CN110880072A - Real-time power grid static security risk disposal optimization method and device and storage medium - Google Patents
Real-time power grid static security risk disposal optimization method and device and storage medium Download PDFInfo
- Publication number
- CN110880072A CN110880072A CN201911120088.XA CN201911120088A CN110880072A CN 110880072 A CN110880072 A CN 110880072A CN 201911120088 A CN201911120088 A CN 201911120088A CN 110880072 A CN110880072 A CN 110880072A
- Authority
- CN
- China
- Prior art keywords
- fault
- decision
- real
- power
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000003068 static effect Effects 0.000 title claims abstract description 65
- 238000000034 method Methods 0.000 title claims abstract description 58
- 238000005457 optimization Methods 0.000 title claims abstract description 38
- 230000035945 sensitivity Effects 0.000 claims description 46
- 238000004364 calculation method Methods 0.000 claims description 28
- 230000006870 function Effects 0.000 claims description 18
- 239000011159 matrix material Substances 0.000 claims description 12
- 230000002441 reversible effect Effects 0.000 claims description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 4
- 230000005283 ground state Effects 0.000 claims description 3
- 238000004590 computer program Methods 0.000 claims description 2
- 238000010276 construction Methods 0.000 claims description 2
- 230000002829 reductive effect Effects 0.000 abstract description 8
- 230000001105 regulatory effect Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003449 preventive effect Effects 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- Power Engineering (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a real-time power grid static safety risk disposal optimization method which is used for carrying out early warning and auxiliary decision-making on power grid operation static safety risks. In an actual data test, when the risk disposal optimization method is used for processing the static safety fault of the power grid, the operation cost and the decision cost can be greatly reduced on the premise of meeting the constraint condition of the safety of the power grid system, and a powerful control scheme can be provided for the actual risk disposal and the assistant decision of the power grid fault. Meanwhile, on the basis of providing a correct risk handling method and ensuring the safe operation of a power grid system, the method and the system can minimize the cost of the risk handling method, reduce the scheduling flow of assistant decision-making, reduce the labor intensity of a dispatcher, provide maximum risk handling method support for the dispatcher, and improve the working efficiency of the dispatcher while reducing the decision-making cost.
Description
Technical Field
The invention relates to the field of power grid fault processing, in particular to a real-time power grid static security risk handling optimization method and device and a storage medium.
Background
In recent years, with the rapid development of domestic economy, the power industry is also developing at a high speed, higher requirements are provided for the reliability and stability of power supply, and the static safety analysis of a power system has important significance for ensuring the reliability and stability of power supply. The out-of-range static power flow of the power grid is a very easy security threat of the power system, and whether the auxiliary decision of the problem reasonably and directly influences the stable operation of the power system or not is determined.
The current online safety and stability analysis system (called online system for short) is embedded in a new generation of intelligent power grid dispatching control system support system basic platform (called D5000 system for short), and is based on real-time tide and synchronous data sharing, regular safety scanning and expected mode analysis are carried out, the operation risk of the current power grid is monitored, practical auxiliary decision is given, the considerable controllability of the power grid can be greatly improved, the power grid operation space is expanded, and the power grid preventive control is really realized. However, the system has certain problems in the assistant decision module. In the current online system, the auxiliary decision module is not perfect in consideration of stability measure strategies, control measures and costs, different types of regulation characteristics, scheme optimization, system fault tolerance and the like, and needs to be further optimized.
An effective solution to the problems in the related art has not been proposed yet.
Disclosure of Invention
Aiming at the technical problems in the related art, the invention provides a real-time power grid static security risk handling optimization method, which can solve the problems of unreasonable auxiliary decision-making strategy output by an online system, weak scheme optimization, low fault tolerance and the like when an accident occurs. Meanwhile, a method for solving a static safety online assistant decision model is provided.
In order to achieve the technical purpose, the technical scheme of the invention is realized as follows:
the invention discloses a real-time power grid static safety risk disposal optimization method which comprises the steps of calculating section data, judging faults, reading fault information, calculating sensitivity, constructing a static safety assistant decision-making model, solving the static safety assistant decision-making model and analyzing an assistant decision-making result.
Still further, the five steps further include the steps of:
step 1, when an accident occurs to a power grid, reading a provincial power grid basic data section from an online safety and stability analysis system, performing static safety analysis, and judging fault conditions, wherein the fault conditions comprise fault lines and details of N-1 out-of-limit conditions;
step 2, according to the fault condition judged in the step 1, taking fault equipment and the fault condition as input, wherein the input fault information comprises active and limit information of the fault equipment; meanwhile, calculating a sensitivity matrix of the fault equipment, selecting a node with a higher absolute value of sensitivity as an input, and inputting the power, the output range and the sensitivity value of the node with the higher absolute value of sensitivity;
step 3, simultaneously inputting the adjustment cost of each type of node according to the fault information, the sensitivity information and the node information input in the step 2, and constructing a static safety aid decision-making model based on a multi-objective optimization function with the aim of minimizing the aid decision-making cost;
step 4, solving the model by adopting a non-convex function nonlinear programming problem solving method based on the static safety assistant decision model constructed in the step 3 to obtain an assistant decision result which meets constraint conditions and has minimum adjusting cost;
and 5, carrying out load flow calculation on the whole network by using the auxiliary decision result output in the step 4 to judge whether a new fault is generated, carrying out iterative processing again or not, and outputting the auxiliary decision result of each iteration.
Furthermore, the cross-section data calculation and fault judgment further comprises the steps of reading various data of a power grid system monitored by a D5000 system in real time, taking the real-time voltage of each bus and trunk line as a main reading object, reading line information with boundary crossing, and outputting the equipment name and the boundary crossing condition of the equipment to the next step after fault judgment.
Still further, the reading the fault information and calculating the sensitivity further comprises: reading the output fault equipment and the fault condition in the last step, calculating a sensitivity matrix related to the fault equipment, selecting a node with a higher absolute value of sensitivity in the sensitivity matrix, inputting the power, the output range and the sensitivity value information of the node to the next step, and constructing a static safety assistant decision model.
Still further, the static security assistant decision model building further comprises: the method aims at the minimum cost of control and regulation, requires the fault information to be given an optimal assistant decision which can ensure the static safety of the whole network after the whole network load flow calculation,
where F is the objective optimization function of the model, C1、C2、C3And C4Respectively representing the adjustment costs of four control measures of unit output adjustment, hydroelectric generating set starting and stopping, regional small power supply output control and load reverse supply; wherein Δ ps-i(1≤i≤a)、Δps-i(i is more than or equal to 1 and less than or equal to a) respectively represents the active power output adjustment quantity of the hydraulic power plant i and the thermal power plant j, and delta px-k、Δpd-kThe method comprises the steps of representing the small power output adjustment and the load dump active amount of a load node k, wherein a, b and m are the number of power nodes and load nodes of the whole network water and thermal power respectively; t is ts-iα number of start-stop units of hydropower plant ivThe cost factor is adjusted for the corresponding control measure.
Still further, the constraint calculation in the static security assistant decision model further comprises: the constraints in the model are divided into load balance constraints, power flow safety constraints and control variable constraints,
wherein, the formula III is the calculation of load balance constraint,
and pwAn active planned value and a real-time value are sent to the current external connecting line; formula IV is the calculation of load flow safety, D and
are respectively provided withControlling the real-time active power of the section and the limit vector thereof for the whole network, wherein the section limit is determined by an on-line system according to the ground state and the off-limit boundary of the N-1 power flow,
indicating nodes of the power supply of the whole network, piGRepresenting power supply node-section sensitivity; and the formula (V) is control variable constraint calculation and respectively represents the small power output adjustment of the hydraulic power plant and the thermal power plant on the load node k and the upper limit and the lower limit of the load active capacity.
Still further, the solving of the aided decision model further comprises: and (3) solving by adopting a local optimization function, calculating by randomly setting an initial value for a plurality of times in a system aided decision model solving process, and then taking the optimal result of the model as the final aided decision content to ensure that the final decision is optimal.
Still further, the result analysis of the assistant decision model further comprises: the main task is to judge whether the auxiliary decision process is stopped and output decision content, after a model solving result is obtained, load flow calculation is carried out on the whole network according to the model solving result to judge whether a new fault is generated, if the new fault is generated, the auxiliary decision result is recorded and the step 1 is carried out again for iterative processing; if no new fault is generated, the faults are all solved, and an auxiliary decision result of each iteration is output.
The invention also discloses an electronic device, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the real-time grid static security risk handling optimization method described above via execution of the executable instructions.
The invention also discloses a computer readable storage medium on which a computer program is stored, which when executed by a processor implements the above-mentioned real-time grid static security risk handling optimization method.
The invention has the beneficial effects that: the real-time power grid static safety risk handling optimization method disclosed by the invention can provide a powerful control scheme for actual power grid fault auxiliary decision. Meanwhile, on the basis of providing a correct assistant decision method and ensuring the safe operation of a power grid system, the cost of the assistant decision method can be minimized, the scheduling flow of the assistant decision is reduced, the labor intensity of a dispatcher is reduced, the assistant decision support is provided to the dispatcher to the greatest extent, and the work efficiency of the dispatcher is improved while the decision cost is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart of a real-time grid static security risk handling optimization method according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention.
The invention provides a real-time power grid static security risk handling optimization method, aims to solve the problems of unreasonable auxiliary decision-making strategy, weak scheme optimization, low fault tolerance and the like output by an online system when an accident occurs, and provides a set of solution thinking of a static security online auxiliary decision-making model.
As shown in fig. 1, the method for optimizing handling of static security risk of a real-time power grid according to an embodiment of the present invention includes the following steps:
(1) calculating section data and judging faults: when the power grid has an accident, reading the provincial power grid basic data section from the online safety and stability analysis system, performing static safety analysis, and judging fault conditions including fault lines, details of N-1 out-of-limit conditions and the like.
(2) Reading fault information and calculating sensitivity: according to the fault condition judged in the step (1), taking fault equipment and the fault condition as input, wherein the input mainly comprises information such as active power and limit value of the fault equipment; and meanwhile, calculating a sensitivity matrix of the fault equipment, selecting a node with a higher absolute value of sensitivity as an input, and inputting the power, the output range and the sensitivity value of the node with the higher absolute value of sensitivity.
(3) Constructing a static safety assistant decision model: and (3) simultaneously inputting the adjustment cost of each type of node according to the fault information, the sensitivity information and the node information input in the step (2), and constructing a static safety aid decision model based on a multi-objective optimization function with the aim of minimizing the aid decision cost.
(4) Solving a static safety assistant decision model: and (4) solving the model by adopting a non-linear programming problem solving method of a non-convex function based on the static safety assistant decision model constructed in the step (3) to obtain an assistant decision result which meets constraint conditions and has the minimum adjusting cost.
(5) And (3) auxiliary decision result analysis: and (4) carrying out load flow calculation on the whole network by using the auxiliary decision result output in the step (4) to judge whether a new fault is generated, carrying out iteration processing again or not, and outputting the auxiliary decision result of each iteration.
Preferably, the method comprises five steps of section data calculation and fault judgment, reading fault information and calculating sensitivity, static safety assistant decision-making model construction, static safety assistant decision-making model solution and assistant decision-making result analysis.
Preferably, the section data is calculated and the fault is judged. And reading various data of the power grid system monitored by the D5000 system in real time, taking the real-time voltage of each bus and trunk line as a main reading object, reading out line information with boundary crossing, and outputting the equipment name, the equipment boundary crossing condition and the like to the next step after fault judgment.
Preferably, the fault information is read and the sensitivity is calculated. Reading the output fault equipment and the fault condition in the last step, calculating a sensitivity matrix related to the fault equipment, selecting a node with a higher absolute value of sensitivity in the sensitivity matrix, inputting information such as power, output range and sensitivity value of the node to the next step, and constructing a static safety assistant decision model.
Preferably, a static security assistant decision model is constructed. The method is characterized in that the minimum cost of control and regulation is taken as a target, an optimization aid decision capable of guaranteeing the static safety of the whole network after the whole network load flow calculation is carried out is required to be given to fault information, and a target optimization function of a model is F and is shown as a formula (I). C1、C2、C3And C4And (3) respectively representing the adjustment cost of four control measures, namely unit output adjustment, hydroelectric generating set starting and stopping, regional small power output control and load reverse supply, as shown in a formula (II). Wherein Δ ps-i(1≤i≤a)、Δps-i(i is more than or equal to 1 and less than or equal to a) respectively represents the active power output adjustment quantity of the hydraulic power plant i and the thermal power plant j, and delta px-k、Δpd-kThe method is characterized by comprising the following steps of representing the small power output adjustment and the load dump active power amount of a load node k, wherein a, b and m are the number of power nodes and load nodes of the whole network water and thermal power respectively. t is ts-iα number of start-stop units for hydropower plant ivThe value of the adjusting cost coefficient for the corresponding control measure is mainly reflected in the two aspects of the execution speed and the influence range and is represented by the unit time cost TimvAnd economic cost per unit EcovDetermined as shown in table 1.
TABLE 1 Tim of the control measuresv，Ecov，αvUsage value
Preferably, the static security aid decision modelConstraint computation in the pattern. The constraints in the model are divided into load balance constraints, power flow safety constraints and control variable constraints. Equation (III) represents the load balancing constraint calculation,
and pwAnd feeding an active planned value and a real-time value for the current outer connecting line. Formula (IV) represents the power flow safety calculation, D and
respectively controlling the real-time active power of the section and a limit vector thereof in the whole network, wherein the section limit is determined by an on-line system according to a ground state and an N-1 power flow out-of-limit boundary,
indicating nodes of the power supply of the whole network, piGRepresenting the power supply node-profile sensitivity. And the formula (V) represents control variable constraint calculation, and respectively represents the small power output adjustment of the hydraulic power plant and the thermal power plant on the load node k and the upper limit and the lower limit of the load active capacity.
Preferably, the solution of the decision model is aided. The task of the static safety assistant decision model is to obtain a decision result which meets the constraint condition of the model and has the minimum assistant decision cost. The problem can be viewed as a non-linear programming problem of non-convex functions, where the tuning cost of the control measure is the objective variable to solve. The adjustment of four control measures, namely unit output adjustment, hydroelectric generating set starting and stopping, regional small power supply output control and load reverse supply, is a decision variable in the solving process. The objective optimization function constructed by connecting the objective variable and the decision variable is a non-convex function, and the objective is to minimize the adjustment cost of the control measure, so that the non-convex function nonlinear programming is adopted for solving. In the process of solving, because the constraint conditions of the model are more, a local optimization function needs to be adopted for solving, so that the initial value setting of the model has great influence on the result of the model. The method adopted in the solution process of the assistant decision model of the system is to calculate by randomly setting an initial value for a plurality of times in the range, and then taking the optimal result of the model as the final assistant decision content to ensure the optimal final decision.
Preferably, the analysis of the results of the decision model is aided. The main task is to judge whether the auxiliary decision process is stopped and output decision content. After a model solving result is obtained, carrying out load flow calculation on the whole network according to the model solving result to judge whether a new fault is generated, if the new fault is generated, recording the auxiliary decision result and transferring to the step (1) to carry out iterative processing again; if no new fault is generated, the faults are all solved, and an auxiliary decision result of each iteration is output.
In order to facilitate understanding of the above-described technical aspects of the present invention, the above-described technical aspects of the present invention will be described in detail below in terms of specific usage.
In specific use, the method for optimizing handling of the static security risk of the real-time power grid comprises the following steps:
(1) calculating section data and judging faults: when the power grid has an accident, reading the provincial power grid basic data section from the online safety and stability analysis system, performing static safety analysis, and judging fault conditions including fault lines, details of N-1 out-of-limit conditions and the like.
(2) Reading fault information and calculating sensitivity: according to the fault condition judged in the step (1), taking fault equipment and the fault condition as input, wherein the input mainly comprises information such as active power and limit value of the fault equipment; and meanwhile, calculating a sensitivity matrix of the fault equipment, selecting a node with a higher absolute value of sensitivity as an input, and inputting the power, the output range and the sensitivity value of the node with the higher absolute value of sensitivity.
(3) Constructing a static safety assistant decision model: and (3) simultaneously inputting the adjustment cost of each type of node according to the fault information, the sensitivity information and the node information input in the step (2), and constructing a static safety aid decision model based on a multi-objective optimization function with the aim of minimizing the aid decision cost.
(4) Solving a static safety assistant decision model: and (4) solving the model by adopting a non-linear programming problem solving method of a non-convex function based on the static safety assistant decision model constructed in the step (3) to obtain an assistant decision result which meets constraint conditions and has the minimum adjusting cost.
(5) And (3) auxiliary decision result analysis: and (4) carrying out load flow calculation on the whole network by using the auxiliary decision result output in the step (4) to judge whether a new fault is generated, carrying out iteration processing again or not, and outputting the auxiliary decision result of each iteration.
The method is based on a D5000 intelligent scheduling support system, the power grid operation data are acquired from a D5000 support platform, and the operation environment is a Linux environment; the development language was Python.
The method comprises the following steps of taking an auxiliary decision after a Hunan-Longping line II is disconnected and a Hunan-Longping line I crosses the boundary as an example for detailed description, wherein after the Hunan-Longping line II is disconnected, an online auxiliary decision system finds the Hunan-Longping line I crosses the boundary through load flow calculation, and then sends the position of a fault line, namely the Hunan-Longping line I and the current active power of the line, namely 152WM and the line limit, namely 100WM, to the auxiliary decision system, and simultaneously calculates the sensitivity matrix of the Hunan-Longping line I and sends the sensitivity matrix information to the auxiliary decision system; reminding a dispatcher to set configuration file information after a fault occurs, setting the maximum number of units to be regulated to be 10, the maximum number of the hydraulic power plants to be regulated to be 5, setting a sensitivity threshold value to be 0.1, setting the regulation cost of water and thermal power plants to be 0.6, setting the start-stop cost of the units of the hydraulic power plants to be regulated to be 16, setting the regulation cost of a small power supply to be 45, setting the regulation cost of load dump to be 100, automatically performing auxiliary decision solving by an auxiliary model system after the setting is finished, performing new power flow calculation by taking auxiliary decision information of five times of random initial quantity optimal solution, finding that no fault is found in the power flow calculation, finishing the operation flow of the system, obtaining the auxiliary decision information which is Hunan A2 plant, reducing the output of 80MW, setting the start-stop amount of the units to be 0, and reducing the output of 110MW of the Hunan A2 plant, the unit start-stop amount is 0, the output of the Hunan Hongjiang A2 plant is reduced by 86MW, the unit start-stop amount is 0, the total cost is 166, and the total time is 0.656 seconds.
After the technical scheme is adopted, a powerful control scheme can be provided for the actual power grid fault auxiliary decision. Meanwhile, on the basis of providing a correct assistant decision method and ensuring the safe operation of a power grid system, the invention can minimize the cost of the assistant decision method, reduce the scheduling flow of the assistant decision, reduce the labor intensity of a dispatcher, provide the maximum assistant decision support for the dispatcher and improve the working efficiency of the dispatcher while reducing the decision cost.
In summary, by means of the technical scheme of the invention, a powerful control scheme can be provided for actual power grid fault auxiliary decision through the real-time power grid static security risk handling optimization method disclosed by the invention. Meanwhile, on the basis of providing a correct assistant decision method and ensuring the safe operation of a power grid system, the cost of the assistant decision method can be minimized, the scheduling flow of the assistant decision is reduced, the labor intensity of a dispatcher is reduced, the assistant decision support is provided to the dispatcher to the greatest extent, and the work efficiency of the dispatcher is improved while the decision cost is reduced.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A real-time power grid static safety risk handling optimization method is characterized by comprising the steps of section data calculation and fault judgment, reading fault information and calculating sensitivity, static safety assistant decision-making model construction, static safety assistant decision-making model solution and assistant decision-making result analysis.
2. The real-time grid static security risk handling optimization method of claim 1, wherein the five steps further comprise the steps of:
step 1, when an accident occurs to a power grid, reading a provincial power grid basic data section from an online safety and stability analysis system, performing static safety analysis, and judging fault conditions, wherein the fault conditions comprise fault lines and details of N-1 out-of-limit conditions;
step 2, according to the fault condition judged in the step 1, taking fault equipment and the fault condition as input, wherein the input fault information comprises active and limit information of the fault equipment; meanwhile, calculating a sensitivity matrix of the fault equipment, selecting a node with a higher absolute value of sensitivity as an input, and inputting the power, the output range and the sensitivity value of the node with the higher absolute value of sensitivity;
step 3, simultaneously inputting the adjustment cost of each type of node according to the fault information, the sensitivity information and the node information input in the step 2, and constructing a static safety aid decision-making model based on a multi-objective optimization function with the aim of minimizing the aid decision-making cost;
step 4, solving the model by adopting a non-convex function nonlinear programming problem solving method based on the static safety assistant decision model constructed in the step 3 to obtain an assistant decision result which meets constraint conditions and has minimum adjusting cost;
and 5, carrying out load flow calculation on the whole network by using the auxiliary decision result output in the step 4 to judge whether a new fault is generated, carrying out iterative processing again or not, and outputting the auxiliary decision result of each iteration.
3. The real-time grid static security risk handling optimization method according to claim 2, wherein the cross-section data calculation and fault judgment further comprises reading each item of data of the grid system monitored by the D5000 system in real time, reading out line information that is out of range with the real-time voltage of each bus and trunk as a main reading object, and outputting the device name and the device out of range condition to the next step after fault judgment.
4. The real-time grid static safety risk handling optimization method according to claim 2, wherein the reading fault information and calculating sensitivity further comprises: reading the output fault equipment and the fault condition in the last step, calculating a sensitivity matrix related to the fault equipment, selecting a node with a higher absolute value of sensitivity in the sensitivity matrix, inputting the power, the output range and the sensitivity value information of the node to the next step, and constructing a static safety assistant decision model.
5. The real-time grid static security risk handling optimization method according to claim 2, wherein the static security assistance decision model building further comprises: the method aims at the minimum cost of control and regulation, requires the fault information to be given an optimal assistant decision which can ensure the static safety of the whole network after the whole network load flow calculation,
where F is the objective optimization function of the model, C1、C2、C3And C4Respectively representing the adjustment costs of four control measures of unit output adjustment, hydroelectric generating set starting and stopping, regional small power supply output control and load reverse supply; wherein Δ ps-i(1≤i≤a)、Δps-i(i is more than or equal to 1 and less than or equal to a) respectively represents the active power output adjustment quantity of the hydraulic power plant i and the thermal power plant j, and delta px-k、Δpd-kThe method comprises the steps of representing the small power output adjustment and the load dump active amount of a load node k, wherein a, b and m are the number of power nodes and load nodes of the whole network water and thermal power respectively; t is ts-iα number of start-stop units of hydropower plant ivThe cost factor is adjusted for the corresponding control measure.
6. The real-time grid static security risk handling optimization method according to claim 2, wherein the constraint calculation in the static security aid decision model further comprises: the constraints in the model are divided into load balance constraints, power flow safety constraints and control variable constraints,
wherein, the formula III is the calculation of load balance constraint,
and pwAn active planned value and a real-time value are sent to the current external connecting line; formula IV is the calculation of load flow safety, D and
respectively controlling the real-time active power of the section and a limit vector thereof in the whole network, wherein the section limit is determined by an on-line system according to a ground state and an N-1 power flow out-of-limit boundary,
indicating the power node of the whole network, nGRepresenting power supply node-section sensitivity; and the formula (V) is control variable constraint calculation and respectively represents the small power output adjustment of the hydraulic power plant and the thermal power plant on the load node k and the upper limit and the lower limit of the load active capacity.
7. The real-time grid static security risk handling optimization method according to claim 2, wherein the solving of the aid decision model further comprises: and (3) solving by adopting a local optimization function, calculating by randomly setting an initial value for a plurality of times in a system aided decision model solving process, and then taking the optimal result of the model as the final aided decision content to ensure that the final decision is optimal.
8. The real-time grid static security risk handling optimization method according to claim 2, wherein the result analysis of the aid decision model further comprises: the main task is to judge whether the auxiliary decision process is stopped and output decision content, after a model solving result is obtained, load flow calculation is carried out on the whole network according to the model solving result to judge whether a new fault is generated, if the new fault is generated, the auxiliary decision result is recorded and the step 1 is carried out again for iterative processing; if no new fault is generated, the faults are all solved, and an auxiliary decision result of each iteration is output.
9. An electronic device, comprising:
a processor; and the number of the first and second groups,
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the real-time grid static security risk handling optimization method of any of claims 1-8 via execution of the executable instructions.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the real-time grid static security risk handling optimization method of any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911120088.XA CN110880072A (en) | 2019-11-15 | 2019-11-15 | Real-time power grid static security risk disposal optimization method and device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911120088.XA CN110880072A (en) | 2019-11-15 | 2019-11-15 | Real-time power grid static security risk disposal optimization method and device and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110880072A true CN110880072A (en) | 2020-03-13 |
Family
ID=69729118
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911120088.XA Pending CN110880072A (en) | 2019-11-15 | 2019-11-15 | Real-time power grid static security risk disposal optimization method and device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110880072A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111539586A (en) * | 2020-06-05 | 2020-08-14 | 中国电力科学研究院有限公司 | Power dispatching center and decision-making assisting method |
| CN112381363A (en) * | 2020-10-28 | 2021-02-19 | 广西电网有限责任公司贺州供电局 | Intelligent navigation method and system for power grid accident handling |
| CN116308887A (en) * | 2023-05-12 | 2023-06-23 | 北京迅巢科技有限公司 | Intelligent power distribution integrated platform model construction method |
| CN117154852A (en) * | 2023-10-27 | 2023-12-01 | 华南理工大学 | Power grid economic dispatching method based on alternate iteration under consideration of N-1 tide constraint |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104868471A (en) * | 2015-06-15 | 2015-08-26 | 国家电网公司 | Static security aid decision making method for provincial power grid |
| US20160077164A1 (en) * | 2014-09-17 | 2016-03-17 | Kabushiki Kaisha Toshiba | Failure sign diagnosis system of electrical power grid and method thereof |
| CN105811401A (en) * | 2016-03-21 | 2016-07-27 | 国家电网公司 | Auxiliary decision making method for provincial power grid accident emergency control |
-
2019
- 2019-11-15 CN CN201911120088.XA patent/CN110880072A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160077164A1 (en) * | 2014-09-17 | 2016-03-17 | Kabushiki Kaisha Toshiba | Failure sign diagnosis system of electrical power grid and method thereof |
| CN104868471A (en) * | 2015-06-15 | 2015-08-26 | 国家电网公司 | Static security aid decision making method for provincial power grid |
| CN105811401A (en) * | 2016-03-21 | 2016-07-27 | 国家电网公司 | Auxiliary decision making method for provincial power grid accident emergency control |
Non-Patent Citations (1)
| Title |
|---|
| 石辉 等: "基于在线系统的电网事故应急辅助决策研究", 《湖南电力》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111539586A (en) * | 2020-06-05 | 2020-08-14 | 中国电力科学研究院有限公司 | Power dispatching center and decision-making assisting method |
| CN112381363A (en) * | 2020-10-28 | 2021-02-19 | 广西电网有限责任公司贺州供电局 | Intelligent navigation method and system for power grid accident handling |
| CN116308887A (en) * | 2023-05-12 | 2023-06-23 | 北京迅巢科技有限公司 | Intelligent power distribution integrated platform model construction method |
| CN116308887B (en) * | 2023-05-12 | 2023-08-11 | 北京迅巢科技有限公司 | Intelligent power distribution integrated platform model construction method |
| CN117154852A (en) * | 2023-10-27 | 2023-12-01 | 华南理工大学 | Power grid economic dispatching method based on alternate iteration under consideration of N-1 tide constraint |
| CN117154852B (en) * | 2023-10-27 | 2024-01-02 | 华南理工大学 | Power grid economic dispatching method based on alternate iteration under consideration of N-1 tide constraint |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110880072A (en) | Real-time power grid static security risk disposal optimization method and device and storage medium | |
| Zhang et al. | Hierarchically-coordinated voltage/VAR control of distribution networks using PV inverters | |
| Feng et al. | A comprehensive approach for preventive and corrective control to mitigate voltage collapse | |
| Kumar et al. | Intelligent controller design and fault prediction using machine learning model | |
| Yan et al. | A new tool to estimate maximum wind power penetration level: In perspective of frequency response adequacy | |
| CN102930344A (en) | Method for forecasting ultra-short term bus load based on load trend changes | |
| CN101888087A (en) | Method for realizing distributed super-short-term area load forecasting in distribution network terminal | |
| KR101219544B1 (en) | Optimised connection interpretive method of distribution line connection | |
| CN108493998B (en) | Robust power transmission network planning method considering demand response and N-1 expected faults | |
| Morillo et al. | Risk-averse stochastic dual dynamic programming approach for the operation of a hydro-dominated power system in the presence of wind uncertainty | |
| CN111079972A (en) | Method, device and medium for planning reliability of active power distribution network | |
| Zhang et al. | Deep reinforcement learning for load shedding against short-term voltage instability in large power systems | |
| Zhang et al. | Research on AGC performance during wind power ramping based on deep reinforcement learning | |
| CN111799793A (en) | Source-grid-load cooperative power transmission network planning method and system | |
| Bento et al. | Analysis of the load growth direction variation in the dynamic security assessment | |
| CN114759579A (en) | Power grid active power optimization control system, method and medium based on data driving | |
| CN113537646A (en) | Power grid equipment power failure maintenance scheme making method, system, equipment and storage medium | |
| CN113452081A (en) | Method and device for evaluating new energy consumption improvement amount | |
| Zhang et al. | Research on the whole life cycle cost management and maintenance decision of secondary equipment based on physical ID | |
| CN112751334A (en) | Power grid online modeling method and system based on memory computing architecture | |
| Zhang et al. | Optimal Scheduling Strategy for Power Systems Containing Offshore Wind Farms Considering Wind Power Uncertainty | |
| Zhang et al. | Probabilistic transmission network expansion planning considering integration of wind power | |
| CN116722549B (en) | Hierarchical control method and device for power distribution network based on high-precision simulation technology | |
| Liu | Distribution Network Optimization Planning Based on Genetic Algorithms | |
| Arestova et al. | Algorithms of in-station optimization in the simulation model of a hydropower plants cascade |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200313 |