Network source collaborative planning method based on multi-type flexible resources and storage medium

Abstract

The invention discloses a network source collaborative planning method based on multi-type flexible resources and a storage medium, and the method comprises the steps: building a flexible supply and demand model based on the multi-type flexible resources, and the multi-type flexible resources comprise a power side, a network side and a load side; constructing a flexibility quantitative index based on the power system based on the power transmission congestion risk and a flexibility supply and demand balance principle, wherein the flexibility quantitative index comprises a flexibility time insufficiency degree and a flexibility balance degree; and constructing a network source cooperation model based on the flexibility supply and demand model and the flexibility quantitative index, and solving the network source cooperation model to obtain a network source planning scheme, the network source planning scheme including a power transmission line expansion scheme and a flexibility unit configuration scheme. According to the method, the synergistic effect of multiple types of flexible resources is fully exerted, and the reliability and economical efficiency of the power system are improved through double-layer optimization solution.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system planning, and in particular relates to a network-source collaborative planning method and storage medium based on multiple types of flexibility resources. Background Art

[0002] As the proportion of renewable energy continues to increase in the power system, the dual uncertainties of both the source and the load are becoming increasingly prominent. The randomness and volatility of renewable energy will inevitably pose severe challenges to the power balance and operational security of the power system. The random fluctuations in renewable energy output make traditional dispatch methods difficult to effectively address. This is especially true during peak loads or when renewable energy output fluctuates significantly, making the system prone to insufficient flexibility resources.

[0003] In related technologies, system flexibility can be improved by optimizing the capacity and location of flexible power sources. However, most existing planning schemes only consider the impact of the installation location and capacity of a specific type of flexible resource on flexibility. This leads to redundant planning, low return on investment, and high planning costs. Summary of the Invention

[0004] The purpose of the present invention is to utilize the supply characteristics of different flexibility resources to achieve coordinated cooperation of multiple types of resources, so as to ensure the reliability and economy of high-proportion new energy system planning.

[0005] In order to achieve the above-mentioned objectives, the present invention proposes a network-source collaborative planning method based on multi-type flexibility resources, which is used in power systems. The method includes: constructing a flexibility supply and demand model based on multi-type flexibility resources, and the multi-type flexibility resources include power supply side, network side and node side; constructing flexibility quantitative indicators based on the power system based on transmission congestion risk and flexibility supply and demand balance principle, and the flexibility quantitative indicators include flexibility time deficiency and flexibility balance; constructing a network-source collaborative model based on the flexibility supply and demand model and the flexibility quantitative indicators; obtaining a network-source planning scheme through the network-source collaborative model, and the network-source planning scheme includes a transmission line expansion scheme and a flexibility unit configuration scheme.

[0006] In an optional embodiment, a flexibility supply and demand model is constructed based on multiple types of flexibility resources, specifically including: collecting modeling parameters based on the power system, the modeling parameters including renewable energy output fluctuation data, load change data, output range of thermal power units, ramp rate and start and stop time, and energy storage charging and discharging time; constructing flexibility requirements based on the power system based on the modeling parameters, the flexibility requirements including system upward flexibility requirements and downward flexibility requirements; constructing a supply model based on the multiple types of flexibility resources; and constructing the flexibility supply and demand model based on the supply model and the flexibility requirements.

[0007] In an optional embodiment, a flexibility demand based on the power system is constructed based on the modeling parameters, specifically including: calculating the current net load power at the current moment based on the new energy output data and the actual load data; calculating the maximum system net load forecast power and the minimum system net load forecast power at the future moment based on the load forecast data and the new energy forecast data; and calculating the flexibility demand based on the difference between the maximum system net load forecast power, the minimum system net load forecast power and the current net load power.

[0008] In an optional implementation, the flexibility requirement is expressed as: Where, F i t,up Indicates the node's upward flexibility margin, F l t,up represents the upward flexibility capacity of line l at time t, PF l t,up represents the upward adjustment flexibility provided by the adjacent line l, represents the upward flexibility demand of the node at time t, P l max represents the maximum transmission power of line l, P l t represents the transmission power of line l at time t, F i t,dn represents the node down-adjustment flexibility margin, F l t,dn represents the downward flexibility capacity of line l at time t, PF l t,dn represents the downward adjustment flexibility provided by the adjacent line l, represents the downward flexibility demand of the node at time t, C b Represents the set of branches connected to node b.

[0009] In an optional implementation, building a supply model based on the flexibility resources specifically includes:

[0010] A power supply flexibility supply model is constructed based on the power supply side; based on the power supply flexibility supply model, the distribution amount on the transmission line and the grid flexibility margin based on the line transmission congestion risk are calculated to construct a grid-side supply model; based on the load-side supply model, a node load supply model is constructed; wherein, the supply model includes a power supply flexibility supply model, a grid-side supply model and a node load supply model.

[0011] In an optional embodiment, the distribution amount on the transmission line and the grid flexibility margin based on the line transmission congestion risk are calculated based on the power supply flexibility supply model, and a grid-side supply model is constructed, which specifically includes: obtaining operating parameters of multiple lines in the power grid, the operating parameters including impedance parameters, thermal stability limits and power flow distribution coefficients of the lines; calculating the line distribution amount when flexible power is injected into multiple lines according to PTDF based on the power supply flexibility supply model; calculating the line flexibility margin based on the line transmission congestion risk coefficient, line transmission power, line thermal stability limit, the flexibility demand and the line distribution amount, the line flexibility margin including line upward flexibility margin and downward flexibility margin; and constructing the grid-side supply model based on the line flexibility margin.

[0012] In an optional embodiment, the flexibility time deficiency is characterized by the ratio of the node flexibility surplus time to the total scheduling time: Where, T D,suf Indicates the total flexibility time, T D,ins Indicates insufficient flexibility total time.

[0013] In an optional implementation, the flexibility balance is characterized by the standard deviation rate of the expected flexibility margins of all lines: Where N represents the total number of lines, represents the flexibility margin of the line at time t, Indicates the expected line flexibility margin.

[0014] In an optional embodiment, the network-source collaboration model includes: upper-level economic optimization objective function: f up =min{f inv +f tc +f fs}; where f inv represents the investment cost, f tc represents the transmission congestion cost, f fs Indicates the cost of insufficient flexibility; the lower layer runs the simulation objective function: f lower =min{C G +C fle +C re +C l}; Where, C G represents the operating cost of thermal power units, C fle represents the operating cost of the flexible unit, C re represents the cost of curtailing electricity from renewable energy sources, and C1 represents the cost of load shedding.

[0015] The present invention also provides a medium storing a computer program, wherein when the computer program is executed by a processor, the method for network source collaborative planning based on multi-type flexibility resources described in any one of the above is implemented.

[0016] The beneficial effects of the present invention are as follows: the present invention fully utilizes the synergistic effect of multiple types of flexibility resources by constructing flexibility supply and demand models and flexibility quantitative indicators on the source side, grid side and node side, proposes a two-stage power generation and transmission coordinated planning model, realizes double-layer optimization solution, ensures the efficient allocation of flexibility resources, significantly improves the overall flexibility of the power system while reducing investment costs, and provides technical support for the planning and operation of high-proportion new energy power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a network resource collaborative planning method based on multiple types of flexible resources provided for the implementation of the present invention;

[0018] Figure 2 A flowchart of a network resource collaborative planning method based on multiple types of flexible resources is provided as another embodiment of the present invention;

[0019] Figure 3 A schematic diagram of upward flexibility demand and a schematic diagram of downward flexibility demand of a system based on a network resource collaborative planning method of multiple types of flexibility resources provided for the implementation of the present invention;

[0020] Figure 4 An improved Garver-18 system structure diagram illustrating an example of a network resource collaborative planning method based on multiple types of flexible resources provided for implementation of the present invention;

[0021] Figure 5 A power supply adjustment and reduction flexibility curve diagram of an example of a network source collaborative planning method based on multiple types of flexibility resources provided for the implementation of the present invention;

[0022] Figure 6 A load rate curve diagram of branch 2-3 and branch 6-7 in an example of a network resource collaborative planning method based on multiple types of flexible resources provided for implementation of the present invention;

[0023] Figure 7 An example of a network resource collaborative planning method based on multiple types of flexibility resources provided for the implementation of the present invention includes a flexibility curve diagram for increasing and decreasing branch 2-3 and a flexibility curve diagram for increasing and decreasing branch 6-7. DETAILED DESCRIPTION

[0024] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] To ensure the safe operation of the system, sufficient flexibility resources need to be deployed to cope with the randomness and volatility of renewable energy. Flexibility reflects the system's ability to dispatch flexible resources according to changes in net load, and flexibility is a crucial consideration in the planning of large-scale renewable energy power systems.

[0026] like Figure 1 and Figure 2 As shown, according to an embodiment of the present invention, on the one hand, a network-source collaborative planning method based on multiple types of flexibility resources is provided for a power system, the method comprising the following steps:

[0027] Step S11: Constructing a flexibility supply and demand model based on multiple types of flexibility resources, where the multiple types of flexibility resources include power supply side, grid side and load side.

[0028] Step S13: constructing a flexibility quantitative index based on the power system based on the transmission congestion risk and the flexibility supply and demand balance principle, wherein the flexibility quantitative index includes flexibility time deficiency and flexibility balance.

[0029] Step S15: Constructing a network-source coordination model based on the flexibility supply and demand model and the flexibility quantitative index.

[0030] Step S17: Obtaining a network-source collaborative planning configuration scheme through the network-source collaborative model. The network-source collaborative planning configuration scheme includes a transmission line expansion scheme and a flexible unit configuration scheme.

[0031] In this embodiment, the sources of flexibility demand and supply of the system are analyzed. Based on the characteristics of different types of flexibility resources, a flexibility analysis model based on the power supply side, grid side, and node side of the power system is established, which can identify and analyze various flexibility resources in the power system. Among them, the power supply side is the power generation side, which mainly includes flexible thermal power units and energy storage power stations that have been modified. The flexibility resources on the power supply side include coal-fired power with adjustable output. The grid side is the transmission side, which involves the topology of transmission lines, substations, and power grids. Flexibility resources need to be used to cope with fluctuations in renewable energy output and load changes. The load side refers to interruptible loads, demand response, etc.

[0032] Previous planning efforts may have focused solely on the configuration of flexible generators on the power source side, while overlooking the potential on the grid and load sides. By considering multiple types of flexibility resources across the power source, grid, and load sides, we can more comprehensively explore the resources available for regulation in the power system. This allows for the rational allocation of various flexibility resources during the planning phase, avoiding overreliance on a single resource or leaving some resources idle, and improving the utilization efficiency of flexibility resources across the entire system.

[0033] Establishing a quantitative indicator of flexibility based on transmission congestion risk enables planning solutions to more effectively address transmission bottlenecks. By optimizing transmission line expansion plans, we can anticipate potential congestion scenarios and rationally schedule transmission line upgrades and new construction, enabling smoother transmission of renewable energy to load centers. This reduces the voltage instability and irrational power flow distribution associated with transmission congestion, thereby improving overall system stability.

[0034] The principle of flexible supply and demand balance refers to achieving a dynamic balance in the power system by flexibly adjusting the output and input on both the supply and demand sides to adapt to real-time changes in power supply and demand. The application of this principle enables the system to maintain a high level of flexibility across different timescales. The two quantitative indicators, flexibility time deficit and flexibility balance, clearly reflect the system's flexibility shortcomings in the temporal dimension and its spatial distribution imbalance. They can optimize the system's flexibility supply and demand balance over time and enhance the system's stability under different operating conditions.

[0035] By constructing a flexibility supply and demand model, we can quantify the supply capacity of different types of flexibility resources, thereby more effectively balancing the supply and demand of the power system, improving the flexibility of the system, helping to cope with the volatility and uncertainty of new energy sources such as wind power and photovoltaics, reducing the peak-to-valley difference in the net load of the power system, and ensuring the dynamic balance of flexibility supply and demand in the power system.

[0036] Building a network-source collaboration model helps optimize the structure and operating characteristics of the power system, achieving optimal resource allocation and thus improving the system's operational efficiency and reliability. Building a network-source collaboration simulation model based on the network-source collaboration model and performing simulations based on operational constraints can evaluate system operation under different planning scenarios, thereby enhancing power system reliability and helping to ensure stable operation when a high proportion of renewable energy is connected.

[0037] By using flexibility time deficit and flexibility balance indicators in planning decisions, and determining the optimal line expansion plan and flexible unit configuration plan through economic optimization objectives, we can achieve coordinated planning of power generation and transmission. This coordinated planning avoids overinvestment in any one area and allows for rational allocation of investment, enabling coordinated coordination between transmission line expansion and flexible unit configuration, reducing system operating costs and achieving the goal of minimizing investment costs while meeting system flexibility requirements.

[0038] Step S11, constructing a flexibility supply and demand model based on multiple types of flexibility resources, specifically includes the following steps:

[0039] Step S111: collecting modeling parameters based on the power system, the modeling parameters including renewable energy output fluctuation data, load change data, output range of thermal power units, ramp rate and start-stop time, and energy storage charging and discharging time.

[0040] Step S113: constructing a flexibility demand based on the power system based on the modeling parameters, where the flexibility demand model includes a system upward flexibility demand and a system downward flexibility demand.

[0041] Step S115: Constructing a supply model based on multiple types of flexible resources.

[0042] Step S117: Constructing a flexibility supply and demand model based on the supply model and flexibility demand.

[0043] Renewable energy sources include wind and solar power. Load change data is derived from an analysis of changing patterns in electricity demand, including daily load curves and seasonal variations. These changes require the power system to be flexible enough to adapt. Parameters such as the output range, ramp rate, and startup time of three types of flexible power sources on the primary power supply side are analyzed: thermal power units, coal-fired units, and energy storage.

[0044] Among them, the supply of flexibility for various power sources can be calculated based on operational characteristics. The Power Transfer Distribution Factor (PTDF) is introduced to calculate the distribution of flexibility on the power source and load sides of the transmission lines. The upward and downward flexibility distribution of each line at time t is derived through the linear superposition method. The grid flexibility margin is calculated considering the risk of line transmission congestion. The flexibility of the node is calculated based on the flexibility supply of the connected lines. Combining the supply model and flexibility demand, a comprehensive flexibility supply and demand model is constructed to quantify the system's flexibility supply and demand status, including indicators such as flexibility time shortage and flexibility balance. This can ensure that the system can respond flexibly and efficiently to changes in supply and demand, improving the system's reliability and economy.

[0045] Combine Figure 3 As shown, step S113, constructing the flexibility demand based on the power system based on the modeling parameters, specifically includes the following steps:

[0046] Step S1131: Calculate the current net load power at the current moment based on the new energy output data and the actual load data.

[0047] Step S1133: Calculate the maximum system net load forecast power and the minimum system net load forecast power at a future moment based on the load forecast data and the new energy forecast data.

[0048] Step S1135: Calculate the flexibility requirement based on the difference between the maximum system net load prediction power, the minimum system net load prediction power and the current net load power.

[0049] Step S1131 involves analyzing the current renewable energy generation and power demand to determine the system's net load power at that moment. Using a forecasting model to estimate future load and renewable energy generation, the system's potential maximum and minimum net loads are determined. By comparing the predicted maximum and minimum net loads with the current net load, the system's required flexibility resources are determined to accommodate potential future load fluctuations.

[0050] First, the actual net load power of the power system at time t is calculated using the new energy output data and load data. The formula is as follows:

[0051]

[0052] Where, Indicates the system load power, Indicates the output power of new energy.

[0053] Then, the maximum system net load forecast power at time t+1 is calculated using the load forecast data and the new energy forecast data. Minimum predicted power of system net load The formula is as follows:

[0054]

[0055] Where, represents the new energy prediction error, Represents the load forecast error.

[0056] Finally, the system flexibility requirement is calculated by the difference between the predicted net load power at time t+1 and the net load power at time t. The formula is as follows:

[0057]

[0058] Where, represents the system's upward flexibility demand at time t, Indicates the system's downward flexibility demand at time t.

[0059] By accurately calculating flexibility needs, the system can better adapt to the volatility of renewable energy and changes in load, thereby improving system reliability and stability. This allows for more rational allocation and dispatch of flexible resources, such as energy storage and demand response, to meet the system's flexibility needs. Precise forecasting and planning can reduce over-allocation of resources to cope with uncertainty, thereby lowering system operating and investment costs. By optimizing the use of flexible resources, the capacity to absorb renewable energy can be increased, curtailment of wind and solar power can be reduced, and energy efficiency can be improved.

[0060] Step S115, building a supply model based on multiple types of flexible resources, specifically includes the following steps:

[0061] Step S1151: Constructing a power supply flexibility model based on the power supply side.

[0062] Step S1153: Calculate the distribution amount on the transmission line and the grid flexibility margin based on the line transmission congestion risk based on the power supply flexibility supply model, and construct a grid-side supply model.

[0063] Step S1155: constructing a node load supply model based on the load side supply model;

[0064] Among them, the supply model includes power flexibility supply model, grid side supply model and node load supply model.

[0065] In this embodiment, the load type is identified, the adjustability and response time of each load type are analyzed, the load flexibility is evaluated, a mathematical model is used to represent the flexibility supply capability of each load, a load-side supply model is constructed, and based on the load-side supply model, the flexibility of the node load is calculated. All load-side supply models are integrated into the node load supply model to facilitate overall analysis and optimization.

[0066] Through these steps, a comprehensive supply model can be constructed that quantifies the flexibility resource supply capacity at different levels—that is, the power source, the grid, and the node. This provides important information for power system planning and operation, helping to optimize resource allocation and improve system flexibility and reliability. Furthermore, by considering flexibility resources at all levels, it is possible to more effectively address the volatility of renewable energy and load fluctuations, improve the absorption capacity of renewable energy, reduce wind and solar power curtailment, and improve energy efficiency.

[0067] To ensure the accuracy of the flexibility supply model, key operating parameters for different types of power units were collected, as shown in Table 1. These parameters include the unit's adjustable capacity range, start-up time, ramp rate, and other technical parameters. Table 1 compares the typical technical characteristics of different flexibility resources. Based on the operating characteristics and technical parameters of different power sources, the power supply-side flexibility supply mode is divided into a thermal power unit / adjustable coal-fired unit flexibility supply mode and an energy storage unit flexibility supply mode.

[0068] Table 1 Comparison of technical characteristics of various flexibility resources

[0069]

[0070] Specifically, step S1151 constructs a power supply flexibility model based on the power supply side, which specifically includes the following steps:

[0071] Step S11511: Divide the power flexibility resources into a first power type and a second power type, the first power type includes thermal power units and adjustable coal-fired units, and the second power type includes energy storage units.

[0072] Step S11513: Calculate the upward flexibility supply amount and the downward flexibility supply amount of the first power source type based on the output range, the ramp rate and the start-up time.

[0073] Step S11515: Calculate the upward flexibility supply amount and the downward flexibility supply amount of the second power source type based on the charge and discharge power, the power constraint, and the charge and discharge efficiency.

[0074] Step S11517: Calculate the total upward flexibility supply and total downward flexibility supply on the power supply side based on the upward flexibility supply and downward flexibility supply of the first power type and the upward flexibility supply and downward flexibility supply of the second power type.

[0075] Step S11519: Calculate the power supply flexibility upward shortfall and downward shortfall based on the flexibility demand and the total upward flexibility supply and the total downward flexibility supply.

[0076] Step S11520: Obtain a power supply flexibility supply model based on the power supply flexibility upward adjustment shortfall amount and downward adjustment shortfall amount.

[0077] The maximum power that a unit can increase is limited by its capacity and ramp rate. To ensure that the unit can safely adjust power within a given time Δt, the upward and downward flexibility supply capacity of thermal power units / adjustable coal-fired units must be calculated. The calculation formula for the upward and downward flexibility supply capacity of thermal power units / adjustable coal-fired units is as follows:

[0078]

[0079] Where, represents the upward flexibility supply of the unit at time t, represents the amount of flexibility provided by the unit at time t, Indicates the upper limit of the output of unit m, Indicates the lower limit of the output of unit m, represents the actual output of unit m at time t, represents the ramp-up rate of unit m at time t, represents the ramp-down rate of unit m at time t, subscript g represents thermal power units, subscript cft represents adjustable coal-fired units, Ω g represents the set of thermal power units, Ω cft represents the set of adjustable coal-fired units, and Δt represents the adjustment time interval.

[0080] Taking into account that the energy storage unit is affected by the charging and discharging power, power constraints, and charging and discharging efficiency, the calculation formula for the upward and downward flexibility supply of the energy storage unit at time t is as follows:

[0081]

[0082] Where, F t,up 、F t,dn They represent the upward and downward flexibility supply provided by energy storage, They represent the maximum charging power and maximum discharging power of energy storage m, respectively. The subscript ps represents the energy storage station. Ω ps represents the collection of energy storage power stations, They represent the charging power and discharging power of the energy storage at time t, They represent the capacity, upper limit and lower limit of energy storage m at time t respectively; They represent the energy storage charging coefficient and discharge coefficient respectively.

[0083] Calculate the total upward and downward flexibility supply capacity of the power source at time t, which includes thermal power units, adjustable coal-fired units, and energy storage units. The formula is as follows:

[0084]

[0085] The power supply flexibility margin is calculated as the difference between the power supply flexibility supply and the flexibility demand, reflecting the overall supply and demand balance of the power system. The calculation formula is as follows:

[0086]

[0087] Where, They represent the power supply flexibility to increase the shortage and decrease the shortage. and When , it indicates that the power supply flexibility can meet the system flexibility requirements.

[0088] Grid flexibility represents the ability of lines to transmit power flexibly.

[0089] Step S1153, based on the power supply flexibility supply model, calculates the distribution amount on the transmission line and the grid flexibility margin based on the line transmission congestion risk, and constructs a grid-side supply model, which specifically includes the following steps:

[0090] Step S11531: Acquire operating parameters of multiple lines in the power grid. These operating parameters include line impedance parameters, thermal stability limits, and power flow distribution coefficients. Line impedance parameters affect power loss and voltage drop across the line. Thermal stability limits characterize the maximum current a line can carry without thermal damage. Power flow distribution coefficients describe the power flow distribution of a line within the power grid.

[0091] Step S11533: Calculate the line allocation amount when the flexible power is injected into multiple lines according to the PTDF based on the power supply flexibility model.

[0092] To calculate the impact of flexible power injection on each line, based on the multi-path resource transmission mechanism between nodes, the amount of flexible power injection will be distributed to each line according to the PTDF. The calculation formula is as follows:

[0093]

[0094] Where, F node represents the flexibility supply injected by the node, F l It represents the distribution of flexible power on the line, which is the superposition of the flexibility injection amount of each node. PTDF is only related to the grid structure and its parameters.

[0095] Step S11535: Calculate the line flexibility margin based on the line transmission congestion risk coefficient, line transmission power, line thermal stability limit, flexibility demand and line allocation. The line flexibility margin includes the line upward flexibility margin and the line downward flexibility margin.

[0096] In this step, the line flexibility margin is calculated, which is expressed as the difference between the current line transmission margin and the upward and downward flexibility requirements. Considering the line transmission congestion risk, the congestion risk threshold coefficient ξ is set:

[0097]

[0098] Where, They represent the upward flexibility margin and downward flexibility margin of the line at time t, respectively. l t represents the transmission power of the line at time t, Pl max Indicates the thermal stability limit of the circuit.

[0099] Step S11537: Construct a grid-side supply model based on the line flexibility margin.

[0100] Step S1155, based on the network-side supply model, calculates the node flexibility, specifically including the following steps:

[0101] Step S11551: Based on the power regulation capability of the node and the transmission capacity constraint of the line, the upward adjustment flexibility and downward adjustment flexibility provided by the adjacent line are calculated:

[0102]

[0103] Where PF l t,up Indicates the upward flexibility provided by the adjacent line l, PF l t,dn represents the downward adjustment flexibility provided by the adjacent line l, P l max Indicates the maximum transmission power of line l, F l t,up represents the upward flexibility capacity of line l at time t, P l t represents the transmission power of line l at time t, F l t,dn represents the downward flexibility capacity of line l at time t.

[0104] Step S11553: Calculate the node flexibility margin based on the upward flexibility and the downward flexibility:

[0105]

[0106] Where, F i t,up Indicates the node's upward flexibility margin, F i t,dn represents the node's downward flexibility margin, C b represents the set of branches connected to node b, represents the upward flexibility demand of the node at time t, Represents the downward flexibility demand of the node at time t.

[0107] Step S11553: Constructing a node flexibility supply and demand balance model based on the upward flexibility margin and the downward flexibility margin.

[0108] In this embodiment, the network topology data of the power system is obtained to determine basic information such as the generator set, load, and energy storage device at each node. Based on the network topology data, line transmission power, line thermal stability limit, and the current line's upward and downward flexibility capacities, the upward and downward flexibility capacities of adjacent lines are calculated. Based on the set of branches connected to the node and the upward and downward flexibility capacities of adjacent lines, the node's upward and downward flexibility margins are calculated.

[0109] Node flexibility is a direct reflection of power system flexibility. Node flexibility demand is directly supplied by connected lines. Considering the power regulation capability of the node and the transmission capacity constraints of the line, the upward and downward flexibility capacity provided by adjacent lines is expressed as follows:

[0110]

[0111] The node flexibility margin is expressed as follows:

[0112]

[0113] Therefore, the flexibility requirement can be expressed as:

[0114]

[0115] Where, F i t,up Indicates the node's upward flexibility margin, F l t,up represents the upward flexibility capacity of line l at time t, PF l t,up represents the upward adjustment flexibility provided by the adjacent line l, represents the upward flexibility demand of the node at time t, P l max represents the maximum transmission power of line l, P l t represents the transmission power of line l at time t, F i t,dn represents the node down-adjustment flexibility margin, F l t,dn represents the downward flexibility capacity of line l at time t, PF l t,dn represents the downward adjustment flexibility provided by the adjacent line l, represents the downward flexibility demand of the node at time t, C b Represents the set of branches connected to node b.

[0116] Step S13, constructing a power system flexibility quantitative index based on the transmission congestion risk and the principle of flexibility supply and demand balance, specifically includes the following steps:

[0117] Step S131: collecting operating status data of the power system during an evaluation period, where the operating status data includes a time distribution of a state of insufficient flexibility and a time distribution of a state of sufficient flexibility.

[0118] Step S133: constructing flexibility quantitative indicators based on the operating status parameters and the flexibility supply and demand model, where the flexibility quantitative indicators include flexibility time shortage and flexibility balance.

[0119] Establish flexibility time shortage and flexibility balance indicators to judge the flexibility of the system. The construction of flexibility quantitative indicators is as follows:

[0120] The definition of flexibility time deficiency refers to the ratio of node flexibility sufficient time to total scheduling time, which measures the relative length of time a node is in a state of insufficient flexibility during the evaluation period and represents the flexibility sufficient situation of the power system. The calculation formula is as follows:

[0121]

[0122] Where, T D,suf Indicates the total time with sufficient flexibility, T D,ins Indicates the total time spent with insufficient flexibility.

[0123] The flexibility balance index (FLEX) measures the distribution balance of the flexibility margins across the system's lines. The expected flexibility margin and the standard deviation of the flexibility margin are calculated for each line. The FLEXIBILITY BALANCING index, expressed as the standard deviation of the expected flexibility margins across all lines, reflects the uniformity of the grid's flexibility distribution. A smaller FLEXIBILITY BALANCING index indicates a more balanced grid flexibility level.

[0124] The calculation formula is as follows:

[0125]

[0126] Where N represents the total number of lines, Indicates the expected line flexibility margin.

[0127] Step S15: Constructing a network-source coordination model based on the flexibility supply and demand model and the flexibility quantitative index, specifically including the following steps:

[0128] Step S151: Calculate the annual penalty cost for insufficient flexibility based on the flexibility quantitative index.

[0129] Obtain data on grid structure, load, power generation, planned investment, and operational flexibility. The annual penalty cost for insufficient flexibility is calculated as follows:

[0130] 1) Calculate the flexibility margin of the system during the evaluation period and calculate the flexibility time deficiency and flexibility balance at each time step.

[0131] 2) Set the penalty coefficient C f1 、C f2 、C f3 , which can be determined based on historical scheduling costs or system operation data.

[0132] 3) Calculate the penalty costs for each component, including the node flexibility shortfall penalty cost, the flexibility shortfall time penalty cost, and the flexibility imbalance penalty cost, and sum them up to calculate the total cost according to the following formula:

[0133]

[0134] Where, f fs represents the annual penalty cost for insufficient flexibility, F i t,up and F i t,dn They represent the flexibility deficit of node i in the upward and downward directions, C f1 、C f2 、C f3 They represent the coefficients of the penalty cost for node flexibility shortage, the coefficients of the penalty cost for insufficient flexibility time, and the coefficients of the penalty cost for flexibility imbalance, respectively.

[0135] Calculate the costs of each component, including investment costs, transmission congestion penalty costs, and inflexibility penalty costs.

[0136] Step S153: Calculate the investment cost and transmission congestion penalty cost based on the flexibility supply and demand model and the flexibility quantitative index.

[0137] Calculate the economic investment cost, including the investment cost of transmission line construction and flexible power generation, using the following formula:

[0138]

[0139] Where, f inv represents the economic investment cost; c l represents the construction cost per unit length of the line; z ij 、l ij 、s ij They represent the number, length and construction status of new lines between nodes ij, γ l , γ h They represent the capital recovery coefficients of new lines and flexible power sources, λ1 and λ2 represent the discount rates, and ψ l , ψ h Represent the repayment period, c p represents the unit construction cost of the p-th type of flexible unit, H h,i represents the capacity of the p-th flexible resource unit at node i, ΩH Represents a set of flexibility unit types.

[0140] Considering the price difference in the market environment, the economic losses caused by transmission congestion are quantified, and the annual penalty cost of transmission congestion is calculated. The calculation formula is:

[0141]

[0142] Where, f ct represents the annual penalty cost of transmission congestion, T year , T represent the annual and daily running time respectively, Indicates the transmission congestion status. They represent the transmission power and maximum transmission power between nodes ij at time t, LMP i 、LMP j represent the node electricity prices of nodes i and j respectively.

[0143] By calculating the annual penalty cost for insufficient flexibility, we can quantify the economic losses caused by the imbalance between supply and demand of system flexibility.

[0144] Calculate the operating cost C of thermal power units G , Flexible unit operating cost C fle 、Cost of abandoned electricity from new energy sources re and load shedding cost C1.

[0145] The cost of thermal power units includes coal consumption cost and start-up and shutdown cost, and the calculation formula is as follows:

[0146]

[0147] Where a G,m 、b G,m 、c G,m represents the coal consumption cost coefficient of thermal power unit m, They represent the startup and shutdown cost coefficients of thermal power unit m respectively.

[0148] Since the coal consumption cost of thermal power generation is a quadratic function, the coal consumption cost is linearized to simplify the calculation.

[0149]

[0150] Where N d Indicates the number of linear segments, K m,d Represents the slope of each segment of the quadratic function.

[0151] Annual operating cost of flexible units:

[0152]

[0153]

[0154] Where, represent the typical daily operating costs of adjustable coal-fired units, gas-fired units, energy storage, and pumped storage, respectively. cft,m 、c gt,m Represent the operating cost coefficients of coal-fired units and gas-fired units respectively; They represent the charging cost and discharging cost of energy storage m respectively; They represent the pumping cost, discharge cost, startup pumping cost, startup pumping status, shutdown status, startup discharge cost, and startup discharge status of the pumped storage unit m respectively.

[0155] Penalty costs for abandoning new energy and load:

[0156]

[0157] Where, Ω RE represents the set of new energy units, c re 、c lc They represent the penalty cost coefficients for curtailed electricity and curtailed load, respectively. represents the maximum new energy of node i, represents the load shedding power of node i at time t.

[0158] The upper layer models the investment cost, transmission congestion cost, and inflexibility cost to construct the objective function of the upper layer line expansion planning, namely the network-source coordination model. The calculation formula is as follows:

[0159] f up =min{f inv +f tc +f fs}

[0160] Where, f up Represents the upper-level planning decision objective function.

[0161] The lower layer is modeled according to the operating costs of thermal power units, flexible units, renewable energy curtailment costs, and load shedding costs to construct the objective function of the lower layer operation simulation, namely the grid-source collaborative simulation model. The calculation formula is as follows:

[0162] f lower =min{C G +C fle +C re +C l};

[0163] Where, f lower represents the lower layer objective function.

[0164] Constraints include line construction loop upper limit constraints, connectivity constraints, unit planning location and capacity constraints, power balance constraints, thermal power unit constraints, flexible unit constraints, and power flow constraints. They are specifically expressed as follows:

[0165] Upper limit constraints for line construction loops:

[0166]

[0167] Where c represents the loop number and z is a 0-1 variable. ijc To describe the operating status of the line, Ω grid represents the set of nodes and branches in the network, represents the number of original loops between nodes ij, They represent the upper and lower limits of the number of new circuits between nodes ij, and Z represents a set of integers.

[0168] To prevent isolated islands from appearing during the construction of new lines, connectivity constraints are introduced:

[0169]

[0170] Where y ijc represents the virtual power flowing from node i to node j, ρ(i) and σ(i) represent the parent node set and child node set of node i respectively, M represents the maximum value, and N I Indicates the number of nodes.

[0171] Location and capacity constraints for new flexible units:

[0172]

[0173] Where, Indicates the upper limit of the flexible unit construction location, They represent the upper and lower limits of the construction capacity of the h-th type unit at node i respectively.

[0174] Power balance constraints:

[0175]

[0176] Where, Represents the output power of the h-th flexible unit m at time t.

[0177] Thermal power units need to meet unit output constraints, start-stop constraints, and ramp constraints:

[0178]

[0179] Where, Indicates the start and stop status of conventional unit m at time t, They represent the minimum start / stop times of thermal power unit m. Coal-fired units are similar to thermal power units and will not be described in detail here.

[0180] Energy storage constraints:

[0181]

[0182] Where, Respectively represent the energy storage charging and discharging status.

[0183] New energy and load shedding constraints:

[0184]

[0185] Where, Indicates the maximum load shedding capacity of node i.

[0186] Power flow constraints:

[0187] A G P G +A fle P fle +A re P re -P ld +P k =Bθ;

[0188] θ min ≤θ i (t)≤θ max ,U min ≤U i (t)≤U max ;-P l,max B(θ i (t)-θ j (t))P l,max ;

[0189] Where A G 、A fle and A re They represent the node-thermal power unit association matrix, node-energy storage association matrix and node-new energy unit association matrix respectively, P G ,P fle ,P re ,P ld ,P k They represent thermal power units, flexible units, new energy, load, and load shedding matrices respectively; B represents the system node admittance matrix, and θ represents the system node phase angle matrix.

[0190] Step S155: construct a network-source coordination model based on the annual penalty cost for insufficient flexibility, investment cost, transmission congestion penalty cost, and planning constraints to obtain topology parameters and unit parameters.

[0191] Step S157: Calculate the thermal power unit operating cost, the flexibility unit operating cost, the renewable energy power curtailment cost and the load shedding cost based on the topology parameters, unit parameters and operating parameters.

[0192] The simulation operation objective function is constructed based on the operating cost of thermal power units, the operating cost of flexible units, the cost of renewable energy power abandonment, the load shedding cost and the operating constraints.

[0193] System flexibility can be quantified by constructing flexibility indicators based on three dimensions: flexibility time deficit, power deficit, and balance. A network-source coordination model that considers multiple types of flexibility resources is constructed. A two-stage generation and transmission coordinated optimization model is implemented using a hybrid solution using the Adaptive Mutation Particle Swarm Optimization (AMPSO) algorithm and the commercial solver GUROBI.

[0194] A hybrid solution method combining AMPSO and the commercial solver GUROBI is used, and an adaptive mutation strategy is used to enhance the global search capability to determine the optimal line expansion plan and flexible unit configuration plan.

[0195] In this embodiment, the system's flexibility requirements include the system's upward flexibility requirements and the system's downward flexibility requirements; the flexibility supply model includes the supply characteristics of power supply-side flexibility resources, grid-side flexibility resources, and node-side flexibility resources; flexibility quantitative indicators include flexibility time shortage and flexibility balance indicators; the upper-level objective function of network-source collaborative planning includes economic investment costs, transmission congestion costs, and flexibility penalty costs; for the lower-level operation simulation, the objective function is the total operating cost including the operating cost of thermal power units, the operating cost of flexibility units, the cost of new energy power abandonment, and the cost of load shedding.

[0196] The present invention proposes a method for collaborative planning of flexible resources across the power supply, grid, and node sides. The power supply side includes thermal power units, coal-fired units, and energy storage, while the grid side includes transmission network expansion. Compared with traditional methods that only focus on optimization at a certain level, this method can more comprehensively enhance the flexibility and reliability of the power system.

[0197] By establishing a flexibility supply and demand model and based on the transmission congestion risk and flexibility supply and demand balance principle, key indicators such as flexibility deficiency, flexibility abundance and flexibility balance are proposed. This avoids the traditional method of relying on only a single indicator, which leads to uneven allocation of flexibility resources or waste of investment, achieves an accurate characterization of the system flexibility supply and demand relationship, and improves the ability to rationally allocate flexibility resources.

[0198] Compared with the method of simply pursuing economic optimization or flexibility optimization, the present invention comprehensively considers economy, transmission congestion risk and flexibility cost, optimizes transmission expansion plan and flexibility unit configuration plan, and can control investment cost while meeting flexibility requirements, thereby improving the overall operating efficiency and economy of the system.

[0199] Example

[0200] In order to verify the effectiveness of the network resource collaborative planning method based on multi-type flexibility resources proposed in this invention, an improved Garver-18 system is used for testing. Figure 4 . The improved Garver-18 system contains 8 expandable nodes and 27 expandable transmission corridors, with a loop limit of 3 for each transmission corridor. The 500MW wind turbine is located at node 16, and the renewable energy penetration rate is 14%. The construction location of the flexibility unit is 3. The penalty costs for wind curtailment and load shedding are 200 yuan / MW and 500 yuan / W. The discount rate is 0.1, and the repayment period is 10. The prediction errors of load and renewable energy are both set to 10%. The line congestion risk threshold is 0.8, the flexibility cost is 32.85, and the congestion penalty cost is 51.1. The daily operating time is 24 hours, and the annual operating time is 8760 hours. The particle dimension is set to 33, the number of iterations is 100 times, the inertia factor range is 0.4-0.95, the learning factors c1 and c2 are both 2, and the variation control parameter is 0.35.

[0201] The method of the present invention was applied to the above-mentioned Garver-18 system, and three cases, namely Case 1, Case 2 and Case 3, were used for comparative analysis.

[0202] In this example, the flexibility resources include coal-fired units and energy storage power stations. The settings for each case are as follows:

[0203] Case 1: The planning model only considers the flexibility provided by energy storage and thermal power units.

[0204] Case 2: The planning model considers multiple types of power supply side flexibility resources, but sets the minimum power supply and grid flexibility deficiency as the flexibility sub-objective function.

[0205] Case 3: The planning model considers multiple flexibility resources and the impact of the transmission line structure on the distribution of flexibility supply and demand. The flexibility sub-objective function includes flexibility time shortage, flexibility balance, and the shortage of power source and node flexibility.

[0206] The optimization results of the optimal transmission line expansion scheme and flexible unit configuration scheme for the three cases are shown in Table 2.

[0207] Table 2 Optimization results of different schemes

[0208]

[0209]

[0210] Table 2 shows that Case 1 has the highest planning and operating costs. Compared to Case 1, Cases 2 and 3 saw total costs decrease by 20.31% and 26.34%, respectively, with significant reductions in flexibility penalty and transmission congestion penalty. This demonstrates that considering the characteristics of different types of flexibility resources can effectively reduce resource construction costs. Although Case 2 sets the minimization of power source and grid flexibility deficiencies as the flexibility sub-objective function, significantly reducing grid flexibility deficiencies, it fails to consider the uniformity of flexibility distribution. This leads to uneven transmission of power source flexibility resources and node flexibility deficits. Avoiding node flexibility deficits requires significant planning costs. Considering the trade-off between flexibility penalty and investment costs, Case 2 in Table 2 still exhibits grid flexibility and node flexibility deficits. In contrast, Case 3 boasts superior investment costs and system flexibility compared to Cases 1 and 2. This is because Case 3 optimizes both the adequacy and uniformity of flexibility resources. By considering the supply characteristics of different flexibility resources, the utilization efficiency of flexible units is improved.

[0211] Further compare the power supply side and key line flexibility of each solution. Figure 5 Provides flexibility for power up and down regulation. Figure 5 As can be seen, Cases 2 and 3 demonstrate sufficient generation-side flexibility. Case 1 exhibits insufficient upward power flexibility at 10:00 AM and 4:00 PM-6:00 PM. This is due to a sudden drop in renewable energy generation and a sharp increase in load during these periods, leading to a surge in flexibility demand. However, in Case 1, flexibility is provided only by energy storage and thermal power units, and the system is unable to provide sufficient upward flexibility in the short term, resulting in a flexibility deficit.

[0212] The role of flexibility uniformity index is further analyzed. Figure 6 The load factor curves of key transmission lines in case 2 and case 3 are drawn; Figure 7 It provides flexibility in adjusting the grid structure upwards and downwards for key lines of the power grid, including branches 6-7 and 2-3.

[0213] It can be observed that while Case 2 has sufficient power generation flexibility, some branches still experience insufficient grid flexibility, such as branches 6-7 and 2-3. Taking branch 2-3 as an example, in Case 2, branch 2-3 exhibits insufficient upward grid flexibility between 00:00 and 04:00 and 21:00 and 22:00, and insufficient downward grid flexibility between 06:00 and 07:00. This is due to the high output of the wind farm at node 16 between 00:00 and 04:00, resulting in a high upward node flexibility demand. Node 16 allocates the positive flexibility transmission demand to branch 6-13 through PTDF. However, branch 6-13 is already heavily or fully loaded at this time, resulting in a shortfall in upward grid flexibility for branch 6-13. The analysis of other time periods and branches is similar and will not be detailed here. In contrast, Case 3 employs transmission congestion penalty costs and flexibility uniformity indicators to prevent insufficient line flexibility, significantly improving the distribution of system flexibility. The load factors of branches 2-3 and 6-7 are lower than the heavy load factor, and there is sufficient flexibility in adjusting them up or down.

[0214] On the other hand, the present invention also provides an electronic device comprising: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so as to enable the at least one processor to execute any one of the grid investment optimization methods based on the hybrid decision model.

[0215] The present invention also proposes a computer storage medium storing a computer program, which implements any one of the network source collaborative planning methods based on multi-type flexibility resources when executed by a processor.

[0216] Computer storage media may be referred to as media for short. Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the program may be stored in a non-volatile computer-readable storage medium, which, when executed, may include processes in the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM). The various embodiments in this specification are described in a progressive manner, and similar parts between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the apparatus, device, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referenced to the partial description of the method embodiments.

[0217] The above embodiments are provided for illustrative purposes only and are not intended to limit the scope of implementation. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to provide an exhaustive list of all implementations. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A network-source collaborative planning method based on multi-type flexibility resources for power systems, characterized in that: The method comprises: Building a flexibility supply and demand model based on multiple types of flexibility resources, including power supply side, grid side and load side; Constructing a flexibility quantitative index based on the power system based on the principle of transmission congestion risk and flexibility supply and demand balance, wherein the flexibility quantitative index includes flexibility time deficiency and flexibility balance; Constructing a network-source coordination model based on the flexibility supply and demand model and the flexibility quantitative index; A network source planning scheme is obtained through the network source collaborative model, and the network source planning scheme includes a transmission line expansion scheme and a flexible unit configuration scheme.

2. The network resource collaborative planning method based on multi-type flexibility resources according to claim 1 is characterized in that: Constructing a flexibility supply and demand model based on multiple types of flexibility resources, specifically including: Based on the power system, modeling parameters are collected, including renewable energy output fluctuation data, load change data, output range of thermal power units, ramp rate and start and stop time, energy storage charging and discharging time, output range, etc. Constructing a flexibility demand based on the power system based on the modeling parameters, wherein the flexibility demand includes a system upward flexibility demand and a system downward flexibility demand; Building a supply model based on the multiple types of flexibility resources; The flexibility supply and demand model is constructed based on the supply model and the flexibility demand.

3. The network resource collaborative planning method based on multi-type flexible resources according to claim 2 is characterized in that: Constructing a flexibility demand based on the power system based on the modeling parameters specifically includes: Calculate the current net load power at the current moment based on the new energy output data and actual load data; Calculate the maximum system net load forecast power and the minimum system net load forecast power at a future moment based on the load forecast data and the new energy forecast data; The flexibility requirement is calculated based on a difference between the maximum system net load forecast power, the minimum system net load forecast power and the current net load power.

4. The network resource collaborative planning method based on multi-type flexibility resources according to claim 3 is characterized in that: The flexibility requirement is expressed as: Where, F i t,up Indicates the node's upward flexibility margin, F l t,up represents the upward flexibility capacity of line l at time t, PF l t,up represents the upward adjustment flexibility provided by the adjacent line l, represents the upward flexibility demand of the node at time t, P l max represents the maximum transmission power of line l, P l t represents the transmission power of line l at time t, F i t,dn represents the node down-adjustment flexibility margin, F l t,dn represents the downward flexibility capacity of line l at time t, PF l t,dn represents the downward adjustment flexibility provided by the adjacent line l, represents the downward flexibility demand of the node at time t, C b Represents the set of branches connected to node b.

5. The network resource collaborative planning method based on multi-type flexible resources according to claim 2, characterized in that: Building a supply model based on the multi-type flexibility resources specifically includes: Build a power supply flexibility model based on the power supply side; Calculating the distribution amount on the transmission line and the grid flexibility margin based on the line transmission congestion risk based on the power supply flexibility supply model, and constructing a grid-side supply model; Based on the load side supply model, a node load supply model is constructed; The supply model includes a power supply flexibility supply model, a grid-side supply model and a node load supply model.

6. The network resource collaborative planning method based on multi-type flexible resources according to claim 5 is characterized in that: Based on the power supply flexibility supply model, the distribution amount on the transmission line and the grid flexibility margin based on the line transmission congestion risk are calculated to construct a grid-side supply model, which specifically includes: Obtaining operating parameters of multiple lines in a power grid, wherein the operating parameters include impedance parameters, thermal stability limits, and power flow distribution coefficients of the lines; Calculating, based on the power supply flexibility supply model, a line allocation amount when the flexible power source is injected into the plurality of lines according to the power transmission distribution factor; Calculating a line flexibility margin based on the line transmission congestion risk coefficient, the line transmission power, the line thermal stability limit, the flexibility demand, and the line allocation, wherein the line flexibility margin includes an upward flexibility margin and a downward flexibility margin; The grid-side supply model is constructed based on the line flexibility margin.

7. The network resource collaborative planning method based on multi-type flexibility resources according to any one of claims 1 to 6, characterized in that: The flexibility time deficiency is characterized by the ratio of the node flexibility surplus time to the total scheduling time: Where, T D,suf Indicates the total flexibility time, T D,ins Indicates insufficient flexibility total time.

8. The network resource collaborative planning method based on multi-type flexibility resources according to any one of claims 1 to 6, characterized in that: The flexibility balance is characterized by the standard deviation ratio of the expected flexibility margins of all lines: Where N represents the total number of lines, represents the flexibility margin of the line at time t, Indicates the expected line flexibility margin.

9. The network resource collaborative planning method based on multi-type flexibility resources according to any one of claims 1 to 6, characterized in that: The network-source collaboration model includes: Upper-level economic optimization objective function: f up =min{f inv +f tc +f fs }; Where, f inv represents the investment cost, f tc represents the transmission congestion cost, f fs Indicates the cost of insufficient flexibility; The lower layer runs the simulation objective function: f lower =min{C G +C fle +C re +C l }; Where C G represents the operating cost of thermal power units, C fle represents the operating cost of the flexible unit, C re represents the cost of curtailing electricity from renewable energy sources, and C1 represents the cost of load shedding.

10. A storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, a network source collaborative planning method based on multi-type flexibility resources as described in any one of claims 1 to 9 is implemented.

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