A power system optimization configuration method considering source-grid-load-storage coordination

Abstract

This invention discloses a power system optimal configuration method considering the coordination of power generation, grid, load, and storage. The steps are as follows: establishing a power system reserve capacity configuration model; establishing a power system total electricity purchase cost objective function; constructing a power system opportunity constraint model based on the power system reserve capacity configuration model; using the Monte Carlo algorithm to verify all generating units in the power system according to the power system opportunity constraint model, obtaining several unit combinations that meet the opportunity constraint requirements; and using the particle swarm optimization algorithm to solve the configuration of each of the several unit combinations, obtaining the unit combination with the optimal power system reserve capacity configuration that satisfies the power system total electricity purchase cost objective function. This invention can reduce the economic cost of power system operation and provides a reference for the optimal configuration of multi-resource capacity in power systems (power generation, grid, load, and storage).

Description

Technical Field

[0001] This invention belongs to the field of power system technology, and specifically relates to a power system optimization configuration method that considers the coordination of power generation, grid, load and storage. Background Technology

[0002] With the rapid increase in the penetration rate of new energy sources, the increase in the capacity of asynchronous generator units has rapidly weakened the peak-shaving capacity of the power system. Furthermore, the large-scale grid connection of interruptible loads with autonomous control on the user side provides a reference for the interactive peak-shaving of the power system's sources and loads. Based on this, considering the coordinated control of multi-level equipment units in the new power system's sources, grid, load, and storage, the capacity optimization configuration of peak-shaving equipment under current dispatching needs to take into account the multi-level coordinated optimization of sources, grid, load, and storage.

[0003] Existing methods for optimizing the configuration of peak-shaving equipment are mostly single-level equipment capacity configuration optimization methods for the power system. For example, they only consider the capacity configuration of equipment on the power source side, without taking load and DC transmission into account. However, the power system will be in a state of coordinated operation of power generation, grid, load and storage. Planning that only considers a single level of equipment cannot meet the design requirements of the new power system and is not suitable for the multi-level optimized dispatching requirements of the new power system. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of the aforementioned background technology and provide a power system optimization configuration method that considers the coordination of power source, grid, load, and storage, thereby solving the scheduling optimization problem for stable operation of the power system under frequency regulation involving multiple resources.

[0005] The technical solution adopted in this invention is: a power system optimization configuration method considering the coordination of power generation, grid, load, and storage, comprising the following steps:

[0006] Establish a power system reserve capacity configuration model;

[0007] Establish an objective function for the total electricity purchase cost of the power system;

[0008] Constructing a power system opportunity constraint model based on a power system reserve capacity configuration model;

[0009] Based on the opportunity constraint model of the power system, the Monte Carlo algorithm is used to verify all units of the power system and obtain several combinations of units that meet the opportunity constraint requirements.

[0010] The particle swarm optimization algorithm is used to solve the configuration of several unit combinations to obtain the optimal unit combination of power system reserve capacity that satisfies the objective function of the total power purchase cost of the power system.

[0011] Furthermore, the power system reserve capacity configuration model includes an active power balance model and a reserve capacity balance model, as well as constraint models for each variable in the active power balance model and the reserve capacity balance model.

[0012] Furthermore, the active power balance model is as follows:

[0013]

[0014] Where, N SG N WF N DC N E These represent the number of conventional generating units, wind turbine units, DC transmission lines, and energy storage units within the power system; P SGi P WFj P DCm P Eq These represent the active power generation of the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively; P L This represents the predicted value of active power consumption by the load.

[0015] Furthermore, the reserve capacity balancing model is as follows:

[0016]

[0017] Where, N SG N WF N DC N E These are the number of conventional generating units, wind turbine units, DC transmission lines, and energy storage units within the power system; The weighting coefficients for providing spinning reserve capacity for the i-th conventional unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit at time t are respectively. These represent the standby capacities that can be dispatched at time t for the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit. Let t be the spinning reserve requirement of the power system at time t.

[0018] Furthermore, the constraint models for each variable in the active power balance model and the reserve capacity balance model are as follows:

[0019] P SGi min ≤P SGi ≤P SGi max

[0020] P WFi min ≤P WFi≤P WFi max

[0021] P DCi min ≤P DCi ≤P DCi max

[0022] P Eqi min ≤P Eqi ≤P Eqi max

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032] in, These are the minimum output limits for the i-th conventional unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively. These are the maximum output limits for the i-th conventional unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively. These represent the minimum reserve capacity indicators that can be dispatched for the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively. This serves as the lower limit indicator for rotating reserve requirements. These represent the upper limits of the standby capacity that can be dispatched for the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively. This is the upper limit indicator for rotating reserve requirements; These represent the standby capacities that can be dispatched at time t for the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit. These represent the standby capacities that can be dispatched at time t' for the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively. These represent the maximum ramp output of the i-th conventional unit, the j-th wind turbine, the m-th DC-DC transmission line, and the q-th energy storage unit during the time interval ΔT, where ΔT is the time interval between time t and time t'.

[0033] Furthermore, the objective function for the total electricity purchase cost of the power system is:

[0034]

[0035] Where F is the total electricity purchase cost of the power system; v b,t Let F be the interruptible load operation state of group b at time t. at F bt These are the cost characteristic functions of the a-th generator set and the b-th interruptible load, respectively; v a,t Let F represent the operating status of the a-th generator unit at time t, with 1 for running and 0 for shut down; T and A represent the total time duration and the total number of units, respectively; a (P a,t () represents the power output P of the a-th generator unit at time t. a,t The relevant cost characteristic function of the a-th generator set.

[0036] Furthermore, the F a (P a,t )for

[0037]

[0038] Where, α i b i c i Let α be the quadratic coefficient, primary coefficient, and constant of the power generation cost of the i-th conventional unit; j b j c j These represent the quadratic coefficient, primary coefficient, and constant of the power generation cost of the j-th wind turbine, respectively; α m b m c m Let α be the quadratic coefficient, primary coefficient, and constant of the generation cost of the m-th DC transmission line, respectively; q b q c q These are the quadratic coefficient, primary coefficient, and constant of the power generation cost of the q-th energy storage unit, respectively; P SGi (t), P WFj (t), PDCm (t), P Eq (t) represents the active power generation of the i-th conventional unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit at time t.

[0039] Furthermore, the constraint model for the variables in the objective function of the total electricity purchase cost of the power system is as follows:

[0040]

[0041] Among them, M b This represents the total interruption time of the interruptible load in group b.

[0042] Furthermore, the opportunity constraint model of the power system is as follows:

[0043]

[0044] Among them, P r {} represents the probability of a certain unit failure event occurring, and α represents the set confidence level for the system to meet the reserve capacity requirements; u i,t u j,t u m,t u q,t These represent the operating status of the i-th conventional unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit at time t, respectively. The spinning reserve requirement of the b-th group of interruptible loads at time t in the power system; These represent the standby capacity that can be dispatched by the i-th conventional unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively.

[0045] Furthermore, the process of verifying all generating units in the power system includes the following steps:

[0046] 1) Set the Monte Carlo simulation counter t' = 0, and the chance constraint holds, so the counter T' = 0;

[0047] 2) Randomly generate random numbers that follow a normal distribution to obtain the predicted value of active power consumption of the power system load;

[0048] 3) For all conventional units, randomly generate pseudo-random numbers x that are uniformly distributed within the range [0, 1]. i If x i ≤y i , then u i,t =0, otherwise u i,t =1, where y i Let be the failure rate of the i-th conventional unit;

[0049] For all wind turbines, randomly generate pseudo-random numbers x that are uniformly distributed within the range [0, 1].j , if x j ≤y j , then u j,t = 0, otherwise u j,t = 1, where y j is the failure rate of the jth wind turbine unit;

[0050] For all DC transmission lines, a pseudo-random number x uniformly distributed in [0, 1] is randomly generated<00000​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​1) The power system reserve capacity configuration model established by this invention takes into account the power characteristics of various types of power sources, energy storage devices and loads in the power grid. It belongs to a multi-level system equipment composition. Combined with the total power purchase cost of the system, it can reduce the economic cost of power system operation while optimizing the capacity of the power system for multi-resource peak shaving. It provides a reference for the multi-resource capacity optimization configuration of power system source, grid, load and storage.

[0061] 2) The opportunity constraint model and Monte Carlo algorithm used in this invention improve the operating efficiency of multi-resource peak shaving constraints.

[0062] 3. The particle swarm optimization algorithm used in this invention improves the timeliness and effectiveness of power system peak shaving by updating the collected real-time active power reserve data. Attached Figure Description

[0063] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0064] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted that these descriptions are for the purpose of aiding understanding the present invention, but do not constitute a limitation thereof. Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0065] like Figure 1 As shown, this invention provides a power system optimization configuration method considering source-grid-load-storage coordination, the steps of which are:

[0066] Step 1: Establish a power system reserve capacity configuration model;

[0067] Step 2: Establish the objective function for the total electricity purchase cost of the power system;

[0068] Step 3: Construct a power system opportunity constraint model based on the power system reserve capacity configuration model;

[0069] Step 4: Based on the opportunity constraint model of the power system, the Monte Carlo algorithm is used to verify all units of the power system to obtain a number of unit combinations that meet the opportunity constraint requirements;

[0070] Step 5: Use the particle swarm optimization algorithm to solve the configuration of several unit combinations to obtain the unit combination with the optimal configuration of power system reserve capacity that satisfies the objective function of the total power purchase cost of the power system.

[0071] In the above scheme, the power system reserve capacity configuration model in step 1 consists of an active power balance model and a reserve capacity balance model for traditional generating units, new energy generating units, DC transmission, and energy storage, as well as an output power constraint model, a reserve capacity constraint model, and a ramp rate constraint model that each variable in the model satisfies respectively.

[0072] The active power balance condition refers to the active power balance between the active power generated by each power generation unit in the power system and the active power consumed by the load. The specific model is as follows:

[0073]

[0074] Where, N SG N WF N DC N E These represent the number of conventional generating units, wind turbine units, DC transmission lines, and energy storage units within the power system; P SGi P WFj P DCm P Eq These represent the active power generation of the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively; P L This represents the predicted value of active power consumption by the load.

[0075] The system reserve capacity balancing model is as follows:

[0076]

[0077] Where, N SG N WF N DC N E These are the number of conventional generating units, wind turbine units, DC transmission lines, and energy storage units within the power system. The weighting coefficients for the spinning reserve capacity of the i-th conventional unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit at time t are respectively. These represent the standby capacities that can be dispatched at time t for the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit. Let t be the spinning reserve requirement of the power system at time t.

[0078] The unit's output power constraint is:

[0079] P SGi min ≤P SGi ≤P SGi max

[0080] P WFi min ≤P WFi ≤P WFi max

[0081] P DCi min ≤P DCi ≤P DCi max

[0082] P Eqi min ≤P Eqi ≤P Eqi max

[0083] in, These are the minimum output limits for the i-th conventional unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively. These are the maximum output limits for the i-th conventional unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively.

[0084] The aforementioned reserve capacity constraint is:

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] in, These represent the minimum reserve capacity indicators that can be dispatched for the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively. This serves as the lower limit indicator for rotating reserve requirements. These represent the upper limits of the standby capacity that can be dispatched for the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively. This is the upper limit indicator for rotating reserve requirements; These represent the standby capacities that can be dispatched for the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively. To meet the spinning reserve requirements of the power system.

[0091] The aforementioned unit ramp rate constraint is:

[0092]

[0093]

[0094]

[0095]

[0096] in, These represent the standby capacities that can be dispatched at time t for the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit. These represent the standby capacities that can be dispatched at time t' for the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit. These represent the maximum ramp output of the i-th conventional unit, the j-th wind turbine, the m-th DC-DC transmission line, and the q-th energy storage unit during the time interval ΔT, where ΔT is the time interval between time t and time t'.

[0097] In the above scheme, the objective function of the total power purchase cost of the power system in step 2 is the sum of the generation cost and the reserve capacity cost, and the constraints it satisfies include the total load interruption time constraint and the load interruption duration constraint.

[0098] The power generation cost model is as follows:

[0099]

[0100] In the formula, C R For the power generation cost model; u a,t Let A represent the operating status of the a-th generator unit at time t, with 1 for running and 0 for stopped; T and A represent the total time duration and the total number of generator units, respectively, where A = N. SG +N WF +N DC +N E This includes all the aforementioned units; F a (P a,t () represents the power output P of the a-th generator unit at time t. a,t The relevant cost characteristic function of the a-th generator set is expressed as a quadratic function:

[0101]

[0102] In the formula, α i b i c iLet α be the quadratic coefficient, primary coefficient, and constant of the power generation cost of the i-th conventional unit; j b j c j These represent the quadratic coefficient, primary coefficient, and constant of the power generation cost of the j-th wind turbine, respectively; α m b m c m Let α be the quadratic coefficient, primary coefficient, and constant of the generation cost of the m-th DC transmission line, respectively; q b q c q These are the quadratic coefficient, primary coefficient, and constant of the power generation cost of the q-th energy storage unit, respectively; P SGi (t), P WFj (t), P DCm (t), P Eq (t) represents the active power generation of the i-th conventional unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit at time t.

[0103] The reserve capacity cost model consists of two parts: one part considers B interruptible loads participating in the reserve, and the other part considers A generator sets. The reserve capacity cost function f is:

[0104]

[0105] Among them, v b,t Let F be the interruptible load operation state of group b at time t. at F bt Let a be the cost characteristic function of the a-th generator unit and the b-th interruptible load, respectively. Let a be any one of the following units from front to back: the i-th conventional unit, the j-th wind turbine unit, the m-th DC transmission line, and the q-th energy storage unit. This means that a needs to traverse i+j+m+q numbers.

[0106] The objective function for the total electricity purchase cost of the power system is:

[0107]

[0108] The total load interruption time constraint and the load interruption duration constraint are as follows:

[0109]

[0110] M b This represents the total interruption time of the interruptible load in group b.

[0111] In the above scheme, the opportunity constraint model in step 3 considers that the total power of each unit's output, reserve capacity, and interruptible load is not less than the load power. The probability of random failure events occurring in each unit and interruptible load must be no less than a pre-set confidence interval, expressed by the following formula:

[0112]

[0113] Among them, P r {} represents the probability of a certain unit failure event occurring, and α is a pre-set confidence level for the system to meet the reserve capacity requirements; u i,t u j,t u m,t u q,t These represent the operating status of the i-th conventional unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit at time t, respectively. The spinning reserve requirement of the b-th group of interruptible loads at time t in the power system.

[0114] In the above scheme, step 4, the process of verifying all dispatched generating units in the power system, includes the following steps:

[0115] 1) Set the Monte Carlo simulation counter t' = 0, and the chance constraint holds, so the counter T' = 0;

[0116] 2) Randomly generate random numbers that follow a normal distribution to obtain the predicted value of active power consumption of the power system load;

[0117] 3) For all conventional units, randomly generate pseudo-random numbers x that are uniformly distributed within the range [0, 1]. i If x i ≤y i , then u i,t =0, meaning the unit is shut down due to a fault; otherwise, u i,t =1, meaning the unit is operating normally, where y i Let be the failure rate of the i-th conventional unit;

[0118] For all wind turbines, randomly generate pseudo-random numbers x that are uniformly distributed within the range [0, 1]. j If x j ≤y j , then u j,t =0, meaning the unit is shut down due to a fault; otherwise u j,t =1, meaning the unit is operating normally, where y j The failure rate of the j-th wind turbine unit;

[0119] For all DC transmission lines, randomly generate pseudo-random numbers x that are uniformly distributed within the range [0, 1]. m If xm ≤ y m , then u m,t = 0, that is, the line fails and stops; otherwise u m,t = 1, that is, the line operates normally, where y m is the failure rate of the m-th DC transmission line;[[ID=A]] [[ID=B]]

[0120] For all energy storage units, a pseudo-random number x uniformly distributed in [0, 1] is randomly generated q , if x q ≤ y q , then u q,t = 0, that is, the unit fails and stops; otherwise u q,t = 1, that is, the unit operates normally, where y q is the failure rate of the q-th energy storage unit;

[0121] For all interruptible loads, a pseudo-random number xx uniformly distributed in [0, 1] is randomly generated μ , if xx μ ≤ yμ, then v b,t = 0, that is, the interruptible load is not interrupted; otherwise v[[ID=A]] b,t = 1, that is, the interruptible load is in an interrupted state, where y μ is the interruption rate of the interruptible load;

[0122] 4), if the random variable satisfies

[0123]

[0124] , then T' = T' + 1, otherwise T' remains unchanged;

[0125] 5), set t' = t' + 1,

[0126] 6), if t' < T, return to step 2; if t' ≥ T, execute step 7), where T is the total time period length;

[0127] 7), judge whether T' / T ≥ α holds. If it holds, the total number of unit combinations meets the requirements, and all unit combinations that satisfy the power system chance-constrained model are the said several unit combinations; if it does not hold, return to step 1 until T' / T ≥ α holds.

[0128] The following is an example for illustration with a county-level power grid system.

[0129] The maximum active load of the system is 830,000 kW, the maximum reserve demand is, and the unit installation situation is shown in Table 1. The system is interconnected with the outside through 1 transmission line, and the line transmission limit is 500,000 kW.

[0130]

[0131]

[0132] Using the Monte Carlo algorithm, random numbers following a normal distribution are generated. Step 5 is executed to obtain multiple sets of unit start-up and standby reserve modes that meet the chance constraints. Table 2 lists a set of data that meets the requirements generated when the Monte Carlo simulation counter t' = 15. The probability P of the unit failure event at this time is shown. r {}=0.12≥0.1, which satisfies the confidence level α=0.1 required for the system to meet the backup capacity requirement.

[0133]

[0134]

[0135] Step 6 involves using a particle swarm optimization algorithm to obtain the recommended reserve capacity of the power system considering the coordination of power generation, grid, load, and storage, as shown in Table 3. Comparing the optimization results with the actual system operation before adopting the method described in this invention, it can be seen that after adopting the method, the system operating cost decreased from 8.29 million yuan per day to 7.12 million yuan. Therefore, the method described in this patent can optimize the reserve capacity reservation value and reduce system operating costs.

[0136] Table 3 Comparison of Power System Operation Before and After Optimization Configuration

[0137]

[0138] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Contents not described in detail in this specification belong to prior art known to those skilled in the art.

Claims

1. A method for optimizing power system configuration considering source-grid-load-storage coordination, characterized in that: It includes the following steps: Establish a power system reserve capacity configuration model; Establish a power system total power purchase cost objective function; Construct a power system chance-constrained model based on the power system reserve capacity configuration model; Verify all units of the power system using the Monte Carlo algorithm according to the power system chance-constrained model to obtain several unit combinations that meet the requirements of chance constraints; Use the particle swarm optimization algorithm to solve the configuration of several unit combinations respectively to obtain the unit combination with the optimal configuration of the power system reserve capacity that meets the power system total power purchase cost objective function; The power system total power purchase cost objective function is: ； Where F represents the total electricity purchase cost of the power system; Let b be the interruptible load operating state at time t. , Let be the cost characteristic functions of the a-th generator set and the b-th interruptible load set, respectively; Let t represent the operating status of the a-th generator unit at time t, with 1 for running and 0 for shut down; T and A represent the total time duration and the total number of units, respectively; B represents the number of interruptible loads participating in the standby. The power output of only the a-th generator unit at time t The relevant cost characteristic function of the a-th generator set; The for: ； in, , , These are the quadratic coefficient, primary coefficient, and constant of the power generation cost of the i-th conventional unit, respectively. , , These are the quadratic coefficient, primary coefficient, and constant of the power generation cost of the j-th wind turbine, respectively. , , These are the quadratic coefficient, primary coefficient, and constant of the power generation cost of the m-th DC transmission line, respectively. , , These are the quadratic coefficient, the primary coefficient, and the constant of the power generation cost of the qth energy storage unit, respectively. , , , These represent the active power generation of the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit at time t, respectively.

2. The power system optimization configuration method considering source-grid-load-storage coordination according to claim 1, characterized in that: The power system reserve capacity configuration model includes an active power balance model and a reserve capacity balance model, as well as constraint models for each variable in the active power balance model and the reserve capacity balance model.

3. The power system optimization configuration method considering source-grid-load-storage coordination according to claim 2, characterized in that: The active power balance model is: ； in, , , , These are the number of conventional generating units, wind turbine units, DC transmission lines, and energy storage units within the power system; , , , These are the active power generation of the i-th conventional unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively. This represents the predicted value of active power consumption by the load.

4. The power system optimization configuration method considering source-grid-load-storage coordination according to claim 3, characterized in that: The reserve capacity balance model is: ； in, , , , These are the number of conventional generating units, wind turbine units, DC transmission lines, and energy storage units within the power system; , , , The weighting coefficients for the spinning reserve capacity of the i-th conventional unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit at time t are respectively. , , , These represent the standby capacities that can be dispatched at time t for the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit. Let t be the spinning reserve requirement of the power system at time t.

5. The power system optimization configuration method considering source-grid-load-storage coordination according to claim 4, characterized in that: The constraint models for each variable in the active power balance model and the reserve capacity balance model are: ； in, , , , These are the minimum output limits for the i-th conventional unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively. , , , These are the maximum output limits for the i-th conventional unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively. , , , These represent the minimum reserve capacity indicators that can be dispatched for the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively. This serves as the lower limit indicator for rotating reserve requirements. , , , These represent the upper limits of the standby capacity that can be dispatched for the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively. This is the upper limit indicator for rotating reserve requirements; , , , These represent the standby capacities that can be dispatched at time t for the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit. , , , These represent the standby capacities that can be dispatched at time t' for the i-th conventional generating unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit. , , , These represent the i-th conventional generating unit, the j-th wind turbine, the m-th DC-DC transmission line, and the q-th energy storage unit, respectively. Maximum climbing output within a time period The time period is the time interval between time t and time t'.

6. The power system optimization configuration method considering source-grid-load-storage coordination according to claim 1, characterized in that: The constraint model for the variable in the power system total power purchase cost objective function is: ； in, This represents the total interruption time of the interruptible load in group b.

7. The power system optimization configuration method considering source-grid-load-storage coordination according to claim 3, characterized in that: The power system chance-constrained model is ,in, Let be the probability that a certain unit failure event occurs. The system needs to meet the confidence level required for backup capacity; i,t u j,t u m,t u q,t These represent the operating status of the i-th conventional unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit at time t, respectively. The spinning reserve requirement of the b-th group of interruptible loads at time t in the power system; , , , These represent the standby capacity that can be dispatched by the i-th conventional unit, the j-th wind turbine, the m-th DC transmission line, and the q-th energy storage unit, respectively.

8. The power system optimization configuration method considering source-grid-load-storage coordination according to claim 7, characterized in that, The process of verifying all units of the power system includes the following steps: 1). Set the Monte Carlo simulation counter t' = 0 and the chance constraint satisfaction counter T' = 0; 2). Randomly generate random numbers obeying the normal distribution to obtain the predicted value of the active power consumed by the load of the power system; 3) For all conventional units, randomly generate pseudo-random numbers uniformly distributed within the range [0, 1]. ,like ,but ,otherwise ,in Let be the failure rate of the i-th conventional unit; For all wind turbine units, randomly generate pseudo-random numbers uniformly distributed within the range [0, 1]. ,like ,but ,otherwise ,in The failure rate of the j-th wind turbine unit; For all DC transmission lines, randomly generate pseudo-random numbers uniformly distributed within the range [0, 1]. ,like ,but ,otherwise ,in Let be the failure rate of the m-th DC transmission line; For all energy storage units, randomly generate pseudo-random numbers uniformly distributed within the range [0, 1]. ,like ,but ,otherwise ,in Let q be the failure rate of the qth energy storage unit; For all interruptible loads, randomly generate pseudo-random numbers uniformly distributed within [0, 1]. ,like ,but ;otherwise ,in Interruption rate of interruptible load; 4). If the random variable satisfies If so, then T' = T' + 1; otherwise, T' remains unchanged. 5). Set t' = t' + 1, 6). If t' < T, return to step 2; if t' ≥ T, execute step 7), where T is the total time period; 7) Judgment If the condition is met, the total number of unit combinations meets the requirement, and all unit combinations that satisfy the power system opportunity constraint model are the aforementioned unit combinations; if not, return to step 1, and so on. Established.

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