Electric power system optimal scheduling method considering multi-type energy storage optimal configuration

Abstract

The invention relates to a power system optimal scheduling method considering multi-type energy storage optimal configuration, and belongs to the field of power system planning and optimal operation. According to the method, a battery energy storage power station, pumped storage and an electric vehicle are respectively configured for three sides of a power grid load to participate in coordination and cooperation, and different energy storage capacity configuration methods are provided; and establishing a multi-scene source network load storage scheduling optimization model considering multi-type energy storage, wherein the multi-scene source network load storage scheduling optimization model takes the highest total efficiency of the scheduling model, the maximum renewable energy consumption amount and the longest charging and discharging service life of an energy storage battery and an electric vehicle battery as targets, and the multi-scene source network load storage scheduling optimization model takes the highest total efficiency of the scheduling model, the maximum renewable energy consumption amount and the longest charging and discharging service life of the energy storage battery and the electric vehicle battery as targets. And the flexibility of the system is enhanced.

Description

Technical Field

[0001] The present invention belongs to the field of power system planning and optimized operation, and specifically relates to a power system optimal scheduling method considering the optimized configuration of multiple types of energy storage. Background Art

[0002] With the large-scale grid integration of renewable energy sources such as wind and solar, the dual carbon goals have triggered changes in the power supply side, the grid side, and the load side. Various forms of energy storage, mainly battery energy storage power stations, pumped storage, and electric vehicle energy storage, have developed rapidly. However, the utilization hours and actual lifespan of energy storage deployed in the current power grid and new energy sites have not met expectations. The regulation cost is higher than that of thermal power units that have undergone flexibility transformation, making it difficult to meet the flexibility and economic requirements of the new power system. Comprehensively optimizing various resources such as source, network, load, and storage in different scenarios, rationally allocating multiple types of energy storage capacity, ensuring the absorption of wind and solar power, improving the economy and flexibility of the system, and enhancing the multi-faceted complementary synergy of source, network, load, and storage are the key to building a new power system. Summary of the Invention

[0003] The purpose of the present invention is to provide an optimal dispatching method for an electric power system that takes into account the optimal configuration of multiple types of energy storage. Battery energy storage power stations, pumped storage, and electric vehicles are configured on the three sides of the power grid and load, respectively. Different energy storage capacity configuration methods are given to establish a multi-scenario source-grid-load-storage dispatching optimization model that takes into account multiple types of energy storage, with the goals of maximizing the overall efficiency of the dispatching model, maximizing the renewable energy absorption capacity, and maximizing the charge and discharge life of energy storage batteries and electric vehicle batteries. The present invention can fully improve the utilization rate of energy storage resources and the absorption capacity of new energy, thereby enhancing the flexibility of the system.

[0004] To achieve the above objectives, the technical solution of the present invention is: a method for optimal dispatching of a power system considering the optimal configuration of multiple types of energy storage, comprising:

[0005] Battery energy storage power stations, pumped storage, and electric vehicles are configured on the three sides of the power grid, and different energy storage capacity configuration methods are given;

[0006] Establish a multi-scenario source-grid-load-storage dispatch optimization model that takes into account multiple types of energy storage, with the goals of maximizing the overall dispatch model efficiency, maximizing renewable energy absorption, and extending the charge-discharge life of energy storage batteries and electric vehicle batteries;

[0007] Solve the multi-scenario source-grid-load-storage scheduling optimization model taking into account multiple types of energy storage, and obtain the optimal output curves of various types of units in the power system.

[0008] Compared with the prior art, the present invention has the following beneficial effects:

[0009] 1) This invention comprehensively considers the uncertainty of renewable energy output and power system emergencies, deploying battery energy storage power stations on the power supply side, pumped hydropower storage on the grid side, and electric vehicles on the load side. This enables capacity configuration of complex energy storage across multiple terminals, including source, grid, and load, in multiple scenarios, enhancing system flexibility and renewable energy absorption capacity.

[0010] 2) This paper addresses the multi-objective optimization problem of coordinated scheduling of sources, grids, loads and storage, and constructs a multi-scenario coordinated optimization scheduling of sources, grids, loads and storage that takes into account multiple types of energy storage capacity configurations. This improves scheduling efficiency, increases renewable energy consumption, and increases the charge and discharge life of energy storage batteries and electric vehicle batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 This is a flow chart of the optimal dispatching method for a power system considering the optimal configuration of multiple types of energy storage according to the present invention. DETAILED DESCRIPTION

[0012] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0013] The present invention provides a method for optimal dispatching of a power system considering the optimal configuration of multiple types of energy storage, comprising:

[0014] Battery energy storage power stations, pumped storage, and electric vehicles are configured on the three sides of the power grid, and different energy storage capacity configuration methods are given;

[0015] Establish a multi-scenario source-grid-load-storage dispatch optimization model that takes into account multiple types of energy storage, with the goals of maximizing the overall dispatch model efficiency, maximizing renewable energy absorption, and extending the charge-discharge life of energy storage batteries and electric vehicle batteries;

[0016] Solve the multi-scenario source-grid-load-storage scheduling optimization model taking into account multiple types of energy storage, and obtain the optimal output curves of various types of units in the power system.

[0017] The following is a specific implementation process of the present invention.

[0018] like Figure 1 As shown, the present invention provides an optimal dispatching method for a power system considering the optimal configuration of multiple types of energy storage, comprising the following steps:

[0019] Step 1: Configure the capacity of three types of energy storage: power supply side, grid side, and load side

[0020] 1.1. Capacity Configuration of Battery Energy Storage Stations on the Power Supply Side

[0021] Considering the instability of wind and solar power output, a battery energy storage station is introduced to smooth out the fluctuations of renewable energy. Electrochemical batteries have fast charge and discharge rates, with a charge rate of 0.25-1C and a discharge rate of 5-10C, and high energy conversion efficiency. The range of energy storage power variation can be inferred from the power variation of renewable energy output, as shown in Equation (1).

[0022] (1)

[0023] Where: P es,t is the output power of the battery energy storage station at time t; is the maximum power fluctuation value of the new energy before stabilization; It is the maximum allowable change in power after wind and solar energy are input into the grid.

[0024] According to the normal distribution The probability of the value being distributed in this interval is 0.9973, and the energy storage power configuration obeys the normal distribution. Therefore, the output power of the energy storage system required to smooth the fluctuation of renewable energy power under the 99.73% scenario is as shown in formulas (2)-(4). Based on historical output data, combined with the energy storage output time △T es , the capacity configuration E of the battery energy storage power station can be obtained es See formula (5).

[0025] (2)

[0026] (3)

[0027] (4)

[0028] (5)

[0029] Where: N c is the number of scenes; P es is the energy storage power configuration value; is the average value of the energy storage power; is the mean of the scene data; is the standard deviation of the scene data.

[0030] 1.2. Grid-side pumped storage capacity configuration

[0031] Pumped storage units have many advantages, such as large capacity, fast response, and long life. By converting potential energy and electrical energy between the upper and lower reservoirs to store excess electricity, they effectively compensate for power shortages. The overall efficiency of pumped storage is as high as 75% to 80%, meeting the large-scale peak-shaving and valley-filling needs of the power grid. The maximum peak-shaving power of the power grid is selected for peak-shaving and valley-filling, and the pumped storage configuration power is shown in Equation (6). Based on the pumped storage configuration power obtained from Equation (6), the capacity configuration can be obtained as shown in Equations (7)-(9).

[0032] (6)

[0033] (7)

[0034] (8)

[0035] (9)

[0036] Where: △P1, △P2, ..., △P t , represents the actual power value of pumped storage at time 1~t, P ew is the pumped storage configuration power; |△P ew,t | is the calculated pumped storage power demand at time t; E ew Configure capacity for pumped storage; 1~t1, t2~t3,…,t j ~t n is the charging and discharging time of pumped storage, △T ew is the sampling time interval.

[0037] 1.3. Electric vehicle capacity configuration on the load side

[0038] As a potentially dispatchable energy storage resource on the load side, electric vehicles play a vital role in regulating grid load balance and achieving energy storage and dispatch. Based on the grid dispatch strategy, the dispatch capacity that electric vehicles can provide can be determined. This capacity is related to the maximum charge and discharge power and the behavior of the vehicle owner. Data shows that the daily mileage of electric vehicles follows the probability density function shown in Equation (10). Therefore, the load side needs to configure the electric vehicle capacity as shown in Equation (11).

[0039] (10)

[0040] Where: l i is the daily mileage of the i-th electric vehicle; R(l i ) is the probability density function; is the average daily mileage of electric vehicles, =3.47; is the standard deviation of the daily mileage of electric vehicles, and =0.88.

[0041] (11)

[0042] Where: E EV is the required configuration capacity of electric vehicles; The maximum charging and discharging power required for electric vehicles to participate in dispatching; is the number of electric vehicles; E km The energy required for an electric vehicle to travel per kilometer.

[0043] Step 2: Establish a multi-objective grid optimization dispatch model considering multiple types of energy storage configurations

[0044] Step 2.1: Establish the objective function

[0045] (1) Objective function for maximum scheduling efficiency

[0046] The efficiency of the dispatch model under multiple scenarios includes the operating efficiency of thermal power units C G , Wind and Solar Operation Efficiency C RE 、Multi-type energy storage C E The power generation efficiency is shown in equations (12) and (13).

[0047] (12)

[0048] (13)

[0049] Where: N T is the number of unit scheduling periods; N c is the number of scenes; is the scenario probability; △t is the unit scheduling time; N G is the number of thermal power units; a i 、b i and c i is the output cost coefficient of thermal power unit i; P Gi,t,c is the total power generation of the i-th thermal power unit in the c-th scenario and time period t; is the startup cost of thermal power unit i in the cth scenario and time period t, is the shutdown cost of thermal power unit i in the cth scenario and time period t; P wt,t,c is the wind power curtailment in the cth scenario during period t; P pv,t,c is the abandoned optical power in the cth scenario during the t period; P WT,t,c is the wind turbine output in the cth scenario during period t; P PV,t,c is the output of the photovoltaic unit in the cth scenario during the t period; k WT 、k PV are the operating coefficients of wind and solar units respectively; k is the penalty fee for curtailing wind and solar power per unit power; es 、k ew and k EV These are the power generation efficiency coefficients of battery energy storage power stations, pumped storage, and electric vehicles; They represent the pumped storage, electrochemical energy storage, and electric vehicle power at time t in the cth scenario respectively;

[0050] (2) Maximum target for renewable energy consumption

[0051] (14)

[0052] Where: P RE,t,c is the output power of the wind and solar power station in the cth scenario and period t.

[0053] (3) Target of longest charge and discharge life of energy storage batteries and electric vehicle batteries

[0054] The cycle life of a battery is mainly affected by the depth of discharge of the battery. It decreases with the increase of the depth of discharge and is negatively exponentially correlated with e. The relationship is shown in formula (16). In this paper, lithium batteries are selected as energy storage batteries and electric vehicle batteries. The maximum number of charge and discharge cycles within the life cycle of the energy storage system is used to represent the energy storage life.

[0055] (15)

[0056] Where: j, l, m, r are the relationship coefficients between battery cycle life and discharge depth. Taking lithium battery as an example, j=553.444, l=-15.16, m=463.216, r=-2.676; D x,t,c is the battery discharge depth; x represents the battery type. When x=1, it represents an energy storage battery; when x=2, it represents an electric vehicle battery.

[0057] (16)

[0058] Where: P Bx,t is the battery discharge power in the cth scenario during the t period; E Bx Configure the total battery capacity for the cth scenario.

[0059] Step 2.2: Establish constraints

[0060] (1) System power conservation constraint

[0061] (17)

[0062] Where: P f,t is the load forecast power in the cth scenario during period t.

[0063] (2) Multi-type energy storage constraints

[0064] 1) Constraints of battery energy storage power stations

[0065] The output power of a battery energy storage station must meet the upper and lower limits of the maximum charge and discharge power and the state of charge (SOC) of the energy storage battery. Its output depends on the SOC and charge and discharge behavior at the previous moment. The duration of the charge and discharge behavior is △t, and it is generally in three states: charging, discharging, and idle. The SOC value of the battery after the scheduling cycle ends should be consistent with the initial moment.

[0066] (18)

[0067] Where: SOC c (t), SOC c (t-1) are the state of charge of the battery energy storage station in the cth scenario at period t and period (t-1); They are the charging and discharging efficiency of the battery energy storage power station respectively.

[0068] (19)

[0069] Where: They are the minimum and maximum output power of the energy storage battery; SOC min , SOC max They are the minimum and maximum state of charge of the energy storage battery; SOC c (0), SOC c (T) are the SOC values ​​at the initial and final moments of the c-th scenario scheduling.

[0070] 2) Pumped storage constraints

[0071] The power of the pumped storage unit and the reservoir water level must meet the upper and lower limit constraints. They can only be in pumping or power generation conditions at the same time, and the water pumping volume must be balanced before and after the scheduling cycle, with a pumping conversion efficiency of 80%.

[0072] (20)

[0073] Where: are the minimum and maximum output power of pumped storage at time t respectively.

[0074] (twenty one)

[0075] Where: the storage capacity constraints of the upper and lower reservoirs are the same; are the upper and lower limits of the water level of the pumped storage power station reservoir; W ew,t,c 、W ew,t+1,care the water levels of the pumped storage power station reservoir in the cth scenario at period t and period t+1, respectively; 、 are the power conversion coefficients for pumping and power generation respectively; P ewc,t,c 、P ewd,t,c are the pumping power and power generation power in the cth scenario at time t, respectively; W ew,0,c 、W ew,T,c are the water levels of the power station reservoir at the initial and end moments of the cth scenario scheduling, respectively.

[0076] 3) Electric vehicle constraints

[0077] To prevent electric vehicle batteries from overcharging, electric vehicles must meet the upper and lower limit constraints and capacity constraints of the state of charge (SOC) at any time.

[0078] (twenty two)

[0079] SOC EV,t+1,c , SOC EV,t,c are the state of charge of the electric vehicle battery at the cth scenario (t+1) and time period t, respectively; 、 They are the minimum and maximum state of charge of electric vehicle batteries; SOC EV,t0,c SOC is the initial state of charge of the electric vehicle at the time of travel in the cth scenario; EV,c is the expected battery state of charge of the electric vehicle in the cth scenario.

[0080] (3) Constraints on wind, solar and thermal power generation units

[0081] 1) Constraints on thermal power units

[0082] (twenty three)

[0083] Where: are the minimum and maximum power generation of the i-th thermal power unit respectively; u Gi,t,c Represents the start and stop status of thermal power unit i in the cth scenario. When u Gi,t,c =0, it means that the thermal power unit i is shut down. Gi,t,c =1, indicating that thermal power unit i is turned on; are the startup and shutdown time of thermal power unit i in the cth scenario t-1 time period; The shortest startup time for thermal power units; is the shortest shutdown time of thermal power unit i.

[0084] (twenty four)

[0085] Where: is the maximum downward ramp rate of the thermal power unit in the cth scenario i; is the maximum upward ramp rate of the thermal power unit in the cth scenario i.

[0086] 2) Wind and solar power unit constraints

[0087] There are upper and lower limits on the output of wind and solar power units, and the amount of wind and solar power curtailed is less than the actual power generation.

[0088] (25)

[0089] Where: P WTF,t,c is the maximum predicted output of the wind turbine in the cth field during the tth period; P PVF,t,c is the maximum predicted output of the PV unit in the cth field and period t.

[0090] Step 3: Solve the optimization model to obtain the optimal output curves of various types of units in the power system.

[0091] The multi-objective genetic algorithm is used to solve the multi-objective optimal model. First, the decision variables are coded into 0-1 binary to form the initial population, and the three objective function values ​​in the initial state population are calculated to obtain the optimal values ​​of the three objective functions in the population. , then perform crossover and mutation operations on the population, and calculate the three objective function values ​​of each individual in the next generation , compared with For more optimized individuals, repeated crossover and mutation are performed until the objective function cannot be further improved, and the optimal decision variables are obtained.

[0092] The above are preferred embodiments of the present invention. Any changes made according to the technical solution of the present invention, as long as the resulting functions and effects do not exceed the scope of the technical solution of the present invention, shall fall within the scope of protection of the present invention.

Claims

1. A method for optimal dispatching of a power system considering the optimal configuration of multiple types of energy storage, characterized in that: include: Battery energy storage power stations, pumped storage, and electric vehicles are configured on the three sides of the power grid, and different energy storage capacity configuration methods are given; Establish a multi-scenario source-grid-load-storage dispatch optimization model that takes into account multiple types of energy storage, with the goals of maximizing the overall dispatch model efficiency, maximizing renewable energy absorption, and extending the charge-discharge life of energy storage batteries and electric vehicle batteries; Solve the multi-scenario source-grid-load-storage scheduling optimization model taking into account multiple types of energy storage, and obtain the optimal output curves of various types of units in the power system.

2. The method for optimal dispatching of a power system considering the optimal configuration of multiple types of energy storage according to claim 1, characterized in that: The three sides of the power grid refer to the power supply side, the grid side and the load side. Among them, the power supply side is equipped with a battery energy storage power station, the grid side is equipped with pumped storage, and the load side is equipped with electric vehicles.

3. The method for optimal dispatching of a power system considering the optimal configuration of multiple types of energy storage according to claim 2, characterized in that: The specific method for configuring the capacity of the battery energy storage station on the power supply side is as follows: The power variation range of the battery energy storage power station can be inferred based on the power variation of the new energy output, as shown in the following formula: P es,t is the output power of the battery energy storage station at time t; is the maximum power fluctuation value of the new energy before stabilization; It is the maximum allowable change value after wind and solar energy new energy grid input power; According to the normal distribution The principle of energy storage power configuration follows the normal distribution. The output power of the energy storage system required to smooth out the fluctuation of renewable energy power is as shown in the following formula: Based on historical output data, combined with energy storage output time △T es , and obtain the capacity configuration E of the battery energy storage power station es : N c is the number of scenes; P es is the energy storage power configuration value; is the average value of the energy storage power; is the mean of the scene data; is the standard deviation of the scene data.

4. The method for optimal dispatching of a power system considering the optimal configuration of multiple types of energy storage according to claim 3, characterized in that: The specific configuration method of grid-side pumped storage capacity is as follows: The maximum value of the grid peak load regulation power is selected for peak load shaving and valley filling. The pumped storage power configuration is shown in the following formula: Based on the pumped storage configuration power obtained from the above formula, the pumped storage capacity configuration is as follows: △P1, △P2, ..., △P t , represents the actual power value of pumped storage at time 1~t, P ew is the pumped storage configuration power; |△P ew,t | is the calculated pumped storage power demand at time t; E ew Configure capacity for pumped storage; 1~t1, t2~t3,…,t j ~t n is the charging and discharging time of pumped storage, △T ew is the sampling time interval.

5. The method for optimal dispatching of a power system considering the optimal configuration of multiple types of energy storage according to claim 4, characterized in that: The specific method for configuring the capacity of electric vehicles on the load side is as follows: According to the grid dispatching strategy, the dispatching capacity that electric vehicles can provide is determined. The capacity is related to the maximum charge and discharge power and the behavior of the vehicle owner. The data shows that the daily mileage of electric vehicles follows the probability density function shown in the following formula: Where: l i is the daily mileage of the i-th electric vehicle; R(l i ) is the probability density function; is the average daily mileage of electric vehicles; is the standard deviation of the daily mileage of electric vehicles; The configuration capacity of electric vehicles is shown as follows: Where: E EV is the electric vehicle configuration capacity; The maximum charging and discharging power required for electric vehicles to participate in dispatching; is the number of electric vehicles; E km E is the energy required for electric vehicles to travel per kilometer. dr Represents the total energy required by all electric vehicles on that day.

6. The method for optimal dispatching of a power system considering the optimal configuration of multiple types of energy storage according to claim 5, characterized in that: The construction method of the multi-scenario source-grid-load-storage scheduling optimization model considering multiple types of energy storage is as follows: 1) Establish the objective function (1) The most efficient objective function of the scheduling model The efficiency of the multi-scenario source-grid-load-storage scheduling optimization model includes the operating efficiency of thermal power units C G , Wind and Solar Operation Efficiency C RE 、Multi-type energy storage C E The power generation efficiency is shown in the following formula: N T is the number of unit scheduling periods; N c is the number of scenes; is the scenario probability; △t is the unit scheduling time; N G is the number of thermal power units; a i 、b i and c i is the output cost coefficient of thermal power unit i; P Gi,t,c is the total power generation of the i-th thermal power unit in the c-th scenario and time period t; is the startup cost of thermal power unit i in the cth scenario and time period t, is the shutdown cost of thermal power unit i in the cth scenario and time period t; P wt,t,c is the wind power curtailment in the cth scenario during period t; P pv,t,c is the abandoned optical power in the cth scenario during the t period; P WT,t,c is the wind turbine output in the cth scenario during period t; P PV,t,c is the output of the photovoltaic unit in the cth scenario during the t period; k WT 、k PV are the operating coefficients of wind and solar units respectively; k is the penalty fee for curtailing wind and solar power per unit power; es 、k ew and k EV These are the power generation efficiency coefficients of battery energy storage power stations, pumped storage, and electric vehicles; They represent the pumped storage, electrochemical energy storage, and electric vehicle power at time t in the cth scenario respectively; (2) Objective function for maximizing renewable energy consumption P RE,t,c The output power of the wind and solar power station in the cth scenario and time period t; (3) Objective function for the longest charge and discharge life of energy storage batteries and electric vehicle batteries The cycle life of the battery is affected by the depth of discharge of the battery. It decreases with the increase of the depth of discharge, showing a negative exponential correlation with e. The maximum number of charge and discharge cycles within the life cycle of the energy storage system is used to represent the energy storage life. The specific relationship is shown in the following formula: j, l, m, r are the relationship coefficients between battery cycle life and discharge depth; D x,t,c is the battery discharge depth, x represents the battery type, when x=1, it represents energy storage battery; when x=2, it represents electric vehicle battery; P Bx,t is the battery discharge power in the cth scenario during the t period; E Bx Configure the total capacity of the battery for the cth scenario; 2) Establish constraints (1) System power conservation constraint P f,t,c is the load forecast power in the cth scenario during period t; (2) Multi-type energy storage constraints (2.1) Constraints of battery energy storage power stations The output power of the battery energy storage station must meet the upper and lower limits of the maximum charge and discharge power and the state of charge (SOC) of the energy storage battery. Its output depends on the SOC and charge and discharge behavior at the previous moment, and the duration of the charge and discharge behavior is Δt. SOC c (t), SOC c (t-1) are the charge states of the battery energy storage station in period t and period t-1 of scenario c respectively; They are the charging efficiency and discharging efficiency of the battery energy storage power station respectively; They are the minimum and maximum output power of the energy storage battery; SOC min , SOC max They are the minimum and maximum state of charge of the energy storage battery; SOC c (0), SOC c (T) are the SOC values ​​at the initial and final moments of the c-th scenario scheduling; (2.2) Pumped storage constraints The power of the pumped storage unit and the reservoir water level must meet upper and lower limit constraints. The unit can only be in pumping or generating mode at the same time, and the water volume pumped before and after the scheduling cycle must be balanced. The pumping conversion efficiency is 80%; are the minimum and maximum output power of pumped storage at time t respectively; The storage capacity constraints of the upper and lower reservoirs are the same; are the upper and lower limits of the water level of the pumped storage power station reservoir; W ew,t,c 、W ew,t+1,c are the water levels of the pumped storage power station reservoir in the cth scenario at period t and period t+1, respectively; 、 are the power conversion coefficients for pumping and power generation respectively; P ewc,t,c 、P ewd,t,c are the pumping power and power generation power in the cth scenario at time t, respectively; W ew,0,c 、W ew,T,c are the water levels of the power station reservoir at the initial and end moments of the cth scenario scheduling, respectively; (2.3) Electric vehicle constraints To prevent electric vehicle batteries from overcharging, electric vehicles must meet the upper and lower limit constraints and capacity constraints for the state of charge (SOC) at any time. SOC EV,t+1,c , SOC EV,t,c are the state of charge of the electric vehicle battery at the cth scenario (t+1) and time period t, respectively; 、 They are the minimum and maximum state of charge of electric vehicle batteries; SOC EV,t0,c SOC is the initial state of charge of the electric vehicle at the time of travel in the cth scenario; EV,c is the expected battery state of charge of the electric vehicle in the cth scenario; (3) Constraints on wind, solar and thermal power generation units (3.1) Constraints on thermal power units are the minimum and maximum power generation of the i-th thermal power unit respectively; u Gi,t,c Represents the start and stop status of thermal power unit i in the cth scenario. When u Gi,t,c =0, it means that the thermal power unit i is shut down. Gi,t,c =1, indicating that thermal power unit i is turned on; are the startup and shutdown time of thermal power unit i in the cth scenario t-1 time period; The shortest startup time for thermal power units; is the shortest shutdown time of thermal power unit i; is the maximum downward ramp rate of the thermal power unit in the cth scenario i; is the maximum upward ramp rate of the thermal power unit in the cth scenario i; (3.2) Wind and solar power generation constraints The output of wind and solar power units has upper and lower limits, and the amount of wind and solar power curtailed is less than the actual power generation; P WTF,t,c is the maximum predicted output of the wind turbine in the cth field during the tth period; P PVF,t,c is the maximum predicted output of the PV unit in the cth field and time period t.

7. The method for optimal dispatching of a power system considering the optimal configuration of multiple types of energy storage according to claim 1, characterized in that: A multi-objective genetic algorithm is used to solve the multi-scenario source-grid-load-storage scheduling optimization model that takes into account multiple types of energy storage. The specific method is as follows: The decision variables are encoded into 0-1 binary to form the initial population, and the three objective function values ​​in the initial state population are calculated to obtain the optimal values ​​of the three objective functions in the population. , then perform crossover and mutation operations on the population, and calculate the three objective function values ​​of each individual in the next generation , compared with For more optimized individuals, repeated crossover and mutation are performed until the objective function cannot be further improved, and the optimal decision variables are obtained.

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