Wind power output attenuation reserve demand rolling correction and market clearing method and system

Abstract

The application discloses a wind and light output attenuation reserve demand rolling correction and market clearing method and system, and the method comprises the following steps: acquiring basic data including new energy field station, weather state, power grid node and reserve resource data; calculating new energy benchmark output under undisturbed state; constructing weather disturbance propagation kernel to generate multi-scenario disturbance state; determining new energy output attenuation under corresponding scene based on new energy benchmark output and multi-scenario disturbance state, and aggregating according to dispatching area to calculate partition rolling reserve demand; constructing reserve market clearing model based on partition rolling reserve demand to obtain clearing result; performing power flow calculation based on the clearing result; when power flow calculation exists power flow divergence, line load rate out of limit or node voltage out of limit, performing reserve resource safety correction. Through the application, the reserve configuration adaptability of high proportion new energy system under sudden weather disturbance, the market clearing rationality and the operation safety can be improved.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching and operation and power market optimization technology, specifically to a method and system for rolling correction of wind and solar power output attenuation and reserve demand and market clearing. Background Technology

[0002] Currently, with the increasing proportion of installed capacity of new energy sources such as wind power and photovoltaics, new energy has gradually transformed from a supplementary power source into an important power source type affecting system power balance and operational safety. Compared with conventional units, wind power and photovoltaic output are highly dependent on meteorological conditions such as wind speed, irradiance, temperature, and cloud movement, exhibiting randomness, volatility, and spatial correlation. When regional meteorological disturbances occur, multiple new energy power plants may experience synchronous output reduction within a similar time period, creating a significant power gap in a short period and altering system power flow distribution and reserve requirements.

[0003] Existing methods for assessing reserve demand and clearing the reserve market are mostly based on fixed proportions, historical forecast errors, or static scenarios over a single time period. These methods fail to adequately depict the propagation process of meteorological disturbances among different wind and solar power plants and the dynamic evolution path of power output attenuation. Furthermore, reserve resource allocation typically focuses on capacity adequacy, neglecting the impact on the region where the reserve resources are located, access nodes, and power flow security. This can lead to problems such as purchased reserve capacity but electrical mismatch, local line overload, or low node voltage.

[0004] Therefore, there is an urgent need to construct a backup configuration method that integrates meteorological disturbance propagation, wind and solar power output attenuation scenarios, regional backup requirements, market clearing and tidal current safety verification. In the rolling process, the risk of new energy power output will be transformed into backup procurement and safety correction decisions, realize the spatiotemporal coordinated allocation of backup resources, and improve the system's ability to cope with meteorological disturbances and synchronous attenuation risks. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a method and system for rolling adjustment of reserve demand and market clearing in the context of wind and solar power output attenuation, enabling the continuous transformation of new energy power output risk into reserve procurement volume, reserve clearing price, and reserve space allocation scheme.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0007] The wind and solar power output attenuation reserve demand rolling correction and market clearing method of the present invention includes:

[0008] Acquire basic data including new energy power plants, meteorological conditions, power grid nodes and backup resources, and establish mapping relationships between power plants, regions, nodes and backup resources;

[0009] The baseline output of new energy sources under undisturbed conditions is calculated based on meteorological data.

[0010] Meteorological disturbance propagation kernels are constructed based on inter-station distances, disturbance propagation directions, and regional correlations, and disturbance states in multiple scenarios are generated.

[0011] Based on the baseline output of new energy sources and the disturbance status of multiple scenarios, the output attenuation of new energy sources under the corresponding scenarios is determined, and aggregated according to the scheduling area to calculate the rolling reserve demand of the partition.

[0012] A reserve market clearing model is constructed based on the partitioned rolling reserve demand to obtain the clearing results;

[0013] Based on the power output attenuation of new energy sources and the clearing results, the power output attenuation of new energy sources and reserve compensation are mapped to grid nodes, and power flow calculations are performed.

[0014] When power flow calculations show power flow non-convergence, line load rate exceeding limits, or node voltage exceeding limits, backup resource safety correction is performed.

[0015] A further improvement of this invention lies in: calculating the benchmark output of new energy sources under undisturbed conditions based on meteorological state data, including:

[0016] For wind farms, the benchmark wind power output is calculated based on wind speed data and wind turbine power curves.

[0017] For photovoltaic power plants, the photovoltaic baseline output is calculated based on solar irradiance and ambient temperature.

[0018] A further improvement of this invention lies in: constructing a meteorological disturbance propagation kernel to generate multiple disturbance states, including:

[0019] Based on the distance between stations, the direction of disturbance propagation, and regional correlation, a meteorological disturbance propagation kernel is constructed and normalized. The expression is as follows:

[0020]

[0021] In the formula, For meteorological disturbances caused by the station Spread to the station The perturbation affects the weights, i.e., the propagation of the core elements. For meteorological disturbances caused by the station The weight of the perturbation effect from self-propagation. For station With station The spatial distance between stations. This is a parameter representing the spatial attenuation scale of meteorological disturbance propagation. Influenced by the direction of propagation Due to regional correlation, For the station;

[0022] A time-varying meteorological disturbance front is constructed, and a recursive model including disturbance persistence, spatial propagation, external shock, and random noise terms is used to generate disturbance states for multiple scenarios, expressed as follows:

[0023]

[0024] In the formula, For station In the scene Time period The disturbance state. This is the disturbance duration coefficient. For the scene Next station During the period The disturbance state. The spatial propagation coefficient, For meteorological disturbances caused by the station Spread to the station The perturbation affects the weight. For the scene Next station During the period The disturbance state. For the moving disturbance front to the station The direct impact, For station In the scene Time period External impact strength coefficient, For station In the scene Time period random noise term, This is for amplitude limiting.

[0025] A further improvement of this invention lies in: based on the baseline output of new energy sources and the disturbance states in multiple scenarios, determining the attenuation of new energy output under the corresponding scenario, and aggregating it according to the scheduling region, as expressed in the following expression:

[0026]

[0027] In the formula, For station In the scene Time period The amount of output attenuation, As a benchmark, For installed capacity, The attenuation sensitivity corresponding to the type of station. For station In the scene Time period The disturbance state. For scheduling area The loss of new energy after aggregation For scheduling area The collection of stations, For station During the period The baseline output.

[0028] A further improvement of the present invention is that: calculating the partition rolling reserve requirement includes:

[0029] Within each rolling period, a rolling forecast window starting from the current period is selected, and the maximum renewable energy output attenuation of each scenario in the corresponding scheduling area within the rolling forecast window is calculated.

[0030] The maximum renewable energy output attenuation is calculated using empirical quantiles based on a preset information level, yielding the risk quantile loss value for each dispatch area in the current time period.

[0031] Subtract the allocated reserve capacity for the corresponding scheduling area from the risk quantile loss value, and add the engineering margin to obtain the new reserve demand for the corresponding scheduling area during the current rolling period.

[0032] When the new reserve requirement is less than zero, the new reserve requirement will be corrected to zero.

[0033] A further improvement of this invention lies in: constructing a reserve market clearing model based on partitioned rolling reserve demand, including:

[0034] A regional risk signal is constructed based on the rolling reserve requirements of each scheduling region and the upper limit of the regional reserve requirements.

[0035] The dynamic price of the standby resource is updated based on the regional risk signal of the scheduling region to which the standby resource belongs, the response characteristics of the standby resource, and the basic price.

[0036] For any reserve resource and any demand scheduling region, calculate the effective clearing price, which includes the dynamic price of reserve resources and the regional transfer penalty;

[0037] The reserve market is cleared in order of effective clearing price from low to high, while simultaneously satisfying the maximum capacity constraint and ramp-up capability constraint of reserve resources during the clearing process.

[0038] When the reserve demand in the dispatch area is not fully met, the corresponding reserve shortage amount in the dispatch area is recorded, and the reserve shortage penalty is included in the calculation of the total clearing cost and the regional clearing price for that period.

[0039] A further improvement of the present invention is that, based on the clearing results, power flow calculation is performed, including:

[0040] The risk quantile loss value of the dispatch area is allocated to the corresponding new energy power station access node according to the proportion of the installed capacity of each power station, and is used as the increase in active power load of the corresponding node;

[0041] The winning bid capacity of the reserve resources is mapped to the reduction in active load according to the access node of the reserve resources, which represents the compensation of the reserve resources for the power gap caused by the reduction of new energy output.

[0042] Based on the modified node active load, AC power flow calculation is performed to obtain whether the power flow converges, the voltage amplitude of each node, the active and reactive power flow of the line, and the line load rate.

[0043] The number of node voltage overruns, the number of lines overloaded, the maximum line load rate, and the minimum node voltage are statistically analyzed based on the node voltage upper and lower limits and line capacity limits.

[0044] A further improvement of this invention is that, when power flow calculations result in power flow non-convergence, line load rate exceeding limits, or node voltage exceeding limits, backup resource safety correction is performed, specifically including:

[0045] When power flow fails to converge, line load rate exceeds limits, or node voltage exceeds limits, a safety correction objective function is constructed, and local reserve transfer is executed within the allocated reserve capacity. Specifically, when power flow calculation fails to converge, the safety correction objective function is assigned a preset non-convergence penalty value; otherwise, the safety correction objective function is calculated based on the degree of line load rate exceeding limits, node voltage deviation, and the number of exceeding limits. The expression is as follows:

[0046]

[0047] In the formula, To pre-determine the non-convergence penalty value, , , These are the penalty weights for exceeding line load rate limits, node voltage deviation, and the number of exceeding limits, respectively. To safely correct the objective function, For the maximum line load rate, For line load rate limits, The lowest node voltage, This is the lower limit of voltage. and These are the number of line overloads and the number of node voltage overruns, respectively.

[0048]

[0049] In the formula, As backup resources To the station The spare capacity corresponding to the local calibration location transfer, To preset the correction step size, As backup resources Adjustable reserve capacity that has already been awarded in the bidding process. For station The amount of output attenuation, For station Local security correction backup capacity already obtained.

[0050] A further improvement of the present invention is that the method further includes a rolling update process, which includes: writing back the backup bidding results of the current time period as the resource availability boundary of the next rolling time period.

[0051]

[0052] In the formula, As backup resources The total winning bid capacity in the current period, As backup resources Assigned to scheduling area The winning bid capacity, For the next rolling period, i.e., the period Backup resources Available spare boundaries, This is the upper limit of the resource reserve capacity. For resource climbing ability, This represents the time interval between adjacent rolling periods.

[0053] The wind and solar power output attenuation reserve demand rolling correction and market clearing system of the present invention includes:

[0054] The data acquisition module is used to acquire basic data including new energy power stations, meteorological conditions, power grid nodes and backup resources, and to establish a mapping relationship between power stations, regions, nodes and backup resources.

[0055] The benchmark output calculation module is used to calculate the benchmark output of new energy sources under undisturbed conditions.

[0056] The disturbance state generation module is used to construct the meteorological disturbance propagation kernel and generate disturbance states for multiple scenarios;

[0057] The reserve demand calculation module is used to determine the amount of new energy output attenuation in the corresponding scenario based on the new energy baseline output and the disturbance status of multiple scenarios, and to aggregate the data by scheduling area to calculate the partitioned rolling reserve demand.

[0058] The clearing module is used to build a backup market clearing model and obtain clearing results;

[0059] The power flow calculation module is used to perform power flow calculations based on the clearing results.

[0060] The correction module is used to perform backup resource safety correction when power flow calculations show power flow non-convergence, line load rate exceeding limits, or node voltage exceeding limits.

[0061] The beneficial effects of this invention are as follows: This invention transforms the spatial propagation, continuous evolution, and differences in power station types of meteorological disturbances into a scenario of synchronous wind and solar power attenuation, reflecting the relevant output losses of multiple renewable energy power stations under the same disturbance frontier. It replaces the fixed-proportion reserve rule with risk quantile losses within a rolling forecast window, enabling reserve demand to be dynamically adjusted according to disturbance intensity and regional loss risk. In the reserve market clearing process, it simultaneously considers bidding, ramp-up, response characteristics, inter-regional transfer penalties, and shortage penalties, enabling the output of regional prices, winning bid capacity, and shortage quantities. This invention further extends reserve capacity adequacy to electrical location feasibility verification through power flow verification and local security correction at the grid node level. Attached Figure Description

[0062] Figure 1 This is a flowchart of the method in an embodiment of the present invention; Figure 2 This is a schematic diagram of meteorological disturbance propagation in an embodiment of the present invention; Figure 3 This is a new energy loss response diagram in an embodiment of the present invention; Figure 4 This is a graph showing the partitioned rolling reserve requirement and total system requirement in an embodiment of the present invention; Figure 5 This is a partitioned standby market clearing price curve diagram in an embodiment of the present invention; Figure 6 This is a comparison chart of the composition of the reserve market winning bid capacity and the total reserve demand in an embodiment of the present invention; Figure 7 This is a comparison chart of the maximum line load rate of the IEEE 30-node system during the critical period in an embodiment of the present invention; Figure 8 This is a comparison chart of the lowest node voltage in the critical period of the IEEE 30-node system in an embodiment of the present invention; Figure 9 This is a line load rate safety trajectory diagram of an IEEE 30-node system in an embodiment of the present invention; Figure 10 This is a voltage safety trajectory diagram of the IEEE 30-node system in an embodiment of the present invention; Figure 11 This is a spatiotemporal distribution diagram of local security correction backup in an embodiment of the present invention; Figure 12 This is a field-level correction backup contribution diagram in an embodiment of the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0064] Combination Figure 1 As shown, this embodiment provides a method for rolling correction and market clearing of wind and solar power output attenuation reserve demand. This method addresses the problem of synchronous power output attenuation caused by regional meteorological disturbances under conditions of high proportion of wind and solar power grid connection. Within the same rolling link, it sequentially completes meteorological disturbance characterization, renewable energy loss scenario generation, regional reserve demand correction, reserve market clearing, node power flow verification, and local security correction, enabling meteorological risks to be transformed into actionable reserve capacity, reserve price, and reserve space allocation results. During implementation, the calculation results of the previous step are used as inputs for the next step, thus ensuring a consistent data foundation between risk identification, reserve procurement, and security correction.

[0065] This embodiment uses 24 scheduling periods as the research time domain, with a time step of 1 hour, 300 disturbance propagation scenarios, a reserve demand risk quantile of 95%, and a rolling forecast window length of 6 hours. The above values ​​are only used as calculation values ​​for this embodiment to illustrate the implementation of the method; in engineering applications, adjustments can be made based on the actual refresh cycles of the meteorological forecasting system, scheduling planning system, market reporting system, and power grid model database without affecting the technical implementation logic of this invention. The specific steps of this embodiment include:

[0066] S1. Collect basic data, including data on renewable energy power plants, meteorological conditions, grid nodes, and reserve resources. During data collection, wind farms and photovoltaic power plants are divided according to their respective dispatch areas, and the installed capacity, plant type, dispatch area, and grid connection node of each renewable energy power plant are recorded. This is used to establish meteorological disturbance propagation relationships, regional renewable energy loss aggregation relationships, and node power flow mapping relationships. Reserve resources are divided according to their respective dispatch areas, and the maximum reserve capacity, unit reserve price, ramp-up capability, response characteristics, and grid connection node of each reserve resource are recorded. This is used for reserve market clearing and grid safety correction. Reserve resources include one or more of hydropower units, energy storage resources, gas turbine units, and demand response resources. Available reserve capacity is used to compensate for the risk of renewable energy output attenuation caused by meteorological disturbances. In this embodiment, the set of renewable energy power plants is denoted as […]. The set of backup resources is denoted as And according to the dispatch area, a subset of new energy power stations and a subset of backup resources are formed, represented as follows:

[0067]

[0068] In the formula, and They belong to the first The set of new energy power stations and the set of backup resources in each dispatch area. For station The area to which it belongs, As backup resources The data is categorized by its assigned dispatch area. This categorization allows meteorological losses, reserve requirements, and market clearing to be unified under a single partitioning framework. In this embodiment, renewable energy power plants, reserve resources, and their access nodes are all included in the same index system, ensuring continuous connection between subsequent disturbance propagation, regional aggregation, market transactions, and node power flow verification. This step provides a unified data foundation for subsequent calculations, allowing all subsequent regional losses, reserve requirements, and node compensation to be traced back to the corresponding power plant or reserve resource.

[0069] S2 calculates the benchmark output of new energy sources.

[0070] This embodiment establishes a baseline power output for new energy sources under undisturbed conditions based on hourly meteorological conditions. Wind power output is determined by the wind speed versus turbine power curve, while photovoltaic output is determined by both irradiance and temperature correction. The baseline power outputs for both wind and photovoltaic power are uniformly expressed as follows:

[0071]

[0072] In the formula, For station The baseline output at time period t, and They are respectively a collection of wind farms and a collection of photovoltaic power stations. For station The installed capacity, , and Stations Wind speed, irradiance, and temperature for the corresponding time period. and For reference irradiance and reference temperature in photovoltaic calculations, This is the temperature correction factor for photovoltaic power plants. This indicates that the proportion of photovoltaic power output will be limited to between 0 and 1. This is the baseline output calculation relationship corresponding to the wind farm.

[0073] The baseline output represents both the available renewable energy generation capacity under current weather conditions and serves as the upper limit for renewable energy attenuation, specifically wind and solar attenuation. Therefore, when solar output is low at night or wind power is unavailable, it will not generate reserve demand exceeding the available output; conversely, when wind and solar resources are abundant, it provides a reasonable capacity boundary for assessing disturbance losses.

[0074] S3 constructs a meteorological disturbance propagation kernel and generates disturbance states for multiple scenarios.

[0075] In this embodiment, a meteorological disturbance propagation kernel is constructed based on the distance between power stations, the direction of disturbance propagation, and regional correlation to describe the spatial correlation of disturbances among different new energy power stations. This kernel is then normalized, and its expression is:

[0076]

[0077] In the formula, For meteorological disturbances caused by the station Spread to the station The perturbation affects the weights, i.e., the propagation of the core elements. For meteorological disturbances caused by the station The weight of the perturbation effect from self-propagation. For station With station The spatial distance between stations. For station With station The spatial distance between stations. This is a parameter representing the spatial attenuation scale of meteorological disturbance propagation. , Influenced by the direction of propagation , Due to regional correlation, For the station.

[0078] Therefore, stations that are closer in distance, located downstream of disturbance propagation, and have stronger regional correlations have a higher synchronous attenuation correlation weight in the disturbance scenario, i.e., a disturbance impact weight. This setting means that the disturbance impact is no longer regarded as a random fluctuation that is independent of each station, but can reflect the characteristics of regional weather processes spreading along the spatial direction and causing coordinated losses at multiple stations.

[0079] A time-varying meteorological disturbance front is constructed, and a recursive model including disturbance persistence, spatial propagation, external shock, and random noise terms is used to generate disturbance states for multiple scenarios, expressed as follows:

[0080]

[0081] In the formula, For station In the scene Time period The disturbance state. This is the disturbance duration coefficient. For the scene Next station During the period The disturbance state. The spatial propagation coefficient, For meteorological disturbances caused by the station Spread to the station The perturbation affects the weight. For the scene Next station During the period The disturbance state. For the moving disturbance front to the station The direct impact, For station In the scene Time period External impact strength coefficient, For station In the scene Time period random noise term, This is for amplitude limiting.

[0082] This embodiment retains the three key elements of disturbance persistence, spatial propagation, and external impact, and limits the disturbance state to a reasonable range. Combined with... Figure 2 As shown, the disturbance state of each station progresses over time and is propagated and diffused in space, which can reflect the linkage effect of regional meteorological processes on multiple stations.

[0083] S4 converts the power station disturbance state into renewable energy output attenuation, aggregates it by dispatch area, and calculates the zonal rolling reserve demand. Power station-level losses are jointly determined by the disturbance state, power station capacity, and resource type, and are also limited by baseline output. Regional-level renewable energy losses are obtained by summing the losses of all power stations within the corresponding dispatch area.

[0084]

[0085] In the formula, For station In the scene Time period The amount of output attenuation, As a benchmark, For installed capacity, The attenuation sensitivity corresponding to the type of station. For station In the scene Time period The disturbance state. For scheduling area The loss of new energy after aggregation For scheduling area The collection of stations, For station During the period The baseline output.

[0086] Combination Figure 3 As shown, the average loss in the dispatch area and the system risk quantile loss rise synchronously during periods of high disturbance frequency, reflecting the driving effect of the propagation scenario on reserve demand. Through step S4, the meteorological disturbance state is converted into a power gap that can be directly used by the market and dispatch.

[0087] Within each rolling period, a future prediction window is selected, i.e., the rolling prediction window, and new reserve requirements are formed based on the high quantile level of the regional loss scenario:

[0088]

[0089] In the formula, For scheduling area During the current rolling period The corresponding risk quantile for new energy losses, For the scene Lower scheduling area During the period The reduction in the output of new energy sources For a set of perturbation scenarios, For risk confidence level, For a rolling forecast window starting from the current time period, For the next scheduling area The new backup demand, For scheduling area The allocated reserves are represented by B, which represents the engineering margin. Compared to a fixed reserve ratio, this embodiment can dynamically adjust reserve requirements based on the intensity of disturbance propagation, the degree of regional impact, and changes in the rolling forecast window.

[0090] In this embodiment, if existing reserves can cover risk quantile losses, the additional reserve requirement is automatically adjusted to 0, avoiding duplicate procurement. This step emphasizes the maximum potential renewable energy gap within the future window, rather than determining reserve capacity solely based on current single-point losses. Therefore, it is better suited to the characteristics of weather disturbances, which have both early propagation and delayed impacts.

[0091] S5, construct a backup market clearing model.

[0092] After the regional rolling reserve demand is formed, the reserve resource price is adjusted according to the regional risk signal, and the regional transfer penalty is added to form an effective clearing price:

[0093]

[0094] In the formula, As backup resources Service Dispatch Area Effective clearing price at that time The price is based on backup resources. This is a regional risk pricing adjustment factor. For the area where the backup resource belongs during the time period The risk signal is used to characterize the degree of shortage of reserve resources in the dispatch area under meteorological disturbances. For resource response characteristics, Impact of supply transfer when providing backup across regions.

[0095] The pricing of reserve resources can simultaneously reflect the resource base cost, response value, regional scarcity, and the impact of inter-regional transfer. The higher the regional risk, the better the price signal of the relevant reserve resources reflects the adjustment value of the reserve resources during that period, thus linking the market clearing results with the level of meteorological risk.

[0096] The clearing of the spare parts market is constrained by meeting the regional spare parts demand, and aims to minimize the sum of procurement costs and shortage penalties.

[0097]

[0098] In the formula, As backup resources Assigned to scheduling area The winning bid capacity, For scheduling area Unmet reserve shortages As a backup shortage penalty, This objective function addresses regional reserve requirements. It aims to control procurement costs and shortage risks while satisfying regional reserve needs.

[0099] During the clearing process, transactions are executed from low to high based on the effective price. Under constraints of capacity, ramp-up, and shortage, the winning bid capacity for each reserve resource, the regional clearing price, and the reserve shortage amount are obtained. For demand that cannot be fully covered by existing reserve resources, a reserve shortage penalty is used to reflect the operational risk of insufficient reserves, thus giving the clearing result both economic and safety constraints. Figures 4 to 6 As shown, the standby price and winning bid capacity vary with risk quantile loss and resource responsiveness. Step S5 ensures that standby resources are not only competitively priced but also subject to constraints such as location, response speed, and transfer costs, thereby preventing over-reliance on low-priced resources with insufficient electrical location or responsiveness.

[0100] S6, Node power flow calculation and verification.

[0101] The reduction in renewable energy and the awarded capacity of reserve resources are mapped to a node model, i.e., the IEEE 30-node system, for power flow verification. Renewable energy loss is equivalent to an increase in the active power load of access nodes, while reserve compensation and local security correction are equivalent to a decrease in the active power load of nodes. Through this mapping, the clearing results of the reserve market in the scheduling area are converted into node power changes that can be used for power flow calculation.

[0102]

[0103] In the formula, The corrected node active power load, For station The equivalent loss of new energy sources, As backup resources Equivalent active power compensation. Local compensation formed near the disturbed station is used for safe correction. , These are designated as new energy power stations and backup resource access nodes, used to establish connections between power stations or resources and nodes. The mapping relationship between them.

[0104] AC power flow calculations are performed based on the modified node active load to obtain whether the power flow converges, the voltage amplitude of each node, the active and reactive power flow of the lines, and the line load. The number of node voltage overruns, the number of lines overloaded, the maximum line load rate, and the minimum node voltage are statistically analyzed based on the node voltage upper and lower limits and the line capacity limits. This data is used to determine the safety and feasibility of the standby market clearing results at the node level.

[0105] When power flow fails to converge, line load rate exceeds limits, or node voltage exceeds limits, a safety correction objective function is constructed, and local reserve transfer is performed within the allocated reserve capacity, i.e., safety correction is implemented. Specifically, when power flow calculation fails to converge, the safety correction objective function is assigned a preset non-convergence penalty value; otherwise, the safety correction objective function is calculated based on the degree of line load rate exceeding limits, node voltage deviation, and the number of exceeding limits. The expression is as follows:

[0106]

[0107] In the formula, To pre-determine the non-convergence penalty value, , , These are the penalty weights for exceeding line load rate limits, node voltage deviation, and the number of exceeding limits, respectively. To safely correct the objective function, For the maximum line load rate, For line load rate limits, The lowest node voltage, This is the lower limit of voltage. and These are the number of line overloads and the number of node voltage exceeding limits, respectively.

[0108]

[0109] In the formula, As backup resources Towards new energy power stations The spare capacity corresponding to the local calibration location transfer, To preset the correction step size, As backup resources Adjustable reserve capacity that has already been awarded in the bidding process. For new energy power stations The amount of output attenuation, For new energy power stations The local safety correction reserve capacity already obtained. The single transfer capacity is taken as the minimum of the above three values ​​to simultaneously meet the constraints of correction step size, resource adjustable capacity, and site remaining compensation requirements.

[0110] Local safety correction adjusts the spatial distribution of remote reserve and reserve near disturbed sites within the already contracted reserve capacity, prioritizing the reduction of line over-limit, voltage deviation, and the number of over-limit incidents. Combined with... Figures 7 to 12 As shown, rolling reserves and safety correction can improve power flow safety indicators after renewable energy degradation and reduce the objective function of safety correction. Power flow safety indicators include the maximum line load rate, minimum node voltage, number of line overloads, and number of node voltage exceedances statistically analyzed during power flow verification. By performing local safety correction based on the clearing results, the problem of insufficient support near disturbed nodes can be avoided, even if the reserve requirements are met only in the total IEEE 30-node system.

[0111] S7, Rolling Backup Market Update and Security Correction Results Output.

[0112] After completing S1 to S6, the output includes partitioned rolling reserve demand, effective clearing price, reserve resource winning bid capacity, reserve shortage, power flow safety indicators, and local safety correction reserve capacity, i.e., the local compensation formed by safety correction reserve near the disturbed site. The system incorporates safety correction iteration information and writes back the current bidding results, i.e., the winning capacity in the reserve resources, as the resource availability boundary for the next time period. The safety correction iteration information records each round of adjustments and improvements in power flow indicators during the safety correction process. This write-back ensures that the continuous responsiveness of resources is reflected between adjacent rolling time periods.

[0113]

[0114] In the formula, As backup resources The total winning bid capacity in the current period, As backup resources Assigned to scheduling area The winning bid capacity, For the next rolling period, i.e., the period Backup resources Available spare boundaries, This is the upper limit of the resource reserve capacity. For resource climbing ability, This represents the time interval between adjacent rolling periods.

[0115] After entering the next time period, the meteorological status, new energy benchmark output, disturbance status, reserve resource status and market quotation parameters are reacquired, and disturbance scenarios are regenerated according to S2 to S6, the rolling reserve demand of the partition is calculated, the reserve market clearing and node security verification are performed.

[0116] Through the aforementioned rolling updates, the partitioned rolling reserve demand, effective clearing price, reserve resource winning bid capacity, reserve shortage, power flow safety indicators, and local safety correction reserve capacity can be continuously corrected as weather disturbances propagate and resource status changes. The clearing and correction results of the current period also serve as the status input for the calculation of the next period, ensuring the temporal continuity of the reserve configuration scheme.

[0117] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.

[0118] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for rolling adjustment of reserve demand due to wind and solar power output attenuation and market clearing, characterized in that, include: Acquire basic data including new energy power plants, meteorological conditions, power grid nodes and backup resources, and establish mapping relationships between new energy power plants, regions, nodes and backup resources; The baseline output of new energy sources under undisturbed conditions is calculated based on meteorological data. Meteorological disturbance propagation kernels are constructed based on inter-station distances, disturbance propagation directions, and regional correlations, and disturbance states in multiple scenarios are generated. Based on the baseline output of new energy sources and the disturbance status of multiple scenarios, the output attenuation of new energy sources under the corresponding scenarios is determined, and aggregated according to the scheduling area to calculate the rolling reserve demand of the partition. A reserve market clearing model is constructed based on the partitioned rolling reserve demand to obtain the clearing results; Based on the power output attenuation of new energy sources and the clearing results, the power output attenuation of new energy sources and reserve compensation are mapped to grid nodes, and power flow calculations are performed. When power flow calculations show power flow non-convergence, line load rate exceeding limits, or node voltage exceeding limits, backup resource safety correction is performed.

2. The method for rolling adjustment of wind and solar power output attenuation reserve demand and market clearing according to claim 1, characterized in that, The baseline output of new energy sources under undisturbed conditions is calculated based on meteorological data, including: For wind farms, the benchmark wind power output is calculated based on wind speed data and wind turbine power curves. For photovoltaic power plants, the photovoltaic baseline output is calculated based on solar irradiance and ambient temperature.

3. The method for rolling adjustment of wind and solar power output attenuation reserve demand and market clearing according to claim 1, characterized in that, Construct a meteorological disturbance propagation kernel to generate disturbance states in multiple scenarios, including: Based on the distance between stations, the direction of disturbance propagation, and regional correlation, a meteorological disturbance propagation kernel is constructed and normalized. The expression is as follows: ； In the formula, For meteorological disturbances caused by the station Spread to the station The perturbation affects the weights, i.e., the propagation of the core elements. For meteorological disturbances caused by the station The weight of the perturbation effect from self-propagation. For station With station The spatial distance between stations. This is a parameter representing the spatial attenuation scale of meteorological disturbance propagation. Influenced by the direction of propagation Due to regional correlation, For the station; A time-varying meteorological disturbance front is constructed, and a recursive model including disturbance persistence, spatial propagation, external shock, and random noise terms is used to generate disturbance states for multiple scenarios, expressed as follows: ； In the formula, For station In the scene Time period The disturbance state. This is the disturbance duration coefficient. For the scene Next station During the period The disturbance state. The spatial propagation coefficient, For meteorological disturbances caused by the station Spread to the station The perturbation affects the weight. For the scene Next station During the period The disturbance state. For the moving disturbance front to the station The direct impact, For station In the scene Time period External impact strength coefficient, For station In the scene Time period random noise term, This is for amplitude limiting.

4. The method for rolling adjustment of wind and solar power output attenuation reserve demand and market clearing according to claim 1, characterized in that, Based on the baseline output of new energy sources and the disturbance states in multiple scenarios, the output attenuation of new energy sources in the corresponding scenarios is determined and aggregated according to the scheduling region. The expression is as follows: ； In the formula, For station In the scene Time period The amount of output attenuation, As a benchmark, For installed capacity, The attenuation sensitivity corresponding to the type of station. For station In the scene Time period The disturbance state. For scheduling area The loss of new energy after aggregation For scheduling area The collection of stations, For station During the period The baseline output.

5. The method for rolling adjustment of wind and solar power output attenuation reserve demand and market clearing according to claim 4, characterized in that, Calculate the partition rolling standby requirement, including: Within each rolling period, a rolling forecast window starting from the current period is selected, and the maximum renewable energy output attenuation of each scenario in the corresponding scheduling area within the rolling forecast window is calculated. The maximum renewable energy output attenuation is calculated using empirical quantiles based on a preset information level, yielding the risk quantile loss value for each dispatch area in the current time period. Subtract the allocated reserve capacity for the corresponding scheduling area from the risk quantile loss value, and add the engineering margin to obtain the new reserve demand for the corresponding scheduling area during the current rolling period. When the new reserve requirement is less than zero, the new reserve requirement will be corrected to zero.

6. The method for rolling adjustment of wind and solar power output attenuation reserve demand and market clearing according to claim 1, characterized in that, A reserve market clearing model is constructed based on partitioned rolling reserve demand, including: A regional risk signal is constructed based on the rolling reserve requirements of each scheduling region and the upper limit of the regional reserve requirements. The dynamic price of the standby resource is updated based on the regional risk signal of the scheduling region to which the standby resource belongs, the response characteristics of the standby resource, and the basic price. For any reserve resource and any demand scheduling region, calculate the effective clearing price, which includes the dynamic price of reserve resources and the regional transfer penalty; The reserve market is cleared in order of effective clearing price from low to high, while simultaneously satisfying the maximum capacity constraint and ramp-up capability constraint of reserve resources during the clearing process. When the reserve demand in the dispatch area is not fully met, the corresponding reserve shortage amount in the dispatch area is recorded, and the reserve shortage penalty is included in the calculation of the total clearing cost and the regional clearing price for that period.

7. The method for rolling adjustment of wind and solar power output attenuation reserve demand and market clearing according to claim 1, characterized in that, Based on the clearing results, power flow calculations are performed, including: The risk quantile loss value of the dispatch area is allocated to the corresponding new energy power station access node according to the proportion of the installed capacity of each power station, and is used as the increase in active power load of the corresponding node; The winning bid capacity of the reserve resources is mapped to the reduction in active load according to the access node of the reserve resources, which represents the compensation of the reserve resources for the power gap caused by the reduction of new energy output. Based on the modified node active load, AC power flow calculation is performed to obtain whether the power flow converges, the voltage amplitude of each node, the active and reactive power flow of the line, and the line load rate. The number of node voltage overruns, the number of lines overloaded, the maximum line load rate, and the minimum node voltage are statistically analyzed based on the node voltage upper and lower limits and line capacity limits.

8. The method for rolling adjustment of wind and solar power output attenuation reserve demand and market clearing according to claim 1, characterized in that, When power flow calculations show power flow non-convergence, line load rate exceeding limits, or node voltage exceeding limits, backup resource safety correction is performed, specifically including: When power flow fails to converge, line load rate exceeds limits, or node voltage exceeds limits, a safety correction objective function is constructed, and local reserve transfer is executed within the allocated reserve capacity. Specifically, when power flow calculation fails to converge, the safety correction objective function is assigned a preset non-convergence penalty value; otherwise, the safety correction objective function is calculated based on the degree of line load rate exceeding limits, node voltage deviation, and the number of exceeding limits. The expression is as follows: ； In the formula, To pre-determine the non-convergence penalty value, , , These are the penalty weights for exceeding line load rate limits, node voltage deviation, and the number of exceeding limits, respectively. To safely correct the objective function, For the maximum line load rate, For line load rate limits, The lowest node voltage, This is the lower limit of voltage. and These are the number of line overloads and the number of node voltage overruns, respectively. ； In the formula, As backup resources To the station The spare capacity corresponding to the local calibration location transfer, To preset the correction step size, As backup resources Adjustable reserve capacity that has already been awarded in the bidding process. For station The amount of output attenuation, For station Local security correction backup capacity already obtained.

9. The method for rolling adjustment of wind and solar power output attenuation reserve demand and market clearing according to claim 1, characterized in that, The method also includes a rolling update process, which involves writing back the standby bid results for the current time period as the resource availability boundary for the next rolling time period. ； In the formula, As backup resources The total winning bid capacity in the current period, As backup resources Assigned to scheduling area The winning bid capacity, For the next rolling period, i.e., the period Backup resources Available spare boundaries, This is the upper limit of the resource reserve capacity. For resource climbing ability, This represents the time interval between adjacent rolling periods.

10. A wind and solar power output attenuation reserve demand rolling correction and market clearing system based on the methods described in claims 1 to 9, characterized in that: include: The data acquisition module is used to acquire basic data including new energy power stations, meteorological conditions, power grid nodes and backup resources, and to establish a mapping relationship between power stations, regions, nodes and backup resources. The benchmark output calculation module is used to calculate the benchmark output of new energy sources under undisturbed conditions. The disturbance state generation module is used to construct the meteorological disturbance propagation kernel and generate disturbance states for multiple scenarios; The reserve demand calculation module is used to determine the amount of new energy output attenuation in the corresponding scenario based on the new energy baseline output and the disturbance status of multiple scenarios, and to aggregate the data by scheduling area to calculate the partitioned rolling reserve demand. The clearing module is used to build a backup market clearing model and obtain clearing results; The power flow calculation module is used to perform power flow calculations based on the clearing results. The correction module is used to perform backup resource safety correction when power flow calculations show power flow non-convergence, line load rate exceeding limits, or node voltage exceeding limits.

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