Industrial park source-load-storage collaborative optimization system and method oriented to flexibility improvement

Abstract

The invention belongs to the technical field of source-load-storage collaborative optimization, and particularly relates to an industrial park source-load-storage collaborative optimization system and method oriented to flexibility improvement, and the system is provided with a dynamic resource portrait modeling module, a space-time elastic evaluation solving module, a quantum collaborative optimization module, a virtual-real linkage verification module and a collaborative optimization instruction updating module. The problem that the supply and demand balance of a traditional industrial park is not in a controllable range is solved; the limitation of a traditional static equipment model is broken through, the source-load-storage resource characteristics are described more accurately, the elasticity of resources in time and space dimensions and the game relationship among different subjects (source, load and storage) are considered, and compared with a traditional optimization mode only from a single dimension or ignoring a subject game, the method is more in line with a complex actual operation scene of an industrial park, and the method is more suitable for popularization and application. And the reliability of the optimization instruction is ensured. According to the method, accurate description, collaborative optimization and reliable execution of the source-load-storage resources of the industrial park are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of source-load-storage collaborative optimization, and specifically relates to a source-load-storage collaborative optimization system and method for industrial parks aiming at flexibility improvement. Background Technique

[0002] The proportion of renewable energy power generation in the total power generation of the power system is increasing continuously. Renewable energy is bound to become an important part of the power system, and building a new power system has become an important development direction. Industrial parks are concentrated areas of industrial production, with large energy consumption and carbon emissions. Therefore, realizing source-load-storage collaborative optimization can improve energy utilization efficiency, reduce the use of fossil energy, lower carbon emissions, and contribute to the green and low-carbon development of the park, which is the future development demand.

[0003] In the existing research on source-load-storage collaborative optimization, there is a scheduling method of "source following load", such as CN115833255A, a source-load collaborative optimization scheduling method and terminal for flexible supply-demand balance. Although it analyzes the source-load collaborative optimization scheduling of flexible supply-demand balance, it only considers how to achieve flexibility on both the source and load sides, and does not consider the uncertainty factors existing when analyzing the flexible supply-demand balance of the new power system.

[0004] Another example is CN115222195A, a distribution network optimization scheduling method considering flexible resources of source-network-load-storage. Although it mentions the upward flexibility deficiency index and the downward flexibility deficiency index, it does not consider the characteristics of flexible balance of this index in specific scenarios. Therefore, it cannot ensure that the flexible supply-demand balance of the new power system is within a controllable range. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the present invention provides a source-load-storage collaborative optimization system and method for industrial parks aiming at flexibility improvement. Through innovative technologies such as dynamic modeling, quantum optimization, and virtual-real linkage verification, it realizes the accurate characterization, collaborative optimization, and reliable execution of source-load-storage resources in industrial parks, and significantly improves the system flexibility and operation efficiency.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A source-load-storage collaborative optimization system for industrial parks aiming at flexibility improvement, comprising: A dynamic resource portrait modeling module, used to collect source-load-storage data of industrial parks, perform dynamic resource portrait modeling, and output a set of dynamic device efficiencies; A spatio-temporal elasticity evaluation and solution module, used to obtain grid constraints and market price signals, establish constraint conditions in combination with the set of dynamic device efficiencies, calculate a dynamic cost function, perform spatio-temporal elasticity evaluation, and output an elastic boundary surface through dynamic game solution; A quantum collaborative optimization module, which is used to consider the elastic boundary surface and collect the real-time monitoring data of the industrial park, and perform quantum collaborative optimization to obtain the optimal policy vector; A virtual-real linkage verification module, which is used to perform virtual-real linkage verification based on the optimal policy vector to obtain a collaborative optimization verification instruction set and a collaborative optimization credibility label; A collaborative optimization instruction update module, which is used to obtain the integrity characteristics of the data collected in the industrial park and the reliability characteristics of the data transmission in the industrial park, analyze them, eliminate the untrusted collaborative optimization instructions in combination with the collaborative optimization credibility label, and perform collaborative optimization adjustment on the source, load and storage of the industrial park based on the remaining trusted collaborative optimization instructions.

[0007] Preferably, in the dynamic resource portrait modeling module, the data of the source, load and storage of the industrial park are collected, and the dynamic resource portrait modeling is carried out. The specific process is as follows: Collect the data of the source, load and storage of the industrial park, including the photovoltaic output of the industrial park, the ambient temperature of the industrial park, the SOC of the energy storage in the industrial park, the rated power of the equipment in the industrial park, the actual power of the equipment in the industrial park, the rated efficiency of the equipment in the industrial park, the installation area of the photovoltaic panels in the industrial park, and the irradiance of the industrial park; Perform dynamic resource portrait modeling. The modeling method is as follows: Set the long short-term memory network LSTM formula: ; In the formula, is the hidden state, is the photovoltaic output of the industrial park at time t, is the ambient temperature of the industrial park at time t, is the SOC of the energy storage in the industrial park at time t; For the SOC of the energy storage: Use particle swarm optimization to screen features and maximize the particle swarm optimization objective function, where the particle swarm optimization objective function is: ; In the formula, is the efficiency of the energy storage system for charge and discharge operations at time t, is the time decay factor, and e is the natural constant; Normalize the actual power of the equipment in the industrial park: ; In the formula, is the normalized power of the equipment in the industrial park at time t, is the actual power of the equipment in the industrial park at time t, is the rated power of the equipment in the industrial park; Establish an efficiency decay model, combine the influence of aging and load, and calculate the real-time efficiency of the equipment in the industrial park: ; In the formula, is the real-time efficiency of the industrial park equipment at time t, is the rated efficiency of the industrial park equipment, is the aging factor stored in the database, is the first correction coefficient of load fluctuation on efficiency, is the second correction coefficient of load fluctuation on efficiency, is the historical load average value of the industrial park equipment; Photovoltaic output correction: Based on the irradiance of the industrial park and the ambient temperature of the industrial park, correct the weather deviation of historical data: ; In the formula, is the irradiance of the industrial park, A is the installation area of the photovoltaic panels in the industrial park, is the photoelectric conversion efficiency.

[0008] Preferably, in the dynamic resource portrait modeling module, the dynamic equipment effectiveness set is output, and the specific process is as follows: Obtain the maximum power data of the industrial park equipment and the response time data of the industrial park equipment and the maintenance cost data of the industrial park equipment ; Based on the power data of the past 24 hours, analyze the equipment health index, where the power data includes the actual power of the industrial park equipment and the efficiency of the industrial park equipment: ; In the formula, is the equipment health index of the industrial park equipment at time k, is the actual power of the industrial park equipment at time k, is the energy conversion efficiency of the industrial park equipment at time k, that is, the ratio of the output energy to the input energy; Combine , , and into a dynamic equipment effectiveness set .

[0009] Preferably, in the spatio-temporal elasticity evaluation and solution module, the grid constraints and market price signals are obtained, and the constraint conditions are established and the dynamic cost function is calculated in combination with the dynamic equipment effectiveness set. The specific process is as follows: Response time constraint, extract the energy storage response time of the industrial park from the response time data of the industrial park equipment and the response time of the gas turbine in the industrial park : ; In the formula, is the dispatching instruction response time; Extract the highest maintenance cost of each device in the industrial park from the industrial park equipment maintenance cost data , and the maintenance cost constraint is that the equipment maintenance cost of the dispatching instruction shall not be higher than the highest maintenance cost of the corresponding industrial park equipment ; ; Obtain the power grid constraint and voltage limit, and convert them into quadratic programming conditions: ; In the formula, is the base voltage, is the line impedance of the th node, is the injection power of the th node, and n represents the number of nodes participating in the calculation; Define the power feasible region: ; In the formula, is the minimum power value allowed to be transmitted by the line, is the actual power transmitted by the line, is the maximum power value allowed to be transmitted by the line; Obtain the market price signal, and the dynamic cost function is: ; In the formula, is the dynamic cost at time t, is the peak electricity price, is the actual power transmitted by the line at time t, is the time-of-use electricity price, is the start time of the time-of-use electricity price calculation, is the end time of the time-of-use electricity price calculation.

[0010] Preferably, in the spatio-temporal elasticity evaluation and solution module, spatio-temporal elasticity evaluation is performed, and the dynamic game solution outputs the elastic boundary surface. The specific process is as follows: Elastic margin calculation: ; Among them, the calculation method of the source-side regulation ability is: ; The load elasticity is calculated as: ; In the formula, For the flexibility evaluation index, T is the total time period, m is the number of source-side devices, is the maximum power output value of the sth source-side device, is the minimum power output value of the sth source-side device, is the number of the load side, is the power change of the lth load, is the time decay factor, e is the natural constant, is the efficiency of the sth source-side for its power partial derivative, is the reciprocal of the response time, is the load transfer cost, is the maximum power of the lth load, t is the time index; Solve the Nash equilibrium by dynamic game, and the three parties of source, load and storage reach the optimal strategy through virtual iteration: ; In the formula, is the strategy variable of the fth party in the (K + 1)th iteration, is the strategy variable of the fth party, is the strategy combination of other parties except the fth party in the Kth iteration, is the utility function of the fth party; The convergence condition is that the strategy change rate is less than 1%; Output the elastic boundary surface ; is the time boundary, that is, the allowable adjustment time window for solving the update, is the power boundary, that is, the maximum power schedulable range for solving the update, is the cost boundary, that is, the cost constraint for solving the update.

[0011] Preferably, in the quantum collaborative optimization module, considering the elastic boundary surface and collecting the real-time monitoring data of the industrial park, the quantum collaborative optimization obtains the optimal strategy vector. The specific process is as follows: Input the elastic boundary surface, and map the optimal solution set to the quantum state amplitude , providing the initial population for the quantum chromosome of the quantum collaborative optimization: ; In the formula, is the power of the jth strategy of the elastic boundary surface, is the total power of the strategies of the elastic boundary surface, j is the index of the strategies of the elastic boundary surface, and J is the number of strategies of the elastic boundary surface; Real-time monitoring data, establish the energy storage SOC constraint: ; In the formula, is the maximum value of the power of the industrial park equipment, is the rated power of the industrial park equipment, and SOC is the state of charge of the industrial park equipment; Quantum collaborative optimization: Chromosome coding is performed, and the rotation angle formula of the dynamic rotation door strategy is: ; In the formula, is the rotation angle at the current iteration number , is the maximum number of iterations, is the best fitness at the th iteration, is the best fitness at the 0th iteration; Fitness evaluation: ; In the formula, is the fitness evaluation value, is the power generation of the equipment, is the maintenance cost of the dispatching instruction equipment, is the flexibility evaluation index, is the hyperbolic tangent function, is the change in carbon dioxide emissions, is the baseline value of carbon dioxide emissions; The strategy corresponding to the highest fitness evaluation value is recorded as the optimal strategy, and the optimal strategy vector is output.

[0012] Preferably, in the virtual-real linkage verification module, virtual-real linkage verification is performed based on the optimal strategy vector to obtain a collaborative optimization verification instruction set and a collaborative optimization credibility label. The specific process is as follows: The optimal strategy vector is decomposed into instructions, and a digital twin is established for verification, including a photovoltaic model and a energy storage model: The photovoltaic model is a single-diode model, and the energy storage model is an RC equivalent circuit; Deviation calculation is performed through virtual-real comparison: ; In the formula, is the comprehensive deviation degree, Q is the number of samples, is the actual measurement value of the qth sample, is the predicted value of the virtual model of the qth sample, is the rated power; If the comprehensive deviation degree is higher than the deviation degree threshold stored in the database, correction is triggered; The sliding window least squares method is used to correct the parameters to ensure the consistency between the digital twin and the entity: ; In the formula, is the updated model parameter vector, is the model parameter vector, is the actual measurement value at time t, is the model prediction value at time t based on the model parameters ; After correction, recalculate the comprehensive deviation degree, obtain the comprehensive deviation degree - collaborative optimization credibility mapping set stored in the database, and find the matching collaborative optimization credibility based on the comprehensive deviation degree, denoted as the collaborative optimization credibility label; Output the collaborative optimization verification instruction set and the corresponding collaborative optimization credibility label.

[0013] Preferably, in the collaborative optimization instruction update module, obtain the integrity characteristics of the collected data in the industrial park and the reliability characteristics of the data transmission in the industrial park and analyze them. The specific process is as follows: Obtain the integrity characteristics of the collected data in the industrial park and the reliability characteristics of the data transmission in the industrial park, including the integrity ratio of the collected data in the industrial park, the error rate of the collected data in the industrial park, and the packet loss rate of the data transmission in the industrial park; Analyze the integrity characteristics of the collected data in the industrial park and the reliability characteristics of the data transmission in the industrial park to obtain the credibility verification index, and the credibility verification index is used as the analysis basis for eliminating untrustworthy collaborative optimization instructions; The credibility verification index, the specific process is as follows: ; In the formula, is the credibility verification index, is the integrity ratio of the collected data in the industrial park, is the error rate of the collected data in the industrial park, is the packet loss rate of the data transmission in the industrial park, and e is the natural constant; Obtain the credibility verification index - collaborative optimization credibility update value mapping set stored in the database, and find the matching collaborative optimization credibility update value based on the credibility verification index.

[0014] Preferably, in the collaborative optimization instruction update module, combine the collaborative optimization credibility label to eliminate untrustworthy collaborative optimization instructions, and perform collaborative optimization adjustment on the source, load, and storage in the industrial park based on the remaining trustworthy collaborative optimization instructions. The specific process is as follows: Add the collaborative optimization credibility update value and the collaborative optimization credibility to obtain the collaborative optimization credible feature, and compare the collaborative optimization credible feature with the collaborative optimization credible threshold stored in the database; If the collaborative optimization trust feature is not lower than the collaborative optimization trust threshold, the collaborative optimization instruction corresponding to the collaborative optimization trust feature is trustworthy; If the collaborative optimization trust feature is lower than the collaborative optimization trust threshold, the collaborative optimization instruction corresponding to the collaborative optimization trust feature is untrustworthy, and the untrustworthy collaborative optimization instructions are eliminated; Based on the remaining trustworthy collaborative optimization instructions, collaborative optimization adjustment is performed on the source-load-storage in the industrial park.

[0015] A collaborative optimization method for source-load-storage in an industrial park aiming at flexibility improvement, based on the above system, includes the following steps: Collect data of the source-load-storage in the industrial park, model the dynamic resource portrait, and output the dynamic equipment efficiency set; Obtain grid constraints and market price signals, establish constraint conditions and calculate the dynamic cost function in combination with the dynamic equipment efficiency set, perform spatio-temporal elasticity evaluation, and solve the dynamic game to output the elastic boundary surface; Consider the elastic boundary surface and collect real-time monitoring data of the industrial park, and obtain the optimal strategy vector through quantum collaborative optimization; Based on the optimal strategy vector, perform virtual-real linkage verification to obtain the collaborative optimization verification instruction set and the collaborative optimization credibility label; Obtain the integrity feature of the data collected in the industrial park and the reliability feature of the data transmission in the industrial park, analyze them, eliminate the untrustworthy collaborative optimization instructions in combination with the collaborative optimization credibility label, and perform collaborative optimization adjustment on the source-load-storage in the industrial park based on the remaining trustworthy collaborative optimization instructions.

[0016] The present invention has the following beneficial effects: The system of the present invention constructs multiple functional modules such as dynamic resource portrait modeling, spatio-temporal elasticity evaluation and solution, quantum collaborative optimization, virtual-real linkage verification, and collaborative optimization instruction update, breaking the traditional single optimization method, and collaborating in the whole process from data modeling, evaluation and solution, strategy optimization to verification and update, which is an innovation of the collaborative optimization system architecture for source-load-storage. Collecting data of the source-load-storage in the industrial park for dynamic modeling and outputting the dynamic equipment efficiency set breaks through the limitations of the traditional static equipment model, can reflect the efficiency state of the equipment in different working conditions and at different times in real time, and can more accurately depict the characteristics of source-load-storage resources. Establishing constraint conditions and dynamic cost functions in combination with grid constraints and market price signals, and performing spatio-temporal elasticity evaluation and dynamic game solution. Considering the elasticity of resources in the time and space dimensions and the game relationship between different entities (source, load, storage), compared with the traditional optimization method that only considers a single dimension or ignores the game between entities, it is more in line with the complex actual operation scenario of the industrial park.

[0017] The present invention obtains an optimal strategy vector through collaborative optimization using quantum-related technologies. Quantum computing has unique advantages in dealing with complex optimization problems, can search the solution space more efficiently, find better strategies, and provide a new technical path for the collaborative optimization of source-load-storage. Through virtual-real linkage verification, a collaborative optimization verification instruction set and credibility labels are obtained, and untrusted instructions are eliminated by combining data feature analysis. The reliability of optimization instructions is ensured by using the linkage verification between the virtual model and the actual system and introducing a credibility management mechanism. Brief Description of the Drawings

[0018] Figure 1 It is a schematic diagram of the connection of the system modules of the present invention; Figure 2 It is a schematic diagram of the method step flow of the present invention; Figure 3 It is a schematic diagram of the complete process of the method of the present invention. Detailed Embodiments

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention.

[0020] Embodiment 1: As Figure 1 shown, a collaborative optimization system for source-load-storage in an industrial park aiming at flexibility improvement includes: a dynamic resource portrait modeling module, a spatio-temporal elasticity evaluation and solution module, a quantum collaborative optimization module, a virtual-real linkage verification module, and a collaborative optimization instruction update module.

[0021] The dynamic resource portrait modeling module is used to collect source-load-storage data in the industrial park, model the dynamic resource portrait, and output a set of dynamic device efficiencies.

[0022] The specific process is as follows: Collect source-load-storage data in the industrial park, including photovoltaic output in the industrial park, ambient temperature in the industrial park, energy storage SOC in the industrial park, rated power of industrial park equipment, actual power of industrial park equipment, rated efficiency of industrial park equipment, installation area of industrial park photovoltaic panels, and irradiance in the industrial park; Perform dynamic resource portrait modeling, and the modeling method is as follows: Set the long short-term memory network LSTM formula: ; where is the hidden state, is the photovoltaic output in the industrial park at time t, is the ambient temperature in the industrial park at time t, is the energy storage SOC in the industrial park at time t; For the energy storage SOC: Use particle swarm optimization to screen key features and maximize the particle swarm optimization objective function, where the particle swarm optimization objective function is: ; where, is the efficiency of the energy storage system for charge and discharge operations at time t, is the time decay factor, and e is the natural constant; Normalize the actual power of the industrial park equipment to ensure the comparability of the output of different equipment: ; where, is the normalized power of the industrial park equipment at time t, is the actual power of the industrial park equipment at time t, is the rated power of the industrial park equipment; Reserve a 20% safety margin to avoid exceeding the limit; Establish an efficiency decay model, combine the effects of aging and load, and calculate the real-time efficiency of the industrial park equipment: ; where, is the real-time efficiency of the industrial park equipment at time t, is the rated efficiency of the industrial park equipment, is the aging factor stored in the database, is the first correction coefficient of load fluctuation on efficiency, is the second correction coefficient of load fluctuation on efficiency, is the historical load average value of the industrial park equipment; ln is the logarithm with base e; Correct the photovoltaic output: Based on the irradiance and ambient temperature of the industrial park, correct the weather deviation of historical data: ; where, is the irradiance of the industrial park, A is the installation area of the photovoltaic panels in the industrial park, is the photoelectric conversion efficiency.

[0023] Collect multi-dimensional data such as photovoltaic output, ambient temperature, energy storage SOC, and equipment power in the industrial park, comprehensively covering the key information of the source, load, and storage, providing a rich and accurate data basis for subsequent analysis and optimization, and avoiding analysis deviations caused by data missing.

[0024] Using the LSTM formula combined with information such as photovoltaic output, temperature, and energy storage SOC can effectively capture the dynamic characteristics of photovoltaic affected by weather and seasons, such as the change of photovoltaic output at different times and different weathers, providing a basis for the reasonable scheduling and prediction of photovoltaic power sources.

[0025] Screen the key features of the energy storage SOC through particle swarm optimization. Considering the decay of charge-discharge efficiency over time helps to accurately evaluate the performance and health status of the energy storage system, reasonably arrange the charge-discharge strategies of the energy storage, and improve the utilization efficiency and lifespan of the energy storage.

[0026] Normalize the actual power of the equipment to ensure the comparability of the outputs of different equipment, which is convenient for unified analysis and scheduling. Establish an efficiency decay model to calculate the real-time efficiency by comprehensively considering the impacts of aging and load, which can more accurately evaluate the actual effectiveness of the equipment at different operation stages and provide references for equipment maintenance and scheduling.

[0027] Correct the photovoltaic output based on irradiance and temperature, considering the influence of meteorological factors on the photovoltaic, making the photovoltaic output prediction more in line with the actual situation and improving the rationality of photovoltaic energy consumption and utilization.

[0028] The dynamic resource portrait modeling module can, through precise data processing and resource characteristic description, better understand the resource status and change laws of each link of the power source, load, and energy storage. Then, in subsequent links such as spatio-temporal elasticity evaluation and solution, and quantum collaborative optimization, it can achieve the optimal allocation of power source, load, and energy storage resources, improve energy utilization efficiency, and reduce resource waste. Accurately grasping the dynamic characteristics of resources such as photovoltaic and energy storage enables the system to better cope with the intermittency, volatility of renewable energy, and load changes, enhancing the flexibility and response ability of the system. At the same time, making decisions based on reliable data and models ensures the reliability of system operation and reduces operation risks caused by inaccurate grasp of resource characteristics.

[0029] Output the dynamic equipment effectiveness set. The specific process is as follows: Obtain the maximum power data of the industrial park equipment , the response time data of the industrial park equipment , and the maintenance cost data of the industrial park equipment ; Based on the power data of the past 24 hours, analyze the equipment health index, where the power data includes the actual power of the industrial park equipment and the efficiency of the industrial park equipment: ; In the formula, is the equipment health index of the industrial park equipment at time k, is the actual power of the industrial park equipment at time k, is the energy conversion efficiency of the industrial park equipment at time k, that is, the ratio of the output energy to the input energy; Combine , , and into the dynamic equipment effectiveness set .

[0030] The dynamic device efficiency set covers multi-dimensional information such as the maximum power of the device, response time, maintenance cost, health index, etc. The maximum power reflects the maximum power generation or work ability of the device; the response time reflects the response speed of the device to instructions and is related to the overall response efficiency of the system; the maintenance cost is used to measure the economic operation of the device; the health index comprehensively considers the actual power and efficiency, etc., to evaluate the health status of the device. The multi-dimensional characterization can comprehensively and accurately grasp the device status and provide a reliable basis for subsequent decision-making.

[0031] Based on the dynamic device efficiency set, the scheduling can be reasonably arranged according to the device capabilities and status. Priority is given to using devices with high maximum power, fast response speed, and good health status to improve the overall operation efficiency of the system; for devices with high maintenance costs and low health indexes, the usage strategy can be adjusted, such as reducing the usage frequency or arranging maintenance, to optimize resource utilization.

[0032] The spatio-temporal elasticity evaluation and solution module is used to obtain grid constraints and market price signals, establish constraint conditions in combination with the dynamic device efficiency set, calculate the dynamic cost function, perform spatio-temporal elasticity evaluation, and output the elastic boundary surface through dynamic game solution.

[0033] The specific process is as follows: For the response time constraint, extract the energy storage response time in the industrial park and the gas turbine response time in the industrial park from the industrial park device response time data to ensure the feasibility of the scheduling instruction: ; where is the response time of the scheduling instruction; ; From the industrial park device maintenance cost data extract the highest maintenance cost of each device in the industrial park , and the maintenance cost constraint is that the maintenance cost of the scheduling instruction device is not higher than the corresponding highest maintenance cost of the industrial park device ; Obtain the grid constraints and voltage limits and transform them into quadratic programming conditions: ; where is the reference voltage, ; where is the reference voltage, is the line impedance of the th node, is the injection power of the th node, and n represents the number of nodes participating in the calculation; Define the power feasible region: ; where is the minimum power value allowed to be transmitted by the line, is the actual power transmitted by the line, is the maximum power value allowed to be transmitted by the line; Obtain the market price signal, and the dynamic cost function is: ; In the formula, is the dynamic cost at time t, is the peak electricity price, is the power actually transmitted by the line at time t, is the time-of-use electricity price, is the start time of the time-of-use electricity price calculation, is the end time of the time-of-use electricity price calculation.

[0034] Through the response time constraint, extract the response times of equipment such as energy storage and gas turbines to ensure that the response time of the dispatching instruction meets the requirements, avoid the ineffective execution of the dispatching instruction due to slow equipment response, ensure the timeliness and effectiveness of the system dispatching operation, and maintain the stable operation of the system.

[0035] Extract the highest maintenance cost from the equipment maintenance cost as a constraint to limit the maintenance cost of the equipment involved in the dispatching instruction, prevent the maintenance cost from being too high due to overuse of the equipment or unreasonable dispatching, ensure that the system operates within the economically feasible range, and improve the service life of the equipment and the overall economy of the system.

[0036] Convert the grid voltage limit into a quadratic programming condition, comprehensively consider factors such as the reference voltage, line impedance, and node injection power to ensure that the grid voltage fluctuates within a safe range. Avoid damage to grid equipment caused by too high or too low voltage, ensure the safe and reliable operation of the grid, and provide a stable power supply for various equipment in the industrial park.

[0037] Combined with the above grid constraints and cost function, in the process of spatio-temporal elasticity evaluation and dynamic game solution, encourage the main bodies of source, load, and storage to optimize resource allocation according to their own characteristics and market price signals. For example, energy storage equipment charges during the low electricity price period and discharges during the high electricity price period to achieve reasonable allocation of resources in time and space, and improve the overall operation efficiency and economy of the system.

[0038] Conduct spatio-temporal elasticity evaluation, and the dynamic game solution outputs the elastic boundary surface. The specific process is: Elastic margin calculation: ; Among them, the regulation ability of the source side is calculated as: ; Load elasticity is calculated as: ; In the formula, is the flexibility evaluation index, T is the total time period, m is the number of source-side devices, is the maximum power output value of the s-th source-side device, is the minimum power output value of the s-th source-side device, is the number of the load side, is the power change of the l-th load, is the time decay factor, and e is the natural constant, is the s-th source-side efficiency for its power partial derivative, is the reciprocal of the response time, is the load transfer cost, is the maximum power of the l-th load, and t is the time index; Solving the Nash equilibrium by dynamic game, the three parties of source, load and storage achieve the optimal strategy through virtual iteration: ; where, is the strategy variable of the f-th party in the (K + 1)-th iteration, is the strategy variable of the f-th party, is the strategy combination of other parties except the f-th party in the K-th iteration, is the utility function of the f-th party; The convergence condition is that the strategy change rate is less than 1%; Output the elastic boundary surface ; is the time boundary, that is, the allowable adjustment time window for solving the update, is the power boundary, that is, the maximum schedulable range of power for solving the update, is the cost boundary, that is, the cost constraint for solving the update.

[0039] The flexibility evaluation index is calculated through the elastic margin. Considering factors such as the source-side regulation ability and load elasticity, the flexibility of the source-load-storage system in the industrial park during the total time period is quantified, providing a specific numerical basis for the evaluation of the system operation state and facilitating an intuitive understanding of the system's flexible regulation ability level.

[0040] By solving the Nash equilibrium through dynamic game, the three parties of source, load and storage continuously adjust their strategies during the virtual iteration process, aiming to maximize their own utility functions. Finally, the convergence condition that the strategy change rate is less than 1% is achieved, and the relatively stable optimal strategies of the three parties are found, realizing the maximization of the interests of each participant and improving the overall operation efficiency of the system. The dynamic game process prompts the three parties of source, load and storage to consider the strategies of other parties and seek balance in mutual influence and restriction. It breaks the independent operation state of each party, strengthens collaborative cooperation, improves the system's ability to cope with complex working conditions and external changes, and realizes the efficient collaborative operation of the source-load-storage system.

[0041] Output the elastic boundary surface, and clarify the time boundary (adjustable time window), power boundary (maximum schedulable power range), and cost boundary (cost constraint). Provide clear boundary conditions for the operation of the source-load-storage system, guide the system to adjust and optimize within a reasonable range, and avoid risks and losses caused by out-of-range operation.

[0042] The quantum collaborative optimization module is used to consider the elastic boundary surface and collect real-time monitoring data of the industrial park, and obtain the optimal policy vector through quantum collaborative optimization.

[0043] The specific process is as follows: Input the elastic boundary surface, and map the optimal solution set to the quantum state amplitude , providing an initial population for the quantum chromosomes of quantum collaborative optimization: ; where is the power of the j-th policy of the elastic boundary surface, is the total power of the elastic boundary surface policy, j is the index of the elastic boundary surface policy, and J is the number of elastic boundary surface policies; Collect real-time monitoring data and establish the energy storage SOC constraint: ; where is the maximum value of the power of the industrial park equipment, is the rated power of the industrial park equipment, and SOC is the state of charge of the industrial park equipment; Quantum collaborative optimization: Perform chromosome coding, and the formula for the rotation angle of the dynamic rotation gate strategy is: ; where is the rotation angle at the current iteration number , is the index of the iteration number, is the maximum number of iterations, is the best fitness at the -th iteration, is the best fitness at the 0-th iteration (initial state); Fitness evaluation: ; where is the fitness evaluation value, is the power generation of the equipment, is the maintenance cost of the scheduling instruction equipment, is the flexibility evaluation index, is the hyperbolic tangent function, is the change in carbon dioxide emissions, is the baseline value of carbon dioxide emissions; Record the policy corresponding to the highest fitness evaluation value as the optimal policy, and output the optimal policy vector.

[0044] The quantum collaborative optimization module uses the characteristics of quantum computing to map the optimal solution set into quantum state amplitudes, providing an initial population for quantum chromosomes. By utilizing the characteristics of quantum state superposition and entanglement, parallel searches are performed in the solution space. Compared with traditional optimization algorithms, it can more quickly explore better solutions and greatly improve the efficiency of optimization solutions. It is especially suitable for complex multivariable optimization problems such as source-load-storage in industrial parks.

[0045] The dynamic revolving door strategy uses the rotation angle formula to provide a wide-area search with a large angle in the early stages of algorithm iteration, quickly locating the optimal solution area; in the later iterations, a small-angle fine optimization is used to further explore more accurate optimal solutions in the areas found in the early stages. By adaptively adjusting the search range and accuracy, quantum collaborative optimization is assisted to converge to the global optimal solution more efficiently.

[0046] Considering the time, power, and cost boundaries determined by the elastic boundary surface, the energy storage SOC constraints are established in combination with real-time monitoring data. The power upper limit of the equipment is flexibly adjusted according to the energy storage charge state to ensure the safe and stable operation of the energy storage, and to make the optimization strategy meet the multiple constraints of the actual operation of the system, thus enhancing the feasibility and practicality of the strategy.

[0047] The fitness evaluation function integrates factors such as power generation, dispatching instruction equipment maintenance costs, flexibility evaluation indicators, and changes in carbon dioxide emissions. It comprehensively measures the system's performance in terms of economy, flexibility, and environmental protection, so that the optimization strategy is not limited to a single goal, but pursues overall benefit maximization, promoting the sustainable development of the source-load-storage system in the industrial park.

[0048] The virtual-reality linkage verification module is used to perform virtual-reality linkage verification based on the optimal strategy vector to obtain the collaborative optimization verification instruction set and the collaborative optimization credibility label.

[0049] The specific process is: decomposing the optimal strategy vector into instructions and establishing a digital twin for verification, including a photovoltaic model and an energy storage model: the photovoltaic model is a single diode model, and the energy storage model is an RC equivalent circuit; Calculate the deviation between virtual and real comparison: ; In the formula, is the comprehensive deviation, Q is the number of samples, is the actual measured value of the qth sample, is the virtual model prediction value of the qth sample, is the rated power; If the comprehensive deviation is higher than the deviation threshold stored in the database, a correction is triggered; the sliding window least squares method is used to correct the parameters to ensure that the digital twin is consistent with the entity: ; In the formula, is the updated model parameter vector, is the model parameter vector, is the actual measurement value at time t, is based on the model parameters at time t of the model prediction value; Recalculate the comprehensive deviation degree after correction, obtain the comprehensive deviation degree - collaborative optimization credibility mapping set stored in the database, find the matching collaborative optimization credibility based on the comprehensive deviation degree, and record it as the collaborative optimization credibility label; output the collaborative optimization verification instruction set and the corresponding collaborative optimization credibility label.

[0050] Construct a digital twin by establishing a photovoltaic single - diode model and a storage RC equivalent circuit model, and simulate the optimal strategy vector decomposition instruction in the virtual model. Using the comprehensive deviation degree calculation formula, compare the actual measurement value with the virtual model prediction value to accurately measure the difference between the virtual and the actual, providing a quantitative basis for judging the reliability of the instruction.

[0051] According to the comprehensive deviation degree, determine the collaborative optimization credibility label from the comprehensive deviation degree - collaborative optimization credibility mapping set. Intuitively reflect the reliability of the instruction, which is convenient for subsequent screening and use. Eliminate the instructions with low credibility and retain the instructions with high credibility to ensure the reliability of the output instruction set and reduce the possibility of system operation risks caused by incorrect instructions.

[0052] When the comprehensive deviation degree is higher than the threshold, trigger the sliding window least - squares method to correct the model parameters. Continuously adjust the parameters according to the difference between the actual measurement value and the model prediction value to ensure that the digital twin is consistent with the actual entity, so that the virtual model can continuously and accurately reflect the actual system operation state, providing a reliable simulation environment for instruction verification and optimization.

[0053] Through virtual - real linkage verification and credibility management, screen out reliable instructions to form a collaborative optimization verification instruction set. Provide high - quality instructions for the operation of the source - load - storage system in the industrial park, guide the reasonable operation of source - load - storage equipment, realize the optimal allocation of resources, and improve the overall stability and operation efficiency of the system.

[0054] The collaborative optimization instruction update module is used to obtain the integrity characteristics of the data collected in the industrial park and the reliability characteristics of the data transmission in the industrial park and analyze them, eliminate the untrusted collaborative optimization instructions in combination with the collaborative optimization credibility label, and perform collaborative optimization adjustment on the source - load - storage in the industrial park based on the remaining trusted collaborative optimization instructions.

[0055] The specific process is as follows: Obtain the integrity characteristics of the data collected in the industrial park and the reliability characteristics of the data transmission in the industrial park, including the integrity ratio of the data collected in the industrial park, the error rate of the data collected in the industrial park, and the packet loss rate of the data transmission in the industrial park; Analyze the integrity characteristics of the data collected in the industrial park and the reliability characteristics of the data transmission in the industrial park to obtain a credibility verification index, and the credibility verification index is used as the analysis basis for eliminating untrusted collaborative optimization instructions. The credibility verification index, the specific process is as follows: ; Wherein, is the credibility verification index, is the integrity ratio of the data collected in the industrial park, is the error rate of the data collected in the industrial park, is the packet loss rate of the data transmission in the industrial park; Obtain the mapping set of the credibility verification index - collaborative optimization credibility update value stored in the database, and based on the credibility verification index, find the matching collaborative optimization credibility update value.

[0056] Accumulate the collaborative optimization credibility update value and the collaborative optimization credibility to obtain the collaborative optimization credibility feature, and compare the collaborative optimization credibility feature with the collaborative optimization credibility threshold stored in the database; If the collaborative optimization credibility feature is not lower than the collaborative optimization credibility threshold, the collaborative optimization instruction corresponding to the collaborative optimization credibility feature is credible; If the collaborative optimization credibility feature is lower than the collaborative optimization credibility threshold, the collaborative optimization instruction corresponding to the collaborative optimization credibility feature is not credible, and eliminate the untrusted collaborative optimization instruction; Based on the remaining trusted collaborative optimization instructions, perform collaborative optimization adjustment on the source-load-storage in the industrial park.

[0057] Obtain the characteristics such as the integrity ratio, error rate, and transmission packet loss rate of the data collected in the industrial park, and evaluate the data quality from multiple dimensions of data integrity, accuracy, and transmission reliability. Comprehensively understand the data status and provide a rich information basis for the analysis of instruction credibility.

[0058] Calculate the credibility verification index, comprehensively consider the characteristics of each dimension of the data. Based on this index, find the corresponding update value from the mapping set of the credibility verification index - collaborative optimization credibility update value, and accumulate it with the original credibility to obtain the credibility feature. Scientifically quantify the instruction credibility, provide an objective basis for eliminating untrusted instructions, and ensure the reliability of the retained instructions.

[0059] According to the comparison result of the credibility feature and the credibility threshold, accurately eliminate the untrusted collaborative optimization instructions and only retain the reliable instructions. Avoid the interference or incorrect guidance of the unreliable instructions to the operation of the source-load-storage system, improve the execution effect of the instructions, and ensure the stability and efficiency of the system operation.

[0060] Based on the remaining trusted instructions, the source-load-storage in the industrial park is coordinated and optimized to enable the source-load-storage equipment to operate reasonably according to reliable instructions. The effective coordination of power sources, loads, and energy storage resources is achieved, the energy utilization efficiency is improved, the operating cost is reduced, and the efficient and economic operation of the energy system in the industrial park is promoted.

[0061] Embodiment 2: As Figure 2 , Figure 3 shown, this embodiment provides a method for coordinating and optimizing the source-load-storage in the industrial park for enhancing flexibility. Based on the system in Embodiment 1, it includes the following steps: Collect the source-load-storage data in the industrial park, model the dynamic resource portrait, and output the set of dynamic device efficiencies.

[0062] Obtain the grid constraints and market price signals, establish constraints and calculate the dynamic cost function in combination with the set of dynamic device efficiencies, conduct spatio-temporal flexibility assessment, and solve the dynamic game to output the flexible boundary surface.

[0063] Consider the flexible boundary surface and collect the real-time monitoring data in the industrial park, and obtain the optimal strategy vector through quantum collaborative optimization.

[0064] Based on the optimal strategy vector, conduct virtual-real linkage verification to obtain the collaborative optimization verification instruction set and the collaborative optimization credibility label.

[0065] Obtain the integrity characteristics of the collected data in the industrial park and the reliability characteristics of the data transmission in the industrial park and conduct analysis. Combine the collaborative optimization credibility label to eliminate untrusted collaborative optimization instructions, and conduct collaborative optimization adjustment on the source-load-storage in the industrial park based on the remaining trusted collaborative optimization instructions.

Claims

1. An industrial park source-load-storage collaborative optimization system for improving flexibility, characterized in that Including: A dynamic resource portrait modeling module, which is used to collect source-load-storage data of the industrial park, model the dynamic resource portrait, and output a set of dynamic device efficiencies; A spatio-temporal elasticity evaluation and solution module, which is used to obtain grid constraints and market price signals, establish constraint conditions by combining the set of dynamic device efficiencies, calculate the dynamic cost function, conduct spatio-temporal elasticity evaluation, and output the elastic boundary surface through dynamic game solution; A quantum collaborative optimization module, which is used to consider the elastic boundary surface and collect real-time monitoring data of the industrial park, and obtain the optimal strategy vector through quantum collaborative optimization; A virtual-real linkage verification module, which is used to conduct virtual-real linkage verification based on the optimal strategy vector to obtain a collaborative optimization verification instruction set and a collaborative optimization credibility label; A collaborative optimization instruction update module, which is used to obtain and analyze the integrity characteristics of the data collected in the industrial park and the reliability characteristics of data transmission in the industrial park, eliminate untrustworthy collaborative optimization instructions in combination with the collaborative optimization credibility label, and conduct collaborative optimization adjustment on the source-load-storage of the industrial park based on the remaining trustworthy collaborative optimization instructions.

2. The system for collaborative optimization of power generation, load and energy storage in industrial parks for improving flexibility according to claim 1, characterized in that: In the dynamic resource portrait modeling module, the process of collecting source-load-storage data of the industrial park and modeling the dynamic resource portrait is as follows: Collect source-load-storage data of the industrial park, including photovoltaic power output in the industrial park, ambient temperature in the industrial park, energy storage SOC in the industrial park, rated power of industrial park equipment, actual power of industrial park equipment, rated efficiency of industrial park equipment, installation area of industrial park photovoltaic panels, and irradiance in the industrial park; Conduct dynamic resource portrait modeling, and the modeling method is as follows: Set the long short-term memory network (LSTM) formula: ； In the formula, is the hidden state, is the photovoltaic output of the industrial park at time t, is the environmental temperature of the industrial park at time t, is the energy storage SOC of the industrial park at time t; For energy storage SOC: Use particle swarm optimization to screen features and maximize the particle swarm optimization objective function, where the particle swarm optimization objective function is: ； In the formula, is the efficiency of the energy storage system for charge and discharge operations at time t, is the time decay factor, and e is the natural constant; Normalize the actual power of industrial park equipment: ； Wherein, is the normalized power of the industrial park equipment at time t, is the actual power of the industrial park equipment at time t, is the rated power of the industrial park equipment; Establish an efficiency decay model, and calculate the real-time efficiency of industrial park equipment by combining the effects of aging and load: ； Wherein, is the real-time efficiency of the industrial park equipment at time t, is the rated efficiency of the industrial park equipment, is the aging factor stored in the database, is the first correction coefficient of the load fluctuation on the efficiency, is the second correction coefficient of the load fluctuation on the efficiency, is the historical load average value of the industrial park equipment; Correct the photovoltaic power output: Based on the irradiance and ambient temperature in the industrial park, correct the weather deviation of historical data: ； In the formula, is the irradiance of the industrial park, A is the installation area of photovoltaic panels in the industrial park, is the photoelectric conversion efficiency.

3. The collaborative optimization system for the source-load-storage in the industrial park for improving flexibility according to claim 2, wherein: In the dynamic resource portrait modeling module, the process of outputting a set of dynamic device efficiencies is as follows: Obtain the maximum power data of the industrial park equipment and the response time data of the industrial park equipment and the maintenance cost data of the industrial park equipment ; Analyze the device health index based on the power data of the past 24 hours, where the power data includes the actual power of industrial park equipment and the efficiency of industrial park equipment: ； Wherein, is the equipment health index of the industrial park at time k, is the actual power of the equipment in the industrial park at time k, is the energy conversion efficiency of the equipment in the industrial park at time k, that is, the ratio of the output energy to the input energy; Combine , , and into a dynamic device performance set .

4. The collaborative optimization system for power generation, load and energy storage in industrial parks for improving flexibility according to claim 1, wherein: In the spatio-temporal elasticity evaluation and solution module, the process of obtaining grid constraints and market price signals, establishing constraint conditions by combining the set of dynamic device efficiencies, and calculating the dynamic cost function is as follows: Response time constraints, extracting the energy storage response time and gas turbine response time in the industrial park from the response time data of industrial park equipment in the industrial park and the gas turbine response time in the industrial park : ； In the formula, is the dispatching instruction response time; Extract the highest maintenance cost of each device in the industrial park from the equipment maintenance cost data of the industrial park Extract the highest maintenance cost of each device in the industrial park The maintenance cost constraint is that the equipment maintenance cost of the scheduling instruction does not exceed the highest maintenance cost of the corresponding industrial park equipment The maintenance cost constraint is that the equipment maintenance cost of the scheduling instruction does not exceed the highest maintenance cost of the corresponding industrial park equipment ; Obtain grid constraints and voltage limits, and transform them into quadratic programming conditions: ； In the formula, is the reference voltage, is the line impedance of the -th node, is the injection power of the -th node, and n represents the number of nodes participating in the calculation; Define the power feasible region: ; In the formula, is the minimum power value allowed for line transmission, is the power actually transmitted by the line, is the maximum power value allowed for line transmission; Obtain market price signals, and the dynamic cost function is: ； Wherein, is the dynamic cost at time t, is the peak electricity price, is the power actually transmitted by the line at time t, is the time-of-use electricity price, is the start time for calculating the time-of-use electricity price, is the end time for calculating the time-of-use electricity price.

5. The collaborative optimization system for power generation, load and energy storage in industrial parks for improving flexibility according to claim 4, wherein: In the spatio-temporal elasticity evaluation and solution module, the process of conducting spatio-temporal elasticity evaluation and outputting the elastic boundary surface through dynamic game solution is as follows: Calculate the elastic margin: ； Among them, the source-side regulation ability is calculated as follows: ; Load elasticity The calculation method is as follows: ; In the formula, is the flexibility evaluation index, T is the total time period, m is the number of source-side devices, is the maximum power output value of the s-th source-side device, is the minimum power output value of the s-th source-side device, is the number of the load side, is the power change of the l-th load, is the time decay factor, e is the natural constant, is the efficiency of the s-th source side with respect to its power partial derivative, is the reciprocal of the response time, is the load transfer cost, is the maximum power of the l-th load, t is the time index; Conduct dynamic game solution to find the Nash equilibrium, and the three parties of source, load, and storage reach the optimal strategy through virtual iteration: ； wherein, is the strategy variable of the f-th party in the (K + 1)-th iteration, is the strategy variable of the f-th party, is the strategy combination of other parties except the f-th party at the K-th iteration, is the utility function of the f-th party; The convergence condition is that the strategy change rate is less than 1%; Output elastic boundary surface ; is the time boundary, that is, to solve the updated allowable adjustment time window, is the power boundary, that is, to solve the updated maximum schedulable range of power, is the cost boundary, that is, to solve the updated cost constraint.

6. The collaborative optimization system for power generation, load and energy storage in industrial parks for improving flexibility according to claim 1, characterized in that: In the quantum collaborative optimization module, the process of considering the elastic boundary surface and collecting real-time monitoring data of the industrial park, and obtaining the optimal strategy vector through quantum collaborative optimization is as follows: Input the elastic boundary surface and map the optimal solution set to the quantum state amplitude , providing an initial population for quantum collaborative optimization of quantum chromosomes: ； In the formula, is the power of the j-th strategy of the elastic boundary surface, is the total power of the elastic boundary surface strategy, j is the index of the elastic boundary surface strategy, and J is the number of elastic boundary surface strategies; Based on the real-time monitoring data, establish an energy storage SOC constraint: ； In the formula, is the maximum value of the power of the industrial park equipment, is the rated power of the industrial park equipment, and SOC is the state of charge of the industrial park equipment; Quantum collaborative optimization: Chromosome coding is performed, and the rotation angle formula of the dynamic roulette wheel strategy is: ； Wherein, is the rotation angle at the current iteration number , is the maximum number of iterations, is the best fitness at the -th iteration, is the best fitness at the 0-th iteration; Fitness evaluation: ； In the formula, is the fitness evaluation value, is the power generation power of the device, is the maintenance cost of the dispatching instruction device, is the flexibility evaluation index, is the hyperbolic tangent function, is the change in carbon dioxide emissions, is the baseline value of carbon dioxide emissions; The strategy corresponding to the highest fitness evaluation value is recorded as the optimal strategy, and the optimal strategy vector is output.

7. The collaborative optimization system for power generation, load, and energy storage in an industrial park for enhancing flexibility according to claim 1, wherein: In the virtual-real linkage verification module, virtual-real linkage verification is performed based on the optimal strategy vector to obtain a collaborative optimization verification instruction set and a collaborative optimization credibility label. The specific process is as follows: Decompose the optimal strategy vector into instructions and establish a digital twin for verification, including a photovoltaic model and an energy storage model: The photovoltaic model is a single-diode model, and the energy storage model is an RC equivalent circuit; Calculate the deviation degree through virtual-real comparison: ； In the formula, is the comprehensive deviation degree, Q is the number of samples, is the actual measured value of the q-th sample, is the predicted value of the virtual model for the q-th sample, is the rated power; If the comprehensive deviation degree is higher than the deviation degree threshold stored in the database, trigger correction; Use the sliding window least squares method to correct the parameters to ensure that the digital twin is consistent with the entity: ； In the formula, is the updated model parameter vector, is the model parameter vector, is the actual measured value at time t, is the model prediction value at time t based on the model parameter ; Recalculate the comprehensive deviation degree after correction, obtain the comprehensive deviation degree - collaborative optimization credibility mapping set stored in the database, and find the matching collaborative optimization credibility based on the comprehensive deviation degree, which is recorded as the collaborative optimization credibility label; Output the collaborative optimization verification instruction set and the corresponding collaborative optimization credibility label.

8. The collaborative optimization system for power generation, load and energy storage in industrial parks for improving flexibility according to claim 1, characterized in that: In the collaborative optimization instruction update module, obtain the integrity characteristics of the industrial park collection data and the reliability characteristics of the industrial park data transmission and analyze them. The specific process is as follows: Obtain the integrity characteristics of the industrial park collection data and the reliability characteristics of the industrial park data transmission, including the integrity ratio of the industrial park collection data, the error rate of the industrial park collection data, and the packet loss rate of the industrial park data transmission; Analyze the integrity characteristics of the industrial park collection data and the reliability characteristics of the industrial park data transmission to obtain a credibility verification index, which is used as the analysis basis for eliminating untrustworthy collaborative optimization instructions; The credibility verification index, the specific process is as follows: ； In the formula, is the credibility verification index, is the integrity ratio of the data collected in the industrial park, is the error rate of the data collected in the industrial park, is the packet loss rate of data transmission in the industrial park, and e is the natural constant; Obtain the credibility verification index - collaborative optimization credibility update value mapping set stored in the database, and find the matching collaborative optimization credibility update value based on the credibility verification index.

9. The system for collaborative optimization of power generation, load, and energy storage in an industrial park for improving flexibility according to claim 8, wherein: In the collaborative optimization instruction update module, eliminate untrustworthy collaborative optimization instructions in combination with the collaborative optimization credibility label, and perform collaborative optimization adjustment on the industrial park source-load-storage based on the remaining trustworthy collaborative optimization instructions. The specific process is as follows: Accumulate the collaborative optimization credibility update value and the collaborative optimization credibility to obtain a collaborative optimization credibility feature, and compare the collaborative optimization credibility feature with the collaborative optimization credibility threshold stored in the database; If the collaborative optimization credibility feature is not lower than the collaborative optimization credibility threshold, the collaborative optimization instruction corresponding to the collaborative optimization credibility feature is trustworthy; If the collaborative optimization credibility feature is lower than the collaborative optimization credibility threshold, the collaborative optimization instruction corresponding to the collaborative optimization credibility feature is untrustworthy, and eliminate the untrustworthy collaborative optimization instructions; Perform collaborative optimization adjustment on the industrial park source-load-storage based on the remaining trustworthy collaborative optimization instructions.

10. An industrial park source-load-storage collaborative optimization method for improving flexibility, based on the system according to any one of claims 1-9, characterized in that, It includes the following steps: Collect the industrial park source-load-storage data, build a dynamic resource portrait model, and output a dynamic device efficiency set; Obtain the grid constraints and market price signals, establish constraint conditions in combination with the dynamic device efficiency set, calculate the dynamic cost function, perform spatio-temporal elasticity evaluation, and solve the dynamic game to output the elastic boundary surface; Consider the elastic boundary surface and collect real-time monitoring data of the industrial park, and obtain the optimal policy vector through quantum collaborative optimization; Based on the optimal policy vector, conduct virtual-real linkage verification to obtain a collaborative optimization verification instruction set and a collaborative optimization credibility label; Obtain the integrity characteristics of the collected data in the industrial park and the reliability characteristics of data transmission in the industrial park, and conduct analysis. Combine the collaborative optimization credibility label to eliminate untrusted collaborative optimization instructions, and based on the remaining trusted collaborative optimization instructions, conduct collaborative optimization adjustment on the source, load, and storage in the industrial park.

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