Method for optimizing the energy management of a solar equipment with storage member and charging an electric vehicle and

Abstract

The present invention relates to a method for energy management in a home having solar installations with storage means using simplex scheduling linear programming in conjunction with charging electric vehicles using different charging modes while applying user-defined charging functions to optimize energy costs over a 24-hour period. Software utilizes an optimization algorithm to manage the charging based on the generated solar energy, the stored energy, consumption requirements of the home, predictions generated by artificial intelligence from weather forecasts, and minimum charging requirements of the vehicle.

Description

Technical Field

[0001] The invention belongs to the field of energy management systems for installations comprising photovoltaic solar energy generation, in particular installations comprising storage media, and for charging electric vehicles.

[0002] In particular, the invention describes a procedure for optimizing the energy management of an installation with solar power generation means and energy storage means, in which an electric vehicle is connected for charging according to charging parameters set by the user to a minimum value. Background Art

[0003] Currently, energy management systems are well known that include both the use of self-generated solar energy and the charging of electric vehicles.

[0004] By way of example, document US2016164299 can be found, which describes a device for energy conversion and management, the device comprising: an input terminal coupled to an input DC voltage; an inverter having a first DC-DC conversion stage and a second DC-AC conversion stage, having a battery charger module; and an associated battery, which stores energy from the first DC-DC stage and supplies energy to the second DC-AC stage during recharging.

[0005] The device comprises: a power meter that monitors input power and output power from the inverter module, and power absorbed or supplied to the network; and a switching unit that is directly connected to the control unit. The control unit adjusts the operation of the inverter module and the switching unit based on a predefined algorithm.

[0006] Document US2019351783 describes a system for controlling multiple mobile EV charging platforms, which includes: a central server; a communication interface for transmitting and receiving control data between the central server and the system object of the present invention; database data that stores information about the system and the EVs connected to it; and a controller that includes AI software, which generates control data in response to the position and data of the platform and the position and data of the EV to plan the meeting point between the EV and the mobile charging platform.

[0007] That is, the aforementioned invention aims to optimize the charging locations of EVs from a charging system having multiple charging platforms, which indicate the locations where the EVs must be recharged from an initial location. The starting point of the two elements.

[0008] Document US2017361717 can also be found, which describes an EV fast charging system using solar energy, which comprises: an EV fast charging system, which is installed in any service area; and a solar power generation system, which is used as an energy source for the fast charging system.

[0009] It is an object of the present invention to achieve a system that enables rapid charging of the EV, particularly within minutes rather than hours, for those EV users whose batteries are depleted while traveling.

[0010] Document DE102016109181 describes a method for controlling the charging process of a rechargeable battery comprising at least one photovoltaic module, such that the charging energy will depend on the instantaneous radiation on the photovoltaic module, which will be detected by using a photodetector. This information will be sent to a control unit, which will always manage the charging power.

[0011] Likewise, a device for controlling the aforementioned charging process is described, said device comprising: an accumulator; a photovoltaic module; and a control device, connected to a photodetector, said control device will include the same orientation as the photovoltaic module. In this way, the control unit will manage the power delivery, for example the start time of the charging process.

[0012] However, none of the state of the art documents describe a system that allows different charging modes to be applied when applying a user defined charging function that will be managed by software using an optimization algorithm based on the solar energy generated, the energy stored, the needs of the equipment and the minimum charging needs of the vehicle. Summary of the invention

[0013] The optimized system for charging electric vehicles and the associated program proposed by the present invention are therefore configured as being of significant novelty in the field of their application, since, according to their embodiments and in an exhaustive manner, the objectives are satisfactorily achieved as previously indicated, the characterizing details that make it possible and that distinguish them being conveniently contained in the final claims annexed to this description.

[0014] In particular, the invention describes a system for optimizing the costs of the charging process of electric vehicles, as well as the consumption of households or the like, the management of auxiliary batteries and the generation of solar energy by processing external information and acting on system operating parameters.

[0015] To achieve this, the invention comprises a direct connection socket to the general electricity grid, means for generating electricity from photovoltaic energy and storage means such as batteries.

[0016] Likewise, the present invention includes processing means located on an external server, either at the facility site or in the cloud, which will be responsible for receiving external information, processing the external information as well as the operating parameters of the system itself, and acting upon what is necessary to achieve the optimization of the energy costs for charging the electric vehicle and the consumption requirements of the household.

[0017] This control unit will comprise communication means, preferably wireless, which will receive information on: the history of electricity generation using photovoltaic energy; the history of residential consumption; and weather forecasts, such as wind, temperature, humidity or radiation.

[0018] In this case, the control unit will process the information by applying a prediction model and will optimize the requirements for charging the electric vehicle and managing the electrical energy consumption of the auxiliary battery, as well as the residential consumption.

[0019] Specifically, the model used is based on the application of an artificial intelligence algorithm (hereinafter referred to as AI) that aims to predict the amount of energy required by the household over the next 24-hour period and the generation of electrical energy through photovoltaic energy.

[0020] In this way, the AI ​​algorithm seeks to optimize the energy costs for the day following its application.

[0021] To predict the amount of energy a home requires, data from the home's consumption history will be used, taking into account average consumption; weather information that may affect the habits of the home's occupants; and the corresponding calendar day, which may change consumption requirements depending on whether it is a holiday or a weekday.

[0022] Based on this information, and by applying AI algorithms, a forecast is made for the household's consumption over the next 24 hours, without taking into account the consumption for charging the electric vehicle.

[0023] To predict the energy production of a home, the production history, the solar position and meteorological data associated with the estimated production, and future weather forecasts are used, both solar position and meteorological data, such as radiation level, humidity, temperature, wind speed and direction.

[0024] Based on this information and by applying AI algorithms, a forecast is made for the minimum residential production in the next 24 hours.

[0025] In parallel with the aforementioned models used, the control unit will also receive information about other parameters external to the home itself, and its associated energy consumption and production.

[0026] On the one hand, information will be received on: the energy price per kilowatt-hour for each hour in the next 24 hours; and the energy export price, ie the price paid per kilowatt-hour of excess generation sent to the grid.

[0027] These data allow estimating the total cost of energy for the next 24 hours, taking into account consumption and surplus.

[0028] On the other hand, and considering the charging of an electric vehicle, the user defines a charging mode for it, in which a charging ramp is applied, which will represent a minimum percentage of the charging power relative to the total power. System charger required.

[0029] Once the relevant calculations have been made and the necessary external information has been received, the system optimizes the costs based on an expression that takes into account all the previous parameters, analyzing which of the parameters to act on to obtain a lower energy cost in the next 24 hours.

[0030] Specifically, an optimization of a two-hour decision vector will be performed, involving the amount of charging to be done on the EV, how much energy to take from the network to meet the demand, and how much battery storage.

[0031] The described optimized system for charging electric vehicles, the associated program and the set of elements represent an innovation of hitherto unknown structural and constituent characteristics, which, together with its practical use, provide a sufficient basis for it to obtain the requested exclusive rights. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] To supplement the ongoing description and to help better understand the characteristics of the invention, a set of drawings is attached as an integral part of said description, in which the following contents, of an illustrative and non-limiting nature, are presented.

[0033] Figure 1 —A diagram showing a program object of the present invention. DETAILED DESCRIPTION

[0034] In the following detailed description of preferred embodiments, reference is made to the accompanying drawings forming a part of this specification in which specific preferred embodiments in which the invention may be practiced are shown by way of illustration. These embodiments are described in sufficient detail to allow one skilled in the art to practice the invention, and it is understood that other embodiments may be used and that logical structural, mechanical, electrical and / or chemical changes may be made without departing from the scope of the invention. The detailed description should therefore not be construed as limiting in order to avoid details that those skilled in the art need not implement.

[0035] Specifically, the present invention describes an optimization program for energy management in the home and charging of electric vehicles, which includes at least the following: components for charging electric vehicles; components for connecting to the electricity network; components for generating electricity, such as photovoltaic energy sources, such as solar panels; and electrical energy storage components, such as batteries, all of the above components are connected to a control unit with processing components; the entire set of equipment is installed in the home.

[0036] The program is configured to optimize the charging of electric vehicles within a given time period, preferably 24 hours in advance, and comprises at least the following steps:

[0037] First, necessary information is received about at least the following items within a given time period:

[0038] i. The historical record of generating electrical energy through the power generation components;

[0039] ii. Historical records of residential consumption;

[0040] iii. Historical meteorological data associated with historical production and consumption

[0041] iv. Date;

[0042] v.Energy price;

[0043] vi. and weather forecasts and / or other relevant data;

[0044] Second, a forecast of residential consumption for a given time period is calculated based on:

[0045] i. Historical household consumption and weather data;

[0046] ii. Weather forecast;

[0047] iii. and the date of the calculation period.

[0048] Third, a forecast of the household’s energy production for a given time period is calculated based on:

[0049] i. Historical records of residential production and associated meteorological data

[0050] ii. The relative position of the sun at each moment in the device, preferably the altitude and azimuth;

[0051] iii. And weather forecast, hourly radiation, temperature, wind speed and direction.

[0052] Finally, start the cost optimization process.

[0053] As preliminary information, it is worth considering the requirements established regarding the vehicle loading process, according to which users define their needs in this regard. In this sense, the increase in the load of the electric vehicle (SQi) for each hour i is configured according to the load function of the electric vehicle, so that the cumulative charge of the electric vehicle in each hour i cannot be less than the charge indicated by the charging function.

[0054] To this end, preferably, a "ramp" function is defined, to which a condition is applied to be satisfied by the program and to ensure that a minimum amount of energy is accumulated in the electric vehicle within each calculation hour, so that the charging amount at a certain time can be predicted when the energy cost is low or a surplus is generated, avoiding charging the electric vehicle when the cost is highest.

[0055] That is, for any k between 1 and 24, the following must hold:

[0056]

[0057] in:

[0058] -RM is the minimum load slope defined by the user (between 0 and 1).

[0059] -PQ max is the maximum power of the charger.

[0060] -Q i is the charging capacity of the charger per hour.

[0061] The equation to be optimized is the energy cost for the following period, preferably 24 hours, which meets all the requirements established based on the demand:

[0062]

[0063] where Ei is the cost of exchanging energy with the network such that:

[0064] - If grid energy is required, Ri>0; and Ei=Ri Pi

[0065] - Otherwise, ie, because of excess generation relative to consumption, energy is dumped into the grid, then Ri<0, and Ei=Ri PXi

[0066] Where Ri is the energy demand of the grid, Pi is the unit price of energy acquisition in hour i, and PXi is the unit price of energy output / compensation in hour i.

[0067] In order to optimize this equation, a connection must be established between the elements that make up the system. To do this, the optimization function will use a second equation related to the loading of the storage medium. In this sense:

[0068] SOC i =SOC i-1 +[R i +ηB i +G i -Q i -C i ] / BATT;

[0069] in:

[0070] - The state of charge of the battery in the previous hour is (SOCi-1), and y BATT is the total capacity of the battery pack.

[0071] - The predicted energy demand of the power grid in the next 24 hours (Ri), the vector to be optimized.

[0072] - The increase and / or decrease in the load of the storage medium (Bi).

[0073] - The planned power generation (Gi) for the next 24 hours.

[0074] - The increase in the load of the electric vehicle (Qi) of the vector to be optimized, and the planned consumption (Ci) in the household,

[0075] - H is the battery charge / discharge efficiency

[0076] - η is the battery charge / discharge efficiency

[0077] That is, the program must optimize the energy cost function:

[0078]

[0079] Comply with the equations that govern the relationships between the variables of the regulation system:

[0080] SOC i = SOC i-1 + [R i + ηB i + G i - Q i - C i / BATT(i:1..2)

[0081] Under the following boundary conditions:

[0082] - SOCmin < SOCi < 100%, that is, the load of the storage medium must be kept higher than the previously established value and obviously not higher than 100%

[0083] - Pdmax < Bi < Pcmax, where Pcmax is the maximum charging power of the battery rack and Pdmax is the maximum discharging power of the battery rack.

[0084] - Ri < Rimax, where Rimax is the maximum power used by the network (contract power)

[0085] - And the boundary conditions established in the minimum load criterion of the electric vehicle ramp.

[0086]

[0087] The optimization results are SOC, battery, network and EV charging vector (SOC i , B i , R i , Q i ), where i 1..24

[0088] Using Qi vectors, the charging of EVs is planned by modulating the charging power hour by hour to meet the demand or, in the case of a constant power charger, alternating charging periods with stop periods to adjust the required load according to the results of the optimization algorithm.

[0089] In the case of storage management (Bi, SOCi), the inverter operation mode will be planned every hour so that, according to the algorithm's request, the battery is charged to a certain SOC, maintaining the battery capacity for future periods, or operating normally so that the battery capacity also meets consumption.

[0090] This allows to optimize the energy taken from the network within 24 hours of calculation, based on the established parameters, predicting the needs of each generation system and thus minimizing the impact of purchasing electricity from the general grid.

[0091] Preferably, the optimization process is performed continuously at least once an hour, and the optimal EV management and charging decisions are recalculated over the next 24 hours, and actions are taken accordingly.

[0092] That is, through this last embodiment, not only a better intelligent management of energy storage is achieved, but it is also possible to optimize the charging costs of the electric vehicle in response to its charging functions requested by the user of the electric vehicle itself, wherein the program ensures that the minimum load of the vehicle will be at least the load required by the user per hour.

[0093] The present invention also describes a system to which the procedures of the previous embodiments will be applied, said system to include at least the following:

[0094] - connection means to the general electricity network;

[0095] - Power generation components;

[0096] - electrical energy storage components;

[0097] - means for charging electric vehicles;

[0098] - a wireless communication member configured to receive external information from the system

[0099] - and information processing means, operatively connected to the aforementioned means, which may be located directly in the control unit, which is included in the home device, or may be located in an external server located in the cloud.

[0100] Wherein the processing means is configured to receive information about: a history of household electricity generation; a history of household consumption; a date; a price per kilowatt-hour of electricity from a general electricity network; and weather.

[0101] and applying AI algorithms configured to manage components connected to a power grid and components storing electrical energy, and optimize electrical energy consumption.

[0102] That is, the system will be prepared to receive relevant information within each computing hour and process it to optimize energy costs.

[0103] Preferably, the AI ​​algorithm applied by the control unit will be responsible for calculating the cost based on the results of the optimization algorithm when applying the optimization procedure described above. That is, as explained previously, the value of Ri depends on the load (SOCi) of the electrical energy storage medium within each hour i. And it can vary according to two operating conditions.

[0104] Having fully described the nature of the invention and the manner in which it can be put into practice, it is considered unnecessary to explain the invention more extensively so that any expert in the field understands the scope of the invention and the advantages derived from it, indicating that within its essence, the invention can be put into practice in other forms of embodiments than those indicated as examples in details and that the protection sought will be achieved without changing, altering or modifying the basic principle thereof.

Claims

1. A program for optimizing the energy management of a solar system with storage means and the load of an electric vehicle, characterized in that The optimization procedure includes at least the following stages: a. Receive information about at least the following items within a given time period: i. The historical record of electricity generated by the power generation components; ii. The historical records of residential consumption and their associated historical meteorological data; iii. Date; iv. Purchase and export / compensation energy prices; v. Weather forecast, horizontal radiation, wind speed, wind direction, temperature and / or humidity; b. Calculating a forecast of said consumption of said residence during said given time period based on: i. a record of the consumption of the residence and its associated historical meteorological data; ii. Weather forecast, horizontal radiation, wind speed, wind direction, temperature and / or humidity; iii. and the date of the calculation period; c. Calculate a forecast of energy production for the residence during the given time period based on: i. the production history of the residence and its solar orbit position data, as well as associated historical meteorological data of horizontal radiation, wind speed, wind direction, temperature and / or humidity; ii. the sun's position at each moment of the period to be predicted; iii. and weather forecast, horizontal radiation, wind speed, wind direction, temperature and / or humidity; d. The user introduces a charging requirement for the electric vehicle according to the electric vehicle charging function, and the charging requirement is configured as a lower limit condition for charging the electric vehicle; e. Optimize the following: i. The energy cost function requires the power grid, which is based on the following formula: ii. Use the equation governing the relationship between the parameters to be optimized according to the formula SOCi=SOCi-1+[Ri+ηBi+Gi-Qi-Ci] / BATT, and observe the boundary conditions: ●SOCmin <SOCi<100%, -Pdmax <Bi<Pcmax, ●Ri <Rimax ●Charging function for electric vehicles f. Obtain the following parameters from the above optimization: i. Loading of storage medium (Bi, SOCi); ii. Electric vehicle charging (Qi); iii. and the energy exchanged with the grid (Ri) The parameters are obtained for each computing hour.

2. Optimization program for energy management of solar installations with storage media and loads of electric vehicles according to the preceding claim, characterized in that The optimization is performed by applying the simplex method.

3. A program for energy management of a solar system with storage means and for optimizing the charge level of an electric vehicle according to any one of the preceding claims, characterized in that The charging function of the electric vehicle is configured as a ramp, which is based on the following formula: The charging amount of the electric vehicle in each hour i cannot be less than the charging amount in the same hour indicated by the charging function.

4. The program for optimizing the energy management of a solar system with storage means and the load of an electric vehicle according to any one of the preceding claims, characterized in that The optimization is performed within the next 24 operating hours.

5. The program for energy management of solar systems with storage media and optimization of loads of electric vehicles according to the preceding claim, characterized in that The optimization procedure is repeated continuously every hour, so that an updated optimization is performed during the following 24 operating hours.

6. A system for energy management of a home and optimization of loads of electric vehicles using solar energy and storage media, characterized in that The optimization system is configured to be installed in a home and includes at least the following: a. Connection components with the general power grid; b. Solar power generation components; c. Components for storing electrical energy; d. Components for charging electric vehicles; e. A wireless medium configured to receive external information from the system f. and an information processing member, which is operatively connected to the aforementioned member, The information processing component is configured to: - receiving information on: the history of electricity generation of the residence, the history of consumption of said residence, the date, the price per kilowatt-hour of said universal electricity grid, and the meteorological conditions; - and applying an AI algorithm configured to manage said connection means with said electrical grid and said means for storing electrical energy, and to optimize electrical energy consumption.

7. System for energy management of a home by solar energy and storage means and optimization of loads of electric vehicles according to claim 8, characterized in that The AI ​​algorithm of the processing means comprises an optimization procedure for energy management of a solar installation with storage means and loads of an electric vehicle, the optimization procedure comprising at least the following stages: a. Receive information about at least the following items within a given time period: i. A historical record of electrical energy generated by the power generation component; ii. said history of consumption of said residence and its associated historical meteorological data; iii. the date stated; iv. Purchase and export / compensation energy prices; v. Weather forecast, horizontal radiation, wind speed, wind direction, temperature and / or humidity; b. Calculating a forecast of said consumption of said residence during said given time period based on: i. the historical record of the consumption of the residence and its associated historical meteorological data; ii. Weather forecast, horizontal radiation, wind speed, wind direction, temperature and / or humidity; iii. and the date of the calculation period; c. Calculate a forecast of energy production for the residence during the given time period based on: i. the production history of the residence and its solar orbit position data, as well as associated historical meteorological data of horizontal radiation, wind speed, wind direction, temperature and / or humidity; ii. the sun's position at each moment of the period to be predicted; iii. and weather forecast, horizontal radiation, wind speed, wind direction, temperature and / or humidity; d. The user introduces a charging requirement for the electric vehicle according to the electric vehicle charging function, and the charging requirement is configured as a lower limit condition for charging the electric vehicle; e. Optimize the following: i. The energy cost function requires the grid, which is according to the following formula: ii. Use the equation governing the relationship between the parameters to be optimized according to the formula SOCi=SOCi-1+[Ri+ηBi+Gi-Qi-Ci] / BATT, and observe the boundary conditions: ●SOCmin <SOCi<100%, -Pdmax <Bi<Pcmax, ●Ri <Rimax ●Charging function for electric vehicles f. Obtain the following parameters from the above optimization: i. Loading of storage medium (Bi, SOCi); ii. Electric vehicle charging (Qi); iii. and the energy exchanged with the grid (Ri) The parameters are obtained for each computing hour.

8. System for energy management in a household and optimization of the charge amount of electric vehicles using solar energy and storage media according to any of the preceding claims, characterized in that The processing means are contained in a control unit and / or are located on a cloud server.

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