System for controlling energy consumption in an industrial building

Abstract

The invention relates to a system for controlling energy consumption in an industrial building (10), the system comprising rooms (11a-11c) that incorporate temperature-regulating members (12a-12c), and / or process stations (13a-13b); - electrical power sources (14a-14c); - thermal energy generation means (15a-15c) supplying an amount of thermal energy (Qet); and - a distribution network (16) for distributing the thermal energy to the various regulating members (12a-12c) and / or process stations (13a-13b). The control system comprises a smart management module (20) for planning the generation and distribution of the amount of thermal energy (Qet), taking into account a key criterion.

Description

[0001] SYSTEM FOR CONTROLLING ENERGY CONSUMPTION IN A

[0002] INDUSTRIAL BUILDING

[0003] Technical field

[0004] The invention relates to a system for controlling energy consumption in an industrial building comprising several rooms or spaces with varied thermal regulation needs and heterogeneous constraints.

[0005] State of the art

[0006] Typically, an industrial site may have several sources of production and consumption of electrical and thermal energy.

[0007] For example, as illustrated in Figure 1 of the prior art, an industrial site 100 incorporates one or more rooms lla-llc containing temperature control devices 12a-12c, and / or process stations (or production stations) 13a-13b. Each room lla-llc can thus contain several zones, each zone being able to be a work shop, an office space, production stations including energy-consuming devices such as refrigerator dryers, etc. Thus, the control devices 12a-12c can include air diffusers, air conditioning and heating systems, or even equipment necessary for production (ovens, dryers, cold generation devices, etc.)

[0008] Electrical energy sources 14a-14c and thermal energy generation means 15a-15c are also present on the site. Conventionally, electrical energy can be supplied by the electrical network, but also by production devices such as solar panels and wind turbines. Thermal energy can be generated by different systems such as a heat pump PAC, an electric, coal or gas boiler, thermal panels, etc. The distribution of thermal energy to the various control devices 12a-12c and process stations 13a-13b is ensured by a distribution network 16 generally consisting of pipes, heat transfer fluid circulation pumps, valves, etc. Conventionally, the temperature regulation of the rooms and stations of an industrial site is carried out individually.To do this, each room and each station is equipped with a control system, for example a thermostat or air flow control. In addition, the production of thermal energy is adapted at each moment according to the evolution of the energy demand of the different rooms and the different process stations. In the example of Figure 1, the supervisory body 200 manages the distribution of the production of electrical and thermal energy according to the energy available over time by the different production sources.

[0009] Thus, the production of thermal energy is sized to meet the maximum load that the industrial site can demand, and therefore requires a subscription to the electricity network which is significant in terms of cost and oversized equipment most of the time.

[0010] There is therefore a need to optimize production sources.

[0011] Statement of the invention

[0012] The invention proposes an alternative approach to optimizing the energy consumption of an industrial site.

[0013] The invention aims in particular to propose a global regulation solution for all the stations or spaces of an industrial site so as to optimize the use of the available energy sources (electricity, gas, etc.) of the industrial site according to a constraint chosen from a set of predefined constraints or key criteria, for example the financial cost or the carbon impact.

[0014] The invention thus relates to a system for controlling energy consumption in an industrial building. The industrial building comprises:

[0015] - several rooms containing temperature control devices and process stations;

[0016] - one or more energy sources, including at least one source of electrical energy;

[0017] - one or more means of generating thermal energy configured to supply a quantity of thermal energy; and - at least one network for distributing said quantity of thermal energy to the various regulation bodies and / or process stations.

[0018] According to the invention, the control system further comprises an intelligent management module configured to plan, over a future period of time divided into time steps, the generation and distribution of said quantity of thermal energy.

[0019] The intelligent management module includes:

[0020] - a module for estimating the available electrical power for each time step of the future time period for each source of electrical energy based on weather forecasts, each available electrical power being associated with a key criterion, such as the financial cost or the carbon cost of obtaining said available electrical power;

[0021] - a module for estimating the available thermal power for each time step of said future time period, for each means of generating thermal energy based on weather forecasts, each available thermal power being associated with said key criterion;

[0022] - a listing module configured to determine, from said available electrical power and said available thermal power, all possible combinations of generation of said quantity of thermal energy by classifying the possible combinations according to said key criterion, for each time step of said future time period;

[0023] - a simulation module, for each time step of said future time period, of the temperature drift of the rooms in the absence of activation of the regulation devices, and of estimation of a criticality level of each room representative of the temperature difference between the simulated temperature drift and the acceptable temperature limits of each room;

[0024] - a module for estimating the thermal energy requirements of each process station for each time step of said future time period based on at least one operating schedule of said process stations or a history of the thermal energy requirements of each process station;

[0025] - a module for calculating the total thermal power required by all of said process stations for each time step of said future time period;

[0026] - a search module, for each time step of said future time period, of the combination meeting the total thermal power requirement of the process stations and presenting the lowest key criterion;

[0027] - a distribution optimization module configured to perform the following steps, for each time step of said future time period:

[0028] 37a / determine the difference between said quantity of thermal energy and said total thermal power required;

[0029] 37b / carry out iteratively in decreasing order of the criticality level, the simulation of the temperature drift of the room corresponding to the criticality level considered by activating its regulation organ, until consumption of the entire difference between the quantity of thermal energy and the total thermal power required;

[0030] - a ranking module configured to rank, for each time step of the entire future time period, the combinations by increasing key criterion among those not selected by the search module (36) and meeting the total thermal power demand required

[0031] - a final optimization module configured to determine the best combinations meeting the needs of all process stations and rooms for the lowest possible value of the key criterion by iteratively performing the following steps, and in ascending order of the value of the key criterion in the ranking of the ranking module, until all the thermal regulation needs of all process stations and rooms are met for the future time period:

[0032] 39a / determine whether there are rooms whose simulated temperature evolution is not included in the acceptable temperature limits for the time step corresponding to the value of the key criterion considered;

[0033] 39b / if so, carrying out the simulation by the distribution optimization module using said combination corresponding to the value of the key criterion considered, at said time step considered;

[0034] 39c / if no, move on to the next key criterion value;

[0035] - the intelligent management module being configured to indicate to the thermal energy generation means the power and temperature necessary to be supplied, on the basis of a selected schedule which includes the best combinations determined by the final optimization module. The optimization proposed by the invention is thus based on an anticipation (for example daily) of the thermal energy requirements to regulate the temperature of all the spaces or process stations of an industrial site, while taking into account constraints linked to the thermal devices or circuits (for example reduction of the financial cost linked to the production of thermal energy) but also constraints specific to each of the spaces and process stations, for example critical or priority stations.

[0036] An energy source available at an industrial site can be electricity, gas, or any other type of energy delivered or deliverable to an industrial site. An electrical energy source can be the electrical grid, a solar panel, or a wind system. A means of generating thermal energy can be an electric or fossil-fuel boiler, a thermal panel, a heat pump, or any other means of heat recovery.

[0037] The time period can be 24 hours, and the time step can be one hour.

[0038] The key criterion may be the carbon impact, the financial cost due to electricity consumption. In practice, the selected key criterion may be different depending on the time period range, for example, on a monthly or annual range.

[0039] Advantageously, the final optimization module is further configured to carry out, after step 39b, the maintenance of said combination corresponding to the value of the key criterion considered, if the thermal regulation needs of all the rooms at said time step considered are met.

[0040] The solution of the present invention thus makes it possible to obtain planning that optimizes the consumption of the different energy sources (electrical, thermal, etc.) available in an industrial site over a period of time. This planning meets the different thermal regulation needs of the industrial site, while guaranteeing the lowest value of a specific key criterion. The planning thus makes it possible to anticipate thermal needs, for example by forcing, or by shedding, or by shifting the operation of certain stations and / or regulation devices to specific times, so as to operate the thermal production units at their best efficiency with optimal consumption of the energy sources in terms of cost and / or CO2 emissions, for example.

[0041] Brief description of the figures

[0042] Other characteristics and advantages of the invention will emerge clearly from the description given below, given for information purposes only and in no way limiting, with reference to the appended figures, in which:

[0043] [Fig 1] Figure 1 is a schematic representation of a state-of-the-art industrial site;

[0044] [Fig 2] Figure 2 is a schematic representation of an industrial site integrating a control system according to one embodiment;

[0045] [Fig 3] Figure 3 is a schematic representation of the data determined by the available electrical power estimation module, the available thermal power estimation module, and the listing module, according to one embodiment;

[0046] [Fig 4] Figure 4 is a schematic representation of the data determined by the room temperature drift simulation module in the absence of activation of the regulation organs, according to one embodiment;

[0047] [Fig 5] Figure 5 is a schematic representation of the data determined by the module for estimating the thermal energy requirements of each process station, the module for calculating the total thermal power required by all the process stations, and the module for searching for combinations, according to one embodiment;

[0048] [Fig 6] Figure 6 is a schematic representation of the data determined by the distribution optimization module, according to one embodiment;

[0049] [Fig 7] Figure 7 is a schematic representation illustrating an update of the room temperature drift simulation tables, after simulation by the distribution optimization module of Figure 6;

[0050] [Fig 8] Figure 8 is a schematic representation of the data determined by the classification module, according to one embodiment;

[0051] [Fig 9] Figure 9 is a schematic representation illustrating the simulation of the drift of the rooms before implementation of the final optimization module, according to one embodiment; [Fig 10] Figure 10 is a schematic representation illustrating the positioning of the final optimization module on the first line of the classification table;

[0052] [Fig 11] Figure 11 is a schematic representation illustrating the positioning of the final optimization module on the second row of the ranking table;

[0053] [Fig 12] Figure 12 is a schematic representation illustrating the simulation of room drift in the case where the room regulation needs are all met.

[0054] Detailed description of the embodiments

[0055] The intelligent management module 20 according to an embodiment of the invention and implemented in an industrial site 10 is illustrated in Figure 2. The industrial site 10 is similar to the industrial site 100 of the prior art and the intelligent management module 20 of the invention is coupled to the temperature regulation members 12a-12c, to the process stations 13a-13b, to the electrical energy sources 14a-14c, to the thermal energy generation means 15a-15c, to the members constituting the thermal distribution network 16.

[0056] The intelligent management module 20 includes different modules which make it possible to plan the management of thermal energy production resources by anticipating the thermal energy needs of all the industrial equipment on the site, while taking into account specific constraints, such as cost, carbon footprint, criticality of process stations, etc.

[0057] In the following, the planning will be described for a future time period Ptf of 24 hours, divided into time steps H+i of one hour.

[0058] The different modules of the intelligent management module 20 as well as the data estimated or calculated by each of these modules according to a particular embodiment, are illustrated in figures 3 to 12.

[0059] With reference to Figure 3, the intelligent management module 20 comprises:

[0060] - a module 30 for estimating the available electrical power Ped;

[0061] - a module 31 for estimating the available thermal power Ptd; and - a module 32 for listing the different possible combinations for generating a quantity of thermal energy Qet.

[0062] The estimation module 30 determines the available electrical power Ped, for each electrical energy source 14a-14c. The estimation of the available electrical power Ped can take into account weather forecasts when the electrical energy production device is dependent on weather conditions. This is the case, for example, with solar panels. In addition, the estimation module 30 associates with each available electrical power Ped the value of a key criterion Ce predefined or chosen by the user. The key criterion Ce can be the financial cost, the carbon impact of obtaining said available electrical power Ped. The key criterion corresponding to the financial cost is particularly interesting when the subscription to the electricity network takes into account so-called off-peak and peak hours. In what follows, the key criterion Ce chosen will be the carbon footprint.

[0063] Thus, in the example given in Figure 3, the source 14a which is for example a solar panel can provide an available electrical power Ped of 78 kW at H+1 with a key criterion value equal to 2, a power of 150 kW at H+2 with a key criterion value equal to 2, etc.

[0064] Similarly, source 14b, which is for example a wind turbine, can provide an available electrical power Ped of 100kW at H+1 with a key criterion value equal to 5, a power of 70kWh at H+2 with a key criterion value equal to 5, etc.

[0065] The source 14c which is for example the electrical network can provide an available electrical power Ped of 200MW at H+1 with a key criterion value equal to 5, a power of 70MW at H+2 with a key criterion value equal to 5, etc.

[0066] The estimation module 31 determines the available thermal power Ptd over the 24-hour period Ptf for each thermal energy generation means 15a-15c. The estimation of the available thermal power Ptd can also take into account weather forecasts when the thermal energy production device is dependent on weather conditions. This is the case, for example, for thermal panels or heat pumps. The estimation module 31 also determines the value of the key criterion Ce for each available thermal power Ptd.

[0067] Thus, in the example given in Figure 3, the means 15a which is for example a thermal solar panel can generate a thermal power Ptd of 125kW at H+1 with a key criterion value equal to 3, and an electrical consumption Ce of 5kW, a thermal power Ptd of 100kW at H+2 with a key criterion value equal to 3, and an electrical consumption Ce of 4kW, and so on for the following hours.

[0068] Similarly, the means 15b which is for example a gas boiler can generate at H+1 a thermal power Ptd of 300kW and 1000kW according to the PCS efficiency of the boiler with a key criterion value equal to 236 and 241 respectively. At H+2, the thermal power Ptd of the boiler 15b is still estimated at 300 and 1000 kW with a key criterion value equal to 236 and 241 respectively, and so on for the following hours.

[0069] The 15c medium, which is for example a heat pump, generates at H+1 a thermal power Ptd of 250kW and 600kW depending on the COP efficiency of the heat pump with a key criterion value equal to 30 and 33 respectively. At H+2, the thermal power Ptd of the 15c heat pump is still estimated at 250kW and 600kW with a key criterion value equal to 32 and 35 respectively, and so on for the following hours.

[0070] The listing module 32 determines, from the estimates of the available electrical powers Ped and the available thermal powers Ptd made by the modules 30 and 31, all the possible combinations Com for generating a quantity of thermal energy Qet by assigning to each combination the corresponding value of the key criterion Ce. The listing module 32 can thus classify the combinations Com according to the value of the key criterion Ce. The combinations Com are determined for each hour, and over the period Ptf of 24 hours.

[0071] Thus, as illustrated in Figure 3, for time step H+l:

[0072] - a first combination Com consists of operating means 15a (the thermal panel) alone, making it possible to generate a quantity of thermal energy Qet of 125kW with a key criterion value Ce of 3; - a second combination Corn consists of operating means 15a and 15c (the thermal panel and the heat pump according to the efficiency providing 250kW), which would make it possible to generate a total quantity of thermal energy Qet of 375kW with a total key criterion value Ce equal to 33;

[0073] - a third combination Com consists of operating means 15a and 15c (the thermal panel and the heat pump according to the efficiency providing 600kW), which would make it possible to generate a total quantity of thermal energy Qet of 725kW with a total value of key criterion Ce equal to 36;

[0074] - etc.

[0075] Similarly, for the time step H+2:

[0076] - a first combination Com consists of operating the means 15a (the thermal panel) alone, making it possible to generate a quantity of thermal energy Qet of 100kW with a key criterion value Ce of 3;

[0077] - a second combination Com consists of operating means 15a and 15c (the thermal panel and the heat pump according to the efficiency providing 250kW), which would make it possible to generate a total quantity of thermal energy Qet of 350kW with a total value of key criterion Ce equal to 35;

[0078] - a third combination Corn consists of operating means 15a and 15c (the thermal panel and the heat pump according to the efficiency providing 600kW), which would allow generating a total quantity of thermal energy Qet of 700kW with a total value of key criterion Ce equal to 38;

[0079] - etc.

[0080] With reference to Figure 4, the intelligent management module 20 further comprises a simulation module 33. This simulation module 33 is configured to simulate, over the 24-hour period Ptf, the temperature drift of rooms 11a-11c in the absence of activation of the regulation members 12a-12c. In addition, this simulation module 33 estimates the criticality level Ncrit of each room 11a-11c. This criticality level Ncrit is representative of the temperature difference between the simulated temperature drift and the acceptable temperature limits of each room 11a-11c. The simulation may in particular be based on the history of the rooms or on the habits of the occupants of these rooms or on production forecasts. In the example illustrated in Figure 4, room 11a, which is for example a design office, will be:

[0081] - at H+l, at a set temperature of 18°C, and tolerates a variation between 10°C (T°min) and 20°C (T°max). The simulation module 33 estimates that the temperature will be 10°C at H+l and its criticality level is evaluated at 0;

[0082] - at H+2, at a set temperature of 18°C, and tolerates a variation between 10°C (T°min) and 20°C (T°max). The simulation module 33 estimates that the temperature will be 15°C at H+2 and its criticality level is evaluated at 0. This period H+2 corresponds for example to one hour before the arrival of the occupants and to mild weather;

[0083] - at H+3, at a set temperature of 18°C, and tolerates a variation between 10°C (T°min) and 20°C (T°max). The simulation module 33 estimates that the temperature will be 25°C at H+2 and its criticality level is evaluated at 5. This period H+3 corresponds for example to the arrival of the occupants and to an increase in the temperature of the room induced by the sun's rays for example;

[0084] - etc.

[0085] Similarly, room 11b which contains for example a process station 13a, will be:

[0086] - at H+l, at a set temperature of 20°C, and tolerates a variation between 18°C ​​(T°min) and 24°C (T°max). The simulation module 33 estimates that the temperature will be 10°C at H+l and its criticality level is evaluated at 8;

[0087] - at H+2, at a set temperature of 20°C, and tolerates a variation between 18°C ​​(T°min) and 24°C (T°max). The simulation module 33 estimates that the temperature will be 25°C at H+2 and its criticality level is evaluated at 1;

[0088] - at H+3, at a set temperature of 20°C, and tolerates a variation between 18°C ​​(T°min) and 24°C (T°max). The simulation module 33 estimates that the temperature will also be 25°C at H+3 and its criticality level is evaluated at 1;

[0089] - etc.

[0090] Room 11c, which contains, for example, another process station 13b, will be:

[0091] - at H+1, at a set temperature of 6°C, and tolerates a variation between 5°C (T°min) and 8°C (T°max). The simulation module 33 estimates that the temperature will be 5°C at H+1 and its criticality level is evaluated at 8. For example, the process station is a cold zone (presence of refrigerator, etc.); - at H+2, at a set temperature of 6°C, and tolerates a variation between 5°C (T°min) and 8°C (T°max). The simulation module 33 estimates that the temperature will be 10°C at H+2 and its criticality level is evaluated at 2. This period H+2 corresponds for example to one hour of start-up of this process station 13b;

[0092] - at H+3, at a set temperature of 6°C, and tolerates a variation between 5°C (T°min) and 8°C (T°max). The simulation module 33 estimates that the temperature will be 10°C at H+2 and its criticality level is evaluated at 2. This period H+3 corresponds for example to the maintenance of operating conditions;

[0093] - etc.

[0094] With reference to Figure 5, the intelligent management module 20 further comprises:

[0095] - a module 34 for estimating the thermal energy requirements Bet of each process station 13a-13b;

[0096] - a calculation module 35 of the total thermal power required Pttr by all process stations 13a-13b, and therefore the minimum production in thermal energy for all process stations 13a-13b;

[0097] - a search module 36, over said future time period Ptf and for each time step H+i, of the combination Com meeting the total thermal power requirement Pttr of the process stations 13a-13b and presenting the lowest key criterion Ce.

[0098] In the example illustrated in Figure 5, the profile of the thermal energy requirements Bet of the process stations 13a and 13b established by the estimation module 34 is illustrated. Thus, the thermal energy requirement Bet of the process station 13a at H+1 is estimated at 250kW, and estimated at 250kW at H+2, etc. The thermal energy requirement Bet of the process station 13b at H+1 is estimated at 125kW, and estimated at 500kW at H+2, etc. This profile can be a function, for example, of an operating schedule of the process stations 13a-13b for the 24-hour period considered, or by learning via a history of the thermal energy requirements of each process station 13a-13b.

[0099] The calculation module 35 then determines the total thermal power required Pttr by all process stations 13a-13b over the future time period Ptf of 24 hours. The total thermal energy requirement for the two process stations 13a and 13b for each hour corresponds to the sum of the thermal energy requirements Bet of each process station 13a and 13b determined by the estimation module 34. Thus, for H+1 the total thermal power required Pttr is 375kW, and 750kW at H+2, etc.

[0100] The search module 36 determines, for each hour, from the list of combinations produced by the listing module 32, the combination Com meeting the total thermal power requirement Pttr of the process stations and presenting the lowest key criterion Ce. A table 360 ​​listing the combinations retained by the search module 36 is illustrated in figure 5:

[0101] - at H+l, the combination Com 2 is the most suitable for the thermal energy requirement with a key criterion value Ce equal to 33;

[0102] - at H+2, the Com 7 combination is the most suitable for the thermal energy requirement with a key criterion value Ce equal to 350;

[0103] - etc.

[0104] With reference to Figures 6 and 7, the intelligent management module 20 further comprises a distribution optimization module 37. This distribution optimization module 37 is configured to carry out the following steps, for each hour and over the 24-hour period, based on the list established by the search module 36:

[0105] 37a / determine the difference Qet-Pttr between the quantity of thermal energy Qet and the total thermal power required Pttr;

[0106] 37b / carry out iteratively in decreasing order of the criticality level Ncrit, the simulation of the temperature drift of the room lla-llc corresponding to the criticality level Ncrit considered, over the period of 24 hours by activating its regulation organ 12a-12c, until consumption of the entire difference between the quantity of thermal energy Q and the total thermal power required Pttr, or until the remaining thermal power is no longer sufficient for the activation of a regulation organ. In practice, it is possible to define a minimum remaining power for the future time period or for each time step, necessary to carry out step 37b.

[0107] In the example given in Figure 6, it is clear from Table 370 listing the remaining thermal powers Qet-Pttr that at time step H+1 all the thermal power produced is consumed, and that at time step H+2 the thermal power still available is 100kW.

[0108] Thus, referring to the tables in Figure 4, at time step H+2, room 11c has the highest criticality level (Ncrit = 2) among those of the other rooms 11a and 11b at this same time step H+2. Thus, a new simulation of the temperature drift of this room 11c taking into account the activation of the regulating member 12c of this room 11c is therefore carried out. This new simulation of the temperature drift of room 11c is represented by table 370_llc. Considering that the power consumed by the regulating member 12c of this room 11c is 50kW, there will still be 50kW of thermal power available. Therefore, the optimization module 37 refers again to the tables in Figure 4 to identify, at time step H+2, the room having the highest criticality level among the other rooms that have not yet been considered.In the example of Figure 4, among rooms 11a and 11b, room 11b has the highest criticality level at H+2 (Ncrit = 1). A new simulation of the temperature drift of this room 11b taking into account the activation of the regulating device 12b of this room 11b is therefore carried out. This new simulation of the temperature drift of room 11b is represented by table 370_llb. Following this second simulation, if we consider that the power consumed by the regulating device 12b of this room 11b is also 50kW, and therefore, all the thermal power that was available (Qet-Pttr) is consumed.

[0109] The optimization module 37 will perform the simulation in a similar way for the following time step H+3 which has a remaining thermal power of 50kW, etc.

[0110] Figure 7 illustrates an update of the simulation tables over the 24-hour Ptf period, the temperature drift of the lla-llc rooms.

[0111] With reference to Figure 8, the intelligent management module 20 further comprises a ranking module 38 configured to rank, over the entire 24-hour time period Ptf, the combinations by increasing key criterion among those not selected by the search module 36 and meeting the total required thermal power demand Pttr. This ranking makes it possible to prioritize production at the most profitable hours or at times with a low key criterion level while still meeting thermal needs. Thus, the same hour can be listed several times in the ranking table 380 generated by the ranking module 38 but with a different combination. In the example given in Figure 8, the time step H+1 is indicated in the ranking table 380 a first time with the combination 3 and a key criterion value of 36 then a second time with the combination 4 and a key criterion value of 269.

[0112] The intelligent management module 20 further comprises a final optimization module 39 configured to iteratively carry out the following steps, and in ascending order of the value of the key criterion Ce in the ranking of the ranking module 38, until all the thermal regulation needs of all the process stations and rooms are provided for the future time period Ptf:

[0113] 39a / determine whether there are any rooms lla-llc whose simulated temperature evolution is not included in the upper and lower limits of acceptable temperatures for the time step H+i corresponding to the value of the key criterion Ce considered;

[0114] 39b / if so, carrying out the simulation by the optimization module 37 at the time step H+i identified using said combination Corn corresponding to the value of the key criterion Ce considered;

[0115] 39c / if no, go to the next line.

[0116] Figure 9 illustrates as an example the simulation of the drift of the lla-llc rooms before implementation of the final optimization module 39.

[0117] As illustrated in Figure 10, the optimization module 39 is positioned on the first line L1 of the classification table 380 in which the values ​​of the key criteria are listed in ascending order. For this first line L1, the time step to be considered is H+4, and the corresponding combination Com is 4 with a quantity of supplied energy Qet of 523kW.

[0118] However, this quantity of energy Qet of 523kW is not sufficient for the step H+4, since according to table 360, the minimum necessary thermal power Pttr is 800kW. The final optimization module 39 will therefore not take this line L1 into account and go directly to the next line of table 380.

[0119] As illustrated in Figure 11, for this second line L2 of the classification table 380, the time step to be considered is H+l, and the corresponding combination Com is 3 with a quantity of supplied energy Qet of 725kW, which is greater than the minimum necessary thermal power Pttr of 375kW of the time step H+l of the table 360. As a result, the final optimization module 39 determines from the tables resulting from the last simulation by the optimization module 37 whether at H+l, there are rooms for which the temperature is out of limit. If such rooms are not identified, the optimization module 39 goes directly to the next line of the classification table 38. Otherwise, the combination at time step H+l of the table 360 ​​is replaced by the combination at H+l of the classification table 380 and the final optimization module 39 orders the distribution optimization module 37 to redo the simulation of the temperature drifts.In the particular case of the tables illustrated in Figure 9, room 11b has a simulated temperature outside the limit at H+l. The distribution optimization module 37 therefore carries out a new simulation using the combination 3 indicated in line L2 of the classification table 380. In Figure 11, tables 370_lla, 370_llb, and 370_llc on the right illustrate the result of this simulation of the Halle rooms by the distribution optimization module 37.

[0120] If after this simulation, there are still rooms with temperatures outside the limits for this time step H+l, this row L2 is not retained. On the contrary, if after this simulation all the temperatures at H+l are within the limits then this combination of row L2 of table 380 for this time step H+l is retained. In the example illustrated in figure 11, it is considered that at H+l, there are no longer any simulated temperatures outside the limits. Therefore, the replacement of the initial combination (Corn = 2) at H+l in table 360 ​​by the new combination (Corn = 3) is maintained.

[0121] The optimization module 39 then moves on to the next row of the classification table 380, and repeats the above steps. Thus for row L3, the time step H+6 is reconsidered and resimulated by the distribution optimization module 37, and the corresponding combination is retained if all temperatures are within acceptable limits. For row L4, the time step H+1 is again considered. However, since the combination of row L2 makes it possible to meet the needs of all rooms for the time step H+1, the combination of row L4 is not retained. If the combination of row L2 had not made it possible to meet the needs of all rooms at H+1 (i.e., there are still rooms for which the simulated temperatures are out of limits), the combination of row L4 would be considered and a simulation based on the combination of row L4 is carried out.

[0122] And so on, until all the needs of all the process stations and rooms are provided for the future time period Ptf (figure 12). In other words, the final optimization module 39 does not need to take into consideration all the rows Li of the classification table 380, and the iteration by the final optimization module 39 stops as soon as all the thermal energy needs of the industrial site are provided.

[0123] The final optimization module 39 thus makes it possible to determine the best combinations meeting the needs of all the process stations and rooms for the lowest possible value of the key criterion.

[0124] The planning by the control system of the invention can be renewed one hour after the end of a previous planning, taking into account any new data relating to the process stations, and / or the rooms, and / or the external climatic conditions.

[0125] Once the planning over the future time period Ptf has been carried out, the intelligent management module 20 of the invention interacts with the various equipment of the industrial building (regulation devices, process station, electrical energy sources, thermal energy generation means) by following the selected planning. In particular, the intelligent management module will indicate to the various energy production systems the power and temperature required to be supplied, on the basis of the modeling or planning selected.

[0126] The modeling implemented makes it possible to determine the thermal power and temperature level instructions necessary to optimize thermal energy consumption by acting on all energy consumption items on the industrial site, taking into account certain high and low thresholds and a key criterion selected (financial costs, consumption (kwh), CO2 emissions, etc.) by the user.

[0127] Thus, the intelligent management system of the invention makes it possible to coordinate all the electrical and thermal energy sources available in the industrial site in order to plan their production and distribution according to the actual thermal energy needs of the industrial site while respecting a predefined key criterion (financial cost, carbon impact, etc.), over a predefined period of time. The thermal energy produced during the predefined period of time is therefore produced as closely as possible to the needs of the industrial site with a minimum value of the key criterion.

[0128] In particular, the invention makes it possible to determine a first list of possible combinations for generating a quantity of thermal energy making it possible to meet the needs of the process stations, then to adjust this list of combinations by determining for each time step, the combination which also makes it possible to cover the needs of all the rooms with the lowest possible key criterion value.

[0129] In other words, the solution of the invention makes it possible, on the basis of an estimate of the thermal energy needs of all the equipment on the industrial site or building over a 24-hour period, to achieve efficient production and consumption of the energy sources available by the various equipment, particularly in terms of the lowest value of key criteria such as cost, kilowatt-hour or even carbon impact.

[0130] For an industrial site with pre-installed conventional optimization modules, the addition of the solution of the invention thus makes it possible to reduce the impact of the industrial site on a selected key criterion by an additional 15 to 20%. For a site without a pre-installed optimization device, the solution of the invention makes it possible to reduce the impact of the industrial site on a selected key criterion by at least 40%. The solution of the invention can thus be implemented to manage pre-existing thermal and electrical installations of an industrial site, including installations integrating energy-saving measures, such as floating HP / LP regulation, with modulation of the heating capacity, or dynamic setpoints, etc.

Claims

CLAIMS 1. System for controlling energy consumption in an industrial building (10), said industrial building (10) comprising: - several rooms (1 la- 1 le) containing temperature regulation devices (12a- 12c), and process stations (13a- 13b); - one or more sources of electrical energy (14a-14c); - one or more thermal energy generating means (15a-15c) configured to provide a quantity of thermal energy (Qet); and - at least one distribution network (16) of said quantity of thermal energy (Qet) to the various regulation organs (12a-12c) and / or process stations (13a-13b), the control system comprising an intelligent management module (20) configured to plan, over a future time period (Ptf) divided into time steps (H+i), the generation and distribution of said quantity of thermal energy (Qet), the intelligent management module (20) comprising: - an estimation module (30) of the available electrical power (Ped) for each time step (H+i) of said future time period (Ptf) for each source of electrical energy (14a-14c) based on weather forecasts, each available electrical power (Ped) being associated with a key criterion (Ce), such as the financial cost or the carbon cost of obtaining said available electrical power (Ped); - a module (31) for estimating the available thermal power (Ptd) for each time step (H+i) of said future time period (Ptf) over said future time period (Ptf), for each means of generating thermal energy (15a-15c) as a function of weather forecasts, each available thermal power (Ptd) being associated with said key criterion (Ce); characterized in that the control system further comprises: - a listing module (32) configured to determine, from said available electrical power (Ped) and said available thermal power (Ptd), all possible combinations (Corn) of generation of said quantity of thermal energy (Qet) by classifying the possible combinations (Corn) according to said key criterion (Ce), for each time step (H+i) of said future time period (Ptf); - a simulation module (33), for each time step (H+i) of said future time period (Ptf), of the temperature drift of the rooms (1 la-1 le) in the absence of activation of the regulatory bodies, and estimation of a criticality level (Ncrit) of each room (1 la-1 le) representative of the temperature difference between the simulated temperature drift and the acceptable temperature limits of each room (1 la-1 le); - an estimation module (34) of the thermal energy requirements (Bet) of each process station (13a-13b) for each time step (H+i) of said future time period (Ptf) as a function of at least one operating schedule of said process stations (13a-13b) or a history of the thermal energy requirements of each process station (13a-13b); - a calculation module (35) of the total thermal power required (Pttr) by all of said process stations (13a-13b) for each time step (H+i) of said future time period (Ptf); - a search module (36), for each time step (H+i) of said future time period (Ptf), of the combination (Corn) meeting the total thermal power requirement (Pttr) of the process stations and presenting the lowest key criterion (Ce); - a distribution optimization module (37) configured to carry out the following steps, for each time step (H+i) of said future time period (Ptf): 37a / determine the difference between said quantity of thermal energy (Qet) and said total thermal power required (Pttr); 37b / carry out iteratively in decreasing order of the criticality level (Ncrit), the simulation of the temperature drift of the room (1 la-1 le) corresponding to the criticality level (Ncrit) considered by activating its regulation organ (12a-12c), until consumption of the entire difference between the quantity of thermal energy (Qet) and the total thermal power required (Pttr); - a classification module (38) configured to classify, for each time step (H+i) of the entire future time period (Ptf), the combinations by increasing key criterion among those not selected by the search module (36) and meeting the demand for total thermal power required (Pttr); - a final optimization module (39) configured to determine the best combinations meeting the needs of all the process stations and rooms for the lowest possible value of the key criterion by iteratively carrying out the following steps, and in ascending order of the value of the key criterion (Ce) in the ranking of the ranking module (38), until all the thermal regulation needs of all the process stations and rooms are provided for the future time period (Ptf): 39a / determine whether there are rooms (1 la-1 le) whose simulated temperature evolution is not included in the acceptable temperature limits for the time step (H+i) corresponding to the value of the key criterion (Ce) considered; 39b / if so, carrying out the simulation by the distribution optimization module (37) using said combination corresponding to the value of the key criterion (Ce) considered, at said time step (H+i) considered; 39c / if not, move on to the next key criterion value (Ce); - the intelligent management module being configured to indicate to the thermal energy generation means the power and temperature required to be supplied, on the basis of a selected schedule which includes the best combinations determined by the final optimization module.

2. Control system according to claim 1, characterized in that the source of electrical energy is the electrical network, a solar panel, a wind system, and a means of generating thermal energy is an electric or fossil energy boiler, a thermal panel, a heat pump.

3. Control system according to claim 1 or 2, characterized in that the future time period (Ptf) corresponds to 24 hours, and the time step (H+i) corresponds to one hour.

4. Control system according to one of claims 1 to 3, characterized in that the key criterion is the carbon impact, or the financial cost due to electricity consumption.

5. Control system according to one of claims 1 to 4, characterized in that the final optimization module (39) is further configured to carry out, after step 39b, the maintenance of said combination (Corn) corresponding to the value of the key criterion (Ce) considered, if the thermal regulation needs of all the rooms at said time step (H+i) considered are met.

Images