Day-ahead active and reactive integrated scheduling method and system for large power grid

Abstract

The invention discloses a day-ahead active and reactive integrated scheduling method and system for a large power grid, and the method comprises the steps: collecting operation parameters of the large power grid, and recognizing key weak nodes and key energy storage nodes in the large power grid through load flow calculation; and for the nodes, establishing a target function taking the total cost minimization as a target, and solving the target function through a Schwander optimization algorithm to obtain a conventional unit start-stop plan, active power output distribution and reactive power resource scheduling strategy of the large power grid. The scheduling scheme generated by the invention can synchronously optimize a conventional unit start-stop plan, active power output distribution and a reactive power resource scheduling strategy, remarkably improves the voltage quality on the premise of ensuring the safe operation of the system, and provides key technical support for the safe and economical operation of the novel power system.

Description

Technical Field

[0001] This invention relates to the field of large power grid dispatching technology, and in particular to a method and system for integrated day-ahead active and reactive power dispatching of large power grids. Background Technology

[0002] With the continuous and rapid growth of installed capacity of renewable energy sources such as wind power and photovoltaics, "new energy + energy storage" has become the core development path for building a new power system. However, in the operation of large power grids with a high proportion of new energy, traditional reactive power regulation methods alone are no longer sufficient to effectively solve the problem of system voltage stability. The limited installed capacity of conventional power sources and the long transmission distances result in high line impedance, and simply relying on reactive power compensation cannot completely eliminate the risk of voltage exceeding limits.

[0003] Current academic research in this field suffers from a significant technological gap: First, existing studies are largely limited to single active or reactive power optimization, lacking a system-level integrated control framework. For example, optimization models only consider photovoltaic active power participation, neglecting the active power regulation role of conventional unit combinations; or they introduce energy storage active power regulation functions, but the research scope is limited to local optimization at the distribution network level, without addressing active power dispatch strategies for conventional units in large power grids. This research limitation makes it difficult for existing methods to meet the global optimization needs of high-proportion renewable energy power systems. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to address the prominent problem of voltage control in large power grids under the current high proportion of renewable energy grid connection, and to propose a day-ahead active and reactive power integrated dispatching method and system for large power grids to achieve coordinated optimization of unit combination and reactive power equipment.

[0005] Technical solution: The integrated day-ahead active and reactive power dispatching method for large power grids described in this invention includes the following steps:

[0006] Obtain generator operating parameters, reactive power compensation equipment status parameters, and energy storage system characteristic parameters from the large power grid;

[0007] Based on the generator operating parameters, reactive power compensation equipment status parameters, and energy storage system characteristic parameters, key weak nodes and key energy storage nodes in the power grid are identified through power flow calculation.

[0008] For the aforementioned critical weak nodes and critical energy storage nodes, an objective function is established with the goal of minimizing the total cost, which includes the total cost of electricity purchase, the cost of starting and stopping the synchronous machine, the pumping cost of the pumped storage unit, and the charging and discharging cost of the battery energy storage system.

[0009] The objective function is solved using the Xueyan optimization algorithm to obtain the start-up and shutdown plan of conventional generating units, active power output allocation, and reactive power resource scheduling strategy for the large power grid.

[0010] Furthermore, the constraints of the objective function include constraints on conventional generating units and power limit constraints on reactive power equipment;

[0011] Conventional generator unit constraints include upper and lower limits of active power for synchronous generator units, ramping constraints for synchronous generator units, and start-stop logic constraints for synchronous generator units.

[0012] The power limit constraints for reactive power equipment include upper and lower limits of reactive power for reactive power equipment, reactive power constraints for capacitor banks, and reactive power constraints for new energy inverters.

[0013] Furthermore, the upper and lower limits of the active power of the synchronous generator set are constrained as follows: P i,min u i,t ≤P i,t ≤P i,max u i,t ;

[0014] Among them, P i,t P represents the active power of synchronous generator set i during time period t; i,min and P i,max These represent the upper and lower limits of the active power output of synchronous generator set i; u i,t The variable is 0-1, where 0 indicates that synchronous generator set i is in a stopped state during time period t, and 1 indicates that synchronous generator set i is in a running state during time period t.

[0015] The synchronous generator ramping constraint is: |P i,t -P i,t-1 |≤ΔP i ;

[0016] Where, ΔP i For synchronous generator set i, the ramp rate is limited;

[0017] The start-stop logic constraints for synchronous generator sets are as follows:

[0018] in, and T represents the continuous start-up time and continuous shutdown time of synchronous generator unit i during time period t; i on and T i off These are the minimum continuous operating time and minimum continuous shutdown time of synchronous generator set i, respectively.

[0019] The upper and lower limits of reactive power of reactive equipment are constrained as follows: Q j,min ≤Q j,t ≤Q j,max ;

[0020] Among them, Q j,tQ represents the reactive power of dynamic reactive power equipment j during time period t; j,max and Q j,min The upper and lower limits of reactive power output for dynamic reactive power device j;

[0021] The reactive power constraint of the capacitor bank is: Q s,min ≤Q s,t ≤Q s,max ,

[0022] Among them, Q s,t Q represents the capacity of capacitor bank s during time period t; s,min and Q s,max These represent the upper and lower limits of the capacitance of capacitor bank S, respectively; C s (t) and C s (t-1) represents the connected capacity of the capacitor bank during time period t and time period t-1, respectively; N s,max This represents the maximum number of switching operations for capacitor bank s.

[0023] The reactive power constraint of the new energy inverter is:

[0024] Among them, P w,t Q w,t and P v,t Q v,t S represents the active and reactive power of wind power (w) and photovoltaic power (v) during time period t, respectively; w,inv and S v,inv These are the rated inverter capacities for wind power (w) and photovoltaic (v), respectively.

[0025] Furthermore, the constraints of the objective function also include active and reactive power balance constraints for key weak nodes and key energy storage nodes, positive and negative spinning reserve constraints, pumped hydro storage constraints, battery energy storage constraints, and grid security constraints.

[0026] Furthermore, the objective function is:

[0027] minF=F en +F g +F pump +F ess ;

[0028]

[0029] Where, N G N represents the total number of synchronous generator sets; Pump N represents the total number of pumped storage units; ESS N represents the total number of battery energy storage devices; w and N v N represents the total number of wind turbines and the total number of photovoltaic power units, respectively; sNumber of segments for the unit's quotation; ρ i,k ρ w and ρ v These are the prices for the k-th segment of synchronous generator set i, wind power (w) generation price, and photovoltaic power (v) generation price, respectively, in yuan / (MWh); P i,t,k P represents the active power of synchronous generator unit i in the k-th segment during time period t; w,t and P v,t ΔT represents the active power of wind power (w) and photovoltaic power (v) during time period t, respectively; T is the total number of time periods, and ΔT is the time interval of one time period. and Represents the start-up and shutdown cost of synchronous generator set i; u i,t The variable is 0-1, where 0 indicates that unit i is in a shutdown state during time period t, and 1 indicates that unit i is in a running state during time period t; Cost coefficient of battery energy storage device e; The charging or discharging power of the battery energy storage device e during time period t; The cost coefficient for pumped water by the pumped storage unit m; Let m be the pumping power of the pumped storage unit during time period t.

[0030] Furthermore, the step of identifying key weak nodes and key energy storage nodes in the large power grid through power flow calculation based on the generator operating parameters, reactive power compensation equipment status parameters, and energy storage system characteristic parameters includes:

[0031] Calculate the first sensitivity of voltage at each node of the large power grid to reactive power changes, and screen nodes with the first sensitivity higher than their corresponding threshold as key weak nodes;

[0032] Calculate the second sensitivity of active power of each energy storage node in the large power grid to voltage changes, and select nodes with the second sensitivity higher than their corresponding threshold as key energy storage nodes.

[0033] The integrated active and reactive power dispatching system for large power grids according to the present invention includes:

[0034] The data acquisition unit is used to acquire generator operating parameters, reactive power compensation equipment status parameters, and energy storage system characteristic parameters of the large power grid.

[0035] The critical node identification unit is used to identify critical weak nodes and critical energy storage nodes in the power grid by power flow calculation based on the generator operating parameters, reactive power compensation equipment status parameters and energy storage system characteristic parameters.

[0036] The scheduling strategy generation unit is used to establish an objective function for the key weak nodes and key energy storage nodes, with the goal of minimizing the total cost. The total cost includes the total cost of electricity purchase, the start-up and shutdown cost of synchronous machines, the pumping cost of pumped storage units, and the charging and discharging cost of battery energy storage systems. The objective function is solved by the Xueyan optimization algorithm to obtain the start-up and shutdown plan of conventional units, the allocation of active power output, and the scheduling strategy of reactive power resources of the large power grid.

[0037] The electronic device of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded onto the processor, it implements the day-ahead integrated active and reactive power dispatching method of a large power grid.

[0038] The computer-readable storage medium of the present invention stores a computer program, which, when executed by a processor, implements the day-ahead integrated active and reactive power dispatching method for a large power grid.

[0039] Beneficial effects: Compared with the prior art, the advantages of this invention are as follows: This invention establishes a day-ahead active-reactive integrated dispatching theoretical system that considers unit combination, various energy storage devices and traditional reactive power regulation equipment. It breaks through the limitations of traditional single time scale control, and innovatively combines the active power dispatching of unit combination with the regulation capability of traditional reactive power equipment. It constructs a multi-time scale voltage control system that takes into account both transient response and steady-state optimization. The generated dispatching scheme can simultaneously optimize conventional unit start-up and shutdown plans, active power output allocation and reactive power resource dispatching strategies. Under the premise of ensuring the safe operation of the system, it significantly improves voltage quality and provides key technical support for the safe and economical operation of new power systems. Attached Figure Description

[0040] Figure 1 This is a flowchart of the integrated active and reactive power dispatching method for large power grids according to the present invention. Detailed Implementation

[0041] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0042] like Figure 1As shown, the integrated active and reactive power dispatching method for the large power grid first acquires basic data such as generator parameters, reactive power equipment status, and characteristics of various energy storage systems through power grid status monitoring and data acquisition. Then, initial power flow calculations are performed to analyze voltage-reactive power and voltage-active power sensitivity and identify key weak nodes and key energy storage nodes in the power grid. Next, a collaborative decision-making model integrating generator combination, economic dispatch, and reactive power optimization is established. A multi-objective optimization framework is adopted to coordinate power generation economics, system reserve requirements, and voltage stability requirements, and spatiotemporal coupling constraints are introduced to characterize the volatility of new energy sources and the cross-period energy balance characteristics of energy storage. Finally, the high-dimensional nonlinear mixed integer programming problem is solved using the Xueyan optimization algorithm. This method includes the following steps.

[0043] Step 1: Real-time acquisition of basic data such as generator set operating parameters, reactive power compensation equipment status (SVC / SVG dynamic compensation range, synchronous condenser operating status, and capacitor bank switching status) and various energy storage system characteristics through the power grid status monitoring and data acquisition system.

[0044] Step 2: Perform initial power flow calculations, analyze voltage-reactive and voltage-active power sensitivity, and identify key weak nodes and key energy storage nodes in the power grid. Step 2 includes the following processes:

[0045] Step 2-1: Conduct a comprehensive assessment of the power grid's operating status through initial power flow calculations, focusing on analyzing the sensitivity of each node's voltage to reactive power changes to identify key control nodes in the system. Based on the sensitivity analysis, select nodes with significant voltage response characteristics (i.e., high sensitivity) and list them as critical weak nodes, as these nodes typically have a significant impact on system voltage stability.

[0046] Step 2-2: Addressing the deep coupling relationship between source, load, and storage in new power systems, a voltage-active power sensitivity analysis method is introduced. This method focuses on examining the sensitivity of energy storage active power changes to the voltage of each node, thereby identifying key energy storage control nodes in the system. Based on the sensitivity analysis, key energy storage nodes with significant responses to energy storage active power regulation are identified, forming a set of energy storage control nodes.

[0047] Steps 2-3: Output a list of critical weak nodes and a list of critical energy storage nodes that have been rigorously screened, providing an important basis for subsequent optimization scheduling measures such as generator start-up and shutdown plans, reactive power compensation strategies, and energy storage system charging and discharging schemes.

[0048] Step 3: Establish a collaborative decision-making model integrating unit combination, economic dispatch, and reactive power optimization based on key weak nodes and key energy storage nodes. This model adopts a multi-objective optimization framework to coordinate power generation economics, system reserve requirements, and voltage stability requirements, and introduces spatiotemporal coupling constraints to characterize the volatility of new energy sources and the cross-period energy balance characteristics of energy storage. Step 3 includes the following processes:

[0049] Step 3-1: Establish the objective function.

[0050] The objective function is to minimize the total system cost, which includes the total cost of purchasing electricity, the start-up and shutdown cost of the synchronous machine, the pumping cost of the pumped storage unit, and the charging and discharging cost of the battery energy storage system.

[0051] minF=F en +F g +F pump +F ess (1)

[0052]

[0053] Where, N G N represents the total number of synchronous generator sets; Pump N represents the total number of pumped storage units; ESS N represents the total number of battery energy storage devices; w and N v N represents the total number of wind turbines and the total number of photovoltaic power units, respectively; s Number of segments for the unit's quotation; ρ i,k ρ w and ρ v These are the prices for the k-th segment of synchronous generator set i, wind power (w) generation price, and photovoltaic power (v) generation price, respectively, in yuan / (MWh); P i,t,k P represents the active power of synchronous generator unit i in the k-th segment during time period t; w,t and P v,t ΔT represents the active power of wind power (w) and photovoltaic power (v) during time period t, respectively; T is the total number of time periods, and ΔT is the time interval of one time period. and Represents the start-up and shutdown cost of synchronous generator set i; u i,t The variable is 0-1, where 0 indicates that unit i is in a shutdown state during time period t, and 1 indicates that unit i is in a running state during time period t; Cost coefficient of battery energy storage device e; The charging or discharging power of the battery energy storage device e during time period t; The cost coefficient for pumped water by the pumped storage unit m; Let m be the pumping power of the pumped storage unit during time period t.

[0054] Step 3-2: Establish constraints

[0055] (2) Constraints

[0056] 1) Active and reactive power balance constraints at each node in each time period

[0057]

[0058] In the formula, P n,g,t and Q n,g,t P represents the active and reactive power of the synchronous generator unit at node n during time period t; n,load,t and Q n,load,t These represent the active and reactive loads at node n during time period t; P n,pump,t and Q n,pump,t These represent the active and reactive power of the pumped-storage unit during time period t at node n; P n,ESS,t and Q n,ESS,t These represent the active and reactive power stored at node n during time period t, respectively; P n,w,t Q n,w,t and P n,v,t Q n,v,t V represents the active and reactive power of wind and solar power units at node n during time period t; n,t and V m,t Let G be the voltage amplitudes of nodes n and m during time period t, respectively; nm and B nm These represent the mutual conductance and mutual susceptance between nodes n and m, respectively; θ nm,t The phase angle difference between nodes n and m in time period t.

[0059] 2) System positive and negative rotational spare constraints

[0060]

[0061] In the formula, R L,t R is the active power reserve required for the load corresponding to time period t; w,t,up With R w,t,down These represent the positive and negative rotary reserve required for wind power during time period t. Where R... L,t R w,t,up and R w,t,down Each value is taken as a certain proportion of the load output and wind power output.

[0062] 3) Constraints of conventional units

[0063] ①Active power upper and lower limit constraints of synchronous generator sets

[0064] P i,min u i,t ≤P i,t ≤P i,max u i,t (9)

[0065] In the formula, P i,tP represents the active power of synchronous generator set i during time period t; i,min and P i,max These represent the upper and lower limits of the active power output of synchronous generator set i, respectively.

[0066] ②Synchronous generator ramping constraints

[0067] |P i,t -P i,t-1 |≤ΔP i (10)

[0068] In the formula, ΔP i The ramp rate limit (MW / h) for synchronous generator set i.

[0069] ③ Synchronous generator set start-stop logic

[0070]

[0071] In the formula, and These represent the continuous start-up time and continuous shutdown time of synchronous generator unit i during time period t; T i on and T i off These are the minimum continuous operating time and minimum continuous shutdown time of synchronous generator set i, respectively.

[0072] 4) Multiple energy storage constraints

[0073] ① Pumped storage

[0074]

[0075] E m,min ≤E m,t ≤E m,max (14)

[0076]

[0077] In the formula, E m,t The reservoir energy storage capacity (MW / h) of pumped storage unit m during time period t; E m,min and E m,max These are the minimum and maximum reservoir capacities of pumped-storage unit m, respectively. and These are the pumping efficiency and power generation efficiency of pumped-storage unit m, respectively. and These represent the maximum pumping power and maximum generating power of pumped-storage unit m, respectively; b m,t This represents the operating mode of pumped storage unit m during time period t, where 1 indicates pumping mode and 0 indicates power generation mode.

[0078] ② Battery energy storage

[0079]

[0080] SOC e,min ≤SOC e,t ≤SOC e,max (18)

[0081] SOC e,0 =SOC e,end (19)

[0082]

[0083] In the formula, SOC e,t The state of charge (SOC) of battery e during time period t. e,min and SOC e,min These represent the minimum and maximum states of charge of battery e, respectively; E e,cap The rated capacity (MWh) of battery e; and These represent the charging efficiency and discharging efficiency of battery e, respectively; μ e,t This represents the operating mode of battery e during time period t, where 1 indicates the charging state and 0 indicates the discharging state. and These represent the charging and discharging power of battery e during time period t, respectively. and These represent the maximum charge / discharge power of battery e; and the State of Charge (SOC). e,0 SOC e,end These represent the state of charge of battery e at the beginning and end of each day.

[0084] 5) Power limit constraints for reactive power equipment

[0085] ① Upper and lower limits of reactive power of SVC / SVG / generator / synchronous condenser / pumped-storage unit

[0086] Q j,min ≤Q j,t ≤Q j,max (twenty two)

[0087] In the formula, Q j,t Q represents the reactive power of dynamic reactive power equipment j during time period t. j,max and Q j,min These are the upper and lower limits of the reactive power output of the dynamic reactive power device j.

[0088] ②Reactive power constraint of capacitor bank

[0089] Q s,min ≤Q s,t ≤Q s,max (twenty three)

[0090]

[0091] In the formula, Q s,t Q represents the capacity of capacitor bank s connected during time period t; s,min and Q s,max These represent the upper and lower limits of the capacitance of capacitor bank S, respectively; C s (t) and C s (t-1) represents the connected capacity of the capacitor bank during time period t and time period t-1, respectively; N s,max This represents the maximum number of switching operations for capacitor bank s.

[0092] ③ Reactive power constraint of new energy inverters

[0093]

[0094] In the formula, P w,t Q w,t and P v,t Q v,t S represents the active and reactive power of wind power (w) and photovoltaic power (v) during time period t; w,inv and S v,inv These are the inverter rated capacities (MVA) for wind power (w) and photovoltaic (v), respectively.

[0095] 6) Power grid security constraints

[0096] ① Voltage safety

[0097] V min ≤V n,t ≤V max (27)

[0098] In the formula, V max and V n,t These represent the upper and lower limits of the voltage, respectively.

[0099] ②Line thermal constraint

[0100]

[0101] In the formula, P l,t and Q l,t These represent the active and reactive power of line l during time period t; S l,max Let be the maximum apparent power of line l.

[0102] Step 4: Solve this high-dimensional nonlinear mixed-integer programming problem using the Snow Goose optimization algorithm. The generated scheduling scheme can simultaneously optimize conventional unit start-up and shutdown plans, active power output allocation, and reactive power resource scheduling strategies. Step 4 includes the following process:

[0103] Step 4-1: Initialization.

[0104] N individual snow geese are randomly generated, including continuous variables (such as the active and reactive power output of the synchronous generator, the charging and discharging power of the energy storage device, the reactive power of the dynamic reactive power compensation device, etc.) and discrete variables (the start-up and shutdown status of the synchronous machine, the pumping and storage status of the pumped storage unit, the charging and discharging status of the energy storage and the number of capacitor banks). The maximum number of iterations M and the population split ratio (such as 20% leading group) are set.

[0105] Step 4-2: Assess fitness.

[0106] The objective function (1) in the mathematical model of the integrated active and reactive power dispatch of a large power grid based on high proportion of new energy and multiple energy storage calculates the fitness value of each individual, and sorts the individuals according to the fitness value, dividing the top 20% into the leading group and the bottom 80% into the following group. The fitness function is:

[0107]

[0108] In the formula, α is the voltage deviation penalty coefficient; β is the line overload penalty coefficient; S l,t Let be the line capacity of line l during time period t.

[0109] Step 4-3: Group update.

[0110] The top 20% of individuals in terms of fitness are grouped into the leading group, and a local search is performed. The group update method is as follows:

[0111] X new =X leader +η(X rand1 -X rand2 (31)

[0112] In the formula, X new Newly generated candidate solutions (offspring individuals); X new The elite individual (the one with the best fitness) in the current leading group; X new and X new Two distinct individuals are randomly selected from 80% of the total; η is the step size control factor, which determines the breadth of the search range.

[0113] The bottom 80% of individuals are classified as a follower group, and a global exploration is performed. The group update method is as follows:

[0114] X new =X current +α·Levy(λ)(X best -X current (32)

[0115] In the formula, X current For individuals in the current follower group (with low fitness); X bestThe current global optimal individual; α is the step size scaling factor, which controls the intensity of Levy flight; Levy(λ) is the Levy random step size, a heavy-tailed random number following the Levy distribution; λ is the exponential parameter of the Levy distribution, which controls the step size distribution characteristics, and is usually taken as 1.5.

[0116] Step 4-4: Crossover and Mutation.

[0117] In order to combine the superior genes of the parents to generate diverse offspring while avoiding the existence of local optima, crossover and mutation operations are performed on continuous and discrete variables.

[0118] For continuous variables, the simulated binary crossover method is used to obtain their position update formula as follows:

[0119]

[0120] In the formula, X new The new solution (offspring individuals) generated after crossover; X1 and X2 are two parent individuals selected from the population to participate in the crossover; p c η represents the crossover probability (e.g., 0.8); c The distribution index (e.g., 2) determines the concentration of the solutions in the offspring; the β expansion factor controls the deviation between the offspring and the parent; u is a uniform random number in the [0,1] range; when u ≤ 0.5, When u > 0.5,

[0121] After performing a Gaussian mutation operation combined with adaptive step size adjustment, the position update formula is obtained as follows:

[0122] X new =X+σ·N(0,1) (34)

[0123]

[0124] In the formula, X new The new solution after mutation; X (original individual, solution to be mutated); σ (adaptive variable asynchronous length); σ max and σ min denoted as initial maximum asynchronous length and minimum asynchronous length, respectively; m is the current iteration count; M is the maximum iteration count; N(0,1) is a standard normal distribution.

[0125] For discrete variables, a two-point crossover method is used, randomly selecting two positions to swap parent segments, and then a bit-flipping mutation method is used to process them.

[0126] Steps 4-5: Constraint Repair.

[0127] After updating the individual's position, it is necessary to ensure that the individual's position satisfies all constraints. Forced corrections are needed for variables that exceed limits; for example, if a variable exceeds its upper or lower limits, the out-of-bounds value should be forcibly set as the boundary; if power is unbalanced, the unbalanced amount should be allocated according to weights, and so on.

[0128] Steps 4-6: Termination conditions.

[0129] Determine if the iteration conditions are met. If they are met, output the globally optimal objective function value and optimization variables, end the calculation, and output the result. Otherwise, return to step 4-2 and continue iterative optimization.

[0130] The integrated active and reactive power dispatching system for large power grids according to the present invention includes:

[0131] The data acquisition unit is used to acquire generator operating parameters, reactive power compensation equipment status parameters, and energy storage system characteristic parameters of the large power grid.

[0132] The critical node identification unit is used to identify critical weak nodes and critical energy storage nodes in the power grid by power flow calculation based on the generator operating parameters, reactive power compensation equipment status parameters and energy storage system characteristic parameters.

[0133] The scheduling strategy generation unit is used to establish an objective function for the key weak nodes and key energy storage nodes, with the goal of minimizing the total cost. The total cost includes the total cost of electricity purchase, the start-up and shutdown cost of synchronous machines, the pumping cost of pumped storage units, and the charging and discharging cost of battery energy storage systems. The objective function is solved by the Xueyan optimization algorithm to obtain the start-up and shutdown plan of conventional units, the allocation of active power output, and the scheduling strategy of reactive power resources of the large power grid.

[0134] The electronic device of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded onto the processor, it implements the day-ahead integrated active and reactive power dispatching method of a large power grid.

[0135] The computer-readable storage medium of the present invention stores a computer program, which, when executed by a processor, implements the day-ahead integrated active and reactive power dispatching method for a large power grid.

[0136] The computer-readable storage medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage devices, magnetic disk storage devices or other magnetic storage devices, flash memory or any other medium that can be used to store program code in the form of instructions or data structures and is accessible by a computer.

[0137] The processor is used to execute a computer program stored in memory to implement the various steps in the methods described in the above embodiments.

Claims

1. A method for integrated day-ahead active and reactive power dispatching of a large power grid, characterized in that, include: Obtain generator operating parameters, reactive power compensation equipment status parameters, and energy storage system characteristic parameters from the large power grid; Based on the generator operating parameters, reactive power compensation equipment status parameters, and energy storage system characteristic parameters, key weak nodes and key energy storage nodes in the power grid are identified through power flow calculation. For the aforementioned critical weak nodes and critical energy storage nodes, an objective function is established with the goal of minimizing the total cost, which includes the total cost of electricity purchase, the cost of starting and stopping the synchronous machine, the pumping cost of the pumped storage unit, and the charging and discharging cost of the battery energy storage system. The objective function is solved using the Xueyan optimization algorithm to obtain the start-up and shutdown plan of conventional generating units, active power output allocation, and reactive power resource scheduling strategy for the large power grid.

2. The integrated day-ahead active and reactive power dispatching method for large power grids according to claim 1, characterized in that, The constraints of the objective function include constraints on conventional generating units and power limit constraints on reactive power equipment; The constraints of the conventional generating units include upper and lower limits of active power of synchronous generator sets, ramping constraints of synchronous generator sets, and start-stop logic constraints of synchronous generator sets. The power limit constraints of the reactive power equipment include upper and lower limits of reactive power of the reactive power equipment, reactive power constraints of capacitor banks and reactive power constraints of new energy inverters.

3. The integrated day-ahead active and reactive power dispatching method for large power grids according to claim 2, characterized in that, The upper and lower limits of active power of synchronous generator sets are constrained as follows: P i,min u i,t ≤P i,t ≤P i,max u i,t ; Among them, P i,t P represents the active power of synchronous generator set i during time period t; i,min and P i,max These represent the upper and lower limits of the active power output of synchronous generator set i; u i,t The variable is 0-1, where 0 indicates that synchronous generator set i is in a stopped state during time period t, and 1 indicates that synchronous generator set i is in a running state during time period t. The synchronous generator ramping constraint is: |P i,t -P i,t-1 |≤ΔP i ; Where, ΔP i For synchronous generator set i, the ramp rate is limited; The start-stop logic constraints for synchronous generator sets are as follows: in, and T represents the continuous start-up time and continuous shutdown time of synchronous generator unit i during time period t; i on and T i off These are the minimum continuous operating time and minimum continuous shutdown time of synchronous generator set i, respectively. The upper and lower limits of reactive power of reactive equipment are constrained as follows: Q j,min ≤Q j,t ≤Q j,max ; Among them, Q j,t Q represents the reactive power of dynamic reactive power equipment j during time period t; j,max and Q j,min The upper and lower limits of reactive power output for dynamic reactive power device j; The reactive power constraint of the capacitor bank is: Q s,min ≤Q s,t ≤Q s,max , Among them, Q s,t Q represents the capacity of capacitor bank s during time period t; s,min and Q s,max These represent the upper and lower limits of the capacitance of capacitor bank S, respectively; C s (t) and C s (t-1) represents the connected capacity of the capacitor bank during time period t and time period t-1, respectively; N s,max This represents the maximum number of switching operations for capacitor bank s. The reactive power constraint of the new energy inverter is: Among them, P w,t Q w,t and P v,t Q v,t S represents the active and reactive power of wind power (w) and photovoltaic power (v) during time period t, respectively; w,inv and S v,inv These are the rated inverter capacities for wind power (w) and photovoltaic (v), respectively.

4. The integrated day-ahead active and reactive power dispatching method for large power grids according to claim 2, characterized in that, The constraints of the objective function also include active and reactive power balance constraints for key weak nodes and key energy storage nodes, positive and negative spinning reserve constraints, pumped hydro storage constraints, battery energy storage constraints, and grid security constraints.

5. The integrated day-ahead active and reactive power dispatching method for large power grids according to claim 1, characterized in that, The objective function is: minF=F en +F g +F pump +F ess ; Where, N G N represents the total number of synchronous generator sets; Pump N represents the total number of pumped storage units; ESS N represents the total number of battery energy storage devices; w and N v N represents the total number of wind turbines and the total number of photovoltaic power units, respectively; s Number of segments for the unit's quotation; ρ i,k ρ w and ρ v These are the prices for the k-th segment of synchronous generator set i, wind power (w) generation price, and photovoltaic power (v) generation price, respectively, in yuan / (MWh); P i,t,k P represents the active power of synchronous generator unit i in the k-th segment during time period t; w,t and P v,t ΔT represents the active power of wind power (w) and photovoltaic power (v) during time period t, respectively; T is the total number of time periods, and ΔT is the time interval of one time period. and Represents the start-up and shutdown cost of synchronous generator set i; u i,t The variable is 0-1, where 0 indicates that unit i is in a shutdown state during time period t, and 1 indicates that unit i is in a running state during time period t; Cost coefficient of battery energy storage device e; The charging or discharging power of the battery energy storage device e during time period t; The cost coefficient for pumped water by the pumped storage unit m; Let m be the pumping power of the pumped storage unit during time period t.

6. The integrated day-ahead active and reactive power dispatching method for large power grids according to claim 1, characterized in that, The step of identifying key weak nodes and key energy storage nodes in the large power grid through power flow calculation based on the generator operating parameters, reactive power compensation equipment status parameters, and energy storage system characteristic parameters includes: Calculate the first sensitivity of voltage at each node of the large power grid to reactive power changes, and screen nodes with the first sensitivity higher than their corresponding threshold as key weak nodes; Calculate the second sensitivity of active power of each energy storage node in the large power grid to voltage changes, and select nodes with the second sensitivity higher than their corresponding threshold as key energy storage nodes.

7. A large power grid day-ahead active and reactive power integrated dispatching system, characterized in that, include: The data acquisition unit is used to acquire generator operating parameters, reactive power compensation equipment status parameters, and energy storage system characteristic parameters of the large power grid. The critical node identification unit is used to identify critical weak nodes and critical energy storage nodes in the power grid by power flow calculation based on the generator operating parameters, reactive power compensation equipment status parameters and energy storage system characteristic parameters. The scheduling strategy generation unit is used to establish an objective function for the key weak nodes and key energy storage nodes, with the goal of minimizing the total cost. The total cost includes the total cost of electricity purchase, the start-up and shutdown cost of synchronous machines, the pumping cost of pumped storage units, and the charging and discharging cost of battery energy storage systems. The objective function is solved by the Xueyan optimization algorithm to obtain the start-up and shutdown plan of conventional units, the allocation of active power output, and the scheduling strategy of reactive power resources of the large power grid.

8. The integrated day-ahead active and reactive power dispatching system for large power grids according to claim 7, characterized in that, In the scheduling strategy generation unit, the constraints of the objective function include constraints on conventional generating units and power limit constraints on reactive power equipment. Conventional generator unit constraints include upper and lower limits of active power for synchronous generator units, ramping constraints for synchronous generator units, and start-stop logic constraints for synchronous generator units. The power limit constraints for reactive power equipment include upper and lower limits of reactive power for reactive power equipment, reactive power constraints for capacitor banks, and reactive power constraints for new energy inverters.

9. The integrated day-ahead active and reactive power dispatching system for large power grids according to claim 8, characterized in that, The upper and lower limits of active power of synchronous generator sets are constrained as follows: P i,min u i,t ≤P i,t ≤P i,max u i,t ; Among them, P i,t P represents the active power of synchronous generator set i during time period t; i,min and P i,max These represent the upper and lower limits of the active power output of synchronous generator set i; u i,t The variable is 0-1, where 0 indicates that synchronous generator set i is in a stopped state during time period t, and 1 indicates that synchronous generator set i is in a running state during time period t. The synchronous generator ramping constraint is: |P i,t -P i,t-1 |≤ΔP i ; Where, ΔP i For synchronous generator set i, the ramp rate is limited; The start-stop logic constraints for synchronous generator sets are as follows: in, and T represents the continuous start-up time and continuous shutdown time of synchronous generator unit i during time period t; i on and T i off These are the minimum continuous operating time and minimum continuous shutdown time of synchronous generator set i, respectively. The upper and lower limits of reactive power of reactive equipment are constrained as follows: Q j,min ≤Q j,t ≤Q j,max ; Among them, Q j,t Q represents the reactive power of dynamic reactive power equipment j during time period t; j,max and Q j,min The upper and lower limits of reactive power output for dynamic reactive power device j; The reactive power constraint of the capacitor bank is: Q s,min ≤Q s,t ≤Q s,max , Among them, Q s,t Q represents the capacity of capacitor bank s during time period t; s,min and Q s,max These represent the upper and lower limits of the capacitance of capacitor bank S, respectively; C s (t) and C s (t-1) represents the connected capacity of the capacitor bank during time period t and time period t-1, respectively; N s,max This represents the maximum number of switching operations for capacitor bank s. The reactive power constraint of the new energy inverter is: Among them, P w,t Q w,t and P v,t Q v,t S represents the active and reactive power of wind power (w) and photovoltaic power (v) during time period t, respectively; w,inv and S v,inv These are the rated inverter capacities for wind power (w) and photovoltaic (v), respectively.

10. The integrated day-ahead active and reactive power dispatching system for large power grids according to claim 8, characterized in that, In the scheduling strategy generation unit, the constraints of the objective function also include active and reactive power balance constraints, positive and negative spinning reserve constraints, pumped hydro storage constraints, battery energy storage constraints, and grid security constraints for key weak nodes and key energy storage nodes.

11. The integrated day-ahead active and reactive power dispatching system for large power grids according to claim 7, characterized in that, In the scheduling policy generation unit, the objective function is: min F=F en +F g +F pump +F ess ; Where, N G N represents the total number of synchronous generator sets; Pump N represents the total number of pumped storage units; ESS N represents the total number of battery energy storage devices; w and N v N represents the total number of wind turbines and the total number of photovoltaic power units, respectively; s Number of segments for the unit's quotation; ρ i,k ρ w and ρ v These are the prices for the k-th segment of synchronous generator set i, wind power (w) generation price, and photovoltaic power (v) generation price, respectively, in yuan / (MWh); P i,t,k P represents the active power of synchronous generator unit i in the k-th segment during time period t; w,t and P v,t ΔT represents the active power of wind power (w) and photovoltaic power (v) during time period t, respectively; T is the total number of time periods, and ΔT is the time interval of one time period. and Represents the start-up and shutdown cost of synchronous generator set i; u i,t The variable is 0-1, where 0 indicates that unit i is in a shutdown state during time period t, and 1 indicates that unit i is in a running state during time period t; Cost coefficient of battery energy storage device e; The charging or discharging power of the battery energy storage device e during time period t; The cost coefficient for pumped water by the pumped storage unit m; Let m be the pumping power of the pumped storage unit during time period t.

12. The integrated day-ahead active and reactive power dispatching system for large power grids according to claim 7, characterized in that, In the critical node identification unit, the step of identifying critical weak nodes and critical energy storage nodes in the large power grid through power flow calculation based on the generator operating parameters, reactive power compensation equipment status parameters, and energy storage system characteristic parameters includes: Calculate the first sensitivity of voltage at each node of the large power grid to reactive power changes, and screen nodes with the first sensitivity higher than their corresponding threshold as key weak nodes; Calculate the second sensitivity of active power of each energy storage node in the large power grid to voltage changes, and select nodes with the second sensitivity higher than their corresponding threshold as key energy storage nodes.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is loaded into the processor, it implements a day-ahead integrated active and reactive power dispatching method for a large power grid according to any one of claims 1-6.

14. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a day-ahead integrated active and reactive power dispatching method for a large power grid according to any one of claims 1-6.

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