Effective reserve capacity optimization method and system for new energy output uncertainty

Abstract

The invention discloses an effective reserve capacity optimization method and system for new energy output uncertainty, and the method comprises the steps: constructing a first power flow constraint in a prediction state according to the historical operation data of a power system and a preset prediction state; according to historical operation data of the power system and a preset extreme severe state, constructing a second power flow constraint in the extreme severe state; according to the first power flow constraint and the second power flow constraint, constructing a relation constraint between the prediction state and the extremely severe state; and constructing a target function by taking the minimum starting capacity of the conventional unit as a target, and then obtaining the optimal effective reserve capacity according to the relation constraint and the target function. According to the method, the sufficient effective reserve capacity is accurately evaluated by considering the predicted operation state and the extremely severe state, and the method is used for solving the problem of insufficient unit output in the extremely severe state, so that safe operation and continuous and reliable power supply of a power system are ensured.

Description

Technical Field

[0001] The present application belongs to the technical field of power systems, and specifically relates to an effective backup capacity optimization method and system for renewable energy output uncertainty. Background Art

[0002] In order to cope with risk factors such as the output of new energy sources falling short of expectations, oversupply of loads, and reduction in external electricity, the units need to reserve a certain amount of spare capacity when arranging the operation mode. For medium- and long-term reserve calculations, the maximum possible output of the unit minus the actual output of the unit is generally used as the reserve that the unit can provide. For short-term reserve calculations (such as 5-minute or 15-minute reserve), the ramp rate constraint of the unit must also be considered. When considering grid congestion, some units cannot operate at full output due to grid security constraints, and their spare capacity cannot be actually called upon under the condition of ensuring grid security after the disturbance. This part of the spare capacity is invalid. The effective spare capacity assessment is to ensure that the spare capacity reserved by the unit is a spare capacity assessment that is not restricted by grid security constraints and can be actually called upon.

[0003] The existing effective reserve configuration methods generally rely on manual experience, deduct the network constraint capacity of specific units in advance, or adopt the means of partitioned reserve configuration, finely partition the power grid according to the grid congestion situation, and then reserve reserve capacity for each partition to ensure that the reserved reserve capacity is as free from grid security constraints as possible, and is an effective reserve capacity that can be actually called. This method generally relies on manual experience, but with the greatly increased uncertainty of the source and load on the power generation and power consumption sides of new energy, it is difficult to exhaust the possible operation modes of the power grid based on manual experience alone, so a more accurate method is urgently needed to evaluate the effective reserve capacity of the power grid. Summary of the invention

[0004] This application proposes a method and system for optimizing effective reserve capacity for the uncertainty of new energy output. By considering the predicted operating status and extremely harsh conditions, sufficient effective reserve capacity is accurately evaluated to solve the problem of insufficient unit output under extremely harsh conditions, so as to ensure safe operation and continuous and reliable power supply.

[0005] A first aspect of the present application provides an effective reserve capacity optimization method for renewable energy output uncertainty, the method comprising:

[0006] According to the historical operation data of the power system and the preset forecast state, constructing the first power flow constraint under the forecast state;

[0007] According to the historical operation data of the power system and the preset extreme bad state, constructing the second power flow constraint under the extreme bad state;

[0008] According to the first power flow constraint and the second power flow constraint, constructing a relationship constraint between the predicted state and the extremely severe state;

[0009] Taking the minimum startup capacity of conventional units as the goal, an objective function is constructed, and then the optimal effective reserve capacity is obtained according to the relationship constraint and the objective function; wherein the optimal effective reserve capacity is used to supplement the unit output of the power system.

[0010] The above scheme considers two power grid states: the predicted state and the extremely bad state, and calculates the first power flow constraint and the second power flow constraint that the power grid needs to meet under these two states. Since the output of conventional units needs to be increased to balance the safe operation of the power grid under extremely bad conditions, by comparing the difference between the first power flow constraint and the second power flow constraint, the power flow change relationship between the predicted state and the extremely bad state can be obtained, the corresponding relationship constraint can be determined, and the effective spare capacity corresponding to the active output that needs to be increased under extremely bad conditions can be clarified to ensure that the power grid can be operated safely and continuously and reliably under extremely bad conditions. Then, considering the economic efficiency of power grid operation, the objective function is constructed with the minimum startup capacity of conventional units as the goal. The optimal effective spare capacity obtained can not only ensure that the power grid maintains safe operation and continuous and reliable power supply under extremely bad conditions, but also ensure that the increased effective spare capacity can reduce the required cost as much as possible.

[0011] In a possible implementation method of the first aspect, according to historical operation data of the power system and a preset forecast state, a first power flow constraint under the forecast state is constructed, specifically:

[0012] constructing the output constraint of the first conventional unit in the predicted state according to the historical operation data;

[0013] According to the historical operation data and the predicted state, respectively constructing a first normal safety constraint when no fault occurs in the predicted state and a first fault safety constraint when a fault occurs; wherein the first normal safety constraint includes a first node power balance constraint and a first static safety constraint;

[0014] The first power flow constraint is constructed according to the first conventional unit output constraint, the first normal safety constraint and the first fault safety constraint.

[0015] The above scheme first calculates the first flow constraint that the power grid needs to meet in order to enable the output of conventional units to reach the expected level when a fault occurs and when no fault occurs under the preset forecast state, thereby determining that the flow distribution of the power system under the forecast state can ensure the safe operation of the power grid, and provide data support for the subsequent comparison of the flow difference between the forecast state and the extremely severe state.

[0016] In a possible implementation method of the first aspect, the first power flow constraint is specifically:

[0017] Among them, the output constraint of the first conventional unit is specifically expressed as:

[0018]

[0019] In the formula, u Gi is the start and stop status of the conventional unit at node i, P Gi is the active power output of the conventional unit at node i under the predicted state, P Gi and are the lower and upper limits of the active output of the conventional unit at node i, S G is a collection of conventional unit end nodes;

[0020] The first node power balance constraint is specifically expressed as:

[0021]

[0022] Where P WDi is the active power output of the wind turbine at node i in the prediction state, P PVi is the active power output of the photovoltaic unit at node i in the prediction state, P DCi is the DC external power input power of node i, P LDi is the active load of node i, S Ni is the set of node i and its adjacent nodes, B ij is the element in the i-th row and j-th column of the node susceptance matrix, is the voltage phase angle of node j when no fault occurs in the predicted state;

[0023] The first static safety constraint is specifically expressed as follows:

[0024]

[0025] In the formula, is the long-term current carrying capacity allowed by branch ij, is the active power flow value flowing into the branch ij from node i, S A is the collection of all branches;

[0026] The first fault safety constraint is specifically expressed as:

[0027]

[0028] In the formula, is the short-time allowable current carrying capacity of branch ij, is the set of branches disconnected under the kth fault in the fault set, L ij-mn is the distribution factor of branch ij when branch mn is disconnected, is the active power flow value of branch mn from node m into the branch when no fault occurs, is the set of branches that operate normally under the kth fault in the fault set, k is the fault number of the fault set under the predicted state, S K is the fault set under the predicted state.

[0029] In a possible implementation method of the first aspect, according to historical operation data of the power system and a preset extreme state, a second power flow constraint under the extreme state is constructed, specifically:

[0030] According to the historical operation data and the output change of the conventional unit under the extremely bad state, constructing the output constraint of the second conventional unit under the extremely bad state;

[0031] According to the historical operation data and the power flow distribution changes under the extremely severe state, a second normal safety constraint when no fault occurs under the extremely severe state and a second fault safety constraint when a fault occurs are respectively constructed; wherein the second normal safety constraint includes a second node power balance constraint and a second static safety constraint; the power flow distribution changes include changes in the output of new energy units, changes in load power and changes in DC external power;

[0032] The second power flow constraint is constructed according to the second conventional unit output constraint, the second normal safety constraint and the second fault safety constraint.

[0033] The above scheme calculates the second power flow constraint that the power grid needs to meet to enable the output of conventional units to reach the expected level when a fault occurs or does not occur under extremely severe conditions. Because the output of conventional units under extremely severe conditions is often lower than that under the predicted state, more effective reserve capacity needs to be added. Therefore, according to the second power flow constraint, it can be accurately determined how much effective reserve capacity needs to be added to enable the power grid to maintain safe operation under extremely severe conditions.

[0034] In a possible implementation method of the first aspect, a relationship constraint between the predicted state and the extremely severe state is constructed according to the first power flow constraint and the second power flow constraint, specifically:

[0035] Compare the predicted state with the power flow distribution under the extremely severe state to obtain a power flow change result; wherein the power flow distribution includes the output of the new energy unit, the load power and the DC external power;

[0036] According to the power flow change result, the relationship constraint is obtained through the first power flow constraint and the second power flow constraint.

[0037] The above scheme compares the predicted state with the flow distribution under the extremely severe state to obtain the deviation caused by the flow change and source load change under the extremely severe state, thereby determining the range of effective reserve capacity that needs to be increased under the extremely severe state and obtaining the relationship constraint.

[0038] In a possible implementation method of the first aspect, taking the minimum startup capacity of conventional units as the goal, an objective function is constructed, and then the optimal effective spare capacity is obtained according to the relationship constraint and the objective function, specifically:

[0039] Taking the minimum startup capacity of conventional units as the goal, the objective function is constructed according to the operation status of conventional units;

[0040] According to the relationship constraint, a penalty weight is introduced into the objective function, and then the optimal effective spare capacity is calculated.

[0041] The above scheme constructs the objective function with the minimum startup capacity of conventional units as the goal, and introduces penalty weights in the objective function according to the relational constraints. While ensuring that the optimal effective reserve capacity can fully support the safe operation and continuous and reliable power supply of the power grid under extremely harsh conditions, it can also reduce the cost of the increased effective reserve capacity as much as possible, greatly improving the economy of the power grid.

[0042] In a possible implementation method of the first aspect, a penalty weight is introduced into the objective function, specifically:

[0043] Among them, the objective function after the penalty weight is introduced is specifically expressed as:

[0044]

[0045] Where W G is the penalty weight of the startup capacity of conventional units, S G is the set of conventional unit end nodes, is the maximum technical output of the conventional unit at node i, u Gi is the start and stop variable of the conventional unit at node i, P RES is the effective reserve capacity of the power system, W LD is the penalty weight when the load power is less than the load oversupply rate, S LD is the set of load nodes, ΔP LDi It is the part of the load power of node i that is less than the load oversupply rate.

[0046] The second aspect of the present application provides an effective spare capacity optimization system for new energy output uncertainty, the system comprising: a prediction state constraint condition building module, an extreme state constraint condition building module, a relationship constraint building module and an optimal effective spare capacity calculation module;

[0047] The forecast state constraint condition building module is used to build the first power flow constraint under the forecast state according to the historical operation data of the power system and the preset forecast state;

[0048] The extremely severe state constraint condition building module is used to build the second power flow constraint under the extremely severe state according to the historical operation data of the power system and the preset extremely severe state;

[0049] The relationship constraint building module is used to build a relationship constraint between the predicted state and the extremely severe state according to the first power flow constraint and the second power flow constraint;

[0050] The optimal effective reserve capacity calculation module is used to construct an objective function with the minimum startup capacity of conventional units as the goal, and then according to the relationship constraints and the objective function; wherein the optimal effective reserve capacity is used to supplement the unit output of the power system.

[0051] A third aspect of the present application provides a terminal device, the device comprising: a terminal device comprising a processor and a memory, the memory storing a computer program, and the processor implementing the steps of an effective spare capacity optimization method for new energy output uncertainty as described in any one of the embodiments of the present application when executing the computer program.

[0052] A fourth aspect of the present application provides a storage medium storing computer-readable program code, which, when executed, implements the steps of an effective spare capacity optimization method for new energy output uncertainty as described in any one of the embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the implementation manner will be briefly introduced below. Obviously, the drawings described below are only some implementation manners of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 It is a specific flow chart of an effective spare capacity optimization method for renewable energy output uncertainty provided by a certain embodiment of the present application;

[0055] Figure 2It is a specific structural diagram of an effective spare capacity optimization system for renewable energy output uncertainty provided by a certain embodiment of the present application;

[0056] Figure 3 A structural diagram of a terminal device is provided for a certain embodiment of the present application. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0058] It should be understood that the step numbers used in this article are only for the convenience of description and are not intended to limit the order in which the steps are executed.

[0059] First embodiment

[0060] The effective reserve capacity in the power system is used to provide additional unit output when the output of new energy units is insufficient, the load is oversupplied, and the external power is reduced, so as to avoid load limitation. However, with the large-scale grid connection of wind power and photovoltaic power, the uncertainty of source and load on the power generation side and the power consumption side of the current new energy units has been greatly enhanced. It is mainly dependent on manual experience to determine the arrangement of effective reserve capacity and distribution. It is no longer possible to accurately meet the backup needs of the power grid, especially in extremely harsh conditions. Accurately assessing the effective reserve capacity required by the unit is often affected by whether the branches and nodes fail, the distribution of power grid currents, etc. Therefore, how to comprehensively consider the operating conditions that the power grid may face to reasonably configure the effective reserve capacity and its distribution, so that the units of new energy units can provide output that meets the demand at any time to ensure the safe operation of the power system, is the main research direction of the embodiments of this application.

[0061] like Figure 1 As shown, Figure 1 A specific flow chart of an effective spare capacity optimization method for renewable energy output uncertainty is provided for a certain embodiment of the present application. The effective spare capacity optimization method for renewable energy output uncertainty of this embodiment includes steps S1 to S4, which are described in detail as follows:

[0062] Step S1, constructing a first power flow constraint under the predicted state according to historical operation data of the power system and a preset predicted state.

[0063] In the embodiment of the present application, the historical operation data of the power system is first obtained to clarify the power flow data of the power system in all previous different operation states. The power flow data mainly includes node power data, branch current data and conventional unit output data.

[0064] Among them, the conventional units shown include thermal power units, hydropower units, nuclear power units, etc., and also new energy units.

[0065] Then, according to the output data of the conventional units, under the preset forecast state, the first conventional unit output constraint under the forecast state is constructed, that is, the output condition that the conventional unit needs to meet under the forecast state, and the specific expression is:

[0066]

[0067] In the formula, u Gi is the start and stop status of the conventional unit at node i, P Gi is the active power output of the conventional unit at node i under the predicted state, P Gi and are the lower and upper limits of the active output of the conventional unit at node i, S G It is a collection of conventional unit end nodes.

[0068] Specifically, the forecast state is the power system operation state arranged based on load forecast and new energy output forecast, which can be considered as the normal operation state of the power system under normal circumstances. The flow data in the forecast state mainly refers to the historical operation data of the power system.

[0069] Then, according to the predicted state and the historical operation data, a first normal safety constraint when no fault occurs in the predicted state and a first fault safety constraint when a fault occurs are constructed respectively. The first normal safety constraint includes a first node power balance constraint and a first static safety constraint.

[0070] Furthermore, the power balance constraint of the first node in the predicted state is usually calculated using a DC power flow model, and the specific expression is:

[0071]

[0072] Where P WDi is the active power output of the wind turbine at node i in the prediction state, P PVi is the active power output of the photovoltaic unit at node i in the prediction state, P DCi is the DC external power input power of node i, P LDi is the active load of node i, S Ni is the set of node i and its adjacent nodes, Bij is the element in the i-th row and j-th column of the node susceptance matrix, is the voltage phase angle of node j when no fault occurs in the predicted state.

[0073] The first node power balance constraint is a power demand that the nodes of the power system must meet under the predicted state to ensure that the node power of the power system is normal.

[0074] When the power system is operating normally, it is required that the active power flow of each branch does not exceed the long-term current carrying capacity of the equipment. Based on this, the first static safety constraint can be constructed. The specific expression is:

[0075]

[0076] In the formula, is the long-term current carrying capacity allowed by branch ij, is the active power flow value flowing into the branch ij from node i, S A is the collection of all branches.

[0077] According to the DC power flow model, the branch active power flow can be described as:

[0078]

[0079] In the formula, x ij is the reactance of branch ij.

[0080] Then, for the preset fault set under the predicted state, the first fault safety constraint is constructed when a fault occurs. The main method is to use the branch disconnection distribution factor to describe the power flow after the fault as a linear function of the power flow without fault, that is:

[0081]

[0082] Where k is the fault number in the fault set, S K is the fault set under the predicted state, is the active power flow from node i into branch ij under fault k, L ij-mn is the distribution factor of branch ij when branch mn is disconnected, is the set of branches that operate normally under the kth fault in the fault set, is the set of branches disconnected under the kth fault in the fault set, It is the active power flow value flowing into the branch mn from the node m when no fault occurs.

[0083] To ensure the safe operation of the power grid, the branch current should not exceed the short-term overload capacity of the equipment after a fault occurs, that is:

[0084]

[0085] In the formula, is the short-time allowable current-carrying capacity of branch ij.

[0086] According to the above formula, the first fault safety constraint can be obtained, and the specific expression is:

[0087]

[0088] The first power flow constraint is constructed according to the first conventional unit output constraint, the first normal safety constraint and the first fault safety constraint.

[0089] Step S2: constructing a second power flow constraint under the extremely severe state according to the historical operation data of the power system and the preset extremely severe state.

[0090] In an embodiment of the present application, a second power flow constraint is first constructed under a preset extremely harsh state. The difference between the predicted state and the extremely harsh state is mainly in the output, load and external power of the new energy unit. The grid structure and startup mode of the two are the same, but the output of the conventional unit is different. Due to the difference in the output of conventional units, the output of new energy units, the load and external power, the power flow distribution of the predicted state and the extremely harsh state is different, and the voltage phase of each node will also be different. Therefore, in the extremely harsh state, while maintaining the same conventional unit startup mode as the predicted state, it is necessary to increase the output of the conventional unit to meet the second power flow constraint. Therefore, it is necessary to increase the effective spare capacity of the power system to increase the output of the conventional unit to maintain the safe operation of the power system and continuous and reliable power supply.

[0091] First, according to the historical operation data and the output change of the conventional unit under the extremely bad state, the output constraint of the second conventional unit under the extremely bad state is constructed. The specific expression is:

[0092]

[0093] In the formula, u Gi is the start and stop status of the conventional unit at node i, P' Gi is the active power output of the conventional unit at node i under the extremely bad condition, P Gi and are the lower and upper limits of the active output of the conventional unit at node i, S G It is a collection of conventional unit end nodes.

[0094] Then, based on the historical operating data and the changes in the power flow distribution under the extremely severe state, a second normal safety constraint when no fault occurs under the extremely severe state and a second fault safety constraint when a fault occurs are constructed respectively; wherein the second normal safety constraint includes a second node power balance constraint and a second static safety constraint.

[0095] Among them, when the power system is in an extremely bad state and no fault occurs, the second node power balance constraint that needs to be satisfied to maintain node power balance is specifically expressed as:

[0096]

[0097] Where P' WDi is the active power output of the wind turbine at node i under the extreme condition, P' PVi is the active power output of the photovoltaic unit at node i under the extremely bad condition, P' DCi is the DC external power input power of node i under the extremely bad condition, P' LDi is the active load of node i under the extreme condition, S Ni is the set of node i and its adjacent nodes, B ij is the element in the i-th row and j-th column of the node susceptance matrix, θ′ j (0) is the voltage phase angle of node j when no fault occurs in the extremely severe state.

[0098] When the power system is in an extremely bad state and no fault occurs, the active power flow of each branch is required not to exceed the long-term current carrying capacity of the equipment, that is, the second static safety constraint constructed, which is specifically expressed as:

[0099]

[0100] In the formula, is the long-term current carrying capacity allowed by branch ij, is the active power flow value of branch ij flowing from node i into branch ij when no fault occurs in the extreme condition, S A is the collection of all branches.

[0101] In the DC power flow model, the active power flow value of the branch can be described as:

[0102]

[0103] In the formula, x ij is the reactance of branch ij.

[0104] Then, for the preset fault set under extremely severe conditions, the second fault safety constraint is constructed when a fault occurs. The main method is to use the branch disconnection distribution factor to describe the power flow after the fault as a linear function of the power flow without fault, that is:

[0105]

[0106] Where k is the fault number in the fault set, S K is the fault set under the predicted state, is the active power flow from node i into branch ij under fault k in the extreme condition, L ij-mn is the distribution factor of branch ij when branch mn is disconnected, is the set of branches that operate normally under the kth fault in the fault set, is the set of branches disconnected under the kth fault in the fault set, It is the active power flow value flowing from node m into branch mn when no fault occurs in branch mn under the extremely bad state.

[0107] To ensure the safe operation of the power grid, the power flow after a fault should not exceed the short-term overload capacity of the equipment, that is:

[0108]

[0109] In the formula, is the short-time allowable current-carrying capacity of branch ij.

[0110] According to the above formula, the second fault safety constraint can be obtained, and the specific expression is:

[0111]

[0112] The second power flow constraint is constructed according to the second conventional unit output constraint, the second normal safety constraint and the second fault safety constraint.

[0113] Step S3: constructing a relation constraint between the predicted state and the extremely severe state according to the first power flow constraint and the second power flow constraint.

[0114] In the embodiment of the present application, the predicted state is compared with the power flow distribution under the extremely bad state to obtain a power flow change result. Then, according to the power flow change result, the relationship constraint is obtained through the first power flow constraint and the second power flow constraint.

[0115] Among them, the relationship constraints include new energy unit output relationship constraints, load power relationship constraints, conventional unit output relationship constraints and minimum reserve capacity demand constraints.

[0116] Because under extremely bad conditions, the output of each new energy unit is less than the output of the new energy unit under the predicted state, based on this, the output relationship constraint of the new energy unit can be constructed. The specific expression is:

[0117] P′ PVi ≤P PVi ;

[0118] P′ WDi ≤P WDi ;

[0119] Because under extremely bad conditions, the power of each load increases approximately in the same proportion as the load power under the predicted state, based on this, the load power relationship constraint can be constructed. The specific expression is:

[0120]

[0121] In the formula, λ LD is the load oversupply rate, ΔP LDi is the part of the load power of node i that is less than the load oversupply rate, ΔP LDi ≥0; Introduce ΔP LDi This is to avoid the situation where load growth is limited due to local overload, because the effective spare capacity configuration mainly focuses on the situation where power output is limited, and is not particularly concerned about the situation where local load is limited.

[0122] Because under extremely bad conditions, the active output of each conventional unit should be greater than the output of the conventional unit under the predicted state, based on this, the output relation constraint of the conventional unit can be constructed, and the specific expression is:

[0123] P′ Gi ≥P Gi ;

[0124] Compared with the predicted state, the additional active power output of the conventional units under extremely severe conditions is the effective spare capacity of the units, so:

[0125]

[0126] Where P RES It is the effective reserve capacity of the power system.

[0127] Because the power system itself has certain restrictions on the size of the effective reserve capacity, the effective reserve capacity of the power system should not be less than the minimum effective reserve capacity set by the power system. The specific expression of the minimum reserve capacity requirement constraint is:

[0128] P RES ≥P RES ;

[0129] In the formula, P RESIt is the minimum effective reserve capacity required for the power system.

[0130] Step S4, taking the minimum startup capacity of the conventional unit as the goal, constructing an objective function, and then obtaining the optimal effective spare capacity according to the relationship constraint and the objective function to improve the unit output of the new energy unit.

[0131] In the embodiment of the present application, in the configuration of the reserve capacity of the power system, it is generally desired to minimize the startup capacity of the conventional units while satisfying the first power flow constraint, the second power flow constraint and the relationship constraint, so as to improve the economic efficiency of the power system operation. The objective function can be described as:

[0132]

[0133] Where W G is the penalty weight of the startup capacity of the conventional unit, which is generally a large positive number greater than 1, and is 10 in the embodiment of the present application; S G is the set of conventional unit end nodes, P G i is the maximum technical output of the conventional unit at node i, u Gi is the start / stop variable of the conventional unit of node i, 1 means the unit is on, 0 means the unit is off, P RES It is the effective reserve capacity of the power system.

[0134] According to the relationship constraints, the above objective function is weighted to obtain:

[0135]

[0136] Where W LD is the penalty weight when the load power is less than the load oversupply rate, and in actual situations, it can be taken as a value close to 1; S LD is the set of load nodes, ΔP LDi It is the part of the load power of node i that is less than the load oversupply rate.

[0137] By calculating based on the objective function after introducing the penalty weight, the optimal effective spare capacity can be obtained, which is used to increase additional capacity for the unit when the unit output does not meet expectations, so as to improve the unit output of the new energy unit.

[0138] Implementing the embodiments of the present application has the following beneficial effects:

[0139] The embodiment of the present application considers two power grid states: the predicted state and the extremely bad state, and calculates the first power flow constraint and the second power flow constraint that the power grid needs to meet in these two states. Since the output of conventional units needs to be increased to balance the safe operation of the power grid in extremely bad states, by comparing the difference between the first power flow constraint and the second power flow constraint, the power flow change relationship between the predicted state and the extremely bad state can be obtained, the corresponding relationship constraint can be determined, and the effective spare capacity corresponding to the active output that needs to be increased in the extremely bad state can be clarified to ensure safe operation and continuous and reliable power supply in extremely bad states. Then, considering the economy of the power grid operation, the objective function is constructed with the minimum startup capacity of conventional units as the goal, and the optimal effective spare capacity obtained can not only ensure the safe operation and continuous and reliable power supply of the power grid in extremely bad states, but also ensure that the increased effective spare capacity can reduce the required cost as much as possible.

[0140] Second embodiment

[0141] Furthermore, in order to implement the effective spare capacity optimization system for renewable energy output uncertainty corresponding to the above method embodiment to achieve corresponding functions and technical effects, Figure 2 A structural diagram of an effective spare capacity optimization system for renewable energy output uncertainty is provided. For ease of explanation, only the parts related to this embodiment are shown. The effective spare capacity optimization system for renewable energy output uncertainty provided by the embodiment of the present application includes:

[0142] The forecast state constraint condition building module 201 is used to build the first power flow constraint under the forecast state according to the historical operation data of the power system and the preset forecast state, so as to improve the unit output of the new energy unit.

[0143] In an embodiment of the present application, based on the historical operating data, a first conventional unit output constraint under the predicted state is constructed; based on the historical operating data and the predicted state, a first normal safety constraint when no fault occurs in the predicted state and a first fault safety constraint when a fault occurs are respectively constructed; wherein the first normal safety constraint includes a first node power balance constraint and a first static safety constraint; based on the first conventional unit output constraint, the first normal safety constraint and the first fault safety constraint, the first flow constraint is constructed.

[0144] The extremely severe state constraint condition building module 202 is used to build a second power flow constraint under the extremely severe state according to the historical operation data of the power system and the preset extremely severe state.

[0145] In an embodiment of the present application, based on the historical operating data and the changes in the output of conventional units under the extremely harsh conditions, a second conventional unit output constraint under the extremely harsh conditions is constructed; based on the historical operating data and the changes in the flow distribution under the extremely harsh conditions, a second normal safety constraint when no fault occurs in the extremely harsh conditions and a second fault safety constraint when a fault occurs are respectively constructed; wherein the second normal safety constraint includes a second node power balance constraint and a second static safety constraint; and the second flow constraint is constructed based on the second conventional unit output constraint, the second normal safety constraint and the second fault safety constraint.

[0146] The relationship constraint building module 203 is used to build a relationship constraint between the predicted state and the extremely severe state according to the first power flow constraint and the second power flow constraint.

[0147] In an embodiment of the present application, the predicted state is compared with the power flow distribution under the extremely severe state to obtain a power flow change result; based on the power flow change result, the relationship constraint is obtained through the first power flow constraint and the second power flow constraint.

[0148] The optimal effective spare capacity calculation module 204 is used to construct an objective function with the minimum startup capacity of conventional units as the goal, and then obtain the optimal effective spare capacity according to the relationship constraint and the objective function.

[0149] In the embodiment of the present application, the objective function is constructed according to the operating conditions of the conventional units with the goal of minimizing the startup capacity of the conventional units; according to the relationship constraints, penalty weights are introduced into the objective function, and then the optimal effective spare capacity is calculated.

[0150] In some embodiments, the prediction state constraint condition building module 201 further includes:

[0151] First, the historical operation data of the power system is obtained to clarify the power flow data of the power system under all previous different operating conditions. The power flow data mainly includes node power data, branch current data and conventional unit output data.

[0152] Then, according to the output data of the conventional units, under the preset forecast state, the first conventional unit output constraint under the forecast state is constructed, that is, the output condition that the conventional unit needs to meet under the forecast state, and the specific expression is:

[0153]

[0154] In the formula, u Gi is the start and stop status of the conventional unit at node i, P Giis the active power output of the conventional unit at node i under the predicted state, P Gi and are the lower and upper limits of the active output of the conventional unit at node i, S G It is a collection of conventional unit end nodes.

[0155] Specifically, the predicted state includes other operating states of the power system except the extreme operating state as comprehensively as possible, and can be considered as the normal operating state of the power system under normal circumstances. The flow data in the predicted state mainly refers to the historical operating data of the power system.

[0156] Then, according to the predicted state and the historical operation data, a first normal safety constraint when no fault occurs in the predicted state and a first fault safety constraint when a fault occurs are constructed respectively. The first normal safety constraint includes a first node power balance constraint and a first static safety constraint.

[0157] Furthermore, the power balance constraint of the first node in the predicted state is usually calculated using a DC power flow model, and the specific expression is:

[0158]

[0159] Where P WDi is the active power output of the wind turbine at node i in the prediction state, P PVi is the active power output of the photovoltaic unit at node i in the prediction state, P DCi is the DC external power input power of node i, P LDi is the active load of node i, S Ni is the set of node i and its adjacent nodes, B ij is the element in the i-th row and j-th column of the node susceptance matrix, is the voltage phase angle of node j when no fault occurs in the predicted state.

[0160] The first node power balance constraint is a power demand that the nodes of the power system must meet under the predicted state to ensure that the node power of the power system is normal.

[0161] When the power system is operating normally, it is required that the active power flow of each branch does not exceed the long-term current carrying capacity of the equipment. Based on this, the first static safety constraint can be constructed. The specific expression is:

[0162]

[0163] In the formula, is the long-term current carrying capacity allowed by branch ij, is the active power flow value flowing into the branch ij from node i, SA is the collection of all branches.

[0164] According to the DC power flow model, the branch active power flow can be described as:

[0165]

[0166] In the formula, x ij is the reactance of branch ij.

[0167] Then, for the preset fault set under the predicted state, the first fault safety constraint is constructed when a fault occurs. The main method is to use the branch disconnection distribution factor to describe the power flow after the fault as a linear function of the power flow without fault, that is:

[0168]

[0169] Where k is the fault number in the fault set, S K is the fault set under the predicted state, is the active power flow from node i into branch ij under fault k, L ij-mn is the distribution factor of branch ij when branch mn is disconnected, is the set of branches that operate normally under the kth fault in the fault set, is the set of branches disconnected under the kth fault in the fault set, It is the active power flow value flowing into the branch mn from the node m when no fault occurs.

[0170] To ensure the safe operation of the power grid, the branch current should not exceed the short-term overload capacity of the equipment after a fault occurs, that is:

[0171]

[0172] In the formula, is the short-time allowable current-carrying capacity of branch ij.

[0173] According to the above formula, the first fault safety constraint can be obtained, and the specific expression is:

[0174]

[0175] The first power flow constraint is constructed according to the first conventional unit output constraint, the first normal safety constraint and the first fault safety constraint.

[0176] In some embodiments, the extremely severe state constraint condition building module 202 further includes:

[0177] First, construct the second power flow constraint under the preset extreme state. The difference between the predicted state and the extreme state is mainly the difference in the output, load and external power of the new energy unit. The grid structure and startup mode of the two are the same, but the output of the conventional unit is different. Due to the difference in the output of conventional units, the output of new energy units, the load and external power, the power flow distribution of the predicted state and the extreme state is different, and the voltage phase of each node will also be different. Therefore, in the extreme state, while maintaining the same conventional unit startup mode as the predicted state, it is necessary to increase the output of conventional units to meet the second power flow constraint. Therefore, it is necessary to increase the effective reserve capacity of the power system to increase the output of conventional units to maintain the safe operation of the power system and continuous and reliable power supply.

[0178] First, according to the historical operation data and the output change of the conventional unit under the extremely bad state, the output constraint of the second conventional unit under the extremely bad state is constructed. The specific expression is:

[0179]

[0180] In the formula, u Gi is the start and stop status of the conventional unit at node i, P Gi P Gi The upper limit, P' Gi is the active power output of the conventional unit at node i under the extremely bad condition, P Gi The lower limit of S G It is a collection of conventional unit end nodes.

[0181] Then, based on the historical operating data and the changes in the power flow distribution under the extremely severe state, a second normal safety constraint when no fault occurs under the extremely severe state and a second fault safety constraint when a fault occurs are constructed respectively; wherein the second normal safety constraint includes a second node power balance constraint and a second static safety constraint.

[0182] Among them, when the power system is in an extremely bad state and no fault occurs, the second node power balance constraint that needs to be satisfied to maintain node power balance is specifically expressed as:

[0183]

[0184] Where P' WDi is the active power output of the wind turbine at node i under the extreme condition, P' PVi is the active power output of the photovoltaic unit at node i under the extremely bad condition, P' DCi is the DC external power input power of node i under the extremely bad condition, P' LDiis the active load of node i under the extreme condition, S Ni is the set of node i and its adjacent nodes, B ij is the element in the i-th row and j-th column of the node susceptance matrix, θ′ j (0) is the voltage phase angle of node j when no fault occurs in the extremely severe state.

[0185] When the power system is in an extremely bad state and no fault occurs, the active power flow of each branch is required not to exceed the long-term current carrying capacity of the equipment, that is, the second static safety constraint constructed, which is specifically expressed as:

[0186]

[0187] In the formula, is the long-term current carrying capacity allowed by branch ij, is the active power flow value of branch ij flowing from node i into branch ij when no fault occurs in the extreme condition, S A is the collection of all branches.

[0188] In the DC power flow model, the active power flow value of the branch can be described as:

[0189]

[0190] In the formula, x ij is the reactance of branch ij.

[0191] Then, for the preset fault set under extremely severe conditions, the second fault safety constraint is constructed when a fault occurs. The main method is to use the branch disconnection distribution factor to describe the power flow after the fault as a linear function of the power flow without fault, that is:

[0192]

[0193] Where k is the fault number in the fault set, S K is the fault set under the predicted state, is the active power flow from node i into branch ij under fault k in the extreme condition, L ij-mn is the distribution factor of branch ij when branch mn is disconnected, is the set of branches that operate normally under the kth fault in the fault set, is the set of branches disconnected under the kth fault in the fault set, It is the active power flow value flowing from node m into branch mn when no fault occurs in branch mn under the extremely bad state.

[0194] To ensure the safe operation of the power grid, the power flow after a fault should not exceed the short-term overload capacity of the equipment, that is:

[0195]

[0196] In the formula, is the short-time allowable current-carrying capacity of branch ij.

[0197] According to the above formula, the second fault safety constraint can be obtained, and the specific expression is:

[0198]

[0199] The second power flow constraint is constructed according to the second conventional unit output constraint, the second normal safety constraint and the second fault safety constraint.

[0200] In some embodiments, the relationship constraint building module 203 further includes:

[0201] The predicted state is compared with the power flow distribution under the extremely bad state to obtain a power flow change result. Then, according to the power flow change result, the relationship constraint is obtained through the first power flow constraint and the second power flow constraint.

[0202] Among them, the relationship constraints include new energy unit output relationship constraints, load power relationship constraints, conventional unit output relationship constraints and minimum reserve capacity demand constraints.

[0203] Because under extremely bad conditions, the output of each new energy unit is less than the output of the new energy unit under the predicted state, based on this, the output relationship constraint of the new energy unit can be constructed. The specific expression is:

[0204] P′ PVi ≤P PVi ;

[0205] P′ WDi ≤P WDi ;

[0206] Because under extremely bad conditions, the power of each load increases approximately in the same proportion as the load power under the predicted state, based on this, the load power relationship constraint can be constructed. The specific expression is:

[0207]

[0208]

[0209] In the formula, λ LD is the load oversupply rate, ΔP LDi is the part of the load power of node i that is less than the load oversupply rate, ΔP LDi ≥0; Introduce ΔP LDiThis is to avoid the situation where load growth is limited due to local overload, because the effective spare capacity configuration mainly focuses on the situation where power output is limited, and is not particularly concerned about the situation where local load is limited.

[0210] Because under extremely bad conditions, the active output of each conventional unit should be greater than the output of the conventional unit under the predicted state, based on this, the output relation constraint of the conventional unit can be constructed, and the specific expression is:

[0211] P′ Gi ≥P Gi ;

[0212] Compared with the predicted state, the additional active power output of the conventional units under extremely severe conditions is the effective spare capacity of the units, so:

[0213]

[0214] Where P RES It is the effective reserve capacity of the power system.

[0215] Because the power system itself has certain restrictions on the size of the effective reserve capacity, the effective reserve capacity of the power system should not be less than the minimum effective reserve capacity set by the power system. The specific expression of the minimum reserve capacity requirement constraint is:

[0216] P RES ≥ P RES ;

[0217] In the formula, P RES It is the minimum effective reserve capacity required for the power system.

[0218] In some embodiments, the optimal effective spare capacity calculation module 204 further includes:

[0219] In the configuration of power system reserve capacity, it is generally hoped that the startup capacity of conventional units will be minimized while satisfying the first power flow constraint, the second power flow constraint and the relationship constraint, so as to improve the economic efficiency of power system operation. The objective function can be described as:

[0220]

[0221] Where W G is the penalty weight of the startup capacity of the conventional unit, which is generally a large positive number greater than 1, and is 10 in the embodiment of the present application; S G is the set of conventional unit end nodes, is the maximum technical output of the conventional unit at node i, u Gi is the start / stop variable of the conventional unit of node i, 1 means the unit is on, 0 means the unit is off, PRES It is the effective reserve capacity of the power system.

[0222] According to the relationship constraints, the above objective function is weighted to obtain:

[0223]

[0224] Where W LD is the penalty weight when the load power is less than the load oversupply rate, and in actual situations, it can be taken as a value close to 1; S LD is the set of load nodes, ΔP LDi It is the part of the load power of node i that is less than the load oversupply rate.

[0225] By calculating based on the objective function after introducing the penalty weight, the optimal effective spare capacity can be obtained, which is used to increase additional capacity for the unit when the unit output does not meet expectations, so as to improve the unit output of the new energy unit.

[0226] Implementing the embodiments of the present application has the following beneficial effects:

[0227] The embodiment of the present application considers two power grid states: the predicted state and the extremely bad state, and calculates the first power flow constraint and the second power flow constraint that the power grid needs to meet in these two states. Since the output of conventional units needs to be increased to balance the safe operation of the power grid in extremely bad states, by comparing the difference between the first power flow constraint and the second power flow constraint, the power flow change relationship between the predicted state and the extremely bad state can be obtained, the corresponding relationship constraint can be determined, and the effective spare capacity corresponding to the active output that needs to be increased in the extremely bad state can be clarified to ensure that the power grid can operate safely and continuously and reliably supply power in extremely bad states. Then, considering the economy of the power grid operation, the objective function is constructed with the minimum startup capacity of conventional units as the goal, and the optimal effective spare capacity obtained can not only ensure that the power grid maintains safe operation and continuous and reliable power supply in extremely bad states, but also ensure that the increased effective spare capacity can reduce the required cost as much as possible.

[0228] Furthermore, Figure 3 This is a structural diagram of a terminal device provided in one embodiment of the present application. Figure 3 As shown, the terminal device 3 of this embodiment includes: at least one processor 30 (in Figure 3 Only one is shown) and a memory 31 and a computer program 32 stored in the memory 31 and executable on the at least one processor. When the processor 30 executes the computer program 32, the steps of an effective standby capacity optimization method for new energy output uncertainty described in any one of the embodiments of the present application can be implemented.

[0229] The terminal device 3 may be a computing device such as a desktop computer, a cloud server, or a laptop computer. The computing device may include but is not limited to a processor 30 and a memory 31 . Figure 3 This is merely an example of the terminal device 3 and does not constitute a limitation on the terminal device 3 , which may include more or fewer components than those shown in the figure.

[0230] An embodiment of the present application provides a storage medium storing computer-readable program code, which, when executed, implements the steps of the above-mentioned method for optimizing effective spare capacity for uncertainty of new energy output.

[0231] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims

1. An effective spare capacity optimization method for renewable energy output uncertainty, characterized in that: include: According to the historical operation data of the power system and the preset forecast state, constructing the first power flow constraint under the forecast state; According to the historical operation data of the power system and the preset extreme bad state, constructing the second power flow constraint under the extreme bad state; According to the first power flow constraint and the second power flow constraint, constructing a relationship constraint between the predicted state and the extremely severe state; Taking the minimum startup capacity of conventional units as the goal, an objective function is constructed, and then the optimal effective reserve capacity is obtained according to the relationship constraint and the objective function; wherein the optimal effective reserve capacity is used to supplement the unit output of the power system.

2. The effective spare capacity optimization method for renewable energy output uncertainty according to claim 1 is characterized in that: The first power flow constraint under the predicted state is constructed according to the historical operation data of the power system and the preset predicted state, specifically: constructing the output constraint of the first conventional unit in the predicted state according to the historical operation data; According to the historical operation data and the predicted state, respectively constructing a first normal safety constraint when no fault occurs in the predicted state and a first fault safety constraint when a fault occurs; wherein the first normal safety constraint includes a first node power balance constraint and a first static safety constraint; The first power flow constraint is constructed according to the first conventional unit output constraint, the first normal safety constraint and the first fault safety constraint.

3. The effective spare capacity optimization method for renewable energy output uncertainty according to claim 2 is characterized in that: The first power flow constraint is specifically: Among them, the output constraint of the first conventional unit is specifically expressed as: In the formula, u Gi is the start and stop status of the conventional unit at node i, P Gi is the active power output of the conventional unit at node i under the predicted state, P Gi and are the lower and upper limits of the active output of the conventional unit at node i, S G is a collection of conventional unit end nodes; The first node power balance constraint is specifically expressed as: Where P WDi is the active power output of the wind turbine at node i in the prediction state, P PVi is the active power output of the photovoltaic unit at node i in the prediction state, P DCi is the DC external power input power of node i, P LDi is the active load of node i, S Ni is the set of node i and its adjacent nodes, B ij is the element in the i-th row and j-th column of the node susceptance matrix, is the voltage phase angle of node j when no fault occurs in the predicted state; The first static safety constraint is specifically expressed as follows: In the formula, is the long-term current carrying capacity allowed by branch ij, is the active power flow value flowing into the branch ij from node i, S A is the collection of all branches; The first fault safety constraint is specifically expressed as: In the formula, is the short-time allowable current carrying capacity of branch ij, is the set of branches disconnected under the kth fault in the fault set, L ij-mn is the distribution factor of branch ij when branch mn is disconnected, is the active power flow value of branch mn from node m into the branch when no fault occurs, is the set of branches that operate normally under the kth fault in the fault set, k is the fault number of the fault set under the predicted state, S K is the fault set under the predicted state.

4. The effective spare capacity optimization method for renewable energy output uncertainty according to claim 1 is characterized in that: The second power flow constraint under the extremely severe state is constructed according to the historical operation data of the power system and the preset extremely severe state, specifically: According to the historical operation data and the output change of the conventional unit under the extremely bad state, constructing the output constraint of the second conventional unit under the extremely bad state; According to the historical operation data and the power flow distribution changes under the extremely severe state, a second normal safety constraint when no fault occurs under the extremely severe state and a second fault safety constraint when a fault occurs are respectively constructed; wherein the second normal safety constraint includes a second node power balance constraint and a second static safety constraint; the power flow distribution changes include changes in the output of new energy units, changes in load power and changes in DC external power; The second power flow constraint is constructed according to the second conventional unit output constraint, the second normal safety constraint and the second fault safety constraint.

5. The effective spare capacity optimization method for renewable energy output uncertainty according to claim 1 is characterized in that: The constructing of the relationship constraint between the predicted state and the extremely bad state according to the first power flow constraint and the second power flow constraint is specifically: Compare the predicted state with the power flow distribution under the extremely severe state to obtain a power flow change result; wherein the power flow distribution includes the output of the new energy unit, the load power and the DC external power; According to the power flow change result, the relationship constraint is obtained through the first power flow constraint and the second power flow constraint.

6. The effective spare capacity optimization method for renewable energy output uncertainty according to claim 1 is characterized in that: The objective function is constructed with the minimum startup capacity of the conventional units as the goal, and then the optimal effective spare capacity is obtained according to the relationship constraint and the objective function, which is specifically: Taking the minimum startup capacity of conventional units as the goal, the objective function is constructed according to the operation status of conventional units; According to the relationship constraint, a penalty weight is introduced into the objective function, and then the optimal effective reserve capacity is calculated.

7. The effective spare capacity optimization method for renewable energy output uncertainty according to claim 6 is characterized in that: The penalty weight is introduced into the objective function, specifically: The objective function after introducing the penalty weight is specifically expressed as follows: Where W G is the penalty weight of the startup capacity of conventional units, S G is the set of conventional unit end nodes, is the maximum technical output of the conventional unit at node i, u Gi is the start and stop variable of the conventional unit at node i, P RES is the effective reserve capacity of the power system, W LD is the penalty weight when the load power is less than the load oversupply rate, S LD is the set of load nodes, ΔP LDi It is the part of the load power of node i that is less than the load oversupply rate.

8. An effective spare capacity optimization system for renewable energy output uncertainty, characterized in that: include: Prediction state constraint condition building module, extreme state constraint condition building module, relationship constraint building module and optimal effective spare capacity calculation module; The forecast state constraint condition building module is used to build the first power flow constraint under the forecast state according to the historical operation data of the power system and the preset forecast state; The extremely severe state constraint condition building module is used to build the second power flow constraint under the extremely severe state according to the historical operation data of the power system and the preset extremely severe state; The relationship constraint building module is used to build a relationship constraint between the predicted state and the extremely severe state according to the first power flow constraint and the second power flow constraint; The optimal effective reserve capacity calculation module is used to construct an objective function with the minimum startup capacity of conventional units as the goal, and then according to the relationship constraints and the objective function; wherein the optimal effective reserve capacity is used to supplement the unit output of the power system.

9. A terminal device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of an effective spare capacity optimization method for renewable energy output uncertainty according to any one of claims 1 to 7 are implemented.

10. A storage medium, characterized in that: The storage medium stores computer-readable program codes, which, when executed, implement the steps of an effective reserve capacity optimization method for renewable energy output uncertainty according to any one of claims 1 to 7.

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