Method, device and medium for optimizing proportioning of grid-connected and grid-forming equipment and determining energy storage capacity of new energy base

Abstract

The application provides a new energy base follow-grid type and grid-constructing type equipment optimal matching and energy storage capacity determination method, equipment and medium, the method comprises the following steps: constructing a multi-source dynamic complementary relationship model; establishing a constraint condition to determine the minimum configuration capacity of the grid-constructing type energy storage system; combining the multi-source dynamic complementary relationship model and the minimum configuration capacity to construct a multi-scenario collaborative optimization configuration model; and solving the multi-scenario collaborative optimization configuration model to obtain the optimal capacity matching of the follow-grid type equipment and the grid-constructing type equipment and the optimal configuration parameters of the energy storage system. The method can scientifically determine the optimal matching of the follow-grid type and the grid-constructing type equipment and the minimum capacity of the grid-constructing type energy storage, and realizes the optimal investment cost while ensuring the safe and stable operation of the system in multiple scenes.

Description

Technical Field

[0001] This application relates to the field of new energy power system planning technology, and in particular to a method, equipment and medium for optimizing the allocation of grid-connected and grid-structured equipment in new energy bases and determining energy storage capacity. Background Technology

[0002] Currently, new energy bases, as the core carriers for achieving the "dual carbon" goal, are widely used for large-scale clean energy transmission from desert and Gobi regions. With the high proportion of new energy integration, related technologies have constructed an integrated source-grid-load-storage technology system through the coordinated operation of grid-connected converters, grid-connected equipment, and energy storage systems. Specifically, this system covers the entire process from power fluctuation mitigation to frequency and voltage support, including key aspects such as inertia simulation, short-circuit capacity provision, and multi-timescale energy balance, aiming to solve the system stability problems caused by the lack of traditional synchronous generator units.

[0003] However, the planning and configuration schemes in related technologies directly adopt single-dimensional estimation or static scenario calculation, without establishing a dynamic complementary model and multi-dimensional capacity coupling constraints for grid-type and grid-based equipment. This may lead to a mismatch between the minimum capacity of grid-based energy storage and the power, energy, and inertia support requirements, or the configuration scheme may fail safety verification under extreme weather and low-output scenarios, thereby affecting the frequency stability and voltage safety of the new energy base under all operating conditions, resulting in investment redundancy or a significant increase in system operation risks. Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, the first objective of this application is to propose a method for optimizing the allocation of grid-type and grid-structured equipment and determining the energy storage capacity of new energy bases.

[0006] The second objective of this application is to propose an electronic device.

[0007] The third objective of this application is to provide a computer-readable storage medium.

[0008] To achieve the above objectives, the first aspect of this application is to propose a method for optimizing the allocation of energy storage capacity in new energy bases and grid-type and grid-structured equipment, including the following steps:

[0009] Based on the output characteristics of new energy sources, load characteristics, and power interaction relationships of various power supply equipment, a multi-source dynamic complementary relationship model is constructed, and a complementary coordination coefficient is defined to characterize the ability of active support equipment to mitigate total power fluctuations. Constraints were established from three dimensions: instantaneous power support capability, continuous energy support capability, and equivalent inertia support capability. The capacity requirements that meet the constraints of each dimension were calculated, and the maximum capacity requirement was determined as the minimum configuration capacity of the grid-type energy storage system. Combining the aforementioned multi-source dynamic complementary relationship model with the aforementioned minimum configuration capacity, and taking the minimization of the system's total lifecycle cost as the objective function, and frequency safety, voltage safety, and short-circuit capacity under multiple typical operating scenarios as constraints, a multi-scenario collaborative optimization configuration model is constructed. Solving the multi-scenario collaborative optimization configuration model yields the optimal capacity ratio of grid-connected devices and network-building devices, as well as the optimal configuration parameters of the energy storage system. These optimal configuration parameters are then substituted into various typical operating scenarios for stability verification. If any constraints are violated, the constraints are updated and the solution is recalculated until all scenarios pass the verification.

[0010] To achieve the above objectives, a second aspect of this application also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the method for optimizing the allocation of new energy base grid-type and grid-structured equipment and determining energy storage capacity as described in any one of the first aspects above.

[0011] To achieve the above objectives, the third aspect of this application also proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method for optimizing the allocation of new energy base and grid-type equipment and determining energy storage capacity as described in any of the first aspects above.

[0012] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects: This application can scientifically determine the optimal ratio of grid-connected and grid-structured equipment and the minimum capacity of grid-structured energy storage, and achieve the optimal investment cost under the premise of ensuring the stability of system frequency, voltage and inertia in multiple scenarios.

[0013] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0014] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a method for optimizing the allocation of grid-connected and grid-structured equipment and determining energy storage capacity in a new energy base, as proposed in an embodiment of this application. Figure 2This is a schematic diagram of a system for optimizing the allocation of grid-type and grid-structured equipment and determining energy storage capacity in a new energy base, as proposed in an embodiment of this application. Detailed Implementation

[0015] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0016] The following description, with reference to the accompanying drawings, describes a method, equipment, and medium for optimizing the allocation of grid-type and grid-structured equipment and determining energy storage capacity in a new energy base, as proposed in an embodiment of this application.

[0017] Example 1 Figure 1 This is a flowchart illustrating a method for optimizing the allocation of grid-type and grid-structured equipment and determining energy storage capacity in a new energy base, as proposed in an embodiment of this application. Figure 1 As shown, the method includes the following steps: Step S101: Based on the output characteristics of new energy sources, load characteristics, and power interaction relationships of various power supply devices, construct a multi-source dynamic complementary relationship model, and define a complementary coordination coefficient that characterizes the ability of active support equipment to mitigate total power fluctuations.

[0018] Specifically, constructing a multi-source dynamic complementary relationship model aims to quantitatively describe the power interaction and complementary characteristics of different power source types within a new energy base on a time scale, providing a foundation for subsequent optimized configuration. This step establishes a basic power characteristic function that reflects power fluctuation patterns across multiple time scales, from seconds to hours, by collecting and analyzing the output characteristics of new energy sources such as wind power and photovoltaics, as well as the electricity consumption characteristics of loads.

[0019] Based on this, considering the power interaction relationships between grid-connected equipment, grid-linked equipment, and energy storage systems, a system power balance equation is constructed to characterize the dynamic power exchange between the system and the external power grid or transmission channel. To scientifically assess the system's ability to mitigate power fluctuations, this step further defines a complementary coordination coefficient. This coefficient is calculated based on the integral ratio of the power change of equipment with active support capabilities to the total system power fluctuation, and is used to quantitatively characterize the ability of active support equipment to absorb total power fluctuations and the system's disturbance rejection level.

[0020] As a specific implementation method, wind power output characteristic functions, photovoltaic power output characteristic functions, and load characteristic functions can be established separately. The specific value of the complementary coordination coefficient can be determined by calculating the proportion of the integral value of the power fluctuation jointly smoothed by the grid-connected equipment and the energy storage system to the total power fluctuation integral value of the wind, solar and grid-connected equipment. This coefficient will serve as a key performance indicator for measuring the system's coordinated regulation capability.

[0021] Therefore, by constructing a multi-source dynamic complementary relationship model and defining complementary coordination coefficients, the synergistic effect between different devices can be scientifically quantified, accurately depicting the dynamic matching relationship between new energy output fluctuations and the support capabilities of various power sources. This method not only reveals the mechanism by which active support devices mitigate system power fluctuations, but also provides precise theoretical basis and data support for achieving the optimal capacity ratio of grid-connected and grid-building devices while ensuring system safety, effectively avoiding configuration redundancy or instability problems caused by the lack of a collaborative model.

[0022] Step S102: Establish constraints from three dimensions: instantaneous power support capability, continuous energy support capability, and equivalent inertia support capability. Calculate the capacity requirements that satisfy the constraints of each dimension and determine the maximum capacity requirement as the minimum configuration capacity of the grid-type energy storage system.

[0023] Specifically, this step aims to quantify the minimum configuration capacity of a grid-based energy storage system through multi-dimensional physical constraints, addressing the issue that single-dimensional estimation cannot fully cover the dynamic stability requirements of the system. Its core lies in constructing a constraint model comprising three independent yet coupled dimensions: instantaneous power support, continuous energy support, and equivalent inertia support. In the instantaneous power support dimension, the rated power lower limit of the energy storage system is determined based on the maximum potential power deficit of the system, ensuring sufficient instantaneous power injection or absorption during severe grid disturbances. In the continuous energy support dimension, the minimum energy capacity required to maintain grid-based operation is calculated by considering the duration required for the system to overcome transient processes or cope with long-term power fluctuations. In the equivalent inertia support dimension, based on the system's requirement for the minimum equivalent inertia time constant, the equivalent inertia that the grid-based energy storage system needs to provide is deducted after subtracting the inertia contributions of other synchronization sources or virtual synchronization sources, thus transforming it into a capacity constraint that satisfies this inertia characteristic.

[0024] Finally, the capacity requirements calculated from the above three dimensions are compared, and the maximum value is selected as the minimum configuration capacity of the grid-type energy storage system, thereby ensuring that the energy storage system meets the safe operation boundary in terms of power response speed, continuous energy supply and frequency stability support.

[0025] As a specific implementation method, the capacity requirements in the power, energy and inertia dimensions can be calculated by using the system's maximum power deficit, the preset support duration integral, and the difference between the system's baseline capacity and the minimum inertia requirement divided by the equivalent inertia time constant of the energy storage unit, and the maximum value of the three can be taken to determine the final capacity.

[0026] Therefore, by establishing a coupled constraint model of power, energy and inertia in three dimensions, this method overcomes the limitation of existing technologies that estimate energy storage capacity from only a single dimension. It effectively avoids the risk of insufficient configuration due to ignoring the hard constraint of inertia support, or the investment redundancy caused by overly conservative estimation. It ensures that the grid-type energy storage system has the ability to respond quickly to power, maintain continuous energy balance and support system frequency stability under complex operating conditions.

[0027] Step S103: Combining the multi-source dynamic complementary relationship model and the minimum configuration capacity, with the goal of minimizing the system's total lifecycle cost and with the constraints of frequency safety, voltage safety, and short-circuit capacity under multiple typical operating scenarios, a multi-scenario collaborative optimization configuration model is constructed.

[0028] Specifically, the core of constructing a multi-scenario collaborative optimization configuration model lies in taking the multi-source dynamic complementary relationship model that characterizes the dynamic interaction characteristics between power sources and the minimum configuration capacity that quantifies the basic support capability of energy storage as the pre-constraint conditions, and then establishing a mathematical programming framework that covers the goal of minimizing the cost of the entire system life cycle and the safety and stability constraints under multiple typical operating scenarios.

[0029] This step aims to comprehensively consider system performance requirements under different time scales and operational boundary conditions through a unified optimization architecture. The objective function comprehensively considers the investment, construction, operation, and maintenance costs of various equipment, as well as power curtailment losses caused by power fluctuations. The constraints must ensure that the system's frequency deviation, voltage fluctuation range, and short-circuit capacity of key nodes meet safe operating thresholds under various preset typical operating scenarios. This modeling approach is not a static calculation for a single operating condition, but rather introduces a set of scenarios to cover multiple boundary conditions such as high renewable energy output, low renewable energy output, and extreme weather disturbances. This allows for the search in the solution space for the optimal combination of equipment capacity that simultaneously meets the stability requirements of all scenarios and is economically optimal.

[0030] As a specific implementation method, three typical scenarios can be defined: high wind and solar power output, low wind and solar power output, and extreme weather. The risks of voltage exceeding limits, frequency stability, and transient power angle stability are examined respectively. A weighted objective function is constructed that includes the cost of grid-connected equipment, grid-connected equipment, and energy storage system. At the same time, hard constraints such as frequency safety, voltage safety, inertia, and short-circuit ratio are applied to form a solvable nonlinear optimization model.

[0031] Therefore, this step, by constructing a multi-scenario collaborative optimization configuration model, effectively overcomes the shortcomings of traditional single-scenario planning methods in adapting to complex operating environments, ensuring that the final determined equipment allocation scheme has sufficient robustness and safety across all operating conditions. Simultaneously, by integrating multi-source dynamic complementarity and multi-dimensional capacity constraints into a unified objective optimization framework, it achieves a globally optimal balance between system technical feasibility and economic efficiency, avoiding configuration redundancy or security shortcomings caused by incomplete scenario coverage.

[0032] Step S104: Solve the multi-scenario collaborative optimization configuration model to obtain the optimal capacity ratio of grid-connected devices and network-building devices, as well as the optimal configuration parameters of the energy storage system. Substitute the optimal configuration parameters into various typical operating scenarios for stability verification. If there is a constraint violation, update the constraint conditions and re-solve until all scenarios pass the verification.

[0033] Specifically, solving the multi-scenario collaborative optimization configuration model aims to find the optimal capacity ratio of grid-connected and grid-connected devices and the optimal configuration parameters of the energy storage system within a multi-dimensional constraint space using numerical optimization algorithms. This process, based on the constructed objective function and constraints, uses an iterative algorithm with global search capabilities to update the device capacity variables until convergence to a solution space that satisfies the safety and stability requirements of all scenarios.

[0034] After obtaining the initial optimal configuration parameters, they need to be substituted into various typical operating scenarios covering boundary conditions such as high wind and solar power output, low wind and solar power output, and extreme weather to conduct time-domain simulation stability verification, in order to verify the effectiveness of constraints such as frequency safety, voltage safety, and short-circuit capacity under all operating conditions. If any constraint violation is found in any scenario during the verification process, the most severe violation is identified and its corresponding limit state is transformed into a new hard constraint condition. Then, the multi-scenario collaborative optimization configuration model is updated and the solution and verification process is re-executed, forming a closed-loop iterative mechanism, until the stability verification under all typical operating scenarios passes.

[0035] As a specific implementation method, an improved particle swarm optimization algorithm incorporating dynamic inertia weights and mutation operations can be used to solve the model, quickly converging to obtain the optimal capacity ratio. and optimal energy storage configuration parameters , And when a constraint violation is detected, the robustness of the solution is ensured through iterative correction.

[0036] Therefore, by establishing a closed-loop iterative mechanism for solving and verifying, this step effectively overcomes the shortcomings of single static calculation in adapting to complex and ever-changing operating environments, ensuring that the final determined equipment ratio and energy storage capacity have sufficient safety and stability support capabilities under all operating conditions, and significantly improving the engineering practicality and risk resistance of the new energy base planning scheme.

[0037] Example 2 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S101, "constructing a multi-source dynamic complementary relationship model based on the output characteristics of new energy sources, load characteristics, and power interaction relationships of various power supply devices, and defining a complementary coordination coefficient that characterizes the ability of active support equipment to mitigate total power fluctuations."

[0038] In this embodiment, the specific process of constructing a multi-source dynamic complementary relationship model and calculating the complementary coordination coefficient is as follows: First, the input source is historical meteorological data or typical meteorological year data, and the processing action is to respectively fit and generate wind power output characteristic functions. Photovoltaic power output characteristic function and load characteristic function These functions can accurately reflect the power fluctuation characteristics at different time scales such as seconds, minutes and hours, and the output results are time series data sequences that characterize the basic fluctuation laws of each source load.

[0039] Next, based on the aforementioned source-load characteristic data, and combined with the preset power output of the network-type equipment, , and the output of network equipment and energy storage system output The processing action is to execute the power balance equation. The calculation specifies that when the energy storage system discharges... The value is positive during charging and negative during charging. The output result is the power exchanged between the system and the external power grid or transmission channel at any given time. .

[0040] Then, based on the above power timing data, we proceed to the complementary coordination coefficient. The calculation process uses a selected time interval as the input source. arrive Power change per unit time of each device The specific processing steps are divided into two parts: the first step is to obtain the power change of the network-type equipment. With the power change of the energy storage system The sum of the absolute values ​​of these values, integrated over time, yields the total amount of power fluctuation absorbed by the equipment with active support capabilities, i.e., the numerator. The second step is to obtain the change in wind power within the same time interval. Photovoltaic power change Power variation of grid-connected equipment The sum of the absolute values ​​of the terms, integrated over time, yields the total power fluctuation of the system, which is the denominator. .

[0041] The final processing step is to divide the total amount of power consumed by the total power fluctuation of the system, according to the formula... The complementary coordination coefficient was calculated. This coefficient serves as a key performance indicator output for the ability of grid-type equipment and energy storage systems to jointly mitigate wind and solar power fluctuations. The larger the value, the stronger the system's resistance to disturbances.

[0042] Therefore, this implementation method scientifically reveals the synergistic regulation mechanism between grid-type equipment and energy storage system by quantifying the proportion of power fluctuations “digested” by active support equipment, providing an accurate performance evaluation basis for subsequent optimization configuration, and effectively avoiding the problems of insufficient system stability or investment redundancy caused by configuration based solely on experience.

[0043] Example 3 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S102, which involves "establishing constraints from three dimensions: instantaneous power support capability, continuous energy support capability, and equivalent inertia support capability, calculating the capacity requirements that satisfy the constraints of each dimension, and determining the maximum capacity requirement as the minimum configuration capacity of the grid-type energy storage system".

[0044] In this embodiment, the process of determining the minimum configuration capacity of a grid-type energy storage system first involves calculating the power support dimension. The input source is a wind power output characteristic function generated based on historical data or typical meteorological year data. Photovoltaic power output characteristic function , and the output of network equipment Power output of network-type equipment and load characteristic function The processing action involves traversing the time series. Calculate the absolute value of the difference between the load power and the sum of the output of all power sources at each time point, and extract the maximum value as the system's maximum power deficit. Its calculation expression is: The rated power of the energy storage system will then be... The output is set to be no less than the maximum power deficit, and the power support constraint capacity is obtained. Its value is equal to .

[0045] Next, the energy support dimension is calculated. The input source is the previously determined rated energy storage power. And the continuous support time of the network configuration mode set according to the system's transient recovery needs or the duration of extreme weather events. This time range is, for example, from 15 minutes to 1 hour. The processing action is to... exist to Integral calculations are performed over a time period to obtain the minimum total energy required to maintain system equilibrium. The output is the energy-supported constraint capacity. Its value is equal to the minimum energy capacity. And satisfy .

[0046] Then, the inertia support dimension is calculated. Input sources include the system baseline capacity. The minimum inertia time constant required by the system The equivalent inertia time constant that the energy storage unit can provide under the set control strategy. And the statistical parameters of all synchronous machines and virtual synchronous machines in the system except for energy storage, i.e., the first Rated capacity of the equipment With inertia time constant The first step in the processing is to calculate the total inertia contribution of non-energy storage devices. Next, calculate the total system inertia requirement. Subtract the two to obtain the inertia gap, and finally divide the inertia gap by... The output is the inertia support constraint capacity that satisfies the system's dynamic stability requirements. The calculation is based on .

[0047] Finally, the overall capacity is determined. The input sources are the values ​​calculated from the three dimensions mentioned above. , and The processing involves comparing these three values ​​and selecting the maximum value. The output is the final minimum configuration capacity for grid-type energy storage. ,Right now This ensures that the energy storage system simultaneously meets the triple functional requirements of instantaneous power support, continuous energy support, and equivalent inertia support.

[0048] Therefore, this implementation method overcomes the defects of configuration redundancy or instability caused by traditional single-dimensional estimation by constructing a constraint model that couples power, energy and inertia in three dimensions, accurately quantifies the minimum capacity of grid-type energy storage, and effectively ensures the frequency safety and dynamic stability of high-proportion new energy bases under complex operating conditions.

[0049] Example 4 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S103 above, which involves "combining the multi-source dynamic complementary relationship model with the minimum configuration capacity, taking the minimization of the system's total lifecycle cost as the objective function, and using frequency security, voltage security, and short-circuit capacity under multiple typical operating scenarios as constraints to construct a multi-scenario collaborative optimization configuration model".

[0050] In this embodiment, the specific process of constructing a multi-scenario collaborative optimization configuration model first involves defining typical operating scenarios and preparing input data. The input sources are historical meteorological data, load forecast curves, and equipment operating parameters from the new energy base. The processing involves clustering this data to generate three representative scenarios: a high wind and solar power output scenario to simulate the voltage over-limit risk during periods of high new energy generation; a low wind and solar power output scenario to simulate the frequency stability and short-circuit capacity support issues of a near-passive system; and an extreme weather scenario to simulate the transient power angle stability and frequency security challenges during continuous rain or windless conditions accompanied by DC blocking. The output is a set of scenarios containing time-series power data for each scenario, serving as boundary conditions for subsequent optimization calculations.

[0051] Furthermore, based on the aforementioned set of scenarios, an objective function is constructed with the goal of minimizing the system's total lifecycle cost. The input source is the capacity of network-based devices. Network equipment capacity Energy storage system power capacity and energy capacity The processing action is to substitute the objective function shown in the following formula. Calculations are performed, in which, , , These respectively represent the investment and operation and maintenance costs of various types of equipment, and are related to their capacity. S and energy E Related, Characterizes the cost of losses caused by wind and solar power curtailment. , , , To reflect the coefficients with different cost weights, the output is a scalar value characterizing the economics of the current configuration. .

[0052] Then, constraints under multiple typical operating scenarios are set and verified to ensure system safety and stability. The input sources are real-time power flow, frequency change rate, and node voltage data for each scenario. The processing involves performing multiple inequality checks: first, verifying the power balance constraints. Secondly, the frequency safety constraints are checked, requiring the system's maximum frequency deviation to be within acceptable limits. and the rate of change of frequency Re-verify the voltage safety constraints and limit the voltage deviation at each node. Simultaneously verify inertia constraints. And short-circuit capacity constraints, requiring the short-circuit ratio of critical nodes in the system to be... The output is a feasible solution space that satisfies all constraints. The configuration schemes in this space are the optimal candidate sets that can ensure the safe and economical operation of the new energy base under multiple scenarios.

[0053] Therefore, this implementation method defines typical scenarios covering high, low output and extreme operating conditions, and combines a three-dimensional cost objective function with multi-dimensional safety constraints to achieve synergistic optimization of the economic efficiency and safety under all operating conditions of the new energy base configuration scheme throughout its entire life cycle, effectively avoiding redundant investment or stability risks caused by single-scenario design.

[0054] Example 5 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S104: "Solve the multi-scenario collaborative optimization configuration model to obtain the optimal capacity ratio of grid-connected devices and grid-building devices and the optimal configuration parameters of the energy storage system, and substitute the optimal configuration parameters into various typical operating scenarios for stability verification. If there is a constraint violation, update the constraint conditions and solve again until all scenarios pass the verification."

[0055] In this embodiment, the specific implementation process of solving the multi-scenario collaborative optimization configuration model is as follows: First, the input source is the multi-scenario collaborative optimization configuration model constructed in step S103. This model includes an objective function aimed at minimizing the total system cost and a multi-dimensional constraint set covering frequency safety, voltage safety, short-circuit capacity and inertia constraints. At the same time, the parameters of the particle swarm algorithm are initialized, including the particle population size, maximum number of iterations, initial inertia weight range and learning factor.

[0056] Next, an improved particle swarm optimization algorithm is used for iterative optimization. In each iteration, the algorithm dynamically adjusts the inertia weight based on the current particle's fitness value, giving particles a higher velocity in the early stages of the search to enhance global exploration capabilities, while reducing the velocity in the later stages to improve local exploration accuracy. Simultaneously, a mutation operation is introduced to randomly perturb the positions of particles trapped in local optima, helping them escape local extremes. In each iteration, the algorithm substitutes the capacity of the grid-connected devices, the capacity of the network-structured devices, and the power and energy parameters of the energy storage system represented by the current particle into the objective function to calculate the cost value, and verifies whether the safety and stability constraints under all typical operating scenarios are met. If they are met, the individual optimal solution and the global optimal solution are updated.

[0057] Then, when the algorithm reaches the maximum number of iterations or the global optimal solution converges, it outputs the final global optimal solution, which is the optimal capacity of the network device. Optimal capacity of network-type equipment Optimal power of energy storage system and optimal energy The optimal capacity ratio between network-connected and network-structured devices is calculated based on the optimal capacity. At the same time, determine the optimal configuration capacity of the energy storage system. .

[0058] Finally, the optimal configuration parameters output above will be used. As input data, the typical operating scenarios defined in step S103 are resubmitted for time-domain simulation stability verification. If the verification results show that all scenarios meet the constraints, the set of parameters is determined as the final configuration scheme. If any scenario violates the constraints, the most severely violated constraint is transformed into a new hard constraint and updated in the multi-scenario collaborative optimization configuration model. The process is then returned to the input of the improved particle swarm optimization algorithm to re-execute the solution process until all scenarios pass the verification and a logical closed loop is formed.

[0059] Therefore, this implementation method, by introducing an improved particle swarm optimization algorithm with dynamic inertia weights and mutation operations, effectively overcomes the defect of traditional algorithms that are prone to getting trapped in local optima when dealing with high-dimensional nonlinear optimization problems. It significantly improves the global search capability and convergence speed for solving the optimal ratio of network devices and the minimum energy storage capacity, ensuring a precise balance between technical feasibility and economic efficiency in the configuration scheme.

[0060] Furthermore, in this embodiment, the specific implementation process of substituting the optimal configuration parameters into various typical operating scenarios for stability verification and iterative updates is as follows. First, the input source is the optimal configuration scheme obtained in the preceding part of this step, and its specific data structure includes the optimal capacity of the network-type devices. Optimal capacity of network-type equipment Optimal power of energy storage system and optimal energy The system includes a pre-defined set of typical operating scenarios, covering three boundary conditions: high wind and solar power output, low wind and solar power output, and extreme weather. The processing involves loading these optimal configuration parameters as fixed input variables into the power system time-domain simulation model, and performing transient stability simulation calculations for each typical operating scenario. During the simulation, the system frequency deviation is monitored in real time. Rate of change of frequency Node voltage deviation and the short-circuit ratio of critical nodes Key indicators, etc. The output results are simulation time series data and constraint satisfaction judgment indicators for each scenario.

[0061] Next, logical judgments are made based on the simulation output results. If the monitoring indicators under all typical operating scenarios meet the frequency safety constraints (i.e., the maximum frequency deviation)... and Voltage safety constraints (i.e.) ) and short-circuit capacity constraints (i.e. If the current configuration scheme passes the verification, it will be output directly. This serves as the final configuration. If a constraint violation occurs in any scenario, a correction loop is initiated. The process involves iterating through all violation scenarios, quantifying the extent of each constraint violation, and identifying the most severe constraint violation scenario with the largest violation or the smallest system stability margin.

[0062] For example, if the voltage deviation of a node reaches 8% under extreme weather conditions, far exceeding the 5% limit, then this scenario is identified as the most severe scenario. Subsequently, the corresponding limit constraint value under this most severe scenario is extracted and transformed into a new hard constraint condition. For instance, the upper limit of the voltage deviation of this node is forcibly updated to a more stringent value, or the power balance equation form under this scenario is directly locked. Finally, this new hard constraint condition is substituted back into the multi-scenario collaborative optimization configuration model constructed in step S103, updating the constraint set of the original model, and the improved particle swarm optimization algorithm is called again for iterative solution to generate new optimal configuration parameters.

[0063] The closed-loop process of “simulation verification—violation identification—constraint update—re-solution” will be repeated until the generated configuration scheme has no constraint violations in all preset typical operating scenarios.

[0064] Therefore, by establishing a multi-scenario closed-loop verification mechanism based on time-domain simulation, this implementation method can accurately identify extreme operating condition risks that are difficult to cover by a single static optimization model. By dynamically adding hard constraints, it forces the optimization model to converge to a more robust solution space, thereby ensuring that the final determined grid-connected equipment ratio and energy storage capacity have sufficient frequency, voltage, and short-circuit capacity support under all operating conditions, significantly improving the engineering robustness and safety of the new energy base planning scheme.

[0065] In summary, the method for optimizing the allocation of grid-type and grid-structured equipment and determining energy storage capacity in new energy bases, as described in this application, achieves the best balance between technical feasibility and economic efficiency. It quantifies the multi-dimensional coupling constraints of the minimum capacity of grid-structured energy storage, ensures the adaptability of the configuration scheme to multiple scenarios, and improves planning calculation efficiency and engineering practicality. This application ensures that the final determined configuration scheme has sufficient stability support capability under all operating conditions, significantly improves the robustness of new energy bases in dealing with complex operating environments and sudden disturbances, and achieves the best balance between technical feasibility and economic efficiency.

[0066] To achieve the above embodiments, this application also proposes a system for optimizing the allocation of grid-connected and grid-structured equipment and determining energy storage capacity in new energy bases. Figure 2 This is a schematic diagram of a system for optimizing the allocation of grid-type and grid-structured equipment and determining energy storage capacity in a new energy base, as proposed in an embodiment of this application. Figure 2 As shown, the system includes: Module 100 is used to construct a multi-source dynamic complementary relationship model based on the output characteristics of new energy sources, load characteristics and power interaction relationships of various power supply equipment, and to define the complementary coordination coefficient that characterizes the ability of active support equipment to smooth out total power fluctuations. The constraint module 200 is used to establish constraint conditions from three dimensions: instantaneous power support capability, continuous energy support capability, and equivalent inertia support capability, calculate the capacity requirement that meets the constraints of each dimension, and determine the maximum capacity requirement as the minimum configuration capacity of the grid-type energy storage system. The optimization module 300 is used to combine the multi-source dynamic complementary relationship model with the minimum configuration capacity, take the minimization of the system's total life cycle cost as the objective function, and take frequency safety, voltage safety and short-circuit capacity under multiple typical operating scenarios as constraints to construct a multi-scenario collaborative optimization configuration model. The solver module 400 is used to solve the multi-scenario collaborative optimization configuration model to obtain the optimal capacity ratio of grid-connected devices and grid-building devices, as well as the optimal configuration parameters of the energy storage system. The optimal configuration parameters are then substituted into various typical operating scenarios for stability verification. If there is a constraint violation, the constraint conditions are updated and the solution is re-solved until all scenarios pass the verification.

[0067] It should be noted that the explanation of the aforementioned embodiment of the method for optimizing the allocation of grid-type and grid-structured equipment and determining energy storage capacity in new energy bases also applies to the system of this embodiment, and will not be repeated here.

[0068] In summary, the method and system for optimizing the ratio of grid-connected and grid-structured equipment and determining energy storage capacity in new energy bases according to the embodiments of this application can scientifically determine the optimal ratio of grid-connected and grid-structured equipment and the minimum capacity of grid-structured energy storage, achieving optimal investment costs while ensuring the safe and stable operation of the system in multiple scenarios.

[0069] To implement the above embodiments, this application also proposes an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the method for optimizing the allocation of new energy base grid-type and grid-structured equipment and determining energy storage capacity as described in any of the first aspect embodiments above.

[0070] To implement the above embodiments, this application also proposes a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the method for optimizing the allocation of new energy base grid-type and grid-structured equipment and determining energy storage capacity as described in any one of the first aspect embodiments above.

[0071] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0072] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0073] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0074] 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-included 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. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), 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). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0075] It should be understood that various parts of this application 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.

[0076] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0077] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0078] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for optimizing the allocation of grid-type and grid-structured equipment and determining energy storage capacity in a new energy base, characterized in that, Includes the following steps: Based on the output characteristics of new energy sources, load characteristics, and power interaction relationships of various power supply equipment, a multi-source dynamic complementary relationship model is constructed, and a complementary coordination coefficient is defined to characterize the ability of active support equipment to mitigate total power fluctuations. Constraints were established from three dimensions: instantaneous power support capability, continuous energy support capability, and equivalent inertia support capability. The capacity requirements that meet the constraints of each dimension were calculated, and the maximum capacity requirement was determined as the minimum configuration capacity of the grid-type energy storage system. Combining the aforementioned multi-source dynamic complementary relationship model with the aforementioned minimum configuration capacity, and taking the minimization of the system's total lifecycle cost as the objective function, and frequency safety, voltage safety, and short-circuit capacity under multiple typical operating scenarios as constraints, a multi-scenario collaborative optimization configuration model is constructed. Solving the multi-scenario collaborative optimization configuration model yields the optimal capacity ratio of grid-connected devices and network-building devices, as well as the optimal configuration parameters of the energy storage system. These optimal configuration parameters are then substituted into various typical operating scenarios for stability verification. If any constraints are violated, the constraints are updated and the solution is recalculated until all scenarios pass the verification.

2. The method according to claim 1, characterized in that, The construction of a multi-source dynamic complementary relationship model, and the definition of complementary coordination coefficients characterizing the ability of active support equipment to mitigate total power fluctuations, includes: Establish wind power output characteristic functions respectively Photovoltaic power output characteristic function and load characteristic function To reflect the power fluctuation characteristics at different time scales; Based on the output of network-type equipment , and the output of network equipment and energy storage system output Construct the power balance equations for the exchange between the system and the external power grid. ; Utilizing the power change of each device per unit time Calculate the complementarity and compatibility coefficient The complementary coordination coefficient is used as a key performance indicator for quantifying the ability of grid-type equipment and energy storage systems to jointly mitigate wind and solar power fluctuations.

3. The method according to claim 2, characterized in that, The method utilizes the power change of each device per unit time. Calculating the complementarity coefficient includes: Get Time Range arrive Power variation of internal network equipment With the power change of the energy storage system The sum of the absolute values ​​and the integration over time yields the total amount of power fluctuation absorbed by the equipment with active support capabilities. Obtain the change in wind power within the same time interval Photovoltaic power change Power variation of grid-connected equipment The sum of the absolute values ​​and the integration over time yield the total power fluctuation of the system. The complementary coordination coefficient is obtained by dividing the total digestion amount by the total system power fluctuation amount. The larger the value of the complementary coordination coefficient, the stronger the system's ability to resist disturbances.

4. The method according to claim 2, characterized in that, The constraints are established from three dimensions: instantaneous power support capability, continuous energy support capability, and equivalent inertia support capability. The capacity requirements that satisfy the constraints of each dimension are calculated, including: Based on the system's maximum power deficit Determine the instantaneous rated power of the energy storage system The power support constraint capacity is obtained based on the instantaneous rated power. ; Based on the continuous support time under the network construction mode Integrate the rated power of the energy storage to calculate the minimum energy capacity. The energy support constraint capacity is obtained based on the minimum energy capacity. ; Based on the system's equivalent inertia Requirements, using formula Calculate the time constant that satisfies the minimum inertia. Required inertia support constraint capacity .

5. The method according to claim 4, characterized in that, Determining the maximum capacity requirement as the minimum configuration capacity of a grid-type energy storage system includes: Comparison of the power support constraint capacity The energy support constraint capacity and the inertia support constraint capacity The numerical value; Based on the comparison results, the maximum value among the various capacities is selected as the minimum configuration capacity for grid-type energy storage. This is to enable the energy storage system to simultaneously meet the triple functional requirements of instantaneous power support, continuous energy support, and equivalent inertia support.

6. The method according to claim 1, characterized in that, The construction of the multi-scenario collaborative optimization configuration model includes: Define high wind and solar power output scenarios, low wind and solar power output scenarios, and extreme weather scenarios; Construct the objective function of the multi-scenario collaborative optimization configuration model as shown in the following formula: in, These are the investment and operation and maintenance costs of various types of equipment, and their respective capacities. S and energy E Related, The cost of curtailing wind and solar power , , and These are different weighting coefficients; The multi-scenario collaborative optimization configuration model is subject to several constraints, including power balance constraints, frequency security constraints, voltage security constraints, inertia constraints, short-circuit capacity constraints, and equipment capacity constraints.

7. The method according to claim 1, characterized in that, The process of solving the multi-scenario collaborative optimization configuration model yields the optimal capacity ratio of grid-connected devices and network-structured devices, as well as the optimal configuration parameters of the energy storage system, including: An improved particle swarm optimization algorithm is used to solve the multi-scenario collaborative optimization configuration model. By introducing dynamic inertia weights and mutation operations, the global search capability and convergence speed are improved. The optimal capacity ratio of network-connected devices to network-building devices was calculated. And the optimal configuration parameters of the energy storage system , and ,in, and These are the optimal capacities for network-connected devices and network-building devices, respectively. , and These are the optimal power capacity, optimal energy capacity, and minimum capacity, respectively.

8. The method according to claim 1, characterized in that, The process involves substituting the optimal configuration parameters into various typical operating scenarios for stability verification. If a constraint violation occurs, the constraint conditions are updated and the solution is recalculated until all scenarios pass verification, including: The optimal configuration parameters are substituted into all preset typical operating scenarios for time-domain simulation verification. Determine whether the safety and stability constraints are met in all scenarios. If so, output the current solution as the final configuration solution. If there is a constraint violation in any scenario, identify the most serious constraint violation scenario and use the limit constraint under the constraint violation scenario as a new hard constraint condition. The newly added hard constraints are added to the multi-scenario collaborative optimization configuration model and the verification process is iterated until all scenarios pass the verification.

9. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the method for optimizing the allocation of grid-type and grid-structured equipment and determining energy storage capacity in new energy bases as described in any one of claims 1-8.

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 method for optimizing the allocation ratio of grid-type and grid-structured equipment and determining the energy storage capacity of new energy bases as described in any one of claims 1-8.

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