Cooperative scheduling method and device of power system, medium and electronic equipment

Abstract

The invention relates to a collaborative scheduling method and device of a power system, a medium and electronic equipment. The method comprises the steps of obtaining wind speed prediction data; determining predicted output power of the wind turbine generator according to the wind speed prediction data; and determining the day-ahead market bidding amount and power system decision parameters according to the power system balance constraint, the wind turbine generator set predicted output power, the storage battery energy storage model, the electric vehicle energy storage model, the thermal power generating unit constraint and the load constraint by taking the maximum expected income as a target. The electric vehicle energy storage model comprises a constraint condition enabling the average state of charge of the electric vehicle to be within a preset range. The average state of charge of the electric vehicles is determined according to the average state of charge of the electric vehicles accessing the power grid and the average state of charge of the electric vehicles leaving the power grid, and the difference between the average state of charge of the electric vehicles leaving the power grid and the average state of charge of the electric vehicles accessing the power grid is larger than or equal to the average state of charge difference value of the electric vehicles.

Description

Technical Field

[0001] The present disclosure relates to the field of electric power dispatching, and in particular, to a coordinated dispatching method, device, medium and electronic equipment for an electric power system. Background Art

[0002] With the increasing development of electric vehicles, the number of electric vehicles is increasing. In addition to helping the low-carbon transformation of the transportation industry, the large-scale use of electric vehicles can also be used as energy storage equipment to participate in peak load reduction and valley filling of the power grid and improve the efficiency of the energy system. How to better use electric vehicles for energy storage to optimize the dispatch of the power system is a hot topic in related research fields. Summary of the invention

[0003] The purpose of the present disclosure is to provide a coordinated dispatching method, device, medium and electronic equipment for an electric power system, which can ensure the travel of electric vehicles in the electric power system while improving the energy consumption capacity.

[0004] In order to achieve the above object, the present disclosure provides a coordinated dispatching method of a power system, comprising:

[0005] Get wind speed forecast data;

[0006] Determining the predicted output power of the wind turbine generator set according to the wind speed prediction data;

[0007] According to the power system balance constraint, the predicted output power of the wind turbine, the battery energy storage model, the electric vehicle energy storage model, the thermal power unit constraint and the load constraint, the day-ahead market bidding amount and the power system decision parameters are determined with the maximum expected benefit as the goal, wherein the power system decision parameters include the total output of the battery, the total output of the thermal power unit, the power load power, and the total output of the electric vehicle;

[0008] Among them, the electric vehicle energy storage model includes electric vehicle state of charge constraints, in which the electric vehicle state of charge constraints, the average state of charge of the electric vehicle is greater than or equal to a preset minimum value of the average state of charge of the electric vehicle, the average state of charge of the electric vehicle is less than or equal to a preset maximum value of the average state of charge of the electric vehicle, the average state of charge of the electric vehicle is determined according to the average state of charge of the electric vehicles connected to the power grid and the average state of charge of the electric vehicles leaving the power grid, the difference between the average state of charge of the electric vehicles leaving the power grid and the average state of charge of the electric vehicles connected to the power grid is greater than or equal to the difference in the average state of charge of the electric vehicle, and the expected benefit is determined according to the electric vehicle charging benefit, the electric vehicle discharge expenditure cost, the power generation cost of the thermal power unit, and the battery energy storage operation cost.

[0009] Optionally, determining the predicted output power of the wind turbine according to the wind speed prediction data includes:

[0010] The predicted output power of the wind turbine is determined according to the following formula:

[0011]

[0012] Among them, v r is the rated wind speed of the wind turbine, v ci is the cut-in wind speed for wind turbine generation, v co is the cut-out wind speed of wind turbine generator, P r is the rated output power of the wind turbine, P wind Predict the output power of the wind turbine, v f To predict wind speed.

[0013] Optionally, the power system balance constraint is determined according to the following formula:

[0014] P DA =∑P wind +P MT +∑P bat +P EV -P Load

[0015] Among them, ∑P wind Forecast the total output power of the wind turbine, P MT is the total output of thermal power units, ∑P bat is the total output of the battery, P EV is the total output of electric vehicles, P Load is the power load power, P DA is the day-ahead market bid volume.

[0016] Optionally, the battery energy storage model is expressed according to the following formula:

[0017]

[0018] SOE min ≤SOE(t)≤SOE max

[0019]

[0020] Among them, SOE(t) represents the state of charge of the battery during period t, p ch (t) represents the charging power of the battery during the period t, p dis (t) represents the discharge power of the battery during the period t, n c is the charging efficiency of the battery, n D is the discharge efficiency of the battery, Δt is the time change, SOE min is the minimum capacity of the battery, SOE max is the maximum capacity of the battery, Pbat (t) is the output of the battery to the power system, is the maximum discharge power of the battery, The maximum charging power of the battery.

[0021] Optionally, the electric vehicle energy storage model is expressed according to the following formula:

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029] soc min ≤soc t ≤soc max

[0030]

[0031] Among them, f DM (x) is the daily mileage of electric vehicles, x is a random variable, σ DM is the standard deviation, μ DM is the expected value of travel mileage, is the amount of electricity required for the jth electric car to travel, is the travel mileage of the jth electric car, W 100 The amount of electricity required for an electric vehicle to travel 100 kilometers. represents the electric vehicle discharge power during period t, represents the charging power of the electric vehicle during period t, is the total output of electric vehicles during period t, The state of discharging electric vehicles connected to the grid, To charge electric vehicles to access the grid, soc t is the average state of charge of the electric vehicle during period t, is the number of electric vehicles connected to the grid during period t, B is the average battery capacity of electric vehicles, is the average state of charge of electric vehicles connected to the grid during period t, is the number of electric vehicles connected to the grid during period t, is the average state of charge of electric vehicles leaving the grid during period t, is the average state of charge of electric vehicles leaving the grid during period t, is the discharge power of each electric vehicle, is the charging power of each electric vehicle, soc min is the minimum average state of charge of electric vehicles, soc max is the maximum value of the average state of charge of electric vehicles, soc demand is the minimum value of the average state of charge of electric vehicles leaving the grid, Δsoc is the difference between the average state of charge of electric vehicles, and N J is the number of electric vehicle trips per day.

[0032] Optionally, the thermal power unit constraint is expressed according to the following formula:

[0033]

[0034] in, is the minimum output of the thermal power unit, is the maximum output of the thermal power unit, P MT is the total output of thermal power units;

[0035] The load constraint is expressed according to the following formula

[0036]

[0037] in, is the minimum load, is the maximum load, P Load is the electrical load power.

[0038] Optionally, the expected return is determined according to the following formula:

[0039]

[0040] F1 DA For the expected return, is the day-ahead market price, P t DA is the day-ahead market bidding volume, C1 is the unit electricity revenue of electric vehicle charging, C2 is the unit electricity cost of electric vehicle discharging, C3 is the unit electricity cost of thermal power generation unit, C4 is the unit electricity cost of battery discharging, represents the charging power of the electric vehicle during period t, represents the electric vehicle discharge power during period t, P MT is the total output of thermal power units, P bat (t) is the output of the battery to the power system.

[0041] The present disclosure also provides a coordinated dispatching device for a power system, comprising:

[0042] An acquisition module is configured to acquire wind speed prediction data;

[0043] A first determination module is configured to determine the predicted output power of the wind turbine according to the wind speed prediction data;

[0044] The second determination module is configured to determine the day-ahead market bidding amount and the power system decision parameters according to the power system balance constraint, the predicted output power of the wind turbine, the battery energy storage model, the electric vehicle energy storage model, the thermal power unit constraint and the load constraint with the maximum expected benefit as the goal, wherein the power system decision parameters include the total output of the battery, the total output of the thermal power unit, the power load power, and the total output of the electric vehicle;

[0045] Among them, the electric vehicle energy storage model includes electric vehicle state of charge constraints, in which the electric vehicle state of charge constraints, the average state of charge of the electric vehicle is greater than or equal to a preset minimum value of the average state of charge of the electric vehicle, the average state of charge of the electric vehicle is less than or equal to a preset maximum value of the average state of charge of the electric vehicle, the average state of charge of the electric vehicle is determined according to the average state of charge of the electric vehicles connected to the power grid and the average state of charge of the electric vehicles leaving the power grid, the difference between the average state of charge of the electric vehicles leaving the power grid and the average state of charge of the electric vehicles connected to the power grid is greater than or equal to the difference in the average state of charge of the electric vehicle, and the expected benefit is determined according to the electric vehicle charging benefit, the electric vehicle discharge expenditure cost, the power generation cost of the thermal power unit, and the battery energy storage operation cost.

[0046] The present disclosure also provides a non-temporary computer-readable storage medium having a computer program stored thereon, which implements the steps of the above-mentioned coordinated dispatching method of the power system when executed by a processor.

[0047] The present disclosure also provides an electronic device, comprising:

[0048] a memory having a computer program stored thereon;

[0049] A processor is used to execute the computer program in the memory to implement the steps of the above-mentioned coordinated dispatching method of the power system.

[0050] Through the above technical scheme, the day-ahead market bidding amount and power system decision parameters are determined with the maximum expected benefit as the goal according to the power system balance constraint, the predicted output power of the wind turbine, the battery energy storage model, the electric vehicle energy storage model, the thermal power unit constraint and the load constraint. The electric vehicle state of charge constraint is included in the electric vehicle energy storage model to ensure that the average state of charge of the electric vehicle is within a predetermined range. Moreover, in the electric vehicle energy storage model, the average state of charge of the electric vehicle is determined according to the average state of charge of the electric vehicle connected to the power grid and the average state of charge of the electric vehicle leaving the power grid. The difference between the average state of charge of the electric vehicle leaving the power grid and the average state of charge of the electric vehicle connected to the power grid is greater than or equal to the difference in the average state of charge of the electric vehicle. In this way, while ensuring the power demand of electric vehicle travel, the power system can be better coordinated and dispatched.

[0051] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0053] Figure 1 The present invention is a flowchart of a coordinated dispatching method of a power system according to an exemplary embodiment.

[0054] Figure 2 It is a block diagram of a coordinated dispatching device of a power system according to an exemplary embodiment.

[0055] Figure 3 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0056] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0057] It should be noted that all actions of acquiring signals, information or data in the present disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the device is located and with the authorization given by the owner of the corresponding device.

[0058] Figure 1 FIG. 1 is a flow chart showing a method for coordinated dispatching of a power system according to an exemplary embodiment. Figure 1 As shown, the coordinated dispatching method of the power system includes steps S101 to S103.

[0059] In step S101, wind speed prediction data is obtained.

[0060] In step S102, the predicted output power of the wind turbine is determined according to the wind speed prediction data.

[0061] In step S103, the day-ahead market bidding amount and power system decision parameters are determined based on the power system balance constraints, predicted output power of wind turbines, battery energy storage model, electric vehicle energy storage model, thermal power unit constraints and load constraints with the goal of maximizing expected benefits, wherein the power system decision parameters include total battery output, total thermal power unit output, power load power, and total electric vehicle output.

[0062] Among them, the electric vehicle energy storage model includes electric vehicle state of charge constraints. In the electric vehicle state of charge constraints, the average state of charge of the electric vehicle is greater than or equal to the preset minimum value of the average state of charge of the electric vehicle, and the average state of charge of the electric vehicle is less than or equal to the preset maximum value of the average state of charge of the electric vehicle. The average state of charge of the electric vehicle is determined according to the average state of charge of the electric vehicles connected to the power grid and the average state of charge of the electric vehicles leaving the power grid. The difference between the average state of charge of the electric vehicles leaving the power grid and the average state of charge of the electric vehicles connected to the power grid is greater than or equal to the difference in the average state of charge of the electric vehicles. The expected benefit is determined according to the electric vehicle charging income, the electric vehicle discharge expenditure cost, the power generation cost of the thermal power unit, and the battery energy storage operation cost.

[0063] The wind speed prediction data may be determined based on the historical wind speed data of the location of the wind turbine. For example, in one embodiment, the wind speed prediction data may be determined based on the historical wind speed data of each month in the area where the wind turbine is located.

[0064] The predicted output power of the wind turbine can be determined based on the wind speed prediction data.

[0065] After the wind speed prediction data is obtained in step S101, the output power of the wind turbine can be predicted based on the predicted wind speed corresponding to each time period. When the predicted wind speed in a certain time period in the area where the wind turbine is located is less than the cut-in wind speed of the wind turbine generator or the predicted wind speed in a certain time period in the area where the wind turbine is located is greater than or equal to the cut-out wind speed of the wind turbine generator, the predicted output power of the wind turbine in the time period is determined to be 0. In the case where the predicted wind speed in a certain time period in the area where the wind turbine is located is greater than or equal to the cut-in wind speed of the wind turbine generator and less than the rated wind speed of the wind turbine generator, the predicted output power of the wind turbine in the time period is determined according to the rated wind speed of the wind turbine generator, the cut-in wind speed of the wind turbine generator, and the predicted wind speed in a certain time period in the area where the wind turbine is located. In the case where the predicted wind speed in a certain time period in the area where the wind turbine is located is less than the cut-out wind speed of the wind turbine generator and greater than or equal to the rated wind speed of the wind turbine generator, the predicted output power of the wind turbine in the time period is determined to be the rated output power of the wind turbine.

[0066] In one embodiment, determining the predicted output power of a wind turbine generator system according to wind speed prediction data includes:

[0067] The predicted output power of the wind turbine is determined according to the following formula:

[0068]

[0069] Among them, v r is the rated wind speed of the wind turbine, v ci is the cut-in wind speed for wind turbine generation, v co is the cut-out wind speed of wind turbine generator, P r is the rated output power of the wind turbine, P wind Predict the output power of the wind turbine, v f is the predicted wind speed. In formula (1), v r 、v ci 、v co , P r is a known quantity.

[0070] The power system balance constraint is a preset constraint condition. In the power system balance constraint, the power that the power system can dispatch outward is equal to the difference between the power provided by each device in the power system and the power consumed by each device in the power system.

[0071] The electric power that the power system can dispatch externally can participate in the day-ahead market bidding. In one embodiment, the power system balance constraint is determined according to the following formula:

[0072] P DA =∑P wind +PMT +∑P bat +P EV -P Load (2)

[0073] Among them, ∑P wind Forecast the total output power of the wind turbine, P MT is the total output of thermal power units, ∑P bat is the total output of the battery, P EV is the total output of electric vehicles, P Load is the power load power, P DA is the day-ahead market bid volume.

[0074] ∑P wind The value of can be the sum of the predicted output powers of the wind turbines connected to the power system. For example, the wind turbines connected to the power system include wind turbine A, wind turbine B, and wind turbine C. The predicted output power of wind turbine A is P1, the predicted output power of wind turbine B is P2, and the predicted output power of wind turbine C is P3, then ∑P wind The value of P is P1+P2+P3. MT That is, the total output of thermal power units connected to the power system.

[0075] In the power system, batteries can be used for peak load regulation. When the power supply is tight during peak hours, batteries can release the stored electric energy. When the power supply is sufficient, batteries can be used to store electric energy to absorb the electric energy generated by each unit in the power system. bat That is, the sum of the electric power output from all batteries connected to the power system. bat When it is positive, it means that the battery outputs electric power to the power system. bat When it is a negative value, it means that the battery receives the electric power output by the power system. In one embodiment, for each group of batteries, the battery energy storage model is expressed according to the following formula:

[0076]

[0077] SOE min ≤SOE(t)≤SOE max (4)

[0078]

[0079] Among them, SOE(t) represents the state of charge of the battery during period t, p ch (t) represents the charging power of the battery during the period t, p dis (t) represents the discharge power of the battery during the period t, n c is the charging efficiency of the battery, nD is the discharge efficiency of the battery, Δt is the time change, SOE min is the minimum capacity of the battery, SOE max is the maximum capacity of the battery, P bat (t) is the output of the battery to the power system, is the maximum discharge power of the battery, The maximum charging power of the battery.

[0080] In the battery energy storage model represented by formula (3), formula (4), and formula (5), SOE min 、SOE max , It can be preset according to the characteristics of the battery itself and is a known quantity. SOE(t-Δt) represents the state of charge of the battery during the period t-Δt. When the battery is in the charging state, P bat The absolute value of (t) and p ch (t) is equal; when the battery is in the discharge state, P bat The value of (t) and p dis The values ​​of (t) are equal. c and n D It is related to the characteristics of the battery itself and is a known quantity.

[0081] Formula (4) can be used as a constraint condition for constraining the state of charge of the battery, that is, during the operation of the power system, the state of charge of the battery should be less than or equal to the maximum capacity of the battery and greater than or equal to the minimum capacity of the battery.

[0082] Formula (5) can be used as a constraint condition for the output of the battery to the power system, that is, during the operation of the power system, the output of the battery to the power system should be less than or equal to the maximum discharge power of the battery and greater than or equal to the maximum charging power of the battery (the maximum charging power of the battery is represented by a negative value).

[0083] The initial value of SOE(t-Δt) can be predetermined. ch (t) and p dis (t) is a decision variable, which can be determined based on formula (3) and the constraints shown in formula (4) and formula (5). ch (t) and p dis The total battery output is the sum of the outputs of the batteries connected to the power system to the power system.

[0084] In the power system, electric vehicles can also be used for peak load regulation. When the power supply is tight during peak hours, the batteries in electric vehicles can release the stored electric energy; when the power supply is sufficient, electric vehicles can be charged. Therefore, electric vehicles have the function of peak load regulation for the power system.

[0085] In another embodiment, the electric vehicle energy storage model is expressed according to the following formula:

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093] soc min ≤soc t ≤soc max (13)

[0094]

[0095] Among them, f DM (x) is the daily mileage of electric vehicles, x is a random variable, σ DM is the standard deviation, μ DM is the expected value of travel mileage, is the amount of electricity required for the jth electric car to travel, is the travel mileage of the jth electric car, W 100 The amount of electricity required for an electric vehicle to travel 100 kilometers. represents the electric vehicle discharge power during period t, represents the charging power of the electric vehicle during period t, is the total output of electric vehicles during period t, The state of discharging electric vehicles connected to the grid, To charge electric vehicles to access the grid, soc t is the average state of charge of the electric vehicle during period t, is the number of electric vehicles connected to the grid during period t, B is the average battery capacity of electric vehicles, is the average state of charge of electric vehicles connected to the grid during period t, is the number of electric vehicles connected to the grid during period t, is the average state of charge of electric vehicles leaving the grid during period t, is the average state of charge of electric vehicles leaving the grid during period t, is the discharge power of each electric vehicle, is the charging power of each electric vehicle, soc min is the minimum average state of charge of electric vehicles, soc max is the maximum value of the average state of charge of electric vehicles, soc demand is the minimum value of the average state of charge of electric vehicles leaving the grid, Δsoc is the difference between the average state of charge of electric vehicles, and N J is the number of electric vehicle trips per day.

[0096] Formula (6) is a formula used in the electric vehicle energy storage model to represent the daily mileage of the electric vehicle. As shown in formula (6), in one embodiment, the daily mileage of the electric vehicle can be determined based on the lognormal distribution. In one embodiment, σ DM The value of is 0.88, μ DM is 3.2.

[0097] The daily mileage of electric vehicles can be determined based on the historical data of electric vehicles. If there are M electric vehicles participating in the peak load regulation of the power system, it is estimated that N J Electric vehicles need to travel. J For each of the electric vehicles, the predicted daily mileage of the electric vehicle can be determined based on formula (6). J The daily mileage of each electric vehicle.

[0098] After determining the daily mileage of each electric vehicle, the amount of electricity required for each electric vehicle to travel (i.e., the amount of electricity consumed during the travel of the electric vehicle) can be determined to meet the travel needs of the electric vehicle. The amount of electricity required for each electric vehicle to travel can be determined according to formula (7).

[0099] For N J For each of the electric vehicles that need to travel, the amount of electricity required for the electric vehicle to travel is determined according to formula (7).

[0100] The difference in the average state of charge of the electric vehicle can be determined based on formula (8). Since electric vehicles consume electricity when traveling, the state of charge of the electric vehicle when leaving the power system needs to be greater than the state of charge of the electric vehicle when entering the power system to meet the travel needs of the electric vehicle. In one embodiment, N J The sum of the electricity required by the electric vehicles is taken as the dividend, and the number of electric vehicles that need to travel (i.e. N JThe product of ) and the average battery capacity of electric vehicles is used as the divisor, and the quotient obtained is the difference in the average state of charge of electric vehicles. The average battery capacity of electric vehicles and the amount of electricity required for electric vehicles to travel every 100 kilometers can be determined based on existing data and are known quantities.

[0101] The state of the electric vehicle being discharged being connected to the power grid, that is, the state of the electric vehicle being discharged being connected to the power system. If the electric vehicle being discharged is connected to the power system, then The value of is 1. If the electric vehicle being discharged is not connected to the power system, then The value of is 0.

[0102] The state of the charging electric vehicle connected to the power grid is the state of the charging electric vehicle connected to the power system. If the charging electric vehicle is connected to the power system, then The value of is 1. If the electric vehicle being discharged is not connected to the power system, then The value of is 0.

[0103] In formula (9), is a decision variable and can be based on formula (9), Determine This also determines the total output of the electric vehicle.

[0104] Formula (10) describes the charging and discharging process of electric vehicles. In formula (10), Predictions can be made based on historical data of electric vehicles. is the number of electric vehicles connected to the grid during period t+1, which can be based on and Sure. The determination can be made based on historical data of electric vehicles. In one embodiment, Can be and Δsoc. t+1 is the average state of charge of the electric vehicle during the t+1 period. soc t and soc t+1 It can be calculated based on the initial value of the average state of charge of the electric vehicle according to the variables involved in formula (10).

[0105] Formula (11) to Formula (14) show the constraints that electric vehicles connected to the power system must meet, where Formula (13) describes the state of charge constraints of electric vehicles. The power allowed to be transmitted to the power grid for each electric vehicle can be preset, and the value of this power is the preset The power that can be preset for each electric vehicle to obtain electricity from the power grid for charging can be preset. The power value is the preset The value of soc min 、soc max and soc demand The value of can be preset by relevant personnel according to actual conditions. In order to ensure that the electric energy stored in electric vehicles can meet travel needs, The value should be greater than or equal to soc demand In one embodiment, if according to and Δsoc determined Less than soc demand , then soc demand The value of The value of is involved in the calculation.

[0106] In one embodiment, the thermal power unit constraint is expressed according to the following formula:

[0107]

[0108] in, is the minimum output of the thermal power unit, It is the maximum output of thermal power unit.

[0109] The total output of the thermal power units refers to the power generated by the thermal power units connected to the power system. The minimum output and maximum output of the thermal power units can be set according to actual conditions.

[0110] In one embodiment, the load constraint is expressed according to the following formula:

[0111]

[0112] in, is the minimum load, The power load power is the power consumption of the load connected to the power system (excluding batteries and electric vehicles). The minimum load and maximum load can be preset according to the actual situation.

[0113] The expected revenue is the revenue remaining after deducting the cost from the revenue obtained by the power system through bidding in the power market.

[0114] In one embodiment, the expected return is determined according to the following formula:

[0115]

[0116] F1 DA For expected revenue, is the day-ahead market price, P t DAis the day-ahead market bidding volume, C1 is the revenue per unit of electric energy from charging electric vehicles, C2 is the cost per unit of electric energy from discharging electric vehicles, C3 is the cost per unit of electric energy from thermal power units, and C4 is the cost per unit of electric energy from batteries. The revenue per unit of electric energy from charging electric vehicles refers to the fee charged to electric vehicle users when the power system provides electric vehicles with a unit of electric energy, and the cost per unit of electric energy from discharging electric vehicles refers to the cost paid to electric vehicle users when the power system receives a unit of electric energy from electric vehicles. Battery loss occurs after charging and discharging, that is, the service life of the battery is limited. The loss cost of the battery charging and discharging process can be amortized over the entire life of the battery. The cost per unit of electric energy from discharging a battery is the battery loss cost allocated to each unit of electric energy discharged by the battery. In formula (17), C1, C2, C3, and C4 are known quantities.

[0117] Based on the power system balance constraints, wind turbine forecast output power, battery energy storage model, electric vehicle energy storage model, thermal power unit constraints and load constraints, the maximum expected profit is taken as the goal to determine the day-ahead market bidding amount and power system decision parameters, so that the determined day-ahead market bidding amount can be used to participate in the market to obtain higher profits, and the wind turbines, batteries, electric vehicles, and thermal power units in the power system can be dispatched according to the determined power system decision parameters. Among them, the total output of the thermal power unit P MT 、Total battery output ∑P bat 、Total output of electric vehicles P EV , Power load power P Load is the decision variable.

[0118] Through the above technical scheme, the day-ahead market bidding amount and power system decision parameters are determined with the maximum expected benefit as the goal according to the power system balance constraint, the predicted output power of the wind turbine, the battery energy storage model, the electric vehicle energy storage model, the thermal power unit constraint and the load constraint. The electric vehicle state of charge constraint is included in the electric vehicle energy storage model to ensure that the average state of charge of the electric vehicle is within a predetermined range. Moreover, in the electric vehicle energy storage model, the average state of charge of the electric vehicle is determined according to the average state of charge of the electric vehicle connected to the power grid and the average state of charge of the electric vehicle leaving the power grid. The difference between the average state of charge of the electric vehicle leaving the power grid and the average state of charge of the electric vehicle connected to the power grid is greater than or equal to the difference in the average state of charge of the electric vehicle. In this way, while ensuring the power demand of electric vehicle travel, the power system can be better coordinated and dispatched.

[0119] Figure 2 FIG. 1 is a block diagram of a coordinated dispatching device for a power system according to an exemplary embodiment. Figure 2As shown, the coordinated dispatching device of the power system includes an acquisition module 201, a first determination module 202 and a second determination module 203.

[0120] The acquisition module 201 is configured to acquire wind speed prediction data.

[0121] The first determination module 202 is configured to determine the predicted output power of the wind turbine according to the wind speed prediction data.

[0122] The second determination module 203 is configured to determine the day-ahead market bidding amount and the power system decision parameters according to the power system balance constraints, the predicted output power of the wind turbine, the battery energy storage model, the electric vehicle energy storage model, the thermal power unit constraints and the load constraints with the maximum expected profit as the goal, wherein the power system decision parameters include the total output of the battery, the total output of the thermal power unit, the power load power, and the total output of the electric vehicle.

[0123] Among them, the electric vehicle energy storage model includes electric vehicle state of charge constraints, in which the electric vehicle state of charge constraints, the average state of charge of the electric vehicle is greater than or equal to a preset minimum value of the average state of charge of the electric vehicle, the average state of charge of the electric vehicle is less than or equal to a preset maximum value of the average state of charge of the electric vehicle, the average state of charge of the electric vehicle is determined according to the average state of charge of the electric vehicles connected to the power grid and the average state of charge of the electric vehicles leaving the power grid, the difference between the average state of charge of the electric vehicles leaving the power grid and the average state of charge of the electric vehicles connected to the power grid is greater than or equal to the difference in the average state of charge of the electric vehicle, and the expected benefit is determined according to the electric vehicle charging benefit, the electric vehicle discharge expenditure cost, the power generation cost of the thermal power unit, and the battery energy storage operation cost.

[0124] In yet another embodiment, the first determination module 202 is further configured to determine the predicted output power of the wind turbine generator system according to the following formula:

[0125]

[0126] Among them, v r is the rated wind speed of the wind turbine, v ci is the cut-in wind speed for wind turbine generation, v co is the cut-out wind speed of wind turbine generator, P r is the rated output power of the wind turbine, P wind Predict the output power of the wind turbine, v f To predict wind speed.

[0127] In yet another embodiment, the power system balance constraint is determined according to the following formula:

[0128] P DA =∑Pwind +P MT +∑P bat +P EV -P Load

[0129] Among them, ∑P wind Forecast the total output power of the wind turbine, P MT is the total output of thermal power units, ∑P bat is the total output of the battery, P EV is the total output of electric vehicles, P Load is the power load power, P DA is the day-ahead market bid volume.

[0130] In another embodiment, the battery energy storage model is expressed according to the following formula:

[0131]

[0132] SOE min ≤SOE(t)≤SOE max

[0133]

[0134] Among them, SOE(t) represents the state of charge of the battery during period t, p ch (t) represents the charging power of the battery during the period t, p dis (t) represents the discharge power of the battery during the period t, n c is the charging efficiency of the battery, n D is the discharge efficiency of the battery, Δt is the time change, SOE min is the minimum capacity of the battery, SOE max is the maximum capacity of the battery, P bat (t) is the output of the battery to the power system, is the maximum discharge power of the battery, The maximum charging power of the battery.

[0135] In another embodiment, the electric vehicle energy storage model is expressed according to the following formula:

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143] soc min ≤soc t ≤soc max

[0144]

[0145] Among them, f DM (x) is the daily mileage of electric vehicles, x is a random variable, σ DM is the standard deviation, μ DM is the expected value of travel mileage, is the amount of electricity required for the jth electric car to travel, is the travel mileage of the jth electric car, W 100 The amount of electricity required for an electric vehicle to travel 100 kilometers. represents the electric vehicle discharge power during period t, represents the charging power of the electric vehicle during period t, is the total output of electric vehicles during period t, The state of discharging electric vehicles connected to the grid, To charge electric vehicles to access the grid, soc t is the average state of charge of the electric vehicle during period t, is the number of electric vehicles connected to the grid during period t, B is the average battery capacity of electric vehicles, is the average state of charge of electric vehicles connected to the grid during period t, is the number of electric vehicles connected to the grid during period t, is the average state of charge of electric vehicles leaving the grid during period t, is the average state of charge of electric vehicles leaving the grid during period t, is the discharge power of each electric vehicle, is the charging power of each electric vehicle, soc min is the minimum average state of charge of electric vehicles, soc max is the maximum value of the average state of charge of electric vehicles, soc demand is the minimum value of the average state of charge of electric vehicles leaving the grid, Δsoc is the difference between the average state of charge of electric vehicles, and N J is the number of electric vehicle trips per day.

[0146] In yet another embodiment, the thermal power unit constraint is expressed according to the following formula:

[0147]

[0148] in, is the minimum output of the thermal power unit, is the maximum output of the thermal power unit, P MT is the total output of thermal power units;

[0149] The load constraint is expressed according to the following formula:

[0150]

[0151] in, is the minimum load, is the maximum load, P Load is the electrical load power.

[0152] In yet another embodiment, the expected return is determined according to the following formula:

[0153]

[0154] F1 DA For the expected return, is the day-ahead market price, P t DA is the day-ahead market bidding volume, C1 is the unit electricity revenue of electric vehicle charging, C2 is the unit electricity cost of electric vehicle discharging, C3 is the unit electricity cost of thermal power unit, and C4 is the unit electricity cost of battery discharging.

[0155] The present disclosure also provides a non-temporary computer-readable storage medium having a computer program stored thereon, which implements the steps of the above-mentioned coordinated dispatching method of the power system when executed by a processor.

[0156] The present disclosure also provides an electronic device, comprising:

[0157] a memory having a computer program stored thereon;

[0158] A processor is used to execute the computer program in the memory to implement the steps of the above-mentioned coordinated dispatching method of the power system.

[0159] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0160] Figure 3 FIG. 7 is a block diagram of an electronic device 700 according to an exemplary embodiment. Figure 3As shown, the electronic device 700 may include: a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input / output (I / O) interface 704, and a communication component 705.

[0161] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above-mentioned coordinated dispatching method of the power system. The memory 702 is used to store various types of data to support the operation of the electronic device 700, and these data may include, for example, instructions for any application or method used to operate on the electronic device 700, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 702 or sent through the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, and the other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 705 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0162] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned coordinated scheduling method of the power system.

[0163] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned coordinated dispatching method of the power system are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 702 including program instructions, and the above-mentioned program instructions can be executed by the processor 701 of the electronic device 700 to complete the above-mentioned coordinated dispatching method of the power system.

[0164] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0165] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0166] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A coordinated dispatching method for a power system, characterized in that: include: Get wind speed forecast data; Determining the predicted output power of the wind turbine generator set according to the wind speed prediction data; According to the power system balance constraint, the predicted output power of the wind turbine, the battery energy storage model, the electric vehicle energy storage model, the thermal power unit constraint and the load constraint, the day-ahead market bidding amount and the power system decision parameters are determined with the maximum expected benefit as the goal, wherein the power system decision parameters include the total output of the battery, the total output of the thermal power unit, the power load power, and the total output of the electric vehicle; Among them, the electric vehicle energy storage model includes electric vehicle state of charge constraints, in which the electric vehicle state of charge constraints, the average state of charge of the electric vehicle is greater than or equal to a preset minimum value of the average state of charge of the electric vehicle, the average state of charge of the electric vehicle is less than or equal to a preset maximum value of the average state of charge of the electric vehicle, the average state of charge of the electric vehicle is determined according to the average state of charge of the electric vehicles connected to the power grid and the average state of charge of the electric vehicles leaving the power grid, the difference between the average state of charge of the electric vehicles leaving the power grid and the average state of charge of the electric vehicles connected to the power grid is greater than or equal to the difference in the average state of charge of the electric vehicle, and the expected benefit is determined according to the electric vehicle charging benefit, the electric vehicle discharge expenditure cost, the power generation cost of the thermal power unit, and the battery energy storage operation cost.

2. The method according to claim 1, characterized in that: The step of determining the predicted output power of the wind turbine generator set according to the wind speed prediction data comprises: The predicted output power of the wind turbine is determined according to the following formula: Among them, v r is the rated wind speed of the wind turbine, v ci is the cut-in wind speed for wind turbine generation, v co is the cut-out wind speed of wind turbine generator, P r is the rated output power of the wind turbine, P wind Predict the output power of the wind turbine, v f To predict wind speed.

3. The method according to claim 1, characterized in that The power system balance constraint is determined according to the following formula: P DA =∑P wind +P MT +∑P bat +P EV -P Load Among them, ∑P wind Forecast the total output power of the wind turbine, P MT is the total output of thermal power units, ∑P bat is the total output of the battery, P EV is the total output of electric vehicles, P Load is the power load power, P DA is the day-ahead market bid volume.

4. The method according to claim 1, characterized in that: The battery energy storage model is expressed according to the following formula: SOE min ≤SOE(t)≤SOE max Among them, SOE(t) represents the state of charge of the battery during period t, p ch (t) represents the charging power of the battery during the period t, p dis (t) represents the discharge power of the battery during the period t, n c is the charging efficiency of the battery, n D is the discharge efficiency of the battery, Δt is the time change, SOE min is the minimum capacity of the battery, SOE max is the maximum capacity of the battery, P bat (t) is the output of the battery to the power system, is the maximum discharge power of the battery, The maximum charging power of the battery.

5. The method according to claim 1, characterized in that The electric vehicle energy storage model is expressed according to the following formula: soc min ≤soc t ≤soc max Among them, f DM (x) is the daily mileage of electric vehicles, x is a random variable, σ DM is the standard deviation, μ DM is the expected value of travel mileage, is the amount of electricity required for the jth electric car to travel, is the travel mileage of the jth electric car, W 100 The amount of electricity required for an electric vehicle to travel 100 kilometers. represents the electric vehicle discharge power during period t, represents the charging power of the electric vehicle during period t, is the total output of electric vehicles during period t, The state of discharging electric vehicles connected to the grid, To charge electric vehicles to access the grid, soc t is the average state of charge of the electric vehicle during period t, is the number of electric vehicles connected to the grid during period t, B is the average battery capacity of electric vehicles, is the average state of charge of electric vehicles connected to the grid during period t, is the number of electric vehicles connected to the grid during period t, is the average state of charge of electric vehicles leaving the grid during period t, is the average state of charge of electric vehicles leaving the grid during period t, is the discharge power of each electric vehicle, is the charging power of each electric vehicle, soc min is the minimum average state of charge of electric vehicles, soc max is the maximum value of the average state of charge of electric vehicles, soc demand is the minimum value of the average state of charge of electric vehicles leaving the grid, Δsoc is the difference between the average state of charge of electric vehicles, and N J is the number of electric vehicle trips per day.

6. The method according to claim 1, characterized in that The thermal power unit constraint is expressed according to the following formula: in, is the minimum output of the thermal power unit, is the maximum output of the thermal power unit, P MT is the total output of thermal power units; The load constraint is expressed according to the following formula in, is the minimum load, is the maximum load, P Load is the electrical load power.

7. The method according to claim 1, characterized in that The expected return is determined according to the following formula: F1 DA For the expected return, is the day-ahead market price, P t DA is the day-ahead market bidding volume, C1 is the unit electricity revenue of electric vehicle charging, C2 is the unit electricity cost of electric vehicle discharging, C3 is the unit electricity cost of thermal power generation unit, C4 is the unit electricity cost of battery discharging, represents the charging power of the electric vehicle during period t, P represents the electric vehicle discharge power during period t, MT is the total output of thermal power units, P bat (t) is the output of the battery to the power system.

8. A coordinated dispatching device for a power system, characterized in that: include: An acquisition module is configured to acquire wind speed prediction data; A first determination module is configured to determine the predicted output power of the wind turbine according to the wind speed prediction data; The second determination module is configured to determine the day-ahead market bidding amount and the power system decision parameters according to the power system balance constraint, the predicted output power of the wind turbine, the battery energy storage model, the electric vehicle energy storage model, the thermal power unit constraint and the load constraint with the maximum expected benefit as the goal, wherein the power system decision parameters include the total output of the battery, the total output of the thermal power unit, the power load power, and the total output of the electric vehicle; Among them, the electric vehicle energy storage model includes electric vehicle state of charge constraints, in which the electric vehicle state of charge constraints, the average state of charge of the electric vehicle is greater than or equal to a preset minimum value of the average state of charge of the electric vehicle, the average state of charge of the electric vehicle is less than or equal to a preset maximum value of the average state of charge of the electric vehicle, the average state of charge of the electric vehicle is determined according to the average state of charge of the electric vehicles connected to the power grid and the average state of charge of the electric vehicles leaving the power grid, the difference between the average state of charge of the electric vehicles leaving the power grid and the average state of charge of the electric vehicles connected to the power grid is greater than or equal to the difference in the average state of charge of the electric vehicle, and the expected benefit is determined according to the electric vehicle charging benefit, the electric vehicle discharge expenditure cost, the power generation cost of the thermal power unit, and the battery energy storage operation cost.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 7.

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