A renewable energy grid connection planning method considering network capacity in power market environment

Abstract

The present invention discloses a new energy grid connection planning method considering network capacity in an electric power market environment, which involves the field of renewable energy planning and includes the following steps: S1: establishing an upper-level optimization problem with the goal of maximizing the access capacity of renewable energy, following the constraint that the income obtained in the day-ahead electricity market and the green certificate trading market is greater than or equal to the investment cost of renewable energy; S2: establishing a lower-level day-ahead wholesale market clearing model with the goal of maximizing social welfare, and listing the constraints of the lower-level market model under the conditions of satisfying power balance, unit output constraints, DC optimal power flow, etc.; S3: converting a double-layer optimization problem into a mathematical programming problem with balance constraints by using the KKT condition; when the present invention is used, the grid connection planning of RES is carried out by considering the impact of network capacity and green certificate trading under the renewable energy quota system through a double-layer optimization model, and the overall optimum can be achieved by transferring decision variables between the upper and lower layers.

Description

Technical Field

[0001] The present invention relates to the field of renewable energy planning, and in particular to a new energy grid connection planning method taking network capacity into consideration in an electric power market environment. Background Art

[0002] As global climate change and environmental issues become increasingly prominent, low-carbonization has become an inevitable trend in the development of the world's energy industry, and constructing a new power system with renewable energy as the main body has become a new trend in the development of future energy systems. my country has gradually reduced or even cancelled fiscal subsidies to encourage the development of new energy, and now has transitioned to the market means of promoting the trading of green electricity certificates (referred to as "green certificates"). A green certificate is a tradable green electricity certificate that can be converted into monetary returns. In 2021, my country has begun to implement green electricity certificate trading under the quota system, that is, all power generation companies must complete the prescribed proportion of renewable energy quotas, and surplus green certificates can be traded. Under this policy, the value of green certificates has been greatly improved, which has promoted renewable energy investment, grid optimization planning, and incentives for the optimal operation of renewable energy.

[0003] my country is facing multiple opportunities such as the rapid development of renewable energy, the steady progress of power market reform and the construction of the green certificate market. Investing in RES is a trend in the power market, which is not only conducive to optimizing the allocation of power resources, but also one of the important means to reduce carbon emissions. The power spot market is a market based on marginal cost competition, which naturally favors RES with extremely low marginal costs to participate in competition and promote RES consumption. However, if RES with high investment costs and extremely low marginal costs participate in the market clearing as a full price taker on the day before, the electricity price will be lower and the volatility will increase. In the context of the "dual carbon" goal and the development of a new power system with new energy as the main body, if the system invests in RES on a large scale, whether the electricity income brought by marketization can guarantee the recovery of RES investment costs is worth studying. At the same time, how to plan the grid connection of RES and determine the optimal access volume in the power market environment, considering the impact of network capacity and green certificate trading, is an urgent issue to study. Summary of the invention

[0004] In order to solve the deficiencies mentioned in the above background technology, the purpose of the present invention is to provide a new energy grid connection planning method considering network capacity in the power market environment. The two-layer optimization model of the present invention can reflect the planning of RES in the power market environment, considering the impact of network capacity and green certificate trading. The overall optimization can be achieved by transferring decision variables between the upper and lower layers, and finally the grid connection planning of RES is realized and the optimal access quantity is determined.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A new energy grid connection planning method considering network capacity in an electric power market environment comprises the following steps:

[0007] S1: Establish an upper-level optimization problem with the goal of maximizing renewable energy access capacity and following the constraint that the income obtained in the day-ahead power market and green certificate trading market is greater than or equal to the renewable energy investment cost;

[0008] S2: Establish a lower-level day-ahead wholesale market clearing model with the goal of maximizing social welfare. List the constraints of the lower-level market model under the conditions of satisfying power balance, unit output constraints, and DC optimal power flow.

[0009] S3: Use KKT conditions to transform the bi-level optimization problem into a mathematical programming problem with equilibrium constraints;

[0010] S4: KKT conditions and SOS1 are used to convert mathematical programming problems with equilibrium constraints into mixed integer linear programming and obtain the global optimal solution. Since two types of nonlinear terms will appear in the upper objective function, it is difficult for the solver to guarantee the global optimal solution. First, the SOS1 method is used to deal with a large number of complementary relaxation constraints brought by the KKT conditions. Then, for the nonlinear terms of the multiplication of electricity price and renewable energy output in the constraints of the upper problem, the KKT condition equations are transformed and linearized. Finally, commercial optimization solvers such as Gurobi are used to solve the global optimal solution.

[0011] Furthermore, the step S1 specifically includes the following steps:

[0012] Establish an optimization model with the upper layer aiming at maximizing the access capacity of renewable energy and the constraint of investment cost recovery:

[0013] Objective function:

[0014] constraint:

[0015] Where: W n represents the access capacity of renewable energy at node n (kW); λ n,t is the marginal electricity price of node n at time t (yuan / kWh); w d,t represents the output of renewable energy at node n at time t (kW); β is the renewable energy consumption responsibility weight coefficient; E is the clean energy power generation required by a single green certificate; γ is the green certificate transaction price (yuan / cost); K is the investment cost of renewable energy (yuan / kW / hour).

[0016] Furthermore, the step S2 is to establish a lower-level day-ahead wholesale market clearing model with the goal of maximizing social welfare, and to list the constraints of the lower-level market model under the conditions of satisfying node power balance, DC optimal power flow, unit output constraints, etc., including the following steps:

[0017] (1) Objective function:

[0018]

[0019] in:

[0020] V ll = {g i,t ,w n,t ,θ n,t} (4)

[0021] Where: V ll is a set of lower-level decision variables; a i represents the first-order coefficient of the cost of the i-th traditional unit (yuan / kWh); b i is the quadratic coefficient of the cost of the i-th traditional unit (yuan / kWh2); g i,t represents the output of the i-th traditional unit at time t (kW); θ n,t is the phase angle of node n at time t;

[0022] (2) Node power balance constraints:

[0023]

[0024] Where: d n,t is the total load of node n at time t; I n represents the set of traditional generators at node n; m is the node number connected to node n; M n is the set of nodes connected to node n; θ m,t is the phase angle of node m at time t; x n,m is the impedance value of the line connecting node n to node m;

[0025] (3) DC optimal power flow:

[0026]

[0027]

[0028]

[0029] Where: is the line capacity from node n to node m; is the Lagrange multiplier corresponding to both ends of inequality (6); is the Lagrange multiplier corresponding to both ends of inequality (7); θ 1,t is the phase angle of node 1, and node 1 is set as the equilibrium node; is the Lagrange multiplier corresponding to equation (8);

[0030] (4) Generator group output constraints:

[0031]

[0032]

[0033] Where: are the lower and upper limits of the output of the traditional unit i; is the Lagrange multiplier corresponding to both ends of inequality (9); ω n,t represents the renewable energy load factor of node n at time t; are the Lagrange multipliers corresponding to both ends of inequality (10).

[0034] Furthermore, the step S3 is to convert the two-level optimization problem into a mathematical programming problem with a balance constraint by using the KKT condition, and specifically comprises the following steps:

[0035] Objective function:

[0036]

[0037] in, is the set of all variables in the converted mathematical programming problem with equilibrium constraints;

[0038] constraint:

[0039] Formula (2), (5), (8)

[0040] Construct the Lagrangian function L:

[0041]

[0042] Find the gradient of the decision variable in the Lagrangian function L and form the stability condition:

[0043]

[0044]

[0045]

[0046] Complementary slack conditions:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055] Furthermore, the step S4 is to convert the mathematical programming problem with balance constraints into mixed integer linear programming by using KKT conditions and SOS1.

[0056] Since the upper objective function will have two types of nonlinear terms, it is difficult for the solver to guarantee the global optimal solution. First, SOS1 is used to process a large number of complementary relaxation constraints brought by KKT conditions, where SOS1 refers to the first type of special ordered set, which means that there is at most one non-zero value in a set of ordered sets. For the nonlinear terms of the multiplication of electricity price and renewable energy output in the constraints of the upper problem, KKT conditions are used to linearize them. Finally, commercial optimization solvers such as Gurobi are used to solve the global optimal solution. The specific steps include:

[0057] For the complementary relaxation constraints of equations (16-23), taking equation (16) as an example, SOS1 can be used to linearize it to obtain equality constraints in the form of equations (24-28).

[0058]

[0059]

[0060]

[0061]

[0062]

[0063] in, S i,t It is an auxiliary variable in SOS1;

[0064] Since there are nonlinear terms in the constraints of the upper optimization problem, Representing the income of renewable energy in the electricity market, that is, the multiplication of the two decision variables of electricity price and renewable energy output, can be linearized using the KKT condition, which specifically includes the following steps:

[0065] By summing both sides of equation (5) for each n and t, and shifting the terms, we can obtain:

[0066]

[0067] By multiplying both sides of equation (29) by λ n,t , we can get:

[0068]

[0069] Multiply both sides of equation (13) by g i,t , and summing each i and t gives:

[0070]

[0071] By transforming equation (31) using equation (16) and equation (17), we can obtain:

[0072]

[0073] Multiply both sides of equation (15) by θ n,t , sum each n and t, and use formula (10) to shift the terms to get:

[0074]

[0075] Using formula (20-21) and substituting it into formula (33), we can get:

[0076]

[0077] Substituting equation (32) and equation (34) into equation (30), we can obtain equation (35):

[0078]

[0079] Finally, the obtained objective function and constraints are input into the Gurobi solver to obtain the global optimal solution.

[0080] Beneficial effects of the present invention:

[0081] The two-layer optimization model proposed in the present invention can consider the impact of network capacity and green certificate trading under the renewable energy quota system to plan the grid connection of RES. The overall optimality can be achieved by transferring decision variables between the upper and lower layers. While ensuring that the profit of investing in RES is non-negative, the line congestion caused by line capacity and the economic incentives brought by green certificate trading are considered, and finally the access capacity of RES is maximized. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] The present invention will be further described below in conjunction with the accompanying drawings.

[0083] Figure 1 It is a schematic diagram of the structure of the proposed two-layer optimization model;

[0084] Figure 2 This is a sample diagram of a 6-node system in the example;

[0085] Figure 3 This is a schematic diagram of the optimal photovoltaic access quantity for each node under different network capacities;

[0086] Figure 4 This is a schematic diagram of thermal power and photovoltaic output in each region under different network capacities;

[0087] Figure 5 It is a schematic diagram of the average node electricity price in each region under different network capacities;

[0088] Figure 6 This is a schematic diagram of the impact of green certificate prices on the growth rate of optimal photovoltaic access under different network capacities.

[0089] In the figure: 1, 10, 2, 3, 31, 32, 33, 4, 5, 6,. DETAILED DESCRIPTION

[0090] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0091] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "thickness", "top", "middle", "length", "inside", "all around" and the like indicating orientation or positional relationship are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the components or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0092] A new energy grid connection planning method considering network capacity in an electric power market environment comprises the following steps:

[0093] S1: Establish an upper-level optimization problem with the goal of maximizing renewable energy access capacity and following the constraint that the income obtained in the day-ahead power market and green certificate trading market is greater than or equal to the renewable energy investment cost;

[0094] S2: Establish a lower-level day-ahead wholesale market clearing model with the goal of maximizing social welfare. List the constraints of the lower-level market model under the conditions of satisfying power balance, unit output constraints, and DC optimal power flow.

[0095] S3: Convert the bi-level optimization problem into MPEC using KKT conditions;

[0096] S4: Use KKT conditions and SOS1 to convert MPEC into a MILP problem and obtain the global optimal solution. Since the upper objective function will have two types of nonlinear terms, it is difficult for the solver to guarantee the global optimal solution. First, SOS1 is used to handle the large number of complementary relaxation constraints brought by the KKT conditions. Then, for the nonlinear terms of the multiplication of electricity price and renewable energy output in the constraints of the upper problem, the obtained KKT condition equation is transformed and linearized. Finally, commercial optimization solvers such as Gurobi are used to solve the global optimal solution.

[0097] Step S1 specifically includes the following steps:

[0098] Establish an optimization model with the upper layer aiming at maximizing the access capacity of renewable energy and the constraint of investment cost recovery:

[0099] Objective function:

[0100]

[0101] constraint:

[0102]

[0103] Where: W n represents the access capacity of renewable energy at node n (kW); λ n,t is the locational marginal prices (LMP) of node n at time t (yuan / kWh); w d,t represents the output of renewable energy at node n at time t (kW); β is the renewable energy consumption responsibility weight coefficient; E is the clean energy power generation required by a single green certificate; γ is the green certificate transaction price (yuan / cost); K is the investment cost of renewable energy (yuan / kW / hour).

[0104] Furthermore, the step S2 is to establish a lower-level day-ahead wholesale market clearing model with the goal of maximizing social welfare, and to list the constraints of the lower-level market model under the conditions of satisfying node power balance, unit output constraints, and DC optimal power flow, including the following steps:

[0105] (1) Objective function:

[0106]

[0107] in:

[0108] V ll = {gi,t ,w n,t ,θ n,t} (4)

[0109] Where: V ll is a set of lower-level decision variables; a i represents the first-order coefficient of the cost of the i-th traditional unit (yuan / kWh); b i is the quadratic coefficient of the cost of the i-th traditional unit (yuan / kWh2); g i,t represents the output of the i-th traditional unit at time t (kW); θ n,t is the phase angle of node n at time t.

[0110] (2) Node power balance constraints:

[0111]

[0112] Where: d n,t is the total load of node n at time t; I n represents the set of traditional generators at node n; m is the node number connected to node n; M n is the set of nodes connected to node n; θ m,t is the phase angle of node m at time t; x n,m is the impedance value of the line connecting node n to node m.

[0113] (3) DC optimal power flow:

[0114]

[0115]

[0116]

[0117] Where: is the line capacity from node n to node m; is the Lagrange multiplier corresponding to both ends of inequality (6); is the Lagrange multiplier corresponding to both ends of inequality (7); θ 1,t is the phase angle of node 1, and node 1 is set as the equilibrium node; is the Lagrange multiplier corresponding to equation (8).

[0118] (4) Generator group output constraints:

[0119]

[0120]

[0121] Where: are the lower and upper limits of the output of the traditional unit i; is the Lagrange multiplier corresponding to both ends of inequality (9); ω n,t represents the renewable energy load factor of node n at time t; are the Lagrange multipliers corresponding to both ends of inequality (10).

[0122] Step S3 is to convert the bi-level optimization problem into an MPEC problem using the KKT condition, which specifically includes the following steps:

[0123] Objective function:

[0124]

[0125] in, is the set of all variables in the converted mathematical programming problem with equilibrium constraints.

[0126] constraint:

[0127] Formula (2), (5), (8)

[0128] Construct the Lagrangian function L:

[0129]

[0130] Find the gradient of the decision variable in the Lagrangian function L and form the stability condition:

[0131]

[0132]

[0133]

[0134] Complementary slack conditions:

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143] Furthermore, the step S4 is to convert the MPEC problem into MILP using KKT conditions and SOS1 etc.

[0144] Since two types of nonlinear terms will appear in the upper-level objective function, it is difficult for the solver to guarantee the global optimal solution. First, SOS1 is used to process a large number of complementary relaxation constraints brought by the KKT conditions, where SOS1 refers to the first type of special ordered set, which means that there is at most one non-zero value in a set of ordered sets. For the nonlinear terms that multiply the electricity price and the output of renewable energy in the constraints of the upper-level problem, the KKT conditions are used to linearize them, and finally commercial optimization solvers such as Gurobi are used to solve the global optimal solution. The specific steps include:

[0145] For the complementary relaxation constraints of equations (16-23), taking equation (16) as an example, SOS1 can be used to linearize it to obtain equality constraints in the form of equations (24-28).

[0146]

[0147]

[0148]

[0149]

[0150]

[0151] in, S i,t It is an auxiliary variable in SOS1.

[0152] Since there are nonlinear terms in the constraints of the upper optimization problem, Represents the income of renewable energy in the electricity market, which is the product of the two decision variables of electricity price and renewable energy output. It can be linearized using the KKT condition, which includes the following steps:

[0153] By summing both sides of equation (5) for each n and t, and shifting the terms, we can obtain:

[0154]

[0155] By multiplying both sides of equation (29) by λ n,t , we can get:

[0156]

[0157] Multiply both sides of equation (13) by g i,t , and summing each i and t gives:

[0158]

[0159] By transforming equation (31) using equation (16) and equation (17), we can obtain:

[0160]

[0161] Multiply both sides of equation (15) by θ n,t , sum each n and t, and use formula (10) to shift the terms to get:

[0162]

[0163] Using formula (20-21) and substituting it into formula (34), we can get:

[0164]

[0165] Substituting equation (32) and equation (34) into equation (30), we can obtain equation (35):

[0166]

[0167] Finally, the obtained objective function and constraints are input into the Gurobi solver to obtain the global optimal solution.

[0168] Simulation experiment

[0169] The example selection is as follows Figure 2 The 6-node network shown is used as a scenario analysis, and the parameter settings of the embodiment are shown in Table 1. In this embodiment, the capacity of the two connecting lines (2, 4) and (3, 5) in the north and south regions is increased from 0 to 3000 kW at intervals of 60 MW, and a typical hour is selected to represent the whole year to analyze the impact of network capacity on the optimal access amount of photovoltaic power.

[0170] Table 1 Variable parameter settings for each node

[0171]

[0172] Figure 3 The figure is a schematic diagram of the optimal photovoltaic access quantity for each node under each network capacity. According to the change trend, it can be divided into the following four stages and analyzed separately.

[0173] Phase 1: When the total capacity of the north-south regional connecting lines (2,4) and (3,5) is between 0 and 600 kW, the total optimal access capacity of photovoltaics shows a downward trend. Figure 3 As shown in Figure 2, the initial growth of network capacity hindered the development potential of photovoltaic power in the southern region because the northern region has cheaper thermal power resources and more abundant photovoltaic resources, which is more economically advantageous than the southern region. Figure 4 As shown in the figure, due to the expansion of interconnection lines, the southern region tends to develop more economical thermal power in the north rather than invest in photovoltaic power generation in the south, because the network capacity is relatively limited at this time, and it is more economical to use the existing power generation capacity than to increase investment in photovoltaic power generation, which in turn makes the total optimal access capacity of photovoltaic power generation show a downward trend. Figure 4 It can be seen that due to the north-south congestion and the difference in the cost of thermal power units in the two regions, the average values ​​of LMP in the north and south are quite different.

[0174] Phase 2: When the total capacity of the north-south regional connecting lines (2,4) and (3,5) is between 600 and 2040kW, the total optimal access to photovoltaic power shows an upward trend. At this time, the network capacity is more abundant than before, and the more economical thermal power resources and more abundant photovoltaic resources in the north have been further developed. The expansion of the interconnection line network capacity has significantly increased the transmission power. At this time, photovoltaic investment in the north is more attractive than before, and it replaces the thermal power resources in the south, so the optimal access volume gradually increases. Secondly, with the increase in network capacity and the easing of congestion, the gap between the average LMP of the north and south regions is gradually narrowing.

[0175] Phase 3: When the total capacity of the north-south regional connecting lines (2,4) and (3,5) is between 2040 and 2460 kW, the total optimal access capacity of photovoltaic power still shows an upward trend, but the growth rate slows down. The capacity of the interconnection line network is further expanded, the congestion in the north and south regions is eliminated, and the electricity prices in the two regions tend to be consistent, making the thermal power and photovoltaic resources in the south completely uncompetitive, so the output of each unit in the south is greatly reduced. At the same time, the large-scale access of low-marginal-cost photovoltaic power has caused the priority order in the power market to change, causing the transaction price to gradually decrease. As the northern photovoltaic power has reached the upper limit of the access capacity at nodes 1 and 2, the growth rate of the total optimal access capacity has slowed down.

[0176] Phase 4: When the total capacity of the north-south regional connecting lines (2,4) and (3,5) is between 2460 and 3000 kW, the total optimal access capacity of photovoltaic power tends to remain unchanged. This is because after the congestion is eliminated, the photovoltaic resources of each node in the north are close to saturation at this stage, so the electricity prices in the two regions are consistent and the output of each unit remains unchanged.

[0177] Furthermore, the example uses the standard of 1 certificate corresponding to 1000kWh of electricity settlement, selects different green certificate prices, and analyzes their impact on the optimal access volume of RES under different network capacities. Figure 6As shown, the horizontal axis is still the total line capacity of the two connecting lines (2,4) and (3,5) in the north and south regions, and the vertical axis is the percentage increase of the optimal photovoltaic access under different green certificate prices. The results show that the higher the price of green certificates, the more obvious the economic incentive effect on RES investment, and therefore the faster the growth rate of RES optimal access. In addition, the higher the price of green certificates, the greater the fluctuation of the growth rate of optimal photovoltaic access due to network capacity and line congestion. Therefore, it is necessary for decision makers to consider the dual impact of network capacity and green certificate transactions and determine the appropriate green certificate price to maximize the economic incentive of RES.

[0178] The two-layer optimization model proposed in the method of the present invention can consider the impact of network capacity and green certificate trading to plan the grid connection of renewable energy. The overall optimality can be achieved by transferring decision variables between the upper and lower layers. While ensuring that the profit of investing in renewable energy is non-negative, the line congestion caused by line capacity and the economic incentives brought by green certificate trading are considered, and ultimately the access capacity of renewable energy is maximized.

[0179] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0180] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A renewable energy grid connection planning method in an electricity market environment, characterized in that: The following steps are involved: S1: Establish an upper-level optimization problem with the goal of maximizing renewable energy access capacity and following the constraint that the income obtained in the day-ahead power market and green certificate trading market is greater than or equal to the renewable energy investment cost; S2: Establish a lower-level day-ahead wholesale market clearing model with the goal of maximizing social welfare. Under the conditions of satisfying power balance, unit output constraints, and DC optimal power flow, list the constraints of the lower-level market model. S3: Use KKT conditions to transform the bi-level optimization problem into a mathematical programming problem with equilibrium constraints; S4: The mathematical programming problem with equilibrium constraints is converted into a mixed integer linear programming using KKT conditions and SOS1, and the global optimal solution is obtained. Since the upper objective function will have two types of nonlinear terms, it is difficult for the solver to guarantee the global optimal solution. First, the SOS1 method is used to deal with a large number of complementary relaxation constraints brought by the KKT conditions. Then, for the nonlinear terms of the multiplication of electricity price and renewable energy output in the constraints of the upper problem, the KKT condition equation is transformed and linearized. Finally, the Gurobi commercial optimization solver is used to solve the global optimal solution. The step S2 is to establish a lower-level day-ahead wholesale market clearing model with the goal of maximizing social welfare, and to list the constraints of the lower-level market model under the conditions of satisfying node power balance, DC optimal power flow, and unit output constraints, including the following steps: (1) Objective function: (3) in: (4) Where: is a set of lower-level decision variables; Indicates The first-order coefficient of the cost of a traditional unit, unit: yuan / kWh; For the The quadratic coefficient of the cost of a traditional unit, unit: yuan / kWh 2 ; Indicates Traditional units in Output at the moment, unit: kW; For Node exist The phase angle of the moment; Renewable energy at node exist Output at the moment, unit: kW; (2) Node power balance constraints: (5) Where: For Node exist Total load at the moment; Indicates that at the node A collection of traditional generator sets located at For the node The number of a connected node; For the node A collection of connected nodes; For Node exist The phase angle of the moment; For Node To Node Impedance value of the connecting line; For Node exist The node marginal electricity price at the time, in yuan / kWh; (3) DC optimal power flow: (6) (7) (8) Where: For Node To Node The line capacity; is the Lagrange multiplier corresponding to both ends of inequality (6); is the Lagrange multiplier corresponding to both ends of inequality (7); is the phase angle of node 1, and node 1 is set as the equilibrium node; is the Lagrange multiplier corresponding to equation (8); (4) Generator group output constraints: (9) (10) Where: For traditional units The lower and upper limits of output; is the Lagrange multiplier corresponding to both ends of inequality (9); Representation Node exist Renewable energy load factor at each moment; are the Lagrange multipliers corresponding to both ends of inequality (10); Indicated in The access capacity of renewable energy at the node, in kW.

2. The renewable energy grid connection planning method in the power market environment according to claim 1 is characterized in that: The step S1 specifically includes the following steps: Establish an optimization model with the upper layer aiming at maximizing the access capacity of renewable energy and the constraint of investment cost recovery: Objective function: (1) constraint: (2) Where: is the renewable energy consumption responsibility weight coefficient; The clean energy power generation required for a single green certificate; The transaction price of green certificates, in yuan / book; is the investment cost of renewable energy, unit: Yuan / kW / hour.

3. The renewable energy grid connection planning method in the power market environment according to claim 2 is characterized in that: The step S3 is to convert the two-layer optimization problem into a mathematical programming problem with a balance constraint by using the KKT condition, and specifically comprises the following steps: Objective function: (11) in, , is the set of all variables in the converted mathematical programming problem with equilibrium constraints; constraint: Formula (2), (5), (8) Constructing the Lagrangian function : (12) For the Lagrangian function Find the gradient of the decision variable and form the stability condition: (13) (14) (15) Complementary slack conditions: (16) (17) (18) (19) (20) (21) (22) (23)。 4. The renewable energy grid connection planning method in the power market environment according to claim 3 is characterized in that: The step S4 is to convert the mathematical programming problem with balance constraints into mixed integer linear programming by using KKT conditions and SOS1; Since the upper objective function will have two types of nonlinear terms, it is difficult for the solver to guarantee the global optimal solution. First, SOS1 is used to process a large number of complementary relaxation constraints brought by KKT conditions, where SOS1 refers to the first type of special ordered set, which means that there is at most one non-zero value in a set of ordered sets. For the nonlinear terms of the multiplication of electricity price and renewable energy output in the constraints of the upper problem, KKT conditions are used to linearize them. Finally, the Gurobi commercial optimization solver is used to solve the global optimal solution, which includes the following steps: For the complementary relaxation constraints of equations (16-23), taking equation (16) as an example, linearizing it using SOS1 can obtain the equality constraints in the form of equations (24-28): (24) (25) (26) (27) (28) in, , It is an auxiliary variable in SOS1; Since there are nonlinear terms in the constraints of the upper optimization problem, , represents the income of renewable energy in the electricity market, that is, the multiplication of the two decision variables of electricity price and renewable energy output, and linearization is performed using the KKT condition, which specifically includes the following steps: For each side of equation (5), and Sum and transpose to get: (29) By multiplying both sides of equation (29) by , we can get: (30) Multiply both sides of equation (13) by , and for each and The sum is: (31) By transforming equation (31) using equation (16) and equation (17), we can obtain: (32) Multiply both sides of equation (15) by , for each and Sum and use formula (10) to shift the terms and get: (33) Using formula (20-21) and substituting it into formula (33), we can get: (34) Substituting equation (32) and equation (34) into equation (30), we can obtain equation (35): (35) Finally, the obtained objective function and constraints are input into the Gurobi solver to obtain the global optimal solution.

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