A stochastic optimization scheduling method considering heterogeneous flexible resources and grid resilience enhancement

Abstract

This invention discloses a stochastic optimization scheduling method that considers heterogeneous flexible resources and enhances grid resilience. The method includes constructing a resource operation model based on the operational characteristics of various heterogeneous flexible resources in the power system; considering the changes in grid operating state under extreme weather conditions; constructing a two-stage stochastic optimization scheduling model with the goal of minimizing power system operating costs; determining the constraints of the two-stage stochastic optimization scheduling model; obtaining the optimal scheduling decision result based on the resource operation model and the constraints of the two-stage stochastic optimization scheduling model; and configuring the actual power system operating state according to the scheduling decision result. This invention utilizes a probabilistic model to simulate the randomness of fault scenarios under extreme conditions and achieves effective enhancement of power system resilience through the coordinated response of flexible power sources and load resources and resilience index constraints.

Description

Technical Field

[0001] This invention belongs to the field of power grid optimization scheduling, and in particular relates to a stochastic optimization scheduling method that considers heterogeneous flexible resources and the improvement of power grid resilience. Background Technology

[0002] In recent years, frequent extreme weather events worldwide have seriously threatened the safe operation of power systems. Global climate and the environment have deteriorated dramatically in recent years, with frequent extreme weather events such as droughts, floods, hurricanes, extreme heat, and extreme cold, leading to continuously escalating disaster losses. As the most important infrastructure, the energy and power system is severely affected by extreme weather. Extreme weather not only causes a surge in load in a short period, but the sudden deterioration of operating conditions also increases the failure rate of equipment on the generation side and transmission lines. Natural disasters accompanying extreme weather can also damage critical facilities such as power plants, transmission lines, electrical equipment, and dispatching and communication systems. Therefore, research on power system resilience has emerged in the field of power system safety and stability. Unlike the fault types and characteristics addressed by the "Guidelines for Power System Safety and Stability" and the "three lines of defense," power system resilience theory addresses faults like extreme weather, which have extremely low probability but extremely high damage.

[0003] On the other hand, the flexibility of my country's power system is insufficient, and the coordinated response capabilities of flexible power sources and load resources have not been fully utilized. From the perspective of power system flexibility, flexible power sources on the power supply side and flexible load resources on the load side are collectively categorized as heterogeneous flexible resources, such as flexible power sources like natural gas power generation, pumped storage, and new energy storage, and flexible load resources like temperature-controlled loads and electric vehicles. These various flexible resources can play a crucial role in the power system's recovery process under extreme conditions. However, current research has not yet addressed dispatch strategies for the coordinated response of these heterogeneous flexible resources to enhance power system resilience. Therefore, there is an urgent need to develop an optimized dispatch model that considers improving grid resilience to further enhance the efficiency of flexible resource allocation across time and space and effectively unleash the multi-timescale flexibility of the power system. Summary of the Invention

[0004] To address the problems existing in the background art, the purpose of this invention is to provide a stochastic optimization scheduling method that considers heterogeneous flexible resources and grid resilience enhancement. This invention can fully utilize the adjustment capabilities of various flexible resources (including flexible power sources and load resources) under extreme weather conditions.

[0005] The technical solution adopted in this invention includes the following steps:

[0006] Step S1: First, construct a resource operation model based on the operating characteristics of various heterogeneous flexible resources in the power system; the heterogeneous flexible resources include gas generator sets, pumped storage power stations, battery energy storage power stations, electric vehicle charging stations, and temperature-controlled load systems.

[0007] Step S2: Then, considering the change process of power grid operation status under extreme weather conditions, and with the goal of minimizing the operating cost of the power system, a two-stage stochastic optimization scheduling model is constructed. The first stage of the two-stage stochastic optimization scheduling model is the preventive scheduling stage before the power system fails, and the second stage is the emergency recovery stage after the power system fails.

[0008] Step S3: Determine the constraints of the two-stage stochastic optimization scheduling model described in step S2;

[0009] Step S4: Based on the constraints of the resource operation model and the two-stage stochastic optimization scheduling model, the optimized scheduling result of the preventive scheduling stage is obtained. Then, the optimized scheduling result of the preventive scheduling stage is used as the boundary condition of the emergency recovery stage. Combined with the resource operation model, the best scheduling decision result of the emergency recovery stage is obtained as the final scheduling decision result of the power system. The actual power system operation state is configured according to the final scheduling decision result.

[0010] The climbing speed of the gas generator set k in step S1 satisfies the following constraints:

[0011] -Rp k,max ≤ i,k,t -P i,k,t+1 ≤p k,max

[0012] In the formula, P i,k,t and P i,k,t+1 Rp represents the power output level of gas generator set k at node i at time t and time t+1, respectively. k,max This represents the maximum ramp rate of gas generator set k.

[0013] The power generation of the pumped storage power station pu during the pumping and power generation process is obtained according to the following formula:

[0014]

[0015] Among them, S pu,t Let pu be the amount of electricity that a pumped-storage hydroelectric power station can generate during time period t, μ represent the efficiency coefficient of the pumping process, and x represent the power generation capacity of the pumped-storage hydroelectric power station. pu,t The 0-1 variable is used to characterize the state of a pumped storage power station, where 0 represents the pumping state and 1 represents the power generation state. Let pu represent the power generation and pumping power of the pumped storage power station at time t, respectively. Let pu represent the power generation flow rate and pumping flow rate of the pumped storage power station at time t, respectively. ρ represents the calculated values ​​of the generator head (pu) and pump head (pu) of the pumped storage power station, respectively. wη represents the density of water. M η G η P η T These represent the efficiency of the electric motor, the efficiency of the generator, the hydraulic efficiency of the pump, and the hydraulic efficiency of the generator, respectively. pu,max r represents the maximum head of the pumped storage power station pu. pu,min This indicates the minimum head (pu) of a pumped storage power station.

[0016] The expression for the charging and discharging process of a battery energy storage power station (es) is as follows:

[0017]

[0018] In the formula, η represents the state of charge of the battery energy storage power station es during time period t. loss x represents the loss coefficient of the battery energy storage power station es. es,t The 0-1 variables characterize the state of a battery energy storage power station, where 0 represents the charging state and 1 represents the discharging state. and η represents the charging power and discharging power of the battery energy storage power station es during time period t, respectively. ch and η dis For the charging and discharging efficiency of battery energy storage power stations, C Es For the rated capacity of energy storage, and These are the upper and lower limits of the state of charge (es) of a battery energy storage power station.

[0019] The charging and discharging capacity model of the electric vehicle charging station in step S1 is as follows:

[0020]

[0021] in, The battery capacity of an electric vehicle charging station (evs) during time period t. Let m represent the discharge battery capacity of the electric vehicle charging station at time t, and m represent the number of electric vehicles that the charging station (evs) can charge and discharge during time period t. and Let C represent the charging capacity and discharging capacity of the electric vehicle ev during time period t, respectively. ev,bat It refers to the battery capacity of the electric vehicle (EV). This is the maximum battery capacity. This represents the state of charge (ev) of the electric vehicle during time period t. The initial state of charge of the battery when the electric vehicle (EV) begins to interact with the grid.

[0022] The specific response model of the temperature-controlled load system is as follows:

[0023] RV A1 =P init -P min

[0024] RC A2 =P max -P init

[0025] RR A1 =RC A1 ·(1-α%) / RT A1

[0026] RR A2 =RC A2 ·(1-α%) / RT A2

[0027] Among them, RC A1 RC represents the downward adjustable capacity of the temperature-controlled load system AC. A2 P represents the upward adjustable capacity of the temperature-controlled load system. init P represents the initial power of the temperature-controlled load system. max P represents the maximum operating power of the temperature-controlled load system. min Represents the minimum operating power of the temperature-controlled load system, RR A1 RR represents the rate of climb during the downward adjustment process. A2 The percentage represents the ramp rate during the upward adjustment process, and % represents the capacity deviation rate generated by the rebound effect. RT A1 RT is the response time for power down-adjustment. A2 This is the response time for power upward adjustment.

[0028] In step S2, the two-stage stochastic optimization scheduling model performs the following specific actions during the preventive scheduling phase:

[0029]

[0030] in, This represents the operating cost of the power system before a failure occurs. This represents the expected value of the sum of operating costs and adjustment costs of various heterogeneous flexible resources in each failure scenario with probability p under the influence of extreme weather.

[0031] In the above formula, the operating cost of the power system before a fault occurs. Specifically as follows:

[0032]

[0033] Where t0 represents the time when the fault occurs, N g N k Np N es N evs and N d These represent the number of coal-fired power generating units, gas-fired power generating units, pumped storage power stations, battery energy storage power stations, electric vehicle charging stations, and loads in the power grid, respectively. and Let g and k represent the unit power generation costs of coal-fired power generation unit g and gas-fired power generation unit k, respectively. and Let g and k represent the power output levels of coal-fired power generating unit g and gas-fired power generating unit k respectively during time period t under normal grid operation conditions. and Let g and k represent the standby costs of coal-fired power generation unit g and gas-fired power generation unit k, respectively. These represent the upper and lower rotating reserve capacities of the coal-fired power generating unit g, respectively. These represent the upper and lower spinning reserve capacities of the gas generator set k, respectively. and These represent the unit costs of pumped storage and power generation in a pumped storage power station, respectively. and These represent the power output of a pumped-storage hydroelectric power station (pu) during time period t, respectively, under normal grid operation. and These represent the unit charging cost and unit discharging cost of the battery energy storage power station es, respectively. and These represent the unit charging cost and unit discharging cost of an electric vehicle charging station (EVS), respectively. and These represent the charging power and discharging power of the battery energy storage station es during time period t under normal grid operation. and These represent the charging power and discharging power of the electric vehicle charging station (evs) during time period t under normal grid operation. This indicates cost reduction per unit load. This represents the load reduction at node i at time t under normal power grid operation.

[0034] In the above formula, the expected value is the sum of the operating cost and the adjustment cost of various heterogeneous flexible resources in each failure scenario with probability p. Specifically as follows:

[0035]

[0036] Where, N s N represents the number of fault scenarios, T represents the total number of time periods in the power system operation simulation process, and N represents the total number of time periods in the simulation process. acp represents the number of temperature-controlled load systems. s Let represent the probability of failure scenario s. and Let G and K represent the estimated power output levels of coal-fired generator unit g and gas-fired generator unit k during time period t, respectively, under fault scenario s. and Let represent the estimated power outputs of the pumped storage power station pu during time period t under grid fault scenario s, specifically the power generated by pumping and releasing water. and Let represent the estimated charging power and estimated discharging power of the battery storage power station es during time period t under grid fault scenario s, respectively. and Let c represent the estimated charging power and estimated discharging power of an electric vehicle charging station evs during time period t under grid fault scenario s. ac This represents the unit cost of adjusting the air conditioning load system. This represents the estimated power adjustment value of the air conditioning load system under fault scenario s. This represents the estimated load reduction at node i at time t under fault scenario s.

[0037] In step S2, the two-stage stochastic optimization scheduling model in the emergency recovery phase is as follows:

[0038]

[0039] Among them, C em This indicates the cost of emergency power system restoration. and Let g and k represent the power output levels of coal-fired generator set g and gas-fired generator set k respectively during time period y under fault scenario s. and Let represent the power output of the pumped storage power station pu during time period t under grid fault scenario s, specifically the power output of pumped storage and power generation. and Let represent the charging power and discharging power of the battery storage power station es during time period t under grid fault scenario s. and Let evs represent the charging power and discharging power of electric vehicle charging station evs during time period t under grid fault scenario s. This represents the power value adjusted by the temperature-controlled load system ac during the time period t under fault scenario s. This represents the load reduction at node i at time t under fault scenario s.

[0040] In step S3, the operational constraint adjustment of the two-stage stochastic optimization scheduling model during the preventive scheduling phase is as follows:

[0041] i) Power balance constraints at each node of the power grid:

[0042]

[0043] Where EG, GG, PU, ​​ES, EVS, and AC represent the sets of coal-fired power generating units, gas-fired power generating units, pumped storage power stations, battery energy storage power stations, electric vehicle charging stations, and temperature-controlled load systems connected to node i, respectively, and EL represents the set of branches connected to node i. and Let g and k represent the power output levels of coal-fired generator unit g and gas-fired generator unit k at node i at time t, respectively, under normal grid operation conditions. and This represents the power output of the pumped storage power station pu at node i during time period t, under normal grid operation. and These represent the charging power and discharging power of the battery storage station es at node i during time period t, respectively, under normal grid operation. and These represent the charging power and discharging power of electric vehicle charging station evs at node i during time period t, respectively, under normal grid operation. This represents the power of the temperature-controlled load system ac at node i during time period t under normal grid operation. This represents the load power of node i at time t under normal grid operation. This represents the power flow of line l at time t under normal power grid operation.

[0044] In the above formula, the power flow of line l The relationship with the phase angle of the node is as follows:

[0045]

[0046] in, and Let x represent the phase angles of node i and node j at time t under normal grid operation, respectively. l This represents the impedance of line l;

[0047] Power constraints on transmission lines:

[0048]

[0049] Among them, f l,max This represents the maximum power transmitted by line l;

[0050] Node phase angle constraint:

[0051]

[0052] Where, θ i,max This represents the maximum allowable deviation of the power angle at node i;

[0053] ii) Standby constraints for coal-fired and gas-fired power generation units:

[0054]

[0055] in, and These represent the upper and lower spinning reserve capacities of coal-fired generating unit g at node i under normal grid operation. and P represents the upper and lower spinning reserve capacity of gas generator set k at node i under normal grid operation. ig,max and P ig,max P represents the minimum and maximum power output of a coal-fired power generating unit, respectively. ik,min and P ik,max These represent the minimum and maximum power output of the gas generator set, respectively.

[0056] iii) Operational constraints of pumped storage power stations:

[0057] Among them, the constraints on the power generation of pumped storage power stations and the pumping power and power generation are:

[0058]

[0059] Pumped storage power station power consumption constraints and power generation constraints:

[0060]

[0061]

[0062] in, and Let pi represent the pumped storage power and the power generation from the release of water by the pumped storage power station during the time period t, respectively, under normal grid operation. and These represent the upper and lower limits of the power output during PU charging in a pumped storage power station, respectively. and These represent the upper and lower limits of the power output during PU discharge in a pumped storage power station, respectively.

[0063] Pumped storage power stations cannot charge and discharge simultaneously due to constraints.

[0064]

[0065] Where M is a preset constant, and M takes the largest possible value;

[0066] Power generation constraints of pumped storage power plants:

[0067]

[0068] Among them, S pu, S represents the equivalent power generation of the maximum reservoir capacity of a pumped storage power station (pu). pu, This represents the equivalent power generation of the reservoir's minimum capacity.

[0069] iv) Operational constraints of battery energy storage power stations:

[0070] Among them, the constraints on the state of charge and charging / discharging power of battery energy storage power stations are:

[0071]

[0072] Upper and lower limits of charging and discharging power constraints for battery energy storage power stations:

[0073]

[0074] in, and These represent the charging power and discharging power of the battery energy storage station es during time period t under normal grid operation. and These represent the upper and lower limits of the power output during charging of the battery energy storage power station (es), respectively. and These represent the upper and lower limits of the power output of the battery energy storage power station during discharge, respectively.

[0075] Battery energy storage power stations cannot be charged and discharged simultaneously due to constraints:

[0076]

[0077] Upper and lower limits of state of charge constraints for battery energy storage power stations:

[0078]

[0079] in, and These represent the minimum and maximum states of charge of the battery energy storage power station es, respectively.

[0080] v) Operational constraints of electric vehicle charging stations:

[0081] Among them, the constraints on the state of charge of electric vehicle charging stations and the power during the charging and discharging process are:

[0082]

[0083] In the formula, η represents the state of charge of the electric vehicle charging station evs during time period t. loss′ This represents the loss coefficient of an electric vehicle charging station. evs,t The 0-1 variables characterize the EVS state of electric vehicle charging stations, where 0 represents the charging state and 1 represents the discharging state. and η represents the charging power and discharging power of the electric vehicle charging station evs during time period t, respectively. ch′ and η dis′ The charging and discharging efficiency of electric vehicle charging stations;

[0084] Upper and lower limits of charging and discharging power constraints for electric vehicle charging stations:

[0085]

[0086] in, and These represent the charging power and discharging power of the electric vehicle charging station (evs) during time period t under normal grid operation. and These represent the upper and lower limits of the power output during charging at an electric vehicle charging station (EVS), respectively. and These represent the upper and lower limits of the power output of an electric vehicle charging station during EVS discharge, respectively.

[0087] Electric vehicle charging stations cannot charge and discharge simultaneously due to constraints:

[0088]

[0089] Upper and lower limits of the state of charge (SOC) of batteries in electric vehicle charging stations:

[0090]

[0091] in, and These represent the minimum and maximum states of charge (evs) of the electric vehicle charging station, respectively.

[0092] vi) Climbing constraints for various heterogeneous flexible resources:

[0093]

[0094]

[0095] Among them, Rp g,max and Rp k,max Let g and k represent the maximum climbing speeds of the coal-fired generator set and the gas-fired generator set, respectively. and These represent the maximum ramp speeds of the pumped storage power station (PU) during the charging and discharging processes, respectively. and These represent the maximum ramp rate of the battery energy storage power station es during the charging and discharging processes, respectively. and Rp represents the maximum ramp speed of an electric vehicle charging station during both the charging and discharging processes. ac, This indicates that the AC response ramp rate of the air conditioning load system is the highest;

[0096] vii) Load shedding constraint:

[0097]

[0098] in, This represents the load reduction at node i at time t under normal grid operation. This represents the load power of node i at time t under normal grid operation.

[0099] In step S3, the operational constraints of the two-stage stochastic optimization scheduling model during the emergency recovery phase are adjusted as follows:

[0100] i) Power balance constraints at each node of the power grid:

[0101]

[0102] in, and Let G and K represent the estimated power output levels of coal-fired generator unit g and gas-fired generator unit k at node i at time t, respectively, under fault scenario s. and This represents the estimated power output of the pumped storage power station pu at node i during time period t under fault scenario s. and Let represent the estimated charging power and estimated discharging power of the battery energy storage station es at node i during time period t, respectively, under fault scenario s. and Let represent the estimated charging power and estimated discharging power of electric vehicle charging station evs at node i during time period t, respectively, under fault scenario s. This represents the estimated power of the temperature-controlled load system ac at node i during time period t under fault scenario s. This represents the estimated load power of node i at time t under power grid fault scenario s. This represents the estimated load reduction at node i at time t under the power grid fault scenario s. This represents the power flow estimate of line l at time t under the power grid fault scenario s;

[0103] In the above formula, the estimated line power flow value The relationship with the phase angle of the node is as follows:

[0104]

[0105] in, and Let represent the estimated phase angles of node i and node j at time t under fault scenario s, respectively;

[0106] Power constraints on transmission lines:

[0107]

[0108] Node phase angle constraint:

[0109]

[0110] ii) Standby constraints for coal-fired and gas-fired units:

[0111]

[0112] iii) Operational constraints of pumped storage power stations:

[0113] Among them, the constraints on the power generation of pumped storage power stations and the pumping power and power generation are:

[0114]

[0115] in, and These represent the estimated charging and discharging power of the pumped storage power station pu during time period t, respectively.

[0116] Pumped storage power station power consumption constraints and power generation constraints:

[0117]

[0118] Pumped storage power stations cannot charge and discharge simultaneously due to constraints.

[0119]

[0120] in, Let be a 0-1 variable representing the state of the pumped storage power station under fault scenario s, where 0 represents the pumping state and 1 represents the power generation state.

[0121] Power generation constraints of pumped storage power plants:

[0122]

[0123] in, S represents the amount of electricity that a pumped storage power station (PU) can generate during time period t under fault scenario s. pu,max S represents the equivalent power generation of the maximum reservoir capacity of a pumped storage power station (pu). pu,min The equivalent power generation of the reservoir's minimum capacity;

[0124] iv) Operational constraints of battery energy storage power stations:

[0125] Among them, the constraints on the state of charge of battery energy storage power stations and the power during their charging and discharging processes are:

[0126]

[0127] in, and These represent the estimated charging and discharging power of the battery energy storage power station es during time period t, respectively.

[0128] Upper and lower limits of charging and discharging power constraints for battery energy storage power stations:

[0129]

[0130] in, and These represent the upper and lower limits of the power output during charging of the battery energy storage power station (es), respectively. and These represent the upper and lower limits of the power output of the battery energy storage power station during discharge, respectively.

[0131] Battery energy storage power stations cannot be charged and discharged simultaneously due to constraints:

[0132]

[0133] in, For the state of the battery energy storage power station under fault scenario s, there are 0-1 variables, where 0 represents the pumping state and 1 represents the power generation state.

[0134] Upper and lower limits of state of charge constraints for battery energy storage power stations:

[0135]

[0136] v) Operational constraints of electric vehicle charging stations:

[0137] Among them, the constraints on the state of charge of electric vehicle charging stations and the power during their charging and discharging processes are as follows:

[0138]

[0139] In the formula, and These represent the estimated charging power and discharging power of the electric vehicle charging station evs during time period t, respectively.

[0140] Upper and lower limits of charging and discharging power constraints for electric vehicle charging stations:

[0141]

[0142] Electric vehicle charging stations cannot charge and discharge simultaneously due to constraints:

[0143]

[0144]

[0145] in, Let evs be a 0-1 variable representing the state of the electric vehicle charging station under fault scenario s, where 0 represents the charging state and 1 represents the power generation state.

[0146] Upper and lower limits of the state of charge (SOC) of batteries in electric vehicle charging stations:

[0147]

[0148] in, The state of charge of the electric vehicle charging station evs at time t under fault scenario s;

[0149] vi) Ramp-up constraints for various adjustment resources:

[0150]

[0151] vii) Load shedding constraint:

[0152]

[0153] viii) Risk constraints:

[0154]

[0155] EENS T ≤EENS set

[0156] Where, p s EENS represents the probability of failure scenario s. T EENS represents the expected value of insufficient electrical energy. set This represents the preset expected threshold.

[0157] The specific steps of S4 are as follows:

[0158] Step S4.1: Based on the constraints of the resource operation model and the two-stage stochastic optimization scheduling model, the optimized scheduling result of the two-stage stochastic optimization scheduling model in the preventive scheduling phase is obtained. The optimized scheduling result Ω of the two-stage stochastic optimization scheduling model in the preventive scheduling phase is... pre Specifically as follows:

[0159]

[0160] Among them, Ω pre This indicates the optimized scheduling result during the preventive scheduling phase;

[0161] Step S4.2 Next, the power output level and reserve capacity of the coal-fired generator set g and the gas-fired generator set k at time t0 before the fault occurred are used as boundary conditions for the two-stage stochastic optimization scheduling model to make optimization scheduling decisions in the emergency recovery phase.

[0162] Step S4.3: Combining the resource operation model, with the total operating cost C of the power system... all The goal is to obtain the best dispatch decision result during the emergency recovery phase, with the lowest possible value as the final dispatch decision result for the power system.

[0163] Total operating cost of power system C all The following formula is used to obtain the result:

[0164]

[0165] Among them, C em This indicates the cost of emergency power system restoration. This represents the operating cost of the power system before a failure occurs. This represents the expected value of the sum of operating costs and adjustment costs of various heterogeneous flexible resources in each failure scenario with probability p under the influence of extreme weather.

[0166] Step S4.4 Finally, the operating status of the actual power system is set according to the final scheduling decision result.

[0167] This method first analyzes and models the operational characteristics of various heterogeneous flexible resources, then considers the changes in grid operation status under extreme weather conditions, and finally establishes a two-stage stochastic optimization scheduling model that considers resilience enhancement. The first stage of this two-stage stochastic optimization scheduling model is the pre-fault prevention scheduling stage. Based on extreme weather warning information, a probabilistic model considers line disconnection scenarios under different fault conditions, decides on the conventional power output plan and reserve requirements before the fault occurs, and prepares various heterogeneous flexible resources, such as reserving reserve capacity for flexible power sources and charging various energy storage devices during the preparation stage to ensure sufficient generating capacity. The decision results of the first stage are used as the boundary conditions for the optimization decision in the second stage. The second stage is the emergency recovery stage after the fault occurs, considering various heterogeneous flexible resources and performing corrective scheduling for the fault scenario. This method utilizes the scheduling of various types of flexible resources to formulate grid resilience enhancement strategies, calculates grid resilience indices using load shedding results of corresponding scenarios, and realizes the interaction between the two stages of scheduling before and after extreme events by introducing resilience index constraints into the model, thereby effectively improving the resilience of the power system.

[0168] The beneficial effects of this invention are as follows:

[0169] This invention proposes a stochastic optimization scheduling method that considers heterogeneous flexible resources and enhances grid resilience. The method first analyzes and models the operational characteristics of various flexible resources. Then, it considers grid operation and scheduling strategies under extreme weather conditions, dividing the optimization process into two stages based on the pre-fault prevention scheduling stage and the post-fault emergency recovery stage. Finally, it uses a probabilistic model to simulate the stochasticity of fault scenarios under extreme conditions and achieves effective enhancement of power system resilience through coordinated response of flexible power sources and load resources and resilience index constraints. Attached Figure Description

[0170] Figure 1 This is a flowchart of the invention. Detailed Implementation

[0171] The present invention will be described in detail below with reference to specific implementation examples. These examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any way.

[0172] The specific steps of this invention are as follows:

[0173] Step S1: First, construct a resource operation model based on the operating characteristics of various heterogeneous flexible resources in the power system; heterogeneous flexible resources include gas generator sets, pumped storage power stations, battery energy storage power stations, electric vehicle charging stations, and temperature-controlled load systems.

[0174] Step S2: Then, considering the change process of power grid operation status under extreme weather conditions, and with the goal of minimizing the operating cost of the power system, a two-stage stochastic optimization scheduling model is constructed. The first stage of the two-stage stochastic optimization scheduling model is the preventive scheduling stage before the power system fails, and the second stage is the emergency recovery stage after the power system fails.

[0175] Step S3: Determine the constraints of the two-stage stochastic optimization scheduling model in step S2;

[0176] Step S4: Based on the constraints of the resource operation model and the two-stage stochastic optimization scheduling model, the optimized scheduling result of the preventive scheduling stage is obtained. Then, the optimized scheduling result of the preventive scheduling stage is used as the boundary condition of the emergency recovery stage. Combined with the resource operation model, the best scheduling decision result of the emergency recovery stage is obtained as the final scheduling decision result of the power system. The actual power system operation state is configured according to the final scheduling decision result.

[0177] Compared to coal-fired power generators, gas-fired power generators are more energy-efficient, cleaner, and lower in carbon emissions. They also offer advantages such as rapid ramp-up, flexible operation, and strong peak-shaving capabilities. Therefore, they play an increasingly important role in power supply and can quickly respond to load fluctuations and rapid dispatch adjustments after faults. The ramp-up speed of gas-fired power generator k in step S1 satisfies the following constraints:

[0178] -Rp k,max ≤ i,k,t -P i,k,t+1 ≤p k,max

[0179] In the formula, P i,k,t and P i,k,t+1 Rp represents the power output level of gas generator unit k at grid node i at time t and time t+1, respectively. k,max This represents the maximum ramp rate of gas generator set k.

[0180] Pumped-storage hydroelectric power stations are flexible and reliable in operation. They can switch freely between pumping water to consume electricity and releasing water to generate electricity, achieving peak shaving and valley filling. Furthermore, they can assist in grid frequency and voltage regulation when the grid load changes. The power generation capacity of a pumped-storage hydroelectric power station is related to the reservoir capacity. The calculated power generation capacity based on the reservoir capacity will affect the power supply capacity during the accident recovery process.

[0181] The power generation of a pumped-storage hydroelectric power station (PU) during the pumping and power generation processes is obtained using the following formula:

[0182]

[0183] Among them, S pu,tLet pu be the power generation capacity of a pumped storage power station during time period t, μ represent the given pumping efficiency coefficient, and x be the power generation capacity of the pumped storage power station during time period t. pu,t The 0-1 variable is used to characterize the state of a pumped storage power station, where 0 represents the pumping state and 1 represents the power generation state. Let pu represent the power generation and pumping power of the pumped storage power station at time t, respectively. Let pu represent the power generation flow rate and pumping flow rate of the pumped storage power station at time t, respectively. ρ represents the calculated values ​​of the generator head (pu) and pump head (pu) of the pumped storage power station, respectively. w η represents the density of water. M η G η P η T These represent the efficiency of the electric motor, the efficiency of the generator, the hydraulic efficiency of the pump, and the hydraulic efficiency of the generator, respectively. pu,max The maximum head (pu) of a pumped storage power station is the difference between the highest water level of the upper reservoir and the dead water level of the lower reservoir. pu,min The minimum head of a pumped storage power station (PU) is the difference between the dead water level of the upper reservoir and the highest water level of the lower reservoir. Generally, the head and pump head of a pumped storage power station are equal. The calculated values ​​of the power generation head and the pump pump head are the average of the maximum head and the minimum head.

[0184] Battery energy storage power stations and pumped storage power stations share similar characteristics, both enabling peak shaving and valley filling of the power grid and assisting in frequency regulation. Their operational performance primarily depends on the State of Charge (SoC) of the battery energy storage system. The expression for the charging and discharging process of a battery energy storage power station is as follows:

[0185]

[0186] In the formula, η represents the state of charge of the battery energy storage power station es during time period t. loss This represents the loss coefficient of the battery energy storage power station es. es, The 0-1 variables characterize the state of a battery energy storage power station, where 0 represents the charging state and 1 represents the discharging state. and η represents the charging power and discharging power of the battery energy storage power station es during time period t, respectively. ch and η dis For the charging and discharging efficiency of battery energy storage power stations, C Es To achieve the rated capacity of the energy storage system and ensure its stable operation, the state of charge (SOC) of the battery energy storage power station needs to be limited to a certain range. and These are the upper and lower limits of the state of charge (es) of a battery energy storage power station.

[0187] With the rapid development and widespread adoption of electric vehicles, large-scale electric vehicle charging has become an indispensable load in power grid operation. Intelligent regulation can provide the power grid with significant adjustment capacity, enhancing its response and recovery capabilities under extreme weather conditions. Modeling the charging and discharging capacity of electric vehicle charging stations involves two steps: first, modeling the adjustable capacity of the power grid based on the charging and discharging process of a single electric vehicle; and then, modeling the capacity of the charging station itself.

[0188] The charging and discharging capacity model of the electric vehicle charging station in step S1 is as follows:

[0189] The formulas for the charging and discharging amounts that can be exchanged with the power grid at a certain moment at an electric vehicle charging station are as follows:

[0190]

[0191] The formula for the battery capacity of a single electric vehicle when it has excess power that can participate in grid interaction is as follows:

[0192]

[0193] in, The battery capacity of an electric vehicle charging station (evs) during time period t. Let m represent the discharge battery capacity of the electric vehicle charging station at time t, and m represent the number of electric vehicles that the charging station (evs) can charge and discharge during time period t. and Let C represent the charging capacity and discharging capacity of the electric vehicle ev during time period t, respectively. ev,bat It refers to the battery capacity of the electric vehicle (EV). This is the maximum battery capacity. This represents the state of charge (ev) of the electric vehicle during time period t. The initial state of charge of the battery at which an electric vehicle (EV) can begin interacting with the grid.

[0194] Power systems contain a large number of fixed-frequency and variable-frequency air conditioning loads, with peak loads accounting for over 50% in some economically developed regions. These loads possess high regulation capacity and fast response speeds. Controlling these air conditioning loads and adjusting their operation modes will enhance the power grid's ability to regulate power supply under extreme conditions. The response model for temperature-controlled load systems can reference the operating characteristics of generators and consider the unique rebound effect during power recovery after air conditioning load regulation. This leads to the establishment of a flexible resource regulation characteristic evaluation index system that includes indicators such as adjustable capacity, response speed, ramp rate, and regulation duration.

[0195] The response model for a temperature-controlled load (air conditioning) system is as follows:

[0196] RC A1 =P init -P min

[0197] RC A2 =P max -P init

[0198] RR A1 =RC A1 ·(1-α%) / RT A1

[0199] RR A2 =RC A2 ·(1-α%) / RT A2

[0200] Among them, RC A1 RC represents the downward adjustable capacity of the temperature-controlled load system AC. A2 P represents the upward adjustable capacity of the temperature-controlled load system. init P represents the initial power of the temperature-controlled load system. max P represents the maximum operating power of the temperature-controlled load system. min Represents the minimum operating power of the temperature-controlled load system, RR A1 RR represents the rate of climb during the downward adjustment process. A2 The ramp rate represents the rate of increase during the upward adjustment process, α% represents the capacity deviation rate generated in the rebound effect, and RT represents the ramp rate. A1 RT is the response time for power down-adjustment. A2 This is the response time for power upward adjustment.

[0201] The objective function of the two-stage stochastic optimization scheduling model is to minimize the total operating cost of the power system. This total operating cost includes the preventative scheduling cost in the first stage and the emergency recovery cost under extreme conditions in the second stage. Under a predicted extreme weather condition, the objective is to minimize the power system operating cost, including the costs of the preventative scheduling stage and the emergency recovery stage. However, due to the randomness of grid faults caused by extreme conditions and the coupling relationship between the costs of the two stages, it is necessary to consider various scenarios of potential faults during the optimization process in the first stage. In step S2, the two-stage stochastic optimization scheduling model performs the preventative scheduling stage as follows:

[0202]

[0203] in, This represents the operating cost of the power system before a failure occurs. This represents the expected value of the sum of operating costs and adjustment costs of various heterogeneous flexible resources (i.e., flexible power sources and load resources) in each fault scenario with probability p under the influence of extreme weather.

[0204] In the above formula, the operating cost of the power system before a fault occurs. Specifically as follows:

[0205]

[0206] Where t0 represents the time when the fault occurs, N g N k N p N es N evs and N d These represent the number of coal-fired power generating units, gas-fired power generating units, pumped storage power stations, battery energy storage power stations, electric vehicle charging stations, and loads in the power grid, respectively. and Let g and k represent the unit power generation costs of coal-fired power generation unit g and gas-fired power generation unit k, respectively. and Let g and k represent the power output levels of coal-fired power generating unit g and gas-fired power generating unit k respectively during time period t under normal grid operation conditions. and Let g and k represent the standby costs of coal-fired power generation unit g and gas-fired power generation unit k, respectively. These represent the upper and lower rotating reserve capacities of the coal-fired power generating unit g, respectively. These represent the upper and lower spinning reserve capacities of the gas generator set k, respectively. and These represent the unit costs of pumped storage and power generation in a pumped storage power station, respectively. and These represent the power output of a pumped-storage hydroelectric power station (pu) during time period t, respectively, under normal grid operation. and These represent the unit charging cost and unit discharging cost of the battery energy storage power station es, respectively. and These represent the unit charging cost and unit discharging cost of an electric vehicle charging station (EVS), respectively. and These represent the charging power and discharging power of the battery energy storage station es during time period t under normal grid operation. and These represent the charging power and discharging power of the electric vehicle charging station (evs) during time period t under normal grid operation. This indicates cost reduction per unit load. This represents the load reduction at node i at time t under normal grid operation; g, k, pu, es, evs, and ac represent the index values ​​of coal-fired power generating units, gas-fired power generating units, pumped storage power stations, battery energy storage power stations, electric vehicle charging stations, and temperature-controlled load systems, respectively.

[0207] In the above formula, the expected value is the sum of the operating cost and the adjustment cost of various heterogeneous flexible resources in each failure scenario with probability p. Specifically as follows:

[0208]

[0209] Where, N s N represents the number of fault scenarios, T represents the total number of time periods in the power system operation simulation process, and N represents the total number of time periods in the simulation process. ac p represents the number of temperature-controlled load systems. s Let represent the probability of failure scenario s. and Let G and K represent the estimated power output levels of coal-fired generator unit g and gas-fired generator unit k during time period t, respectively, under fault scenario s. and Let represent the estimated power outputs of the pumped storage power station pu during time period t under grid fault scenario s, specifically the power generated by pumping and releasing water. and Let represent the estimated charging power and estimated discharging power of the battery storage power station es during time period t under grid fault scenario s, respectively. and Let c represent the estimated charging power and estimated discharging power of an electric vehicle charging station evs during time period t under grid fault scenario s. ac This represents the unit cost of adjusting the air conditioning load system. This represents the estimated power adjustment value of the air conditioning load system under fault scenario s. This represents the estimated load reduction at node i at time t under fault scenario s; s represents the index value of the fault scenario.

[0210] In step S2, the two-stage stochastic optimization scheduling model performs as follows during the emergency recovery phase:

[0211]

[0212] Among them, C em This indicates the cost of emergency power system restoration. and Let g and k represent the power output levels of coal-fired generator set g and gas-fired generator set k respectively during time period t under fault scenario s. and Let represent the power output of the pumped storage power station pu during time period t under grid fault scenario s, specifically the power output of pumped storage and power generation. and Let represent the charging power and discharging power of the battery storage power station es during time period t under grid fault scenario s. and Let evs represent the charging power and discharging power of electric vehicle charging station evs during time period t under grid fault scenario s. This represents the power adjusted by the air conditioning load system ac during time period t under fault scenario s. This represents the load reduction at node i at time t under fault scenario s;

[0213] The two-stage stochastic optimization scheduling model in the preventive scheduling phase mainly considers the power system operation constraints based on the DC power flow model and the operation constraints of various heterogeneous flexible resources.

[0214] In step S3, the operational constraints of the two-stage stochastic optimization scheduling model during the preventive scheduling phase are adjusted as follows:

[0215] i) Power balance constraints at each node of the power grid:

[0216]

[0217] Where EG, GG, PU, ​​ES, EVS, and AC represent the sets of coal-fired power generating units, gas-fired power generating units, pumped storage power stations, battery energy storage power stations, electric vehicle charging stations, and temperature-controlled load systems connected to node i, respectively, and EL represents the set of branches connected to node i. and Let g and k represent the power output levels of coal-fired generator unit g and gas-fired generator unit k at node i at time t, respectively, under normal grid operation conditions. and This represents the power output of the pumped storage power station pu at node i during time period t, under normal grid operation. and These represent the charging power and discharging power of the battery storage station es at node i during time period t, respectively, under normal grid operation. and These represent the charging power and discharging power of electric vehicle charging station evs at node i during time period t, respectively, under normal grid operation. This represents the power of the temperature-controlled load system ac at node i during time period t under normal grid operation. This represents the load power of node i at time t under normal grid operation. This represents the power flow of line l at time t under normal grid operation; l represents the line number.

[0218] In the above formula, the power flow of line l The relationship with the phase angle of the node is as follows:

[0219]

[0220] in, and Let x represent the phase angles of node i and node j at time t under normal grid operation, respectively. l This represents the impedance of line l;

[0221] Power constraints on transmission lines:

[0222]

[0223] Among them, f l,max This represents the maximum power transmitted by line l;

[0224] Node phase angle constraint:

[0225]

[0226] Where, θ i,max This represents the maximum allowable deviation of the power angle at node i;

[0227] ii) Standby constraints for coal-fired and gas-fired power generation units:

[0228]

[0229] in, and These represent the upper and lower spinning reserve capacities of coal-fired generating unit g at node i under normal grid operation. and P represents the upper and lower spinning reserve capacity of gas generator set k at node i under normal grid operation. ig,max and P ig,max P represents the minimum and maximum power output of a coal-fired power generating unit, respectively. ik,min and P ik,max These represent the minimum and maximum power output of the gas generator set, respectively.

[0230] iii) Operational constraints of pumped storage power stations: The power generation capacity of a pumped storage power station is related to the reservoir capacity.

[0231] Among them, the constraints on the power generation of pumped storage power stations and the pumping power and power generation are:

[0232]

[0233] Pumped storage power station power consumption constraints and power generation constraints:

[0234]

[0235] in, and These represent the power output of a pumped-storage hydroelectric power station (pu) during time period t, respectively, under normal grid operation. and These represent the upper and lower limits of the power output during PU charging in a pumped storage power station, respectively. and These represent the upper and lower limits of the power output during PU discharge in a pumped storage power station, respectively.

[0236] Pumped storage power stations cannot charge and discharge simultaneously due to constraints.

[0237]

[0238] Where M is a preset constant, and M takes the largest preset number;

[0239] Power generation constraints of pumped storage power plants:

[0240]

[0241] Among them, S pu,max S represents the equivalent power generation of the reservoir at its maximum capacity (pu) in a pumped storage power station, i.e., the equivalent power generation of the reservoir under the highest permissible water level. pu,min This represents the equivalent power generation of the reservoir's minimum capacity.

[0242] iv) Operational constraints of battery energy storage power stations:

[0243] Among them, the constraints on the state of charge and charging / discharging power of battery energy storage power stations are:

[0244]

[0245] Upper and lower limits of charging and discharging power constraints for battery energy storage power stations:

[0246]

[0247] in, and These represent the charging power and discharging power of the battery energy storage station es during time period t under normal grid operation. and These represent the upper and lower limits of the power output during charging of the battery energy storage power station (es), respectively. and These represent the upper and lower limits of the power output of the battery energy storage power station during discharge, respectively.

[0248] Battery energy storage power stations cannot be charged and discharged simultaneously due to constraints:

[0249]

[0250] Upper and lower limits of state of charge constraints for battery energy storage power stations:

[0251]

[0252] in, and These represent the minimum and maximum states of charge of the battery energy storage power station es, respectively.

[0253] v) Operational constraints of electric vehicle charging stations:

[0254] Among them, the constraints on the state of charge of electric vehicle charging stations and the power during the charging and discharging process are:

[0255]

[0256] In the formula, η represents the state of charge of the electric vehicle charging station evs during time period t. loss′ This represents the loss coefficient of an electric vehicle charging station. evs,t The 0-1 variables characterize the EVS state of electric vehicle charging stations, where 0 represents the charging state and 1 represents the discharging state. and η represents the charging power and discharging power of the electric vehicle charging station evs during time period t, respectively. ch′ and η dis′ The charging and discharging efficiency of electric vehicle charging stations;

[0257] Upper and lower limits of charging and discharging power constraints for electric vehicle charging stations:

[0258]

[0259] in, and These represent the charging power and discharging power of the electric vehicle charging station (evs) during time period t under normal grid operation. and These represent the upper and lower limits of the power output during charging at an electric vehicle charging station (EVS), respectively. and These represent the upper and lower limits of the power output of an electric vehicle charging station during EVS discharge, respectively.

[0260] Electric vehicle charging stations cannot charge and discharge simultaneously due to constraints:

[0261]

[0262] Upper and lower limits of the state of charge (SOC) of batteries in electric vehicle charging stations:

[0263]

[0264] in, and These represent the minimum and maximum states of charge (evs) of the electric vehicle charging station, respectively.

[0265] vi) Climbing constraints for various heterogeneous flexible resources:

[0266]

[0267] Among them, Rp g,max and Rp k,max Let g and k represent the maximum climbing speeds of the coal-fired generator set and the gas-fired generator set, respectively. and These represent the maximum ramp speeds of the pumped storage power station (PU) during the charging and discharging processes, respectively. and These represent the maximum ramp rate of the battery energy storage power station es during the charging and discharging processes, respectively. and Rp represents the maximum ramp speed of an electric vehicle charging station during both the charging and discharging processes. ac, This indicates that the response ramp rate of the air conditioning load system AC is the highest; the ramp constraints of the bidirectional charging and discharging process are considered for pumped storage power stations, battery energy storage power stations and electric vehicle charging stations respectively.

[0268] vii) Load shedding constraint:

[0269]

[0270] in, This represents the load reduction at node i at time t under normal grid operation. This represents the load power of node i at time t under normal grid operation.

[0271] In step S3, the operational constraints of the two-stage stochastic optimization scheduling model during the emergency recovery phase are adjusted as follows:

[0272] i) Power balance constraints at each node of the power grid:

[0273]

[0274] in, and Let G and K represent the estimated power output levels of coal-fired generator unit g and gas-fired generator unit k at node i at time t, respectively, under fault scenario s. and This represents the estimated power output of the pumped storage power station pu at node i during time period t under fault scenario s. and Let represent the estimated charging power and estimated discharging power of the battery energy storage station es at node i during time period t, respectively, under fault scenario s. and Let represent the estimated charging power and estimated discharging power of electric vehicle charging station evs at node i during time period t, respectively, under fault scenario s. This represents the estimated power of the temperature-controlled load system ac at node i during time period t under fault scenario s. This represents the estimated load power of node i at time t under power grid fault scenario s. This represents the estimated load reduction at node i at time t under the power grid fault scenario s. This represents the power flow estimate of line l at time t under the power grid fault scenario s;

[0275] In the above formula, the estimated line power flow value The relationship with the phase angle of the node is as follows:

[0276]

[0277] in, and Let represent the estimated phase angles of node i and node j at time t under fault scenario s, respectively;

[0278] Power constraints on transmission lines:

[0279]

[0280] Node phase angle constraint:

[0281]

[0282] ii) Standby constraints for coal-fired and gas-fired units:

[0283]

[0284] iii) Operational constraints of pumped storage power stations: The power generation capacity of a pumped storage power station is related to the reservoir capacity.

[0285] Among them, the constraints on the power generation of pumped storage power stations and the pumping power and power generation are:

[0286]

[0287] in, and These represent the estimated charging and discharging power of the pumped storage power station pu during time period t, respectively.

[0288] Pumped storage power station power consumption constraints and power generation constraints:

[0289]

[0290] Pumped storage power stations cannot charge and discharge simultaneously due to constraints.

[0291]

[0292] in, Let be a 0-1 variable representing the state of the pumped storage power station under fault scenario s, where 0 represents the pumping state and 1 represents the power generation state.

[0293] Power generation constraints of pumped storage power plants:

[0294]

[0295] in, S represents the amount of electricity that a pumped storage power station (PU) can generate during time period t under fault scenario s. pu, S represents the equivalent power generation of the reservoir at its maximum capacity (pu) in a pumped storage power station, i.e., the equivalent power generation of the reservoir under the highest permissible water level. pu, The equivalent power generation of the reservoir's minimum capacity;

[0296] iv) Operational constraints of battery energy storage power stations:

[0297] Among them, the constraints on the state of charge of battery energy storage power stations and the power during their charging and discharging processes are:

[0298]

[0299] in, and These represent the estimated charging and discharging power of the battery energy storage power station es during time period t, respectively.

[0300] Upper and lower limits of charging and discharging power constraints for battery energy storage power stations:

[0301]

[0302] in, and These represent the upper and lower limits of the power output during charging of the battery energy storage power station (es), respectively. and These represent the upper and lower limits of the power output of the battery energy storage power station during discharge, respectively.

[0303] Battery energy storage power stations cannot be charged and discharged simultaneously due to constraints:

[0304]

[0305] in, For the state of the battery energy storage power station under fault scenario s, there are 0-1 variables, where 0 represents the pumping state and 1 represents the power generation state.

[0306] Upper and lower limits of state of charge constraints for battery energy storage power stations:

[0307]

[0308] v) Operational constraints of electric vehicle charging stations:

[0309] Among them, the constraints on the state of charge of electric vehicle charging stations and the power during their charging and discharging processes are as follows:

[0310]

[0311] In the formula, and These represent the estimated charging power and discharging power of the electric vehicle charging station evs during time period t, respectively.

[0312] Upper and lower limits of charging and discharging power constraints for electric vehicle charging stations:

[0313]

[0314] Electric vehicle charging stations cannot charge and discharge simultaneously due to constraints:

[0315]

[0316] in, Let be a 0-1 variable representing the state of the electric vehicle charging station under fault scenario s, where 0 represents the charging state and 1 represents the power generation state.

[0317] Upper and lower limits of the state of charge (SOC) of batteries in electric vehicle charging stations:

[0318]

[0319] in, The state of charge of the electric vehicle charging station evs at time t under fault scenario s;

[0320] vi) Ramp-up constraints for various adjustment resources:

[0321]

[0322]

[0323] vii) Load shedding constraint:

[0324]

[0325] viii) Risk constraints. Risk constraints are an important indicator for considering grid resilience, represented by the expected energy deficit (EENS). Based on the estimated load shedding, the EENS index can be calculated using the following formula:

[0326]

[0327] EENS T ≤EENS set

[0328] Where, p s EENS represents the probability of failure scenario s. T EENS represents the expected value of insufficient electrical energy. set This represents the preset expected threshold.

[0329] In specific implementation, step S4 is as follows:

[0330] Step S4.1: Based on the constraints of the resource operation model and the two-stage stochastic optimization scheduling model, the optimized scheduling result of the two-stage stochastic optimization scheduling model in the preventive scheduling phase is obtained. The optimized scheduling result Ω of the two-stage stochastic optimization scheduling model in the preventive scheduling phase is... pre Specifically as follows:

[0331]

[0332] Among them, Ω pre This indicates the optimized scheduling result during the preventive scheduling phase.

[0333] Step S4.2 Next, the power output level and reserve capacity of the coal-fired generator set g and the gas-fired generator set k at time t0 before the fault occurred are used as boundary conditions for the two-stage stochastic optimization scheduling model to make optimization scheduling decisions in the emergency recovery phase.

[0334] Step S4.3: Combining the resource operation model, with the total operating cost C of the power system... all The goal is to obtain the best dispatch decision result during the emergency recovery phase, with the lowest possible value as the final dispatch decision result for the power system.

[0335] Total operating cost of power system C all The following formula is used to obtain the result:

[0336]

[0337] Among them, C em This indicates the cost of emergency power system restoration. This represents the operating cost of the power system before a failure occurs. This represents the expected value of the sum of operating costs and adjustment costs of various heterogeneous flexible resources in each failure scenario with probability p under the influence of extreme weather.

[0338] The optimal dispatch decision results during the emergency recovery phase include the power curves of coal-fired and gas-fired generator units from the time of the fault (t0) to the process of the power system regaining balance, the power curves of pumped storage power stations, battery energy storage power stations, electric vehicle charging stations, and the power curves that need to be adjusted for temperature-controlled load systems.

[0339] Step S4.4 Finally, the operating status of the actual power system is set according to the final scheduling decision result.

[0340] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A stochastic optimization scheduling method considering heterogeneous flexible resources and grid resilience improvement, characterized in that, Includes the following steps: Step S1: First, construct a resource operation model based on the operating characteristics of various heterogeneous flexible resources in the power system; the heterogeneous flexible resources include gas generator sets, pumped storage power stations, battery energy storage power stations, electric vehicle charging stations, and temperature-controlled load systems. Step S2: Then, considering the change process of power grid operation status under extreme weather conditions, and with the goal of minimizing the operating cost of the power system, a two-stage stochastic optimization scheduling model is constructed. The first stage of the two-stage stochastic optimization scheduling model is the preventive scheduling stage before the power system fails, and the second stage is the emergency recovery stage after the power system fails. Step S3: Determine the constraints of the two-stage stochastic optimization scheduling model described in step S2; Step S4: Based on the constraints of the resource operation model and the two-stage stochastic optimization scheduling model, the optimized scheduling result of the preventive scheduling stage is obtained. Then, the optimized scheduling result of the preventive scheduling stage is used as the boundary condition of the emergency recovery stage. Combined with the resource operation model, the best scheduling decision result of the emergency recovery stage is obtained as the final scheduling decision result of the power system. The actual power system operation state is configured according to the final scheduling decision result. 2.The random optimization scheduling method considering heterogeneous flexible resources and power grid resilience enhancement according to claim 1, wherein: gas generator set in step S1 The climbing speed satisfies the following constraints: In the formula, and Representing nodes respectively Gas generator set exist Time and Power output level at any given time Indicates gas generator set The maximum rate of ascent; The pumped storage power station The amount of power generated during pumping and power generation is processed according to the following formula: in, Pumped storage power station exist Electricity generation during the period This represents the efficiency coefficient of the pumping process. The 0-1 variable is used to characterize the state of a pumped storage power station, where 0 represents the pumping state and 1 represents the power generation state. , These represent pumped storage power stations. exist The power generation and pumping power at any given time, , These represent pumped storage power stations. exist The power generation flow and pumping flow at any given time, , These represent pumped storage power stations. The calculated values ​​of the generator head and the pump head. This indicates the density of water. , , , These represent the efficiency of the electric motor, the efficiency of the generator, the hydraulic efficiency of the water pump, and the hydraulic efficiency of the generator, respectively. Indicates pumped storage power station Maximum water head, Indicates pumped storage power station Minimum head; Battery energy storage plant The expression of the charge and discharge process is as follows: In the formula, Indicates battery energy storage power station exist State of charge during a period of time Indicates battery energy storage power station Loss coefficient, The 0-1 variables characterize the state of a battery energy storage power station, where 0 represents the charging state and 1 represents the discharging state. and These represent battery energy storage power stations. exist Charging power and discharging power during the time period and For the charging and discharging efficiency of battery energy storage power stations, For the rated capacity of energy storage, and Battery energy storage power station Upper and lower limits of state of charge.

3. The stochastic optimization scheduling method considering heterogeneous flexible resources and grid resilience improvement according to claim 2, characterized in that: The charging and discharging capacity model of the electric vehicle charging station in step S1 is as follows: in, Electric vehicle charging stations exist Battery capacity during the period of time, Electric vehicle charging stations The discharge battery capacity at any given time, where m represents the electric vehicle charging station. exist The number of electric vehicles available for charging and discharging during a given time period. and They represent electric vehicles. exist Charging and discharging capacity during a given period It is an electric car Battery capacity, This is the maximum battery capacity. Indicates electric vehicles exist State of charge during the period For electric vehicles The initial state of charge of the battery as it begins to interact with the grid; The specific response model of the temperature-controlled load system is as follows: in, Indicates temperature control load system Downward adjustable capacity This indicates the upward adjustable capacity of the temperature-controlled load system. This indicates the initial power of the temperature-controlled load system. This indicates the maximum operating power of the temperature-controlled load system. This indicates the minimum operating power of the temperature-controlled load system. This indicates the ramp rate during the downward adjustment process. This represents the ramp rate during the upward adjustment process. This refers to the capacity deviation rate generated during the rebound effect. The response time for power downward adjustment. This is the response time for power upward adjustment. 4.The random optimization scheduling method considering heterogeneous flexible resources and power grid resilience enhancement according to claim 3, characterized in that: In step S2, the two-stage stochastic optimization scheduling model performs the following specific actions during the preventive scheduling phase: in, This represents the operating cost of the power system before a failure occurs. This represents the expected value of the sum of operating costs and adjustment costs of various heterogeneous flexible resources in each failure scenario with probability p under the influence of extreme weather. In the above formula, the operating cost before the power system failure In detail as follows: in, Indicates the time when the fault occurred. , , , , and These represent the number of coal-fired power generating units, gas-fired power generating units, pumped storage power stations, battery energy storage power stations, electric vehicle charging stations, and loads in the power grid, respectively. and These represent coal-fired power generating units. and gas generator sets The unit cost of electricity generation and These represent coal-fired power generating units under normal grid operation conditions. and gas generator sets exist Power output level during a given period and These represent coal-fired power generating units. and gas generator sets Backup costs, , These represent coal-fired power generating units. The upper and lower rotating spare capacity, , They represent gas generator sets The upper and lower rotating spare capacity, and These represent pumped storage power stations. The unit cost of pumped storage and hydroelectric power generation. and These represent pumped storage power stations under normal grid operation conditions. exist The power output of pumped storage and hydroelectric power generation during the specified time period. and These represent battery energy storage power stations. The unit charging cost and unit discharging cost, and These represent electric vehicle charging stations. The unit charging cost and unit discharging cost, and These represent battery energy storage power stations under normal grid operation conditions. exist Charging power and discharging power during the time period and These represent electric vehicle charging stations under normal grid operation conditions. exist Charging power and discharging power during the time period This indicates cost reduction per unit load. Indicates the normal operating state of the power grid Time Node Load reduction amount; In the above formula, the expected value is the sum of the operating cost and the adjustment cost of various heterogeneous flexible resources in each failure scenario with probability p. Specifically as follows: in, Indicates the number of failure scenarios. This represents the total number of time periods in the power system operation simulation process. Indicates the number of temperature-controlled load systems. Indicates the fault scenario The probability, and These represent the fault scenarios respectively. Coal-fired power generation units and gas generator sets exist Estimated power output levels for the time period. and These represent power grid fault scenarios. Pumped storage power station exist Estimated power output of pumped storage and hydroelectric power generation during the specified period. and These represent power grid fault scenarios. Battery energy storage power station exist Estimated charging power and estimated discharging power during the time period. and These represent power grid fault scenarios. Electric vehicle charging station exist Estimated charging power and estimated discharging power during the time period. This represents the unit cost of adjusting the air conditioning load system. Indicates the fault scenario Estimated power for air conditioning load system regulation. Indicates the fault scenario Down Time Node The estimated amount of load reduction.

5. The stochastic optimization scheduling method considering heterogeneous flexible resources and grid resilience improvement according to claim 4, characterized in that: In step S2, the two-stage stochastic optimization scheduling model in the emergency recovery phase is as follows: in, This indicates the cost of emergency power system restoration. and These represent the fault scenarios respectively. Coal-fired power generation units and gas generator sets exist Power output level during a given period and These represent power grid fault scenarios. Pumped storage power station exist The power output of pumped storage and hydroelectric power generation during the specified time period. and These represent different power grid fault scenarios. Battery energy storage power station exist Charging power and discharging power during the time period and These represent power grid fault scenarios. Electric vehicle charging station exist Charging power and discharging power during the time period Indicates the fault scenario Lower temperature control load system exist The power value adjusted within the time period, Indicates the fault scenario Down Node of time Load reduction amount. 6.The random optimization scheduling method considering heterogeneous flexible resources and power grid resilience enhancement according to claim 5, characterized in that: In step S3, the operational constraint adjustment of the two-stage stochastic optimization scheduling model during the preventive scheduling phase is as follows: i) Power balance constraints at each node of the power grid: Where EG, GG, PU, ​​ES, EVS, and AC represent the nodes respectively. The connected set of coal-fired power generating units, gas-fired power generating units, pumped storage power stations, battery storage power stations, electric vehicle charging stations, and temperature-controlled load systems, EL represents the set of nodes. A set of connected branches, and These represent nodes under normal power grid operation. Upper coal-fired power generation unit and gas generator sets exist Power output level at any given time and Represents the nodes under normal power grid operation. Upper pumped storage power station exist The power output of pumped storage and hydroelectric power generation during the specified time period. and These represent nodes under normal power grid operation. Battery energy storage station exist Charging power and discharging power during the time period and These represent nodes under normal power grid operation. Electric vehicle charging station exist Charging power and discharging power during the time period Represents the nodes under normal power grid operation. Upper temperature control load system exist Power during the time period Indicates the normal operating state of the power grid Time Node The load power, Indicates the normal operating state of the power grid Timetable Power flow; In the above equation, the power flow of the line is related to the angle of the nodes as follows: in, and Representing nodes respectively and nodes At any time during normal grid operation phase angle, Indicates the line impedance; Power constraints on transmission lines: wherein indicates a line maximum value of the transmitted power; Node phase angle constraint: in, Represents a node The maximum allowable deviation of the power angle; ii) Standby constraints for coal-fired and gas-fired power generation units: in, and These represent nodes under normal power grid operation. Upper coal-fired power generation unit The upper and lower rotating spare capacity, and These represent nodes under normal power grid operation. Gas generator set The upper and lower rotating spare capacity, and These represent the minimum and maximum power generation of the coal-fired power generating unit, respectively. and These represent the minimum and maximum power output of the gas generator set, respectively. iii) Operational constraints of pumped storage power stations: Among them, the constraints on the power generation of pumped storage power stations and the pumping power and power generation are: Pumped storage power station power consumption constraints and power generation constraints: in, and These represent pumped storage power stations under normal grid operation conditions. exist The power output of pumped storage and hydroelectric power generation during the specified time period. and These represent pumped storage power stations. The upper and lower limits of charging power. and These represent pumped storage power stations. Upper and lower limits of power during discharge; Pumped storage power stations cannot charge and discharge simultaneously due to the following constraint: wherein is a predetermined constant; Power generation constraints of pumped storage power plants: in, Pumped storage power station The maximum capacity of the reservoir, equivalent to the amount of electricity generated. The equivalent power generation of the reservoir's minimum capacity; iv) Operational constraints of battery energy storage power stations: Among them, the constraints on the state of charge and charging / discharging power of battery energy storage power stations are: Upper and lower limits of charging and discharging power constraints for battery energy storage power stations: in, and These represent battery energy storage power stations under normal grid operation conditions. exist Charging power and discharging power during the time period and These represent battery energy storage power stations. The upper and lower limits of charging power. and These represent battery energy storage power stations. Upper and lower limits of power during discharge; Battery energy storage power stations cannot be charged and discharged simultaneously due to constraints: Upper and lower limits of state of charge constraints for battery energy storage power stations: wherein, and respectively represent the minimum state of charge and the maximum state of charge of the battery energy storage plant ; v) Operational constraints of electric vehicle charging stations: Among them, the constraints on the state of charge of electric vehicle charging stations and the power during the charging and discharging process are: In the formula, Indicates electric vehicle charging station exist State of charge during a period of time This represents the loss coefficient of an electric vehicle charging station. To characterize electric vehicle charging stations The state is a 0-1 variable, where 0 represents the charging state and 1 represents the discharging state. and These represent electric vehicle charging stations. exist Charging power and discharging power during the time period and The charging and discharging efficiency of electric vehicle charging stations; Upper and lower limits of charging and discharging power constraints for electric vehicle charging stations: in, and These represent electric vehicle charging stations under normal grid operation conditions. exist Charging power and discharging power during the time period and These represent electric vehicle charging stations. The upper and lower limits of charging power. and These represent electric vehicle charging stations. Upper and lower limits of power during discharge; Electric vehicle charging stations cannot charge and discharge simultaneously due to constraints: Upper and lower limits of the state of charge (SOC) of batteries in electric vehicle charging stations: wherein, and respectively represent the minimum state of charge and the maximum state of charge of an electric vehicle charging station ; vi) Climbing constraints for various heterogeneous flexible resources: in, and These represent coal-fired power generating units. and gas generator sets The maximum climbing speed, and These represent pumped storage power stations. The maximum ramp speed during the charging and discharging processes. and These represent battery energy storage power stations. The maximum ramp speed during the charging and discharging processes. and Indicates electric vehicle charging station The maximum ramp speed during the charging and discharging processes. Indicates the air conditioning load system The response ramp speed is the greatest; vii) Load shedding constraint: in, Indicates the normal operating state of the power grid Time Node Load reduction amount, Indicates the normal operating state of the power grid Time Node The load power.

7. The random optimization scheduling method of claim 6, wherein: In step S3, the operational constraints of the two-stage stochastic optimization scheduling model during the emergency recovery phase are adjusted as follows: i) Power balance constraints at each node of the power grid: in, and These represent the fault scenarios respectively. Next node Upper coal-fired power generation unit and gas generator sets exist The estimated power output level at any given time. and Indicates the fault scenario Next node Upper pumped storage power station exist Estimated power output of pumped storage and hydroelectric power generation during the specified period. and These represent the fault scenarios respectively. Next node Battery energy storage station exist Estimated charging power and estimated discharging power during the time period. and These represent the fault scenarios respectively. Next node Electric vehicle charging station exist Estimated charging power and estimated discharging power during the time period. Indicates the fault scenario Next node Upper temperature control load system exist Power estimates for the time period Representing power grid fault scenarios Down Time Node The estimated load power Indicates power grid fault scenarios Down Time Node The estimated load reduction amount, Indicates power grid fault scenarios Down Timetable The power flow estimate; In the above equation, the line power flow estimate The relationship to the node angle is as follows: in, and Representing nodes respectively and nodes In failure scenarios Next moment The estimated phase angle; Power constraints on transmission lines: Node phase angle constraint: ii) Standby constraints for coal-fired and gas-fired units: iii) Operational constraints of pumped storage power stations: Among them, the constraints on the power generation of pumped storage power stations and the pumping power and power generation are: in, and These represent pumped storage power stations. exist Estimated values ​​of charging and discharging power over a given period; Pumped storage power station power consumption constraints and power generation constraints: Pumped storage power stations cannot charge and discharge simultaneously due to constraints. wherein, for the fault scenario 0-1 variable for the state of the pumped storage power plant, 0 indicating the pumping state and 1 indicating the generating state; Power generation constraints of pumped storage power plants: in, For pumped storage power station In failure scenarios Down Electricity generation during the period Pumped storage power station The maximum capacity of the reservoir, equivalent to the amount of electricity generated. The equivalent power generation of the reservoir's minimum capacity; iv) Operational constraints of battery energy storage power stations: Among them, the constraints on the state of charge of battery energy storage power stations and the power during their charging and discharging processes are: in, and These represent battery energy storage power stations. exist Estimated values ​​of charging and discharging power over a given period; Upper and lower limits of charging and discharging power constraints for battery energy storage power stations: in, and These represent battery energy storage power stations. The upper and lower limits of charging power. and These represent battery energy storage power stations. Upper and lower limits of power during discharge; Battery energy storage power stations cannot be charged and discharged simultaneously due to constraints: wherein, for the fault scenario 0-1 variable of the state of the battery energy storage plant, 0 indicating the pumping state and 1 indicating the generating state; Upper and lower limits of state of charge constraints for battery energy storage power stations: v) Operational constraints of electric vehicle charging stations: Among them, the constraints on the state of charge of electric vehicle charging stations and the power during their charging and discharging processes are as follows: In the formula, and These represent electric vehicle charging stations. exist Estimated values ​​of charging and discharging power over a given period; Upper and lower limits of charging and discharging power constraints for electric vehicle charging stations: Electric vehicle charging stations cannot charge and discharge simultaneously due to constraints: in, For fault scenarios Electric vehicle charging station The state is a 0-1 variable, where 0 represents the charging state and 1 represents the power generation state; Upper and lower limits of the state of charge (SOC) of batteries in electric vehicle charging stations: wherein, Charging station for electric vehicles In a fault scenario Below State of charge at the moment; vi) Ramp-up constraints for various adjustment resources: vii) Load shedding constraint: viii) Risk constraints: wherein represents a probability of a failure scenario represents an expected value of an energy deficit represents a preset expected threshold value.​ 8. The stochastic optimization scheduling method considering heterogeneous flexible resources and grid resilience improvement according to claim 7, characterized in that: The specific steps of S4 are as follows: Step S4.1: Based on the constraints of the resource operation model and the two-stage stochastic optimization scheduling model, obtain the optimized scheduling result of the two-stage stochastic optimization scheduling model in the preventive scheduling phase. Specifically as follows: wherein, represents the optimized scheduling result of the prevention scheduling stage; Step S4.2: Next, before the circuit system fails... Coal-fired power generating units at all times and gas generator sets The power output level and reserve capacity are used as boundary conditions for the two-stage stochastic optimization scheduling model to make optimal scheduling decisions during the emergency recovery phase. Step S4.3: Combining the resource operation model, with the total operating cost of the power system... The goal is to obtain the best dispatch decision result during the emergency recovery phase, with the lowest possible value as the final dispatch decision result for the power system. Total cost of power system operation The processing is done according to the following formula: in, This indicates the cost of emergency power system restoration. This represents the operating cost of the power system before a failure occurs. This represents the expected value of the sum of operating costs and adjustment costs of various heterogeneous flexible resources in each failure scenario with probability p under the influence of extreme weather. Step S4.4 Finally, based on the final scheduling decision result, the operating characteristics of heterogeneous flexible resources in the actual power system are set.

9. An electronic device, comprising: It includes a memory and a processor coupled to each other, wherein the memory stores program data and the processor invokes the program data to perform the method as described in any one of claims 1-8.

10. A computer-readable storage medium storing program data thereon, characterized in that: When the program data is executed by the processor, it implements the method as described in any one of claims 1-8.

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