Flexible resource capacity optimization configuration method based on node marginal benefit tracing

Abstract

The application discloses a kind of based on the flexible resource capacity optimization configuration method of node marginal benefit traceability, the joint optimization model of node is constructed, and the trade-off relationship of investment cost, operating cost, reliability cost and environmental cost in optimization process is uniformly depicted, and combined with node marginal benefit and iterative increment algorithm, marginal value quantification of flexible resource can be realized at different nodes, different constructed capacity level, to support the systematic comparison of multi-node, multi-capacity combination scheme, not only can the optimal total capacity and node capacity optimal configuration matrix be obtained, the total amount optimal and node distribution optimal of resource can be simultaneously depicted, and the capacity increment vector sequence arranged in iteration order can be obtained, so that the reason of node selection, capacity benefit how to change can be clearly shown, the interpretability of optimization result is greatly improved, and data basis is provided for forming executable staged construction sequence.

Description

Technical Field

[0001] This invention relates to the field of power system planning and operation optimization technology, and in particular to a method and system for optimizing the allocation of flexible resource capacity based on node marginal benefit tracing, as well as electronic equipment and computer-readable storage media. Background Technology

[0002] With the rapid growth of new energy installed capacity, the randomness, volatility, and uncertainty faced by the power system in operation have significantly increased. To ensure power system security and high-proportion consumption of new energy, it is essential to rationally allocate flexible resources such as energy storage, peak-shaving power sources, and demand response. In actual engineering planning, planners generally focus on the following issues: 1) How much capacity should be allocated for each type of flexible resource to achieve economic optimization? 2) Which nodes should flexible resources be connected to, and how should their capacity be allocated among different nodes? 3) How to make a unified comparison of the system value of different flexible resources and different nodes? Existing flexible resource capacity optimization schemes typically include single-cost planning models aimed at minimizing investment or operating costs, economic evaluation methods for flexible resources based on experience or scenario comparisons, and planning models that indirectly reflect flexibility needs by setting constraints such as reserve rate and failure probability. However, these schemes do not uniformly characterize the trade-off between investment costs, operating costs, reliability costs, and environmental costs. The system value of flexible resources also lacks a clear physical definition and traceable source, making it difficult to simultaneously characterize the "total optimal" and "node allocation optimal" of flexible resources. They cannot support the systematic comparison of multi-node and multi-capacity combination schemes. Furthermore, existing technologies usually use joint optimization solutions, which can only provide the optimal capacity result at the end of the planning period. They cannot explain why each node is prioritized, how capacity benefits change with the construction scale, or form an executable phased construction sequence. Summary of the Invention

[0003] This invention provides a method and system for optimizing the allocation of flexible resource capacity based on node marginal benefit tracing, as well as electronic devices and computer-readable storage media. It uniformly characterizes the trade-offs between investment costs, operating costs, reliability costs, and environmental costs during the optimization process, and realizes the quantification of the marginal value of flexible resources at different nodes and different levels of existing capacity. This supports the systematic comparison of multi-node and multi-capacity combination schemes, and can simultaneously characterize the "optimal total amount" and "optimal node allocation" of resources. It can also clearly show the reasons for node selection and how capacity benefits change, greatly improving the interpretability of optimization results and providing a data foundation for forming an executable phased construction sequence.

[0004] According to one aspect of the present invention, a flexible resource capacity optimization allocation method based on node marginal benefit tracing is provided, comprising the following: Collect configuration data of the target power system; Based on the configuration data, a node-based joint optimization model is constructed; wherein, the optimization objective of the joint optimization model is to minimize the total system cost, which includes investment cost, operating cost, reliability cost and environmental cost; Define the marginal benefit of nodes and use an iterative allocation algorithm to iteratively solve the joint optimization model based on the marginal benefit of nodes until the iteration termination condition is met. Output the optimal total capacity of flexibility resources, the optimal node capacity configuration matrix, and the capacity allocation vector sequence arranged in the iteration order.

[0005] Furthermore, the formula for calculating the marginal benefit of the node is as follows: ; in, Represents the node capacity configuration matrix. Indicates the step size for capacity expansion. Represents the marginal benefit of a node, used to characterize the impact of a node's marginal benefit on its performance, assuming other conditions remain unchanged. n flexibility resources i If the actual configuration capacity increases by 1 unit, the optimal reduction in system operating losses is the amount by which this reduction occurs. Indicates only A capacity configuration identity matrix where all components are 1 and the rest are 0. Represents the configuration matrix of a given node capacity. Below, the optimal value of system operating loss excluding investment costs, , , , These represent the node capacity configuration matrices respectively. The optimal operating cost, optimal reliability cost, and optimal environmental cost under the given conditions. Represents the capacity configuration matrix for a given node. Add only nodes on the basis The system operating losses were obtained after capacity was increased and the operating plan was re-optimized.

[0006] Furthermore, the process of iteratively solving the joint optimization model based on the marginal benefits of nodes using the iterative allocation algorithm includes the following: Set the capacity increment granularity and the initial capacity values ​​for all nodes, and set the initial number of iterations to 0; With the current node capacity configuration matrix fixed, solve the operation optimization subproblem to obtain the optimal value of system operation loss under the current node capacity configuration matrix, and calculate the node marginal benefit and net marginal revenue of each candidate node. Select the candidate node with the largest net marginal revenue as the optimal node for this iteration. The iteration continues until the candidate node set is empty or the net marginal revenue of all candidate nodes is less than or equal to 0. The iteration stops, and the optimal total capacity of flexibility resources, the optimal node capacity configuration matrix, and the capacity increase vector sequence arranged in the iteration order are obtained.

[0007] Furthermore, the net marginal revenue for each candidate node is calculated based on the following formula: ; in, Indicates net marginal revenue, Indicates the marginal benefit of a node. Flexibility Resources i At the node n The annualized investment cost per unit capacity.

[0008] Furthermore, the objective function of the joint optimization model is: ; in, C total Indicates the total system cost. C inv Indicates investment cost, , F Represents a collection of flexible resources. N i Represents the set of candidate access nodes. Indicating flexibility resources i At the node n Annualized investment cost per unit capacity Indicating flexibility resources i At the node n Configuration capacity, C op Indicates operating costs, , Represents a set of scenes. Representing a scene The probability of the scenario Indicates the length of time. T Represents a time set, Indicates conventional units g The marginal cost of generating electricity, G This represents a collection of conventional generating units. p g,t,ω Indicates conventional units g of efforts, Indicates new energy power units r In the scene Next period The upper limit of available output, p r,t,ω Indicates new energy power units r In the scene Next period of efforts, Indicates new energy power units r The cost coefficient of abandoned electricity, R It represents a collection of new energy generating units. C rel Indicates reliability cost, , N Represents the set of busbars. Indicates the value of load loss. Represents a node n In the scene Next period The power of the load shedding C env Indicates environmental costs, , Indicates carbon price. Indicates conventional units g Emission factors.

[0009] Furthermore, it also includes the following: The priority sequence for flexibility resource development is determined based on the capacity expansion vector sequence.

[0010] In addition, the present invention also provides a flexible resource capacity optimization allocation system based on node marginal benefit tracing, comprising: The data acquisition module is used to collect configuration data of the target power system; The model building module is used to build a node-based joint optimization model based on the configuration data; wherein, the optimization objective of the joint optimization model is to minimize the total system cost, which includes investment cost, operating cost, reliability cost and environmental cost; The iterative solution module is used to define the marginal benefits of nodes and use an iterative allocation algorithm to iteratively solve the joint optimization model based on the marginal benefits of nodes until the iteration termination condition is met. It outputs the optimal total capacity of flexibility resources, the optimal configuration matrix of node capacity, and the sequence of capacity allocation vectors arranged in the iteration order.

[0011] Furthermore, it also includes: The priority determination module is used to determine the priority sequence for flexible resource construction based on the capacity expansion vector sequence.

[0012] In addition, the present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method described above by calling the computer program stored in the memory.

[0013] In addition, the present invention provides a computer-readable storage medium for storing a computer program for optimizing resource capacity allocation based on node marginal benefit tracing, wherein the computer program executes the steps of the method described above when running on a computer.

[0014] The present invention has the following beneficial effects: The flexible resource capacity optimization allocation method based on node marginal benefit tracing of this invention constructs a node-based joint optimization model, uniformly characterizing the trade-offs between investment costs, operating costs, reliability costs, and environmental costs during the optimization process. Furthermore, by combining node marginal benefits and iterative allocation algorithms, it can quantify the marginal value of flexible resources at different nodes and different existing capacity levels, supporting the systematic comparison of multi-node, multi-capacity combination schemes. It not only obtains the optimal total capacity and node capacity optimal allocation matrices, simultaneously characterizing both "optimal total resource allocation" and "optimal node allocation," but also generates a capacity allocation vector sequence arranged in iterative order. This clearly demonstrates the reasons for node selection and how capacity benefits change, greatly improving the interpretability of the optimization results and providing a data foundation for forming an executable phased construction sequence.

[0015] In addition, the flexible resource capacity optimization and allocation system based on node marginal benefit tracing of the present invention also has the above-mentioned advantages.

[0016] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0017] The accompanying drawings, which form part of this application, 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 of the invention. In the drawings: Figure 1 This is a flowchart illustrating a preferred embodiment of the flexible resource capacity optimization allocation method based on node marginal benefit tracing in this application. Figure 2 yes Figure 1 A schematic diagram of the sub-process of step S3; Figure 3 This is another flowchart illustrating the flexible resource capacity optimization allocation method based on node marginal benefit tracing according to a preferred embodiment of this application; Figure 4 This is a schematic diagram of the module structure of a flexible resource capacity optimization and allocation system based on node marginal benefit tracing, according to another embodiment of this application. Detailed Implementation

[0018] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] Reference Figure 1 A preferred embodiment of this application provides a flexible resource capacity optimization allocation method based on node marginal benefit tracing, including the following: Step S1: Collect configuration data of the target power system; Step S2: Construct a node-based joint optimization model based on the configuration data; wherein, the optimization objective of the joint optimization model is to minimize the total system cost, which includes investment cost, operating cost, reliability cost and environmental cost; Step S3: Define the marginal benefits of nodes, and use an iterative allocation algorithm to iteratively solve the joint optimization model based on the marginal benefits of nodes until the iteration termination condition is met. Output the optimal total capacity of flexibility resources, the optimal configuration matrix of node capacity, and the sequence of capacity allocation vectors arranged in the iteration order.

[0020] It is understood that the flexible resource capacity optimization allocation method based on node marginal benefit tracing in this embodiment constructs a node-based joint optimization model, uniformly characterizing the trade-offs between investment costs, operating costs, reliability costs, and environmental costs in the optimization process. Furthermore, by combining node marginal benefits and iterative allocation algorithms, it can quantify the marginal value of flexible resources at different nodes and different levels of existing capacity, thus supporting the systematic comparison of multi-node and multi-capacity combination schemes. It can not only obtain the optimal total capacity and node capacity optimal allocation matrix, but also simultaneously characterize the "optimal total amount" and "optimal node allocation" of resources. Moreover, it can obtain a capacity allocation vector sequence arranged in iterative order, thereby clearly showing the reasons for node selection and how capacity benefits change, greatly improving the interpretability of optimization results and providing a data foundation for forming an executable phased construction sequence.

[0021] In step S1, the target power system is a new type of power system primarily based on new energy sources, characterized by a high proportion of wind power, photovoltaic power, and other renewable energy sources connected to the grid. It ensures safe, economical, and low-carbon operation through the coordinated configuration of multiple flexible resources. Flexible resources refer to resources capable of adjusting the operating state of the power system in terms of power or energy, including but not limited to electrochemical energy storage, pumped storage, gas-fired peak-shaving units, and demand response resources. The collected configuration data specifically includes grid topology and network parameters, basic power source and load data, typical scenario set data, candidate sets of flexible resources and access conditions, and cost and value parameters (using a unified standard). The grid topology and network parameters include the bus set. Branch set (Line / Transformer Set), Line Suspension / Impedance, Thermal Stability Limit, Transformer Turns Ratio, Node Voltage Upper and Lower Limits ( and Parameters such as ) are used for power flow / transmission constraint modeling; power supply and load basic data include a set of conventional generating units. Including each conventional unit g Parameters such as rated capacity, minimum output, ramp rate, and start-stop cost of new energy vehicles. (Wind power / solar power, etc.), including each new energy source r Available output time series (divided by scenario), load set For example, the demand time series for each type of load (divided by scenario), where nodes During the period Scene The baseline load power is denoted as Typical scenario set data includes typical scenario sets. Each scenario probability ,satisfy For each scenario, the available inputs include power output and load level; the candidate set of flexible resources and access conditions include the flexible resource set. (Including energy storage (ES), demand response (DR), electric vehicle aggregation (EV), interruptible load (IL), etc.), candidate access node set (i.e., the set of nodes that can be connected to each type of flexible resource), and the maximum node access capacity. (i.e., flexibility resources) At the node (Maximum buildable capacity); cost parameters and value parameters include discount rate. Planning period in years Annualized investment cost per unit capacity (Unit: RMB / MW·year, or RMB / MWh·year), fuel cost and operation and maintenance cost parameters, carbon price. (Unit: Yuan / Ton), Loss of Load Value Time set (In units of hours / 15 minutes, etc., this invention uses hours as the unit), and time weighting. (That is, the number of hours represented by this period).

[0022] Furthermore, in step S2, after obtaining the configuration data of the target power system, a node-based joint optimization model is constructed to solve for the minimum overall system cost in terms of planned capacity and operating mode. To achieve systematic comparison of multi-node, multi-capacity combination schemes, this invention sets the following decision variables: 1) Node capacity variables, , Indicating flexibility resources At the node 1) Configuration capacity, in MW; if energy capacity, in MWh; 2) Operating variables, including output of conventional units: , p g,t,ω Indicates conventional units g The output of new energy sources: , p r,t,ω Indicates new energy power units r In the scene Next period of efforts, Indicates new energy power units r In the scene Next period 3) Maximum available output; 4) Loss of load: , For nodes In the scene Next period The power loss under load, in MW. This represents the corresponding baseline load demand; the power loss due to load change is... Calculated.

[0023] The objective function of the node-based joint optimization model constructed in this invention is: ; in, C total This represents the total system cost, used to characterize the sum of all economic costs incurred within a given planning period to meet electricity demand, system security, and environmental constraints. C inv Indicates investment cost, , F Represents a collection of flexible resources. N i Represents the set of candidate access nodes. Indicating flexibility resources i At the node n The annualized investment cost per unit capacity, expressed in yuan / MW·year or yuan / MWh·year. Indicating flexibility resources i At the node n Configuration capacity, in MW or MWh , C op This represents the operating cost (calculated by weighting the cost across different scenarios). , Represents a set of scenes. Representing a scene The probability of the scenario Indicates the length of time. T Represents a time set, Indicates conventional units g The marginal cost of generating electricity, expressed in yuan / MWh. G This represents a collection of conventional generating units. p g,t,ω Indicates conventional units g of efforts, Indicates new energy power units r In the scene Next period The upper limit of available output, p r,t,ω Indicates new energy power units r In the scene Next period of efforts, Indicates new energy power units r The cost factor for power curtailment, expressed in yuan / MWh. R It represents a collection of new energy generating units. This means the amount of electricity wasted. C rel Indicates reliability cost, , N Represents the set of busbars. The value of power outage is expressed in yuan / MWh and is used to characterize the socioeconomic losses caused by power outages. Represents a node n In the scene Next period The power of the load shedding This means there is a lack of power supply. C env Indicates environmental costs, , This indicates the carbon price, expressed in yuan / ton. Indicates conventional units g The emission factor is expressed in tons per MWh.

[0024] In addition, several key constraints were set for the joint optimization model, including node power balance constraints, line transmission constraints, upper limit constraints on node access capacity for flexibility resources, and resource operation and capacity coupling constraints. For the node power balance constraint, taking DC power flow as an example... , and Representing access nodes A collection of conventional generating units and a collection of new energy generating units. For flexibility resources At the node nThe equivalent net injection is such that energy storage discharge and demand response load reduction are positive, while charging is negative. For example, the equivalent net injection of energy storage satisfies... , Indicates discharge power. Indicates charging power, when At that time, it was stipulated , Indicates the line The trend It represents the elements of the node-branch association matrix and determines the positive and negative directions of the power flow according to a unified convention.

[0025] In addition, the line transmission constraint (thermal stability limit) can be expressed as: , Indicates the line l The capacity, in MW; the upper limit constraint on the node access capacity of flexible resources can be expressed as: , Indicating flexibility resources At the node n The maximum buildable capacity, expressed in MW or MWh.

[0026] In addition, resource operation and capacity coupling constraints include capacity-limited charging and discharging power: And energy balance constraints: , , This indicates the energy storage capacity, measured in MW. Energy storage capacity (MWh). These represent charging efficiency and discharging efficiency, respectively. Indicates the duration of time (in hours), used for converting power to electricity. N ES This represents the set of nodes that can be connected to energy storage. Indicates the energy state. When energy storage uses a fixed duration... When modeling, general capacity variables Refers to power capacity, i.e. and satisfy .

[0027] It is understandable that the optimization problem represented by the joint optimization model in step S2 is a typical linear programming problem, which can be solved using common joint programming algorithms, i.e. As a decision variable, with the goal of minimizing the overall system cost, the optimal node capacity under continuous capacity conditions is directly obtained. The optimal operating scheme and various costs are obtained. However, joint programming can only directly obtain the overall optimal final state, but it cannot output the construction sequence of capacity growth from zero to the final state, nor can it directly show the value source and diminishing returns trajectory of each capacity block. Therefore, in step S3, this invention defines the node marginal benefit and proposes an iterative allocation algorithm to iteratively solve the above joint optimization model based on the node marginal benefit.

[0028] In this invention, the marginal benefit of a node is defined as, under other conditions remaining unchanged, if the node... n flexibility resources i The amount by which the system operating loss can be reduced by 1 unit (MW or MWh) when the actual configured capacity increases (in yuan / year or yuan / discount period), which can also be called the node value. n Medium flexibility resources i The marginal value per unit capacity can be used to support economically optimal capacity criteria, node allocation, and construction priority ranking. System operating loss is the marginal reduction in the sum of operating costs, reliability costs, and environmental costs brought about by adding unit capacity; investment costs are not included in system operating loss. Given a node capacity configuration matrix... With the capacity of each node fixed and the operation plan re-optimized, the optimal system operating loss excluding investment costs is denoted as... Then the marginal benefit of a node can be expressed as: Then, using the finite difference method based on parameter perturbation, the node... n The node capacity configuration matrix after capacity expansion is as follows: Then the marginal benefit of a node can be approximated as: ,in, Represents the node capacity configuration matrix. This indicates the step size for capacity expansion, for example, set to 10MW or 50MW. Indicates the marginal benefit of a node. Indicates only A capacity configuration identity matrix where all components are 1 and the rest are 0. Represents the configuration matrix of a given node capacity. Below, the optimal value of system operating loss excluding investment costs, , , , These represent the node capacity configuration matrices respectively. The optimal operating cost, optimal reliability cost, and optimal environmental cost under the given conditions. Represents the capacity configuration matrix for a given node. Add only nodes on the basis The system operating loss is obtained after capacity adjustment and re-optimization of the operating plan. Additionally, parameter perturbations must satisfy... .

[0029] After defining the marginal benefits of nodes, the iterative allocation algorithm proposed in this invention is used for iterative solution, wherein, as... Figure 2 As shown, the process of iteratively solving the joint optimization model using the iterative allocation algorithm based on the marginal benefits of nodes includes the following: Step S31: Set the capacity increment granularity and the initial capacity values ​​of all nodes, and set the initial number of iterations to 0; Step S32: Fix the current node capacity configuration matrix, solve the running optimization subproblem, obtain the optimal value of system running loss under the current node capacity configuration matrix, and calculate the node marginal benefit and net marginal revenue of each candidate node. Select the candidate node with the largest net marginal revenue as the optimal node for this iteration. Step S33: Iterate continuously until the candidate node set is empty or the net marginal revenue of all candidate nodes is less than or equal to 0, then stop the iteration and obtain the optimal total capacity of flexibility resources, the optimal node capacity configuration matrix, and the capacity increase vector sequence arranged in the iteration order.

[0030] Specifically, first initialize the capacity of all nodes. Or, given a lower bound, let the iteration count be... And given the capacity to increase particle size .

[0031] Then, the iteration process is initiated, fixing the current node capacity configuration matrix. Solving the operational optimization subproblem, that is, minimizing the objective function after removing the investment cost, yields the following results: , , Optimal running variables, and , , and Based on the above formula for calculating the marginal benefit of nodes, the marginal benefit of each candidate node is calculated, wherein the candidate node must satisfy... ,get Then, the net marginal revenue for each candidate node is calculated based on the following formula: ,in, Indicates net marginal revenue, Indicates the marginal benefit of a node. Flexibility Resources i At the node n The annualized investment cost per unit capacity, if ,Right now A value greater than 0 indicates that it is worthwhile to further increase the flexibility resource capacity of this node, because the marginal benefit outweighs the marginal cost. ,Right now <0 means that it is not cost-effective to continue increasing the flexibility resource capacity of this node. When the capacity is continuous and has not reached the upper or lower limit, the economically optimal boundary point satisfies approximately: When using discrete capacity granularity, the stopping criterion is "the marginal value of the last accepted capacity block is higher than the cost, and the marginal value of the next capacity block is not higher than the cost." Then, the candidate node with the largest net marginal benefit is selected as the optimal node for this iteration. , ( i , n ) indicates the first k Selecting the optimal node in rounds of iteration n Optimize flexibility resources i Capacity expansion is carried out, and the amount of capacity expansion is the granularity of capacity expansion. , , indicating the first The set of candidate nodes that can still be increased in capacity during the next iteration.

[0032] Iterate continuously until Empty, or Stop iterating; the final capacity configuration is the economically optimal solution. Output the economically optimal node capacity for each node. Economically optimal total capacity and the sequence of capacity expansion vectors arranged in iterative order. , among which, the k The selected capacity block of the wheel can be represented as All capacity blocks are sorted sequentially according to the iteration order to generate a capacity expansion vector sequence. For each capacity block, the following information can be recorded synchronously: 1) The resource type, access node, and its net marginal benefit comparison with other candidate locations; this capacity block is prioritized because its current net marginal benefit is the largest in the feasible candidate set, which answers the question "Why build this node first"; 2) The gross marginal value of this capacity block. 1) Quantifying the system value brought by new capacity; 2) Operational, reliability, and environmental value components, used to explain the source of system value; 3) Current capacity level, value of the previous capacity block, and value of this capacity block, used to demonstrate the process of diminishing marginal returns; 4) Unit investment cost and net marginal return. 6) The cumulative construction capacity, cumulative investment and cumulative system cost reduction are used to form phased implementation recommendations under funding constraints.

[0033] It is understood that the essence of the iterative capacity expansion algorithm proposed in this invention is to reselect the capacity block with the largest net marginal benefit after each update of the system state. The sorting basis of the capacity expansion vector sequence is not a static ranking formed all at once in the initial state, but a dynamic priority after considering the interaction of existing capacity, network constraints and resources. This can clearly show the reasons for node selection and how capacity benefits change, greatly improving the interpretability of the optimization results and providing a data foundation for forming an executable phased construction sequence.

[0034] Optional, such as Figure 3 As shown, the flexible resource capacity optimization allocation method based on node marginal benefit tracing also includes the following: Step S4: Determine the priority sequence for flexibility resource construction based on the capacity expansion vector sequence.

[0035] It is understandable that, under conditions of limited funds, for example, a certain construction phase may have an available amount equivalent to the investment cost. At that time, the capacity expansion vector sequence can be used. As a priority sequence for flexible resource construction, capacity blocks are sequentially extracted from the sequence for flexible resource construction until the cumulative cost exceeds a certain threshold after the addition of a certain capacity block. However, in the case of phased implementation, for example, multiple phases may use different quotas. In this case, the capacity expansion vector sequence can be divided into multiple construction batches, as long as the total cost of each construction batch does not exceed the quota of the corresponding stage. This allows for the phased implementation of capacity blocks. In addition, after each phase of construction is completed, the actual built capacity should be used as the new capacity. The system re-executes marginal iterations based on updated load, new energy sources, equipment costs, and network conditions, and reorders the capacity blocks that have not yet been built. Therefore, the construction sequence can both maintain the traceability of the selection basis at each step and adapt to changes in planning conditions.

[0036] In addition, the cost sources of the marginal benefits of nodes can be decomposed, and the nodes... The marginal benefit of a node can be decomposed into: When increasing capacity and re-optimizing sub-problems at each node, the individual costs are recorded simultaneously. , and Then, we can calculate them separately: , , ,in, This indicates the marginal contribution to reducing operating costs (such as reducing fuel consumption, curtailing electricity, etc.). This indicates the marginal contribution to reducing reliability costs (such as reducing load shedding). This indicates the marginal contribution to reducing environmental costs (such as the cost of reducing carbon emissions).

[0037] In addition, to verify the effectiveness of the flexible resource capacity optimization allocation method of the present invention, this application also conducted simulation verification and constructed a simplified 8-node power grid example. The example adopted a DC power flow model with line phase angle and transmission limit, set a typical 24-hour day and four types of wind and solar load scenarios, and calculated the annual cost according to the scenario probability. The candidate resources include energy storage ES with a fixed energy duration of 2 hours, and demand response DR available from 17:00 to 22:00 without energy replenishment. The load loss value is taken as 30,000 yuan / MWh, the carbon price is taken as 120 yuan / ton, the cost of new energy curtailment is taken as 80 yuan / MWh, and the capacity expansion step size is taken as 50MW. Two optimization schemes were designed: 1) Inflexibility-free baseline scheme: set all flexibility resource capacity to 0, and solve for system operating cost, reliability cost and environmental cost; 2) Marginal iteration scheme of the present invention: starting from zero capacity, apply 50MW capacity perturbation to each candidate node in sequence, calculate the difference between marginal benefit and annualized investment cost per unit capacity, select the candidate node with the largest net marginal benefit to add, until the net marginal benefit of all candidate nodes that can be added is not greater than 0. The economic capacity configuration obtained by the marginal iteration is as follows: (1) Energy storage ES: Node 8 is configured with 500MW, Node 5 with 500MW, Node 4 with 200MW, and the remaining candidate nodes are 0, for a total of 1200MW; (2) Demand response DR: Node 2 with 350MW, Node 3 with 300MW, Node 1 with 100MW, and the remaining candidate nodes are 0, for a total of 750MW; In addition to the above final capacity, the marginal iteration scheme also retains the selection order and marginal value record of each 50MW capacity block. This record can explain why a certain node is selected first in a certain stage, how much the system cost can be reduced by the capacity block, which parts of the value are composed of operation improvement, reliability improvement and emission reduction, and how the benefits decrease when the same flexibility resources are further increased. When the stage funds are insufficient to complete the entire economic capacity at once, the high net benefit capacity blocks at the front of the sequence can be implemented first in this order, instead of simply reducing the construction scale of each node proportionally according to the final capacity. In addition, the results of the two optimization schemes are compared as shown in Table 1: Table 1. Comparison results of the two optimization schemes ; As shown in Table 1, compared to the baseline scheme without flexibility, the marginal iterative scheme of this invention, although increasing investment costs by RMB 874 million / year, reduces operating costs by RMB 1.18 billion / year, reliability costs by RMB 531 million / year, and environmental costs by RMB 260 million / year, resulting in a net reduction of RMB 1.097 billion / year in total system costs. Simultaneously, the renewable energy curtailment rate decreases from 6.8% to 2.1%, EENS decreases from 21.3 GWh / year to 3.6 GWh / year, and carbon dioxide emissions decrease from 29.80 Mt / year to 27.63 Mt / year. Therefore, the simulation results demonstrate that the iterative configuration based on node marginal benefit tracing can determine the economic capacity and access location of flexible resources while taking investment costs into account, significantly reducing overall system costs, load shedding risk, renewable energy curtailment, and carbon emissions. The marginal value decomposition and stopping conditions simultaneously ensure the economic efficiency and traceability of the configuration results.

[0038] In addition, such as Figure 4 As shown, another embodiment of the present invention also provides a flexible resource capacity optimization allocation system based on node marginal benefit tracing, comprising: The data acquisition module is used to collect configuration data of the target power system; The model building module is used to build a node-based joint optimization model based on the configuration data; wherein, the optimization objective of the joint optimization model is to minimize the total system cost, which includes investment cost, operating cost, reliability cost and environmental cost; The iterative solution module is used to define the marginal benefits of nodes and use an iterative allocation algorithm to iteratively solve the joint optimization model based on the marginal benefits of nodes until the iteration termination condition is met. It outputs the optimal total capacity of flexibility resources, the optimal configuration matrix of node capacity, and the sequence of capacity allocation vectors arranged in the iteration order.

[0039] It is understood that the flexible resource capacity optimization and allocation system based on node marginal benefit tracing in this embodiment constructs a node-based joint optimization model, uniformly characterizing the trade-offs between investment costs, operating costs, reliability costs, and environmental costs in the optimization process. Furthermore, by combining node marginal benefits and iterative allocation algorithms, it can quantify the marginal value of flexible resources at different nodes and different levels of existing capacity, thus supporting the systematic comparison of multi-node and multi-capacity combination schemes. It can not only obtain the optimal total capacity and node capacity optimal allocation matrix, but also simultaneously characterize the "optimal total amount" and "optimal node allocation" of resources. Moreover, it can obtain a capacity allocation vector sequence arranged in iterative order, thereby clearly showing the reasons for node selection and how capacity benefits change, greatly improving the interpretability of optimization results and providing a data foundation for forming an executable phased construction sequence.

[0040] In addition, the flexible resource capacity optimization allocation system based on node marginal benefit tracing also includes: The priority determination module is used to determine the priority sequence for flexible resource construction based on the capacity expansion vector sequence.

[0041] It is understood that each module of this system embodiment corresponds to each step of the above method embodiment. Therefore, the specific working principle of each module will not be repeated here, and you can refer to the steps of the above method embodiment.

[0042] In addition, another embodiment of the present invention provides an electronic device including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method described above by calling the computer program stored in the memory.

[0043] In addition, another embodiment of the present invention provides a computer-readable storage medium for storing a computer program for optimizing resource capacity allocation based on node marginal benefit tracing, wherein the computer program executes the steps of the method described above when running on a computer.

[0044] Common computer-readable storage media include: floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, any other optical media, punch cards, paper tape, any other physical media with perforated patterns, random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), flash erasable programmable read-only memory (FLASH-EPROM), any other memory chips or cartridges, or any other media readable by a computer. Instructions may further be transmitted or received by a transmission medium. The term transmission medium can include any tangible or intangible medium used to store, encode, or carry instructions for execution by a machine, and includes digital or analog carrier communication signals or intangible media that facilitate communication of such instructions. Transmission media include coaxial cables, copper wires, and optical fibers, which contain conductors for transmitting a bus of computer data signals.

[0045] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0046] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0047] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0048] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0049] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0050] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0051] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A flexible resource capacity optimization allocation method based on node marginal benefit tracing, characterized in that, Includes the following: Collect configuration data of the target power system; Based on the configuration data, a node-based joint optimization model is constructed; wherein, the optimization objective of the joint optimization model is to minimize the total system cost, which includes investment cost, operating cost, reliability cost and environmental cost; Define the marginal benefit of nodes and use an iterative allocation algorithm to iteratively solve the joint optimization model based on the marginal benefit of nodes until the iteration termination condition is met. Output the optimal total capacity of flexibility resources, the optimal node capacity configuration matrix, and the capacity allocation vector sequence arranged in the iteration order.

2. The flexible resource capacity optimization allocation method based on node marginal benefit tracing as described in claim 1, characterized in that, The formula for calculating the marginal benefit of the node is as follows: ； in, Represents the node capacity configuration matrix. Indicates the step size for capacity expansion. Represents the marginal benefit of a node, used to characterize the impact of a node's marginal benefit on its performance, assuming other conditions remain unchanged. n flexibility resources i If the actual configuration capacity increases by 1 unit, the optimal reduction in system operating losses is the amount by which this reduction occurs. Indicates only A capacity configuration identity matrix where all components are 1 and the rest are 0. Represents the configuration matrix of a given node capacity. Below, the optimal value of system operating loss excluding investment costs, , , , These represent the node capacity configuration matrices respectively. The optimal operating cost, optimal reliability cost, and optimal environmental cost under the given conditions. Represents the capacity configuration matrix for a given node. Add only nodes on the basis The system operating losses were obtained after capacity was increased and the operating plan was re-optimized.

3. The flexible resource capacity optimization allocation method based on node marginal benefit tracing as described in claim 2, characterized in that, The process of iteratively solving the joint optimization model using the iterative allocation algorithm based on the marginal benefits of nodes includes the following: Set the capacity increment granularity and the initial capacity values ​​for all nodes, and set the initial number of iterations to 0; With the current node capacity configuration matrix fixed, solve the operation optimization subproblem to obtain the optimal value of system operation loss under the current node capacity configuration matrix, and calculate the node marginal benefit and net marginal revenue of each candidate node. Select the candidate node with the largest net marginal revenue as the optimal node for this iteration. The iteration continues until the candidate node set is empty or the net marginal revenue of all candidate nodes is less than or equal to 0. The iteration stops, and the optimal total capacity of flexibility resources, the optimal node capacity configuration matrix, and the capacity increase vector sequence arranged in the iteration order are obtained.

4. The flexible resource capacity optimization allocation method based on node marginal benefit tracing as described in claim 3, characterized in that, The net marginal revenue for each candidate node is calculated based on the following formula: ； in, Indicates net marginal revenue, Indicates the marginal benefit of a node. Flexibility Resources i At the node n The annualized investment cost per unit capacity.

5. The flexible resource capacity optimization allocation method based on node marginal benefit tracing as described in claim 1, characterized in that, The objective function of the joint optimization model is: ； in, C total Indicates the total system cost. C inv Indicates investment cost, , F Represents a collection of flexible resources. N i Represents the set of candidate access nodes. Indicating flexibility resources i At the node n Annualized investment cost per unit capacity Indicating flexibility resources i At the node n Configuration capacity, C op Indicates operating costs, , Represents a set of scenes. Representing a scene The probability of the scenario Indicates the length of time. T Represents a time set, Indicates conventional units g The marginal cost of generating electricity, G This represents a collection of conventional generating units. p g,t,ω Indicates conventional units g of efforts, Indicates new energy power units r In the scene Next period The upper limit of available output, p r,t,ω Indicates new energy power units r In the scene Next period of efforts, Indicates new energy power units r The cost coefficient of abandoned electricity, R It represents a collection of new energy generating units. C rel Indicates reliability cost, , N Represents the set of busbars. Indicates the value of load loss. Represents a node n In the scene Next period The power of the load shedding C env Indicates environmental costs, , Indicates carbon price. Indicates conventional units g Emission factors.

6. The flexible resource capacity optimization allocation method based on node marginal benefit tracing as described in claim 1, characterized in that, Also includes the following: The priority sequence for flexibility resource development is determined based on the capacity expansion vector sequence.

7. A flexible resource capacity optimization allocation system based on node marginal benefit tracing, characterized in that, include: The data acquisition module is used to collect configuration data of the target power system; The model building module is used to build a node-based joint optimization model based on the configuration data; wherein, the optimization objective of the joint optimization model is to minimize the total system cost, which includes investment cost, operating cost, reliability cost and environmental cost; The iterative solution module is used to define the marginal benefits of nodes and use an iterative allocation algorithm to iteratively solve the joint optimization model based on the marginal benefits of nodes until the iteration termination condition is met. It outputs the optimal total capacity of flexibility resources, the optimal configuration matrix of node capacity, and the sequence of capacity allocation vectors arranged in the iteration order.

8. The flexible resource capacity optimization allocation system based on node marginal benefit tracing as described in claim 7, characterized in that, Also includes: The priority determination module is used to determine the priority sequence for flexible resource construction based on the capacity expansion vector sequence.

9. An electronic device, characterized in that, The method includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method as described in any one of claims 1 to 6 by calling the computer program stored in the memory.

10. A computer-readable storage medium for storing a computer program for optimizing resource capacity allocation based on node marginal benefit tracing, characterized in that, The computer program, when run on a computer, performs the steps of the method as described in any one of claims 1 to 6.

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