Flexible resource optimization scheduling method and system for promoting improvement of photovoltaic bearing capacity

Abstract

The invention discloses a flexible resource optimization scheduling method and system for promoting improvement of photovoltaic bearing capacity, and the method comprises the steps: firstly obtaining related data of flexible resources in a power grid, classifying the flexible resources in the power grid according to source-grid-load-storage, and selecting typical flexible resources of a supply side, a power grid side, a demand side and an energy storage side for modeling analysis; establishing a source-network-load-storage flexible resource optimization scheduling model through the linkage and coordination between typical flexible resources; a source-network-load-storage flexible resource optimization scheduling model is associated with power grid operation, a flexible resource clustering power grid coordination model is solved through a technical economic feasible region of flexible resources, and an optimal scheduling scheme of source-network-load-storage coordination optimization on the premise of meeting power grid operation economic indexes is obtained. Aiming at the problem that huge bearing pressure is brought to the power distribution network due to the fact that photovoltaic is difficult to consume, the method gives consideration to the two aspects of technical constraint and economic characteristics, carries out the combined scheduling of the flexible resources of the four parts of source-network-load-storage in the power grid, and considers the improvement of the photovoltaic bearing capacity from the perspective of the flexible resources.

Description

Technical Field

[0001] The present invention belongs to the field of photovoltaic carrying capacity, and particularly relates to a flexible resource optimal scheduling method and system for promoting the improvement of photovoltaic carrying capacity. Background Art

[0002] Distributed photovoltaics have seen a sharp increase, bringing huge carrying pressure to low-voltage distribution networks. How to improve the in-situ consumption capacity of distributed photovoltaics is a huge challenge in the development process of distributed photovoltaics, which means that for the grid side, it has become extremely urgent to improve the photovoltaic carrying capacity of the distribution network.

[0003] With the advancement of the construction of the new power system, the distributed flexible resources of the distribution network show the characteristics of diversification and scale. Therefore, how to fully tap and utilize the flexible regulation potential of these resources is crucial for promoting the in-situ consumption of photovoltaics. Summary of the Invention

[0004] The purpose of the present invention is to provide a flexible resource optimal scheduling method and system for promoting the improvement of photovoltaic carrying capacity in view of the above problems existing in the prior art.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] In the first aspect, the present invention proposes a flexible resource optimal scheduling method for promoting the improvement of photovoltaic carrying capacity, including:

[0007] S1. Obtain the relevant data of flexible resources in the power grid, classify the flexible resources in the power grid according to the four aspects of source-network-load-storage, and based on the classification results of the flexible resources, select typical flexible resources on the supply side, grid side, demand side, and energy storage side for modeling and analysis;

[0008] S2. Based on the modeling and analysis results of the typical flexible resources on the supply side, grid side, demand side, and energy storage side, establish a source-network-load-storage flexible resource optimal scheduling model through the linkage and coordination between the typical flexible resources;

[0009] S3. Connect the source-network-load-storage flexible resource optimal scheduling model with the power grid operation, construct a flexible resource clustering power grid coordination model, and solve the flexible resource clustering power grid coordination model through the technical and economic feasible region of the flexible resources to obtain the optimal scheduling plan for the coordinated optimization of source-network-load-storage that meets the economic indicators of power grid operation.

[0010] In the above S2, the objective function of the source-network-load-storage flexible resource optimal scheduling model includes:

[0011] textmaxF = α·F - β·C + γ·RM;

[0012]

[0013] C=C S +C Rpc +C L +C St ;

[0014]

[0015] In the above formula, textmaxF is the potential for the power system to flexibly regulate various resources, α, β, and γ are all weight coefficients, F is the grid flexibility, C is the total cost of construction, operation, and maintenance control, RM is the reliability; ∝ represents a positive correlation, l S is the range of reactive power regulation on the power supply side, t S is the response time for regulating reactive power on the power supply side, c Rpc is the total reactive power compensation capacity on the grid side, Δ Rpc is the degree to which the reactive power compensation device on the grid side can accurately regulate reactive power, l L is the range within which users can reduce or increase the load, t L is the time for users to respond to the dispatching instruction, c St is the total capacity on the energy storage side, v St is the charge and discharge rate on the energy storage side, C S is the operation and maintenance cost of the reactive power control and regulation device on the power supply side, C Rpc is the operation and maintenance cost of the reactive power compensation device on the grid side, C L is the subsidy for motivating users to participate in demand-side response and the management and coordination cost for implementing demand-side plans, C St is the operation and maintenance cost of the energy storage side equipment and the cost required for replacement considering the performance decay of the energy storage equipment over time, P w (S) is the failure probability of the reactive power regulation device on the power supply side, τ S is the repair time after the reactive power regulation device on the power supply side fails, P w (Rpc) is the failure probability of the reactive power compensation device on the grid side, τ Rpc is the repair time after the reactive power compensation device on the grid side fails, λ L is the proportion of users participating in demand-side response, δ L is the proportion of users successfully responding to the dispatching instruction, θ St is the energy storage capacity redundancy reserved by the energy storage system to cope with emergencies, P w (St) is the failure probability of the energy storage system, τ St is the repair time after the energy storage system fails;

[0016] Among them, textmaxF, as the potential for the power system to flexibly regulate various resources, can also be jointly determined by the power regulation capabilities of multiple adjustable flexible resources. Therefore, it can also be expressed as:

[0017]

[0018] In the above formula, N is the total number of adjustable flexible resources, and w n is the weight of the nth adjustable flexible resource, which is related to factors such as its capacity, response speed, and reliability. is the maximum adjustable power of the nth adjustable flexible resource.

[0019] In S2, the constraint conditions of the flexible resource optimal scheduling model for source-network-load-storage include:

[0020] P gen，t = P load，t + P storage，t ;

[0021]

[0022] C total ≤ B;

[0023]

[0024] E total ≤ E lim it ;

[0025] In the above formula, P gen，t is the total power generated at time t, P load，t is the load demand at time t, P storage，t is the charging and discharging power of the energy storage device at time t, negative for charging and positive for discharging, F x is the power flow of power grid line x, are the lower and upper limits of the power flow of power grid line x respectively, V j is the voltage of power grid node j, are the lower and upper limits of the voltage of power grid node j respectively, C total is the total project investment, V is the total investment budget, C op，t is the operating cost at time t, is the operating expense threshold, E total is the total project emissions, E lim it is the emission limit.

[0026] In S3, the specific steps to solve the flexible resource clustering power grid coordination model through the technical and economic feasible region of flexible resources and obtain the optimal scheduling plan for source-network-load-storage coordination optimization under the premise of meeting the economic indicators of power grid operation are as follows:

[0027] S31. In the technical and economic feasible region of flexible resources, define the projection variables of equivalent projection aggregation as P and C. P is the adjustable power of adjustable flexible resources at the equivalent aggregation port, and C is the total cost of construction, operation, and regulation, including the operation and maintenance cost C S of the reactive power control and regulation device on the power supply side, the operation and maintenance cost C Rpc of the reactive power compensation device on the power grid side, the subsidy for motivating users to participate in demand-side response and the management and coordination cost C L of implementing the demand-side plan, the operation and maintenance cost of energy storage side equipment, and the cost C St required for replacing energy storage equipment considering the performance decay over time. Project the feasible region with the projection variables P and C to obtain the feasible region of the external characteristics of technical and economic aggregation, that is, the technical and economic feasible region is:

[0028]

[0029] In the above formula, Ω PC is the technical and economic feasible region of flexible resources, A Ω is the coefficient matrix of the technical and economic feasible region, b Ω is the constant vector of the technical and economic feasible region, S PC is the scheduling plan, and Φ is the safety operation constraint of the distribution network;

[0030] S32. Under the specified adjustable power P, the total cost C of construction, operation, and regulation of the power system is related to the scheduling plan S PC . The most economical total cost C of construction, operation, and regulation must be on a certain hyperplane of the technical and economic feasible region. Therefore, define the hyperplane where the operating point with the lowest cost within the adjustable power range is located as the optimal economic operation hyperplane, and the scheduling plan on the optimal economic operation hyperplane is the optimal scheduling plan:

[0031]

[0032] In the above formula, Ψ B is the optimal economic operation hyperplane, A Ψ is the matrix coefficient of the optimal economic operation hyperplane, and b Ψ is the constant vector of the optimal economic operation hyperplane.

[0033] In S3, the optimal scheduling model of source-network-load-storage flexible resources is linked to the power grid operation to construct a flexible resource clustering power grid coordination model;

[0034] The flexible resource clustering power grid coordination model includes a source-network-load-storage flexible resource optimal scheduling model, an optimal power flow model for power grid operation, a load forecasting model, a power market model, and a demand response model.

[0035] In a second aspect, the present invention proposes a flexible resource optimal scheduling system for promoting the improvement of photovoltaic carrying capacity, including a typical flexible resource analysis module, a flexible resource optimal scheduling model establishment module, and an optimal scheduling plan solving module;

[0036] The typical flexible resource analysis module is used to obtain relevant data of flexible resources in the power grid, classify the flexible resources in the power grid according to the four aspects of source-network-load-storage, and based on the classification results of the flexible resources, select typical flexible resources on the supply side, grid side, demand side, and energy storage side for modeling analysis;

[0037] The flexible resource optimal scheduling model establishment module is used to establish a source-network-load-storage flexible resource optimal scheduling model based on the modeling analysis results of typical flexible resources on the supply side, grid side, demand side, and energy storage side through the linkage and coordination between typical flexible resources;

[0038] The optimal scheduling plan solving module is used to connect the source-network-load-storage flexible resource optimal scheduling model with power grid operation, construct a flexible resource clustering power grid coordination model, and solve the flexible resource clustering power grid coordination model through the technical and economic feasible region of flexible resources to obtain an optimal scheduling plan for source-network-load-storage coordinated optimization that meets the economic indicators of power grid operation.

[0039] The flexible resource optimal scheduling model establishment module includes an objective function construction unit;

[0040] The objective function construction unit is used to construct the following objective function of the flexible resource optimal scheduling model for source-network-load-storage:

[0041] textmaxF = α·F - β·C + γ·RM;

[0042]

[0043] C = C S +C Rpc +C L +C St ;

[0044]

[0045] In the above formula, textmaxF is the potential for the power system to flexibly adjust various resources, α, β, and γ are all weight coefficients, F is the power grid flexibility, C is the total construction and operation and maintenance control cost, and RM is the reliability; ∝ represents a positive correlation, lS is the reactive power regulation range on the power supply side, t S is the response time for regulating reactive power on the power supply side, c Rpc is the total reactive power compensation capacity on the grid side, Δ Rpc is the degree to which the reactive power compensation device on the grid side can accurately regulate reactive power, l L is the range within which users can reduce or increase the load, t L is the time for the user side to respond to the dispatching instruction, c St is the total capacity of the energy storage side, v St is the charge and discharge rate of the energy storage side, C S is the operation and maintenance cost of the reactive power control and regulation device on the power supply side, C Rpc is the operation and maintenance cost of the reactive power compensation device on the grid side, C L is the subsidy for motivating users to participate in demand-side response and the management and coordination cost for implementing demand-side plans, C St is the operation and maintenance cost of the energy storage side equipment and the cost required for replacement considering the performance degradation of the energy storage equipment over time, P w (S) is the failure probability of the reactive power regulation device on the power supply side, τ S is the repair time after the reactive power regulation device on the power supply side fails, P w (Rpc) is the failure probability of the reactive power compensation device on the grid side, τ Rpc is the repair time after the reactive power compensation device on the grid side fails, λ L is the proportion of users participating in demand-side response, δ L is the proportion of users successfully responding to the dispatching instruction, θ St is the energy storage capacity redundancy reserved by the energy storage system to cope with emergencies, P w (St) is the failure probability of the energy storage system, τ St is the repair time after the energy storage system fails;

[0046] Among them, textmaxF, as the potential for the power system to flexibly regulate various resources, can also be jointly determined by the power regulation capabilities of multiple adjustable flexible resources. Therefore, it can also be expressed as:

[0047]

[0048] In the above formula, N is the total number of adjustable flexible resources, w n is the weight of the nth adjustable flexible resource, which is related to factors such as its capacity, response speed, reliability, etc., is the maximum adjustable power of the nth adjustable flexible resource.

[0049] The flexible resource optimal scheduling model establishment module further includes a constraint condition construction unit;

[0050] The constraint condition construction unit is used to construct the constraint conditions of the flexible resource optimization scheduling model for the source-network-load-storage as follows:

[0051] P gen，t =P load，t +P storage，t ;

[0052]

[0053] C rotal ≤B;

[0054]

[0055] E total ≤E lim it ;

[0056] In the above formula, P gen，t is the total power generated at time t, P load，t is the load demand at time t, P storage，t is the charge and discharge power of the energy storage device at time t, negative for charging and positive for discharging, F x is the power flow of grid line x, are the lower and upper limits of the power flow of grid line x respectively, V j is the voltage of grid node j, are the lower and upper limits of the voltage of grid node j respectively, C total is the total project investment, B is the total investment budget, C op，t is the operating cost at time t, is the operating expense threshold, E total is the total project emissions, E limit is the emission upper limit.

[0057] The optimal scheduling scheme solving module includes a technical and economic feasible region establishment unit and an optimal economic operation hyperplane establishment unit;

[0058] The technical and economic feasible region establishment unit is used to define the projection variables of the equivalent projection aggregation as P and C in the flexible resource technical and economic feasible region. P is the adjustable power of the adjustable flexible resource at the equivalent aggregation port, and C is the total construction, operation and maintenance regulation cost, including the operation and maintenance cost C S of the reactive power control and regulation device on the power supply side, the operation and maintenance cost C Rpc of the reactive power compensation device on the grid side, the subsidy for motivating users to participate in demand-side response and the management and coordination cost C L for implementing the demand-side plan, the operation and maintenance cost of the energy storage side equipment and the cost C St, project the feasible region with the projection variables P and C to obtain the feasible region of the technical and economic aggregation external characteristics, that is, the technical and economic feasible region is:

[0059]

[0060] In the above formula, Ω PC is the technical and economic feasible region of flexible resources, A Ω is the coefficient matrix of the technical and economic feasible region, b Ω is the constant vector of the technical and economic feasible region, S PC is the scheduling scheme, and Φ is the security operation constraint of the distribution network;

[0061] The optimal economic operation hyperplane establishment unit is used to, under the specified adjustable power P, the total construction, operation and maintenance control cost C of the power system is related to the scheduling scheme S PC The most economical total construction, operation and maintenance control cost C must be on a certain hyperplane of the technical and economic feasible region. Therefore, the hyperplane where the operating point with the lowest cost within the adjustable power range is located is defined as the optimal economic operation hyperplane, and the scheduling scheme on the optimal economic operation hyperplane is the optimal scheduling scheme:

[0062]

[0063] In the above formula, Ψ B is the optimal economic operation hyperplane, A Ψ is the matrix coefficient of the optimal economic operation hyperplane, and b Ψ is the constant vector of the optimal economic operation hyperplane.

[0064] The optimal scheduling scheme solving module further includes a flexible resource clustering power grid coordination model construction unit;

[0065] The flexible resource clustering power grid coordination model construction unit is used to construct a flexible resource clustering power grid coordination model including a source-network-load-storage flexible resource optimal scheduling model and an optimal power flow model, a load forecasting model, a power market model, and a demand response model for power grid operation.

[0066] Compared with the prior art, the beneficial effects of the present invention are:

[0067] The present invention proposes a flexible resource optimal scheduling method and system for promoting the improvement of photovoltaic carrying capacity. The method first obtains relevant data of flexible resources in the power grid, classifies the flexible resources in the power grid according to four aspects of source-grid-load-storage, and based on the classification results of the flexible resources, selects typical flexible resources on the supply side, grid side, demand side, and energy storage side for modeling and analysis; then, based on the modeling and analysis results of the typical flexible resources on the supply side, grid side, demand side, and energy storage side, an optimal scheduling model for source-grid-load-storage flexible resources is established through the linkage and coordination between the typical flexible resources; finally, the optimal scheduling model for source-grid-load-storage flexible resources is linked with the power grid operation to construct a flexible resource clustering grid coordination model, and the flexible resource clustering grid coordination model is solved through the technical and economic feasible region of the flexible resources to obtain the optimal scheduling plan for the coordinated optimization of source-grid-load-storage that meets the economic indicators of the power grid operation. On the one hand, aiming at the problem that the difficulty in consuming photovoltaic power brings huge carrying pressure to the distribution network, the method jointly optimizes and schedules the flexible resources in the four aspects of source-grid-load-storage in the power grid, and considers the improvement of photovoltaic carrying capacity from the perspective of flexible resources; on the other hand, the method takes into account both technical constraints and economic characteristics. By solving the flexible resource clustering grid coordination model through the technical and economic feasible region of the flexible resources, the internal variables in the entire power system model are eliminated. By extracting the external equivalent technical and economic characteristics, the optimal economic operation hyperplane information within the feasible region is determined, and the concise and observable coordinated optimization boundary information is provided to the superior, greatly reducing the number of optimization variables and constraints in the system model and reducing the computational complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is the overall flowchart of the method described in the present invention.

[0069] Figure 2 It is the equivalent grid connection schematic diagram of the distributed photovoltaic described in Embodiment 1.

[0070] Figure 3 It is the maximum and minimum load power curve graphs at time t on different dates in the heating season and cooling season described in Embodiment 1;

[0071] Figure 4 It is the structure diagram of the system described in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] The present invention will be further described in detail below in conjunction with the specific embodiments and the drawings.

[0073] The present invention provides a flexible resource optimal scheduling method and system for promoting the improvement of photovoltaic carrying capacity. First, relevant data of flexible resources in the power grid are obtained, and the flexible resources in the power grid are classified according to four aspects: source, grid, load, and energy storage. Typical flexible resources on the supply side, grid side, demand side, and energy storage side are selected for modeling and analysis. The flexible resource optimal scheduling model of source-grid-load-storage is linked with the operation of the power grid to construct a flexible resource clustering power grid coordination model. The flexible resource clustering power grid coordination model is solved through the technical and economic feasible region of flexible resources, and concise and observable boundary information is provided after superior coordination and optimization. The improvement of photovoltaic carrying capacity is considered from the perspective of flexible resource clustering.

[0074] Embodiment 1:

[0075] As Figure 1 shown, a flexible resource optimal scheduling method for promoting the improvement of photovoltaic carrying capacity is carried out in sequence according to the following steps:

[0076] 1. Obtain relevant data of flexible resources in the power grid, and clearly define and classify the flexible resources in the power grid according to the coordinated optimization mode of the four aspects of source, grid, load, and energy storage;

[0077] The "source-grid-load-storage" coordinated optimization mode refers to the operation mode and technology in which the four parts of power supply, power grid, load, and energy storage use various interaction means to more economically, efficiently, and safely improve the power dynamic balance ability of the power system, so as to achieve the maximum utilization of energy resources. Based on the concept of the "source-grid-load-storage" coordinated optimization mode in the power system, the flexible resources in the active distribution network are classified according to four aspects: supply side, grid side, demand side, and energy storage side;

[0078] The flexible resources on the supply side include the power injection from the superior power grid and various distributed power sources. The power injection from the superior power grid can be reasonably adjusted through daily, day-ahead, and medium- and long-term scheduling; the power generation of various distributed power sources is located on the user side, close to the load center, which can effectively reduce the construction cost and loss of the distribution network. In the distributed power generation system, advanced medium- and small-sized modular equipment is mostly used, which has the advantages of fast start-up and shutdown, convenient maintenance and management, and flexible adjustment. Moreover, various power sources are relatively independent and can meet different needs such as peak shaving and valley filling and power supply to important users. Its types mainly include conventional distributed power sources and distributed new energy. Conventional distributed power sources include reciprocating engines, micro gas turbines, fuel cells, etc., and distributed new energy includes photovoltaic power generation, wind power generation, biomass power generation, gas turbines, and tidal energy power generation, etc. Reasonable utilization of various distributed power sources is an important flexible resource in the distribution network;

[0079] Flexible resources on the grid side include reactive power compensation devices and network reconfiguration switches. Among them, reactive power compensation devices include on-load tap-changing transformers (OTLCs), switched capacitor banks, static var compensators (SVCs), static var generators (SVG), and reactive power compensation devices for new energy equipment; network reconfiguration switches include tie switches, sectionalizing switches, and flexible soft switches. During normal operation, the distribution network needs to meet requirements such as the power flow limit of transmission lines and bus voltage constraints. When the power flow distribution in the system is unreasonable, it will increase the active power loss of the line and limit the transmission capacity of active power on the line. However, with the gradual increase of distributed generation equipment connected to the distribution network, such as renewable energy generation equipment, energy storage systems, electric vehicle chargers, etc., these generation devices can flexibly inject or absorb reactive power into the grid through grid-connected inverters. Through network reconfiguration, the adjustment of on-load tap-changing transformers, and the coordination of reactive power compensation devices, the power flow distribution in the network can be effectively adjusted, the power quality can be improved, and the system operation cost and network loss can be reduced;

[0080] Flexible resources on the demand side include incentive-responsive loads, shiftable loads, controllable loads, etc. Through strategies such as time-of-use tariffs, real-time tariffs, and peak-load tariffs, the time-series characteristics of incentive-responsive loads and shiftable loads are changed to achieve the effect of "peak shaving and valley filling". Electricity consumption is reduced during peak load periods and low new energy output, and shifted to periods of low load and high new energy output, so as to be able to achieve the accommodation of new energy at a lower cost. And through direct load control, emergency demand response, and ancillary service projects, etc., directly control the controllable loads, which can provide a certain degree of operating flexibility for the system;

[0081] Flexible resources on the energy storage side include mechanical energy storage, such as compressed air energy storage, flywheel energy storage, etc., chemical energy storage, such as sodium-sulfur batteries, lead-acid batteries, supercapacitors, etc., electromagnetic energy storage, such as superconducting magnetic energy storage, etc., and phase change energy storage, such as ice storage. Through large-capacity and high-efficiency energy storage devices, the volatility of new energy generation can be eliminated, and the ability to absorb new energy and connect to the grid can be improved, such as electrical energy storage; the peak-valley difference of the load can be reduced, the utilization rate of power equipment can be improved, the power supply reliability can be enhanced, and the power quality can be improved, such as pumped-storage energy storage, compressed air energy storage; it can promote the development of microgrids, act as an efficient on-vehicle power source, and promote the development of electric vehicles, such as electric vehicle energy storage, hybrid energy storage, etc., to achieve effective energy storage and improve the flexibility and reliability of the operation of the distribution network.

[0082] 2. Based on the classification results of flexible resources, select typical flexible resources on the supply side, grid side, demand side, and energy storage side for modeling and analysis;

[0083] For the supply side, the equivalent grid connection schematic diagram of distributed photovoltaics connected to the grid through an inverter is as Figure 2As shown, while grid-connected distributed PV provides active power to the grid, it can also output reactive power. It can utilize the capacity of the grid-connected inverter itself to provide reactive power regulation ability to the grid. Therefore, as a typical flexible resource on the supply side, the reactive power control of distributed PV inverters can effectively improve the carrying capacity of distributed PV;

[0084] When the distributed PV inverter participates in reactive power regulation, the maximum reactive power it can provide is:

[0085]

[0086] In the above formula, Q max is the maximum reactive power that the distributed PV inverter can provide when participating in reactive power regulation, S max is the capacity of the PV inverter, P DG is the current active power output;

[0087] In the typical scenario of distributed PV, the range of reactive power regulation on the power supply side is calculated using the following formula:

[0088]

[0089] In the above formula, l S is the range of reactive power regulation on the power supply side, S inv is the rated capacity of the distributed PV inverter, in kVA or MVA, P PV is the current active power output of the distributed PV inverter, in kW or MW;

[0090] Within its reactive power regulation range, the distributed PV inverter outputs reactive power according to the requirements of reactive power-voltage optimization control. When the voltage is low, the PV inverter is adjusted to operate in the leading mode to provide reactive power to the power system. Conversely, when the voltage is high, the PV inverter is adjusted to operate in the lagging mode to absorb reactive power from the power system. By utilizing the reactive power regulation ability of the distributed PV inverter in the leading or lagging operation mode, the node voltage is controlled to improve the carrying capacity of distributed PV in the distribution network;

[0091] For the grid side, when the reactive power regulation ability of the distributed PV inverter alone cannot meet the voltage control requirements in the power system, it is necessary for the grid side to further improve the voltage level through the minimum additional reactive power compensation device, thereby enhancing the carrying capacity of distributed PV in the distribution network. Since the access location and access capacity of the additional reactive power compensation device will affect the voltage control effect, a method based on network loss sensitivity is used to determine the optimal access location of the additional reactive power compensation device, and the following formula is used to calculate the total reactive power compensation capacity and the degree to which the reactive power compensation device can accurately regulate reactive power:

[0092]

[0093]

[0094] In the above formula, c Rpc is the total reactive power compensation capacity on the grid side, Q comp，m is the rated reactive power capacity of the m-th reactive power compensation device, with the unit of kVar or MVAr, M is the total number of additional reactive power compensation devices in the grid, Δ Rpc is the degree to which the reactive power compensation device on the grid side can accurately adjust reactive power, Q actual is the reactive power that the reactive power compensation device can accurately adjust during actual operation, with the unit of kVar or MVAr, Q rated is the rated reactive power capacity of the reactive power compensation device, with the unit of kVar or MVAr;

[0095] The network loss sensitivity comprehensively considers the influence of the nonlinearity of the power flow equation and the additional reactive power compensation on the candidate compensation nodes. The following formula is used to calculate the network loss increment of the system based on the network loss sensitivity matrix:

[0096]

[0097] In the above formula, ΔP loss is the network loss increment of the system, P loss is the total active network loss of the system, u is the node input power, Δu is the node power increment, Δu T is the node power increment matrix, is the first-order network loss sensitivity, is the second-order network loss sensitivity;

[0098] For the demand side, the demand response represents the actual response and adjustable ability of the flexible resources on the demand side. The heating and cooling loads in traditional response resources can be used as typical flexible resources on the demand side to promote the improvement of the photovoltaic carrying capacity. The electricity consumption behavior of heating and cooling loads has a certain regularity. When air conditioners and electric heating equipment are frequently turned on and off, there will be differences in the overall load curve. Therefore, the spring and winter seasons can be regarded as the heating seasons, and the summer and autumn seasons can be regarded as the cooling seasons. Based on the load curves of different dates in different seasons of the year, the maximum and minimum load power curves at time t on different dates in the heating season and cooling season as shown in Figure 3 can be obtained respectively, and the following formula is used to approximately measure the total maximum heating and cooling loads:

[0099] ΔP Tsmax，t =P Tsmax，t -P Tsmin，t ;

[0100] ΔP Twmax，t =P Twmax，t -P twmin，t ;

[0101] In the above formula, ΔP Tsmax，t is the maximum load difference at time t in the daily power curve of the cooling user load, that is, the total maximum load of the cooling load, P Tsmax，t and P Tsmin，t are the maximum and minimum loads at time t on different dates of the cooling day respectively. ΔP Twmax，t is the maximum load difference at time t in the daily power curve of the heating user load, that is, the total maximum load of the heating load, P Twmax，t and P Twmin，t are the maximum and minimum loads at time t on different dates of the heating day respectively;

[0102] The range within which the user can reduce or increase the load is the maximum load difference at each time t in the daily power curve of the cooling / heating user load:

[0103]

[0104] In the above formula, l L is the range within which the user can reduce or increase the load;

[0105] Considering the discreteness of the loads at time t in the daily load curves of all days in a year, the probability of the total maximum heating and cooling loads occurring is small. Directly using them as the adjustable capacities of heating and cooling loads will result in an overly large evaluation result of the response potential. Therefore, in order to obtain a more reasonable total maximum heating and cooling loads in most cases, the load mean square deviations at each time in the heating season and cooling season can be calculated based on the daily load curves of regional users to measure and identify the fluctuations of the corresponding loads and the average adjustable degree that is likely to occur. That is, the average total heating and cooling loads are respectively compared with the total maximum heating and cooling loads, and the adjustable coefficients are used to quantify the average adjustable degrees of heating and cooling:

[0106]

[0107] In the above formula, E w / s (t) is the load adjustable coefficient of the winter heating / summer cooling load in the region at time t, M w / s，D is the number of days of winter heating / summer cooling, is the normalized load of the users in this region at time t on the d-th day of winter heating / summer cooling, which is normalized based on the maximum and minimum loads on each day in winter and summer during this period, is the average per-unit load of the users in this region at time t during winter heating / summer cooling days. w represents winter heating, and s represents summer cooling;

[0108] For the energy storage side, taking battery energy storage as the typical flexible resource and the modeling object of the energy storage system on the energy storage side, a response characteristic model of the energy storage system is constructed:

[0109]

[0110]

[0111] In the above formula, P i ess is the charge-discharge power value of the i-th energy storage device, with discharge being positive and charge being negative, is the charge-discharge power value of the i-th energy storage device at time t, and T is the total time, are respectively the upper and lower limits of the charge-discharge power of the i-th energy storage device, and Δt is the time change amount, is the total dispatching incentive cost of the i-th energy storage device, is the charge-discharge electricity price of the i-th energy storage device.

[0112] 3. Based on the modeling analysis results of typical flexible resources on the supply side, grid side, demand side, and energy storage side, establish a flexible resource optimal dispatching model for source-grid-load-storage through the linkage and coordination among typical flexible resources;

[0113] To make full use of flexible resources in the distribution network, the source-grid-load-storage operation mode needs to pay attention to the following three aspects:

[0114] Source-source complementarity: "Source-source complementarity" emphasizes the effective coordination and complementarity between different power sources. Through the coordination and complementarity between flexible power generation resources and clean energy, overcome the randomness and volatility problems caused by the influence of environmental and meteorological factors on the output of clean energy generation;

[0115] Source-grid coordination: "Source-grid coordination" requires improving the acceptance ability of the distribution network to diverse power sources, using advanced control technologies to optimize and combine decentralized and centralized energy supplies, highlighting the complementary coordination between different combinations, and giving play to the buffering role of microgrid and intelligent distribution network technologies to reduce the adverse impacts on the safe and stable operation of the distribution network caused by the acceptance of new energy power;

[0116] Grid-load-storage interaction: "Grid-load-storage interaction" requires further expanding the definition of demand-side resources, regarding energy storage and distributed energy as generalized demand-side resources, so that demand-side resources can participate in the system regulation and operation as resources equivalent to the supply side, guiding the demand side to actively pursue the fluctuations in the output of renewable energy, and cooperating with the intelligent and orderly charge and discharge of energy storage resources, thereby enhancing the system's ability to accept new energy;

[0117] The objective function of the flexible resource optimal dispatching model for source-grid-load-storage is:

[0118] textmaxF = α·F - β·C + γ·RM;

[0119]

[0120] Q < l S ;

[0121] t S < t req ;

[0122] C = C S + C Rpc + C L + C St ;

[0123]

[0124] In the above formula, textmaxF is the potential for the power system to flexibly regulate various resources, and α, β, and γ are all weight coefficients used to balance the importance of different objectives. They are dynamically adjusted according to resource capacity, response speed, and reliability. Resources with a faster response speed, such as energy storage, have a higher weight and can be preferentially dispatched in emergency scheduling to reduce the cost of fault repair. Resources with low reliability, such as reactive power compensation devices with a high failure probability, have a reduced weight to minimize their negative impact on operation and maintenance costs. F is the grid flexibility. By enhancing it, the capabilities of various resources in coping with load fluctuations, uncertainties of renewable energy, etc. can be maximally improved. C is the total cost of construction and operation and maintenance regulation, including power generation cost, energy storage cost, grid transformation cost, etc. The cost can be reduced by clustering and optimizing resource allocation. RM is the reliability. Enhancing it can ensure the stability and continuity of power supply and reduce the risk of power outages; ∝ represents a positive correlation, l S is the range of reactive power regulation on the power source side. The larger the range, the higher the flexibility. t S is the response time for the power source side to regulate reactive power. The shorter the time, the higher the flexibility. This data can be directly obtained from the power grid. c Rpc is the total reactive power compensation capacity on the grid side. The larger the capacity, the higher the flexibility. Δ Rpc is the degree to which the reactive power compensation device on the grid side can accurately regulate reactive power. The higher the accuracy, the higher the flexibility. l L is the range within which users can reduce or increase the load. The larger the range, the higher the flexibility. t L is the time for the user side to respond to the dispatching instruction. The shorter the time, the higher the flexibility. This data can be directly obtained from the power grid. c St is the total capacity on the energy storage side. The larger the capacity, the higher the flexibility. v St is the charge and discharge rate on the energy storage side. The faster the charge and discharge rate on the energy storage side, the stronger the system's ability to respond to load changes and the higher the flexibility. Based on the characteristics of the typical flexible resource of battery energy storage, c St and v StParameter data to ensure that the regulation ability and economy of the energy storage device are fully reflected in the optimization process. Q is the actual reactive power regulation amount on the power supply side, and t req is the maximum allowable response time for the dispatching demand, C S is the purchase and operation and maintenance cost of the reactive power control and regulation device on the power supply side, C Rpc is the operation and maintenance cost of the reactive power compensation device on the grid side, C L is the subsidy for motivating users to participate in demand-side response and the management and coordination cost of implementing the demand-side plan, C St is the operation and maintenance cost of the energy storage side equipment and the cost required for replacement considering the decay of the energy storage device performance over time, P w (S) is the failure probability of the reactive power regulation device on the power supply side. The lower the probability, the higher the reliability, τ S is the repair time after the reactive power regulation device on the power supply side fails. The shorter the time, the higher the reliability, P w (Rpc) is the failure probability of the reactive power compensation device on the grid side. The lower the probability, the higher the reliability, τ Rpc is the repair time after the reactive power compensation device on the grid side fails. The shorter the time, the higher the reliability, λ L is the proportion of users participating in demand-side response, δ L is the proportion of users successfully responding to the dispatching instruction, θ St is the energy storage capacity redundancy reserved by the energy storage system to cope with emergencies. The higher the redundancy, the higher the reliability, P w (St) is the failure probability of the energy storage system. The lower the probability, the higher the reliability, τ St is the repair time after the energy storage system fails. The shorter the time, the higher the reliability;

[0125] The flexible resource optimization scheduling model of source-grid-load-storage reduces the redundant cost of equipment by linking the reactive power regulation range on the power supply side with the reactive power compensation capacity on the grid side, and coordinates the adjustable range of the load on the demand side with the charge and discharge rate of the energy storage to reduce the operation and maintenance pressure brought by the peak-valley difference;

[0126] Among them, textmaxF, as the potential for the power system to flexibly regulate various resources, can also be jointly determined by the power regulation capabilities of multiple adjustable flexible resources. Therefore, it can also be expressed as:

[0127]

[0128] In the above formula, N is the total number of adjustable flexible resources, w n is the weight of the nth adjustable flexible resource, which is related to factors such as its capacity, response speed, and reliability, is the maximum adjustable power of the nth adjustable flexible resource;

[0129] The constraint conditions of the flexible resource optimal scheduling model for source-network-load-storage are as follows:

[0130] Power constraint:

[0131] P gen，t = P load，t + P storage，t；

[0132] Power flow constraint:

[0133]

[0134] Voltage constraint:

[0135]

[0136] Cost constraint:

[0137] C total ≤ B;

[0138]

[0139] Emission constraint:

[0140] E total ≤ E lim it ;

[0141] In the above formula, P gen，t is the total power generated at time t, P load，t is the load demand at time t, P storage，t is the charging and discharging power of the energy storage device at time t, negative for charging and positive for discharging, F x is the power flow of grid line x, are the lower and upper limits of the power flow of grid line x respectively, V j is the voltage of grid node j, are the lower and upper limits of the voltage of grid node j respectively, C total is the total project investment, B is the total investment budget, C op，t is the operating cost at time t, is the operating expense threshold, E total is the total project emissions, E lim it is the emission upper limit.

[0142] 4. Link the flexible resource optimal scheduling model of source-network-load-storage with grid operation to construct a flexible resource clustering grid coordination model;

[0143] The flexible resource clustering grid coordination model includes the flexible resource optimal scheduling model of source-network-load-storage and the optimal power flow model, load forecasting model, power market model, demand response model, etc. of grid operation;

[0144] 5. Solve the flexible resource clustering power grid coordination model through the technical and economic feasible region of flexible resources to obtain the optimal scheduling plan for source-network-load-storage coordinated optimization that meets the economic indicators of power grid operation;

[0145] The technical and economic feasible region of flexible resources refers to the aggregated feasible range of technology and economy at the interactive nodes in the distribution area of flexible resources when meeting the user's electricity demand and the safe operation constraints of the distribution network, including power constraints, power flow constraints, and voltage constraints, considering their flexible adjustable capabilities. Compared with most existing power feasible regions, the technical and economic feasible region simultaneously considers the aggregated equivalent characteristics of power and cost, provides feasible information when participating in power grid operation regulation, and meets the technical constraints and economic requirements of the system;

[0146] In the technical and economic feasible region of flexible resources, define the projection variables of equivalent projection aggregation as P and C. P is the adjustable power of the adjustable flexible resources at the equivalent aggregation port, which is an intermediate variable in the solution process of linking the source-network-load-storage flexible resource optimal scheduling model with power grid operation. C is the total cost of construction, operation, and maintenance regulation, including the operation and maintenance cost C S of the reactive power control and regulation device on the power supply side, the operation and maintenance cost C Rpc of the reactive power compensation device on the power grid side, the subsidy for motivating users to participate in demand-side response and the management and coordination cost C L of implementing the demand-side plan, the operation and maintenance cost of the energy storage side equipment, and the cost C St required to replace the energy storage equipment considering the performance decay over time. Perform feasible region projection with the projection variables P and C to obtain the feasible region of the external characteristics of technical and economic aggregation, that is, the technical and economic feasible region is:

[0147]

[0148] In the above formula, Ω PC is the technical and economic feasible region of flexible resources, A Ω is the coefficient matrix of the technical and economic feasible region, b Ω is the constant vector of the technical and economic feasible region, S PC is the scheduling plan, that is, other variables except the projection variables, including power grid operation parameters such as voltage level and current magnitude; time variables. Since the output of photovoltaic power is intermittent and the power demand in different periods is also different, time variables such as time periods and seasons are also important factors affecting the scheduling of flexible resources; external environment variables such as weather conditions, policy factors, and failure probabilities, and Φ is the safe operation constraint of the distribution network;

[0149] At a specified adjustable power P, the total cost C of construction, operation, and maintenance regulation of the power system and the scheduling plan S PCRegarding this, the most economical total construction, operation, and maintenance regulation cost C must be on a certain hyperplane within the technical and economic feasible region. Therefore, the hyperplane where the operating point with the lowest cost within the adjustable power range is located is defined as the optimal economic operation hyperplane, and the scheduling plan on the optimal economic operation hyperplane is the optimal scheduling plan:

[0150]

[0151] In the above formula, Ψ B is the optimal economic operation hyperplane, A Ψ is the matrix coefficient of the optimal economic operation hyperplane, and b Ψ is the constant vector of the optimal economic operation hyperplane;

[0152] The optimal scheduling plan includes the scheduling and control of four aspects: source - grid - load - storage. For distributed generation resources, the actual output values of various distributed generation resources are obtained in real - time to provide a basis for grid scheduling. According to the grid demand and the characteristics of distributed generation resources, an optimized scheduling strategy is formulated. By adjusting the output of distributed generation resources, the supply - demand balance of the grid is achieved, and the resource utilization rate is improved. For load regulation, according to the grid demand, the operating state of flexible loads is adjusted, such as adjusting the air - conditioner temperature setting, the charging and discharging time of electric vehicles, etc., to balance the grid supply and demand, and the transferred load quantity of transferable load resources is restricted to ensure that the load can be transferred when the grid needs it, reducing the grid pressure. For energy storage system control, the charge - discharge efficiency and charge - discharge power of the energy storage system are restricted to ensure that the energy storage system can provide or absorb electric energy when the grid needs it, realizing peak shaving and valley filling and stable operation of the grid. For other controls, considering the characteristics of AC - DC distribution networks, weighted node flexibility margin indicators and line flexibility margin indicators are proposed. Through these indicators, the flexibility requirements of the grid are evaluated, and corresponding control strategies are formulated;

[0153] The technical and economic feasible region of flexible resources considers system operation constraints and costs, converts complex internal variables such as voltage, power flow, time variables, etc. into boundary information of the external feasible region, extracts the equivalent external technical and economic characteristics through the information of the optimal economic operation hyperplane within the feasible region, quickly locates the operating point with the lowest cost, and obtains the concise and intuitive "adjustable power - cost" boundary information after superior coordination and optimization, greatly reducing the number of optimization variables and constraints in the system model, reducing the computational complexity, and enhancing the decision - making transparency; through the optimal scheduling plan, the proportional parameters related to the grid flexibility F and reliability RM are controlled and improved, so that the potential textmaxF of the power system to flexibly adjust various resources is enhanced, and finally the improvement of the photovoltaic carrying capacity is realized on the premise of meeting the grid operation economic indicators.

[0154] Embodiment 2:

[0155] Such asFigure 4 As shown in the figure, a flexible resource optimal scheduling system for promoting the improvement of photovoltaic carrying capacity includes a typical flexible resource analysis module, a flexible resource optimal scheduling model establishment module, and an optimal scheduling plan solving module;

[0156] The typical flexible resource analysis module is used to obtain relevant data of flexible resources in the power grid, classify the flexible resources in the power grid according to the four aspects of source-network-load-storage, and based on the classification results of the flexible resources, select typical flexible resources on the supply side, grid side, demand side, and energy storage side for modeling and analysis;

[0157] The flexible resource optimal scheduling model establishment module is used to establish a source-network-load-storage flexible resource optimal scheduling model based on the modeling and analysis results of typical flexible resources on the supply side, grid side, demand side, and energy storage side through the linkage and coordination between typical flexible resources;

[0158] The optimal scheduling plan solving module is used to link the source-network-load-storage flexible resource optimal scheduling model with the power grid operation, construct a flexible resource clustering power grid coordination model, and solve the flexible resource clustering power grid coordination model through the technical and economic feasible region of the flexible resources to obtain an optimal scheduling plan for source-network-load-storage coordinated optimization that meets the economic indicators of power grid operation.

[0159] The flexible resource optimal scheduling model establishment module includes an objective function construction unit;

[0160] The objective function construction unit is used to construct the following objective function of the flexible resource optimal scheduling model of source-network-load-storage:

[0161] textmaxF = α·F - β·C + γ·RM;

[0162]

[0163] C = C S +C Rpc +C L +C St ;

[0164]

[0165] In the above formula, textmaxF is the potential for the power system to flexibly adjust various resources, α, β, and γ are all weight coefficients, F is the power grid flexibility, C is the total construction and operation and maintenance regulation cost, and RM is the reliability; ∝ represents a positive correlation, l S is the range of reactive power regulation on the power supply side, t S is the response time for regulating reactive power on the power supply side, c Rpc is the total reactive power compensation capacity on the grid side, Δ RpcFor the degree to which the reactive power compensation device on the grid side can accurately regulate reactive power, t L For the range within which users can reduce or increase the load, t L For the time when users respond to dispatching instructions, c St For the total capacity on the energy storage side, v St For the charge and discharge rate on the energy storage side, C S For the operation and maintenance cost of the reactive power control and regulation device on the power source side, C Rpc For the operation and maintenance cost of the reactive power compensation device on the grid side, C L For the subsidy for motivating users to participate in demand-side response and the management and coordination cost for implementing demand-side plans, C St For the operation and maintenance cost of the energy storage side equipment and the cost required for replacement considering the performance decay of the energy storage equipment over time, P w (S) For the failure probability of the reactive power regulation device on the power source side, τ S For the repair time after the reactive power regulation device on the power source side fails, P w (Rpc) For the failure probability of the reactive power compensation device on the grid side, τ Rpc For the repair time after the reactive power compensation device on the grid side fails, λ L For the proportion of users participating in demand-side response, δ L For the proportion of users successfully responding to dispatching instructions, θ St For the energy storage capacity redundancy reserved by the energy storage system to cope with emergencies, P w (St) For the failure probability of the energy storage system, τ St For the repair time after the energy storage system fails;

[0166] Among them, textmaxF, as the potential for the power system to flexibly regulate various resources, can also be jointly determined by the power regulation capabilities of multiple adjustable flexible resources. Therefore, it can also be expressed as:

[0167]

[0168] In the above formula, n is the total number of adjustable flexible resources, w n is the weight of the nth adjustable flexible resource, which is related to factors such as its capacity, response speed, reliability, etc., is the maximum adjustable power of the nth adjustable flexible resource.

[0169] The flexible resource optimal scheduling model establishment module further includes a constraint condition construction unit;

[0170] The constraint condition construction unit is used to construct the following constraint conditions for the flexible resource optimal scheduling model of source-network-load-energy storage:

[0171] P gen，t =P load，t+P storage，t ;

[0172]

[0173] C total ≤B;

[0174]

[0175] E total ≤E lim it ;

[0176] In the above formula, P gen,t is the total power emitted at time t, P load,t is the load demand at time t, P storage，t is the charge and discharge power of the energy storage device at time t, negative for charging and positive for discharging, F x is the power flow of grid line x, are the lower and upper limits of the power flow of grid line x respectively, V j is the voltage of grid node j, are the lower and upper limits of the voltage of grid node j respectively, C total is the total project investment, B is the total investment budget, C op，t is the operating cost at time t, is the operation expense threshold, E total is the total project emissions, E lim it is the emission upper limit.

[0177] The optimal scheduling scheme solving module includes a flexible resource clustering power grid coordination model construction unit, a technical and economic feasible region establishment unit, and an optimal economic operation hyperplane establishment unit;

[0178] The flexible resource clustering power grid coordination model construction unit is used to construct a flexible resource clustering power grid coordination model including a source-network-load-storage flexible resource optimal scheduling model, an optimal power flow model of power grid operation, a load forecasting model, a power market model, and a demand response model;

[0179] The technical and economic feasible region establishment unit is used to define the projection variables of equivalent projection aggregation as P and C in the flexible resource technical and economic feasible region. P is the adjustable power of the adjustable flexible resources at the equivalent aggregation port, and C is the total construction, operation and maintenance regulation cost, including the operation and maintenance cost C S of the reactive power control and regulation device on the power supply side, the operation and maintenance cost C Rpc of the reactive power compensation device on the grid side, and the subsidy for motivating users to participate in demand-side response and the management and coordination cost C L, the operation and maintenance cost of energy storage side equipment and the cost C required for replacement considering the performance decay of energy storage equipment over time St , project the feasible region with the projection variables P and C to obtain the feasible region of the technical and economic aggregation external characteristics, that is, the technical and economic feasible region is:

[0180]

[0181] In the above formula, Ω PC is the technical and economic feasible region of flexible resources, A Ω is the coefficient matrix of the technical and economic feasible region, b Ω is the constant vector of the technical and economic feasible region, S PC is the scheduling scheme, and Φ is the safe operation constraint of the distribution network;

[0182] The optimal economic operation hyperplane establishment unit is used to determine the total construction, operation, and maintenance control cost C of the power system and the scheduling scheme S PC at a specified adjustable power P. The most economical total construction, operation, and maintenance control cost C must be on a certain hyperplane of the technical and economic feasible region. Therefore, the hyperplane where the operating point with the lowest cost within the adjustable power range is located is defined as the optimal economic operation hyperplane, and the scheduling scheme on the optimal economic operation hyperplane is the optimal scheduling scheme:

[0183]

[0184] In the above formula, Ψ B is the optimal economic operation hyperplane, A Ψ is the matrix coefficient of the optimal economic operation hyperplane, and b Ψ is the constant vector of the optimal economic operation hyperplane.

Claims

1. A flexible resource optimal scheduling method for promoting the improvement of photovoltaic carrying capacity, characterized in that the method includes: S1. Obtain the relevant data of flexible resources in the power grid, classify the flexible resources in the power grid according to the four aspects of source-grid-load-storage, and based on the classification results of the flexible resources, select typical flexible resources on the supply side, grid side, demand side, and energy storage side for modeling and analysis; S2. Based on the modeling and analysis results of typical flexible resources on the supply side, grid side, demand side, and energy storage side, establish a source-grid-load-storage flexible resource optimal scheduling model through the linkage and coordination between typical flexible resources; S3. Link the source-grid-load-storage flexible resource optimal scheduling model with the power grid operation, construct a flexible resource clustering power grid coordination model, and solve the flexible resource clustering power grid coordination model through the technical and economic feasible region of the flexible resources to obtain the optimal scheduling scheme for the coordinated optimization of source-grid-load-storage under the premise of meeting the economic indicators of power grid operation.

2. The flexible resource optimal scheduling method for promoting the improvement of photovoltaic carrying capacity according to claim 1, characterized in that in S2, the objective function of the source-grid-load-storage flexible resource optimal scheduling model includes: textmaxF = α·F - β·C + γ·RM; C=C S +C Rpc +C L +C St ; In the above formula, textmaxF is the potential for the power system to flexibly regulate various resources, α, β, and γ are all weight coefficients, F is the grid flexibility, C is the total cost of construction, operation, and maintenance of regulation, RM is the reliability; ∝ represents a positive correlation, l S is the range of reactive power regulation on the power source side, t S is the response time for regulating reactive power on the power source side, c Rpc is the total reactive power compensation capacity on the grid side, Δ Rpc is the degree to which the reactive power compensation device on the grid side can precisely regulate reactive power, l L is the range within which users can reduce or increase the load, t L is the time for users to respond to the dispatching instruction on the user side, c St is the total capacity on the energy storage side, v St is the charge and discharge rate on the energy storage side, C S is the operation and maintenance cost of the reactive power control and regulation device on the power source side, C Rpc is the operation and maintenance cost of the reactive power compensation device on the grid side, C L is the subsidy for motivating users to participate in demand-side response and the management and coordination cost for implementing demand-side plans, C St is the operation and maintenance cost of the energy storage side equipment and the cost required for replacement considering the performance decay of the energy storage equipment over time, P w (S) is the failure probability of the reactive power regulation device on the power source side, τ S is the repair time after the reactive power regulation device on the power source side fails, P w (Rpc) is the failure probability of the reactive power compensation device on the grid side, τ Rpc is the repair time after the reactive power compensation device on the grid side fails, λ L is the proportion of users participating in demand-side response, δ L is the proportion of users successfully responding to the dispatching instruction, θ St is the energy storage capacity redundancy reserved by the energy storage system to cope with emergencies, P w (St) is the failure probability of the energy storage system, τ St is the repair time after the energy storage system fails; where textmaxF, as the potential for the power system to flexibly adjust various resources, can also be jointly determined by the power regulation capabilities of multiple adjustable flexible resources, so it can also be expressed as: In the above formula, N is the total number of adjustable flexible resources, and w n is the weight of the nth adjustable flexible resource, which is related to factors such as its capacity, response speed, reliability, etc., and is the maximum adjustable power of the nth adjustable flexible resource.

3. The flexible resource optimal scheduling method for promoting the improvement of photovoltaic carrying capacity according to claim 1, characterized in that in S2, the constraint conditions of the source-grid-load-storage flexible resource optimal scheduling model include: P gen，t = P load，t + P storage，t ; V j min ≤V j ≤V j max ; C total ≤B; E total ≤E limit ; In the above formula, P gen，t is the total power generated at time t, P load,t is the load demand at time t, P storage,t is the charging and discharging power of the energy storage device at time t, negative for charging and positive for discharging, F x is the power flow of grid line x, are the lower and upper limits of the power flow of grid line x respectively, V j is the voltage of grid node j, V j min and V j max are the lower and upper limits of the voltage of grid node j respectively, C total is the total project investment, B is the total investment budget, C is the operating cost at time t, is the operating expense threshold, E total is the total project emissions, E limit is the emission limit.

4. The flexible resource optimal scheduling method for promoting the improvement of photovoltaic carrying capacity according to claim 1, characterized in that in S3, the specific steps of solving the flexible resource clustering power grid coordination model through the technical and economic feasible region of the flexible resources to obtain the optimal scheduling scheme for the coordinated optimization of source-grid-load-storage under the premise of meeting the economic indicators of power grid operation include: S31. In the technical and economic feasible region of flexible resources, define the projection variables for equivalent projection aggregation as P and C. P is the adjustable power of the adjustable flexible resources at the equivalent aggregation port, and C is the total cost of construction, operation, and regulation, including the operation and maintenance cost C of the reactive power control and regulation device on the power supply side S , the operation and maintenance cost C of the reactive power compensation device on the grid side Rpc , the subsidy for motivating users to participate in demand-side response and the management and coordination cost C for implementing demand-side plans L , the operation and maintenance cost of energy storage side equipment and the cost C required for replacement considering the performance decay of energy storage equipment over time St . Project the feasible region with the projection variables P and C to obtain the feasible region of the external characteristics of technical and economic aggregation, that is, the technical and economic feasible region is: In the above formula, Ω PC is the technical and economic feasible region of flexible resources, A Ω is the coefficient matrix of the technical and economic feasible region, b Ω is the constant vector of the technical and economic feasible region, S PC is the scheduling scheme, and Φ is the security operation constraint of the distribution network; S32. At the specified adjustable power P, the total construction, operation, and maintenance control cost C of the power system is related to the dispatching scheme S PC and the most economical total construction, operation, and maintenance control cost C must be on a certain hyperplane in the technical and economic feasible region. Therefore, the hyperplane where the operating point with the lowest cost within the adjustable power range is located is defined as the optimal economic operation hyperplane, and the dispatching scheme on the optimal economic operation hyperplane is the optimal dispatching scheme: In the above formula, Ψ B is the optimal economic operation hyperplane, A Ψ is the matrix coefficient of the optimal economic operation hyperplane, and b Ψ is the constant vector of the optimal economic operation hyperplane.

5. The flexible resource optimal scheduling method for promoting the improvement of photovoltaic carrying capacity according to claim 1, characterized in that in S3, link the source-grid-load-storage flexible resource optimal scheduling model with the power grid operation to construct a flexible resource clustering power grid coordination model; the flexible resource clustering power grid coordination model includes the source-grid-load-storage flexible resource optimal scheduling model and the optimal power flow model, load forecasting model, power market model, and demand response model of power grid operation.

6. A flexible resource optimal scheduling system for promoting the improvement of photovoltaic carrying capacity, characterized in that the system includes a typical flexible resource analysis module, a flexible resource optimal scheduling model establishment module, and an optimal scheduling scheme solving module; the typical flexible resource analysis module is used to obtain the relevant data of flexible resources in the power grid, classify the flexible resources in the power grid according to the four aspects of source-grid-load-storage, and based on the classification results of the flexible resources, select typical flexible resources on the supply side, grid side, demand side, and energy storage side for modeling and analysis; The flexible resource optimal scheduling model establishment module is used to establish a source-network-load-storage flexible resource optimal scheduling model based on the modeling analysis results of typical flexible resources on the supply side, grid side, demand side, and energy storage side through the linkage and coordination among typical flexible resources; The optimal scheduling plan solving module is used to connect the source-network-load-storage flexible resource optimal scheduling model with grid operation, construct a flexible resource clustering grid coordination model, and solve the flexible resource clustering grid coordination model through the technical and economic feasible region of flexible resources to obtain an optimal scheduling plan for source-network-load-storage coordinated optimization that meets the economic indicators of grid operation.

7. The flexible resource optimal scheduling system for promoting the improvement of photovoltaic carrying capacity according to claim 6, characterized in that The flexible resource optimal scheduling model establishment module includes an objective function construction unit; The objective function construction unit is used to construct the objective function of the following source-network-load-storage flexible resource optimal scheduling model: textmaxF = α·F - β·C + γ·RM; C=C S +C Rpc +C L +C St ; In the above formula, textmaxF represents the potential for the power system to flexibly regulate various resources, α, β, and γ are all weight coefficients, F is the grid flexibility, C is the total cost of construction, operation, and maintenance of regulation and control, and RM is the reliability; ∝ represents a positive correlation, l S is the range of reactive power regulation on the power source side, t S is the response time for regulating reactive power on the power source side, c Rpc is the total reactive power compensation capacity on the grid side, Δ Rpc is the degree to which the reactive power compensation device on the grid side can accurately regulate reactive power, l L is the range within which users can reduce or increase the load, t L is the time for users to respond to dispatching instructions on the user side, c St is the total capacity on the energy storage side, v St is the charge and discharge rate on the energy storage side, C S is the operation and maintenance cost of the reactive power control and regulation device on the power source side, C Rpc is the operation and maintenance cost of the reactive power compensation device on the grid side, C L is the subsidy for motivating users to participate in demand-side response and the management and coordination cost for implementing demand-side plans, C St is the operation and maintenance cost of the energy storage side equipment and the cost required for replacement considering the performance decay of the energy storage equipment over time, P w (S) is the failure probability of the reactive power regulation device on the power source side, τ S is the repair time after the reactive power regulation device on the power source side fails, P w (Rpc) is the failure probability of the reactive power compensation device on the grid side, τ Rpc is the repair time after the reactive power compensation device on the grid side fails, λ L is the proportion of users participating in demand-side response, δ L is the proportion of users successfully responding to dispatching instructions, θ St is the energy storage capacity redundancy reserved by the energy storage system to cope with emergencies, P w (St) is the failure probability of the energy storage system, τ St is the repair time after the energy storage system fails; Among them, textmaxF, as the potential for the power system to flexibly regulate various resources, can also be jointly determined by the power regulation capabilities of multiple adjustable flexible resources. Therefore, it can also be expressed as: In the above formula, N is the total number of adjustable flexible resources, and w n is the weight of the nth adjustable flexible resource, which is related to factors such as its capacity, response speed, reliability, etc. is the maximum adjustable power of the nth adjustable flexible resource.

8. The flexible resource optimal scheduling system for promoting the improvement of photovoltaic carrying capacity according to claim 6, characterized in that The flexible resource optimal scheduling model establishment module further includes a constraint condition construction unit; The constraint condition construction unit is used to construct the constraint conditions of the following source-network-load-storage flexible resource optimal scheduling model: P gen，t = P load，t + P storage，t ; V j min ≤V j ≤V j max ; C total ≤ B; E total ≤E limit ; In the above formula, P gen，t is the total power generated at time t, P load，t is the load demand at time t, P storage，t is the charging and discharging power of the energy storage device at time t, negative for charging and positive for discharging, F x is the power flow of grid line x, are the lower and upper limits of the power flow of grid line x respectively, V j is the voltage of grid node j, Vj min 、V j max are the lower and upper limits of the voltage of grid node j respectively, C total is the total project investment, B is the total investment budget, C op，t is the operating cost at time t, is the operating expense threshold, E total is the total project emissions, E limit is the emission limit.

9. The flexible resource optimal scheduling system for promoting the improvement of photovoltaic carrying capacity according to claim 6, characterized in that The optimal scheduling plan solving module includes a technical and economic feasible region establishment unit and an optimal economic operation hyperplane establishment unit; The technical and economic feasible region establishing unit is used to define the projection variables of the equivalent projection aggregation as P and C in the technical and economic feasible region of flexible resources. P is the adjustable power of the adjustable flexible resources at the equivalent aggregation port, and C is the total cost of construction, operation and maintenance regulation, including the operation and maintenance cost C of the reactive power control and regulation device on the power supply side S , the operation and maintenance cost C of the reactive power compensation device on the grid side Rpc , the subsidy for motivating users to participate in demand-side response and the management and coordination cost C of implementing demand-side plans L , the operation and maintenance cost of the energy storage side equipment and the cost C required for replacement considering the performance decay of the energy storage equipment over time St , project the feasible region with the projection variables P and C to obtain the feasible region of the external characteristics of technical and economic aggregation, that is, the technical and economic feasible region is as follows: In the above formula, Ω PC is the flexible resource technical and economic feasible region, A Ω is the coefficient matrix of the technical and economic feasible region, b Ω is the constant vector of the technical and economic feasible region, S PC is the scheduling plan, and Φ is the distribution network safe operation constraint; The optimal economic operation hyperplane establishing unit is used to determine the total construction, operation, and maintenance control cost C and the scheduling plan S of the power system at a specified adjustable power P. PC The most economical total construction, operation, and maintenance control cost C must lie on a certain hyperplane within the technically and economically feasible region. Therefore, the hyperplane passing through the operating point with the lowest cost within the adjustable power range is defined as the optimal economic operation hyperplane, and the scheduling plan on the optimal economic operation hyperplane is the optimal scheduling plan: In the above formula, Ψ B is the optimal economic operation hyperplane, A Ψ is the matrix coefficient of the optimal economic operation hyperplane, and b Ψ is the constant vector of the optimal economic operation hyperplane.

10. The flexible resource optimal scheduling system for promoting the improvement of photovoltaic carrying capacity according to claim 6, characterized in that The optimal scheduling plan solving module further includes a flexible resource clustering grid coordination model construction unit; The flexible resource clustering grid coordination model construction unit is used to construct a flexible resource clustering grid coordination model including the source-network-load-storage flexible resource optimal scheduling model and the optimal power flow model, load forecasting model, power market model, and demand response model of grid operation.

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