New energy base distributed energy storage configuration optimization method and device and electronic equipment

Abstract

This application provides a method, apparatus, and electronic equipment for optimizing the distributed energy storage configuration in a new energy base. The method includes: determining a set of candidate distributed energy storage ratios for the new energy base to be configured with an energy storage system; acquiring operational data of the new energy base within a preset time period; constructing an energy storage configuration optimization model; for each candidate distributed energy storage ratio, solving the problem based on the operational data and the energy storage configuration optimization model to determine the total high-voltage side power supply and total operation and maintenance cost corresponding to that ratio; determining a target distributed energy storage ratio from the set of candidate distributed energy storage ratios based on the total high-voltage side power supply and total operation and maintenance cost corresponding to each candidate distributed energy storage ratio; and configuring energy storage in the new energy base based on the target distributed energy storage ratio. This application achieves a reasonable distributed energy storage configuration without changing the total planned energy storage capacity of the base, reducing dependence on high-voltage side power supply.

Description

Technical Field

[0001] This application relates to the field of energy storage configuration technology, and in particular to a method, device and electronic equipment for optimizing the decentralized energy storage configuration in a new energy base. Background Technology

[0002] With the increasing proportion of wind and solar power generation in the power system, configuring energy storage in renewable energy bases has become a necessary means to ensure high-proportion consumption and stable transmission of renewable energy. Currently, centralized energy storage configurations are commonly used, which involve centralized operation, scheduling, and maintenance management. Under the current scheduling and settlement rules, all charging power is supplied from the high-voltage side of the system, resulting in grid connection fees and reduced economic efficiency. Distributed energy storage can directly absorb local renewable energy on the low-voltage side, reducing the proportion of grid connection fees in the energy storage system's charging power. However, distributed energy storage deployment significantly increases the number of sites, operational complexity, and system construction and management costs.

[0003] Therefore, there is an urgent need to provide a method for optimizing the energy storage configuration of new energy bases that can reasonably balance centralized and decentralized configurations, so as to achieve a comprehensive balance between the electricity generated on the high-voltage side and the total operation and maintenance cost. Summary of the Invention

[0004] This application provides a method, device, and electronic equipment for optimizing the distributed energy storage configuration in new energy bases, in order to solve the technical problem of how to coordinate the economy and operation and maintenance complexity of centralized energy storage configuration and distributed energy storage configuration.

[0005] In a first aspect, embodiments of this application provide a method for optimizing the distributed energy storage configuration in a new energy base, the method comprising: For a new energy base to be configured with an energy storage system, a set of candidate energy storage distribution ratios is determined; wherein, the new energy base includes multiple booster stations, and each candidate energy storage distribution ratio in the set of candidate energy storage distribution ratios includes a first distribution ratio of the total energy storage capacity corresponding to the energy storage system in the multiple booster stations and a second distribution ratio of the total energy storage power corresponding to the energy storage system in the multiple booster stations; Obtain operational data of the new energy base within a preset time period; An energy storage configuration optimization model is constructed, which includes multiple operational constraints. The energy storage configuration optimization model takes the minimum total power-down of the energy storage system on the high-voltage side and the minimum total operation and maintenance cost of the energy storage system as its objective functions within the preset time period. For each candidate energy storage distribution ratio, the total high-voltage side power-off volume and total operation and maintenance cost corresponding to the candidate energy storage distribution ratio are determined by solving the problem based on the operating data and the energy storage configuration optimization model. Based on the total high-voltage side power-off volume and total operation and maintenance cost corresponding to each of the candidate energy storage distribution ratios in the candidate energy storage distribution ratio set, the target energy storage distribution ratio is determined from the candidate energy storage distribution ratio set. Based on the target energy storage distribution ratio, the energy storage capacity and energy storage power of each booster station in the new energy base are configured to obtain the energy storage configuration results corresponding to each booster station included in the new energy.

[0006] Secondly, embodiments of this application also provide a distributed energy storage configuration optimization device for new energy bases, the device comprising: The first determining module is used to determine a set of candidate energy storage distribution ratios for a new energy base to which an energy storage system is to be configured; wherein, the new energy base includes multiple booster stations, and each candidate energy storage distribution ratio in the set of candidate energy storage distribution ratios includes a first distribution ratio of the total energy storage capacity corresponding to the energy storage system in the multiple booster stations and a second distribution ratio of the total energy storage power corresponding to the energy storage system in the multiple booster stations. The acquisition module is used to acquire the operational data of the new energy base within a preset time period; The model building module is used to build an energy storage configuration optimization model. The energy storage configuration optimization model includes multiple operating constraints. The energy storage configuration optimization model takes the minimum total power-off volume on the high-voltage side of the energy storage system and the minimum total operation and maintenance cost of the energy storage system as its objective functions within the preset time period. The second determining module is used to solve, based on the operating data and the energy storage configuration optimization model, for each candidate energy storage distribution ratio, to determine the total high-voltage side power-down volume and total operation and maintenance cost corresponding to the candidate energy storage distribution ratio. The third determining module is used to determine the target energy storage distribution ratio from the set of candidate energy storage distribution ratios based on the total high-voltage side power-off volume and total operation and maintenance cost corresponding to each candidate energy storage distribution ratio in the set of candidate energy storage distribution ratios. The energy storage configuration module is used to configure the energy storage capacity and energy storage power of each booster station in the new energy base based on the target energy storage distribution ratio, so as to obtain the energy storage configuration results corresponding to each booster station included in the new energy.

[0007] Thirdly, embodiments of this application also provide an electronic device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the above-mentioned method for optimizing the distributed energy storage configuration of new energy bases.

[0008] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the aforementioned method for optimizing the distributed energy storage configuration in new energy bases.

[0009] The embodiments of this application include at least the following technical effects: The technical solution of this application embodiment determines the set of candidate energy storage distribution ratios, acquires operational data, and constructs an energy storage configuration optimization model that includes multiple operational constraints and a dual objective function. This model then determines the total high-voltage side power supply and total operation and maintenance cost for each candidate scheme, selects the target energy storage distribution ratio, and carries out energy storage configuration. Through this solution, without changing the planned total energy storage scale of the new energy base, a reasonable distributed configuration of energy storage across multiple booster stations can be achieved, reducing dependence on high-voltage side power supply, rationalizing operation and maintenance and management pressure, and achieving a comprehensive balance between economic efficiency and operational controllability. This, in turn, improves the level of new energy consumption and reduces power curtailment. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0011] Figure 1 This is a flowchart illustrating the method for optimizing the distributed energy storage configuration in new energy bases provided in the embodiments of this application; Figure 2 This is a schematic diagram of the structure of the distributed energy storage configuration optimization device for new energy bases provided in the embodiments of this application; Figure 3 A block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0014] In the various embodiments of this application, it should be understood that the sequence number of each process described below does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0015] In related technologies, wind and solar power bases generally adopt centralized energy storage configurations. While their operation, scheduling, and maintenance are relatively centralized, they cannot absorb renewable energy from various booster stations locally. Energy storage charging must be done from the high-voltage side, resulting in grid connection fees for all electricity generated under current scheduling and settlement rules, thus reducing economic efficiency. Fully distributed energy storage can directly absorb local renewable energy and reduce the proportion of grid connection fees in energy storage charging, but it significantly increases the number of sites, operational complexity, and system construction and management costs. Therefore, there is an urgent need to provide an optimization method for renewable energy base energy storage configuration that can reasonably balance centralized and distributed configurations, achieving a comprehensive balance between electricity generated from the high-voltage side and total operational costs.

[0016] like Figure 1 As shown in the figure, this application provides a method for optimizing the configuration of distributed energy storage in a new energy base. The method includes: Step 101: For the new energy base to be configured with energy storage system, determine the set of candidate energy storage distribution ratios; the new energy base includes multiple booster stations, and each candidate energy storage distribution ratio in the set of candidate energy storage distribution ratios includes a first distribution ratio of the total energy storage capacity corresponding to the energy storage system in the multiple booster stations and a second distribution ratio of the total energy storage power corresponding to the energy storage system in the multiple booster stations.

[0017] The distributed energy storage configuration optimization method for new energy bases provided in this application is used to optimize the energy storage configuration of new energy bases. Specifically, it is a distributed energy storage configuration optimization method for new energy bases that fully considers the economics of operation and maintenance without changing the total planned energy storage scale of the base.

[0018] The new energy base includes multiple booster stations. The energy storage capacity of the energy storage system required for the new energy base can be preset. In other words, the total energy storage capacity and total energy storage power of the energy storage system are predetermined.

[0019] For new energy bases that require the configuration of energy storage systems, a set of candidate energy storage distribution ratios is determined. This set encompasses candidate schemes with varying degrees of distribution. The set of candidate energy storage distribution ratios includes multiple candidate energy storage distribution ratios, which are also candidate energy storage distribution schemes. Each candidate energy storage distribution ratio includes two distribution ratios: the first is the first distribution ratio of the total energy storage capacity across various booster stations, and the second is the second distribution ratio of the total energy storage power across various booster stations.

[0020] Specifically, each candidate energy storage distribution ratio must meet the following constraints: the total energy storage capacity allocated to each booster station is equal to the total energy storage capacity of the base; the total energy storage power allocated to each booster station is equal to the total energy storage power of the base.

[0021] Step 102: Obtain the operation data of the new energy base within the preset time period.

[0022] When configuring energy storage for a new energy base, it is also necessary to obtain the base's operational data within a preset time period, including at least the output and power transmission data of each booster station within that period. This data will provide support for subsequent model building and solving. The preset time period can be 8760 hours throughout the year, covering output fluctuations caused by seasonal, diurnal, and weather changes.

[0023] Specifically, the operational data should include: the output range and ramp-up constraints of thermal power units at various times, the single-sided charging and discharging efficiency of energy storage equipment, and the wind and solar installed capacity of substation i. New energy output sequence within the preset time period Overall power transmission curve of the base within the preset time period The number of substations is N, where i is an integer from 1 to N. The number of thermal power units is M, where j is an integer from 1 to M.

[0024] Step 103: Construct an energy storage configuration optimization model. The energy storage configuration optimization model includes multiple operating constraints. The objective function of the energy storage configuration optimization model is to minimize the total power supply on the high-voltage side of the energy storage system and the total operation and maintenance cost of the energy storage system within a preset time period.

[0025] An energy storage configuration optimization model is constructed with two objective functions: minimizing the total power supply to the high-voltage side within a preset time period and minimizing the total operation and maintenance cost. This model incorporates multiple operational constraints, achieving a balance between objective orientation and constraint adaptation. The model considers both the economic viability of the optimization objectives and the engineering feasibility of the constraints, avoiding a disconnect between objectives and constraints.

[0026] High-voltage side power supply refers to the electricity obtained from the high-voltage power grid during energy storage charging, which requires paying grid access fees. The essence of this objective is to prioritize the use of local renewable energy for energy storage charging. At any given moment, the total charging capacity of energy storage configured under any booster station is reduced by the local renewable energy's on-site charging capacity at that moment; the remaining portion must be obtained from the high-voltage side. The total high-voltage side power supply is calculated by summing the high-voltage side power supply at each moment within a preset time period according to a preset time step. The total operation and maintenance cost is directly related to the degree of energy storage dispersion; the more dispersed the sites, the higher the costs of inspection, maintenance, and management. The operation and maintenance cost of a single booster station includes fixed operation and maintenance expenses related to the number of sites and capacity operation and maintenance costs related to the energy storage capacity. This yields the annualized unit capacity operation and maintenance cost, which varies with the degree of energy storage dispersion. For example, as the number of dispersed sites increases and the capacity of a single site decreases, the fixed operation and maintenance expenses are distributed more widely across the unit capacity, thus demonstrating the correlation between the annualized unit capacity operation and maintenance cost and the degree of dispersion.

[0027] Multiple operational constraints are a prerequisite for the model's feasibility, and these constraints must cover three core categories: power balance, equipment characteristics, and engineering operation boundaries. Power balance constraints: The overall power transmission capacity of the base = the cumulative output of wind and solar power at each booster station (excluding curtailed power) + the cumulative discharge power of energy storage at each booster station + the output of thermal power - the cumulative charging power of energy storage at each booster station, ensuring real-time balance between power supply and demand; Energy storage equipment constraints: Energy storage capacity constraints (energy storage load does not exceed energy storage capacity), charging and discharging power constraints (charging / discharging power does not exceed the power allocated to a single station), energy balance constraints (the energy storage load at the end of a preset period returns to the initial value to avoid long-term power shortages or full charges); New energy utilization constraints: Preset lower limits for new energy utilization rate (e.g., 95%) and new energy power proportion (e.g., 80%) to ensure that energy storage configuration does not reduce the base's new energy consumption level; Engineering operation boundary constraints: Including main transformer capacity constraints (the total output power of power sources including energy storage discharge at any booster station does not exceed the main transformer's carrying capacity), thermal power ramping constraints (the ability of thermal power units to increase / decrease output per hour), energy storage dispatch priority constraints (e.g., prioritizing local wind and solar power to charge energy storage, and then cutting power from the high-voltage side when insufficient), etc.

[0028] It should be noted that custom constraints can be added according to project requirements, such as upper limit constraints on energy storage capacity for remote booster stations to avoid management risks caused by inconvenience in operation and maintenance; and energy storage backup capacity constraints under extreme weather conditions to ensure power supply stability.

[0029] Step 104: For each candidate energy storage distribution ratio, solve the problem based on the operating data and the energy storage configuration optimization model to determine the total high-voltage side power supply and total operation and maintenance cost corresponding to the candidate energy storage distribution ratio.

[0030] Each scheme in the set of candidate energy storage distribution ratios, i.e. each candidate energy storage distribution ratio, is input into the constructed energy storage configuration optimization model one by one. Combined with the acquired operating data, the total high-voltage side power-down and total operation and maintenance costs corresponding to each scheme, i.e. each candidate energy storage distribution ratio, are calculated to provide a quantitative basis for subsequent scheme selection.

[0031] It should be noted that for each candidate energy storage distribution ratio, the essence is to determine the allocation of energy storage capacity and energy storage power of each booster station. After substituting the allocation result into the energy storage configuration optimization model, the energy storage configuration optimization model will simulate the hourly operation state of the preset period under the premise of satisfying all operating constraints, and thus obtain the total power supply of the high-voltage side during the preset period; at the same time, the total operation and maintenance cost is calculated based on the energy storage capacity allocated to each booster station.

[0032] This application embodiment solves each scheme one by one, transforming the abstract dispersion ratio into a quantifiable total power supply and total operation and maintenance cost on the high-voltage side, providing a basis for comparison in subsequent screening and avoiding the bias of subjective judgment.

[0033] Step 105: Determine the target energy storage distribution ratio from the set of candidate energy storage distribution ratios based on the total high-voltage side power supply and total operation and maintenance cost corresponding to each candidate energy storage distribution ratio in the set of candidate energy storage distribution ratios.

[0034] Specifically, based on the total high-voltage side power supply and total operation and maintenance cost corresponding to each candidate energy storage distribution ratio, a non-dominated solution set is determined; then, the schemes in the non-dominated solution set are configured and scored according to preset evaluation weights, and the scheme with the lowest configuration score is determined as the target energy storage distribution ratio.

[0035] The non-dominated solution is used to eliminate inferior solutions that are simultaneously superior to other solutions in both objectives, thus preventing the optimization of one indicator from causing a significant deterioration in the other. For any two solutions, if the total high-voltage power supply of solution A is no greater than that of solution B and the total operation and maintenance cost is no higher than that of solution B, and at least one of them is strictly superior, then solution B is dominated by solution A and is eliminated from the candidates; if the two solutions each have their advantages and disadvantages in the two objectives, then both are retained in the non-dominated solution set.

[0036] After obtaining the non-dominated solution set, to ensure comparability of objectives with different dimensions, it is preferable to use range normalization to perform dimensionless processing on both objectives, and then calculate the configuration score according to the preset evaluation weights. Let the non-dominated solution set be... For any scheme within the set Its normalization result can be expressed as:

[0037] in, For the plan Total voltage on the high-voltage side For non-dominated solution set The minimum value among the total high-voltage side power supply of each scheme; For non-dominated solution set The maximum value among the total high-voltage side power supply of each scheme; For the plan The normalized result of the total voltage on the high-voltage side.

[0038] For the plan Total maintenance cost For non-dominated solution set The minimum total operation and maintenance cost among all the proposed solutions; For non-dominated solution set The maximum value among the total operation and maintenance costs of each solution; For the plan The normalized result of the total operation and maintenance cost.

[0039] When a certain objective is in the non-dominated solution set When the values ​​within the same range are completely identical, resulting in a denominator of zero, the normalization result of the objective is set to 0 to avoid division by zero and maintain score comparability.

[0040] Furthermore, let the first weight of the total voltage on the high-voltage side be... The second weight of total operation and maintenance cost is ,and Then the solution Configuration rating for:

[0041] The non-dominated scheme with the lowest configuration score is determined as the target distributed energy storage ratio. By setting reasonable weights, the optimal performance of a single indicator can be avoided, which could lead to poor overall results.

[0042] Step 106: Configure the energy storage capacity and energy storage power of each booster station in the new energy base based on the target energy storage distribution ratio, and obtain the energy storage configuration results corresponding to each booster station included in the new energy.

[0043] Based on the determined target energy storage distribution ratio, the total energy storage capacity and total energy storage power are allocated to each booster station in proportion to complete the energy storage configuration of the new energy base.

[0044] This application's embodiments determine the set of candidate energy storage distribution ratios, acquire operational data, and construct an energy storage configuration optimization model that includes multiple operational constraints and a dual objective function. This model then determines the total high-voltage side power supply and total operation and maintenance cost for each candidate scheme, selects the target energy storage distribution ratio, and carries out energy storage configuration. Guided by a dual objective function and combined with multiple operational constraints, this application ensures that the optimization process aligns with engineering realities, prevents schemes from deviating from safety and operational boundaries, achieves a reasonable distributed energy storage configuration, reduces dependence on high-voltage side power supply, lowers economic costs, avoids the operational complexity and management pressure caused by extreme distribution, achieves a dynamic balance between economic efficiency and operational controllability, improves the level of new energy consumption, and avoids energy waste.

[0045] In an optional embodiment of this application, for a new energy base to which an energy storage system is to be configured, determining a set of candidate distributed energy storage ratios includes: Obtain the number of booster stations included in the new energy base, and the energy storage configuration boundary conditions corresponding to each booster station; Obtain the total energy storage capacity and total energy storage power of the energy storage system; Based on the number of booster stations, and provided that the energy storage configuration boundary conditions corresponding to each booster station are satisfied, the sum of the energy storage capacities of each booster station equals the total energy storage capacity, and the sum of the energy storage powers of each booster station equals the total energy storage power, the total energy storage capacity and total energy storage power corresponding to the energy storage system are allocated to each booster station included in the new energy base, resulting in multiple candidate energy storage distribution ratios; wherein, the energy storage configuration boundary conditions corresponding to each booster station are used to limit the feasible range of energy storage capacity and power allocated to the booster station; the candidate energy storage distribution ratios include the distribution ratio of the total energy storage capacity corresponding to the energy storage system among the multiple booster stations and the distribution ratio of the total energy storage power corresponding to the energy storage system among the multiple booster stations; The set of candidate energy storage distribution ratios is obtained based on the plurality of candidate energy storage distribution ratios.

[0046] In order to provide sufficient differentiated options for subsequent energy storage configuration optimization and screening, and to avoid missing the optimal energy storage configuration mode due to a single solution, this application provides a solid and feasible basic candidate pool for the entire energy storage configuration optimization by determining the set of candidate energy storage distribution ratios.

[0047] By constructing a set of candidate energy storage distribution ratios, differentiated candidate schemes can be provided for subsequent energy storage configuration optimization. Specifically, the boundary conditions for energy storage configuration may include the upper limit of energy storage capacity and the upper limit of energy storage power corresponding to each booster station, to ensure that the candidate schemes meet the requirements of engineering feasibility. Based on this, multiple candidate schemes can be generated according to preset rules. The preset rules can be determined based on one or more of the characteristics of new energy output, historical operating characteristics, or preset distribution gradients, generally ensuring that the generated schemes meet the boundary conditions of each booster station and the conservation conditions of total energy storage capacity and total energy storage power.

[0048] Finally, all the generated and verified candidate energy storage distributed ratio schemes are integrated to form a standardized candidate set, providing a unified and standardized input format for subsequent substitution into the optimization model.

[0049] Specifically, the expression for the set of candidate distributed energy storage ratios is as follows:

[0050] in, This is a set of candidate distributed energy storage ratios. For the k-th candidate distributed energy storage scheme, the allocation matrix of different schemes across each booster station is as follows:

[0051] Where N represents the number of booster stations.

[0052] The proposed distributed energy storage schemes must meet the following constraints:

[0053] And it meets the energy storage configuration boundary conditions for each booster station:

[0054] in, The total energy storage capacity of the energy storage system. The total energy storage capacity of the energy storage system. For the first The upper limit of energy storage capacity for each booster station For the first The upper limit of energy storage capacity for each booster station. and It can be determined by one or more of the following: substation site conditions, main transformer capacity, power distribution equipment capacity, line current carrying capacity, and fire protection conditions.

[0055] The above-mentioned implementation scheme of this application, by taking the actual carrying capacity of the booster station as a constraint and the total energy storage scale as a benchmark, and combining multi-allocation logic and gradient design to generate a set of candidate energy storage distribution ratios, ensures that each scheme meets the boundary constraints of engineering implementation, and provides a foundation for subsequent energy storage configuration optimization.

[0056] In an optional embodiment of this application, the objective function includes a first objective function; Construct an energy storage configuration optimization model, including: Determine the energy storage charging power, new energy local charging power, and high-voltage side power output for each booster station at each moment within the preset time period; wherein, the high-voltage side power output is taken as the larger of the difference between the energy storage charging power and the new energy local charging power and zero; Based on the high-voltage side power output of each booster station at each moment within the preset time period, the total high-voltage side power output within the preset time period is accumulated according to the preset time step. The minimum value of the total voltage on the high-voltage side is determined as the first objective function.

[0057] The objective function of the energy storage configuration optimization model constructed in this application embodiment includes a first objective function. Specifically, the first objective function is to minimize the total power consumption on the high-voltage side of the energy storage system within a preset time period.

[0058] When constructing the first objective function, it is necessary to determine the expression for the total amount of electricity supplied by the energy storage system on the high-voltage side within a preset time period. Specifically, the electricity obtained by the energy storage system from the booster station includes two parts: one part is obtained through on-site charging, and the other part is obtained through electricity supplied on the high-voltage side. The total amount of electricity supplied on the high-voltage side is the sum of the electricity supplied by the energy storage system from the high-voltage side of each booster station within the preset time period.

[0059] First, the energy storage charging power, high-voltage side power supply, and local charging power corresponding to each booster station at each moment within a preset time period are determined. Specifically, the energy storage charging power is determined by the energy storage configuration boundary conditions corresponding to the booster station, which limit the feasible range of energy storage power allocated to the booster station; the local charging power is the charging power absorbed by the energy storage system from the booster station's local renewable energy source, which must match the real-time output of the renewable energy source and not exceed the charging power requirement; the high-voltage side power supply is the supplementary charging power obtained by the energy storage system from the high-voltage side of the booster station, generated only when local charging cannot meet the total charging demand.

[0060] The energy storage charging power, the high-voltage side power supply, and the local charging power must meet the following constraints to ensure that the high-voltage side power supply is only used as a supplement when local charging is insufficient.

[0061]

[0062] in, Let be the energy storage charging power of the i-th boost station at time t. Let be the high-voltage side power output of the i-th booster station at time t. Let be the local charging power of the i-th boost station at time t.

[0063] Then, based on the high-voltage side power output of each booster station at each moment within the preset time period, the total high-voltage side power output of the energy storage system within the preset time period is determined. The expression for the total high-voltage side power output is as follows:

[0064] in, This is the total voltage output on the high-voltage side. Let be the high-voltage side power output of the i-th booster station at time t. The length of the preset time period, This is the preset time step. When the preset time step is 1 hour, the above formula... This can be omitted.

[0065] Finally, the minimum value of the total voltage drop on the high-voltage side is determined as the first objective function. The expression is as follows:

[0066] The above-described implementation scheme of this application, by determining the charging power, the power supplied to the high-voltage side, the local charging power of new energy sources and the constraint relationship, obtains the total power supplied to the high-voltage side, and minimizes it as the first objective function. This can guide the priority of local charging of energy storage, reduce dependence on the high-voltage grid, improve the local consumption level of new energy sources, and reduce grid access costs in the energy storage configuration optimization model, while ensuring that the energy storage charging demand is not affected.

[0067] In an optional embodiment of this application, the objective function includes a second objective function; Construct an energy storage configuration optimization model, including: The energy storage capacity, fixed annualized operation and maintenance cost of each substation, and unit capacity operation and maintenance cost calculated based on the annualized unit capacity operation and maintenance cost are determined. The fixed annualized operation and maintenance cost of each substation is related to the degree of dispersion of energy storage configuration. The total operation and maintenance cost of the energy storage system is determined based on the energy storage capacity corresponding to each booster station, the fixed annualized operation and maintenance cost of the station, and the unit capacity operation and maintenance cost calculated based on the annualized unit capacity operation and maintenance cost. The minimum value of the total operation and maintenance cost is determined as the second objective function.

[0068] The objective function of the energy storage configuration optimization model constructed in this application embodiment also includes a second objective function. Specifically, the second objective function aims to minimize the total operation and maintenance cost of the energy storage system within a preset time period. To ensure that the total operation and maintenance cost reflects the increased number of sites and the increased complexity of inspection and management resulting from the distributed configuration of energy storage, this application sets the total operation and maintenance cost as the sum of two parts: "fixed site operation and maintenance expenses" and "capacity-related operation and maintenance costs."

[0069] The expression for total operation and maintenance cost is as follows:

[0070] in, For total operation and maintenance costs, The number of booster stations. For the first The energy storage capacity of each booster station For the first The unit capacity operation and maintenance cost of each booster station is calculated based on the annualized unit capacity operation and maintenance cost. For the first The fixed annualized operation and maintenance costs of each booster station. For the first The status variables are enabled at the site of the first booster station; when the first... When configuring energy storage for a booster station When the first When a booster station is not equipped with energy storage .

[0071] Among them, capacity-related operation and maintenance costs Used to characterize the costs of routine maintenance, testing, and component replacement resulting from increased energy storage capacity; fixed annualized operation and maintenance costs of the site. This is used to characterize the fixed operation and maintenance costs incurred once a site is equipped with energy storage, including but not limited to site inspection, on-duty management, security and fire protection maintenance, and communication and monitoring maintenance costs. Therefore, with a fixed total energy storage capacity, as the number of booster stations equipped with energy storage increases, the total fixed operation and maintenance costs of the sites will increase, thus enabling the second objective function to effectively distinguish the operation and maintenance economics of different distributed solutions.

[0072] The second objective function is defined as minimizing the total operation and maintenance cost. The expression is as follows:

[0073] The above-described implementation scheme of this application sets the total operation and maintenance cost as the sum of the fixed operation and maintenance expenses of the site and the capacity-related operation and maintenance costs, and minimizes it as the second objective function. This enables the energy storage configuration optimization results to meet the consumption demand while taking into account the differences in operation and maintenance complexity and economic costs brought about by the distributed configuration. It avoids weakening the optimization effect due to the convergence of operation and maintenance costs of different schemes caused by only using the capacity cost item.

[0074] The energy storage configuration optimization model constructed in this application includes multiple operational constraints, which at least include: power balance constraints, renewable energy output constraints, energy storage balance constraints, thermal power operation constraints, renewable energy utilization rate and renewable energy power ratio constraints, energy storage dispatch constraints, and booster station power constraints. The construction of each operational constraint will be described below.

[0075] In an optional embodiment of this application, the plurality of operational constraints include power balance constraints; Construct an energy storage configuration optimization model, including: The power transmission capacity, high-voltage power output, energy storage charging capacity, energy storage discharging capacity, and thermal power output of the new energy base are determined at various times within the preset time period. Specifically, at any given time, the high-voltage power output of the new energy base is the sum of the high-voltage power outputs of all booster stations within the new energy base; the energy storage charging capacity is the sum of the energy storage charging capacity of all booster stations within the new energy base; the energy storage discharging capacity is the sum of the energy storage discharging capacity of all booster stations within the new energy base; and the thermal power output is the sum of the thermal power output corresponding to the thermal power units at all booster stations within the new energy base. The power balance constraint is constructed based on the fact that the sum of the high-voltage transmission power of the base's renewable energy, the discharge power of the base's energy storage, and the output power of the base's thermal power at each time point equals the sum of the base's power transmission power and the base's energy storage charging power at the corresponding time point. To ensure the power balance of the power system, the power balance constraint is established at the overall level of the renewable energy base. Specifically, the demand-side power includes the base's power transmission and energy storage charging power, while the supply-side power includes the base's high-voltage transmission power of renewable energy, the base's energy storage discharge power, and the base's thermal power output power.

[0076] The expression for the power balance constraint is as follows:

[0077] in, The power output of the new energy base at time t. Let represent the high-voltage power output of the new energy source at time t for the i-th booster station. Let be the energy storage discharge power of the i-th booster station at time t. Let be the energy storage charging power of the i-th boost station at time t. Let N be the output power of the thermal power unit at time t of the i-th substation, that is, the thermal power output power corresponding to the i-th substation, and N be the number of substations.

[0078] The above-mentioned implementation scheme of this application constructs power balance constraints by determining the high-voltage transmission power of new energy, the discharge power of energy storage, the output power of thermal power and the transmission power, to ensure real-time supply and demand matching of the power system, and to delineate a safe and feasible domain for dual-objective optimization of energy storage configuration, so as to avoid the theoretically optimal but unworkable scheme.

[0079] In an optional embodiment of this application, the multiple operational constraints include booster station power constraints; constructing an energy storage configuration optimization model includes: For each booster station, obtain the maximum power that the high-voltage side of the booster station can send out; For each booster station, determine the new energy high-voltage transmission power and energy storage discharge power corresponding to each moment within the preset time period; The power constraint of the booster station is constructed based on the fact that the sum of the high-voltage transmission power of the new energy source and the discharge power of the energy storage does not exceed the maximum power that the high-voltage side of the booster station can bear.

[0080] When optimizing energy storage configuration for new energy bases, the main transformer capacity of the booster station, the capacity of the power distribution equipment, and the carrying capacity of the high-voltage transmission channel constitute the station-level physical boundary. Without explicit power constraints on the booster station, an infeasible scenario may arise where the sum of the high-voltage transmission power from new energy sources and the discharge power from energy storage exceeds the booster station's carrying capacity at any given time. Therefore, power constraints on the booster station are set in the energy storage configuration optimization model to ensure that the optimization results meet the conditions for station-level engineering implementation.

[0081] The expression for the power constraint of the booster station is as follows:

[0082] in, Let i be the maximum power that the high-voltage side of the i-th booster station can handle. Let represent the high-voltage power output of the new energy source at time t for the i-th booster station. Let be the energy storage discharge power of the i-th booster station at time t.

[0083] The above-mentioned implementation scheme of this application constructs a power constraint for the booster station to limit the combined power output of "new energy transmission + energy storage transmission" at the dispatch level, thereby avoiding overload of the booster station's transmission channel and improving the engineering feasibility of the optimization results.

[0084] In an optional embodiment of this application, the plurality of operational constraints include renewable energy output constraints; Construct an energy storage configuration optimization model, including: For each booster station, determine the renewable energy output power, renewable energy high-voltage transmission power, renewable energy local charging power, and renewable energy curtailment power corresponding to each moment within the preset time period. The new energy output constraint is constructed based on the fact that the new energy output power at each time moment is equal to the sum of the new energy high-voltage transmission power, the new energy local charging power, and the new energy curtailment power at the corresponding time moment.

[0085] The actual output power of new energy sources will be broken down into three destinations: high-voltage transmission, local charging, and curtailment. By constructing constraints on new energy output, rigid boundaries for new energy utilization can be defined to optimize energy storage configuration, ensuring the achievement of new energy consumption targets. Specifically, new energy output power refers to the actual output power of local wind, solar, and other new energy power generation equipment at the booster station. High-voltage transmission power is the power directly transmitted to the high-voltage grid after deducting local charging and curtailment; it represents the contribution of new energy to the external power grid. Local charging power is the power absorbed by energy storage devices at the booster station. Curtailed power is the power that is forcibly discarded due to transmission limitations and insufficient local consumption capacity.

[0086] The expression for the power output constraint of new energy sources is as follows:

[0087] in, Let be the renewable energy output power of the i-th booster station at time t. Let t represent the local charging power of new energy sources at the i-th booster station at time t. Let represent the high-voltage power output of the new energy source at time t for the i-th booster station. Let represent the amount of renewable energy curtailed at time t for the i-th booster station.

[0088] The above-mentioned implementation scheme of this application establishes constraints on renewable energy output by determining renewable energy power output, renewable energy high-voltage transmission power, renewable energy local charging power, and renewable energy curtailment power, thus clarifying the various destinations of renewable energy output, defining boundaries for energy storage configuration optimization, and avoiding sacrificing renewable energy consumption efficiency in order to reduce costs.

[0089] In an optional embodiment of this application, the plurality of operational constraints include energy storage balance constraints; Construct an energy storage configuration optimization model, including: For each booster station, determine the energy storage capacity and energy storage power allocated to the booster station, as well as the energy storage charging power, energy storage discharging power, energy storage charge, and energy storage charge change value at each moment within the preset time period; Based on the energy storage charging power, charging efficiency, and preset time step, determine the energy storage charging amount corresponding to each moment; based on the energy storage discharging power, discharging efficiency, and preset time step, determine the energy storage discharging amount corresponding to each moment. Based on the energy storage load satisfying the update relationship determined by the energy storage charging and discharging at adjacent time points, where the energy storage load is greater than or equal to zero and less than or equal to the energy storage capacity, the energy storage charging power and the energy storage discharging power are both greater than or equal to zero and less than or equal to the energy storage power, and the energy storage load at the end of the preset time period is equal to the initial energy storage load, the energy storage balance constraint is constructed. After energy storage equipment is configured in the booster station, it is necessary to ensure that the energy storage equipment meets the energy balance at each time point and is within the physical boundary to avoid overcharging and over-discharging. The energy storage scale includes energy storage capacity and energy storage power, corresponding to the upper limit of energy and the upper limit of power, respectively. Energy storage charging power and energy storage discharging power reflect the energy inflow and outflow rates, and charging and discharging efficiency reflects energy conversion losses. In addition, to avoid obtaining unreasonable optimization results by net emptying or net charging of energy storage at the end of the preset time period, the energy storage load of each booster station at the end of the preset time period is also constrained to return to the initial energy storage load.

[0090] The expression for the energy storage balance constraint is as follows:

[0091]

[0092]

[0093] in, Let be the energy storage charge of the i-th booster station at time t. Let be the change in energy storage charge at time t+1 for the i-th booster station. Let be the charging power of the i-th boost station at time t. Let be the energy storage discharge power of the i-th booster station at time t. For charging efficiency, For discharge efficiency, Let i be the energy storage capacity of the i-th booster station. Let be the energy storage capacity of the i-th booster station. This represents the number of moments corresponding to the preset time period. Preset time step. When the preset time step is 1 hour, the above formula... This can be omitted.

[0094] The above-described implementation scheme of this application, by constructing energy storage balance constraints, ensures that the operation of energy storage equipment meets physical boundary conditions, thereby improving the feasibility and accuracy of energy storage configuration optimization results.

[0095] In an optional embodiment of this application, the plurality of operational constraints include thermal power plant operational constraints; Construct an energy storage configuration optimization model, including: Obtain the maximum ramp rate of each thermal power unit in the new energy base and the output range corresponding to each moment within the preset time period; For each thermal power unit, the output power at each moment within a preset time period is determined, and the output power change value is determined based on the output power at adjacent moments. Based on the fact that the output power is within the output range and the absolute value of the output power change is less than or equal to the ramp limit, the thermal power operation constraints are constructed.

[0096] A new energy base may include thermal power units that provide basic support. The output range and ramp-up capability of these units constitute their operational boundaries. When optimizing energy storage configuration for a new energy base, the operational constraints of thermal power should be incorporated into the energy storage configuration optimization model to avoid infeasible solutions that require thermal power units to exceed their safe operating boundaries.

[0097] The expressions for the operating constraints of thermal power plants are as follows:

[0098]

[0099] in, Let be the output power of the j-th thermal power unit at time t. and Let be the upper and lower limits of the output range of the j-th thermal power unit. is the upper limit of the ramp for the j-th thermal power unit, and represents the maximum output change of thermal power unit j allowed between adjacent time points.

[0100] To maintain in accordance with power balance constraints Consistency, in one embodiment, can be the first The thermal power output corresponding to a single substation is defined as the sum of the outputs of the set of thermal power units connected to that substation:

[0101] in, This refers to the set of thermal power units electrically connected to the i-th substation; when thermal power units are centrally connected to a single substation, only those corresponding to that substation are considered. This set represents all thermal power units, while the remaining substation sets are empty.

[0102] The above-mentioned implementation scheme of this application, by constructing thermal power operation constraints and keeping them consistent with the thermal power output term in the power balance constraints, can ensure that the optimized scheduling results meet the output boundary and ramp boundary of thermal power units, thereby improving the engineering feasibility of the energy storage configuration optimization results.

[0103] In an optional embodiment of this application, the multiple operational constraints include constraints on the utilization rate of new energy sources and the proportion of new energy power generation. Construct an energy storage configuration optimization model, including: Obtain the lower limit of new energy utilization rate, the lower limit of new energy power ratio, and the base power transmission power of the new energy base at each time within the preset time period; For each booster station, determine the corresponding renewable energy output power, renewable energy curtailment power, and thermal power output power for each booster station at each moment within the preset time period; The total renewable energy output power of each booster station at each moment within the preset time period is accumulated according to a preset time step to obtain the total renewable energy output power. The total amount of abandoned renewable energy is obtained by summing the renewable energy power at each booster station at each moment within the preset time period according to the preset time step. Determine the new energy utilization rate corresponding to the new energy base, wherein the new energy utilization rate is the ratio of the difference between the total output power of the new energy and the total abandoned power of the new energy to the total output power of the new energy; The thermal power output of each booster station at each moment within the preset time period is accumulated according to the preset time step to obtain the total thermal power generation. The total power transmission capacity of the new energy base is obtained by summing the base power transmission capacity at each moment within the preset time period according to the preset time step. Determine the proportion of new energy power generation corresponding to the new energy base, wherein the proportion of new energy power generation is the ratio of the difference between the total power transmission of the base and the total power generation of thermal power to the total power transmission of the base; Based on the fact that the new energy utilization rate is greater than or equal to the lower limit of the new energy utilization rate and the new energy power ratio is greater than or equal to the lower limit of the new energy power ratio, the constraints of the new energy utilization rate and the new energy power ratio are constructed.

[0104] When optimizing the energy storage configuration of new energy bases, in order to avoid problems such as reduced energy storage utilization leading to increased power curtailment and decreased new energy utilization, or reliance on thermal power to supplement power supply resulting in insufficient proportion of new energy power, constraints on new energy utilization and proportion of new energy power are set in the operating constraints of the energy storage configuration optimization model.

[0105] When calculating the renewable energy utilization rate of a renewable energy base, it is necessary to summarize the renewable energy output power and abandoned power of all booster stations within the renewable energy base during a preset time period. The formula for calculating the renewable energy utilization rate is as follows:

[0106] in, To improve the utilization rate of new energy sources, Let be the renewable energy output power of the i-th booster station at time t. Let represent the amount of renewable energy curtailed at time t for the i-th booster station.

[0107] When calculating the proportion of renewable energy power in renewable energy bases, it is necessary to summarize the total power transmission capacity of renewable energy bases and the output capacity of thermal power within a preset time period. Based on the fact that all power transmission capacity of renewable energy bases, excluding thermal power, is provided by renewable energy, the formula for calculating the proportion of renewable energy power is as follows:

[0108] in, The proportion of electricity generated by new energy sources. Let be the thermal power output of the i-th booster station at time t. The power output of the new energy base at time t.

[0109] The expressions for the constraints on the utilization rate of new energy sources and the proportion of new energy electricity are as follows:

[0110]

[0111] in, To improve the utilization rate of new energy sources, This is the lower limit of energy utilization rate. The proportion of electricity generated by new energy sources. This represents the lower limit for the proportion of electricity generated from new energy sources.

[0112] The above-mentioned implementation scheme of this application determines the power output of new energy, the power of abandoned electricity, the power output of thermal power and the total power transmission power of the base at each time by obtaining the lower limit of the utilization rate of new energy and the lower limit of the proportion of new energy power by obtaining the lower limit of the utilization rate of new energy and the proportion of new energy power. It also determines the utilization rate of new energy and the proportion of new energy power, thereby constructing constraints on the utilization rate of new energy and the proportion of new energy power, defining the boundary for the optimization of energy storage configuration, and avoiding sacrificing the effect of clean energy substitution in pursuit of economic goals.

[0113] In an optional embodiment of this application, the plurality of operational constraints include energy storage scheduling constraints; Construct an energy storage configuration optimization model, including: For each booster station, determine the corresponding new energy output power, energy storage charging power, new energy local charging power, and high-voltage side power supply power for each moment within the preset time period. The energy storage scheduling constraint is constructed based on the fact that the local charging power of the new energy source is less than or equal to the smaller of the output power of the new energy source and the charging power of the energy storage source, and the power output on the high-voltage side is determined to be the difference between the charging power of the energy storage source and the local charging power of the new energy source.

[0114] Among them, the local charging power of new energy sources refers to the charging power of new energy sources used for energy storage on-site. The local charging power of new energy sources is less than or equal to the smaller of the output power of new energy sources and the charging power of energy storage, so that the charging power of energy storage is preferentially met by the local charging power of new energy sources.

[0115] When optimizing energy storage configuration in new energy bases, to avoid over-reliance on grid power and improve the efficiency of local new energy consumption, energy storage scheduling constraints are set in the operational constraints of the energy storage configuration optimization model. Specifically, the energy storage scheduling constraints clarify the power balance relationship between local charging and high-voltage side power supply, limit the boundary of local charging power, and guide energy storage to prioritize absorbing local new energy output from the booster station. Only when local output is insufficient or system indicators fail to meet standards will power be fed back from the high-voltage side.

[0116] The expression for the energy storage scheduling constraint is as follows:

[0117]

[0118] in, Let be the energy storage charging power of the i-th boost station at time t. Let t represent the local charging power of new energy sources at the i-th booster station at time t. Let be the high-voltage side power output of the i-th booster station at time t. Let be the renewable energy output power of the i-th booster station at time t.

[0119] The above-mentioned implementation scheme of this application constructs energy storage scheduling constraints by determining the output power of new energy, charging power, local charging power of new energy, and power output of high voltage side at each time of the booster station. This sets boundaries for the optimization of energy storage configuration, avoids problems such as unreasonable energy storage charging strategies and excessive local charging power of new energy, as well as the resulting increase in new energy curtailment and loss of control over the total power output of high voltage side, thus deviating from the dual-objective optimization direction.

[0120] In an optional embodiment of this application, the target energy storage distribution ratio is determined from the set of candidate energy storage distribution ratios based on the total high-voltage side power-off volume and total operation and maintenance cost corresponding to each candidate energy storage distribution ratio, including: Based on the total high-voltage side power supply and total operation and maintenance cost corresponding to each of the selected energy storage distribution ratios in the set of selected energy storage distribution ratios, a set of non-dominated solutions is determined; wherein, when there is no other selected energy storage distribution ratio such that the total high-voltage side power supply and the total operation and maintenance cost are both not greater than and at least one is less than, the selected energy storage distribution ratio is determined as a non-dominated solution. For each candidate energy storage distribution ratio in the non-dominated solution set, the range normalization process is performed on the total power supply on the high-voltage side and the total operation and maintenance cost, and the configuration score corresponding to the candidate energy storage distribution ratio is determined according to the preset evaluation weight; wherein, the preset evaluation weight is a non-negative weight and the sum of the weights is 1; The candidate energy storage distribution ratio with the lowest configuration score in the non-dominated solution set is determined as the target energy storage distribution ratio.

[0121] After obtaining the total high-voltage side power consumption and total operation and maintenance cost corresponding to each of the candidate energy storage distribution ratios in the candidate energy storage distribution ratio set, a Pareto screening is performed on the candidate energy storage distribution ratio set. Dominated solutions that are inferior to other solutions in both the total high-voltage side power consumption and total operation and maintenance cost objectives are eliminated, and the set of non-dominated solutions is retained. A non-dominated solution (Pareto optimal solution) is one in the candidate energy storage distribution ratio set that does not have another solution that simultaneously satisfies both a lower total high-voltage side power consumption and a lower total operation and maintenance cost.

[0122] Specifically, non-dominated solutions can be determined by comparing each solution in the set of selected distributed energy storage ratios pairwise. For example, if the total high-voltage power output of solution A is less than or equal to that of solution B and the total operation and maintenance cost is less than or equal to that of solution B, and at least one objective is strictly better, then solution B is dominated by solution A and solution B is eliminated. If the two objectives of solution A and solution B have their own advantages and disadvantages (A has a lower total power output but a higher operation and maintenance cost, and B has the opposite), then both are non-dominated solutions and are retained in the set. If both objectives of solution C are strictly worse than at least one solution in the set, then solution C is dominated and is eliminated.

[0123] After obtaining the set of non-dominated solutions, to avoid weight distortion caused by the different dimensions and numerical ranges of the total power supply on the high-voltage side and the total operation and maintenance cost, range normalization is performed on each non-dominated solution. Let the th solution be... The total voltage-side current corresponding to each non-dominated solution is: The total operation and maintenance cost is The normalized expression is as follows:

[0124]

[0125] in, and These represent the maximum and minimum values ​​of the total voltage on the high-voltage side in the non-dominated solution set, respectively. and These are the maximum and minimum values ​​of the total operation and maintenance cost in the non-dominated solution set, respectively.

[0126] When the range of a certain objective in the non-dominated solution set is zero, the normalized value corresponding to that objective can be uniformly set to zero to avoid the denominator being zero.

[0127] After normalization, the configuration score is calculated according to the preset evaluation weights. Let the total power supply on the high-voltage side correspond to the first weight as follows: The total operation and maintenance cost corresponds to the second weight. ,and Then the first The configuration score of a non-dominated solution can be expressed as:

[0128] in, The configuration score is the score for the k-th non-dominated solution. The lower the score, the better the overall performance of the solution.

[0129] The above-mentioned implementation scheme of this application transforms the multi-objective decision-making process into an interpretable and quantifiable screening process through the method of "Pareto screening + range normalization + weighted scoring". While taking into account the total power supply control on the high-voltage side and the optimization of total operation and maintenance costs, it reduces subjective judgment bias.

[0130] The above describes the method for optimizing the distributed energy storage configuration of new energy bases provided in the embodiments of this application. The following will describe the device for optimizing the distributed energy storage configuration of new energy bases provided in the embodiments of this application with reference to the accompanying drawings.

[0131] like Figure 2 As shown in the figure, this embodiment of the invention also provides a distributed energy storage configuration optimization device for new energy bases, the device comprising: The first determining module 210 is used to determine a set of candidate energy storage distribution ratios for a new energy base to which an energy storage system is to be configured; wherein, the new energy base includes multiple booster stations, and each candidate energy storage distribution ratio in the set of candidate energy storage distribution ratios includes a first distribution ratio of the total energy storage capacity corresponding to the energy storage system in the multiple booster stations and a second distribution ratio of the total energy storage power corresponding to the energy storage system in the multiple booster stations. Module 220 is used to acquire the operating data of the new energy base within a preset time period; The model building module 230 is used to build an energy storage configuration optimization model. The energy storage configuration optimization model includes multiple operating constraints. The energy storage configuration optimization model takes the minimum total power-off volume of the energy storage system on the high-voltage side and the minimum total operation and maintenance cost of the energy storage system as its objective functions within the preset time period. The second determining module 240 is used to solve, based on the operating data and the energy storage configuration optimization model, for each candidate energy storage distribution ratio, to determine the total high-voltage side power-down volume and total operation and maintenance cost corresponding to the candidate energy storage distribution ratio. The third determining module 250 is used to determine the target energy storage distribution ratio from the set of candidate energy storage distribution ratios based on the total high-voltage side power-off volume and total operation and maintenance cost corresponding to each candidate energy storage distribution ratio in the set of candidate energy storage distribution ratios. The energy storage configuration module 260 is used to configure the energy storage capacity and energy storage power of each booster station in the new energy base based on the target energy storage distribution ratio, so as to obtain the energy storage configuration results corresponding to each booster station included in the new energy.

[0132] Optionally, the first determining module includes: The first acquisition submodule is used to acquire the number of booster stations included in the new energy base, and the energy storage configuration boundary conditions corresponding to each booster station; The second acquisition submodule is used to acquire the total energy storage capacity and total energy storage power of the energy storage system. The allocation submodule is used to allocate the total energy storage capacity and total energy storage power of the energy storage system to the various booster stations included in the new energy base, based on the number of booster stations, provided that the energy storage configuration boundary conditions corresponding to each booster station are satisfied, the sum of the energy storage capacities of each booster station equals the total energy storage capacity, and the sum of the energy storage power of each booster station equals the total energy storage power. This results in multiple candidate energy storage distribution ratios. The energy storage configuration boundary conditions corresponding to each booster station are used to limit the feasible range of energy storage capacity and power allocated to the booster station. The candidate energy storage distribution ratios include the distribution ratio of the total energy storage capacity of the energy storage system among the multiple booster stations and the distribution ratio of the total energy storage power of the energy storage system among the multiple booster stations. The proportion set determination submodule is used to obtain the set of candidate energy storage distribution ratios based on the multiple candidate energy storage distribution ratios.

[0133] Optionally, the objective function includes a first objective function; The model building module includes: The first determining submodule is used to determine the energy storage charging power, the new energy local charging power, and the high-voltage side power output corresponding to each booster station at each moment within the preset time period; wherein, the high-voltage side power output is taken as the larger of the difference between the energy storage charging power and the new energy local charging power and zero; The second determining submodule is used to accumulate the total power supply of the high-voltage side of each booster station at each moment within the preset time period according to a preset time step to obtain the total power supply of the high-voltage side within the preset time period. The first objective function determination submodule is used to determine the minimum value of the total voltage drop on the high-voltage side as the first objective function. Optionally, the objective function includes a second objective function; The model building module includes: The third determining submodule is used to determine the energy storage capacity corresponding to each booster station, the fixed annualized operation and maintenance cost of the station, and the unit capacity operation and maintenance cost calculated based on the annualized unit capacity operation and maintenance cost. The fixed annualized operation and maintenance cost of the station is related to the degree of dispersion of the energy storage configuration. The fourth determination submodule is used to determine the total operation and maintenance cost of the energy storage system based on the energy storage capacity corresponding to each booster station, the fixed annualized operation and maintenance cost of the station, and the unit capacity operation and maintenance cost calculated based on the annualized unit capacity operation and maintenance cost. The second objective function determination submodule is used to determine the minimum value of the total operation and maintenance cost as the second objective function.

[0134] Optionally, the multiple operational constraints include power balance constraints; The model building module includes: The fifth determining submodule is used to determine the base power transmission capacity, base new energy high-voltage transmission capacity, base energy storage charging capacity, base energy storage discharging capacity, and base thermal power output capacity of the new energy base at each moment within the preset time period; wherein, the base new energy high-voltage transmission capacity is the sum of the new energy high-voltage transmission capacity of each booster station in the new energy base; the base energy storage charging capacity is the sum of the energy storage charging capacity of each booster station in the new energy base; the base energy storage discharging capacity is the sum of the energy storage discharging capacity of each booster station in the new energy base; and the base thermal power output capacity is the sum of the thermal power output capacity corresponding to the thermal power units of each booster station in the new energy base; The first constraint construction module is used to construct the power balance constraint based on the fact that the sum of the base's new energy high-voltage transmission power, base energy storage discharge power and the base's thermal power output power at each time moment is equal to the sum of the base's power transmission power and base energy storage charging power at the corresponding time moment.

[0135] Optionally, the multiple operational constraints include new energy output constraints; The model building module includes: The sixth determining submodule is used to determine, for each booster station, the corresponding renewable energy output power, renewable energy high-voltage transmission power, renewable energy local charging power, and renewable energy curtailment power at each moment within the preset time period. The second constraint construction module is used to construct the new energy output constraint based on the fact that the output power of the new energy at each time is equal to the sum of the high-voltage transmission power of the new energy, the local charging power of the new energy, and the curtailment power of the new energy at the corresponding time.

[0136] Optionally, the multiple operational constraints include energy storage balance constraints; The model building module includes: The seventh determination submodule is used to determine, for each booster station, the energy storage capacity and energy storage power allocated to the booster station, as well as the energy storage charging power, energy storage discharging power, energy storage charge and energy storage charge change value corresponding to each moment within the preset time period; The eighth determining submodule is used to determine the energy storage charging amount corresponding to each moment based on the energy storage charging power, charging efficiency and preset time step; and to determine the energy storage discharging amount corresponding to each moment based on the energy storage discharging power, discharging efficiency and preset time step. The third constraint construction module is used to construct the energy storage balance constraint based on the energy storage charge satisfying the update relationship characterized by the energy storage charging amount and the energy storage discharging amount at adjacent time moments, wherein the energy storage charge is greater than or equal to zero and less than or equal to the energy storage capacity, and the energy storage charging power and the energy storage discharging power are both less than or equal to the energy storage power.

[0137] Optionally, the multiple operational constraints include thermal power plant operational constraints; The model building module includes: The third acquisition submodule is used to acquire the upper limit of the slope of each thermal power unit in the new energy base and the output range range corresponding to each moment in the preset time period. The ninth determination submodule is used to determine the output power corresponding to each time within a preset time period for each thermal power unit, and to determine the output power change value based on the output power at adjacent times. The fourth constraint construction module is used to construct the thermal power operation constraints based on the fact that the output power is within the output range and the absolute value of the output power change is less than or equal to the ramp limit.

[0138] Optionally, the multiple operational constraints include constraints on the utilization rate of new energy sources and the proportion of new energy power generation. The model building module includes: The fourth acquisition submodule is used to acquire the lower limit of the new energy utilization rate, the lower limit of the proportion of new energy power, and the base power transmission power of the new energy base at each time within the preset time period; The tenth determination submodule is used to determine the renewable energy output power, renewable energy curtailment power and thermal power output power of each booster station at each moment within the preset time period. The eleventh determination submodule is used to accumulate the new energy power output of each booster station at each moment within the preset time period according to a preset time step to obtain the total new energy power output. The twelfth determination submodule is used to accumulate the renewable energy curtailment power of each booster station at each moment within the preset time period according to a preset time step to obtain the total renewable energy curtailment power. The thirteenth determining submodule is used to determine the new energy utilization rate corresponding to the new energy base, wherein the new energy utilization rate is the ratio of the difference between the total output power of the new energy and the total abandoned power of the new energy to the total output power of the new energy. The fourteenth determination submodule is used to accumulate the thermal power output of each booster station at each moment within the preset time period according to a preset time step to obtain the total thermal power generation. The fifteenth determination submodule is used to accumulate the base power transmission power corresponding to each moment of the new energy base within the preset time period according to the preset time step to obtain the total power transmission of the base. The sixteenth determination submodule is used to determine the proportion of new energy power corresponding to the new energy base, wherein the proportion of new energy power is the ratio of the difference between the total power transmission of the base and the total power generation of thermal power to the total power transmission of the base; The fifth constraint construction module is used to construct constraints on the new energy utilization rate and the new energy power ratio based on the new energy utilization rate being greater than or equal to the lower limit of the new energy utilization rate and the new energy power ratio being greater than or equal to the lower limit of the new energy power ratio.

[0139] Optionally, the multiple operational constraints include energy storage scheduling constraints; The model building module includes: The seventeenth determination submodule is used to determine, for each booster station, the corresponding new energy output power, energy storage charging power, new energy local charging power and high voltage side power supply at each moment within the preset time period; The sixth constraint construction module is used to construct the energy storage scheduling constraint based on the fact that the local charging power of the new energy source is less than or equal to the smaller of the output power of the new energy source and the charging power of the energy storage source, and the power output on the high-voltage side is determined to be the difference between the charging power of the energy storage source and the local charging power of the new energy source.

[0140] Optionally, the multiple operational constraints include booster station power constraints; The model building module includes: The fifth acquisition submodule is used to acquire the maximum power that the high-voltage side of each booster station can send out. The eighteenth determination submodule is used to determine the new energy high-voltage transmission power and energy storage discharge power of each booster station at each moment within the preset time period; The seventh constraint construction module is used to construct the power constraint of the booster station based on the fact that the sum of the high-voltage transmission power of the new energy source and the discharge power of the energy storage does not exceed the maximum power that the high-voltage side of the booster station can bear.

[0141] Optionally, the third determining module includes: The non-dominated solution set determination submodule is used to determine the non-dominated solution set based on the total high-voltage side power supply and total operation and maintenance cost corresponding to each of the candidate energy storage distribution ratios in the candidate energy storage distribution ratio set; wherein, when there is no other candidate energy storage distribution ratio such that the total high-voltage side power supply and the total operation and maintenance cost are both not greater than and at least one is strictly less than, the candidate energy storage distribution ratio is determined as a non-dominated solution. The processing submodule is used to perform range normalization processing on the total power supply and total operation and maintenance cost of the high-voltage side for each candidate energy storage distribution ratio in the non-dominated solution set, and determine the configuration score corresponding to the candidate energy storage distribution ratio according to the preset evaluation weight; wherein, the preset evaluation weight is a non-negative weight and the sum of the weights is 1; The target energy storage distribution ratio determination submodule is used to determine the candidate energy storage distribution ratio with the smallest configuration score in the non-dominated solution set as the target energy storage distribution ratio.

[0142] The distributed energy storage configuration optimization device for new energy bases provided in this application determines the set of candidate distributed energy storage ratios, acquires operational data, and constructs an energy storage configuration optimization model that includes multiple operational constraints and a dual objective function. This model then determines the total high-voltage side power supply and total operation and maintenance cost for each candidate scheme, selects the target distributed energy storage ratio, and carries out energy storage configuration. Guided by a dual objective function and combined with multiple operational constraints, this application ensures that the optimization process aligns with engineering realities, prevents schemes from deviating from safety and operational boundaries, achieves reasonable distributed energy storage configuration, reduces dependence on high-voltage side power supply, lowers economic costs, avoids the operational complexity and management pressure caused by extreme dispersion, achieves a dynamic balance between economic efficiency and operational controllability, improves the level of new energy consumption, and avoids energy waste.

[0143] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0144] This application also provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the various processes of the above-described method for optimizing the distributed energy storage configuration in new energy bases and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0145] For example, Figure 3 A schematic diagram of the physical structure of an electronic device is shown. (For example...) Figure 3 As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330, and a communication bus 340. The processor 310, communication interface 320, and memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions in the memory 330. The processor 310 is used to execute the following steps: for a new energy base to which an energy storage system is to be configured, determine a set of candidate energy storage distribution ratios; wherein the new energy base includes multiple booster stations, and each candidate energy storage distribution ratio in the set includes a first distribution ratio of the total energy storage capacity corresponding to the energy storage system among the multiple booster stations and a second distribution ratio of the total energy storage power corresponding to the energy storage system among the multiple booster stations; acquire the operating data of the new energy base; the operating data includes at least the total power transmission data of the base and the output data of each booster station within a preset time period; construct an energy storage configuration optimization model, the energy storage configuration optimization model including multiple operating constraints. The energy storage configuration optimization model takes minimizing the total high-voltage side power consumption and the total operation and maintenance cost of the energy storage system within the preset time period as its objective functions. For each candidate energy storage distribution ratio, the model is solved based on the operating data and the optimization method to determine the total high-voltage side power consumption and total operation and maintenance cost corresponding to that ratio. Based on the total high-voltage side power consumption and total operation and maintenance cost corresponding to each candidate energy storage distribution ratio in the set, a target energy storage distribution ratio is determined. Based on the target energy storage distribution ratio, the energy storage capacity and power of each booster station in the new energy base are configured to obtain the energy storage configuration results corresponding to each booster station included in the new energy source. The processor 310 can also execute other schemes in the embodiments of this application, which will not be further elaborated here.

[0146] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion 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 described in the various embodiments of this application.

[0147] This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described method for optimizing the distributed energy storage configuration in new energy bases, achieving the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0148] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0149] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0150] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0151] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0152] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0153] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0154] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0155] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0156] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion 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 described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0157] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing the distributed energy storage configuration in a new energy base, characterized in that, The method includes: For a new energy base to be configured with an energy storage system, a set of candidate energy storage distribution ratios is determined; wherein, the new energy base includes multiple booster stations, and each candidate energy storage distribution ratio in the set of candidate energy storage distribution ratios includes a first distribution ratio of the total energy storage capacity corresponding to the energy storage system in the multiple booster stations and a second distribution ratio of the total energy storage power corresponding to the energy storage system in the multiple booster stations; Obtain operational data of the new energy base within a preset time period; An energy storage configuration optimization model is constructed, which includes multiple operational constraints. The energy storage configuration optimization model takes the minimum total power-down of the energy storage system on the high-voltage side and the minimum total operation and maintenance cost of the energy storage system as its objective functions within the preset time period. For each candidate energy storage distribution ratio, the total high-voltage side power-off volume and total operation and maintenance cost corresponding to the candidate energy storage distribution ratio are determined by solving the problem based on the operating data and the energy storage configuration optimization model. Based on the total high-voltage side power-off volume and total operation and maintenance cost corresponding to each of the candidate energy storage distribution ratios in the candidate energy storage distribution ratio set, the target energy storage distribution ratio is determined from the candidate energy storage distribution ratio set. Based on the target energy storage distribution ratio, the energy storage capacity and energy storage power of each booster station in the new energy base are configured to obtain the energy storage configuration results corresponding to each booster station included in the new energy.

2. The method for optimizing the distributed energy storage configuration in new energy bases according to claim 1, characterized in that, For new energy bases where energy storage systems are to be configured, a set of candidate distributed energy storage ratios is determined, including: Obtain the number of booster stations included in the new energy base, and the energy storage configuration boundary conditions corresponding to each booster station; Obtain the total energy storage capacity and total energy storage power of the energy storage system; Based on the number of booster stations, and provided that the energy storage configuration boundary conditions corresponding to each booster station are satisfied, the sum of the energy storage capacities of each booster station equals the total energy storage capacity, and the sum of the energy storage powers of each booster station equals the total energy storage power, the total energy storage capacity and total energy storage power corresponding to the energy storage system are allocated to each booster station included in the new energy base, resulting in multiple candidate energy storage distribution ratios; wherein, the energy storage configuration boundary conditions corresponding to each booster station are used to limit the feasible range of energy storage capacity and power allocated to the booster station; the candidate energy storage distribution ratios include the distribution ratio of the total energy storage capacity corresponding to the energy storage system among the multiple booster stations and the distribution ratio of the total energy storage power corresponding to the energy storage system among the multiple booster stations; The set of candidate energy storage distribution ratios is obtained based on the plurality of candidate energy storage distribution ratios.

3. The method for optimizing the distributed energy storage configuration in new energy bases according to claim 1, characterized in that, The objective function includes a first objective function; Construct an energy storage configuration optimization model, including: Determine the energy storage charging power, new energy local charging power, and high-voltage side power output for each booster station at each moment within the preset time period; wherein, the high-voltage side power output is taken as the larger of the difference between the energy storage charging power and the new energy local charging power and zero; Based on the high-voltage side power output of each booster station at each moment within the preset time period, the total high-voltage side power output within the preset time period is accumulated according to the preset time step. The minimum value of the total voltage on the high-voltage side is determined as the first objective function.

4. The method for optimizing the distributed energy storage configuration in new energy bases according to claim 1, characterized in that, The objective function includes a second objective function; Construct an energy storage configuration optimization model, including: The energy storage capacity, fixed annualized operation and maintenance cost of each substation, and unit capacity operation and maintenance cost calculated based on the annualized unit capacity operation and maintenance cost are determined. The fixed annualized operation and maintenance cost of the substation is related to the degree of dispersion of energy storage configuration. The total operation and maintenance cost of the energy storage system is determined based on the energy storage capacity corresponding to each booster station, the fixed annualized operation and maintenance cost of the station, and the unit capacity operation and maintenance cost calculated based on the annualized unit capacity operation and maintenance cost. The minimum value of the total operation and maintenance cost is determined as the second objective function.

5. The method for optimizing the distributed energy storage configuration in new energy bases according to claim 1, characterized in that, The aforementioned multiple operational constraints include power balance constraints. Construct an energy storage configuration optimization model, including: The following parameters are determined for each moment within the preset time period: base power transmission capacity, base high-voltage power transmission capacity, base energy storage charging capacity, base energy storage discharging capacity, and base thermal power output capacity. Specifically, the base high-voltage power transmission capacity is the sum of the high-voltage power transmission capacity of all booster stations within the new energy base; the base energy storage charging capacity is the sum of the energy storage charging capacity of all booster stations within the new energy base; the base energy storage discharging capacity is the sum of the energy storage discharging capacity of all booster stations within the new energy base; and the base thermal power output capacity is the sum of the thermal power output capacity corresponding to the thermal power units at all booster stations within the new energy base. The power balance constraint is constructed based on the fact that the sum of the base's new energy high-voltage transmission power, base energy storage discharge power, and base thermal power output power at each time moment is equal to the sum of the base's power transmission power and base energy storage charging power at the corresponding time moment.

6. The method for optimizing the distributed energy storage configuration in new energy bases according to claim 1, characterized in that, The aforementioned multiple operational constraints include new energy output constraints; Construct an energy storage configuration optimization model, including: For each booster station, determine the renewable energy output power, renewable energy high-voltage transmission power, renewable energy local charging power, and renewable energy curtailment power corresponding to each moment within the preset time period. The new energy output constraint is constructed based on the fact that the new energy output power at each time moment is equal to the sum of the new energy high-voltage transmission power, the new energy local charging power, and the new energy curtailment power at the corresponding time moment.

7. The method for optimizing the distributed energy storage configuration in new energy bases according to claim 1, characterized in that, The aforementioned multiple operational constraints include energy storage balance constraints; Construct an energy storage configuration optimization model, including: For each booster station, determine the energy storage capacity and energy storage power allocated to the booster station, as well as the energy storage charging power, energy storage discharging power, energy storage charge, and energy storage charge change value at each moment within the preset time period; Based on the energy storage charging power, charging efficiency, and preset time step, determine the energy storage charging amount corresponding to each moment; based on the energy storage discharging power, discharging efficiency, and preset time step, determine the energy storage discharging amount corresponding to each moment. Based on the fact that the energy storage charge satisfies the update relationship characterized by the energy storage charging amount and the energy storage discharging amount at adjacent time points, the energy storage charge is greater than or equal to zero and less than or equal to the energy storage capacity, and the energy storage charging power and the energy storage discharging power are both less than or equal to the energy storage power, the energy storage balance constraint is constructed.

8. The method for optimizing the distributed energy storage configuration in new energy bases according to claim 1, characterized in that, The aforementioned multiple operational constraints include thermal power plant operational constraints. Construct an energy storage configuration optimization model, including: Obtain the maximum ramp rate of each thermal power unit in the new energy base and the output range corresponding to each moment within the preset time period; For each thermal power unit, the output power at each moment within a preset time period is determined, and the output power change value is determined based on the output power at adjacent moments. Based on the fact that the output power is within the output range and the absolute value of the output power change is less than or equal to the ramp limit, the thermal power operation constraints are constructed.

9. The method for optimizing the distributed energy storage configuration in new energy bases according to claim 1, characterized in that, The aforementioned multiple operational constraints include constraints on the utilization rate of new energy sources and the proportion of new energy power generation. Construct an energy storage configuration optimization model, including: Obtain the lower limit of new energy utilization rate, the lower limit of new energy power ratio, and the base power transmission power of the new energy base at each time within the preset time period; For each booster station, determine the corresponding renewable energy output power, renewable energy curtailment power, and thermal power output power for each booster station at each moment within the preset time period; The total renewable energy output power of each booster station at each moment within the preset time period is accumulated according to a preset time step to obtain the total renewable energy output power. The total amount of abandoned renewable energy is obtained by summing the renewable energy power at each booster station at each moment within the preset time period according to the preset time step. Determine the new energy utilization rate corresponding to the new energy base, wherein the new energy utilization rate is the ratio of the difference between the total output power of the new energy and the total abandoned power of the new energy to the total output power of the new energy; The thermal power output of each booster station at each moment within the preset time period is accumulated according to the preset time step to obtain the total thermal power generation. The total power transmission capacity of the new energy base is obtained by summing the base power transmission capacity at each moment within the preset time period according to the preset time step. Determine the proportion of new energy power generation corresponding to the new energy base, wherein the proportion of new energy power generation is the ratio of the difference between the total power transmission of the base and the total power generation of thermal power to the total power transmission of the base; Based on the fact that the new energy utilization rate is greater than or equal to the lower limit of the new energy utilization rate and the new energy power ratio is greater than or equal to the lower limit of the new energy power ratio, the constraints of the new energy utilization rate and the new energy power ratio are constructed.

10. The method for optimizing the distributed energy storage configuration in new energy bases according to claim 1, characterized in that, The aforementioned multiple operational constraints include energy storage scheduling constraints; Construct an energy storage configuration optimization model, including: For each booster station, determine the corresponding new energy output power, energy storage charging power, new energy local charging power, and high-voltage side power supply power for each moment within the preset time period. The energy storage scheduling constraint is constructed based on the fact that the local charging power of the new energy source is less than or equal to the smaller of the output power of the new energy source and the charging power of the energy storage source, and the power supplied from the high-voltage side is determined to be the difference between the charging power of the energy storage source and the local charging power of the new energy source.

11. The method for optimizing the distributed energy storage configuration in new energy bases according to claim 1, characterized in that, The aforementioned operational constraints include power constraints for the booster station; an energy storage configuration optimization model is constructed, including: For each booster station, obtain the maximum power that the high-voltage side of the booster station can send out; For each booster station, determine the new energy high-voltage transmission power and energy storage discharge power corresponding to each moment within the preset time period; The power constraint of the booster station is constructed based on the fact that the sum of the high-voltage transmission power of the new energy source and the discharge power of the energy storage does not exceed the maximum power that the high-voltage side of the booster station can bear.

12. The method for optimizing the distributed energy storage configuration in new energy bases according to claim 1, characterized in that, Based on the total high-voltage side power consumption and total operation and maintenance cost corresponding to each candidate energy storage distribution ratio in the candidate energy storage distribution ratio set, the target energy storage distribution ratio is determined from the candidate energy storage distribution ratio set, including: Based on the total high-voltage side power supply and total operation and maintenance cost corresponding to each of the selected energy storage distribution ratios in the set of selected energy storage distribution ratios, a set of non-dominated solutions is determined; wherein, when there is no other selected energy storage distribution ratio such that the total high-voltage side power supply and the total operation and maintenance cost are both not greater than and at least one is strictly less than, the selected energy storage distribution ratio is determined as a non-dominated solution. For each candidate energy storage distribution ratio in the non-dominated solution set, the total power supply and total operation and maintenance cost on the high-voltage side are normalized according to the range, and the configuration score corresponding to the candidate energy storage distribution ratio is determined according to the preset evaluation weight; wherein, the preset evaluation weight is a non-negative weight and the sum of the weights is 1; The candidate energy storage distribution ratio with the lowest configuration score in the non-dominated solution set is determined as the target energy storage distribution ratio.

13. A distributed energy storage configuration optimization device for a new energy base, characterized in that, include: The first determining module is used to determine a set of candidate energy storage distribution ratios for a new energy base to which an energy storage system is to be configured; wherein, the new energy base includes multiple booster stations, and each candidate energy storage distribution ratio in the set of candidate energy storage distribution ratios includes a first distribution ratio of the total energy storage capacity corresponding to the energy storage system in the multiple booster stations and a second distribution ratio of the total energy storage power corresponding to the energy storage system in the multiple booster stations. The acquisition module is used to acquire the operational data of the new energy base within a preset time period; The model building module is used to build an energy storage configuration optimization model. The energy storage configuration optimization model includes multiple operating constraints. The energy storage configuration optimization model takes the minimum total power-off volume on the high-voltage side of the energy storage system and the minimum total operation and maintenance cost of the energy storage system as its objective functions within the preset time period. The second determining module is used to determine the total high-voltage side power-off volume and total operation and maintenance cost corresponding to each candidate energy storage distribution ratio, based on the operating data and the energy storage configuration optimization model. The third determining module is used to determine the target energy storage distribution ratio from the set of candidate energy storage distribution ratios based on the total high-voltage side power-off volume and total operation and maintenance cost corresponding to each candidate energy storage distribution ratio in the set of candidate energy storage distribution ratios. An energy storage configuration module is used to configure energy storage for the new energy base based on the target energy storage distribution ratio.

14. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the method for optimizing the distributed energy storage configuration of new energy bases as described in any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method for optimizing the distributed energy storage configuration of new energy bases as described in any one of claims 1 to 12.

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