Method, device and equipment for making decision based on unified adjustment of all units in power grid and medium

Abstract

The present application belongs to the technical field of power grid dispatching, and particularly relates to a decision method, device, equipment and medium based on unified adjustment of all units in a power grid. The present application takes the minimum unit comprehensive adjustment amount as the target, considers the constraints of power grid safety, potential risk, partition backup, all-grid backup, cross-regional mutual aid, and the like, constructs a model of all-unit coordination and mutual aid, uses a high-performance solver to quickly solve the model, mines the coordination and mutual aid capability of regional resources of the power grid under the premise of not increasing the operation risk of the power grid, forms a more reasonable and safe power grid operation mode, and provides effective auxiliary decision for real-time operation control of the power grid.

Description

Technical Field

[0001] This invention belongs to the field of power grid dispatching technology, specifically relating to a decision-making method, device, equipment, and medium based on unified adjustment of all generating units in the power grid. Background Technology

[0002] In recent years, the power grid has undergone profound changes on the power generation side, grid side, and demand side. On the power generation side, new energy sources, represented by wind power, have been widely integrated into the grid. As their proportion continues to rise, the proportion of conventional thermal power sources has decreased accordingly, highlighting the contradiction between power balance and new energy consumption. Maintaining grid balance and ensuring the consumption of new energy are becoming increasingly difficult. On the grid side, with the construction of ultra-high-voltage direct current (UHVDC) transmission channels, regional power grids have completed DC interconnection, making system optimization and scheduling more complex. UHVDC blocking faults and other issues will cause severe power shortages in the receiving areas. In the case of insufficient reserves, cross-regional resource coordination and mutual assistance are needed to ensure grid security.

[0003] On the demand side, in the electricity market system, the demand side is not just the electricity consumption side. It can adjust its load consumption and usage time to participate in grid operation based on market price signals or incentive mechanisms. Due to uncertainties such as electricity prices, environment and users' subjective willingness, there is a large deviation between the actual and expected demand load, which causes large load fluctuations. As a result, the system reserve reserved in the day-ahead does not meet the actual operation needs of the grid.

[0004] Under the inter-regional interconnected power grid collaborative dispatch model, based on information such as load levels at the sending and receiving ends, wind and solar forecasts, and the operational status of thermal power units, the power of the tie lines is considered as a coordination factor for the transfer and distribution of power resources between regions. Dispatch plans are formulated for resources on both the source and grid sides of the interconnected power grid, with optimization targets including inter-regional tie lines, inter-provincial tie lines, and thermal and hydropower units. The sending-end power grid plans the output of wind, solar, and hydropower units for each time period, providing the planned power of the tie lines for each time period while ensuring its own power supply, and transmitting surplus power to other regions. Supported by the power transmitted through the tie lines, the receiving-end power grid plans the output of thermal power units according to the load and thermal power unit status within the region to ensure the power supply to the load.

[0005] When the power grid's reserve is insufficient and the safe operation of the grid cannot be guaranteed by adjusting local generating units alone, the safe and reliable operation of the grid can be ensured by transmitting or receiving power resources. Through the safe and economical dispatch of power on interconnected grid tie lines, complementary advantages between regional grids can be achieved, enhancing the safe and economical operation of interconnected grids and their capacity for renewable energy absorption.

[0006] Under the new power system, the uncertainty of active power on both the power grid source and load sides has increased significantly, making it more difficult to predict new energy sources and loads. The prediction results contain uncertain errors, which leads to deviations between the actual operation mode and the day-ahead plan. The day-ahead reserve may be insufficient, and situations such as transmission channel blockage and equipment failure will result in power shortages in the power grid generation and reception, making it difficult to ensure the safe operation of the power grid. Summary of the Invention

[0007] The purpose of this invention is to provide a decision-making method, device, equipment, and medium based on unified adjustment of all generating units in the network, in order to solve the problems in the prior art where the actual operation mode during the day deviates from the day-ahead plan, the day-ahead reserve may be insufficient, and when there are situations such as transmission channel blockage or equipment failure, the power generation and receiving of the power grid will be short, making it difficult to ensure the safe operation of the power grid.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] In a first aspect, the present invention provides a decision-making method based on unified adjustment of all generating units across the network, comprising the following steps:

[0010] When the target power grid experiences a power deficit, determine the set of adjustable generating units, tie-line data set, over-limit constraint data set, and region set within the target power grid.

[0011] Construct unit adjustment variables, tie-line slack variables, over-limit slack variables, and reserve slack variables within the target power grid;

[0012] Based on the adjustable unit set, tie line data set, over-limit constraint data set, region set, and unit adjustment variables, tie line slack variables, over-limit slack variables, and reserve slack variables, a regional resource coordination and mutual assistance model is constructed; wherein, the regional resource coordination and mutual assistance model aims to minimize the overall adjustment amount;

[0013] The constraints of the regional resource coordination and mutual assistance model are determined; wherein the constraints include upper and lower limits of unit output, power balance constraints, network security constraints, tie line slack variable constraints, tie line planning constraints, unit ramping constraints, and reserve constraints.

[0014] Based on the aforementioned constraints, the regional resource coordination and mutual assistance model is solved to obtain a regional resource mutual assistance unit adjustment scheme.

[0015] Furthermore, in the step of determining whether there is a power deficit in the target power grid, the method is as follows:

[0016]

[0017] If any of the above conditions are not met, a power deficit is determined to have occurred.

[0018] Among them, P G，max P represents the upper limit of output for all units within the region. G,min P represents the lower limit of the output of all units in the region. G P represents the current output value of all generating units in the region. LOAD P represents the predicted load power, Δ represents the given maximum load fluctuation, and P represents the load power. positive-spare P is the specified value for positive standby. negtive-spare The specified value is for negative backup.

[0019] Furthermore, in the step of constructing the regional resource coordination and mutual assistance model, the constructed regional resource coordination and mutual assistance model is as follows:

[0020]

[0021] Where n is the number of adjustment periods; VG t A collection of adjustable generating units; L i Adjust the penalty factor for the output of unit i. The output adjustment amount at time t. V is the adjustment amount of output at time t; tieline For the tie-line data set; N tie The relaxation penalty factor for the tie. Slack variables on the connection line For the slack variable in the connection line; V l1 For branch boundary constraint data set; R l Slack is the active power adjustment relaxation penalty factor for the over-limit branch l. l,t V is a branch slack variable. section1 For cross-sectional limit constraint data set; M s Slack is the active power adjustment relaxation penalty factor for the over-limit section s. s,t V is the section relaxation variable; district For a set of regions; Q spare-d The spare relaxation penalty factor for region d; For region d, use the spare upper slack variable; This is a spare slack variable for region d.

[0022] Furthermore, in the steps of constructing unit adjustment variables, tie-line slack variables, over-limit slack variables, and reserve slack variables within the target power grid, the over-limit slack variables specifically include: branch slack variables and cross-sectional slack variables.

[0023] Secondly, the present invention provides a decision-making device based on unified adjustment of all generating units across the network, comprising:

[0024] The first determination module is used to determine the set of adjustable generating units, tie line data set, over-limit constraint data set, and regional set within the target power grid when a power deficit occurs.

[0025] The variable construction module is used to construct unit adjustment variables, tie-line slack variables, over-limit slack variables, and reserve slack variables within the target power grid.

[0026] The model building module is used to construct a regional resource coordination and mutual assistance model based on the adjustable unit set, tie line data set, over-limit constraint data set, regional set, as well as unit adjustment variables, tie line slack variables, over-limit slack variables, and reserve slack variables; wherein, the regional resource coordination and mutual assistance model aims to minimize the overall adjustment amount;

[0027] The second determining module is used to determine the constraints of the regional resource coordination and mutual assistance model; wherein, the constraints include upper and lower limits of unit output, power balance constraints, network security constraints, tie line slack variable constraints, tie line planning constraints, unit ramping constraints, and reserve constraints.

[0028] The model solving module is used to solve the regional resource coordination and mutual assistance model based on the constraints, and obtain the regional resource mutual assistance unit adjustment scheme.

[0029] Furthermore, in the first determining module, the method for determining whether there is a power deficit is as follows:

[0030]

[0031] If any of the above conditions are not met, a power deficit is determined to have occurred.

[0032] Among them, P G，max P represents the upper limit of output for all units within the region. G,min P represents the lower limit of the output of all units in the region. G P represents the current output value of all generating units in the region. LOAD P represents the predicted load power, Δ represents the given maximum load fluctuation, and P represents the load power. positive-spare P is the specified value for positive standby. negtive-spare The specified value is for negative backup.

[0033] Furthermore, the regional resource coordination and mutual assistance model constructed in the model construction module is as follows:

[0034]

[0035] Where n is the number of adjustment periods; VG t A collection of adjustable generating units; L iAdjust the penalty factor for the output of unit i. The output adjustment amount at time t. V is the adjustment amount of output at time t; tieline For the tie-line data set; N tie The relaxation penalty factor for the tie. Slack variables on the connection line For the slack variable in the connection line; V l1 For branch boundary constraint data set; R l Slack is the active power adjustment relaxation penalty factor for the over-limit branch l. l,t V is a branch slack variable. section1 For cross-sectional limit constraint data set; M s Slack is the active power adjustment relaxation penalty factor for the over-limit section s. s,t V is the section relaxation variable; district For a set of regions; Q spare-d The spare relaxation penalty factor for region d; For region d, use the spare upper slack variable; This is a spare slack variable for region d.

[0036] Furthermore, in the variable construction module, the over-limit relaxation variables specifically include: branch relaxation variables and cross-sectional relaxation variables.

[0037] Thirdly, the present invention provides an electronic device, including a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the above-mentioned decision-making method based on unified adjustment of all network units.

[0038] Fourthly, the present invention provides a computer-readable storage medium storing at least one instruction, which, when executed by a processor, implements the above-described decision-making method based on unified adjustment of all network units.

[0039] The beneficial effects of this invention are as follows:

[0040] The decision-making method provided by this invention incorporates provincial reserve constraints, regional power grid reserve constraints, and potentially risky branches and sections into the regional resource coordination and mutual assistance model during the process of power grid power deficit coordination and mutual assistance. This method can maximize the coordination and mutual assistance of network resources and improve the safe operation level of the power grid without adding new risks of exceeding limits. The results can be obtained directly by solving the regional resource coordination and mutual assistance model, which is fast and highly interpretable. Attached Figure Description

[0041] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0042] Figure 1 This is a flowchart illustrating a decision-making method based on unified adjustment of all generating units across the network, according to an embodiment of the present invention.

[0043] Figure 2 This is a flowchart illustrating another embodiment of the decision-making method based on unified adjustment of all generating units across the network.

[0044] Figure 3 This is a structural block diagram of a decision-making device based on unified adjustment of all generating units across the network, according to an embodiment of the present invention.

[0045] Figure 4 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0046] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0047] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0048] Example 1

[0049] like Figure 1 As shown, a decision-making method based on unified adjustment of all generating units across the network includes the following steps:

[0050] S100. When the target power grid experiences a power deficit, determine the set of adjustable generating units, tie line data set, over-limit constraint data set, and regional set within the target power grid.

[0051] Specifically, the method for determining whether there is a power deficit in this solution is as follows:

[0052]

[0053] If any of the above conditions are not met, a power deficit is determined to have occurred.

[0054] Among them, P G，max P represents the upper limit of output for all units within the region. G,min P represents the lower limit of the output of all units in the region.G P represents the current output value of all generating units in the region. LOAD P represents the predicted load power, Δ represents the given maximum load fluctuation, and P represents the load power. positive-spare P is the specified value for positive standby. negtive-spare The specified value is for negative backup.

[0055] Specifically, the over-limit constraint data set in this scheme includes: the over-limit section constraint data set and the over-limit branch data set.

[0056] S200: Construct unit adjustment variables, tie-line slack variables, over-limit slack variables, and reserve slack variables within the target power grid.

[0057] Specifically, the over-limit relaxation variables in this scheme include: branch relaxation variables and cross-sectional relaxation variables.

[0058] S300. Based on the set of adjustable generating units, the set of tie line data, the set of over-limit constraint data, the set of regions, as well as the unit adjustment variables, tie line slack variables, over-limit slack variables, and reserve slack variables, a regional resource coordination and mutual assistance model is constructed; among them, the regional resource coordination and mutual assistance model aims to minimize the overall adjustment amount.

[0059] Among them, the standby slack variables include standby upper slack variables and standby lower slack variables.

[0060] Specifically, the regional resource coordination and mutual assistance model constructed in this plan is as follows:

[0061]

[0062] Where n is the number of adjustment periods; when n = 1, single-time regional resource coordination and mutual assistance are performed; when n > 1, continuous-time regional resource mutual assistance and adjustment are performed; VG t A collection of adjustable generating units; L i Adjust the penalty factor for the output of unit i. The output adjustment amount at time t. V is the adjustment amount of output at time t; tieline For the tie-line data set; N tie The relaxation penalty factor for the tie. Slack variables on the connection line For the slack variable in the connection line; V l1 For branch boundary constraint data set; R l Slack is the active power adjustment relaxation penalty factor for the over-limit branch l. l,t V is a branch slack variable. section1 For cross-sectional limit constraint data set; M s Slack is the active power adjustment relaxation penalty factor for the over-limit section s.s,t V is the section relaxation variable; district For a set of regions; Q spare-d The spare relaxation penalty factor for region d; For region d, use the spare upper slack variable; N is the slack variable for region d under standby conditions. Compared with the unit output adjustment penalty factor, N tie R l M s Q spare-d The penalty factor is set relatively large, and the order of size is generally set to Q. spare-d ≥M s ≥R l ≥N tie ≥L i This guides the solver towards achieving maximum regional resource exchange; by setting different L... i It can also prioritize the adjustment of unit output; the smaller the penalty factor is set, the higher the priority of output adjustment.

[0063] S400. Determine the constraints of the regional resource coordination and mutual assistance model; among which, the constraints include upper and lower limits of unit output, power balance constraints, network security constraints, tie line slack variable constraints, tie line planning constraints, unit ramping constraints, and reserve constraints.

[0064] S500. Based on the constraints, the regional resource coordination and mutual assistance model is solved to obtain the regional resource mutual assistance unit adjustment scheme.

[0065] Specifically, this solution uses a high-performance solver to solve the regional resource coordination and mutual assistance model. The results include four parts: unit output adjustment values, reserve slack values, tie line slack values, and over-limit branch / section slack values. Based on the unit output adjustment values, unit adjustment suggestions can be directly derived. Based on the reserve slack values, it can be directly determined whether regional resource mutual assistance can be achieved. Based on the tie line slack values, the amount of coordinated and mutually assisted power resources between regions can be determined. Based on the over-limit branch / section slack values, it can be determined which risks cannot be eliminated after regional resource coordination and mutual assistance.

[0066] The decision-making method provided by this invention first obtains data such as power flow section data, section limits, unit parameters participating in regional resource sharing, provincial reserve requirements, and regional reserve requirements to determine whether a power deficit will occur in the future. If a power deficit occurs or is predicted, power flow calculations are performed to statistically analyze power flow exceedance situations and potential operational risks during regional resource sharing. Combined with provincial and regional reserve requirements, and with the goal of minimizing the overall unit adjustment, a network-wide unit coordination and sharing model is constructed to satisfy power grid security constraints (power grid security, potential risks, inter-regional sharing, etc.) and reserve constraints. The network-wide unit coordination and sharing model is then solved to obtain a network-wide resource coordination strategy. Regional resource coordination and sharing instructions are generated based on the network-wide resource coordination strategy. Without increasing power grid operational risks, this method leverages the regional resource coordination and sharing capabilities of the power grid to form a more rational and safer power grid operation mode, providing effective auxiliary decision-making for real-time power grid operation control.

[0067] like Figure 2 As shown, as a possible implementation of this solution, a decision-making method based on unified adjustment of all generating units across the network includes the following steps:

[0068] Step S1: Obtain power flow section data and physical model parameters of the target power grid.

[0069] Specifically, the power flow cross-section data includes: power grid model, planned unit output and load power, reserve requirements for each province, and regional reserve requirements. Physical model parameter information includes: intelligent quotas for cross-sections, cross-section member composition, tie-line composition, and unit parameters participating in regional resource coordination and mutual assistance; among which, unit parameter information includes maximum technical output, minimum technical output, and ramp rate. Based on the above power flow cross-section data and physical model parameters, the set of unit variables for the regional resource coordination and mutual assistance model is determined.

[0070] Step S2: Determine whether there is a power deficit in the target power grid within the province or region based on the reserve requirements of each province and region. If there is a power deficit, proceed to step S3; otherwise, exit the calculation.

[0071] Specifically, power deficit can be determined based on the following criteria:

[0072]

[0073] If any of the above formulas are not satisfactory, it is determined that a power deficit has occurred.

[0074] Among them, P G，max P represents the upper limit of output for all units within the region. G,min P represents the lower limit of the output of all units in the region. G P represents the current output value of all generating units in the region.LOAD P represents the predicted load power, Δ represents the given maximum load fluctuation, and P represents the load power. positive-spare P is the specified value to be prepared for each province or region. negtive-spare The negative reserve value is specified for each province or region.

[0075] In other embodiments, the criteria for determining power deficit may be changed according to actual circumstances.

[0076] Step S3: Perform power flow calculation on the target power grid to determine the overload information of branches and sections, as well as the information of branches and sections that may exceed the limit during the resource coordination process of the whole network.

[0077] Specifically, power flow calculation is performed based on the power flow cross-section data obtained in step S1, and power flow data of the cross-section and connecting lines are statistically analyzed based on information such as cross-section and connecting line members.

[0078] More specifically, based on the branch / section overload threshold, a judgment threshold σ for branch / section overload is set. overload The threshold for assessing potential operational risks, σ risky When the measured value of the branch / section exceeds σ overload If the measured value of the branch / section exceeds the overload threshold σ, then the branch / section is considered overloaded. risky If so, it is considered that the branch / section may be operating at risk during the process of resource sharing and coordination across the entire network.

[0079] Furthermore, considering that when the unit's output increases, its outgoing lines or step-up transformers may experience overload or exceed limits, the set of branch data at risk can be represented as follows:

[0080] D brch-risky =∪{B risky B plant-close} (2)

[0081] In the formula: B risky B represents the set of branches that may face operational risks during the coordination and mutual assistance of resources across the entire network. plant-close This represents the set of branches near the power plant. The data that enters the constraints of the whole network resource mutual assistance and coordination model is the union of the branch data of the two sets.

[0082] The set of cross-sectional data with potential risks can be represented as:

[0083] D section-risky =∪{S risky ,S plant-related} (3)

[0084] In the formula: S risky S represents the set of cross-sections that may exceed the limits during the correction process. plant-relatedThis represents the set of cross-sections related to power plants. The data included in the constraints of the whole network resource mutual assistance and coordination model is the union of the cross-section data of the two sets.

[0085] By calculating power flow and statistics on exceeding limits, we can obtain information on branch / section exceeding limits and overload conditions, as well as information on branch / sections that may exceed limits during the process of coordinating resources across the entire network.

[0086] Based on the data from the above steps, the variables that will ultimately enter the regional resource coordination and mutual assistance model, as well as the data sources participating in constraint construction, are determined. The above data is then processed as follows to generate the set of variables and data participating in constraint construction for the regional resource coordination and mutual assistance model:

[0087] 1) Determine the regulating capacity of the unit based on its output value, minimum technical output, maximum technical output, and other information, and set up the adjustable unit set VG. t ;

[0088] 2) Determine the set of branch road over-limit constraint data V in the regional resource coordination and mutual assistance model based on the information of over-limit branch roads. l1 ;

[0089] 3) Determine the set of heavy-load constraint data V for branch roads in the regional resource coordination and mutual assistance model based on the heavy-load branch road information. l2 ;

[0090] 4) Based on formula (2), the risk branch data set V in the regional resource coordination and mutual assistance model is determined by the branch data set that may pose operational risks during the whole network resource coordination process. l3 ;

[0091] 5) Determine the set of boundary exceedance constraint data V for the regional resource coordination and mutual assistance model based on the boundary exceedance information. section1 ;

[0092] 6) Determine the set of heavy load constraint data V for the regional resource coordination and mutual assistance model based on the heavy load section information. section2 ;

[0093] 7) Based on formula (3), determine the risk section data set V by the set of section data that may cause operational risks during the whole network resource coordination process. section3 ;

[0094] 8) Determine the data set V of the connecting lines in the regional resource coordination and mutual assistance model based on the connecting line information. tieline .

[0095] Step S4: Calculate the quasi-steady-state sensitivity values ​​of the generating units participating in the whole network power adjustment to over-limit, overloaded branches (sections), and branches (sections) that may over-limit during the whole network resource coordination process; calculate the quasi-steady-state sensitivity values ​​of the generating units participating in the power adjustment to tie lines.

[0096] Specifically, it includes the following:

[0097] 1) Calculate VG at time t t Unit i in the set to V l1 The sensitivity α of the overlimited branch l1 in the set i,l1,t ;

[0098] 2) Calculate VG at time t t Unit i in the set to V l2 The sensitivity α of the overloaded branch l2 in the set i,l2,t ;

[0099] 3) Calculate VG at time t t Unit i in the set to V l3 Sensitivity α of risk branch l3 in the set i,l3,t ;

[0100] 4) Calculate VG at time t t Unit i in the set to V section1 The sensitivity α of the cross section s1 in the set i,s1,t ;

[0101] 5) Calculate VG at time t t Unit i in the set to V section2 The sensitivity α of the overloaded section s2 in the set i,s2,t ;

[0102] 6) Calculate VG at time t t Unit i in the set to V section3 Sensitivity α of risk section s3 in the set i,s3,t ;

[0103] 7) Calculate VG at time t t Unit i in the set to V tieline The sensitivity α of the tie in the set i,tie,y .

[0104] This scheme provides the formula for calculating sensitivity:

[0105]

[0106] In the formula, G k-G Let I be the conventional active power sensitivity coefficient of generator G to branch k. G For n G ×n G An identity matrix of order α G The generator's contribution factor to unbalanced power is 1. G is n G A 1×1 dimension vector consisting of all 1 columns.

[0107] Step S5: Divide the entire network of generating units and loads by region, determine the composition of generating units and loads in each province, and provide a data source for the construction of provincial and regional reserve constraints.

[0108] Specifically, the entire network of generating units and loads is geographically divided to form regional aggregates V. district Regional unit collection Regional load aggregation This provides a data source for subsequent provincial and regional backup constraints.

[0109] Step S6: Based on the data obtained in Steps S1 and S3, and the quasi-steady-state sensitivity numerical information calculated in Step S4, construct power grid security constraints, construct backup constraints based on the data proposed in Step S5, and construct a regional resource coordination and mutual assistance model based on the power grid security constraints and backup constraints.

[0110] Specifically, the method for constructing a regional resource coordination and mutual assistance model is as follows:

[0111] 1) Create unit adjustment variables, namely the output adjustment amount at time t. and output adjustment amount

[0112] During resource coordination, the above two sets of variables must satisfy the upper and lower limits of unit output constraints:

[0113]

[0114] in, This represents the planned active power output of unit i at time t. They represent unit G respectively i Maximum and minimum technical output, G i ∈VG t Indicates unit G i The unit can be adjusted.

[0115] 2) Based on the branch / section over-limit information obtained in step S3, create corresponding over-limit relaxation variables.

[0116] If an out-of-limit branch l exists, then create a branch slack variable Slack. l,t If there is an over-limit section s, then create a corresponding section relaxation variable Slack. s,t Slack l,t and Slack s,t The following conditions must be met:

[0117] 0≤Slack l,t ≤Pl vio Branch l exceeds limit, Pl vio For the branch with active power exceeding the limit, l∈Vl1 ;

[0118] 0≤Slack s,t ≤Ps vio Cross-section s exceeds limit, Ps vio For the cross-sectional active power exceeding the limit, s∈V section1 .

[0119] 3) Construct power balance constraints.

[0120] In the process of coordinating and assisting resources across the entire network, in order to ensure the power balance of the entire network, the total upward adjustment of the active power output of the generating units should be equal to the total downward adjustment of the active power output of the generating units, that is:

[0121]

[0122] 4) Based on the active power limit exceedance and overload information of branches and sections, as well as the information of branches and sections that may pose operational risks during the network-wide resource coordination process, network security constraints are constructed. Considering that the network risk is only eliminated during the network-wide resource coordination process without changing the power flow direction, the lower limit of the security constraints for branches and sections is set to 0, as follows:

[0123]

[0124]

[0125]

[0126]

[0127]

[0128]

[0129] In the formula, Let PL be the absolute value of the active power of branch l at time t. l,max Let l be the maximum active power that branch l can carry. PS is the absolute value of the active power flowing through section s at time t. s,max For the power flow limit at section s, when the power flow at section t is positive, then PS s,max For the positive limit of section s, when the t-current flow at section s is negative, then PS s,max is the absolute value of the reverse limit of section s.

[0130] 5) Create tie line slack variables.

[0131] The tie-line power is a coordination quantity for the transfer and distribution of power resources between regions. Changes in power can directly reflect the situation of resource mutual assistance. At the same time, considering the possibility that regional resource mutual assistance may not be achieved, slack variables are set on the tie-line and lower slack variables to ensure that the regional resource coordination and mutual assistance model has a solution. The tie-line slack variables should satisfy the following formal constraints:

[0132]

[0133] In the formula, taking a tie line connecting region A and region B as an example, according to the tie line plan, the tie line transfers the power resources of region A to region B. Upper and lower slack values ​​are set for this tie line, with the upper limit of the positive slack value being the additional power that region A can transmit based on the tie line plan, P. A→B(max) P represents the maximum power that the link line between region A and region B can transmit. A→B,t This represents the current power of the link between region A and region B, and the reverse relaxation limit is the maximum power that region B can transmit to region A.

[0134] 6) Establish constraints for the connection line plan.

[0135] The tie-line planning constraints consist of two parts: one part is the overall change in tie-lines caused by unit adjustments, and the other part reflects the inter-regional mutual assistance situation; the tie-line planning constraints can be expressed as:

[0136]

[0137] 7) If regional resource sharing across consecutive time periods is considered, it is also necessary to construct ramp-up constraints for units at adjacent time points. The calculation formula is as follows:

[0138]

[0139] In the formula, λ i,rampup , λ i,rampdn These represent the uphill and downhill rates of the adjustable unit i, respectively.

[0140] 8) Based on the unit and load area division information and standby specifications obtained in step S5, construct standby constraints.

[0141] The spare constraint can be expressed as:

[0142]

[0143] Where A∈V district P G∈A,max P represents the maximum output of all units within region A. LOAD-A,t This represents the load data within region A, Δ A,t P represents the maximum fluctuation value of the load within region A. positive-spare-AP represents the reserve specification value within region A. negtive-spare-A P represents the negative reserve specification value within region A. O→A,t P represents the power transmitted from other regions to region A, and P represents the sum of the power transmitted between other regions and region A via connecting lines. A→O,t This refers to the power transmitted from region A to other regions. This indicates that region A has a reserve of slack. This indicates the negative reserve slack in region A.

[0144] The smallest unit of regional division should satisfy the above-mentioned reserve constraints. Similarly, the entire region as a whole also needs to satisfy the regional reserve constraints. By setting tie-line slack and reserve slack, the tie-line slack represents the resource mutual assistance between inter-provincial power grids or regional power grids, and whether the reserve slack is 0 represents whether the resource mutual assistance is successful. The results can be obtained directly by solving the whole-network unit coordination and mutual assistance model. The solution is fast and highly interpretable.

[0145] 9) Constructing a regional resource coordination and mutual assistance model with the objective of minimizing the overall adjustment of generating units within the region. The regional resource coordination and mutual assistance model is set as follows:

[0146]

[0147] In the formula, n represents the number of adjustment periods. When n = 1, single-time regional resource coordination and mutual assistance are performed; when n > 1, continuous-time regional resource mutual assistance and adjustment are performed. t A collection of adjustable generating units. L i Adjust the penalty factor for the output of unit i. The output adjustment amount at time t. V is the adjustment amount of output at time t; tieline For the tie-line data set, N tie The relaxation penalty factor for the tie. Slack variables on the connection line For the slack variable in the connection line; V l1 For the branch limit constraint data set, R l Slack is the active power adjustment relaxation penalty factor for the over-limit branch l. l,t V is a branch slack variable. section1 M is the dataset of cross-sectional limit constraints. s Slack is the active power adjustment relaxation penalty factor for the over-limit section s. s,t V is the section relaxation variable; district For the region set Q spare-d Q is a spare relaxation penalty factor. spare-d The spare relaxation penalty factor for region d; For region d, use the spare upper slack variable; For the standby slack variable of region d; compared with the unit output adjustment penalty factor, N tie R l M s Q spare-d The penalty factor is set relatively large, and the order of size is generally set to Q. spare-d ≥M s ≥R l ≥N tie ≥L i This guides the solver towards achieving maximum regional resource exchange; by setting different L... i It can also prioritize the adjustment of unit output; the smaller the penalty factor is set, the higher the priority of output adjustment.

[0148] Step S7: Use a high-performance solver to solve the regional resource coordination and mutual assistance model, and provide suggestions for adjusting the regional resource mutual assistance of the power grid based on the solution results.

[0149] Specifically, the regional resource coordination and mutual assistance model is solved using a high-performance solver, and the results include four parts:

[0150] Unit output adjustment values, standby slack values, tie line slack values, and over-limit branch / section slack values.

[0151] Based on the unit output adjustment values, unit adjustment suggestions can be directly derived. Based on the reserve slack value, it can be directly seen whether regional resource mutual assistance can be achieved. Based on the tie line slack value, the amount of power resources for inter-regional coordination and mutual assistance can be obtained. Based on the over-limit branch / section slack value, it can be seen what risks cannot be eliminated after regional resource coordination and mutual assistance.

[0152] Based on the above four parts, reasonable power grid dispatching and allocation will generate regional resource coordination and mutual assistance instructions to ensure the safe operation of the power grid.

[0153] Example 2

[0154] like Figure 3 As shown, a decision-making device based on unified adjustment of all generating units in the network includes:

[0155] The first determination module is used to determine the set of adjustable generating units, tie line data set, over-limit constraint data set, and regional set within the target power grid when a power deficit occurs.

[0156] The variable construction module is used to construct unit adjustment variables, tie-line slack variables, over-limit slack variables, and reserve slack variables within the target power grid.

[0157] The model building module is used to construct a regional resource coordination and mutual assistance model based on the set of adjustable units, tie line data, over-limit constraint data, regional set, as well as unit adjustment variables, tie line slack variables, over-limit slack variables, and reserve slack variables; the regional resource coordination and mutual assistance model aims to minimize the overall adjustment amount.

[0158] The second determination module is used to determine the constraints of the regional resource coordination and mutual assistance model; among which, the constraints include upper and lower limits of unit output, power balance constraints, network security constraints, tie line slack variable constraints, tie line planning constraints, unit ramping constraints, and reserve constraints.

[0159] The model solving module is used to solve the regional resource coordination and mutual assistance model based on constraints, and obtain the regional resource mutual assistance unit adjustment scheme.

[0160] In the first determination module, the method for determining whether there is a power shortage is as follows:

[0161]

[0162] If any of the above conditions are not met, a power deficit is determined to have occurred.

[0163] Among them, P G，max P represents the upper limit of output for all units within the region. G,min P represents the lower limit of the output of all units in the region. G P represents the current output value of all generating units in the region. LOAD P represents the predicted load power, Δ represents the given maximum load fluctuation, and P represents the load power. positive-spare P is the specified value for positive standby. negtive-spare The specified value is for negative backup.

[0164] The regional resource coordination and mutual assistance model constructed in the model building module is shown below:

[0165]

[0166] Where n is the number of adjustment periods; VG t A collection of adjustable generating units; L i Adjust the penalty factor for the output of unit i. The output adjustment amount at time t. V is the adjustment amount of output at time t; tieline For the tie-line data set; N tie The relaxation penalty factor for the tie. Slack variables on the connection line For the slack variable in the connection line; V l1 For branch boundary constraint data set; R lSlack is the active power adjustment relaxation penalty factor for the over-limit branch l. l,t V is a branch slack variable. section1 For cross-sectional limit constraint data set; M s Slack is the active power adjustment relaxation penalty factor for the cross-section s that exceeds the limit. s,t V is the section relaxation variable; district For a set of regions; Q spare-d The spare relaxation penalty factor for region d; For region d, use the spare upper slack variable; For region d, use the slack variable as a backup.

[0167] In the variable construction module, the over-limit relaxation variables specifically include: branch relaxation variables and cross-sectional relaxation variables.

[0168] Example 3

[0169] like Figure 4 As shown, the present invention also provides an electronic device 100 for implementing a decision-making method based on unified adjustment of all network units according to the above embodiments. The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104. The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the decision-making method based on unified adjustment of all network units according to Embodiment 1 by running or executing the computer program stored in the memory 101 and calling data stored in the memory 101.

[0170] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0171] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0172] The memory 101 in the electronic device 100 stores multiple instructions to implement a decision-making method based on unified adjustment of all network units, and the processor 102 can execute multiple instructions to achieve the following:

[0173] When the target power grid experiences a power deficit, determine the set of adjustable generating units, tie line data set, over-limit constraint data set, and regional set within the target power grid.

[0174] Construct unit adjustment variables, tie-line slack variables, over-limit slack variables, and reserve slack variables within the target power grid;

[0175] Based on the set of adjustable generating units, the set of tie line data, the set of over-limit constraint data, the set of regions, and the unit adjustment variables, tie line slack variables, over-limit slack variables, and reserve slack variables, a regional resource coordination and mutual assistance model is constructed; the regional resource coordination and mutual assistance model aims to minimize the overall adjustment amount.

[0176] The constraints of the regional resource coordination and mutual assistance model are determined. These constraints include upper and lower limits of unit output, power balance constraints, network security constraints, tie line slack variable constraints, tie line planning constraints, unit ramping constraints, and reserve constraints.

[0177] Based on the constraints, the regional resource coordination and mutual assistance model is solved to obtain the regional resource mutual assistance unit adjustment scheme.

[0178] Example 4

[0179] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0180] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0181] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0182] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0183] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0184] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0185] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A decision-making method based on unified adjustment of all generating units across the network, characterized in that, Includes the following steps: When the target power grid experiences a power deficit, determine the set of adjustable generating units, tie-line data set, over-limit constraint data set, and region set within the target power grid. Construct unit adjustment variables, tie-line slack variables, over-limit slack variables, and reserve slack variables within the target power grid; Based on the adjustable unit set, tie line data set, over-limit constraint data set, region set, and unit adjustment variables, tie line slack variables, over-limit slack variables, and reserve slack variables, a regional resource coordination and mutual assistance model is constructed; wherein, the regional resource coordination and mutual assistance model aims to minimize the overall adjustment amount; The constraints of the regional resource coordination and mutual assistance model are determined; wherein the constraints include upper and lower limits of unit output, power balance constraints, network security constraints, tie line slack variable constraints, tie line planning constraints, unit ramping constraints, and reserve constraints. Based on the aforementioned constraints, the regional resource coordination and mutual assistance model is solved to obtain a regional resource mutual assistance unit adjustment scheme. In the step of constructing the regional resource coordination and mutual assistance model, the constructed regional resource coordination and mutual assistance model is as follows: in, To adjust the number of time periods; A collection of adjustable generating units; For the unit The output adjustment penalty factor, for Constantly adjust the output. for Adjust the amount of force applied at all times; For the tie-line data set; For connecting lines The relaxation penalty factor, Slack variables on the connection line For slack variables in the connection line; This is the data set for branch road over-limit constraints; For over-limit branch roads Active relaxation penalty factor, For branch slack variables, This is a set of cross-sectional limit constraint data; For cross-sections exceeding the limit Active relaxation penalty factor, For cross-sectional relaxation variables; For regional collections; For the region The spare relaxation penalty factor; For the region The spare slack variable; For the region The spare slack variable.

2. The decision-making method based on unified adjustment of all generating units across the network as described in claim 1, characterized in that, In the step of determining whether there is a power deficit in the target power grid, the method is as follows: If any of the above conditions are not met, a power deficit is determined to have occurred. in, This represents the upper limit of output for all units within the region. This represents the lower limit of the output of all units within the region. This represents the current output value of all generating units within the region. For the predicted load power, Given the maximum load fluctuation, The specified value is for backup. The specified value is for negative backup.

3. The decision-making method based on unified adjustment of all generating units across the network as described in claim 1, characterized in that, In the steps of constructing unit adjustment variables, tie line relaxation variables, over-limit relaxation variables, and reserve relaxation variables within the target power grid, the over-limit relaxation variables specifically include: branch relaxation variables and cross-sectional relaxation variables.

4. A decision-making device based on unified adjustment of all generating units across the network, characterized in that, include: The first determination module is used to determine the set of adjustable generating units, tie line data set, over-limit constraint data set, and regional set within the target power grid when a power deficit occurs. The variable construction module is used to construct unit adjustment variables, tie-line slack variables, over-limit slack variables, and reserve slack variables within the target power grid. The model building module is used to construct a regional resource coordination and mutual assistance model based on the adjustable unit set, tie line data set, over-limit constraint data set, regional set, as well as unit adjustment variables, tie line slack variables, over-limit slack variables, and reserve slack variables; wherein, the regional resource coordination and mutual assistance model aims to minimize the overall adjustment amount; The second determining module is used to determine the constraints of the regional resource coordination and mutual assistance model; wherein, the constraints include upper and lower limits of unit output, power balance constraints, network security constraints, tie line slack variable constraints, tie line planning constraints, unit ramping constraints, and reserve constraints. The model solving module is used to solve the regional resource coordination and mutual assistance model based on the constraints to obtain the regional resource mutual assistance unit adjustment scheme; The regional resource coordination and mutual assistance model constructed in the model building module is shown below: in, To adjust the number of time periods; A collection of adjustable generating units; For the unit The output adjustment penalty factor, for Constantly adjust the output. for Adjust the amount of force applied at all times; For the tie-line data set; For connecting lines The relaxation penalty factor, Slack variables on the connection line For slack variables in the connection line; This is the data set for branch road over-limit constraints; For over-limit branch roads Active relaxation penalty factor, For branch slack variables, This is a set of cross-sectional limit constraint data; For cross-sections exceeding the limit Active relaxation penalty factor, For cross-sectional relaxation variables; For regional collections; For the region The spare relaxation penalty factor; For the region The spare slack variable; For the region The spare slack variable.

5. The decision-making device based on unified adjustment of all generating units across the network as shown in claim 4, characterized in that, In the first determining module, the method for determining whether there is a power deficit is as follows: If any of the above conditions are not met, a power deficit is determined to have occurred. in, This represents the upper limit of output for all units within the region. This represents the lower limit of the output of all units within the region. This represents the current output value of all generating units within the region. For the predicted load power, Given the maximum load fluctuation, The specified value is for backup. The specified value is for negative backup.

6. The decision-making device based on unified adjustment of all generating units across the network as shown in claim 4, characterized in that, In the variable construction module, the over-limit relaxation variables specifically include: branch relaxation variables and cross-sectional relaxation variables.

7. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the decision-making method based on unified adjustment of all network units as described in any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the decision-making method based on unified adjustment of all network units as described in any one of claims 1 to 3.

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