Energy resource auditing device for digitization and tokenization

Abstract

A digital energy auditing device for collecting energy information, comprising: a processing unit; communication means for receiving energy (VERD) and / or meteorological information (VM), and sending information to a cloud; and a power source for electrically powering the elements of the auditing device. A power cell for digitalizing energy information comprising said Digital Energy Auditor Device. A system for the tokenization of energy information comprising at least one power cell. A method for the digitalization and tokenization of energy information.

Description

ENERGY RESOURCE AUDITING DEVICE FOR DIGITIZATION AND TOKENIZATIONTECHNICAE FIEED OF THE INVENTION

[0001] The present invention falls within the technical field of the digitization and tokenization of renewable energy, which complements energy auditing systems that, in general terms, monitor / audit energy production, consumption, and / or storage. In particular, regarding energy production, this invention enables distributed monitoring / auditing of production at distributed generation plants interconnected to an electrical grid, such as wind generation plants, photovoltaic generation plants, and mini-hydroelectric power plants.

[0002] This invention allows for the distributed monitoring, verification, and tracing of power generation or storage facilities that have power electrical devices such as inverters, storage systems, energy meters, and sub-meters, among others. It can also monitor consumption and loads in residential, commercial, and industrial facilities.

[0003] To achieve monitoring and / or auditing, this invention relates to mechanisms for collecting energy resource information (such as information on generation, storage, and / or electricity consumption) through appropriate technologies, such as the Internet of Things (loT); mechanisms for processing such information for digitization and tokenization, e.g., via Blockchain; mechanisms for managing distributed renewable energy resources, e.g., through non-invasive Virtual Aggregation; and mechanisms for optimizing processing and analytics, e.g., through artificial intelligence (Al), machine learning, and / or automatic learning.BACKGROUND OF THE INVENTION

[0004] Currently, renewable power generation plants require the use of energy converters, such as DC / AC (direct current / alternating current) or AC / AC (alternating current / altemating current) before delivering energy to the distribution grid. These converters have user interfaces to display information, either on-site or online, regarding instantaneous and accumulated power generation. The information provided by these interfaces allows for monitoring, verification, and reporting. The objective of monitoring is visualization; identification of improvement opportunities; optimization in operation / generation; and decision-making based on the collected information. Regarding verification and reporting, these processes aim to audit the progress in decarbonizing electricity consumption.

[0005] The information generated by these audits is of great interest to governments, corporate entities, and public and private funds, where auditing processes require ensuring theintegrity, accuracy, and security of the collected data. However, audits are currently not sufficiently automated and rely heavily on human trust, making them susceptible to intentional or accidental alterations. Additionally, issues of double accounting may arise when generation is not "tagged" nor associated with a specific consumption.

[0006] The energy transition aims to decarbonize the energy used in human daily activities (such as food, transportation, comfort, etc.). The decarbonization process involves replacing generation based on fossil fuels or other polluting energies with clean or renewable energy (RE). Governments, foundations, and the private sector have established decarbonization goals within staggered timeframes. However, these goals depend on what is considered clean, renewable, or "green" energy in each jurisdiction (e.g., nuclear energy or natural gas energy, which are considered "green” in some jurisdictions). Significant investments have been made worldwide in RE, which necessitates monitoring, verifying, and reporting (MVR) the progress in meeting established goals, thereby moving toward a sustainable development model.

[0007] Moreover, today there are players in the electrical energy system whose primary activity is not necessarily electricity generation. These players participate in the system as consumers but implement generation means (e.g., clean and renewable means) to reduce their consumption of external generation (e.g., users or companies that install solar panels or industries with cogeneration capacities) and may even generate an energy surplus that they inject into the electrical system. In the Chilean market, these distributed electricity producers (especially small producers), although they can inject their surpluses into the distribution grid (thanks to the enactment of Law 20.571, updated with Law 21.118), find it challenging to certify their clean or low-emission generation due to complications in the recognition and certification of the energy they generate. Additionally, small producers individually cannot afford the costs of accessing these environmental markets. Small producers face primarily economic barriers to obtaining certification for their generation as clean or low-emission energy.

[0008] This inability to recognize and certify such generation occurs because their electrical generation is located downstream of the consumption meter (since these actors are consumers). However, the information used for such recognition and certification must be collected upstream of the meter. For this reason, tracking and certifying renewable energy that is self-generated and consumed on the spot, i.e., at the same site, currently presents challenges.

[0009] It has been validated with multiple stakeholders in the electricity market that the behavior of consumption and distributed energy resources "behind, the meter", i.e., downstream from the meter, is unknown or "invisible" to distribution network operators. There is no real-time monitoring of power systems that could have positive effects on the quality of supply in thedistribution network if operated and managed in an integrated and digital manner. This would allow understanding the behavior of these distributed energy resources to benefit the optimal operation of the grid. Additionally, there is a bidirectional benefit, understanding that if the grid parameters are more stable and within normal ranges, individual power systems will perform better.

[0010] Each small energy producer individually cannot access clean energy certificate or emissions reduction certificate trading markets due to the transaction costs of these markets. The proposed technology and method allow, through the virtual aggregation of these distributed energy resources, to share transaction costs and distribute the benefits obtained from the issuance and sale of clean energy certificates and / or CO2 emissions reduction certificates.

[0011] To monitor small generators or distributed generators that may correspond to actors like those mentioned above, there are currently devices that collect data on energy and climate resources, such as those disclosed in U.S. patent applications published under numbers 2017 / 0005515, 2018 / 0143037, 2016 / 0028231, and 2012 / 0161527 (described below for quick reference). However, these devices lack means and processes for optimizing generation systems, or for tokenizing, digitizing generation, storage, and / or electricity consumption.

[0012] Patent application US2017 / 0005515 discloses a software platform in communication with a distributed energy resource storage device in a network, configured to offer several specific applications related to compensation demand control, virtual power plant methods and orchestration, as well as load modeling services, aggregate-level demand reduction methods, prioritization of software programs related to the virtual energy group, cloud energy controller methods, orchestration plans for electric vehicle charging and discharging, distributed energy resources, machine learning predictive algorithms, value optimization algorithms, autonomous detection event awareness, mode selection methods, capacity reserve monitoring, and virtual power plant methods.

[0013] Patent applications US2018 / 0143037 and US2016 / 0028231 disclose a system for analyzing energy use that measures one or more parameters indicative of energy use for multiple sub circuits, where the sampling rate for the measurement is substantially continuous and automatically transmits information related to at least one of the measured parameters at a rate that allows monitoring of current energy use. The system also detects a significant change in a measured parameter, determines whether the significant change in the measured parameter is caused by a change in energy use, and automatically transmits information related to the significant change in the measured parameter caused by the change in energy use after detecting the significant change.

[0014] Patent application US2012 / 0161527 discloses a photovoltaic (PV) power generation system and a control system for managing series-connected photovoltaic modules.

[0015] Currently, there is no non-invasive and automated monitoring, reporting, and verification (MRV) method or process that ensures the integrity and accuracy of data regarding electricity generated by distributed renewable energy resource plants, storage system management, and power consumption.

[0016] Additionally, existing products in the market either do not support or hinder interoperability between devices, platforms, and / or systems from different manufacturers, such as current inverters, storage systems, and smart meters. The devices and procedures described here enable such interoperability, among other advantages. It is noted that generally, each current inverter manufacturer tends to have its own platform, which stores and displays information differently (e.g., different time ranges, accumulated information, or daily accumulated information), and in practice, these platforms are not interoperable. In this context, it is noted that the platform (commercially named APIS) would enable the direct integration of operational data from different manufacturers of current inverters, storage systems, and smart meters into a single platform (standardizing languages and creating a cloud database).

[0017] The proposed technology is based on disruptive technologies such as industrial loT and blockchain, and incorporates others such as artificial intelligence (Al) processing, including machine learning algorithms and / or automatic learning algorithms for operation optimization.

[0018] The Internet of Things (loT) technology allows for remote MRV functions online. Blockchain-based tokens are technology that ensure transparency and immutability of the information.

[0019] Regarding the context of the systems, devices, and procedures described here, the following should be considered:

[0020] - Renewable energy installations consist of a power generation system and an energy conversion system, also known as an inverter, which conditions the energy in terms of amplitude and frequency and extracts the maximum available power from the generator. Inverters that meet certification standards for public utility interconnection integrate standard wired and wireless digital communication ports. These ports provide access to all electrical variables of the installation via standard protocols such as MODBUS or Sunspec.

[0021] - To track renewable electricity generation and perform monitoring, verification, and reporting functions of the generated and delivered energy to the grid, there are two options: manual or automatic via a digital meter.The latter is invasive, meaning it requires physical modifications to the installations for measurements, and in both options, data can be altered. Renewable energy generating installations are typically complemented by a digital hardware / software ecosystem that enables an information management platform for power installations.

[0022] To address the aforementioned issues, an external device to the inverter is described here that (1) collects / extracts information about the electrical energy generated (e.g., delivered to the grid), consumed, and stored; (2) digitizes the information; (3) tokenizes it for registration in a blockchain, maintaining the integrity, security, and accuracy of the obtained information; and (4) allows optimization based on artificial intelligence. The collected information is displayed on a Digital Web Platform, enabling real-time monitoring of renewable power generation, consumption, and storage.SUMMARY OF THE INVENTION

[0023] This document describes systems, devices, and procedures designed to address the previously outlined issues.

[0024] In the field of electrical generation, particularly for renewable energy installations, the present invention involves a device that can be implemented in a hardware package and methods implemented by computer software, which together establish a platform and / or system for energy digitization. Regarding the device, it includes a non-invasive Digital Energy Auditor that, through blockchain technology, digitizes and tokenizes the following:

[0025] - Electricity generation data from distributed renewable plants (corresponding to energy resources mainly based on smart current inverters);

[0026] - Distribution network information (e.g., information on the quality of the electrical supply network) provided by photovoltaic current inverters (e.g., Grid-following inverters that "follow" distribution network parameters) and / or energy meters;

[0027] - power storage systems; and

[0028] - consumption (mainly obtained through smart energy meters and smart appliances').

[0029] This enables small energy producers to participate, associatively and virtually, in energy markets for ancillary services, obtain clean energy certificates, or provide traceability and visibility to energy transition efforts based on energy communities and small-scale distributed renewable generation.

[0030] The digitization process provides structure, integrity, accuracy, and security of data, enabling individual monitoring of operations, as well as virtual aggregation and management of generation, storage, and consumption assets. Depending on the national regulations of each jurisdiction, this allows for the development of ancillary services for electric grid operations. For example, modem electric grids have entities such as Virtual Generators that can offer flexibility / ancillary services, locally or systemically, depending on the size of the systems and the virtual aggregation obtained.

[0031] Through the digitized monitoring of distributed energy resources, it is possible to configure a Virtual Generation Plant, which can be designed to offer ancillary services to the distribution network, flexibility services, and / or auxiliary services. These services enable the electrical system to adapt to the variability and uncertainty of generation and demand in a reliable and cost-effective manner across all time scales. For example, ancillary services related to "System Security," frequency and voltage regulation, among other services.

[0032] The specific applications to distributed energy resources described in this document, digitally coordinated, can offer services related to demand response control, virtual power plant and orchestration methods, load modeling services, aggregated demand reduction methods, prioritization of software programs related to the virtual energy group, cloud energy controllers, electromobility and plans for electric vehicle load and discharge orchestration, management of distributed energy resources, predictive machine learning algorithms, economic value optimization algorithms, autonomous event detection awareness, mode selection methods, capacity reserve monitoring, aggregated cloud energy methods considering origin certification standards and CO2 and other local pollutant emission reductions, inverter-battery-photovoltaic module-charge controller lifecycle degradation, cost-economic analysis for investment recovery and decision-making based on information, and other auxiliary and complementary services for electrical grids.

[0033] The technology based on blockchain ensures the accuracy of the collected data, enabling immutable Monitoring, Reporting, and Verification (MRV) for renewable electricity generation plants, while also making it possible to certify clean power generation and CO2 emission reductions. Additionally, the non-invasive Digital Energy Auditor includes a secondary module for collecting climatic and meteorological variables, which allows for the analysis of energy production and forecasting future generation considering meteorological variables. Furthermore, this meteorological information is supplemented with data from third-party databases, such as NASA, for comparing modeled, measured, and generated data.

[0034] All data collected and stored by the Digital Energy Auditor is displayed on a Digital Web Platform, enabling real-time monitoring of renewable power generation, consumption, and storage. The platform features permissions and credentials based on user type and, in addition to monitoring, allows for verification and reporting in both aggregated and granular formats through blockchain transactions and nodes (Blockchain).

[0035] Specifically, the method described involves data collection through the Internet of Things (loT), storage of this data on the blockchain, and analytics and deployment on a Web Platform (Software) that facilitates the creation of various services based on data aggregation, management, and analysis.

[0036] The non-invasive Digital Energy Auditor has interoperability attributes between devices, systems, and / or platforms from different manufacturers, such as current inverters, storage systems, and smart meters, allowing the consolidation of energy and climatic data into a single platform.

[0037] Some advantages of the present invention include:

[0038] - Non-invasive Data Collection: The collection of electrical generation, consumption, and storage data, as well as climatic data, is performed via a digital auditor that connects non-invasively (i.e., it can be connected and disconnected without substantially altering the system or stopping its operation). This Digital Energy Auditor preferably uses wireless connections between the devices it collects data from (e.g., smart inverters) or to which it sends information (e.g., to a cloud infrastructure) to facilitate system integration and maintain its non-invasive nature.

[0039] Data Digitalization and Tokenization: Collected data is digitized, encoded, and recorded on a blockchain system.

[0040] - Monitoring, Verification, and Reporting (MVR): The structuring, integrity, accuracy, and security of data provided by blockchain enables an MVR mechanism for distributed renewable generation plants.

[0041] Local Climatic Data Collection: The described platform or system includes a meteorological data collection module that can be supplemented with third-party climatic data.

[0042] - Incorporation of Third-Party Meteorological Databases.

[0043] Virtual aggregation of multiple energy resources distributed to act as a generation plant through a Virtual Power Plant (VPP) to offer services similar to those of a traditional generation plant.

[0044] - Real-time Data Sampling and Visualization.

[0045] Optimization of System Operation Based on Artificial Intelligence.FIGURE DESCRIPTION

[0046] Figure 1 shows an embodiment of a power cell 100, comprising a Digital Energy Auditor 110, It illustrates how this cell connects to the cloud infrastructure 140 (for transmitting and / or receiving data) and to the electrical distribution network 910 (for transmitting and / or receiving electrical energy).

[0047] Figure 2 shows how multiple power cells (CEn, CE12, CEin, CE21, CE22, CE2n, CEni, CEn2, CEnn) can form one or more Virtual Power Plants (PVi, PV2, PVn) and connect to a common digital web platform 141 of the cloud infrastructure 140.

[0048] Figure 3 shows an embodiment of a Digital Energy Auditor 110, highlighting some of its components.

[0049] Figure 4 shows part of the system in which the Digital Energy Auditor 110 operates, connecting to the cloud 145 and an inverter 122.

[0050] Figure 5 shows what the digital web platform 141 supports, including blockchain support 151, data visualization and analysis 152, Geographic Information System (GIS) 153, and / or integration of meteorological data 154.

[0051] Figure 6 shows details on the processes of data digitalization and tokenization.

[0052] Figure 7 shows details of the data visualization and analysis module 152, involving artificial intelligence (Al) and machine learning.DETAILED DESCRIPTION OF THE INVENTION

[0053] The invention consists of a system comprising: (1) a product (Digital Energy Auditor 110 of loT), 2) a process of digitalization and tokenization of distributed energy resource data (generation, storage, and consumption), and (3) procedures, implemented by computer and software, that automate this digitalization and tokenization process, perform analysis on collected data, and optimize cells using artificial intelligence.

[0054] A distributed energy system is organized into multiple power cells 100, where several cells make up the distributed system. In this regard, a power cell 100 is a set of elements and / or devices that make up the cell 100 and relate to at least one element or device for power generation, consumption, and / or storage. Generally, but not necessarily, these elements or devices are located in, near, or around the power generation, consumption, and / or storage element, and allow for the collection, analysis, and / or transmission of information regarding that generation, consumption, and / or storage. In this regard, each power cell has the capability to generate, consume, or storeenergy, or a combination of these capabilities. When it is mentioned that elements or devices are located in, near, or around, it means that the elements composing the cell 100 are not more than 300 meters apart or, more preferably, not more than 100 meters apart.

[0055] As illustrated in Figure 2, the multiplicity of power cells (100, CEn, CE12, CEin, CE21, CE22, CE2n, CEni, CEn2, CEnn) communicate with a digital platform 141 (e.g., a digital web platform) to collect data from various cells 100. Several cells 100 can form a Virtual Power Plant (PVi, PV2, PVn), where the generation, consumption, or storage of each virtual plant (PVi, PV2, PVn) is seen as the respective sum of the generation, consumption, and / or storage of all its cells, effectively offering services similar to those of a traditional generation plant. For example, a first virtual plant PVi can be composed of one or more cells 100, such as a number n of cells (CEn, CE12, . . . CEin), and the system can have one or more virtual plants, such as a number n of virtual plants (PVi, PV2, ... PVn).

[0056] A power cell 100 is a system, or an arrangement of elements, that includes a Digital Energy Auditor 110; one or more power generation elements 121, one or more energy consumption elements, and / or one or more power storage elements 131; communication means (unidirectional or bidirectional) for the Digital Energy Auditor 110 to receive information from one or more additional elements comprising the cell 100; and communication elements (unidirectional or bidirectional) to transmit information to a cloud infrastructure 140, which is part of the cloud 145, where the information from each cell is processed and analyzed 100. The Digital Energy Auditor can communicate via any wired or wireless communication medium existing to date, such as Wi-Fi or cellular technologies.

[0057] The cloud infrastructure 140 includes a smart contracts module 142, a digital reading module 147, and a chaining module 144 for inserting data into the blockchain 143.

[0058] The smart contract module 142 may correspond to programmed instructions or steps that, when executed by the processing means, tokenize the received data.

[0059] The digital reading module 147 may correspond to programmed instructions or steps that, when executed by processing means, allow reading the stored data. The digital reading module 147 includes routines (e.g., functions in programming terms) that enable the smart contract to read and write data.

[0060] The chaining module 144 consists of programmed instructions or steps that, when executed by processing means 111 insert data into a blockchain 143.

[0061] The smart contract modules 142, digital reading module 147, and chaining module 144 may correspond to software or computer programs.

[0062] Additionally, the cloud infrastructure 140 may include visualization means, such as a digital dashboard or publishing interface 146.

[0063] In addition, the cloud infrastructure 140 can have a reporting module to generate report on the analyzed data.

[0064] Optionally, data analysis may be conducted using artificial intelligence (Al) (see Figure 7).

[0065] One or more power generation elements 121 can be one, multiple, or a combination of the following: solar energy capture means; wind energy capture means; photovoltaic cells; wind turbines; hydraulic energy capture means; geothermal energy capture means. One or more power generation elements 121 may be any existing means of electric generation as of the current date. In a preferred embodiment, one or more power generation elements 121 is one or more photovoltaic cells.

[0066] When one or more power generation elements 121 deliver direct current (DC) 125, as is the case with photovoltaic cells, it is necessary to convert said DC current to alternating current (AC) 126 via an inverter 122, which may be a smart inverter that collects data on the energy delivered by the power generation elements 121 (e.g., information on current, voltage, power, inductive energy, reactive energy, etc.).

[0067] One or more energy consumption elements may be one, multiple, or a combination of the following: Lighting elements; heating elements; household appliances; electrical devices; electrical transmission means (e.g., cables); and / or smart appliances 162. One or more energy consumption elements may be any existing means of power consumption as of the current date.

[0068] One or more power storage elements 131 may be one or a multiplicity of batteries or capacitors; or an element that converts electrical energy into another type of energy for storage, e.g., thermal storage, flywheel, compressed air storage, pumped hydroelectric storage, or hydrogen fuel cell. One or more power storage elements 131 may be any existing means of power storage as of the current date.

[0069] One or several additional element(s) comprising the cell 100 are one or a combination of the following: energy data collection means; energy data collection devices; climate and / or weather data collection means 161; inverter or smart inverter 122; meter or smart meter 124 (for power consumption); and / or load centre 123.

[0070] The smart meter 124 may communicate with the Digital Energy Auditor 110 using, e.g., Modbus.

[0071] The climate and / or meteorological information collection means 161 may be any or a combination of the following: weather station; pressure sensor; humidity sensor; radiation sensor; precipitation sensor; temperature sensor; and / or air quality sensor (e.g., CO2 quantity sensor).

[0072] The power cell has transmission means 911 (e.g., electrical cables) to transmit the generated energy to the distribution network 910 and / or to other entities.

[0073] The power cell has communication means 921, either wireless or wired, bidirectional or unidirectional, to transmit and / or receive information to or from other entities.

[0074] The Digital Energy Auditor 110 consists of one or more processing means 111 that communicate with devices collecting information from a power cell 100 (e.g., energy information collection means and / or energy information collection devices) to collect data on generation, consumption, and / or power storage; and climate and / or meteorological information collection means 161 that collect local climate and / or meteorological information (i.e., climate / metrological information from the site and / or surroundings of the point of electrical generation, consumption, and / or storage) such as, e.g., a weather station.

[0075] The Digital Energy Auditor 110 may optionally include a visual interface, e.g., it may include a built-in display; and / or communication with a mobile application or web-based management / configuration interface (viewable from a web browser) for remote control, management, and / or configuration of the Digital Energy Auditor 110.

[0076] The energy information collection means and / or energy information collection devices may correspond to one or a combination of the following: an energy generation information collection device, an energy consumption information collection device, an energy storage information collection device, an inverter 122, a smart inverter, smart appliances 162, a load centre 123, consumption meters and / or smart consumption meters 124.

[0077] One or more processing means 111 may be one, multiple, or a combination of the following: a processor, microprocessor, computer, microcomputer, CPU, GPU, FPGA, chip, microchip or any processing means existing at the time.

[0078] These processing means 111 include an encoding module 112 that encodes the information obtained from at least one of the elements comprising the cell 100 (e.g., encodes the information obtained from the inverter 122).

[0079] The encoding module 112 may correspond to instructions or programmed steps that, when executed by the processing means 111, encode the information received from the energy information collection means, the meteorological information collection means 161, and / or the elements comprising the cell 100.

[0080] The encoding module 112 may correspond to software or a computer program.

[0081] The Digital Energy Auditor 110 includes communication means (113A, 113B, 114A, 114B) to communicate with the cloud 145 and with at least one of the elements comprising the cell 100 (e.g., to communicate with an inverter 122, with power storage means 131, with smart appliances 162, with climate and / or meteorological information collection means 161, with a load center 123; and / or with meters 124).

[0082] These communication means (113A, 113B, 114A, 114B) of the Digital Energy Auditor 110 may be wired or wireless, and / or may correspond to a communication module (113A) with its respective wired or wireless communication port (114A) for sending data to the cloud 145; and to a second communication module (113B) with its respective wired or wireless communication port (114B) for communicating with any element comprising the cell (e.g., to communicate with an inverter 122; with power storage means 131; with smart appliances 162; with climate and / or meteorological information collection means 161; with a load center 123; and / or with meters 124).

[0083] These communication means (113A, 113B, 114A, 114B) of the Digital Energy Auditor 110 may be connection means to a WiFi access point 118 or cellular technologies. The Digital Energy Auditor 110 may alternatively operate via cellular networks through a SIM module.

[0084] Although in Figures 3 and 4 it is shown that communications between the Digital Energy Auditor 110 and the inverter 122, and between the Digital Energy Auditor 110 and the cloud 145 are wireless, each of these communications may be wired and / or wireless.

[0085] The Digital Energy Auditor 110 is powered by a power source 115 to electrically power the elements of the Digital Energy Auditor 110. This power source may include an AC / DC converter to draw power from the electrical grid and connection means 117 to connect to the electrical grid or any power source (e.g. to connect to an outlet). The power source 115 may alternatively include batteries. For example, the power source 115 may include a plug 117 to connect to the electrical supply (e.g., a public electrical supply) and an AC / DC power converter 116 to convert AC electricity from the electrical supply into DC electricity used by the components of the Digital Energy Auditor 110.

[0086] The Digital Energy Auditor 110 may be housed in a casing (e.g., an industrial casing).

[0087] The Digital Energy Auditor 110 may be configured remotely, receiving configuration parameters through its communication means.

[0088] The digital auditor 110 may generate notifications, alerts, or alarms which it communicates via its communication means. These notifications, alerts, or alarms pertain to the operation and / or maintenance of the Digital Energy Auditor 110; the operation and / or maintenanceof the cell 100 or one of its components; and / or the operation and / or maintenance of the power generation, storage, and / or consumption systems.

[0089] One embodiment is a Digital Energy Auditor 110 that functions as a communication gateway that links and encodes the information provided by the renewable power generation facility via an inverter 122 with a digital cloud 145 accessible via the internet.

[0090] The Digital Energy Auditor 110 may be placed inside the site where the power generation installation is mounted (e.g., the power generation elements 121). The Digital Energy Auditor 110 can be connected to a power outlet and to the internet at the site where it is installed. The Digital Energy Auditor 110 may be contained in a casing or enclosure that protects its internal components, e.g., one that meets the safety regulations corresponding to the IEC60529 standard, which recommends an IP65 protection rating for electronic system enclosures. The IEC60529 standard defines the degrees of protection provided by enclosures of electrical equipment against access to hazardous parts, entry of solid foreign objects, permeation of water.

[0091] In an alternative embodiment, the Digital Energy Auditor 110 comprises a device that includes a computer 111, connection means 117 to connect to electrical power (e.g., a plug 117 to connect to an outlet of the public electrical supply), an AC / DC power converter 116, a power source 115, a first communication port 114B, wired or wireless, for communication with an inverter 122, and a second communication port 114A, wired or wireless, for data transfer to the cloud 145. In the preferred embodiment of the Digital Energy Auditor 110, both communication ports are wireless type.

[0092] Additionally, the non-invasive Digital Energy Auditor 110 may have attributes of semantic interoperability implemented, e.g., through the Modbus protocol. This allows communication between devices from different manufacturers, brands, and / or models. For example, these interoperability attributes enable communication between current inverters 122, storage systems 131, and smart meters 124 from different manufacturers, brands, and / or models. This allows the aggregation of operational, generation, storage, and consumption data in real time on a single platform, without the need to access multiple platforms from different inverter brands and suppliers individually.

[0093] To allow non-invasive data collection, this is performed by the Digital Energy Auditor 110, which communicates with the inverters 122 (devices that convert energy in distributed plants), storage systems 131, and consumption (energy consumption measurement devices) via standardized communication protocols. Additionally, the Digital Energy Auditor allows data collection from a weather station 161 and an inverter 122, both preferably located near the power generation station 121 (e.g., a photovoltaic station). This method of data collection does notnecessarily require physical modifications / adjustments to the generation plants (e.g., no additional meters or intervention in the generation lines are needed).

[0094] In another alternative embodiment, the Digital Energy Auditor 110 may alternatively include a communication module 113B for Auditor- Inverter communication via MODBUS / Sunspec, wired or wireless, a coding module 112 that encodes information obtained from the inverter in a computer 111, and a second communication module 113 A for data transfer to the cloud 145.

[0095] Another alternative for the communication 113B of the Auditor- Inverter is Ethernet.

[0096] Alternatively, the Digital Energy Auditor 110 comprises an Ethernet port or a set of Ethernet ports corresponding to one or a combination of USB, micro USB, and / or mini hdmi.

[0097] The Digital Energy Auditor 110 may be agnostic to the technology of the inverters 122, meaning it is agnostic to the manufacturer of the inverters, allowing it to communicate with any commercial inverter that complies with a defined standard (e.g., the UL1741 standard).

[0098] The data obtained by the Digital Energy Auditor 110 is transmitted through wireless or wired means, e.g., data transmission can be performed via WiFi, Ethernet, or Bluetooth and / or using Modbus TCP or Modbus RTU.

[0099] The proposed and described Digital Energy Auditor includes a second module, connected via Power Over Ethernet (POE) with a climatic data collection module 161 (e.g., a weather station) that includes sensors for evaluating other variables affecting the operation of the power generation plants, such as pressure, humidity, radiation, temperature, etc. In this way, the Digital Energy Auditor collects weather values (WV) corresponding to humidity, precipitation, temperature, and solar radiation. The weather station 161 includes one or a combination of humidity sensors, precipitation sensors, temperature sensors, and solar radiation sensors.

[0100] The Digital Energy Auditor 110 collects, from the inverter (which can be a smart inverter), values (VERD) regarding energy and the state of the distribution network 910 to which the power generation plant (e.g., a photovoltaic plant) is connected. VERD values include one or a combination of electrical generation, AC voltage, DC voltage, AC current, DC current, distribution network frequency, generated power, and / or consumed power.

[0101] Additionally, the digital platform 141 or the cell 100 can communicate with third-party meteorological databases (e.g., NASA, Solar Explorer, meteorological offices, etc.) for more comprehensive climatic information and / or can communicate with a geographic information system for enhanced geographic information. The climatic or meteorological information received from third-party databases can complement or replace the meteorological information collected by the meteorological data collection means 161.

[0102] In a preferred embodiment, the Digital Energy Auditor device 110 connects to an inverter 122 of a photovoltaic generation plant.

[0103] In other alternative embodiments, the Digital Energy Auditor device 110 connects to an inverter of a plant based on inverter-based resources, such as wind turbines, mini-hydro, and batteries requiring a bidirectional inverter (also known in the market as a charger inverter), used also in off-grid or grid-connected hybrid applications.

[0104] In other alternative embodiments, the Digital Energy Auditor device 110 connects to one or a combination of Electric Vehicles (EV), Smart Appliances, Battery Management Systems (BMS), and Battery Energy Storage Systems (BESS).

[0105] The process of digitization and tokenization involves the collection of data from the Digital Energy Auditor 110 and its subsequent storage in a blockchain 143 via a smart contract managed by a smart contract module 142. In this regard, the auditor 110 becomes the manager of a power cell 100 composed of generation 121, consumption, and power storage 131 elements. Similarly, a digital platform 141 (e.g., a digital web platform 141) allows for the aggregation of Digital Energy Auditors 110 to configure virtual plants (see, Figure 2). The digital platform 141 has bidirectional communication with the Digital Energy Auditors 110 and can also interact with the smart contract to access the data stored in the blockchain 143. The tokenization process generates one or more tokens that may contain information certifying that the power generation is clean energy, renewable energy, and / or low-emission energy.

[0106] The data collected / retrieved referred to in this description are one or a combination of the following:

[0107] 1) DC Side Variables: such as accumulated connected power of the photovoltaic inverter (W); photovoltaic power limitation via communication (%); direct marketing photovoltaic power limitation via communication (%); internal photovoltaic power limitation (%).

[0108] 2) Variables on the Alternating Current (AC) side such as total energy injected today across all line conductors (Wh); current active photovoltaic injection power across all phase conductors (W); reactive power across all line conductors (VAr); energy injection to the grid (from any or a combination of the three lines LI, L2, L3); reactive power injection to the grid (from any or a combination of the three lines LI, L2, L3); active power of the system at PCC (W); and / or reactive power of the system at PCC (VAr).

[0109] 3) Grid Variables such as grid frequency at PCC, in Hz; displacement power factor at PCC; average line-to-neutral voltage at PCC, in V; average line-to-line voltage at PCC, in V; system voltage between two of the three lines (LI, L2, L3); system voltage of any of the three lines (LI, L2, L3); and / or system current of any of the three lines (LI, L2, L3) at PCC.

[0110] 4) Battery and Storage Variables such as current battery state of charge (SOC),(%); current battery charge, in W; current battery discharge, in W; battery charge, in Wh; and / or battery discharge, in Wh.

[0111] 5) Power and Limitation Variables such as power limitation via digital input (%); active power set point (Rho) limitation via analog input, in %; maximum active power set point, in %; available active power from all inverters, in W; internal reactive photovoltaic power limitation (%); available under excited reactive power, in Var; available overexcited reactive power, in Var; theoretically available output power, in W; system generation availability, in %; and / or external active power limitation, in %.

[0112] 6) Control or Climatic Variables: such as ambient temperature (°C); global wind speed (m / s); photovoltaic module temperature (°C); total irradiation on the external irradiation sensor / pyranometer (W / m2); ambient temperature (°F); ambient temperature (K); photovoltaic module temperature (°F); photovoltaic module temperature (K); global wind speed (km / h); global wind speed (mph).

[0113] 7) System State and Health Variables: such as system UTC time(s); current health status (general inverter status, connection, alerts, etc.); and / or power value of the generating system when all generating units are in operation, in W.

[0114] The collected data are digitized in a structured manner using standards such as Modbus Sunspec. Once the data is available, the Digital Energy Auditor 110 encodes them and sends them to a smart contract module 142 that generates a smart contract registered in a blockchain 143 hosted in the cloud 145. Data encoding is performed using cryptographic methods and transmission can be done via WiFi-Internet or other Internet of Things technologies such as GPRS, Sigfox, or Lora.

[0115] When the information is received in the cloud 145, the smart contract system 142 decodes and registers the information in the blockchain 143 used. The structuring, integrity, accuracy, and security of the data provided by the blockchain 143 enable the operation of a Monitoring, Verification, and Reporting (MVR) mechanism for distributed renewable power generation plants.

[0116] The digital web platform 141 also integrates meteorological data from available internet databases (e.g., NASA Power Project); and / or implements a geographic information system (GIS) to visualize the geographic location of the Digital Energy Auditors 110 and to crosscheck with modeled meteorological information from third parties.

[0117] Data Collection: The Digital Energy Auditor 110 allows for the collection of data from a weather station 161 and an inverter 122, both situated near the generation station 121 (e.g.,near a photovoltaic station). Data collection can be performed via WiFi, Ethernet, or Bluetooth, utilizing Modbus TCP or Modbus RTU. The Digital Energy Auditor 110 collects, from the weather station 161, climate values or meteorological variables 601 (VM), such as humidity, precipitation, temperature, and solar radiation. The Digital Energy Auditor 110 collects, from the inverter 122, "values related to energy and the distribution network" 602 (VERD), to which the generation plant 121 is connected. The values include power generation, AC voltage, DC voltage, distribution network frequency contained in the VERDs (i.e. in the values on energy and status of the distribution network). These VM and VERD values are sent together in a consolidated data group 603 (comprising the VMs and VERDs) to the cloud infrastructure 140.

[0118] Data Transmission: Once the information is collected, the Digital Energy Auditor 110 encrypts the variables to obtain an encrypted data group 604 (which includes encrypted VM and VERD) and sends them over the Internet to an API {Application Programming Interface) using one of the following options:

[0119] - Option 1 (Opl) involves a WiFi access point for communication to the Internet.

[0120] - Option 2 (Op2, link via cellular network) involves the use of communications based on cellular technology (2G, 3G, 4G or 5G) that allows communication to the Internet from the Digital Energy Auditor 110.

[0121] Data Reception and Tokenization: The data (e.g., VM and VERD data) sent from the Digital Energy Auditor 110 is received by an API, which subsequently transfers it to a smart contract module 142. The smart contract module 142 performs the tokenization of these data to produce a tokenized data group 605 (which includes tokenized VM and VERD), creating a digital asset representing this data using existing standards for the generation of non-fungible tokens to date. The tokens generated from this data are then stored on a blockchain 143. To facilitate querying of this data (e.g., VM and VERD data), a web platform 141 is available for end-users 901 (e.g., photovoltaic system installers, energy analysts, among others). The API, smart contract module 142, blockchain 143, and web platform 141 are deployed within a digital infrastructure 140 in the cloud 145 accessible via the Internet. The web platform 141 enables querying 606 and reporting.

[0122] Additionally, artificial intelligence is employed primarily through machine learning techniques to identify, classify, and promptly notify of factors influencing the performance of distributed power generation systems (e.g., photovoltaic generation systems). Machine learning techniques will help distinguish between climate -related factors (e.g., high humidity or high temperatures) or distribution network factors (e.g., energy quality) in which generation systems operate.

[0123] In a preferred embodiment of the described method, after data fusion 702, artificial intelligence is applied (e.g., processing with artificial intelligence of a combination of energy information (VERD), meteorological information (VM), blockchain- stored information, and / or other historical data). This process mainly involves the use of machine learning techniques to identify, classify, and notify timely causes affecting the performance of distributed photovoltaic systems. Machine learning techniques will help differentiate causes related to climate (e.g., high humidity or high temperatures) or with the distribution network on which the photovoltaic systems operate (e.g., operation frequency). This allows operators of distributed photovoltaic systems to optimize their operation through monitoring via a supervision module 724 (see Figure 7).

[0124] Artificial intelligence involves the following aspects:

[0125] • Data Engineering: Once the data has been tokenized and stored on the blockchain143, a data fusion process 702 is performed. This process involves combining blockchain- stored data 143 with other historical data 701 (e.g., satellite data, and / or historical meteorological data such as precipitation, UV, wind speed, etc.). Such other data may be obtained from global third party databases (e.g., satellite data from public sources such as NASA available at h s: / / po er.larc masa.gov ), and / or local databases developed through open source or paid options (e.g., solar explorers in Chile or other climate modeling tools available in various countries / regions / cities).

[0126] • Data Analysis and Modeling 710: This encompasses data analysis, representation, and modeling using exploratory data techniques, including:

[0127] — Data Analysis and Engineering 711; e

[0128] Pattern Identification and Data Labeling 712: It involves the use of unsupervised data analysis and processing techniques (e.g. principal components - PCA) and unsupervised learning techniques (e.g. clustering). This phase also includes automatic labeling of generation system data (e.g., photovoltaic systems) based on the analyses from the previous phase (e.g., systems with normal or abnormal operation). E.g., the automatic tagging of the data may be done based on previous processing or previous artificial intelligence processing. E.g., the labeling of data may label data with one or a combination of energy information (VERD), weather information (VM), and / or the information stored on the blockchain.

[0129] • Learning Models in Operation 720: Labelled data is used to train machine learning models and predict the performance of generation systems. Three types of models are trained in this phase:

[0130] 1) Regression Models to forecast electricity generation expected from generation systems (e.g., photovoltaic systems) for upcoming minutes, 15 minutes, hours (e.g., 1 hour), 12 hours, and 24 hours.

[0131] 2) Classification Models to identify whether photovoltaic systems are operating according to one of the behavior categories (i.e., normal or abnormal).

[0132] 3) Regression models to forecast distribution network performance for upcoming minutes, 15 minutes, hours (e.g., 1 hour), 12 hours, and 24 hours (e.g., forecasting voltage levels, AC or frequency values). Once trained 723, the models are deployed 722 for autonomous operation, with performance continuously monitored 721.

[0133] Optimization of distributed photovoltaic systems using artificial intelligence and / or machine learning can be achieved through the deployed learning models 722 (which may include one or a combination of regression models for forecasting power generation, classification models for identifying photovoltaic system behavior, and regression models for forecasting distribution network performance). These models feed into a monitoring module 724 with information about the state of photovoltaic systems and the distribution network. The monitoring module 724 allows for visualization of future photovoltaic system generation, identification of photovoltaic system behavior (i.e., normal or abnormal), and forecasting of potential distribution network behavior in terms of frequency, voltage, and current values. This enables operators to optimize the operation of photovoltaic systems by adjusting system elements. E.g. operators can perform simple tasks such as cleaning the photovoltaic panels or even adjusting the tolerated operating values (e.g., frequency, voltage, current) for photovoltaic systems.

[0134] The described methods or procedures may also include certifying energy units (e.g., certifying the number of watt-hours (Wh), kilowatt-hours (kWh), and / or megawatt-hours (MWh)).

[0135] Additionally, the described methods or procedures may aggregate data from multiple cells 100 and / or different inverters 122 for a more comprehensive data analysis.

[0136] The methods or procedures described here may additionally include smart contract netting actions that consist of automatically calculating the energy consumed and generated by PV cell, by distribution circuit and even by geographical area in order to have an understanding of how balanced consumption and generation is.

[0137] Alongside the data stored on the blockchain 143, previously collected by Digital Energy Auditors 110, internet-available meteorological data, and GIS data, the digital web platform allows for data visualization and analysis.

[0138] Finally, when digitalization and tokenization processes are implemented through software, the software is modularized and distributed into the following components:

[0139] - Digital Energy Auditor: Software / firmware for data collection and transmission to the smart contract.

[0140] - Smart Contract: Code that allows for storing and querying data on the blockchain.

[0141] - Blockchain: Code that manages the operation of nodes within the network and the creation of blocks with transactions.

[0142] - Web Digital Platform Includes:

[0143] - Meteorological Data Integration: Code that extracts meteorological data from online databases.

[0144] GIS: Code that implements functionality for the geolocation ofDigital Energy Auditors 110; and

[0145] Data Visualization and Analysis: Code that displays data in the form of graphs and performs statistical analysis on data stored on the blockchain 143 and integrated meteorological data.

[0146] The proposed method, based on the hardware, software, and firmware technology package, enables the virtual aggregation of multiple distributed energy resources through a Virtual Power Plant (VPP). This virtual aggregation allows for providing ancillary services to electrical grids, based on the coordination and management of distributed energy resources. Additionally, it enables aggregated access to certificate trading markets such as Clean Energy Certificates and / or CO2 Emission Reduction Certificates.

[0147] The described platform and / or system has the potential to provide real-time data on the behavior of distributed energy resources and the distribution network, information that can be key for distribution network operators to analyze operational performance and make quick decisions based on system signals.

[0148] In an alternative embodiment corresponding to a method of auditing renewable energy generation using loT and blockchain 143. In this method, a smart contract module 142 is used for cloud-blockchain decoding and symbolization, and a digital dashboard or interface for publishing 146. This forms an integrated system comprising the inverter 122, Digital Energy Auditor 110, cloud storage 145, a smart contract module 142 managing a record via blockchain 143, and a publishing interface 146 hosted on a website.

[0149] Several examples of the methods described here are presented below:

[0150] - A method that starts with local communication between the Digital Energy Auditor110 and the inverter 122 via wired communication means (e.g., through ports 485,Ethernet, or PLC) or wireless communication (e.g., via WiFi or Bluetooth). In the case of wired communication, the Digital Energy Auditor may have a connector adapted to a port of the inverter. In the case of wireless communication, the Digital Energy Auditor 110 may have one of the following two options: (a) Through a wireless network or providing the necessary credentials; or (b) Direct connection to the inverter 122 via point-to-point connection using a WiFi access point enabled by the Digital Energy Auditor 110.

[0151] - A method in which, once the Digital Energy Auditor 110 and the inverter 122 have been interconnected (wired or wirelessly), the Digital Energy Auditor 110 will establish communication using standard communication protocols (e.g., MODBUS or Sunspec) and collect data from the inverter 122 via a first communication module 113B (e.g., via an API communication module) for local communication. Once the Digital Energy Auditor 110 has obtained the data from the inverter 122, it encodes it using the encoding module 112 (e.g., an API encoding module) and sends the encoded information to the cloud 145 with the support of the second communication module 113A. The encoded information is in a format that can only be read by a digital reading module 147, which is embedded in the blockchain chaining module 144 of blockchain 143 that is part of the smart contract hosted on remote servers.

[0152] - A method where, to send the encoded information to the cloud 145, WiFi-Intemet technology or any other loT technology service such as GPRS, Sigfox, Lora, or any other available service is used.

[0153] - A method where, in the tokenization process, once the information is received in the cloud 145, the smart contract module 142 decodes and records the information in the blockchain 143 used, adding one more transaction to the chain. In this case, hyperledger fabric technology can be used, but it could also be any blockchain with support for smart contracts.

[0154] - A method where data reading, selective extraction, and exposure are performed via a digital interface or dashboard 146 hosted on a website, accessible only to blockchain participants (e.g., government, citizens, investors, companies, organizations, end-users, etc.).

[0155] Through the methodologies described in the previous examples, data extraction is automated directly from the inverter 122 without undergoing any other treatment than encoding. The information is sent to the cloud 145 and subsequently added to a smart contract via an application, adding a transaction to the blockchain 143. The information stored is integral, secure, reliable, available, visible, and incorruptible for blockchain participants.

[0156] Data communications and transmissions described here use "data pipelining" processes.

[0157] For the user interface controlling the digital platform 141, a voice assistant may be used. Due to the structured and standardized representation of data on the blockchain, it is suitable to integrate a voice assistant (possibly developed by third parties, e.g., Amazon's Alexa) so that a user can query generation and / or consumption data in their cell, or an administrator of the entire platform can ask questions about consumption and generation in the cells.

[0158] Where appropriate, the connections, communications, and / or means of communication described herein may correspond to, or be replaced with, one or a combination of the following: wired means of communication, wireless means of communication, two-way means of communication, one-way means of communication, Bluetooth, WiFi, Ethernet, Internet, Modbus TCP, Modbus RTU Bluetooth and / or cellular technologies (2G, 3G, 4G or 5G) or any other communication technology existing to date.

[0159] Throughout this description, when referring to "energy" or "energetic", without specifying the type of energy, it generally refers to electrical energy or the capacity to produce electrical energy.

[0160] Throughout this description, when referring to the reduction of pollutants such as CO2 reduction, the invention may adapt to other types of pollutant reductions such as the reduction of Particulate Matter (PM) less than 10 microns and 2.5 microns (PM 10 and PM2.5, respectively), reduction of Nitrogen Oxides (NOx), and / or reduction of Sulfur Oxides (SO2). The examples and embodiments described above are illustrative but not limiting, and therefore do not limit the matter claimed, which is defined in the claims.

[0161] Note that two or more of all the variations, embodiments, and descriptions presented above may be combined to obtain other forms of implementing the invention described here.

[0162] Implementations of this invention may take various specific forms by making modifications to what is described in this description without departing from the inventive essence of the present application. For this reason, the embodiments depicted in this description or shown in the figures should be taken as illustrative and not restrictive. The scope of the matter claimed in this application is defined by its claims and not by the limitations described in the description and / or figures.EXAMPLES OF APPLICATION

[0163] The proposed platform is considered m-sided, in the sense that it can offer multiple and varied services, depending on the selected customer segment. Thus, the application examples will depend on this.Example 1

[0164] The following example describes a case focused on renewable energy certification. A company X that has a renewable power generation system produces products and / or services. To make its business more sustainable, it decides to invest in a photovoltaic power generation system to self-generate its electricity and use it in the production of its products and / or services. Currently, to demonstrate to its customers that it uses 100% renewable energy, this company must prove that all the consumed energy is renewable. Currently, certification bodies in Chile do not recognize self-consumption, as certification uses data from the National Electric Coordinator, which does not recognize "behind, the meter" so self-generation and self-consumption are not being recognized, mainly due to limitations in monitoring and verification capabilities of the available certification schemes in Chile.

[0165] With the previously described platform and / or system, a method is proposed where self-generation and self-consumption are recognized, monitored, and verified as data is extracted directly from the source (photovoltaic inverter). Interviews with key stakeholders have validated the need for a monitoring and verification mechanism for small energy producers and for self- consumption based on the Netbilling Law.Example 2

[0166] Another example of application is the monitoring of operation and electrical generation where, based on the data analytics of the Platform / system, flexible alerts with recommendations can be raised to improve operation and electrical generation. Based on historical data and projected data from meteorological databases or sensors integrated into the hardware and software package.

[0167] In the context of VPP, it can be noted that, based on the virtual aggregation of small distributed renewable energy resources, complementary services can be offered aimed at greater flexibility of the distribution network, such as frequency and voltage regulation, spinning reserve, among others.Example 3

[0168] Another example of application, for the public good for local and / or national governments and decision-makers, corresponds to the massive deployment of distributed energy resources, often involving "non-traditional" actors such as homes and / or businesses that install their own power generation means and inject surpluses into the electrical grid, requiring the creation, organization, and management of national (or ad-hoc jurisdictional) databases on technical details of all distributed generation systems comprising the grid. Such databases serve both for scientific research in the dimension of energy transition (i.e.: network operation modeling using digital twins) as well as for public policy and corporate decision-making. Understanding that the electrical grid and the quality of electricity supply is a public good, using the Digital Energy Auditor solution and the APIS web platform, it is possible to create BBDD as a public good considering the relevant data protection regulations to make key granular information available to the community for research, decision-making, and the development of energy policy and investment in energy infrastructure.Example 4

[0169] DSOs (Distribution System Operators) are interested in having real-time information on network analysis and its variables, specifically in terms of frequency and voltage. Having distributed information, both on network parameters and on the behavior of individual distributed energy systems connected to distribution networks. Through information management and power device management, key information or services associated with the following can be provided: Service Quality Management, Network Stability; efficient integration of distributed energy resources; demand response and network flexibility; early identification, prevention, and resolution of issues leading to supply interruptions; network planning and optimization; and / or regulatory compliance and reporting (There are technical standards for quality and service for distribution systems). Thus, through the Digital Energy Auditor solution and the APIS Platform, multiple solutions and services associated with the points listed above can be offered, for which monthly, annual, spot, BBDD sale, etc., reporting contracts can be subscribed to.Example 5

[0170] Energy marketers or traders have portfolios of both consumers and generators and often manage clients' generation plants in terms of negotiating Power Purchase Agreements (PPAs) or in the spot market. They also face challenges in monitoring and tracing renewable energy that may be self-consumed or injected into the grid depending on the type and scale of the project (inChilean taxonomy, these can be netbilling or PMGD). Consumers, end customers, also seek to trace and certify that their consumption comes from renewable electricity generation sources. Therefore, certification, monitoring, verification, and reporting services become particularly relevant when demonstrating corporate sustainability efforts, national environmental commitments, etc. Through the Digital Energy Auditor solution and the APIS web platform, services of this kind can be provided, offering accountability for generation projects and delivering the necessary verification and reporting.Example 6

[0171] Another application example is the peer-to-peer (P2P) marketplace where P2P energy trading is a model in which an electricity consumer (or a group of them) generates their own electricity, which in this context is through distributed energy systems. In this context, excess generation can be sold directly to other consumers without the traditional mediation of large electric companies operating the electrical distribution network. From a commercial perspective, the APIS platform, or a module of it, can act as a P2P energy marketplace platform, serving as a key facilitator in the trade of renewable energy between independent producers and consumers. The platform can generate revenue through transaction commissions, subscriptions for access to advanced features, and additional services such as generation and consumption data analysis, energy consulting (with access to other modules / services offered by APIS).

Claims

CLAIMS1. A digital energy auditing device (110) for collecting energy information, characterized in that it comprises: a processing unit (111); communication means for receiving energy (VERD) and / or meteorological (VM) information, and sending information to a cloud (145); and a power source (115) for electrically powering the elements of the auditing device (110).

2. The digital energy auditing device (110) according to claim 1, which is characterized in that the power supply 115 comprises a plug 117 for connecting to the electrical supply and an AC / DC power converter 116 to convert AC electricity from the electrical supply into DC electricity used to power the components of the digital energy auditing device 110; the processing unit 111 comprises an encoding module 112 for encoding the energy and / or meteorological information received by the digital energy auditing device (110); the communication means comprise a first communication module (113 A) with its respective first communication port (114A) for communicating with the cloud (145); and a second communication module (113B) with its respective second communication port (114B) for receiving energy (VERD) and / or meteorological (VM) information.

3. A power cell (110) for digitalizing energy information comprising the Digital Energy Auditor Device (110) according to any of claims 1 or 2, characterized in that it further comprises: at least one energy information collection device communicatively connected to the Digital Energy Auditor Device (110); at least one meteorological information collection device (161) communicatively connected to the Digital Energy Auditor Device (110); wherein the Digital Energy Auditor Device (110) is configured to: receive from the energy information collection device, values about energy and the state of the distribution network (VERD), and to receive from the meteorological information collection device, meteorological values (VERD); aggregate both pieces of information into a consolidated data set (603); send the consolidated data set (603) to the cloud (145).

4. The power cell (100) according to claim 3, characterized in that the at least one energy information collection device is one or a combination of the following: a power generation information collection device, an energy consumption information collection device, a power storage information collection device, an inverter, a smart inverter (122), smart appliances 162, a charge center 123, consumption meters, and / or smart consumption meters 124.

5. The power cell (100) according to any of claims 3 or 4, characterized by additionally comprising at least one energy generation element 121; and at least one energy information collection device, which is a smart inverter 122 connected to the energy generation element 121 and configured to obtain data from the power generation element 121 corresponding to one or a combination of current, voltage, and / or power; and where the energy and distribution network state values (VERD) comprise values corresponding to one or a combination of current values, voltage values, or power values.

6. The power cell (100) according to claim 5, characterized in that the energy generation element 121 is one, multiple, or a combination of photovoltaic cells, wind turbines, and / or hydroelectric turbines.

7. The power cell (100) according to any of claims 3 to 6, characterized in that the meteorological information collection device (161) comprises one or a combination of the following: a weather station; pressure sensor; humidity sensor; radiation sensor; precipitation sensor; temperature sensor; and / or CO2 quantity sensor.

8. A system for the tokenization of energy information comprising at least one power cell (100) according to any of claims 3 to 7, characterized in that it further comprises: a digital platform (141) implemented in the cloud (145) which includes a smart contract module (142) and is configured to receive the consolidated data set (603) from the digital energy auditing device (110); tokenize the consolidated data set (603) via the smart contract module (142) to obtain at least one token representing the consolidated data set (603); and store such token on a blockchain (143).

9. The system according to claim 8, characterized in that at least one cell (100) comprises at least one energy generation element (121) and the token representing the consolidated datagroup (603) contains information certifying that the generation from said energy generation element (121) is clean energy, renewable energy, and / or low-emission energy.

10. The system according to any of claims 8 or 9, where the system further includes multiple power cells (100) grouped into one or more virtual power plants (PV).

11. A method for the digitalization and tokenization of energy information comprising: collecting energy information (VERD); collecting meteorological information (VM); combining the meteorological information (VM) with the energy information (VERD) to obtain a consolidated data set (603); tokenizing the consolidated data set (603) to obtain at least one token representing the consolidated data set (603); and store such token on a blockchain (143).

12. The method according to claim 11, characterized in that after the consolidated data set (603) has been tokenized and stored on the blockchain 143, a data fusion process 702 is performed, which includes obtaining other historical data and combining the data stored on the blockchain 143 with said other historical data 701.

13. The method according to any of claims 11 or 12, characterized in that the token representing the consolidated data set (603) contains information certifying that the generation of a power generation element 121 is clean energy, renewable energy, and / or low-emission energy.

14. The method according to any of claims 12 or 13, characterized in that it is executed within a distributed photovoltaic system and after the data fusion 702, artificial intelligence processing is performed on one or a combination of the energy information (VERD), the meteorological information (VM), the information stored on the blockchain, and / or other historical data to identify, classify, and / or notify causes that may affect the performance of distributed photovoltaic systems.

15. The method according to claim 14, characterized in that the artificial intelligence processing applies machine learning techniques.

16. The method according to any of claims 14 or 15, characterized in that the results of the artificial intelligence processing are sent to a supervision module 724 for monitoring.

17. The method according to any of claims 14 to 16, characterized in that the artificial intelligence processing includes one or a combination of pattern identification; automatic data labeling based on previous processing; and / or data analysis.

18. The method according to any of claims 11 to 17, characterized in that it further includes a data-labeling step and where the labeled data is used to train machine learning models and / or forecast the performance of power generation systems.

19. The method according to claim 18, where the labeled data is used to train machine learning models and the method is characterized in that the trained models are one or a combination of the following models: 1) Regression models to forecast expected electricity generation by power generation systems;2) Classification models to identify if photovoltaic systems operate according to normal or abnormal behavior categories; and / or3) Regression models to forecast the performance of the distribution network.

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