A scheduling method, a computer system, a readable storage medium and a program product for distributed risk robust reserve optimization operation of an AC-DC hybrid microgrid system

Abstract

The application belongs to the technical field of electrical engineering, and relates to a scheduling method for distributed risk robust reserve optimization operation of an AC-DC hybrid microgrid system, comprising the following steps: constructing an operation risk and two-stage robust optimization model of the AC-DC hybrid microgrid system; decomposing the two-stage robust optimization model of the AC-DC hybrid microgrid system into a sending-end subproblem and a receiving-end subproblem by using ADMM decomposition; simplifying the sending-end subproblem and the receiving-end subproblem into a compact form, and decomposing them into a main problem and a subproblem; solving the main problem and the subproblem by using a C&CG algorithm to obtain a coupling variable value and a system scheduling scheme; updating a penalty factor and a Lagrange multiplier by using the coupling variable value; modifying the main problem and the subproblem according to the penalty factor and the Lagrange multiplier, performing next iteration, and obtaining an optimal scheduling scheme if an ADMM convergence condition is met.

Description

Technical Field

[0001] This invention belongs to the field of electrical engineering technology, and more specifically, relates to a scheduling method, computer system, readable storage medium, and program product for distributed risk robust backup optimized operation of AC / DC hybrid microgrid systems. Background Technology

[0002] In recent years, my country has faced severe environmental challenges such as dwindling reserves of traditional fossil fuels and intensified global warming. Therefore, the country is committed to building a new type of power grid system, with non-fossil energy as the primary supply source and fossil fuels as a safety net. AC / DC hybrid microgrid systems can improve the absorption of new energy sources and the reliability of power transmission, making microgrid operation more flexible. However, with the increasing number of distributed new energy sources, DC loads, and DC power sources connected to AC microgrids, the power quality of AC microgrids has been affected, reducing their operational economics. At the same time, while large-scale grid connection of new energy sources brings significant environmental and economic benefits, the strong volatility and uncertainty of these new energy sources also pose challenges to the scheduling and operation of microgrid systems.

[0003] For the scheduling problem of distributed risk-robust reserve optimization operation in AC / DC hybrid microgrid systems, centralized scheduling methods can be used. However, for AC / DC hybrid microgrids composed of multiple coupled subsystems, the centralized solution of the above methods suffers from drawbacks such as privacy leakage and poor computational performance as the scale of the microgrid system increases. Therefore, solving the optimal scheduling problem of AC / DC hybrid microgrid systems requires the use of distributed scheduling methods. Distributed optimization scheduling methods can achieve the same effect as centralized optimization by only collecting local information and communicating between adjacent partitions, thus avoiding the aforementioned privacy issues.

[0004] In distributed solution methods, the ADMM-based optimal scheduling computation method is an effective solution to the scheduling problem of distributed risk-robust reserve optimization operation in AC / DC hybrid microgrid systems. However, the original ADMM method struggles to balance accuracy and efficiency during the solution process. Summary of the Invention

[0005] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a scheduling method, computer system, readable storage medium and program product for distributed risk robust reserve optimization operation of AC / DC hybrid microgrid system, the purpose of which is to balance the accuracy and solution efficiency of ADMM optimal scheduling results.

[0006] To achieve the above objectives, according to one aspect of the present invention, a scheduling method for distributed risk-robust reserve optimization operation of an AC / DC hybrid microgrid system is provided, comprising: S1. Construct a two-stage robust optimization model for operational risks and AC / DC hybrid microgrid systems; S2. The two-stage robust optimization model of the AC / DC hybrid microgrid system is decomposed into sending-end and receiving-end problems using ADMM decomposition. S3. Simplify the sending and receiving terminal problems into a compact form and decompose them into a main problem and sub-problems; S4. Use the C&CG algorithm to solve the main problem and subproblems, and obtain the values ​​of the coupled variables and the system scheduling scheme; S5. Update the penalty factor and Lagrange multipliers using the values ​​of the coupled variables; S6. Modify the main problem and subproblems according to the penalty factor and Lagrange multipliers, and proceed to the next iteration. If the ADMM convergence condition is met, the optimal scheduling scheme is obtained.

[0007] Furthermore, the operational risk and the two-stage robust optimization model are constructed as follows: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) Equations (1) and (2) represent the risks of abandoning new energy sources and the risk of load shedding, respectively. For new energy generators w exist tThe constant risk of abandoning new energy sources For new energy generators w exist t Risk of loss of load at any moment , These are the upper and lower boundaries of the renewable energy absorption capacity, respectively. For new energy generators w The upper limit of output, , These are the power output of new energy sources and their probability density functions, respectively. Equation (3) represents the objective function for the first stage. , These refer to gas turbine units in DC microgrids and AC microgrids, respectively. , , Gas turbine units g exist t The cost coefficients for operation, standby, and backup at any given time; , , Gas turbine units g exist t The operating output, reserve output, and standby output at all times; , These are wind and solar power generators in DC and AC microgrids, respectively. , These are the cost coefficients for wind and solar curtailment and load shedding, respectively. This is the first phase of the overall system; Total running time; Equation (4) represents the power constraint of the AC microgrid node. For nodes s branch road at the beginning k ; For nodes s For the terminal branch k ; , and Branch roads k At any moment t Active power, reactive power, and current; and At time respectively t Active and reactive power input to the node; and Branch roads k Resistance and reactance; This is the sum of the branches of the AC microgrid; Equation (5) represents the power constraint of a DC microgrid node. This represents the total number of branches in a DC microgrid. Equations (6) and (7) represent the voltage and current constraints of the system nodes, respectively. i and j branch road k The starting point and the ending point and Starting point i At any moment t voltage and endpoint j At any moment t The voltage; Equation (8) represents the output constraint of new energy sources during the pre-adjustment phase. New energy generators in the pre-adjustment phase w At any moment t contribution; New energy generators in the pre-adjustment phase w At any moment t The upper limit of output; Equation (9) represents the standby capacity constraint of the unit during the pre-adjustment phase. For coal-fired units in the system g At any moment t Output at the preset stage; and These are the coal-fired units in the system. g At any moment t Upper and lower spares; and These are the coal-fired units in the system. g Maximum power limit and minimum power limit; Equation (10) represents the power flow constraint of the converter. and They are respectively in t Branches in an AC microgrid that are constantly connected to the converter l Input active power and reactive power; In order to be in t Branches in an AC microgrid that are constantly connected to the converter l The square of the current transmitted upwards; and These are the branches in the AC microgrid connected to the converter. l Resistance and reactance; In order to be in t DC microgrid branches that are always connected to the converter m The output active power; for t DC microgrid branches that are always connected to the converter m Voltage amplitude at the connection point; for t Time flows through the communication line u reactive power; for t The magnitude of the equivalent internal electromotive force at both ends of the converter at any given time; DC voltage efficiency; for t The modulation scheme of the converter; Equation (11) represents the line transmission power constraint in the system. Indicates the lines in an AC microgrid k At any moment t The maximum power that can be transmitted; Equation (12) represents the active power constraint on the tie line. for t Timeline u Active power during the pre-scheduling phase; , They are respectively t Timeline u The upper and lower limits of the adjustable range of active power; For connecting lines u Maximum power that can be transmitted; The maximum value of the upper and lower limits of the adjustable range; Equation (13) represents the objective function for the second stage. For new energy generators w exist t The amount of adjustment at any given time; For load d exist t The amount of adjustment at any given time; , These are the loads in DC and AC microgrids, respectively; This is the second phase of the overall system; It is of the uncertainty level; Equation (14) represents the adjusted line transmission power constraint within the system. After the power output of the generator unit was adjusted k exist t The amount of power transmitted at any given time; Equation (15) represents the adjusted new energy output constraint. for t Shike New Energy Generator w Output during the pre-scheduling phase, After adjustment t Shike New Energy Generator w The upper limit of output; Equations (16)-(17) represent the uncertainty constraints of the adjusted new energy sources. This represents the upper limit of new energy output during the pre-schedule phase. , These are the maximum and minimum values ​​of the upper limit for new energy power output, respectively. Equation (18) represents the adjusted generator output constraint. After adjustment t Shike Gas Turbine Unit g The output value; Equation (19) represents the power balance of the nodes after adjustment. , They are respectively t The starting point and the ending point are points. s The communication line u Adjusted active power; , They are respectively t The starting point and the ending point are nodes. s The route k Adjusted transmission power; For load d exist t The first phase of effort at any given moment; For connection at node s The load on.

[0008] Furthermore, in step S2: The expression for the terminal delivery problem is as follows: (20) (twenty one) The expression for the terminal problem is as follows: (twenty two) (twenty three) The formula for calculating the average value of the coupled variables is as follows: (twenty four) in, , These represent the total costs of the sending-end system and the receiving-end system in the first phase, respectively. The sum of the connecting lines; , These are the first and second phases of the overall sending-end system, respectively. , These are the receiving-end systems for the first and second phases, respectively. , These are the uncertainty sets for the sending and receiving systems, respectively. , , The first n Coupling variables during the next iteration , , Lagrange multipliers; , , The first n Coupling variables during the next iteration , , Lagrange multipliers; , , Then they are respectively the first n The corresponding penalty factor for the corresponding coupling variable during each iteration; , , The first n- The average value of the corresponding coupling variables after one iteration.

[0009] Furthermore, in step S3: The sender and receiver problems have the same compact form, and their expression is: (25) (26) (27) (28) in, a , b These are the variables within the first-stage sending-end system and the coupling variables on the tie line, respectively. These are variables within the second-stage sending system; Uncertainty regarding the contribution of new energy sources in the second phase; , The uncertainties lie in the power output from new energy sources and the power on the interconnection lines; , These are Lagrange multipliers and penalty factors, respectively. , , , , , , , , , , , , , These are the corresponding constant matrices in the objective function and constraints, respectively; for The constant matrix adjusted in the second stage; symbol For Hadamah accumulation; Decompose the compact form of the sending and receiving terminal problem into a main problem and sub-problems; The main problem expression is: (29) (30) (31) (32) The subproblem expression is: (33) (34) (35) (36) in, , , , , The corresponding variables are in the th j The values ​​in the next iteration; The constants in the objective function of the main problem. This is the constant matrix in the objective function of the subproblem.

[0010] Furthermore, the calculation of coupling variables and optimal scheduling scheme using the C&CG algorithm includes the following steps: S401, Initialize the number of iterations i =1, setting the lower bound of the objective function for the main problem and subproblems to be 1. The upper boundary is ; S402. Solve for the optimal solution of the objective function (29) of the main problem. and the corresponding scheduling scheme Update the lower bound of the objective function to ; S403, according to The optimal solution to the subproblem objective function (33) is obtained by solving the problem. Scheduling schemes for the worst operating scenarios Modify the correlation coefficients of constraints (31)-(32) in the main problem, and update the upper bound of the objective function to... ; S404. Convergence test, if... If the objective function converges, the iteration ends; otherwise... i = i +1, and return S402; where e The preset threshold; The C&CG algorithm is used to solve the main problem and subproblems, obtaining the values ​​of the coupling variables and the system scheduling scheme. Then, the Lagrange multipliers and penalty factors are updated based on the values ​​of the coupling variables to determine whether convergence has occurred.

[0011] Furthermore, the process of updating the Lagrange multipliers and the penalty factor is as follows: S501. Update the Lagrange multipliers based on the values ​​of the coupling variables: (37) in, for and The sum; for and The sum; for and The sum; S502. Calculate the original residual and dual residual using coupling variables: (38) in, For the first n Dual residuals of coupled variables after the next iteration For the first n The original residual of the coupling variable of the terminal problem after the next iteration For the first n The original residuals of the coupling variables affected by the terminal problem after the next iteration; S503, Compare the original residual and the dual residual to update the penalty factor: (39) in, x and y All are correction factors; The coefficients are constants. S504. Perform a convergence test on the obtained original residual and dual residual: (40) in, and These represent the limits of the original residual and the dual residual, respectively.

[0012] If the convergence condition is not met, the updated Lagrange multipliers and penalty factors are substituted into the main problem and subproblems for correction, and the iteration is repeated.

[0013] Furthermore, in step S6, when the convergence condition is met, the iteration stops and the optimal system scheduling scheme is output.

[0014] According to another aspect of the invention, a computer system is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the scheduling method as described in any of the preceding claims.

[0015] According to another aspect of the invention, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps of the scheduling method as described in any of the preceding claims.

[0016] According to another aspect of the invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the steps of the scheduling method as described in any of the preceding claims.

[0017] In general, the above-mentioned technical solutions conceived in this invention can achieve the following beneficial effects compared with the prior art.

[0018] (1) The scheduling method for distributed risk robust reserve optimization operation of AC / DC hybrid microgrid system provided by the present invention solves the converter power setpoint and its dynamic adjustment range simultaneously in the new energy pre-scheduling stage, which improves the adaptive ability of AC / DC hybrid microgrid system to cope with regional new energy fluctuations and reduces system operating costs.

[0019] (2) The scheduling method for distributed risk-robust reserve optimization operation of AC / DC hybrid microgrid systems provided by this invention expresses uncertainty by constraining the uncertainty of new energy output through a robust optimization model with risk quantification. It avoids the over-conservative problem of traditional fixed-boundary robust optimization models by using a risk linearization function. Compared with deterministic scheduling methods, this method, by incorporating extreme operating conditions and optimizing reserve resource allocation, can achieve a more secure scheduling scheme with lower operating costs.

[0020] (3) The scheduling method for distributed risk robust standby optimization operation of AC / DC hybrid microgrid system provided by the present invention constructs a distributed scheduling framework and its regional decomposition mechanism for AC / DC hybrid microgrid system that protects the privacy of regional information. It can decouple the original problem into sub-problems that can be solved in parallel, thereby realizing the fully distributed parallel solution of the system. Attached Figure Description

[0021] Figure 1 This is a flowchart of the scheduling method for distributed risk robust reserve optimization operation of AC / DC hybrid microgrid system provided in Embodiment 1 of the present invention; Figure 2 This is a topology diagram of a 2-region, 12-node AC / DC hybrid microgrid system, based on the scheduling method for distributed risk robust reserve optimized operation of AC / DC hybrid microgrid systems provided in Embodiment 1 of the present invention. Figure 3 This is a schematic diagram of the tie-line power and adjustable range of a 2-region, 12-node AC / DC hybrid microgrid system in the pre-scheduling stage, provided in Embodiment 1 of the present invention.

[0022] Figure 4 This is a schematic diagram of the convergence curve of Example 1 obtained by using the improved ADMM method and the original ADMM method of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0024] Example 1 The scheduling method for distributed risk-robust reserve optimization operation of AC / DC hybrid microgrid systems provided in this embodiment was tested in a 2-region, 12-node AC / DC hybrid microgrid system. The topology of the 2-region, 12-node AC / DC hybrid microgrid system is shown below. Figure 2 As shown, the DC microgrid has one gas turbine, one photovoltaic generator, one wind turbine, five transmission lines, and six nodes. The AC microgrid is the same as the DC microgrid, and the AC and DC microgrids are connected by an inverter. Three scenarios are set up to illustrate the effectiveness of the invention: First scenario: There is no energy flow between the AC microgrid and the DC microgrid, and the two microgrids adopt an islanded independent dispatch mode; Second scenario: AC microgrids and DC microgrids only coordinate dispatch during the pre-dispatch decision-making phase; Third scenario: AC microgrids and DC microgrids are simultaneously coordinated during the pre-dispatch decision and re-dispatch decision phases; In this 2-region, 12-node AC / DC hybrid microgrid system, the third case proposes a scheduling method for distributed risk robust reserve optimization operation of the AC / DC hybrid microgrid system, such as... Figure 1 As shown, the specific steps include: S1. Construct a two-stage robust optimization model for operational risks and AC / DC hybrid microgrid systems; The mathematical expression for the two-stage robust optimization model of the AC / DC hybrid microgrid system is as follows: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) Among them, equations (41) and (42) represent the risks of abandoning new energy sources and the risks of load shedding, respectively. For new energy generators w exist t The constant risk of abandoning new energy sources For new energy generators w exist t Risk of loss of load at any moment , These are the upper and lower boundaries of the renewable energy absorption capacity, respectively. For new energy generators w The upper limit of output, , These are the power output of new energy sources and their probability density functions, respectively. Equation (43) represents the objective function for the first stage. , These refer to gas turbine units in DC microgrids and AC microgrids, respectively. , , Gas turbine units g exist t The cost coefficients for operation, standby, and backup at any given time; , , Gas turbine units g exist t The operating output, reserve output, and standby output at all times; , These are wind and solar power generators in DC and AC microgrids, respectively. , These are the cost coefficients for wind and solar curtailment and load shedding, respectively. This is the first phase of the overall system; Total running time; Equation (44) represents the power constraint of the AC microgrid node. For nodes s branch road at the beginning k ; For nodes s For the terminal branch k ; , and Branch roads k At any moment t Active power, reactive power, and current; and At time respectively t Active and reactive power input to the node; and Branch roads k Resistance and reactance; This is the sum of the branches of the AC microgrid; Equation (45) represents the power constraint of a DC microgrid node. This represents the total number of branches in a DC microgrid. Equations (46) and (47) represent the voltage and current constraints of the system nodes, respectively. i and j branch road k The starting point and the ending point and Starting point i At any moment t voltage and endpoint j At any moment t The voltage; Equation (48) represents the output constraint of new energy sources during the pre-adjustment phase. New energy generators in the pre-adjustment phase w At any moment t contribution; New energy generators in the pre-adjustment phase w At any moment t The upper limit of output; Equation (49) represents the standby capacity constraint of the unit during the pre-adjustment phase. For coal-fired units in the system g At any moment t Output at the preset stage; and These are the coal-fired units in the system. g At any moment t Upper and lower spares; and These are the coal-fired units in the system. g Maximum power limit and minimum power limit; Equation (50) represents the power flow constraint of the converter. and They are respectively in t Branches in an AC microgrid that are constantly connected to the converter l Input active power and reactive power; In order to be in t Branches in an AC microgrid that are constantly connected to the converter l The square of the current transmitted upwards; and These are the branches in the AC microgrid connected to the converter. l Resistance and reactance; In order to be in t DC microgrid branches that are always connected to the converter m The output active power; for t DC microgrid branches that are always connected to the converter m Voltage amplitude at the connection point; for t Time flows through the communication line u reactive power; for t The magnitude of the equivalent internal electromotive force at both ends of the converter at any given time; DC voltage efficiency; for t The modulation scheme of the converter; Equation (51) represents the line transmission power constraint in the system. Indicates the lines in an AC microgrid k At any moment t The maximum power that can be transmitted; Equation (52) represents the active power constraint on the tie line. for t Timeline u Active power during the pre-scheduling phase; , They are respectively t Timeline u The upper and lower limits of the adjustable range of active power; For connecting lines u Maximum power that can be transmitted; The maximum value of the upper and lower limits of the adjustable range; Equation (53) represents the objective function for the second stage. For new energy generators w exist t The amount of adjustment at any given time; For load d exist t The amount of adjustment at any given time; , These are the loads in DC and AC microgrids, respectively; This is the second phase of the overall system; It is of the uncertainty level; Equation (54) represents the line transmission power constraint in the adjusted system. After the power output of the generator unit was adjusted k exist t The amount of power transmitted at any given time; Equation (55) represents the adjusted new energy output constraint. for t Shike New Energy Generator w Output during the pre-scheduling phase, After adjustment t Shike New Energy Generator w The upper limit of output; Equations (56)-(57) represent the uncertainty constraints of the adjusted new energy source. This represents the upper limit of new energy output during the pre-schedule phase. , These are the maximum and minimum values ​​of the upper limit for new energy power output, respectively. Equation (58) represents the adjusted generator output constraint. After adjustment t Shike Gas Turbine Unit g The output value; Equation (59) represents the power balance of the nodes after adjustment. , They are respectively t The starting point and the ending point are points. s The communication line u Adjusted active power; , They are respectively t The starting point and the ending point are nodes. s The route k Adjusted transmission power; For load d exist t The first phase of effort at any given moment; For connection at node s The load on.

[0025] S2. The two-stage robust optimization model of the AC / DC hybrid microgrid system is decomposed into sending-end and receiving-end problems using ADMM decomposition. The expression for the terminal delivery problem is as follows: (20) (twenty one) The expression for the terminal problem is as follows: (twenty two) (twenty three) in, , These represent the total costs of the sending-end system and the receiving-end system in the first phase, respectively. , These are the first and second phases of the overall sending-end system, respectively. , These are the receiving-end systems for the first and second phases, respectively. , These are the uncertainty sets for the sending and receiving systems, respectively. , , The first n Coupling variables during the next iteration , , Lagrange multipliers; , , The first n Coupling variables during the next iteration , , Lagrange multipliers; , , Then they are respectively the first n The corresponding penalty factor for the corresponding coupling variable during each iteration; , , The first n- The average value of the corresponding coupling variables after one iteration.

[0026] The formula for calculating the average value of the coupled variables is as follows: (twenty four) S3. Simplify the sending and receiving terminal problems into a compact form and decompose them into a main problem and sub-problems; Taking the terminal feeding problem as an example, the compact form of the expression is: (25) (26) (27) (28) in, a , b These are the variables within the first-stage sending-end system and the coupling variables on the tie line, respectively. These are variables within the second-stage sending system; Uncertainty regarding the contribution of new energy sources in the second phase; , The uncertainties lie in the power output from new energy sources and the power on the interconnection lines; , These are Lagrange multipliers and penalty factors, respectively. , , , , , , , , , , , , , These are the corresponding constant matrices in the objective function and constraints, respectively; for The constant matrix adjusted in the second stage; symbol For Hadamah accumulation; The compact form of the sending and receiving terminal problem is decomposed into the main problem and sub-problems.

[0027] The main problem expression is: (29) (30) (31) (32) The subproblem expression is: (33) (34) (35) (36) in, , , , , The corresponding variables are in the th j The values ​​in the next iteration; The constants in the objective function of the main problem, This is the constant matrix in the objective function of the subproblem.

[0028] S4. Use the C&CG algorithm to solve the main problem and subproblems, and obtain the values ​​of the coupled variables and the system scheduling scheme; The steps for calculating coupling variables and scheduling schemes using the C&CG algorithm are as follows: S401, Initialize the number of iterations i =1, setting the lower bound of the objective function for the main problem and subproblems to be 1. The upper boundary is ; S402. Solve for the optimal solution of the objective function (29) of the main problem. and the corresponding scheduling scheme Update the lower bound of the objective function to ; S403, according to The optimal solution to the subproblem objective function (33) is obtained by solving the problem. Scheduling schemes for the worst operating scenarios Modify the correlation coefficients of constraints (31)-(32) in the main problem, and update the upper bound of the objective function to... ; S404. Convergence test, if... If the objective function converges, the iteration ends; otherwise... i = i +1, and return S402; where e The preset threshold; The C&CG algorithm is used to solve the main problem and subproblems, obtaining the values ​​of the coupling variables and the system scheduling scheme. Then, the Lagrange multipliers and penalty factors are updated based on the values ​​of the coupling variables to determine whether convergence has occurred.

[0029] S5. Update the penalty factor and Lagrange multipliers using the values ​​of the coupled variables; The steps to update the Lagrange multipliers and penalty factors are as follows: S501. Update the Lagrange multipliers based on the values ​​of the coupling variables: (37) in, for and The sum; for and The sum; for and The sum; S502. Calculate the original residual and dual residual using coupling variables: (38) in, For the first n Dual residuals of coupled variables after the next iteration For the first n The original residual of the coupling variable of the terminal problem after the next iteration For the first n The original residuals of the coupling variables affected by the terminal problem after the next iteration; S503, Compare the original residual and the dual residual to update the penalty factor: (39) in, x and y All are correction factors; The coefficients are constants. S504. Perform a convergence test on the obtained original residual and dual residual: (40) in, and These represent the limits of the original residual and the dual residual, respectively.

[0030] If the convergence condition is not met, the updated Lagrange multipliers and penalty factors are substituted into the main problem and subproblems for correction, and the iteration is repeated.

[0031] S6. Correct the main problem and subproblems based on the penalty factor and Lagrange multipliers, and proceed to the next iteration. If the ADMM convergence condition is met, the optimal scheduling scheme is obtained. When the convergence condition is met, the iteration stops, and the optimal system scheduling scheme is output.

[0032] According to calculations, as shown in Table 1, the third scenario in the 2-region, 12-node AC / DC hybrid microgrid system has the lowest operating cost. The cost reductions in the second and third scenarios are 21.67% and 23.75% respectively compared to the first scenario, indicating that coordinated scheduling of the AC / DC hybrid microgrid system yields better economic benefits. A comparative analysis of the total costs of the second and third scenarios shows that the two-stage coordinated scheduling achieves optimal global cost, verifying the effectiveness of the invention.

[0033] Table 1

[0034] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements 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 scheduling method for distributed risk-robust reserve optimization operation of an AC / DC hybrid microgrid system, characterized in that, include: S1. Construct a two-stage robust optimization model for operational risks and AC / DC hybrid microgrid systems; The operational risks and the two-stage robust optimization model are constructed as follows: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) Equations (1) and (2) represent the risks of abandoning new energy sources and the risk of load shedding, respectively. For new energy generators w exist t The constant risk of abandoning new energy sources For new energy generators w exist t Risk of loss of load at any moment , These are the upper and lower boundaries of the renewable energy absorption capacity, respectively. For new energy generators w The upper limit of output, , These are the power output of new energy sources and their probability density functions, respectively. Equation (3) represents the objective function for the first stage. , These refer to gas turbine units in DC microgrids and AC microgrids, respectively. , , Gas turbine units g exist t The cost coefficients for operation, standby, and backup at any given time; , , Gas turbine units g exist t The operating output, reserve output, and standby output at all times; , These are wind and solar power generators in DC and AC microgrids, respectively. , These are the cost coefficients for wind and solar curtailment and load shedding, respectively. This is the first phase of the overall system; Total running time; Equation (4) represents the power constraint of the AC microgrid node. For nodes s branch road at the beginning k ; For nodes s For the terminal branch k ; , and Branch roads k At any moment t Active power, reactive power, and current; and At time respectively t Active and reactive power input to the node; and Branch roads k Resistance and reactance; This is the sum of the branches of the AC microgrid; Equation (5) represents the power constraint of a DC microgrid node. This represents the total number of branches in a DC microgrid. Equations (6) and (7) represent the voltage and current constraints of the system nodes, respectively. i and j branch road k The starting point and the ending point and Starting point i At any moment t voltage and endpoint j At any moment t The voltage; Equation (8) represents the output constraint of new energy sources during the pre-adjustment phase. New energy generators in the pre-adjustment phase w At any moment t contribution; New energy generators in the pre-adjustment phase w At any moment t The upper limit of output; Equation (9) represents the standby capacity constraint of the unit during the pre-adjustment phase. For coal-fired units in the system g At any moment t Output at the preset stage; and These are the coal-fired units in the system. g At any moment t Upper and lower spares; and These are the coal-fired units in the system. g Maximum power limit and minimum power limit; Equation (10) represents the power flow constraint of the converter. and They are respectively in t Branches in an AC microgrid that are constantly connected to the converter l Input active power and reactive power; In order to be in t Branches in an AC microgrid that are constantly connected to the converter l The square of the current transmitted upwards; and These are the branches in the AC microgrid connected to the converter. l Resistance and reactance; In order to be in t DC microgrid branches that are always connected to the converter m The output active power; for t DC microgrid branches that are always connected to the converter m Voltage amplitude at the connection point; for t Time flows through the communication line u reactive power; for t The magnitude of the equivalent internal electromotive force at both ends of the converter at any given time; DC voltage efficiency; for t Modulation of the converter at any time; Equation (11) represents the line transmission power constraint in the system. Indicates the lines in an AC microgrid k At any moment t The maximum power that can be transmitted; Equation (12) represents the active power constraint on the tie line. for t Timeline u Active power during the pre-scheduling phase; , They are respectively t Timeline u The upper and lower limits of the adjustable range of active power; For connecting lines u Maximum power that can be transmitted; The maximum value of the upper and lower limits of the adjustable range; Equation (13) represents the objective function for the second stage. For new energy generators w exist t The amount of adjustment at any given time; For load d exist t The amount of adjustment at any given time; , These are the loads in DC and AC microgrids, respectively; This is the second phase of the overall system; It is of the uncertainty level; Equation (14) represents the adjusted line transmission power constraint within the system. After the power output of the generator unit was adjusted k exist t The amount of power transmitted at any given time; Equation (15) represents the adjusted new energy output constraint. for t Shike New Energy Generator w Output during the pre-scheduling phase, After adjustment t Shike New Energy Generator w The upper limit of output; Equations (16)-(17) represent the uncertainty constraints of the adjusted new energy sources. This represents the upper limit of new energy output during the pre-schedule phase. , These are the maximum and minimum values ​​of the upper limit for new energy power output, respectively. Equation (18) represents the adjusted generator output constraint. After adjustment t Shike Gas Turbine Unit g The output value; Equation (19) represents the power balance of the nodes after adjustment. , They are respectively t The starting point and the ending point are points. s The communication line u Adjusted active power; , They are respectively t The starting point and the ending point are nodes. s The route k Adjusted transmission power; For load d exist t The first phase of effort at any given moment; To connect at node s The load on; S2. The two-stage robust optimization model of the AC / DC hybrid microgrid system is decomposed into sending-end and receiving-end problems using ADMM decomposition. The expression for the terminal delivery problem is as follows: (20) (21) The expression for the terminal problem is as follows: (22) (23) The formula for calculating the average value of the coupled variables is as follows: (24) in, , These represent the total costs of the sending-end system and the receiving-end system in the first phase, respectively. The sum of the connecting lines; , These are the first and second phases of the overall sending-end system, respectively. , These are the receiving-end systems for the first and second phases, respectively. , These are the uncertainty sets for the sending and receiving systems, respectively. , , The first n Coupling variables during the next iteration , , Lagrange multipliers; , , The first n Coupling variables during the next iteration , , Lagrange multipliers; , , Then they are respectively the first n The corresponding penalty factor for the corresponding coupling variable during each iteration; , , The first n- The average value of the corresponding coupling variables after one iteration; S3. Simplify the sending and receiving terminal problems into a compact form and decompose them into a main problem and sub-problems; S4. Use the C&CG algorithm to solve the main problem and subproblems, and obtain the values ​​of the coupled variables and the system scheduling scheme; S5. Update the penalty factor and Lagrange multipliers using the values ​​of the coupled variables; S6. Modify the main problem and subproblems according to the penalty factor and Lagrange multipliers, and proceed to the next iteration. If the ADMM convergence condition is met, the optimal scheduling scheme is obtained.

2. The scheduling method for distributed risk-robust reserve optimization operation of an AC / DC hybrid microgrid system according to claim 1, characterized in that, In step S3: The sender and receiver problems have the same compact form, and their expression is: (25) (26) (27) (28) in, a , b These are the variables within the first-stage sending-end system and the coupling variables on the tie line, respectively. These are variables within the second-stage sending system; Uncertainty regarding the contribution of new energy sources in the second phase; , The uncertainties lie in the power output from new energy sources and the power on the interconnection lines; , These are Lagrange multipliers and penalty factors, respectively. , , , , , , , , , , , , , These are the corresponding constant matrices in the objective function and constraints, respectively; for The constant matrix adjusted in the second stage; symbol For Hadamah accumulation; Decompose the compact form of the sending and receiving terminal problem into a main problem and sub-problems; The main problem expression is: (29) (30) (31) (32) The subproblem expression is: (33) (34) (35) (36) in, , , , , The corresponding variables are in the th j The values ​​in the next iteration; These are constants in the objective function of the main problem. This is the constant matrix in the objective function of the subproblem.

3. The scheduling method for distributed risk-robust reserve optimization operation of an AC / DC hybrid microgrid system according to claim 2, characterized in that, The calculation of coupling variables and optimal scheduling scheme using the C&CG algorithm includes the following steps: S401, Initialize the number of iterations i =1, setting the lower bound of the objective function for the main problem and subproblems to be 1. The upper boundary is ; S402. Solve to obtain the optimal solution of the objective function (29) of the main problem. and the corresponding scheduling scheme Update the lower bound of the objective function to ; S403, according to The optimal solution to the subproblem objective function (33) is obtained by solving the problem. Scheduling schemes for the worst operating scenarios Modify the correlation coefficients of constraints (31)-(32) in the main problem, and update the upper bound of the objective function to... ; S404. Convergence test, if... If the objective function converges, the iteration ends; otherwise... i = i +1, and return S402; where e The preset threshold; The C&CG algorithm is used to solve the main problem and subproblems, obtaining the values ​​of the coupling variables and the system scheduling scheme. Then, the Lagrange multipliers and penalty factors are updated based on the values ​​of the coupling variables to determine whether convergence has occurred.

4. The scheduling method for distributed risk-robust reserve optimization operation of an AC / DC hybrid microgrid system according to claim 3, characterized in that, The process of updating the Lagrange multipliers and penalty factors is as follows: S501. Update the Lagrange multipliers based on the values ​​of the coupling variables: (37) in, for and The sum; for and The sum; for and The sum; S502. Calculate the original residual and dual residual using coupling variables: (38) in, For the first n Dual residuals of coupled variables after the next iteration For the first n The original residual of the coupling variable of the terminal problem after the next iteration For the first n The original residuals of the coupling variables affected by the terminal problem after the next iteration; S503, Compare the original residual and the dual residual to update the penalty factor: (39) in, x and y All are correction factors; The coefficients are constants. S504. Perform a convergence test on the obtained original residual and dual residual: (40) in, and These represent the limits of the original residual and the dual residual, respectively; If the convergence condition is not met, the updated Lagrange multipliers and penalty factors are substituted into the main problem and subproblems for correction, and the iteration is repeated.

5. The scheduling method for distributed risk-robust reserve optimization operation of an AC / DC hybrid microgrid system according to claim 1, characterized in that, In step S6, when the convergence condition is met, the iteration stops and the optimal system scheduling scheme is output.

6. A computer system comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the scheduling method according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the scheduling method according to any one of claims 1 to 5.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the scheduling method according to any one of claims 1 to 5.

Images