Method for determining an optimized electrical energy self-generation capacity associable with a user

Abstract

A method is proposed for determining an electrical energy self-generation capacity associable with a user. The method comprises: receiving consumption data (CC, CD) indicative of an expected consumption of electrical energy by the user; receiving production data (PE, PC) indicative of an expected production of electrical energy associated with a plurality of groups of virtual fractions (G1, G2), wherein each virtual fraction (FVQ represents a fraction (PXE, PXS) of a generation capacity of a power generation plant (50, 51), and wherein the groups of virtual fractions are representative of fractions of power generation plants different from each other; determining a virtual-fraction combination (TB) comprising virtual fractions of at least two groups of virtual fractions different from each other, such as to make the combination unique and not existing in nature, based on the consumption data and the production data; and providing the user with data indicative of the virtual-fraction combination (TB). The virtual- fraction combination is configured, in response to an association with the user, to constitute an amount of electrical energy self-generated by the user during a first time interval (DTB).

Description

[0001] “METHOD FOR DETERMINING AN OPTIMIZED ELECTRICAL ENERGY SELF-GENERATION CAPACITY ASSOCIABLE WITH A USER”

[0002] * * *

[0003] Field of the invention

[0004] The present invention concerns a method for determining an optimized electrical energy self-generation capacity associable with a user.

[0005] Background

[0006] As is known, the electrification of consumption has led to an ever increasing demand for electrical energy and therefore to the need to increase and optimize the generation and usage of electrical energy.

[0007] Concurrently, the electrification of consumption has resulted in an ever increasing expenditure for users, linked to the ever increasing consumption of electrical energy.

[0008] At the same time, there is a need to increase the spread of power generation plants that are based on renewables.

[0009] However, renewable-based power generation plants are not always achievable for individual users.

[0010] In particular, not all users may have the possibility of equipping themselves with private / personal power generation plants, such as for example solar (photovoltaic) type private plants, due to a lack of available space or limited exposure to the renewable resource.

[0011] There is therefore the need to promote the spread of renewable energy generation plants to as many users as possible, and to keep the costs linked to the consumption of electrical energy low.

[0012] Centralized renewable energy generation systems with fractional ownership are known, as disclosed for example in document US 2010 / 293045 Al . This document describes a system in which a user can purchase a share of ownership of a centralized renewable energy generation plant and receive a credit on their electricity bill corresponding to the energy generated by their share. The system described in US 2010 / 293045 Al provides an optimization process to recommend and sell a share of ownership in a renewable energy system based on historical information of the user’s past bills. However, this system has several disadvantages and limitations. In particular, the system of US 2010 / 293045 Al performs a static sizing of the generation capacity (expressed in kilowatts) to be associated with the user based on historical aggregated consumption data, without considering the time variations of the user’s electrical consumption and of the energy generation during the investment validity period. This static approach does not allow to effectively optimize the combination of different generation technologies (e.g., solar and wind) taking into account the complementarity of their generation time profiles with respect to the user’s consumption time profile. In addition, the system of US 2010 / 293045 Al does not provide for a dynamic assessment of the balance between consumption and production for a plurality of different time instants (e.g., different hours of the day, different days, months or seasons) during the entire validity period, nor does it consider the time variations in the prices of electrical energy in the market. Consequently, the system of US 2010 / 293045 Al is unable to optimize the combination of fractions of plants with different technologies in order to maximize self-consumption and minimize any non-consumed production surpluses under the different time conditions that occur during the validity period.

[0013] In addition, the system of US 2010 / 293045 Al is primarily focused on the sale of ownership shares of a single generation plant, without providing for a multitechnology optimization that simultaneously considers fractions of generation plants based on different energy sources (e.g., solar, wind, hydroelectric) and / or located in different geographic locations, so as to guarantee a more constant production of electrical energy, independent of local weather conditions and specific timeframes.

[0014] There is therefore the need for a method and system that allow to determine an optimal combination of fractions of power generation plants with different technologies and / or locations, by means of a dynamic assessment that takes into account the time variations of the user’s electrical consumption, of the energy production of the different technologies, and of the electrical energy prices, so as to maximize a user’s self-consumption and economic savings throughout the entire validity period of the investment.

[0015] The purpose of the present invention is to at least partly overcome the disadvantages of the state of the art and meet at least the above-mentioned requirements. Summary

[0016] According to the present invention, a method, a data processing system and a computer program are therefore provided, as defined in the attached claims.

[0017] In particular, the present invention allows to provide a tool useful to optimize the mix of fractions of power generation plants, preferably of the renewable type, associable with the user so as to satisfy the specific needs of the user, and which are actually acquirable by the user.

[0018] Brief description of the drawings

[0019] For a better understanding of the present invention, some embodiments thereof are described below, given purely as a non-restrictive example, with reference to the attached drawings wherein:

[0020] - fig. 1 shows a block diagram of a data processing system, according to the present invention;

[0021] - fig. 2 shows a flowchart of a part of a method for determining an electrical energy self-generation capacity associable with a user, according to the present invention;

[0022] - fig. 3 shows a detailed flowchart of an optimization step of the method of fig. 2, according to one embodiment;

[0023] - fig. 4 shows an example of a time profile of a user’s monthly electric demand;

[0024] - fig. 5 shows an example of different hourly trends of the cost of electrical energy;

[0025] - fig. 6 shows an example of a comparison between the hourly profile of a user’s monthly electric demand and the hourly profile of the electrical energy generation associable with the user;

[0026] - fig. 7 shows an example of a graphic interface;

[0027] - fig. 8 shows a flowchart of a further part of the method according to the present invention;

[0028] - fig. 9 shows a system for implementing the method of fig. 8, according to one embodiment.

[0029] Description of some embodiments

[0030] Fig. 1 shows a block diagram of a data processing system 1 comprising processing resources 3 and a user interface 5, operationally coupled together. The processing resources 3 can comprise electronic control, computing and / or processing resources of the centralized or distributed type, for example comprising one or more servers, electronic devices and suchlike, for example created in whole or in part in the cloud.

[0031] The user interface 5 can be, for example, an electronic, mobile or fixed device, configured to be used by an individual (user) and comprising input / output means such as a screen, a display or other known visualization, data entry, etc. means.

[0032] The data processing system 1 is configured to implement a method, described below in detail with reference to fig. 2, for determining an electrical energy selfgeneration capacity associable with a user (individual) during a specific future time interval (e.g., of a few years).

[0033] In particular, as described in detail below, the data processing system 1 determines one or more token boxes that can be associable with (e.g., acquirable by) the user, wherein each token box has a specific time duration DTB and represents a specific combination of fractions of power generation plants that, following the purchase, will be associated with the user so that the electrical energy generated by the specific combination of fractions of power generation plants which is associated with the purchased token box constitutes, during part or all of the time duration DTB of the token box, an amount of self-generated electrical energy to be associated with the user.

[0034] In detail, according to the present description, a user refers to a specific individual, person, entity, etc. with which one or more electrical utilities of an electricity grid, in particular a low voltage (LV) electricity grid, can be associated.

[0035] Each electrical utility can have one or more points of distribution (PODs) configured to couple the user’s electrical utility, for example a domestic or industrial utility depending on the user type, to the electricity grid.

[0036] Each electrical utility comprises an electrical energy meter coupled to respective points of distribution and configured to measure the electrical energy consumed, for example expressed in kWh, by the electrical utility.

[0037] In particular, the user can be associated with a single electrical energy meter and / or point of distribution (POD) in the case of a single electrical utility, or they can be associated with a plurality of electrical energy meters and / or points of distribution (PODs) - in this case we speak of a multi-POD user - in the event a plurality of electrical utilities are associated with one same user, the consumption of electrical energy is measured through corresponding meters.

[0038] According to the present invention, power generation or production plants are understood as plants that are not owned by the user; preferably, the plants are large sized plants and not of the private / domestic type, in particular based on technologies that differ from each other.

[0039] With reference to the flowchart of fig. 2, the data processing system 1 receives, step SI, at a current time instant, consumption data indicative of an expected consumption of electrical energy by the user, in particular the expected consumption during a future time interval compared to the current instant.

[0040] Preferably, the consumption data can be indicative of the user’s electric demand CD, for example expressed in kWh, and of the cost Cc of the electrical energy consumed, for example expressed in € / kWh.

[0041] The consumption data can be provided in whole or in part by the user himself by means of the user interface 5; in addition or alternatively, the consumption data can be retrieved in whole or in part from a specific archive or memory in which it was previously stored.

[0042] In detail, the consumption data comprises data on the user’s electric demand CD, which is indicative of one or more of either:

[0043] - a region of consumption of the user, that is, the geographic region in which the user’s utility(s) are located. By way of example only, in the case of the Italian territory, the consumption regions can correspond to the regions of Italy (e.g., Sicily, Lazio, Lombardy, etc.) or the respective provinces, municipalities, or other areas or macro-areas comprising for example multiple regions;

[0044] - user’s historical consumption, for example historical consumption on a fixed time basis, for example on a yearly basis and expressed for example in kWh and / or the capacity of a meter associated with the user. The total consumption can be a consumption referred to a past time interval or preferably can comprise a consumption estimate referred to a future time interval; and

[0045] - the possible presence of personal power generation plants, for example a domestic photovoltaic system, and / or the presence of specific electrical energy consumption devices, such as an electric car for example, which are already associated with the user’s electrical utility. According to one embodiment, the consumption data further comprises the data on cost Cc that are indicative of the expected electrical energy costs that the user will have to face; in particular, the cost data Cc can be indicative of the expected cost of the electrical energy in a specific future time interval. In particular, the cost data can include one or more of either:

[0046] - average energy component tariff, for example expressed in € / kWh, and which can be customizable by the user himself, for example by means of the user interface 5;

[0047] - expected increase in the cost of electrical energy across a given time base, for example it can be an expected average annual increase of the tariff, for example expressed on a percentage basis; or

[0048] - time duration DTB of the token box, for example equal to 10, 15 or 20 years.

[0049] For example, the consumption data can also be indicative of one or more of either: technical characteristics of one or more of the points of distribution (PODs) associated with the user; personal or statistical trends of the electrical energy consumption profile associable with the user; user’s socio-economic data.

[0050] The method of fig. 2 also provides to receive, step S2, production data indicative of the expected production PE of electrical energy associated with a plurality of groups of virtual fractions Gi, ..., GN of power generation plants.

[0051] In particular, the production data PE can comprise, for each group of virtual fractions, data indicative of an expected production time profile, that is, data indicative of the expected production of electrical energy for a plurality of different time instants during the time duration DTB of the token box. For example, for a group of virtual fractions associated with solar plants, the expected production time profile can indicate the expected production for several hours of the day and several months of the year, taking into account solar radiation variability. Similarly, for a group of virtual fractions associated with wind plants, the expected production time profile can indicate the expected production as a function of the expected wind conditions at different time instants.

[0052] For example, for each plant, the production data can comprise one or more of either: total plant operating capacity, production capacity available to be fractionated (tokenized), geographic location, production capacity manufacturability and volatility, plant technology, useful life, efficiency, decay, etc.

[0053] For example, each group of virtual fractions Gi can comprise a single virtual fraction FV or a plurality of virtual fractions FVi, FVN, depending on the number of generation plants associated with the respective group Gi or depending on the percentage value of the plant associated with each virtual fraction FVi.

[0054] Each virtual fraction FVi of a group of virtual fractions Gi represents a fraction of the production capacity, for example expressed in W, of a specific power generation plant.

[0055] According to the present invention, the production data PE are indicative of groups of virtual fractions Gi, ..., GN of power generation plants different from each other.

[0056] According to the present description, the expression “power generation plants different from each other” indicates generation plants capable of guaranteeing a generation of power that is as constant and / or independent of time, date, weather conditions, etc. as possible. For example, power generation plants different from each other can be generation plants based on different energy sources from each other and / or plants located in different geographic locations.

[0057] Additionally or alternatively, each group of virtual fractions Gi can comprise virtual fractions of two or more power generation plants located in geographic locations different from each other.

[0058] Furthermore, in addition or alternatively, each group of virtual fractions Gi can comprise virtual fractions of two or more power generation plants that are based on energy sources that differ from each other.

[0059] This can allow to further increase flexibility in meeting users’ needs.

[0060] Preferably, the power generation plants with which the aforementioned virtual fractions are associated are renewable energy generation plants. However, the aforementioned virtual fractions can also concern non-renewable plants, for example thermoelectric plants, etc., which can be useful to increase flexibility of use on behalf of the user.

[0061] For the purposes of the present disclosure, a renewable energy generation (or production) plant is understood as a solar (photovoltaic), wind, hydroelectric, nuclear, biomass, geothermal or other renewable technology plant.

[0062] Purely by way of example, the production data can comprise at least two groups of virtual fractions, of which a first group Gi is associated with one or more solartype plants and a second group G2 is associated with one or more wind-type plants.

[0063] The production data can further comprise, for each virtual fraction FVi, respective cost data Pc indicative of a purchase cost of the respective virtual fraction, that is, the sale price associated with it that the user will have to bear to purchase the virtual fraction FVi.

[0064] Preferably, the method according to the present invention can also comprise receiving, step S3, market data indicative of economic indices associated with the sale and / or purchase of electrical energy in one or more specific markets. For example, market data can comprise one or more of either: economic indices as a function of specific zones and regions, energy prices, energy price volatility, electricity grid losses, self-consumption coefficients, interest rate curves, etc.

[0065] According to one embodiment, the market data can comprise:

[0066] - sales data indicative of the expected national and zonal sale prices Pv of the electrical energy produced by the aforementioned virtual fractions, in particular expected during the duration DTB of the token box TB; and / or

[0067] - purchase data indicative of the expected supply prices PA for the electrical energy by the user, in particular expected during the duration DTB of the token box TB.

[0068] The market data can for example be retrieved from one or more memories, databases, archives, etc., for example on a national, supranational, regional, zonal basis or suchlike, depending on the specific implementation.

[0069] The present method further provides, step S4, to determine one or more combinations of the aforementioned virtual fractions, hereafter indicated as a whole with TB, on the basis of the consumption data and production data received in steps SI and S2, and in particular also of the market data received in step S3. In particular, according to some embodiments, the determination of the virtual- fraction combination TB can comprise a dynamic assessment, for a plurality of different time instants within the time duration DTB of the token box, of the balance between the user’s expected consumption of electrical energy and the expected production of electrical energy associated with the virtual fractions. This dynamic assessment allows to optimize the virtual-fraction combination by taking into account the time variations of both the user’s electric demand and also the energy production of the different generation plants.

[0070] In practice, step S4 provides to determine the virtual-fraction combinations, that is, the optimal token boxes, TB associable with the user.

[0071] Hereafter, unless explicitly indicated otherwise, the expressions “token box” and “virtual-fraction combinations” are to be understood as synonymous and therefore usable interchangeably.

[0072] The virtual-fraction combinations TB are configured to be associated with the user so that, in response to the purchase of a specific token box TBj, the respective virtual-fraction combination provides the user with an optimal amount of selfgenerated electrical energy for the time duration DTB of the token box.

[0073] In other words, the association of the token box with the user corresponds to the association of the token box with at least one of the user’s electrical utilities. This allows to count the amount of energy associated with the virtual fractions of the token box as energy generated by the user, and therefore determine the selfgeneration from renewable plants consumed and / or sold, and thus quantify overall the active contribution to the decarbonization of the electrical system. In addition, the aforementioned association allows to reduce the billing cost of the user’s energy component.

[0074] According to one embodiment, the one or more virtual-fraction combinations TB determined in step S4 are such as to maximize the user’s bill savings, against the cost of the token box TB, during the token box TB validity period, based on the consumption data, production data and market data; that is, in such a way as to minimize a difference between expected consumption by the user and expected generation of electrical energy associated with the virtual fractions of the token box, and to optimize the cost and risk which are associated with the exposure of the excess amount of self-generated energy for the specific technology of the plants (e.g., solar or wind) exposed to the market price risk arising from the sale of energy, during the duration DTB of the token box.

[0075] With reference to fig. 3, a detailed embodiment of step S4 of fig. 2 is described.

[0076] To determine the virtual-fraction combinations TB, the method can provide to determine an expected profile of the user’s electrical energy demand, step SI 1, based on the consumption data received in step S 1.

[0077] In detail, step Si l can comprise associating the user with a specific user cluster, based on the demand data CD associated with the user received in step S 1.

[0078] Each user cluster is characterized by a specific electric demand time profiling PT,D, for example on an hourly, quarter-hourly, etc. basis. The time profiling PT,D comprises expected electric demand values for a plurality of different time instants, for example on an hourly or quarter-hourly basis, during the time duration DTB of the token box. In particular, the time profiling PT,D can indicate how the user’s electric demand varies at different times of the day and on different days, months or seasons of the year.

[0079] Consumption clusters can be retrieved from a memory or specific archive in which they were previously stored following specific initialization steps.

[0080] Purely by way of example, fig. 4 shows an example of the hourly profiling PT,D of the electric demand (cumulative on a monthly basis) associated with the user in step Si l.

[0081] The example of fig. 4, which shows the hourly trend of the user’s monthly electric demand during a day, can refer to a specific day of the year, if appropriately reparameterized.

[0082] However, the hourly demand can be different from day to day, and depend for example on the month or season considered.

[0083] As shown in the example of fig. 4, the user’s electric demand (monthly progressive) is relatively low in the morning and reaches a peak in the evening period, in the specific example between 8 pm and 10 pm.

[0084] Then, step SI 2, a price time profile PT,C of the energy is determined based on the cluster that has been associated with the user.

[0085] The price time profile PT,C can be determined starting from market data and can depend, for example, on the region the user belongs to, the energy price and the expected increase in energy price. The price time profile PT,C comprises electrical energy price values for a plurality of different time instants during the time duration DTB of the token box, taking into account the variations in the energy price at different times of the day and at different periods of the year, as shown for example in fig. 5.

[0086] In fact, although the consumption data may indicate the average tariff of the electrical energy entered by the user himself, the level of the price of the zonal electricity on the spot market must also be considered, in order to assign value to any excess electrical energy produced by the token box.

[0087] Fig. 5 shows, purely by way of example, the hourly trend of the spot market electricity prices associated with the user’s cluster, for example based on the user’s consumption zone, in the different months of the year (wherein the winter months are indicated by a dashed line).

[0088] In the example of fig. 5 it can be seen that in the various months the price is higher in the evening and in the morning (in particular around 9 am). This trend in the price of energy can derive from the joint trend of the relationship between demand and total generation of electrical energy.

[0089] Moreover, in the example of fig. 5, the month of January has the highest profile of all the other months, and overall the cost of energy is higher in the winter months (indicated by a dashed line).

[0090] Subsequently, step S 13, the data processing system 1 seeks the virtual-fraction combination capable of optimally responding to the user’s expected electrical energy consumption, optimizing the cost of the token box and minimizing the economic risk deriving from the market price of the sale of any surpluses (that is, self-generation not actually consumed).

[0091] The search for the optimal combination of virtual fractions in step S13 comprises a dynamic assessment of the balance between expected consumption and expected production for a plurality of different time instants within the time duration DTB. In particular, for each time instant (e.g., for each hour or fraction of an hour), the data processing system 1 assesses the balance between the user’s expected electric demand, determined on the basis of the time profile PT,D, and the expected production of electrical energy associated with the virtual fractions of the different groups, which is determined on the basis of the respective production time profiles. This dynamic assessment allows to identify, for each time instant, any production surpluses (when production exceeds consumption) or production deficits (when consumption exceeds production), and to optimize the virtual- fraction combination so as to minimize the surpluses and maximize selfconsumption during the course of the entire period DTB-

[0092] In particular, the processing system 1 can be configured to determine the virtual- fraction combination TB that would be able to provide the user, for the time duration DTB of the virtual-fraction combination TB, an electrical energy production capacity configured to optimize, that is, to maximize at the lowest possible cost, the coverage of the user’s expected consumption. In other words, the token box TB is configured to minimize the difference between the amount of electrical energy associated with the user as self-generated and the amount of energy consumed by the user, and / or configured to minimize the expected economic risk deriving from the surplus of non-consumed self-generated energy which, since it is sold on the market, is therefore exposed to the volatility of the energy market.

[0093] In fact, different energy production technologies may have different capture prices from each other. For example, considering that the token box TB comprises fractions of a solar plant and a wind plant, the solar plant can have a lower levelized cost of electricity (LCOE) than the wind plant and would therefore be more cost effective for the user, but at the same time in the case of excess non-consumed selfgeneration, the sale of the electricity generated by the solar plant could have a lower capture price on the market, due to the production of solar energy being concentrated only in daylight hours, at a lower remuneration.

[0094] In particular, the dynamic assessment for different time periods allows to take into account the complementarity of the production profiles of the different generation technologies. For example, as shown in fig. 6, production from solar plants is concentrated in daylight hours, while production from wind plants can be more distributed throughout the day. The dynamic assessment therefore allows to determine an optimal combination of virtual fractions of solar and wind plants that maximizes the coverage of the user’s consumption at different times of the day, minimizing both production deficits (which would require the purchase of energy from the grid) and also production surpluses (which would be sold at potentially unfavorable prices).

[0095] The optimization algorithm of step SI 3 can be an iterative or other algorithm, depending on the specific implementation.

[0096] According to one embodiment, each virtual-fraction combination TB determined comprises at least:

[0097] - the total amount of production capacity, for example in terms of electrical power expressed in Watts, that the virtual- fraction combination TB will be able to provide; - a total amount of energy produced by the virtual-fraction combination determined; and

[0098] - for each group of virtual fractions, a respective number of virtual fractions capable of providing a respective portion of the aforementioned total amount of self-generated energy, so that the set of virtual fractions of the virtual-fraction combination is capable of providing the optimal total amount of energy.

[0099] Fig. 6 shows, by way of example, on a monthly cumulative basis, the user’s time profile of electric demand PT,D as determined in step Si l, compared with the hourly profile of energy production PETB associated with the token box TB that was determined in step S13. In practice, the hourly profile of energy production PETB indicates the optimal total amount of energy that the specific token box TB will be able to generate.

[0100] As shown in fig. 6, the hourly profile of energy production PETB and the time profile of electric demand PT,D vary throughout the day. The dynamic assessment performed in step S13 allows to optimize the virtual-fraction combination taking into account these time variations. In particular, the solar energy component PEs is greater in daylight hours, while the wind energy component PEE can also contribute during other hours. The optimal combination of virtual fractions is determined in such a way that, for each time instant (e.g., for each hour), the sum PETB = PEs + PEE is as close as possible to the user’s electric demand PT,D at that time instant, thereby minimizing production surpluses and deficits over the entire period DTB.

[0101] In the example of fig. 6, the token box TB comprises, as a virtual-fraction combination, a first group of virtual fractions Gi which is associated with one or more solar-type power generation plants, and a second group of virtual fractions G2 which is associated with one or more wind-type power generation plants.

[0102] The energy production PETB associated with the token box TB therefore comprises the supply of a solar energy component having a specific hourly generation profile PEs and of a wind energy component having a specific hourly generation profile PEE.

[0103] In particular, the energy production PETB of the token box TB is given by the sum of the hourly production profiles PEE, PES.

[0104] Again with reference to fig. 2, the method according to the present invention further comprises, step S5, providing the user with the virtual-fraction combination TB determined, so that the user himself can choose whether and which of the proposed combinations to purchase and therefore associate with his utility.

[0105] The virtual-fraction combinations determined can be provided to the user by means of the user interface 5, in particular by means of a graphical interface.

[0106] According to one embodiment, the user interface 5 can show the user, for each optimal token box, data indicative of one or more of either:

[0107] - total amount of production capacity, for example in terms of electrical power expressed in Watts, proposed;

[0108] - total amount of electrical energy, for example expressed in kWh / year, that the token box will be able to produce during the time duration of the token box;

[0109] - percentage of self-generation relative to the total electrical energy consumption that the token box will be able to guarantee the user during the time duration of the token box; or

[0110] - portion of the total amount of electrical energy capacity associated with each group of virtual fractions; for example, in the event the groups of virtual fractions comprise a first group Gi associated with solar-type plants and a second group G2 associated with wind-type plants, the portion of the total amount of capacity resulting from solar-type plants and the portion of the total amount of capacity resulting from wind-type plants.

[0111] An example of such a graphical interface is shown in fig. 7.

[0112] According to one embodiment, the user interface 5 can show the user a plurality of virtual-fraction combinations different from each other. For example, the virtual-fraction combinations proposed to the user can have different levels of selfgeneration that the different combinations can be able to provide to the user in response to the purchase. This allows to further customize the offer given to the user.

[0113] Furthermore, the user interface 5 can be configured to allow the user to vary the purchase cost of one of the token boxes shown, and in response to the change in the purchase cost indicated by the user, determine one or more new token boxes.

[0114] The advantages of the method disclosed above will be clear to the person of skill in the art.

[0115] In fact, the fact that the user can choose to associate with their own electrical utility, or several utilities, a token box TB whose virtual fractions are representative of the power generation of power generation plants with different technologies (wind, solar, etc.), allows to guarantee that a self-generation of energy is guaranteed to the user even if the user does not directly own personal generation systems (e.g., domestic type personal solar plants).

[0116] In particular, in the event that the plants belonging to the virtual fractions are renewable energy generation plants, the method contributes to promoting the spread of renewable type energy generation plants through a multifactorial optimization.

[0117] Moreover, the fact that the virtual-fraction combination is chosen from groups of virtual fractions representative of power generation plants that differ from each other allows for a high degree of customization and coverage of the user’s consumption.

[0118] In particular, the combination of virtual fractions of plants that differ from each other allows to make the virtual-fraction combination determined unique and not existing in nature.

[0119] According to one aspect of the present invention, the present method can comprise, in response to the proposal to the user of the virtual-fraction combinations determined (step S5 of fig. 2), receiving (step S21 of fig. 8) from the user selection data indicative of the specific virtual-fraction combination TBi chosen by the user.

[0120] The data processing system 1 then associates with the user the specific virtual- fraction combination TB selected, step S22.

[0121] In response, step S23, the token box TBi associated with the user is used to determine, during the time duration DTB of the token box TBi, the electrical energy consumed by the user that is not covered by the self-generation provided by the token box (also indicated as non-self-generated consumption) and the electrical energy produced that however is not consumed and is therefore sold.

[0122] In detail, fig. 9 shows a system 40 comprising two power generation plants, in particular a solar generation plant 50 and a wind generation plant 51.

[0123] The solar plant 50 comprises a detection system 53, for example comprising one or more sensors, configured to provide actual production data indicative of the electrical energy SR actually generated by the solar plant 50. The wind plant 51 comprises a detection system 55, for example comprising one or more sensors, configured to provide actual production data indicative of the electrical energy ER actually generated by the wind plant 51.

[0124] The system 40 further comprises a point of distribution, POD, 57 and a respective electrical energy meter 58 which are associated with a user.

[0125] The electrical energy meter 58 is configured to detect the electrical energy C consumed by the user, for example consumed by the user’s electrical appliances coupled to the POD 57, and provide user consumption data indicative of the energy Cu detected.

[0126] The system 40 further comprises a data processing system comprising electronic resources 60 which are configured to receive the actual production data of the plants 50, 51 and the user consumption data detected by the meter 58.

[0127] In addition, the electronic resources 60 also receive data indicative of the token box TB that has been selected by the user and associated with the user.

[0128] In the example of fig 9, the token box TB can comprise a first group of virtual fractions Gi comprising one or more virtual fractions associated with the solar plant 50 and representing a percentage PXs of the production capacity of the solar plant 50, and a second group of virtual fractions G2 comprising virtual fractions associated with the wind plant 51 and representing a percentage PXE of the production capacity of the wind plant 51.

[0129] During use, during the time duration DTB of the token box TB, the electronic resources 60 determine the user’s non-self-generated consumption CE and the production of non-consumed and sold energy Ev, as a function of the user consumption Cu detected by the meter 58 and the actual energy production SR, E of the plants 50, 51 detected by the detection systems 53, 55.

[0130] In detail, the electronic resources 60 calculate an amount of electrical energy AU self-generated by the user on the basis of the actual production S , ER of the plants 50, 51 and of the percentage portions PXE, PXS indicated in the token box TB.

[0131] In particular, the amount of electrical energy AU self-generated by the user depends on: SR-PXS + ER-PXE.

[0132] The non-self-generated consumption CE is a function of the difference between the detected user consumption Cu and the amount of self-generated energy AU consumed on a time basis (hourly, quarter hourly, etc.).

[0133] The production of energy that is not consumed and sold Ev is a function of the difference between the amount of self-generated energy AU and the detected user consumption Cu, on a time basis (hourly, quarter hourly, etc.).

[0134] The control unit 60 can also determine, starting from the user consumption Cu, the amount of energy consumed and self-generated.

[0135] It is therefore clear to the person of skill in the art that the present invention allows to associate an amount of self-generation energy with the user, even if they are not equipped with private / domestic generation systems, thus allowing to generate in the user interest in using electrical energy from renewable sources.

[0136] In particular, according to one aspect of the present invention, the virtual fractions of the power generation plants can be associated with one or more tokens based on blockchain technology. Consequently, all or some of the operations described above with reference to the virtual fractions, such as for example the association of the token box with the user, can be based on blockchain technology; this allows to increase the security of the present method.

[0137] Finally, it is clear that modifications and variants may be made to what described and illustrated heretofore, without thereby departing from the field of protection of the present invention, as defined in the attached claims.

[0138] For example, the electrical energy sale and / or purchase prices described with reference to figs. 2 and 3 can be determined on a national basis or on a local basis (regional, provincial, etc.), depending on the specific application. For example, they can be derived on the basis of the national single price (PUN) or another local index determined on a zonal basis.

[0139] For example, although explicit reference has been made above to user utilities of a low voltage (LV) electricity grid, the invention according to the present invention is also intended to be extensible to a utility of a medium voltage (MV) or high voltage (HV) electricity grid.

[0140] For example, with reference to fig. 9, the detection of the energy generated by the plants 50, 51 and / or the detection of the user consumption by the meter 58 can be performed on a fixed or variable time basis, hourly or in fractions of an hour, depending on the specific implementation.

[0141] For example, what described with reference to fig. 9 can also be applied in the case of a multi-pod user.

[0142] For example, the different embodiments described above can be combined so as to provide additional solutions.

Claims

CLAIMS1. A method, implemented by a data processing system (1, 60), for determining an electrical energy self-generation capacity associable with a user, the method comprising: receiving (SI) consumption data (Cc, CD) indicative of an expected consumption of electrical energy by the user; receiving (S2) production data (PE, PC) indicative of an expected production of electrical energy associated with a plurality of groups of virtual fractions (Gi, G2), wherein each virtual fraction (FVi) represents a fraction (PXE, PXS) of a generation capacity of a power generation plant (50, 51), and wherein the groups of virtual fractions are representative of fractions of power generation plants different from each other; determining (S4) a virtual-fraction combination (TB) comprising virtual fractions of at least two groups of virtual fractions (Gi, G2) different from each other, based on the consumption data and the production data; and providing (S5) the user with data indicative of the virtual-fraction combination (TB), wherein the virtual-fraction combination is configured, in response to an association of the virtual-fraction combination with the user, to constitute an amount of electrical energy self-generated by the user during a first time interval (DTB), the amount of self-generated electrical energy being a function of an actual production of electrical energy associated with the virtual fractions of the virtual- fraction combination during the first time interval.

2. The method according to claim 1, wherein determining the virtual-fraction combination comprises dynamically assessing, for a plurality of different time instants within said first time interval (DTB), a balance between the expected consumption of electrical energy by the user and the expected generation of electrical energy associated with the virtual fractions, so that the virtual-fraction combination is optimized based on said dynamically assessed balance for said different time instances.

3. The method according to claim 1 or 2, wherein each group of virtual fractions (Gi, G2) is representative of fractions of one or more power generation plants based on a respective energy source and / or located in a respective geographic location,so that different groups of virtual fractions (Gi, G2) are representative of power generation plants based on energy sources different from each other and / or located in geographic locations different from each other.

4. The method according to claim 1, 2 or 3, wherein the power generation plants comprise plants from renewable energy sources and, optionally, thermoelectric.

5. The method according to any of the preceding claims, further comprising, in response to receiving data indicative of the selection by the user of the determined virtual-fraction combination, associating (S22) the user with the selected virtual- fraction combination.

6. The method according to any of the preceding claims, further comprising, in response to associating the virtual-fraction combination with the user: receiving first data indicative of a real electrical energy consumption (Cu) measured at one or more points of distribution (57) of the user; receiving second data indicative of a real energy production (SR, ER) of the power generation plants associated with the virtual fractions of the virtual-fraction combination; and determining one or more of either: a non-self-generated consumption (CE) of electrical energy and a non-consumed and sold production (Ev) of electrical energy, by the user based on the first data, the second data, and the fractions of generation capacity (PXE, PXS) indicated by the virtual fractions of the virtual- fraction combination (TB).

7. The method according to any of the preceding claims, wherein the virtual- fraction combination has a validity time duration (DTB) including the first time interval.

8. The method according to any of the preceding claims, wherein providing the user with data indicative of the virtual-fraction combination (TB) comprises providing the user, through a user interface (5), with data indicative of one or more of either:- an expected total production capacity that the virtual-fraction combination is able to provide during the validity time duration of the virtual-fraction combination;- a validity time duration (DTB) of the virtual-fraction combination;- a total amount of expected electrical energy that the virtual-fractioncombination is able to provide during the validity time duration of the virtual- fraction combination;- a percentage of self-generation, out of the total expected electrical energy consumption of the user, that the virtual-fraction combination is able to provide to the user during the validity time duration of the virtual-fraction combination; and- for each group of virtual fractions of the virtual-fraction combination, a respective portion of the total amount of electrical energy.

9. The method according to any of the preceding claims, further comprising receiving (S3) market data indicative of expected purchase and / or sale prices of electrical energy, wherein the virtual-fraction combination is also determined based on the market data, so as to minimize, during the first time interval, an expected economic risk of the user resulting from the surplus of non-consumed self-generated energy.

10. The method according to any of the preceding claims, wherein the virtual- fraction combination is determined so as to minimize, during the first time interval, a difference in absolute value dynamically assessed for a plurality of different time instants and on a certain time basis, for example in each hour or fraction of hour, between expected consumption by the user and expected production of electrical energy associated with the virtual fractions of the virtual-fraction combination.

11. The method according to any of the preceding claims, wherein the consumption data includes at least one of either: a region of consumption of the user and a historical consumption of electrical energy by the user, the method further comprising determining an expected time profile of electric demand (PT,D) of the user based on the consumption data, wherein the expected time profile of electric demand comprises expected electric demand values for a plurality of different time instants within the first time interval (DTB), the virtual-fraction combination being determined based on the expected time profile of electric demand by dynamically assessing, for said different time instances, a balance between the expected electric demand and the expected production of electrical energy associated with the virtual fractions.

12. The method according to claim 10, further comprising determining, for each group of virtual fractions (Gi, G2), a respective expected production time profile (PES, PEE) indicative of expected production values for said different time instanceswithin the first time interval (DTB), wherein the virtual-fraction combination is determined by dynamically assessing, for said different time intervals, a contribution of each group of virtual fractions to the balance between the expected electric demand and the total expected production of electrical energy.

13. The method according to any of the preceding claims, further comprising receiving one or more of either: data indicative of an expected supply tariff (PA, Cc) of electrical energy; data indicative of an expected selling price (Pv) of electrical energy; or data indicative of the unit cost (Pc) of each virtual fraction, wherein the data indicative of the expected supply tariff and / or of the expected selling price comprises tariff and / or price values for a plurality of different time instances within the first time interval (DTB), the virtual-fraction combination being further determined based on one or more of either: the expected electrical energy supply tariff, the expected electrical energy selling price, and the unit cost of each virtual fraction, dynamically assessing, for said different time instances, an economic value of the balance between expected consumption and expected production.

14. The method according to any of the preceding claims, wherein the virtual fractions (FVi) and the virtual-fraction combination (TB) correspond to tokens based on blockchain technology.

15. The method according to any of the preceding claims, wherein the virtual- fraction combination (TB) is determined, simultaneously, based on data relative to a plurality of users.

16. The method according to any of the preceding claims, wherein at least one of the groups of virtual fractions (Gi, G2) is representative of two or more power generation plants that are located in different geographical locations from each other.

17. The method according to any of the preceding claims, wherein at least one of the groups of virtual fractions (Gi, G2) is representative of two or more power generation plants that are based on different energy sources from each other.

18. A computer program comprising instructions that, when implemented by a data processing system (1, 60), cause the data processing system to execute the method according to any of the preceding claims.

19. A data processing system ( 1 , 60) configured to perform the method according to any of claims 1-17.

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