Power system tight balance handling method and system and storage medium

Abstract

The application discloses a power system tight balancing treatment method and system and a storage medium, wherein the method comprises the following steps: S1, constructing an electric vehicle charging station model considering the uncertainty of the number of electric vehicles in the electric vehicle charging station; S2, regarding the electric vehicle charging station as a flexible load in a power system, and establishing an electric vehicle charging station constraint according to the electric vehicle charging station model; S3, taking the minimum total operation cost of the power system as an objective function, and establishing a tight balancing treatment model of the power system, wherein the constraint condition of the tight balancing treatment model of the power system comprises the flexible climbing capacity constraint provided by the electric vehicle charging station in step S2; and S4, bringing real-time data of the power system into the tight balancing treatment model of the power system established in step S3, and solving to obtain a tight balancing treatment strategy for minimizing the total operation cost of the power system. The electric vehicle charging station is regarded as a flexible resource in the method, the abandoned wind and the cut-off load of the system during the tight balancing treatment are reduced, and the economy of the system is improved.

Description

Technical Field

[0001] This invention belongs to the field of power systems, and specifically relates to a method, system, and storage medium for handling tight balance in power systems. Background Technology

[0002] Increasing the proportion of renewable energy in the power system is a global consensus aimed at addressing fossil fuel depletion and environmental pollution. However, renewable energy sources such as wind power are highly volatile and uncertain, requiring the system to have sufficient flexibility to cope with fluctuations in net load (load minus renewable energy output). Simultaneously, facing short-term peak loads, the system's installed generating capacity is insufficient, leading to supply and demand imbalances during specific periods and placing the system in a tight balance. This tight balance further limits the system's ability to provide flexibility. Once the system cannot meet flexibility requirements, measures such as wind curtailment and load shedding become necessary, severely jeopardizing the system's operational safety and economic efficiency.

[0003] Electric vehicle charging stations, as a flexible resource, while inherently subject to uncertainty due to the behavior of electric vehicle owners, contribute to reducing carbon emissions and protecting the environment through their widespread use. Electric vehicle charging stations can provide system flexibility, alleviate the regulation burden on thermal power units, reduce wind curtailment and load shedding, and ensure the safe and economical operation of the system.

[0004] Existing research on power system flexibility largely focuses on improving system flexibility, with little in-depth study of the insufficient flexibility of power grids with high wind power penetration and the tight balance problem caused by insufficient installed power generation capacity. In systems with low wind power penetration, the demand for system flexibility is not significant. However, as wind power penetration continues to increase, the strong volatility and uncertainty of wind power lead to a continuous increase in the demand for system flexibility. Thermal power units must meet both the ever-growing system flexibility demands and the load's power demand, which easily leads to wind curtailment and load shedding. At this point, more flexibility resources are needed to increase system flexibility and ensure the safe and economical operation of the system. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to propose a method for handling tight balance in power systems. By establishing an electric vehicle charging station model that considers the uncertainties of electric vehicle charging stations, electric vehicle charging stations are treated as flexible resources, reducing wind curtailment and load shedding in the system, improving system flexibility, and ensuring the safe and economical operation of the power system.

[0006] Another object of the present invention is to provide a power system tight balance handling system that can implement the above-described handling method, and a storage medium storing a computer program that instantiates the above-described handling method.

[0007] Technical solution: The power system tight balance handling method of the present invention includes the following steps:

[0008] S1: Construct an electric vehicle charging station model that considers the uncertainty of the number of electric vehicles in the charging station;

[0009] S2: Treat electric vehicle charging stations as flexible loads in the power system and establish constraints for electric vehicle charging stations based on the electric vehicle charging station model;

[0010] S3: With the goal of minimizing the total operating cost of the power system, establish a tight balance handling model for the power system. The constraints of the tight balance handling model for the power system include the flexible ramping capacity constraint that electric vehicle charging stations can provide in step S2.

[0011] S4: Input the real-time data of the power system into the tight balance handling model of the power system established in step S3, and solve for the tight balance handling strategy that minimizes the total operating cost of the power system.

[0012] Furthermore, the electric vehicle charging station model in step S1 includes:

[0013] Number of electric vehicles available at electric vehicle charging stations:

[0014]

[0015]

[0016]

[0017]

[0018] in, Indicates in Arrive at the charging station in time and The total number of electric vehicles that leave the charging station at any given time; Indicates that electric vehicle n is in Arrive at the charging station in time and Always leave the charging station; Indicates in The total number of electric vehicles arriving at the charging station at any given time; Indicates in The total number of electric vehicles that leave the charging station at any given time; This indicates the total number of electric vehicles in the charging station;

[0019] Energy state of charging stations considering the impact of uncertainty in the number of electric vehicles:

[0020]

[0021]

[0022]

[0023] in, Indicates in The total energy state of electric vehicles arriving at charging stations at any given time; Indicates in Arrive at the charging station in time and The state of charge of electric vehicle n that leaves the charging station at any given time; Indicates in Arrive at the charging station in time and The capacity of electric vehicle n that leaves the charging station at any given time; Indicates in The total energy state of an electric vehicle that is constantly away from a charging station; This indicates the total energy status of the charging station;

[0024] Total capacity of charging stations considering the uncertainty of the number of electric vehicles:

[0025]

[0026]

[0027]

[0028] in, Indicates in The total capacity of electric vehicles arriving at charging stations at any given time; Indicates in The total capacity of electric vehicles that are constantly leaving the charging station; This indicates the total capacity of the charging station.

[0029] Furthermore, the constraints on the electric vehicle charging station in step S2 include:

[0030] Constraints on charging stations regarding energy, backup capacity, and ramp flexibility:

[0031]

[0032]

[0033]

[0034] in, and These represent the power from the charging station pl to the grid and the power from the grid to the charging station during time period t, respectively. and These represent the upward and downward flexible ramp capacity of the charging station pl during time period t, respectively. and These represent the spinning reserve and non-spinning reserve capacity of charging station pl during time period t, respectively. and These represent the upward and downward frequency regulation reserve capacity of the charging station pl during time period t, respectively; This indicates the replacement reserve capacity of charging station pl during time period t; γ discharge and γ charge These represent the discharge efficiency and charging efficiency of an electric vehicle, respectively. and These are 0-1 state variables, representing the states of energy at charging station pl during time period t: energy flowing from the charging station to the grid or from the grid to the charging station.

[0035] Energy state constraints of charging stations:

[0036]

[0037] in, This indicates the energy state of charging station pl during time period t; and These represent the charging efficiency and discharging efficiency of the charging station, respectively.

[0038] The maximum power exchange constraint between electric vehicles and the power grid when energy is transferred from the vehicle to the grid:

[0039]

[0040] Where, ψ pl,t This indicates the net percentage of discharge resulting from the electric vehicle owner's contract regarding the required state of charge.

[0041] Maximum and minimum range constraints for the state of charge of charging stations:

[0042]

[0043]

[0044] Maximum and minimum range constraints for the energy state of charging stations:

[0045]

[0046] Furthermore, in step S3, the total operating cost of the power system includes the total operating cost of thermal power units, the total operating cost of electric vehicle charging stations, and the penalty cost for wind curtailment and load shedding.

[0047] Furthermore, in step S3, the constraints of the tight balance handling model of the power system also include thermal power unit constraints, wind power unit constraints, system flexibility constraints, and power demand constraints.

[0048] Furthermore, in step S3, the objective function is:

[0049] min C = C1 + C2 + C3

[0050]

[0051]

[0052]

[0053] Where: C represents the total operating cost of the system; C1 represents the total operating cost of the thermal power unit, including the operating cost of the thermal power unit, conventional ancillary services, and flexible ramp-up backup costs; N T N represents the total number of periods for tight balance handling. I N represents the total number of thermal power units; C2 represents the total operating cost of electric vehicle charging stations, including the discharge cost of electric vehicle charging stations, routine ancillary services, and flexible ramping backup costs. PL N represents the total number of electric vehicle charging stations; C3 represents the penalty cost for system curtailment and load shedding; N represents the total number of electric vehicle charging stations. J and N W These represent the total load and the total number of wind turbine units, respectively; p i,t MPC represents the active power of thermal power unit i during time period t; i,t (·) represents the operating cost of thermal power unit i during time period t; and This represents the coefficient of spinning reserve cost and non-spinning reserve cost for thermal power unit i during time period t; and These represent the spinning reserve capacity and non-spinning reserve capacity of thermal power unit i during time period t, respectively. and These represent the frequency regulation reserve cost coefficient and the replacement reserve cost coefficient of thermal power unit i during time period t, respectively. and These represent the upward frequency regulation reserve capacity and the downward frequency regulation reserve capacity of thermal power unit i during time period t, respectively. This represents the replacement reserve capacity of thermal power unit i during time period t; and These represent the upward flexible ramp cost coefficient and the downward flexible ramp cost coefficient of thermal power unit i during time period t, respectively. and These represent the upward and downward flexible ramping capacities of thermal power unit i during time period t, respectively. This represents the discharge cost coefficient of charging station pl during time period t; This represents the power supplied from the charging station pl to the grid during time period t; and These represent the spinning reserve cost coefficient and non-spinning reserve cost coefficient of charging station pl during time period t, respectively. and These represent the frequency regulation reserve cost coefficient and the replacement reserve cost coefficient of the charging station pl during time period t, respectively. and These represent the upward and downward flexible ramp cost coefficients of the charging station pl during time period t, respectively. and These represent the system's wind curtailment penalty cost coefficient and load shedding penalty cost coefficient, respectively; LS j,t This indicates the load shedding generated by load j during time period t; This indicates the wind curtailment generated by wind power w during time period t.

[0054] The power system tight balance handling system of the present invention includes: an electric vehicle charging station module, used to establish an electric vehicle charging station model that considers the uncertainty of the number of electric vehicles in the electric vehicle charging station; and a system tight balance handling module, used to establish a power system tight balance handling model with the objective function of minimizing the total operating cost of the power system, treating the electric vehicle charging station as a flexible load of the power system, and solving for the optimal tight balance handling strategy of the power system under the constraints of each generator set, load, flexibility requirement and power demand of the power system.

[0055] Furthermore, it also includes: a thermal power unit module, used to build thermal power unit models; and a wind turbine module, used to build wind turbine models.

[0056] The storage medium of the present invention stores a computer program, which is configured to implement the above-described power system tight balance handling method when running.

[0057] Beneficial effects: Compared with the prior art, the present invention has the following advantages: By modeling the uncertainty of electric vehicle charging stations, a flexible ramping capacity model provided by electric vehicle charging stations is obtained, realizing the integration of electric vehicle charging stations into the power system, improving system flexibility, thereby reducing wind curtailment and load shedding during tight balance disposal, and improving system economy. Attached Figure Description

[0058] Figure 1 This is a flowchart of a power system tight balance handling method according to an embodiment of the present invention;

[0059] Figure 2 This is a schematic diagram illustrating the impact of electric vehicle scale on the total system operating cost and the savings in thermal power plant ramp-up costs according to an embodiment of the present invention.

[0060] Figure 3 This is a schematic diagram illustrating the impact of the scale of electric vehicles on the total load shedding of the system according to an embodiment of the present invention;

[0061] Figure 4 These are schematic diagrams illustrating wind power processing in different typical scenarios according to embodiments of the present invention. Detailed Implementation

[0062] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0063] Reference Figure 1 The power system tight balance handling method according to an embodiment of the present invention includes the following steps:

[0064] S1: Construct an electric vehicle charging station model that considers the uncertainty of the number of electric vehicles in the charging station;

[0065] S2: Treat electric vehicle charging stations as flexible loads in the power system and establish constraints for electric vehicle charging stations based on the electric vehicle charging station model;

[0066] S3: With the goal of minimizing the total operating cost of the power system, establish a tight balance handling model for the power system. The constraints of the tight balance handling model for the power system include the flexible ramping capacity constraint that electric vehicle charging stations can provide in step S2.

[0067] S4: Input the real-time data of the power system into the tight balance handling model of the power system established in step S3, and solve for the tight balance handling strategy that minimizes the total operating cost of the power system.

[0068] The above method, by establishing an electric vehicle charging station model that considers the uncertainties of electric vehicle charging stations, can obtain a constrained model that incorporates electric vehicle charging stations as flexible loads into the power system, and calculate the ramp-up capacity that electric vehicle charging stations can provide. By introducing electric vehicle charging stations as a flexible resource into the power system, during tight balance operations, the ramp-up of thermal power units can be reduced, wind curtailment and load shedding can be reduced, and the economic efficiency of the system can be improved.

[0069] The uncertainty of electric vehicle charging stations is mainly reflected in the fact that the number of electric vehicles in the charging station is affected by the subjective consciousness of the car owners. Therefore, the model considering the uncertainty of electric vehicle charging stations mainly includes the number of available electric vehicles in the charging station, as well as the state of energy (SOE) of the charging station and the total capacity of the charging station under the influence of the flow of electric vehicles in the charging station.

[0070] In this embodiment, the electric vehicle charging station model that considers the uncertainty of the number of electric vehicles includes:

[0071] 1) Number of electric vehicles available at electric vehicle charging stations:

[0072]

[0073]

[0074]

[0075]

[0076] in, Indicates in Arrive at the charging station in time and The total number of electric vehicles that leave the charging station at any given time; Indicates that electric vehicle n is in Arrive at the charging station in time and Always leave the charging station; Indicates in The total number of electric vehicles arriving at the charging station at any given time; Indicates in The total number of electric vehicles that leave the charging station at any given time; This indicates the total number of electric vehicles in the charging station.

[0077] 2) The impact of electric vehicle arrivals / departures on the state of energy (SOE) of charging stations:

[0078]

[0079]

[0080]

[0081] in, Indicates in The total state of energy (SOE) of electric vehicles arriving at charging stations at any given time; Indicates in Arrive at the charging station in time, and The state of charge (SOC) of electric vehicle n when it leaves the charging station; Indicates in Arrive at the charging station in time and The capacity of electric vehicle n that leaves the charging station at any given time; Indicates in Total state of energy (SOE) of an electric vehicle that is constantly leaving a charging station; This indicates the total state of energy (SOE) of a charging station due to the arrival / departure of electric vehicles.

[0082] 3) The impact of electric vehicle arrivals / departures on the total capacity of charging stations:

[0083]

[0084]

[0085]

[0086] in, Indicates in The total capacity of electric vehicles arriving at charging stations at any given time; Indicates in The total capacity of electric vehicles that are constantly leaving the charging station; This indicates the total capacity of the charging station due to the arrival / departure of electric vehicles.

[0087] Based on the above model of electric vehicle charging stations, we can derive the constraints that electric vehicle charging stations, acting as flexible loads that can be mobilized within the power system, should be subject to, considering the uncertainty of electric vehicle flow. The constraints of electric vehicle charging stations are similar to those of energy storage power stations, the difference being that the capacity and state of energy of an electric vehicle charging station are both related to the number of electric vehicles within the station. Therefore, according to the above model, the constraints of electric vehicle charging stations include:

[0088] 1) Constraints on energy, backup capacity, and ramp-up flexibility for charging stations:

[0089]

[0090]

[0091]

[0092] in, This represents the power from the charging station pl to the grid and the power from the grid to the charging station during time period t; This indicates the upward and downward flexible ramping capacity of the charging station pl during time period t; This indicates the spinning reserve and non-spinning reserve capacity of the charging station pl during the time period t. This indicates the upward and downward frequency regulation reserve capacity of the charging station PL during time period t; This indicates the replacement reserve capacity of charging station pl during time period t; γ discharge γ charge This indicates the discharge efficiency and charging efficiency of electric vehicles; The variable is 0-1, indicating that the energy of charging station pl is in the state of either going from the charging station to the grid or from the grid to the charging station during time period t.

[0093] 2) Energy State (SOE) constraints for charging stations:

[0094]

[0095] in, This indicates the state of energy (SOE) of charging station pl during time period t; This indicates the charging and discharging efficiency of the charging station.

[0096] 3) Maximum power exchange constraint between electric vehicle owners and the grid when using vehicle-to-grid (V2G) mode:

[0097]

[0098] Where, ψ pl,t This represents the net discharge percentage (%) resulting from the electric vehicle owner's contract regarding the required state of charge (SOC), assuming a contract between the charging station and the electric vehicle owner allowing the charging station to use the electric vehicle's energy in a vehicle-to-grid (V2G) mode. The charging station should then aggregate the required SOC allocated for each time period in the contract to limit the maximum power exchange with the grid.

[0099] 4) Maximum and minimum range constraints for the state of charge (SOC) of the charging station:

[0100]

[0101]

[0102] 5) Maximum and minimum range constraints for the state of energy (SOE) of the charging station:

[0103]

[0104] Due to the high volatility and uncertainty of wind power, power systems with high wind power penetration often experience significant wind curtailment during tight balancing due to insufficient ramp-up of thermal power units, resulting in substantial economic losses. Therefore, the method of this invention is more effective in power systems with high wind power penetration. In this embodiment, the power system includes wind turbines, thermal power units, electric vehicle charging stations, and other loads. The total operating cost of the power system includes the operating cost of thermal power units (C1), the total operating cost of electric vehicle charging stations (C2), and the penalty cost of wind curtailment and load shedding (C3). The objective function of the constructed tight balancing model for the power system is:

[0105] min C=C1+C2+C3 (19)

[0106]

[0107]

[0108]

[0109] Where C represents the total operating cost of the system; the total operating cost of a thermal power unit includes the operating cost of the thermal power unit, conventional ancillary services, and flexible ramp-up backup costs, N T N represents the total number of periods for tight balance handling. I N represents the total number of thermal power units; the total operating cost of electric vehicle charging stations includes the discharge cost of electric vehicle charging stations, routine ancillary services, and flexible ramping backup costs. PL N represents the total number of electric vehicle charging stations; J N W This indicates the total load and the total number of wind turbine units; p i,t MPC represents the active power of thermal power unit i during time period t; i,t (·) represents the operating cost of thermal power unit i during time period t; This represents the cost coefficients of the spinning reserve and non-spinning reserve of thermal power unit i during time period t. This represents the spinning reserve and non-spinning reserve capacity of thermal power unit i during time period t. This represents the cost coefficient of frequency regulation reserve and replacement reserve for thermal power unit i during time period t. This indicates the upward and downward frequency regulation reserve capacity of thermal power unit i during time period t; This represents the replacement reserve capacity of thermal power unit i during time period t; This represents the upward and downward flexible ramp-up cost coefficients of thermal power unit i during time period t. This represents the upward and downward flexible ramping capacity of thermal power unit i during time period t; This represents the discharge cost coefficient of charging station pl during time period t; This represents the power supplied from the charging station pl to the grid during time period t; This represents the cost coefficients of the spinning reserve and non-spinning reserve of the charging station pl during time period t; This represents the cost coefficient of frequency regulation reserve and replacement reserve for charging station pl during time period t; This represents the upward and downward flexible ramp cost coefficients of the charging station pl during time period t; This represents the system's wind curtailment and load shedding penalty cost coefficient; LS j,t This indicates the load shedding generated by load j during time period t; This indicates the wind curtailment generated by wind power w during time period t.

[0110] The constraints of the tight balance handling model of the power system are determined by the system composition. In this embodiment, in addition to the above-mentioned electric vehicle charging station constraints, the constraints also include thermal power unit constraints, wind power unit constraints, system flexibility constraints, and power demand constraints.

[0111] The constraints of thermal power units include:

[0112] 1) Constraints on the upward and downward climbing ability of thermal power units:

[0113]

[0114]

[0115] 2) Maximum and minimum active power constraints for thermal power units:

[0116]

[0117]

[0118] in, τ represents the upward and downward ramp rates of thermal power unit i; τ represents the time resolution in minutes. This represents the maximum and minimum active power of thermal power unit i.

[0119] Wind turbines, system flexibility, and power demand constraints include:

[0120] 1) Maximum and minimum active power of wind turbines and system curtailment constraints:

[0121]

[0122]

[0123] in, This represents the active power of wind power w during time period t; This represents the maximum active power of wind power w during time period t.

[0124] 2) Wind turbine units provide flexible ramping capacity constraints:

[0125]

[0126] in, This indicates the upward and downward flexible ramping capacity of wind power w during time period t.

[0127] 3) System flexibility requirement modeling:

[0128]

[0129]

[0130] Among them, P t NL P represents the net load during time period t; t NLmax Pt NLmin κ represents the upper and lower limits of net load fluctuation during period t; κ represents the net load fluctuation coefficient, which is defined as the maximum error rate between predicted and actual data; FRUN t FRDN t This indicates the upward and downward flexible ramp-up demand during time period t.

[0131] 4) Constraints on the system's upward and downward flexible ramping requirements:

[0132]

[0133]

[0134]

[0135] in, This indicates that the upward flexible ramp provided by load shedding during time period t and the downward flexible ramp provided by wind curtailment can provide flexibility to the system in extreme cases, but this will significantly increase operating costs.

[0136] 5) System power balance constraints:

[0137]

[0138] Where D j,t This represents the electricity demand of load j during time period t.

[0139] 6) Power flow constraints on transmission lines:

[0140]

[0141] Among them, P l Lmax G represents the active power transmission limit of line l; l-i G represents the power transfer distribution factor of thermal power plant i to line l; l-w G represents the power transfer distribution factor of wind power w to line l; l-pl G represents the power transfer distribution factor of the charging station pl to line l; l-j This represents the power transfer distribution factor of load j to line l.

[0142] To more clearly illustrate the effect of the power system tight balance handling method of the present invention, a simulation and analysis based on the IEEE 118-node system was set up, and simulation case 1, which only considers thermal power units and wind power units participating in the system tight balance handling, and simulation case 2, which considers thermal power units, wind power units and electric vehicle charging stations participating in the system tight balance handling simultaneously, were set up.

[0143] The system comprises 54 thermal power units, 186 branch lines, and 91 load nodes. A wind turbine is connected at node 43. Two electric vehicle charging stations, each with 135,000 charging positions, are located at nodes 58 and 72. The penalty costs for wind curtailment and load shedding are $40 / MWh and $200 / MWh, respectively. The total number of electric vehicles in each charging station, the state of energy (SOE) of the charging station, and the available electric vehicle capacity of the charging station are modeled based on the uncertainty of electric vehicle owner behavior. Other parameters of the electric vehicle charging stations are shown in Table 1.

[0144] Table 1 Parameters of Electric Vehicle Charging Stations

[0145]

[0146] Table 2 shows the total cost, total wind curtailment, total load shedding, thermal power ramp-up, and power saving ramp-up results for different case studies. As can be seen from Table 2, compared to Case 1, Case 2, after connecting to the electric vehicle charging station, saw a 11.72% reduction in total operating cost, a 17.88% reduction in total wind curtailment, and a 59.42% reduction in total load shedding. The electric vehicle charging station provides flexibility, improving the safety and economy of system operation.

[0147] Table 2 Comparison of Tight Balance Handling Costs in Case 1 and Case 2

[0148]

[0149] Furthermore, to study the impact of electric vehicle scale on the system's tight balance handling results, based on the original system's electric vehicle capacity of 135,000, ten scenarios were set up with scales of 20%, 40%, 60%, 80%, 100%, 120%, 140%, 160%, 180%, and 200% of the original capacity. The changes in total system operating cost, savings in thermal power plant flexible ramping, and total load shedding were compared under these ten scenarios. Figure 2-3 As shown. By Figure 2-3 It is evident that as the scale of electric vehicles continues to increase, the total operating cost and total load shedding of the system continue to decline, and the flexible ramp-up of electric vehicle charging stations to save thermal power is showing an upward trend.

[0150] Meanwhile, in order to illustrate the effect of the treatment method of the present invention under different wind power penetration rates, the following three typical wind power output scenarios are set up, and the specific output is shown in Figure 4.

[0151] Typical scenario 1: Wind power penetration rate is 9.68%, with low power output fluctuation; Typical scenario 2: Wind power penetration rate is 19.35%, with relatively strong power output fluctuation; Typical scenario 3: Wind power penetration rate is 38.70%, with very strong power output fluctuation.

[0152] Table 3 shows the total operating cost, total wind curtailment, total load shedding, thermal power ramping, and thermal power ramping savings results for different typical wind power output scenarios, with or without the above-mentioned tight balance handling method.

[0153] Table 3 Comparison of disposal costs for Case 1 and Case 2 under typical scenarios with different wind power outputs

[0154]

[0155] As shown in Table 3, without the connection to electric vehicle charging stations, Scenario 1 has the highest total operating cost and thermal power ramp-up; Scenario 2 has a moderate total operating cost and thermal power ramp-up; and Scenario 3 has the lowest total operating cost and thermal power ramp-up. After connecting to electric vehicle charging stations, the total operating cost, total wind curtailment, total load shedding, and thermal power ramp-up all decreased to varying degrees in each scenario. Under different wind power penetration rates, electric vehicle charging stations can improve system flexibility and enhance the safe and economical operation of the system.

[0156] In summary, the handling method of this invention addresses the tight balance problem caused by insufficient flexibility and insufficient installed power generation capacity in high wind power penetration power grids. It establishes a tight balance handling model for power systems considering the uncertainties of electric vehicle charging stations. Analysis of the embodiments yields the following conclusions: 1) Electric vehicle charging stations, as a flexible resource, participate in the tight balance handling of the system, helping thermal power units to share some of the system's flexible ramp-up requirements. This allows thermal power units to have more adjustment range to absorb wind power and meet load demands, reducing wind curtailment and load shedding caused by insufficient system flexibility and installed power generation capacity, thus improving system flexibility and ensuring the safe and economical operation of the power system. 2) Under different wind power penetration rates, electric vehicle charging stations can improve system flexibility and enhance the level of safe and economical system operation. As the scale of electric vehicles continues to increase, system flexibility increases, while the total system operating cost and total load shedding show a downward trend. This indicates that the increase in the scale of electric vehicles can reduce load shedding caused by insufficient system flexibility and installed power generation capacity, improving system flexibility and economy.

[0157] The power system tight balance handling system according to an embodiment of the present invention includes an electric vehicle charging station module and a system tight balance handling module. The electric vehicle charging station module is used to establish an electric vehicle charging station model considering the uncertainty of the number of electric vehicles in the charging station; the system tight balance handling module is used to establish a power system tight balance handling model with the objective function of minimizing the total operating cost of the power system, treating the electric vehicle charging station as a flexible load of the power system, and solving for the optimal tight balance handling strategy of the power system under the constraints of each generator unit, load, flexibility requirement, and power demand. In this embodiment, the system also includes a thermal power unit module and a wind power unit module, used to establish wind power unit models and thermal power unit models, respectively. The storage medium according to an embodiment of the present invention stores a computer program instantiated from the above-described power system tight balance handling method.

[0158] Each module in the above system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0159] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0160] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for handling tight balance in a power system, characterized in that, Includes the following steps: S1: Construct an electric vehicle charging station model that considers the uncertainty of the number of electric vehicles in the electric vehicle charging station; S2: Treat electric vehicle charging stations as flexible loads in the power system and establish electric vehicle charging station constraints based on the electric vehicle charging station model; S3: With the goal of minimizing the total operating cost of the power system, establish a tight balance handling model for the power system. The constraints of the tight balance handling model for the power system include the flexible ramping capacity constraint that electric vehicle charging stations can provide in step S2. S4: Input the real-time data of the power system into the tight balance handling model of the power system established in step S3, and solve for the tight balance handling strategy that minimizes the total operating cost of the power system. The electric vehicle charging station model in step S1 includes: Number of electric vehicles available at electric vehicle charging stations: in, Indicates in Arrive at the charging station in time and The total number of electric vehicles that leave the charging station at any given time; Indicates that electric vehicle n is in Arrive at the charging station in time and Always leave the charging station; Indicates in The total number of electric vehicles arriving at the charging station at any given time; Indicates in The total number of electric vehicles that leave the charging station at any given time; This indicates the total number of electric vehicles in the charging station; Energy state of charging stations considering the impact of uncertainty in the number of electric vehicles: in, Indicates in The total energy state of electric vehicles arriving at charging stations at any given time; Indicates in Arrive at the charging station in time and The state of charge of electric vehicle n that leaves the charging station at any given time; Indicates in Arrive at the charging station in time and The capacity of electric vehicle n that leaves the charging station at any given time; Indicates in The total energy state of an electric vehicle that is constantly away from a charging station; This indicates the total energy state of the charging station; Total capacity of charging stations considering the uncertainty of the number of electric vehicles: in, Indicates in The total capacity of electric vehicles arriving at charging stations at any given time; Indicates in The total capacity of electric vehicles that are constantly leaving the charging station; This indicates the total capacity of the charging station; The constraints on electric vehicle charging stations in step S2 include: Constraints on charging stations regarding energy, backup capacity, and ramp flexibility: in, and These represent the power from the charging station pl to the grid and the power from the grid to the charging station during time period t, respectively. and These represent the upward and downward flexible ramp capacity of the charging station pl during time period t, respectively. and These represent the spinning reserve and non-spinning reserve capacity of charging station pl during time period t, respectively. and These represent the upward and downward frequency regulation reserve capacity of the charging station pl during time period t, respectively; This indicates the replacement reserve capacity of charging station pl during time period t; γ discharge and γ charge These represent the discharge efficiency and charging efficiency of an electric vehicle, respectively. and These are 0-1 state variables, representing the states of energy at charging station pl during time period t: energy flowing from the charging station to the grid or from the grid to the charging station. Energy state constraints of charging stations: in, This indicates the energy state of charging station pl during time period t; and These represent the charging efficiency and discharging efficiency of the charging station, respectively. The maximum power exchange constraint between electric vehicles and the power grid when energy is transferred from the vehicle to the grid: Where, ψ pl,t This indicates the net percentage of discharge resulting from the electric vehicle owner's contract regarding the required state of charge. Maximum and minimum range constraints for the state of charge of charging stations: Maximum and minimum range constraints for the energy state of charging stations: In step S3, the objective function is: min C = C1 + C2 + C3 Where: C represents the total operating cost of the system; C1 represents the total operating cost of the thermal power unit, including the operating cost of the thermal power unit, conventional ancillary services, and flexible ramp-up backup costs; N T N represents the total number of periods for tight balance handling. I N represents the total number of thermal power units; C2 represents the total operating cost of electric vehicle charging stations, including the discharge cost of electric vehicle charging stations, routine ancillary services, and flexible ramping backup costs. PL N represents the total number of electric vehicle charging stations; C3 represents the penalty cost for system curtailment and load shedding; N represents the total number of electric vehicle charging stations. J and N W These represent the total load and the total number of wind turbine units, respectively; p i,t MPC represents the active power of thermal power unit i during time period t; i,t (·) represents the operating cost of thermal power unit i during time period t; and This represents the coefficient of spinning reserve cost and non-spinning reserve cost for thermal power unit i during time period t; and These represent the spinning reserve capacity and non-spinning reserve capacity of thermal power unit i during time period t, respectively. and These represent the frequency regulation reserve cost coefficient and the replacement reserve cost coefficient of thermal power unit i during time period t, respectively. and These represent the upward frequency regulation reserve capacity and the downward frequency regulation reserve capacity of thermal power unit i during time period t, respectively. This represents the replacement reserve capacity of thermal power unit i during time period t; and These represent the upward flexible ramp cost coefficient and the downward flexible ramp cost coefficient of thermal power unit i during time period t, respectively. and These represent the upward and downward flexible ramping capacities of thermal power unit i during time period t, respectively. This represents the discharge cost coefficient of charging station pl during time period t; This represents the power supplied from the charging station pl to the grid during time period t; and These represent the spinning reserve cost coefficient and non-spinning reserve cost coefficient of charging station pl during time period t, respectively. and These represent the frequency regulation reserve cost coefficient and the replacement reserve cost coefficient of the charging station pl during time period t, respectively. and These represent the upward flexible ramp cost coefficient and the downward flexible ramp cost coefficient of the charging station pl during time period t, respectively. and These represent the system's wind curtailment penalty cost coefficient and load shedding penalty cost coefficient, respectively; LS j,t This indicates the load shedding generated by load j during time period t; This indicates the wind curtailment generated by wind power w during time period t.

2. The method for handling tight balance in a power system according to claim 1, characterized in that, In step S3, the total operating cost of the power system includes the total operating cost of thermal power units, the total operating cost of electric vehicle charging stations, and the penalty cost for wind curtailment and load shedding.

3. The method for handling tight balance in a power system according to claim 1, characterized in that, In step S3, the constraints of the tight balance handling model of the power system also include thermal power unit constraints, wind power unit constraints, system flexibility constraints, and power demand constraints.

4. A power system tight balance handling system, characterized in that, include: Electric vehicle charging station module, used to establish an electric vehicle charging station model that takes into account the uncertainty of the number of electric vehicles in the charging station; The system tight balance handling module is used to establish a tight balance handling model for the power system with the objective function of minimizing the total operating cost of the power system. It treats electric vehicle charging stations as flexible loads of the power system and solves for the optimal tight balance handling strategy of the power system under the constraints of each generator set, load, flexibility requirements and power demand.

5. The power system tight balance handling system according to claim 4, characterized in that, Also includes: Thermal power unit module, used to build thermal power unit models; The wind turbine module is used to build wind turbine models.

6. A storage medium storing a computer program, characterized in that, The computer program is configured to implement the power system tight balance handling method according to any one of claims 1 to 3 when it is run.

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