A power distribution network multi-element energy storage robust optimization configuration method and system

Abstract

The application discloses a kind of multi-element energy storage robust optimization configuration method and system of distribution network, it is related to multi-element energy storage technical field of distribution network, including acquisition distribution network data, the data of acquisition is input into deterministic multi-element energy storage optimization configuration model of distribution network, generates deterministic optimal solution;The weight of uncertain variable is calculated by objective weight assignment method, and deviation factor is set;Robust optimization configuration model of distribution network multi-element energy storage based on objective weight assignment-information gap decision method is constructed, and robust configuration scheme is output.The method improves the efficiency of optimization configuration, reduces the operation cost and equipment investment cost of distribution network, lays a solid foundation for improving the economic benefit of technology, improves the applicability and robustness of model, so that the output configuration scheme has higher flexibility and reliability when coping with uncertainty, improves the robustness and adaptability of whole system, lays a foundation for improving the overall efficiency of multi-element energy storage system of distribution network.

Description

Technical Field

[0001] This invention relates to the field of multi-element energy storage technology in power distribution networks, specifically to a robust optimization configuration method and system for multi-element energy storage in power distribution networks. Background Technology

[0002] In the development of power systems, the distribution network, as a crucial link in power transmission, bears the key task of efficiently transmitting electricity to users. With the rapid development of renewable energy and the diversification of electricity demand, traditional distribution networks are facing increasing challenges. In recent years, the application of diversified energy storage technologies in distribution networks has gradually demonstrated its importance. Combining different types of energy storage devices can effectively balance supply and demand fluctuations, improve system stability and robustness. Through data collection and analysis, the construction of intelligent optimization configuration models enables distribution networks to better cope with emergencies and uncertainties during operation, improving overall resource utilization efficiency. The integration of modern information technology and artificial intelligence technology provides new ideas and methods for the optimization of diversified energy storage in distribution networks.

[0003] While some models and algorithms have improved the stability and efficiency of power systems to a certain extent, several shortcomings remain. Current optimization methods largely rely on static models, failing to effectively consider the dynamic changes and uncertainties faced by distribution networks in actual operation. Using historical data for prediction makes them susceptible to data timeliness issues, leading to significant discrepancies between the optimized scheme and actual needs. When dealing with uncertain variables, most existing technologies employ subjective weighting methods, relying on expert experience or historical data for weight settings, lacking objectivity and scientific rigor. Risk assessments during decision-making cannot fully reflect the actual system situation, reducing the effectiveness of optimization schemes. In the face of complex environmental changes, subjective weight settings may lead to decision-making errors, preventing energy storage systems from playing their expected role at critical moments and affecting the stable operation of the distribution network. The operation of energy storage devices is affected by constraints such as power, upper and lower limits of energy storage, and economic factors. Inadequate integration of these constraints may significantly reduce the practical benefits of the optimization scheme, failing to achieve optimal energy storage system configuration. Research on robustness in existing technologies is relatively scarce; many optimization algorithms do not fully consider the system's ability to cope with emergencies, resulting in reduced system reliability under unforeseen circumstances. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that existing multi-element energy storage optimization configuration technologies have insufficient robustness, poor objectivity in uncertain decision-making, and insufficient dynamic environmental response capabilities, as well as the problem of how to achieve an efficient energy storage system configuration scheme under complex operating conditions.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a robust optimization configuration method for multi-element energy storage in a distribution network, comprising: collecting distribution network data; inputting the collected data into a deterministic multi-element energy storage optimization configuration model for a distribution network to generate a deterministic optimal solution; calculating the weights of uncertain variables using an objective weighting method and setting a deviation factor; constructing a robust optimization configuration model for multi-element energy storage in a distribution network based on the objective weighting-information gap decision method, and outputting a robust configuration scheme.

[0007] As a preferred embodiment of the robust optimization configuration method for multi-element energy storage in distribution networks described in this invention, the distribution network data includes power grid investment data and operation data.

[0008] Investment data includes the equipment models and investment costs per unit for both electric and hydrogen energy storage units.

[0009] The operational data includes power distribution network system parameters, predicted output data of photovoltaic and wind turbine units on typical days throughout the four seasons, and load data on typical days throughout the four seasons.

[0010] As a preferred embodiment of the robust optimization configuration method for multi-energy storage in distribution networks described in this invention, the generation of deterministic optimal solutions includes inputting collected power grid investment data and operation data into a deterministic multi-energy storage optimization configuration model for distribution networks, solving the model, and generating a deterministic optimal solution that does not consider uncertainties in wind and solar power output and load power fluctuations. A deterministic distribution network multi-element energy storage optimization configuration model is constructed with the goal of minimizing the overall cost, including investment cost and operating cost. It is based on the location and capacity determination of electric energy storage and hydrogen energy storage units and the steady-state optimization scheduling operation of the distribution network. The constraints include equipment investment constraints and simulation operation constraints. Calculate the objective function , is represented as: in, Investment costs, including annualized energy storage investment costs. Investment cost of hydrogen energy storage units , Operating costs, including the annual operating costs of distributed power sources. Operating costs of energy storage Operating costs of hydrogen energy storage units Cost of purchasing electricity from the upper-level power grid Costs of wind and solar power curtailment Network loss cost .

[0011] Calculate the investment cost of electric energy storage , is represented as: in, For the entire life cycle of electrical energy storage, This is a set of candidate installation nodes for electric energy storage. The investment cost of a single energy storage unit, Energy storage at nodes The number of installations.

[0012] Investment cost of hydrogen energy storage units , is represented as: in, For the entire life cycle of the hydrogen energy storage unit, This is a set of candidate installation nodes for hydrogen energy storage units. The investment cost of a single water electrolysis unit, For the water electrolysis device at the node The number of installations, The investment cost for a single hydrogen storage tank, For hydrogen storage tanks at nodes The number of installations, The investment cost for a single hydrogen fuel cell, This represents the number of hydrogen fuel cells installed at a node.

[0013] Annual operating cost of distributed power sources , is represented as: in, For the season Total number of days, The set of nodes where the photovoltaic units are located. The set of nodes where the wind turbines are located. The operating cost per unit of electricity generated by a photovoltaic power unit. The operating cost per unit of electricity generated by a wind turbine. for season Time-of-use photovoltaic unit injection node active power, for season Time-of-use wind turbine injection node active power, express Duration of the period, for .

[0014] Operating costs of electric energy storage , is represented as: in, For the set of nodes to install electrical energy storage, The operating cost per unit charge / discharge capacity of electrical energy storage. for season Energy storage at nodes The charging power for season Energy storage at nodes The magnitude of the discharge power.

[0015] Operating costs of hydrogen energy storage units , is represented as: in, For the set of nodes to install hydrogen energy storage units, The operating cost per unit of electricity consumed by the water electrolysis unit. for season Input node at any time The electrical power of the water electrolysis device The operating cost of injecting or extracting hydrogen per unit volume flow rate into a hydrogen storage tank. for season Time Node The volumetric flow rate of hydrogen injected into the hydrogen storage tank. for season Time Node The volumetric flow rate of hydrogen injected into or extracted from the hydrogen storage tank. The operating cost per unit of electricity generated by a hydrogen fuel cell. for season Momentary hydrogen fuel cell injection node The electrical power.

[0016] Cost of purchasing electricity from the upstream power grid , is represented as: in, for Time-of-use electricity pricing for different time periods This refers to the set of nodes where the system interacts with other power grids. for season Interaction nodes between time period and power grid Active power injected into the local power grid.

[0017] Costs of wind and solar power curtailment , is represented as: in, Cost per unit of wind curtailment Cost per unit of electricity curtailed For nodes Photovoltaic units in season The ability to predict and contribute at all times For nodes Wind turbine units in season The effort and contribution can be predicted at any time.

[0018] Network loss cost , is represented as: in, for season The amount of network loss during a given time period.

[0019] Equipment investment constraints include constraints on the number of devices to be installed at candidate installation nodes, expressed as: in, For nodes The electrical energy storage output of the device, For energy storage devices at nodes Maximum number of installations, This is a set of candidate installation nodes for energy storage devices. For nodes The output of the electro-hydrogen conversion equipment, For the water electrolysis device at the node Maximum number of installations, This is the set of candidate installation nodes for hydrogen energy storage devices. For nodes The output of the energy storage device of the equipment. For hydrogen storage tanks at nodes Maximum number of installations, For nodes The output of the hydrogen fuel cell in the device, For hydrogen fuel cells at the node Maximum number of installations.

[0020] The simulated operation constraints include AC power flow constraints of the distribution network, system security constraints, power interaction constraints with the external power grid, operation constraints of photovoltaic and wind turbine units, operation constraints of electrical energy storage, operation constraints of hydrogen energy storage units, and hydrogen volume flow balance constraints.

[0021] AC power flow constraints in a distribution network are represented as: in, Indicated by This is the set of terminal nodes of the AC branch with the first terminal node. Indicated by Let be the set of the starting nodes of the AC branch with the terminal node. , and , They are respectively season Timetable and Active and reactive power on the surface , for season External power grid and wind turbine injection nodes at all times reactive power, and They are respectively season Time Node The magnitude of active and reactive power of the load, For nodes The voltage magnitude and system safety constraints are expressed as: in, and They are nodes The upper and lower limits of the voltage. For the safe current of the line, For nodes At any moment The square of the current represents the value at time t. Energy storage device or circuit Real-time current status.

[0022] The power constraint for interaction with the external power grid is expressed as: in, for season Interaction nodes between time period and power grid The reactive power injected into the local power grid, and These are the interaction nodes with the power grid. Upper and lower limits of active power injected into the grid. and These are the interaction nodes with the power grid. Upper and lower limits of reactive power injected into the power grid. This represents the set of all candidate nodes that exchange power with the power grid.

[0023] The operating constraints of photovoltaic units and wind turbine units are expressed as follows: in, The power factor angle of the wind turbine generator. This represents the set of all nodes containing photovoltaic (PV) units. This represents the set of all nodes containing wind turbine units.

[0024] The operational constraints of energy storage are expressed as follows: in, and These are the state variables for charging and discharging electrical energy storage, and are 0-1 variables. This represents the upper limit of the charging and discharging power of a single energy storage unit. for season Time period nodes The amount of electricity stored. This refers to the maximum capacity of a single energy storage unit. The positive efficiency of an energy storage device represents the efficiency with which energy is output from the energy storage device to the power grid. The reverse efficiency of an energy storage device represents the efficiency when energy is input from the grid into the energy storage device.

[0025] The operational constraints of hydrogen energy storage units include the operational constraints of water electrolysis devices, hydrogen storage tanks, and hydrogen fuel cells.

[0026] The operating constraints of a water electrolysis unit are expressed as follows: in, The hydrogen production efficiency of the water electrolysis unit, Hydrogen has a high calorific value. , These represent the upper and lower limits of the input electrical power for a single water electrolysis unit. For season Time Node The volumetric flow rate of hydrogen produced by the water electrolysis device.

[0027] The operating constraints of the hydrogen storage tank are expressed as follows: in, , All variables are 0-1, representing decision variables for the hydrogen storage tank's hydrogen injection and extraction process. A value of 0 indicates that the hydrogen storage tank has not been injected or extracted, while a value of 1 indicates that the hydrogen storage tank has been injected or extracted. for season Time Node The hydrogen volume of the hydrogen storage tank, The maximum permissible hydrogen volumetric flow rate for a single hydrogen storage tank. This refers to the maximum volumetric flow rate of hydrogen that can be extracted from a single hydrogen storage tank. The efficiency of hydrogen injection into the hydrogen storage tank. The efficiency of hydrogen extraction from the hydrogen storage tank. , These are the maximum and minimum volumes of a single hydrogen storage tank, respectively. For set The edge set represents the system boundary participating in energy storage configuration optimization.

[0028] The operating constraints of hydrogen fuel cells are expressed as follows: in, For the efficiency of hydrogen fuel cells, for season Time Node The required hydrogen volumetric flow rate for hydrogen fuel cells , These represent the upper and lower limits of the output electrical power of a single hydrogen fuel cell, respectively.

[0029] The hydrogen volume flow rate balance constraint is expressed as: The nonlinear terms in the deterministic distribution network multi-element energy storage optimization configuration model are linearized through variable substitution, McCormick linearization method, and Big-M method, and relaxed using second-order cone technique, transforming it into a mixed-integer second-order cone programming model, which is then solved using the GUROBI solver on the GAMS platform.

[0030] As a preferred embodiment of the robust optimization configuration method for multi-element energy storage in distribution networks described in this invention, the calculation of the weights of uncertain variables includes data preprocessing, standardizing the predicted data of uncertain variables for wind power, photovoltaic power generation, and load, as follows: in, Uncertain variables At any moment The value below, Indicates the uncertainty of variables The values ​​at various time points are compared, and the minimum value is selected. Indicates the uncertainty of variables The values ​​at various time points are compared, and the maximum value is selected. Indicates the first Each device in the time period Performance metrics.

[0031] Calculate the variability and conflict of each uncertainty measure, and express the variability of each uncertain variable through standard deviation. Characterize and calculate the first The conflict coefficient of the three uncertain variables is expressed as: in, The correlation coefficient between uncertain variables. For the first The cost of each configuration option.

[0032] Calculating the weighting coefficients involves calculating the weighting coefficients based on the variability and conflict of each uncertain variable, and calculating the first... The weight coefficients of the uncertain variables are expressed as follows: The corresponding uncertain variables in the total uncertainty The portion that occupies is represented as: in, For the first The robustness coefficient of each configuration scheme For the first The weighting coefficients of each configuration scheme.

[0033] As a preferred embodiment of the robust optimization configuration method for multi-element energy storage in distribution networks described in this invention, the deviation factor includes a measure of the difference between the expected investment cost of the planning scheme and the deterministic optimal solution, outputting the additional investment cost accepted by investors in the face of uncertainties, and setting the value of the deviation factor based on the degree of risk aversion of investors, with the deviation factor value ranging from 1% to 5%.

[0034] As a preferred embodiment of the robust optimization configuration method for multi-element energy storage in distribution networks according to the present invention, the construction of the robust optimization configuration model for multi-element energy storage in distribution networks includes constructing a mathematical model of an optimization problem containing uncertain variables, expressed as: in, Let be the objective function. For the decision variable matrix, For an uncertain variable matrix, and These are equality constraints and inequality constraints, respectively.

[0035] IGDT assigns uncertainty to each uncertain variable, describes the size of the set of uncertain variables, and aims to maximize the uncertainty of each uncertain variable while satisfying a preset target value. It constructs a model based on the uncertainty. The determined set of uncertain variables , is represented as: in, For the predicted value, the uncertainty is... Greater than 0 and less than 1.

[0036] Under deterministic optimization model Pick The optimal solution at time is ,definition To determine the maximum acceptable target value for decision-makers after introducing uncertainty, for problems with multiple uncertain variables, a CRITIC-IGDT model is constructed using an uncertainty weighting method, expressed as: Based on the CRITIC-IGDT model, the planning variables , , , As a decision variable Run variables , , , , , , , , , , , , , , , , , , , , , , , , As a decision variable .

[0037] A robust optimization configuration model for multi-element energy storage in distribution networks based on CRITIC-IGDT is constructed, represented as follows: in, , and The variables are uncertain, representing the predicted wind power output, photovoltaic power output, and load, respectively. This represents the determined optimal solution obtained.

[0038] As a preferred embodiment of the robust optimization configuration method for multi-energy storage in power distribution networks described in this invention, the output robust configuration scheme includes, based on the generated multi-energy storage configuration scheme, outputting the power distribution network investment cost, operating cost, number of installed electrical energy storage units and hydrogen energy storage units and their configuration capacity.

[0039] Another objective of this invention is to provide a robust optimization configuration system for multi-element energy storage in distribution networks, which can calculate the weights of uncertain variables and set deviation factors through an objective weighting method, thereby solving the problem of inaccurate subjective weight allocation in current multi-element energy storage optimization technologies for distribution networks.

[0040] As a preferred embodiment of the robust optimization configuration system for multi-element energy storage in power distribution networks described in this invention, it includes a data acquisition module, a weight calculation module, and a model output module.

[0041] The data acquisition module is used to collect data from the distribution network, input the collected data into a deterministic distribution network multi-element energy storage optimization configuration model, and generate a deterministic optimal solution; the weight calculation module is used to calculate the weights of uncertain variables through an objective weighting method and set a deviation factor; the model output module is used to construct a robust optimization configuration model for distribution network multi-element energy storage based on the objective weighting-information gap decision method and output a robust configuration scheme.

[0042] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement a robust optimization configuration method for multi-energy storage in a power distribution network.

[0043] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a robust optimization configuration method for multi-energy storage in a power distribution network.

[0044] The beneficial effects of this invention are as follows: The robust optimization configuration method for multi-element energy storage in distribution networks provided by this invention collects distribution network data, inputs the collected data into a deterministic multi-element energy storage optimization configuration model for distribution networks, generates a deterministic optimal solution, improves the efficiency of optimization configuration, thereby reducing the operating cost and equipment investment cost of the distribution network, and lays a solid foundation for improving the economic benefits of the technology. By calculating the weights of uncertain variables through the objective weighting method and setting the deviation factor, the applicability and robustness of the model are improved, making the output configuration scheme more flexible and reliable in dealing with uncertainties, creating more favorable conditions for the stable operation of the power system. The robust optimization configuration model for multi-element energy storage in distribution networks based on the objective weighting-information gap decision method is constructed, and a robust configuration scheme is output, improving the robustness and adaptability of the entire system, laying the foundation for improving the overall efficiency of the multi-element energy storage system in distribution networks. This invention achieves better results in terms of operating efficiency, decision-making scientificity, robustness, and flexibility. Attached Figure Description

[0045] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 The first embodiment of the present invention provides an overall flowchart of a robust optimization configuration method for multi-element energy storage in a power distribution network.

[0047] Figure 2 The second embodiment of the present invention provides a system implementation flowchart of a robust optimization configuration method for multi-element energy storage in a power distribution network.

[0048] Figure 3 The following is an overall flowchart of a robust optimization configuration system for multi-element energy storage in a power distribution network, provided as a third embodiment of the present invention. Detailed Implementation

[0049] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0050] Example 1, referring to Figure 1 As an embodiment of the present invention, a robust optimization configuration method for multi-element energy storage in a distribution network is provided, comprising: S1: Collect data from the distribution network, input the collected data into the deterministic distribution network multi-element energy storage optimization configuration model, and generate a deterministic optimal solution.

[0051] Furthermore, distribution network data includes power grid investment data and operation data.

[0052] Investment data includes the equipment models and investment costs per unit for both electric and hydrogen energy storage units.

[0053] The operational data includes power distribution network system parameters, predicted output data of photovoltaic and wind turbine units on typical days throughout the four seasons, and load data on typical days throughout the four seasons.

[0054] It should be noted that generating a deterministic optimal solution involves inputting the collected power grid investment data and operation data into a deterministic distribution network multi-element energy storage optimization configuration model, solving the model, and generating a deterministic optimal solution that does not consider uncertainties such as wind and solar power output and load power fluctuations.

[0055] A deterministic multi-element energy storage optimization configuration model for distribution networks is constructed with the goal of minimizing overall costs, including investment and operating costs. It is based on the location and capacity determination of electric and hydrogen energy storage units and the steady-state optimization scheduling operation of the distribution network. The constraints include equipment investment constraints and simulation operation constraints.

[0056] Calculate the objective function , is represented as: in, Investment costs, including annualized energy storage investment costs. Investment cost of hydrogen energy storage units , Operating costs, including the annual operating costs of distributed power sources. Operating costs of energy storage Operating costs of hydrogen energy storage units Cost of purchasing electricity from the upper-level power grid Costs of wind and solar power curtailment Network loss cost .

[0057] Calculate the investment cost of electric energy storage , is represented as: in, For the entire life cycle of electrical energy storage, This is a set of candidate installation nodes for electric energy storage. The investment cost of a single energy storage unit, Energy storage at nodes The number of installations.

[0058] Investment cost of hydrogen energy storage units , is represented as: in, For the entire life cycle of the hydrogen energy storage unit, This is a set of candidate installation nodes for hydrogen energy storage units. The investment cost of a single water electrolysis unit, For the water electrolysis device at the node The number of installations, The investment cost for a single hydrogen storage tank, For hydrogen storage tanks at nodes The number of installations, The investment cost for a single hydrogen fuel cell, This represents the number of hydrogen fuel cells installed at a node.

[0059] Annual operating cost of distributed power sources , is represented as: in, For the season Total number of days, The set of nodes where the photovoltaic units are located. The set of nodes where the wind turbines are located. The operating cost per unit of electricity generated by a photovoltaic power unit. The operating cost per unit of electricity generated by a wind turbine. for season Time-of-use photovoltaic unit injection node active power, for season Time-of-use wind turbine injection node active power, express Duration of the period, for ; Operating costs of electric energy storage , is represented as: in, For the set of nodes to install electrical energy storage, The operating cost per unit charge / discharge capacity of electrical energy storage. for season Energy storage at nodes The charging power for season Energy storage at nodes The magnitude of the discharge power.

[0060] Operating costs of hydrogen energy storage units , is represented as: in, For the set of nodes to install hydrogen energy storage units, The operating cost per unit of electricity consumed by the water electrolysis unit. for season Input node at any time The electrical power of the water electrolysis device The operating cost of injecting or extracting hydrogen per unit volume flow rate into a hydrogen storage tank. for season Time Node The volumetric flow rate of hydrogen injected into the hydrogen storage tank. for season Time Node The volumetric flow rate of hydrogen injected into or extracted from the hydrogen storage tank. The operating cost per unit of electricity generated by a hydrogen fuel cell. for season Momentary hydrogen fuel cell injection node The electrical power.

[0061] Cost of purchasing electricity from the upstream power grid , is represented as: in, for Time-of-use electricity pricing for different time periods This refers to the set of nodes where the system interacts with other power grids. for season Interaction nodes between time period and power grid Active power injected into the local power grid.

[0062] Costs of wind and solar power curtailment , is represented as: in, Cost per unit of wind curtailment Cost per unit of electricity curtailed For nodes Photovoltaic units in season The ability to predict and contribute at all times For nodes Wind turbine units in season The effort and contribution are predicted in real time.

[0063] Network loss cost , is represented as: in, for season The amount of network loss during a given time period.

[0064] Equipment investment constraints include constraints on the number of devices to be installed at candidate installation nodes, expressed as: in, For nodes The electrical energy storage output of the device, For energy storage devices at nodes Maximum number of installations, This is a set of candidate installation nodes for energy storage devices. For nodes The output of the electro-hydrogen conversion equipment, For the water electrolysis device at the node Maximum number of installations, This is the set of candidate installation nodes for hydrogen energy storage devices. For nodes The output of the energy storage device of the equipment. For hydrogen storage tanks at nodes Maximum number of installations, For nodes The output of the hydrogen fuel cell in the device, For hydrogen fuel cells at the node Maximum number of installations.

[0065] The simulated operation constraints include AC power flow constraints of the distribution network, system security constraints, power interaction constraints with the external power grid, operation constraints of photovoltaic and wind turbine units, operation constraints of electrical energy storage, operation constraints of hydrogen energy storage units, and hydrogen volume flow balance constraints.

[0066] AC power flow constraints in a distribution network are represented as: in, Indicated by This is the set of terminal nodes of the AC branch with the first terminal node. Indicated by Let be the set of the starting nodes of the AC branch with the terminal node. , and , They are respectively season Timetable and Active and reactive power on , for season External power grid and wind turbine injection nodes at all times reactive power, and They are respectively season Time Node The magnitude of active and reactive power of the load, For nodes The voltage magnitude and system safety constraints are expressed as: in, and They are nodes The upper and lower limits of the voltage. For the safe current of the line, For nodes At any moment The square of the current represents the value at time t. Energy storage device or circuit Real-time current status.

[0067] The power constraint for interaction with the external power grid is expressed as: in, for season Interaction nodes between time period and power grid The reactive power injected into the local power grid, and These are the interaction nodes with the power grid. Upper and lower limits of active power injected into the grid. and These are the interaction nodes with the power grid. Upper and lower limits of reactive power injected into the power grid. This represents the set of all candidate nodes that exchange power with the power grid.

[0068] The operating constraints of photovoltaic units and wind turbine units are expressed as follows: in, The power factor angle of the wind turbine generator. This represents the set of all nodes containing photovoltaic (PV) units. This represents the set of all nodes containing wind turbine units.

[0069] The operational constraints of energy storage are expressed as follows: in, and These are the state variables for charging and discharging electrical energy storage, and are 0-1 variables. This represents the upper limit of the charging and discharging power of a single energy storage unit. for season Time period nodes The amount of electricity stored. This is the maximum capacity of a single energy storage unit; The positive efficiency of an energy storage device represents the efficiency with which energy is output from the energy storage device to the power grid. The reverse efficiency of an energy storage device represents the efficiency when energy is input from the grid into the energy storage device.

[0070] The operational constraints of hydrogen energy storage units include the operational constraints of water electrolysis devices, hydrogen storage tanks, and hydrogen fuel cells.

[0071] The operating constraints of a water electrolysis unit are expressed as follows: in, The hydrogen production efficiency of the water electrolysis unit, Hydrogen has a high calorific value. , These represent the upper and lower limits of the input electrical power for a single water electrolysis unit. For season Time Node The volumetric flow rate of hydrogen produced by the water electrolysis device.

[0072] The operating constraints of the hydrogen storage tank are expressed as follows: in, , All variables are 0-1, representing decision variables for the hydrogen storage tank's hydrogen injection and extraction process. A value of 0 indicates that the hydrogen storage tank has not been injected or extracted, while a value of 1 indicates that the hydrogen storage tank has been injected or extracted. for season Time Node The hydrogen volume of the hydrogen storage tank, The maximum permissible hydrogen volumetric flow rate for a single hydrogen storage tank. This refers to the maximum volumetric flow rate of hydrogen that can be extracted from a single hydrogen storage tank. The efficiency of hydrogen injection into the hydrogen storage tank. The efficiency of hydrogen extraction from the hydrogen storage tank. , These are the maximum and minimum volumes of a single hydrogen storage tank, respectively. For set The edge set represents the system boundary participating in energy storage configuration optimization.

[0073] The operating constraints of hydrogen fuel cells are expressed as follows: in, For the efficiency of hydrogen fuel cells, for season Time Node The required hydrogen volumetric flow rate for hydrogen fuel cells , These represent the upper and lower limits of the output electrical power of a single hydrogen fuel cell, respectively.

[0074] The hydrogen volume flow rate balance constraint is expressed as: The nonlinear terms in the deterministic distribution network multi-element energy storage optimization configuration model are linearized through variable substitution, McCormick linearization method, and Big-M method, and relaxed using second-order cone technique, transforming it into a mixed-integer second-order cone programming model, which is then solved using the GUROBI solver on the GAMS platform.

[0075] It should also be noted that by comprehensively collecting data from the distribution network, including investment and operational data, and inputting this data into a deterministic distribution network multi-element energy storage optimization configuration model, a deterministic optimal solution was generated. Data collection provides a solid foundation for subsequent analysis, ensuring that the model can reflect the actual operating conditions of the distribution network, including the predicted output and load data of typical days for photovoltaic and wind turbines throughout the four seasons. By comprehensively considering these variables, the model can more accurately reflect the dynamic characteristics of the power system, thereby maximizing the utilization of renewable energy and improving the reliability and stability of power supply. The generated deterministic optimal solution can provide clear guidance for the site selection and capacity determination of energy storage devices, reducing future investment risks caused by incorrect equipment selection, optimizing the economics of the configuration scheme, and ensuring the scientific nature of investment decisions. Through optimized configuration, the total cost of grid operation, including operating and investment costs, is effectively reduced, thereby improving the economic and environmental benefits of the power system. This makes the entire distribution network more adaptable and flexible in the face of complex power demands, providing strong support for the sustainable development of the power market.

[0076] S2: Calculate the weights of uncertain variables using the objective weighting method and set the deviation factor.

[0077] Furthermore, calculating the weights of uncertain variables includes data preprocessing, standardizing the predicted data for uncertain variables in wind power, photovoltaic power generation, and load, as follows: in, It is an uncertain variable At any moment The value below, Indicates the uncertainty of variables The values ​​at various time points are compared, and the minimum value is selected. Indicates the uncertainty of variables The values ​​at various time points are compared, and the maximum value is selected. Indicates the first Each device in the time period Performance metrics.

[0078] Calculate the variability and conflict of each uncertainty measure, and express the variability of each uncertain variable through standard deviation. Characterize and calculate the first The conflict coefficient of the three uncertain variables is expressed as: in, The correlation coefficient between uncertain variables. For the first The cost of each configuration option.

[0079] Calculating the weighting coefficients involves calculating the weighting coefficients based on the variability and conflict of each uncertain variable, and calculating the first... The weight coefficients of the uncertain variables are expressed as follows: The corresponding uncertain variables in the total uncertainty The portion that occupies is represented as: in, For the first The robustness coefficient of each configuration scheme For the first The weighting coefficients of each configuration scheme.

[0080] It should be noted that the deviation factor measures the degree of difference between the expected investment cost of the planning scheme and the deterministic optimal solution, outputting the additional investment cost that investors are willing to accept in the face of uncertainty factors. The value of the deviation factor is set based on the degree of risk aversion of investors, and the deviation factor ranges from 1% to 5%.

[0081] It should also be noted that CRITIC (Criteria Importance Through) IntercriteriaCorrelation (ICC) is an objective weighting method for multi-objective decision analysis. It calculates the weights of each decision indicator by analyzing the relative importance and interrelationships between evaluation indicators, thus better reflecting the importance of each indicator during the decision-making process. Infinite Graph Theory (IGDT) is a decision-making tool for handling uncertainty and fuzziness, applicable to situations with frequent environmental changes and multiple risk factors. IGDT introduces an uncertainty model to analyze the robustness and adaptability of decisions, thereby optimizing decision schemes to meet needs under different conditions. The CRITIC-IGDT model combines the CRITIC method with IGDT. By first assigning weights to each objective and then using IGDT for optimization, it generates feasible configuration schemes, making decisions more comprehensive. It not only considers the importance of different objectives but also flexibly responds to changes in the external environment, effectively solving the complexity and uncertainty problems brought about by multiple objectives. When facing multiple objectives and uncertain conditions, the CRITIC-IGDT model provides decision-makers with a scientific basis, ensuring that the optimized scheme has strong robustness and adaptability. In the optimal configuration of multi-element energy storage in distribution networks, the CRITIC-IGDT model can effectively handle the uncertainty of power supply and demand, providing support for the stable operation and economy of the power system.

[0082] It should also be noted that the use of objective weighting to calculate the weights of uncertain variables and the setting of deviation factors ensures a scientific assessment of uncertain factors. The application of objective weighting eliminates the subjective bias that may exist in traditional methods, allowing uncertain variables to be quantified and reasonably weighted in the model. Uncertainties from different sources are treated equally, avoiding decision-making errors caused by expert experience or inaccurate historical data. Setting deviation factors allows investors to flexibly adjust their strategies for dealing with uncertainty according to their own risk tolerance. By reflecting the difference between expected investment costs and the deterministic optimal solution, investors can make more informed investment decisions in uncertain environments, reduce potential economic losses, improve the transparency and operability of the model, and make the decision-making process more systematic. The combination of scientifically calculated weights and deviation factors enhances the adaptability and robustness of the multi-energy storage optimization configuration scheme for the distribution network, and improves the stability of the overall system under uncertain conditions.

[0083] S3: Based on the objective weighting and information gap decision method, construct a robust optimization configuration model for multi-element energy storage in the distribution network and output a robust configuration scheme.

[0084] Furthermore, constructing a robust optimization configuration model for multi-element energy storage in the distribution network includes building a mathematical model of an optimization problem with uncertain variables, expressed as: in, Let be the objective function. For the decision variable matrix, For an uncertain variable matrix, and These are equality constraints and inequality constraints, respectively.

[0085] IGDT assigns uncertainty to each uncertain variable, describes the size of the set of uncertain variables, and aims to maximize the uncertainty of each uncertain variable while satisfying a preset target value. It constructs a model based on the uncertainty. The determined set of uncertain variables , is represented as: in, For the predicted value, the uncertainty is... Greater than 0 and less than 1.

[0086] Under deterministic optimization model Pick The optimal solution at time is ,definition To determine the maximum acceptable target value for decision-makers after introducing uncertainty, for problems with multiple uncertain variables, a CRITIC-IGDT model is constructed using an uncertainty weighting method, expressed as: Based on the CRITIC-IGDT model, the planning variables , , , As a decision variable Run variables , , , , , , , , , , , , , , , , , , , , , , , , As a decision variable .

[0087] A robust optimization configuration model for multi-element energy storage in distribution networks based on CRITIC-IGDT is constructed, represented as follows: in, , and The variables are uncertain, representing the predicted wind power output, photovoltaic power output, and load, respectively. This represents the determined optimal solution obtained.

[0088] It should be noted that the output robust configuration scheme includes the generated multi-energy storage configuration scheme, outputting the distribution network investment cost, operating cost, number of installed electrical energy storage and hydrogen energy storage units and configuration capacity.

[0089] It should also be noted that, based on objective weighting and information gap decision-making, a robust optimization configuration model for multi-element energy storage in the distribution network was constructed, and a robust configuration scheme was output. The model considers uncertain variables, ensuring that the optimization scheme not only focuses on cost minimization but also guarantees the stable operation of the distribution network in the face of sudden events and environmental changes. By maximizing the uncertainty of uncertain variables, the model can effectively evaluate the effectiveness and adaptability of operating strategies under complex grid conditions, improving the robustness of the system. The output of the robust configuration scheme not only provides investment costs, operating costs, and configuration indicators for electrical and hydrogen energy storage units but also ensures the practical feasibility of all configurations under specific constraints. By organically combining the results of the theoretical model with practical operational challenges, the final scheme becomes practical during implementation. The constructed robust optimization model provides effective support for the power system to cope with external uncertainties, ensuring that the system can achieve the expected power supply effect with the lowest risk under any circumstances. This comprehensive design approach optimizes the flexibility and economy of power system operation, lays the foundation for the efficient utilization of clean energy, and promotes the sustainable development of the entire distribution network.

[0090] Example 2, refer to Figure 2 As an embodiment of the present invention, a robust optimization configuration method for multi-element energy storage in distribution networks is provided. To verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculations and simulation experiments.

[0091] First, the system consists of six collaborative modules distributed across three key layers: a data acquisition layer, a core computing layer, and a scheme output layer. The data acquisition layer includes a data acquisition module; the core computing layer includes a deterministic optimal solution generation module, an uncertainty weight calculation module, a deviation factor setting module, and a configuration scheme generation module; and the scheme output layer includes a scheme output module. The interaction process between each layer is detailed below: The data acquisition layer acquires investment data such as equipment models and single-unit investment costs for electric and hydrogen energy storage units, as well as operational data such as distribution network parameters, predicted output data for typical days in all four seasons for photovoltaic and wind turbine units, and load data for typical days in all four seasons, through the data acquisition module. This data is then transmitted to the core computing layer as the basis for optimization calculations by the core computing module. The core computing layer receives the data transmitted from the data acquisition layer, performs analysis and calculations, and the data is then passed to the deterministic optimal solution generation module, which is responsible for, given the data, performing multi-element energy storage optimization on the deterministic distribution network. The optimized configuration model is transformed and solved to generate a deterministic optimal solution. Typical daily operating data is transmitted to the uncertainty weight calculation module. This module standardizes the wind and solar load forecast data, calculates variability and conflict, and calculates weight coefficients to generate uncertainty weights for each uncertainty variable. The core calculation layer transmits the deterministic optimal solution obtained from the deterministic optimal solution generation module, the uncertainty weights obtained from the uncertainty weight calculation module, and the deviation factors set by the deviation factor setting module to the configuration scheme generation module. This module further combines the CRITIC-IGDT model to construct a deterministic distribution network multi-element energy storage optimal configuration model and solves the model to obtain a robust configuration scheme. The scheme output layer receives the optimal planning scheme transmitted from the core calculation layer and outputs the received optimal planning scheme. The output includes investment costs, operating costs, the number of electrical energy storage units and hydrogen energy storage units installed, and their configuration capacity, for investors to further analyze and make decisions. Based on the comprehensive experimental results, it can be concluded that… Example 3, referring to Figure 3 As an embodiment of the present invention, a robust optimization configuration system for multi-element energy storage in a power distribution network is provided, including a data acquisition module, a weight calculation module, and a model output module.

[0092] The data acquisition module is used to collect data from the distribution network and input the collected data into a deterministic distribution network multi-element energy storage optimization configuration model to generate a deterministic optimal solution. The weight calculation module is used to calculate the weights of uncertain variables and set the deviation factor through the objective weighting method. The model output module is used to construct a robust optimization configuration model for distribution network multi-element energy storage based on the objective weighting-information gap decision method and output a robust configuration scheme.

[0093] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0094] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0095] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0096] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0097] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A robust optimization configuration method for multi-element energy storage in a distribution network, characterized in that, include: Collect data from the distribution network, input the collected data into a deterministic distribution network multi-element energy storage optimization configuration model, and generate a deterministic optimal solution; The weights of uncertain variables are calculated using the objective weighting method, and a deviation factor is set. Based on the objective weighting and information gap decision-making method, a robust optimization configuration model for multi-element energy storage in the distribution network is constructed, and a robust configuration scheme is output.

2. The robust optimization configuration method for multi-element energy storage in distribution networks as described in claim 1, characterized in that: The power distribution network data includes power grid investment data and operation data; Investment data includes the equipment models and investment costs per unit for both electrical and hydrogen energy storage units; The operational data includes power distribution network system parameters, predicted output data of photovoltaic and wind turbine units on typical days throughout the four seasons, and load data on typical days throughout the four seasons.

3. The robust optimization configuration method for multi-element energy storage in distribution networks as described in claim 2, characterized in that: The process of generating a deterministic optimal solution involves inputting the collected power grid investment data and operation data into a deterministic distribution network multi-electrode energy storage optimization configuration model, solving the model, and generating a deterministic optimal solution that does not consider uncertainties such as wind and solar power output and load power fluctuations. A deterministic distribution network multi-element energy storage optimization configuration model is constructed with the goal of minimizing the overall cost, including investment cost and operating cost. It is based on the location and capacity determination of electric energy storage and hydrogen energy storage units and the steady-state optimization scheduling operation of the distribution network. The constraints include equipment investment constraints and simulation operation constraints. Calculate the objective function , is represented as: in, Investment costs, including annualized energy storage investment costs. Investment cost of hydrogen energy storage units , Operating costs, including the annual operating costs of distributed power sources. Operating costs of energy storage Operating costs of hydrogen energy storage units Cost of purchasing electricity from the upper-level power grid Costs of wind and solar power curtailment Network loss cost ; Calculate the investment cost of electric energy storage , is represented as: in, For the entire life cycle of electrical energy storage, This is a set of candidate installation nodes for electric energy storage. The investment cost of a single energy storage unit, Energy storage at nodes The number of installations; Investment cost of hydrogen energy storage units , is represented as: in, For the entire life cycle of the hydrogen energy storage unit, This is a set of candidate installation nodes for hydrogen energy storage units. The investment cost of a single water electrolysis unit, For the water electrolysis device at the node The number of installations, The investment cost for a single hydrogen storage tank, For hydrogen storage tanks at nodes The number of installations, The investment cost for a single hydrogen fuel cell, This refers to the number of hydrogen fuel cells installed at a node; Annual operating cost of distributed power sources , is represented as: in, For the season Total number of days, The set of nodes where the photovoltaic units are located. The set of nodes where the wind turbines are located. The operating cost per unit of electricity generated by a photovoltaic power unit. The operating cost per unit of electricity generated by a wind turbine. for season Time-of-use photovoltaic unit injection node active power, for season Time-of-use wind turbine injection node active power, express Duration of the period, for ; Operating costs of electric energy storage , is represented as: in, For the set of nodes to install electrical energy storage, The operating cost per unit charge / discharge capacity of electrical energy storage. for season Energy storage at nodes The charging power for season Energy storage at nodes The magnitude of the discharge power; Operating costs of hydrogen energy storage units , is represented as: in, For the set of nodes to install hydrogen energy storage units, The operating cost per unit of electricity consumed by the water electrolysis unit. for season Input node at any time The electrical power of the water electrolysis device The operating cost of injecting or extracting hydrogen per unit volume flow rate into a hydrogen storage tank. for season Time Node The volumetric flow rate of hydrogen injected into the hydrogen storage tank. for season Time Node The volumetric flow rate of hydrogen injected into or extracted from the hydrogen storage tank. The operating cost per unit of electricity generated by a hydrogen fuel cell. for season Momentary hydrogen fuel cell injection node The electrical power; Cost of purchasing electricity from the upstream power grid , is represented as: in, for Time-of-use electricity pricing for different time periods This refers to the set of nodes where the system interacts with other power grids. for season Interaction nodes between time period and power grid Active power injected into the local power grid; Costs of wind and solar power curtailment , is represented as: in, Cost per unit of wind curtailment Cost per unit of electricity curtailed For nodes Photovoltaic units in season The ability to predict and contribute at all times For nodes Wind turbine units in season The potential for positive results can be predicted at any time; Network loss cost , is represented as: in, for season Network loss during a given time period; Equipment investment constraints include constraints on the number of devices to be installed at candidate installation nodes, expressed as: in, For nodes The electrical energy storage output of the device, For energy storage devices at nodes Maximum number of installations, This is a set of candidate installation nodes for energy storage devices. For nodes The output of the electro-hydrogen conversion equipment, For the water electrolysis device at the node Maximum number of installations, This is the set of candidate installation nodes for hydrogen energy storage devices. For nodes The output of the energy storage device of the equipment. For hydrogen storage tanks at nodes Maximum number of installations, For nodes The output of the hydrogen fuel cell in the device, For hydrogen fuel cells at the node Maximum number of installations; The simulated operation constraints include AC power flow constraints of the distribution network, system security constraints, power interaction constraints with the external power grid, operation constraints of photovoltaic and wind turbine units, operation constraints of electric energy storage, operation constraints of hydrogen energy storage units, and hydrogen volume flow balance constraints. AC power flow constraints in a distribution network are represented as: in, Indicated by This is the set of terminal nodes of the AC branch with the first terminal node. Indicated by Let be the set of the starting nodes of the AC branch with the terminal node. , and , They are respectively season Timetable and Active and reactive power on , for season External power grid and wind turbine injection nodes at all times reactive power, and They are respectively season Time Node The magnitude of active and reactive power of the load, For nodes The voltage magnitude and system safety constraints are expressed as: in, and They are nodes The upper and lower limits of the voltage, For the safe current of the line, For nodes At any moment The square of the current represents the value at time t. Energy storage device or circuit Real-time current status; The power constraint for interaction with the external power grid is expressed as: in, for season Interaction nodes between time period and power grid The reactive power injected into the local power grid, and These are the interaction nodes with the power grid. Upper and lower limits of active power injected into the grid. and These are the interaction nodes with the power grid. Upper and lower limits of reactive power injected into the power grid. This represents the set of all candidate nodes that exchange power with the power grid; The operating constraints of photovoltaic units and wind turbine units are expressed as follows: in, The power factor angle of the wind turbine generator. This represents the set of all nodes containing photovoltaic (PV) units. This represents the set of all nodes containing wind turbine units; The operational constraints of energy storage are expressed as follows: in, and These are the state variables for charging and discharging electrical energy storage, and are 0-1 variables. This represents the upper limit of the charging and discharging power of a single energy storage unit. for season Time period nodes The amount of electricity stored. This refers to the maximum capacity of a single energy storage unit. The positive efficiency of an energy storage device represents the efficiency with which energy is output from the energy storage device to the power grid. The reverse efficiency of an energy storage device represents the efficiency when energy is input from the grid into the energy storage device. The operational constraints of hydrogen energy storage units include the operational constraints of water electrolysis devices, hydrogen storage tanks, and hydrogen fuel cells. The operating constraints of a water electrolysis unit are expressed as follows: in, The hydrogen production efficiency of the water electrolysis unit, Hydrogen has a high calorific value. , These represent the upper and lower limits of the input electrical power for a single water electrolysis unit. For season Time Node The volumetric flow rate of hydrogen produced by the water electrolysis device; The operating constraints of the hydrogen storage tank are expressed as follows: in, , All variables are 0-1, representing decision variables for the hydrogen storage tank's hydrogen injection and extraction process. A value of 0 indicates that the hydrogen storage tank has not been injected or extracted, while a value of 1 indicates that the hydrogen storage tank has been injected or extracted. for season Time Node The hydrogen volume of the hydrogen storage tank, The maximum permissible hydrogen volumetric flow rate for a single hydrogen storage tank. This refers to the maximum volumetric flow rate of hydrogen that can be extracted from a single hydrogen storage tank. The efficiency of hydrogen injection into the hydrogen storage tank. The efficiency of hydrogen extraction from the hydrogen storage tank. , These are the maximum and minimum volumes of a single hydrogen storage tank, respectively. For set The edge set represents the system boundary participating in energy storage configuration optimization; The operating constraints of hydrogen fuel cells are expressed as follows: in, For the efficiency of hydrogen fuel cells, for season Time Node The required hydrogen volumetric flow rate for hydrogen fuel cells , These represent the upper and lower limits of the output power of a single hydrogen fuel cell, respectively. The hydrogen volume flow rate balance constraint is expressed as: The nonlinear terms in the deterministic distribution network multi-element energy storage optimization configuration model are linearized through variable substitution, McCormick linearization method, and Big-M method, and relaxed using second-order cone technique, transforming it into a mixed-integer second-order cone programming model, which is then solved using the GUROBI solver on the GAMS platform.

4. The robust optimization configuration method for multi-element energy storage in distribution networks as described in claim 3, characterized in that: The calculation of the weights of uncertain variables includes data preprocessing, standardizing the predicted data of uncertain variables for wind power, photovoltaic power generation, and load, as follows: in, Uncertain variables At any moment The value below, Indicates the uncertainty of variables The values ​​at various time points are compared, and the minimum value is selected. Indicates the uncertainty of variables The values ​​at various time points are compared, and the maximum value is selected. Indicates the first Each device in the time period Performance metrics; Calculate the variability and conflict of each uncertainty measure, and express the variability of each uncertain variable through standard deviation. Characterize and calculate the first The conflict coefficient of the three uncertain variables is expressed as: in, The correlation coefficient between uncertain variables. For the first The cost of each configuration option; Calculating the weighting coefficients involves calculating the weighting coefficients based on the variability and conflict of each uncertain variable, and calculating the first... The weight coefficients of the uncertain variables are expressed as follows: The corresponding uncertain variables in the total uncertainty The portion that occupies is represented as: in, For the first The robustness coefficient of each configuration scheme For the first The weighting coefficients of each configuration scheme.

5. The robust optimization configuration method for multi-element energy storage in distribution networks as described in claim 4, characterized in that: The deviation factor measures the difference between the expected investment cost of the planning scheme and the deterministic optimal solution, outputting the additional investment cost that investors are willing to accept in the face of uncertainty. The value of the deviation factor is set based on the degree of risk aversion of investors, and the deviation factor ranges from 1% to 5%.

6. The robust optimization configuration method for multi-element energy storage in distribution networks as described in claim 5, characterized in that: The construction of a robust optimization configuration model for multi-element energy storage in the distribution network includes constructing a mathematical model of an optimization problem with uncertain variables, expressed as: in, Let be the objective function. For the decision variable matrix, For an uncertain variable matrix, and These are equality constraints and inequality constraints, respectively. IGDT assigns uncertainty to each uncertain variable, describes the size of the set of uncertain variables, and aims to maximize the uncertainty of each uncertain variable while satisfying a preset target value. It constructs a model based on the uncertainty. The determined set of uncertain variables , is represented as: in, For the predicted value, the uncertainty is... Greater than 0 and less than 1; Under deterministic optimization model Pick The optimal solution at time is ,definition To determine the maximum acceptable target value for decision-makers after introducing uncertainty, for problems with multiple uncertain variables, a CRITIC-IGDT model is constructed using an uncertainty weighting method, expressed as: Based on the CRITIC-IGDT model, the planning variables , , , As a decision variable Run variables , , , , , , , , , , , , , , , , , , , , , , , , As a decision variable ; A robust optimization configuration model for multi-element energy storage in distribution networks based on CRITIC-IGDT is constructed, represented as follows: in, , and The variables are uncertain, representing the predicted wind power output, photovoltaic power output, and load, respectively. This represents the determined optimal solution obtained.

7. The robust optimization configuration method for multi-element energy storage in distribution networks as described in claim 6, characterized in that: The output robust configuration scheme includes a generated multi-element energy storage configuration scheme, which outputs the power distribution network investment cost, operating cost, number of installed electrical energy storage units and hydrogen energy storage capacity.

8. A system employing the robust optimization configuration method for multi-element energy storage in a distribution network as described in any one of claims 1 to 7, characterized in that: It includes a data acquisition module, a weight calculation module, and a model output module; The data acquisition module is used to collect data from the distribution network, input the collected data into the deterministic distribution network multi-element energy storage optimization configuration model, and generate a deterministic optimal solution. The weight calculation module is used to calculate the weights of uncertain variables using an objective weighting method and to set a deviation factor. The model output module is used to construct a robust optimization configuration model for multi-element energy storage in distribution networks based on the objective weighting-information gap decision method, and output a robust configuration scheme.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the robust optimization configuration method for multi-element energy storage in the distribution network as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the robust optimization configuration method for multi-element energy storage in the distribution network as described in any one of claims 1 to 7.

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