Optical storage multi-mode control method

Abstract

The invention provides an optical storage multi-mode control method. The optical storage multi-mode control method comprises the following steps of 1, collecting multi-source data of photovoltaic output, energy storage SOC, load requirements and electricity price signals in real time; 2, dividing operation modes based on fuzzy logic, such as an economic mode, an emergency power supply mode and a peak value adjustment mode; 3, generating an optimal power distribution strategy in each mode through model predictive control (MPC); and step 4, designing a mode switching logic based on Lyapunov stability to realize non-impact transition, and relates to the technical field of photovoltaic power generation and energy storage system cooperative control, and through multi-source data fusion, fuzzy logic mode division, model prediction control and the Lyapunov stability switching logic, the mode switching logic based on the Lyapunov stability is realized. Self-adaptive efficient operation of the optical storage system under complex working conditions is achieved, the problems that a traditional control method is single in mode, lagged in response and low in energy utilization rate are solved, and the method is suitable for improving the economical efficiency and stability of the optical storage system.

Description

Technical Field

[0001] The present invention relates to the technical field of coordinated control of photovoltaic power generation and energy storage systems, and particularly to a multi-modal control method for photovoltaic and energy storage systems. Background Art

[0002] Existing photovoltaic power generation systems are affected by factors such as light intensity and weather, and have significant problems of intermittency and volatility. They need to operate in coordination with energy storage systems to suppress power fluctuations. Traditional control methods (such as constant power control, peak shaving and valley filling control, etc.) have the following defects:

[0003] Single mode: It can only achieve simple control in fixed scenarios and cannot adapt to complex working conditions such as grid electricity price fluctuations and load sudden changes;

[0004] Poor dynamic response: The adjustment speed for sudden changes in photovoltaic output or transient changes in load is slow, which easily leads to system power imbalance;

[0005] Low energy utilization rate: It does not fully combine electricity price signals and energy storage state (SOC) to optimize energy scheduling, resulting in photovoltaic power abandonment or overcharging / discharging of energy storage.

[0006] The above problems lead to insufficient operation economy of the photovoltaic and energy storage system and it is difficult to meet the requirements of the power grid for power supply stability. Summary of the Invention

[0007] In order to overcome the existing problems, the embodiments of the present application provide a multi-modal control method for photovoltaic and energy storage systems. Through multi-source data fusion, fuzzy logic modal division, model predictive control and Lyapunov stability switching logic, it realizes the adaptive and efficient operation of the photovoltaic and energy storage system under complex working conditions, solves the problems of single mode, response lag and low energy utilization rate of traditional control methods, and is applicable to improving the economy and stability of the photovoltaic and energy storage system.

[0008] The technical solution adopted by the embodiments of the present application to solve its technical problems is:

[0009] A multi-modal control method for photovoltaic and energy storage systems, the multi-modal control method for photovoltaic and energy storage systems includes the following steps:

[0010] Step 1: Real-time acquisition of multi-source data

[0011] Real-time obtain multi-dimensional data such as photovoltaic output power, energy storage battery SOC, load demand power, grid time-of-use electricity price signal, etc., to provide a basis for modal division and control strategy generation. The source data acquisition frequency is not less than 10 Hz, and it includes preprocessing of data filtering and noise reduction;

[0012] Among them, the output of the photovoltaic array (such as an average of 500 kW during the day, with a fluctuation of ±20%), the energy storage SOC (currently 60%), the real-time load (300 kW in the office area, 200 kW in the production line), and the electricity price signal (currently in the peak period, electricity price of 1.2 yuan / kWh) are obtained in real time through sensors;

[0013] Step 2: Operation mode division based on fuzzy logic

[0014] Construct a fuzzy logic controller, with the volatility of photovoltaic output, the energy storage SOC level, the load priority, and the peak-valley period of electricity price as inputs, and output operation modes such as economic mode, emergency power supply mode, peak regulation mode, etc., to achieve adaptive classification of working conditions. The operation modes include economic mode, emergency power supply mode, and peak regulation mode. The input variables of the mode division include the volatility of photovoltaic output, the energy storage SOC level, the load priority, and the peak-valley period of electricity price;

[0015] Among them, when the fuzzy logic controller determines it as the "peak regulation mode" (high electricity price + high load + medium SOC) according to the input data, it preferentially calls the energy storage to discharge to reduce the electricity purchase cost;

[0016] Step 3: Model Predictive Control (MPC) generates the optimal power distribution

[0017] For each operation mode, an optimization model including photovoltaic output prediction, load prediction, and energy storage charge and discharge constraints is established. Through the MPC algorithm, the optimal power distribution strategy among the photovoltaic array, the energy storage system, and the load is solved to maximize the photovoltaic accommodation or minimize the electricity consumption cost. The optimization objectives of the MPC algorithm include maximizing the photovoltaic accommodation amount or minimizing the user's electricity consumption cost, and the constraint conditions include the upper and lower limits of the energy storage SOC and the power limit of the converter;

[0018] Among them, the MPC algorithm calculates that the photovoltaic supplies full power (500 kW), the energy storage discharges 100 kW, and the insufficient part (100 kW) is supplemented by the power grid. At the same time, 20% of the energy storage capacity is reserved to cope with sudden loads;

[0019] Step 4: Lyapunov stability mode switching logic

[0020] Design a switching criterion based on the Lyapunov function to ensure that the system power fluctuation is less than the threshold during mode switching, and achieve shockless switching between modes through a smooth transition algorithm to improve the system stability. The shockless transition is achieved through a smooth transition algorithm to ensure that the system power fluctuation is less than 5% of the rated power during the switching process;

[0021] Among them, when the photovoltaic output at night is 0 and the electricity price enters the valley period (0.3 yuan / kWh), the Lyapunov logic trigger mode switches to the "economic mode", controlling the energy storage to charge with maximum efficiency, and the power grid supplies electricity in full to meet the load demand. During the switching process, the system power fluctuation is less than 3%.

[0022] Preferably, the process of dividing the fuzzy logic modes includes: constructing a fuzzy membership function, establishing a mode division rule base, and outputting the mode type through fuzzy inference synthesis operation.

[0023] Preferably, the trigger conditions of the mode switching logic include at least one of the SOC threshold, the electricity price period switching signal, and the load mutation signal.

[0024] Preferably, the fuzzy inference synthesis operation adopts the method, and the calculation formula is:

[0025] μ C (z) = ∨x∈X[μ A (x) ∧ μ B (y) ∧ μ R (x, y, z)]

[0026] Among them, μ A (x), μ B (y) are the membership functions of the input variables, μ R (x, y, z) is the membership relationship of the fuzzy rule, and μ C (z) is the membership function of the output mode.

[0027] Preferably, the SOC threshold includes the charging upper limit threshold SOC max (such as 90%) and the discharging lower limit threshold SOC min (such as 20%). When the measured SOC breaks through the threshold, the mode switching is triggered.

[0028] Preferably, the Lyapunov stability analysis is realized by constructing a positive definite function V(ΔP), satisfying:

[0029] V(ΔP) = ΔP T QΔP, V1(ΔP) < 0

[0030] Among them, ΔP is the system power deviation vector, and Q is a positive definite matrix to ensure that the system state is asymptotically stable during the mode switching process.

[0031] It includes a photovoltaic array module, an energy storage battery module, a bidirectional converter module, and a multi-mode controller module;

[0032] The photovoltaic array module converts solar energy into electrical energy and outputs it to the converter and the load end.

[0033] The energy storage battery module realizes charging and discharging through a bidirectional converter to suppress the fluctuation of photovoltaic power output.

[0034] The bidirectional converter module controls the bidirectional flow of energy, adjusts the power matching between the AC and DC sides. The response time of the bidirectional converter module is ≤100 ms, and the power regulation accuracy is ≤±2% of the rated power.

[0035] The multimodal controller module includes:

[0036] Data fusion unit: Collect and preprocess multi-source data to achieve data synchronization and noise reduction;

[0037] Decision-making unit: Execute fuzzy logic modal division and MPC power optimization calculation;

[0038] Switching execution unit: Trigger modal switching based on Lyapunov logic and output control instructions to the converter.

[0039] The advantages of the embodiments of this application are:

[0040] In the present invention, through multi-source data fusion, fuzzy logic modal division, model predictive control, and Lyapunov stability switching logic, the adaptive and efficient operation of the photovoltaic energy storage system under complex working conditions is realized, solving the problems of single control method mode, response lag, and low energy utilization rate in traditional control methods, and is applicable to improving the economy and stability of the photovoltaic energy storage system. Brief Description of the Drawings

[0041] Figure 1 It is a schematic flow chart of the photovoltaic energy storage multimodal control method of the present invention. Detailed Embodiments

[0042] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. In addition, for the convenience of description below, the "upper", "lower", "left", "right", etc. cited are the same as the upper, lower, left, right, etc. of the drawings themselves. The "first", "second", etc. in the following text are for descriptive distinction and have no other special meanings.

[0043] The embodiments of this application provide a photovoltaic energy storage multimodal control method to solve the problems in the prior art. Through multi-source data fusion, fuzzy logic modal division, model predictive control, and Lyapunov stability switching logic, the adaptive and efficient operation of the photovoltaic energy storage system under complex working conditions is realized, solving the problems of single control method mode, response lag, and low energy utilization rate in traditional control methods, and is applicable to improving the economy and stability of the photovoltaic energy storage system.

[0044] The technical solutions in the embodiments of the present application to solve the above problems are generally as follows:

[0045] Embodiment

[0046] This embodiment provides a photovoltaic-storage multimodal control method. As Figure 1 shown, the photovoltaic-storage multimodal control method includes the following steps:

[0047] Step 1: Real-time acquisition of multi-source data

[0048] Real-time obtain multi-dimensional data such as photovoltaic output power, energy storage battery SOC, load demand power, grid time-of-use electricity price signal, etc., to provide a basis for mode division and control strategy generation. The source data acquisition frequency is not less than 10 Hz, and includes preprocessing of data filtering and noise reduction;

[0049] Among them, the photovoltaic array output (such as an average of 500 kW during the day, with a fluctuation of ±20%), energy storage SOC (currently 60%), real-time load (300 kW in the office area, 200 kW in the production line), and electricity price signal (currently in the peak period, electricity price 1.2 yuan / kWh) are obtained in real time through sensors;

[0050] Step 2: Operation mode division based on fuzzy logic

[0051] Build a fuzzy logic controller, with the volatility of photovoltaic output, energy storage SOC level, load priority, and electricity price peak-valley period as inputs, and output operation modes such as economic mode, emergency power supply mode, peak regulation mode, etc., to achieve adaptive classification of working conditions. The operation modes include economic mode, emergency power supply mode, and peak regulation mode. The input quantities for mode division include the volatility of photovoltaic output, energy storage SOC level, load priority, and electricity price peak-valley period;

[0052] Among them, the fuzzy logic controller determines it as the "peak regulation mode" (high electricity price + high load + medium SOC) according to the input data, and preferentially calls the energy storage to discharge to reduce the electricity purchase cost;

[0053] Step 3: Model Predictive Control (MPC) to generate the optimal power distribution

[0054] For each operation mode, establish an optimization model including photovoltaic output prediction, load prediction, and energy storage charge and discharge constraints, and solve the optimal power distribution strategy among the photovoltaic array, energy storage system, and load through the MPC algorithm to maximize photovoltaic consumption or minimize electricity consumption cost. The optimization objectives of the MPC algorithm include maximizing the photovoltaic consumption or minimizing the user's electricity consumption cost, and the constraint conditions include the upper and lower limits of the energy storage SOC and the power limit of the converter;

[0055] Among them, the MPC algorithm calculates that the photovoltaic system supplies full power (500 kW), the energy storage discharges 100 kW, and the insufficient part (100 kW) is supplemented by the power grid. At the same time, 20% of the energy storage capacity is reserved to cope with sudden loads;

[0056] Step 4: Lyapunov stability mode switching logic

[0057] Design a switching criterion based on the Lyapunov function to ensure that the system power fluctuation is less than the threshold during mode switching. Achieve shockless switching between modes through a smooth transition algorithm to improve system stability. The shockless transition is realized through a smooth transition algorithm to ensure that the system power fluctuation is less than 5% of the rated power during the switching process;

[0058] Among them, when the photovoltaic output is 0 at night and the electricity price enters the valley period (0.3 yuan / kWh), the Lyapunov logic triggers the mode to switch to the "economic mode", controls the energy storage to charge with the maximum efficiency, and the power grid supplies full power to meet the load demand. The system power fluctuation is less than 3% during the switching process.

[0059] The process of dividing modes by fuzzy logic includes: constructing a fuzzy membership function, establishing a mode division rule base, and outputting the mode type through fuzzy inference synthesis operation.

[0060] The triggering conditions of the mode switching logic include at least one of the SOC threshold, the electricity price period switching signal, and the load mutation signal.

[0061] The fuzzy inference synthesis operation adopts a method, and the calculation formula is:

[0062] μ C (z) = ∨x∈X[μ A (x) ∧ μ B (y) ∧ μ R (x, y, z)]

[0063] Among them, μ A (x), μ B (y) are the membership functions of the input variables, μ R (x, y, z) is the membership relationship of the fuzzy rule, and μ C (z) is the membership function of the output mode.

[0064] The SOC threshold includes the charging upper limit threshold SOC max (such as 90%) and the discharging lower limit threshold SOC min (such as 20%). When the measured SOC breaks through the threshold, the mode switching is triggered.

[0065] The Lyapunov stability analysis is realized by constructing a positive definite function V(ΔP), which satisfies:

[0066] V(ΔP) = ΔPT QΔP, V1(ΔP) < 0

[0067] Where ΔP is the system power deviation vector, and Q is a positive definite matrix to ensure the asymptotic stability of the system state during the mode switching process.

[0068] It includes a photovoltaic array module, an energy storage battery module, a bidirectional converter module, and a multi-modal controller module;

[0069] The photovoltaic array module converts solar energy into electrical energy and outputs it to the converter and the load end.

[0070] The energy storage battery module realizes charge and discharge through a bidirectional converter to suppress the fluctuation of photovoltaic output.

[0071] The bidirectional converter module controls the bidirectional flow of energy, regulates the power matching between the AC and DC sides. The response time of the bidirectional converter module ≤ 100 ms, and the power regulation accuracy ≤ ±2% of the rated power.

[0072] The multi-modal controller module includes:

[0073] Data fusion unit: Collect and preprocess multi-source data to achieve data synchronization and noise reduction;

[0074] Decision-making unit: Execute fuzzy logic mode partitioning and MPC power optimization calculation;

[0075] Switching execution unit: Trigger mode switching based on Lyapunov logic and output control instructions to the converter.

[0076] By adopting the above technical solutions:

[0077] Residential user photovoltaic energy storage system (mainly in economic mode)

[0078] Scenario background

[0079] Photovoltaic array capacity: 10 kWp, daily power generation: 40 kWh;

[0080] Energy storage configuration: 15 kWh lithium battery, initial SOC value: 70%;

[0081] Load characteristics: Low load during the day (lighting, home appliances on standby, about 2 kW), high load at night (air conditioner, water heater, about 5 kW);

[0082] Time-of-use electricity price: Peak period (17:00 - 22:00, 1.0 yuan / kWh), valley period (23:00 - 7:00 the next day, 0.3 yuan / kWh).

[0083] Implementation steps

[0084] Data collection

[0085] Real-time monitoring:

[0086] Photovoltaic output: The average is 8 kW from 9:00 to 15:00 in the morning and drops to 5 kW at 16:00 in the afternoon;

[0087] Energy storage SOC: Currently 70% (remaining 10.5 kWh);

[0088] Load power: The average is 2 kW during the day and is expected to be 5 kW at night;

[0089] Electricity price signal: Currently in the valley period (0.3 yuan / kWh), entering the peak period at 17:00.

[0090] 2. Mode division

[0091] Fuzzy logic input:

[0092] Volatility of photovoltaic output: Low (stable irradiation in the morning);

[0093] Energy storage SOC: Medium to high level (70%);

[0094] Load priority: Low (non-critical load);

[0095] Electricity price period: Valley period (low electricity price).

[0096] Output mode: Economic mode (prioritize charging with low-price grid electricity and maximize self-use of photovoltaic power).

[0097] 3. Power distribution

[0098] Optimization goal: Minimize the user's electricity cost and avoid purchasing electricity during the peak period.

[0099] Strategy execution:

[0100] Valley period (current):

[0101] Photovoltaic output is 8 kW. After meeting the current load of 2 kW, the remaining 6 kW is used to charge the energy storage (it takes about 0.75 hours to reach SOC 100%);

[0102] After the energy storage is full, the excess photovoltaic power (8 - 2 = 6 kW) is fed back to the grid (assuming surplus electricity can be sold back to the grid at a price of 0.4 yuan / kWh).

[0103] Peak period (after 17:00):

[0104] Photovoltaic output drops to 5 kW, load is 5 kW, and the photovoltaic power supplies the full load;

[0105] The energy storage discharges to supplement the night load (for example, at 20:00, the load increases to 5 kW, photovoltaic output is 0, and the energy storage discharges at 5 kW until the lower limit of SOC 20%, lasting for 3 hours);

[0106] Avoid purchasing electricity from the power grid (high peak electricity price).

[0107] 4. Mode Switching

[0108] Trigger condition: The electricity price enters the peak period at 17:00 and the energy storage SOC ≥ 30%.

[0109] Switching process:

[0110] Start the mode switching logic 5 minutes in advance, gradually reduce the energy storage charging power, and increase the discharge preparation;

[0111] Instantaneous power fluctuation during switching: Photovoltaic output 5kW → Load 5kW, energy storage discharge 0 → 5kW, system power difference controlled within ±0.5kW (<5% of the rated power), voltage fluctuation <2%, meeting the stable operation requirements of household appliances.

[0112] Implementation effect

[0113] Economy: Completely use photovoltaic + energy storage power supply during peak periods, with the daily average electricity purchase cost reduced by 60% (compared with traditional constant power control);

[0114] Energy utilization rate: The photovoltaic curtailment rate is reduced from 15% to 3%, and the energy storage charge-discharge efficiency is increased to 95%;

[0115] Stability: The mode switching is imperceptible, and household appliances (such as air conditioners) do not show abnormal start-stop.

[0116] Finally, it should be noted that: Obviously, the above embodiments are only examples for clearly explaining the present invention, rather than limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation manners here. And the obvious changes or modifications derived therefrom are still within the protection scope of the present invention.

Claims

1. A photovoltaic and energy storage multimodal control method, characterized in that, The described photovoltaic-storage multimodal control method includes the following steps: Step 1: Collect multi-source data of photovoltaic output, energy storage SOC, load demand, and electricity price signals in real time; Step 2: Divide the operation modes based on fuzzy logic, such as economic mode, emergency power supply mode, and peak regulation mode; Step 3: Generate the optimal power distribution strategy in each mode through model predictive control (MPC); Step 4: Design a mode switching logic based on Lyapunov stability to achieve shock-free transition.

2. The multimodal control method for optical storage according to claim 1, characterized in that, The operation modes in Step 2 include economic mode, emergency power supply mode, and peak regulation mode. The input variables for mode division include photovoltaic output volatility, energy storage SOC level, load priority, and electricity price peak-valley period.

3. The multimodal control method for optical storage as claimed in claim 1, wherein, The process of dividing modes by fuzzy logic includes: constructing a fuzzy membership function, establishing a mode division rule base, and outputting the mode type through fuzzy inference synthesis operation.

4. The multimodal control method for optical storage according to claim 1, wherein, The optimization objectives of the MPC algorithm in Step 3 include maximizing photovoltaic accommodation or minimizing the user's electricity cost. The constraint conditions include the upper and lower limits of energy storage SOC and the power limit of the converter.

5. The multimodal control method for optical storage according to claim 3, characterized in that, The triggering conditions of the mode switching logic include at least one of the SOC threshold, electricity price period switching signal, and load mutation signal.

6. The multimodal control method for optical storage according to claim 1, wherein The shock-free transition in Step 4 is achieved through a smooth transition algorithm to ensure that the system power fluctuation during the switching process is less than 5% of the rated power.

7. The optical storage multimodal control method according to claim 3, characterized in that The fuzzy inference synthesis operation adopts a method, and the calculation formula is: μ C μ(z) = ∨x∈X [μ A (x) ∧ μ B (y) ∧ μ R (x, y, z)] where, μ A (x), μ B (y) are membership functions of input variables, μ R (x, y, z) is the membership relation of fuzzy rules, and μ C (z) is the membership function of the output modality.

8. The optical storage multimodal control method according to claim 5, wherein The SOC threshold includes a charging upper limit threshold SOC max (such as 90%) and a discharging lower limit threshold SOC min (such as 20%). When the measured SOC breaks through the threshold, mode switching is triggered.

9. A photovoltaic energy storage multimodal control method according to claim 1, characterized in that The Lyapunov stability analysis is realized by constructing a positive definite function V(ΔP), satisfying: V(ΔP) = ΔP T QΔP, V1(ΔP) < 0 where ΔP is the system power deviation vector and Q is a positive definite matrix to ensure the asymptotic stability of the system state during mode switching.

10. A photovoltaic and energy storage multimodal control system, characterized in that, It includes a photovoltaic array module, an energy storage battery module, a bidirectional converter module, and a multimodal controller module; The photovoltaic array module converts solar energy into electrical energy and outputs it to the converter and the load end. The energy storage battery module realizes charge and discharge through a bidirectional converter to suppress the fluctuation of photovoltaic output. The bidirectional converter module controls the bidirectional flow of energy and adjusts the power matching of the AC and DC sides. The multimodal controller module includes: Data fusion unit: Collect and preprocess multi-source data to achieve data synchronization and noise reduction; Decision unit: Execute fuzzy logic mode division and MPC power optimization calculation; Switching execution unit: Trigger mode switching based on Lyapunov logic and output control instructions to the converter.

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