Autonomous power generation type multi-system energy management and surplus control system

Abstract

This invention provides an autonomous power generation type multi-system energy management and profitability control system that autonomously generates, stores, and discharges energy without relying on commercial power sources. [Solution] This system comprises an autonomous power generation unit 20 that generates electricity based on natural laws such as mechanical energy, thermal energy, pressure energy, and magnetic energy; a first battery system 30 mainly for supplying power to the load and a second battery system 40 mainly for generating, receiving, and backing up power; an AI energy management unit 50 that controls the power generation, charging, and discharging of these systems; and an economic information acquisition unit 52 that acquires economic information such as electricity unit price information, subsidy information, and loan repayment information.

Description

[Technical Field]

[0001] This invention relates to an energy management system that autonomously generates, stores, discharges, and controls economically, without relying on the commercial power supply of a power company. In particular, it relates to energy management and profitability control that simultaneously optimizes both technical power control and economically profitable operation by integrating an autonomous power generation unit, two or more battery systems, energy control by AI (artificial intelligence), and profitability control including subsidy, electricity rate, and loan information. [Background technology]

[0002] Traditionally, solar power generation, home battery storage, all-electric systems, and fuel generators have been individually introduced to enable energy autonomy in homes and businesses (see Patent Documents 1 and 2).

[0003] However, the following problems existed: (1) Power generation, energy storage, and load were controlled separately. Solar power generation prioritized power generation amount, battery storage prioritized remaining charge, and home equipment prioritized comfort, resulting in different "objective functions" and thus the system as a whole was not optimized. (2) Economic efficiency (profitable operation) was managed separately. That is, subsidies, electricity rates, time-of-use rates (peak / off-peak), loan repayments, etc., were often managed separately using "Excel or paper" and were not reflected in the actual charge / discharge control or power generation operation. As a result, there were cases where "the equipment was state-of-the-art, but the household was in the red." (3) Dependence on external power sources. Many systems are based on commercial power (grid power), and sufficient autonomy cannot be obtained during long-term power outages or in areas with unstable power supply such as remote islands and mountainous areas. There is a risk of a single battery system. In conventional systems, the entire load is dependent on a single battery system, so there is a high risk that the entire system will shut down in the event of battery abnormality, failure, or deterioration.

[0004] Thus, conventional energy management has been characterized by a disconnect between technical control (managing electricity) and economic control (surplus / deficit), and has remained incompletely autonomous, dependent on the power grid. [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2020-61867 [Patent Document 2] Japanese Patent Publication No. 2015-186277 [Overview of the Initiative] [Problems that the invention aims to solve]

[0006] The present invention aims to solve the above problems by achieving the following:

[0007] <1. Ensuring power autonomy through autonomous power generation and multi-system batteries> This system utilizes an autonomous power generation unit that uses non-commercial power sources (mechanical, thermal, pressure, magnetic, etc.) and two or more independent or interconnected battery systems to enable stable power supply for extended periods even if the commercial power supply fails.

[0008] <2. Simultaneous optimization of energy and economy using AI> The AI ​​learns electricity unit prices, time-of-use rates, load patterns, autonomous power generation output, subsidy information, loan repayment amounts, etc., and performs charging, discharging, power generation, and load control to simultaneously satisfy "stable power supply" and "profitability for households and businesses."

[0009] <3. A structure that can generate a profit both with and without subsidies> This system aims for profitable operation using only autonomous power generation and multi-system battery control, regardless of the availability of subsidies or regional differences, while providing a variable profitability control system that maximizes the benefits of subsidies when they are available.

[0010] <4. Easily implementable hardware configuration and general-purpose algorithm configuration> It provides a clear wiring diagram and configuration diagram, a highly expandable hardware configuration that can connect to existing solar power generation systems, storage batteries, eco-friendly water heaters, EVs, etc., and a control algorithm that can adapt to various load patterns in homes, shops, factories, and municipal facilities. [Means for solving the problem]

[0011] The autonomous power generation type multi-system energy management and surplus control system according to the first invention of the present invention is an autonomous power generation type multi-system energy management and surplus control system comprising: an autonomous power generation unit that converts at least one of mechanical energy, thermal energy, pressure energy, or magnetic energy into electrical energy; a first battery system that mainly supplies power to a load; a second battery system that mainly receives power from the autonomous power generation unit and provides backup power to the first battery system; a power measurement unit that measures the voltage, current, and remaining charge of the autonomous power generation unit, the first battery system, and the second battery system; an economic information acquisition unit that acquires at least one of time-of-day electricity rate information, electricity sales price information, subsidy information, and loan repayment information; and an AI energy management unit that calculates a surplus evaluation index R based on information from the power measurement unit and the economic information acquisition unit, and controls the power generation, charging, and discharging of the autonomous power generation unit, the first battery system, and the second battery system so that the surplus evaluation index R is equal to or greater than a predetermined value.

[0012] The autonomous power generation type multi-system energy management and profit control system according to the second invention of the present invention is, in the first invention, the AI ​​energy management unit, daytime electricity unit price P day and nighttime electricity unit price P night The difference ΔP and the shift energy E, which is charged at night and discharged during the day. shift And, the amount of electricity sold E sell And, the electricity selling price P sell Based on the monthly equivalent value G of the subsidy amount, the loan repayment amount L, and the maintenance costs M including inspection fees, insurance premiums, and repair reserve funds,

[0013]

Number

[0014] The self - generating multi - system energy management and black - lettering control system according to the third invention of the present invention, in the first invention or the second invention, when the black - letter evaluation index R is less than the first threshold value, the AI energy management unit generates at least one of a candidate for changing the operation time zone or operation output of the self - generating unit, a candidate for changing the charge - discharge schedule of the first battery system and the second battery system, and a candidate for changing the loan repayment period or repayment amount, and presents the candidate to the user terminal or the financial institution terminal.

[0015] The self - generating multi - system energy management and black - lettering control system according to the fourth invention of the present invention, in the first invention or the second invention, when the economic information acquisition unit cannot acquire the subsidy information, the AI energy management unit calculates the black - letter evaluation index R with the subsidy amount G set to 0, and executes a self - black - letter mode of controlling so that the black - letter evaluation index R becomes not less than the first threshold value using only the power generation, charging, and discharging of the self - generating unit, the first battery system, and the second battery system.

[0016] The self - generating multi - system energy management and black - lettering control system according to the fifth invention of the present invention, in the first invention or the second invention, the self - generating unit includes at least one of a mechanical power generation mechanism that converts rotational motion energy into electrical energy based on the centrifugal force acting on a rotating body and Faraday's law of electromagnetic induction, a thermoelectric power generation mechanism that converts thermal energy into electrical energy based on the temperature difference between a high - temperature part and a low - temperature part, a pressure power generation mechanism that converts pressure energy into electrical energy based on a change in water pressure or air pressure, and a magnetic power generation mechanism that converts magnetic energy into electrical energy based on a change in magnetic flux, and the AI energy management unit determines the operation priority according to the power generation capacity and efficiency of each power generation mechanism.

[0017] The self - generating multi - system energy management and black - lettering control system according to the sixth invention of the present invention, in the first invention or the second invention, the AI energy management unit monitors the remaining battery capacity SOC, temperature, and degree of deterioration of the first battery system and the second battery system, and when the SOC of the first battery system is less than a second threshold value and the SOC of the second battery system exceeds a third threshold value, it is characterized in that energy transfer from the second battery system to the first battery system is performed.

[0018] The self - generating multi - system energy management and black - lettering control system according to the seventh invention of the present invention, in the first invention or the second invention, when the AI energy management unit receives power outage information indicating that the grid power from the power company has stopped, it switches to an emergency operation mode that prioritizes loads necessary for life support over economic efficiency, selects lighting, communication equipment, water supply pumps, and refrigeration equipment as priority loads, and continues to supply power to the priority loads using at least the self - generating unit, the first battery system, and the second battery system.

[0019] The self - generating multi - system energy management and black - lettering control system according to the eighth invention of the present invention , the In the second invention, in a house where home all - electric appliances, solar power generation equipment, and this system are connected, the AI energy management unit controls the operating time zones of loads such as water heaters, IH cooking heaters, and washing machines to match the solar power generation output and the discharge of the first battery system, and controls so that the black - letter evaluation index R becomes positive by comparing the reduction amount of the household electricity bill, the loan repayment amount, and the maintenance cost.

[0020] The self - generating multi - system energy management and black - lettering control system according to the ninth invention of the present invention is, in the first invention or the second invention, in the factory - oriented system connecting the waste heat source and the water pressure source of the cooling water pipe in the factory with the self - generating power unit, predicting a load profile according to the production schedule and the power generation capacity obtained from the waste heat temperature and water pressure, generating an operation plan for the self - generating power unit, the first battery system, and the second battery system based on the prediction result, and controlling so as to reduce the energy cost of the entire factory in the long term.

[0021] The self - generating multi - system energy management and black - lettering control method according to the tenth invention of the present invention includes steps of generating electrical energy from natural energy by a self - generating power unit and charging the second battery system; obtaining the remaining charge amounts and power unit price information of the first battery system and the second battery system while supplying power from the first battery system to a load; calculating a black - letter evaluation index R, and determining the operation timing of the self - generating power unit and the charge - discharge schedules of the first battery system and the second battery system so that the black - letter evaluation index R is not less than a predetermined value; and controlling power generation, charging, and discharging based on the determination result.

[0022] The self - generating multi - system energy management and black - lettering control method according to the eleventh invention of the present invention is, in the method of the tenth invention, in the calculation of the black - letter evaluation index R, at least Daytime electricity unit price P day and nighttime electricity unit price P night It is the difference between the two. the power unit price difference ΔP, It charges at night and discharges during the day. the shifted power amount E shift the power amount sold E sell the selling unit price P sell the subsidy amount G, the loan repayment amount L, and the maintenance cost M are included.

[0023] The program according to the twelfth invention of the present invention is a program that causes a computer to execute the autonomous power generation type multi-system energy management and surplus control method of the tenth or eleventh invention, wherein the program calculates a surplus evaluation index R based on power measurement data and economic information, and enables the computer to implement a function to automatically update the operating parameters of the autonomous power generation unit, the first battery system and the second battery system so that the surplus evaluation index R satisfies predetermined conditions.

[0024] The computer-readable recording medium according to the 13th invention of the present invention is a non-temporary computer-readable recording medium on which the program described in the 12th invention is recorded.

[0025] The autonomous power generation type multi-system energy management and surplus control system according to the 14th invention of the present invention is characterized in that, in the system described in the first or second invention, the AI ​​energy management unit learns past power usage history, autonomous power generation history and power unit price history to construct a demand forecasting model and a power generation forecasting model, and simultaneously optimizes at least two objective functions: a first objective function aimed at stable power supply and a second objective function aimed at maximizing the surplus evaluation index R.

[0026] The energy management system according to the 15th invention of the present invention is characterized in that the autonomous power generation type multi-system energy management and surplus control system described in the first or second invention is installed in multiple buildings or facilities within a region, a central management server acquires the surplus evaluation index R and power generation / storage status from each system, and adjusts the operating conditions of each system based on the overall energy demand and subsidy budget status of the region, thereby optimizing energy autonomy and economic efficiency on a regional basis. [Effects of the Invention]

[0027] The present invention, with the above configuration, provides the following effects. 1. Energy autonomy through autonomous power generation based on natural laws and multi-system energy storage. By combining energy sources that follow natural laws, such as centrifugal force (rotational motion), heat (temperature gradient), pressure (water pressure / air pressure), and magnetism (magnetic flux change), power can be continuously generated even in environments where commercial power has completely shut down. By independently / interlockingly controlling two or more battery systems, high reliability is achieved, allowing power to continue being supplied from one system even if the other system fails.

[0028] 2. Simultaneous optimization of "technology x economy" with high novelty and inventiveness. While conventional energy management focused solely on "power control," this invention is novel and progressive in that it calculates a surplus evaluation index R and changes the technical control based on the result.

[0029] 3. A universal surplus model regardless of the presence or absence of subsidies. Even in areas without subsidies, long-term profitable operation can be achieved simply by optimizing autonomous power generation and multi-system energy storage. Conversely, if subsidies are available, the control logic can be modified by simply adding a G term. [Brief explanation of the drawing]

[0030] The drawings illustrate specific embodiments of the present invention as disclosed herein, including not only essential components of the invention but also optional and preferred embodiments. [Figure 1] A block diagram of the overall configuration of an autonomous power generation type multi-system energy management and surplus control system showing embodiments of the present invention. [Figure 2] This is a diagram illustrating the principle of an autonomous power generation unit that utilizes natural laws. (a) is an example of a mechanical power generation mechanism, (b) is an example of a thermoelectric power generation mechanism, (c) is an example of a pressure power generation mechanism, and (d) is an example of a magnetic power generation mechanism. [Figure 3] Diagram showing the connection configuration of the two battery systems and the autonomous power generation unit. [Figure 4] Flowchart for profitability control by the AI ​​energy management unit in an embodiment of the present invention. [Figure 5] An example configuration for a residential application (solar power + all-electric system + this autonomous power generation type multi-system energy management and profitability control system). [Figure 6] An example configuration for a factory application (waste heat + hydrostatic autonomous power generation + this autonomous power generation type multi-system energy management and profitability control system). [Figure 7] An example configuration for a disaster prevention base (priority load control during long-term power outages). [Figure 8] A diagram illustrating a regional energy management system that shows another embodiment of the present invention. [Modes for carrying out the invention]

[0031] The embodiments illustrated below, applying the present invention, will be described with reference to the drawings.

[0032] <Embodiment 1> <Overview of Embodiment 1> The outline of Embodiment 1 will now be described. The autonomous power generation type multi-system energy management and profitability control system of Embodiment 1 includes at least an autonomous power generation unit, a main battery (first battery system), a secondary battery (second battery system), an AI energy management device (AI-EMS), an economic control module, and a communication module.

[0033] The autonomous power generation unit is an energy conversion mechanism based on natural laws such as centrifugal force, thermoelectricity, hydrostatic pressure, wind pressure, and magnetism. The main battery system (first battery system) is primarily intended to supply power to loads (homes, shops, factories). The secondary battery system (second battery system) is primarily intended to charge from the autonomous power generation unit and provide backup power to the main system. The AI ​​Energy Management System (AI-EMS) acquires power sensors, voltage and current meters, temperature sensors, time-of-use pricing information, subsidy information, loan information, etc., and optimizes charging, discharging, power generation, and load control. The economic control module integrates monthly electricity bill reductions, electricity sales, subsidy amounts, and loan repayments to calculate the "profitability evaluation index R = (reductions + electricity sales + subsidies) - (loans + maintenance costs)". The communication module communicates with smart meters, the cloud, financial institutions, and local government servers.

[0034] The following describes the characteristic basic control logic (profitability control) of this embodiment 1. The AI-EMS performs at least power optimization, economic optimization (profitability determination), and automatic switching between having subsidies or not.

[0035] <Power optimization> During off-peak hours when electricity rates are low, the auxiliary battery is prioritized for charging using grid power or an autonomous power generation unit. During off-peak hours when electricity rates are high, the primary battery is discharged to the load to reduce the amount of electricity purchased from the grid. The State of Charge (SoC) of the primary and auxiliary systems is constantly monitored, and if the primary system falls below a threshold (e.g., 20%) and the auxiliary system exceeds a threshold (e.g., 50%), energy is transferred from the auxiliary system to the primary system.

[0036] <Economic Optimization (Supplement Judgment)> The monthly surplus evaluation index R is calculated from the difference in electricity unit price ΔP (day-night), daily electricity consumption, amount of electricity sold, subsidies, loan repayments, maintenance reserves, etc.

[0037]

number

[0038] <Automatic switching between subsidy availability and non-subsidy availability> If no subsidy information exists (G=0), the AI ​​switches to "autonomous surplus mode," aiming to achieve profitability solely through autonomous power generation and the difference in electricity unit prices. If subsidy information exists (G>0), the AI ​​considers the subsidy period, amount, and conditions, and proposes and adjusts the loan term, repayment amount, and equipment specifications so that profitability continues even after the subsidy ends.

[0039] <Effects of Embodiment 1> <Energy autonomy through autonomous power generation based on natural laws and multi-system energy storage> By combining energy sources that follow natural laws, such as centrifugal force (rotational motion), heat (temperature gradient), pressure (water pressure / air pressure), and magnetism (magnetic flux change), power can be continuously generated even in environments where commercial power has completely shut down. By independently / interlockingly controlling two or more battery systems, high reliability is achieved, allowing power to continue being supplied from one system even if the other system fails.

[0040] <Highly innovative and progressive simultaneous optimization of "technology x economy"> While conventional energy management focused solely on "power control," this invention is novel and progressive in that it calculates a surplus evaluation index R and changes the technical control based on the result.

[0041] Even in areas without subsidies, long-term profitable operation can be achieved simply by optimizing autonomous power generation and multi-system energy storage. Conversely, if subsidies are available, the control logic can be modified by simply adding a G term.

[0042] <Applicability to various specific cases> The following are some specific examples.

[0043] <Case Study 1: All-electric home + solar power + the present invention system> In a typical household with a monthly electricity consumption of 800kWh, a 5kW solar power generation system, 10kWh of battery storage, and the equivalent of 1kW of autonomous power generation are installed. The AI ​​prioritizes using solar power and battery power for hot water heating, induction cooking, and laundry during the day, and supplements only the necessary amount from the grid or autonomous power generation during off-peak hours when electricity rates are lower. As a result, electricity cost reduction + electricity sales + subsidies - loan - maintenance costs > 0, resulting in a profit from the first month.

[0044] <Case Study 2: No subsidies, existing factory, autonomous power generation only> In a local factory in an area where grid power is expensive and unstable. An autonomous power generation unit (cogeneration using waste heat + hydraulic power generation) and a multi-system battery of the present invention are introduced, and AI performs power generation control in conjunction with the load of the production line. Although there is no subsidy, by taking advantage of the power price difference and using waste heat and hydraulic pressure, the total annual energy cost is reduced, and the equipment investment can be recovered within several years.

[0045] <Case 3: Disaster Prevention Base (Shelter / Autonomous Body Facility)> Even if the grid power is cut off during a disaster, lighting, communication, water supply pumps, refrigerators, etc. can operate for several days with autonomous power generation + multi-system batteries. Based on the remaining energy and the priority of important loads, AI automatically optimizes which equipment to operate in which time period. It has a two-sided nature of cost reduction by taking advantage of the power price difference during normal times and specializing in power maintenance during disasters.

[0046] <Embodiment of Hardware Configuration> Item and connection example of each unit (autonomous generator, main system battery, sub-system battery, inverter, DC / DC converter, sensors, relay / switching element, communication module). Safety measures (overcurrent protection, overcharge / overdischarge protection, insulation monitoring, leakage circuit breaker, etc.).

[0047] <Embodiment of AI-EMS Processing Procedure> A series of processing flows: sensor data acquisition → preprocessing → prediction model (demand, power generation, price) → formulation of optimization problem → generation of charge / discharge, power generation, load control commands → log / learning update.

[0048] <Numerical Example of Black-ink Judgment Algorithm> When the power price is 30 yen / kWh during the day, 10 yen / kWh at night, the monthly shifted power quantity is 300 kWh, the sold power is 100 kWh, the subsidy in monthly conversion is 20,000 yen, the loan is 11,000 yen, and the maintenance cost is 5,000 yen, R = ((30 - 10) × 300) + (100 × 8) + 20,000 - (11,000 + 5,000) = 6,000 + 800 + 20,000 - 16,000 = 10,800 > 0 is calculated and determined to be in the black.

[0049] <Specific Link to Natural Law> The autonomous power generation unit converts mechanical energy, thermal energy, potential energy, etc., into electrical energy based on the laws of conservation of energy, Joule's law, Faraday's law of electromagnetic induction, etc. AI control adjusts the operating timing, rotation speed, and load of the autonomous power generation unit, taking into account the efficiency of these energy conversions and losses (such as heat generation due to internal resistance).

[0050] The following will provide a detailed explanation of the autonomous power generation type multi-system energy management and profit-generating control system using specific diagrams. In the following explanation, the focus will be on the distinctive features of the invention, and sensors, relays, switching elements, etc., which are necessary for the conventional electrical equipment mentioned above, will not be shown in the diagrams.

[0051] Figure 1 is a block diagram of the overall configuration of an autonomous power generation type multi-system energy management and profitability control system showing an embodiment of the present invention. Figure 1 shows the overall system configuration including an autonomous power generation unit 20 based on natural laws, two battery systems (main system 30 / sub-system 40), an AI energy management unit 50, a group of loads 60 such as homes, factories, and disaster prevention systems, a power company grid 70, and a financial / subsidy server 80.

[0052] The autonomous power generation unit 20 generates electricity based on natural laws (centrifugal force, thermal gradient, pressure difference, and magnetic flux change). Specifically, it has a mechanical power generation mechanism (centrifugal force + electromagnetic induction) 21, a thermoelectric power generation mechanism (using temperature difference) 22, a pressure power generation mechanism (water pressure / air pressure) 23, and a magnetic power generation mechanism (magnetic flux change) 24. The autonomous power generation unit 20 only needs to be equipped with one or more of these power generation mechanisms. An example of a principle explanation of the power generation mechanism of the autonomous power generation unit 20 is shown in Figures 2(a), (b), (c), and (d). Figure 2(a) is an example of the mechanical power generation mechanism 21, (b) is an example of the thermoelectric power generation mechanism 22, (c) is an example of the pressure power generation mechanism 23, and (d) is an example of the magnetic power generation mechanism 24.

[0053] The autonomous power generation unit 20 is connected to the second battery system (sub-system) 40, and the power generated by the autonomous power generation unit 20 is preferentially powered, received, and backed up. The second battery system (sub-system) 40 is connected to the first battery system (main system) 30 via a bidirectional DC / DC converter 44, forming a power supply line to the load group 60.

[0054] The AI ​​Energy Management Unit (AI-EMS) 50 comprises a power measurement unit (voltage, current, SoC) 51, an economic information acquisition unit (subsidies, rates, loans) 52, and a control calculation unit (profit evaluation index R calculation) 53. The power measurement unit 51 measures voltage, current, SoC (remaining energy storage capacity), etc. The economic information acquisition unit 52 acquires economic information such as electricity rates, subsidies, and repayment information (loan information) from financial and subsidy servers 80 of banks and local governments. The control calculation unit 53 performs calculations for the profit evaluation index R. The calculation formula is calculated using the formula in Equation 1.

[0055] According to the profitability control system implemented by the AI ​​Energy Management Unit (AI-EMS) 50, households can achieve profitability in the first month through day / night shifts plus autonomous power generation, factories can reduce electricity purchases by reusing waste heat and water pressure, and disaster prevention bases can maintain operation for several days through priority load control during power outages.

[0056] Load group 60 includes loads from either a home, a factory, or a disaster prevention base. In the example in Figure 1, a home load 61, a factory load 62, and a disaster prevention load 63 are shown as examples, but loads are installed according to the use of the building or facility, such as a residence, factory building, or disaster prevention base. For example, in the case of home load 61, loads such as an eco-friendly water heater, induction cooktop, and lighting are provided. In the case of factory load 62, loads such as motors and production line loads are provided. In the case of disaster prevention load 63, communication equipment, water supply pumps, and refrigerated medical storage are provided.

[0057] The power company grid (commercial power source) 70 is connected to the commercial power source of the power company (grid power). By efficiently supplying electricity from the autonomous power generation unit 20 based on natural laws (selling or buying electricity), it is possible to achieve profitability.

[0058] The financial and subsidy server 80 is a server installed in banks and local governments.

[0059] According to the embodiment shown in Figure 1, unlike conventional single-energy storage or solar control, it offers the advantage of integrated management of multiple power sources based on natural laws, multiple battery systems, AI, and economic information. In other words, this new structure of AI control that performs "two-axis optimization" of power and economy is a novel concept unlike anything seen before. It demonstrates that it can be used in homes, factories, and disaster prevention centers.

[0060] The structure shown in Figure 1 can be applied to all of the following massive markets. For example, housing (housing with a surplus in utility costs), factories (reusing waste heat and water pressure to achieve cost surplus), disaster relief centers (72-hour operation), regional microgrids, island power, new power businesses, EVs / charging stations, agriculture, livestock, and fisheries.

[0061] Figure 2 is a diagram illustrating the principle of an autonomous power generation unit utilizing natural laws. Figure 2(a) is an example of a mechanical power generation mechanism 21, (b) is an example of a thermoelectric power generation mechanism 22, (c) is an example of a pressure power generation mechanism 23, and (d) is an example of a magnetic power generation mechanism 24. Figure 2(a) shows an example of utilizing natural laws based on the centrifugal force of a small rotating body (wind turbine). Figure 2(b) shows an example of utilizing natural laws of the thermal gradient (Seebeck effect) transitioning from a high-temperature area to a low-temperature area. Figure 2(c) shows an example of utilizing natural laws of pressure difference due to a turbine (steam or water flow). Figure 2(d) shows an example of utilizing natural laws of magnetic flux change due to the rotation of an electromagnetic coil.

[0062] Figure 3 is a diagram illustrating the connection configuration of the two battery systems and the autonomous power generation unit. The example in Figure 3 shows the specific configuration of the power path in this embodiment, which is "autonomous power generation unit 20 (utilizing natural laws) → second battery system (sub-system) 40 → first battery system (main system) 30 → load group 60" and "bidirectional energy transfer between the first battery system (main system) 30 and the second battery system (sub-system) 40".

[0063] In Figure 3, the autonomous power generation unit 20 includes a mechanical power generation mechanism (centrifugal force + electromagnetic induction) 21, a thermoelectric power generation mechanism (using temperature difference) 22, a pressure power generation mechanism (water pressure / air pressure) 23, and a magnetic power generation mechanism (magnetic flux change) 24. The power generated by each power generation mechanism 21 to 24 is converted from AC to DC in a rectifier (AC→DC) 25, and then boosted or stepped down in a step-up / step-down DC / DC converter (power generation voltage optimization) 26 to optimize the power generation voltage. The step-up / step-down DC / DC converter 26 is connected to a second battery system (sub-system) 40, and the autonomous power generated from the step-up / step-down DC / DC converter 26 is preferentially charged and backed up to the second battery system (sub-system) 40 (battery module 41).

[0064] The second battery system (sub-system) 40 includes a battery module 41, a protective switch (circuit breaker) 42, and a current sensor 43.

[0065] The first battery system (main system: load supply) 30 includes a battery module 31, a protective switch 32, and a current sensor 33. The first battery system (main system) 30 provides load supply.

[0066] The bidirectional DC / DC converter 44 is positioned between the second battery system (sub-system) 40 and the first battery system (main system) 30, and has a connection structure that enables bidirectional energy transfer between the second battery system (sub-system) 40 and the first battery system (main system) 30.

[0067] The output of the first battery system (main system) 30 is converted from DC to AC by the inverter 45, and power is supplied to the household loads 61 of the load group 60 via the main system switch 46. In the example in Figure 3, it is connected to household loads 61, but it could also be a factory load or a load at a disaster prevention base.

[0068] Meanwhile, the output of the inverter 45 is connected to the power company grid (commercial power supply) 70 via the grid connection breaker 47. Bidirectional transmission is possible between the power company grid (commercial power supply) 70 and the inverter 45, allowing for the purchase of electricity from the power company or the sale of surplus electricity to the power company. The power measurement unit (installed in the AI-EMS in Figure 1) 51 receives sensor input from each power generation mechanism 21-24, current sensors 43 and 33, and the inverter 45, and can acquire various data.

[0069] This enables bidirectional power transmission between the multi-battery system and the bidirectional DC / DC converter 44. Conventional single-battery configurations have problems with the loss of surplus power generation and vulnerability during power outages. In this embodiment, the two-system structure of the first battery system 30 and the second battery system 40, along with the bidirectional DC / DC converter 44, provides surplus energy recovery, backup, and redundancy.

[0070] For example, by generating electricity at night, storing it in the second battery system (sub-system) 40, and then supplying it to the load group 60 from the first battery system (main system) 30 during peak hours in the daytime, electricity costs can be reduced, and long-term profitability can be achieved.

[0071] Furthermore, when the SoC of the first battery system (main system) 30 degrades, it can be reverse-charged from the second battery system (sub-system) 40 via the bidirectional DC / DC converter 44.

[0072] The integrated structure of the four elements, "autonomous power generation unit 20 (natural laws x multiple mechanisms) + second battery system (sub-system) 40 + first battery system (main system) 30 + bidirectional DC / DC converter 44," is a unique structure not found in conventional home energy storage, UPS, or solar control systems. Energy flow and redundancy are multi-systemized, and AI (in conjunction with Figure 1) enables integrated optimization of natural power generation, electricity costs, SoC, and loan repayment, realizing a two-layer control system of technology and economy.

[0073] In terms of industrial applicability, it is a versatile DC bus structure that can be implemented in a wide range of locations, including homes, factories, disaster prevention, off-grid environments, and remote islands.

[0074] Figure 4 is a flowchart of the profitability control performed by the AI ​​Energy Management Unit (AI-EMS) 50. Figure 4 shows the profitability control process performed by the AI ​​Energy Management Unit 50, which is the core of the present invention, as processing steps (S100 to S190). It shows that information such as natural power generation amount, battery SoC, electricity rate unit price, subsidy information, loan repayment information, and maintenance costs (inspection, insurance, repair reserves) is integrated to simultaneously optimize two axes: "stable power supply (technical objective)" and "economic profitability (economic objective)".

[0075] As shown in Figure 1, the AI ​​Energy Management Unit (AI-EMS) 50 includes a power measurement unit 51, an economic information acquisition unit 52, and a control calculation unit (surplus evaluation index R calculation) 53. The surplus control process shown in Figure 4 will be explained below with reference to Figures 1 and 3 as appropriate.

[0076] The surplus control process has the following steps: S100: acquire power measurement data, S110: acquire economic information, S120: forecast demand and power generation, S130: calculate surplus evaluation index R, S140: determine surplus (R ≥ threshold?), S150: generate improvement candidates (power generation, charging / discharging, load shift, loan conditions), S160: determine optimal schedule, S170: output control signals (power generation, charging, discharging, load control), S180: save log and update learning data, S190: transition to the next control cycle.

[0077] <S100: Power measurement data acquisition> Voltage, current, and SoC of the self - generating unit 20, main battery 30, sub - battery 40, and power company system 70 are acquired from the input of the power measurement unit 51.

[0078] <S110: Economic information acquisition> Inputs (subsidy, tariff unit price, loan repayment amount, maintenance cost) from the economic information acquisition unit 52 are acquired from the finance and subsidy server 80.

[0079] <S120: Demand prediction and power generation prediction> The AI energy management unit (AI - EMS) 50 learns the fluctuations based on the power usage history (either home, factory, or disaster prevention), self - generating capacity (temperature difference, pressure difference, rotation speed, speed, magnetic flux change, etc.), and natural laws of weather, temperature, and time zone. A learning model is created from the learning results. The learning model is used to perform power generation prediction and demand prediction based on natural conditions (temperature, pressure, rotation speed).

[0080] <S130: Calculation of black - letter evaluation index R> The black - letter evaluation index R is calculated from the following formula.

[0081]

Equation

[0082] <S140: Black - letter determination (R ≥ threshold?)> In the black - letter determination, the black - letter evaluation index R is compared with the threshold. When the black - letter evaluation index R is greater than or equal to the threshold (YES), S180: Log storage and learning data update step is executed, and S190: Transition to the next control cycle is performed.

[0083] When the black - letter evaluation index R is less than the threshold value (NO), execute S150: Improvement candidate generation step.

[0084] <S150: Improvement candidate generation> "Candidates" for black - lettering are shown below. Generate candidates such as changing the operation output / operation timing of the autonomous power generation unit, changing the charge - discharge schedule of the main / sub - batteries, shifting the operating time zone of the load (e.g., moving hot water supply and washing to the solar time zone), adjustment plans for loan repayment periods and amounts, subsidy utilization plans (two control modes with or without subsidies), etc.

[0085] <S160: Optimal schedule determination> Select a plan that simultaneously satisfies the two objective functions of stable power supply (technical objective) and black - lettering (economic objective) from the group of candidates generated in step S150.

[0086] <S170: Control signal output> Output control signals to the autonomous power generation unit 20, the first battery system 30, the second battery system 40, and the load group 60. That is, transmit "power generation / charging / discharging / load control" signals.

[0087] <S180: Log storage and learning data update> Accumulate past power generation, power storage, and load data. Perform machine learning and prediction model update (including time - series variations based on natural laws).

[0088] <S190: Transition to the next control cycle> The process ends and returns to step S100.

[0089] Through the above process, the autonomous power generation amount is predicted in S120, utilizing natural laws and depending on natural conditions such as temperature difference, pressure difference, rotational speed, and magnetic flux change. The difference in waste heat temperature can also be a linear function of the power generation amount. Conventionally, "power optimization" and "household budget surplus" were controlled separately, but simultaneous optimization can be achieved with the two-stage AI control in S130-S160. Conventionally, operation was limited to a single threshold or fixed schedule, but this invention offers high flexibility with a two-stage process of candidate generation (S150) → optimization (S160).

[0090] The AI ​​control flow, which calculates a surplus evaluation index R that integrates power and economic information and then modifies power generation, storage, and load control based on that, is unprecedented. It's not just simple peak shifting; the AI ​​can perform dual-objective optimization (stable power supply + surplus). The two-stage structure of candidate generation → optimization is clearly sophisticated.

[0091] <Industrial applicability> It can be implemented in a wide range of locations, including homes, factories, disaster prevention centers, and remote islands.

[0092] <Household Embodiment: Solar Power Generation + All-Electric Home + Energy Management System of the Present Invention> Figure 5 shows an example configuration of a residential embodiment (solar power + all-electric + this system). Figure 5 is a diagram showing the specific installation configuration of the present invention system in a typical household. It visualizes the connection relationships of solar power generation (PV), all-electric equipment (EcoCute, IH, etc.), the present invention system (autonomous power generation + multi-system battery + AI), the power company grid (commercial power), the household distribution board, and each load (household load), and explains that "profitable operation can be achieved in a household" with this embodiment.

[0093] In Figure 5, the entire house 100 includes the autonomous power generation type multi-system energy management and surplus control system 10 of this embodiment, an autonomous power generation unit 20, a first battery system (main system) 30, a second battery system (sub-system) 40, an AI-EMS (AI Energy Management Unit) 50, and household loads 61. Here, the autonomous power generation type multi-system energy management and surplus control system 10 has been explained in detail in Figures 1 and 3, so it is not shown here. The explanation will focus on solar power generation equipment and household loads that are generally installed in homes.

[0094] The household load 61 includes an EcoCute (hot water heater) 611, an IH cooking heater 612, a washing machine 613, a refrigerator, lighting, and other appliances 614, and an air conditioner 615.

[0095] Solar power generation panels (PV) 81 are installed on the roof of house 100, and a PV inverter (grid-connected inverter) 82 is installed inside the house or on the exterior wall of the house. The power company grid (commercial power) is connected to the distribution board 90 inside the house, and the output of the PV inverter 82 is also connected to the distribution board 90 inside the house. This clearly defines three paths: grid → load, the present invention system → load, and PV → distribution board → load.

[0096] In such an installed equipment environment, load time-based control can be performed using AI-EMS (AI) 50. For example, time-based shifting can be performed for the EcoCute (hot water heater) 611 using AI.

[0097] <Natural law> The autonomous power generation unit 20 generates electrical energy using centrifugal force (rotating body), temperature difference (thermoelectric element), pressure difference (water pressure / air pressure), and magnetic flux change (electromagnetic induction).

[0098] <Improving household issues> Conventional "solar power + single battery storage" systems often result in higher loan repayments and a net loss. This system utilizes autonomous power generation and multiple battery systems, with AI optimally managing fluctuations in solar power and nighttime price differences to automatically maintain a profitability index R>0.

[0099] <Specific example> Example: 30 yen / kWh during daytime, 10 yen / kWh at night Example: PV 5 kW, energy storage 10 kWh, self - generating power 1 kW Example: Shift load: Eco - cut (for hot water supply) 611, IH cooking heater 612, washing machine 613

[0100] <Embodiment for factories: Self - generating power by waste heat and water pressure + multi - system battery + AI control> Figure 6 is a configuration example of the embodiment for factories (waste heat + water - pressure self - generating power + this system). Figure 6 is a configuration diagram of the "embodiment for factories" showing how the present invention realizes energy reuse, power cost reduction, and profitability in a factory (industrial environment). In particular, it shows the connection relationships among the waste heat (thermal energy) of the factory, the cooling water and water pressure (pressure energy) of the factory, the large - scale loads (motors, heaters, compressors) of the factory, the self - generating power unit 20, the multi - system battery (the first battery system (main system) 30 / the second battery system (sub - system) 40), and the profitability control by the AI - EMS 50.

[0101] In the factory building 200, there are installed a waste heat source (such as a boiler, firing furnace, drying furnace, etc.) 201, a heat exchanger + thermoelectric power generation module (a part of the thermoelectric power generation mechanism 22) 202, a cooling water pipe line (water - pressure power source) 203, and a water - pressure turbine + generator (a part of the pressure power generation mechanism 23) 204.

[0102] In Figure 6, the factory building 200 has a self - generating power type multi - system energy management and profitability control system 10 of this embodiment, a self - generating power unit 20, a first battery system (main system) 30, a second battery system (sub - system) 40, an AI - EMS (AI energy management unit) 50, and a factory load 62. Here, since the details of the self - generating power type multi - system energy management and profitability control system 10 have been described in Figures 1 and 3, they are shown here with illustration omitted. The description will be centered around the waste heat source utilization power generation equipment, the water - pressure power source utilization power generation equipment, and the factory load installed in the factory building 200.

[0103] The factory load 62 includes a production line motor 621, an industrial heater 622, and a factory air conditioning compressor 623.

[0104] The power grid (commercial power supply) 70 is connected to the factory's internal distribution board 91, and power is supplied to each load of the factory load 62 via the internal distribution board 91. In addition, the power generated by the heat exchanger + thermoelectric power generation module 202 and the hydraulic turbine + generator 204 is aggregated in the autonomous power generation unit 20 and charged to the second battery system (sub-system) 40. The power from the battery (storage battery) is converted from DC to AC by the inverter 45 and supplied to the factory load 62. Power is also supplied to the factory load 62 as auxiliary power (auxiliary power supply) from the power grid (commercial power supply) 70 via a separate line.

[0105] <Natural law> The heat exchanger + thermoelectric power generation module 202 performs "thermoelectric power generation: temperature difference → power conversion based on the Seebeck effect." The hydraulic turbine + generator 204 performs "water pressure → turbine rotation (kinetic energy) → conversion to electricity via electromagnetic induction."

[0106] <Reflection of natural laws> The heat exchanger + thermoelectric power generation module 202 includes a thermoelectric element that generates a voltage proportional to the temperature difference ΔT (Seebeck effect). The hydraulic turbine + generator 204 generates electricity through electromagnetic induction, with the turbine rotation speed determined by the pressure and flow rate of the cooling water.

[0107] <Technical challenges faced by factories> Traditionally, most waste heat was discharged unused, and hydraulic energy was also discarded through pressure reducing valves. Consequently, peak power consumption was high, resulting in high electricity bills, and energy storage was a single, unredundant system.

[0108] <Improvements according to the present invention> The improvements shown in Figure 6 enable autonomous power generation by converting waste heat and water pressure into renewable energy resources. The generated electricity is first stored in a second (sub-system) and then transferred to the first system as needed. The first and second systems are redundant with bidirectional DC / DC converters. AI optimizes the load by monitoring "electricity costs," "power generation," and "production plans." As a result, annual electricity purchases can be reduced, contributing to overall cost surplus for the factory.

[0109] <Implementation for disaster prevention bases: Prioritized load selection during power outages + autonomous power generation + long-term operation with multiple battery systems> Figure 7 is an explanatory diagram of an embodiment for disaster prevention bases (priority load control during long-term power outages). Figure 7 shows the operational configuration of the present invention system in disaster prevention bases such as municipal facilities, evacuation centers, and hospitals. It shows a structure that enables continuous operation of critical loads (lighting, communication, water supply, refrigeration, etc.) even during long-term power outages during disasters by combining autonomous power generation, multi-system batteries, and AI-based load priority control.

[0110] In Figure 7, the disaster prevention base building (shelter, government building, hospital, etc.) 300 includes the autonomous power generation type multi-system energy management and surplus control system 10 of this embodiment, an autonomous power generation unit 20, a first battery system (main system) 30, a second battery system (sub-system) 40, an AI-EMS (AI energy management unit) 50, and a disaster prevention load group 63. Here, the details of the autonomous power generation type multi-system energy management and surplus control system 10 have been explained in Figures 1 and 3, so they are omitted from the illustration here. The disaster prevention load group 63 installed in the disaster prevention base building 300 will be explained in detail.

[0111] Disaster prevention load group (critical load) 63 includes emergency lighting (priority 1) 631, communication equipment (Wi-Fi, satellite phones, base station auxiliary power, etc.) (priority 2) 632, water supply pumps (priority 3) 633, medical refrigerators / food coolers (priority 4) 634, and charging stations (smartphones, etc.) (priority 5) 635. Disaster prevention load group 63 is assigned a priority label as the target of priority control. The priority labels are, for example, "priority 1": lighting, "priority 2": communication, "priority 3": water supply, "priority 4": refrigeration, "priority 5": other / charging, etc. Since energy is limited, priority control is performed according to the importance of the load.

[0112] The power company grid (commercial power supply) 70 is connected to the disaster prevention base distribution board 92, and under normal circumstances, power is supplied to each load of the disaster prevention load group 63 via the disaster prevention base distribution board 92. If the power company grid 70 is in a state of prolonged power outage 701, the line between the power company grid 70 and the disaster prevention base distribution board 92 is cut off, so power cannot be supplied from the power company grid 70. Power from the battery (storage battery) is converted from DC to AC by the inverter 45 and supplied to the disaster prevention load group 63 by priority control. Specifically, power is supplied in the following order: emergency lighting (priority 1) 631, communication equipment (priority 2) 632, water supply pump (priority 3) 633, medical refrigerator / food cooler (priority 4) 634, and charging station (smartphone, etc.) (priority 5) 635.

[0113] <Problem → Solution> Conventional emergency power generators have the problem of stopping when fuel runs out during power outages. This invention enables long-term operation without refueling by using autonomous power generation and multiple battery systems. AI determines load priority and executes control to keep the most important loads running for the longest possible time.

[0114] <Specific Examples> For example, with 1kW of autonomous power generation + 30kWh of the main system + 20kWh of the secondary system, lighting, communication, and water supply can be maintained for 72 hours.

[0115] <Advantages of the present invention> Conventional disaster prevention systems rely solely on diesel power generation, a single battery, and lack load-priority control. This combination of natural power generation, multiple battery systems, and AI-based priority control is unprecedented. The concept of "load priority" itself is new in disaster prevention systems. The control system, in which AI monitors the SoC and autonomous power generation capacity to switch between loads to run and loads to shut down, is a highly advanced optimization technique. This system can be used in disaster prevention, by local governments, hospitals, and evacuation centers. In particular, "securing power for 72 hours or more" is a national guideline and has very strong industrial significance.

[0116] <Other Embodiments> Another embodiment of the autonomous power generation type multi-system energy management and surplus control method is an autonomous power generation type multi-system energy management and surplus control method characterized by comprising the steps of: generating electrical energy from natural energy using an autonomous power generation unit and charging a second battery system; supplying power to a load from the first battery system while acquiring the remaining charge amount and power unit price information of the first battery system and the second battery system; calculating a surplus evaluation index R and determining the operating timing of the autonomous power generation unit and the charge / discharge schedule of the first battery system and the second battery system so that the surplus evaluation index R is equal to or greater than a predetermined value; and controlling power generation, charging, and discharging based on the determination result.

[0117] The autonomous power generation type multi-system energy management and surplus control method, in the method described in another embodiment, includes at least the difference in electricity unit price ΔP and the shift amount E in the calculation of the surplus evaluation index R. shift , amount of electricity sold E sell electricity sales price P sell It is characterized by including the subsidy amount G, the loan repayment amount L, and the maintenance cost M.

[0118] The present invention provides a program for a computer to execute an autonomous power generation type multi-system energy management and surplus control method described in other embodiments, wherein the program enables the computer to calculate a surplus evaluation index R based on power measurement data and economic information, and to automatically update the operating parameters of the autonomous power generation unit, the first battery system and the second battery system so that the surplus evaluation index R satisfies predetermined conditions.

[0119] The computer-readable recording medium of the present invention is a non-temporary computer-readable recording medium on which the program described above is recorded.

[0120] In the autonomous power generation type multi-system energy management system described in the first or second invention, the AI ​​energy management unit learns past power usage history, autonomous power generation history, and power unit price history to construct a demand forecasting model and a power generation forecasting model, and simultaneously optimizes at least two objective functions: a first objective function aimed at stable power supply and a second objective function aimed at maximizing the surplus evaluation index R.

[0121] Figure 8 is a diagram illustrating the configuration of a regional energy management system, showing another embodiment of the present invention. The example in Figure 8 realizes regional energy management, and the autonomous power generation type multi-system energy management and surplus control system 10 (see Figures 1 and 3) and AI-EMS 50 (AI) of the embodiment are installed in multiple buildings (Building A 500, Building B 600) or facilities (Facility C 700, Facility D 800) within the region. The central management server 400 acquires the surplus evaluation index R and power generation / storage status from each system and adjusts the operating conditions of each system based on the overall energy demand and subsidy budget status of the region, thereby optimizing energy autonomy and economic efficiency on a regional basis.

[0122] Thus, this embodiment includes an autonomous power generation unit that generates electricity based on natural laws such as mechanical energy, thermal energy, pressure energy, and magnetic energy; a first battery system mainly for supplying power to the load and a second battery system mainly for generating, receiving, and backing up power; an AI energy management device that controls the power generation, charging, and discharging of these systems; and a module that acquires economic information such as electricity unit price information, subsidy information, and loan repayment information.

[0123] The AI ​​energy management system calculates a surplus evaluation index R based on time-of-use electricity rates, demand forecasts, autonomous power generation output, remaining energy storage capacity, subsidy amounts, loan repayments, and maintenance costs, and optimizes the operating modes of the autonomous power generation unit and the first and second battery systems so that R is positive. This enables homes, factories, disaster prevention centers, etc., to simultaneously achieve power autonomy during long-term power outages and energy cost surpluses during normal operation, regardless of the presence or absence of subsidies or the electricity price structure, providing high reliability and economic efficiency that could not be obtained with conventional single-system energy storage or simple peak-cut control. [Explanation of Symbols]

[0124] 10. Autonomous power generation type multi-system energy management and surplus control system 20 Autonomous power generation unit 30. First battery system (main system) 40. Second battery system (sub-system) 50 AI Energy Management Department (AI-EMS) 51 Power Measurement Unit 52 Economic Information Acquisition Department

Claims

1. An autonomous power generation type multi-system energy management and surplus control system, An autonomous power generation unit that converts at least one of mechanical energy, thermal energy, pressure energy, or magnetic energy into electrical energy, A first battery system that primarily supplies power to the load, A second battery system primarily receives power from the autonomous power generation unit and provides backup power to the first battery system, The autonomous power generation unit, the first battery system and the second battery system, and a power measurement unit that measures the voltage, current and remaining charge of the battery system, An economic information acquisition unit that acquires at least one of the following: time-of-use electricity rate information, electricity sales price information, subsidy information, and loan repayment information. Based on the information from the power measurement unit and the economic information acquisition unit, the AI ​​energy management unit calculates a surplus evaluation index R and controls the power generation, charging, and discharging of the autonomous power generation unit, the first battery system, and the second battery system so that the surplus evaluation index R is equal to or greater than a predetermined value. An autonomous power generation type multi-system energy management and surplus control system characterized by comprising the following features.

2. The aforementioned AI energy management unit calculates the daytime electricity unit price P ｄａｙ and nighttime electricity unit price P ｎｉｇｈｔ The difference ΔP and the shift energy E, which is charged at night and discharged during the day. ｓｈｉｆｔ And, the amount of electricity sold E ｓｅｌｌ And, the electricity selling price P ｓｅｌｌ Based on the monthly equivalent value G of the subsidy amount, the loan repayment amount L, and the maintenance costs M including inspection fees, insurance premiums, and repair reserve funds, [Math 1] The profitability indicator R is calculated as follows: The autonomous power generation type multi-system energy management and profitability control system according to feature 1.

3. The AI ​​energy management unit, when the profitability evaluation index R is less than the first threshold, generates at least one of the following candidates: a candidate to change the operating time period or operating output of the autonomous power generation unit; a candidate to change the charging and discharging schedule of the first battery system and the second battery system; or a candidate to change the loan repayment period or repayment amount, and presents the said candidate to the user terminal or financial institution terminal. The autonomous power generation type multi-system energy management and profitability control system according to claim 1 or 2.

4. The autonomous power generation type multi-system energy management and surplus control system according to claim 1 or 2, characterized in that the AI ​​energy management unit calculates the surplus evaluation index R with the subsidy amount G set to 0 when the economic information acquisition unit is unable to acquire subsidy information, and executes an autonomous surplus mode that controls the surplus evaluation index R to be equal to or greater than a first threshold using only the power generation, charging, and discharging of the autonomous power generation unit and the first battery system and the second battery system.

5. The autonomous power generation unit includes at least one of the following: a mechanical power generation mechanism that converts rotational kinetic energy into electrical energy based on the centrifugal force acting on a rotating body and Faraday's law of electromagnetic induction; a thermoelectric power generation mechanism that converts thermal energy into electrical energy based on the temperature difference between a high-temperature part and a low-temperature part; a pressure power generation mechanism that converts pressure energy into electrical energy based on a change in water pressure or air pressure; and a magnetic power generation mechanism that converts magnetic energy into electrical energy based on a change in magnetic flux. The autonomous power generation type multi-system energy management and profitability control system according to claim 1 or 2, characterized in that the AI ​​energy management unit determines the operating priority according to the power generation capacity and efficiency of each power generation mechanism.

6. The autonomous power generation type multi-system energy management and profit control system according to claim 1 or 2, characterized in that the AI ​​energy management unit monitors the remaining charge state of charge (SOC), temperature, and degree of degradation of the first battery system and the second battery system, and performs energy transfer from the second battery system to the first battery system when the SOC of the first battery system is below a second threshold and the SOC of the second battery system exceeds a third threshold.

7. The autonomous power generation type multi-system energy management and profitability control system according to claim 1 or 2, characterized in that when the AI ​​energy management unit receives power outage information from the power company indicating that grid power has been interrupted, it switches to an emergency operation mode that prioritizes loads necessary for life support over economic efficiency, selects lighting, communication equipment, water supply pumps and refrigeration equipment as priority loads, and continues to supply power to these priority loads using at least the autonomous power generation unit and the first battery system and the second battery system.

8. The AI ​​energy management unit controls the operating time of loads such as water heaters, induction cooktops, and washing machines in a house connected to a household all-electric appliance, solar power generation equipment, and this autonomous power generation type multi-system energy management and surplus control system to match the solar power generation output and the discharge of the first battery system, and controls the system to make the surplus evaluation index R positive by comparing the amount of electricity bill savings for the household with the loan repayment amount and the maintenance costs, as described in claim 2.

9. The AI ​​energy management unit, in a factory system connecting a waste heat source and a water pressure source for cooling water piping in a factory with the autonomous power generation unit, predicts a load profile according to the production schedule and the amount of power that can be generated from the waste heat temperature and water pressure, generates an operation plan for the autonomous power generation unit, the first battery system and the second battery system based on the prediction results, and controls the system so that the energy costs of the entire factory are reduced in the long term, as described in claim 1 or 2.

10. An autonomous power generation type multi-system energy management and surplus control method, The process involves generating electrical energy from natural energy sources using an autonomous power generation unit and charging the second battery system, The steps include supplying power to a load from the first battery system while acquiring the remaining charge and power unit price information of the first and second battery systems, The steps include: calculating a profit evaluation index R, and determining the operating timing of the autonomous power generation unit and the charging / discharging schedules of the first battery system and the second battery system so that the profit evaluation index R is equal to or greater than a predetermined value; Based on the decision result, the steps include controlling power generation, charging, and discharging, An autonomous power generation type multi-system energy management and surplus control method characterized by having the following features.

11. In the method according to claim 10, The calculation of the aforementioned surplus evaluation index R includes at least the difference in electricity unit price ΔP, which is the difference between the daytime electricity unit price P day and the nighttime electricity unit price P night, and the shift energy amount E, which is charged at night and discharged during the day. ｓｈｉｆｔ Electricity sold E ｓｅｌｌ electricity selling price P ｓｅｌｌ An autonomous power generation type multi-system energy management and profit control method characterized by including subsidy amount G, loan repayment amount L, and maintenance costs M.

12. A program for causing a computer to execute the autonomous power generation type multi-system energy management and surplus control method described in claim 10 or 11, The program enables a computer to perform a function that calculates a surplus evaluation index R based on power measurement data and economic information, and automatically updates the operating parameters of the autonomous power generation unit, the first battery system, and the second battery system so that the surplus evaluation index R satisfies predetermined conditions.

13. A non-temporary computer-readable recording medium on which the program described in claim 12 is recorded.

14. In the system according to claim 1 or 2, The AI ​​energy management unit is characterized by learning past power usage history, autonomous power generation history, and power unit price history to construct a demand forecasting model and a power generation forecasting model, and simultaneously optimizing at least two objective functions: a first objective function aimed at stable power supply and a second objective function aimed at maximizing the surplus evaluation index R. This is an autonomous power generation type multi-system energy management and surplus control system.

15. An energy management system characterized in that the autonomous power generation type multi-system energy management and surplus control system described in claim 1 or 2 is installed in multiple buildings or facilities within a region, a central management server acquires the surplus evaluation index R and power generation / storage status from each system, and adjusts the operating conditions of each system based on the overall energy demand and subsidy budget status of the region, thereby optimizing energy autonomy and economic efficiency on a regional basis.

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