Risk optimization method and system for medium-short-term standby configuration

Abstract

The invention relates to the technical field of power systems, and discloses a medium and short term standby configuration risk optimization method, which comprises the following steps: collecting resource data of a power generation side, a demand side and an energy storage side under the influence of extreme weather; constructing a cooperative standby pre-scheduling model of a power generation side, a demand side and an energy storage side; constructing a target function and a constraint condition of multi-scene random optimization of medium-short-term standby configuration; and optimizing the short-term standby configuration risk in the power system by using the collaborative standby pre-scheduling model, the target function and the constraint condition. A power system of source-load-storage flexible resource coordination is considered, the system safety is guaranteed, and intra-weekly coordination of resources is realized; multi-scene random optimization is adopted, a standby configuration strategy can be coordinated according to the extreme weather change condition, and the system risk in the extreme weather period is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and system for optimizing short- to medium-term standby configuration risks. Background Art

[0002] In recent years, the penetration rate of renewable energy in new power systems has continued to increase, and the impact of extreme weather on the safety and stability of power systems has increased, requiring a large amount of flexible power regulation capabilities. The high-risk events with low probability are prone to occur during extreme weather, and insufficient system backup resources will lead to insufficient redundancy of the power system, which in turn threatens the safety of the power system. Before predictable extreme weather, power system operators have sufficient time to make long-term flexible resource reserves and emergency plans. Therefore, advance control and multi-day coordinated scheduling of power system backup resources are required. Only short-term power system scheduling on the day before or within the day may lead to insufficient system regulation capabilities during extreme weather.

[0003] At present, the regulation resources of the power system are mainly provided by the power generation side. The regulation rate of coal-fired power units is slow and the peak regulation depth is limited. Hydropower calls need to comprehensively consider multiple factors such as the water level of reservoirs in cascade basins, ecological flow, and DC section matching. Energy storage calls have high requirements for the coordinated planning of electricity over a long period of time. The ability of existing regulation resources to cope with the uncertainty of fluctuations in new energy power generation such as wind power and photovoltaic power generation, or even during extreme weather with rapid changes, is still large. There is still a lot of room for improvement. The unified optimization and coordination of demand-side flexibility resources and source-grid resources is realized, and the evaluation and optimization methods of the adequacy of power systems with a high proportion of renewable energy are studied, which will be conducive to the safer, more reliable, economical and environmentally friendly operation of future energy and power systems. Summary of the invention

[0004] The present invention provides a method and system for optimizing the risk of medium- and short-term standby configurations to solve the problems existing in the background technology.

[0005] The present invention provides a method for optimizing medium- and short-term standby configuration risks, comprising:

[0006] S1: Collect resource data on the power generation side, demand side and energy storage side under the influence of extreme weather;

[0007] S2: Construct a coordinated reserve pre-dispatch model on the power generation side, demand side and energy storage side;

[0008] S3: Construct the objective function and constraints for multi-scenario stochastic optimization of short- and medium-term standby configuration;

[0009] S4: Optimize the short-term reserve configuration risk in the power system using the collaborative reserve pre-dispatch model along with the objective function and constraints.

[0010] Preferably, in step S2, constructing a collaborative standby pre-dispatching model for the power generation side, the demand side and the energy storage side includes:

[0011] Construct a primary energy fuel pre-dispatching sub-model for power generation side backup resources;

[0012] Construct a sub-model for pre-scheduling the number of calls of demand-side backup resources;

[0013] Construct an initial power pre-dispatch sub-model for backup resources on the energy storage side.

[0014] Preferably, the construction of the primary energy fuel pre-scheduling sub-model for power generation side backup resources includes the following constraints:

[0015] Pre-dispatch the remaining fuel of the units on the day before, on the day of, and after the extreme weather, and construct the constraints on the initial remaining fuel of the day:

[0016] Additional fuel purchases before short- to medium-term dispatch to cope with power generation and reserve during extreme weather, and constraints on traditional units to build fuel inventory for thermal power units during extreme weather;

[0017] The spare capacity configuration of traditional units at each moment is the decision variable of the model, which constructs the operation constraints of traditional units.

[0018] Preferably, the operation constraints of constructing the traditional unit are specifically:

[0019] Traditional units build scheduling ramp constraints, start and stop time constraints, and N-1 scenario constraints when optimizing capacity reserve.

[0020] Preferably, the construction of the call number pre-scheduling sub-model for demand-side standby resources includes the following constraints:

[0021] Coordinate demand-side reserve resources during the week, pre-schedule the number of daily calls for demand-side reserve resources, and establish constraints on the number of calls for demand-side reserve resources;

[0022] The demand-side reserve capacity configuration at each moment is the decision variable of the model, and the demand-side reserve operation constraints are constructed;

[0023] According to the credibility difference of demand-side reserve during scheduling, the security constraints of the demand-side reserve Nx uncertainty set are constructed;

[0024] Based on the capacity reserve optimization of demand-side backup resources, the minimum interruption interval time constraint and the upper and lower limit time constraints of continuous interruption are constructed.

[0025] Preferably, the construction of the initial power pre-dispatching sub-model for energy storage side backup resources includes the following constraints:

[0026] Construct daily initial power constraints and weekly power consistency constraints for daily power of energy storage;

[0027] The energy storage reserve capacity configuration at each moment is the decision variable of the model, which constructs the energy storage reserve operation constraints;

[0028] When optimizing energy storage capacity reserve, energy storage charging and discharging power constraints and energy storage charge state constraints are established.

[0029] Preferably, in step S3, the objective function of constructing the short- to medium-term standby configuration multi-scenario stochastic optimization includes:

[0030] Considering the randomness of multiple scenarios, an objective function is established to minimize the total cost of the system;

[0031] The total cost includes the capacity cost of various resources providing backup, the cost of starting and stopping traditional units and purchasing additional fuel, the cost of electricity provided by various resources for backup, the cost of unit operation, carbon emission cost and risk cost;

[0032] The risk cost includes load loss cost and wind and solar power abandonment cost.

[0033] Preferably, in step S3, the constraints for constructing the multi-scenario random optimization of the short- to medium-term standby configuration include:

[0034] Power balance constraints and network security constraints;

[0035] In the power balance constraint, the generation power is equal to the sum of the load power and the network losses;

[0036] In network security constraints, the DC power flow model and the upper limit constraint of line transmission power are considered to ensure that the power grid operates within a safe range and avoid line overload.

[0037] Preferably, a medium- and short-term standby configuration risk optimization system comprises:

[0038] Data collection module, used to collect resource data on the power generation side, demand side and energy storage side under the influence of extreme weather;

[0039] Model building module, used to build collaborative reserve pre-dispatch models on the generation side, demand side and energy storage side;

[0040] Function construction module, used to construct the objective function and constraints for multi-scenario stochastic optimization of short- and medium-term standby configurations;

[0041] The risk optimization module is used to optimize the short-term reserve configuration risk in the power system by using the collaborative reserve pre-dispatch model as well as the objective function and constraints.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention discloses a method and system for optimizing the risk of medium- and short-term standby configuration, which considers a power system with coordinated source-load-storage flexible resources, ensures system security, and realizes intra-week coordination of resources. It adopts multi-scenario random optimization, can coordinate the standby configuration strategy according to extreme weather changes, and reduce system risks during extreme weather periods. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A flow chart of a short- to medium-term standby configuration risk optimization method considering the synergy of source-load-storage flexible resources;

[0045] Figure 2 This is a diagram of the operation optimization results of the power generation side, demand side, and energy storage side under multiple scenarios within a week in the embodiment;

[0046] Figure 3 This is a diagram showing the risk optimization results of the backup configuration on the power generation side, demand side, and energy storage side under multiple scenarios within a week in the embodiment;

[0047] Figure 4 A module diagram of a medium- and short-term standby configuration risk optimization system considering the coordination of source-load-storage flexible resources. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] like Figure 1 As shown, the present application provides a method for optimizing the risk of medium- and short-term standby configuration, comprising the following steps:

[0050] S1: Collect resource data on the power generation side, demand side and energy storage side under the influence of extreme weather;

[0051] S2: Construct a coordinated reserve pre-dispatch model on the power generation side, demand side and energy storage side;

[0052] S3: Construct the objective function and constraints for multi-scenario stochastic optimization of short- and medium-term standby configuration;

[0053] S4: Optimize the short-term reserve configuration risk in the power system using the collaborative reserve pre-dispatch model along with the objective function and constraints.

[0054] Preferably, S2, constructs a coordinated reserve pre-dispatch model for the power generation side, the demand side, and the energy storage side:

[0055] 1) Primary energy fuel pre-scheduling sub-model for power generation side backup resources

[0056] Pre-dispatch the remaining fuel of the units on the day before, during and after the extreme weather, and the initial remaining fuel of the day meets the following constraints:

[0057]

[0058] in, is the traditional unit T at time t in scenario s i The remaining fuel inventory; Q Ti,t,s is the traditional unit T in scenario s i Fuel consumption; t 0,d The starting time of D-day; is the pre-scheduled minimum remaining fuel for day d.

[0059] Additional fuel is purchased before short- and medium-term scheduling to cope with power generation and standby during extreme weather. During extreme weather, traditional units need to meet the fuel inventory constraints of thermal power units, that is, the upper limit of power generation for a period of time:

[0060]

[0061] Among them, T E Gather for fuel supply periods affected by extreme weather; For traditional unit T i Existing inventory during periods of extreme weather; For traditional unit T i Additional fuel purchases prior to short- to medium-term pre-dispatch.

[0062] In the backup configuration risk optimization model, the backup capacity configuration of traditional units at each moment is also used as the decision variable of the model. The operation constraints of traditional units can be expressed as:

[0063]

[0064] in, is the traditional unit T at time t in scenario s i The power generation capacity; is the traditional unit T at time t in scenario s i Fixed output; and They are respectively the traditional unit T at time t in scenario s i Upper and lower standby output; and Respectively represent the traditional unit T i The upper and lower limits of technical output; Indicates the time t of the traditional unit T i The operating status of and They are respectively the traditional units T i Upper and lower spare capacity.

[0065] At the same time, traditional units should meet the scheduling ramp constraints, start-stop time constraints, and N-1 scenario constraints when optimizing capacity reserve, as follows:

[0066] a) Traditional unit climbing constraints

[0067]

[0068] in, and Respectively represent the traditional unit T i The maximum value of the landslide and climbing power.

[0069] b) Minimum start-up and shutdown time constraints for traditional units

[0070]

[0071]

[0072] in, It represents the traditional unit T i A flag to start the action; It represents the traditional unit T i The sign of the stop action; The traditional unit T at time t i The boot duration, The traditional unit T at time t i Duration of downtime; and For traditional unit T i Minimum start and stop time.

[0073] c) Unit N-1 scenario constraints

[0074]

[0075] in, For scenes m The serial number of the faulty unit; For scenes m The actual output of unit i at the next time t; For scenes m The actual operating status of unit i at the next time t; and Respectively represent scenes m The upper spinning reserve power and the lower spinning reserve power of unit i at the next time t.

[0076] 2) Pre-scheduling sub-model for the number of calls of demand-side backup resources

[0077] In order to ensure the comfort of power users on the demand side, the number of calls of interruptible loads within a week is limited. In order to achieve the coordination of demand-side reserve resources within a week, the daily call number of demand-side reserve resources is pre-scheduled. The demand-side reserve call number constraint can be expressed as:

[0078]

[0079] in, is the demand-side reserve D at time t in scenario s. i Flags for response actions; and They are demand side reserve D i The weekly and daily maximum calls. is the demand side reserve D scheduled on day d i The daily maximum number of calls.

[0080] In the reserve configuration risk optimization model, the demand-side reserve capacity configuration at each moment is also used as the decision variable of the model, and the demand-side reserve operation constraint can be expressed as:

[0081]

[0082] in, is the demand side reserve D at time t in scenario s i The calling power; represents the demand side reserve D at time t in scenario s i The call interruption status; and They are demand side reserve D i The minimum and maximum interruptible capacity; Demand side resource D i of spare capacity.

[0083] Considering the credibility difference of demand-side reserve in scheduling, the demand-side reserve Nx uncertainty set is constructed. The situation that the user does not respond to the scheduling instruction is regarded as a fault state. D is the demand side reserve i The credibility of is the probability of user failure, with the user failure state as the index, and the Nx uncertainty set taking into account the user backup credibility is set as:

[0084]

[0085] in, For the overall failure probability parameter, the Nx safety constraints are determined to limit the probability of occurrence of the failure conditions contained in the uncertainty set.

[0086] At the same time, the capacity reserve optimization of demand-side reserve resources should also meet the interruption interval time constraints and continuous interruption time constraints, as follows:

[0087] a) Minimum interruption interval constraint

[0088]

[0089] in, is the demand-side reserve D at time t in scenario s. i Respond to the sign of stop action; is the demand side reserve D at time t in scenario s i The interruption interval time; D is the demand side reserve i The minimum interruption interval.

[0090] b) Upper and lower limits of continuous interruption time

[0091]

[0092] in, is the demand side reserve D at time t in scenario s i duration of the outage; and They are demand side reserve D i The minimum and maximum interruption duration.

[0093] 3) Initial power pre-dispatch sub-model for energy storage side backup resources

[0094] The daily initial energy storage capacity is set. According to the extreme weather forecast information, different energy storage capacity lower limits are set at the beginning of the day before, the current period, and the day after the extreme weather. The energy capacity at the beginning and the end of the week is kept the same. Therefore, the daily energy storage capacity consistency constraint is improved into the daily initial energy capacity constraint and the weekly energy capacity consistency constraint, which can be expressed as:

[0095]

[0096] in, Represents the energy storage device S at time t in scenario s i The state of charge; and respectively

[0097] is the energy storage device S on day d i Upper and lower limits of state of charge; is the state of charge at the beginning of day d under scenario s.

[0098] In the backup configuration risk optimization model, the energy storage backup capacity configuration at each moment is also used as the decision variable of the model, and the energy storage backup operation constraint can be expressed as:

[0099]

[0100] in, and They are respectively the energy storage device S at time t in scenario s. i Upper and lower standby output; and They are energy storage devices S i Discharge power and charge power; and They are energy storage devices S i Upper and lower spare capacity.

[0101] At the same time, energy storage should also meet the charging and discharging power constraints and charge state constraints when optimizing capacity reserve, as follows:

[0102] a) Energy storage charging and discharging power constraints

[0103]

[0104] in, and They are energy storage devices S i The maximum discharge power and maximum charging power.

[0105] b) Energy storage state of charge constraints

[0106]

[0107] in, The energy storage device S i The capacity, and They are energy storage devices S i The charge and discharge efficiency; and They are energy storage devices S i Upper and lower limits of the state of charge.

[0108] S3, construct the objective function of multi-scenario random optimization of medium- and short-term standby configuration:

[0109]

[0110] Among them, C RC,t The cost of providing backup capacity for various resources at time t; C TC,t is the cost of starting and stopping the traditional unit and purchasing additional fuel at time t; C RE,t,sThe cost of providing backup power for various resources at time t in scenario s; C TE,t,s is the unit operating cost at time t under scenario s; C C,t,s is the carbon emission cost at time t under scenario s; C K,t,s is the risk cost at time t under scenario s. S is the total number of scenarios in the output scenario set to be regulated under the uncertainty of typhoon path and intensity, p s is the probability of scene s appearing.

[0111]

[0112] C K,t,s =E EENS,t,s C VOLL +E EWC,t,s C VOW

[0113] in, They are respectively the traditional units T i and energy storage device S i The up / down spare unit capacity cost; Demand side resource D i The cost of spare unit capacity; For traditional unit T i Unit fuel cost of existing inventory; For traditional unit T i Unit cost of additional fuel; For traditional unit T i The start-stop cost of E r,t,s For traditional unit T i Carbon emissions; E f,t is the regional carbon quota; K C is the carbon emission rights trading price; For traditional unit T i The carbon emission coefficient; η is the carbon quota coefficient. They are respectively the traditional units T i and energy storage device S i The unit electricity cost of up / down reserve; Demand side resource D i The standby unit electricity cost; E EENS,t,s is the system load loss at time t under scenario s, satisfying E EENS,t,s ≥0; C VOLL is the unit loss of load cost; E EWC,t,s is the system abandoned wind and solar power at time t in scenario s, satisfying E EWC,t,s ≥0; C VOW The unit cost of curtailing wind and solar power.

[0114] S3, constructs constraints for random optimization of short- and medium-term standby configuration in multiple scenarios:

[0115] 1) Power balance constraints

[0116]

[0117] 2) Network security constraints

[0118] -P l,km,max ≤P l,km,t ≤P l,km,max

[0119]

[0120] Among them, X ki , X mi and X km Represents the element of the network node impedance matrix; P i,t and D s,t They represent the power generation and load power of node i at time t respectively; Ψ and L represent the power supply and load node set of the system respectively; P l,km,t represents the transmission power of branch km in the DC power flow model at time t; P l,km,max Indicates the upper limit of the transmission power of branch km.

[0121] The operation optimization results of the power generation side, energy storage side, and demand side and the backup configuration risk optimization results under multiple scenarios within one week using the calculation example of the present invention are as follows: Figure 2 and Figure 3 As shown. Figure 2 Actual typhoon scenes and Figure 3 It can be seen from the actual call of the reserve that the risk optimization result of the medium- and short-term reserve configuration considering the uncertainty of typhoons can meet the sufficiency in the actual typhoon scenario, and there is no load loss in all scheduling periods.

[0122] like Figure 4 As shown, a medium- and short-term standby configuration risk optimization system includes:

[0123] Data collection module, used to collect resource data on the power generation side, demand side and energy storage side under the influence of extreme weather;

[0124] Model building module, used to build collaborative reserve pre-dispatch models on the generation side, demand side and energy storage side;

[0125] Function construction module, used to construct the objective function and constraints for multi-scenario stochastic optimization of short- and medium-term standby configurations;

[0126] The risk optimization module is used to optimize the short-term reserve configuration risk in the power system by using the collaborative reserve pre-dispatch model as well as the objective function and constraints.

[0127] Preferably, a device comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the method for optimizing the risk of medium- and short-term standby configurations when executing the computer program.

[0128] Preferably, a computer-readable storage medium comprises a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the short- to medium-term standby configuration risk optimization method.

[0129] Preferably, the computer program can be divided into one or more modules / units (such as computer programs), and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0130] The processor may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor. The processor is the control center of the terminal device, and various parts of the terminal device are connected using various interfaces and lines.

[0131] The memory mainly includes a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function, etc., and the data storage area can store related data, etc. In addition, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, and a flash card (Flash Card), etc., or the memory can also be other volatile solid-state storage devices.

[0132] It should be noted that the above-mentioned terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that the above-mentioned terminal device is merely an example and does not constitute a limitation on the terminal device. It may include more or fewer components, or a combination of certain components, or different components.

[0133] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for optimizing short- to medium-term standby configuration risk, characterized in that: include: S1: Collect resource data on the power generation side, demand side and energy storage side under the influence of extreme weather; S2: Construct a coordinated reserve pre-dispatch model on the power generation side, demand side and energy storage side; S3: Construct the objective function and constraints for multi-scenario stochastic optimization of short- and medium-term standby configuration; S4: Optimize the short-term reserve configuration risk in the power system using the collaborative reserve pre-dispatch model along with the objective function and constraints.

2. A method for optimizing short- to medium-term standby configuration risk according to claim 1, characterized in that: In step S2, the construction of the collaborative reserve pre-dispatching model of the power generation side, the demand side and the energy storage side includes: Construct a primary energy fuel pre-dispatching sub-model for power generation side backup resources; Construct a sub-model for pre-scheduling the number of calls of demand-side backup resources; Construct an initial power pre-dispatch sub-model for backup resources on the energy storage side.

3. A method for optimizing short- to medium-term standby configuration risk according to claim 2, characterized in that: The construction of the primary energy fuel pre-scheduling sub-model for power generation side backup resources includes the following constraints: Pre-dispatch the remaining fuel of the units on the day before, on the day of, and after the extreme weather, and construct the constraints on the initial remaining fuel of the day: Additional fuel purchases before short- to medium-term dispatch to cope with power generation and reserve during extreme weather, and constraints on traditional units to build fuel inventory for thermal power units during extreme weather; The spare capacity configuration of traditional units at each moment is the decision variable of the model, which constructs the operation constraints of traditional units.

4. A method for optimizing short- to medium-term standby configuration risk according to claim 3, characterized in that: The operation constraints for constructing the traditional unit are specifically: Traditional units build scheduling ramp constraints, start and stop time constraints, and N-1 scenario constraints when optimizing capacity reserve.

5. A method for optimizing short- to medium-term standby configuration risk according to claim 2, characterized in that: The construction of the call number pre-scheduling sub-model for demand-side standby resources includes the following constraints: Coordinate demand-side reserve resources during the week, pre-schedule the number of daily calls for demand-side reserve resources, and establish constraints on the number of calls for demand-side reserve resources; The demand-side reserve capacity configuration at each moment is the decision variable of the model, and the demand-side reserve operation constraints are constructed; According to the credibility difference of demand-side reserve during scheduling, the security constraints of the demand-side reserve Nx uncertainty set are constructed; Based on the capacity reserve optimization of demand-side backup resources, the minimum interruption interval time constraint and the upper and lower limit time constraints of continuous interruption are constructed.

6. A method for optimizing short- to medium-term standby configuration risk according to claim 2, characterized in that: The construction of the initial power pre-dispatching sub-model for energy storage side backup resources includes the following constraints: Construct daily initial power constraints and weekly power consistency constraints for daily power of energy storage; The energy storage reserve capacity configuration at each moment is the decision variable of the model, which constructs the energy storage reserve operation constraints; When optimizing energy storage capacity reserve, energy storage charging and discharging power constraints and energy storage charge state constraints are established.

7. A method for optimizing short- to medium-term standby configuration risk according to claim 2, characterized in that: In step S3, the objective function of constructing the short- to medium-term standby configuration multi-scenario stochastic optimization includes: Considering the randomness of multiple scenarios, an objective function is established to minimize the total cost of the system; The total cost includes the capacity cost of various resources providing backup, the cost of starting and stopping traditional units and purchasing additional fuel, the cost of electricity provided by various resources for backup, the cost of unit operation, carbon emission cost and risk cost; The risk cost includes load loss cost and wind and solar power abandonment cost.

8. A method for optimizing short- to medium-term standby configuration risk according to claim 7, characterized in that: In step S3, the constraints for constructing the multi-scenario random optimization of the short-term and medium-term standby configuration include: Power balance constraints and network security constraints; In the power balance constraint, the generation power is equal to the sum of the load power and the network losses; In network security constraints, the DC power flow model and the upper limit constraint of line transmission power are considered to ensure that the power grid operates within a safe range and avoid line overload.

9. A medium- and short-term standby configuration risk optimization system, characterized in that: include: Data collection module, used to collect resource data on the power generation side, demand side and energy storage side under the influence of extreme weather; Model building module, used to build collaborative reserve pre-dispatch models on the generation side, demand side and energy storage side; Function construction module, used to construct the objective function and constraints for multi-scenario stochastic optimization of short- and medium-term standby configurations; The risk optimization module is used to optimize the short-term reserve configuration risk in the power system by using the collaborative reserve pre-dispatch model as well as the objective function and constraints.

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