A multi-resource coordinated new energy power system two-stage scheduling method and device

Abstract

A kind of new energy power system two-stage scheduling method and device of multi-resource cooperation, the method constructs stage optimization scheduling model according to the uncertainty and intermittence characteristics of wind power;Set day-ahead scheduling stage constraint, through stage optimization scheduling model, according to the predicted value of next day load demand and renewable energy output, optimize power transmission network structure and determine unit start-stop strategy;Set real-time scheduling stage constraint, through stage optimization scheduling model, according to the scenario of next day load and renewable energy output, carry out real-time PST regulation to voltage power angle and adjust unit output;Through setting solver, stage optimization scheduling model is solved, and the optimal scheduling strategy of day-ahead scheduling stage and the optimal scheduling strategy of real-time scheduling stage are obtained.The present application can effectively improve renewable energy consumption capacity, so as to promote the overall economy of power grid operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind power system scheduling, and particularly relates to a multi-resource coordinated new energy power system two-stage scheduling method and device. BACKGROUND

[0002] Developing renewable energy such as wind power has become an important means for the world to deal with energy resource shortage, environmental degradation, and climate warming. In this context, the proportion of renewable energy such as wind power connected to the grid is rising. Compared with traditional power sources such as thermal power and hydropower, renewable energy generation has the characteristics of strong intermittency, randomness, and uncertainty, and wind-solar renewable energy generation shows intraday and seasonal fluctuation characteristics, which do not match the load demand. These factors make the power flow distribution characteristics of the power grid more complex, the operation mode more variable, and pose a serious challenge to the safety of the power grid, restricting the large-scale development and grid connection of new energy. Therefore, it is of great significance to improve the flexibility of the power grid, ensure the safe operation of the system, achieve economic dispatching, and improve the consumption of renewable energy by optimizing control based on the existing grid structure.

[0003] On the power supply side, Unit Commitment (UC) takes the start-stop state and output of each generator in the system as control variables, and under the constraints of meeting system load and reserve requirements, line power flow limits, unit ramping rate, minimum start-stop time, and other constraints, it improves the consumption of renewable energy and effectively improves the flexibility of the power grid. On the grid side, how to fully tap the flexibility of the grid side and improve the consumption of new energy has attracted more and more attention. Transmission Topology Optimization (TTO) is an important means to improve the flexibility of the power system. By optimizing the transmission network structure, the transmission equipment including transmission lines can adjust the operating state during the operation of the power grid, dynamically select the optimal network topology structure, and improve the flexibility, economy, and safety of system operation. The transmission network structure optimization model introduces 0-1 integer variables to represent the operating state of the transmission equipment, and the transmission network structure optimization model considering only the change of line operating state is called Optimal Transmission Switching (OTS) model.

[0004] At present, in addition to the optimization of the power transmission network structure, at the power grid side, flexible AC transmission equipment such as a phase shifting transformer (PST) has important value for improving the operation flexibility of the power grid. By configuring the phase shifting transformer, the initial and final phase angles of the line voltage, that is, the power angle, can be changed to control and improve the distribution of active power between different power grids, thereby playing a role in controlling the active power flow of the power grid, eliminating the electromagnetic circulation of the loop network, and improving the transmission of the section. Related technologies have carried out a lot of related research on unit commitment, power transmission network structure optimization, and phase shifting transformer. However, on the one hand, there is a lack of a scheme for improving renewable energy consumption by using a phase shifting transformer; on the other hand, the existing technology does not simultaneously consider the coordination of unit commitment, power transmission network structure optimization, and phase shifting transformer in power grid dispatching at different time scales, making it difficult to achieve efficient consumption of renewable energy and economic operation of the power grid. Therefore, how to invent a two-stage optimal dispatching method for the power transmission network that comprehensively considers unit commitment, power transmission network structure optimization, and phase shifting transformer regulation has become a problem to be solved. SUMMARY

[0005] To this end, the present application provides a multi-resource coordinated new energy power system two-stage dispatching method and device, which can comprehensively consider unit commitment, power transmission network structure optimization, and phase shifting transformer regulation, and ensure reliable consumption of renewable energy by comprehensively coordinating flexible resources on the power supply side and the network side. At the same time, it can effectively improve the renewable energy consumption capacity, thereby promoting the overall economy of the power grid operation.

[0006] In order to achieve the above purpose, the present application provides the following technical scheme: a multi-resource coordinated new energy power system two-stage dispatching method, comprising:

[0007] According to the uncertainty and intermittency characteristics of wind power, a stage optimization dispatching model is constructed;

[0008] Setting day-ahead dispatching stage constraints, optimizing the power transmission network structure and determining the unit start-stop strategy according to the predicted values of the next day's load demand and renewable energy output through the stage optimization dispatching model;

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

[0010] Solving the stage optimization dispatching model through a solver to obtain the optimal dispatching strategy of the day-ahead dispatching stage and the optimal dispatching strategy of the real-time dispatching stage.

[0011] As a preferred scheme of the multi-resource coordinated new energy power system two-stage scheduling method, the objective function of the stage optimization scheduling model is the sum of the total operation cost of the wind power system being minimum; the expression of the objective function is:

[0012]

[0013] In the formula, C total is the total operation cost of the system; C bran is the transmission line opening cost; C start is the unit start-up cost; Ω S is the system operation scenario set, that is, the system typical operation scenario set formed by considering the uncertainty of the renewable energy output and the load; ρ s represents the probability of the scenario s; and respectively represent the system generation cost, the renewable energy abandonment penalty cost and the load shedding cost coefficient under the predicted scenario; and respectively represent the system generation cost, the renewable energy abandonment penalty cost and the load shedding cost coefficient under the scenario s; a and b are the cost coefficients of the day-ahead scheduling and the real-time scheduling respectively.

[0014] As a preferred scheme of the multi-resource coordinated new energy power system two-stage scheduling method, the day-ahead scheduling stage constraints include: line whether to allow opening constraint, opening line upper limit constraint, unit start state constraint, minimum start-up time constraint, minimum shutdown time constraint, node power constraint, branch power flow constraint considering branch opening state and PST regulation mode at the same time, generator output constraint, generator climbing constraint, system upward reserve constraint, system downward reserve constraint, node phase angle constraint, branch phase angle regulation constraint of the branch installed with PST, reference node phase angle constraint, load shedding constraint and active power output reduction constraint of the wind farm.

[0015] As a preferred scheme of the multi-resource coordinated new energy power system two-stage scheduling method, the real-time scheduling stage constraints include: node power constraint under each operation scenario, branch power flow constraint considering branch opening state and PST regulation mode at the same time under each operation scenario, generator output constraint under each operation scenario, generator climbing constraint under each operation scenario, system upward reserve constraint under each operation scenario, system downward reserve constraint under each operation scenario, node phase angle constraint under each operation scenario, branch phase angle regulation constraint of the branch installed with PST under each operation scenario, reference node phase angle constraint, node load shedding constraint under each operation scenario and wind farm active power output reduction constraint under each operation scenario.

[0016] As a preferred scheme of a new energy power system two-stage scheduling method of multi-resource cooperation, in the process of solving the stage optimization scheduling model by setting a solver, the solver includes: Gurobi, CPLEX solver.

[0017] The application also provides a new energy power system two-stage scheduling device of multi-resource cooperation, based on the above-mentioned new energy power system two-stage scheduling method of multi-resource cooperation, comprising:

[0018] A stage optimization scheduling model construction module is configured to construct a stage optimization scheduling model according to the uncertainty and intermittence of wind power;

[0019] A day-ahead scheduling stage optimization module is configured to set day-ahead scheduling stage constraints, and to optimize a power transmission network structure and determine a unit start-stop strategy according to predicted values of next-day load demand and renewable energy output through the stage optimization scheduling model;

[0020] A real-time scheduling stage optimization module is configured to set real-time scheduling stage constraints, and to perform real-time PST adjustment on voltage and power angle and adjust unit output according to scenarios of next-day load and renewable energy output through the stage optimization scheduling model;

[0021] A stage optimization scheduling model solving module is configured to solve the stage optimization scheduling model through a set solver to obtain optimal scheduling strategies of the day-ahead scheduling stage and the real-time scheduling stage.

[0022] As a preferred scheme of a new energy power system two-stage scheduling device of multi-resource cooperation, in the stage optimization scheduling model construction module, the objective function of the stage optimization scheduling model is the sum of total operation costs of the wind power system; and the expression of the objective function is:

[0023]

[0024] In the formula, C total is the total operation cost of the system; C bran is the power transmission network line opening cost; C start is the unit start cost; Ω S is a system operation scenario set, i.e., a typical system operation scenario set formed by considering the uncertainty of renewable energy output and load; ρ s represents the probability of scenario s; and respectively represent the system generation cost, renewable energy penalty cost and load shedding cost coefficient under the predicted scenario; and respectively represent the system generation cost, the renewable energy curtailment penalty cost and the load shedding cost coefficient under the scenario s; a, b are respectively the cost coefficients of the day-ahead scheduling and the real-time scheduling phase.

[0025] As a preferred scheme of the multi-resource coordinated new energy power system two-stage scheduling device, in the day-ahead scheduling phase optimization module, the day-ahead scheduling phase constraints include: line disconnection permission constraint, disconnection line upper limit constraint, unit start state constraint, minimum start-up time constraint, minimum shutdown time constraint, node power constraint, branch power flow constraint considering branch disconnection state and PST adjustment mode, generator output constraint, generator ramping constraint, system up-regulation reserve constraint, system down-regulation reserve constraint, node phase angle constraint, branch phase angle adjustment constraint of the branch installed with PST, reference node phase angle constraint, load shedding constraint and wind farm active power output reduction constraint.

[0026] As a preferred scheme of the multi-resource coordinated new energy power system two-stage scheduling device, in the real-time scheduling phase optimization module, the real-time scheduling phase constraints include: node power constraint under each operating scenario, branch power flow constraint considering branch disconnection state and PST adjustment mode under each operating scenario, generator output constraint under each operating scenario, generator ramping constraint under each operating scenario, system up-regulation reserve constraint under each operating scenario, system down-regulation reserve constraint under each operating scenario, node phase angle constraint under each operating scenario, branch phase angle adjustment constraint of the branch installed with PST under each operating scenario, reference node phase angle constraint, node load shedding constraint under each operating scenario and wind farm active power output reduction constraint under each operating scenario.

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

[0028] The application has the following advantages: according to the uncertainty and intermittence characteristics of wind power, a stage optimization scheduling model is constructed; day-ahead scheduling stage constraints are set, the power transmission network structure is optimized and the unit start-stop strategy is determined according to the predicted values of the next day's load demand and renewable energy output through the stage optimization scheduling model; real-time scheduling stage constraints are set, the real-time PST adjustment of voltage and power angle and the adjustment of unit output are performed according to the scenarios of the next day's load and renewable energy output through the stage optimization scheduling model; the stage optimization scheduling model is solved through the setting of a solver, and the optimal scheduling strategy of the day-ahead scheduling stage and the optimal scheduling strategy of the real-time scheduling stage are obtained. The application proposes a two-stage optimization scheduling method of the power transmission network, which comprehensively considers unit combination, power transmission network structure optimization and phase-shifting transformer adjustment, and guarantees the reliable consumption of renewable energy by comprehensively coordinating the flexible resources on the power supply side and the network side. In the day-ahead scheduling stage, the power transmission network structure optimization and the unit start-stop scheme are determined based on the predicted values of the load demand and the renewable energy output; in the real-time scheduling stage, the real-time PST adjustment and the unit output adjustment are performed based on the possible scenarios of the load and the renewable energy output. Through the above two-stage scheduling method, the renewable energy consumption capacity is effectively improved, thereby promoting the overall economy of the power grid operation. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can be derived from the provided drawings without creative labor.

[0030] The structures, proportions, sizes, etc. shown in the specification are only used to cooperate with the content disclosed in the specification, to be understood and read by those skilled in the art, and do not define the limiting conditions for the implementation of the application, so they do not have technical significance. Any modification of the structure, change of the proportion relationship or adjustment of the size, without affecting the effect and purpose that can be achieved by the application, should still fall within the scope covered by the disclosed technical content.

[0031] Figure 1 A multi-resource coordinated new energy power system two-stage scheduling method flowchart provided in embodiment 1 of the application;

[0032] Figure 2 A multi-resource coordinated new energy power system two-stage scheduling method flowchart provided in embodiment 1 of the application;

[0033] Figure 3A schematic diagram of IEEE RTS-24 system wiring in a possible embodiment provided in Embodiment 1 of the present application;

[0034] Figure 4 A schematic diagram of system load demand in a possible embodiment provided in Embodiment 1 of the present application;

[0035] Figure 5 A schematic diagram of node 17 wind farm output in a possible embodiment provided in Embodiment 1 of the present application;

[0036] Figure 6 A schematic diagram of unit operation state of node 18 in a possible embodiment provided in Embodiment 1 of the present application;

[0037] Figure 7 A schematic diagram of unit output result of node 18 in a possible embodiment provided in Embodiment 1 of the present application;

[0038] Figure 8 A schematic diagram of PST phase angle regulation result of line L15-16 in a possible embodiment provided in Embodiment 1 of the present application;

[0039] Figure 9 A schematic diagram of a multi-resource coordinated new energy power system two-stage scheduling device architecture in Embodiment 2 of the present application. DETAILED DESCRIPTION

[0040] The embodiments of the present application are described below by specific embodiments, and those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in the specification. Obviously, the described embodiments are part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0041] Embodiment 1

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

[0043] S1, according to the uncertainty and intermittency characteristics of wind power, a stage optimization scheduling model is constructed;

[0044] S2, set the day-ahead scheduling stage constraint, through the stage optimization scheduling model, according to the predicted value of the next day load demand and renewable energy output, optimize the power transmission network structure and determine the unit start-stop strategy;

[0045] S3, setting a real-time scheduling stage constraint, optimizing a scheduling model through the stage, adjusting voltage and power angle in real-time PST and adjusting unit output according to a next-day load and a renewable energy output scenario;

[0046] S4, solving the stage optimization scheduling model through a solver to obtain a day-ahead scheduling stage optimal scheduling strategy and a real-time scheduling stage optimal scheduling strategy.

[0047] In this embodiment, in step S1, a stage optimization scheduling model is constructed according to the uncertainty and intermittence of wind power.

[0048] Specifically, in actual operation of the power grid, it is not appropriate to frequently open and close the transmission line, therefore, the network topology is considered unchanged within a day, and the network topology scheme of the next day and the unit scheduling plan of each hour are determined according to the prediction of renewable energy generation and system load of the next day in the day-ahead. According to the uncertainty and intermittence of wind power, a stage optimization scheduling model is constructed.

[0049] The objective function of the stage optimization scheduling model is the sum of the total operation cost of the wind power system, and the expression of the objective function is:

[0050]

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

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

[0053] The line opening and closing cost is shown in formula (2):

[0054]

[0055] In the formula, cbran is the line outage cost coefficient; a l is a 0-1 variable, indicating the status of branch l, 1 means branch l is closed, 0 means branch l is open; Ω B is the set of all branches in the system.

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

[0057]

[0058] In the equation, is the startup cost of unit k; χ k,t is a 0-1 variable, indicating whether unit k starts at time period t; Ω T is the unit commitment optimization period; Ω G is the set of traditional generating units.

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

[0060]

[0061]

[0062] In the equation, and are the quadratic term coefficient, the linear term coefficient and the constant term coefficient of the generation cost function of unit k respectively; and are the active power output of generating unit k at time period t in the prediction scenario and scenario s respectively; γ k,t is a 0-1 variable, indicating the operation status of unit k at time period t under scenario s, 1 means starting operation, otherwise 0.

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

[0064]

[0065]

[0066]

[0067]

[0068] In the equation, Ω W is the set of wind farms; Ω L is the set of loads; c wind and c loadrespectively represent the penalty cost coefficient of renewable energy rejection and load shedding; and represents the wind power rejection of wind farm k in time period t in the prediction scenario and scenario s; and represents the active load shedding amount of load i in time period t in the prediction scenario and scenario s.

[0069] In the embodiment, in step S2, a day-ahead scheduling stage constraint is set, and the power transmission network structure is optimized and the unit start-stop strategy is determined according to the predicted values of the next-day load demand and renewable energy output by optimizing the scheduling model of the stage;

[0070] The day-ahead scheduling stage constraint includes the following constraints: line disconnection permission constraint, disconnection line upper limit constraint, unit start state constraint, minimum start-up time constraint, minimum shutdown time constraint, node power constraint, branch power flow constraint considering the disconnection state of the branch and PST adjustment mode, generator output constraint, generator ramp constraint, system up-regulation reserve constraint, system down-regulation reserve constraint, node phase angle constraint, phase angle adjustment constraint of the branch with PST, reference node phase angle constraint, load shedding constraint, and active power output reduction constraint of the wind farm.

[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 for the number of disconnected lines. The line disconnection permission constraint is shown in formula (10):

[0072]

[0073] In the formula, α l is a 0-1 variable representing the opening and closing state of line l, and is 1 if the line is closed and 0 otherwise; β l is a 0-1 variable representing whether line l is allowed to be disconnected, and is 1 if allowed and 0 otherwise; Ω B is a line set.

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

[0075]

[0076] In the formula, N open represents the upper limit of the number of lines allowed to be disconnected.

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

[0078]

[0079] According to the unit start state constraint as shown in formula (12), the values of the variables are shown in Table 1.

[0080] gamma k,t-1 ]] gamma k,t ]] χ k,t ]]> 0 0 0 0 1 1 1 0 0 1 1 0

[0081] Table 1 Unit on / off state constraints Variable values of formula (12)

[0082] Where, only when γ k,t-1 = 0 and γ k,t = 1, χ k,t = 1, otherwise χ k,t = 0. That is, if the kth unit is on at the tth time period, χ k,t = 1; otherwise, χ k,t = 0.

[0083] The minimum on-time constraint and the minimum off-time constraint are shown in formula (13) and formula (14) respectively:

[0084]

[0085]

[0086] In the formula, T and T represent the minimum continuous operation time and the minimum continuous off-time of the unit k respectively.

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

[0088]

[0089] In the formula, Ω g (i) represents the generator set of access node i; Ω w (i) represents the wind farm set of access node i; Ω p (i) represents the bus set of node i; Ω c (i) represents the sub-line set of i; P represents the active power flow of branch l at time period t; represents the active power load demand of node i at time period t; represents the planned active power output of generator k at time period t.

[0090] The branch power flow constraint considering both branch on / off state and PST regulation mode is shown in formula (16):

[0091]

[0092] In the formula, node i and node j are the starting node and the ending node of branch l respectively; M is a maximum positive number; r l and x lRl and Xl represent the resistance and reactance of branch l, respectively; θ pre,i,t θi,t represents the phase angle of node i at period t; pre,j,t θj,t represents the phase angle of node j at period t; Plst represents whether branch l is equipped with PST, 1 if yes, otherwise 0; θp represents the regulating phase angle of PST.

[0093] Generator output constraint, i.e. the active power output level of generator k needs to be within its allowed limit, as shown in equation (17):

[0094]

[0095] In equation (17), and Rk,max and Rk,min represent the allowed maximum and minimum active power output of generator k, respectively.

[0096] Generator ramping constraint is shown in equation (18):

[0097]

[0098] In equation (18), Rk,max represents the maximum downward ramping power allowed for generator k; Rk,max represents the maximum upward ramping power allowed for generator k.

[0099] The up-regulation reserve constraint of the system and the down-regulation reserve constraint of the system are shown in equations (19) and (20), respectively:

[0100]

[0101]

[0102] In equation (19), Rup represents the up-regulation reserve of the system at period t; Rdown represents the down-regulation reserve of the system at period t.

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

[0104] -θ max ≤θ pre,i,t ≤θ max ,i∈Ω D ,t∈Ω T (21)

[0105] In equation (21), θ max represents the maximum phase angle allowed for the node.

[0106] The phase angle regulating constraint of the branch equipped with PST is shown in equation (22):

[0107]

[0108] wherein, denotes the maximum regulation phase angle of the PST located at branch l.

[0109] The reference node phase angle constraint, i.e., the voltage phase angle of the slack node in the system is 0, is shown in equation (23):

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

[0111] The cut load constraint is shown in equation (24):

[0112]

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

[0114]

[0115] In this embodiment, in step S3, a real-time scheduling stage constraint is set, and the voltage power angle is adjusted in real time and the unit output is adjusted according to the next day load and renewable energy output scenario by optimizing the scheduling model of the stage;

[0116] The real-time scheduling stage constraint includes: node power constraints under each operating scenario, branch power flow constraints under each operating scenario considering branch opening state and PST regulation mode at the same time, generator output constraints under each operating scenario, generator climbing constraints under each operating scenario, system up-regulation standby constraints under each operating scenario, system down-regulation standby constraints under each operating scenario, phase angle constraints of each node under each operating scenario, phase angle regulation constraints of the branch with the PST under each operating scenario, reference node phase angle constraints, node cut load constraints under each operating scenario, and wind farm active power output reduction constraints under each operating scenario.

[0117] Specifically,

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

[0119]

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

[0121] The branch power flow constraints in each operation scenario are considered simultaneously with the branch open state and the PST regulation mode, as shown in equation (27):

[0122]

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

[0124] The generator output constraint in each operation scenario is shown in equation (28):

[0125]

[0126] The generator ramp constraint in each operation scenario is shown in equation (29):

[0127]

[0128] The system up-regulation reserve constraint and down-regulation reserve constraint in each operation scenario are shown in equations (30) and (31) respectively:

[0129]

[0130]

[0131] In the formula, represents the up-regulation reserve of the system at time period t in scenario s; represents the down-regulation reserve of the system at time period t in scenario s.

[0132] The phase angle constraint of each node in each operation scenario is shown in equation (32):

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

[0134] The phase angle regulation constraint of the branch with PST in each operation scenario is shown in equation (33):

[0135]

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

[0137] θ s,i,t= 0, t e Q T i is the balanced node (34)

[0138] The node load shedding constraint in each operation scenario is shown in equation (35):

[0139]

[0140] The wind farm active power output reduction constraint in each operation scenario is shown in equation (36):

[0141]

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

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

[0144] The effectiveness of the present application is verified by an IEEE RTS-24 system. The computer configuration used is Intel Core i5-10400 (2.90 GHz), 16 GB RAM, and the model establishment and solution are realized by MATLAB and GUROBI respectively.

[0145] The IEEE RTS-24 system connection is shown in FIG. 3. The system contains 24 nodes and 33 lines. Five transformers are respectively located at lines L3-24, L9-11, L9-12, L10-11 and L10-12, which divide the system into a 138 kV low-voltage area and a 230 kV high-voltage area. The wind farms are respectively located at nodes 17 and 22, with a capacity of 800 MW and 1000 MW respectively, and each wind power output scenario is proportional to its capacity. The line outage cost coefficient, the renewable energy penalty cost coefficient and the load shedding cost coefficient are respectively 1×10 3 , 1×10 3 and 1×10 6 . The OTS line and PST configuration position are shown in FIG. 4. The upper limit of the number of lines allowed to be opened is 3, Figure 3 and the angle is 10°. The system load demand and the wind farm output of node 17 in each scenario are shown in FIGS. 5 and 6. Figure 4 Figure 5

[0146] ​​​Based on the model of the application, in the day-ahead scheduling stage, the two branches of line L20-23 are disconnected, and through analysis, it can be known that lines L13-23 and L12-23 are in full load or close to full load in most scenarios and time periods, so the two branches of line L20-23 are disconnected to prevent the wind power plant and other related units from delivering more power to node 23 and flowing through lines L13-23 and L12-23 to inject low-voltage areas, resulting in line overload.

[0147] In the day-ahead scheduling stage, each unit is formulated according to the predicted scenario, taking the unit located at node 18 as an example, the running state change curve thereof is as shown in Figure 6 It can be seen from the system load demand and wind power output curve that in the first 1-7 time period, the system load demand level is low and the wind power output level is high, so the unit located at node 18 is closed to ensure the maximum wind power consumption; with the increase of the system load demand level and the decrease of the wind power output level, the unit is started to meet the system load demand.

[0148] Taking the unit located at node 18 as an example, the unit output results in the day-ahead scheduling and real-time scheduling stages are as shown in Figure 7 In the 0-7 time period and the 24 time period, the unit is closed, so the output level is 0. For the predicted scenario in the day-ahead scheduling stage and different scenarios in the real-time scheduling stage, the unit output is adjusted differently, thereby improving the flexibility of the power supply side. For example, taking scenario 1 as an example, in the 18-20 time period, the wind power output level is low, at this time, the unit output of the unit located at node 18 is increased to a high level, thereby effectively guaranteeing the load demand.

[0149] Taking the PST phase angle regulation result of line L15-16 as an example, the day-ahead scheduling and real-time scheduling results are as shown in Figure 8 Overall, in each scenario, in the time period with high wind power output level (such as the 1-7 time period), by adjusting the PST phase angle (negative value), the phase angle difference between nodes 16 and 15 is increased, so that more power flows from node 16 to node 15, thereby ensuring that wind power can be more consumed by other regional loads; and in the time period with low wind power output level (such as the 10-15 time period), the PST phase angle regulation degree is small. It can be seen that for the predicted scenario in the day-ahead scheduling stage and different scenarios in the real-time scheduling stage, the phase angle of the PST is adjusted differently, thereby improving the flexibility of the power grid side.

[0150] In order to verify the effectiveness of the method proposed in the application, the method proposed in the application is compared with the optimization scheduling method considering only unit commitment and OTS. Wherein, the OTS optimization result and the unit operation state result in the day-ahead scheduling stage are obtained by using the optimization scheduling method considering only unit commitment and OTS, and the result is applied to the model proposed in the application to verify the effectiveness of the coordinated consideration of OST, unit commitment, and PST phase angle regulation.

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

[0152]

[0153] After calculation, R I =-5.89%, which shows that compared with the traditional method, the method proposed in the application can effectively improve the overall economy of power grid operation through the coordination among unit commitment, OTS, and PST phase angle regulation.

[0154] In summary, according to the uncertainty and intermittency characteristics of wind power, the stage optimization scheduling model is constructed; the day-ahead scheduling stage constraint is set, the power transmission network structure is optimized and the unit start-stop strategy is determined according to the predicted value of the next day's load demand and renewable energy output through the stage optimization scheduling model; the real-time scheduling stage constraint is set, the real-time PST regulation of voltage and power angle and the adjustment of unit output are carried out according to the scenario of the next day's load and renewable energy output through the stage optimization scheduling model; the stage optimization scheduling model is solved by setting the solver to obtain the optimal scheduling strategy in the day-ahead scheduling stage and the optimal scheduling strategy in the real-time scheduling stage. The two-stage optimization scheduling method of the power transmission network is proposed, which comprehensively considers unit commitment, power transmission network structure optimization, and phase-shifting transformer regulation, and guarantees the reliable consumption of renewable energy by comprehensively coordinating the flexible resources on the power supply side and the network side. In the day-ahead scheduling stage, the power transmission network structure optimization and unit start-stop scheme are determined based on the predicted value of the load demand and the renewable energy output; in the real-time scheduling stage, the real-time PST regulation and unit output adjustment are carried out based on the possible scenario of the load and the renewable energy output. Through the above two-stage scheduling method, the renewable energy consumption capacity is 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 executed by a single device, such as a computer or a server, etc. The method of the embodiments can also be applied to a distributed scenario, and be completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only execute one or more steps in the method of the embodiments of the present disclosure, and the multiple devices can interact with each other to complete the method.

[0156] It should be noted that some embodiments of the present disclosure have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown or sequential order to achieve desirable results. In certain implementations, multitasking and parallel processing can be advantageous.

[0157] Embodiment 2

[0158] Referring to Figure 9 Embodiment 2 of the present application also provides a multi-resource coordinated new energy power system two-stage scheduling device, comprising:

[0159] A stage optimization scheduling model construction module 001 is configured to construct a stage optimization scheduling model according to the uncertainty and intermittency characteristics of wind power;

[0160] A day-ahead scheduling stage optimization module 002 is configured to set day-ahead scheduling stage constraints, and to optimize the power transmission network structure and determine the unit start-stop strategy according to the predicted values of the next-day load demand and renewable energy output through the stage optimization scheduling model;

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

[0162] A stage optimization scheduling model solving module 004 is configured to solve the stage optimization scheduling model through a solver to obtain the optimal scheduling strategy of the day-ahead scheduling stage and the optimal scheduling strategy of the real-time scheduling stage.

[0163] In the present 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; and the expression of the objective function is:

[0164]

[0165] In the formula, Ctotal Ctotal is the total operation cost of the system; C bran Cline is the transmission line outage cost; C start Cstart is the unit start-up cost; Ω S ρ is the set of system operation scenarios, i.e., the set of typical operation scenarios of the system formed by considering the uncertainty of renewable energy output and load; s ρs represents the probability of scenario s; and Cg, Cpen, and Ch represent the system generation cost, the renewable energy curtailment penalty cost, and the load shedding cost coefficient under the predicted scenario, respectively; and Cg, Cpen, and Ch represent the system generation cost, the renewable energy curtailment penalty cost, and the load shedding cost coefficient under scenario s, respectively; a and b are the cost coefficients of the day-ahead scheduling and real-time scheduling phases, respectively.

[0166] In this embodiment, in the day-ahead scheduling phase optimization module 002, the day-ahead scheduling phase constraints include: line whether to allow outage constraints, line outage upper limit constraints, unit start-up state constraints, minimum start-up time constraints, minimum shutdown time constraints, node power constraints, branch power flow constraints considering the branch outage state and PST adjustment mode, generator output constraints, generator ramping constraints, system up-regulation reserve constraints, system down-regulation reserve constraints, node phase angle constraints, branch phase angle adjustment constraints of the branch installed with PST, reference node phase angle constraints, load shedding constraints, and active power output reduction constraints of the wind farm.

[0167] In this embodiment, in the real-time scheduling phase optimization module 003, the real-time scheduling phase constraints include: node power constraints under each operation scenario, branch power flow constraints considering the branch outage state and PST adjustment mode under each operation scenario, generator output constraints under each operation scenario, generator ramping constraints under each operation scenario, system up-regulation reserve constraints under each operation scenario, system down-regulation reserve constraints under each operation scenario, phase angle constraints of each node under each operation scenario, branch phase angle adjustment constraints of the branch installed with PST under each operation scenario, reference node phase angle constraints, node load shedding constraints under each operation scenario, and active power output reduction constraints of the wind farm under each operation 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 setting solver includes: Gurobi, CPLEX solver.

[0169] It is to be explained that the information interaction and execution process between the modules of the system described above are based on the same concept as the method embodiment in Embodiment 1 of the present application, and the technical effects brought by them are the same as those of the method embodiment of the present application. For specific content, refer to the description in the method embodiment described above.

[0170] Embodiment 3

[0171] Embodiment 3 of the present application provides a non-transitory computer readable storage medium, which stores a program code of a multi-resource coordinated new energy power system two-stage scheduling method, the program code includes instructions for executing the multi-resource coordinated new energy power system two-stage scheduling method of embodiment 1 or any possible implementation manner thereof.

[0172] The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)) and the like.

[0173] Embodiment 4

[0174] Embodiment 4 of the present application provides an electronic device, comprising a memory and a processor.

[0175] The processor and the memory complete mutual communication through a bus; the memory stores program instructions that can be executed by the processor, and the processor calling the program instructions can execute the multi-resource coordinated new energy power system two-stage scheduling method of embodiment 1 or any possible implementation manner thereof.

[0176] Specifically, the processor can be implemented by hardware or 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 realizes by reading software codes stored in a memory. The memory can be integrated in the processor or exist independently outside the processor.

[0177] In the embodiments described above, all or some of the modules / units can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or some of the modules / units can be implemented 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 into and executed by a computer, all or some of the procedures or functions as described in the embodiments of the present application are generated. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable system. 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 one website, computer, server or data center to another website, computer, server or data center through a wired (for example, coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (for example, infrared, wireless, microwave, etc.) manner.

[0178] It is obvious that those skilled in the art should understand that the modules or steps of the present application described above can be implemented by a general computing system, which can be concentrated on a single computing system or distributed on a network composed of multiple computing systems, and optionally, they can be implemented by program codes executable by a computing system, so that they can be stored in a storage system and executed by a computing system, and in some cases, the steps shown or described can be executed in different order, or they can be made into individual integrated circuit modules, or multiple modules or steps can be made into a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.

[0179] Although the present application has been described in detail by the above general description and specific embodiments, some modifications or improvements can be made on the basis of the present application, which is obvious to those skilled in the art. Therefore, these modifications or improvements made on the basis of not deviating from the spirit of the present application are within the scope of the present application.

Claims

1. A two-stage dispatching method for a multi-resource collaborative new energy power system, characterized in that, include: Based on the uncertainty and intermittent nature of wind power, a phase-optimized scheduling model is constructed; Set day-ahead scheduling stage constraints, and optimize the transmission network structure and determine the unit start-up and shutdown strategy based on the day-ahead forecast of the next day's load demand and renewable energy output through the stage-optimized scheduling model. Set real-time scheduling stage constraints, and through the stage-optimized scheduling model, adjust the voltage power angle and unit output in real time according to the next day's load and renewable energy output scenario; The optimal scheduling strategy for the day-ahead scheduling stage and the optimal scheduling strategy for the real-time scheduling stage are obtained by solving the stage optimization scheduling model using a solver. The total operating cost of the system is the weighted sum of line disconnection costs, unit start-up costs, power generation costs, renewable energy curtailment costs, and load shedding costs. The day-ahead scheduling stage constraints include: line disconnection constraints, upper limit constraints for disconnected lines, unit start-up status constraints, minimum start-up time constraints, minimum downtime constraints, node power constraints, branch power flow constraints considering both branch disconnection status and PST regulation mode, generator output constraints, generator ramping constraints, system up-reserve constraints, system down-reserve constraints, node phase angle constraints, branch phase angle regulation constraints with PST installed, reference node phase angle constraints, load shedding constraints, and active power output reduction constraints of wind farms. The constraints of the real-time scheduling phase include: node power constraints under each operating scenario, branch power flow constraints considering both branch open state and PST adjustment mode under each operating scenario, generator output constraints under each operating scenario, generator ramping constraints under each operating scenario, system reserve increase constraints under each operating scenario, system reserve decrease 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 power output reduction constraints under each operating scenario.

2. The two-stage dispatching method for a multi-resource collaborative new energy power system according to claim 1, characterized in that, The objective function of the stage-based optimization scheduling model is to minimize the sum of the total operating costs of the wind power system; the expression for the objective function is: In the formula, C total C represents the total operating cost of the system. bran Cost of disconnecting power transmission lines; C start For unit start-up costs; Ω S The system operation scenario set refers to a set of typical system operation scenarios that take into account the uncertainties of renewable energy output and load; ρ s Represents the probability of scenario s; and These represent the system generation cost, renewable energy curtailment penalty cost, and load shedding cost parameters, respectively, under the predicted scenario; and denoted as system generation cost, renewable energy abandonment penalty cost, and load shedding cost parameters under scenario s, respectively; a and b are the cost coefficients for day-ahead scheduling and real-time scheduling stages, respectively.

3. The two-stage dispatching method for a multi-resource collaborative new energy power system according to claim 1, 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.

4. A two-stage dispatching device for a multi-resource collaborative new energy power system, employing the two-stage dispatching method for a multi-resource collaborative new energy power system as described in any one of claims 1-3, characterized in that, include: The phase-optimized scheduling model construction module is used to construct a phase-optimized scheduling model based on the uncertainty and intermittency characteristics of wind power. 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. 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 of the next day, and the unit output is adjusted. The phase optimization scheduling model solving module is used to solve the phase optimization scheduling model by setting a solver, and obtain the optimal scheduling strategy for the day-ahead scheduling phase and the optimal scheduling strategy for the real-time scheduling phase.

5. A two-stage dispatching device for a multi-resource collaborative new energy power system according to claim 4, characterized in that, In the stage-based optimization scheduling model construction module, the objective function of the stage-based 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 C represents the total operating cost of the system. bran Cost of disconnecting power transmission lines; C start For unit start-up costs; Ω S The system operation scenario set refers to a set of typical system operation scenarios that take into account the uncertainties of renewable energy output and load; ρ s Represents the probability of scenario s; and These represent the system generation cost, renewable energy curtailment penalty cost, and load shedding cost parameters, respectively, under the predicted scenario; and denoted as system generation cost, renewable energy abandonment penalty cost, and load shedding cost parameters under scenario s, respectively; a and b are the cost coefficients for day-ahead scheduling and real-time scheduling stages, respectively.

6. A two-stage dispatching device for a multi-resource collaborative new energy power system according to claim 5, characterized in that, In the day-ahead scheduling phase optimization module, the day-ahead scheduling phase constraints include: whether line interruption is allowed, upper limit constraint for interrupted lines, unit start-up status constraint, minimum start-up time constraint, minimum downtime constraint, node power constraint, branch power flow constraint considering both branch interruption status and PST adjustment mode, generator output constraint, generator ramping constraint, system upward reserve constraint, system downward reserve constraint, node phase angle constraint, branch phase angle adjustment constraint with PST installed, reference node phase angle constraint, load shedding constraint, and active power output reduction constraint of wind farm.

7. A two-stage dispatching device for a multi-resource collaborative new energy power system according to claim 6, characterized in that, In the real-time scheduling phase optimization module, the real-time scheduling phase constraints include: node power constraints under each operating scenario, branch power flow constraints considering both branch opening status and PST adjustment mode under each operating scenario, generator output constraints under each operating scenario, generator ramping constraints under each operating scenario, system reserve increase constraints under each operating scenario, system reserve decrease 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 power output reduction constraints under each operating scenario.

8. A two-stage dispatching device for a multi-resource collaborative new energy power system according to claim 7, characterized in that, In the phase optimization scheduling model solving module, during the process of solving the phase optimization scheduling model by setting a solver, the set solver includes: Gurobi and CPLEX solvers.

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