A transmission network planning method, system, device and medium

Abstract

The present disclosure relates to a transmission network planning method, system, device and medium, and belongs to the field of transmission network planning technology. The transmission network planning method includes the following steps: constructing a net load effective safety domain model; constructing a transmission network planning model based on the net load effective safety domain model, the transmission network planning model is a three-layer model, the first layer is a planning model under the net load prediction value, the second layer is an effective safety domain model under the net load disturbance value, and the third layer is a joint planning model under the net load disturbance value; the transmission network planning model is processed, converted into a linear programming model and solved. The transmission network planning method proposed in the present disclosure can ensure the safe and stable operation of the power system and the power supply quality, and has important theoretical significance and practical value.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of power transmission network planning, and in particular, relates to a power transmission network planning method, system, device and medium. Background Art

[0002] Power system transmission network planning is one of the important tasks of the power system. A reasonable and efficient transmission network planning scheme is an important guarantee for the balance of power supply and demand and the safe and economical operation of the system. Against the backdrop of energy shortage and environmental deterioration, the penetration rate of renewable energy such as wind power and photovoltaic power in the power grid continues to increase. The strong uncertainty of its own randomness, intermittency, volatility and uncontrollability has greatly increased the difficulty of transmission network planning. How to effectively improve the grid's ability to accept renewable energy, achieve the reasonable allocation of system flexibility resources, and promote the coordination of the economy and safety of transmission network planning is an urgent problem to be solved.

[0003] In traditional planning models, most of them require power generation and load balance based only on the net load forecast value at a certain point in time. The simulation of power system operation is relatively rough, and the fluctuation of net load at a certain time is not considered. The real-time balance of system power generation and load is considered too simply, and it is impossible to ensure that the actual climbing ability of the system can keep up with the fluctuation of net load, resulting in insufficient system reliability.

[0004] As an optimization method, information gap decision theory can effectively deal with the uncertainty problem in power system transmission network planning. This method does not require a complete probability curve or fuzzy model, but directly optimizes the gap of uncertain parameters for the target and studies the impact of the predicted values ​​of these parameters. However, this method is too rough and does not take into account the disturbance phenomenon that may exist in the node. It only determines the total amount of parameters, which will inevitably lead to the inability of the net load to fully match the net load disturbance. In other words, the system's spare resources may cause great waste and generate a large amount of redundancy, or it may make the actual net load disturbance insufficient to cope with the required capacity, and even these two situations may occur at the same time. Therefore, when the traditional information gap decision theory is applied to the power system transmission network planning, it needs to be appropriately improved. Direct application without modification will inevitably lead to unreasonable allocation of spare resources, thereby destroying the economy and safety of the power system.

[0005] At present, the existing technology has at least the following shortcomings and deficiencies:

[0006] 1. The existing transmission network reference grid joint planning scheme does not take into account the structural changes in the transmission network brought about by the rapid increase in the penetration rate of renewable energy, and the planning scheme has not completed the transition from full absorption of renewable energy to partial absorption of renewable energy;

[0007] 2. The existing allocation method of backup resources has not yet taken into account the mismatch between the supply and demand of the new power system transmission network, which will lead to a mismatch between the actual disturbance range of the system after the decision and the new energy range that can be absorbed;

[0008] 3. The existing planning scheme does not reflect the issue of restrictions on backup resources. No resources are unlimited. Restrictions on backup resources are an inevitable requirement for the development of a high proportion of new energy. If the planning results do not restrict them, the economy and safety of the power system will inevitably be damaged. Summary of the invention

[0009] The purpose of the present disclosure is to provide a transmission network planning method in order to solve the above problems.

[0010] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0011] A transmission network planning method comprises the following steps:

[0012] Construct a payload effective security domain model;

[0013] A transmission network planning model is constructed based on the net load effective safety domain model, wherein the transmission network planning model is a three-layer model, wherein the first layer is a planning model under a net load prediction value, the second layer is an effective safety domain model under a net load disturbance value, and the third layer is a joint planning model under a net load disturbance value;

[0014] The transmission network planning model is processed, converted into a linear programming model and solved.

[0015] As a further optimization solution of the present disclosure, constructing a payload effective security domain model includes:

[0016] A net load acceptable domain mathematical model is constructed, an acceptable net load disturbance value is introduced, and a net load effective safety domain model is constructed based on the net load acceptable domain mathematical model and the acceptable net load disturbance value.

[0017] As a further optimization scheme of the present disclosure, the planning model under the net load prediction value of the first layer is a deterministic model, and the effective safety domain model under the net load disturbance value of the second layer and the joint planning model under the net load disturbance value of the third layer are uncertain models.

[0018] As a further optimization scheme of the present disclosure, the planning model under the net load prediction value of the first layer does not consider the uncertainty of load and new energy output, and constructs a deterministic model based on the prediction value to pre-plan the network to obtain a benchmark value of economic cost; the effective safety domain model under the net load disturbance value of the second layer relaxes the benchmark value of economic cost obtained by the first layer model and adds it to the constraint conditions of the second layer model to ensure that the information of the first layer model is fully utilized and that the effective safety domain of the second layer model does not tend to infinity; the joint planning model under the net load disturbance value of the third layer considers the reference power grid planning with uncertainty, and at the same time relaxes both the benchmark value of economic cost and the effective safety domain, and adds them to the constraint conditions of the third layer model to obtain the final joint planning result.

[0019] As a further optimization scheme of the present disclosure, the objective function of the planning model under the net load forecast value is to minimize the total cost under the net load forecast value.

[0020] As a further optimization scheme of the present disclosure, the total cost under the net load forecast value includes the investment cost and the operating cost under the net load forecast value.

[0021] As a further optimization scheme of the present disclosure, the constraints of the planning model under the net load forecast value include total power balance constraints under the net load forecast value, line flow constraints under the net load forecast value, line capacity constraints, generator-related constraints and capacity reserve constraints under the net load forecast value.

[0022] As a further optimization solution of the present disclosure, the objective function of the effective safety domain model under the net load disturbance value is to maximize the total net load effective safety domain of the system.

[0023] As a further optimization scheme of the present invention, the constraints of the effective safety domain model under the net load disturbance value include the total cost constraint of joint planning, the total power balance constraint of the net load prediction value, the branch flow constraint under the disturbance scenario, the generator set capacity reserve constraint and the generator set constraint.

[0024] As a further optimization scheme of the present disclosure, the objective function of the joint planning model under the net load disturbance value is to minimize the total cost under the net load disturbance value.

[0025] As a further optimization scheme of the present disclosure, the total cost under the net load disturbance value includes the investment cost, operation cost and standby cost under the net load disturbance value.

[0026] As a further optimization scheme of the present invention, the constraints of the joint planning model under the net load disturbance value include the total cost constraint of the joint planning, the relaxation constraint of the net load effective safety domain, the total power balance constraint of the net load prediction value, the branch flow constraint under the disturbance scenario, the generator set capacity reserve constraint and the generator related constraints.

[0027] As a further optimization scheme of the present disclosure, the transmission network planning model is processed, converted into a linear programming model and solved, including:

[0028] Linearizing the transmission network planning model to convert it into a linear planning model;

[0029] The constraints of the transmission network planning model are deterministically transformed, and the transformed transmission network planning model is solved using a commercial solver.

[0030] A transmission network planning system, comprising:

[0031] A payload effective security domain model construction module is used to construct a payload effective security domain model;

[0032] A transmission network planning model construction module, used to construct a transmission network planning model based on the net load effective safety domain model, wherein the transmission network planning model is a three-layer model, wherein the first layer is a planning model under a net load prediction value, the second layer is an effective safety domain model under a net load disturbance value, and the third layer is a joint planning model under a net load disturbance value;

[0033] The conversion and solving module is used to process the transmission network planning model, convert it into a linear programming model and solve it.

[0034] An electronic device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0035] Memory, for storing computer programs;

[0036] The processor is used to implement the transmission network planning method when executing the program stored in the memory.

[0037] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the transmission network planning method is implemented.

[0038] The beneficial effects of the present disclosure are:

[0039] (1) The present invention improves the traditional information gap decision theory to be applicable to planning problems. The improvement is that the solution of the first-level planning model under the net load forecast value is defined as the decision gap of the maximum effective safety domain of the net load of the second-level model; in the second-level model, the net load disturbance value is used to replace the uncertain set of load and new energy output introduced by the traditional information gap decision theory, and the optimization results of the first and second levels are relaxed and defined again as the decision gap of the third-level model to better configure the flexibility resources in the system.

[0040] (2) The optimization model proposed in the present disclosure based on the improved information gap decision theory is: the reference power grid joint planning model of the maximum effective safety domain. First, only the net load forecast value is considered to plan the reference power grid and obtain a benchmark value of the economic cost. Enhancing the node's ability to accept net load disturbances will inevitably make the system's economy worse. The cost benchmark value obtained in the first layer of the model needs to be relaxed to obtain the upper limit of the economic cost. Under the condition that the total investment of the system is limited, the system's maximum acceptance capacity for node net load disturbances is found. This model is called the reference power grid joint planning model under the net load disturbance value, which is divided into two layers: the planning layer of the maximum effective safety domain and the planning layer of the joint planning total cost.

[0041] (3) The model proposed in this disclosure is a planning strategy for the initial stage of new energy investment in the region. In the stage when the proportion of new energy penetration is not large, the use of the maximum absorption model can improve the overall security of the power system and make good reserves of flexible resources; after the proportion of new energy rises to a certain level, the model still has strong flexibility and can be transformed into a compromise model with economy as the main focus. This optimization model can realize the progressive planning of the transmission network and achieve the complementarity of safety and economy.

[0042] (4) The present invention uses branch power flow based on the power generation load transfer factor. Compared with the traditional DC power flow method, this method eliminates the phase angle variables existing in the DC power flow model, simplifies the model variables, and avoids the solution obstacles caused by the node power balance formula with uncertain variables.

[0043] (5) The present invention adopts an affine strategy to merge the uncertainties in the model, and uses the duality theory to replace the random parameter of the net load disturbance value with the endpoint value representation, thereby converting the entire model into a deterministic model, which greatly improves the computational efficiency of the model and the robustness of the planning results. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a schematic diagram of a method flow in an embodiment of the present disclosure;

[0045] Figure 2 is a security domain diagram of the maximum effective security domain of two nodes in an embodiment of the present disclosure;

[0046] Figure 3 is a diagram of an improved IEEE-RTS 24-node system in an embodiment of the present disclosure;

[0047] Figure 4 is a comparison diagram of line capacity planning results in an embodiment of the present disclosure;

[0048] Figure 5 is a system structure block diagram in an embodiment of the present disclosure;

[0049] Figure 6 It is a block diagram of the device structure in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0050] The present application is further described in detail below in conjunction with the accompanying drawings. It is necessary to point out here that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technical personnel in this field can make some non-essential improvements and adjustments to the present application based on the above application content.

[0051] like Figure 1 As shown, a transmission network planning method comprises the following steps:

[0052] Construct a payload effective security domain model;

[0053] A transmission network planning model is constructed based on the net load effective safety domain model, wherein the transmission network planning model is a three-layer model, wherein the first layer is a planning model under a net load prediction value, the second layer is an effective safety domain model under a net load disturbance value, and the third layer is a joint planning model under a net load disturbance value;

[0054] The transmission network planning model is processed, converted into a linear programming model and solved.

[0055] In this embodiment, the following contents are specifically included:

[0056] 1. Construct a payload effective security domain model

[0057] In the operation of the power system, uncertainty mainly includes uncertainty of load, uncertainty of renewable energy output, uncertainty caused by equipment failure, etc. The present disclosure takes into account the uncertainty of load and renewable energy output, and combines the two together, which is called the uncertainty of net load (i.e., actual load minus renewable energy output).

[0058] Next, we introduce the mathematical model of the net load admissible domain. The fluctuating random net load on each node can be expressed by the predicted value of the net load and the actual disturbance value, as shown in formula (1):

[0059] ; (1)

[0060] ; (2)

[0061] ; (3)

[0062] In the formula, and Respectively represent t Nodes within a time period i The load and renewable energy output are uncertain parameters with clear upper and lower boundaries; Indicated in t Nodes within a time period i The net load on is an uncertain parameter; Indicated in t Nodes within a time period i The net load forecast value on is used to determine the parameters; Indicated in t Nodes within a time period i The disturbance value of the net load on is an uncertain parameter; and They represent the lower and upper limits of the net load respectively and are determined parameters.

[0063] Subtract the net load forecast value from equation (3) The interval description form of the net load disturbance can be obtained, as shown in formula (4):

[0064] ; (4)

[0065] In the formula, and Respectively expressed in t Nodes within a time period i The upper and lower limits of the net load disturbance on the grid are determined as parameters. Their values ​​are derived from the load data of previous years and the forecast data of renewable energy.

[0066] The reference grid joint planning can consider both line capacity planning and generator capacity planning. However, due to the limitations of network topology and total investment cost, the disturbance that the system can actually absorb may be less than the upper and lower limits of the net load disturbance. Therefore, the concept of acceptable net load disturbance value is introduced. The acceptable net load disturbance value represents the disturbance acceptance capacity of the unit mapped to the net load node through the unit reserve. This concept will be established through the following formula:

[0067] ; (5)

[0068] ; (6)

[0069] ; (7)

[0070] ; (8)

[0071] In the formula, represent g Unit No. t The output at a given moment is an uncertain variable because it is affected by the uncertainty of the net load; For the unit t Nodes within a time period i The acceptable net load is an uncertain parameter; is obtained under the net load forecast value g The output of unit No. at time t; For the unit t Nodes within a time period i The acceptable net load is an uncertain parameter, but its upper and lower limits are not certain; is obtained under acceptable net load g Unit No. t The disturbance of the output at the moment.

[0072] According to the total power balance, the generator output disturbance is equal to the acceptable net load disturbance, as shown in formula (9):

[0073] ; (9)

[0074] In order to obtain the relationship between the unit output disturbance and the acceptable net load disturbance, the participation factor is introduced. , as shown in equations (10) and (11):

[0075] ; (10)

[0076] ; (11)

[0077] Because the disturbance of generator output needs to be overcome by the capacity reserve of the generator set, the relationship as shown in formula (12) is obtained:

[0078] ; (12)

[0079] In the formula, and is the upward and downward capacity reserve of the g generator at time t; is the participation factor of generator g at time t.

[0080] Finally, by assuming that the acceptable net load disturbance has upper and lower limits, as shown in formula (13), the relationship between the unit reserve and the acceptable net load disturbance can be calculated, as shown in formula (14):

[0081] ; (13)

[0082] ; (14)

[0083] In the formula, and Respectively represent t Nodes within a time period i The upper and lower limits of the acceptable net load disturbance are variables.

[0084] Formula (4) describes the net load disturbance method by the interval method, and formula (13) describes the acceptable net load disturbance method by the interval method. Obviously, only the overlapping part of the two is the ability of the net load node to effectively accept the net load disturbance, that is, the net load effective safety domain. For this reason, simply expanding the acceptable net load disturbance interval on a single node does not necessarily mean that the net load effective safety domain is expanded, and appropriately giving up part of the acceptable net load disturbance interval does not necessarily mean reducing the net load effective safety domain.

[0085] The size of the effective safety domain of the net load on any net load node can be calculated by the following formula (15): Figure 2 As shown:

[0086] ; (15)

[0087] In the formula, Indicated in t Nodes within a time period i The size of the effective security field of the payload.

[0088] 2. Constructing a transmission network planning model based on the net load effective safety domain model

[0089] The transmission network planning model is a three-layer model. The first layer is the planning model under the net load forecast value, the second layer is the effective safety domain model under the net load disturbance value, and the third layer is the joint planning model under the net load disturbance value.

[0090] 2.1 The first-level model is the planning model under the net load forecast value

[0091] The present disclosure draws on the information gap decision theory, and therefore needs to construct the first-level planning model through the net load forecast value to set the benchmark value of the investment cost, and relax the benchmark value to a certain extent, transform it into a constraint and send it to the second and third layers of the model to achieve information transmission between layers. Since the first-level model only uses the net load forecast value, the first-level model can also be called a model under the forecast value.

[0092] The objective function of the first layer model is shown in formula (16):

[0093] ; (16)

[0094] In the formula represents the total cost of minimizing the net load forecast value, and They represent the investment cost and system operation cost under the net load forecast value respectively. The specific expressions are shown in equations (17) and (18):

[0095] ; (17)

[0096] ; (18)

[0097] In the formula, d is the number of typical days in a year, which is set to 365 here; For transmission line collection; k i For line i Investment cost per unit length per unit capacity; L l is the length of line l; T l is the maximum transmission capacity of line l; NT represents the set of time intervals within the optimization time scale; NG represents a collection of generator sets; NG + Represents the set of generator sets to be built; , , For generator g operating cost factor.

[0098] The constraints of the first layer model are as follows:

[0099] (1) Total power balance formula under net load forecast value:

[0100] ; (19)

[0101] Equation (19) ensures that in any time period, the total generator output is equal to the total net load forecast value.

[0102] (2) Line power flow calculation formula under net load forecast value:

[0103] ; (20)

[0104] In the formula, Indicates the time period under the net load forecast value t Internal Line l The trend on , Respectively represent the unit g and payload nodes i For branch road l The above two parameters can be calculated by knowing the network topology and line impedance, and do not involve internal optimization issues.

[0105] (3) Line capacity constraints:

[0106] ;(twenty one)

[0107] In the formula, Indicates line l The capacity upper limit is the variable to be planned.

[0108] Formula (21) ensures that the planned line capacity is the maximum value of the line flow in each period.

[0109] (4) Generator-related constraints:

[0110] ; (twenty two) ;(twenty three)

[0111] ;(twenty four)

[0112] In the formula, and They represent the upper and lower output limits of unit g respectively; and g is the downward and upward climbing ability of the generator.

[0113] (5) Capacity reserve constraints under net load forecast value:

[0114] ; (25)

[0115] Formula (25) is the generator under the net load forecast value g Provide upper and lower limits for upward and downward reserve capacity.

[0116] The above is the planning model under the net load forecast value. It is a deterministic model. The total cost forecast value it decides will be passed to the second and third layer models as a constraint after relaxation.

[0117] 2.2 Effective safety domain model under net load disturbance value

[0118] Starting from the second layer of the model, it is a mathematical model containing uncertain parameters. Since such models contain uncertain parameters, countless constraints can be written from a mathematical perspective. Therefore, it is impossible to solve them directly. It is necessary to use certain mathematical methods to transform them into deterministic models before they can be solved. The second layer model takes maximizing the total net load effective safety domain of the system as the objective function, and the expression is shown in formula (26):

[0119] ; (26)

[0120] Where AR represents the sum of the net load effective safety areas at each node in each time period of the system.

[0121] The constraints of the second layer model are:

[0122] (1) Joint planning total cost constraint:

[0123] ; (27)

[0124] In the formula, represents the total cost under the net load disturbance value, The optimal result determined by the first-layer model is passed to the second-layer model as known parameter information; It represents the investment cost relaxation factor of the second-level model, which represents the expansion investment capacity allowed to accommodate net load disturbance capacity; and They represent the operating cost and investment cost of the system under the net load disturbance value respectively, and their expressions are as follows:

[0125] ; (28)

[0126] ; (29)

[0127] In the formula, It represents the implicit function form of the line capacity under the net load disturbance value, that is, the maximum value of the line capacity under the net load disturbance value; It represents the implicit function form of the generator capacity under the net load disturbance value, that is, the maximum value of the generator capacity under the net load disturbance value.

[0128] Formula (27) indicates that under limited cost, the maximum effective safety region determined by the second-level model is of practical significance, because if there is no effective restriction on the cost, the planning results will develop in an infinite non-convergent direction until all net load disturbance values ​​are covered.

[0129] (2) Net load forecast value total power balance constraint:

[0130] This constraint is the same as that shown in formula (19).

[0131] Formula (19) ensures that a reliable generator operation base point can be constructed through the net load forecast value in any time period under the condition of net load disturbance.

[0132] (3) Branch flow constraints under disturbance scenarios:

[0133] ; (30)

[0134] ; (31)

[0135] In the formula, Indicates the time period under the net load disturbance value t Internal Line l The trend of Representation and l The set of generators at the nodes related to the line; ND represents l The net load node set on the line related nodes.

[0136] Formula (31) represents the inequality relationship between line capacity and line flow when the line flow is also affected by the net load disturbance value, and further shows the causal function relationship between the net load disturbance value and the line capacity.

[0137] (4) Generator capacity reserve constraints:

[0138] This constraint is the same as that shown in equation (11) and equation (14).

[0139] (5) Generator set constraints:

[0140] ; (32)

[0141] ; (33)

[0142] ; (34)

[0143] In the formula, and They are the maximum values ​​of the upward and downward capacity reserves of the generator respectively.

[0144] The above is the second-level model of the reference grid joint planning method considering the net load effective safety domain based on the improved information gap decision theory. This model indicates that in the reference grid planning of the power system, the planning decision maker ensures that the disturbance acceptance capacity of the net load nodes of the system is maximized under limited cost.

[0145] 2.3 Joint planning model under net load disturbance value

[0146] The third layer of the model takes minimizing the total cost under the net load disturbance value as the objective function. This objective function is different from the total cost under the net load forecast value in the first layer. The reason is that considering that the effective safety domain has a tendency to make the capacity reserve develop towards infinity, this objective function should consider limiting this trend. Therefore, in addition to the investment cost and operating cost, the reserve cost is also added to the objective function of this layer.

[0147] ; (35)

[0148] ; (36)

[0149] (36) In the formula and They represent the price of adjusting the capacity reserve upward and the price of adjusting the capacity reserve downward respectively.

[0150] Constraints of the third layer model:

[0151] (1) Joint planning total cost constraint:

[0152] This constraint is the same as equation (27), equation (28), and equation (29).

[0153] The purpose of introducing this constraint is also to limit the total investment cost from developing towards infinity and to ensure that the planning information of the first layer can act on the third layer model.

[0154] (2) Relaxation constraints of net load effective safety domain:

[0155] ; (37)

[0156] In the formula, AR is the optimal result determined by the second-layer model, which is passed to the third-layer model as known parameter information; It represents the net load effective safety domain relaxation factor of the third-layer model, with a value between 0 and 1, indicating an appropriate degree of abandonment of the net load node's anti-disturbance capability in exchange for higher economic benefits.

[0157] (3) Net load forecast value total power balance constraint:

[0158] This constraint is the same as that shown in formula (19).

[0159] (4) Branch flow constraints under disturbance scenarios:

[0160] This constraint is the same as equation (30) and equation (31).

[0161] (5) Generator capacity reserve constraints:

[0162] This constraint is the same as that shown in equation (11) and equation (14).

[0163] (6) Generator related constraints:

[0164] This constraint is the same as shown in equation (32), equation (33), and equation (34).

[0165] 3. Process the transmission network planning model, convert it into a linear programming model and solve it:

[0166] (1) Linearization of quadratic terms in the objective function:

[0167] The quadratic term of the operating cost part of the objective function of the transmission network planning model can be piecewise linearized. This method is a common technique and will not be described in detail here.

[0168] (2) Deterministic transformation of constraints

[0169] The transmission network planning model takes into account that the uncertainty of net load disturbance will cause implicit function relationships between related variables of various equality constraints in the model, thereby making the number of constraints infinite. In order to avoid such solution obstacles, the present invention uses a dual method to transform the uncertain model into a deterministic model for solution.

[0170] According to the corresponding relationship of formula (10), the line flow constraint can be transformed to obtain a new expression of the line flow constraint, as shown in formula (38) and formula (39):

[0171] (38)

[0172] (39)

[0173] At this time, the uncertainty contained in the branch flow constraint is converted into a single form, and the equality constraint is relaxed into an inequality constraint. Therefore, the Soyster robust optimization method can be used to transform the constraint, eliminate the uncertainty, and transform it into a deterministic model. The conversion method is shown in the following formula (42).

[0174] ; (40)

[0175] In the formula, x represents the only uncertainty and satisfies the following constraints:

[0176] ; (41)

[0177] It can be transformed into a linear constraint as follows:

[0178] ; (42)

[0179] (3) Linearization of logical operations in the objective function

[0180] The calculation of the effective safety region involved in the second-level objective function of the transmission network planning model involves the logical operation of taking the minimum value. Such nonlinear terms will greatly increase the difficulty of calculation and reduce the speed of the solver. However, the model structure designed in this disclosure is special, and equation (15) can be directly linearly equivalent, thereby avoiding the introduction of binary variables. The equivalent method is as follows:

[0181] Introduce two non-negative continuous variables and , modify the objective function to:

[0182] ; (43)

[0183] ; (44)

[0184] ; (45)

[0185] Maximization in the objective function requires that the two sets of inequalities in equations (44) and (45) be equal to the smaller one on the right side. It can be proved that the above equations are equivalent to the original objective function.

[0186] Finally, after linearization and deterministic transformation, the model is converted into a linear programming model, which can be efficiently solved using mature commercial solvers.

[0187] In the embodiment, the IEEE-RTS 24-node system is appropriately modified: two new lines are added between node 2 and node 7 and between node 5 and node 7, respectively. The topology diagram is as follows: Figure 3 The system includes 24 nodes, 26 units and 40 lines to be selected, of which 17 nodes have load distribution. Node 1 is the access node of the upper power grid. A wind farm with a capacity of 170MW is connected to nodes 2, 8, 13 and 16 respectively, and a wind farm with a capacity of 200MW is connected to node 14. The wind power penetration rate is about 60%.

[0188] 1. Comparative analysis of configuration models

[0189] In order to verify the superiority of the planning model proposed in this model, a comparative analysis is performed by setting the control group 1 as the traditional transmission network planning model considering N-1. After the optimization is completed, the cost comparison of the two groups of examples is shown in Table 1, and the planning results of the line capacity are compared. Figure 4 shown.

[0190] Table 1 Cost comparison of two groups of examples

[0191] cost This public model Control group 1 Total cost / million yuan 1109.7539 985.0579 Line investment cost / million yuan 184.9607 60.2647 Unit operating cost / million yuan 924.7932 924.7932

[0192] As can be seen from Table 1, the line investment cost of the model disclosed in this paper is higher than that of the traditional planning model. This is because the traditional planning model only requires the system to meet the equal generation and load, without analyzing the actual operation of the future power system and the high proportion of new energy penetration, and the planning results are too optimistic. Although the planning model proposed in this paper is more costly than the traditional planning model in terms of economy, the model takes into account the complete absorption of new energy in the transmission network planning, ensuring that there will be no problems such as load shedding and line flow exceeding the limit due to the high proportion of new energy access when the transmission network is put into operation in the future. Its reliability is better than the traditional model, which reflects the superiority of this model.

[0193] In addition, by Figure 4 Comparison of the line capacities of the two models shows that the model disclosed in this disclosure has higher requirements for line capacity than the traditional planning model. This is because this model takes into account the impact of unit operation and standby constraints and the maximum safety domain constraints brought by the superior model on the transmission network planning, resulting in changes in the system flow. In addition, the impact of line maintenance and line N-1 disconnection accidents are also considered in this model, resulting in a higher planning result for line capacity and improving the power supply reliability of the system.

[0194] 2. Analysis of optimization configuration results

[0195] In order to simulate the current situation of large-scale transmission grids gradually increasing the penetration rate of new energy, this paper sets up control groups with various wind power penetration rates for simulation. The control group settings are as follows:

[0196] Control group 3: The wind power penetration rate is 0, and no wind turbines are connected to the system.

[0197] Control group 4: The wind power penetration rate is 20%, with a 170MW wind farm connected at node 2 and a 200MW wind farm connected at node 14.

[0198] Control group 5: The wind power penetration rate is 40%. A 170MW wind farm is connected to each node 2 and node 16, and a 200MW wind farm is connected to each node 8 and node 14.

[0199] Control group 6: The wind power penetration rate is 80%, with a 200MW wind farm connected to nodes 2 and 14, and a 300MW wind farm connected to nodes 8, 13, 16 and 20.

[0200] The cost results of the five groups of examples are shown in Table 2:

[0201] Table 2 Comparison of five groups of examples

[0202] cost Control group 3 Control group 4 Control group 5 This public model Control group 6 Total cost / million yuan 985.0579 1431.1846 1325.0444 1109.7539 1373.8340 Line investment cost / million yuan 60.2647 188.1406 191.7631 184.9607 188.1316 Unit operating cost / million yuan 924.7932 1243.0440 1133.2812 924.7932 1185.7025

[0203] Since wind power fluctuates violently in the actual operation of the power system, its large-scale access puts higher requirements on the robustness of the system; and as the penetration rate of wind power increases, the output of wind turbines increases accordingly, and the number of units turned on during the operation of the power system decreases, which also limits the system's climbing ability, and also means that the system can have less reserve. In addition, line failures will also have an adverse effect on the robustness of the system, so insufficient reserve, wind abandonment, load shedding, etc. may occur in the lower-level model, which requires a more detailed design for the establishment of the maximum safety domain in the upper-level model.

[0204] From the analysis of the costs under various scenarios in Table 2, it can be seen that with the continuous increase in wind power penetration, the power system has higher and higher requirements for the net load safety domain. When the wind power penetration rate is 0 and 20%, the system can still cope with the fluctuation of the net load, and the requirements for line capacity are relatively low. When the wind power penetration rate reaches 40%, the system's requirements for robustness increase, and it is necessary to maintain a high level of unit operation and increase the line capacity. With the increase in wind power penetration, the line capacity and the units that the system needs to maintain operation are also constantly changing. When the wind power penetration rate reaches 60%, the model works best. The reason is that, first, a large number of wind power access reduces the total operating cost of the system, and a large number of thermal power units maintain a low operating state and are always ready to meet the backup demand caused by wind power fluctuations. Secondly, because the wind power penetration rate of the system has exceeded a critical value, the system does not need to maintain the operation of some low-quality thermal power unit nodes for a long time to maintain the power balance of the entire system. The resource allocation of the system is more rational, which also reduces the demand for line capacity slightly. When the wind power penetration rate reaches 80%, in order to meet the robustness of the system, it is necessary to significantly increase the number of standing units to improve the reserve level to meet the uncertainty brought by a large amount of wind power, forcibly increase the operating cost of the system to expand the safety domain, which also leads to an increase in the system's demand for line capacity to ensure that there will be no serious wind curtailment and load shedding. In summary, at the current stage, that is, new energy is largely connected to the power system, the penetration rate is gradually increasing, but it has not yet reached the stage of becoming a pillar energy source. The superiority of the present disclosure is significantly reflected, and it has strong practical significance and application value.

[0205] like Figure 5 As shown, an embodiment of the present disclosure provides a transmission network planning system, including:

[0206] A payload effective security domain model construction module 11 is used to construct a payload effective security domain model;

[0207] The transmission network planning model construction module 12 is used to construct a transmission network planning model based on the net load effective safety domain model, wherein the transmission network planning model is a three-layer model, wherein the first layer is a planning model under a net load prediction value, the second layer is an effective safety domain model under a net load disturbance value, and the third layer is a joint planning model under a net load disturbance value;

[0208] The conversion and solving module 13 is used to process the transmission network planning model, convert it into a linear programming model and solve it.

[0209] The implementation process of the functions and effects of each module in the above system is specifically described in the implementation process of the corresponding steps in the above method, which will not be repeated here.

[0210] For the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The system embodiment described above is only schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0211] In the above embodiments, any number of all modules can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. At least one of all modules can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in a suitable combination of any of them. Alternatively, at least one of all modules can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0212] See also Figure 6 , an electronic device provided by an embodiment of the present disclosure includes a processor 1110, a communication interface 1120, a memory 1130 and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140;

[0213] Memory 1130, for storing computer programs;

[0214] The processor 1110 is used to implement the following transmission network planning method when executing the program stored in the memory 1130.

[0215] The communication bus 1140 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus 1140 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0216] The communication interface 1120 is used for communication between the above electronic device and other devices.

[0217] The memory 1130 may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory 1130 may also be at least one storage device located away from the processor 1110.

[0218] The above-mentioned processor 1110 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0219] The embodiment of the present disclosure further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the transmission network planning method described above is implemented.

[0220] The computer-readable storage medium may be included in the device / apparatus described in the above embodiment; or it may exist independently without being assembled into the device / apparatus. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the transmission network planning method according to the embodiment of the present disclosure is implemented.

[0221] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus, or device.

[0222] The above-mentioned embodiments only express several implementation methods of the present disclosure, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present disclosure. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present disclosure, and these all belong to the protection scope of the present disclosure.

Claims

1. A transmission network planning method, characterized in that: The following steps are involved: Constructing a net load effective safety domain model, including constructing a net load acceptable domain mathematical model, introducing an acceptable net load disturbance value, and constructing the net load effective safety domain model based on the net load acceptable domain mathematical model and the acceptable net load disturbance value; A transmission network planning model is constructed based on the net load effective safety domain model. The transmission network planning model is a three-layer model, wherein the first layer is a planning model under a net load prediction value, the second layer is an effective safety domain model under a net load disturbance value, and the third layer is a joint planning model under a net load disturbance value; the planning model under the net load prediction value of the first layer does not consider the uncertainty of load and new energy output, and constructs a deterministic model based on the prediction value to pre-plan the network to obtain a benchmark value of economic cost; the effective safety domain model under the net load disturbance value of the second layer relaxes the benchmark value of economic cost obtained by the first layer model and adds it to the constraint condition of the second layer model, to ensure that the information of the first layer model is fully utilized, and also to ensure that the effective safety domain of the second layer model does not tend to infinity; the joint planning model under the net load disturbance value of the third layer considers the reference grid planning of uncertainty, and at the same time relaxes both the benchmark value of economic cost and the effective safety domain, and adds them to the constraint condition of the third layer model to obtain the final joint planning result; The transmission network planning model is processed, converted into a linear programming model and solved.

2. A transmission network planning method according to claim 1, characterized in that: The planning model under the net load prediction value of the first layer is a deterministic model, and the effective safety domain model under the net load disturbance value of the second layer and the joint planning model under the net load disturbance value of the third layer are uncertain models.

3. A transmission network planning method according to claim 1, characterized in that: The objective function of the planning model under the net load forecast value is to minimize the total cost under the net load forecast value.

4. A transmission network planning method according to claim 1, characterized in that: The total cost under the net load forecast value includes the investment cost and the operating cost under the net load forecast value.

5. A transmission network planning method according to claim 1, characterized in that: The constraints of the planning model under the net load forecast value include total power balance constraints under the net load forecast value, line flow constraints under the net load forecast value, line capacity constraints, generator related constraints and capacity reserve constraints under the net load forecast value.

6. A transmission network planning method according to claim 1, characterized in that: The objective function of the effective safety domain model under the net load disturbance value is to maximize the total net load effective safety domain of the system.

7. A transmission network planning method according to claim 1, characterized in that: The constraints of the effective safety domain model under the net load disturbance value include the total cost constraint of joint planning, the total power balance constraint of the net load forecast value, the branch flow constraint under the disturbance scenario, the generator set capacity reserve constraint and the generator set constraint.

8. A transmission network planning method according to claim 1, characterized in that: The objective function of the joint planning model under the net load disturbance value is to minimize the total cost under the net load disturbance value.

9. A transmission network planning method according to claim 1, characterized in that: The total cost under the net load disturbance value includes the investment cost, operation cost and standby cost under the net load disturbance value.

10. A transmission network planning method according to claim 1, characterized in that: The constraints of the joint planning model under the net load disturbance value include the joint planning total cost constraint, the net load effective safety domain relaxation constraint, the net load predicted value total power balance constraint, the branch flow constraint under the disturbance scenario, the generator set capacity reserve constraint and the generator related constraints.

11. A transmission network planning method according to claim 1, characterized in that: Processing the transmission network planning model, converting it into a linear programming model and solving it includes: Linearizing the transmission network planning model to convert it into a linear planning model; The constraints of the transmission network planning model are deterministically transformed, and the transformed transmission network planning model is solved using a commercial solver.

12. A transmission network planning system, characterized in that: include: A net load effective safety domain model construction module is used to construct a net load effective safety domain model, including constructing a net load acceptable domain mathematical model, introducing an acceptable net load disturbance value, and constructing a net load effective safety domain model based on the net load acceptable domain mathematical model and the acceptable net load disturbance value; The transmission network planning model construction module is used to construct a transmission network planning model based on the net load effective safety domain model. The transmission network planning model is a three-layer model, the first layer is a planning model under the net load prediction value, the second layer is an effective safety domain model under the net load disturbance value, and the third layer is a joint planning model under the net load disturbance value; the planning model under the net load prediction value of the first layer does not consider the uncertainty of load and new energy output, and constructs a deterministic model based on the prediction value to pre-plan the network to obtain a benchmark value of economic cost; the effective safety domain model under the net load disturbance value of the second layer relaxes the benchmark value of economic cost obtained by the first layer model and adds it to the constraint condition of the second layer model, ensuring that the information of the first layer model is fully utilized and that the effective safety domain of the second layer model does not tend to infinity; the joint planning model under the net load disturbance value of the third layer considers the reference grid planning of uncertainty, and at the same time relaxes the benchmark value of economic cost and the effective safety domain, and adds them to the constraint condition of the third layer model to obtain the final joint planning result; The conversion and solving module is used to process the transmission network planning model, convert it into a linear programming model and solve it.

13. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, for storing computer programs; The processor is used to implement the power transmission network planning method described in any one of claims 1-11 when executing the program stored in the memory.

14. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the transmission network planning method according to any one of claims 1 to 11 is implemented.

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