Method for controlling an electricity distribution network and control system for the implementation of said method

Abstract

Method for controlling an electricity distribution network, which network comprises one or more electrical energy generation units that power said network and a plurality of separate and independent withdrawal points of said electrical energy, in particular distributed over a pre-established geographical area and wherein each withdrawal point is provided with a storage unit for storing the electrical energy withdrawal curve measured as a function of time from the corresponding withdrawal unit; a central control unit that executes a control program of the electricity distribution network as a function of said withdrawal curves of the individual withdrawal units and of said electrical energy generation curves of the individual generation units, and a program for predicting the time trend of the energy consumption of at least part, or of each, of the withdrawal units and / or for predicting the time trend of the energy generated by at least part, or by each, of said electrical energy generation units, on the basis of said predictions there being earned out actions of compensation of the network's operating conditions.

Description

[0001] “METHOD FOR CONTROLLING AN ELECTRICITY DISTRIBUTION NETWORK AND CONTROL SYSTEM FOR THE IMPLEMENTATION OF SAID METHOD”

[0002] FIELD OF THE INVENTION

[0003] The present invention concerns a method for controlling an electricity distribution network and a control system for the implementation of this method, the network comprising one or more electrical energy generation units that power the network, the electrical energy generation units being based on alternative energy sources, in particular sources operating on the basis of photovoltaic processes and / or sources driven by wind power, and a plurality of separate and independent withdrawal points of the electrical energy, in particular distributed over a pre-established geographical area, and wherein each withdrawal point is provided with a storage unit for storing the electrical energy withdrawal curve measured as a function of time from the corresponding withdrawal unit and with an access port for reading the withdrawal curve, and wherein the withdrawal curves are stored in at least a part of the withdrawal units of the plurality of withdrawal units and are supplied to a central control unit, the central control unit being further connected to measurement units for measuring the electrical energy generation curve of each of the electrical energy generation units and the operating conditions of the electrical energy generation units, in the central control unit there being stored and executable a control program for controlling the electricity distribution network as a function of the withdrawal curves of the individual withdrawal units and of the electrical energy generation curves of the individual generation units.

[0004] BACKGROUND OF THE INVENTION

[0005] The progressive abandonment of fossil energy sources and the transition toward energy sources with less environmental impact and toward user devices with higher energy efficiency have led to substantial structural changes in electricity distribution networks.

[0006] Sources of electrical energy generation alternative to fossil and / or hydroelectric sources, such as solar and / or wind and / or other types for example, have made it possible to create small electrical energy generation units, configured to meet at least part of the energy demands of each individual user or small groups of users. Unlike traditional sources of electrical energy, that is, based on the combustion of fossil fuels and / or on the transformation of water energy and / or on the exploitation of nuclear energy, these alternative energy sources, also known as renewable sources, have the characteristic of atomizing the energy sources and distributing said sources throughout the territory. In addition, the currently most widespread alternative energy sources, that is, those sources operating on the basis of photovoltaic processes and / or those driven by wind power, suffer from the fact that they are extremely sensitive to climatic conditions in terms of the amount of energy produced. For photovoltaic sources in particular, there are also problems related to the fact that they do not guarantee an energy flywheel effect that allows to deal with maximum and minimum power peaks, so that in the presence of strong solar radiation, a large amount of energy is generated that is fed into the networks and that can lead to high power peaks, both localized in sub-areas of the distribution network and also affecting larger zones of the distribution network. On the contrary, in the case of low solar radiation, the energy produced by photovoltaic systems drops drastically, while often in parallel the energy demand is greater than in conditions where solar radiation is greater. A possible energy flywheel to control positive and / or negative energy peaks that occur in the form of fast transients is the use of accumulators such as traditional accumulators and / or even super capacitors. However, these devices are relatively expensive, have relatively short average lives, and require constant functional monitoring.

[0007] As far as wind generators are concerned, these require a certain force of the winds to be efficient, but they have an advantage that they can act as power flywheels that allow to control and compensate for power peaks, within certain limits.

[0008] It is evident that in a control method and system of an electricity distribution network, or at least a part of such network, it is essential to constantly monitor the operating conditions of the network and intervene in a very short time to implement measures that allow to deal with relatively rapid changes in operating conditions, both from the point of view of the energy required and also from the point of view of the energy generated.

[0009] In relation to this requirement, it should also be considered that current climatic changes mean that, in many cases, climatic effects are extreme, in relation to their stability over time, that is, climatic changes are more extreme and temporally limited, and this entails a further rapid variability of the power generated and / or of that withdrawn or demanded from a distribution network.

[0010] In the context of controlling, above all, the energy withdrawals by the different withdrawal units, it is also important to be able to detect anomalies regarding the peaks of energy withdrawn in order to establish whether there are conditions of dispersion of electrical energy caused by damage to the network infrastructure and / or abusive withdrawals of electrical energy.

[0011] Document AU 2017368470 describes a method for controlling an energy storage system. The method comprises receiving measurement data from a measurement device positioned between a utility distribution system and an electric infrastructure. The function of the measurement device is to track the energy withdrawn from the utility distribution system. The method comprises using historical and current electricity consumption data and real-time or near-realtime generators with a plurality of layered nodes configured to form an artificial neural network, generating a predicted transmission level load and a confidence value for an entire jurisdiction of the utility distribution system. The method comprises identifying a coincident peak potential for the utility distribution system on the basis of the predicted transmission level load and the confidence value generated by the artificial neural network. The method comprises, upon identification of a coincident peak potential, transmitting signals to cause the electric infrastructure to consume energy stored in the energy storage system, thereby reducing the energy withdrawn from the utility distribution system during the coincident peak potential identified. The system described in AU 2017368470 provides to use a deep learning algorithm, such as neural networks, to predict the future consumption of a user device or a network, in order to identify future peaks in energy consumption and intervene by connecting storage systems to the network in order to buffer the peaks. The action taken by the system, that is, connecting the storage systems to the network, is not the result of a comparison between a current value and a predicted value, but simply the response of the system to the predicted value. However, even this known method does not take into account electrical energy generation units based on alternative or renewable energy sources, and therefore suffers from the disadvantage of not being able to deal with relatively rapid changes in operating conditions, both from the point of view of the energy required and also from the point of view of the energy generated, which are found in the case of alternative or renewable energy sources, as identified above, which are extremely sensitive to weather conditions in what pertains to the amount of energy produced, or which occur in the event of brief and extreme weather changes.

[0012] SUMMARY OF THE INVENTION

[0013] The present invention aims to provide a control method and a system for implementing such control method that, thanks to relatively simple measures, allows to deal with the greater dynamism of energy demand and generation in an electricity distribution network, or in a part thereof, and that allows to keep the network under control, down to the detail of the individual user devices, both in terms of energy consumption and also in terms of energy production, as well as allowing to implement structural changes to the network in a timely fashion, which can prevent transient overloads of the network, whether positive or negative, and / or to detect failures of the network infrastructures that cause dispersions, as well as any abusive energy withdrawals.

[0014] The present invention solves the problem outlined with a method for controlling an electricity distribution network, which network comprises one or more electrical energy generation units that power the network, the electrical energy generation units being based on alternative energy sources, in particular sources operating on the basis of photovoltaic processes and / or driven by wind power, and a plurality of separate and independent withdrawal points of the electrical energy, in particular distributed over a pre-established geographical area and wherein each withdrawal point is provided with a storage unit for storing the electrical energy withdrawal curve measured as a function of time from the corresponding withdrawal unit, that is, the time trend of the electrical energy withdrawn from the network, and with an access port for reading the withdrawal curve, and wherein the withdrawal curves are stored in at least a part of the withdrawal units of the plurality of withdrawal units and are supplied to, or can be retrieved from, a central control unit, which central control unit is further connected to measurement units for measuring the curve as a function of the electrical energy generation time of at least part, or of each, of the electrical energy generation units and the operating conditions of the electrical energy generation units, that is, the time trend of the electrical energy generated, a control program being stored and executable in the central control unit for controlling the electricity distribution network as a function of the withdrawal curves of the individual withdrawal units and of the electrical energy generation curves of the individual generation units, while a program is stored and executable in the central control unit for predicting the time trend of the energy consumption of at least part, preferably of each, of the withdrawal units within a pre-established future time period and / or for predicting the time trend of the energy generated by at least part, or by each, of the electrical energy generation units which are connected to the network in the pre-established future time period, which prediction program comprises the instructions for the processor of the central control unit, which are related to the execution steps of an algorithm predictive of the time trend of the consumption relating to at least part, or to each, of the withdrawal units and / or predictive of the time trend of the quantity of energy generated by at least part, or by each, of the generation units in the pre-established future time period as a function of the curves as a function of the time of withdrawal and / or generation of the electrical energy, respectively, from at least the part, or from each, of the withdrawal units and / or the generation units stored therein and relating to a time period prior to the future time period, on the basis of the predictions there being carried out actions of compensation of transitory peaks of withdrawal and / or generation of electrical energy, which actions are synchronized with the instant in which the occurrence of the peaks has been predicted and / or the planning, in advance of the instant in which the occurrence of the transitory peaks is predicted, of structural measures and / or configuration of the connection and / or connections between the withdrawal units and / or the generation units of the network.

[0015] These measures can consist of the presence of connections with further distribution networks and / or further zones of the distribution network, which connections can be activated and deactivated by means of servo-controlled change- over switches, for example, by the central control unit or by a command unit, which is in turn controlled by the central control unit, and as a function of the prediction relating to the time trend of the withdrawals and / or the generation of electrical energy.

[0016] In this case, for example, an electricity distribution network comprising a plurality of electrical energy withdrawal units and / or a plurality of electrical energy generation units can be divided into sub-networks, each of which is characterized by a different geographical location area of the corresponding withdrawal and / or generation units, and also by the number of such units, as well as by the type of withdrawal units with reference to the energy withdrawal curves of the individual withdrawal units, as well as by the type of electrical energy generation units, while between one or more of the sub-networks there are provided electrical connections able to be switched between a closed and an open condition, the switching state of the one or more connections between the sub-networks being automatically commanded into a closed or open condition as a function of the results of the prediction relating to the withdrawal of electrical energy and / or the generation of electrical energy for the future time period, and the closed and / or open condition of the connections between the sub-networks being varied in the future time period as a function of the variation in the time trend of the withdrawal and / or the time trend of the generation of electrical energy for the various sub-networks.

[0017] Thanks to the aforementioned steps, it is possible to keep the network’s operating conditions stable, preventing transient peaks of excess electricity production or excess demand for electrical energy, by promptly connecting, on the basis of the trends resulting from the aforementioned prediction, parts of an electricity distribution network which, on the basis of the aforementioned predictions, compensate each other in relation to the energy produced and the energy withdrawn. This stabilization measure occurs without delay with respect to the onset of instabilities, and prevents excessive stress on the network’s operating conditions or the need to intervene more drastically in order to stop a positive or negative drift of the energy present in the network.

[0018] The above mentioned measures can also comprise, alternatively or in combination, other types of interventions, such as for example the timely creation, before the occurrence of transient events of excessive withdrawal or excessive generation of energy, of compensation stations, such as electrical energy storage stations functioning as an electrical energy flywheel to level out negative and / or positive energy peaks in the network or in a specific sub-network.

[0019] The results of the prediction allow to provide a sizing of such storage stations and possibly also a modular arrangement to expand the capacity of these stations.

[0020] According to a further characteristic of the method, which improves the precision and reliability of the predictions on the future time trend of consumption, that is, of the withdrawals from the individual withdrawal units and / or the energy generated by the individual generation units, the method according to one or more of the embodiments described above provides to use further data as training data of the predictive algorithm, such as registry data of the users of each withdrawal and / or generation unit, geographic data for each withdrawal and / or generation unit, weather data relating to each withdrawal and / or generation unit, and / or time data such as month, week and / or time, or a combination of part, or of all, types of this data.

[0021] The registry data allow to introduce into the calculation of the prediction, data relating to the type of supply associated with a specific withdrawal unit, such as for example the type of current, the maximum power that can be delivered on the basis of the supply contract, the combination of the withdrawal unit with a local electrical energy generation unit and the technical specifications of the local electrical energy production unit.

[0022] The data relating to the geographical position also have relevance in relation to whether the withdrawal and / or generation unit belong to a sub-network, as well as for example the seasonal and / or daily trend of the withdrawal of energy from the network and / or the generation and feed of energy into the network.

[0023] With regard to the weather data, these can be related to the time trend of the weather conditions that have been recorded and stored for a pre-established geographical position of an energy withdrawal unit and / or an energy generation unit, and which is related to a pre-established period of time prior to that of the prediction.

[0024] With regard to the time data, these may be important to establish the conditions under which the energy withdrawal was performed, such as day or night for example, or the month to which some of the data refer, and in particular the data relating to the measurements of the energy withdrawn, so that the measurements are automatically related to conditions such as seasonal conditions, for example the duration of the day, average external temperatures and other climatic conditions, and also in order to allow to detect the time coincidence with other functional conditions of the network, such as for example transient failures, maintenance interventions or other.

[0025] The predictive calculation as a function of the time trend of the withdrawal of electrical energy and / or of the generation of electrical energy, and optionally of a combination or sub-combination of the further data relating to the registry conditions of the users relating to the withdrawal and / or generation units, to the geographical position and / or to the weather conditions, or to the trend of the weather conditions over time, can occur thanks to various predictive algorithms, such as for example machine learning algorithms and / or other types of algorithms.

[0026] Preferably, the algorithm model used is a so-called supervised algorithm.

[0027] For the specific application, the use of a regression and statistical classification machine learning algorithm called gradient boosting proved to be advantageous. This algorithm is known and is described in greater detail in several publications including, for example, the document published on the internet page This algorithm is particularly suited to performing predictive operations, in particular relating to the trend of energy consumption, or the loads to which a pre-established electricity distribution network is subject in a pre-established period of time.

[0028] The program executed by the central control unit to determine the prediction is stored in a memory of the central control unit and is executable by the central control unit, and it comprises the instructions for training the algorithm, as well as the instructions for enabling the central control unit to execute the computation steps provided by the algorithm in its trained condition.

[0029] According to one embodiment, the withdrawal units are configured to keep in memory the curves relating to the time trend of the electrical energy withdrawn as a function of time and / or the electrical energy fed into the network only for a time window having a pre-established length with reference to a time instant in which the data are red, this time window being moved forward in time with a pre- established updating frequency of the stored data, while the reading of the memory is also repeated in time according to a pre-established reading sequence, and the reading data of the memory in correspondence with a pre-established time instant of the reading sequence being used to perform supervised adjustment steps of the training condition of the predictive algorithm, that is, the regression algorithm, in particular the gradient boosting algorithm.

[0030] Optionally, in correspondence with each time instant of reading of the memory, it can also be provided to perform a new reading of the registry data and / or of the geographical data and / or of the weather data and / or of the time data, such as date, month, week, time, etc. and to use these data for the process of supervised training of the predictive algorithm.

[0031] The future time period with respect to the act of reading the data stored and relating to the consumption curves within the above defined pre-established time window and / or also the data relating to the production of electrical energy in the same time window, can be defined at will.

[0032] In one embodiment, the period to which the prediction of the time trend of the energy withdrawn and / or generated refers is of the order of at least a few days.

[0033] According to a further characteristic, the method also provides steps of evaluating the reliability of the prediction, which are performed on the basis of the comparison between the predictions calculated by means of the predictive algorithm and the data relating to the time trend of the withdrawals of electrical energy and / or of the electrical energy generated actually measured in the period corresponding to that for which the predictions of the corresponding data were calculated.

[0034] According to one embodiment, it is possible to provide different comparison metrics, which can be used to adjust and refine the algorithm’s predictive effectiveness.

[0035] According to one embodiment, a first comparison metric provides to calculate the difference between the predicted values and the real value weighted with respect to the available power, averaging the data between the day’s quarters of an hour, according to the following formula:

[0036] A second comparison metric is the object of an alternative embodiment that provides to calculate the difference between the predicted value and the real value weighted with respect to the available power, by performing, for each withdrawal unit, the sum of the values between the day’s quarters of an hour and then averaging the withdrawal units according to the following formula:

[0037] In which UP is a numbering index of the individual withdrawal units provided in the network.

[0038] A third alternative comparison metric provides to calculate the difference between the predicted value and the value measured squared obtained by averaging the values relating to the day’s quarters of an hour according to the following formula:

[0039] From the comparison between the predicted values for electrical energy withdrawal and / or generation and the values actually measured, it is possible to draw considerations, not only on the quality of the prediction, but also identifying conditions of potential failures of the network infrastructures that lead to energy losses and / or possibly also electrical energy withdrawals carried out by bypassing the passage by means of the supply units.

[0040] When the variations between predicted values and values actually measured according to the different metrics exceed a pre-established limit value, it is possible to plan inspection visits to the individual installation sites of the withdrawal and / or generation units. Since the predictions are uniquely correlated to each withdrawal and / or generation unit provided in the network, or to at least some of them, it is possible for the on-site verification interventions to be aimed at one or a just a few intervention sites, that is, at the installation sites of the withdrawal units and / or generation units for which the differences between predicted values and actually measured values exceed the pre-established threshold.

[0041] The invention also concerns a system for implementing the aforementioned method according to one or more of the embodiments and variants of the method described above, which system comprises: an electricity distribution network, which network in turn comprises one or more electrical energy generation units that power the network, the electrical energy generation units being based on alternative energy sources, in particular sources operating on the basis of photovoltaic processes and / or driven by wind power, and a plurality of separate and independent withdrawal points of the electrical energy, in particular distributed over a pre-established geographical area, and wherein each withdrawal point is provided with a storage unit for storing the electrical energy withdrawal curve measured as a function of time from the corresponding withdrawal unit, that is, the time trend of the electrical energy withdrawn from the network, and with an access port for reading the withdrawal curve, and wherein the withdrawal curves are stored in at least a part of the withdrawal units of the plurality of withdrawal units and are supplied to, or can be retrieved from, a central control unit, which central control unit is further connected to measurement units for measuring the curve as a function of the electrical energy generation time of at least part, or of each, of the electrical energy generation units and the operating conditions of the electrical energy generation units, that is, the time trend of the electrical energy generated, a control program being stored and executable in the central control unit for controlling the electricity distribution network as a function of the withdrawal curves of the individual withdrawal units and of the electrical energy generation curves of the individual generation units, while in the central control unit a program is stored and executable for predicting the time trend of the energy consumption of at least part, preferably of each, of the withdrawal units within a pre-established future time period and / or for predicting the time trend of the energy generated by at least part, or by each, of the electrical energy generation units connected to the network in the pre-established future time period, which prediction program comprises the instructions for the central control unit’s processor which are related to the execution steps of an algorithm predictive of the time trend of the consumption relating to at least part, or to each, of the withdrawal units and / or predictive of the time trend of the quantity of energy generated by at least part, or by each, of the generation units in the pre-established future time period as a function of the curves as a function of the time of withdrawal and / or generation, respectively, of the electrical energy from at least the part, or from each, of the withdrawal units and / or the generation units stored therein and relating to a time period prior to the future time period, on the basis of these predictions there being activated units for compensating for the transient peaks of electrical energy withdrawal and / or electrical energy generation synchronized with the time instant in which these transient peaks are predicted.

[0042] The compensation units can consist of a plurality of electrical energy storage / transfer flywheels, such as one or more storage / transfer stations for example, comprising one or more electric accumulators or similar devices and / or one or more units of electrical energy generators comprising electromechanical transducers.

[0043] Alternatively or in combination, the distribution network can be provided in combination with a structural configuration that provides the division of the distribution network comprising a plurality of electrical energy withdrawal units and / or a plurality of electrical energy generation units into two or more subnetworks, each of which sub-networks is characterized by a different geographical location area of the corresponding withdrawal and / or generation units, and also by the number of such units, as well as by the type of withdrawal units with reference to the energy withdrawal curves of the individual withdrawal units, as well as by the type of electrical energy generation units, while between one or more of the sub-networks there are provided electrical connections able to be switched between a closed and an open condition, the switching state of the one or more connections between the sub-networks being automatically commanded into a closed or open condition as a function of the results of the prediction relating to the withdrawal of electrical energy and / or the generation of electrical energy for the future time period, and the closed and / or open condition of the connections between the sub-networks being varied in the future time period as a function of the prediction of the time trend of the withdrawal and / or the time trend of the generation of electrical energy for the various sub-networks.

[0044] DESCRIPTION OF THE DRAWINGS

[0045] These and other characteristics and advantages of the present invention will become apparent from the following description of a non-limiting example embodiments, shown in the attached drawings wherein:

[0046] Figure 1 shows a high-level block diagram of an electricity distribution network provided with a plurality of electrical energy withdrawal units and a plurality of electrical energy generation units and, in combination, with a central control unit according to an embodiment of the present invention.

[0047] Figure 2 shows a high-level block diagram of an example embodiment of a unit for withdrawing energy from an electricity distribution network.

[0048] Figure 3 shows a high-level block diagram of an example embodiment of a unit for generating energy from an electricity distribution network.

[0049] Figure 4 shows a high-level block diagram of an example embodiment of a central control unit according to the present invention.

[0050] Figure 5 shows an example of curves representing the daily time trend of the energy withdrawn by a withdrawal unit on different dates.

[0051] Figure 6 shows a block diagram of the steps according to an embodiment of the method performed by the system of the present invention.

[0052] DESCRIPTION OF SOME EMBODIMENTS

[0053] In some embodiments, a method for controlling an electricity distribution network provides to estimate the future consumption of an electricity user device (POD) by means of an algorithm, e.g. gradient boosting type, which receives as input (i) historical consumption data and (ii) registry data (position, type, weather, etc.) of the user device.

[0054] The predictions of future consumption are then compared (e.g. step 611) with actually measured consumption, both in order to verify the performance of the algorithm and make any corrections to the algorithm itself (e.g. step 612), and also to verify the accuracy of the consumption (e.g. step 613) in order to initiate control inspections (e.g. step 614). In some embodiments, the method can therefore provide to link or correlate an action of the system to the result of the comparison between predicted values and measured values. In some embodiments, the action performed by the method can for example be the implementation or planning of control inspections (e.g. step 614).

[0055] Favorably, the measurement of the energy withdrawn by a withdrawal unit and / or the energy fed into the network by a generation unit is performed at intervals comprised between 1 and 15 minutes. In this way, the method described here can provide that the consumption estimates are carried out with a desired granularity, which can be between 1 and 15 minutes, for example every 15 minutes.

[0056] With reference to figure 1, this schematically shows an electricity distribution network that is divided into a plurality of sub-networks SUBnet 1, SUBnet 2 to SUBnet N, wherein N is any natural number whatsoever.

[0057] Each sub-network comprises a plurality of withdrawal units indicated with POD1, POD2, POD3, POD4, PODn, wherein n is a natural number greater than 4.

[0058] Similarly, each sub-network can comprise one or a plurality of electrical energy generation units indicated with Genl, Gen2, Gen3, Gen4, Genn, wherein n is any natural number whatsoever greater than 4. These electrical energy generation units are based on alternative energy sources, also called renewable energy sources, in particular sources operating on the basis of photovoltaic processes and / or driven by wind power.

[0059] The above notation is intended to indicate that each sub-network can have a pre- established number greater than 1 of electrical energy withdrawal units generically called PODs, and any number whatsoever of electrical energy generation units. The electrical energy generation units are shown for completeness in the embodiment of figure 1, but may also not be provided in one or more or all of the sub-networks.

[0060] Typically, the withdrawal units consist of the so-called “PODs” which are associated with an electrical energy supply contract, the conditions of the supply and the subjects for whom the supply is intended, as well as the payment tariffs for the energy supplied and the payment terms.

[0061] With regard to the generation units, these are increasingly widespread and serve the same user devices served by a withdrawal unit. There is no limitation regarding the type of electrical energy generators which can be, for example, photovoltaic panels and / or wind generators and / or even hydrodynamic generators or other types of so-called energy harvesting systems.

[0062] According to a non-limiting embodiment, the example embodiment shown further presents a plurality of change-over switches indicated overall by the block 100, which change-over switches allow two or more sub-networks to be connected to each other, or to be connected to or isolated from each other. The change-over switches are an example of implementation of members for compensating network instability caused, for example, by transient peaks in the withdrawal of electrical energy or the feed of electrical energy generated by the one or more generation units into the network.

[0063] An alternative provided in the present example configuration can provide one or more energy accumulators 101, 201, M which are stably connected to one or more of the sub-networks, or which can be connected, each one or in any combination of them whatsoever, to one or more of the sub-networks by means of associated connection / isolation change-over switches 100.

[0064] By the term “energy accumulators” we mean energy storage devices in a broad sense, which are not limited to static accumulators of the electrochemical type but also comprise inertial-type accumulators, such as electromechanical accumulators or suchlike, which in some cases can also consist of the alternators of wind generators connected to the network.

[0065] The individual withdrawal units POD1, POD2, POD3, POD4, PODn and / or one or more generators Genl, Gen2, Gen3, Gen4, Genn, have communication interfaces for communicating with a central control unit 110.

[0066] With reference to the withdrawal units, these are provided with a management processor that runs a firmware comprising the instructions to make the management processor capable of performing the functionalities provided for the withdrawal units.

[0067] According to an essential characteristic of the present invention, the withdrawal units comprise a meter of the time trend of the withdrawal of electrical energy or, alternatively, they are configured by means of the firmware to execute that function.

[0068] The measured values are stored as a function of time, in the form of energy withdrawal time curves, in a memory of the corresponding withdrawal unit, which memory is accessible to the central control unit 110 by means of the communication interface indicated above.

[0069] The central control unit 110 is further connected with maintenance activity planning units 120 and / or an upgrade planning unit 130 and / or a unit for verifying the accuracy of the consumption detected for each withdrawal and / or generation unit 140.

[0070] Similarly, it is also possible to provide that the energy generation units are provided with local management units that are provided with sensors to measure the time trend of the energy generated and fed into the respective sub-network. Also in this case, the sensors can be of a hardware type controlled by the generation unit’s management unit, or they can consist of a firmware that configures the management unit in such a way as to make it capable of performing the measurement functions.

[0071] The time trend of the energy generated by a generation unit is stored in a memory of the same generation unit, which memory is accessible to the central control unit by means of a communication interface for communicating with each, or at least part, of the electrical energy generation units provided in the subnetworks.

[0072] With reference to figure 2, this shows a non-limiting embodiment example of a possible configuration of a withdrawal unit according to the present invention.

[0073] The withdrawal unit comprises a CPU 200 which runs a firmware for managing various peripherals provided in the withdrawal unit. The firmware is stored in a dedicated memory or in a dedicated memory area of a common mass memory of the withdrawal unit, indicated with 210. It is possible to communicate with the CPU 200 and with the various peripherals by means of a human-machine interface, indicated overall with 220. This can comprise various devices, such as display devices, selection devices, data string input devices and / or command devices. These devices can consist of one or more human-machine interface devices provided in current computers or computer systems.

[0074] The CPU 200 controls a unit 230 for measuring the energy delivered by the withdrawal unit for a pre-established time interval. In addition, the CPU 200 controls a communication interface, preferably for two-way communication with the central control unit 110, which communication interface is indicated with 240.

[0075] In dedicated memories 250 and 260, or in dedicated memory areas of a common mass memory, there are respectively stored the data relating to the curves of the time trend of the energy that has been withdrawn by means of the withdrawal unit, or delivered thereby, and optionally diagnostic data that indicate the functional status of the withdrawal unit itself and / or of one or more of the peripherals that compose it.

[0076] This hardware configuration can also be provided for the energy generation units, being suitably configured to operate in combination with electrical energy generation devices. These devices can be of different types and operate according to different physical and / or physical / chemical principles, and are indicated overall with the block 370. Even in the case of the electrical energy generation units, these comprise a management processor 300 that controls the functionalities of the various peripherals provided in such units. In particular, according to the more generic embodiment shown in figure 3, the functional control of the peripherals is implemented by means of a firmware that is stored in a dedicated memory or in a dedicated memory area represented by block 310.

[0077] The CPU 300 controls a unit 330 for measuring the energy produced by the generator 370 and fed into the network during a pre-established time interval. In addition, the CPU 300 controls a communication interface, preferably for two-way communication with the central control unit 110, the communication interface being indicated with 340.

[0078] It is possible to communicate with the CPU 300 and with the various peripherals by means of a human-machine interface indicated overall with 320. This can comprise various devices, such as display devices, selection devices, data string input devices and / or command devices. These devices can consist of one or more human-machine interface devices provided in current computers or computer systems.

[0079] Dedicated memories 350 and 360, or dedicated memory areas of a common mass memory, are provided, in which there are respectively stored the data relating to the curves of the time trend of the energy that has been generated and fed into the network by means of the generation unit, or by the generator 370, and optionally diagnostic data that indicate the functional status of the generation unit itself and / or of one or more of the peripherals that compose it.

[0080] Figure 4 shows a high-level block diagram of an embodiment of the central control unit. Also in this case, the central control unit 110 has at least one CPU 400 that manages several peripherals. In different areas of a common mass memory, or in dedicated memories indicated with 410, 450, 460, 480, there are stored, respectively, a firmware for managing the peripherals that contain the instructions for making the peripherals capable of performing the functions provided, for training the predictive algorithm, a software comprising the instructions for making the CPU capable of performing the steps provided by the predictive algorithm, and optionally diagnostic data relating to the central control unit’s peripherals, or other units associated with or controlled by the central control unit.

[0081] It is possible to communicate with the CPU 400 and with the various peripherals by means of a human-machine interface indicated overall with 420. This can comprise various devices, such as display devices, selection devices, data string input devices and / or command devices. These devices can consist of one or more human-machine interface devices provided in current computers or computer systems.

[0082] The CPU 400 controls one or more drivers 430 for commanding one or more members for automatic intervention on the network for the execution of operations aimed, for example, at compensating for conditions of network instability and / or transient peaks of network load and / or feed of energy into the network itself, such as for example those described with reference to figure 2, and which are in the form of servo-controlled change-over switches and / or stations for storing / releasing the electrical energy delivered by the withdrawal unit for a pre-established time interval. In addition, the CPU 400 controls a communication interface, preferably for two-way communication with the central control unit 110, which communication interface is indicated with 440 and through which it can access the memories of the withdrawal units and / or of the generation units in order to retrieve the data relating to the time trend curves of the energy withdrawn by the withdrawal units and that generated and fed into the network by the generation units.

[0083] According to yet a further characteristic, the central control unit can provide a memory, or a memory area, indicated with 490 and in which there is stored a software for planning network interventions and / or inspections for verifying the actual conditions of the network and / or of the withdrawal units and / or of the generation units.

[0084] With reference to the data stored in the memories of the withdrawal units relating to the curves that represent the time trend of the energy withdrawn through the withdrawal units, figure 5 shows these curves that represent the electrical energy withdrawn from the network through a withdrawal unit and over the course of a time interval of essentially one day and on different dates.

[0085] According to a further characteristic, the central control unit comprises a comparator 491 which can be either of the hardware or software type and which is configured to perform a comparison between the time trends of the energy withdrawn and / or that generated of a pre-established withdrawal unit and / or of a pre-established generation unit, as defined by the predictive algorithm, and the data actually measured in coincidence with the time instants which these values refer to.

[0086] Figure 6 shows a flowchart illustrating the steps of the method object of the present invention and implemented by means of the system according to the present invention.

[0087] As already described above, the method provides to estimate the trend of the withdrawal of electrical energy and / or of the feed of electrical energy in future time instants, through a prediction of the withdrawal of electrical energy and / or the generation of electrical energy or the feed of electrical energy into the network in one or more future time instants, on the basis of historical data, that is, stored data relating to the time trend of the withdrawal of electrical energy and / or the feed of electrical energy by each of at least a part of the electrical energy withdrawal units and / or generation units provided in an electricity distribution network and / or in a sub-network thereof, in a time window prior to the instant such data is collected.

[0088] In order to perform the aforementioned prediction, a preferred embodiment of the invention provides to use a machine learning algorithm suitably trained on the basis of this data.

[0089] Among the various possible machine learning algorithms, it has proven advantageous to use an algorithm called “gradient boosting”, which determines the predictive values by means of a regression based on known data. The algorithm is known, and more detailed information can be found from various publications, including the one referenced at the beginning.

[0090] Since the acquisition of the data relating to the time trend of the energy withdrawn by the individual withdrawal units and / or fed into the network by the individual generation units continues according to a pre-established measurement sequence, the data on the electrical energy withdrawn and / or on that generated which are calculated by means of the predictive algorithm can in turn be compared with the data actually measured. This allows to verify the accuracy between these values, from which not only to test the quality of the prediction, but also to extract indications on possible anomalies that may be related to failures of the units that make up the network infrastructure and / or even related to malicious energy withdrawal actions that exclude the withdrawal devices.

[0091] In addition, the predictions also allow to deal in a timely and near real-time maimer with the occurrence of conditions of instability or excessive load on the network that may compromise its operation or cause damage or other malfunctions.

[0092] There are multiple possible variants. Figure 6 shows the block diagram of an example embodiment and, in relation to what has been illustrated, we will indicate the possible optional steps of the process which in the drawing are instead explicitly shown for the purpose of completeness of the embodiment with reference to the possible variants.

[0093] The method shown is a dynamic method that provides to measure the time trend of the electrical energy withdrawn by each of the withdrawal units that are provided in a network or in a sub-network thereof, or by a part of these units in a time window of pre-established length, that is, which, with respect to a pre- established instant, extends backward in time for a pre-established time distance.

[0094] The time window typically has a substantially predetermined length that can be modified but is kept fixed during the execution of the method and moves forward in time continuously or at pre-established time intervals, so that the stored data are constantly updated by adding new data measured in the time interval in which the time window moves forward, and by deleting the older data.

[0095] Therefore, according to this embodiment, the process begins as shown in step 600, with the definition of the time sequence of the movement in time of the time window for the storage of the data deriving from the measurement of the energy withdrawn from the network and / or the energy fed into the network for each or for part of the withdrawal units and / or the generation units.

[0096] In step 601, a time sequence of instances in which the stored data is red is defined. In each instant of the reading sequence, the data stored by the individual withdrawal and / or generation units present in the network or in a sub-network are retrieved.

[0097] Steps 602 through to 604 are optional steps which, however, in the most refined and preferred embodiment, integrate the information relating to the stored data relating to the energy withdrawn and the energy generated and fed into the network, with information useful for generating more reliable predictions.

[0098] In particular, in step 602, so-called registry data are added to the data obtained from the curves of the time trend measured for the energy withdrawn and / or that fed into the network. These data are fixed and provide the indications relating to the settings of the withdrawal units in relation to the maximum values of energy that can be withdrawn by them, and to other settings of the withdrawal and / or generation units.

[0099] In step 603, information relating to the geographical positioning of the various withdrawal and / or generation units is added, while in step 604 data relating to the predictions of the weather conditions for the geographical positions in which the withdrawal units and / or the generation units are installed are added, and in step 615 time data relating to the date, such as month, week, and / or time, when certain events occurred, such as the beginning of the measurement readings or time instants to which the readings or other data refer, are added.

[0100] In this way, both the average climatic conditions of the installation zone and also those specifically forecast, which can be affected by considerable variations compared to the conditions that would be expected according to seasonal averages, are taken into account.

[0101] These data are supplied to the predictive algorithm for its training in the execution of the predictive calculation, and in particular for the calculation of the prediction values by means of a regression process performed in particular and preferably by means of a so-called gradient boosting algorithm, as indicated in steps 605 and 606. In step 607, a time period is defined for which to calculate, by means of the predictive algorithm, the values of the time trend of the energy withdrawn and / or that generated by a certain withdrawal unit and / or by a certain generation unit. In step 608, the algorithm is executed, and the prediction curves are generated for the time trend of the energy withdrawn and / or that fed into the network in the prediction time period defined in step 607.

[0102] Subsequently, the data subject to the prediction undergo post-processing in order to assess the need to perform planning for, or changes to, actions of maintenance and / or upgrade and / or stabilization of the network’s operating conditions, as indicated in step 609.

[0103] The process of measuring the data relating to the time trends of the energy withdrawn and that fed for each withdrawal unit and for each generation unit is continued according to the time sequence of forward movement of the time window, defined in step 600. The data measured and stored in the memories of the withdrawal units and / or of the generation units are then updated, as indicated in step 610, while for the data measured at the same time instants for which the prediction values have been calculated, in step 611, it is provided to carry out a comparison between the data actually measured at these instants and the data predicted for such instants, the results of this comparison can be used either to verify the performances of the predictive algorithm, and possibly make corrections, as indicated in step 612, or they can be used to perform a step 613 of verifying the accuracy of the energy consumption of the individual withdrawal units and / or of the energy fed by the individual generation units with predictive data in order to establish whether the network’s infrastructure is malfunctioning and / or damaged, or whether the energy withdrawal or the energy fed are subject to abusive withdrawal activities that exclude the passage through the electrical energy withdrawal units or generation units, for which a step of implementing or planning control inspections is performed, as indicated in step 614.

[0104] It is possible to set a threshold value for the result of the accuracy verification that can be, for example, in the form of a comparison between values obtained with the prediction process and values actually measured. When this threshold value is exceeded, then it is possible to provide a step of planning an inspection at the withdrawal unit and / or the generation unit for which this comparison has been calculated. This step is indicated with reference number 614.

[0105] One embodiment provides that the measurement of the energy withdrawn by a withdrawal unit and / or that fed into the network by a generation unit is carried out at intervals comprised between 1 and 15 minutes, while the time window within which to store the measured data is of about two months from the time of the last measurement.

[0106] Also in one embodiment, the time period for which to determine the prediction of the time trend of the energy withdrawn and / or that fed can vary from about one day up to a few days, for example three or more days.

Claims

CLAIMS1. Method for controlling an electricity distribution network, which network comprises one or more electrical energy generation units that power said network, said electrical energy generation units being based on alternative energy sources, in particular sources operating on the basis of photovoltaic processes and / or sources driven by wind power, and a plurality of separate and independent withdrawal points of said electrical energy, in particular distributed over a pre-established geographical area and wherein each withdrawal point is provided with a storage unit for storing the electrical energy withdrawal curve measured as a function of time from the corresponding withdrawal unit, that is, the time trend of the electrical energy withdrawn from the network, and with an access port for reading said withdrawal curve and wherein said withdrawal curves are stored in at least a part of the withdrawal units of said plurality of withdrawal units and are supplied to, or can be recalled from, a central control unit, which central control unit is further connected to measurement units of the curve as a function of the electrical energy generation time of at least part or each of said electrical energy generation units and the operating conditions of said electrical energy generation units, that is, of the time trend of the electrical energy generated, in said central control unit there being stored and executable a control program for controlling the electricity distribution network as a function of said withdrawal curves of the individual withdrawal units and of said electrical energy generation curves of the individual generation units, while in said central control unit there is stored and executable a program for predicting the time trend of the energy consumption of at least part, preferably of each of the withdrawal units, within a pre-established future time period and / or for predicting the time trend of the energy generated by at least part or by each of said electrical energy generation units which are connected to said network in said pre-established future time period, which prediction program comprises the instructions for the processor of said central control unit, which are related to the executionsteps of an algorithm predictive of said time trend of the consumption relating to at least part or each of said withdrawal units and / or predictive of said time trend of the quantity of energy generated by at least part or each of said generation units in said pre-established future time period as a function of said curves as a function of the time of withdrawal and / or generation of the electrical energy, respectively, from at least said part or from each of said withdrawal units and / or of said generation units stored therein and relating to a time period prior to said future time period, on the basis of said predictions there being carried out actions of compensation of transitory peaks of withdrawal and / or generation of electrical energy, which actions are synchronized with the instant in which the occurrence of said peaks has been expected and / or the planning, in advance of the instant in which the occurrence of said transitory peaks is expected, of structural measures and / or configuration of the connection and / or connections between said withdrawal units and / or said generation units of said network.

2. Method according to claim 1, which method provides to divide the network into two or more sub-networks which can be electrically connected to each other by means of connection members which can be switched between an open condition and a closed condition and / or one or more stations for accumulating and / or transferring electrical energy which can also be connected, by means of connection members which can be switched between an open condition and a closed condition, to one or more of said sub-networks; each of said sub-networks is characterized by a different geographical location area of the corresponding withdrawal and / or generation units and also by the number of said units as well as by the type of withdrawal units with reference to the energy withdrawal curves of the individual withdrawal units, as well as the type of electrical energy generation units the switching state of said one or more switchable connections, provided between said sub-networks and / or between said one or more sub-networks and said one or more accumulation / transfer stations, being automatically commanded in a closed or open condition as a function of the results of the prediction relating to the withdrawal of electrical energy and / or the generation of electrical energy forsaid future time period, and said closed and / or open condition of said connections being varied in said future time period as a function of the variation in the time trend of withdrawal and / or the time trend of generation of electrical energy for the various sub-networks.

3. Method according to one or more of the previous claims, characterized in that it provides to use, as training data of the predictive algorithm, further data, such as registry data of the users of each withdrawal and / or generation unit, geographical data of each withdrawal and / or generation unit, weather data relating to each withdrawal and / or generation unit, and / or time data such as date, month, week, and / or hour relating to different events or data, or a combination of part or all types of this data.

4. Method according to one or more of the previous claims, wherein the predictive algorithm consists of a regression algorithm according to the model called “gradient Boosting”.

5. Method according to one or more of the previous claims, wherein the withdrawal units are configured to keep in memory the curves relating to the time trend of the electrical energy withdrawn as a function of time and / or of the electrical energy fed into the network only for a time window having a pre- established length with reference to a time instant of reading of said data, said time window being moved forward in time with a pre-established update frequency of the stored data, while the reading of said memory is also repeated in time according to a pre-established reading sequence and the reading data of said memory in correspondence with a pre-established time instant of said reading sequence being used for the execution of supervised adjustment steps of the training condition of the predictive algorithm, that is, of the regression algorithm, in particular the “gradient boosting” algorithm.

6. Method according to one or more of the previous claims, which method further provides steps of evaluating the reliability of the prediction which are carried out on the basis of the comparison of the predictions calculated by means of the predictive algorithm and the data relating to the time trend of the withdrawals of electrical energy and / or the electrical energy generated actually measured in the period corresponding to that for which the predictions of the corresponding data were calculated.

7. Method according to one or more of the previous claims, wherein the predicted values and / or the comparison between said predicted values and the values actually measured at the corresponding time instants are analyzed to identify conditions of potential failures of the network infrastructures leading to energy dispersions and / or possibly also electrical energy withdrawals carried out bypassing the passage by means of the supply units.

8. Method according to one or more of the previous claims, wherein the measurement of the energy withdrawn by a withdrawal unit and / or the energy fed into the network by a generation unit is performed at intervals comprised between 1 and 15 minutes.

9. System for the implementation of said method according to one or more of the previous claims, which system comprises: an electricity distribution network, which network in turn comprises one or more electrical energy generation units that power said network, said electrical energy generation units being based on alternative energy sources, in particular sources operating on the basis of photovoltaic processes and / or sources driven by wind power, and a plurality of separate and independent withdrawal points of said electrical energy, in particular distributed over a pre-established geographical area and wherein each withdrawal point is provided with a storage unit for storing the electrical energy withdrawal curve measured as a function of time from the corresponding withdrawal unit, that is, the time trend of the electrical energy withdrawn from the network, and with an access port for reading said withdrawal curve and wherein said withdrawal curves are stored in at least a part of the withdrawal units of said plurality of withdrawal units and are supplied to, or can be recalled from, a central control unit, which central control unit is further connected to measurement units of the curve as a function of the electrical energy generation time of at least part or each of said electrical energy generation units and the operating conditions of said electrical energy generation units, that is, of the time trend of the electrical energy generated, in said central control unit there being stored and executable a controlprogram for controlling the electricity distribution network as a function of said withdrawal curves of the individual withdrawal units and of said electrical energy generation curves of the individual generation units, while in said central control unit there is stored and executable a program for predicting the time trend of the energy consumption of at least part, preferably of each of the withdrawal units, within a pre-established future time period and / or for predicting the time trend of the energy generated by at least part or by each of said electrical energy generation units which are connected to said network in said pre-established future time period, which prediction program comprises the instructions for the processor of said central control unit, which are related to the execution steps of an algorithm predictive of said time trend of the consumption relating to at least part or each of said withdrawal units and / or predictive of said time trend of the quantity of energy generated by at least part or each of said generation units in said pre-established future time period as a function of said curves as a function of the time of withdrawal and / or generation of the electrical energy, respectively, from at least said part or from each of said withdrawal units and / or of said generation units stored therein and relating to a time period prior to said future time period, on the basis of said predictions there being activated units for compensating for the transient peaks of electrical energy withdrawal and / or electrical energy generation synchronized with the time instant in which said transient peaks are expected.

10. System according to claim 9, wherein said compensation units can consist of one or of a plurality of electrical energy accumulation / transfer flywheels, such as for example one or more accumulation / transfer stations, comprising one or more electric accumulators or similar devices and / or one or more electrical energy generating units comprising electromechanical transducers, which compensation units are connectable to the network by means of connections switchable into a closed or open condition.

11. System according to claim 9 or 10, wherein the distribution network can be provided in combination with a structural configuration that provides the subdivision of said distribution network, comprising a plurality of electrical energywithdrawal units and / or a plurality of electrical energy generation units, into two or more sub-networks, each of which sub-networks is characterized by a different geographical location area of the corresponding withdrawal and / or generation units and also by the number of said units as well as by the type of withdrawal units with reference to the energy withdrawal curves of the individual withdrawal units, as well as the type of electrical energy generation units, while between one or more of said sub-networks there are provided electrical connections that can be switched between a closed condition and an open condition, the switching state of said one or more connections between said sub-networks being automatically commanded in a closed or open condition as a function of the results of the prediction relating to the withdrawal of electrical energy and / or the generation of electrical energy for said future time period, and said closed and / or open condition of said connections between the sub-networks being varied in said future time period as a function of the prediction of the time trend of withdrawal and / or the time trend of generation of electrical energy for the various sub-networks.

12. System according to one or more of claims from 9 to 11, wherein the measurement of the energy withdrawn by a withdrawal unit and / or the energy fed into the network by a generation unit is performed at intervals comprised between 1 and 15 minutes.

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