A method and system for coordinated control of photovoltaic storage and charging based on multi-energy complementation

Abstract

This invention proposes a photovoltaic-storage-charging coordinated control method and system based on multi-energy complementarity. The method includes the following sub-steps: establishing a photovoltaic power generation model, an energy storage system model, and a charging load model respectively; acquiring historical solar irradiance information, charging information, and electricity price information, and constructing a prediction model based on a neural network to predict and output the solar irradiance, charging load demand power, and electricity price for future periods; constructing an objective optimization function with annual net cost and power deviation rate as optimization objectives; formulating a photovoltaic-storage-charging coordinated control strategy, using an improved whale optimization algorithm to solve the objective optimization function, generating a charging and discharging power sequence based on the obtained optimal solution, and inputting it into the system for execution. This method achieves coordinated optimization control of photovoltaic power generation, energy storage, and charging systems, reduces annual net cost, reduces power deviation, and improves the overall system operating efficiency and reliability.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic-storage-charging coordinated control technology, and in particular to a photovoltaic-storage-charging coordinated control method and system based on multi-energy complementarity. Background Technology

[0002] The rapid growth in the number of new energy vehicles has made the charging problem increasingly prominent. In essence, this is a contradiction between the insufficient power distribution capacity of charging stations and the ever-increasing demand for charging. Introducing energy storage units, utilizing photovoltaic power generation and self-consumption, and coordinating energy transfer and load dispatch can increase power capacity during peak electricity consumption periods. This is undoubtedly a key way to solve the charging problem in hot cities. The combination of energy storage, photovoltaics, and charging stations is called a photovoltaic-storage-charging integrated system.

[0003] A multi-energy coupled complementary photovoltaic-storage-charging management system, disclosed in CN119726855A, includes a photovoltaic power generation module, an energy storage module including a lithium battery pack, a grid input terminal, an energy conversion module for converting photovoltaic power into storable electrical energy, an energy storage management module for monitoring and controlling the charging and discharging process of the energy storage device, an energy dispatch module for allocating power from the photovoltaic power generation, energy storage module, and grid input through a multi-energy coupling algorithm, a charging management module for controlling the stability of the output power, system control software integrating a multi-energy complementary control model and energy efficiency optimization algorithm, and a data acquisition module for real-time acquisition of photovoltaic power generation, energy storage status, grid power supply, and load demand data.

[0004] Existing supplemental photovoltaic and energy storage systems employ traditional multi-energy coupling algorithms, which suffer from insufficient global search capabilities, susceptibility to local optima, and slow convergence speed. This results in insufficient energy dispatch accuracy and a lack of dynamic SOC threshold adaptive adjustment mechanisms and load priority management strategies. Consequently, batteries are overcharged and over-discharged, leading to accelerated lifespan degradation and limited renewable energy absorption, thus reducing the overall system's operating efficiency and reliability. Summary of the Invention

[0005] In view of this, the present invention proposes a photovoltaic-storage-charging coordinated control method and system based on multi-energy complementarity. By constructing an objective function with annual net cost and power deviation rate as optimization objectives, and by adopting an improved whale optimization algorithm to enhance global optimization capability, combined with neural network prediction and dynamic SOC management, the coordinated optimization control of photovoltaic power generation, energy storage and charging system is realized, which reduces annual net cost, reduces power deviation and improves the overall system operating efficiency and reliability.

[0006] The technical solution of the present invention is implemented as follows: In a first aspect, the present invention provides a method for coordinated control of photovoltaic storage and charging based on multi-energy complementarity, the method comprising the following sub-steps:

[0007] S1. Establish corresponding photovoltaic power generation models, energy storage system models, and charging load models based on photovoltaic power generation systems, energy storage systems, and charging systems, respectively.

[0008] S2 acquires historical lighting information, historical charging information, and historical electricity price information, and builds a prediction model based on a neural network to predict and output the lighting intensity, charging load demand power, and electricity price for future periods, respectively.

[0009] S3 uses annual net cost and power deviation rate as optimization objectives and employs a linear weighting method to construct the objective optimization function.

[0010] S4. Based on the predicted future electricity price, charging load demand power and light intensity data, a photovoltaic-storage-charging coordinated control strategy is formulated. The predicted data is used as the input of the control strategy. An improved whale optimization algorithm is used to solve the objective optimization function. The optimal solution is used to generate a charging and discharging power sequence and input it into the system for execution.

[0011] Based on the above technical solutions, preferably, in step S1, corresponding photovoltaic power generation models, energy storage system models, and charging load models are established according to the photovoltaic power generation system, energy storage system, and charging system, respectively.

[0012] The photovoltaic power generation model expression is:

[0013] P PV (t)=η PV (t)·A PV ·G(t)·[1-α(T(t))·(T(t)-T ref )]·f s (t)

[0014] In the formula, η PV η is the photoelectric conversion efficiency coefficient. PV (t)=η PV，0 ·e -λt λ is the attenuation rate, η PV，0 A represents the initial value of the photoelectric conversion efficiency coefficient. PV Let G(t) be the total area of ​​the photovoltaic panel, G(t) be the light intensity at time t, and T(t) be the ambient temperature at time t. ref The reference temperature for photovoltaic panel efficiency testing is α, where α is the temperature coefficient and P is the reference temperature. PV (t) represents the photovoltaic output power;

[0015]

[0016] In the formula, f s (t) is the local shading correction factor at time t, S i (t) represents the area ratio of the i-th occluded region at time t, and k iThis is the occlusion coefficient;

[0017] The energy storage system model expression is:

[0018]

[0019] In the formula, SOC(t) represents the state of charge of the energy storage battery at time t, and η ch For the charging efficiency of energy storage batteries, η dis For the discharge efficiency of energy storage batteries, E max P represents the total capacity of the energy storage battery, Δt is the time interval, and P is the total capacity of the energy storage battery. ch (t) represents the charging power of the energy storage battery at time t, P dis (t) represents the discharge power of the energy storage battery at time t. This represents the maximum charging power of the energy storage battery. The maximum discharge power of the energy storage battery, SOC min The SOC (State of Charge) is the dynamic threshold that limits the state of charge of an energy storage battery. min =SOC min，0 +ΔSOC SOH (t), SOC min，0 ΔSOC is the initial threshold value for the state of charge limit of the energy storage battery. SOH (t) represents the threshold offset of SOH, and SOC max The SOC is the upper limit dynamic threshold of the state of charge of the energy storage battery. max =SOC max，0 +ΔSOC SOH (t), ΔSOC SOH (t)=-γ(1-SOH), where γ is the adjustment coefficient;

[0020] The charging load model expression is:

[0021]

[0022] In the formula, δ i (t) represents the charging state variable of the i-th electric vehicle, taking values ​​of 0 or 1, δ i When (t) = 1, it indicates that the i-th vehicle is charging at time t; δ i When (t) = 0, it means that the i-th vehicle is not charging at time t; N EV The total number of electric vehicles connected to the system; The rated charging power of the i-th electric vehicle.

[0023] Based on the above technical solutions, preferably, step S2 includes the following sub-steps:

[0024] Historical illumination information is obtained, including hourly illumination intensity, temperature and humidity, cloud cover, wind speed, and weather type.

[0025] Historical illumination information is preprocessed and time-series data is aligned to obtain an illumination training dataset;

[0026] A light intensity prediction model is constructed based on the CNN-LSTM network structure. The light intensity training dataset is input into the CNN-LSTM network structure for iterative training, and the Adam optimization algorithm is used to adjust the model parameters to obtain the optimal light intensity prediction model, which outputs the predicted light intensity values ​​for future periods.

[0027] Based on the above technical solution, preferably, step S2 further includes the following sub-steps:

[0028] Obtain historical charging information, which includes charging start time, end time, power timing data of charging pile, vehicle ID, vehicle battery capacity, SOC history record, vehicle type, holiday marking code information, and geographical location;

[0029] Historical charging information is preprocessed and historical charging feature information is extracted to obtain a charging demand feature training set.

[0030] A charging demand prediction model is constructed based on the Transformer network structure. The charging demand feature training set is input into the Transformer network structure for iterative training, and the Adam optimization algorithm is used to adjust the model parameters to obtain the optimal charging demand prediction model, which outputs the charging load demand power for future periods.

[0031] Based on the above technical solution, preferably, step S2 further includes the following sub-steps:

[0032] Obtain historical electricity price information, which includes real-time time-of-use electricity price, day-ahead market electricity price data, holiday marker codes, renewable energy output ratio, and historical charging load;

[0033] Historical electricity price information is preprocessed and historical electricity price feature information is extracted to obtain an electricity price information feature training set;

[0034] A power price prediction model is constructed based on the Prophet network structure. The power price information feature training set is input into the Prophet network structure for iterative training, and the Adam optimization algorithm is used to adjust the model parameters to obtain the optimal power price prediction model, and output the power price prediction value for future periods.

[0035] Based on the above technical solutions, preferably, in step S3, the annual net cost and power deviation rate are used as optimization objectives, and a linear weighted method is used to construct the objective optimization function, the expression of which is:

[0036] minF=λ1CANC +λ2PDR

[0037] In the formula, C ANC The annual net cost is given by PDR, which represents the power deviation rate. λ1 and λ2 are the weighting coefficients for the annual net cost and the power deviation rate, respectively.

[0038] C ANC =C int +C o&m +C rep +C buy -(I sale +I EVsale )

[0039] In the formula, C int For the initial cost, C o&m For operating costs, C rep For equipment replacement costs, C buy To the cost of purchasing electricity, I sale For the sales revenue from the sale of electricity, I EVsale Revenue from charging electric vehicles;

[0040]

[0041] In the formula, EER(t) is the energy surplus rate, LLR(t) is the load shedding rate, and t s The total number of hours in a year; P su P(t) represents the unused excess power in the system at time t. oload (t) represents the power demand of other loads at time t; P sh (t) represents the load power that the system cannot satisfy at time t.

[0042] Based on the above technical solutions, preferably, the photovoltaic-storage-charging coordinated control strategy formulated in step S4 based on predicted future electricity prices, charging load demand power, and solar irradiance data includes the following steps:

[0043] S41, Input the predicted future electricity price, charging load demand power, and solar intensity data, initialize the time step t=1, and determine whether the current time step t is less than t0. s If t <t s Based on the predicted charging load demand power and light intensity data, the net load ΔP(t) is calculated, expressed as: ΔP(t) = P EV (t)-P PV (t);

[0044] S42, determine whether the net load ΔP(t) is greater than 0. If ΔP(t)>0, then determine whether the state of charge of the energy storage battery at time t is greater than the minimum dynamic threshold of the state of charge of the energy storage battery.

[0045] S43, if SOC(t) > SOC min Then, the discharge power at time step t is calculated for energy storage discharge, and the SOC is updated. Based on the net load and the discharge power at time step t, the remaining power gap ΔP is calculated. r1 (t), if ΔP r1 If SOC(t) > 0, then electricity is purchased from the grid; if SOC(t) ≤ SOC min In this case, electricity is purchased directly from the power grid;

[0046] S44, calculate the power P that the system purchases from the grid at time step t. bug (t), and calculate and update the remaining power gap ΔP based on the remaining power gap and the power purchased by the system from the grid at time step t. r1 (t), if ΔP r1 If (t)>0, calculate the load loss rate, increment the time step, and return to step S41 for a loop; if ΔP r1 If (t)≤0, then increase the time step by one and return to step S41 for looping;

[0047] S45, if ΔP(t)≤0, then determine whether the state of charge of the energy storage battery at time t is greater than the minimum dynamic threshold of the state of charge of the energy storage battery.

[0048] S46, if SOC(t) <SOC max Then, the charging power at time step t is calculated for energy storage charging, and the SOC is updated. Based on the net load and the charging power at time step t, the remaining excess power ΔP is calculated. r2 (t), if ΔP r1 If SOC(t) > 0, then electricity is sold to the grid. max Then, they sell electricity directly to the power grid;

[0049] S47, Calculate the power P sold by the system to the grid at time step t. bug (t), and calculate and update the remaining excess power ΔP based on the remaining excess power and the power sold to the grid by the system at time step t. r2 (t), if ΔP r2 If (t)>0, calculate the energy surplus rate, increment the time step by one, and return to step S41 for a loop; if ΔP r2 If (t)≤0, then increase the time step by one and return to step S41 for looping.

[0050] Based on the above technical solutions, preferably, step S4 involves using an improved whale optimization algorithm to solve the objective optimization function, generating a charge / discharge power sequence based on the obtained optimal solution, and inputting it into the system for execution. This includes the following sub-steps:

[0051] Input the predicted future electricity price, charging load demand power, and light intensity data, and set the number of whales N and the maximum number of iterations T. max Objective function weights λ1 and λ2 and search space boundary [X] min ,X max ];

[0052] Chaotic sequences are generated using chaotic mapping, and these sequences are mapped to the search space to obtain the initial positions of individual whales. The fitness value of each individual whale is then calculated based on the objective optimization function.

[0053] The inertia weight and energy transformation matrix parameters are dynamically adjusted based on the number of iterations, where,

[0054] The expression for updating the inertia weight is:

[0055]

[0056] In the formula, t is the current iteration number, T max ω represents the maximum number of iterations. max ω is the maximum value of the inertia weight. min This represents the minimum value of the inertia weight.

[0057] The matrix update expression is:

[0058] A = 2a·ra

[0059] a=2-2·t / T max

[0060] In the formula, A is the transform matrix, a is the convergence factor that decreases linearly from 2 to 0, and r is a random number in the interval [0,1].

[0061] Generate a random probability threshold p, p∈[0,1]. When p<0.5, determine whether the absolute value of the transformable matrix is ​​less than 1. If |A|<1, then use the shrinking encirclement strategy to update the whale position. If |A|≥1, then use the spiral approximation strategy to update the whale position. When p≥0.5, then use the shrinking encirclement strategy to update the whale position.

[0062] Apply boundary constraints to the updated position, calculate the fitness values ​​of all individuals, select the current best individual, and perturb the current best individual using Gaussian difference mutation to obtain the current best solution;

[0063] Once the maximum number of iterations is reached, the optimal solution is output, the charging and discharging power sequence is decoded, and then input into the system for execution.

[0064] Secondly, the present invention also provides a multi-energy complementary photovoltaic-storage-charging coordinated control system, implemented using a multi-energy complementary photovoltaic-storage-charging coordinated control method, the system comprising:

[0065] The modeling module is used to establish corresponding photovoltaic power generation models, energy storage system models, and charging load models based on the photovoltaic power generation system, energy storage system, and charging system, respectively.

[0066] The data prediction module is used to acquire historical light intensity, historical charging information and historical electricity price information, and build a prediction model based on neural network to predict and output the light intensity, charging load demand power and electricity price for future periods respectively.

[0067] The objective function construction module is used to construct the objective optimization function using the annual net cost and power deviation rate as optimization objectives and employing a linear weighting method.

[0068] The optimized control module formulates a photovoltaic-storage-charging coordinated control strategy based on predicted future electricity prices, charging load demand power, and solar irradiance data. The predicted data is used as the input to the control strategy, and an improved whale optimization algorithm is used to solve the objective optimization function. The optimal solution is then used to generate a charging and discharging power sequence, which is then input into the system for execution.

[0069] Thirdly, the present invention also provides a computer-readable storage medium storing a program for a multi-energy complementary optical storage and charging coordinated control method, wherein when the program is executed, the multi-energy complementary optical storage and charging coordinated control method implements the multi-energy complementary optical storage and charging coordinated control method.

[0070] The photoelectric storage-charging coordinated control method and system based on multi-energy complementarity of the present invention have the following advantages over the prior art:

[0071] (1) By constructing photovoltaic power generation, energy storage system and charging load models in sequence, and using historical data combined with neural networks to accurately predict the light intensity, charging load demand power and electricity price in future periods, the objective function is constructed with annual net cost and power deviation rate as optimization objectives. The improved whale optimization algorithm is used to solve the objective function to formulate a photovoltaic-storage-charging coordinated control strategy. Finally, the optimal charging and discharging power sequence is generated and input into the system for execution, which effectively improves the economic efficiency and stability of the system operation, realizes the coordinated optimization control of photovoltaic power generation, energy storage and charging system, reduces annual net cost, reduces power deviation, and improves the overall system operation efficiency and reliability.

[0072] (2) The dynamic characteristics of photovoltaic power generation are described by photoelectric conversion efficiency coefficient and temperature correction term. The shadow correction factor, shading area ratio and shading coefficient are introduced to quantify the impact of local shading on power generation efficiency, avoid the linear assumption error of traditional model and improve the accuracy of power generation prediction.

[0073] (3) Generating the initial population through chaotic mapping effectively avoids premature convergence. The introduction of nonlinear inertial weights and dynamic spiral constants enables the algorithm to have stronger global exploration capabilities in the early stages of iteration. The combination of linear decrease of the convergence factor and random numbers accelerates the convergence speed. Gaussian difference mutation is used to perturb the optimal individual to avoid local optima. This allows for a more accurate solution of the objective optimization function, resulting in a better charging and discharging power sequence, which improves the system's operating efficiency, reduces operating costs, and may extend the equipment's service life. Attached Figure Description

[0074] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0075] Figure 1 This is a flowchart of the photoelectric storage-charging coordinated control method based on multi-energy complementarity of the present invention;

[0076] Figure 2 This is a flowchart illustrating the target optimization process of the photoelectric storage-charging coordinated control method based on multi-energy complementarity of the present invention. Detailed Implementation

[0077] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0078] like Figure 1 and Figure 2 As shown, in a first aspect, the present invention provides a method for coordinated control of photovoltaic storage and charging based on multi-energy complementarity, the method comprising the following sub-steps:

[0079] S1. Establish corresponding photovoltaic power generation models, energy storage system models, and charging load models based on the photovoltaic power generation system, energy storage system, and charging system, respectively.

[0080] The photovoltaic power generation model expression in step S1 is:

[0081] P PV (t)=η PV (t)·A PV ·G(t)·[1-α(T(t))·(T(t)-Tref )]·f s (t)

[0082] In the formula, η PV η is the photoelectric conversion efficiency coefficient. PV (t)=η PV，0 ·e -λt λ is the attenuation rate, η PV，0 A represents the initial value of the photoelectric conversion efficiency coefficient. PV Let G(t) be the total area of ​​the photovoltaic panel, G(t) be the light intensity at time t, and T(t) be the ambient temperature at time t. ref The reference temperature for photovoltaic panel efficiency testing is α, where α is the temperature coefficient and P is the reference temperature. PV (t) represents the photovoltaic output power;

[0083]

[0084] In the formula, f s (t) is the local shading correction factor at time t, S i (t) represents the area ratio of the i-th occluded region at time t, and k i This is the occlusion coefficient;

[0085] It should be noted that photovoltaic output power is affected by light intensity, ambient temperature, and local shading. The dynamic characteristics of photovoltaic power generation are described by the photoelectric conversion efficiency coefficient and temperature correction term. A shading correction factor is introduced to quantify the impact of local shading on power generation efficiency by using the proportion of shaded area and shading coefficient. This avoids the linear assumption error of traditional models and improves the accuracy of power generation prediction.

[0086] The energy storage system model expression is:

[0087]

[0088] In the formula, SOC(t) represents the state of charge of the energy storage battery at time t, and η ch For the charging efficiency of energy storage batteries, η dis For the discharge efficiency of energy storage batteries, E max P represents the total capacity of the energy storage battery, Δt is the time interval, and P is the total capacity of the energy storage battery. ch (t) represents the charging power of the energy storage battery at time t, P dis (t) represents the discharge power of the energy storage battery at time t. This represents the maximum charging power of the energy storage battery. The maximum discharge power of the energy storage battery, SOC min The SOC (State of Charge) is the dynamic threshold that limits the state of charge of an energy storage battery. min =SOC min，0 +ΔSOC SOH (t), SOCmin，0 ΔSOC is the initial threshold value for the state of charge limit of the energy storage battery. SOH (t) represents the threshold offset of SOH, and SOC max The SOC is the upper limit dynamic threshold of the state of charge of the energy storage battery. max =SOC max，0 +ΔSOC SOH (t), ΔSOC SOH (t)=-γ(1-SOH), where γ is the adjustment coefficient;

[0089] It should be noted that the energy storage battery achieves bidirectional regulation through charging efficiency, discharging efficiency, and dynamic threshold. The dynamic threshold is related to the battery's health status and is dynamically adjusted by the threshold offset to avoid overcharging and over-discharging, extend the battery's service life, and constrain the charging and discharging power. The energy storage system adjusts the charging and discharging strategy in real time according to the maximum charging and discharging power and the state of charge (SOC) to ensure stable system operation.

[0090] The charging load model expression is:

[0091]

[0092] In the formula, δ i (t) represents the charging state variable of the i-th electric vehicle, taking values ​​of 0 or 1, δ i When (t) = 1, it indicates that the i-th vehicle is charging at time t; δ i When (t) = 0, it means that the i-th vehicle is not charging at time t; N EV The total number of electric vehicles connected to the system; The rated charging power of the i-th electric vehicle.

[0093] It should be noted that the charging load model adjusts the charging priority and power allocation of multiple electric vehicles in real time through charging state variables and rated charging power. Combined with photovoltaic output and energy storage status, it prioritizes the consumption of renewable energy and reduces dependence on the grid.

[0094] S2 acquires historical light intensity, historical charging information, and historical electricity price information, and builds a prediction model based on a neural network to predict and output the light intensity, charging load demand power, and electricity price for future periods.

[0095] Step S2 includes the following sub-steps:

[0096] Historical illumination information is obtained, including hourly illumination intensity, temperature and humidity, cloud cover, wind speed, and weather type.

[0097] Historical illumination information is preprocessed and time-series data is aligned to obtain an illumination training dataset;

[0098] A light intensity prediction model is constructed based on the CNN-LSTM network structure. The light intensity training dataset is input into the CNN-LSTM network structure for iterative training, and the Adam optimization algorithm is used to adjust the model parameters to obtain the optimal light intensity prediction model, which outputs the predicted light intensity values ​​for future periods.

[0099] It should be noted that the model integrates multi-dimensional spatiotemporal data such as light intensity, temperature and humidity, cloud cover, wind speed, and weather type to capture the coupled impact of environmental factors on photovoltaic power generation. The CNN layer is used to extract local spatiotemporal features and reduce data dimensionality. The LSTM layer is used to learn the long-term temporal dependence of light intensity. The CNN-LSTM combined model has both spatial feature extraction and time series modeling capabilities, making it suitable for nonlinear, high-dimensional meteorological data prediction. It captures the dynamic changes in light intensity through multi-channel input and outputs hourly predictions for the next 1-24 hours, providing a foundation for photovoltaic power generation prediction.

[0100] Obtain historical charging information, which includes charging start time, end time, power timing data of charging pile, vehicle ID, vehicle battery capacity, SOC history record, vehicle type, holiday marking code information, and geographical location;

[0101] Historical charging information is preprocessed and historical charging feature information is extracted to obtain a charging demand feature training set.

[0102] A charging demand prediction model is constructed based on the Transformer network structure. The charging demand feature training set is input into the Transformer network structure for iterative training, and the Adam optimization algorithm is used to adjust the model parameters to obtain the optimal charging demand prediction model, which outputs the charging load demand power for future periods.

[0103] It should be noted that by integrating charging behavior data with external factors such as holidays and geographical location, the user's charging habits and scenario dependencies are characterized. The self-attention mechanism in the Transformer network structure can capture the long-term dependence and sudden patterns of charging demand. Furthermore, the location encoding introduces timestamps and geographic location encoding to enhance the awareness of sequence order and spatial context. As a result, the Transformer performs well in long sequence prediction, can be parallelized, and is suitable for load prediction of large-scale charging pile clusters. By capturing the periodicity and suddenness of user charging behavior through the multi-head attention mechanism, the charging load demand power for future periods is output.

[0104] Obtain historical electricity price information, which includes real-time time-of-use electricity price, day-ahead market electricity price data, holiday marker codes, renewable energy output ratio, and historical charging load;

[0105] Historical electricity price information is preprocessed and historical electricity price feature information is extracted to obtain an electricity price information feature training set;

[0106] A power price prediction model is constructed based on the Prophet network structure. The power price information feature training set is input into the Prophet network structure for iterative training, and the Adam optimization algorithm is used to adjust the model parameters to obtain the optimal power price prediction model, and output the power price prediction value for future periods.

[0107] It should be noted that the Prophet model is used to decompose the electricity price series into trend, seasonal and holiday terms, automatically fit nonlinear changes, and integrate features such as real-time time-of-use electricity price, day-ahead market electricity price, and renewable energy output ratio to construct a feature training set containing multi-source data. Through Bayesian parameter estimation and adaptive trend adjustment in the Prophet model, the predicted electricity price values ​​for the next 1-7 days are output.

[0108] S3 uses annual net cost and power deviation rate as optimization objectives and employs a linear weighting method to construct the objective optimization function.

[0109] The objective optimization function expression is:

[0110] minF=λ1C ANC +λ2PDR

[0111] In the formula, C ANC The annual net cost is given by PDR, which represents the power deviation rate. λ1 and λ2 are the weighting coefficients for the annual net cost and the power deviation rate, respectively.

[0112] C ANC =C m +C o&m +C rep +C buy -(I sale +I EVsale )

[0113] In the formula, C int For the initial cost, C o&m For operating costs, C rep For equipment replacement costs, C buy To the cost of purchasing electricity, I sale For the sales revenue from the sale of electricity, I EVsale Revenue from charging electric vehicles;

[0114] The initial cost expression is:

[0115]

[0116] In the formula, N i Let C be the number of devices of type i. iLet m be the unit price of equipment of type i, r be the discount rate, and m be the unit price of equipment of type i. i For the service life of the i-th type of equipment, C csp N represents the unit price of special equipment. cs The number of special equipment;

[0117] The expression for operating cost is:

[0118]

[0119] In the formula, C io&m Let i be the annual operating cost of the i-th type of equipment;

[0120] The expression for equipment replacement cost is:

[0121]

[0122] In the formula, N bat C represents the number of energy storage batteries. bat_rep For the replacement cost of a single energy storage battery, m bat This refers to the lifespan of the energy storage battery.

[0123] The cost expression for purchasing electricity is:

[0124]

[0125] In the formula, P buy To purchase electrical energy, P buy (t) represents the purchase price of electricity at time t;

[0126] The expression for sales revenue from the sale of electricity is:

[0127]

[0128] In the formula, P sale The power of electricity sold, P sale (t) represents the electricity price at time t;

[0129] The formula for electric vehicle charging revenue is:

[0130]

[0131] In the formula, P csp For revenue related to electric vehicle charging services, P EV (t) represents the electric vehicle charging load power at time t;

[0132]

[0133] In the formula, EER(t) is the energy surplus rate, LLR(t) is the load shedding rate, and t s The total number of hours in a year; Psu P(t) represents the unused excess power in the system at time t. oload (t) represents the power demand of other loads at time t; P sh (t) represents the load power that the system cannot satisfy at time t.

[0134] S4. Based on the predicted future electricity price, charging load demand power and light intensity data, a photovoltaic-storage-charging coordinated control strategy is formulated. The predicted data is used as the input of the control strategy. An improved whale optimization algorithm is used to solve the objective optimization function. The optimal solution is used to generate a charging and discharging power sequence and input it into the system for execution.

[0135] Step S4, which involves formulating a photovoltaic-storage-charging coordinated control strategy based on predicted future electricity prices, charging load demand, and solar irradiance data, includes the following steps:

[0136] S41, Input the predicted future electricity price, charging load demand power, and solar intensity data, initialize the time step t=1, and determine whether the current time step t is less than t0. s If t <t s Based on the predicted charging load demand power and light intensity data, the net load ΔP(t) is calculated, expressed as: ΔP(t) = P EV (t)-P PV (t);

[0137] S42, determine whether the net load ΔP(t) is greater than 0. If ΔP(t)>0, then determine whether the state of charge of the energy storage battery at time t is greater than the minimum dynamic threshold of the state of charge of the energy storage battery.

[0138] S43, if SOC(t) > SOC min Then, the discharge power at time step t is calculated for energy storage discharge, and the SOC is updated. Based on the net load and the discharge power at time step t, the remaining power gap ΔP is calculated. r1 (t), if ΔP r1 If SOC(t) > 0, then electricity is purchased from the grid; if SOC(t) ≤ SOC min In this case, electricity is purchased directly from the power grid;

[0139] The expression for the discharge power with a time step t in the energy storage discharge calculation is as follows:

[0140]

[0141] Update the SOC expression to:

[0142]

[0143] Calculate the residual power gap ΔP based on the net load and the discharge power at time step t.r1 (t), the expression is:

[0144] △P r1 (t)=△P(t)-P dis (t);

[0145] S44, calculate the power P that the system purchases from the grid at time step t. bug (t), and calculate and update the remaining power gap ΔP based on the remaining power gap and the power purchased by the system from the grid at time step t. r1 (t), if ΔP r1 If (t)>0, calculate the load loss rate, increment the time step, and return to step S41 for a loop; if ΔP r1 If (t)≤0, then increase the time step by one and return to step S41 for looping;

[0146] Among them, the power P purchased by the system from the grid at time step t bug The expression for (t) is:

[0147]

[0148] The updated expression for the remaining power gap is:

[0149] △P r1 (t)=△P r1 (t)-P buy (t)

[0150] S45, if ΔP(t)≤0, then determine whether the state of charge of the energy storage battery at time t is greater than the minimum dynamic threshold of the state of charge of the energy storage battery.

[0151] S46, if SOC(t) <SOC max Then, the charging power at time step t is calculated for energy storage charging, and the SOC is updated. Based on the net load and the charging power at time step t, the remaining excess power ΔP is calculated. r2 (t), if ΔP r1 If SOC(t) > 0, then electricity is sold to the grid. max Then, they sell electricity directly to the power grid;

[0152] The expression for the charging power at time step t is:

[0153]

[0154] Update the SOC expression to:

[0155]

[0156] The updated expression for remaining excess power is:

[0157] △P r2 (t)=|△P(t)|-P ch (t);

[0158] S47, Calculate the power P sold by the system to the grid at time step t. sale (t), and calculate and update the remaining excess power ΔP based on the remaining excess power and the power sold to the grid by the system at time step t. r2 (t), if ΔP r2 If (t)>0, calculate the energy surplus rate, increment the time step by one, and return to step S41 for a loop; if ΔP r2 If (t)≤0, then increase the time step by one and return to step S41 for looping.

[0159] Among them, the power P sold by the system to the grid at time step t sale The expression for (t) is:

[0160]

[0161] The updated expression for remaining excess power is:

[0162] △P r2 (t)=△P r2 (t)-P sell (t);

[0163] It should be noted that by dynamically adjusting the energy storage charging and discharging and grid interaction strategies through real-time forecast data, the goal is to minimize the annual net cost and reduce the power deviation rate. By judging whether the system's power supply and demand status is in short supply or surplus, the system prioritizes the use of energy storage to regulate power based on the dynamic threshold of SOC and charging and discharging efficiency. When energy storage regulation is insufficient, the system balances the power gap / surplus by purchasing or selling electricity, updating the status step by step until full-time optimization is achieved.

[0164] In this embodiment, step S4 involves using an improved whale optimization algorithm to solve the objective optimization function, generating a charge / discharge power sequence based on the obtained optimal solution, and inputting it into the system for execution. This includes the following sub-steps:

[0165] Input the predicted future electricity price, charging load demand power, and light intensity data, and set the number of whales N and the maximum number of iterations T. max Objective function weights λ1 and λ2 and search space boundary [X] min ,X max ];

[0166] Chaotic sequences are generated using chaotic mapping, and these sequences are mapped to the search space to obtain the initial positions of individual whales. The fitness value of each individual whale is then calculated based on the objective optimization function.

[0167] The inertia weight and energy transformation matrix parameters are dynamically adjusted based on the number of iterations, where,

[0168] The expression for updating the inertia weight is:

[0169]

[0170] In the formula, t is the current iteration number, T max ω represents the maximum number of iterations. max ω is the maximum value of the inertia weight. min This represents the minimum value of the inertia weight.

[0171] The matrix update expression is:

[0172] A = 2a·ra

[0173] a=2-2·t / T max

[0174] In the formula, A is the transform matrix, a is the convergence factor that decreases linearly from 2 to 0, and r is a random number in the interval [0,1].

[0175] Generate a random probability threshold p, p∈[0,1]. When p<0.5, determine whether the absolute value of the transformable matrix is ​​less than 1. If |A|<1, then use the shrinking encirclement strategy to update the whale position. If |A|≥1, then use the spiral approximation strategy to update the whale position. When p≥0.5, then use the shrinking encirclement strategy to update the whale position.

[0176] Apply boundary constraints to the updated position, calculate the fitness values ​​of all individuals, select the current best individual, and perturb the current best individual using Gaussian difference mutation to obtain the current best solution;

[0177] Once the maximum number of iterations is reached, the optimal solution is output, the charging and discharging power sequence is decoded, and then input into the system for execution.

[0178] It should be noted that generating the initial population through Cubic chaotic mapping effectively increases population diversity, thus preventing the algorithm from getting stuck in local optima in the early stages of iteration. The introduction of nonlinear inertial weights gives the algorithm stronger global exploration capabilities in the early stages of iteration. The inertial weights are dynamically adjusted according to the number of iterations, allowing the algorithm to explore the search space more extensively in the early stages of search, thereby increasing the probability of finding the global optimum. The combination of linearly decreasing convergence factor and random numbers allows the algorithm to dynamically adjust the search strategy during iteration. In the early stages of iteration, the convergence factor is large, and the algorithm tends to perform a global search. As the number of iterations increases, the convergence factor gradually decreases, and the algorithm gradually shifts to a local search. This dynamic adjustment strategy helps accelerate the convergence speed of the algorithm. By perturbing the optimal individual using Gaussian difference mutation, the algorithm can perform a more detailed search around the optimal solution, thus avoiding getting trapped in local optima. Simultaneously, this perturbation helps the algorithm find a better balance between global and local searches, thereby enabling a more accurate solution to the objective function, resulting in a better charging and discharging power sequence, improving system efficiency, reducing operating costs, and potentially extending the equipment's lifespan.

[0179] In this embodiment, photovoltaic power generation, energy storage system, and charging load models are constructed sequentially. Historical data is combined with neural networks to accurately predict the future solar intensity, charging load demand power, and electricity price. Then, an objective function is constructed with annual net cost and power deviation rate as optimization objectives. An improved whale optimization algorithm is used to solve the objective function to formulate a photovoltaic-storage-charging coordinated control strategy. Finally, the optimal charging and discharging power sequence is generated and input into the system for execution. This method effectively improves the economic efficiency and stability of the system operation, realizes the coordinated optimization control of photovoltaic power generation, energy storage, and charging systems, reduces annual net cost, reduces power deviation, and improves the overall system operating efficiency and reliability.

[0180] Secondly, the present invention also provides a multi-energy complementary photovoltaic-storage-charging coordinated control system, implemented using a multi-energy complementary photovoltaic-storage-charging coordinated control method, the system comprising:

[0181] The modeling module is used to establish corresponding photovoltaic power generation models, energy storage system models, and charging load models based on the photovoltaic power generation system, energy storage system, and charging system, respectively.

[0182] The data prediction module is used to acquire historical light intensity, historical charging information and historical electricity price information, and build a prediction model based on neural network to predict and output the light intensity, charging load demand power and electricity price for future periods respectively.

[0183] The objective function construction module is used to construct the objective optimization function using the annual net cost and power deviation rate as optimization objectives and employing a linear weighting method.

[0184] The optimized control module formulates a photovoltaic-storage-charging coordinated control strategy based on predicted future electricity prices, charging load demand power, and solar irradiance data. The predicted data is used as the input to the control strategy, and an improved whale optimization algorithm is used to solve the objective optimization function. The optimal solution is then used to generate a charging and discharging power sequence, which is then input into the system for execution.

[0185] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0186] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0187] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0188] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0189] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0190] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0191] Furthermore, it should be noted that in the system and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent solutions of the present invention. Moreover, the steps performing the above series of processes can naturally be executed in the order described, but are not necessarily required to be executed in chronological order; some steps can be executed in parallel or independently of each other. Those skilled in the art will understand that all or any step or component of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or network of computing devices, in hardware, firmware, software, or a combination thereof. This is something that those skilled in the art can achieve by using their basic programming skills after reading the description of the present invention.

[0192] Therefore, the object of the present invention can also be achieved by running a program or a set of programs on any computing system. The computing system can be a known general-purpose system. Therefore, the object of the present invention can also be achieved simply by providing a program product containing program code for implementing the method or apparatus. That is, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any known storage medium or any storage medium developed in the future. It should also be noted that in the apparatus and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent to the present invention. Furthermore, the steps for performing the above series of processes can naturally be performed in the order described, but are not necessarily required to be performed in chronological order. Some steps can be performed in parallel or independently of each other.

[0193] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for coordinated control of photovoltaic storage and charging based on multi-energy complementarity, characterized in that, The method includes the following sub-steps: S1. Establish corresponding photovoltaic power generation models, energy storage system models, and charging load models based on photovoltaic power generation systems, energy storage systems, and charging systems, respectively. The photovoltaic power generation model expression is: P PV ( t )= η PV ( t )· A PV · G ( t )·[1− α ( T ( t ))·( T ( t )− T ref )]· f s ( t ) In the formula, η PV The photoelectric conversion efficiency coefficient. η PV ( t )= η PV，0 · e −λt , λ The attenuation rate, η PV，0 This is the initial value of the photoelectric conversion efficiency coefficient; A PV The total area of ​​the photovoltaic panels. G ( t )for t Light intensity at any given time T ( t )for t The ambient temperature at any given time T ref This is the reference temperature for testing the efficiency of photovoltaic panels. α For temperature coefficient, P PV ( t (This refers to photovoltaic power output;) ； In the formula, f s ( t )for t Local shadow correction factor at time. S i ( t )for t The first moment i The area ratio of each obstructed area k i This is the occlusion coefficient; S2 acquires historical lighting information, historical charging information, and historical electricity price information, and builds a prediction model based on a neural network to predict and output the lighting intensity, charging load demand power, and electricity price for future periods, respectively. S3 uses annual net cost and power deviation rate as optimization objectives, and constructs the objective optimization function using a linear weighting method; the expression is: ； In the formula, C ANC Annual net cost, PDR Indicates the power deviation rate. λ 1 and λ 2 represents the weighting coefficients for annual net cost and power deviation rate, respectively; ； In the formula, C int For initial cost, C o&m For operating costs, C rep For equipment replacement costs, C buy To the cost of purchasing electricity, I sale Revenue from the sale of electricity I EVsale Revenue from charging electric vehicles; ； In the formula, EER(t) is the energy surplus rate. LLR ( t ) represents the load loss rate. t s The total number of hours in a year; P su ( t (time) t Unutilized excess power in the system P oload ( t (time) t Other load power requirements; P sh ( t (time) t The system cannot meet the load power requirements; S4. Based on the predicted future electricity price, charging load demand power and light intensity data, a photovoltaic-storage-charging coordinated control strategy is formulated. The predicted data is used as the input of the control strategy. An improved whale optimization algorithm is used to solve the objective optimization function. The optimal solution is used to generate a charging and discharging power sequence and input it into the system for execution.

2. The photovoltaic-storage-charging coordinated control method based on multi-energy complementarity as described in claim 1, characterized in that, In step S1, corresponding photovoltaic power generation models, energy storage system models, and charging load models are established based on the photovoltaic power generation system, energy storage system, and charging system, respectively. The energy storage system model expression is: ； In the formula, SOC ( t ) is an energy storage battery t State of charge at time t, η ch For the charging efficiency of energy storage batteries, η dis For the discharge efficiency of energy storage batteries, E max Δ is the total capacity of the energy storage battery. t For time intervals, P ch ( t )for t The charging power of the energy storage battery at any time. P dis ( t )for t The discharge power of the energy storage battery at any given time. This represents the maximum charging power of the energy storage battery. This represents the maximum discharge power of the energy storage battery. SOC min This is the dynamic threshold that limits the state of charge of the energy storage battery. SOC min = SOC min，0 + Δ SOC SOH ( t ), SOC min，0 Δ is the initial threshold value for the state of charge limit of the energy storage battery. SOC SOH ( t )for SOH The threshold offset, SOC max This is the dynamic threshold for the upper limit of the state of charge of the energy storage battery. SOC max = SOC max，0 + Δ SOC SOH ( t ), Δ SOC SOH ( t )=- γ (1− SOH ), γ For adjustment coefficients; The charging load model expression is: ； In the formula, δ i ( t ) is the first i The charging state variable of an electric vehicle takes the value of 0 or 1. δ i ( t When )=1, it means the first i The car is t It is constantly charging; δ i ( t When )=0, it means the first i The car is t Not charged at all; N EV The total number of electric vehicles connected to the system; For the first i The rated charging power of an electric vehicle.

3. The photovoltaic-storage-charging coordinated control method based on multi-energy complementarity as described in claim 2, characterized in that: Step S2 includes the following sub-steps: Historical illumination information is obtained, including hourly illumination intensity, temperature and humidity, cloud cover, wind speed, and weather type. Historical illumination information is preprocessed and time-series data is aligned to obtain an illumination training dataset; A light intensity prediction model is constructed based on the CNN-LSTM network structure. The light intensity training dataset is input into the CNN-LSTM network structure for iterative training, and the Adam optimization algorithm is used to adjust the model parameters to obtain the optimal light intensity prediction model, which outputs the predicted light intensity values ​​for future periods.

4. The photoelectric storage-charging coordinated control method based on multi-energy complementarity as described in claim 2, characterized in that: Step S2 also includes the following sub-steps: Obtain historical charging information, which includes charging start time, end time, power timing data of charging pile, vehicle ID, vehicle battery capacity, SOC history record, vehicle type, holiday marking code information, and geographical location; Historical charging information is preprocessed and historical charging feature information is extracted to obtain a charging demand feature training set. A charging demand prediction model is constructed based on the Transformer network structure. The charging demand feature training set is input into the Transformer network structure for iterative training, and the Adam optimization algorithm is used to adjust the model parameters to obtain the optimal charging demand prediction model, which outputs the charging load demand power for future periods.

5. The photovoltaic-storage-charging coordinated control method based on multi-energy complementarity as described in claim 2, characterized in that, Step S2 also includes the following sub-steps: Obtain historical electricity price information, which includes real-time time-of-use electricity price, day-ahead market electricity price data, holiday marker codes, renewable energy output ratio, and historical charging load; Historical electricity price information is preprocessed and historical electricity price feature information is extracted to obtain an electricity price information feature training set; A power price prediction model is constructed based on the Prophet network structure. The power price information feature training set is input into the Prophet network structure for iterative training, and the Adam optimization algorithm is used to adjust the model parameters to obtain the optimal power price prediction model, and output the power price prediction value for future periods.

6. The photovoltaic-storage-charging coordinated control method based on multi-energy complementarity as described in claim 5, characterized in that: Step S4, which involves formulating a photovoltaic-storage-charging coordinated control strategy based on predicted future electricity prices, charging load demand, and solar irradiance data, includes the following steps: S41, Input the predicted future electricity price, charging load demand power, and solar irradiance data, and initialize the time step. t =1, determine the current time step. t Is it less than t s ,like t <t s Based on the predicted charging load demand power and light intensity data, the net load Δ is calculated. P ( t The expression is: Δ P ( t )= P EV ( t )- P PV ( t ); S42, Determine the net load Δ P ( t Is Δ greater than 0? P ( t If )>0, then determine the energy storage battery. t Whether the state of charge at any given time is greater than the dynamic threshold of the state of charge limit of the energy storage battery; S43, if SOC ( t )> SOC min The time step for energy storage discharge calculation is then... t The discharge power is calculated, and the SOC is updated based on the net load and time step. t Calculate the discharge power and the remaining power gap Δ. P r1 ( t If Δ P r1 ( t If )>0, then electricity is purchased from the grid; if SOC ( t )≤ SOC min In this case, electricity is purchased directly from the power grid; S44, Calculate and obtain the time step. t Power purchased by the system from the grid P bug ( t ), and based on the remaining power gap and time step t The system purchases power from the grid, calculates and updates the remaining power gap Δ. P r1 ( t If Δ P r1 ( t If Δ > 0, calculate the load loss rate, increment the time step by one, and return to step S41 for a loop; if Δ P r1 ( t If )≤0, then increase the time step by one and return to step S41 to loop; S45, if Δ P ( t If )≤0, then determine the energy storage battery t Whether the state of charge at any given time is greater than the dynamic threshold of the state of charge limit of the energy storage battery; S46, if SOC ( t )< SOC max The time step for energy storage charging calculation is then... t The charging power is adjusted, and the SOC is updated based on the net load and time step. t Calculate the remaining excess power Δ based on the charging power. P r2 ( t If Δ P r1 ( t If )>0, then electricity is sold to the grid. SOC ( t )> SOC max Then, they sell electricity directly to the power grid; S47, Calculate and obtain the time step t Power sold by the system to the grid P bug ( t ), and based on the remaining excess power and time step. t The system sells power to the grid, calculates and updates the remaining excess power Δ. P r2 ( t If Δ P r2 ( t If Δ > 0, calculate the energy surplus rate, increment the time step by one, and return to step S41 for a loop; if Δ P r2 ( t If )≤0, then increase the time step by one and return to the loop in step S41.

7. The photovoltaic-storage-charging coordinated control method based on multi-energy complementarity as described in claim 6, characterized in that: Step S4, which involves using an improved whale optimization algorithm to solve the objective function and generating a charge / discharge power sequence based on the optimal solution, includes the following sub-steps: Input the predicted future electricity price, charging load demand, and solar irradiance data, and set the number of individual whales. N and maximum number of iterations T max Objective function weights λ 1 and λ 2 and search space boundary[ X min , X max ]; Chaotic sequences are generated using chaotic mapping, and these sequences are mapped to the search space to obtain the initial positions of individual whales. The fitness value of each individual whale is then calculated based on the objective optimization function. The inertia weight and energy transformation matrix parameters are dynamically adjusted based on the number of iterations, where, The expression for updating the inertia weight is: ； In the formula, t This represents the current iteration number. T max The maximum number of iterations, ω max This represents the maximum value of the inertia weight. ω min This represents the minimum value of the inertia weight. The matrix update expression is: A =2 a · r − a a =2−2· t / T max In the formula, A is the transform matrix. a The convergence factor decreases linearly from 2 to 0. r for[ 0,1 Random numbers within the specified interval; Generate random probability threshold p, p∈ [0,1], when p<0.5 At that time, determine whether the absolute value of the matrix that can be transformed is less than 1. If |A| < When |A| is 1, the whale's position is updated using a contraction and encirclement strategy; when |A| ≥ 1, the whale's position is updated using a spiral approximation strategy. p When the value is ≥0.5, the shrinking encirclement strategy is used to update the whale's position; Apply boundary constraints to the updated position, calculate the fitness values ​​of all individuals, select the current best individual, and perturb the current best individual using Gaussian difference mutation to obtain the current best solution; Once the maximum number of iterations is reached, the optimal solution is output, the charging and discharging power sequence is decoded, and then input into the system for execution.

8. A photovoltaic-storage-charging coordinated control system based on multi-energy complementarity, implemented using the photovoltaic-storage-charging coordinated control method based on any one of claims 1-7, characterized in that: The system includes: The modeling module is used to establish corresponding photovoltaic power generation models, energy storage system models, and charging load models based on the photovoltaic power generation system, energy storage system, and charging system, respectively. The data prediction module is used to acquire historical light intensity, historical charging information and historical electricity price information, and build a prediction model based on neural network to predict and output the light intensity, charging load demand power and electricity price for future periods respectively. The objective function construction module is used to construct the objective optimization function using the annual net cost and power deviation rate as optimization objectives and employing a linear weighting method. The optimized control module formulates a photovoltaic-storage-charging coordinated control strategy based on predicted future electricity prices, charging load demand power, and solar irradiance data. The predicted data is used as the input to the control strategy, and an improved whale optimization algorithm is used to solve the objective optimization function. The optimal solution is then used to generate a charging and discharging power sequence, which is then input into the system for execution.

9. A computer-readable storage medium, characterized in that, The storage medium stores a program for a multi-energy complementary optical storage and charging coordinated control method. When the program is executed, it implements the multi-energy complementary optical storage and charging coordinated control method as described in any one of claims 1 to 7.

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