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CN-122000957-A - Energy storage cooperative control method and system based on photovoltaic efficient digestion

CN122000957ACN 122000957 ACN122000957 ACN 122000957ACN-122000957-A

Abstract

The invention provides an energy storage cooperative control method and system based on photovoltaic efficient digestion. The method comprises the steps of obtaining 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-built optical storage capacity optimization model to conduct energy storage configuration prediction to generate an energy storage capacity configuration scheme set, selecting the energy storage capacity configuration scheme which accords with preset conditions 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 operation constraint conditions in the joint operation task, so that the technical problem that full-chain closed-loop control from optimal configuration to cooperative operation of an energy storage system in the prior art is achieved is solved, and the photovoltaic consumption level and the power grid operation economy are effectively improved.

Inventors

  • XU ANG

Assignees

  • 南京邮电大学

Dates

Publication Date
20260508
Application Date
20260126

Claims (10)

  1. 1. The energy storage cooperative control method based on the photovoltaic high-efficiency absorption is characterized by applying an energy storage cooperative control system, wherein the energy storage cooperative control system is in communication connection with a power generation end, an energy storage end and a power grid end, and the method comprises the following steps: Acquiring photovoltaic output data of the power generation end, energy storage configuration data of the energy storage end and power grid demand data of the 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 to perform energy storage configuration prediction, and generating an energy storage capacity configuration scheme set; selecting an energy storage capacity configuration scheme which meets preset conditions 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 operation constraint conditions in the joint operation task; And responding to the energy storage control sequence instruction, and controlling the energy storage cooperative control system to execute corresponding charge and discharge operation on the energy storage end under the first time sequence.
  2. 2. The method according to claim 1, wherein before obtaining 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, specifically comprises: Acquiring historical meteorological data, historical photovoltaic time sequence output data, historical energy storage equipment data and historical power grid demand data of any region; extracting uncertain weather features according to the historical weather data to generate uncertain weather features; Extracting uncertainty output characteristics of the historical photovoltaic time sequence output data based on the deterministic weather characteristics to obtain uncertainty time sequence output characteristics; extracting charge characteristics according to the data type characteristics of the historical energy storage equipment, and generating energy storage type charge characteristics; Training a prediction model based on the uncertainty meteorological features, the uncertainty time sequence output features, the energy storage type charge features and the historical power grid demand data to generate a trained light-storage combined time sequence prediction model, wherein the light-storage combined time sequence prediction model can simulate light-storage combined operation simulation data of any region, and the prediction model to be trained is constructed based on a Markov model; and carrying out optical storage joint operation simulation data on the completed simulation power generation end, simulation energy storage end and simulation power grid end by using the optical storage joint time sequence prediction model.
  3. 3. The method of claim 2, wherein before the inputting the photovoltaic output data, the energy storage configuration data, and the grid demand data into a pre-built optimal model for energy storage configuration prediction, further comprising: The optical storage capacity optimization model is constructed and generated based on a two-layer random mixed integer linear programming framework, wherein the two-layer random mixed integer linear programming framework comprises an upper model of an electric power grid and a lower model of energy storage; The total objective function of the optical storage capacity optimization model is generated by performing objective fusion based on the cost representation parameters corresponding to the energy storage end and the abandoned light cost representation parameters corresponding to the energy storage cooperative control system; The total objective function of the optical storage capacity optimization model is expressed as: Wherein, the Representing the initial money multiplied by the capacity of the purchased energy storage; representing the sum of the operating fees for all years; Representing an average value, and averaging different weather scenes omega; representing a scene Positive part of discharge electricity of lower t-th section photovoltaic electricity reducing The grid side upper layer model is expressed as: wherein, the Representing the electricity price of the t th section; Indicating load gap; representing the capacity charge multiplied by the size; Representing expected loss caused by light rejection in multiple scenes The energy storage side lower layer model is expressed as: wherein, the Represents a charging electricity price (valley period); indicating the discharge electricity price (peak time), For the charging power, the power of the battery, Is the discharge power.
  4. 4. The method of claim 3, wherein the inputting the photovoltaic output data, the energy storage configuration data, and the grid demand data into a pre-built optical storage capacity optimization model for energy storage configuration prediction, and generating an energy storage capacity configuration scheme set specifically comprises: inputting the photovoltaic output data, the energy storage configuration data and the power grid demand data into a pre-constructed light storage capacity optimization model; Solving an energy storage capacity configuration scheme set generated by the optical storage capacity optimization model by adopting an improved particle swarm algorithm to generate the energy storage capacity configuration scheme set, wherein the solving step of the improved particle swarm algorithm comprises the following steps: Step 1, generating a plurality of photovoltaic output scene sequences with different time sequence characteristics by using a Markov chain model based on the photovoltaic output data, and providing random input for subsequent optimization; Step 2, initializing a particle swarm, wherein the position vector xi= [ E, P ] of each particle represents a configuration scheme of a group of candidate energy storage energy capacity E and power capacity P, and the speed and the position of the configuration scheme are randomly generated in a preset feasible region; Step 3-current position for each particle i Substituting the represented energy storage capacity configuration scheme into the light storage capacity optimization model, performing full life cycle simulation based on the multi-photovoltaic scene generated in the step 1, and calculating the total objective function value of the model As a fitness of particles ; And 4, introducing uncertainty weight of the photovoltaic output in a speed updating formula: wherein, the For the time-varying inertial weight, And In order for the learning factor to be a function of, And In the form of a random number, The standard deviation of the current iteration photovoltaic output is used for enhancing the searching capability of the algorithm on the fluctuation scene. After updating, if Updating the individual optimal position ; Step 5, calculating the population diversity index If (if) , wherein, If the threshold value is preset, randomly disturbing the speed or the position of part of particles so as to maintain population diversity and avoid premature convergence; and step 6, if the maximum iteration times are reached, generating the energy storage capacity configuration scheme set, and if the termination condition is not met, returning to the step 3 to continue iteration.
  5. 5. The method of claim 1, wherein generating the energy storage control sequence instruction comprises Acquiring photovoltaic output fluctuation data, power grid frequency deviation data, energy storage charge state data and power grid load data; Dynamically calculating peak regulation weight and frequency modulation weight and normalizing based on the standard deviation of the photovoltaic output fluctuation data and the absolute value of the power grid frequency deviation data by combining a preset baseline coefficient and a frequency reference standard deviation, and constructing a weighted multi-objective function, wherein the weighted multi-objective function is a linear combination of the peak regulation cost function and the frequency modulation cost function; constructing a discrete state space model comprising the energy storage state of charge data and the grid frequency deviation; Inputting the related data into the state space model, and rolling and optimizing the weighted multi-objective function based on a model prediction control framework to generate a peak regulation control instruction and a frequency modulation control instruction; And calculating a dynamic distribution coefficient according to the normalized peak regulation weight and the normalized frequency regulation weight, and generating the energy storage control sequence instruction based on the distribution coefficient linear combination peak regulation control instruction and the frequency regulation control instruction.
  6. 6. The method of claim 5, wherein the model-based predictive control framework roll optimizes the weighted multi-objective function to generate peaking control commands and tuning control commands, comprising: setting a prediction view of the model prediction control framework, wherein the prediction view corresponds to a rolling window with preset duration; Constructing a quadratic optimization objective function, wherein the objective function comprises a deviation cost of a state variable relative to a reference track and a smoothness cost of control input, and the state variable reference track comprises a preset SOC reference value and zero frequency deviation; Applying SOC boundary constraint and power limiting constraint, wherein the SOC boundary comprises preset upper and lower limits, and the power limiting does not exceed the energy storage rated power; solving the quadratic optimization objective function at each time step, and executing only the current step control instruction; and continuously generating a peak regulation control instruction and a frequency modulation control instruction based on the newly acquired real-time data rolling update prediction and optimization process.
  7. 7. The method according to claim 5 or 6, wherein the dynamically calculating and normalizing peak shaver weights and frequency modulation weights specifically comprises: Calculating standard deviation of current photovoltaic output fluctuation data in real time Frequency reference standard deviation of grid frequency deviation data ; Based on preset peak regulation baseline coefficient a, frequency regulation baseline coefficient b and the frequency reference standard deviation Respectively calculating unnormalized peak shaving temporary weights And frequency modulation temporary weights The calculation formula is as follows: Normalizing the temporary weight to obtain a final peak regulation weight And frequency modulation weight The calculation formula is as follows: wherein, the peak regulation baseline coefficient a and the frequency regulation baseline coefficient b are positive real numbers preset according to the system operation preference, and satisfy a+b >0a+b >0.
  8. 8. The method of claim 1, wherein the controlling the energy storage cooperative control system further comprises, before the corresponding charge and discharge operations are performed on the energy storage terminal at the first time period Performing inspection on the operation items corresponding to the combined operation tasks, and judging whether an abnormal inspection item which is characterized as abnormal by the operation items exists or not; When the abnormal inspection item exists, scheduling calculation is carried out on abnormal operation parameters corresponding to the abnormal inspection item by utilizing a pre-constructed abnormal scheduling strategy, and a first cooperative scheduling instruction under a second time sequence is generated; and adjusting the joint operation task by using the first cooperative scheduling instruction so as to control the energy storage cooperative control system to execute corresponding charge and discharge operation on the energy storage end under the second time sequence.
  9. 9. The method of claim 8, wherein the adjusting the joint operation task using the first co-scheduling instruction to control the energy storage co-control system to perform a corresponding charge and discharge operation on the energy storage end at the second time sequence further comprises: monitoring the frequency deviation and the frequency change rate of the power grid in real time; When the absolute value of the frequency deviation exceeds a preset trigger threshold, generating a rapid frequency response power instruction; Acquiring an upper-layer optimization instruction in the energy storage control sequence instruction, and calculating a fusion coefficient based on the absolute value of the current frequency deviation; and carrying out weighted fusion on the fast frequency response power instruction and the upper layer optimization instruction based on the fusion coefficient to obtain a total power instruction.
  10. 10. Energy storage cooperative control system based on high-efficient consumption of photovoltaic, its characterized in that, energy storage cooperative control system communication connection power generation end, energy storage end and electric wire netting end includes: the acquisition module is used for acquiring photovoltaic output data of the power generation end, energy storage configuration data of the energy storage end and power grid demand data of the power grid end; The scheme generating module is used for inputting the photovoltaic output data, the energy storage configuration data and the power grid demand data into a pre-constructed optical storage capacity optimizing model to perform energy storage configuration prediction, and generating an energy storage capacity configuration scheme set; The instruction generation module is used for selecting the energy storage capacity configuration schemes which meet preset conditions 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 operation constraint conditions in the joint operation task; The instruction execution module is used for responding to the energy storage control sequence instruction and controlling the energy storage cooperative control system to execute corresponding charge and discharge operation on the energy storage end under the first time sequence.

Description

Energy storage cooperative control method and system based on photovoltaic efficient digestion Technical Field The invention relates to the technical field of power grid energy storage scheduling control, in particular to an energy storage cooperative control method and system based on photovoltaic efficient digestion. Background Along with the rapid increase of the installed capacity of the photovoltaic power generation, the intermittence and fluctuation of the output of the photovoltaic power generation form a serious challenge for the stable operation and efficient digestion of a power grid. The energy storage system is used as an important flexible adjustment resource, can effectively stabilize photovoltaic power fluctuation and participate in grid peak regulation and frequency modulation, and is a key technical means for improving photovoltaic digestion capability. Currently, research on optical storage combined systems mainly focuses on optimization of a single link, such as energy storage capacity configuration or operation control strategies. However, in practical applications, capacity planning of the energy storage system interacts with real-time operation control. Simple capacity optimization may not be able to accommodate real-time fluctuating operating scenarios. In addition, the uncertainty of the photovoltaic output is often not considered enough, or the adopted optimization algorithm is easy to fall into local optimization, so that the configuration scheme is poor in economy and untimely in control response, and the photovoltaic consumption benefit is difficult to maximize on the premise of ensuring the safety of the power grid. Therefore, there is an urgent need for an energy storage cooperative control method that can perform through planning and operation, comprehensively consider the uncertainty of the photovoltaic output, and realize global optimization. Disclosure of Invention The invention mainly aims to provide an energy storage cooperative control method and system based on photovoltaic high-efficiency digestion, and aims to solve the technical problems of realizing full-chain closed-loop control from optimal configuration to cooperative operation of an energy storage system and effectively improving the photovoltaic digestion level and the power grid operation economy in the prior art. In a first aspect, an embodiment of the present invention provides an energy storage cooperative control method based on efficient photovoltaic absorption, where an energy storage cooperative control system is applied, and the energy storage cooperative control system is communicatively connected to a power generation end, an energy storage end and a power grid end, and the method includes: Acquiring photovoltaic output data of the power generation end, energy storage configuration data of the energy storage end and power grid demand data of the 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 to perform energy storage configuration prediction, and generating an energy storage capacity configuration scheme set; selecting an energy storage capacity configuration scheme which meets preset conditions 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 operation constraint conditions in the joint operation task; And responding to the energy storage control sequence instruction, and controlling the energy storage cooperative control system to execute corresponding charge and discharge operation on the energy storage end under the first time sequence. In a second aspect, an embodiment of the present invention provides an energy storage cooperative control system based on efficient photovoltaic digestion, where the energy storage cooperative control system is communicatively connected to a power generation end, an energy storage end and a power grid end, and includes: the acquisition module is used for acquiring photovoltaic output data of the power generation end, energy storage configuration data of the energy storage end and power grid demand data of the power grid end; The scheme generating module is used for inputting the photovoltaic output data, the energy storage configuration data and the power grid demand data into a pre-constructed optical storage capacity optimizing model to perform energy storage configuration prediction, and generating an energy storage capacity configuration scheme set; The instruction generation module is used for selecting the energy storage capacity configuration schemes which meet preset conditions 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