. The planning consists of in particular to determine a start time for an operating period as well as its duration.

[0007] Global warming is partly due to increasing emissions of carbon dioxide (CO2) or, more generally, CO2 equivalent, linked to human activities. To combat global warming, countries around the world have committed to reducing their CO2 and / or CO2 equivalent emissions in the future. For the sake of simplicity, the term "CO2 emissions" in the remainder of this application is used to refer to CO2 and / or CO2 equivalent emissions. Thus, industrial actors are innovating in various areas such as adapting buildings, for example, in terms of thermal insulation, or producing renewable energy. The production of renewable energy, for example, wind or solar energy, allows for a reduction in CO2 emissions compared to fossil fuels in particular. However, renewable energy production processes are dependent on weather conditions.For example, wind energy is only produced when there is wind. Thus, on the electricity grid, the available electricity is a mix from different sources such as fossil fuels, nuclear, wind, or solar energy.

[0008] The origins of the electricity available on the grid, and therefore the CO2 emitted to produce this electricity, vary greatly over a given period. It is now possible to determine, for a given time and location, the origin of the electricity produced on the grid. From this information, it is also possible to deduce the CO2 emission per kilowatt-hour available in a given area. For example, the French electricity transmission network operator, Réseau de Transport d'Électricité (RTE), provides the "éC02mix" tool, which allows access to such information.

[0009] There is therefore a need to provide a method for optimizing a load curve of one or more electrical equipment in order to reduce CO2 consumption. Summary of the invention

[0010] The invention offers a solution to the problems mentioned above by optimizing a load curve of one or more electrical equipment in order to reduce electricity consumption when the production of energy available on the network has generated a large emission of CO2.

[0011] One aspect of the invention relates to a method implemented by an automated system for optimizing a load curve of at least one electrical equipment comprising: • Obtain, using the automated system, for the future period: • Authorised consumption periods for at least one electrical equipment • A maximum permitted cost for electricity consumption, • A forecast of a load curve for at least one piece of electrical equipment, • A forecast of the evolution of carbon dioxide emissions per unit of electricity consumed, and • A forecast of how the price of electricity will change per unit of electricity consumed, • Determine a set of load curves for at least one electrical equipment for the future period based on the permitted consumption periods for at least one electrical equipment and the forecast of the load curve of at least one electrical equipment for the future period obtained, the determination of each load curve of the set of load curves being carried out by planning consumption periods of said at least one electrical equipment for the future period. • For the future period and for each load curve in the set of load curves: • Calculate a first convolution product taking as input each load curve of at least one electrical piece of equipment and the forecast of the evolution of carbon dioxide emissions for the quantity of electricity consumed obtained, and providing as output a total of the carbon dioxide emissions for the load curve, • Calculate a second convolution product taking as input each load curve of at least one electrical piece of equipment and the forecast of the evolution of the price of electricity for the quantity of electricity consumed obtained (10) and providing as output a total financial cost, • Generate a table associating, for each load curve in the set of load curves, the carbon dioxide emissions, the total financial cost, and an efficiency index based on the financial cost and carbon dioxide emissions. • Identify, from the generated table, a load curve among the set of load curves, having a cost lower than the maximum cost to be respected and minimizing carbon dioxide emissions, the financial cost or an efficiency index based on the financial cost and carbon dioxide emissions.

[0012] Thanks to the invention, the load curve of electrical equipment is optimized by planning the consumption periods of electrical equipment over a specific period. This optimization helps reduce CO2 emissions related to the energy production required for consumption. Indeed, scheduling favors periods of electrical appliance use when the production of electricity available on the grid has required the least CO2 emissions. Shifting the consumption periods of electrical equipment therefore allows for increased electricity consumption during periods when the electricity produced emits the least CO2 over a future period and to reduce electricity consumption over periods during which the electricity produced emits the most CO2 over a future period.

[0013] In addition to the characteristics just mentioned in the preceding paragraph, The process according to one aspect of the invention may have one or more complementary characteristics from among the following, considered individually or according to all technically possible combinations: The entire set of load curves is further determined from a maximum electrical power allowed for at least one electrical equipment. The set of load curves is further determined by dividing temporally at least one of the consumption periods of said at least one electrical equipment. The load curve forecast for the future period is obtained: By receiving, through the automated system, at least one desired consumption period for said at least one electrical piece of equipment for the future period, and / or Based on at least one load curve from at least one electrical piece of equipment for a past period. The process is further implemented via a user interface connected to the PLC and also includes a display of: A schedule of consumption periods for said at least one electrical piece of equipment corresponding to the identified load curve, and / or The identified load curve, and / or Carbon dioxide emissions and / or financial cost and / or efficiency index based on financial cost and carbon dioxide emissions. An automatic implementation of a planning of consumption periods of said at least one electrical equipment corresponding to the identified load curve. • A period of consumption of at least one electrical equipment among the at least one electrical equipment is initiated by a user, the steps of the process are carried out again taking into account this period of charging and / or operation of the electrical equipment.

[0014] A second aspect of the invention relates to an automated system adapted for implementing the method according to the invention, comprising a digital input card configured to receive the forecast of the evolution of carbon dioxide emissions for a quantity of electricity consumed for the future period.

[0015] A third aspect of the invention relates to a program product for an automaton comprising instructions which, when the program is executed by an automaton, lead the latter to implement the process according to the invention.

[0016] A fourth aspect of the invention relates to a recording medium readable by an automaton comprising instructions which, when executed by an automaton, lead the latter to implement the process according to the invention.

[0017] The invention and its various applications will be better understood by reading the following description and examining the accompanying figures. BRIEF DESCRIPTION OF THE FIGURES

[0018] The figures are presented for illustrative purposes only and are in no way limiting of the invention. • Fig. 1 is a synoptic diagram illustrating the sequence of steps of a process according to the invention. • Fig. 2 shows a graphical representation of a set of load curves represented as a curve, the curve being represented in a "Financial cost - CO2 emissions" frame. • Fig. 3 shows a schematic representation of the invention implemented by a 200 automaton. DETAILED DESCRIPTION

[0019] The figures are presented for illustrative purposes only and are in no way limiting of the invention.

[0020] Figure 1 is a synoptic diagram illustrating the sequence of steps of process 100 according to the invention. The invention comprises 6 steps.

[0021] The process 100 can be implemented by a controller 200. The controller 200 is a device that autonomously reproduces a sequence of actions without human intervention. The controller is a programmed object. The controller 200 also receives information via a connection; it is therefore connected to an information source. From this information, the controller 200 then performs calculations in order to plan and implement a sequence of actions. Thus, the controller 200, which enables the implementation of the process 100, includes a digital input card configured to receive various information. The digital input card is specifically configured to receive and process forecast information on the evolution of carbon dioxide emissions for a given amount of electricity consumed over a future period. By "configure," we mean that a software component is specifically dedicated to the task of receiving and processing this information. Furthermore, the PLC 200 is also configured to implement the various steps of process 100. An example of a PLC is a contactor. By "implemented by a PLC 200," we mean that the steps, or at least one step, are executed by at least one PLC 200 or any other similar system. Thus, steps are carried out by the PLC 200, possibly in a fully automatic or semi-automatic manner. In some examples, the invention is implemented by a system comprising a PLC 200. In these examples, the triggering of at least some of the process steps can be achieved through a user-system interaction.This interaction can be performed using a user interface 210 connected to the PLC 200. This user interface 210 can be any object connected to the PLC 200 that includes a graphical interface, such as a screen. A smartphone or a computer could be used, for example. In these examples, the process is therefore implemented by a system comprising a PLC 200 and a user interface 210 that allows for user-system interactions. The information transmitted by the user through interactions with the interface 210 is then passed on to the PLC. The required level of user interaction can depend on the intended level of automation and be balanced against the need to implement the user's requirements. In some examples, this level can be defined by the user and / or predefined.

[0022] The method 100 optimizes the load curve of one or more electrical devices. These electrical devices are controlled by the controller 200, meaning that the controller can start or stop their operation and / or their load according to a predetermined schedule. Establishing this schedule according to the invention allows for the optimization of the load curve of these electrical devices for a future period. According to one embodiment of the invention, the future period is a day J or a period equal to: J + Xh, where J is a calendar day and Xh is a number of hours between -23 and 168. According to another embodiment of the invention, compatible with the preceding embodiment, the start of each period can vary. For example, for a period of duration equal to: J + Xh, the start of each period can be shifted by a number of hours between -24 and 24. The future period can be divided into sub-periods, called "measurement steps."Each measurement step can last one hour. According to another embodiment of the invention, compatible with the previous embodiment, the future period is between 2 and 7 days and each measurement step is one hour or one day.

[0023] The process 100 comprises a first obtaining step 10, for example by data reception by the automaton. Data can be sent by a remote server and received by the automaton 200 or provided to the automaton 200 by a user via a user interface 210. Five types of data are obtained in step 10.

[0024] The first type of data obtained in step 10 is the permitted consumption periods 11 for the electrical equipment for the future period. These permitted consumption periods can be provided by a user to the PLC 200 via the user interface 210, for example. The permitted consumption periods of an electrical piece of equipment can be equal to or greater than the minimum consumption period of that electrical equipment. For example, for charging an electric car, it is possible to specify that the permitted charging period is between 8 p.m. and 7 a.m. Thus, for an electric car with a minimum charging period of 7 hours, the process 100 can schedule the car's charging according to a large number of combinations, such as from 8 p.m. to 4 a.m., or from midnight to 7 a.m., or from 8 p.m. to midnight and then from 3 a.m. to 6 a.m., if the minimum charging period is divisible.In one example, consumption periods can be sent by a user.

[0025] The second type of data obtained in step 10 is the maximum permitted cost 12 for electricity consumption in the future period. This maximum cost can be provided by a user to the PLC 200 in the same way as the permitted operating periods 11. This maximum cost can also be obtained from an average cost for electricity consumption in a past period. For example, the past period could be a year, a month, a week, or a day. From this average cost, a permitted surcharge, for example expressed as a percentage of the average cost, can be provided by the user to the PLC in the same way as the permitted operating periods 11. In one example, the maximum cost can be sent by a user.

[0026] The third type of data obtained in step 10 is the forecast of a load curve 13 for at least one electrical device for the future period. This load curve forecast can be made for each electrical device. This forecast includes, for example, the duration and evolution of energy consumption during a consumption period for each electrical device. Furthermore, depending on the electrical device, the load curves can be constant, as for the operating periods of a swimming pool pump, or change over time, as for the operating periods of a washing machine. Then, from these different load curves, it is possible to obtain a forecast of the load curve for the electrical device(s) for the future period, the load curve of which is optimized with the Method 100 involves adding the load curve of each electrical device. Adding the load curves consists of adding the predicted electrical consumption for each electrical device for each measurement step in the future period, in order to obtain the total predicted electrical consumption for at least one electrical device.

[0027] According to a variant of the invention, compatible with the preceding variants, the load curve 13 of at least one electrical device for the future period is predicted by the controller receiving at least one desired consumption period for the electrical device(s) for the future period. For example, the user can provide the desired consumption period as well as the electrical consumption and / or power required for the electrical device(s) using the user interface 210. Thus, it is possible, for each measurement step of the future period, to predict the electrical consumption for the electrical device(s).

[0028] According to a variant of the invention, compatible with the preceding variants, the load curve 13 of at least one electrical device for the future period is predicted from at least one load curve of at least one electrical device for a past period. The load curve of the electrical device(s) for a past period can be determined by learning. For example, for a past period of one month or one year, learning makes it possible to determine the load curve for the charging period of an electric vehicle or a water heater. Learning makes it possible, for example, to identify the minimum, average, and / or maximum duration of the charging period, as well as the evolution of electrical consumption during this period. It also makes it possible to identify the maximum power reached during this period.From this learned data, it is therefore possible to make a forecast of the load curve of at least one electrical piece of equipment for the future period.

[0029] The fourth type of data obtained in step 10 is the forecast of CO2 emissions for a quantity of electricity consumed 14 for the future period. This forecast can be sent by a remote server and received by the controller. This forecast can also be provided by the user via the user interface 210. This forecast makes it possible, for each measurement step of the future period, to estimate the quantity of CO2 that will be emitted for a quantity of electricity, for example, one kilowatt-hour (kWh) available on the electrical grid. Thus, when one or more electrical devices consume this electricity available on the electrical grid, the quantity of CO2 emitted to supply this electricity is considered to be the quantity of CO2 emitted by the electricity consumed by the electrical device(s). Thus, the invention does not aim to reduce the quantity of CO2 emitted CO2 emissions are generated directly by electrical equipment during operation and / or charging, but their load curves need to be optimized to reduce the amount of CO2 emitted to supply the electricity needed for their operation and / or charging. For example, if part of the electricity available on the grid is supplied by a solar panel, this electricity can be considered as not emitting CO2. Thus, depending on the expected sunshine levels for the future period, the CO2 emission forecast will change.

[0030] The fifth type of data obtained in step 10 is the forecast of the evolution of the electricity price for a quantity of electricity consumed 15 for the future period. This forecast can be sent by a remote server and received by the PLC. This forecast can also be provided by the user via the user interface 210. This forecast makes it possible, for each measurement step, to know the price of one kWh. For example, if part of the electricity available on the grid is supplied by a solar panel, this electricity supplied by the solar panel can be considered free. Thus, depending on the sunshine forecast for the future period, the electricity price forecast will change.

[0031] In one example, a sixth type of data can be obtained in step 10. This data relates to a prioritization of at least one electrical device. For example, this data could include an ordered list of at least one electrical device based on its priority. Thus, the higher the priority considered for an electrical device, the more emphasis will be placed on respecting its permitted consumption period in subsequent steps of the process.

[0032] The process 100 includes a second step 20 of determining a set of load curves for at least one electrical piece of equipment for the future period. This determination is based on the permitted consumption periods and the load curve forecast for the at least one electrical piece of equipment obtained in step 10. Thus, all possible combinations of scheduling consumption periods for the electrical piece(s) during the permitted consumption periods are determined. For example, it is possible to allow consumption periods that begin before the future period and / or end after the future period. In this example, it is possible to consider only those consumption periods where the majority of the consumption period falls within the future period.

[0033] According to a variant of the invention, compatible with the preceding variants, the determination 20 of the set of load curves for at least one electrical device for the future period is also based on a maximum permissible electrical power for at least one electrical device. In this variant, this maximum permissible electrical power can be obtained by the controller during Step 10. This information can, for example, be provided by the user via the user interface 210. It can also be sent from a remote server to the PLC 200. Finally, it can be learned from a past period, for example, by identifying the maximum electrical power reached during that past period. Taking into account the maximum permissible electrical power, the set of determined load curves allows for the planning of operating and / or charging periods for the electrical equipment(s) with a maximum electrical power at any time during the future period that is less than or equal to the maximum permissible power. For example, if the load curve resulting from a plan in which all electrical equipment operates simultaneously does not comply with the maximum permissible power, then this load curve will not be retained in step 20.

[0034] According to a variant of the invention, compatible with the preceding variants, the determination 20 of the set of load curves is carried out by temporally dividing at least one of the consumption periods of the electrical equipment(s). In this variant, the permissible consumption periods 11 for the at least one electrical equipment obtained in step 10 include data on the possible temporal divisions for the electrical equipment(s). For example, the charging of an electric car can be divided into a minimum period of half an hour, whereas the operating period of a dishwasher cannot be divided. By temporally dividing at least one of the consumption periods of the electrical equipment(s), the set of load curves determined is even larger and thus allows for better optimization of the load curve of the electrical equipment(s).

[0035] The method 100 includes a third step 30 of calculating, for the future period and for each load curve in the set of load curves, a first convolution product taking as input each load curve of at least one electrical equipment and the forecast of the evolution of CO2 emissions for the quantity of electricity consumed obtained in step 10, and providing as output a total of the carbon dioxide emissions for the load curve. More precisely, for each load curve in the set of load curves used as the first term, the first convolution product is calculated using as the second term the forecast of the evolution of CO2 emissions for the quantity of electricity consumed. The function obtained by the convolution product therefore corresponds to the evolution of CO2 emissions during the future period for the load curve used as the first term of the convolution product.From this function, it is possible to calculate the total CO2 emissions for the future period by calculating the integral of this function over the future period.

[0036] The process 100 includes a fourth calculation step 40, for the future period and For each load curve in the set of load curves, a second convolution product is performed, taking as input each load curve for at least one piece of electrical equipment and the forecast of the electricity price evolution for the quantity of electricity consumed, obtained in step 10, and providing as output a total financial cost. More precisely, for each load curve in the set of load curves used as the first term, the second convolution product is calculated using as the second term the forecast of the electricity price evolution for the quantity of electricity consumed. The function obtained by the convolution product therefore corresponds to the evolution of the cost during the future period for the load curve used as the first term of the convolution product.From this function, it is possible to calculate the total cost for the future period by calculating the integral of this function over the future period.

[0037] The process 100 includes a fifth step 50 of generating a table associating, for each load curve in the set of determined load curves 20, the CO2 emissions, the total financial cost, and an efficiency index based on the financial cost and the carbon dioxide emissions. In a first example, with the primary objective of minimizing CO2 emissions, the efficiency index can be calculated from the number of grams of CO2 emissions emitted per monetary unit spent. In this first example, the efficiency index increases as the number of grams of CO2 emissions per monetary unit spent decreases. In a second example, with the primary objective of minimizing the financial cost, the efficiency index can be calculated from the number of monetary units spent per gram of CO2 emissions.In a third example, the efficiency index 1 of a load curve is equal to: . [00381 / = ^((c - c„J + (CO2 - cm,J)

[0039] With: • , a square root, • C, the total financial cost of the load curve, • Cmin, the total financial cost of the load curve among all load curves minimizing the total financial cost, • CO2, CO2 emissions from the load curve, • CO2min, the CO2 emissions of the load curve among all load curves minimizing CO2 emissions.

[0040] The generated table may, for example, include a number of rows equal to the number of load curves in the set of load curves and a number of columns equal to three: the first column containing the values ​​of the carbon dioxide emissions of carbon, the second column containing the total financial cost values, and the third column containing the efficiency index values ​​based on financial cost and carbon dioxide emissions. The table can be sorted according to the measurement interval for starting a consumption period of one or more electrical devices. For example, if a first load curve is obtained by planning the consumption period of one or more electrical devices at 1 μh and a second load curve is obtained by planning the consumption period of one or more electrical devices at 1 μh, then the first load curve will be placed before the second load curve in the table.It is also possible to sort the curves in ascending or descending order of one of these three values: carbon dioxide emissions, total financial cost, and the efficiency index based on financial cost and carbon dioxide emissions. Optionally, the table can include a fourth column containing the maximum power values ​​achieved for each load curve.

[0041] From this table, it is possible to obtain a graphical representation such as that illustrated in [Fig. 2]. Thus, [Fig. 2] shows a graphical representation of a set of load curves represented as a curve in a "Financial Cost - CO2 Emissions" coordinate system. In other words, [Fig. 2] shows a graphical representation of the impact of a schedule on the financial cost and on CO2 emissions. Point A on [Fig. 2] is the point on the curve representing the load curve minimizing the total financial cost as well as the efficiency index. Point B on [Fig. 2] is the point on the curve representing the load curve minimizing CO2 emissions. The hatched area of ​​[Fig. 2] begins at a maximum cost value to be respected. Thus, the part of the curve crossing this hatched area is not considered during step 60.

[0042] The process 100 includes a sixth step 60 of identifying, from the table generated in step 50, a load curve 61 among the set of load curves, having a cost lower than the maximum cost to be respected and minimizing carbon dioxide emissions, the financial cost or the efficiency index based on the financial cost and carbon dioxide emissions.

[0043] According to one embodiment of the invention, compatible with the preceding embodiments, the method 100 includes an optional seventh step 70 for displaying information on the user interface 210. The information displayed may be: • Planning the consumption periods of the electrical equipment(s) corresponding to the load curve identified in step 60, and / or • The identified load curve, and / or • Carbon dioxide emissions and / or financial cost and / or efficiency index based on financial cost and carbon dioxide emissions.

[0044] This information can be displayed together or successively. The user is thus informed of the different consumption periods of each of the electrical devices and can verify that this is suitable for them.

[0045] According to a variant of the invention, compatible with the preceding variant, information corresponding to several load curves can be displayed. The load curve identified in step 60 is included among the displayed load curves. The information displayed in this variant can be: the scheduling of consumption periods for the electrical equipment(s) corresponding to the load curves, and / or the load curves themselves, and / or the carbon dioxide emissions, and / or the financial cost, and / or the efficiency index based on the financial cost and carbon dioxide emissions. The different displayed load curves can be identified during an optional step performed between steps 60 and 70. In a first example, this optional step identifies a predetermined number, for example, 2, 3, or 10, of load curves that minimize the criterion used in step 60.In another example, this optional step identifies the load curve(s) that minimize the two other criteria not used in step 60. This information can be displayed together or sequentially. The user is thus informed of the different consumption periods of each electrical device for several possible load curves. They can then decide which one suits them by selecting it via user interface 210. If the user does not make a selection, the load curve identified in step 60 is selected by default.

[0046] According to a variant of the invention, compatible with the previous variants, the method 100 includes an optional step 80 for the automatic implementation of the planning 81 of the consumption periods of said at least one electrical equipment corresponding to the load curve identified 60. This step can be carried out after step 60 or after the optional step 70. By "automatic implementation", we mean that the implementation is carried out by the controller without user intervention.

[0047] According to a variant of the invention, compatible with the preceding variants, process 100 is interrupted by a manual start-up, i.e., by a user, of a charging and / or operating period of an electrical device. In this case, process 100 is restarted automatically. Thus, steps 10 to 60, as well as optional steps 70 and 80, are repeated taking into account the charging and / or operating period of the electrical device(s) that the user manually started.

[0048] Figure 3 shows a schematic representation of the invention implemented by a programmable logic controller (PLC) 200. Thus, the PLC receives, at step 10, authorized consumption periods 11, a maximum authorized cost 12 for electricity consumption, and a forecast of a load curve 13 due to at least one electrical equipment, a forecast of a change in carbon dioxide emissions for a quantity of electricity consumed 14, and a forecast of a change in the price of electricity for a quantity of electricity consumed 15. Finally, the automaton 200 implements steps 20 to 60 and provides as output a load curve 61 from among all the load curves, having a cost lower than the maximum cost to be respected and minimizing carbon dioxide emissions, the financial cost or an efficiency index based on the financial cost and carbon dioxide emissions.

[0049] Unless otherwise specified, the same element appearing on different figures has a unique reference.

Claims

Claims

1. Method (100) implemented by an automaton (200) for optimizing a load curve of at least one electrical device comprising: - Obtain (10), by the automaton (200), for the future period: • Authorized consumption periods (11) for at least one electrical device, • A maximum cost (12) authorized for electricity consumption, • A forecast of a load curve (13) of at least one electrical device, • A forecast of a change in carbon dioxide emissions per quantity of electricity consumed (14), and • A forecast of a change in the price of electricity per quantity of electricity consumed (15), - Determining (20) a set of load curves for the at least one electrical equipment for the future period based on the authorized consumption periods (11) for the at least one electrical equipment and the forecast of the load curve (13) of the at least one electrical equipment for the future period obtained (10), the determination of each load curve of the set of load curves being carried out by planning consumption periods of said at least one electrical equipment for the future period. - For the future period and for each load curve of the set of load curves: • Calculate (30) a first convolution product taking as input said each load curve of the at least one electrical equipment and the forecast of the evolution of carbon dioxide emissions for the quantity of electricity consumed obtained (10) and providing as output a total of the carbon dioxide emissions for the load curve • Calculate (40) a second convolution product taking as input said each load curve of the at least one electrical equipment and the forecast of the evolution of the price of electricity for the quantity of electricity consumed obtained (10) and providing as output a total financial cost - Generate (50) a table associating, for each load curve of the set of load curves, the carbon dioxide emissions the total financial cost and an efficiency index based on the financial cost and the carbon dioxide emissions, - Identify (60), from the generated table (50), a load curve (61) among the set of load curves, having a cost lower than the maximum cost to be respected and minimizing the carbon dioxide emissions, the financial cost or an efficiency index based on the financial cost and the carbon dioxide emissions.

2. Method according to claim 1 in which the set of load curves is further determined (20) from a maximum electrical power authorized for the at least one electrical equipment.

3. Method according to any one of the preceding claims in which the set of load curves is further determined (20) by temporally dividing at least one of the consumption periods of said at least one electrical equipment.

4. Method according to any one of the preceding claims in which the forecast of the load curve for the future period is obtained (10) °: - By reception, by the automaton (200), of at least one period of desired consumption of said at least one electrical equipment for the future period, and / or - From at least one load curve of the at least one electrical equipment for a past period.

5. Method according to any one of the preceding claims further implemented by a user interface (210) connected to the automaton and further comprising a display (70) of: - A planning of the consumption periods of said at at least one electrical equipment corresponding to the identified load curve (60), and / or - The identified load curve (60), and / or - The carbon dioxide emissions and / or the financial cost and / or the efficiency index based on the financial cost and the carbon dioxide emissions.

6. Method according to any one of the preceding claims further comprising an automatic implementation (80) of a planning (81) of the consumption periods of said at least one electrical equipment corresponding to the identified load curve (60).

7. Method according to any one of the preceding claims in which when a period of consumption of at least one electrical equipment among the at least one electrical equipment is initiated by a user, the steps of the method are carried out again taking into account this period of charging and / or operation of the electrical equipment.

8. Automaton (200) adapted for implementing the method according to any one of the preceding claims comprising a digital input card configured to receive the forecast of the evolution of carbon dioxide emissions for a quantity of electricity consumed for the future period.

9. PLC program product (200) comprising instructions which, when the program is executed by a PLC, cause the latter to implement the method according to any one of claims 1 to 7

10. A machine-readable recording medium (200) comprising instructions which, when executed by a machine, cause the machine to implement the method according to any one of claims 1 to 7.

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