Energy storage cooperative control method and system based on photovoltaic efficient absorption

Abstract

The invention provides an energy storage cooperative control method and system based on photovoltaic efficient absorption. The method comprises the following steps: acquiring photovoltaic output data of a power generation end, energy storage configuration data of an energy storage end and power grid demand data of a power grid end; inputting the photovoltaic output data, the energy storage configuration data and the power grid demand data into a pre-constructed optical storage capacity optimization model for energy storage configuration prediction, and generating an energy storage capacity configuration scheme set; selecting energy storage capacity configuration schemes meeting a preset condition in the energy storage capacity configuration scheme set to construct a joint operation task under a first time sequence, and generating an energy storage control sequence instruction according to an operation constraint condition in the joint operation task, the objective of the invention is to solve the technical problems in the prior art that full-chain closed-loop control of an energy storage system from optimal configuration to cooperative operation is realized, and the photovoltaic consumption level and the power grid operation economy are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of power grid energy storage dispatch and control technology, and in particular to a collaborative control method and system for energy storage based on efficient photovoltaic absorption. Background Technology

[0002] With the rapid growth of photovoltaic power generation capacity, the intermittency and volatility of its output pose a severe challenge to the stable operation and efficient absorption of the power grid. Energy storage systems, as an important flexible regulation resource, can effectively smooth photovoltaic power fluctuations and participate in grid peak shaving and frequency regulation, making them a key technological means to improve photovoltaic absorption capacity.

[0003] Currently, research on integrated photovoltaic (PV) and energy storage systems mainly focuses on optimizing single components, such as energy storage capacity configuration or operational control strategies. However, in practical applications, energy storage system capacity planning and real-time operational control are interdependent. Simple capacity optimization may not be suitable for real-time fluctuating operating scenarios. Furthermore, existing methods often fail to adequately consider the uncertainties in PV output, or the optimization algorithms used are prone to getting trapped in local optima, resulting in poor economic efficiency of configuration schemes, untimely control responses, and difficulty in maximizing PV consumption benefits while ensuring grid security.

[0004] Therefore, there is an urgent need for an energy storage collaborative control method that can integrate planning and operation, comprehensively consider the uncertainty of photovoltaic output, and achieve global optimization. Summary of the Invention

[0005] The main objective of this invention is to provide a method and system for coordinated control of energy storage based on efficient photovoltaic absorption, aiming to solve the technical problem of achieving closed-loop control of the entire chain from optimized configuration to coordinated operation of energy storage systems in the prior art, and effectively improving the photovoltaic absorption level and grid operation economy.

[0006] In a first aspect, embodiments of the present invention provide an energy storage collaborative control method based on high-efficiency photovoltaic absorption and an energy storage collaborative control system. The energy storage collaborative control system communicatively connects the power generation end, the energy storage end, and the power grid end. The method includes:

[0007] Acquire photovoltaic output data from the power generation end, energy storage configuration data from the energy storage end, and grid demand data from the grid end;

[0008] The photovoltaic output data, the energy storage configuration data, and the grid demand data are input into a pre-built photovoltaic-storage capacity optimization model to predict energy storage configuration and generate a set of energy storage capacity configuration schemes.

[0009] Select an energy storage capacity configuration scheme that meets the preset conditions from the set of energy storage capacity configuration schemes to construct a joint operation task under the first time sequence, and generate energy storage control sequence instructions according to the operation constraints in the joint operation task;

[0010] In response to the energy storage control sequence command, the energy storage collaborative control system controls the energy storage terminal to perform corresponding charging and discharging operations under the first timing sequence.

[0011] Secondly, embodiments of the present invention provide a photovoltaic-based energy storage collaborative control system, wherein the energy storage collaborative control system communicatively connects the power generation end, the energy storage end, and the grid end, and includes:

[0012] The acquisition module is used to acquire photovoltaic output data at the power generation end, energy storage configuration data at the energy storage end, and grid demand data at the grid end.

[0013] The scheme generation module is used to input the photovoltaic output data, the energy storage configuration data and the grid demand data into a pre-built photovoltaic-storage capacity optimization model to predict the energy storage configuration and generate a set of energy storage capacity configuration schemes.

[0014] The instruction generation module is used to select energy storage capacity configuration schemes that meet preset conditions from the set of energy storage capacity configuration schemes to construct a joint operation task under the first time sequence, and generate energy storage control sequence instructions according to the operation constraints in the joint operation task.

[0015] The instruction execution module is used to respond to the energy storage control sequence instruction and control the energy storage collaborative control system to perform corresponding charging and discharging operations on the energy storage terminal under the first timing sequence.

[0016] Thirdly, embodiments of the present invention provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0017] Fourthly, embodiments of the present invention provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0018] Fifthly, embodiments of the present invention provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0019] In a sixth aspect, embodiments of the present invention provide a computer program product stored in a storage medium, which is executed by at least one processor to implement the method described in the first aspect.

[0020] In this embodiment of the invention, an energy storage collaborative control system is applied. The energy storage collaborative control system communicatively connects the power generation end, the energy storage end, and the grid end. The method includes: acquiring photovoltaic output data from the power generation end, energy storage configuration data from the energy storage end, and grid demand data from the grid end; inputting the photovoltaic output data, the energy storage configuration data, and the grid demand data into a pre-constructed photovoltaic-energy storage capacity optimization model to predict energy storage configuration and generate a set of energy storage capacity configuration schemes; selecting energy storage capacity configuration schemes that meet preset conditions from the set of energy storage capacity configuration schemes to construct a joint operation task under a first time sequence, and generating energy storage control sequence instructions according to the operation constraints in the joint operation task; responding to the energy storage control sequence instructions, controlling the energy storage collaborative control system to perform corresponding charging and discharging operations on the energy storage end under the first time sequence. This invention achieves comprehensive collaboration from long-term capacity planning and short-term operation planning to real-time closed-loop control and abnormal emergency response, significantly improving the economy, safety, and stability of the photovoltaic power system. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of the present invention;

[0022] Figure 2 This is a flowchart of the energy storage collaborative control method based on high-efficiency photovoltaic absorption provided in the embodiments of the present invention;

[0023] Figure 3 This is a schematic diagram of the energy storage collaborative control system provided in an embodiment of the present invention;

[0024] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0026] like Figure 1 As shown, Figure 1 This is a schematic diagram of the terminal structure of the hardware operating environment involved in the embodiments of the present invention.

[0027] The terminal of this invention is a mobile device, and it can also be other terminal devices with storage functions.

[0028] like Figure 1 As shown, the terminal may include: a processor 1001, such as a CPU; a communication bus 1002; a user interface 1003; a network interface 1004; and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0029] Optionally, the terminal may also include a camera, a Wi-Fi module, etc., which will not be described in detail here.

[0030] Those skilled in the art will understand that Figure 1 The terminal structure shown does not constitute a limitation on the terminal and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0031] exist Figure 1 In the terminal shown, the network interface 1004 is mainly used to connect to the backend server and communicate data with it; the user interface 1003 mainly includes an input unit such as a keyboard, which includes a wireless keyboard and a wired keyboard, used to connect to the client and communicate data with it; and the processor 1001 can be used to call the generation program of the energy storage collaborative control system stored in the memory 1005.

[0032] The specific implementation of this terminal is basically the same as the implementations of the energy storage collaborative control method based on high-efficiency photovoltaic absorption described below, and will not be repeated here.

[0033] Please see Figure 2 , Figure 2 This is a flowchart illustrating a photovoltaic-based energy storage collaborative control method according to an embodiment of the present invention. The energy storage collaborative control system provided in this embodiment of the present invention is communicatively connected to the power generation end, the energy storage end, and the power grid end. The method includes the following steps:

[0034] S210: Obtain the photovoltaic output data of the power generation end, the energy storage configuration data of the energy storage end, and the grid demand data of the grid end;

[0035] In this embodiment, the energy storage collaborative control system acquires real-time or periodic output forecast data and historical data of the photovoltaic power plant, current configuration parameters (including type, rated energy capacity, rated power, and charge / discharge efficiency) and real-time status data of the energy storage system, grid load demand data, and time-of-use electricity price information through communication connections with the power generation end, energy storage end, and grid. Simultaneously, the acquired raw data undergoes preprocessing such as cleaning, alignment, and normalization to ensure data quality and consistency.

[0036] In another preferred embodiment, before acquiring the photovoltaic output data of the power generation end, the energy storage configuration data of the energy storage end, and the grid demand data of the grid end, the method further includes:

[0037] Historical meteorological data, historical photovoltaic (PV) time-series output data, historical energy storage device data, and historical grid demand data for any region are acquired. Uncertain meteorological features are extracted from the historical meteorological data to generate uncertain meteorological features. Uncertain output features are extracted from the historical PV time-series output data based on the deterministic meteorological features to obtain uncertain time-series output features. Charge features are extracted from the historical energy storage device data type features to generate energy storage type charge features. A prediction model is trained based on the uncertain meteorological features, uncertain time-series output features, energy storage type charge features, and historical grid demand data to generate a trained PV-storage joint time-series prediction model. This model can simulate PV-storage joint operation simulation data for any region. The prediction model to be trained is constructed based on a Markov model. The PV-storage joint time-series prediction model is used to simulate the PV-storage joint operation data of the completed simulated power generation end, simulated energy storage end, and simulated grid end.

[0038] Specifically, firstly, long-term historical datasets for the target area are acquired, including historical meteorological data, historical photovoltaic power plant time-series output data, historical energy storage device operation data, and historical grid load demand data. It should be noted that when extracting uncertain meteorological features: the historical meteorological data is analyzed, as its uncertainties stem from sources such as cloud cover, rainfall, and snowstorms. Furthermore, weather conditions on a single day are uncertain; it's possible for a day to have heavy snow in the morning and sunny skies in the afternoon, or sunny skies in the morning and heavy rain in the afternoon. These uncertainties cause fluctuations in irradiance and ambient temperature, resulting in reduced photovoltaic power output at the generation end, which affects the conversion efficiency of photovoltaic modules. It is possible to obtain the power generation capacity under different radiation conditions. Therefore, a stochastic process is introduced to simulate the impact of cloud cover changes on solar radiation intensity, using a Markov chain model to describe changes in sky conditions such as clear, partially cloudy, and overcast. The probability transition matrix for each state can be estimated based on historical meteorological data. Under clear conditions, the photovoltaic system receives maximum radiation Gmax, while it receives zero under complete shading. Therefore, the expected power generation at any given time can be expressed as:

[0039]

[0040] Where C(t) represents the cloud cover ratio at time t.

[0041] Furthermore, a photovoltaic (PV) time-series output model is constructed using a Markov model to simulate PV power output. For example, in the simulation, the state space can be defined as a series of discrete values ​​or intervals of PV output. PV output is divided into N states, denoted as S1, S2, ..., SN, where each state represents an output interval. S1 represents the output interval of 0-100kW, S2 represents the output interval of 101-200kW, and so on. Feature vectors characterizing weather uncertainty are generated. Uncertainty-based time-series output feature extraction is performed: based on the extracted meteorological uncertainty features, correlation analysis is conducted on historical PV time-series output data to extract the volatility and randomness characteristics of PV output affected by weather, resulting in uncertainty-based time-series output features. Energy storage type charge characteristics extraction is also performed: based on historical energy storage device data, the mapping relationship between the charge / discharge efficiency, self-discharge rate, lifetime decay, and state of charge of different types of energy storage (such as lithium-ion batteries and flow batteries) is analyzed to generate energy storage type charge characteristics characterizing their properties.

[0042] Then, based on uncertain meteorological characteristics, uncertain time-series power output characteristics, energy storage type charge characteristics, and historical grid demand data as input features, and using historically recorded photovoltaic-storage joint operation scenario data as the training target, a time-series prediction model is trained. The prediction model is preferably constructed based on a Markov decision process or a long short-term memory network. After training, the photovoltaic-storage joint time-series prediction model is obtained. This model can simulate and generate multiple sets of probabilistically representative future operation sequence data of the photovoltaic-storage joint system based on input information such as future weather forecasts, providing input for subsequent stochastic optimization.

[0043] S220: Input the photovoltaic output data, the energy storage configuration data, and the grid demand data into the pre-built photovoltaic-energy storage capacity optimization model to predict the energy storage configuration and generate a set of energy storage capacity configuration schemes.

[0044] In this embodiment, the photovoltaic-storage capacity optimization model is a planning layer model used to determine the optimal energy capacity (E) and power capacity (P) of the energy storage system.

[0045] Before inputting the photovoltaic output data, the energy storage configuration data, and the grid demand data into the pre-built photovoltaic-energy storage capacity optimization model for energy storage configuration prediction, the method further includes:

[0046] The photovoltaic-storage capacity optimization model is constructed based on a two-layer stochastic mixed-integer linear programming framework, which simulates the Stackelberg game relationship between grid operators and energy storage operators. The two-layer stochastic mixed-integer linear programming framework includes an upper-layer grid model and a lower-layer energy storage model.

[0047] The overall objective function of the photovoltaic storage capacity optimization model is generated by fusion of the objective based on the cost characterization parameters corresponding to the energy storage end and the curtailment cost characterization parameters corresponding to the energy storage collaborative control system.

[0048] The overall objective function of the photovoltaic-storage capacity optimization model is expressed as:

[0049]

[0050] in, This represents the initial cost of purchasing energy storage multiplied by the capacity. This represents the sum of operating costs for all years. ω represents the average value, which is the average over different weather scenarios; Representing a scene The positive part of the photovoltaic power reduction and discharge in the t-th segment.

[0051] The upper-level model on the power grid side is represented as follows:

[0052]

[0053] in, This represents the electricity price for segment t. Indicates a load gap; Indicates the capacity cost multiplied by the size; This indicates the expected loss caused by light wastage in multiple scenarios.

[0054] The lower-level model of the energy storage side is represented as follows:

[0055]

[0056] in, Indicates the charging electricity price (off-peak hours); This indicates the discharge electricity price (peak hours). , is the charging power. This represents the discharge power.

[0057] Specifically, in this embodiment, the photovoltaic output data, the energy storage configuration data, and the grid demand data are input into a pre-built photovoltaic-energy storage capacity optimization model;

[0058] An improved particle swarm optimization algorithm is used to solve the energy storage capacity configuration scheme set generated by the photovoltaic energy storage capacity optimization model, thereby generating the energy storage capacity configuration scheme set. The solution steps of the improved particle swarm optimization algorithm are as follows:

[0059] Step 1: Based on the photovoltaic power output data, use the Markov chain model to generate multiple photovoltaic power output scenario sequences with different time series characteristics to provide random input for subsequent optimization;

[0060] Step 2: Initialize the particle swarm. The position vector Xi=[E,P] of each particle represents a set of candidate configuration schemes for energy storage capacity E and power capacity P. Its velocity and position are randomly generated within a preset feasible region.

[0061] Step 3: For the current position of each particle i Substitute the energy storage capacity configuration scheme it represents into the photovoltaic-storage capacity optimization model, perform a full life cycle simulation based on the multiple photovoltaic scenarios generated in step 1, and calculate the total objective function value of the model. As the fitness of particles ;

[0062] Step 4: In the speed update formula, a weight for the uncertainty of photovoltaic output is introduced:

[0063]

[0064] in, For time-varying inertial weights, and As a learning factor, and It is a random number. This represents the standard deviation of the photovoltaic output in the current iteration, used to enhance the algorithm's ability to search for fluctuating scenarios. After the update, if... Then update the individual's optimal position. ;

[0065] Step 5: Calculate population diversity indices ,like ,in, If a preset threshold is set, the velocity or position of some particles is randomly perturbed to maintain population diversity and avoid premature convergence.

[0066] Step 6: If the maximum number of iterations is reached Or the global optimal fitness value The change over K consecutive iterations is less than the threshold. If the iteration fails, the global optimal position is determined. The optimal energy storage capacity configuration scheme is decoded, and based on the final distribution of the particle swarm, multiple schemes with the best fitness values ​​are selected to form the energy storage capacity configuration scheme set; if the termination condition is not met, the process returns to step 3 to continue iterating.

[0067] S230: Select an energy storage capacity configuration scheme that meets the preset conditions from the set of energy storage capacity configuration schemes to construct a joint operation task under the first time sequence, and generate an energy storage control sequence instruction according to the operation constraints in the joint operation task;

[0068] It should be noted that, firstly, from the scheme set generated by S220, an optimal energy storage capacity configuration scheme is selected based on preset conditions (such as the lowest total cost or the highest comprehensive economic and reliability score). Combining the latest ultra-short-term photovoltaic power forecasts and grid dispatch plans, a joint operation task for the next dispatch cycle (e.g., the next 24 hours, referred to as the "first time sequence") is constructed. This task clarifies the expected charge and discharge power plan for energy storage in each time period, the SOC (State of Charge) plan trajectory, and the grid peak-shaving and frequency regulation requirements that need to be met.

[0069] Specifically, in this embodiment, based on the model predictive control framework, a refined energy storage control sequence instruction is dynamically generated. This includes acquiring photovoltaic power output fluctuation data, grid frequency deviation data, energy storage state of charge data, and grid load data; dynamically calculating and normalizing peak-shaving weights and frequency regulation weights based on the standard deviation of the photovoltaic power output fluctuation data and the absolute value of the grid frequency deviation data, combined with a preset baseline coefficient and the frequency benchmark standard deviation, to construct a weighted multi-objective function. This weighted multi-objective function is a linear combination of the peak-shaving cost function and the frequency regulation cost function, and the sum of the peak-shaving weights and frequency regulation weights is 1; constructing a discrete state-space model containing the energy storage state of charge data and the grid frequency deviation; inputting the relevant data into the state-space model, and continuously optimizing the weighted multi-objective function based on the model predictive control framework to generate peak-shaving control instructions and frequency regulation control instructions; calculating dynamic allocation coefficients based on the normalized peak-shaving weights and frequency regulation weights, and linearly combining the peak-shaving control instructions and frequency regulation control instructions based on the allocation coefficients to generate the energy storage control sequence instruction.

[0070] Furthermore, in another preferred embodiment, the rolling optimization of the weighted multi-objective function based on the model predictive control framework to generate peak-shaving control commands and frequency modulation control commands specifically includes:

[0071] The prediction horizon of the model predictive control framework is set, and the prediction horizon corresponds to a scrolling window of a preset duration; a quadratic optimization objective function is constructed, which includes the deviation cost of the state variable relative to the reference trajectory and the smoothness cost of the control input. The reference trajectory of the state variable includes a preset SOC reference value and a zero-frequency deviation; SOC boundary constraints and power limiting constraints are applied, where the SOC boundary includes preset upper and lower limits, and the power limiting does not exceed the rated power of the energy storage; the quadratic optimization objective function is solved at each time step, and only the control command for the current step is executed. The prediction and optimization process is continuously updated based on newly acquired real-time data, and peak-shaving control commands and frequency regulation control commands are continuously generated.

[0072] Specifically, the dynamic calculation and normalization of peak-shaving weights and frequency-shaving weights includes:

[0073] Real-time calculation of the standard deviation of current photovoltaic power output fluctuation data Frequency reference standard deviation of power grid frequency deviation data ;

[0074] Based on the preset peak-shaving baseline coefficient a, frequency-modulation baseline coefficient b, and the frequency reference standard deviation. Calculate the unnormalized peak-shaving temporary weights respectively. and frequency modulation temporary weight The calculation formula is as follows:

[0075]

[0076] The temporary weights are normalized to obtain the final peak-shaving weights. With frequency modulation weight The calculation formula is as follows:

[0077]

[0078] Wherein, the peak-shaving baseline coefficient a and the frequency-shaving baseline coefficient b are positive real numbers preset according to the system operation preference, and satisfy a+b>0.

[0079] S240: In response to the energy storage control sequence command, control the energy storage collaborative control system to perform corresponding charging and discharging operations on the energy storage terminal under the first timing sequence.

[0080] In this embodiment, the grid frequency deviation and frequency change rate are monitored in real time; when the absolute value of the frequency deviation exceeds a preset trigger threshold, a fast frequency response power command is issued; the upper-level optimization command in the energy storage control sequence command is obtained, a fusion coefficient is calculated based on the current absolute value of the frequency deviation, and the fast frequency response power command and the upper-level optimization command are weighted and fused based on the fusion coefficient to obtain the total power command.

[0081] In another preferred embodiment, before the energy storage collaborative control system performs corresponding charging and discharging operations on the energy storage terminal under the first timing sequence, the method further includes: inspecting the operating items corresponding to the joint operation task to determine whether there are any abnormal inspection items characterized as abnormal; if there are abnormal inspection items, using a pre-constructed abnormal scheduling strategy to schedule and calculate the abnormal operating parameters corresponding to the abnormal inspection items, generating a first collaborative scheduling instruction under the second timing sequence; and using the first collaborative scheduling instruction to adjust the joint operation task to control the energy storage collaborative control system to perform corresponding charging and discharging operations on the energy storage terminal under the second timing sequence. The step of using the first collaborative scheduling instruction to adjust the joint operation task to control the energy storage collaborative control system to perform corresponding charging and discharging operations on the energy storage terminal under the second timing sequence is further elaborated.

[0082] Furthermore, this invention also proposes a collaborative control system for energy storage based on efficient photovoltaic power consumption. Please refer to [link to relevant documentation]. Figure 3 , Figure 3 This is a schematic diagram of the energy storage collaborative control system provided in an embodiment of the present invention.

[0083] like Figure 3 As shown, the energy storage collaborative control system 300 includes:

[0084] The acquisition module 310 is used to acquire photovoltaic output data at the power generation end, energy storage configuration data at the energy storage end, and grid demand data at the grid end.

[0085] The scheme generation module 320 is used to input the photovoltaic output data, the energy storage configuration data and the grid demand data into a pre-built photovoltaic-storage capacity optimization model to predict the energy storage configuration and generate a set of energy storage capacity configuration schemes.

[0086] The instruction generation module 330 is used to select energy storage capacity configuration schemes that meet preset conditions from the set of energy storage capacity configuration schemes to construct a joint operation task under the first time sequence, and generate energy storage control sequence instructions according to the operation constraints in the joint operation task.

[0087] The instruction execution module 340 is used to respond to the energy storage control sequence instruction and control the energy storage collaborative control system to perform corresponding charging and discharging operations on the energy storage terminal under the first timing sequence.

[0088] Furthermore, this embodiment of the invention also proposes a computer-readable storage medium storing a generation program for an energy storage collaborative control system. When the generation program for the energy storage collaborative control system is executed by a processor, it implements each step of the above-mentioned energy storage collaborative control method based on high-efficiency photovoltaic absorption, which will not be repeated here.

[0089] The specific embodiments of the computer-readable storage medium of the present invention are basically the same as the embodiments of the above-described energy storage collaborative control method based on high-efficiency photovoltaic absorption, and will not be described in detail here.

[0090] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0091] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0092] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0093] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for coordinated control of energy storage based on efficient photovoltaic power consumption, characterized in that, The method of applying an energy storage collaborative control system, wherein the energy storage collaborative control system communicatively connects the generation end, the energy storage end, and the grid end, includes: Acquire photovoltaic output data from the power generation end, energy storage configuration data from the energy storage end, and grid demand data from the grid end; The photovoltaic output data, the energy storage configuration data, and the grid demand data are input into a pre-built photovoltaic-storage capacity optimization model to predict energy storage configuration and generate a set of energy storage capacity configuration schemes. Select an energy storage capacity configuration scheme that meets the preset conditions from the set of energy storage capacity configuration schemes to construct a joint operation task under the first time sequence, and generate energy storage control sequence instructions according to the operation constraints in the joint operation task; In response to the energy storage control sequence command, the energy storage collaborative control system controls the energy storage terminal to perform corresponding charging and discharging operations under the first timing sequence.

2. The method as described in claim 1, characterized in that, Before acquiring the photovoltaic output data at the power generation end, the energy storage configuration data at the energy storage end, and the grid demand data at the grid end, the process specifically includes: Acquire historical meteorological data, historical photovoltaic power output data, historical energy storage equipment data, and historical grid demand data for any region; Uncertain meteorological features are extracted from historical meteorological data to generate uncertain meteorological features. Based on the deterministic meteorological characteristics, uncertain output characteristics are extracted from the historical photovoltaic time-series output data to obtain uncertain time-series output characteristics; Based on the historical energy storage device data type characteristics, charge characteristics are extracted to generate energy storage type charge characteristics; Based on the uncertain meteorological characteristics, uncertain time-series power output characteristics, energy storage type charge characteristics and the historical power grid demand data, a training prediction model is generated to produce a trained photovoltaic-storage joint time-series prediction model. The photovoltaic-storage joint time-series prediction model can simulate the photovoltaic-storage joint operation simulation data in any region. The prediction model to be trained is constructed based on a Markov model. The photovoltaic-storage joint time-series prediction model is used to simulate the joint operation of photovoltaic and energy storage at the completed simulated power generation end, simulated energy storage end, and simulated grid end.

3. The method as described in claim 2, characterized in that, Before inputting the photovoltaic output data, the energy storage configuration data, and the grid demand data into the pre-built photovoltaic-energy storage capacity optimization model for energy storage configuration prediction, the method further includes: The photovoltaic-storage capacity optimization model is constructed based on a two-layer stochastic mixed-integer linear programming framework, which includes an upper-layer grid model and a lower-layer energy storage model. The overall objective function of the photovoltaic storage capacity optimization model is generated by fusion of the objective based on the cost characterization parameters corresponding to the energy storage end and the curtailment cost characterization parameters corresponding to the energy storage collaborative control system. The overall objective function of the photovoltaic-storage capacity optimization model is expressed as: in, This represents the initial cost of purchasing energy storage multiplied by the capacity. This represents the sum of operating costs for all years. ω represents the average value, which is the average over different weather scenarios; Representing a scene The positive part of the photovoltaic power reduction and discharge in the t-th segment. The upper-level model on the power grid side is represented as follows: in, This represents the electricity price for segment t. Indicates a load gap; Indicates the capacity cost multiplied by the size; This indicates the expected loss caused by light wastage in multiple scenarios. The lower-level model of the energy storage side is represented as follows: in, Indicates the charging electricity price (off-peak hours); This indicates the discharge electricity price (peak hours). , is the charging power. This represents the discharge power.

4. The method as described in claim 3, characterized in that, The step of inputting the photovoltaic output data, the energy storage configuration data, and the grid demand data into a pre-built photovoltaic-energy storage capacity optimization model to predict energy storage configuration and generate an energy storage capacity configuration scheme set specifically includes: The photovoltaic output data, the energy storage configuration data, and the grid demand data are input into a pre-built photovoltaic-energy storage capacity optimization model. An improved particle swarm optimization algorithm is used to solve the energy storage capacity configuration scheme set generated by the photovoltaic energy storage capacity optimization model. The solution steps of the improved particle swarm optimization algorithm are as follows: Step 1: Based on the photovoltaic power output data, use the Markov chain model to generate multiple photovoltaic power output scenario sequences with different time series characteristics to provide random input for subsequent optimization; Step 2: Initialize the particle swarm. The position vector Xi=[E,P] of each particle represents a set of candidate configuration schemes for energy storage capacity E and power capacity P. Its velocity and position are randomly generated within a preset feasible region. Step 3: For the current position of each particle i Substitute the energy storage capacity configuration scheme it represents into the photovoltaic-storage capacity optimization model, perform a full life cycle simulation based on the multiple photovoltaic scenarios generated in step 1, and calculate the total objective function value of the model. As the fitness of particles ; Step 4: In the speed update formula, a weight for the uncertainty of photovoltaic output is introduced: in, For time-varying inertial weights, and As a learning factor, and It is a random number. This represents the standard deviation of the photovoltaic output in the current iteration, used to enhance the algorithm's ability to search for fluctuating scenarios. After the update, if... Then update the individual's optimal position. ; Step 5: Calculate population diversity indices ,like ,in, If a preset threshold is set, the velocity or position of some particles is randomly perturbed to maintain population diversity and avoid premature convergence. Step 6: If the maximum number of iterations is reached, generate the energy storage capacity configuration scheme set; if the termination condition is not met, return to step 3 to continue iterating.

5. The method as described in claim 1, characterized in that, The generated energy storage control sequence instructions specifically include Acquire photovoltaic power output fluctuation data, grid frequency deviation data, energy storage status of charge data, and grid load data; Based on the standard deviation of the photovoltaic power output fluctuation data and the absolute value of the grid frequency deviation data, combined with the preset baseline coefficient and the frequency benchmark standard deviation, the peak shaving weight and frequency regulation weight are dynamically calculated and normalized to construct a weighted multi-objective function. The weighted multi-objective function is a linear combination of the peak shaving cost function and the frequency regulation cost function. Construct a discrete state-space model that includes the energy storage state-of-charge data and the grid frequency deviation; The relevant data is input into the state space model, and the weighted multi-objective function is continuously optimized based on the model predictive control framework to generate peak shaving control commands and frequency modulation control commands. Based on the normalized peak shaving weight and frequency regulation weight, a dynamic allocation coefficient is calculated. Based on the allocation coefficient, the peak shaving control command and the frequency regulation control command are linearly combined to generate the energy storage control sequence command.

6. The method as described in claim 5, characterized in that, The model-based predictive control framework performs rolling optimization of the weighted multi-objective function to generate peak-shaving control commands and frequency modulation control commands, specifically including: The prediction horizon of the model prediction control framework is set, and the prediction horizon corresponds to a scrolling window of a preset duration. Construct a quadratic optimization objective function, which includes the deviation cost of the state variable relative to the reference trajectory and the smoothness cost of the control input. The state variable reference trajectory includes a preset SOC reference value and a zero-frequency deviation. Apply SOC boundary constraints and power limiting constraints, wherein the SOC boundary includes preset upper and lower limits, and the power limiting does not exceed the rated power of the energy storage; Solve the quadratic optimization objective function at each time step, and execute only the control instructions for the current step; Based on the newly acquired real-time data, the prediction and optimization process is continuously updated, and peak-shaving control commands and frequency regulation control commands are continuously generated.

7. The method as described in claim 5 or 6, characterized in that, The dynamic calculation and normalization of peak-shaving weights and frequency-shaving weights specifically includes: Real-time calculation of the standard deviation of current photovoltaic power output fluctuation data Frequency reference standard deviation of power grid frequency deviation data ; Based on the preset peak-shaving baseline coefficient a, frequency-modulation baseline coefficient b, and the frequency reference standard deviation. Calculate the unnormalized peak-shaving temporary weights respectively. and frequency modulation temporary weight The calculation formula is as follows: The temporary weights are normalized to obtain the final peak-shaving weights. With frequency modulation weight The calculation formula is as follows: Wherein, the peak-shaving baseline coefficient a and the frequency-shaving baseline coefficient b are positive real numbers preset according to the system operation preference, and satisfy a+b>0.

8. The method as described in claim 1, further comprising, before controlling the energy storage collaborative control system to perform corresponding charging and discharging operations on the energy storage terminal under the first timing sequence, [further steps]. The operation items corresponding to the joint operation task are inspected to determine whether there are any abnormal inspection items characterized as abnormal. When the abnormal inspection item exists, the abnormal operation parameters corresponding to the abnormal inspection item are scheduled and calculated using a pre-built abnormal scheduling strategy to generate the first collaborative scheduling instruction under the second time sequence. The first coordinated scheduling instruction is used to adjust the joint operation task, so as to control the energy storage coordinated control system to perform corresponding charging and discharging operations on the energy storage terminal under the second timing.

9. The method as described in claim 8, characterized in that, The step of adjusting the joint operation task using the first coordinated scheduling instruction to control the energy storage coordinated control system to perform corresponding charging and discharging operations on the energy storage terminal under the second timing sequence further includes: Real-time monitoring of power grid frequency deviation and frequency change rate; When the absolute value of the frequency deviation exceeds the preset trigger threshold, a fast frequency response power command is generated; Obtain the upper-level optimization instruction from the energy storage control sequence instruction, and calculate the fusion coefficient based on the absolute value of the current frequency deviation; The fast frequency response power command and the upper-layer optimization command are weighted and fused based on the fusion coefficient to obtain the total power command.

10. A photovoltaic-based energy storage collaborative control system, characterized in that, The energy storage collaborative control system communicates with the power generation end, the energy storage end, and the grid end, including: The acquisition module is used to acquire photovoltaic output data at the power generation end, energy storage configuration data at the energy storage end, and grid demand data at the grid end. The scheme generation module is used to input the photovoltaic output data, the energy storage configuration data and the grid demand data into a pre-built photovoltaic-storage capacity optimization model to predict the energy storage configuration and generate a set of energy storage capacity configuration schemes. The instruction generation module is used to select energy storage capacity configuration schemes that meet preset conditions from the set of energy storage capacity configuration schemes to construct a joint operation task under the first time sequence, and generate energy storage control sequence instructions according to the operation constraints in the joint operation task. The instruction execution module is used to respond to the energy storage control sequence instruction and control the energy storage collaborative control system to perform corresponding charging and discharging operations on the energy storage terminal under the first timing sequence.

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