Power distribution network energy storage configuration method and system and storage medium

Abstract

The invention discloses a power distribution network energy storage configuration method and system and a storage medium, and the method comprises the steps: taking a governance demand node of which the time-space complementary parameter is lower than a set threshold value as an insufficient governance node according to the time-space complementary parameter of each governance demand node in a power distribution network region; calculating a power demand and a capacity demand of the energy storage device according to the to-be-governed capacity of the insufficient governed node; an energy storage configuration optimization model is constructed based on the space-time complementary parameters and the power demand and the capacity demand of the energy storage device, and the optimization model takes the minimum energy storage full life cycle cost as an objective function on the basis of considering the space-time complementary parameters before and after comparison treatment. The constraint conditions of the optimization model comprise a power demand constraint, a capacity demand constraint, a charge state constraint and a charge and discharge state constraint; based on the optimization model of energy storage configuration, optimal configuration parameters of energy storage are solved; according to the method, optimal matching of the energy storage resources and the governance gap on the spatial-temporal scale is realized, and the governance efficiency and the utilization rate of existing assets are improved.

Description

Technical Field

[0001] This invention relates to the field of power grid optimization configuration technology, specifically to a distribution network energy storage configuration method, system, and storage medium. Background Technology

[0002] With the rapid advancement of new power system construction and the in-depth implementation of the "dual-carbon" strategic goals, the penetration rate of distributed power sources and power electronic loads in the distribution network continues to rise. While the transformation of the energy structure brings clean energy, it also triggers increasingly prominent power quality problems, such as aggravated harmonic pollution, worsening three-phase imbalance, and reduced power factor. These problems can range from affecting equipment lifespan to causing production equipment failures and economic losses.

[0003] The surplus capacity of widely integrated distributed resources (such as photovoltaic inverters) in power distribution networks offers new insights into power quality management. Meanwhile, dedicated management equipment such as active power filters (APFs) and static var generators (SVGs) are also widely used. However, distributed resources, management equipment, and loads in power distribution networks exhibit distinct spatiotemporal distribution characteristics and complex complementary relationships. For example, the surplus capacity of photovoltaic inverters dynamically changes with solar irradiance, while harmonics and reactive power generated by loads fluctuate temporally and spatially depending on their electricity consumption behavior.

[0004] To address the aforementioned issues, various solutions have been proposed in related technologies. For example, the patent application document with publication number CN120598442A effectively achieves dynamic evaluation of the power quality governance capability of the distribution network by quantifying governance needs, calculating the remaining capacity of governance resources, dividing governance areas based on voltage sensitivity, and finally generating spatiotemporal complementary parameters. This solution provides an advanced quantitative tool for addressing how to evaluate the power quality governance capability of the distribution network.

[0005] However, related technologies, such as the aforementioned assessment methods, are still mainly at the diagnostic level, meaning they can identify and locate nodes or periods of insufficient governance capabilities, but they do not address the issue of how to govern. Specifically, they have the following shortcomings: (1) Lack of guidance on the allocation of governance resources: Existing research has failed to provide specific methods for how to scientifically allocate new governance resources (such as energy storage systems) based on assessment results (such as spatiotemporal complementarity parameters).

[0006] (2) Failure to link assessment and allocation in a closed loop: Assessment and resource allocation are two separate links. There is no perfect solution yet for how to use the key indicators output by assessment (such as governance gaps and capacity to be governed) to directly drive the capacity and power calculation of energy storage devices.

[0007] (3) Neglecting the balance between economic efficiency and governance effectiveness: Existing resource allocation research often focuses on technical feasibility and lacks an optimization model that takes economic efficiency as the goal while also considering governance effectiveness, thus failing to guide grid operators in making economically optimal investment decisions. For example, the patent application literature with publication number CN114723321A focuses on the spatiotemporal state modeling of mobile energy storage, combines conditional risk value quantification of wind and solar uncertainties, proposes an optimization model that considers mobile energy storage, and uses an improved particle swarm optimization algorithm to solve the multi-objective optimization problem. It focuses on the spatiotemporal balance and economic optimization of energy, mainly solving the spatiotemporal optimization scheduling and total cost minimization problem of mobile energy storage in multi-microgrid systems, rather than the power quality governance problem, which is a cost-driven technical path.

[0008] For example, the patent application document with publication number CN116799828A proposes to simulate the energy storage response to multiple scenarios through time-series simulation and construct a pure economic function with the goal of maximizing net benefits for optimization. It is aimed at flexible interconnected distribution networks and solves the problem of economic capacity configuration of energy storage in multiple scenarios such as frequency regulation, power prediction compensation and new energy consumption. It focuses on the economic optimal scheduling or configuration at the system level and is a cost-driven technical path.

[0009] Therefore, there is an urgent need in this field for a power quality governance resource allocation method that can be integrated with advanced assessment methods and generate scientific allocation schemes based on assessment results, so as to form a complete closed loop from "assessment" to "governance", thereby achieving the optimal allocation of governance resources and improving the economic benefits and operational efficiency of distribution network investment. Summary of the Invention

[0010] The technical problem to be solved by this invention is how to optimize the configuration of energy storage in the distribution network based on the spatiotemporal complementary parameters of each governance demand point in the distribution network, so as to achieve a dual improvement in the power quality governance capability and investment economic benefits of the distribution network.

[0011] The present invention solves the above-mentioned technical problems through the following technical means: A method for configuring energy storage in a distribution network is proposed, the method comprising: Based on the spatiotemporal complementary parameters of each governance demand node in the distribution network area, governance demand nodes whose spatiotemporal complementary parameters are lower than the set threshold are identified as governance inadequate nodes. The capacity to be addressed is determined based on the spatiotemporal complementary parameters of the nodes with insufficient governance, and the power and capacity requirements of the energy storage device are calculated based on the capacity to be addressed of the nodes with insufficient governance. An optimization model for energy storage configuration is constructed based on spatiotemporal complementary parameters and the power and capacity requirements of energy storage devices. The optimization model takes minimizing the total life cycle cost of energy storage as the objective function, considering the spatiotemporal complementary parameters before and after the treatment. The constraints of the optimization model include power demand constraints, capacity demand constraints, state of charge constraints, and state of charge / discharge constraints. Based on the optimization model of energy storage configuration, the optimal configuration parameters of energy storage are solved.

[0012] Furthermore, before identifying nodes with spatiotemporal complementary parameters below a set threshold as nodes with insufficient governance based on the spatiotemporal complementary parameters of each governance demand node within the distribution network area, the method further includes: The power quality governance capacity of the governance demand node is calculated based on the harmonic components, negative sequence components, and reactive components obtained from the decoupling at the governance demand node. Quantify the remaining capacity of each power quality management resource and the management scope of each power quality management resource; Based on the remaining capacity and scope of each power quality governance resource, as well as the power quality governance demand capacity of each governance demand node, the spatiotemporal complementary parameters of each governance demand node are calculated.

[0013] Furthermore, the step of determining the capacity to be addressed based on the spatiotemporal complementary parameters of the under-addressed nodes, and calculating the power and capacity requirements of the energy storage device based on the capacity to be addressed of the under-addressed nodes, includes: Based on the power capacity required to compensate for under-managed nodes in order to meet power quality standards and the spatiotemporal complementary parameters corresponding to under-managed nodes, the capacity to be managed for under-managed nodes is calculated. The power requirement of the energy storage device is determined based on the unmanaged capacity of the inadequate nodes at the moment of maximum governance gap. The capacity requirement of the energy storage device is determined based on the total capacity of the nodes with insufficient capacity to be addressed throughout the day.

[0014] Furthermore, determining the power requirement of the energy storage device based on the unmanaged capacity of the undermanaged node at the moment of maximum manifold deficit includes: Based on the unmanaged capacity of each insufficient node within the management range of the energy storage device, calculate the unmanaged capacity at each time point within the management range of the energy storage device. The power requirement of the energy storage device is determined based on the capacity to be addressed at the moment of the maximum governance gap within the governance range of the energy storage device.

[0015] Furthermore, the formula for calculating the unmanaged capacity of the undermanaged nodes is as follows:

[0016] In the formula, For the insufficient nodes of governancek To achieve the power quality standards, the required compensation capacity, For the insufficient nodes of governance k exist t The spatiotemporal complementary parameters at time, for t Addressing shortcomings at all times k The capacity to be addressed.

[0017] Furthermore, the power requirement of the energy storage device is as follows:

[0018]

[0019] In the formula, For energy storage devices i Power at any point in time, for t Real-time energy storage device i The unmanaged capacity of nodes with insufficient governance within the governance scope. for t Addressing shortcomings at all times k The capacity to be treated The treatment range is determined based on voltage sensitivity analysis.

[0020] Furthermore, the formula for determining the capacity requirement of the energy storage device based on the sum of the total capacity of the nodes with insufficient capacity to be treated throughout the day is expressed as follows:

[0021] In the formula, For energy storage devices i At any point in time, for t Real-time energy storage device i The unmanaged capacity of nodes with insufficient governance within the governance scope. For time intervals.

[0022] Furthermore, the objective function is expressed as follows:

[0023]

[0024]

[0025] In the formula, F Cost of energy storage throughout its entire lifecycle; This is a cost item, including investment costs and operation and maintenance costs; For the governance effect item, For the insufficient nodes of governancek Spatiotemporal complementary parameters before treatment To address the insufficient nodes after configuring energy storage k The spatiotemporal complementary parameters, For the set of nodes with insufficient governance, This is a penalty item; The governance effectiveness coefficient, This is the penalty weighting coefficient.

[0026] Furthermore, the formula for the power demand constraint is expressed as:

[0027] The formula for the capacity requirement constraint is expressed as follows:

[0028] The formula for the charge state constraint is expressed as follows:

[0029] The formula for the charge / discharge state constraint is expressed as follows:

[0030] In the formula, For energy storage devices i Power at any point in time, For minimum power, This is the highest power output; For energy storage devices i Capacity at any point in time, For minimum capacity, Maximum capacity; for t Real-time energy storage device i The charging and discharging power, For efficiency, For time intervals, For energy storage devices i exist t- Capacity at time 1 For energy storage devices i exist t The capacity of a given moment; and These are the energy storage charging indicator and the energy release status indicator, respectively, both of which are 0-1 variables.

[0031] Furthermore, the optimization model based on energy storage configuration solves for the optimal configuration parameters of energy storage, including: The optimization model is solved using a mixed-integer linear programming algorithm to obtain the optimal configuration parameters for energy storage.

[0032] Furthermore, after solving for the optimal configuration parameters of energy storage using the optimization model based on energy storage configuration, the method further includes: Configure energy storage according to the optimal configuration parameters, recalculate the spatiotemporal complementary parameters of the nodes with insufficient governance, and verify the governance effect.

[0033] Furthermore, this invention also proposes a power distribution network energy storage configuration system, the system comprising: The node identification module is used to identify nodes with insufficient governance based on the spatiotemporal complementary parameters of each governance demand node in the distribution network area. The demand calculation module is used to determine the capacity to be addressed of nodes with insufficient governance based on their spatiotemporal complementary parameters, and to calculate the power and capacity requirements of energy storage devices based on the capacity to be addressed of nodes with insufficient governance. The model building module is used to build an optimization model for energy storage configuration based on spatiotemporal complementary parameters and the power and capacity requirements of energy storage devices. The optimization model takes minimizing the total life cycle cost of energy storage as the objective function, based on the comparison of spatiotemporal complementary parameters before and after the treatment. The constraints of the optimization model include power demand constraints, capacity demand constraints, state of charge constraints, and state of charge / discharge constraints. The solver module is used to solve for the optimal configuration parameters of energy storage based on the optimization model of energy storage configuration.

[0034] Furthermore, the present invention also proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the distribution network energy storage configuration method as described above.

[0035] The advantages of this invention are: This invention introduces spatiotemporal complementary parameters to accurately quantify the matching degree between governance needs and existing governance resources in the spatiotemporal dimension, effectively identifying weak links and gaps in governance capabilities. Based on this, the aforementioned spatiotemporal complementary parameters are used as input to directly calculate the power and capacity requirements of energy storage devices. An optimization model for energy storage configuration with economic efficiency as the objective is constructed based on the spatiotemporal complementary parameters and the power and capacity requirements of energy storage devices. By solving the optimization model, the invention provides grid operators with the most cost-effective energy storage configuration scheme. This invention establishes a closed-loop solution from "dynamic assessment" to "precise configuration," and scientifically quantifies and optimizes the allocation of energy storage governance resources based on highly accurate governance capability assessment results. This not only achieves the optimal matching of energy storage resources and governance gaps in the spatiotemporal scale, but also greatly improves governance efficiency and the utilization rate of existing assets, ultimately achieving a dual improvement in the power quality governance capability and investment economic benefits of the distribution network.

[0036] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0037] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation thereof. Figure 1 This is a flowchart illustrating a distribution network energy storage configuration method according to an embodiment of the present invention; Figure 2 This is a typical photovoltaic power output data diagram in one embodiment of the present invention; Figure 3 Here is a typical load active power data diagram in one embodiment of the present invention: Figure 4 This is a typical load reactive power data diagram in one embodiment of the present invention; Figure 5 This is a typical power distribution network scenario diagram based on the IEEE 33-node model in one embodiment of the present invention; Figure 6 This is a diagram showing the spatiotemporal complementary parameter results of a typical power distribution network scenario governance requirement node in an embodiment of the present invention; Figure 7 This is a graph showing the spatial complementarity parameters of nodes 25 and 31 in one embodiment of the present invention. Figure 8 This is a schematic diagram of the structure of a power distribution network energy storage configuration system according to an embodiment of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] like Figure 1 As shown, the first embodiment of the present invention proposes a method for configuring energy storage in a distribution network, the method comprising the following steps: S10. Based on the spatiotemporal complementary parameters of each governance demand node in the distribution network area, the governance demand nodes whose spatiotemporal complementary parameters are lower than the set threshold are regarded as governance insufficiency nodes. It should be noted that, in this embodiment, the specific value of the threshold can be 0.8, which is used to compare with the spatiotemporal complementarity parameters of the governance demand nodes. If the spatiotemporal complementarity parameters are lower than the set threshold, it indicates that these governance demand nodes may have significant power quality problems and the current power quality governance equipment cannot effectively address the power quality problems of these nodes. In this embodiment, governance demand nodes with spatiotemporal complementarity parameters lower than the set threshold are identified as insufficiently governed nodes, and then they are governed.

[0040] It should be noted that 0.8 is a common safety margin threshold in power system and control engineering. Setting the threshold at this level has the effects of early warning and providing reliability assurance: setting it too high will lead to overly stringent governance standards, with many nodes being identified as "under-governed," thus requiring excessive energy storage capacity, causing a surge in investment costs and potentially wasting resources. Setting it too low will result in overly lenient governance standards. Although it saves on initial investment, many power quality problems at various nodes will not be addressed in a timely manner, leading to poor governance effectiveness and potential risks such as equipment failure and production losses for users.

[0041] It should be understood that the threshold value set in this embodiment is only for illustrative purposes, and those skilled in the art can select other values ​​according to the actual situation. This embodiment does not impose any specific limitations.

[0042] S20. Determine the capacity to be addressed for nodes with insufficient governance based on their spatiotemporal complementary parameters, and calculate the power and capacity requirements of the energy storage device based on the capacity to be addressed for nodes with insufficient governance. It should be noted that this embodiment quantifies the power and capacity required by energy storage devices based on the unmanaged capacity of nodes with insufficient governance at different time scales. Specifically, it directly calculates the power and capacity requirements of energy storage devices based on the spatiotemporal complementarity parameters of the nodes with insufficient governance. The configuration target is clearly quantified as raising the spatiotemporal complementarity parameters of specific nodes above a threshold. This provides a clear and intuitive standard for verifying the adequacy of resource allocation. Bypassing the intermediate steps of traditional methods that rely on expert experience, multiple trial calculations, or fuzzy judgments, and eliminating the roundabout and uncertainties of traditional solutions, this method establishes a linear technical path from the quantitative indicators of governance gaps to energy storage configuration parameters. Through a calculable and verifiable deterministic relationship, this method achieves precise and efficient allocation of governance resources.

[0043] S30. An optimization model for energy storage configuration is constructed based on spatiotemporal complementary parameters and the power and capacity requirements of energy storage devices. The optimization model takes minimizing the total life cycle cost of energy storage as the objective function, considering the spatiotemporal complementary parameters before and after the treatment. The constraints of the optimization model include power demand constraints, capacity demand constraints, state of charge constraints, and state of charge / discharge constraints. S40. Based on the optimization model of energy storage configuration, solve for the optimal configuration parameters of energy storage.

[0044] This embodiment connects the energy storage configuration of the distribution network with advanced evaluation methods. It can generate a scientific configuration scheme for power quality governance resources based on the high-precision governance capability evaluation results, forming a complete closed loop from "evaluation" to "governance". This enables accurate evaluation of the power governance capability of the distribution network and scientific allocation of energy storage resources, thereby improving the economic benefits and operational efficiency of distribution network investment.

[0045] This embodiment constructs a closed-loop methodology from assessment to governance. It uses spatiotemporal complementary parameters as core inputs, directly driving the determination of energy storage power and capacity requirements by calculating the capacity to be addressed at nodes with insufficient governance. It also constructs an optimization model with the goal of minimizing costs while embedding governance effect improvement terms, ultimately solving the problem using mixed-integer linear programming. Essentially, it is a governance demand-driven technical path that achieves a dual improvement in power quality governance capabilities and investment economic benefits. Through optimal matching of energy storage configuration and governance gaps on a spatiotemporal scale, it significantly improves the power quality level of the distribution network and reduces governance costs.

[0046] As a further preferred technical solution, before step S10: identifying governance demand nodes with spatiotemporal complementary parameters lower than a set threshold as governance-insufficient nodes based on the spatiotemporal complementary parameters of each governance demand node within the distribution network area, the method further includes the following steps: S11. Calculate the power quality governance capacity of the governance demand node based on the harmonic components, negative sequence components and reactive components obtained from the decoupling at the governance demand node. S12. Quantify the remaining capacity of each power quality management resource and the management scope of each power quality management resource; S13. Based on the remaining capacity and scope of each power quality governance resource and the power quality governance demand capacity of each governance demand node, calculate the spatiotemporal complementary parameters of each governance demand node.

[0047] Specifically, in this embodiment, the spatiotemporal complementary parameters are obtained through the following methods: extracting harmonic, reactive, and negative sequence components of the governance demand point based on the synchronous coordinate transformation method; quantifying the governance demand capacity and the remaining governance resource capacity; determining the governance range based on voltage sensitivity analysis; and calculating the spatial complementary parameters. Spatiotemporal complementary parameters are generated by integrating multiple time points. This allows for the quantification of the spatiotemporal matching degree between governance resources and governance needs in the distribution network using spatiotemporal complementary parameters.

[0048] It should be noted that the specific calculation process of the spatiotemporal complementarity parameters in this embodiment can be found in the scheme described in the patent application document with publication number CN120598442A, and will not be described in detail in this embodiment.

[0049] As a further preferred technical solution, step S20: determining the capacity to be addressed based on the spatiotemporal complementary parameters of the under-addressed nodes, and calculating the power and capacity requirements of the energy storage device based on the capacity to be addressed of the under-addressed nodes, specifically includes the following steps: S21. Based on the power capacity required to compensate for the under-managed nodes in order to achieve power quality standards and the spatiotemporal complementary parameters corresponding to the under-managed nodes, calculate the capacity to be managed for the under-managed nodes. Specifically, the formula for calculating the unmanaged capacity of the undermanaged nodes is as follows:

[0050] In the formula, For the insufficient nodes of governance k To achieve the power quality standards, the required compensation capacity, For the insufficient nodes of governance k exist t The spatiotemporal complementary parameters at time, for t Addressing shortcomings at all times k The capacity to be addressed.

[0051] S22. Determine the power requirement of the energy storage device based on the capacity to be addressed at the moment of maximum governance gap of the inadequate governance node; Specifically, the power requirement of the energy storage device is:

[0052]

[0053] In the formula, For energy storage devices i Rated power, for t Real-time energy storage device i The unmanaged capacity of nodes with insufficient governance within the governance scope. for t Addressing shortcomings at all times k The capacity to be treated The treatment range is determined based on voltage sensitivity analysis.

[0054] S23. Determine the capacity requirement of the energy storage device based on the total capacity of the nodes with insufficient capacity to be treated throughout the day.

[0055] Specifically, the formula for calculating the capacity requirement of the energy storage device is as follows:

[0056] In the formula, For energy storage devices i Capacity at any point in time, for t Real-time energy storage device i The unmanaged capacity of nodes with insufficient governance within the governance scope. For time intervals.

[0057] It should be noted that in this embodiment, the power demand is directly taken as the sum of the capacity to be addressed at the moment of maximum governance shortfall. This strictly adheres to the instantaneous requirements of power quality governance, ensuring that energy storage provides sufficient compensation power under the worst operating conditions, thereby eradicating instantaneous power disturbances and preventing governance failures due to insufficient power at the source. The capacity demand is determined by time-series integration of the capacity to be addressed throughout the day. The key is to transform discrete governance tasks into continuous energy demands, ensuring that energy storage has the endurance to support continuous governance throughout the day. These two calculation methods precisely correspond to the two physical dimensions of power and capacity of the energy storage device, enabling the final configuration scheme to cover power quality issues at every point in time while maintaining long-term governance. This achieves targeted governance technically and avoids resource waste economically.

[0058] As a further preferred technical solution, the objective function in the optimization model is:

[0059]

[0060]

[0061]

[0062]

[0063] In the formula, F Cost of energy storage throughout its entire lifecycle; This is a cost item, including investment costs. and operation and maintenance costs , For energy storage devices i Power at any point in time, For energy storage devices i Rated capacity, a This is the unit capacity cost coefficient for energy storage. b This is the unit power cost coefficient for energy storage. N The number of energy storage devices; For the governance effect item, For the insufficient nodes of governance k Spatiotemporal complementary parameters before treatment To address the insufficient nodes after configuring energy storage k The spatiotemporal complementary parameters, For the set of nodes with insufficient governance, This is a penalty item; The governance effectiveness coefficient, This is the penalty weighting coefficient.

[0064] It should be noted that traditional methods usually use "the spatiotemporal complementarity parameter reaching a threshold" as a hard constraint. If the constraint is too strict, the model may have no solution, leading to optimization failure; if the constraint is too loose, a feasible solution with poor governance effect may be obtained.

[0065] This embodiment, by introducing a governance effect term, no longer mandates that parameters immediately reach the threshold after governance. Instead, it encourages them to approach or even exceed the threshold as much as possible. This allows the optimization model to balance cost and governance effectiveness, proactively exploring configurations with better governance outcomes. It provides decision-makers with an intuitive cost-benefit trade-off tool. The governance effect term and the penalty term together form a balance between effectiveness and cost. By adjusting the governance effect coefficient and penalty weight coefficient, decision-makers can clearly control the direction of optimization emphasis.

[0066] As a further preferred technical solution, the formula for the power demand constraint is expressed as:

[0067] The formula for the capacity requirement constraint is expressed as follows:

[0068] The formula for the charge state constraint is expressed as follows:

[0069] The formula for the charge / discharge state constraint is expressed as follows:

[0070] In the formula, For energy storage devices i Power at any point in time, For minimum power, This is the highest power output; For energy storage devices i Capacity at any point in time, For minimum capacity, Maximum capacity; For energy storage devices i exist t The charging and discharging power at any given time For efficiency, For time intervals, For energy storage devices i exist t- Capacity at time 1 For energy storage devices i exist t The capacity of a given moment; and These are the energy storage charging indicator and the energy release status indicator, respectively, both of which are 0-1 variables.

[0071] This embodiment sets power demand constraints to ensure that the power of the energy storage device covers the maximum governance gap, sets capacity demand constraints to meet the total capacity to be governed throughout the day, sets state of charge constraints to prevent overcharging and over-discharging, and sets charge and discharge state constraints to avoid simultaneous charging and discharging.

[0072] As a further preferred technical solution, step S40: based on the optimization model of energy storage configuration, solving for the optimal configuration parameters of energy storage, specifically includes the following steps: The optimization model is solved using the mixed integer linear programming algorithm (MILP) to obtain the optimal configuration parameters for energy storage.

[0073] Specifically, the process of solving the optimization model using the Mixed Integer Linear Programming (MILP) algorithm is as follows: Input data: Input the time series data of the energy storage power demand, capacity demand, and spatiotemporal complementary parameters of all nodes with insufficient governance obtained in the previous calculations into the optimization model.

[0074] Parameter settings: Set the weight coefficients (such as governance effect coefficient, penalty weight) and cost parameters (unit capacity cost a, unit power cost b) in the objective function.

[0075] Construct a mixed-integer linear programming model: Transform nonlinear relationships (such as the coupling between governance effects and energy storage output) into linear constraints using linearization techniques. Introduce constraints to ensure the model conforms to physical logic.

[0076] Iterative calculations are performed using a solver: The MILP solver is used to solve the model. The solver automatically traverses different energy storage configuration combinations and their operating strategies in the feasible solution space that satisfies all constraints (power, capacity, SOC, charge / discharge state), iteratively calculates the objective function value, and finally finds the global optimal solution or a high-quality feasible solution that minimizes the objective function, thereby outputting the optimal configuration parameters.

[0077] As a further preferred technical solution, after step S40: solving the optimization model using a mixed-integer linear programming algorithm to obtain the optimal configuration parameters for energy storage, the method further includes the following steps: Configure energy storage according to the optimal configuration parameters, recalculate the spatiotemporal complementary parameters of the nodes with insufficient governance, and verify the governance effect.

[0078] Specifically, in this embodiment, after configuring energy storage, the spatiotemporal complementary parameters of the nodes with insufficient governance are recalculated, and the recalculated spatiotemporal complementary parameters are verified to see if they reach the set target threshold. If they do, it means that the power governance effect has achieved the target. Otherwise, the difference between the recalculated spatiotemporal complementary parameters and the target threshold is used as a feedback signal and re-inputted into the optimization model. By adjusting the weight of the governance effect in the objective function or relaxing the economic constraints, the required additional energy storage power and capacity are directly calculated, and an expansion plan is generated.

[0079] The following example illustrates the configuration of energy storage in a typical distribution network: Reference Figure 2 and Figure 3 , Figure 2 This is typical photovoltaic power output data. Figure 3 and Figure 4 This is typical load data. Governance resources and loads are added to the IEEE 33-node network model. Governance resources include active power filters, static var generators (SVMs), and photovoltaic (PV) inverters. The output data for the PV inverters is as follows: Figure 2 China Photovoltaic Power Output 1. Simultaneously, add power to the distributed nodes. Figure 3 and Figure 4 Typical load data. Access location as follows: Figure 5 As shown in the table, APF stands for Active Power Filter, SVG for Static Var Generator, and PV for Photovoltaic Inverter. The resource capacity for pollution control is shown in Table 1. Table 1. Parameters and Access Locations of Governance Resources

[0080] refer to Figure 6 , Figure 6 The spatiotemporal complementary parameters for the typical distribution network governance nodes mentioned above are as follows: The spatial complementary parameters for most nodes are close to 1, indicating that the power quality issues at these nodes are well managed, and the remaining capacity of the power quality governance facilities can address these issues. However, some nodes have relatively low spatiotemporal complementary parameters, indicating that these nodes may have significant power quality problems, and current power quality governance equipment cannot effectively address these issues. Specifically, the spatiotemporal complementary parameters for nodes 25 and 31 are both below the set threshold for spatiotemporal complementary parameters. Figure 7 , Figure 7 A daily spatial complementarity parameter curve for nodes 25 and 31.

[0081] Then, the mixed-integer linear programming (MILP) algorithm is used to solve the optimization model. Tables 2 and 3 show the energy storage parameters calculated using spatiotemporal complementary parameters and the energy storage configuration parameters obtained after MILP optimization: Table 2. Verification of Energy Storage Configuration at Node 25

[0082] Table 3. Verification of Energy Storage Configuration at Node 31

[0083] After configuring energy storage, the spatiotemporal complementarity parameters of the nodes with insufficient energy management need to be recalculated to verify whether they have reached the set threshold. The management effect is then evaluated by comparing the changes in each power quality component before and after the management. After configuring energy storage with parameters calculated using the spatiotemporal complementarity parameters at nodes 25 and 31, new spatiotemporal complementarity parameters are obtained. After configuring energy storage with energy storage parameters associated with the spatiotemporal complementarity parameters, the spatiotemporal complementarity parameters of nodes 25 and 31 are both 1.

[0084] In addition, such as Figure 8 As shown, another embodiment of the present invention also proposes a distribution network energy storage configuration system, the system comprising: The node identification module 10 is used to identify nodes with insufficient governance based on the spatiotemporal complementary parameters of each governance demand node in the distribution network area. The demand calculation module 20 is used to determine the capacity to be addressed of the nodes with insufficient governance based on their spatiotemporal complementary parameters, and to calculate the power and capacity requirements of the energy storage device based on the capacity to be addressed of the nodes with insufficient governance. The model building module 30 is used to build an optimization model for energy storage configuration based on spatiotemporal complementary parameters and the power and capacity requirements of energy storage devices. The optimization model takes minimizing the total life cycle cost of energy storage as the objective function, based on the comparison of spatiotemporal complementary parameters before and after the treatment. The constraints of the optimization model include power demand constraints, capacity demand constraints, state of charge constraints, and state of charge / discharge constraints. Solver module 40 is used to solve for the optimal configuration parameters of energy storage based on the optimization model of energy storage configuration.

[0085] As a further preferred technical solution, the demand calculation module 20 specifically includes: The capacity to be addressed calculation unit is used to calculate the capacity to be addressed of the under-governed nodes based on the power capacity required to be compensated by the under-governed nodes in order to meet the power quality standards and the spatiotemporal complementary parameters corresponding to the under-governed nodes. The power calculation unit is used to determine the power requirement of the energy storage device based on the capacity to be addressed at the moment of maximum governance gap of the under-governed node. The capacity calculation unit is used to determine the capacity requirement of the energy storage device based on the total capacity of the nodes with insufficient capacity to be treated throughout the day.

[0086] As a further preferred technical solution, the power calculation unit is specifically used to perform the following steps: Based on the unmanaged capacity of each insufficient node within the management range of the energy storage device, calculate the unmanaged capacity at each time point within the management range of the energy storage device. The power requirement of the energy storage device is determined based on the capacity to be addressed at the moment of the maximum governance gap within the governance range of the energy storage device.

[0087] As a further preferred technical solution, the solving module 40 is specifically used for: The optimization model is solved using a mixed-integer linear programming algorithm to obtain the optimal configuration parameters for energy storage.

[0088] As a further preferred technical solution, the system also includes a verification module, specifically used for: Configure energy storage according to the optimal configuration parameters, recalculate the spatiotemporal complementary parameters of the nodes with insufficient governance, and verify the governance effect.

[0089] It should be noted that other embodiments or specific implementation methods of the distribution network energy storage configuration system described in this invention can refer to the above-mentioned method embodiments, and will not be repeated here.

[0090] Furthermore, the third embodiment of the present invention also proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the distribution network energy storage configuration method as described in the first embodiment above.

[0091] It should be noted that the computer-readable medium disclosed in this embodiment may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, and portable compact disk read-only memory (CD-ROM). ROM, optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0092] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform a zero-sample image anomaly detection method according to the above embodiments.

[0093] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server.

[0094] In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0095] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0096] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0097] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" or "several" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0098] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A power distribution grid energy storage configuration method, characterized in that, The method comprises the following steps: According to the space-time complementary parameters of each management demand node in the power distribution network region, the management demand node with a space-time complementary parameter lower than a set threshold is regarded as a management insufficient node; According to the space-time complementary parameters of the management insufficient node, the management capacity of the management insufficient node is determined, and the power demand and capacity demand of the energy storage device are calculated based on the management capacity of the management insufficient node; Based on the space-time complementary parameters and the power demand and capacity demand of the energy storage device, an optimization model for energy storage configuration is constructed, the optimization model takes minimizing the life cycle cost of the energy storage as an objective function based on the space-time complementary parameters before and after comparison of management, and the constraint conditions of the optimization model include power demand constraint, capacity demand constraint, state of charge constraint and charging and discharging state constraint; Based on the optimization model for energy storage configuration, the optimal configuration parameters of the energy storage are solved.

2. The power distribution grid energy storage configuration method of claim 1, wherein, Before the step of according to the space-time complementary parameters of each management demand node in the power distribution network region, the method further comprises the following steps: According to the harmonic component, negative sequence component and reactive component decoupled from the management demand node, the power quality management demand capacity of the management demand node is calculated; The residual capacity of each power quality management resource and the management range of each power quality management resource are quantified; Based on the residual capacity and management range of each power quality management resource and the power quality management demand capacity of the management demand node, the space-time complementary parameters of each management demand node are calculated.

3. The power distribution grid energy storage configuration method of claim 1, wherein, The step of according to the space-time complementary parameters of the management insufficient node, determining the management capacity of the management insufficient node, and calculating the power demand and capacity demand of the energy storage device based on the management capacity of the management insufficient node comprises the following steps: Based on the power capacity required to compensate for the management insufficient node to reach the power quality standard and the space-time complementary parameters corresponding to the management insufficient node, the management capacity of the management insufficient node is calculated; According to the management capacity of the management insufficient node at the maximum management gap time, the power demand of the energy storage device is determined; According to the sum of the management capacity of the management insufficient node throughout the day, the capacity demand of the energy storage device is determined.

4. The power distribution grid energy storage configuration method of claim 3, wherein, The step of according to the management capacity of the management insufficient node at the maximum management gap time, determining the power demand of the energy storage device comprises the following steps: According to the management capacity of each management insufficient node in the management range of the energy storage device, the management capacity of each time in the management range of the energy storage device is calculated; According to the management capacity of the management insufficient node at the maximum management gap time in the management range of the energy storage device, the power demand of the energy storage device is determined.

5. The power distribution grid energy storage configuration method of claim 3, wherein, The formula for calculating the management capacity of the management insufficient node is: wherein the power capacity needed to compensate for the deficient node k to reach the power quality standard, the power capacity needed to compensate for the deficient node k in t the spatiotemporal complementary parameter at the moment, to reach the power quality standard, t the deficient node at the moment k the capacity to be compensated for the deficient node.

6. The power distribution grid energy storage configuration method of claim 4, wherein, The power demand of the energy storage device is: In the formula, is an energy storage device i power at any point in time, is t energy storage device at the moment i to-be-managed capacity of the insufficiently managed node contained in the management range, is t insufficiently managed node at the moment k to-be-managed capacity of the insufficiently managed node, is the management range determined based on voltage sensitivity analysis.

7. The power distribution grid energy storage configuration method of claim 3, wherein, The formula for determining the capacity demand of the energy storage device according to the sum of the management capacity of the management insufficient node throughout the day is: In the formula, is an energy storage device i capacity at any point in time, is t energy storage device at a time i to-be-treated capacity of the insufficiently treated nodes contained in the governance range, is a time interval.

8. The power distribution grid energy storage configuration method of claim 1, wherein, The formula for the objective function is: In the formula, F is the total life cycle cost of energy storage; is the cost term, including investment cost and operation and maintenance cost; is the governance effect term, is the governance deficiency node k is the space-time complementary parameter before governance, is the space-time complementary parameter of the governance deficiency node k after configuring energy storage, is the set of governance deficiency nodes, is the penalty term; is the governance effect coefficient, is the penalty weight coefficient.

9. The power distribution grid energy storage configuration method of claim 1, wherein, The formula for the power demand constraint is: The formula for the capacity demand constraint is: The formula for the state of charge constraint is: The formula for the charging and discharging state constraint is: In the formula, For energy storage devices i Power at any point in time, For minimum power, This is the highest power output; For energy storage devices i Capacity at any point in time, For minimum capacity, Maximum capacity; for t Real-time energy storage device i The charging and discharging power, For efficiency, For time intervals, For energy storage devices i exist t- Capacity at time 1 For energy storage devices i exist t The capacity of a given moment; and These are the energy storage charging indicator and the energy release status indicator, respectively, both of which are 0-1 variables.

10. The power distribution grid energy storage configuration method of claim 1, wherein, The step of solving the optimal configuration parameters of the energy storage based on the optimization model for energy storage configuration comprises the following steps: The optimization model is solved by using a mixed integer linear programming algorithm to obtain the optimal configuration parameters of the energy storage.

11. The power distribution grid energy storage configuration method of claim 1, wherein, After solving the optimal configuration parameters of the energy storage based on the optimization model of the energy storage configuration, the method further comprises: According to the optimal configuration parameters of the energy storage, the energy storage is configured, and the spatiotemporal complementary parameters of the insufficient governance nodes are recalculated to verify the governance effect.

12. A power distribution grid energy storage configuration system, comprising: Comprise: A node identification module is configured to identify insufficient governance nodes according to the spatiotemporal complementary parameters of each governance demand node in the power distribution network region, and the governance demand nodes with spatiotemporal complementary parameters lower than a set threshold are identified as insufficient governance nodes; A demand calculation module is configured to determine the to-be-governed capacity of the insufficient governance nodes according to the spatiotemporal complementary parameters of the insufficient governance nodes, and to calculate the power demand and capacity demand of the energy storage device based on the to-be-governed capacity of the insufficient governance nodes; A model construction module is configured to construct an optimization model of energy storage configuration based on the spatiotemporal complementary parameters and the power demand and capacity demand of the energy storage device, and to minimize the full life cycle cost of the energy storage as an objective function based on the consideration of the spatiotemporal complementary parameters before and after comparative governance. The constraint conditions of the optimization model include power demand constraints, capacity demand constraints, state of charge constraints, and charging and discharging state constraints; A solving module is configured to solve the optimal configuration parameters of the energy storage based on the optimization model of the energy storage configuration.

13. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the power distribution network energy storage configuration method of any one of claims 1-11.

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