A planning scheme comprehensive performance evaluation method and system based on grid load rate and power utilization hours

Abstract

The application is suitable for the technical field of power grid planning, and provides a planning scheme comprehensive performance evaluation method and system based on power grid load rate and power supply utilization hours, which comprises the following steps: obtaining power grid topology data of a candidate planning scheme and identifying the topology structure type thereof, obtaining hourly load rate sequences of each line and hourly output sequences of each generator set through annual time sequence production simulation; calling a pre-constructed performance evaluation template, calculating power grid load performance indexes by using heavy load determination logic, distinguishing the hourly output sequences into effective power supply output and limited output, attributing the limited output to network transmission constraints and system peak regulation constraints, and obtaining power supply utilization performance indexes containing performance loss causes; and according to a performance aggregation level path, merging and aggregating the above indexes as bottom layer parameters layer by layer to obtain a system level comprehensive performance value and output a scheme ranking result. The comprehensive performance evaluation accuracy of the planning scheme and the reliability of scheme comparison and selection are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of power grid planning technology, and in particular to a method and system for comprehensive performance evaluation of planning schemes based on power grid load rate and power supply utilization hours. Background Technology

[0002] With the continuous increase in the proportion of renewable energy generation, power system planning faces profound changes in power supply structure and grid operation characteristics. Existing evaluation methods for planning schemes typically aim to minimize the total life-cycle investment cost, determining feasibility through verification under typical operating modes. Specifically, several typical load scenarios are first set up, and the line power flow distribution and unit start-up methods under each scenario are calculated to verify whether the line load exceeds the thermal stability limit and whether the node voltage meets operational requirements. Then, the total investment cost and annual operating cost of the scheme are calculated, using net present value or equivalent annual value as the basis for scheme comparison. For considerations regarding renewable energy consumption, the renewable energy curtailment rate is usually used as a constraint; controlling the curtailment rate below a preset threshold is considered a feasible scheme.

[0003] Existing evaluation methods only focus on investment costs and safety constraint verification under typical scenarios, failing to predict grid load balance and power utilization efficiency during the full-time operation of the scheme at the planning stage. This is because typical operating modes cannot cover the continuously changing load and renewable energy output combinations throughout the 8760 hours of the year. The differences between heavy and light load periods, and the temporal coupling relationship between renewable energy output fluctuations and load changes, are averaged or ignored. This leads to problems such as frequent heavy loads on local lines and significant deviations in the utilization hours of some units from design values ​​after the planned scheme is put into operation, resulting in a large discrepancy between the actual performance of the planned scheme during the operation phase and the planning expectations. Summary of the Invention

[0004] This invention provides a method and system for comprehensive performance evaluation of planning schemes based on grid load rate and power utilization hours, which is used to solve the problem that there is a large deviation between the actual performance of the planning scheme and the planning expectation during the operation phase.

[0005] The first aspect of this invention provides a method for comprehensive performance evaluation of planning schemes based on grid load rate and power supply utilization hours, comprising: Obtain power grid topology data of candidate planning schemes and identify the topology type corresponding to the power grid topology data; The candidate planning schemes are simulated throughout the year to obtain the hourly load rate sequence of each line and the hourly output sequence of each generator unit. Retrieve a pre-built performance evaluation template that matches the topology type. The performance evaluation template defines the overload determination logic and the performance aggregation hierarchical path from the device layer to the system layer. Based on the heavy load determination logic, the power grid load efficiency index is calculated using the hourly load rate sequence; the hourly output sequence of each generator set is divided into effective power supply output and limited output, and the limited output is attributed to a variety of predefined constraint types to obtain a power utilization efficiency index that includes the causes of power efficiency loss. According to the efficiency aggregation hierarchy path, the grid load efficiency index and the power utilization efficiency index are used as the bottom-level equipment-level efficiency parameters, and are aggregated layer by layer upwards until the system-level comprehensive efficiency value that characterizes the overall efficiency of the candidate planning scheme is obtained. Based on the system-level comprehensive performance value of each candidate planning scheme, the ranking of scheme performance is output.

[0006] Furthermore, obtaining the hourly load rate sequence for each line includes: Based on the line impedance parameters in the power grid topology data, a power transfer distribution factor matrix is ​​generated. Based on the pre-output plan of the generator units and the predicted node load in the candidate planning scheme, a node injection power vector is formed for each simulation period. Based on the node injected power vector and the power transfer distribution factor matrix, the active power transmission of each line in each simulation period is determined. Based on the ratio of active transmission power to rated capacity of each line, the load rate of each line in each simulation period is determined, and the load rate sequence is formed by combining them in time sequence.

[0007] Furthermore, obtaining the hourly output sequence of each generator unit includes: A safety-constrained unit combination model is constructed with the goal of minimizing system operating costs. The safety-constrained unit combination model includes line power flow constraints, which are expressed by the linear sensitivity relationship between the node injected power vector and the line transmitted power. Obtain the hourly load forecast curve and the renewable energy output forecast curve for the whole year. For each simulation period, solve the safety constraint unit combination model to obtain the output plan of each conventional generator unit and the renewable energy curtailment power caused by network constraints. The actual output of each new energy unit is determined based on the new energy power output prediction curve and the new energy curtailment power. The power output plans of each conventional generator set and the actual power output of each new energy generator set are arranged in time sequence to obtain the hourly power output sequence of all generator sets.

[0008] Furthermore, the retrieval of a pre-built performance evaluation template matching the topology type includes: Obtain historical power grid operation data corresponding to the topology type, and extract overload determination parameters and efficiency aggregation path information based on the historical power grid operation data; The overload determination logic is generated based on the overload determination parameters, and the performance aggregation hierarchical path is determined based on the performance aggregation path information. The overload determination logic and the performance aggregation hierarchical path are combined to generate a pre-built performance evaluation template that matches the topology type.

[0009] Furthermore, the calculation of the power grid load efficiency index based on the overload determination logic and the hourly load rate sequence includes: Based on the overload determination threshold defined in the overload determination logic, the proportion of overload periods exceeding the overload determination threshold in the hourly load rate sequence is statistically analyzed. Based on the key performance sections defined in the overload determination logic, the load rate of each line within the key performance section is extracted, and the load balance of the key performance section is calculated. The proportion of heavy-load periods and the load balance are used as indicators of power grid load efficiency.

[0010] Furthermore, the step of distinguishing the hourly output sequence of each generator set into effective power supply output and limited output includes: For new energy generator sets, the new energy output prediction curve is obtained, the actual output of each time period in the hourly output sequence is determined as the effective power supply output, and the difference between the predicted output and the actual output of the corresponding time period in the new energy output prediction curve is determined as the restricted output. For conventional generator sets, the output plan output by the safety-constrained generator set combination model is obtained. The output that meets the load demand and the demand for renewable energy consumption in the output plan is determined as the effective power supply output, and the output reduction caused by the system peak-shaving demand is determined as the restricted output.

[0011] Furthermore, attributing the limited output to a variety of predefined constraint types to obtain a power utilization efficiency index that includes the causes of power efficiency loss includes: The various constraint types include network transmission constraints and system peak shaving constraints; Based on the boundary state of the line power flow constraints at each time period, the limited output of new energy caused by the line power flow reaching the transmission limit is attributed to the network transmission constraints. Based on the relationship between system load and peak-shaving capacity in each time period, the reduction in output of conventional generator units due to insufficient system peak-shaving capacity is attributed to the aforementioned system peak-shaving constraints. The total amount of restricted power corresponding to each constraint type is calculated, and a power utilization efficiency index containing the attribution results of each constraint type is generated.

[0012] Furthermore, the process of aggregating the grid load performance index and the power utilization performance index as bottom-level equipment-level performance parameters according to the performance aggregation hierarchy path, and then merging and aggregating them layer by layer upwards until a system-level comprehensive performance value representing the overall performance of the candidate planning scheme is obtained, includes: Identify the lowest-level device node defined in the performance aggregation hierarchy path, and associate the grid load performance index and the power utilization performance index with the corresponding device node as performance parameters of the device node; Based on the hierarchical relationship between device nodes and their parent system nodes in the performance aggregation hierarchy path, the performance parameters of each device node under the same parent system node are weighted and aggregated to obtain the performance value of each parent system node. Each of the higher-level system nodes is treated as a new lower-level node, and the weighted aggregation process is repeated until the root node of the performance aggregation hierarchical path is reached. The performance value of the root node is then used as the overall system-level performance value.

[0013] Furthermore, the weighted aggregation of performance parameters of each device node under the same upper-level system node includes: Obtain the aggregation weights defined in the performance aggregation hierarchy path. The aggregation weights are determined based on the capacity ratio or sensitivity factor of each device node in the upper-level system node. Based on the aggregation weight, the performance parameters of each device node are weighted and summed to obtain the performance value of the upper-level system node.

[0014] A second aspect of the present invention provides a comprehensive performance evaluation system for planning schemes based on grid load rate and power supply utilization hours, comprising: The topology type identification module is used to acquire power grid topology data of candidate planning schemes and identify the topology type corresponding to the power grid topology data; The hourly load rate sequence and hourly output sequence determination module is used to perform a year-round time-series production simulation on the candidate planning scheme to obtain the hourly load rate sequence of each line and the hourly output sequence of each generator unit. The performance evaluation template retrieval module is used to retrieve a pre-built performance evaluation template that matches the topology type. The performance evaluation template defines the overload judgment logic and the performance aggregation hierarchical path from the device layer to the system layer. The power grid load efficiency index and power utilization efficiency index determination module is used to calculate the power grid load efficiency index based on the heavy load determination logic and the hourly load rate sequence; to classify the hourly output sequence of each generator set into effective power supply output and limited output, and to attribute the limited output to a variety of predefined constraint types, so as to obtain the power utilization efficiency index containing the causes of power efficiency loss. The system-level comprehensive performance value determination module is used to merge and aggregate the grid load performance index and the power utilization performance index as the bottom-level equipment-level performance parameters according to the performance aggregation hierarchical path, layer by layer, until the system-level comprehensive performance value representing the overall performance of the candidate planning scheme is obtained. The scheme effectiveness ranking result output module is used to output the scheme effectiveness ranking result based on the system-level comprehensive effectiveness value of each candidate planning scheme.

[0015] As can be seen from the above technical solutions, the present invention has the following advantages: This invention first acquires the power grid topology data of candidate planning schemes and identifies their topology types. Through year-round time-series production simulation, it obtains the hourly load rate sequence of each line and the hourly output sequence of each generator unit. Then, it retrieves a pre-built efficiency evaluation template that matches the topology type, uses the overload judgment logic defined in the template to calculate the power grid load efficiency index, and distinguishes the hourly output sequence into effective power supply output and constrained output. The constrained output is attributed to network transmission constraints and system peak-shaving constraints to obtain a power utilization efficiency index that includes the causes of efficiency loss. Finally, according to the efficiency aggregation hierarchy path defined in the template, the above indicators are merged and aggregated layer by layer as bottom parameters to obtain the system-level comprehensive efficiency value and output the scheme ranking result accordingly. Compared with existing technologies, this invention achieves differentiated performance evaluation for different power grid structures by introducing a topology-adaptive performance evaluation template; it captures overload periods and load imbalance issues that typical verification methods cannot detect through year-round time-series simulation and heavy load determination logic; it accurately locates and traces the causes of power supply performance losses by distinguishing between effective and limited output and attributing multiple types of constraints; and it systematically integrates scattered equipment-level indicators into a comprehensive performance value that reflects the overall merits of the scheme through a hierarchical performance aggregation path. This invention enables planning schemes to fully reveal their grid load adaptability and power supply utilization efficiency under full-time operating conditions before commissioning, effectively improving the accuracy of comprehensive performance evaluation of planning schemes and the credibility of scheme comparison. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a comprehensive performance evaluation method for planning schemes based on grid load rate and power utilization hours in this invention. Figure 2This is a flowchart illustrating the process of determining the hourly load rate sequence of each line in this invention. Figure 3 This is a flowchart illustrating the process of determining the hourly output sequence of each generator set in this invention. Figure 4 This is a schematic diagram of the process for retrieving a pre-constructed performance evaluation template in this invention; Figure 5 This is a schematic diagram of the process for calculating the power grid load efficiency index in this invention; Figure 6 This is a flowchart illustrating the process of determining power utilization efficiency indicators in this invention. Figure 7 This is a schematic diagram of the process of merging and aggregating device-level indicators layer by layer upwards in this invention. Detailed Implementation

[0017] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “corresponding to,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0018] Example 1 Please see Figure 1 The comprehensive performance evaluation method for planning schemes based on grid load rate and power utilization hours provided in this application includes the following steps: S1. Obtain the power grid topology data of the candidate planning schemes and identify the topology type corresponding to the power grid topology data; Candidate planning schemes are pre-formulated by planners based on regional load growth forecasts, power generation construction plans, and equipment decommissioning plans. Each candidate scheme includes the start and end nodes, voltage levels, line types, and lengths of new transmission lines; the grid connection nodes, unit types, and installed capacities of new generating units; and a list of units or lines to be decommissioned. When obtaining the grid topology data for the candidate planning schemes, the grid topology data is extracted from the planning scheme design documents. The grid topology data includes node data and branch data. Node data includes the names, voltage levels, power supply zones, and load levels of each substation and power plant. Branch data includes the start and end nodes, line impedance parameters, rated capacity, and line length of each transmission line.

[0019] Based on the node connections and power supply paths in the power grid topology data, this study analyzes the network connectivity and power supply reliability characteristics, classifying the topology into one of the following types: radial structure, ring network structure, and multi-terminal interconnection structure. A radial structure refers to a network structure that radiates outwards from a single power source and has no closed loops; a ring network structure refers to a network structure with one or more closed loops, where any load node has at least two power supply paths; and a multi-terminal interconnection structure refers to a network structure where three or more power sources are interconnected through transmission channels, and each power source has the ability to exchange power.

[0020] By using the above method, candidate planning schemes are given a clear topology type identifier, so that a pre-built performance evaluation template matching the topology type can be retrieved later.

[0021] S2. Perform a full-year time-series production simulation on the candidate planning schemes to obtain the hourly load rate sequence of each line and the hourly output sequence of each generator unit; The annual time-series production simulation uses a year as its cycle, dividing the year into multiple equally spaced time periods. It simulates the output of each generating unit and the transmission power of each line in the power system for each time period, obtaining operational status data covering a continuous time segment throughout the year. This annual time-series production simulation can completely preserve the temporal characteristics of load changes and fluctuations in renewable energy output, as well as the temporal coupling relationship between the two, providing a continuous and complete operational data foundation for the refined evaluation of grid load balance and power utilization efficiency.

[0022] Please see Figure 2 The hourly load rate sequence of each line is obtained, including the following: S211. Generate a power transfer distribution factor matrix based on the line impedance parameters in the power grid topology data; S212. Based on the pre-output plan of generator units and the predicted node load in the candidate planning scheme, form the node injection power vector for each simulation period; S213. Based on the node injected power vector and the power transfer distribution factor matrix, determine the active power transmission of each line in each simulation period; S214. Based on the ratio of active transmission power to rated capacity of each line, determine the load rate of each line in each simulation period, and form an hourly load rate sequence by combining them in time sequence.

[0023] Specifically, line impedance parameters include the resistance, reactance, and susceptance to ground of each transmission line. Assume the power grid includes... Individual nodes and Line, for connecting nodes With nodes The route Its reactance is denoted as The process of generating the power transfer distribution factor matrix is ​​as follows: First, construct the nodal susceptance matrix. Its dimension is The element is defined as: In the formula: This represents the set of all adjacent nodes directly connected to a given node by a path. Select a node as the balancing node and remove it from the matrix. The reduced-order susceptance matrix is ​​obtained by dividing the rows and columns corresponding to the equilibrium nodes. Its dimension is .calculate The inverse matrix is ​​used to obtain the reduced-order nodal impedance matrix. To expand to a complete state, insert zero elements at the corresponding positions of the balanced nodes. 3D nodal impedance matrix It is located in the first Line 1 The elements of a column are denoted as , representing a node With nodes The mutual impedance between them.

[0024] For connection nodes With nodes The route Its power transfer distribution factor matrix Corresponding element The formula for calculation is: In the formula: For nodes With nodes mutual impedance, matrix The dimension is Under the DC power flow approximation, the nodes The line caused by a unit change in injected active power Active power flow change.

[0025] Each simulation period refers to dividing the 8760 hours of the whole year into... Each time slot is of equal length. Based on the planned output of generator units and the predicted node load in the candidate planning schemes, a node injection power vector is formed. , its first Each component Defined as: In the formula: For access nodes All conventional generator sets during the period The sum of the pre-output forces; The sum of the predicted output of all new energy generator units at the access node during the time period; For nodes During the period The load forecast value.

[0026] Based on node-injected power vector and power transfer distribution factor matrix The active power transmission power of each line during a given time period is determined by the following formula: In the formula: For the active power transmission vector of the line, its first... Each component Indicates the line During the period The positive direction of the transmitted active power is defined as from the lower node number to the higher node number.

[0027] The load factor of each line in each simulated time period is defined as the ratio of the absolute value of the actual active power transmitted by the line to its rated capacity. For a line in a time period... Its load rate The formula for calculation is: In the formula: This refers to the rated capacity of the line. Indicates the line During the period The load factor is the absolute value of transmitted active power, a dimensionless quantity or expressed as a percentage. For the same line, the load factors for each time period are arranged in chronological order to obtain the hourly load factor sequence of the line. For all Perform the above operation on all lines to obtain the hourly load rate sequence set of all lines.

[0028] Please see Figure 3 The hourly output sequence of each generator unit is obtained, including the following: S221. Construct a safety-constrained unit combination model with the goal of minimizing system operating costs. The safety-constrained unit combination model includes line power flow constraints, which are expressed by the linear sensitivity relationship between the node injected power vector and the line transmitted power. S222. Obtain the hourly load forecast curve and renewable energy output forecast curve for the whole year. For each simulation period, solve the safety constraint unit combination model to obtain the output plan of each conventional generator unit and the renewable energy curtailment power caused by network constraints. S223. Determine the actual output of each new energy unit based on the new energy power output forecast curve and the new energy curtailment power; S224. Arrange the planned output of each conventional generator set and the actual output of each new energy generator set in a time sequence to obtain the hourly output sequence of all generator sets.

[0029] Specifically, the safety-constrained unit combination model takes minimizing system operating costs as its optimization objective, and the objective function is: In the formula: This simulates the total number of time periods; This represents the total number of conventional generator sets. This represents the total number of new energy generator sets; For conventional units Contributing to the effort during the specified time period; For the unit The fuel cost function, taking the quadratic function ,in , , This is the cost coefficient; and The units The cost of a single startup and the cost of downtime; To activate the indicator variable, when the unit During the period The value is 1 when the machine changes from a stopped state to a running state, and 0 otherwise. As a shutdown indication variable, when the unit During the period The value is 1 when the running state changes to the stop state, and 0 otherwise. For new energy units The amount of power abandoned during a given time period; This is a penalty factor, the value of which is greater than the marginal generation cost of any conventional unit.

[0030] The constraints that the model must satisfy include: 1. System power balance constraints: for each time period , In the formula: For new energy units In terms of time period prediction output, For load nodes Load values ​​during the time period, This represents the total number of load nodes.

[0031] 2. Upper and lower limits of output for conventional generating units: In the formula: For the unit In the runtime status variable, 1 indicates running and 0 indicates stopped; and The units The minimum and maximum technical output.

[0032] 3. Ramp-up constraints for conventional units: In the formula: and The units The upward and downward climbing rates.

[0033] 4. Minimum start-up and shutdown time constraints: Unit Once started, it needs to run continuously for at least This refers to a specific time period; once the machine stops, it must remain shut down for at least [duration missing]. Each time period.

[0034] 5. Line power flow constraints: Each line During the period The transmission power must meet the capacity limit, and the relationship between the transmission power and the injected power of each node is linear, expressed by the power transfer distribution factor. In the formula: Power transfer distribution factor matrix Middle line corresponding nodes Element; This is the transmission capacity limit for the line; For nodes During the period The injection power is expressed as: in: This refers to the set of conventional generator sets used by the access nodes. A collection of new energy generator sets for access nodes; This refers to the load set of the access node.

[0035] The above model is a mixed-integer linear programming or mixed-integer quadratic programming problem. For each time period... The solution is obtained using either a commercial or open-source solver. To reduce computational complexity, a rolling time-domain method is employed for daily or weekly optimization. After solving, the results for each conventional generator set are directly obtained. Output plan for each time period and various new energy units Power curtailment at different time periods .

[0036] Based on the renewable energy output forecast curve and the renewable energy curtailment power, the actual output of each renewable energy unit is determined as follows: For renewable energy units During the period Its actual output Calculate using the following formula: Output plan of each conventional generator unit By unit number and time period Arranged in sequence, conventional units are obtained. Hourly output sequence The actual output of each new energy unit Arranged in the same way, we obtain the new energy units. Hourly output sequence By merging the hourly output sequences of all conventional and renewable energy units, a complete set of hourly output sequences for generator units is obtained, providing a data foundation for distinguishing between effective power output and constrained power output.

[0037] S3. Retrieve a pre-built performance evaluation template that matches the topology type. The performance evaluation template defines the overload judgment logic and the performance aggregation hierarchical path from the device layer to the system layer. The pre-built performance evaluation template is a set of evaluation rules and aggregation paths generated offline and pre-stored. Its function is to enable the performance evaluation process to adaptively adjust to the structural characteristics of different power grid topologies. After identifying the topology type of the candidate planning scheme in step S1, the performance evaluation template uniquely corresponding to that topology type is retrieved from the pre-built template library, providing a structured evaluation framework for subsequent index calculations and layer-by-layer aggregation. Please refer to [link to relevant documentation]. Figure 4 Including the following: S31. Obtain historical power grid operation data corresponding to the topology type, and extract overload judgment parameters and efficiency aggregation path information based on the historical power grid operation data; Historical power grid operation data refers to the actual operation data recorded within the historical operating cycle of an existing power grid with the same or similar topology as the current power grid to be evaluated. This includes measured active power and load rate records for each line in each historical period, voltage operation data for each substation node, actual output records for each generator unit, power consumption data for each load node, and records of historically occurring overload events and corresponding handling measures. For each topology type, at least one full year of historical operation data should be collected as the analysis sample.

[0038] Overload determination parameters are quantitative indicators and threshold conditions used to determine whether a line is under overload and the severity of the overload. These parameters are a set of parameters statistically extracted from historical operating data. The overload determination parameters include a first overload threshold and a second overload threshold. The first overload threshold corresponds to the minimum load rate at which a line enters a state requiring attention, and the second overload threshold corresponds to the minimum load rate at which a line enters a severe overload state. The second overload threshold is greater than the first overload threshold. An overload duration threshold is also included to distinguish between instantaneous overload events and continuous overload events. Events with a load rate exceeding the overload threshold but a duration below the duration threshold are not included in the overload statistics. Finally, a key performance section identifier is defined as a set of lines in the power grid that, under this topology, bear the main power transmission task and whose overload will directly affect power supply reliability. This set is determined by analyzing the distribution patterns of overload events in historical operating data and the grid connectivity structure.

[0039] Efficiency aggregation path information refers to the organizational structure information describing the hierarchical merging of efficiency indicators from the bottom-level equipment to the top-level system. It is a hierarchical evaluation framework extracted from historical power grid operation data and topological characteristics. Efficiency aggregation path information includes: the definition of equipment-level nodes (a single line or generator unit, representing the lowest-level unit for efficiency indicator calculation); the definition of intermediate-level nodes (substations or power supply zones divided according to geographical area or voltage level, with several equipment-level nodes under each intermediate-level node); the definition of top-level nodes (corresponding to the system level of the entire candidate planning scheme); and the affiliation relationships between nodes at each level, i.e., which intermediate-level node each equipment-level node belongs to, and which top-level node each intermediate-level node belongs to. For example, for a radial structure, intermediate-level nodes divide power supply areas hierarchically according to the power supply direction from the power source to the load, and the efficiency aggregation path follows the order of hierarchical merging from the end load equipment to the upstream power source; for a ring network structure, intermediate-level nodes are divided according to the closed loop of the ring network or power supply zones, and the efficiency aggregation path follows the order of first aggregation within the ring, then aggregation between rings.

[0040] S32. Generate overload determination logic based on overload determination parameters, and determine the performance aggregation hierarchical path based on performance aggregation path information; Specifically, for the hourly load rate sequence to be evaluated, the overload determination logic includes: if a line's load rate is greater than the first overload threshold but less than or equal to the second overload threshold during a period exceeding the overload duration threshold consecutively, the line is determined to be in a general overload state; if its load rate is greater than the second overload threshold during a period exceeding the overload duration threshold consecutively, the line is determined to be in a severe overload state; for lines belonging to critical performance sections, if they are in a general or severe overload state, the performance deduction weight of the overload event is higher than that of overload events on non-critical performance section lines. The output of the overload determination logic includes the proportion of overload periods and overload distribution information. The starting point of the performance aggregation hierarchical path is each equipment layer node, that is, each line and each generator set is used as the bottom-level performance parameter input node; the intermediate aggregation node is each intermediate layer node, that is, the performance parameters of equipment nodes belonging to the same substation or the same power supply zone are weighted and aggregated at this layer; the endpoint is the top-level node, that is, the performance values ​​of all intermediate layer nodes are finally aggregated into a system-level comprehensive performance value. The performance aggregation hierarchy path also defines the aggregation operators used in each aggregation layer, such as weighted average operators or weighted summation operators, as well as the weight factor of each device node in the aggregation. This weight factor can be determined based on the capacity ratio of the device node in the previous layer, or based on the sensitivity factor of the device node to the performance index of the previous layer.

[0041] S33. Combine the overload decision logic and the performance aggregation hierarchical path to generate a pre-built performance evaluation template that matches the topology type.

[0042] The overload judgment logic and efficiency aggregation hierarchical path generated above for this topology type are encapsulated into an efficiency evaluation template file according to a preset data structure, and a unique identifier corresponding to the topology type is assigned to the template. The encapsulated efficiency evaluation template contains two functional modules: the first functional module is the load efficiency evaluation module, which stores all rules and parameters of the overload judgment logic; the second functional module is the aggregation path module, which stores all node information and aggregation rules of the efficiency aggregation hierarchical path. This efficiency evaluation template is stored in a template library. When it is necessary to evaluate a new candidate planning scheme, the matching efficiency evaluation template can be directly retrieved according to the topology type identifier identified in step S1, so as to obtain the overload judgment criteria and aggregation path applicable to the topology, thereby realizing the adaptive evaluation of grid load efficiency and power utilization efficiency.

[0043] S4. Based on the heavy load determination logic, the power grid load efficiency index is calculated using the hourly load rate sequence; the hourly output sequence of each generator unit is divided into effective power supply output and limited output, and the limited output is attributed to a variety of predefined constraint types to obtain the power utilization efficiency index that includes the causes of power efficiency loss. This step quantitatively evaluates the operational efficiency of candidate planning schemes from both the grid and power generation perspectives. The core of the grid-side evaluation is to use the hourly load rate sequence obtained in step S2, combined with the overload judgment logic defined in the template, to calculate efficiency indicators reflecting the grid load characteristics. The core of the power generation-side evaluation is to decompose the actual output of generator units into value, distinguishing between effective components that contribute to the system and ineffective components that are constrained, and tracing the causes of the ineffective components. The evaluation results from both parts together constitute the basic parameters for subsequent hierarchical aggregation.

[0044] Please see Figure 5 The calculation of power grid load efficiency indicators includes the following: S411. Based on the overload determination threshold defined in the overload determination logic, calculate the proportion of overload periods exceeding the overload determination threshold in the hourly load rate sequence. S412. Based on the key performance section defined in the overload judgment logic, extract the load rate of each line within the key performance section and calculate the load balance of the key performance section. S413. The proportion of heavy-load periods and load balance are used as indicators of power grid load efficiency.

[0045] The overload judgment thresholds are defined in the overload judgment logic of the performance evaluation template, including a first overload threshold for determining that a line is in a state of concern and a second overload threshold for determining that a line is in a state of severe overload. For a certain line Its hourly load rate sequence is Count the number of time periods during which the load rate of the line exceeds the first overload threshold. If the load rate exceeds the first overload threshold for a certain period but the continuous duration does not reach the overload duration threshold, then that period will not be counted. The percentage of heavy-load periods on a line is defined as follows: The percentage of heavy load periods for all lines across the entire network is statistically analyzed, and the maximum value or weighted average of the percentage of heavy load periods for all lines is taken as the system-level heavy load period percentage indicator. The weights are allocated according to the rated capacity of each line.

[0046] The key performance section has been identified as a set of lines in the template. .for Extract the routes in the same time period. The load rate forms the load rate distribution of key performance sections during that period. When calculating the load balance of key performance sections, the load rate variation coefficient can be used as a measure of balance. For the time period... The average load rate of each line within the key performance section and standard deviation They are respectively: The load balancing degree during the time period Defined as coefficient of variation: The average value over all time periods is used to obtain the system-level load balancing index. , The smaller the value, the more uniform the load distribution among the lines within the critical section, and the more rational the power flow distribution in the power grid structure. and The combination serves as a component of the power grid load performance index.

[0047] In this embodiment, a power utilization efficiency index containing the causes of power efficiency loss is obtained, including the process of distinguishing between effective power output and limited power output, and the process of attributing limited power output to the type of constraint.

[0048] 1. For new energy generator sets, obtain the new energy output prediction curve, determine the actual output of each time period in the hourly output sequence as the effective power supply output, and determine the difference between the predicted output and the actual output of the corresponding time period in the new energy output prediction curve as the restricted output. For new energy generator sets The hourly renewable energy output forecast curve for the whole year has been obtained from step S222, and its forecast output value is The actual output force has been determined from step S223. Actual output The power output is defined as the effective power supply output, meaning this portion of the power contributes to the system's power balance; the difference between the predicted output and the actual output is defined as the limited output. The calculation formula is: .

[0049] 2. For conventional generator sets, obtain the output plan from the safety-constrained unit combination model, determine the output that meets the load demand and the demand for renewable energy consumption in the output plan as the effective power supply output, and determine the output reduction caused by the system peak-shaving demand as the restricted output.

[0050] For conventional generator sets The power output plan has been obtained from step S222. The portion of the planned power output that meets load demand and renewable energy consumption demand is defined as effective power supply output; the reduction in output of conventional units due to system peak-shaving demand to maintain power balance is defined as restricted output. Under the premise of satisfying line power flow constraints and power balance constraints, if conventional units... During the period If the actual output is lower than the lower limit of its economic operating range, and the system experiences both renewable energy curtailment and low output from conventional units, then this portion of output below the economic operating range is identified as peak-shaving-restricted output. If conventional units During the period If a power generation plan is executed normally and the power output provides space for the consumption of new energy sources after meeting the load, then the entire power output plan is considered as effective power supply output.

[0051] Please see Figure 6 By attributing limited output to various predefined constraint types, a power utilization efficiency index is obtained that includes the causes of power efficiency loss. These constraint types include network transmission constraints and system peak-shaving constraints, as follows: S421. Based on the boundary state of the line power flow constraints in each time period, the limited output of new energy caused by the line power flow reaching the transmission limit is attributed to the network transmission constraints. S422. Based on the relationship between system load and peak-shaving capacity in each time period, the reduction in output of conventional generator units due to insufficient system peak-shaving capacity is attributed to system peak-shaving constraints. S423. Calculate the total restricted power corresponding to each constraint type and generate a power utilization efficiency index that includes the attribution results of each constraint type.

[0052] During step S222, when solving the safety-constrained unit combination model, each time period has been recorded. The power flow boundary status information of each line, i.e., the line Transmission power during the time period Has its transmission capacity limit been reached? For new energy units Limited output during the time period If there is at least one related to new energy units during that period If the lines associated with a grid-connected node are at their transmission capacity limits, then the cause of this limited output is determined to be network transmission constraints. This limited power output is then categorized as a network transmission constraint type, denoted as... System peak-shaving capacity refers to the total downward regulation capacity that all operating conventional generator units in the system can provide during a given time period. Let the time period be... The total system load is The minimum technical output of conventional units is The system's downward peak-shaving capacity is then the difference between the current total output and the minimum technical output. When this difference is less than the fluctuation range between the load and the net output of new energy sources, it is determined that the system's peak-shaving capacity is insufficient. The amount by which conventional generating units are forced to reduce their output due to insufficient system peak-shaving capacity will be determined. Attributing the issue to system peak-shaving constraints, this portion of restricted electricity is classified into the system peak-shaving constraint type, denoted as... .

[0053] The total restricted power for each constraint type is calculated, and the total restricted power for the network transmission constraint type is the sum of the restricted power of all renewable energy units attributed to that type: The total restricted power corresponding to the system peak-shaving constraint type is the sum of the restricted power of all conventional units attributed to this type: The generated power utilization efficiency index is a set of indicators containing multi-dimensional information, including: the effective power supply of each renewable energy unit, the restricted power supply of each renewable energy unit and its attribution type identifier, the effective power supply and peak-shaving restricted power supply of each conventional unit, and the total restricted power supply of network transmission constraint type. Total power consumption restricted by system peak shaving constraint type The set of indicators includes the proportion of restricted power generation under the two types of constraints to the total power generation. This set of indicators is a power utilization efficiency indicator that includes the causes of power efficiency loss, providing a complete and traceable power-side efficiency input for subsequent hierarchical aggregation evaluation.

[0054] S5. Following the hierarchical path of efficiency aggregation, the grid load efficiency index and power utilization efficiency index are used as the bottom-level equipment-level efficiency parameters, and are aggregated layer by layer upwards until the system-level comprehensive efficiency value representing the overall efficiency of the candidate planning scheme is obtained. The dispersed and multi-dimensional device-level indicators generated in step S4 are refined into a single numerical value that comprehensively reflects the overall merits of candidate planning schemes through the hierarchical path preset in the template of step S3. The aggregation process follows the hierarchical definition of the performance aggregation path, first aggregating from the lowest-level device nodes to intermediate-level nodes, then using the intermediate-level nodes as new inputs to aggregate to higher levels, ultimately reaching the root node. (See also...) Figure 7 Including the following: S51. Identify the lowest-level device node defined in the performance aggregation hierarchy path, and associate the grid load performance index and power utilization performance index with the corresponding device node as performance parameters of the device node; The device layer node definitions of the efficiency aggregation hierarchical path are read from the efficiency evaluation template. Each line corresponds to a line device node, and each generator set corresponds to a unit device node. For line device nodes, the heavy load period ratio and load balance related components calculated in steps S411 and S412 are used as their load efficiency parameters. For unit device nodes, the effective power supply, restricted power supply, and attribution type identifier of the unit obtained in step S423 are used as their power efficiency parameters.

[0055] S52. Based on the hierarchical relationship between device nodes and their parent system nodes in the performance aggregation hierarchy path, the performance parameters of each device node under the same parent system node are weighted and aggregated to obtain the performance value of each parent system node. The implementation of weighted aggregation includes two steps: weight acquisition and weighted calculation. First, the aggregation weights defined in the performance aggregation hierarchy are acquired. These weights are determined based on the capacity proportion or sensitivity factor of each device node within its parent system node. Capacity proportion refers to the ratio of a device node's rated capacity to the total rated capacity of all similar device nodes within its parent system node. Sensitivity factor refers to the degree of impact of a device node's performance change on the overall performance of the parent system node, which can be obtained from the power transfer distribution factor or correlation analysis of historical operating data.

[0056] Secondly, based on the obtained aggregation weights, the performance parameters of the device nodes are weighted and summed. Let's assume a certain parent system node... Subordinate The device node, the first The performance parameters of each device node are Its corresponding aggregation weight is Then the performance value of the superior system node The formula for calculation is: For grid load efficiency indicators and power utilization efficiency indicators, since the dimensions and numerical ranges are different, each efficiency parameter can be normalized before weighted aggregation and mapped to the [0,1] interval. The normalization benchmark can be based on the preset benchmark values ​​of each indicator in the efficiency evaluation template.

[0057] S53. Treat each higher-level system node as a new lower-level node, repeat the weighted aggregation process until the root node of the performance aggregation hierarchical path is reached, and use the performance value of the root node as the system-level comprehensive performance value.

[0058] After completing S52, the performance parameters of all device nodes directly belonging to a certain intermediate layer node have been aggregated into the performance value of that intermediate layer node. At this point, each intermediate layer node is treated as a new bottom layer node, and its performance value is used as the input of that node. The weighted aggregation operation described in S52 is executed again to aggregate to higher layers. This process is repeated until the top layer node defined by the performance aggregation hierarchy path is reached, i.e., the root node. The performance value of the root node is the system-level comprehensive performance value of the candidate planning scheme. This system-level comprehensive performance value is a scalar between 0 and 1 or within a specific score range. The higher the value, the better the overall performance of the scheme in terms of grid load balance, power utilization efficiency, and source-grid coordination. This completes the entire process from year-round time-series operation simulation to system-level performance evaluation. This comprehensive performance value can be used for horizontal comparison and ranking among multiple candidate schemes.

[0059] S6. Based on the system-level comprehensive performance value of each candidate planning scheme, output the scheme performance ranking result.

[0060] After evaluating the performance of all candidate planning schemes, each scheme receives a system-level comprehensive performance value. This value is a quantifiable, horizontally comparable scalar that comprehensively reflects the scheme's grid load adaptability and power utilization efficiency under continuous year-round operation conditions. The specific process for outputting the scheme performance ranking results is as follows: all candidate schemes are ranked from highest to lowest according to their system-level comprehensive performance values, with higher performance values ​​indicating better overall operational performance. The ranking results are presented in list form, with each scheme entry including the scheme number or name, the system-level comprehensive performance value, and a summary of key sub-indicators for the scheme in both grid load performance and power utilization performance dimensions, such as the proportion of system heavy load periods, load balance of key sections, network transmission constraints on power supply, and system peak-shaving constraints on power supply. This output method allows planners to not only directly obtain the order of merit among the schemes but also intuitively understand the strengths and weaknesses of each scheme, providing sufficient and transparent data support for the final planning decision.

[0061] Example 2 An embodiment of the comprehensive performance evaluation system for planning schemes based on grid load rate and power utilization hours of the present invention includes the following: The topology type identification module is used to acquire power grid topology data of candidate planning schemes and identify the topology type corresponding to the power grid topology data. The hourly load rate sequence and hourly output sequence determination module is used to perform year-round time-series production simulation on candidate planning schemes to obtain the hourly load rate sequence of each line and the hourly output sequence of each generator unit. The performance evaluation template retrieval module is used to retrieve pre-built performance evaluation templates that match the topology type. The performance evaluation templates define the overload judgment logic and the performance aggregation hierarchical path from the device layer to the system layer. The power grid load efficiency index and power utilization efficiency index determination module is used to calculate the power grid load efficiency index based on the heavy load judgment logic and using the hourly load rate sequence; it distinguishes the hourly output sequence of each generator unit into effective power supply output and limited output, and attributes the limited output to a variety of predefined constraint types to obtain the power utilization efficiency index that includes the causes of power efficiency loss. The system-level comprehensive performance value determination module is used to merge and aggregate the grid load performance index and power utilization performance index as the underlying equipment-level performance parameters according to the performance aggregation hierarchy path, layer by layer, until the system-level comprehensive performance value representing the overall performance of the candidate planning scheme is obtained. The scheme effectiveness ranking result output module is used to output the scheme effectiveness ranking result based on the system-level comprehensive effectiveness value of each candidate planning scheme.

[0062] For specific limitations regarding the system, please refer to the method limitations described above, which will not be repeated here. Each module in the above system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0063] It is understood that those skilled in the art can combine various implementation methods in the above embodiments under the guidance of the above examples to obtain technical solutions with multiple implementation methods.

[0064] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A comprehensive performance evaluation method for planning schemes based on grid load rate and power supply utilization hours, characterized in that, include: Obtain power grid topology data of candidate planning schemes and identify the topology type corresponding to the power grid topology data; The candidate planning schemes are simulated throughout the year to obtain the hourly load rate sequence of each line and the hourly output sequence of each generator unit. Retrieve a pre-built performance evaluation template that matches the topology type. The performance evaluation template defines the overload determination logic and the performance aggregation hierarchical path from the device layer to the system layer. Based on the heavy load determination logic, the power grid load efficiency index is calculated using the hourly load rate sequence; the hourly output sequence of each generator set is divided into effective power supply output and limited output, and the limited output is attributed to a variety of predefined constraint types to obtain a power utilization efficiency index that includes the causes of power efficiency loss. According to the efficiency aggregation hierarchy path, the grid load efficiency index and the power utilization efficiency index are used as the bottom-level equipment-level efficiency parameters, and are aggregated layer by layer upwards until the system-level comprehensive efficiency value that characterizes the overall efficiency of the candidate planning scheme is obtained. Based on the system-level comprehensive performance value of each candidate planning scheme, the ranking of scheme performance is output.

2. The comprehensive performance evaluation method for planning schemes based on grid load rate and power utilization hours as described in claim 1, characterized in that, The method of obtaining the hourly load rate sequence for each line includes: Based on the line impedance parameters in the power grid topology data, a power transfer distribution factor matrix is ​​generated. Based on the pre-output plan of the generator units and the predicted node load in the candidate planning scheme, a node injection power vector is formed for each simulation period. Based on the node injected power vector and the power transfer distribution factor matrix, the active power transmission of each line in each simulation period is determined. Based on the ratio of active transmission power to rated capacity of each line, the load rate of each line in each simulation period is determined, and the load rate sequence is formed by combining them in time sequence.

3. The comprehensive performance evaluation method for planning schemes based on grid load rate and power utilization hours as described in claim 2, characterized in that, The process of obtaining the hourly output sequence of each generator set includes: A safety-constrained unit combination model is constructed with the goal of minimizing system operating costs. The safety-constrained unit combination model includes line power flow constraints, which are expressed by the linear sensitivity relationship between the node injected power vector and the line transmitted power. Obtain the hourly load forecast curve and the renewable energy output forecast curve for the whole year. For each simulation period, solve the safety constraint unit combination model to obtain the output plan of each conventional generator unit and the renewable energy curtailment power caused by network constraints. The actual output of each new energy unit is determined based on the new energy power output prediction curve and the new energy curtailment power. The power output plans of each conventional generator set and the actual power output of each new energy generator set are arranged in time sequence to obtain the hourly power output sequence of all generator sets.

4. The comprehensive performance evaluation method for planning schemes based on grid load rate and power utilization hours as described in claim 1, characterized in that, The process of retrieving a pre-built performance evaluation template that matches the topology type includes: Obtain historical power grid operation data corresponding to the topology type, and extract overload determination parameters and efficiency aggregation path information based on the historical power grid operation data; The overload determination logic is generated based on the overload determination parameters, and the performance aggregation hierarchical path is determined based on the performance aggregation path information. The overload determination logic and the performance aggregation hierarchical path are combined to generate a pre-built performance evaluation template that matches the topology type.

5. The comprehensive performance evaluation method for planning schemes based on grid load rate and power utilization hours as described in claim 1, characterized in that, The calculation of the power grid load efficiency index based on the overload determination logic and the hourly load rate sequence includes: Based on the overload determination threshold defined in the overload determination logic, the proportion of overload periods exceeding the overload determination threshold in the hourly load rate sequence is statistically analyzed. Based on the key performance sections defined in the overload determination logic, the load rate of each line within the key performance section is extracted, and the load balance of the key performance section is calculated. The proportion of heavy-load periods and the load balance are used as indicators of power grid load efficiency.

6. The comprehensive performance evaluation method for planning schemes based on grid load rate and power utilization hours according to claim 1, characterized in that, The step of dividing the hourly output sequence of each generator set into effective power supply output and limited output includes: For new energy generator sets, the new energy output prediction curve is obtained, the actual output of each time period in the hourly output sequence is determined as the effective power supply output, and the difference between the predicted output and the actual output of the corresponding time period in the new energy output prediction curve is determined as the restricted output. For conventional generator sets, the output plan output by the safety-constrained generator set combination model is obtained. The output that meets the load demand and the demand for renewable energy consumption in the output plan is determined as the effective power supply output, and the output reduction caused by the system peak-shaving demand is determined as the restricted output.

7. The comprehensive performance evaluation method for planning schemes based on grid load rate and power utilization hours as described in claim 6, characterized in that, The process of attributing the limited output to various predefined constraint types to obtain a power utilization efficiency index that includes the causes of power efficiency loss includes: The various constraint types include network transmission constraints and system peak shaving constraints; Based on the boundary state of the line power flow constraints at each time period, the limited output of new energy caused by the line power flow reaching the transmission limit is attributed to the network transmission constraints. Based on the relationship between system load and peak-shaving capacity in each time period, the reduction in output of conventional generator units due to insufficient system peak-shaving capacity is attributed to the aforementioned system peak-shaving constraints. The total amount of restricted power corresponding to each constraint type is calculated, and a power utilization efficiency index containing the attribution results of each constraint type is generated.

8. The comprehensive performance evaluation method for planning schemes based on grid load rate and power utilization hours as described in claim 1, characterized in that, The process involves following the performance aggregation hierarchy path, using the grid load performance index and the power utilization performance index as bottom-level equipment-level performance parameters, and aggregating them layer by layer upwards until a system-level comprehensive performance value representing the overall performance of the candidate planning scheme is obtained. This includes: Identify the lowest-level device node defined in the performance aggregation hierarchy path, and associate the grid load performance index and the power utilization performance index with the corresponding device node as performance parameters of the device node; Based on the hierarchical relationship between device nodes and their parent system nodes in the performance aggregation hierarchy path, the performance parameters of each device node under the same parent system node are weighted and aggregated to obtain the performance value of each parent system node. Each of the higher-level system nodes is treated as a new lower-level node, and the weighted aggregation process is repeated until the root node of the performance aggregation hierarchical path is reached. The performance value of the root node is then used as the overall system-level performance value.

9. The comprehensive performance evaluation method for planning schemes based on grid load rate and power utilization hours as described in claim 8, characterized in that, The weighted aggregation of performance parameters of each device node under the same upper-level system node includes: Obtain the aggregation weights defined in the performance aggregation hierarchy path. The aggregation weights are determined based on the capacity ratio or sensitivity factor of each device node in the upper-level system node. Based on the aggregation weight, the performance parameters of each device node are weighted and summed to obtain the performance value of the upper-level system node.

10. A comprehensive performance evaluation system for planning schemes based on grid load rate and power supply utilization hours, characterized in that, The method for comprehensive performance evaluation of planning schemes based on grid load rate and power utilization hours as described in any one of claims 1 to 9 includes: The topology type identification module is used to acquire power grid topology data of candidate planning schemes and identify the topology type corresponding to the power grid topology data; The hourly load rate sequence and hourly output sequence determination module is used to perform a year-round time-series production simulation on the candidate planning scheme to obtain the hourly load rate sequence of each line and the hourly output sequence of each generator unit. The performance evaluation template retrieval module is used to retrieve a pre-built performance evaluation template that matches the topology type. The performance evaluation template defines the overload judgment logic and the performance aggregation hierarchical path from the device layer to the system layer. The power grid load efficiency index and power utilization efficiency index determination module is used to calculate the power grid load efficiency index based on the heavy load determination logic and the hourly load rate sequence; to classify the hourly output sequence of each generator set into effective power supply output and limited output, and to attribute the limited output to a variety of predefined constraint types to obtain the power utilization efficiency index that includes the causes of power efficiency loss. The system-level comprehensive performance value determination module is used to merge and aggregate the grid load performance index and the power utilization performance index as the bottom-level equipment-level performance parameters according to the performance aggregation hierarchical path, layer by layer, until the system-level comprehensive performance value representing the overall performance of the candidate planning scheme is obtained. The scheme effectiveness ranking result output module is used to output the scheme effectiveness ranking result based on the system-level comprehensive effectiveness value of each candidate planning scheme.

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