Cross-provincial backup sharing multi-stage coordination method considering channel capacity uncertainty

Abstract

The present application relates to the field of power system and automation, in particular to a multi-level coordination method for cross-provincial reserve sharing considering uncertain channel capacity, the steps are as follows: a dynamic security interval calculation model of cross-provincial tie-line is constructed considering the uncertainty of new energy physical prediction and market behavior, and the dynamic security transmission capacity interval is determined; a cross-provincial reserve coordination transaction optimization model covering the market clearing of grid dispatch cross-provincial reserve and the safety check of provincial dispatch under accident scenario is established respectively, and a cross-provincial reserve coordination optimization model covering the dispatch of national dispatch cross-provincial reserve and the safety check of grid dispatch under accident scenario is established; a multi-level coordination solving model is established based on the alternating direction multiplier method, the two reserve coordination optimization models are solved with the dynamic security transmission capacity interval as the constraint, and the cross-provincial reserve coordination operation scheme is generated. The present application can maximize the release of reserve sharing benefit under the premise of ensuring the safety of power grids at all levels, and improve the system's resilience and economic level.

Description

Technical Field

[0001] This invention relates to the field of power system and automation technology, and more specifically, to a multi-level collaborative method for cross-provincial and regional backup sharing that takes into account channel capacity uncertainty. Background Technology

[0002] With the rapid construction of inter-regional and inter-provincial power transmission channels and the deepening of power market reforms in my country, the sharing of reserve resources under the interconnected grid structure has become an important means to improve the overall reliability of the system, promote the consumption of renewable energy, and achieve optimal resource allocation. However, current practices of inter-regional and inter-provincial reserve coordination and trading face significant challenges: First, the large-scale integration of high-proportion renewable energy sources has dramatically increased the uncertainty of the power system. This is reflected not only in the inherent physical prediction deviations of wind and solar power output, but also in the uncertainty of their bidding and winning behaviors in the market environment. These uncertainties, through power flow correlation, directly affect the available transmission capacity of inter-regional and inter-provincial interconnection lines, transforming it from a fixed value under traditional deterministic planning into a dynamic and fuzzy safety range. Existing reserve sharing models typically treat interconnection line capacity as a deterministic parameter, ignoring this key uncertainty. This may lead to actual channel obstruction during reserve call-up, or be overly conservative, limiting the mutual support potential of reserve resources and failing to achieve a precise balance between security and economy. Second, existing reserve coordination models are difficult to adapt to the complex security and economic coordination needs under the multi-level dispatch system of "national-regional-provincial". From an economic perspective, market clearing or collaborative optimization models need to be built at the grid and national dispatch levels to minimize standby procurement costs and coordinate the use of standby resources across the entire grid. From a security perspective, provincial and grid dispatch centers need to conduct security checks on standby deployment plans issued by higher authorities, taking into account potential accident scenarios within their regions (such as generator failures and line trips), to ensure the deliverability of standby resources and the safe operation of the power grid in the event of an accident. Currently, there is a lack of a unified framework that can organically couple the above-mentioned economic optimization and security verification models, resulting in standby plans often struggling to balance economic optimization and security feasibility, leading to frequent decision-making conflicts between different levels. Finally, standby sharing involves multiple levels of dispatching entities and market operators, including national, regional, and provincial levels. These levels differ in their optimization objectives (such as global economics, regional security, and provincial autonomy), information privacy, and decision-making authority, forming a complex multi-level decision-making system. Traditional centralized optimization methods are ill-suited to this hierarchical and autonomous architecture, while simple decentralized coordination cannot guarantee global optimality and security feasibility under conditions of uncertain tie-line capacity. Summary of the Invention

[0003] The purpose of this invention is to provide a multi-level collaborative method for cross-provincial backup sharing that takes into account the uncertainty of channel capacity, so as to solve the technical problems pointed out in the background art.

[0004] This invention is achieved through the following technical solution: a multi-level collaborative method for cross-provincial / regional backup sharing that takes into account channel capacity uncertainty, comprising the following steps:

[0005] Acquire basic power system operation data, construct a dynamic safety interval calculation model for inter-regional and inter-provincial interconnection lines, and determine the dynamic safety transmission capacity interval of inter-regional and inter-provincial interconnection lines based on the net uncertainty injection scenario that considers the uncertainty of new energy physical prediction and market behavior uncertainty.

[0006] Establish an optimized model for cross-provincial backup collaborative transactions that includes a clearing model for the cross-provincial backup market and a safety verification model for provincial backup under accident scenarios;

[0007] Establish a cross-regional standby collaborative optimization model that encompasses the national dispatch cross-regional standby dispatch model and the grid dispatch safety verification model under accident scenarios;

[0008] A multi-level collaborative solution model for cross-provincial and cross-regional backup sharing based on the alternating direction multiplier method is established. The dynamic security transmission capacity range is used as the transmission constraint of the cross-regional and cross-provincial connection line. The cross-provincial backup collaborative transaction optimization model and the cross-regional backup collaborative optimization model are solved in a distributed iterative manner to generate a cross-provincial and cross-regional backup collaborative operation scheme.

[0009] According to a preferred embodiment, the net uncertainty injection scenario based on considering the uncertainty of new energy physical predictions and market behavior specifically includes:

[0010] The maximum physical power generation capacity of a new energy power station is expressed as the sum of the predicted value and the prediction deviation, generating a set of physical prediction uncertainty scenarios;

[0011] Under the reporting volume without bidding model, the market behavior deviation of new energy power stations is represented as the market reporting deviation power and the market clearing deviation power, generating a set of uncertain market behavior scenarios;

[0012] Based on the condition that the actual power generation of new energy power plants is constrained by both physical upper limits and market clearing plans, and combining the set of physical prediction uncertainty scenarios and the set of market behavior uncertainty scenarios, a set of new energy net uncertainty injection scenarios containing multiple scenarios is generated.

[0013] According to a preferred embodiment, the determination of the dynamic secure transmission capacity range of the inter-regional and inter-provincial connection line specifically includes:

[0014] Based on the power transfer distribution factor of the tie line injected by the node with unit power, calculate the tie line power flow fluctuations for each scenario under the set of net uncertainty injection scenarios.

[0015] Based on conditional value at risk and the set risk tolerance, the risk margins for upward and downward fluctuations of the connection line are calculated respectively.

[0016] The dynamic safe transmission capacity range of inter-regional and inter-provincial interconnection lines is determined by deducting the corresponding risk margin from the physical transmission upper and lower limits of the interconnection line channel and adding a precautionary margin.

[0017] According to a preferred embodiment, the network dispatch cross-provincial standby market clearing model aims to minimize the total network standby dispatch purchase cost, and its constraints include:

[0018] Provincial-level and provincial-level reserve demand constraints are used to ensure that the actual amount of cross-provincial reserve calls and reserved activation amounts do not exceed their market winning bid amounts.

[0019] The reserved capacity constraints of grid-dispatch and provincial-dispatch units are used to constrain the output of each unit after providing inter-provincial backup to be within its minimum and maximum technical output range;

[0020] The inter-provincial tie line transmission capacity constraint is used to constrain the sum of the base state power flow, power flow changes caused by backup calls, and the maximum power flow changes that may be caused by the full activation of reserved capacity to cope with future faults to be within the dynamic safe transmission capacity range of the inter-provincial tie line.

[0021] According to a preferred embodiment, the provincial dispatch safety verification model under accident scenarios aims to minimize the accident dispatch cost under provincial accident scenarios, and its constraints include:

[0022] Power limit constraint is used to ensure that the actual amount of cross-provincial backup power used in the event of an accident does not exceed its market bid volume;

[0023] The cross-provincial reserve supply and demand balance constraint after the accident: the cross-provincial reserve used to constrain the call must meet the power deficit caused by the accident in the province.

[0024] The inter-provincial standby deliverability constraint is used to ensure that when all available standby lines are dispatched in the event of an accident, the power flow of the inter-provincial tie line does not exceed the security threshold determined by the dynamic secure transmission capacity range of the inter-provincial tie line.

[0025] According to a preferred embodiment, the national grid inter-regional reserve dispatch model aims to minimize the total cost of inter-regional upper and lower reserve coordination under the premise of grid security, and its constraints include:

[0026] Regional upper and lower reserve demand constraints are used to prioritize meeting reserve demands by utilizing slack variables, and to minimize reserve deficits when they cannot be met.

[0027] The reserved capacity constraints for generating units at the national, grid, and provincial dispatch levels are used to ensure that the output of generating units at all levels does not exceed the limits after providing cross-regional backup.

[0028] The cross-regional tie line transmission capacity constraint is used to ensure that the sum of power flow changes caused by the base state, backup dispatch, and fault reservation is within the dynamic safe transmission capacity range of the cross-regional tie line.

[0029] According to a preferred embodiment, the network dispatch safety verification model under accident scenarios aims to minimize the accident dispatch cost under regional accident scenarios, and its constraints include:

[0030] The cross-regional backup dispatch power limit constraint is used to ensure that the actual cross-regional backup dispatch volume does not exceed its market bid volume during an accident.

[0031] Post-accident cross-regional reserve supply and demand balance constraints: the amount of cross-regional reserve used to constrain the call must meet the power deficit in the region caused by the accident.

[0032] Cross-regional standby deliverability constraints are used to ensure that, when all available standby lines are scheduled during an incident, the power flow of the cross-regional tie line does not exceed the security threshold determined by the dynamic secure transmission capacity range of the cross-regional tie line.

[0033] According to a preferred embodiment, the cross-provincial backup collaborative transaction optimization model is solved through distributed iterative solution, specifically including:

[0034] Construct a network-based and provincial-based hierarchical optimization objective function that includes the original optimization objective of the network-based survey, the original safety verification objective of the provincial-based survey, and cross-provincial backup collaborative iteration penalty items;

[0035] In each iteration, the network dispatch and each provincial dispatch independently solve and update the reserve coordination quantity, and based on the deviation between the network dispatch and the provincial dispatch in the upper and lower reserve gaps and coordination quantities, update the primary penalty coefficient and the secondary penalty coefficient.

[0036] Determine whether the coordination between the network survey and the provincial survey meets the convergence condition. If not, proceed to the next iteration until convergence, and formulate a cross-provincial backup coordination plan at the network survey and provincial survey levels accordingly.

[0037] According to a preferred embodiment, the cross-regional backup collaborative optimization model is solved in a distributed iterative manner, specifically including:

[0038] Construct a national and regional level optimization objective function that includes the original optimization objective of the national survey, the original safety verification objective of the network survey, and the cross-regional backup collaborative iteration penalty.

[0039] In each iteration, the National Dispatch Center and each Network Dispatch Center independently solve and update the reserve coordination quantity. Based on the deviations between the National Dispatch Center and the Network Dispatch Center in the upper and lower reserve gaps and coordination quantities, the primary penalty coefficient and secondary penalty coefficient of cross-regional reserve coordination are updated.

[0040] Determine whether the coordination quantity between the national and regional surveys meets the convergence condition. If not, proceed to the next iteration until convergence, and formulate a cross-regional backup coordination plan at the national and regional survey levels accordingly.

[0041] According to a preferred embodiment, the generation of the cross-provincial and cross-regional backup collaborative operation scheme specifically includes:

[0042] After the cross-provincial backup coordination at the provincial and national levels and the cross-regional backup coordination at the national and national levels have both reached convergence, the final cross-provincial and cross-regional backup coordination plan is output.

[0043] Based on the final inter-provincial and inter-regional backup coordination plan, the national dispatch center and the grid dispatch center modify and issue the transmission plans for inter-regional and inter-provincial interconnection lines to control the coordinated balance and backup dispatch operation of the physical multi-level power grid.

[0044] The technical solution of the cross-provincial and cross-regional reserve sharing multi-level collaborative method considering channel capacity uncertainty provided by this invention has at least the following advantages and beneficial effects: This invention quantifies the impact of the dual uncertainties of new energy physics and market on the safety boundary of tie lines, constructs a dynamic safety interval calculation model for tie lines, and provides more accurate transmission constraints for reserve sharing; furthermore, it constructs cross-provincial and cross-regional two-level collaborative optimization models covering market clearing and safety verification, realizing hierarchical and collaborative optimization of reserve resources between regions and across the entire network; finally, it introduces ADMM to construct a distributed solution framework, using iterative negotiation to solve complex optimization problems under multi-subject, multi-level, and uncertain constraints, thereby maximizing the benefits of cross-regional and cross-provincial reserve sharing while ensuring the safe operation of power grids at all levels, and improving the resilience and economic operation level of large power grids in the face of uncertainty. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating the cross-provincial / regional backup sharing multi-level collaborative method provided in Embodiment 1 of the present invention.

[0046] Figure 2 This is a framework diagram for determining the dynamic secure transmission capacity range of the tie line according to Embodiment 1 of the present invention;

[0047] Figure 3 This is a diagram of the cross-provincial and cross-regional backup collaborative transaction optimization framework provided in Embodiment 1 of the present invention;

[0048] Figure 4 The flowchart for solving the cross-provincial and cross-regional backup collaborative mechanism provided in Embodiment 1 of the present invention is shown. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0050] Example 1

[0051] This invention provides a multi-level collaborative method for cross-provincial / regional backup sharing that takes into account channel capacity uncertainty. Figure 1 See the flowchart for this cross-provincial / regional backup sharing multi-level collaborative method. Figure 1 As shown, this cross-provincial / regional backup sharing multi-level collaborative method includes the following steps:

[0052] Step S1: Construct a dynamic safety interval calculation model for inter-regional and inter-provincial connecting lines;

[0053] First, basic operational data of the power system is acquired. In some implementations, this includes grid topology, unit parameters, renewable energy output and application data, reserve demand, and market clearing prices. Further, a dynamic safety interval calculation model for inter-regional and inter-provincial power line interconnections is constructed based on this basic operational data. It should be noted that the core of this dynamic safety interval calculation model for inter-regional and inter-provincial power line interconnections lies in considering the net uncertainty injection scenario generated by the uncertainty of renewable energy physical predictions and market behavior, thereby determining the dynamic safety transmission capacity interval of inter-regional and inter-provincial power line interconnections.

[0054] Specifically, the net uncertainty injection scenario, which takes into account the uncertainties of new energy physics predictions and market behavior, includes the following process:

[0055] Step S101: Considering that for a new energy power station, the maximum physical power generation at a specific moment is a random variable, composed of the baseline predicted output and the prediction deviation, this embodiment expresses the maximum physical power generation of the new energy power station as the sum of the predicted value and the prediction deviation, generating a set of physical prediction uncertainty scenarios, as shown in the following expression:

[0056]

[0057] In the above formula, Representing a scene The next node New energy power stations during the time period The maximum physical power that can be generated. Representing a scene The next node New energy power stations during the time period The predicted output Representing a scene The next node New energy power stations during the time period Prediction bias, , These respectively represent the inter-provincial connection lines. and inter-regional connection lines The associated set of network nodes can generate a set of related data based on historical data. A collection of physical prediction uncertainty scenarios Each scene One corresponding group , Representing a scene The probability of occurrence.

[0058] Step S102: In the reporting quantity without bidding mode, the market behavior deviation of new energy power stations is represented as the market declaration deviation power and the market clearing deviation power, generating a set of market behavior uncertainty scenarios. Among them, the market declaration deviation power reflects the aggressive or conservative behavior tendency of new energy power stations in declaration, and the expression is as follows:

[0059]

[0060] In the above formula, Representing a scene The next node New energy power stations during the time period Market declaration deviation power, This indicates that the application for new energy power stations is conservative. This indicates that the application for new energy power stations is aggressive. Representing a scene The next node New energy power stations during the time period The market declaration power, Representing a scene The next node New energy power stations during the time period The predicted output;

[0061] The market clearing deviation power reflects the difference between the planned market clearing output and the declared output, expressed as follows:

[0062]

[0063] In the above formula, Representing a scene The next node New energy power stations during the time period Market clearing deviation power, Representing a scene The next node New energy power stations during the time period The market clearing plan contributes to the effort, and can generate data based on historical data. A collection of scenarios with market behavior uncertainty Each scene One corresponding group , Representing a scene The probability of occurrence.

[0064] Step S103: Based on the condition that the actual power generation of new energy power plants is constrained by both the physical upper limit and the market clearing plan, and combining the set of physical prediction uncertainty scenarios and the set of market behavior uncertainty scenarios, a set of new energy net uncertainty injection scenarios containing multiple scenarios is generated, as expressed below:

[0065]

[0066]

[0067] In the above formula, Representing a scene The next node New energy power stations during the time period Net uncertainty injection after considering physical forecast uncertainty and market behavior uncertainty. Representing a scene The next node New energy power stations during the time period The predicted output Representing a scene The next node New energy power stations during the time period Contribute to the market clearing plan Representing a scene The next node New energy power stations during the time period Actual execution deviation power, Representing a scene The next node New energy power stations during the time period Actual output based on scenario set and , can be obtained containing Net uncertainty injection scenario set for each scenario Each scene One corresponding group , Representing a scene The probability of occurrence.

[0068] Furthermore, the determination of the dynamic secure transmission capacity range of the inter-regional and inter-provincial connection lines is detailed in [reference needed]. Figure 2 As shown, it specifically includes:

[0069] Step S104: Based on the power transfer distribution factor of node-injected unit power to the tie line, calculate the tie line power flow fluctuations corresponding to each scenario in the set of net uncertainty injection scenarios, as shown in the following expression:

[0070]

[0071] In the above formula, Representing a scene Offline The tidal fluctuations are affected by the net uncertainty injection of new energy sources at relevant nodes. Represents a node Injecting unit power to tie line The power transfer distribution factor, Representing a scene Offline During the period Net uncertainty injection after considering physical forecast uncertainty and market behavior uncertainty.

[0072] Step S105: Based on the conditional value at risk and the set risk tolerance, calculate the risk margin for upward and downward fluctuations of the connection line, respectively, as shown in the following expressions:

[0073]

[0074]

[0075]

[0076]

[0077]

[0078]

[0079] In the above formula, , These represent risk tolerance levels. The number of upward and downward fluctuations of the lower connecting line. , These represent the number of upward and downward fluctuations in the connecting line fluctuations, respectively. , These represent risk tolerance levels. Offline At any moment The upward and downward fluctuation thresholds Indicates the contact line During the period The upward volatility risk threshold Indicates the contact line During the period The absolute value of the downward volatility risk threshold. , These respectively represent exceeding the risk tolerance level. The communication line At any moment The mean of the upward and downward extreme values, Indicates the contact line At any moment An upward-fluctuating sorted set. Indicates the contact line At any moment A sorted set of absolute values ​​that fluctuate downwards.

[0080] Step S106: Subtract the corresponding risk margin from the physical transmission upper and lower limits of the tie line channel and add a precautionary margin to determine the dynamic secure transmission capacity range of the cross-regional and cross-provincial tie line, as shown in the following expression:

[0081]

[0082]

[0083] In the above formula, , Separate connecting lines At any moment The upper and lower limits of dynamic secure transmission capacity. , They represent the connecting lines respectively. The upper and lower limits of physical transmission Indicates the contact line The safety transmission capacity range has a preventative margin.

[0084] Step S2: Establish a cross-provincial backup collaborative transaction optimization model;

[0085] like Figure 3 The cross-provincial backup collaborative transaction optimization model established in this embodiment covers the cross-provincial backup market clearing model of network dispatch and the provincial dispatch safety verification model under accident scenarios;

[0086] The network dispatch cross-provincial backup market clearing model aims to minimize the total network backup call purchase cost, and its expression is as follows:

[0087]

[0088] In the above formula, , Representing regions Internal network dispatching units and provincial areas Internal inspection and adjustment of generator units During the period The Inter-provincial backup capacity , Representing regions Internal network dispatching units and provincial areas Internal inspection and adjustment of generator units During the period The The inter-provincial backup capacity was selected through a bidding process. , Representing regions Internal network dispatching units and provincial areas Internal inspection and adjustment of generator units During the period The Inter-provincial standby quote, , Representing regions Internal network dispatching units and provincial areas Internal inspection and adjustment of generator units During the period The Inter-provincial standby quote, Indicates the area Internal network direct dispatching units collection. Indicates province The internal inspection and adjustment of the generating units was carried out. This represents a set of backup provinces across provinces. This indicates the number of segments in the inter-provincial standby call market segmented pricing.

[0089] Its constraints include:

[0090] Provincial-level upper and lower reserve demand constraints are used to ensure that the actual amount of cross-provincial reserve calls and reserved activations do not exceed their market winning bids. The expression is as follows:

[0091]

[0092]

[0093]

[0094] In the above formula, , Representing provinces During the period Cross-regional backup capacity requirements , Representing provinces During the period The cross-provincial upstream and downstream backup capacity demand, , The variables are 0 and 1, representing provinces respectively. During the period The status of cross-provincial reserve shortages;

[0095] The reserved capacity constraints for grid-dispatch and provincial-dispatch units are used to limit the output of each unit after providing inter-provincial reserve to within its minimum and maximum technical output range, as expressed below:

[0096]

[0097]

[0098]

[0099]

[0100] In the above formula, , Representing regions Internal network dispatching units and provincial areas Internal inspection and adjustment of generator units During the period The unit output, , Representing regions Internal network dispatching units and provincial areas Internal inspection and adjustment of generator units During the period The electrical energy of the winning bid power, , Representing regions Internal network dispatching units and provincial areas Internal inspection and adjustment of generator units During the period Maximum technical output , Representing regions Internal network dispatching units and provincial areas Internal inspection and adjustment of generator units Minimum technical output, , The variables are 0 and 1, representing the regions respectively. Internal network dispatching units During the period Provides cross-provincial uplink and downlink backup status. , The variables are 0 and 1, representing provinces respectively. Internal inspection and adjustment of generator units During the period Provides cross-provincial uplink and downlink backup status;

[0101] The inter-provincial tie line transmission capacity constraint is used to ensure that the sum of the base-state power flow, power flow changes caused by backup dispatch, and the maximum power flow changes that may be caused by the full activation of reserved capacity to cope with future faults are within the dynamic safe transmission capacity range of the inter-provincial tie line. The expression is as follows:

[0102]

[0103] In the above formula, Indicates inter-provincial communication line At any moment The ground state current, , The variables are 0 and 1, representing the grid-dispatch units respectively. and provincial dispatch units Located at node state, Represents a node Injecting unit power to tie line The power transfer distribution factor.

[0104] Furthermore, after the network dispatch clearing, provincial dispatch verification is required; the provincial dispatch safety verification model under the accident scenario aims to minimize the accident dispatch cost under the scenario of an accident occurring within the province, and its expression is as follows:

[0105]

[0106] In the above formula, , Representing provinces In the accident Below, network nodes At any moment The actual cross-provincial backup power is being used. , Representing network nodes At any moment The market clearing price for cross-provincial reserve call services. Indicates province The set of related network nodes.

[0107] Its constraints include:

[0108] The power limit constraint is used to ensure that the actual cross-provincial reserve usage during an accident does not exceed its market bid volume. The expression is as follows:

[0109]

[0110]

[0111] In the above formula, , These represent the grid-dispatch units. and provincial dispatch units Located in network node ;

[0112] The cross-provincial reserve supply and demand balance constraint after an accident, which requires the cross-provincial reserve to be mobilized to meet the power deficit in the province caused by the accident, is expressed as follows:

[0113]

[0114] In the above formula, Indicates province Due to the accident At any moment The power deficit;

[0115] The inter-provincial standby deliverability constraint is used to ensure that when all available standby lines are dispatched under an accident, the power flow of the inter-provincial tie line does not exceed the security threshold determined by the dynamic secure transmission capacity range of the inter-provincial tie line. The expression is as follows:

[0116]

[0117]

[0118] Step S3: Establish a cross-regional backup collaborative optimization model;

[0119] Similar to the provincial dispatch level, the cross-regional backup collaborative optimization model established in this embodiment covers the national dispatch cross-regional backup scheduling model and the network dispatch safety verification model under accident scenarios;

[0120] The national grid inter-regional reserve dispatch model aims to minimize the total cost of inter-regional upper and lower reserve coordination under the premise of grid security, and its expression is as follows:

[0121]

[0122] In the above formula, Indicates national dispatch units During the period The cross-regional standby transaction capacity, Indicates national dispatch units During the period The cross-regional standby transaction capacity Indicates national dispatch units During the period The price of standby service across regions, Indicates national dispatch units During the period The price of standby service across regions, This represents the collection of directly dispatched generating units within the national power dispatching area. , Let be slack variables, representing regions respectively. During the period The slack in the upper and lower reserve requirements ensures that the model prioritizes meeting reserve requirements, and minimizes the deficit as much as possible when reserve requirements cannot be met. Represents all the moments of a day. This represents a set of cross-regional backup and collaborative regions.

[0123] Its constraints include:

[0124] The upper and lower reserve demand constraints of the region are used to prioritize meeting reserve demands by utilizing slack variables, and to minimize the reserve deficit when it is not possible to meet them. The expression is as follows:

[0125]

[0126]

[0127]

[0128] In the above formula, , Representing regions During the period Total cross-regional backup capacity requirements , The variables are 0 and 1, representing the regions respectively. During the period The status of cross-regional upper and lower reserve shortages;

[0129] The reserved capacity constraints for generating units at the national, regional, and provincial dispatch levels are used to ensure that the output of units at each level does not exceed the limit after providing inter-regional reserve. The expression is as follows:

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136] In the above formula, Indicates national dispatch units During the period The unit output, Indicates national dispatch units During the period The electrical energy of the winning bid power, Indicates national dispatch units Maximum technical output Indicates national dispatch units Minimum technical output, , The variables are 0 and 1, representing the national dispatch units respectively. During the period Provides cross-regional up and down standby status. , The variables are 0 and 1, representing the regions respectively. Internal network dispatching units At any moment Provides cross-regional up and down standby status. , The variables are 0 and 1, representing provinces respectively. Internal inspection and adjustment of generator units At any moment Provides cross-regional up and down standby status;

[0137] The cross-regional tie line transmission capacity constraint is used to ensure that the sum of power flow changes caused by the base state, standby dispatch, and fault reservation is within the dynamic safe transmission capacity range of the cross-regional tie line. The expression is as follows:

[0138]

[0139]

[0140] In the above formula, Indicates inter-regional connection line At any moment The ground state current, , , The variables are 0 and 1, representing the national dispatch units respectively. Grid-dispatch units and provincial dispatch units Located at node state, Represents a node Injecting unit power to tie line The power transfer distribution factor.

[0141] Based on this, the network dispatcher needs to perform a safety verification on the national dispatcher's results. The network dispatcher safety verification model under the accident scenario aims to minimize the accident scheduling cost under the scenario of regional accident occurrence, and its expression is as follows:

[0142]

[0143] In the above formula, , Representing regions In the accident Below, network nodes At any moment The actual cross-regional upper and lower backups are used. , Representing network nodes At any moment The settlement price for cross-regional backup calls, Indicates the area The set of related network nodes.

[0144] Its constraints include:

[0145] The cross-regional standby scheduling power limit constraint is used to ensure that the actual cross-regional standby usage under an accident does not exceed its market bid volume. The expression is as follows:

[0146]

[0147]

[0148] In the above formula, , , These represent the national dispatch units. Grid-dispatch units and provincial dispatch units Located in network node ;

[0149] The post-accident cross-regional reserve supply and demand balance constraint, used to constrain the amount of cross-regional reserve to be called up, must meet the power deficit in the region caused by the accident, is expressed as follows:

[0150]

[0151] In the above formula, Indicates the area Due to the accident At any moment The power deficit;

[0152] The cross-regional standby deliverability constraint is used to ensure that, when all available standby lines are scheduled during an incident, the power flow of the cross-regional tie line does not exceed a security threshold determined by the dynamic secure transmission capacity range of the cross-regional tie line. The expression is as follows:

[0153]

[0154]

[0155] Step S4: Multi-level cooperative balancing solution and execution based on ADMM;

[0156] Because the above model involves multiple scheduling entities at the national, grid, and provincial levels, information barriers and hierarchical coupling exist. For example... Figure 4 As shown, this embodiment establishes a multi-level collaborative balance solution model for cross-provincial and cross-regional reserve sharing based on the Alternating Direction Multiplier Method (ADMM). The dynamic security transmission capacity range determined in step S1 is used as the transmission constraint of the cross-regional and cross-provincial tie line. The cross-provincial reserve collaborative transaction optimization model described in step S2 and the cross-regional reserve collaborative optimization model described in step S3 are solved in a distributed iterative manner to generate a cross-provincial and cross-regional reserve collaborative operation scheme to achieve multi-level collaborative balance of the power system.

[0157] Specifically, the distributed iterative solution of the cross-provincial backup collaborative transaction optimization model includes:

[0158] Step S401: Construct a network-level and provincial-level optimization objective function that includes the original network-level optimization objective, the original provincial-level safety verification objective, and cross-provincial backup collaborative iteration penalty items. The expression is as follows:

[0159]

[0160]

[0161]

[0162] In the above formula, This indicates the optimization target of the cross-provincial backup collaborative network dispatching system. This indicates the original safety verification target for cross-provincial backup coordination. Indicates cross-provincial backup collaboration The next iteration optimizes the penalty. , These represent the provincial survey times. The upper and lower backup gaps , These represent the times of the network survey. Upper and lower backup coordination quantities;

[0163] Step S402: In each iteration, the network dispatch and each provincial dispatch independently solve and update the reserve coordination quantity. Based on the deviations between the network dispatch and provincial dispatch in the upper and lower reserve gaps and coordination quantities, the primary penalty coefficient and the secondary penalty coefficient are updated, as shown in the following expressions:

[0164]

[0165]

[0166]

[0167]

[0168] In the above formula, , Indicates cross-provincial backup collaboration The penalty coefficient at the next iteration , Indicates cross-provincial backup collaboration The second-order penalty coefficient in the next iteration , The constant for the cross-provincial backup collaborative secondary penalty update is [0,1].

[0169] Step S403: Determine whether the coordination quantity between the network dispatch and the provincial dispatch meets the convergence condition. If not, proceed to the next iteration until convergence, and formulate a cross-provincial backup coordination plan at the network dispatch and provincial dispatch levels accordingly.

[0170] Simultaneously, the cross-regional backup collaborative optimization model is solved in a distributed iterative manner, specifically including:

[0171] Step S404: Construct a national and regional survey-level optimization objective function that includes the original national survey optimization objective, the original network survey safety verification objective, and cross-regional backup collaborative iteration penalty items. The expression is as follows:

[0172]

[0173]

[0174]

[0175] In the above formula, This indicates the goal of cross-regional backup coordination and optimization in China. This indicates the original safety verification target of the cross-regional backup collaborative network dispatching system. Indicates cross-regional backup coordination The next iteration optimizes the penalty. , These represent the times of the network survey. The upper and lower backup gaps , These represent the national survey at time. Upper and lower backup coordination quantities;

[0176] Step S405: In each iteration, the National Dispatch Center (NDC) and each regional dispatch center independently solve and update the reserve coordination quantity. Based on the deviations between the NDC and regional dispatch centers in the upper and lower reserve gaps and coordination quantities, the primary and secondary penalty coefficients of cross-regional reserve coordination are updated, as shown in the following expressions:

[0177]

[0178]

[0179]

[0180]

[0181] In the above formula, , Indicates cross-regional backup coordination The penalty coefficient at the next iteration , Indicates cross-regional backup coordination The second-order penalty coefficient in the next iteration , The constant for the cross-regional backup collaborative secondary penalty update is [0,1].

[0182] Step S406: Determine whether the coordination quantity between the national and regional dispatch centers meets the convergence condition. If not, proceed to the next iteration until convergence, and formulate a cross-regional backup coordination plan at the national and regional dispatch center levels accordingly.

[0183] In some implementations, the generation of the cross-provincial and cross-regional backup collaborative operation plan specifically includes:

[0184] After the cross-provincial backup coordination at the provincial and national levels and the cross-regional backup coordination at the national and national levels have both reached convergence, the final cross-provincial and cross-regional backup coordination plan is output.

[0185] Based on the final inter-provincial and inter-regional backup coordination plan, the national dispatch center and the grid dispatch center modify and issue the transmission plans for inter-regional and inter-provincial interconnection lines to control the coordinated balance and backup dispatch operation of the physical multi-level power grid.

[0186] In summary, this invention quantifies the impact of both physical and market uncertainties of new energy sources on the safety boundary of tie lines, constructs a dynamic safety interval calculation model for tie lines, and provides more accurate transmission constraints for reserve sharing. Furthermore, it constructs two-level collaborative optimization models covering market clearing and safety verification across provinces and regions, respectively, realizing hierarchical and collaborative optimization of reserve resources between regions and across the entire network. Finally, it introduces ADMM to construct a distributed solution framework, using iterative negotiation to solve complex optimization problems under multi-subject, multi-level, and uncertain constraints. Thus, while ensuring the safe operation of power grids at all levels, it can maximize the benefits of cross-regional and cross-provincial reserve sharing, and improve the resilience and economic operation level of the large power grid in the face of uncertainties.

[0187] The above are merely preferred embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-level collaborative method for cross-provincial / regional backup sharing that takes into account channel capacity uncertainty, characterized in that: Includes the following steps: Acquire basic power system operation data, construct a dynamic safety interval calculation model for inter-regional and inter-provincial interconnection lines, and determine the dynamic safety transmission capacity interval of inter-regional and inter-provincial interconnection lines based on the net uncertainty injection scenario that considers the uncertainty of new energy physical prediction and market behavior uncertainty. Establish an optimized model for cross-provincial backup collaborative transactions that includes a clearing model for the cross-provincial backup market and a safety verification model for provincial backup under accident scenarios; Establish a cross-regional standby collaborative optimization model that encompasses the national dispatch cross-regional standby dispatch model and the grid dispatch safety verification model under accident scenarios; A multi-level collaborative solution model for cross-provincial and cross-regional backup sharing based on the alternating direction multiplier method is established. The dynamic security transmission capacity range is used as the transmission constraint of the cross-regional and cross-provincial tie line. The cross-provincial backup collaborative transaction optimization model and the cross-regional backup collaborative optimization model are solved in a distributed iterative manner to generate a cross-provincial and cross-regional backup collaborative operation scheme. The net uncertainty injection scenario, which takes into account the uncertainties of new energy physical predictions and market behavior, specifically includes: The maximum physical power generation capacity of a new energy power station is expressed as the sum of the predicted value and the prediction deviation, generating a set of physical prediction uncertainty scenarios; Under the reporting volume without bidding model, the market behavior deviation of new energy power stations is represented as the market reporting deviation power and the market clearing deviation power, generating a set of uncertain market behavior scenarios; Based on the condition that the actual power generation of new energy power plants is constrained by both physical upper limits and market clearing plans, and combining the set of physical prediction uncertainty scenarios and the set of market behavior uncertainty scenarios, a set of new energy net uncertainty injection scenarios containing multiple scenarios is generated. The determination of the dynamic secure transmission capacity range of the inter-regional and inter-provincial connection lines specifically includes: Based on the power transfer distribution factor of the tie line injected by the node with unit power, calculate the tie line power flow fluctuations for each scenario under the set of net uncertainty injection scenarios. Based on conditional value at risk and the set risk tolerance, the risk margins for upward and downward fluctuations of the connection line are calculated respectively. The dynamic safe transmission capacity range of inter-regional and inter-provincial interconnection lines is determined by deducting the corresponding risk margin from the physical transmission upper and lower limits of the interconnection line channel and adding a precautionary margin.

2. The cross-provincial / regional backup sharing multi-level collaborative method considering channel capacity uncertainty as described in claim 1, characterized in that, The network dispatch cross-provincial backup market clearing model aims to minimize the total network backup dispatch purchase cost, and its constraints include: Provincial-level and provincial-level reserve demand constraints are used to ensure that the actual amount of cross-provincial reserve calls and reserved activation amounts do not exceed their market winning bid amounts. The reserved capacity constraints of grid-dispatch and provincial-dispatch units are used to constrain the output of each unit after providing inter-provincial backup to be within its minimum and maximum technical output range; The inter-provincial tie line transmission capacity constraint is used to constrain the sum of the base state power flow, power flow changes caused by backup calls, and the maximum power flow changes that may be caused by the full activation of reserved capacity to cope with future faults to be within the dynamic safe transmission capacity range of the inter-provincial tie line.

3. The cross-provincial / regional backup sharing multi-level collaborative method considering channel capacity uncertainty as described in claim 1, characterized in that, The provincial dispatch safety verification model under the accident scenario aims to minimize the accident dispatch cost under the scenario of an accident occurring within the province. Its constraints include: Power limit constraint is used to ensure that the actual amount of cross-provincial backup power used in the event of an accident does not exceed its market bid volume; The cross-provincial reserve supply and demand balance constraint after the accident: the cross-provincial reserve used to constrain the call must meet the power deficit caused by the accident in the province. The inter-provincial standby deliverability constraint is used to ensure that when all available standby lines are dispatched in the event of an accident, the power flow of the inter-provincial tie line does not exceed the security threshold determined by the dynamic secure transmission capacity range of the inter-provincial tie line.

4. The cross-provincial / regional backup sharing multi-level collaborative method considering channel capacity uncertainty as described in claim 1, characterized in that, The national grid inter-regional reserve dispatch model aims to minimize the total cost of inter-regional upper and lower reserve coordination under the premise of grid security. Its constraints include: Regional upper and lower reserve demand constraints are used to prioritize meeting reserve demands by utilizing slack variables, and to minimize reserve deficits when they cannot be met. The reserved capacity constraints for generating units at the national, grid, and provincial dispatch levels are used to ensure that the output of generating units at all levels does not exceed the limits after providing cross-regional backup. The cross-regional tie line transmission capacity constraint is used to ensure that the sum of power flow changes caused by the base state, backup dispatch, and fault reservation is within the dynamic safe transmission capacity range of the cross-regional tie line.

5. The cross-provincial / regional backup sharing multi-level collaborative method considering channel capacity uncertainty as described in claim 4, characterized in that, The network dispatch safety verification model under the accident scenario aims to minimize the accident scheduling cost under the scenario of regional accident occurrence, and its constraints include: The cross-regional backup dispatch power limit constraint is used to ensure that the actual cross-regional backup dispatch volume does not exceed its market bid volume during an accident. Post-accident cross-regional reserve supply and demand balance constraints: the amount of cross-regional reserve used to constrain the call must meet the power deficit in the region caused by the accident. Cross-regional standby deliverability constraints are used to ensure that, when all available standby lines are scheduled during an incident, the power flow of the cross-regional tie line does not exceed the security threshold determined by the dynamic secure transmission capacity range of the cross-regional tie line.

6. The cross-provincial / regional backup sharing multi-level collaborative method considering channel capacity uncertainty as described in claim 1, characterized in that, The distributed iterative solution of the cross-provincial backup collaborative transaction optimization model specifically includes: Construct a network-based and provincial-based hierarchical optimization objective function that includes the original optimization objective of the network-based survey, the original safety verification objective of the provincial-based survey, and cross-provincial backup collaborative iteration penalty items; In each iteration, the network dispatch and each provincial dispatch independently solve and update the reserve coordination quantity, and based on the deviation between the network dispatch and the provincial dispatch in the upper and lower reserve gaps and coordination quantities, update the primary penalty coefficient and the secondary penalty coefficient. Determine whether the coordination between the network survey and the provincial survey meets the convergence condition. If not, proceed to the next iteration until convergence, and formulate a cross-provincial backup coordination plan at the network survey and provincial survey levels accordingly.

7. The cross-provincial / regional backup sharing multi-level collaborative method considering channel capacity uncertainty as described in claim 1, characterized in that, The distributed iterative solution of the cross-regional backup collaborative optimization model specifically includes: Construct a national and regional level optimization objective function that includes the original optimization objective of the national survey, the original safety verification objective of the network survey, and the cross-regional backup collaborative iteration penalty. In each iteration, the National Dispatch Center and each Network Dispatch Center independently solve and update the reserve coordination quantity. Based on the deviations between the National Dispatch Center and the Network Dispatch Center in the upper and lower reserve gaps and coordination quantities, the primary penalty coefficient and secondary penalty coefficient of cross-regional reserve coordination are updated. Determine whether the coordination quantity between the national and regional surveys meets the convergence condition. If not, proceed to the next iteration until convergence, and formulate a cross-regional backup coordination plan at the national and regional survey levels accordingly.

8. The cross-provincial / regional backup sharing multi-level collaborative method considering channel capacity uncertainty as described in any one of claims 6 or 7, characterized in that, The generation of the cross-provincial and cross-regional backup collaborative operation plan specifically includes: After the cross-provincial backup coordination at the provincial and national levels and the cross-regional backup coordination at the national and national levels have both reached convergence, the final cross-provincial and cross-regional backup coordination plan is output. Based on the final inter-provincial and inter-regional backup coordination plan, the national dispatch center and the grid dispatch center modify and issue the transmission plans for inter-regional and inter-provincial interconnection lines to control the coordinated balance and backup dispatch operation of the physical multi-level power grid.

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