A photovoltaic all-electric kitchen and energy management optimization system

Abstract

The present invention provides a photovoltaic all-electric kitchen and energy management optimization system, which is composed of a photovoltaic cell, a main control device, a battery, an all-electric kitchen cooker mounted on an AC bus, and an all-electric kitchen water supply system. The main control device of the system uses STM32 as a controller, and an optimization management method is embedded in the STM32 controller. The method first summarizes the operating rules of the water supply system, analyzes the load flexibility and energy storage characteristics of the water supply system, and evaluates its energy storage potential; secondly, the water supply system is regarded as a virtual energy storage system, which together with the battery energy storage system constitutes a multi-source energy storage system; a digital twin model of photovoltaic power generation power prediction and kitchen load prediction is established, and the prediction accuracy is improved through virtual-real interaction; finally, under the premise of considering the classification of source-load-storage scenarios, a non-dominated sorting genetic algorithm is used to optimize the system model, with the goal of improving the economy and stability of the operation of the all-electric kitchen, an all-electric kitchen energy management model with the charging / discharging of the multi-source energy storage system as a scheduling means is established, and an intelligent search algorithm is used to solve the optimal scheduling scheme, which has the function of being incorporated into the local power grid when the power output is in excess. The present invention realizes the intercommunication of virtual and real data while ensuring the economical and stable operation of the all-electric kitchen.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic all-electric kitchens and energy management optimization, and in particular to a photovoltaic all-electric kitchen and energy management optimization technology. Background Art

[0002] As energy crisis and environmental pollution become increasingly serious, solar photovoltaic power generation, as a clean green energy, has become a hot topic of concern and research in countries around the world and has been gradually promoted to people's daily lives. The independent grid connection of distributed power stations has become the main trend of microgrid development.

[0003] Photovoltaic all-electric kitchen refers to a kitchen where all energy-consuming equipment in the kitchen abandons traditional fossil energy and uses photovoltaic power generation as the only energy source, which is capable of meeting all cooking methods and production needs. Its biggest advantage is energy saving, high efficiency, safety and reliability. The thermal efficiency of the electromagnetic cooker is as high as 90% or more, and the heating speed is fast. It is 30% to 60% more efficient than traditional gas cookers, reducing carbon emissions by 30% to 50%, and reducing energy consumption by 68% to 77%. At the same time, the all-electric kitchen uses electricity without open flames, precise temperature control, less oil smoke, and avoids safety hazards such as gas leakage and oil pan explosion. In the all-electric smart home system, short-term power outages of most household loads will not have a drastic impact on user comfort, and the energy consumption is relatively stable, which is an important part of the load scheduling of the power system. However, due to the frequent use and special status of the kitchen, the energy management optimization for kitchen equipment remains at a low level and cannot be scheduled through the existing direct load control. Reasonable optimization of photovoltaic all-electric kitchen energy management enables equipment to participate in demand-side load scheduling, which is of great significance to the development of "grid-user" interactive technology. Summary of the invention

[0004] The present invention provides a photovoltaic all-electric kitchen and energy management optimization system, which includes photovoltaic cells, storage batteries, water supply systems, cookers, and main control equipment with energy management functions, among which the water supply system mainly includes a water supply tank and a water supply treatment device; the energy management method in the main control equipment mainly includes a data processing module, a power prediction module, an optimization scheduling model module, a scheduling scheme generation module, and other parts. In the normal mode, the cooker and the water supply system do not work in staggered peaks. Whether each component operates depends on the current needs of the kitchen. During peak power consumption, the operation of the water supply system increases the pressure on the power supply. When the power production capacity is in excess, the electric energy can be fed back to the local power grid.

[0005] The present invention is achieved by the following technical solutions:

[0006] The photovoltaic all-electric kitchen and energy management optimization system uses STM32 as the main control, and establishes a digital twin model of photovoltaic power generation power, kitchen load, and charge state through the collected historical data, and then performs energy management optimization, so that the results can provide guidance for the operation of the all-electric kitchen at the physical level to ensure the stability and economy of the kitchen operation.

[0007] The optimization method of the photovoltaic all-electric kitchen and energy management optimization system comprises the following steps:

[0008] (1) Summarize the operation rules and energy storage characteristics of the water supply system and evaluate its energy storage potential;

[0009] (2) Establish digital twin models of photovoltaic power generation and batteries and complete model calibration;

[0010] (3) Calculate the cost function and power quality function, and construct the optimization constraints and objective function;

[0011] (4) Establish an all-electric kitchen energy management model in STM32 to provide guidance for physical kitchen operations;

[0012] The step (1) is based on the current system equipment configuration and operating parameters, and summarizes the working rules of the water supply system through historical operating data, including: the working energy consumption of the water supply treatment device and the water storage capacity of the water supply tank, so as to evaluate the energy storage potential of the water supply system. The decision variable is set to the working energy consumption of the water supply system at each moment, and the water supply system power is set to 0 at 7-8, 11-12, and 19-21 every day by default.

[0013] The step (2) performs digital modeling on the photovoltaic power generation and storage battery in the all-electric kitchen, and completes the mathematical equations describing the physical laws of each entity object. The output power model of the photovoltaic cell is:

[0014]

[0015] Where: P PV is the actual output power of the photovoltaic cell, G INC is the actual solar radiation intensity, G STC is the solar radiation intensity under standard test conditions, P STC is the maximum output power of the photovoltaic cell under standard test conditions, k is the power temperature coefficient, Tr is the rated temperature of the battery, and Te is the actual temperature of the battery. The state of charge is an important parameter of the battery, which indicates the ratio of the remaining capacity of the battery to its fully charged state capacity. The relationship between the state of charge and power of the energy storage battery is:

[0016]

[0017] Where: SOC i+1and SOC i are the state of charge of the energy storage battery at two adjacent moments, Cap bat is the total capacity of the energy storage battery. The output state of the device is collected through stm32, and the photovoltaic power generation and charge state are predicted through the prediction algorithm. The difference between the simulated state and the actual measurement is compared, and the difference between the two is minimized by adjusting the parameters of the digital twin model, thereby achieving the purpose of digital twin model parameter calibration.

[0018] The objective function construction process in step (3): Energy management optimization problems generally include stability objectives and economic objectives. The economic objective mainly reflects the power quality, that is, the minimum voltage deviation, while the economic objective includes the highest system economic cost. Therefore, the objective function expression of energy management optimization is

[0019]

[0020] In the formula: ΔU is the voltage deviation, C is the economic cost of the all-electric kitchen. In order to protect the battery and extend its service life, the battery cannot be over-discharged or over-charged, and the maximum and minimum state of charge constraints are:

[0021] SOC min ≤SOC≤SOC max (11)

[0022] Where: SOC min , SOC max Indicates the upper and lower limits of the battery's state of charge.

[0023] The step (4) embeds the all-electric kitchen energy management model into the STM32 controller, simulates the photovoltaic power generation power and charge state required for energy optimization, predicts and deduce to obtain the net water power and heating power of the water supply system at each moment corresponding to the minimum objective function value, and then feeds back the obtained information to the actual physical kitchen system through the hardware system, thereby guiding the efficient operation of the kitchen.

[0024] The beneficial effects of the present invention are as follows: comprehensively considering the above-mentioned all-electric kitchen component model, objective function, and constraint conditions, adopting a non-dominated sorting genetic algorithm, setting data such as the maximum number of iterations, population number, crossover rate, and mutation rate, and embedding the optimization algorithm of the STM32 controller to deduce and optimize the energy management optimization of the all-electric kitchen, guide the operation of the actual kitchen system, and realize the combination of virtual and real. At the same time, digital twins help to deal with the uncertainty in the energy management optimization of all-electric kitchens, improve the power supply quality of photovoltaic-powered all-electric kitchens, and improve the working efficiency and life of kitchen appliances. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The overall framework diagram of the present invention is

[0026] Figure 2 This is the hardware structure diagram of the main control equipment in the all-electric kitchen system

[0027] Figure 3 Schematic diagram of STM32 pins and internal control logic blocks

[0028] Figure 4 Flowchart of energy management optimization algorithm for all-electric kitchens DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] See also Figure 1 A photovoltaic all-electric kitchen and energy management optimization system, the system consists of photovoltaic cells, storage batteries, main control equipment, all-electric kitchen utensils, and all-electric kitchen water supply systems. The positive and negative poles of the storage battery and photovoltaic cell are connected to the positive and negative poles of the DC input side of the main control equipment, and the AC output side of the main control equipment is connected to the AC bus of the kitchen. All-electric kitchen utensils, all-electric kitchen water supply systems and other power equipment are connected to the AC bus, and one side of the AC bus provides an interface for connecting to the power grid. The power energy of the photovoltaic cell can be converted from DC to industrial frequency AC through the main control equipment and transmitted to the AC bus. The kitchen utensils and water supply system can be powered by the AC bus. When no electrical appliances are working and the power generation is in excess, the power can be fed back to the local power grid through the AC bus. The storage battery can supply DC power to the main control equipment on the one hand, and can be charged by the main control equipment on the other hand.

[0031] See also Figure 2The hardware topology structure of the main control device of a photovoltaic all-electric kitchen and energy management optimization system of the present invention mainly includes an STM32 controller, a drive module U1, a Boost boost circuit, a full-bridge inverter unit, an AD acquisition card and a filter; the Boost boost circuit, the full-bridge inverter unit and the filter together constitute an inverter; the AD acquisition card collects the voltage and current information required for energy management optimization, and its structure includes MCP6022 type voltage sensors V1~V3 respectively measuring the input and output voltages of the Boost boost circuit and the AC bus voltage, ACS712ELCTR-20A-1 type Hall current sensors A1~A3 measure the input and output currents of the Boost circuit and the AC bus current respectively; the low-voltage DC positive and negative poles of the Boost circuit serve as the DC input terminals of the main control device, and the high-voltage DC positive and negative poles are connected to the input terminals of the full-bridge inverter unit accordingly. Under the control of the STM32 controller, on the one hand, the photovoltaic cells and batteries provide DC voltage at maximum power, and on the other hand, the low-voltage DC is boosted to the high-voltage DC required for inverter; the output terminal of the full-bridge inverter unit is connected to the input terminal of the filter, and the high-voltage DC is converted into industrial frequency AC and sent to the AC bus through the filter for use in kitchen utensils.

[0032] See also Figure 3 The STM32 controller of a photovoltaic all-electric kitchen and energy management optimization system of the present invention selects Cortex-M3-STM32F103, and its pins GPIO_C1 to GPIO_C6 are connected to the input end of the driving module composed of MCP14E4, and the pins GPIO_A0 to GPIO_A15 are connected to the 16-bit data output end of the AD acquisition card; the output ends Q1 to Q6 of the driving module U1 are respectively connected to the gates of the IRFS4321 field effect tubes in the Boost boost circuit and the full-bridge inverter unit, and the Boost boost circuit and the field effect tubes in the full-bridge inverter unit are turned off and on by PWM signals; the STM32 controller is internally constructed by a program to generate four modules: data processing, optimization model, power prediction, and scheduling scheme.

[0033] See also Figure 4 The optimization algorithm flow of a photovoltaic all-electric kitchen and energy management optimization system of the present invention comprises the following steps (1) to (4):

[0034] Said Figure 4In step (1), based on the equipment composition and operating parameters of the previous system, the working rules of the water supply system are summarized through historical operating data, including: the energy consumption of the water supply treatment device and the water storage capacity of the water supply tank, so as to evaluate the energy storage potential of the water supply system. The decision variables are set as the water purification power and heating power of the water supply system at each moment. The default water supply system power is 0 at 7-8, 11-12, and 19-21 every day. The battery is connected to the water supply system. During the time period when the cooking equipment does not need to work, photovoltaic power generation first meets the needs of the water supply system, including the demand for purified water and hot water. The excess power is stored in the battery to reduce the pressure of photovoltaic power generation during peak power consumption.

[0035] Said Figure 4 In step (2), the photovoltaic power generation, kitchen load, and battery in the all-electric kitchen are digitally modeled to complete the mathematical equations describing the physical laws of each entity object. The output power model of the photovoltaic cell is:

[0036]

[0037] Where: P PV is the actual output power of the photovoltaic cell, G INC is the actual solar radiation intensity, G STC is the solar radiation intensity under standard test conditions, P STC is the maximum output power of the photovoltaic cell under standard test conditions, k is the power temperature coefficient, Tr is the rated temperature of the cell, and Te is the actual temperature of the cell. The load is calculated using the unit index method, and the load model is as follows:

[0038] P C =P e ×N (2)

[0039] In the formula: PC is the all-electric kitchen load, Pe is the unit power consumption index, and N is the number of people dining in the all-electric kitchen at the same time. The state of charge is an important parameter of the battery, which indicates the ratio of the remaining capacity of the battery to its fully charged state capacity. The relationship between the state of charge and power of the energy storage battery is:

[0040]

[0041] Where: SOC i+1 and SOC i are the state of charge of the energy storage battery at two adjacent moments, Cap batis the total capacity of the energy storage battery. For various devices or systems in the photovoltaic all-electric kitchen, although relevant mathematical models have been established according to their operating rules, there are a large number of random parameters in the model (such as power temperature coefficient, solar radiation intensity, etc.) that need to be determined. Setting these parameters through empirical data may cause the set parameters to be inconsistent with the actual parameters, which in turn leads to inaccurate dynamic models. The photovoltaic power generation power and state of charge are predicted through the prediction algorithm, and the difference between the simulated state and the actual measurement is compared. The difference between the two is minimized by adjusting the parameters of the digital twin model, thereby achieving the purpose of digital twin model parameter calibration.

[0042] Said Figure 4 In step (3), the economic cost function and the power quality function are calculated, the objective function of the simulation optimization is constructed, and the constraints of the optimization objective function are determined:

[0043] The objective function expression of optimal energy management optimization is:

[0044]

[0045] Where: C is the economic cost of the all-electric kitchen, ΔU is the voltage deviation. The economic goal is to minimize the economic cost of the photovoltaic all-electric kitchen. The economic cost function is as follows:

[0046] min(C)=C ZJ +C NH +C GS (5)

[0047] Where: C is the economic cost of photovoltaic all-electric kitchen, C ZJ , C NH , C GS They are the annual depreciation of kitchen equipment, annual energy consumption, and annual water supply costs. ZJ It is the annual average value of the initial fixed investment for building or renovating a kitchen minus the residual value, taking into account the time value of money. The project period is the service life of the kitchen equipment. The initial fixed investment includes the purchase cost, installation and commissioning cost, and other supporting equipment costs.

[0048]

[0049] Where: C in is the initial fixed investment, β is the residual value rate, i is the base discount rate, and n is the life of the kitchen. NH The cost of energy consumed by the kitchen for preparing meals in a year. The energy saving is the most obvious advantage of the all-electric kitchen over the traditional kitchen.

[0050] C NH =r×C per×d (7)

[0051] Where: r is the number of people received by the all-electric kitchen per day, C per is the energy cost per person per meal, and d is the number of days in a year when the all-electric kitchen is needed for reception.

[0052]

[0053] Where: C GS For the annual water supply system, C pri is the unit power cost of the water supply system, P m is the water supply system power at time m. The components of the all-electric kitchen are regarded as loads and are uniformly connected to the main line, that is, each node is connected to the load. The objective function of the voltage deviation is to minimize the sum of the voltage deviations of each node, and its expression is:

[0054]

[0055] Where: U i is the true value of the voltage at each node during the optimization process, U sta is the node standard voltage value, and n is the number of nodes. The power flow equation constraint in the all-electric kitchen equivalent node system is:

[0056]

[0057] Where: P is ,Q is is the active and reactive power of node i, U i is the voltage amplitude of node i, j is the node directly connected to node i, G ij ,B ij are the real and imaginary parts of the impedance, θ ij The premise of energy optimization is that the water supply of the water supply system meets the actual production needs, and the minimum working power constraint of the water supply system in one day is:

[0058]

[0059] Where: P m is the water supply system power at time m, P str To meet the power required for one day's water supply. In order to protect the battery and extend its service life, the battery cannot be over-discharged or over-charged. The maximum and minimum constraints of the state of charge are:

[0060] SOC min ≤SOC i ≤SOC max (12)

[0061] Where: SOC min , SOC maxRespectively represent the upper and lower limits of the battery's state of charge.

[0062] Said Figure 4 Step (4) completes the operation analysis. The real-time operation model is developed using the stm32 programming environment, which mainly includes the all-electric kitchen component model, objective function, and constraints. Digital twins are used to deal with uncertainties such as renewable energy generation in the energy management optimization of all-electric kitchens. The non-dominated sorting genetic algorithm is used to set the maximum number of iterations, population number, crossover rate, mutation rate and other data to perform deduction and optimization on the energy management optimization of all-electric kitchens, so as to obtain the water purification power and heating power of the water supply system at each moment corresponding to the minimum objective function value, thereby obtaining the optimal operation plan. The actual kitchen system interacts with the operation system to achieve virtual-real integration.

[0063] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0064] As described above, a photovoltaic all-electric kitchen and energy management optimization system proposed according to the present invention is described by way of example with reference to the accompanying drawings. However, those skilled in the art should understand that various improvements can be made to the implementation details of the photovoltaic all-electric kitchen and energy management optimization system proposed by the present invention without departing from the content of the present invention. Therefore, the protection scope of the present invention should be determined by the content of the attached claims.

Claims

1. A photovoltaic all-electric kitchen and energy management optimization system, characterized in that The system consists of photovoltaic cells, storage batteries, main control equipment, all-electric kitchen cookers, and all-electric kitchen water supply systems. The positive and negative poles of the storage batteries and photovoltaic cells are connected to the positive and negative poles of the DC input side of the main control equipment, and the AC output side of the main control equipment is connected to the AC bus of the kitchen. The power equipment of the all-electric kitchen cookers and the all-electric kitchen water supply system are connected to the AC bus, and one side of the AC bus provides an interface for connecting to the power grid. The STM32 controller has an internal energy management optimization method, which includes the following steps: (1) Summarize the operation rules and energy storage characteristics of the water supply system and evaluate its energy storage potential; (2) Establish digital twin models of photovoltaic power generation and batteries and complete model calibration; (3) Calculate the cost function and power quality function, and construct the optimization constraints and objective function; (4) Establish an all-electric kitchen energy management model in STM32 to provide guidance for physical kitchen operations; Step (1) based on the equipment composition and operating parameters of the current system, summarize the working rules of the water supply system through historical operating data, including: the working energy consumption of the water supply treatment device and the water storage capacity of the water supply tank, so as to evaluate the energy storage potential of the water supply system; Step (2) digitally modeling the photovoltaic power generation and battery in the all-electric kitchen, completing the mathematical equations describing the physical laws of each entity object, and predicting the photovoltaic power generation power and charge state through a prediction algorithm, comparing the difference between the simulated state and the actual measurement, and minimizing the difference between the two by adjusting the parameters of the digital twin model, thereby achieving the purpose of digital twin model parameter calibration; Step (3) includes power quality function and economic cost function. The objective function expression of energy management optimization is: Where: ΔU is the voltage deviation, C is the economic cost of the photovoltaic all-electric kitchen; the all-electric kitchen is equivalent to a node system, and the power flow equation constraint is: Where: P is ,Q is is the active and reactive power of node i, U i is the voltage amplitude of node i, j is the node directly connected to node i, G ij ,B ij are the real and imaginary parts of the impedance, θ ij is the phase angle difference; Step (4) embeds the all-electric kitchen energy management model into the STM32 controller, simulates the photovoltaic power generation and charge state required for energy optimization, predicts and deduce to obtain the net water power and heating power of the water supply system at each moment corresponding to the minimum objective function value, and then feeds back the obtained information to the actual physical kitchen system through the hardware system, thereby guiding the efficient operation of the kitchen.

2. A photovoltaic all-electric kitchen and energy management optimization system according to claim 1, characterized in that The main control device mainly includes an STM32 controller, a driver module U1, a Boost boost circuit, a full-bridge inverter unit, an AD acquisition card, and a filter; the STM32 controller model is Cortex-M3-STM32F103, and its pins GPIO_C1 to GPIO_C6 are connected to the input end of the driver module composed of MCP14E4, and the pins GPIO_A0 to GPIO_A15 are connected to the 16-bit data output end of the AD acquisition card; the output ends Q1 to Q6 of the driver module U1 are respectively connected to the Boost boost circuit and the IRFS4321 field effect tube in the full-bridge inverter unit. Gate; the positive and negative poles of the low-voltage DC of the Boost circuit serve as the DC input of the main control device, and the positive and negative poles of the high-voltage DC are connected to the input of the full-bridge inverter unit accordingly; the output of the full-bridge inverter unit is connected to the input of the filter; the output of the filter is connected to the AC bus as the output of the main control device; the AD acquisition card includes MCP6022 voltage sensors V1~V3 to measure the input and output voltages of the Boost circuit and the AC bus voltage respectively, and ACS712ELCTR-20A-1 Hall current sensors A1~A3 to measure the input and output currents of the Boost circuit and the AC bus current respectively.

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