Multi-resource collaborative new energy power system two-stage scheduling method and device

Abstract

The invention discloses a multi-resource collaborative new energy power system two-stage scheduling method and device, and the method comprises the steps: building a stage optimization scheduling model according to the uncertainty and intermittence characteristics of wind power; day-ahead scheduling stage constraints are set, and a power transmission network structure is optimized and a unit start-stop strategy is determined through a stage optimization scheduling model according to a day-ahead predicted value of a next-day load demand and renewable energy output; setting real-time scheduling stage constraints, and performing real-time PST adjustment on a voltage power angle and adjustment on unit output according to a next-day load and renewable energy output scene through a stage optimization scheduling model; and a solver is set to solve the stage optimization scheduling model, and an optimal scheduling strategy of a day-ahead scheduling stage and an optimal scheduling strategy of a real-time scheduling stage are obtained. According to the invention, the renewable energy consumption capability can be effectively improved, so that the overall economy of power grid operation is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power system scheduling, and in particular to a two-stage scheduling method and device for a multi-resource coordinated new energy power system. Background Art

[0002] Vigorously developing renewable energy sources such as wind power has become a crucial tool for countries around the world to address energy resource constraints, environmental degradation, and global warming. Against this backdrop, the integration rate of wind power and other renewable energy sources continues to rise. Compared with traditional power sources such as thermal and hydropower, renewable energy generation is characterized by significant intermittency, randomness, and uncertainty. Furthermore, wind and solar power generation exhibit intraday and seasonal fluctuations, mismatching load demand. These factors lead to more complex power flow distribution characteristics and more variable operation modes in the power grid, posing severe challenges to grid security and hindering the large-scale development and integration of renewable energy. Therefore, improving grid flexibility through optimized control and other means within the existing grid structure is crucial to ensure safe system operation, achieve economic dispatch, and increase renewable energy consumption.

[0003] On the power supply side, unit commitment (UC) uses the on / off status and output of each generator set in the system as control variables. While meeting constraints such as system load and reserve requirements, line flow limits, unit ramp rates, and minimum on / off times, it aims to improve the energy consumption capacity and effectively enhance the flexibility of the power grid. On the power grid side, there is growing interest in fully exploiting this flexibility and enhancing the capacity to accommodate renewable energy. Transmission topology optimization (TTO) is an important means of improving the operational flexibility of power systems. Through TTO, transmission equipment, including transmission lines, can adjust their operating status during grid operation, dynamically selecting the optimal network topology and improving system flexibility, economy, and safety. TTO models introduce 0-1 integer variables to represent the operating status of transmission equipment. TTO models that only consider changes in line operating status are called optimal transmission switching (OTS) models.

[0004] At present, in addition to optimizing the transmission network structure, flexible AC transmission equipment such as phase-shifting transformers (PSTs) on the grid side also plays an important role in improving the operational flexibility of the grid. By configuring a phase-shifting transformer, the initial and final phase angles of the line voltage, i.e., the power angle, can be changed to control and improve the distribution of active power between different grids, thereby controlling the active power flow of the grid, eliminating electromagnetic circulating currents in the ring network, and improving cross-sectional transmission. Related technologies have conducted extensive research on unit combination, transmission network structure optimization, and phase-shifting transformers. However, on the one hand, there is a lack of solutions to improve the consumption of renewable energy using phase-shifting transformers. On the other hand, existing technologies do not simultaneously consider the coordinated coordination of unit combination, transmission network structure optimization, and phase-shifting transformers in grid scheduling at different time scales, making it difficult to achieve efficient consumption of renewable energy and economic operation of the grid. Therefore, how to develop a two-stage transmission network optimization scheduling method that comprehensively considers unit combination, transmission network structure optimization, and phase-shifting transformer regulation has become an urgent problem to be solved. Summary of the Invention

[0005] To this end, the present invention provides a two-stage dispatching method and apparatus for a new energy power system that coordinates multiple resources. This method comprehensively considers unit combination, transmission network structure optimization, and phase-shifting transformer regulation. By comprehensively coordinating flexible resources on both the power source side and the network side, it ensures the reliable absorption of renewable energy. Furthermore, it effectively improves the renewable energy absorption capacity, thereby promoting the overall economic efficiency of power grid operation.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a two-stage scheduling method for a multi-resource coordinated new energy power system, comprising:

[0007] According to the uncertainty and intermittent characteristics of wind power, the optimization scheduling model is constructed;

[0008] Setting day-ahead scheduling constraints, optimizing the transmission network structure and determining the unit start-up and shutdown strategies based on the day-ahead forecast of the next day's load demand and renewable energy output using the stage-specific scheduling model;

[0009] Setting real-time scheduling stage constraints, optimizing the scheduling model through the stage, and performing real-time PST adjustment of the voltage power angle and the unit output according to the next day's load and renewable energy output scenarios;

[0010] The optimal scheduling model for the stage is solved by setting a solver to obtain the optimal scheduling strategy for the day-ahead scheduling stage and the optimal scheduling strategy for the real-time scheduling stage.

[0011] As a preferred solution for a two-stage scheduling method for a multi-resource coordinated new energy power system, the objective function of the stage optimization scheduling model is to minimize the sum of the total operating costs of the wind power system; the expression of the objective function is:

[0012]

[0013] Where C total is the total operating cost of the system; C bran is the cost of disconnecting the transmission line; C start is the unit startup cost; Ω S is the system operation scenario set, that is, the set of typical system operation scenarios formed by considering the uncertainty of renewable energy output and load; ρ s represents the probability of scene s; and They represent the system power generation cost, renewable energy abandonment penalty cost and load shedding cost coefficient under the forecast scenario respectively; and They represent the system power generation cost, renewable energy abandonment penalty cost and load shedding cost coefficient under scenario s respectively; a and b are the cost coefficients of the day-ahead scheduling and real-time scheduling stages respectively.

[0014] As an optimal solution for a two-stage scheduling method for a multi-resource coordinated new energy power system, the day-ahead scheduling stage constraints include: whether the line is allowed to be disconnected, the upper limit constraint of the disconnected line, the unit startup status constraint, the minimum startup time constraint, the minimum shutdown time constraint, the node power constraint, the branch flow constraint that simultaneously considers the branch disconnection status and the PST adjustment mode, the generator output constraint, the generator climbing constraint, the system's upward reserve constraint, the system's downward reserve constraint, the node phase angle constraint, the branch phase angle adjustment constraint with PST installed, the reference node phase angle constraint, the load shedding constraint, and the wind farm's active output reduction constraint.

[0015] As an optimal solution for the two-stage scheduling method of a multi-resource coordinated new energy power system, the real-time scheduling stage constraints include: node power constraints under each operating scenario, branch flow constraints that simultaneously consider the branch disconnection state and PST adjustment mode under each operating scenario, generator output constraints under each operating scenario, generator climbing constraints under each operating scenario, system upward reserve constraints under each operating scenario, system downward reserve constraints under each operating scenario, phase angle constraints of each node under each operating scenario, branch phase angle adjustment constraints with PST installed under each operating scenario, reference node phase angle constraints, node load shedding constraints under each operating scenario, and wind farm active output reduction constraints under each operating scenario.

[0016] As an optimal solution for a two-stage scheduling method of a multi-resource coordinated new energy power system, in the process of solving the stage optimization scheduling model by setting a solver, the set solver includes: Gurobi and CPLEX solvers.

[0017] The present invention further provides a two-stage dispatching device for a multi-resource coordinated new energy power system, based on the above two-stage dispatching method for a multi-resource coordinated new energy power system, comprising:

[0018] The stage optimization scheduling model construction module is used to build a stage optimization scheduling model based on the uncertainty and intermittent characteristics of wind power;

[0019] The day-ahead scheduling phase optimization module is used to set day-ahead scheduling phase constraints. Through the phase optimization scheduling model, the transmission network structure is optimized and the unit start-up and shutdown strategies are determined based on the day-ahead forecast of the next day's load demand and renewable energy output.

[0020] The real-time scheduling phase optimization module is used to set real-time scheduling phase constraints. Through the phase optimization scheduling model, the voltage power angle is adjusted in real time according to the load and renewable energy output scenarios of the next day, and the unit output is adjusted.

[0021] The stage optimization scheduling model solving module is used to solve the stage optimization scheduling model by setting a solver to obtain the optimal scheduling strategy for the day-ahead scheduling stage and the optimal scheduling strategy for the real-time scheduling stage.

[0022] As a preferred solution for a two-stage dispatching device for a multi-resource coordinated new energy power system, in the stage optimization dispatching model construction module, the objective function of the stage optimization dispatching model is to minimize the sum of the total operating costs of the wind power system; the expression of the objective function is:

[0023]

[0024] Where C total is the total operating cost of the system; C bran is the cost of disconnecting the transmission line; C start is the unit startup cost; Ω S is the system operation scenario set, that is, the set of typical system operation scenarios formed by considering the uncertainty of renewable energy output and load; ρ s represents the probability of scene s; and They represent the system power generation cost, renewable energy abandonment penalty cost and load shedding cost coefficient under the forecast scenario respectively; and They represent the system power generation cost, renewable energy abandonment penalty cost and load shedding cost coefficient under scenario s respectively; a and b are the cost coefficients of the day-ahead scheduling and real-time scheduling stages respectively.

[0025] As an optimal solution for a two-stage dispatching device of a multi-resource coordinated new energy power system, in the day-ahead dispatching stage optimization module, the day-ahead dispatching stage constraints include: whether the line is allowed to be disconnected, the upper limit constraint of the disconnected line, the unit startup status constraint, the minimum startup time constraint, the minimum shutdown time constraint, the node power constraint, the branch flow constraint that takes into account the branch disconnection status and PST adjustment mode, the generator output constraint, the generator climbing constraint, the system's upward reserve constraint, the system's downward reserve constraint, the node phase angle constraint, the branch phase angle adjustment constraint with PST installed, the reference node phase angle constraint, the load shedding constraint and the wind farm's active output reduction constraint.

[0026] As an optimal solution for a two-stage dispatching device of a multi-resource coordinated new energy power system, in the real-time dispatching stage optimization module, the real-time dispatching stage constraints include: node power constraints under each operating scenario, branch flow constraints that simultaneously consider the branch disconnection state and PST adjustment mode under each operating scenario, generator output constraints under each operating scenario, generator climbing constraints under each operating scenario, system upward reserve constraints under each operating scenario, system downward reserve constraints under each operating scenario, phase angle constraints of each node under each operating scenario, branch phase angle adjustment constraints with PST installed under each operating scenario, reference node phase angle constraints, node load shedding constraints under each operating scenario, and wind farm active output reduction constraints under each operating scenario.

[0027] As an optimal solution for a two-stage scheduling device of a multi-resource coordinated new energy power system, in the stage optimization scheduling model solving module, in the process of solving the stage optimization scheduling model by setting a solver, the set solver includes: Gurobi, CPLEX solver.

[0028] The present invention has the following advantages: It constructs a phased optimization scheduling model based on the uncertainty and intermittent characteristics of wind power; sets day-ahead scheduling constraints, using this phased optimization scheduling model to optimize the transmission network structure and determine unit startup and shutdown strategies based on day-ahead forecasts of next-day load demand and renewable energy output; sets real-time scheduling constraints, using this phased optimization scheduling model to perform real-time PST adjustments of the voltage angle and adjust unit output based on next-day load and renewable energy output scenarios; and solves the phased optimization scheduling model by setting a solver to obtain the optimal scheduling strategies for both the day-ahead scheduling phase and the real-time scheduling phase. The present invention proposes a two-phase transmission network optimization scheduling method that comprehensively considers unit combination, transmission network structure optimization, and phase-shifting transformer regulation. By comprehensively coordinating flexible resources on both the power source and network sides, it ensures the reliable absorption of renewable energy. In the day-ahead scheduling phase, the transmission network structure optimization and unit startup and shutdown strategies are determined based on load demand and renewable energy output forecasts; in the real-time scheduling phase, real-time PST adjustments and unit output adjustments are performed based on possible load and renewable energy output scenarios. Through the above two-stage scheduling method, the renewable energy absorption capacity can be effectively improved, thereby promoting the overall economy of power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can, without inventive effort, derive other implementation drawings based on the provided drawings.

[0030] The structures, proportions, sizes, etc. illustrated in this specification are intended solely to complement the contents disclosed herein and to facilitate understanding and reading by persons skilled in the art. They are not intended to limit the conditions under which the present invention may be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportions, or adjustments in sizes, without affecting the efficacy and objectives of the present invention, shall remain within the scope of the technical contents disclosed herein.

[0031] Figure 1 This is a flow chart of a two-stage scheduling method for a multi-resource coordinated new energy power system provided in Example 1 of the present invention;

[0032] Figure 2 This is a schematic diagram of a framework of a two-stage scheduling method for a multi-resource coordinated new energy power system provided in Example 1 of the present invention;

[0033] Figure 3This is a wiring diagram of an IEEE RTS-24 system in a possible embodiment provided in Example 1 of the present invention;

[0034] Figure 4 This is a schematic diagram of system load requirements in a possible embodiment provided in Example 1 of the present invention;

[0035] Figure 5 This is a schematic diagram of wind farm output at node 17 in a possible embodiment provided in Example 1 of the present invention;

[0036] Figure 6 This is a schematic diagram of the unit operation status of node 18 in a possible embodiment provided in Example 1 of the present invention;

[0037] Figure 7 This is a schematic diagram of the unit output result of node 18 in a possible embodiment provided in Example 1 of the present invention;

[0038] Figure 8 This is a schematic diagram of the PST phase angle adjustment result of line L15-16 in a possible embodiment provided in Example 1 of the present invention;

[0039] Figure 9 This is a schematic diagram of the architecture of a two-stage scheduling device for a multi-resource coordinated new energy power system provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0040] The following describes the implementation of the present invention using specific embodiments. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. Obviously, the embodiments described are only a portion of the present invention, not all of it. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0041] Example 1

[0042] See also Figure 1 and Figure 2 Embodiment 1 of the present invention provides a two-stage scheduling method for a multi-resource coordinated new energy power system, comprising the following steps:

[0043] S1. Based on the uncertainty and intermittent characteristics of wind power, a phased optimization scheduling model is constructed;

[0044] S2. Set day-ahead scheduling constraints, optimize the transmission network structure and determine the unit start-up and shutdown strategies based on the day-ahead forecast of the next day's load demand and renewable energy output using the stage-by-day scheduling model.

[0045] S3. Set real-time scheduling stage constraints, optimize the scheduling model through the stage, and perform real-time PST adjustment of the voltage power angle and the unit output according to the next day's load and renewable energy output scenario;

[0046] S4. Solve the stage optimization scheduling model by setting a solver to obtain the optimal scheduling strategy for the day-ahead scheduling stage and the optimal scheduling strategy for the real-time scheduling stage.

[0047] In this embodiment, in step S1, a phase optimization scheduling model is constructed based on the uncertainty and intermittent characteristics of wind power;

[0048] Specifically, frequent opening and closing of transmission lines is not recommended during actual grid operation. Therefore, considering that the network topology remains unchanged within a single day, the next day's network topology and hourly unit dispatch plans are determined based on a day-ahead forecast of renewable energy generation and system load for the next 24 hours. A phased optimization dispatch model is constructed based on the uncertainty and intermittent nature of wind power.

[0049] The objective function of the stage optimization scheduling model is to minimize the total operating cost of the wind power system; the expression of the objective function is:

[0050]

[0051] Where C total is the total operating cost of the system; C bran is the cost of disconnecting the transmission line; C start is the unit startup cost; Ω S is the system operation scenario set, that is, the set of typical system operation scenarios formed by considering the uncertainty of renewable energy output and load; ρ s represents the probability of scene s; and They represent the system power generation cost, renewable energy abandonment penalty cost and load shedding cost coefficient under the forecast scenario respectively; and They represent the system power generation cost, renewable energy abandonment penalty cost and load shedding cost coefficient under scenario s respectively; a and b are the cost coefficients of the day-ahead scheduling and real-time scheduling stages respectively.

[0052] Specifically, the total system operating cost is the weighted sum of line disconnection cost, unit startup cost, power generation cost, renewable energy abandonment cost, and load shedding cost.

[0053] The line disconnection cost is shown in formula (2):

[0054]

[0055] Where cbran is the line disconnection cost coefficient; α l is a 0-1 variable, indicating the state of branch l, 1 indicates branch l is closed, 0 indicates branch l is open; Ω B is the collection of all branches in the system.

[0056] The unit startup cost is shown in formula (3):

[0057]

[0058] Where, represents the startup cost of unit k; k,t is a 0-1 variable, indicating whether unit k is started in time period t; Ω T Represents the unit commitment optimization period; Ω G Represents a collection of traditional generator sets.

[0059] The total power generation cost of the prediction scenario and the operation scenario s, including the power generation cost and startup cost representing the fuel consumption of the unit, are shown in Equations (4) and (5), respectively:

[0060]

[0061]

[0062] Where, and They represent the quadratic coefficient, linear coefficient and constant coefficient of the power generation cost function of unit k respectively; and represents the active power output of generator k in period t in the prediction scenario and scenario s; γ k,t It is a 0-1 variable, indicating the operating status of unit k in time period t under scenario s. If it is running, it is 1, otherwise it is 0.

[0063] The cost of abandoning renewable energy and the cost of load shedding in the prediction scenario s and scenario s are shown in equations (6)-(9) respectively:

[0064]

[0065]

[0066]

[0067]

[0068] Where, Ω W represents the wind farm set; Ω L represents the load set; c wind and c loaddenote the penalty cost coefficients for abandoning renewable energy and shedding load respectively; and represents the wind power curtailment of wind farm k in period t in the prediction scenario and scenario s; and It represents the active load shedding amount of load i in period t in the prediction scenario and scenario s.

[0069] In this embodiment, in step S2, the day-ahead scheduling phase constraints are set, and the transmission network structure is optimized and the unit start-stop strategy is determined based on the day-ahead predicted values ​​of the next day's load demand and renewable energy output through the phase optimization scheduling model.

[0070] Among them, the constraints in the day-ahead scheduling stage include: whether the line is allowed to be disconnected, the upper limit constraint of the disconnected line, the unit startup status constraint, the minimum startup time constraint, the minimum shutdown time constraint, the node power constraint, the branch power flow constraint that takes into account the branch disconnection status and PST adjustment mode, the generator output constraint, the generator climbing constraint, the system's upward reserve constraint, the system's downward reserve constraint, the node phase angle constraint, the branch phase angle adjustment constraint with PST installed, the reference node phase angle constraint, the load shedding constraint, and the wind farm's active output reduction constraint.

[0071] Specifically, in practice, considering the safety and stability requirements of system operation, only certain specific lines are allowed to be disconnected, and there is an upper limit on the number of disconnected lines. The constraint on whether a line is allowed to be disconnected is shown in formula (10):

[0072]

[0073] Where, α l is a 0-1 variable, indicating the open / closed state of line l. If the line is closed, it is 1, otherwise it is 0; l Ω is a 0-1 variable indicating whether line l is allowed to be disconnected. If it is allowed, it is 1, otherwise it is 0; B A collection of lines.

[0074] The upper limit constraint of disconnecting line is shown in formula (11):

[0075]

[0076] Where N open Indicates the upper limit of the number of lines allowed to be disconnected.

[0077] The unit startup state constraint is shown in formula (12):

[0078]

[0079] According to the unit startup state constraint as shown in formula (12), the values ​​of each variable are shown in Table 1.

[0080] <![CDATA[γ k,t-1 ]]> <![CDATA[γ k,t ]]> <![CDATA[χ k,t ]]> 0 0 0 0 1 1 1 0 0 1 1 0

[0081] Table 1 Unit startup status constraints Variable values ​​such as formula (12)

[0082] Among them, only when γ k,t-1 = 0 and γ k,t =1, χ k,t =1, otherwise χ k,t are all 0. That is, if the kth unit is started in the tth period, then k,t =1; otherwise, χ k,t =0.

[0083] The minimum startup time constraint and the minimum shutdown time constraint are shown in equations (13) and (14) respectively:

[0084]

[0085]

[0086] Where, and They represent the minimum continuous operating time and minimum continuous downtime of unit k respectively.

[0087] The node power constraint is shown in formula (15):

[0088]

[0089] Where, Ω g (i) represents the set of generators connected to node i; Ω w (i) represents the set of wind farms connected to node i; Ω p (i) represents the set of bus lines of node i; Ω c (i) represents the sub-circuit set of i; is the predicted active power output of wind farm k in time period t; represents the active power flow of branch l in time period t; represents the active load demand of node i in time period t; It represents the planned active power output of generator k in time period t.

[0090] The branch power flow constraint considering both the branch disconnection state and the PST regulation mode is shown in Equation (16):

[0091]

[0092] Where, node i and node j are the starting node and ending node of branch l respectively; M is a maximum positive number; r l and x lRespectively represent the resistance and reactance of branch l; θ pre,i,t represents the phase angle of node i in time period t; θ pre,j,t represents the phase angle of node j in time period t; Indicates whether branch l is configured with PST, if yes, it is 1, otherwise it is 0; Indicates the adjustment phase angle of PST.

[0093] Generator output constraint, that is, the active output level of the generator needs to be within its allowable limit range, as shown in formula (17):

[0094]

[0095] Where, and They represent the maximum and minimum allowable active output of generator k respectively.

[0096] The generator climbing constraint is shown in formula (18):

[0097]

[0098] Where, Indicates the maximum downward climbing power allowed for unit k; is the maximum upward climbing power allowed for unit k.

[0099] The system's upward reserve constraint and the system's downward reserve constraint are shown in Equation (19) and Equation (20) respectively:

[0100]

[0101]

[0102] Where, It represents the system's reserve capacity in period t; Indicates the system's reserve capacity during period t.

[0103] The phase angle constraint of each node is shown in formula (21):

[0104] -θ max ≤θ pre,i,t ≤θ max ,i∈Ω D ,t∈Ω T (twenty one)

[0105] Where θ max Indicates the maximum phase angle allowed for the node.

[0106] The phase angle adjustment constraint of the branch with PST installed is shown in formula (22):

[0107]

[0108] Where, It represents the maximum adjustment phase angle of the PST located in branch l.

[0109] The reference node phase angle constraint means that the voltage phase angle of the balance node in the system is 0, as shown in Equation (23):

[0110] θ pre,i,t =0,t∈Ω T ,i is the balance node (23)

[0111] The load shedding constraint is shown in formula (24):

[0112]

[0113] The active output reduction constraint of the wind farm is shown in formula (25):

[0114]

[0115] In this embodiment, in step S3, real-time scheduling stage constraints are set, and through the stage optimization scheduling model, the voltage power angle is adjusted in real time PST and the unit output is adjusted according to the next day's load and renewable energy output scenarios;

[0116] Among them, the real-time scheduling stage constraints include: node power constraints under each operating scenario, branch flow constraints that simultaneously consider the branch disconnection state and PST adjustment mode under each operating scenario, generator output constraints under each operating scenario, generator climbing constraints under each operating scenario, system upward reserve constraints under each operating scenario, system downward reserve constraints under each operating scenario, phase angle constraints of each node under each operating scenario, branch phase angle adjustment constraints with PST installed under each operating scenario, reference node phase angle constraints, node load shedding constraints under each operating scenario, and wind farm active output reduction constraints under each operating scenario.

[0117] Specifically,

[0118] The node power constraints under each operation scenario are shown in formula (26):

[0119]

[0120] Where, represents the actual active power output of generator k in time period t; represents the actual active power output of wind farm k in time period t in scenario s; represents the active power flow of branch l in time period t in scenario s; represents the active load of node i in time period t in scenario s.

[0121] The branch power flow constraints considering both the branch disconnection state and the PST regulation mode in each operation scenario are shown in Equation (27):

[0122]

[0123] Where u s,i,t represents the square of the voltage of node i in time period t in scenario s; h s,l,t represents the square of the current of branch l in time period t in scenario s; θ s,i,t represents the phase angle of node i in time period t in scenario s; represents the adjustment phase angle of PST in scene s.

[0124] The generator output constraints under each operating scenario are shown in formula (28):

[0125]

[0126] The generator ramp constraints under each operating scenario are shown in formula (29):

[0127]

[0128] The system reserve increase constraint and reserve decrease constraint under each operation scenario are shown in equations (30) and (31) respectively:

[0129]

[0130]

[0131] Where, represents the system's increased reserve in period t in scenario s; It represents the system's reserve capacity in the tth period in scenario s.

[0132] The phase angle constraints of each node in each operating scenario are shown in formula (32):

[0133] -θ max ≤θ s,i,t ≤θ max ,i∈Ω D ,t∈Ω T ,s∈Ω S (32)

[0134] The phase angle adjustment constraints of the branches with PST installed under various operating scenarios are shown in formula (33):

[0135]

[0136] The reference node phase angle constraint is shown in formula (34):

[0137] θ s,i,t=0,t∈Ω T ,i is the balance node (34)

[0138] The node load shedding constraints under each operation scenario are shown in formula (35):

[0139]

[0140] The wind farm active output reduction constraints under each operation scenario are shown in formula (36):

[0141]

[0142] In this embodiment, in step S4, a solver is set to solve the stage optimization scheduling model to obtain the optimal scheduling strategy for the day-ahead scheduling stage and the optimal scheduling strategy for the real-time scheduling stage.

[0143] In a possible embodiment, an example of optimizing the IEEE RTS-24 system scheduling strategy is provided as follows:

[0144] The effectiveness of the present invention was verified by the IEEE RTS-24 system. The computer configuration used was Intel Core i5-10400 (2.90 GHz) with 16 GB RAM, and the model was established and solved using MATLAB and GUROBI, respectively.

[0145] The IEEE RTS-24 system wiring is shown in Figure 3. The system consists of 24 nodes and 33 lines. Five transformers are located on lines L3-24, L9-11, L9-12, L10-11, and L10-12, dividing the system into a 138kV low-voltage area and a 230kV high-voltage area. Wind farms are located at nodes 17 and 22, with capacities of 800MW and 1000MW, respectively. Each wind power output scenario is proportional to its capacity. The line disconnection cost coefficient, the renewable energy penalty cost coefficient, and the load shedding cost coefficient are 1×10 3 , 1×10 3 and 1×10 6 . OTS lines and PST configuration locations are as follows Figure 3 As shown. The maximum number of lines allowed to be disconnected is 3. Take 10°. The system load demand under each scenario and the wind farm output at node 17 are as follows: Figure 4 and Figure 5 shown.

[0146] Based on the model proposed in this invention, optimized scheduling is performed. During the day-ahead scheduling phase, both branches of line L20-23 are disconnected. Analysis shows that lines L13-23 and L12-23 are fully loaded or nearly fully loaded in most scenarios and time periods. Therefore, the two branches of line L20-23 are disconnected to prevent the wind farm and other related units from delivering more power to node 23, which would then flow through lines L13-23 and L12-23 into the low-voltage area and cause line overload.

[0147] In the day-ahead scheduling phase, each unit formulates a schedule based on the forecast scenario. Taking the unit at node 18 as an example, its operating status change curve is as follows: Figure 6 Combining the system load demand and wind power output curves, we can see that in the initial period 1-7, the system load demand level is low and the wind power output level is high. Therefore, the unit at node 18 is shut down to ensure the maximum wind power consumption. As the system load demand level increases and the wind power output level decreases, the unit is started to meet the system load demand.

[0148] Taking the unit at node 18 as an example, the unit output results in the day-ahead scheduling and real-time scheduling stages are as follows: Figure 7 As shown in the figure, during the 0-7 and 24 hour periods, the wind turbines are shut down, so their output is 0. The wind turbine output is adjusted differently for both the forecasted and real-time scheduling scenarios during the day-ahead scheduling phase, thereby enhancing power supply flexibility. For example, in scenario 1, during the 18-20 hour period, wind power output is low. At this time, the wind turbine at node 18 is increased to a higher level, effectively ensuring load demand.

[0149] Taking the PST phase angle adjustment result of line L15-16 as an example, its day-ahead scheduling and real-time scheduling results are as follows: Figure 8 As shown in the figure. Overall, in each scenario, during periods of high wind power output (such as periods 1-7), adjusting the PST phase angle (negative value) increases the phase angle difference between nodes 16 and 15, allowing more power to flow from node 16 to node 15, thereby ensuring that more wind power can be absorbed by loads in other areas. During periods of low wind power output (such as periods 10-15), the PST phase angle is adjusted to a lesser degree. It can be seen that the PST phase angle is adjusted differently for different scenarios, both in the forecasting and real-time scheduling phases, thereby enhancing grid-side flexibility.

[0150] To verify the effectiveness of the proposed method, we compared it with an optimization scheduling method that only considers unit commitment and OTS. The OTS optimization results and unit operating status results for the day-ahead scheduling phase were obtained using the optimization scheduling method that only considers unit commitment and OTS. These results were then applied to the proposed model to verify the effectiveness of the coordinated consideration of OST, unit commitment, and PST phase angle adjustment.

[0151] Let the cost result obtained by the method proposed in the present invention be RA, and the cost result obtained by only considering the unit combination and OTS method be RB, and the improvement benefit of the method of the present invention is calculated by the following formula.

[0152]

[0153] After calculation, R I =-5.89%, indicating that compared with the traditional method, the method proposed in the present invention can effectively improve the overall economy of power grid operation through the coordination among unit combination, OTS, and PST phase angle adjustment.

[0154] In summary, the present invention constructs a phased optimization scheduling model based on the uncertainty and intermittent characteristics of wind power. It sets constraints for the day-ahead scheduling phase. Using this phased optimization scheduling model, the transmission network structure is optimized and unit startup and shutdown strategies are determined based on the day-ahead forecasts of next-day load demand and renewable energy output. It also sets constraints for the real-time scheduling phase. Using this phased optimization scheduling model, the voltage angle is adjusted in real time, PST, and unit output is adjusted based on the next-day load and renewable energy output scenarios. A solver is then set to solve the phased optimization scheduling model to obtain the optimal scheduling strategies for both the day-ahead scheduling phase and the real-time scheduling phase. The present invention proposes a two-phase transmission network optimization scheduling method that comprehensively considers unit combination, transmission network structure optimization, and phase-shifting transformer regulation. This method ensures the reliable absorption of renewable energy by comprehensively coordinating flexible resources on both the power source and network sides. In the day-ahead scheduling phase, the transmission network structure optimization and unit startup and shutdown strategies are determined based on the forecasts of load demand and renewable energy output. In the real-time scheduling phase, real-time PST adjustments and unit output adjustments are performed based on possible scenarios of load and renewable energy output. Through the above two-stage scheduling method, the renewable energy absorption capacity can be effectively improved, thereby promoting the overall economy of power grid operation.

[0155] It should be noted that the method of the embodiments of the present disclosure can be performed by a single device, such as a computer or server. The method of the embodiments of the present disclosure can also be applied in a distributed scenario, where multiple devices cooperate to perform the method. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiments of the present disclosure, and the multiple devices will interact with each other to complete the method.

[0156] It should be noted that the above description is limited to some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0157] Example 2

[0158] See also Figure 9 Embodiment 2 of the present invention further provides a two-stage dispatching device for a multi-resource coordinated new energy power system, comprising:

[0159] Phase optimization scheduling model construction module 001 is used to construct a phase optimization scheduling model based on the uncertainty and intermittent characteristics of wind power;

[0160] The day-ahead scheduling phase optimization module 002 is used to set day-ahead scheduling phase constraints, optimize the transmission network structure and determine the unit start-up and shutdown strategies based on the day-ahead forecast of the next day's load demand and renewable energy output through the phase optimization scheduling model;

[0161] The real-time scheduling phase optimization module 003 is used to set real-time scheduling phase constraints and, through the phase optimization scheduling model, perform real-time PST adjustment of the voltage power angle and adjust the unit output according to the next day's load and renewable energy output scenarios;

[0162] The stage optimization scheduling model solving module 004 is used to solve the stage optimization scheduling model by setting a solver to obtain the optimal scheduling strategy for the day-ahead scheduling stage and the optimal scheduling strategy for the real-time scheduling stage.

[0163] In this embodiment, in the stage optimization scheduling model construction module 001, the objective function of the stage optimization scheduling model is to minimize the sum of the total operating costs of the wind power system; the expression of the objective function is:

[0164]

[0165] Where Ctotal is the total operating cost of the system; C bran is the cost of disconnecting the transmission line; C start is the unit startup cost; Ω S is the system operation scenario set, that is, the set of typical system operation scenarios formed by considering the uncertainty of renewable energy output and load; ρ s represents the probability of scene s; and They represent the system power generation cost, renewable energy abandonment penalty cost and load shedding cost coefficient under the forecast scenario respectively; and They represent the system power generation cost, renewable energy abandonment penalty cost and load shedding cost coefficient under scenario s respectively; a and b are the cost coefficients of the day-ahead scheduling and real-time scheduling stages respectively.

[0166] In this embodiment, in the day-ahead scheduling phase optimization module 002, the day-ahead scheduling phase constraints include: whether the line is allowed to be disconnected, the upper limit constraint of the disconnected line, the unit startup status constraint, the minimum startup time constraint, the minimum shutdown time constraint, the node power constraint, the branch power flow constraint that considers the branch disconnection status and the PST adjustment mode at the same time, the generator output constraint, the generator climbing constraint, the system's upward reserve constraint, the system's downward reserve constraint, the node phase angle constraint, the branch phase angle adjustment constraint where PST is installed, the reference node phase angle constraint, the load shedding constraint, and the wind farm's active output reduction constraint.

[0167] In this embodiment, in the real-time scheduling stage optimization module 003, the real-time scheduling stage constraints include: node power constraints under each operating scenario, branch flow constraints that simultaneously consider the branch disconnection state and PST adjustment mode under each operating scenario, generator output constraints under each operating scenario, generator climbing constraints under each operating scenario, system upward reserve constraints under each operating scenario, system downward reserve constraints under each operating scenario, phase angle constraints of each node under each operating scenario, branch phase angle adjustment constraints with PST installed under each operating scenario, reference node phase angle constraints, node load shedding constraints under each operating scenario, and wind farm active output reduction constraints under each operating scenario.

[0168] In this embodiment, in the stage optimization scheduling model solving module 004, in the process of solving the stage optimization scheduling model by setting a solver, the set solver includes: Gurobi and CPLEX solvers.

[0169] It should be noted that the information interaction, execution process, etc. between the modules of the above-mentioned system are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown above in the present application, and no further details will be given here.

[0170] Example 3

[0171] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code for a two-stage scheduling method for a new energy power system with multi-resource coordination is stored. The program code includes instructions for executing embodiment 1 or any possible implementation thereof.

[0172] Computer-readable storage media can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0173] Example 4

[0174] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0175] The processor and the memory communicate with each other through a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute a two-stage scheduling method for a multi-resource coordinated new energy power system in accordance with Example 1 or any possible implementation thereof.

[0176] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor, which is implemented by reading software code stored in a memory. The memory can be integrated into the processor or located outside the processor and exist independently.

[0177] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode.

[0178] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing system. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Alternatively, they can be implemented using program code executable by a computing system, and thus, they can be stored in a storage system and executed by the computing system. In some cases, the steps shown or described herein can be performed in a different order than that shown, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0179] Although the present invention has been described in detail above using general descriptions and specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made thereto. Therefore, such modifications and improvements, without departing from the spirit of the present invention, are intended to be within the scope of protection claimed herein.

Claims

1. A two-stage dispatching method for a multi-resource coordinated new energy power system, characterized in that: include: According to the uncertainty and intermittent characteristics of wind power, an optimization dispatch model is constructed in the phase; Set day-ahead dispatch stage constraints, optimize the transmission network structure and determine the start-stop strategy of the unit based on the day-ahead predicted values ​​of the load demand and renewable energy output for the next day through the stage optimization dispatch model; Set real-time dispatch stage constraints, optimize the dispatch model in the stage, and perform real-time PST regulation of the voltage power angle and adjust the unit output according to the scenario of the next day's load and renewable energy output; The solver is set to solve the stage optimization scheduling model to obtain the optimal scheduling strategy for the day-ahead scheduling stage and the optimal scheduling strategy for the real-time scheduling stage.

2. A two-stage dispatching method for a multi-resource coordinated new energy power system according to claim 1, characterized in that: The objective function of the stage optimization scheduling model is to minimize the sum of the total operating costs of the wind power system; the expression of the objective function is: In the formula, C total is the total operating cost of the system; C bran is the cost of disconnecting the transmission line; C start is the unit startup cost; Ω S is the system operation scenario set, that is, the set of typical system operation scenarios formed by considering the uncertainty of renewable energy output and load; ρ s represents the probability of scene s; and They represent the system power generation cost, penalty cost for abandoning renewable energy and load shedding cost coefficient in the forecast scenario respectively; and They represent the system power generation cost, renewable energy abandonment penalty cost and load shedding cost coefficient under scenario s respectively; a and b are the cost coefficients of the day-ahead scheduling and real-time scheduling stages respectively.

3. A two-stage dispatching method for a multi-resource coordinated new energy power system according to claim 2, characterized in that: The day-ahead dispatch stage constraints include: whether the line is allowed to be disconnected, the upper limit of the disconnected line, the unit startup state constraint, the minimum startup time constraint, the minimum shutdown time constraint, the node power constraint, the branch flow constraint that considers the branch disconnection state and the PST adjustment method, the generator output constraint, the generator climbing constraint, the system's upward reserve constraint, the system's downward reserve constraint, the node phase angle constraint, the branch phase angle adjustment constraint with PST installed, the reference node phase angle constraint, the load shedding constraint and the wind farm's active output reduction constraint.

4. A two-stage dispatching method for a multi-resource coordinated new energy power system according to claim 3, characterized in that: The real-time scheduling stage constraints include: node power constraints under various operating scenarios, branch flow constraints that simultaneously consider branch disconnection states and PST adjustment methods under various operating scenarios, generator output constraints under various operating scenarios, generator ramp constraints under various operating scenarios, system upward reserve constraints under various operating scenarios, system downward reserve constraints under various operating scenarios, phase angle constraints of each node under various operating scenarios, branch phase angle adjustment constraints with PST installed under various operating scenarios, reference node phase angle constraints, node load shedding constraints under various operating scenarios, and wind farm active output reduction constraints under various operating scenarios.

5. A two-stage dispatching method for a multi-resource coordinated new energy power system according to claim 4, characterized in that: In the process of solving the stage optimization scheduling model by setting a solver, the set solver includes: Gurobi and CPLEX solvers.

6. A two-stage dispatching device for a new energy power system with multi-resource coordination, adopting a two-stage dispatching method for a new energy power system with multi-resource coordination as described in any one of claims 1 to 5, characterized in that: include: The phase optimization dispatch model construction module is used to construct the phase optimization dispatch model according to the uncertainty and intermittent characteristics of wind power; The day-ahead dispatching stage optimization module is used to set the day-ahead dispatching stage constraints, and optimize the transmission network structure and determine the unit start-stop strategy based on the day-ahead forecast of the next day's load demand and renewable energy output through the stage optimization dispatching model; The real-time scheduling phase optimization module is used to set the real-time scheduling phase constraints, and through the phase optimization scheduling model, the voltage power angle is adjusted in real time PST and the unit output is adjusted according to the scenario of the next day's load and renewable energy output; The stage optimization scheduling model solving module is used to solve the stage optimization scheduling model by setting a solver to obtain the optimal scheduling strategy for the day-ahead scheduling stage and the optimal scheduling strategy for the real-time scheduling stage.

7. A two-stage dispatching device for a new energy power system with multi-resource coordination according to claim 6, characterized in that: In the stage optimization scheduling model construction module, the objective function of the stage optimization scheduling model is to minimize the sum of the total operating costs of the wind power system; the expression of the objective function is: In the formula, C total is the total operating cost of the system; C bran is the cost of disconnecting the transmission line; C start is the unit startup cost; Ω S is the system operation scenario set, that is, the set of typical system operation scenarios formed by considering the uncertainty of renewable energy output and load; ρ s represents the probability of scene s; and They represent the system power generation cost, penalty cost for abandoning renewable energy and load shedding cost coefficient in the forecast scenario respectively; and They represent the system power generation cost, renewable energy abandonment penalty cost and load shedding cost coefficient under scenario s respectively; a and b are the cost coefficients of the day-ahead scheduling and real-time scheduling stages respectively.

8. A two-stage dispatching device for a multi-resource coordinated new energy power system according to claim 7, characterized in that: In the day-ahead dispatching stage optimization module, the day-ahead dispatching stage constraints include: whether the line is allowed to be disconnected, the upper limit constraint of the disconnected line, the unit startup state constraint, the minimum startup time constraint, the minimum shutdown time constraint, the node power constraint, the branch flow constraint that considers the branch disconnection state and the PST adjustment method at the same time, the generator output constraint, the generator climbing constraint, the system's upward reserve constraint, the system's downward reserve constraint, the node phase angle constraint, the branch phase angle adjustment constraint with PST installed, the reference node phase angle constraint, the load shedding constraint and the active output reduction constraint of the wind farm.

9. A two-stage dispatching device for a multi-resource coordinated new energy power system according to claim 8, characterized in that: In the real-time scheduling stage optimization module, the real-time scheduling stage constraints include: node power constraints under each operating scenario, branch flow constraints that simultaneously consider branch disconnection status and PST adjustment mode under each operating scenario, generator output constraints under each operating scenario, generator climbing constraints under each operating scenario, system upward reserve constraints under each operating scenario, system downward reserve constraints under each operating scenario, phase angle constraints of each node under each operating scenario, branch phase angle adjustment constraints with PST installed under each operating scenario, reference node phase angle constraints, node load shedding constraints under each operating scenario, and wind farm active output reduction constraints under each operating scenario.

10. A two-stage dispatching device for a new energy power system with multi-resource coordination according to claim 9, characterized in that: In the stage optimization scheduling model solving module, in the process of solving the stage optimization scheduling model by setting a solver, the set solver includes: Gurobi, CPLEX solver.

Images