IT201800003869U1 - SYSTEM FOR COMPUTERIZED SELECTION OF TECHNOLOGIES TO BE APPLIED TO IMPROVE THE ENERGY EFFICIENCY OF A PLANT - Google Patents

Description

SYSTEM FOR COMPUTERIZED SELECTION OF TECHNOLOGIES

TO BE APPLIED TO IMPROVE THE ENERGY EFFICIENCY OF A

PLANT

DESCRIPTION

TECHNICAL FIELD

The present invention refers to the sector of energy efficiency systems. In particular, the invention refers to a method according to the preamble of claim 1.

STATE OF THE ART

Energy efficiency indicates the ability to adopt physical systems to achieve a given result using less energy. It is necessary to be more and more efficient in the use of energy, exploiting it in a rational way and eliminating waste due to the operation and non-optimal management of systems and plants. Waste and loss of energy represent a hidden "reservoir" and energy efficiency allows them to be recovered and enhanced to obtain substantial economic, environmental and social benefits.

Nowadays, different methods have been proposed to try to achieve energy efficiency in different sectors, from residential to tertiary to industrial.

A first method involves dividing the system to be made more efficient into subsections (related to processes or technologies) and seeking energy efficiency measures chosen from various technologies known to the designer according to a one by one approach in series. According to this method, illustrated in Figure 1, once the input data of the system has been received (step 100), one proceeds to choose a first technology (Technology 1, step 101) and to search (step 102) the most efficient technological solution for this plant technology (or process), for example a boiler. The sizing of the first technology is done in such a way as to maximize (or minimize, as the case may be) an objective function. For example, it leads to maximizing the IRR (Internal Rate of Return) or minimizing the Payback. Subsequently, the consumption of the system is calculated (step 103) if Technology 1 is applied, and the second technology is identified (Technology 2, step 104) and dimensioned (step 105) starting from the residual consumption deriving from the implementation of the Optimized 1 technology. The process is then repeated for all the technologies identified with a sequential and never transversal approach between the technologies until the final calculation (step 106) of the system consumption deriving from the application of all the technologies.

However, this method of making energy efficiency has some disadvantages, such as the need to define an order of priority of the technologies (variable aspect according to the sensitivity of the person who implements the method) and the fact that it does not take into account the combined effect of the different technologies. . It is therefore generally possible to have an oversizing of Technology 1 and a diffusion of energy efficiency which is lower than the condition of "excellent".

A second method (Figure 2) involves dividing the plant into subsections (related to processes or technologies) and seeking energy efficiency measures chosen from various technologies known to the designer according to a one by one parallel approach.

The identification, choice, development and design of the various energy efficiency measures therefore takes place in parallel, without interaction between the various measures, always trying to maximize (or minimize, as appropriate) the objective function.

Therefore, this methodology also has some disadvantages, such as the oversizing of technologies. For example, if Technology 1 is a cogenerator, the first type of approach will tend to install a cogenerator with electricity generation equal to or slightly lower than the electrical load of the plant. If Technology 2 is the replacement of lighting fixtures with LED lighting fixtures, their installation will lead to a drop in the electrical load of the system. Consequently, Technology 2 will lead to a partial load behavior of Technology 1 (which will have a worse business plan than the one assumed).

Both of the above methods are generally carried out by natural persons who, after carrying out an inspection and identifying the technologies or processes on which to make energy efficiency, determine the solutions. This has the consequence that the planning times of the energy optimization intervention are long and the result is not the best.

Recently, however, software has also been proposed that determine the optimal technologies to be used in a plant to achieve a goal, eg. costs and / or consumption.

For example, E.T.A., Energy Track and A udit, is an energy diagnosis support software that uses savings calculation algorithms and has a database of sizes and prices of technologies based on interpolations. The software outputs a series of energy efficiency measures with a recommended sequence based on parameters that are calculated by E.T.A .. These parameters consist of a simple fixed assignment of a score based on the economic parameters of each single technology identified: the technology which, by adding the various scores of its economic parameters, has the highest global score is recommended first. It should be emphasized that E.T.A., just like for the second methodology described above, is not able to dimension the technologies with a transversal approach that prevents the cannibalization of each other.

In the article "Life Cycle Assessment of Mixed Municipal Solid Waste: Multiinput versus multi-output perspective" four management strategies of Mixed Municipal Solid Waste (MMSW) are analyzed in terms of environmental impact and potential advantages from the point of view of the Life Cycle methodology Assessment (LCA). For this purpose, both a multi-input and a multi-output approach are applied, in order to evaluate the effect of these two perspectives on selected impact categories.

In the article "Improving energy efficiency in Malawian tea Industries using an integrated multi-objective optimization method combining IDA, DEA and evolutionary algorithms" a method for generating efficient boundaries in multi-objective optimization problems is presented, using Index Decomposition Analysis (IDA) , Data Envelopment A nalysis (DEA) and evolutionary algorithms. The goal of the DEA is to measure the relative efficiency of various decision-making units and reflect the various preferences expressed in the act of making decisions. IDA, on the other hand, aims to understand the characteristics that reveal changes in specific energy consumption at the plant level.

In the article "Stakeholders perspective on sustainable technology selection to achieve zero carbon retail buildings" we talk about the design and construction of sustainable buildings, with various investors involved with the general purpose of selecting appropriate technologies capable of reducing energy consumption and carbon emissions . Previous studies and literature research indicate that there is no comprehensive selection process that can help investors. This can be classified as a complex multiple criterion decision problem, thanks to the high number of alternatives, potential solutions and variety of investors (e.g. clients, professional consultants, end users) with different objectives, resulting in the slow acceptance of sustainable technologies. Using A H P questionnaires and expert opinions, the identified criteria are weighted and ranked, obtaining risk (37%) as the most important, followed by cost (22%), proven success (20%), time (12%) and sustainability ( 9%) as the least important factor. Although the established criteria are more relevant to the selection of sustainable technologies for existing buildings, they can be useful for new constructions.

In the article "Concurrent optimization of size and switch-on priority of a multisource energy System for a commercial building application" it is defined that the main problems of multi-source systems are:

The strategic allocation of energy demand between the various technologies; - The adequate sizing of each technology.

In this regard, a model capable of considering the profiles of electricity, heat and refrigerator consumption for one year is developed and implemented in the Matlab ÷ environment. The performance of the energy system is modeled through a systematic approach. Finally, the optimization of the size and priority of operation of the different technologies that make up the multi-source energy system is implemented, through a genetic algorithm. In the article "A multi-objective optimization framework for risk-controlled integration of renewable generation into electric power systems" we talk about the optimization by combining different plants for production from renewable sources. The article presents a multi-objective optimization structure for integrations of distributed generation technologies in the electricity grid. The algorithm identifies the size and position of the different technologies, considering uncertainties relating to the availability of renewable sources, faults, demand and electricity production. A non-sequential MCS-OPF (Monte Carlo simulation and optimal power flow) computational model is developed to emulate the operation of the network by generating random scenarios from different sources of uncertainty.

In the article "Evaluation of a proposed optimization method for discrete-event simulation models" we talk about optimization linked to discrete events with genetic algorithms. The suggested procedure is the evaluation an optimizing method for discrete event simulation models based on genetic algorithms that exhibit more efficiency than computational time, when compared to software packages on the market.

In the article "Combining optimization and simulation in an energy systems analysis of a Swedish iron foundry" the analysis of possible variations of industrial production processes is addressed, and to choose the variations to be operated, various modeling tools can be used. This article uses two types of energy analysis tools: Discrete Event Simulation (DES) and Energy Systems Optimization (ESO). The results show that the combination of optimization and simulation tools is useful for providing very detailed information on how the system works and for predicting system behavior in order to minimize system costs.

The article "Systems capacity expansion planning: Novel approach for environmental and energy policy change analysis" describes the use of optimization algorithms to verify and compare different energy policy scenarios (energy incentives).

AIMS AND SUMMARY OF THE INVENTION

It is an object of the present invention to overcome the disadvantages of the known art.

In particular, the purpose of the present invention is to identify and size the technologies to be used in a plant (residential, tertiary or industrial) to seek the best from an energy or economic point of view (in general according to criteria defined by the user) by means of a multi-technology and multi-scenario approach.

It is also an object of the present invention to eliminate the priority logics of the current approaches, allowing instead a systematic combination of the different technologies.

It is also an object of the present invention to reduce the time and energy used for the identification, selection, development and sizing of energy efficiency measures.

It is also an aim of the present invention to present a method which allows to determine a program of energy efficiency measures to be carried out over time, therefore in a manner consistent with the economic possibilities of a subject.

These and other objects of the present invention are achieved by means of a method and a system incorporating the characteristics of the attached claims, which are intended as an integral part of the present description.

In one embodiment, the present invention relates to a method, and the related computer system for the implementation of this method, aimed at the computerized selection of technologies to be applied to improve the energy efficiency of a plant by means of a multi-technology approach and multi-scenario. The method involves acquiring plant input data, defining an objective function to be optimized and using the data of a set of technologies applicable to the plant, each technology comprising at least one device characterized by its own consumption and technical characteristics. Subsequently, an optimization process is implemented which determines an optimal set of apparatuses that optimizes the objective function. The applicable technologies are selected from a broader group of investigable technologies by simulating the use in the system of the devices of these technologies, and selecting as the applicable technology the set of devices of the investigable technology that are compatible with the constraints and consumption of the system. In particular, the selection of applicable technologies takes place using a parallel approach, in which, among the input data of the plant, the data necessary for the sizing of the investigable technology are acquired and, starting from the acquired data, the thermodynamic parameters of the plant are calculated. necessary for the sizing of the investigable technology and the use of the investigable technology devices in the system is simulated considering the calculated parameters.

This solution therefore offers the advantage of preselecting the applicable technologies, so as to reduce the computational time to calculate the optimum of the objective function, for example the mix of devices that reduces the energy consumption of the plant.

Advantageously, in one embodiment the optimization process provides for defining a plurality of scenarios, in which each scenario corresponds to a different order of priority of the applicable technologies. In this embodiment, for each scenario, we proceed to:

a) Select, on the basis of the current energy consumption of the plant, a first apparatus of the applicable technology with a degree of higher priority, said apparatus being the one that optimizes the objective function;

b) Calculate the first new energy consumption of the plant by simulating the use of said first device in the plant;

c) Select a second apparatus of a second applicable technology second in order of priority, said second apparatus being the one that among the apparatuses of the second technology optimizes the objective function in a plant that uses the first apparatus and presents said first new energy consumptions;

d) Calculate second new energy consumption of the system by simulating the use of the first and second equipment in the system;

e) Select, for each remaining technology, a respective apparatus that optimizes the objective function in a plant that uses the apparatuses previously selected by the technologies with higher priority order;

f) Determine a suboptimal solution for the efficiency of the plant as a set of devices selected according to steps a) to e);

The method therefore envisages choosing the suboptimal solution that optimizes the objective function as the optimal set of apparatuses.

In this form, therefore, the method provides for the application of a series approach to search for an optimum of an objective function, so as to exploit and take into account the joint effect of the different technologies in the plant. The proposed method, however, is characterized by the fact that it is independent of the constraint, present in known methods of this type, of defining an order of priority: it is in fact multi-scenario. Furthermore, thanks to an intelligent preselection of the technologies applicable to the plant, the method is flexible and with not excessive computational cost, thus allowing to obtain a rapid and precise mix of technologies applicable to the plant to obtain the best result expected by the user. , be it an energy saving, an economic saving, or other parameter.

Further characteristics and objects of the present invention will become clearer from the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be described hereinafter with reference to some examples, provided for explanatory and non-limiting purposes, and illustrated in the attached drawings. These drawings illustrate different aspects and embodiments of the present invention and, where appropriate, reference numerals illustrating similar structures, components, materials and / or elements in different figures are indicated by similar reference numerals.

Figure 1 illustrates a flow diagram of a first method for making energy efficiency in a plant according to the known art.

Figure 2 illustrates a flow diagram of a second method for making energy efficiency in a plant according to the prior art.

Figure 3 illustrates a computer system capable of implementing the method for making energy efficiency in a plant according to the present invention.

Figure 4 illustrates a generic flow diagram of the method for making energy efficiency in a plant according to the present invention.

Figure 5 illustrates a flow diagram of a process (called Function) for the sizing of a technology to be used in the plant.

Figure 6 illustrates a flow diagram of the optimization step of the method of Figure 4.

Figure 7 illustrates a flow diagram of a block provided in the diagram of Figure 6.

DETAILED DESCRIPTION OF THE INVENTION

While the invention is susceptible to various modifications and alternative constructions, some preferred embodiments are shown in the drawings and will be described in detail below. It must be understood, however, that there is no intention of limiting the invention to the specific embodiment illustrated, but, on the contrary, the invention is intended to cover all modifications, alternative constructions, and equivalents that fall within the scope of the invention as defined in the claims.

The use of "for example", "etc", "or" indicates non-exclusive alternatives without limitation unless otherwise indicated. Use of "include" means "includes, but not limited to" unless otherwise indicated.

In this description, the term "scenario" means a multi-technology proposal for energy efficiency in which the interactions between the various proposed technologies are evaluated, resulting in different work points and operating conditions (possibly in off design) of each single technology within the mix under consideration.

In the present description, the term "objective function" means the mathematical model that describes a parameter of interest as a function of some variables, in this case parameters of the plant. An example of an objective function can be the operating cost of a plant, which depends on personnel costs, depreciation of equipment, but also on energy consumption and the energy sources used to generate energy. Furthermore, an objective function can be the consumption of electrical energy, which depends on the number / type and operating point of the equipment present in the plant.

Figure 3 illustrates a computer system capable of implementing a method for selecting and sizing equipment / technologies to be included in a plant in order to achieve energy efficiency.

In the example of Figure 3, the computer system 1000 comprises a server computer 1 capable of performing services for personal computers 2 or, in general, remote devices (e.g. PC, tablet, smartphone, etc.), to it connected through a local network 3 or a data network 4 such as the internet or a mobile telephone network.

The computer system 1000 allows to implement a method, illustrated below with reference to Figures 4-7, for selecting the technologies to be used in a plant.

The method begins with a "Startup" phase 400 in which the user launches the application from his computer 2 to improve the energy efficiency of a plant.

Using the appropriate user interface, the latter enters the system data necessary for defining the optimal technological scenario (step 401). In one embodiment, the user interface can be offline (for example on Microsoft Excel ÷), therefore the user interacts with this offline interface by uploading the necessary data that will be subsequently processed and transferred to the server for the processing of the case. . Alternatively, the interface can be online. In particular, it can be a web interface, for example developed in the Ruby® environment. In this case the user uploads the data directly to the web and these are acquired and transferred to a database, for example a Microsoft Excel ÷ spreadsheet or an Oracle ÷ table, accessible from the processor 10 of server 1.

As mentioned above, through the interface the user transfers the system data to be optimized to server 1. These data preferably consist of a list of basic system information: for example sector, country, baseline consumption, plate data of the technologies present (e.g. plate data of a boiler or motor), operating logics, etc.

With the acquisition of the input data of the plant (step 402), the processor 10 of the server 1 executes code portions, stored in a memory area 11, of a main program. These portions implement the steps described below. First of all, the plant data are acquired (step 401) and a first selection of searchable technologies is carried out (step 402). For this purpose, the server 1 is equipped with a database 12 containing information relating to a multitude of apparatuses belonging to different technologies. This information also includes possible constraints given by national regulations or past experiences, for example a given technology or a given apparatus may not be usable in a given technological sector or in a given nation.

On the basis of sector and country information, the method therefore envisages excluding some of the technologies and / or devices present in the database from the group of technologies that can be investigated for use in the plant.

In a preferred embodiment, in order to facilitate data entry and limit the acquisition to only the necessary system data (with consequent speeding up and simplification of the input phase), the user interface is configured to automatically delete from the data acquisition screen that information that is used for the analysis of non-applicable technologies. Advantageously, then, some of the constraints for the preselection of investigable technologies can be entered by the user through the user interface. For example, in one embodiment, the user can decide to exclude some technologies a priori (eg for ethical choices). Furthermore, through the user interface, the user can choose whether to include in the economic analyzes the incentives applicable to the various technologies and how much to value them. These incentives will be applied only if the plant studied is in the country in which they are valid, and if the requirements for obtaining them are fully satisfied.

Subsequently, the method provides an "Initialization" phase 404 in which the investigated technologies resulting from Preselection 403 are initialized. The "initialization" phase involves the start of a stand alone simulation of each pre-selected technology, with a parallel approach, similar to that of figure 2, which does not consider the interactions, overlaps and cannibalizations of one technology with the other. This simulation is carried out by starting special programs, hereinafter called Functions, provided for the various technologies in which the energy input data are the general ones of the plant.

The Function of a technology is an algorithm that is proposed as a tool for estimating the feasibility of an energy efficiency intervention applied to a process and to one or more energy flows of the plant, as well as a tool for choosing the supplier, size and optimal operating point within a scenario. The algorithm is prepared in the most generic way possible, taking into consideration the application of the technology to different processes or energy flows that can occur in different plants. The Function, therefore, appears as a black box, which receives system data as input and returns at the output:

1. The data of the devices that implement the technology and can be used in the plant;

2. The impact of these systems on an objective function set by the user or foreseen by default in the system, for example the operating cost of the equipment, or its energy consumption, or both.

Preferably, the Function also returns the operating point of such apparatuses to obtain the supplied objective function at the output.

For example, if the technology is that of cogeneration systems and if the plant requires constant 50 kW of electrical power, the Function will operate the cogenerators with less than 50 kW electric at nominal load and will provide for partial load operation (with relative efficiency due to operation in off design) for cogenerators of more than 50 kW electric.

In detail, a possible Function algorithm is illustrated below with reference to Figure 5. In this example, the algorithm is developed in five sections:

1. Acquisition of inputs (reference 501);

2. Basic calculation of the thermodynamic properties of fluids and pre-intervention parameters (502);

3. Dimensioning (503) of the technology with simulation of all applicable sizes (even in operating conditions in off design) and calculation of the savings achievable using the technology algorithm, their enhancement and creation of the business plan. The procedure can be done by simulating various sizes of several qualified suppliers;

4. Choice (504) of "optimal" size, operating point and supplier on the basis of the objective function set by the user;

5. Creating the outputs.

The Function receives in input (step 501) the system data that are necessary for its sizing. In other words, the Function does not receive as input all the system data entered by the user through the user interface, but only those necessary for the technology to which it refers.

For example, if the technology studied is an economizer, as previously indicated, data are requested relating to:

- Characteristics of the thermal systems involved: requires information such as type of system, size of the flue, potential, yields, energy consumption, fuel and operating hours of both the system to which the economizer is applied and the system currently used for heating of users. In this context, the prices of electricity and fuels relating to the supply contracts for the site in question are also requested;

- Characteristics of the thermal vector: requests information relating to the thermal vector constituting the user of the economizer. They include the average mass flow (in the case of process fluid heating), the average specific heat, the inlet temperature and, if a fixed set-point is available, the outlet temperature. In the case of steam production, the line pressure and the latent enthalpy of specific evaporation are also required; - Characteristics of the fuel: deals with the properties of the fuel, such as calorific value, average density, stoichiometric combustion ratio, stoichiometric production of water vapor;

- Characteristics of the fumes: requires information specifically related to the fumes from which to recover heat. In particular, their composition and temperature, as reported in the combustion analyzes, the minimum acceptable temperature at the outlet of the economizer, linked to the type of fuel used, the average condensation temperature of the contained vapor and the average specific heat, also they are related to the type of fuel used.

Subsequently the Function (step 502) proceeds to calculate the thermodynamic properties of the fluids and of the pre-intervention parameters, ie the parameters of the plant before the application of the technology considered.

Continuing to consider the economizer example, this section is used for the analytical calculation of:

- Specific heat of the fumes;

- Mass flow of fumes;

- Mass flow rate of carrier fluid (in case of use of economizer for preheating of process fluid).

The specific heat of the fumes is calculated by considering them a mixture of CO2, O2, H2O and N 2 according to the following equation:

where cp is the specific heat at constant pressure [kj / kgK], x is the mole fraction [-] and the subscript i indicates the i-th species. The specific heat of the individual compounds is calculated using empirical formulas.

The annual mass flow rate of the fumes is therefore calculated through a mass balance, considering the fuel consumed, the stoichiometric amount of air necessary for its combustion and the excess air at which the boiler operates. The average flow rate is subsequently deduced from the estimated operating hours of the device.

Finally, the mass flow rate of the carrier fluid is calculated through the energy balance, knowing the average power of the boiler, the inlet and outlet temperature of the water and, in the case of steam production, the latent heat of evaporation, as shown in the following equation :

where m is the mass flow rate of the carrier fluid [kg / s], Q is the average power produced by the boiler [kW], ΔΤ is the temperature increase of the carrier fluid inside the boiler [K] and is the enthalpy specific evaporation latent of the heat carrier [kj / kg].

Note that some parameters can be entered by the user in the system data or determined automatically by the system. This solution is particularly convenient because it allows a dynamic adaptation of the method on the basis of the available data.

Once the thermodynamic parameters of the plant on which you want to simulate the application of the technology have been calculated, the method implemented by the Function, step 503, involves simulating the application of various devices of different sizes and / or suppliers and operating in different points of operation of the technology with consequent calculation of the energy and / or economic savings achievable and of the business plan in the event that the objective function to be optimized consists of the economic savings obtainable in a given period of time. The choice is made by ensuring compliance with the operating range of the devices themselves, as indicated by the manufacturer of the technology in question.

For example, in the case of the economizer, the device must be able to simultaneously satisfy the constraints on:

- Smoke passage section;

- Flow rate of heat transfer fluid.

The first restriction prohibits the installation of economizers with a section of passage of the fumes lower than that of the flue of the existing boiler. The second constraint, on the other hand, allows the choice of only the devices that admit a minimum flow rate of heat transfer fluid lower than that previously calculated for the specific case.

Each technology will be sized by searching for the solution in terms of devices and operating points that give the best result of the objective function. To make the most convenient choice, sensitivity analyzes are set by varying the parameters of the Function in appropriate ranges of variability.

In another example, if the objective function is the operating cost of the plant, then the Function algorithm calculates (using data provided by the equipment manufacturer) the energy savings achievable using the "new" technology in the plant . At this point, the algorithm calculates all the cash flows that occur in a given period of time, for example fifteen years, from the investment made. These costs include:

- Cost of the machine body;

- Cost of any optionals;

- Cost of packing and shipping;

- Cost of accounting systems for actual savings;

- Design costs and preparation of any authorizations;

- Management costs;

- Service costs and scheduled (ordinary) or extraordinary maintenance (where requested by the user through an appropriate graphic interface);

- Warranty extension costs (where requested by the user through an appropriate graphic interface);

- Savings attributable to the reduction in consumption;

- Incentives (where present, and only if the user decides to consider them); - Costs due to the increase in the purchase of energy carriers (for example, in the case of the installation of an economizer in a plant that also uses a cogenerator, any higher cost of the electricity no longer produced by the cogenerator if this is switched off) .

On the basis of all these costs, the Function calculates the trend of the management costs of the plant in a given time period if the apparatus of the size considered and operating in the given work point is used.

At the end of this phase 503, therefore, the Function algorithm will have calculated the value of the objective function (eg annual energy saving or costs in the five-year period or more) for each apparatus and for different work points of the Technology.

At the end of the simulations, the Function (step 504) selects, among those of the considered technology, the apparatus (size, supplier) and the operating point that optimize the objective function sought, for example the apparatus that minimizes management costs or that minimizes the energy consumption of the system.

Lastly, step 505, the output data structure is created, which contains the data of all the devices belonging to the technology that are compatible with the system data (e.g. all those devices that are able to provide energy data indicated in input) and which summarize the Technology in question:

- Identification data (eg a code or a progressive number) of the device; - Energy and / or economic data of the system calculated in the simulations of use of the apparatus.

The output data structure then includes information on the technology apparatus that optimizes the objective function (eg reduces the maintenance cost).

Returning to the 404 "initialization" process, as previously mentioned, this involves launching the Functions of the various technologies investigated. These Functions will return, for what has been said above with reference to Figure 5, the identification data of the devices that can be used in the system because they are compatible with the system data entered by the user. The set of apparatuses thus selected will be indicated below as a set of investigated and applicable technologies.

Subsequently to the "Initialization" phase 404, one passes to the "Optimization" phase 405, the purpose of which is to identify, among all the technologies investigated and applicable, the mix that optimizes the objective function.

In a first embodiment, the "Optimization" step 405, illustrated in Figure 6, provides for calculating the value of the objective function for a plurality of defined scenarios by setting different priority orders for the technologies and applying, for each scenario, a serious approach for plant efficiency.

After having acquired (step 601) the information outgoing from the "initialization" phase of the investigated and applicable technologies, the M (with integer M greater than 1) different scenarios are generated (step 602). The different priority orders can be generated in different ways, for example by using for loops that scroll through the technologies investigated and applicable or by using specific routines, such as Matlab ÷ functions for the calculation of the possible combinations (e.g. combntns, nchoosek).

For each scenario (step 603) a search process for an optimization solution of the objective function of the type described below with reference to Figure 7 is launched. Each of these processes involves executing the Function of the first technology (step 702) in order of priority. This Function thus allows to size, on the basis of the system data provided by the user and supplied in input to the Function (step 701), Technology 1. As previously described with reference to Figure 5, the Function returns the devices to the output applicable, and also the indication of the apparatus and the working point that allow to optimize the objective function. The same Function returns the energy consumption of the system that would have been used in the system.

The energy consumptions calculated in this way are used as input data for the Function of Technology 2 in order of priority, which after having been carried out (step 702) will provide the indication of the apparatus that optimizes the objective function in the system. simulated using the first two technologies. The process is repeated for all the other technologies (step 704), each of which is dimensioned on the basis of the energy consumptions received at the input, ie those calculated for a plant that uses all the previous technologies in order of priority.

Cost and / or consumption data are automatically updated each time a Technology is applied. This occurs thanks to a data structure containing the aforementioned cost and / or consumption data, which is updated after each start of a Function. This continuous overwriting allows to minimize the physical database necessary for the simulation, because what is stored is only the output data structure of each scenario (and not all the intermediate steps of the scenario itself).

Once all the functions of the various technologies have been performed, a mix of devices is determined which constitutes a suboptimal solution (scenario) to the efficiency problem of the analyzed plant.

After having calculated the suboptimal solutions for all possible scenarios (i.e. for all combinations of priority order), the "Optimization" phase 405 provides (step 604) to choose as the optimal solution the suboptimal solution which, among all those calculated, optimizes the objective function.

In one embodiment, if there are suboptimal solutions that have the same optimal value as the objective function, the method provides for presenting the user, through the graphical user interface, with the possibility of defining at least one other secondary criterion, preferably three, with which to create a prioritization between the "equal" scenarios from the point of view of the objective function. In this case, the scenario corresponding to the mix of technologies with the best parameters will therefore be selected as the optimal one.

In an alternative embodiment, the "Optimization" step involves the use of an optimization algorithm to search for the scenario that maximizes (or minimizes) a predetermined or user-selected objective function via the user interface ( e.g. IRR, Profitability Index, PayBack, Net Present Value of the total investment). As in the example previously described with reference to figures 6 and 7, if there are solutions that have the same value as the main parameter, the method provides for offering the user, through a graphical interface, the possibility of defining one or more further criteria secondary with which to create a prioritization between the "equal" scenarios from the point of view of the objective function.

Examples of optimization algorithms are the deterministic algorithms that cannot be used for this application and the stochastic ones. Technical deterministic algorithms such as Conjugate Gradient (CG) belong to the category of deterministic algorithms, while genetic algorithms (based on principles of genetics and natural selection), simulated annealing, optimization particle swam optimization, ant colony optimization (based on the cooperative behavior of these insects, which allows them to find the shortest path from nest to food), fuzzy optimization methods, methods based on neural networks . In general, therefore, the optimization algorithms are per se known to the person skilled in the art, and therefore we will not go into detail about them.

In general, this embodiment that searches for the optimum (mathematically the maximum or the minimum) of the objective function has a lower computational cost than the optimization method described above with reference to Figures 6 and 7.

In the preferred embodiment, at the end of the "initialization" step 404, the method provides for estimating the computational time of the optimization step using the method of figures 6 and 7. The estimate is preferably made using tables accessible to the processor 10 which implement the method. These tables are provided for the various user-selectable objective functions and for the various combinations of technologies and retain average calculation times deriving from empirical tests or mathematical estimates. Obviously the physical execution time depends on the hardware of the server 10 which carries out the method of figure 4, therefore in one embodiment through the graphic interface the user can select a type of hardware and impose a maximum time for the computations. Should the maximum time be greater than that envisaged by the tables for the selected hardware, the processor 10 is configured to implement an optimization phase which uses an optimization algorithm other than that of figures 6 and 7.

Once the optimal mix of technologies has been determined, the method ends at step 406 with the presentation to the user of the information on the energy consumption that can be obtained and / or the costs in the management time of the system optimized with the mix of selected devices. This output data can be inserted into a file which is made available to the computer 2 from which the user operates or be printed.

Advantageously, in one embodiment the output data report not only the data relating to the calculated optimal scenario, but also the data of one or more non-optimal scenarios from the point of view of the objective function, to allow the user to choose between different technological mixes.

From the above description it is therefore evident how the technical solution according to the present invention, in its various aspects, allows to achieve the proposed aims.

It is equally evident to a person skilled in the art that it is possible to make modifications and further variations to the solution described with reference to the attached figures, without thereby departing from the teaching of the present invention and from the scope of protection as defined by the attached claims.

In particular, it is evident that the invention refers not only to a generic method for selecting the technologies to be used to improve the energy efficiency of a plant, but is directed to a computer program containing portions of code which, when executed by a computer or, in general, a computer system, make it possible to implement the method. Although the invention has been described with reference to a particular computer system, in which a server and a remote computer collaborate to implement the energy optimization method of a plant, it is evident that this computer system should not be understood as a limiting example of the invention. , the same functions being achievable by a single computer or by a plurality of cooperated computers. The portions of code that allow you to execute the method can therefore be distributed differently on different computers, for example in a cloud environment.

Claims (9)

CLAIMS 1. Computerized system (1000) for the computerized selection of the technologies to be applied in order to improve the energy efficiency of a plant, comprising a processor (10) and a computer (2) connected to each other via a network (3,4) ; the computer (2) being configured for: - implement a user interface; - acquire system data, necessary to define an optimal technological scenario for the system, through the user interface, and - transmit the system data to the server, e in which the processor (10) includes a database (12) storing at least data on a plurality of devices and information necessary to calculate thermodynamic and economic parameters of interest for a technology applicable to the plant, and is configured for: - define an objective function to be optimized; - acquire a set of technologies applicable to the plant (601) from the database (12) based on the plant data, each technology comprising at least one device characterized by its own consumption, technical characteristics and costs; - determine (305) an optimal set of apparatuses belonging to different applicable technologies, called set of apparatuses to the one that optimizes the objective function, and - transmit the optimal set of equipment to the computer (2), characterized by the fact that the processor (10) selects applicable technologies in a searchable technology group stored in a database (12) by, for each searchable technology of that group: i the acquisition (501), among said plant data, the data necessary for the sizing of the investigable technology; ii starting from the acquired data, the calculation (502) thermodynamic and economic parameters of the plant necessary to size the investigable technology; iii simulating (503) the use in the system of the investigable technology equipment using the thermodynamic parameters calculated in point ii); iv selecting (504) as the applicable technology the set of investigable technology apparatuses that are consistent with the consumption and constraints of the plant. Computerized system (1000) according to claim 1, wherein the processor (10) is configured for: - define a plurality of scenarios, in which a different order of priority of the applicable technologies corresponds to each scenario; - for each scenario: a) select (701), on the basis of the energy consumption of the current plant costs, as well as of the technologies currently present with relevant energy performance, a first apparatus of the applicable technology with a high level of priority, said apparatus being the one that optimizes the objective function; b) calculate the first new energy consumption of the system by simulating the use of said first device in the system; c) select (702) a second apparatus of a second applicable technology which is second in the order of priority, said second apparatus is the one among the apparatuses of the second technology which optimizes the target function in a plant which uses the first apparatus and which exhibits said first new energy consumption; d) calculate the second new energy consumption of the system by simulating the use of the first and second equipment in the system; e) select (704), for each remaining technology, a respective apparatus that optimizes the objective function in a plant that uses the apparatuses previously selected by the technologies with higher priority order f) determine a suboptimal efficiency solution for the plant such as the set of devices selected on the basis of steps a) to e); - select (604) as the optimal set of apparatuses the suboptimal solution that optimizes the objective function considering each scenario. 3. Computerized system (1000) according to claim 2, in which the processor (10) is configured to define all the possible permutations of the order of priority of the applicable technologies as said plurality of scenarios. 4. Computerized system (1000) according to claim 2 or 3, in which the processor (10) selects a respective apparatus for each remaining technology, evaluating different operating points in conditions of off-design of the apparatus with the relative energy performance. Computerized system (1000) according to claim 2 or 3 or 4, wherein the processor (10) is configured for - minimize the database (12) necessary to simulate the use of searchable technology devices by overwriting the energy consumption data previously calculated for the selection of the previous technology device in order of priority, with the energy consumption data calculated at the end of the selection of an apparatus of a technology. Computerized system (1000) according to claim 1 or 2 or 3 or 4 or 5, wherein the computer (2) is configured for: - acquire secondary optimization criteria a through the user interface, e - transmitting said secondary optimization criterion to the processor (10), e where the processor (10) is configured for: - select as the optimal solution the solution among several sub-optimal solutions that are equivalent with regard to the optimization of the objective function. Computerized system (100) according to any one of claims 1 to 6, wherein the processor (10) is configured for - estimate, based on the plant data and the objective function, the execution times required to determine the optimal set of devices for an applicable technology by implementing two different methods, and - implementing the method with the lower execution time if said execution time exceeds a predetermined value. Computerized system (1000) according to claim 7, wherein the processor (10) estimates the execution times using established tables before starting the computerized selection of technologies. Computerized system (1000) according to claim 7, wherein one of said methods implemented by the server (10) comprises a stochastic optimization algorithm belonging to the group comprising: genetic algorithms, simulated annealing algorithms, optimization with particle swarm, optimization of ant colonies, fuzzy logic optimization, algorithms based on neural networks.