Power transmission network bi-level expansion planning method based on primary and secondary energy market clearing results

Abstract

The application discloses a kind of based on the power transmission network double-layer extension planning method of main auxiliary energy market clearing result, comprising the following steps: with the minimum net cost of distribution network in planning period as target to build upper layer objective function and upper layer constraint condition of upper layer planning model;With the minimum net cost of operation of typical day as target to build lower layer objective function and lower layer constraint condition of lower layer main auxiliary energy market clearing model;With the KKT condition of the lower layer main auxiliary energy market clearing model, double-layer problem is equivalent to the single-layer linear integer programming problem containing equilibrium constraint and is solved.The purpose of the application is to provide a kind of based on the power transmission network double-layer extension planning method of main auxiliary energy market clearing result, to use reasonable power transmission network extension scheme to guarantee the supply and demand equilibrium situation of power system, reduce grid congestion and improve the fairness of electric power market.

Description

Technical Field

[0001] This invention relates to the field of power grid planning, and specifically to a two-layer extended planning method for transmission networks based on the clearing results of the primary and secondary energy markets. Background Technology

[0002] Transmission network expansion planning, a crucial link in ensuring the safe and stable operation of the power grid, aims to achieve specific planning objectives through the optimization of transmission line types, capacities, and topologies. In traditional transmission network expansion planning, power grid companies tend to increase transmission line capacity at higher economic costs to cope with the demands of renewable energy grid integration and load electricity consumption under extreme and severe scenarios. However, with the gradual opening of the electricity market, the diversification of market participants, and the increasing frequency of market transactions, the flexible interaction between power sources and loads in the power grid operation mode places new demands on the transmission system. Traditional power system planning approaches geared towards the transmission network are no longer compatible with the new characteristics of power grid operation under a high proportion of renewable energy grid integration and a competitive electricity market environment. Summary of the Invention

[0003] The purpose of this invention is to provide a two-layer expansion planning method for power transmission networks based on the clearing results of the primary and secondary energy markets, so as to ensure the supply and demand balance of the power system, reduce grid congestion, and improve the fairness of the power market by using a reasonable power transmission network expansion scheme.

[0004] This invention is achieved through the following technical solution:

[0005] A two-layer expansion planning method for transmission networks based on the clearing results of the primary and secondary energy markets, such as Figure 1 As shown, it includes the following steps:

[0006] S1: Construct the upper-level objective function and upper-level constraints of the upper-level planning model with the goal of minimizing the net cost of the distribution network within the planning period;

[0007] S2: Construct the lower-level objective function and lower-level constraints of the lower-level primary and secondary energy market clearing model with the goal of minimizing the net operating cost on a typical day;

[0008] S3: Use the KKT conditions of the lower-level primary and secondary energy market clearing model to transform the bi-level programming model into a single-level linear integer programming model with equilibrium constraints for solution.

[0009] This application provides a two-layer extended planning method for transmission networks based on the clearing results of the primary and secondary energy markets. It includes an upper-layer planning model and a lower-layer primary and secondary energy market clearing model. The upper-layer planning model primarily considers the investment costs of the power grid company and the system's operational utility costs, while the lower-layer primary and secondary energy market clearing model is a clearing model for the primary energy market and the ancillary service market. By substituting the Karush-Kuhn-Tucker (KKT) conditions from the lower-layer primary and secondary energy market clearing model into the upper-layer extended planning model, a single-layer mixed-integer linear programming problem with equilibrium constraints is constructed and solved. This allows the extended planning of the upper-layer transmission network to be guided by marginal nodal electricity prices and ancillary service market reserve prices.

[0010] Preferably, the upper-level objective function is:

[0011]

[0012] in, For the upper-level objective function, C E (·) represents the power grid system investment cost function, x u For the planning and decision variables of the upper-level transmission lines, ∑ j∈J w j =1, J is the typical day set of the planning period, w j Typical daily weighting, This represents the operating cost function of a typical daily power grid system. This represents the typical daily social utility cost function. This refers to the unit scheduling decisions made under typical daily market clearing at the lower level.

[0013] Preferably, the investment cost of the power grid system is:

[0014]

[0015] Among them, C E (x u The investment cost of the power grid system is... r is the discount rate of the planning scheme, M is the investment planning period, and L is the investment period. e L n These are the candidate sets for new investment routes and the candidate sets for expanded routes, respectively, and L n ∈L o L o This is a collection of existing routes prior to the planning stage. These are the unit investment costs for newly built lines and expanded lines, respectively. These are 0-1 decision variables for system operators regarding newly built and expanded lines. The capacities of the newly built lines and the expanded lines are respectively, and T is the set of time points in a 24-hour period.

[0016] Preferably, the upper-level constraint condition is:

[0017]

[0018]

[0019] Among them, I max Equation (3) ensures that the power grid expansion plan meets the initial budget requirements; Equation (4) constrains the redundant investment planning of new and expanded lines.

[0020] Preferably, the social utility cost is:

[0021]

[0022]

[0023] In the formula, To generate revenue for generating units participating in the primary and secondary energy markets. For users' energy efficiency functions, To contribute to the unit, The backup power supply provided for the generator set. The backup power provided for the generator set. User load rate; α m and β m This is the user's utility coefficient. Marginal nodal electricity prices in the main energy market To support the backup market, To support the lower price of standby equipment in the standby market, The marginal cost component of power generation represents the electricity price at the system boundary node, and h(·) represents the congestion component of the transmission line.

[0024] Preferably, the lower-level objective function is:

[0025]

[0026] In the formula, For the lower-level objective function, For lower-level decision variables, For system load rate, For the amount of electricity abandoned by renewable energy, To provide power to conventional units, The upper reserve capacity provided for conventional units The reserve capacity provided for conventional units, f j，t，l For the power flow on transmission lines l∈L, θ j，t，b For node voltage, For the curtailment of renewable energy, To provide power to conventional units, The unit cost of providing additional standby capacity for conventional units. The unit cost of the reserve capacity provided for conventional units, D is the set of renewable energy nodes, G is the set of conventional unit nodes, and T is the set of time points in a 24-hour day.

[0027] Preferably, the lower-level constraints include unit constraints:

[0028]

[0029]

[0030]

[0031]

[0032]

[0033] In the formula, To contribute to the unit, The backup power supply provided for the generator set. The backup power provided for the generator set. For the maximum output of this conventional unit g∈G, For the minimum output of this conventional unit g∈G, This is the maximum standby capacity of the unit. Equation (8) constrains the maximum lower reserve capacity of the unit, Equation (9) constrains the minimum output of the conventional unit when participating in the main energy market, Equation (10) constrains the maximum upper reserve capacity provided by the unit when participating in the reserve market, and Equation (11) constrains the minimum lower reserve capacity provided by the unit when participating in the reserve market. as well as All are represented as Lagrange multiplier variables.

[0034] Preferably, the lower-level constraints include renewable energy curtailment and load constraints:

[0035]

[0036]

[0037] in, For the amount of electricity abandoned by renewable energy, Let d∈D be the maximum output of the renewable energy unit at time t on a typical day j. Let B be the base load rate and the maximum load rate, respectively, for node b∈B. The actual load on node b∈B and It is represented as a Lagrange multiplier variable.

[0038] Preferably, the lower-level constraints include system constraints, which include:

[0039] Node balance constraints:

[0040]

[0041] Among them, f j，t，l Let L be the line power flow on transmission line l∈L; b∈o(l) be the set of lines at node b (sending end); b∈r(l) be the set of lines at node b (receiving end); and L be the set of all lines in the system under the upper-level planning decision, L=L e ∪L n ∪L o B is the set of nodes. Represented as Lagrange multiplier variables;

[0042] Alternative constraints:

[0043]

[0044]

[0045] Equations (16)-(17) ensure that the upper / lower reserve capacity margins provided by all conventional units in the power transmission system can meet the reserve requirements considering the fluctuations in renewable energy output and load demand; φ G , φ D These are certain reserve ratios for renewable energy and load, respectively. and Represented as Lagrange multiplier variables;

[0046] System operation security constraints:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052] in, For the initial line capacity, θ j，t，b Let θ be the node voltage. min θ max These represent the minimum and maximum values ​​of the node voltage, B. lM is the susceptance of line l. l For a maximum value, equation (18) is the node voltage constraint, equations (17)-(19) are the power flow constraints for newly built lines, and equations (20)-(21) are the power flow constraints for the initial lines considering line expansion. as well as It is represented as a Lagrange multiplier variable.

[0053] Preferably, step S3 includes the following steps:

[0054] S31: Based on the KKT conditions of the lower-level primary and secondary energy market clearing model, the upper-level objective function is transformed into:

[0055]

[0056] St

[0057] Equations (3)-(4), (8)-(22)

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067] in,

[0068] S32: Equations (8)-(22) are used as new constraints for the upper-level planning problem, and equations (24)-(32) are used as constraints for the lower-level market clearing problem based on KKT.

[0069] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0070] (1) The two-level planning model of the power transmission network can effectively balance system investment, system operating costs and user load utility, and optimize the power transmission network expansion planning scheme based on limited investment, which is conducive to incentivizing users to have greater electricity demand to improve the absorption level of renewable energy.

[0071] (2) It helps to reduce the volatility of marginal node prices after the main energy market of the power system is cleared, effectively avoids network congestion under peak load scenarios of the power grid, and ensures the supply and demand balance of the whole system and the fairness of market transactions;

[0072] (3) The method of expressing the upper and lower layer models as single-layer models to realize model optimization solution can realize the expansion planning of the power grid based on the existing network topology of the power system transmission network. It takes into account the clearing results of the flexible main energy market and ancillary service market. The planning results can effectively reduce network congestion and ensure the fair and efficient participation of power system nodes in the power market. Attached Figure Description

[0073] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0074] Figure 1 This is a schematic diagram of the structure of the present invention;

[0075] Figure 2 This is a schematic diagram of the improved Garver-6 node system of the present invention. Detailed Implementation

[0076] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0077] Example 1

[0078] This embodiment provides a two-layer extended planning method for transmission networks based on the clearing results of the primary and secondary energy markets, such as... Figure 1 As shown, it includes the following steps:

[0079] S1: Construct the upper-level objective function and upper-level constraints of the upper-level planning model with the objective of minimizing the net cost of the distribution network within the planning period; specifically:

[0080] Taking into account the clearing results of the primary and reserve power markets and system utility within the planning period, a centralized expansion plan for transmission lines is implemented to determine the topology and feeder capacity of newly built or expanded transmission lines within the planning period. Its objective function can be expressed as:

[0081]

[0082] In the formula, C E(·) represent the typical daily system operating cost function, social utility cost function, and system investment cost function, respectively; x u For the planning and decision-making variables of upper-level transmission lines; For typical days at the lower level, j represents the unit scheduling decision under market clearing; J represents the typical day set of the planning period; w j For typical daily weights, ∑ j∈J w j =1.

[0083] The power grid investment cost C in this embodiment E (·)for:

[0084]

[0085] Its constraints are:

[0086]

[0087]

[0088] In the formula, For system operators, these are 0-1 decision variables for new and expanded lines; For the capacity of newly built and expanded lines; L e L n L o The set includes candidate sets for new investment routes, candidate sets for expanded routes, and the set of existing routes before planning, and L n ∈L o ; Unit investment cost for new / extended lines; I max The maximum investment amount is r; the discount rate of the planning scheme is M; the investment planning period is M; Equation (3) ensures that the power grid expansion planning scheme meets the initial budget requirements; Equation (4) constrains the repeated investment planning of new and expanded lines.

[0089] The social utility cost function in this embodiment The sum of the social costs and utilities of the generating unit and the user can be expressed as:

[0090]

[0091]

[0092] In the formula, the first term is the revenue of the generating unit from participating in the primary and secondary energy markets, and the second term is the energy efficiency function of the user. To provide power to the generator unit; The upper and lower reserves provided for the generator unit; User load rate; α m ,βm This is the user's utility coefficient. Marginal node electricity prices in the main energy market; These are the prices for upper and lower reserves in the auxiliary reserve market, respectively. The generation marginal cost component represents the electricity price at the system boundary node, and h(·) represents the congestion component of the transmission line, which is related to the line power flow constraints (19)-(22).

[0093] S2: Construct the lower-level objective function and lower-level constraints of the lower-level primary and secondary energy market clearing model with the goal of minimizing the net operating cost on a typical day; specifically:

[0094] The lower-level primary and secondary energy market clearing model aims to reduce the system operating cost per typical day while meeting the constraints of system safety operation. Minimum, that is: for any time t, there exists:

[0095]

[0096] In the formula, For lower-level decision variables, These are system load rate, renewable energy curtailment, conventional unit output, upper reserve capacity provided by conventional units, and lower reserve capacity provided by conventional units, respectively. D represents the unit cost of renewable energy curtailment, conventional unit output, and conventional unit backup / standby capacity, respectively; G and D represent the renewable energy node set and the conventional unit node set, respectively; and T represents the set of time points in a 24-hour period.

[0097] In this embodiment, the specific constraints of the lower-level model include unit constraints, renewable energy curtailment and load constraints, and system constraints, among which:

[0098] The unit constraints are:

[0099]

[0100]

[0101]

[0102]

[0103]

[0104] In the formula, To contribute to the unit, The backup power supply provided for the generator set. The backup power provided for the generator set. For the maximum output of this conventional unit g∈G, For the minimum output of this conventional unit g∈G, This is the maximum standby capacity of the unit. Equation (8) constrains the maximum lower reserve capacity of the unit, Equation (9) constrains the minimum output of the conventional unit when participating in the main energy market, Equation (10) constrains the maximum upper reserve capacity provided by the unit when participating in the reserve market, and Equation (11) constrains the minimum lower reserve capacity provided by the unit when participating in the reserve market. as well as All are represented as Lagrange multiplier variables.

[0105] Renewable energy curtailment and load constraints:

[0106]

[0107]

[0108] in, For the amount of electricity abandoned by renewable energy, Let d∈D be the maximum output of the renewable energy unit at time t on a typical day j. Let B be the base load rate and the maximum load rate, respectively, for node b∈B. The actual load on node b∈B and It is represented as a Lagrange multiplier variable.

[0109] System constraints:

[0110] On a typical day j, for any time t, the system operation should satisfy the following constraints:

[0111] a) Node balance constraints:

[0112]

[0113] In the formula, f j，t，l Let L be the line power flow on transmission line l∈L; b∈o(l) be the set of lines at node b (sending end); b∈r(l) be the set of lines at node b (receiving end); and L be the set of all lines in the system under the upper-level planning decision, L=L e ∪L n ∪L o B is the set of nodes. It is represented as a Lagrange multiplier variable.

[0114] b) Alternative constraints:

[0115]

[0116]

[0117] In the formula, equations (16)-(17) ensure that the upper / lower reserve capacity margin provided by all conventional units in the power transmission system can meet the reserve requirements considering the fluctuations in renewable energy output and load demand; φ G , φ D A certain percentage of renewable energy and load reserves are allocated. and It is represented as a Lagrange multiplier variable.

[0118] c) System operation safety constraints:

[0119]

[0120]

[0121]

[0122]

[0123]

[0124] in, For the initial line capacity, θ j，t，b Let θ be the node voltage. min θ max These represent the minimum and maximum values ​​of the node voltage, B. l M is the susceptance of line l. l For a maximum value, equation (18) is the node voltage constraint, equations (17)-(19) are the power flow constraints for newly built lines, and equations (20)-(21) are the power flow constraints for the initial lines considering line expansion. as well as It is represented as a Lagrange multiplier variable.

[0125] S3: Using the KKT conditions of the lower-level primary and secondary energy market clearing model, the bilevel programming model is transformed into a single-level linear integer programming model with equilibrium constraints for solution. Specifically:

[0126] Considering that the lower-level market clearing model is a linear programming problem, in this embodiment, the original two-level programming model is equivalently transformed into a single-level linear integer programming problem with equilibrium constraints using the KKT conditions of the lower-level primary and secondary energy market clearing model. The objective function based on equation (1) can be characterized as:

[0127]

[0128] St

[0129] Equations (3)-(4), (8)-(22)

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139] Equations (8)-(22) are used as new constraints for the upper-level planning problem, and equations (24)-(32) are used as constraints for the lower-level market clearing problem based on KKT. The business optimization software Cplex is used to optimize and solve the problem.

[0140] In this application, the transmission network expansion planning under a power market clearing environment is described as a two-layer optimization problem. The upper-layer model is the power grid planning decision function, realizing the expansion planning of the transmission network. Through reasonable expansion and new transmission line schemes, it maximizes the total utility of power market participants and minimizes system operating costs. The lower-layer model is the power market clearing model, guiding the operation and scheduling of the power grid. Based on market supply and demand, the power grid company determines the nodal marginal price of the primary energy market and the upper and lower reserve prices of the ancillary services market for a typical day, and achieves optimized scheduling of generating units. Conventional generating units conduct energy trading at the nodal marginal price and provide reserve capacity services to the system at the reserve price. Simultaneously, the upper and lower-layer models are described as a single-layer model to achieve model optimization and solution. This method can realize the transmission network expansion planning based on the existing network topology of the power system's transmission network, comprehensively considering the flexible clearing results of the primary energy market and the ancillary services market. The planning results can effectively reduce network congestion and ensure the fair and efficient participation of power system nodes in the power market.

[0141] Example 2

[0142] This embodiment uses the improved Garver-6 system to verify the solution provided in Embodiment 1.

[0143] Take the new energy reserve rate φ G =5%, load reserve rate φ D=3%, the cost per unit length of the line is 50,000 yuan / MW, and the initial parameters of the system and the parameters of the expansion alternative set are shown in Table 1.

[0144] Improved Garver-6 system, such as Figure 2 As shown, it includes one wind farm, two conventional generator sets and six load nodes. Among them, the generator sets G1 = 300MW and G2 = 80MW are connected to bus 1 and bus 2 of the system respectively; the wind farm WT = 200MW is installed on bus 6.

[0145] This system has a total of 7 lines that can be newly built (such as...) Figure 2 (shown by dashed lines) and 5 original and expandable transmission lines (such as...) Figure 2 (As shown by the solid line).

[0146] A comparison is made between transmission line expansion method A, which only considers system operating costs, and transmission line expansion method B, which is driven by electricity market prices, as proposed in this application. The planning results and annual investment results are shown in Table 2, while the typical daily average operating costs and residential utility are shown in Table 3. Compared with method A, method B demonstrates better investment economics in transmission line expansion. Although the system operating cost of method B is slightly higher than that of method A, method B, by comprehensively considering load demand utility and the clearing results of the primary and reserve electricity markets, is conducive to incentivizing user electricity demand, enhancing user market participation, and promoting further absorption of renewable energy.

[0147] Table 1. Garver-6 System Expansion Alternative Set Parameters

[0148] Line number branch road Line capacity (MW) Existing line number of round trips Number of lines that can be expanded 1 1-2 128 1 1 2 1-4 128 1 1 3 2-3 80 1 1 4 3-6 104 1 1 5 4-5 132 1 1 6 2-4 104 0 1 7 5-6 72 0 1 8 1-3 56 0 1 9 2-5 104 0 1 10 3-5 64 0 1 11 3-4 64 0 1 12 1-6 64 0 1

[0149] Table 2. Expansion of Garver-6 Transmission Lines

[0150] plan New Line Extended Line Investment amount (ten thousand yuan) A 1-6、2-5、3-4 3-6 1150 B 5-6 1-4 625

[0151] Table 3 Garver - 6-Day Operation Results

[0152]

[0153] Further comparing the impact of different renewable energy installed capacity ratios on the planning scheme proposed in this application, Table 4 shows the optimized expansion planning schemes for the power transmission network when the renewable energy penetration rate is 20%, 40%, and 60%, respectively:

[0154] Table 4 Comparison of Planning Schemes under Different New Energy Penetration Rates

[0155]

[0156] As shown in Table 4, the increasing proportion of renewable energy has led to a gradual increase in the demand for new and expanded transmission lines, resulting in higher planning costs. The renewable energy penetration rate has a significant impact on the marginal node price of the power grid. Compared to a renewable energy grid-connected system with a 20% penetration rate, a power system with a 40% renewable energy share allows the grid to select higher-quality and more economical conventional generating units due to the relatively abundant renewable energy generation capacity, thus helping to reduce the system's marginal node price. With the gradual increase in the proportion of renewable energy generation, line congestion becomes more prominent, increasing the cost of renewable energy curtailment within the system. This leads to varying degrees of increase in the average node price within the system, and significant differences in node prices between different nodes.

[0157] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for transmission network expansion planning based on the clearing results of primary and secondary energy markets, characterized in that, The method comprises the following steps: S1: constructing an upper target function and upper constraint conditions of an upper planning model with the minimum net cost of the power distribution network in a planning period as a target; S2: constructing a lower target function and lower constraint conditions of a lower main and auxiliary energy market clearing model with the minimum net operation cost of a typical day as a target; S3: converting the double-layer planning model into a single-layer linear integer programming model containing equilibrium constraints by using KKT conditions of the lower main and auxiliary energy market clearing model for solving; The upper target function is: wherein, is the upper-level objective function, C E (·) represents the grid system investment cost function, x u is the upper-level transmission line planning decision variable, ∑ j∈J w j = 1, J is the set of planning period typical days, w j is the typical day weight, represents the typical day j grid system operation cost function, represents the typical day j social utility cost function, represents the lower-level typical day j unit dispatching decision under market clearing. The power grid system investment cost is: where C E (x u ) is the investment cost of the power grid system, r is the discount rate of the planning scheme, M is the investment planning period, L e ,L n are the candidate set of new investment lines and the candidate set of extended lines, respectively, and L n ∈L o , L o is the set of existing lines before planning, are the unit investment costs of the new lines and the extended lines, respectively, are 0-1 decision variables of the system operator for the new lines and the extended lines, respectively, are the capacities of the new lines and the extended lines, respectively, and T is the set of 24-hour time points. The upper constraint conditions are: where I max is the maximum investment amount; equation (3) ensures that the transmission network expansion planning scheme meets the initial budget requirement; equation (4) constrains the repeated investment planning of newly built lines and expanded lines; The social utility cost is: wherein, is the unit's participation in the main and auxiliary energy market revenue, is the user's energy utility function, is the unit's output, is the upper reserve provided by the unit, is the lower reserve provided by the unit, is the user's load rate; a m and β m are the utility coefficients of the user, is the marginal node price of the main energy market, is the upper reserve of the auxiliary reserve market, is the lower reserve price of the auxiliary reserve market, represents the generation marginal cost component of the system boundary node price, h(·) represents the congestion component of the transmission line; The lower target function is: wherein, is the lower-level objective function, is the lower-level decision variable, is the system load rate, is the renewable energy curtailment, is the conventional unit output, is the upper reserve capacity provided by the conventional unit, is the lower reserve capacity provided by the conventional unit, j,t,l is the line flow on transmission line l e L, θ j,t,b is the node voltage, is the renewable energy curtailment, is the conventional unit output, is the unit cost of the upper reserve capacity provided by the conventional unit, is the unit cost of the lower reserve capacity provided by the conventional unit, D is the set of renewable energy nodes, G is the set of conventional unit nodes, and T is the set of 24-hour time points. The lower constraint conditions comprise unit constraints: wherein is the unit output, is the upper reserve provided by the unit, is the lower reserve provided by the unit, is the maximum output of the conventional unit g G, is the minimum output of the conventional unit g G, is the maximum upper reserve capacity of the unit, is the maximum lower reserve capacity of the unit, equation (8) constrains the maximum output of the conventional unit when participating in the main energy market, equation (9) constrains the minimum output of the conventional unit when participating in the main energy market, equation (10) constrains the maximum upper reserve capacity provided by the unit when participating in the reserve market, equation (11) constrains the minimum lower reserve capacity provided by the unit when participating in the reserve market, and both represent Lagrange multiplier variables; The lower constraint conditions comprise renewable energy curtailment and load constraints: wherein, is the renewable energy curtailment, is the maximum output of the renewable energy unit de∈D at the typical day j and time t, are the base load rate and the maximum load rate at the node be∈B, respectively, is the actual load at the node be∈B, and is denoted as the Lagrange multiplier variable; The lower constraint conditions comprise system constraints, and the system constraints comprise: Node balance constraints: where f j,t,l is the line flow on transmission line l e L; b e o(l) is the set of lines sending to node b; b e r(l) is the set of lines receiving from node b; L is the set of all lines in the system under the upper level planning decision, L = L e ∪ L n ∪ L o ; B is the set of nodes, is represented as a Lagrange multiplier variable; Standby constraints: wherein, the equations (16)-(17) guarantee that the up / down reserve capacity margins provided by all conventional units in the power transmission system can meet the reserve demand considering the fluctuation of renewable energy output and load demand; φ G ,φ D are certain reserve proportions of renewable energy and load, respectively, and are Lagrange multiplier variables; System operation safety constraints: where is the initial line capacity, θ j,t,b is the node voltage, θ min , θ max are the minimum and maximum of the node voltage, respectively, B l is the susceptance of line l, M l is a maximum, equation (18) is the node voltage constraint, equations (17)-(19) are the power flow constraints for the new line, and equations (20)-(21) are the power flow constraints for the initial lines considering line expansion, and are represented as Lagrange multiplier variables; The S3 comprises the following steps: S31: converting the upper target function into the following formula based on the KKT conditions of the lower main and auxiliary energy market clearing model: wherein, is the Lagrange multiplier variable for the system up reserve constraint, representing the clearing price of the up reserve in the main and secondary energy market clearing model, is the Lagrange multiplier variable for the system down reserve constraint, representing the clearing price of the down reserve in the main and secondary energy market clearing model; S32: solving with the formula (8)-(22) as new constraint conditions of the upper planning model and the formula (24)-(32) as KKT-based constraint conditions of the lower market clearing problem.

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