A photovoltaic storage hierarchical collaborative optimization control method coupling photovoltaic preferential consumption and time-of-use electricity price

Abstract

The application discloses a kind of coupling photovoltaic priority consumption and time-of-use electricity price's light storage layered collaborative optimization control method, it is related to new energy power system optimization control technical field, the method includes: photovoltaic power generation forecast value, load power consumption forecast value and peak-valley electricity price information are used as input data, using model predictive control framework is carried out rolling optimization, generates light storage schedule;Based on the generated light storage schedule, in combination with the real-time acquisition photovoltaic power generation power and load power consumption power, calculate photovoltaic output regulation value and energy storage charge-discharge power, and using decision tree control mechanism executes corresponding energy storage charge-discharge and photovoltaic output processing, according to the processing result optimization light storage control.The application is reduced by executing photovoltaic priority consumption strategy, and light is reduced and the photovoltaic consumption rate is improved, in combination with time-of-use electricity price dynamic response mechanism and energy storage efficiency compensation and light penalty mechanism, while maximizing peak-valley arbitrage, reduce comprehensive power consumption cost, reduce grid power purchase demand.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy power system optimization control, in particular to a photovoltaic storage hierarchical collaborative optimization control method coupled with photovoltaic preferential consumption and time-of-use electricity price. BACKGROUND

[0002] Under the background of large-scale popularization and application of distributed photovoltaic power generation, the contradiction between the inherent intermittency and volatility of photovoltaic output and the randomness of power load demand is increasingly prominent, not only causing a large amount of photovoltaic output to be abandoned during the noon peak period, which seriously wastes renewable energy, but also increasing the pressure on grid peak regulation, which restricts the efficient use of distributed photovoltaic and the stable operation of the power grid.

[0003] The control strategy currently used by the photovoltaic storage system relies on static rules or simple electricity price response mechanisms, which has exposed obvious technical bottlenecks in actual application: first, the priority of photovoltaic consumption is not effectively guaranteed, and the consumption rate is generally low, with a serious problem of abandoned photovoltaic output during the noon period, which reduces the utilization rate of renewable energy and weakens the overall economy of the photovoltaic storage system; second, the coordination between the charging and discharging scheduling of the energy storage system and the time-of-use electricity price mechanism is insufficient, and the arbitrage space brought by the peak-valley electricity price difference cannot be fully tapped, which cannot effectively reduce the cost of purchasing electricity from the grid; third, the energy storage device frequently performs deep charging and discharging operations during operation, and lacks a targeted efficiency compensation mechanism, which accelerates the decay of the energy storage capacity, shortens the service life of the device, and increases the operation and maintenance cost.

[0004] Although the existing model predictive control (MPC) technology is applied to the multi-period optimization control of the photovoltaic storage system, the constraint processing capability of this technology has limitations and cannot meet the requirements of the mandatory logic of photovoltaic preferential consumption, the physical mutual exclusion constraint of energy storage charging and discharging, and the dynamic response demand of real-time electricity price, and cannot realize the collaborative optimization of the photovoltaic storage system under multiple targets such as consumption, economy, and device life.

[0005] At present, no effective solution has been proposed for the problems in the related art. SUMMARY

[0006] In view of the problems in the related art, the present application proposes a photovoltaic storage hierarchical collaborative optimization control method coupled with photovoltaic preferential consumption and time-of-use electricity price to overcome the above technical problems existing in the prior art.

[0007] To this end, the specific technical solutions adopted by the present application are as follows:

[0008] A photovoltaic storage hierarchical collaborative optimization control method coupled with photovoltaic preferential consumption and time-of-use electricity price, the method comprising:

[0009] S1, taking the photovoltaic power generation prediction value, the load power consumption prediction value and the peak-valley electricity price information as input data, using a model predictive control framework to perform rolling optimization to generate a photovoltaic storage schedule;

[0010] S2, based on the generated photovoltaic storage schedule, combining the real-time acquired photovoltaic power and load power, calculating the photovoltaic output adjustment value and the energy storage charging and discharging power, and using a decision tree control mechanism to execute corresponding energy storage charging and discharging and photovoltaic output processing, and optimizing photovoltaic storage control according to the processing result.

[0011] Further, taking the photovoltaic power generation prediction value, the load power consumption prediction value and the peak-valley electricity price information as input data, using a model predictive control framework to perform rolling optimization to generate a photovoltaic storage schedule includes:

[0012] S11, initializing energy storage system parameters and grid parameters, the energy storage system parameters including minimum capacity, maximum capacity, charging efficiency, discharging efficiency, maximum charging power and maximum discharging power, and the grid parameters including grid maximum power supply power threshold and grid minimum power supply power threshold;

[0013] S12, based on the initialization result, collecting meteorological data, historical photovoltaic power data and power consumption data, and generating a photovoltaic power generation prediction sequence according to photovoltaic module parameters, and using a load prediction algorithm to generate a load demand prediction sequence;

[0014] S13, generating an electricity price prediction sequence according to the change rule of the grid electricity price, and taking the photovoltaic power generation prediction sequence, the load demand prediction sequence and the electricity price prediction sequence as input data, and transmitting them to the control platform;

[0015] S14, the control platform receives the transmission data, uses a model predictive control framework to perform rolling optimization, obtains a charging and discharging plan, and formats the charging and discharging plan to generate a photovoltaic storage schedule.

[0016] Further, based on the initialization result, collecting meteorological data, historical photovoltaic power data and power consumption data, and generating a photovoltaic power generation prediction sequence according to photovoltaic module parameters, and using a load prediction algorithm to generate a load demand prediction sequence includes:

[0017] S121, based on the initialization result, collecting meteorological data and historical photovoltaic power data, and using a machine learning algorithm to establish an association model between the meteorological data and the historical photovoltaic power data;

[0018] S122, according to the photovoltaic module parameters, establishing a physical model, and fusing the output results of the association model and the physical model, constructing a hybrid prediction model through a fusion algorithm, and generating a photovoltaic power generation prediction sequence based on the hybrid prediction model;

[0019] S123、According to the collected power consumption data, statistics are carried out according to time units, and a load demand prediction sequence is generated through a load prediction algorithm.

[0020] Further, according to the collected power consumption data, statistics are carried out according to time units, and a load demand prediction sequence is generated through a load prediction algorithm, which includes:

[0021] S1231, according to the load data in the power consumption data, statistics are carried out according to time units, a historical load curve is generated, and the historical load curve is preprocessed;

[0022] S1232, based on the preprocessed historical load curve, a time series model is constructed by using a long short-term memory neural network, and the load behavior mode difference of each period under different scenes is identified, and the period mode of the load data is processed according to the identification result;

[0023] S1233, the collected meteorological data is taken as an input factor, combined with the load data, a load driving factor model is established;

[0024] S1234, the output results of the time series model, the period mode processing results and the output results of the load driving factor model are fused to generate a load demand prediction sequence.

[0025] Further, the control platform receives the transmission data, uses the model predictive control framework for rolling optimization, obtains the charging and discharging plan, and formats the charging and discharging plan to generate the light storage plan table, which includes:

[0026] S141, the control platform receives input data, and uses a model predictive control algorithm to construct a model predictive control framework;

[0027] S142, based on the constructed model predictive control framework, taking time units as granularity, combining the preset energy storage efficiency compensation and light abandonment penalty mechanism and constraint conditions, using a solver to perform rolling optimization on the input data;

[0028] S143, according to the rolling optimization result, the charging and discharging plan is obtained, which includes the energy storage charging and discharging plan, the photovoltaic consumption plan, the abandoned light power plan and the power grid purchase power plan;

[0029] S144, the obtained charging and discharging plan is formatted to generate a light storage plan table, which includes a charging and discharging time sequence table, a photovoltaic utilization plan table and a power purchase cost table.

[0030] Further, based on the generated light storage plan table, combining the real-time acquired photovoltaic power generation power and load power consumption power, the photovoltaic output adjustment value and the energy storage charging and discharging power are calculated, and the decision tree control mechanism is used to execute the corresponding energy storage charging and discharging and photovoltaic output processing, and the light storage control is optimized according to the processing result, which includes:

[0031] S21, based on the obtained light storage schedule, combining the preset energy storage system parameters and the real-time acquired photovoltaic power and load power, calculating the photovoltaic output adjustment value and the energy storage charging and discharging power;

[0032] S22, according to the calculated photovoltaic output adjustment value and the energy storage charging and discharging power, triggering the decision tree control mechanism, judging the relationship between the photovoltaic power and the load demand, and performing corresponding energy storage charging and discharging and photovoltaic output processing;

[0033] S23, according to the processing result, using the energy storage life extension mechanism and the real-time performance monitoring and correction mechanism to optimize the light storage control.

[0034] Further, according to the calculated photovoltaic output adjustment value and the energy storage charging and discharging power, triggering the decision tree control mechanism, judging the relationship between the photovoltaic power and the load demand, and performing corresponding energy storage charging and discharging and photovoltaic output processing includes:

[0035] When the photovoltaic power is greater than the load demand, the remaining photovoltaic power is calculated, and it is judged whether the energy storage system has charging conditions, and based on the judgment result, the energy storage system is charged or the light is abandoned;

[0036] When the photovoltaic power is less than the load demand, the shortage power of the photovoltaic power is calculated, and the time-of-use price dynamic response mechanism is used for charging and discharging processing combined with the power consumption period of the energy storage system, including peak period, flat period and valley period.

[0037] Further, when the photovoltaic power is greater than the load demand, the remaining photovoltaic power is calculated, and it is judged whether the energy storage system has charging conditions, and based on the judgment result, the energy storage system is charged or the light is abandoned, including:

[0038] When the state of charge of the energy storage system is less than the preset safe upper limit, the energy storage system has charging conditions, the remaining photovoltaic power is compared with the target charging power in the charging and discharging plan, if the remaining photovoltaic power is less than the target charging power, the energy storage system is charged according to the target charging power; if the remaining photovoltaic power is greater than the target charging power, the energy storage system is charged according to the preset safety threshold of the target charging power, and the remaining photovoltaic power is consumed, and the light is abandoned for the remaining photovoltaic power that cannot be consumed;

[0039] When the state of charge of the energy storage system is greater than the preset safe upper limit, the energy storage system does not have charging conditions, and the energy storage system enters standby state and abandons the remaining photovoltaic power.

[0040] Further, when the photovoltaic power is less than the load demand, the shortage power of the photovoltaic power is calculated, and combined with the power consumption period of the energy storage system, a time-of-use dynamic response mechanism is used for charging and discharging processing, and the power consumption period includes a peak period, a flat period and a valley period, including:

[0041] In the peak period, it is judged whether the energy storage system has a discharging condition, and corresponding charging and discharging processing is executed based on the judgment result;

[0042] In the flat period, the energy storage system charges and discharges according to the charging and discharging plan, and if there is still a shortage power after charging and discharging, the power grid supplies power;

[0043] In the valley period, if the charging and discharging plan is charging, the energy storage system charges according to the target charging power, and the grid power is less than the preset demand threshold, if the charging and discharging plan is discharging, the energy storage system discharges according to the minimum value of the target discharging power and the shortage power, and the grid supplies power to supplement the remaining shortage power.

[0044] Further, in the peak period, it is judged whether the energy storage system has a discharging condition, and corresponding charging and discharging processing is executed based on the judgment result, including:

[0045] When the state of charge of the energy storage system is greater than the preset lower limit of safety, the energy storage system has a discharging condition, the shortage power is compared with the target discharging power in the charging and discharging plan, if the shortage power is less than or equal to the target discharging power, the energy storage system discharges according to the shortage power, if the shortage power is greater than the target discharging power, the energy storage system discharges according to the maximum value of the full power and the shortage power, and if there is still a shortage power after charging and discharging, the power grid supplies power;

[0046] When the state of charge of the energy storage system is less than or equal to the preset lower limit of safety, the energy storage system does not have a discharging condition, the energy storage system enters a standby state, discharging is prohibited, and the power grid supplies power.

[0047] The beneficial effects of the present application are:

[0048] 1. The present application reduces the amount of abandoned light and improves the photovoltaic consumption rate by executing the photovoltaic preferential consumption strategy, reduces the comprehensive power consumption cost, reduces the power grid purchase demand, and enhances the system energy self-sufficiency by combining the time-of-use dynamic response mechanism and the energy storage efficiency compensation and abandoned light punishment mechanism while maximizing the peak-valley arbitrage.

[0049] 2. The present application suppresses the damage of deep charging and discharging to the energy storage system by using the charging and discharging optimization, real-time performance monitoring and correction mechanism, prolongs the service life of the system by combining the life extension mechanism, improves the response speed based on the model predictive control and decision tree mechanism, enhances the adaptability to photovoltaic fluctuations and load mutations, and reduces the operation and maintenance complexity. BRIEF DESCRIPTION OF DRAWINGS

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a flowchart of a photovoltaic-storage tiered collaborative optimization control method that couples photovoltaic priority consumption and time-of-use pricing according to an embodiment of the present invention;

[0052] Figure 2 This is a primary wiring diagram of a photovoltaic-storage-load stratified collaborative optimization control method for photovoltaic priority consumption and time-of-use pricing according to an embodiment of the present invention.

[0053] Figure 3 This is a flowchart of the hierarchical optimization control of a photovoltaic-storage hierarchical collaborative optimization control method that couples photovoltaic priority consumption and time-of-use pricing according to an embodiment of the present invention.

[0054] Figure 4 This is a schematic diagram of a real-time control decision tree for a photovoltaic-storage hierarchical collaborative optimization control method that couples photovoltaic priority consumption and time-of-use pricing according to an embodiment of the present invention.

[0055] Figure 5 This is a schematic diagram of the energy storage charge and discharge limit curve of a photovoltaic-storage hierarchical collaborative optimization control method that couples photovoltaic priority consumption and time-of-use pricing according to an embodiment of the present invention;

[0056] Figure 6 This is a functional module interaction diagram of a photovoltaic-storage hierarchical collaborative optimization control method that couples photovoltaic priority consumption and time-of-use pricing according to an embodiment of the present invention. Detailed Implementation

[0057] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention.

[0058] According to an embodiment of the present invention, a photovoltaic-storage tiered collaborative optimization control method that couples photovoltaic priority consumption and time-of-use pricing is provided.

[0059] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figures 1-2 As shown, the photovoltaic-storage tiered collaborative optimization control method according to an embodiment of the present invention, which couples photovoltaic priority consumption and time-of-use pricing, includes:

[0060] S1, taking the photovoltaic power generation prediction value, the load power consumption prediction value and the peak-valley electricity price information as input data, using a model predictive control framework to perform rolling optimization to generate a photovoltaic storage schedule.

[0061] In this optional embodiment, taking the photovoltaic power generation prediction value, the load power consumption prediction value and the peak-valley electricity price information as input data, using a model predictive control framework to perform rolling optimization to generate a photovoltaic storage schedule includes:

[0062] S11, initializing the energy storage system parameters and the grid parameters, the energy storage system parameters including the minimum capacity, the maximum capacity, the charging efficiency, the discharging efficiency, the maximum charging power and the maximum discharging power, and the grid parameters including the grid maximum power supply threshold and the grid minimum power supply threshold.

[0063] It should be noted that the minimum capacity Smin and the maximum capacity Smax of the energy storage system are set, wherein Smin = 10% Smax, to ensure that the energy storage system will not be damaged due to deep discharge; the charging efficiency ηcharge and the discharging efficiency ηdischarge of the energy storage are set to 0.94, indicating that there is a 6% energy loss in the charging and discharging process of the energy storage; the maximum charging power Pmax_charge and the maximum discharging power Pmax_discharge of the energy storage system are set to meet the charging and discharging requirements and hardware capabilities of the system; the grid maximum power supply threshold Gmax and the grid minimum power supply threshold Gmin, wherein the grid maximum power supply threshold limits the upper limit of the power taken by the user side to avoid over-demand, and the grid minimum power supply threshold (preferably 0) limits the lower limit of the power sent back to the grid by the user side to prevent reverse power flow.

[0064] S12, based on the initialization result, collecting meteorological data, historical photovoltaic power data and power consumption data, and generating a photovoltaic power generation prediction sequence according to the photovoltaic module parameters, and generating a load demand prediction sequence using a load prediction algorithm.

[0065] In this optional embodiment, based on the initialization result, collecting meteorological data, historical photovoltaic power data and power consumption data, and generating a photovoltaic power generation prediction sequence according to the photovoltaic module parameters, and generating a load demand prediction sequence using a load prediction algorithm includes:

[0066] S121, based on the initialization result, collecting meteorological data and historical photovoltaic power data, and using a machine learning algorithm to establish a correlation model between the meteorological data and the historical photovoltaic power data.

[0067] S122, according to the photovoltaic module parameters, establishing a physical model, and fusing the output results of the correlation model and the physical model, constructing a hybrid prediction model through a fusion algorithm, and generating a photovoltaic power generation prediction sequence based on the hybrid prediction model.

[0068] It needs to be noted that according to weather, season and other factors, the photovoltaic power generation prediction sequence [P1, P2, …, P24] (unit: kWh) of the next 24 hours is input to guide the energy storage charging and discharging decision, and the steps are as follows: 24 ] (unit: kWh), for guiding the energy storage charging and discharging decision, the steps are as follows:

[0069] 1. Meteorological data collection: Through accessing third-party meteorological platforms or installing local meteorological monitoring devices, auxiliary parameters such as solar radiation intensity, temperature, cloud cover percentage, humidity, wind speed, etc. are obtained.

[0070] 2. Historical photovoltaic output backtracking modeling: A historical power and meteorological data correlation model is established, and machine learning methods such as regression analysis, support vector machine or random forest are used to model the output response of photovoltaic components.

[0071] 3. PV component physical property modeling: According to component parameters such as conversion efficiency, inclination angle, area, etc., a physical model (such as PVWatts or PVsyst model) is established to calculate the ideal output under different meteorological conditions.

[0072] 4. Short-term prediction model fusion: The outputs of the physical model and the data-driven model (i.e. the historical power and meteorological data correlation model) are fused to form a hybrid prediction model; common methods include weighted average method, LSTM neural network, etc.

[0073] 5. Hourly prediction sequence generation: Perform hourly rolling prediction for the next 24 hours to obtain the photovoltaic power generation prediction sequence [P1, P2, …, P24].

[0074] S123, according to the collected electricity data, statistics are performed according to time units, and a load demand prediction sequence is generated through a load prediction algorithm.

[0075] In this optional embodiment, according to the collected electricity data, statistics are performed according to time units, and a load demand prediction sequence is generated through a load prediction algorithm, which includes:

[0076] S1231, according to the load data in the electricity data, statistics are performed according to time units, a historical load curve is generated, and the historical load curve is preprocessed;

[0077] S1232, based on the preprocessed historical load curve, a long short-term memory neural network is used to construct a time series model, and the load behavior mode difference of each period under different scenarios is identified, and the cycle mode of the load data is processed according to the identification result;

[0078] S1233, taking the collected meteorological data as input factors, a load driving factor model is established combined with the load data;

[0079] S1234, the output result of the fusion time series model, the output result of the periodic pattern processing result, and the output result of the load driving factor model are generated to generate a load demand prediction sequence.

[0080] It should be noted that according to the load prediction algorithm, a load demand sequence [L1, L2, …, L24] (unit: kWh) of 24 hours in the future is input for calculating the load gap and the energy storage adjustment strategy, and the steps are as follows: 24

[0081] 1. Load data acquisition and preprocessing: Collect the electricity data in the past 30 to 90 days through the energy management system or the smart meter; form the historical load curve by hourly statistics; perform outlier rejection and smoothing processing.

[0082] 2. Time series modeling: A long short-term memory neural network method is used to construct a time series model.

[0083] 3. Periodic behavior identification: Identify the load behavior differences of daily, weekend, holiday, etc. for industrial park / commercial building scenarios; encode or classify the periodic patterns to improve the prediction accuracy.

[0084] 4. Load driving factor modeling: Weather parameters (temperature, humidity), etc. are taken as input factors to establish a multivariate regression model with the load to improve the prediction accuracy.

[0085] 5. Output hourly prediction value: The future 24-hour load demand prediction sequence [L1, L2, …, L24] is generated by comprehensively outputting the results of the above models.

[0086] S13, according to the change rule of the power grid price, a price prediction sequence is generated, and the photovoltaic power generation prediction sequence, the load demand prediction sequence, and the price prediction sequence are taken as input data and transmitted to the control platform.

[0087] It should be noted that according to the change rule of the power grid price, a price sequence [price1, price2, …, price24] (unit: yuan / kWh) of 24 hours in the future is input for the price period optimization of the energy storage charging and discharging decision. 24

[0088] S14, the control platform receives the transmission data, performs rolling optimization using the model predictive control framework to obtain the charging and discharging plan, and performs format processing on the charging and discharging plan to generate a photovoltaic storage plan table.

[0089] In this optional embodiment, the control platform receives the transmission data, performs rolling optimization using the model predictive control framework to obtain the charging and discharging plan, and performs format processing on the charging and discharging plan to generate a photovoltaic storage plan table, which includes:

[0090] ​​S141, the control platform receives input data and constructs a model predictive control framework using a model predictive control algorithm.

[0091] S142, based on the constructed model predictive control framework, the input data is rolled and optimized in time units, combined with the preset energy storage efficiency compensation and light abandonment penalty mechanism and constraint conditions, and a solver is used.

[0092] It should be noted that the energy storage efficiency compensation and light abandonment penalty mechanism includes dynamically adjusting the light abandonment penalty coefficient, that is:

[0093]

[0094] In the formula, Penalty(t) represents the light abandonment penalty coefficient; η charge represents the energy storage charging efficiency; η discharge represents the energy storage discharging efficiency; δ t represents the dynamic compensation term based on the photovoltaic prediction error.

[0095] S143, according to the rolling optimization result, a charging and discharging plan is obtained, which includes an energy storage charging and discharging plan, a photovoltaic consumption plan, a light abandonment power plan and a power grid power purchase plan.

[0096] It should be noted that the energy storage charging and discharging plan includes outputting a sequence of energy storage charging and discharging capacity [charging and discharging capacity 1, charging and discharging capacity 2, …, charging and discharging capacity 24 ] (unit: kWh) in the next 24 hours; the photovoltaic consumption plan includes outputting a sequence of photovoltaic consumption capacity [photovoltaic consumption capacity 1, photovoltaic consumption capacity 2, …, photovoltaic consumption capacity 24 ] (unit: kWh) in the next 24 hours.

[0097] S144, the obtained charging and discharging plan is formatted to generate a photovoltaic storage plan table, which includes a charging and discharging time sequence table, a photovoltaic utilization plan table and a power purchase cost table.

[0098] It should be noted that the control platform receives transmission data, performs rolling optimization using the model predictive control framework, obtains the charging and discharging plan, and formats the charging and discharging plan to generate a photovoltaic storage plan table, which includes:

[0099] 1, the access and use position of the input data:

[0100] The photovoltaic power generation prediction data is input into the power balance constraint module of the control platform, which is used to calculate the available renewable energy in each period, and is used as a reference value for the light abandonment penalty item to guide the setting of the goal of local consumption of photovoltaic power; the load prediction data is input into the power demand side module as the basic load demand in each period, which is used to construct the key item L​t ; Electricity price data input to the optimization objective function module as the coefficient P of the grid electricity purchase cost term t , at the same time as the guiding factor of energy storage scheduling in the electricity price linkage adjustment module, adjust the discharge strategy to realize peak-valley arbitrage.

[0101] 2. Output data type and generation mechanism: The output data is generated by the model solver under the multi-constraint optimization framework.

[0102] (1) Energy storage charging and discharging plan:

[0103] Format: sequence [C1, C2,..., C 24 ], [D1, D2,..., D 24 ], unit: kWh;

[0104] Generation method: obtained by rolling optimization under multi-objective function (electricity purchase cost, curtailment penalty, SOC compensation) and constraint conditions by model predictive control (MPC) algorithm.

[0105] (2) Photovoltaic consumption plan:

[0106] Format: sequence [PV use,1 , PV use,2 ,..., PV use,24 ], unit: kWh;

[0107] Generation method: dynamically calculated according to photovoltaic prediction and curtailment amount, and matched with energy storage chargeable power and load gap.

[0108] (3) Curtailed photovoltaic power plan:

[0109] Optional output, representing the amount of energy [PV curtail,1 ,..., PV curtail,24 ] that cannot be consumed in each period due to full energy storage or low load.

[0110] (4) Grid electricity purchase plan:

[0111] Output sequence [G1,..., G 24 ], representing the energy purchased from the grid in each period, used for subsequent calculation of operation cost and implementation of scheduling strategy.

[0112] 3. Specific implementation steps:

[0113] (1) Data initialization: after system startup, import the next day's prediction data to the control platform automatically through API or data interface.

[0114] (2) Model loading and configuration: call the optimization module, load the MPC model structure, and configure optimization parameters such as objective function weight, SOC limit, electricity price model, etc.

[0115] (3) Rolling solution process: use the solver to perform 24-hour rolling optimization with hourly time granularity, and obtain the optimal charging power C of the energy storage in each time period t and discharging power D t ; actual photovoltaic utilization, calculated by electric quantity conservation; optimal power purchase, energy storage SOC curve, light abandonment, economic indicators, etc.

[0116] (4) Output plan generation: format the solution results into charge and discharge time sequence table, photovoltaic utilization plan table and power purchase cost table, and import them into database or control interface.

[0117] (5) Plan result publishing: distribute the generated plan data to the real-time control module for execution control; system display interface for visualization; historical data module for comparative analysis and closed-loop correction.

[0118] (6) Verification and fault tolerance processing: perform consistency verification on the output plan, and if conflicts such as charge and discharge exclusion, SOC out-of-bounds, and grid power over-limit are found, trigger the fault tolerance mechanism, and re-solve after adjusting the optimization weight.

[0119] 4. Rolling optimization and model predictive control framework:

[0120] (1) Under the model predictive control (MPC) framework, perform rolling optimization of the energy storage charging and discharging strategy for the next 24*N hours to minimize the overall power cost of the system, avoid the problem of light abandonment caused by excess photovoltaic power generation, and compensate for the final value of the energy storage capacity. The optimization objective is:

[0121] ;

[0122] where Gt represents the grid power purchase at time period t (unit: kWh), which is the amount of power obtained from the grid by the system; Pcurtail represents the light abandonment (unit: kWh), which is the amount of photovoltaic power generation that cannot be absorbed due to insufficient energy storage capacity; ST represents the energy storage capacity at time period T (unit: kWh), which represents the remaining amount of the energy storage system at the last time; S0 represents the initial energy storage capacity (unit: kWh), which is the initial amount of energy storage when the system starts; and the energy storage final value compensation coefficient is used to evaluate the value of the remaining energy storage and the difference in charging remaining energy.

[0123] (2) Introduction of charging and discharging efficiency: In the MPC optimization process, the impact of charging and discharging efficiency on the energy flow of the energy storage system must be considered. Specifically, the actual charging power of the energy storage system during charging is affected by the charging efficiency, while the discharging capacity of the energy storage system during discharging is affected by the discharging efficiency, which can be expressed as:

[0124] ;

[0125] In the formula, S t represents the energy storage capacity at time period t (unit: kWh); C t represents the energy storage charging power at time period t (unit: kWh); η charge represents the energy storage charging efficiency; D t represents the discharge amount at time period t (unit: kWh); η discharge represents the energy storage discharging efficiency.

[0126] (3) Constraint condition:

[0127] A, power balance constraint:

[0128] L t +C t =G t +P t +D t -PV curtail [t];

[0129] In the formula, G t represents the grid power consumption at time period t (unit: kWh); L t represents the load power consumption at time period t (unit: kWh); P t represents the photovoltaic power generation at time period t (unit: kWh); C t represents the energy storage charging power at time period t (unit: kWh); D t represents the energy storage discharging power at time period t (unit: kWh); PV curtail represents the photovoltaic power generation abandoned light at time period t (unit: kWh).

[0130] B, energy storage capacity dynamic constraint:

[0131] ;

[0132] In the formula, S t represents the energy storage capacity at time period t (unit: kWh); C t represents the energy storage charging power at time period t (unit: kWh); η charge represents the energy storage charging efficiency, preferably 0.94; D t represents the discharge amount at time period t (unit: kWh); η discharge represents the energy storage discharging efficiency, preferably 0.94.

[0133] C, charging and discharging exclusion constraint:

[0134] ;

[0135] ;

[0136] Ct= Pct- Pt-1 t represents the energy storage charging power (unit: kWh) of the time period t; P max_charge represents the maximum allowed charging power of the energy storage; y t represents a binary variable, y t = 1 represents charging, y t = 0 represents discharging; D t represents the energy storage discharging power (unit: kWh) of the time period t; P max_discharge represents the maximum allowed discharging power of the energy storage.

[0137] S2, based on the generated light storage schedule, combining the real-time acquired photovoltaic power and load power, calculating the photovoltaic output adjustment value and the energy storage charging and discharging power, and using the decision tree control mechanism to execute the corresponding energy storage charging and discharging and photovoltaic output processing, and optimizing the light storage control according to the processing result.

[0138] In this optional embodiment, based on the generated light storage schedule, combining the real-time acquired photovoltaic power and load power, calculating the photovoltaic output adjustment value and the energy storage charging and discharging power, and using the decision tree control mechanism to execute the corresponding energy storage charging and discharging and photovoltaic output processing, and optimizing the light storage control according to the processing result includes:

[0139] S21, based on the obtained light storage schedule, combining the preset energy storage system parameters and the real-time acquired photovoltaic power and load power, calculating the photovoltaic output adjustment value and the energy storage charging and discharging power.

[0140] It needs to be supplemented that, as Figure 3 shown, the energy storage system parameters include the demand threshold, the anti-backflow threshold, the energy storage capacity, the time-of-use electricity price, and the charging and discharging power threshold.

[0141] S22, according to the calculated photovoltaic output adjustment value and the energy storage charging and discharging power, triggering the decision tree control mechanism, judging the relationship between the photovoltaic power and the load demand, and executing the corresponding energy storage charging and discharging and photovoltaic output processing.

[0142] In this optional embodiment, according to the calculated photovoltaic output adjustment value and the energy storage charging and discharging power, triggering the decision tree control mechanism, judging the relationship between the photovoltaic power and the load demand, and executing the corresponding energy storage charging and discharging and photovoltaic output processing includes:

[0143] When the photovoltaic power is greater than the load demand, the remaining photovoltaic power is calculated, and it is judged whether the energy storage system has charging conditions, and based on the judgment result, the energy storage system is charged or the light is abandoned.

[0144] In the optional embodiment, when the photovoltaic power generation power is greater than the load demand, the residual photovoltaic power generation power is calculated, and it is judged whether the energy storage system has a charging condition, and the energy storage system is charged or the photovoltaic power is abandoned based on the judgment result, including:

[0145] When the state of charge of the energy storage system is less than the preset safe upper limit, the energy storage system has a charging condition, the residual photovoltaic power generation power is compared with the target charging power in the charging and discharging plan, if the residual photovoltaic power generation power is less than the target charging power, the energy storage system is charged according to the target charging power; if the residual photovoltaic power generation power is greater than the target charging power, the energy storage system is charged according to the preset safe threshold of the target charging power, and the residual photovoltaic power generation power is consumed, and the photovoltaic power that cannot be consumed is abandoned;

[0146] When the state of charge of the energy storage system is greater than the preset safe upper limit, the energy storage system does not have a charging condition, the energy storage system enters a standby state, and the residual photovoltaic power generation power is abandoned.

[0147] It should be noted that, as shown in Figure 4 When the photovoltaic power generation power is greater than the load demand, the energy storage can only be charged or standby, and is prohibited to be discharged; the residual photovoltaic power is preferentially used for charging, and the photovoltaic power that cannot be charged is abandoned, specifically including:

[0148] 1. Calculate the residual photovoltaic power (residual photovoltaic power = PV-Load), and judge whether the energy storage is allowed to be charged.

[0149] 2. If the energy storage is allowed to be charged, the SOC (i.e. state of charge) is less than the safe upper limit; if the residual power is less than the planned charging power: charge according to the planned charging power, the photovoltaic power provides part of the power, and the remaining part is provided by the power grid (planned charging power-residual power); if the residual power is greater than the planned charging power: charge according to the safe threshold of the energy storage planned charging power (the maximum of the planned charging power and the charging power safe threshold), the energy storage tries to consume the photovoltaic power generation as much as possible, and the photovoltaic power that cannot be consumed is abandoned (abandoned power = residual power-planned charging power), to avoid the energy storage sending power back.

[0150] 3. If the energy storage cannot be charged, the SOC of the energy storage is greater than the safe upper limit, the energy storage is standby, and all the residual power is abandoned.

[0151] When the photovoltaic power generation power is less than the load demand, the shortage power of the photovoltaic power generation power is calculated, and the dynamic response mechanism of time-of-use price is used for charging and discharging treatment in combination with the power consumption period in which the energy storage system is located, the power consumption period including a peak period, a flat period and a valley period.

[0152] In the optional embodiment, when the photovoltaic power generation power is less than the load demand, the shortage power of the photovoltaic power generation power is calculated, and the charging and discharging processing is performed by using the time-of-use electricity price dynamic response mechanism in combination with the power consumption period in which the energy storage system is located. The power consumption period includes a peak period, a flat period and a valley period.

[0153] In the peak period, it is judged whether the energy storage system has a discharging condition, and corresponding charging and discharging processing is performed based on the judgment result.

[0154] In the optional embodiment, in the peak period, it is judged whether the energy storage system has a discharging condition, and corresponding charging and discharging processing is performed based on the judgment result.

[0155] When the state of charge of the energy storage system is greater than the preset lower limit of safety, the energy storage system has a discharging condition. The shortage power is compared with the target discharging power in the charging and discharging plan. If the shortage power is less than or equal to the target discharging power, the energy storage system discharges according to the shortage power. If the shortage power is greater than the target discharging power, the energy storage system discharges according to the maximum value of the full power and the shortage power. After the charging and discharging, there is still a shortage power, which is supplemented by the power grid.

[0156] When the state of charge of the energy storage system is less than or equal to the preset lower limit of safety, the energy storage system does not have a discharging condition. The energy storage system enters a standby state, discharging is prohibited, and power supply is performed by the power grid.

[0157] In the flat period, the energy storage system discharges according to the charging and discharging plan. If there is still a shortage power after the charging and discharging, the power grid supplements the power supply.

[0158] In the valley period, if the charging and discharging plan is charging, the energy storage system charges according to the target charging power, and the power grid power is less than the preset demand threshold. If the charging and discharging plan is discharging, the energy storage system discharges according to the minimum value of the target discharging power and the shortage power, and the power grid supplies the remaining shortage power.

[0159] It needs to be noted that, as shown in Figure 4 The photovoltaic power generation power is less than the load demand. The energy storage system discharges to supplement the shortage. When the power is insufficient, the power grid supplies power. The discharging strategy is linked with the period (peak / flat / valley). The energy storage system can discharge in the peak period, the peak time arbitrage is maximized, and the energy storage system cannot excessively discharge in the flat and valley periods, resulting in no power to discharge in the peak period. Other conditions need to be met, including the demand constraint and the anti-flow constraint.

[0160] In addition, as shown in Figure 5 When the photovoltaic power generation power is less than the load demand, the shortage power of the photovoltaic power generation power is calculated, and the charging and discharging processing is performed by using the time-of-use electricity price dynamic response mechanism in combination with the power consumption period in which the energy storage system is located. The power consumption period includes a peak period, a flat period and a valley period.

[0161] 1. Calculate the power deficit (deficit power = Load - PV) and control the charging and discharging power according to the peak and valley periods.

[0162] 2. Peak Hours: Discharge is performed according to the planned discharge power deficit (the minimum of the planned discharge power and the deficit power). During peak hours, supplementary discharge can be performed to obtain additional revenue. The energy storage charging and discharging power must meet the discharge power constraint under the current SOC. Further judgment is made on whether the energy storage is allowed to discharge (i.e., SOC > safety lower limit). If the deficit power is less than or equal to the planned discharge power, the energy storage discharges according to the deficit power. If the deficit power is greater than the planned discharge power, the energy storage discharges according to the maximum value of the full power and the deficit power, and the insufficient part is supplied by the grid. If the energy storage cannot discharge (i.e., SOC ≤ safety lower limit), the grid supplies power directly, and the energy storage does not operate.

[0163] 3. Normal period: Charge and discharge according to plan, and the remaining power shortage is provided by the power grid.

[0164] 4. Off-peak hours: If charging is planned, charging will be carried out according to the planned charging power, while ensuring that the grid power is less than the demand threshold; if discharging is planned, the energy storage will discharge according to the planned discharge power and the minimum value of the shortfall power, and the insufficient part will be supplied by the grid.

[0165] During peak electricity price periods (i.e., when the β value is relatively large, β=1.5), energy storage is prioritized for discharge to maximize the release of stored energy; during off-peak electricity price periods (i.e., when the α value is relatively large, α=1.3), energy storage is prioritized for charging to enhance the charging strategy; during flat electricity price periods (i.e., when α=β=1.0), the charging and discharging strategy is executed according to the plan.

[0166] S23. Based on the processing results, optimize the photovoltaic-storage control by using the energy storage life extension mechanism and the real-time performance monitoring and correction mechanism.

[0167] It should be further explained that the energy storage lifespan extension mechanism: when the energy storage state of health (SOH) is less than 0.7, the charging and discharging power is limited to 60% of the rated value to prevent overcharging and discharging from shortening the equipment lifespan. The real-time performance monitoring and correction mechanism: monitors the health status of the energy storage system in real time and dynamically adjusts the charging and discharging strategy according to changes in energy storage capacity; based on changes in photovoltaic power generation, load demand, energy storage capacity, and electricity prices, it adjusts the grid power purchase and energy storage charging and discharging plans in real time.

[0168] In addition, such as Figure 6 As shown, a tiered collaborative optimization control method for photovoltaic and energy storage that couples photovoltaic priority consumption and time-of-use pricing includes the following two control steps:

[0169] The day-ahead planning layer uses the next day photovoltaic power generation prediction value, load power consumption prediction value and peak-valley electricity price, combines with constraint conditions, and generates the next day charging plan through a constraint solving algorithm; the real-time control layer uses a decision tree algorithm, combines with the peak-valley electricity price and the real-time value of the energy storage capacity, and adjusts and controls the energy storage charging and discharging power and the photovoltaic output in real time based on the next day photovoltaic actual power generation and the real-time value of the load.

[0170] In specific embodiments, it is assumed that the initial capacity of the energy storage is 77 kWh, the initial capacity of the energy storage is 30 kWh, the maximum capacity of the energy storage is 100 kWh, the charging efficiency is 0.94, the discharging efficiency is 0.94, the maximum charging power of the energy storage is 50 kW, and the maximum discharging power of the energy storage is 50 kW; according to the prediction values of the next day photovoltaic power generation and load power consumption shown in Table 1, the next day charging and discharging power plan is calculated, the charging and discharging plan table is obtained as shown in Table 2, and the charging and discharging plan is sent to the real-time control module.

[0171] Table 1: Prediction values of next day photovoltaic output and load power consumption

[0172] Period Photovoltaic (kWh) Load (kWh) Electricity price (yuan / kWh) 0-1 0 50 0.3 1-2 0 50 0.3 2-3 0 50 0.3 3-4 0 50 0.3 4-5 0 50 0.3 5-6 5 50 0.3 6-7 20 50 0.3 7-8 30 50 0.3 8-9 40 50 0.5 9-10 40 50 0.5 10-11 40 50 0.9 11-12 40 50 0.3 12-13 40 50 0.3 13-14 40 50 0.9 14-15 40 50 0.9 15-16 40 50 0.5 16-17 40 50 0.5 17-18 20 50 0.5 18-19 5 50 0.9 19-20 0 50 0.9 20-21 0 50 0.9 21-22 0 50 0.9 22-23 0 50 0.3 23-24 0 50 0.3

[0173] Table 2: Next day charging and discharging plan

[0174] Period Abandoned light (kWh) Charging (kWh) Discharging (kWh) Grid electricity (kWh) Electricity price (yuan / kWh) Grid cost (yuan) Decision 0-1 0 50 0 100 0.3 30 Charging 1-2 0 24.47 0 74.47 0.3 22.34 Charging 2-3 0 0 0 50 0.3 15 Standby 3-4 0 0 0 50 0.3 15 Standby 4-5 0 0 0 50 0.3 15 Standby 5-6 0 0 0 45 0.3 13.5 Standby 6-7 0 0 0 30 0.3 9 Standby 7-8 0 0 0 20 0.3 6 Standby 8-9 0 0 10 0 0.5 0 Discharging 9-10 0 0 10 0 0.5 0 Discharging 10-11 0 0 10 0 0.9 0 Discharging 11-12 0 33.95 0 43.95 0.3 13.19 Charging 12-13 0 0 0 10 0.3 3 Standby 13-14 0 0 10 0 0.9 0 Discharging 14-15 0 0 10 0 0.9 0 Discharging 15-16 0 22.63 0 32.63 0.5 16.32 Charging 16-17 0 0 0 10 0.5 5 Standby 17-18 0 0 0 30 0.5 15 Standby 18-19 0 0 45 0 0.9 0 Discharging 19-20 0 0 49 1 0.9 0.9 Discharging 20-21 0 0 0 50 0.9 45 Standby 21-22 0 0 0 50 0.9 45 Standby 22-23 0 0 0 50 0.3 15 Standby 23-24 0 0 0 50 0.3 15 Standby

[0175] The real-time control module calculates the charging and discharging power of the energy storage in real time according to the charging and discharging plan, and combines with the demand threshold, the anti-backflow threshold, the energy storage capacity, the time-of-use electricity price, the charging power threshold, and the real-time values of the load real-time power, the photovoltaic real-time output, the peak-valley period, etc.

[0176] Taking the actual operation of a certain industrial park on a certain day as an example: after the system is initialized, the predicted photovoltaic output (peak value of 0 kW in the afternoon), load demand (50 kW during the day and 30 kW at night), and time-of-use electricity price (peak time 0.9 yuan / kWh, valley time 0.3 yuan / kWh) are input; according to the real-time control decision tree, when the photovoltaic power generation (P t ) exceeds the load (L t ) (such as P t =80 kW, L t =50 kW at 12:00 P t M.), the photovoltaic excess branch of the decision tree is triggered preferentially, and 30 kW of excess photovoltaic is charged into the energy storage (if the energy storage is not full); when the photovoltaic is insufficient (such as P t =0 kW, L t =40 kW at 19:00 P t M.) and is in the peak electricity price period, the decision tree calls the electricity price linkage mechanism, preferentially releases the energy storage (such as discharging 20 kW), and only purchases 20 kW of electricity from the power grid to reduce the cost; the real-time monitoring module dynamically corrects the strategy according to the actual photovoltaic fluctuation (such as a 10% decrease in output due to cloud cover), forming a closed-loop optimization.

[0177] Another example scenario: a certain commercial building has a photovoltaic output of 60 kW at 14:00, and a load demand of 40 kW (P t ≥ L t ), first calculate the net excess power 20 kW, if the remaining capacity of the energy storage is 15 kWh (S max = 100 kWh), then the charging power C t = min (20 kW, 85 kW) = 20 kW, no light waste; if the energy storage is full (S t = 100 kWh), then trigger the light waste of 20 kW; on the contrary, at 18:00 peak electricity price period, photovoltaic output is 5 kW, load demand is 35 kW (P t < L t ), the lack of power is 30 kW, because β = 1.5 (peak time), preferentially discharge D t = min (current energy of energy storage 50 kWh, 30 kW) = 30 kW, and the grid purchase power G t = 0, significantly reducing the cost during the high electricity price period.

[0178] The economic benefits of the electricity price linkage mechanism are shown through 24-hour time sequence; taking a certain microgrid as an example: the system actively charges during the valley electricity price period (22:00-08:00, 11:00-13:00, electricity price 0.3 yuan / kWh, α = 1.3), for example, charging 30 kWh, and preferentially discharges during the peak electricity price period (10:00-11:00, 13:00-15:00, and 18:00-22:00, electricity price 0.9 yuan / kWh, β = 1.5), for example, releasing 30 kWh; through this strategy, 30 kWh of high-price electricity purchase is reduced during the peak time, saving 27 yuan, and only 9 yuan is consumed for low-price charging during the valley time, with a net arbitrage of 18 yuan; at the same time, the energy storage final value compensation model ensures the final capacity of the energy storage, for example, S t = 40 kWh at T time, close to the initial value (S0= 45 kWh), avoiding excessive consumption of the energy storage for arbitrage.

[0179] As shown in Table 3, the energy storage state of health (SOH) monitoring and life protection mechanism is shown; for example, a certain energy storage system has a rated power of 50 kW, and the initial SOH = 0.85, which decreases to 0.68 after long-term operation, lower than the threshold 0.7, the monitoring system immediately triggers the derating instruction, limiting the charging and discharging power to 30 kW (60% × 50 kW); in subsequent decision-making, the decision tree adjusts the strategy in combination with the constraint: for example, when the photovoltaic is excess, the charging power C t cannot exceed 30 kW, instead of the original 50 kW, avoiding high-power impact; when the photovoltaic is insufficient, the discharging power D t is also limited; this mechanism combines the charging and discharging loss quantification model (η charge × η discharge= 0.94 x 0.94 = 0.88), effectively reduces the number of deep charge and discharge times, prolongs the battery life by more than 20%.

[0180] Table 3: Energy storage charge and discharge limit table

[0181] Charging / discharging type Starting SOC (%) Termination SOC (%) Charging power limit (kW) Charging 0 80 150 Charging 80 95 50 Charging 95 100 0 Discharging 0 20 0 Discharging 20 100 150

[0182] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method of layered collaborative optimization control of photovoltaic preferential consumption and time-of-use electricity price coupling, characterized in that, The method comprises: S1, taking the photovoltaic power generation prediction value, the load power consumption prediction value and the peak-valley electricity price information as input data, using a model predictive control framework for rolling optimization to generate a photovoltaic storage schedule, specifically comprising: S11, initializing the energy storage system parameters and the grid parameters, the energy storage system parameters including minimum capacity, maximum capacity, charging efficiency, discharging efficiency, maximum charging power and maximum discharging power, and the grid parameters including the grid maximum power supply threshold and the grid minimum power supply threshold; S12, based on the initialization result, collecting meteorological data, historical photovoltaic power data and power consumption data, and generating a photovoltaic power generation prediction sequence according to photovoltaic module parameters, and using a load prediction algorithm to generate a load demand prediction sequence; S13, generating an electricity price prediction sequence according to the change rule of the grid electricity price, and taking the photovoltaic power generation prediction sequence, the load demand prediction sequence and the electricity price prediction sequence as input data and transmitting them to the control platform; S14, the control platform receives the transmission data, uses a model predictive control framework for rolling optimization to obtain a charging and discharging plan, and formats the charging and discharging plan to generate a photovoltaic storage schedule, specifically comprising: S141, the control platform receives the input data and uses a model predictive control algorithm to build a model predictive control framework; S142, based on the built model predictive control framework, taking time unit as granularity, combining the preset energy storage efficiency compensation and light rejection penalty mechanism and constraint conditions, using a solver to perform rolling optimization on the input data; S143, according to the rolling optimization result, obtaining the charging and discharging plan, the charging and discharging plan including the energy storage charging and discharging plan, the photovoltaic consumption plan, the light rejection power plan and the grid power purchase plan; S144, formatting the obtained charging and discharging plan to generate a photovoltaic storage schedule, the photovoltaic storage schedule including a charging and discharging time sequence table, a photovoltaic utilization plan table and a power purchase cost table; S2, based on the generated photovoltaic storage schedule, combining the real-time acquired photovoltaic power and load power, calculating the photovoltaic output adjustment value and the energy storage charging and discharging power, and using a decision tree control mechanism to execute corresponding energy storage charging and discharging and photovoltaic output processing, and optimizing the photovoltaic storage control according to the processing result. 2.The method of claim 1, wherein, Based on the initialization result, collecting meteorological data, historical photovoltaic power data and power consumption data, and generating a photovoltaic power generation prediction sequence according to photovoltaic module parameters, and using a load prediction algorithm to generate a load demand prediction sequence, comprising: S121, based on the initialization result, collecting meteorological data and historical photovoltaic power data, and using a machine learning algorithm to establish a correlation model between the meteorological data and the historical photovoltaic power data; S122, according to the photovoltaic module parameters, establishing a physical model, and fusing the output results of the correlation model and the physical model to construct a hybrid prediction model through a fusion algorithm, and generating a photovoltaic power generation prediction sequence based on the hybrid prediction model; S123, according to the collected power consumption data, performing statistics according to time unit, and generating a load demand prediction sequence through a load prediction algorithm. 3.The method of claim 2, wherein, According to the collected power consumption data, performing statistics according to time unit, and generating a load demand prediction sequence through a load prediction algorithm, comprising: S1231, according to the load data in the power consumption data, statistical analysis is carried out according to time unit, historical load curve is generated, and historical load curve is pretreated; S1232, based on the pretreated historical load curve, a time series model is constructed by using a long short-term memory neural network, and differences in load behavior patterns of each period in different scenarios are identified, and the cycle mode of the load data is processed according to the identification result; S1233, the collected meteorological data is taken as an input factor, and a load driving factor model is established in combination with the load data; S1234, the output results of the time series model, the cycle mode processing results and the output results of the load driving factor model are fused to generate a load demand prediction sequence. 4.The method of claim 1, wherein, The generated light storage schedule is combined with the real-time acquired photovoltaic power and load power to calculate the photovoltaic output adjustment value and the energy storage charging and discharging power, and a decision tree control mechanism is used to execute corresponding energy storage charging and discharging and photovoltaic output processing, and the light storage control is optimized according to the processing result, including: S21, based on the obtained light storage schedule, the preset energy storage system parameters and the real-time acquired photovoltaic power and load power, the photovoltaic output adjustment value and the energy storage charging and discharging power are calculated; S22, according to the calculated photovoltaic output adjustment value and energy storage charging and discharging power, the decision tree control mechanism is triggered, the relationship between the photovoltaic power and the load demand is judged, and the corresponding energy storage charging and discharging and photovoltaic output processing is executed; S23, according to the processing result, the energy storage life extension mechanism and the real-time performance monitoring and correction mechanism are used to optimize the light storage control.

5. The coupled photovoltaic preferential consumption and time-of-use electricity price photovoltaic storage hierarchical collaborative optimization control method according to claim 4, characterized in that, According to the calculated photovoltaic output adjustment value and energy storage charging and discharging power, the decision tree control mechanism is triggered, the relationship between the photovoltaic power and the load demand is judged, and the corresponding energy storage charging and discharging and photovoltaic output processing is executed, including: When the photovoltaic power is greater than the load demand, the residual photovoltaic power is calculated, and it is judged whether the energy storage system has charging conditions, and the energy storage system is charged or the light is abandoned based on the judgment result; When the photovoltaic power is less than the load demand, the shortage power of the photovoltaic power is calculated, and the time-of-use price dynamic response mechanism is used for charging and discharging processing in combination with the power consumption period of the energy storage system, including peak period, flat period and valley period.

6. The coupled photovoltaic preferential consumption and time-of-use electricity price photovoltaic storage hierarchical collaborative optimization control method according to claim 5, characterized in that, When the photovoltaic power is greater than the load demand, the residual photovoltaic power is calculated, and it is judged whether the energy storage system has charging conditions, and the energy storage system is charged or the light is abandoned based on the judgment result; When the state of charge of the energy storage system is less than the preset safe upper limit, the energy storage system has charging conditions, the residual photovoltaic power is compared with the target charging power in the charging and discharging plan, if the residual photovoltaic power is less than the target charging power, the energy storage system is charged according to the target charging power; if the residual photovoltaic power is greater than the target charging power, the energy storage system is charged according to the preset safe threshold of the target charging power, and the residual photovoltaic power is consumed, and the light of the residual photovoltaic power that cannot be consumed is abandoned; When the state of charge of the energy storage system is greater than the preset upper limit of safety, the energy storage system does not have charging conditions, the energy storage system enters a standby state, and the remaining photovoltaic power generation power is abandoned.

7. The coupled photovoltaic preferential consumption and time-of-use electricity price photovoltaic storage hierarchical collaborative optimization control method according to claim 6, characterized in that, When the photovoltaic power generation power is less than the load demand, the shortage power of the photovoltaic power generation power is calculated, and the charging and discharging process is performed by using a time-of-use dynamic response mechanism in combination with the electricity consumption period of the energy storage system, wherein the electricity consumption period includes a peak period, a flat period, and a valley period. In the peak period, it is judged whether the energy storage system has discharging conditions, and corresponding charging and discharging processes are performed based on the judgment result. In the flat period, the energy storage system charges and discharges according to the charging and discharging plan, and if there is still a shortage power after charging and discharging, the power supply is supplemented by the power grid. In the valley period, if the charging and discharging plan is charging, the energy storage system charges according to the target charging power, and the power grid power is less than the preset demand threshold, if the charging and discharging plan is discharging, the energy storage system discharges according to the minimum value of the target discharging power and the shortage power, and the remaining shortage power is supplemented by the power supply of the power grid.

8. The coupled photovoltaic preferential consumption and time-of-use electricity price photovoltaic storage hierarchical collaborative optimization control method according to claim 7, characterized in that, In the peak period, it is judged whether the energy storage system has discharging conditions, and corresponding charging and discharging processes are performed based on the judgment result. When the state of charge of the energy storage system is greater than the preset upper limit of safety, the energy storage system does not have charging conditions, the energy storage system enters a standby state, and the remaining photovoltaic power generation power is abandoned. When the state of charge of the energy storage system is greater than the preset upper limit of safety, the energy storage system does not have charging conditions, the energy storage system enters a standby state, and the remaining photovoltaic power generation power is abandoned.

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