New energy power system multi-type inertia resource collaborative planning method and system

Abstract

The invention relates to a multi-type inertia resource collaborative planning method and system for a new energy power system, belongs to the technical field of power system operation and planning, and solves the problems that in the prior art, the inertia supporting potential of wind power and photovoltaic virtual synchronous machine transformation is not fully utilized, and inertia space distribution is ignored. And the planning economy and safety are not enough. Comprising the steps of constructing a multi-type inertia resource frequency support model of the current new energy power system; based on a current new energy power system structure, calculation inertia of each node in the power system is obtained, and based on a constructed energy storage site selection model which takes node calculation inertia variance minimization as a target function and takes an energy storage site selection number as a constraint condition, an energy storage optimal site selection in the current power system is obtained. And then constructing a collaborative planning model which takes minimization of annual investment cost and operation cost as an objective function and takes typical daily operation constraint and frequency security constraint, solving to obtain an optimal collaborative planning scheme, and operating in the power system.

Description

Technical Field

[0001] This invention relates to the field of power system operation and planning technology, and in particular to a collaborative planning method and system for multiple types of inertia resources in a new energy power system. Background Technology

[0002] With the continued advancement of the "dual carbon" target, the proportion of wind and solar power in the power system is constantly increasing, leading to a significant decrease in system inertia and making frequency stability issues increasingly prominent. Energy storage systems, with their rapid bidirectional regulation capabilities, can effectively mitigate the fluctuations caused by renewable energy sources. Meanwhile, wind and solar power equipment retrofitted using virtual synchronous machine (VSM) technology can provide inertia and primary frequency regulation support, thereby improving system frequency stability. However, there is an economic trade-off between VSM retrofitting of new energy equipment and deployment of energy storage equipment. VSM retrofitting of wind and solar power increases investment costs, and the power reserves reserved for frequency support reduce the power generation efficiency of wind and solar power units. Therefore, optimizing the coordinated planning of VSM retrofitting of wind and solar power and energy storage while ensuring system frequency stability is of great significance for achieving economical and efficient power system operation.

[0003] Currently, existing multi-resource collaborative planning methods mainly focus on energy storage configuration strategies, emphasizing the optimization of its power regulation capabilities. They fail to fully explore the potential of wind and solar power units to provide virtual inertia support through virtual synchronous machine retrofits, resulting in insufficient economic efficiency and security in planning schemes. Secondly, existing planning models often use the global inertia mean for frequency stability constraints, neglecting the spatial distribution characteristics of inertia under the grid topology. For example, areas with high renewable energy penetration may experience local frequency collapse due to local inertia shortages, and traditional methods, by not considering the spatial distribution characteristics of inertia, cannot avoid such risks. Summary of the Invention

[0004] Based on the above analysis, the embodiments of the present invention aim to provide a collaborative planning method and system for multiple types of inertia resources in new energy power systems, in order to solve the problems that existing planning does not fully utilize the inertia support potential of wind power and photovoltaic virtual synchronous machine retrofits, and ignores the spatial distribution of inertia, resulting in insufficient planning economy and safety.

[0005] On one hand, embodiments of the present invention provide a collaborative planning method for multiple types of inertia resources in a new energy power system, comprising the following steps:

[0006] Construct a frequency support model for multiple types of inertia resources in the current new energy power system, where the inertia resource types include wind turbines, photovoltaic power plants and energy storage;

[0007] Based on the current structure of the new energy power system, the computational inertia of each node in the power system is obtained. Then, based on the constructed energy storage location model with the objective function of minimizing the variance of the node computational inertia and the constraint of the number of energy storage locations, the optimal energy storage location in the current power system is obtained.

[0008] Based on the frequency support model of multiple types of inertia resources and the optimal location of energy storage in the current power system, a collaborative planning model is constructed with the goal of minimizing annual investment cost and operating cost, and with typical daily operation constraints and frequency security constraints. The model is solved to obtain the optimal collaborative planning scheme and put it into operation in the power system. The collaborative planning scheme includes the retrofit capacity of wind power and photovoltaic units and the configuration capacity of energy storage at the optimal location.

[0009] Furthermore, the calculated inertia of each node in the power system is expressed as follows:

[0010]

[0011] In the formula, H m B represents the calculated inertia of node m in a power system. i,m r represents the susceptance between the potential node i and node m of the synchronous generator. m,i H represents the element in the correlation matrix between network node voltages and synchronous generator internal potentials and node voltages, corresponding to the voltage relationship between node m and synchronous generator i. i I represents the inertial time constant of synchronous generator i. m This represents the set of generators associated with node m in the power system.

[0012] Furthermore, the objective function F1 of the energy storage location model is expressed as:

[0013]

[0014] in,

[0015]

[0016] In the formula, H′ m This represents the calculated inertia of node m after taking into account the energy storage configuration; ξ represents the average nodal inertia taking into account energy storage configuration; m This indicates whether energy storage should be deployed at node m. A value of 1 indicates deployment, and a value of 0 indicates no deployment. N represents the number of nodes in the system.

[0017] Furthermore, the node inertia H′ after taking into account the energy storage configuration m , represented as:

[0018]

[0019] In the formula, This represents the equivalent inertial time constant that the energy storage deployed at node m can provide.

[0020] Furthermore, the frequency security constraints include the system frequency minimum point constraint and the system frequency change rate constraint; wherein, the system frequency minimum point constraint and the system frequency change rate constraint are constructed based on a multi-type inertia resource frequency support model.

[0021] Furthermore, the constraint on the lowest system frequency point is expressed as:

[0022] fnadir≥f0-Δf max ,

[0023] in,

[0024]

[0025] in,

[0026] In the formula, fnadir represents the lowest point of the system frequency, H syn H vir These represent the overall inertia levels of the synchronous generator and the virtual inertial resources, respectively. P represents the primary frequency regulation power of energy storage. loss Indicates the system's disturbance power. T represents the total primary frequency regulation power provided by the synchronous generator, and T1, T2, and T3 represent the primary frequency regulation response times of the photovoltaic, synchronous generator, and wind power generation, respectively. del1 T del2 T del3 These represent the delivery times for the regulating power generated by photovoltaic, synchronous generators, and wind power, respectively. This indicates the standby power of the wind turbine. This indicates the reserve power of the photovoltaic power station.

[0027] Furthermore, the system frequency change rate constraint is expressed as:

[0028]

[0029] In the formula, RoCoF lim ΔP represents the limit of the system's rate of change of frequency. loss This indicates the power loss of the system.

[0030] Furthermore, the frequency support model for the inertia resource of the wind turbine is expressed as follows:

[0031]

[0032] in,

[0033]

[0034] In the formula, Indicates virtual inertia support power. Represents the virtual inertial time constant. Represents the rate of change of system frequency. P represents the maximum inertial time constant that a wind turbine can provide. w , These represent the wind turbine's output power, reserve power, and maximum output power, respectively. This represents the power used to compensate for the released virtual inertia response and primary frequency regulation in the wind turbine recovery effect. k represents the response dead time of the virtual inertia. rec This represents the proportional relationship between the frequency modulation demand generated by the recovery effect and the virtual inertia time constant, where t represents time.

[0035] Furthermore, the frequency support model for the inertia resources of the energy storage is expressed as:

[0036]

[0037] in,

[0038]

[0039] Among them, the primary frequency regulation power of energy storage meets the following requirements:

[0040]

[0041] In the formula, Let f0 represent the virtual inertia support power and virtual inertia time constant of the energy storage, respectively; f0 represent the rated frequency of the system; and RoCoF(t) represent the rate of frequency change of the energy storage bus node at time t. RoCoF represents the maximum inertial support power of the energy storage output. max This represents the maximum frequency change rate of the energy storage bus node. Δf max H represents the energy storage capacity and the maximum deviation from the rated frequency during the system frequency response process, respectively. b,max This represents the maximum virtual inertial time constant provided by energy storage. P represents the primary frequency regulation power of energy storage. B This indicates the output power of the energy storage system.

[0042] On the other hand, embodiments of the present invention provide a collaborative planning system for multiple types of inertia resources in a new energy power system, comprising:

[0043] The frequency support model construction module is used to construct frequency support models for various types of inertia resources in the current new energy power system. The types of inertia resources include wind turbines, photovoltaic power plants, and energy storage.

[0044] The energy storage location module is used to obtain the computational inertia of each node in the power system based on the current new energy power system structure, and then obtain the optimal energy storage location in the current power system based on the constructed energy storage location model with minimizing the variance of node computational inertia as the objective function and the number of energy storage locations as the constraint.

[0045] The collaborative planning module is used to construct a collaborative planning model based on the frequency support model of multiple types of inertia resources and the optimal location of energy storage in the current power system. The model has the objective function of minimizing annual investment cost and operating cost, and is subject to typical daily operation constraints and frequency security constraints. The model is solved to obtain the optimal collaborative planning scheme and put it into operation in the power system. The collaborative planning scheme includes the retrofit capacity of wind power and photovoltaic units and the configuration capacity of energy storage at the optimal location.

[0046] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0047] This invention provides a collaborative planning method and system for multiple types of inertia resources in a new energy power system. It constructs a frequency support model for multiple types of inertia resources in the current new energy power system, and based on the current new energy power system structure, obtains the calculated inertia of each node in the power system. Then, based on a constructed energy storage location model with the objective function of minimizing the variance of node calculated inertia and the constraint of the number of energy storage locations, it obtains the optimal energy storage location in the current power system. Next, based on the frequency support model for multiple types of inertia resources and the optimal energy storage location in the current power system, it constructs a collaborative planning model with the objective function of minimizing annual investment cost and operating cost, and with typical daily operating constraints and frequency safety constraints. The model is solved to obtain the optimal collaborative planning scheme, which is then implemented in the power system. This achieves collaborative planning for multiple types of inertia resources in a new energy power system, comprehensively considering the economic synergy optimization of wind power and photovoltaic virtual synchronous machine retrofitting and energy storage deployment, and fully utilizing the influence of the spatial distribution characteristics of grid inertia on frequency stability. It effectively improves the spatial distribution characteristics of system inertia, rationally allocates multiple types of inertia resources, and effectively improves the economy, frequency stability, and security of system operation.

[0048] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0049] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0050] Figure 1 This is a flowchart illustrating the collaborative planning method for multiple types of inertia resources in a new energy power system provided in Embodiment 1 of the present invention.

[0051] Figure 2 This is a schematic diagram of the improved IEEE-39 node system provided in Embodiment 3 of the present invention;

[0052] Figure 3 This describes the system cost composition under different scenarios provided in Embodiment 3 of the present invention;

[0053] Figure 4 The lowest frequency point and RoCoF variation curve provided in Embodiment 3 of the present invention;

[0054] Figure 5 The curve showing the lowest frequency point and RoCoF variation under scenario five provided in Embodiment 3 of the present invention;

[0055] Figure 6 The following is a scenario provided in Embodiment 3 of the present invention: the spatial distribution of node inertia without energy storage optimization.

[0056] Figure 7 The spatial distribution of node inertia is considered when optimizing energy storage in scenario five provided in Embodiment 3 of the present invention. Detailed Implementation

[0057] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0058] Example 1

[0059] A specific embodiment of the present invention discloses a collaborative planning method for multiple types of inertia resources in a new energy power system, such as... Figure 1 As shown, it includes the following steps:

[0060] S1. Construct a frequency support model for multiple types of inertia resources in the current new energy power system, where the inertia resource types include wind turbines, photovoltaic power plants and energy storage.

[0061] In practice, the frequency support model for the inertia resources of the wind turbine is expressed as follows:

[0062]

[0063] in,

[0064]

[0065] In the formula, Indicates virtual inertia support power. Represents the virtual inertial time constant. Represents the rate of change of system frequency. P represents the maximum inertial time constant that a wind turbine can provide. w , These represent the wind turbine's output power, reserve power, and maximum output power, respectively. This represents the power used to compensate for the released virtual inertia response and primary frequency regulation in the wind turbine recovery effect. k represents the response dead time of the virtual inertia. rec This represents the proportional relationship between the frequency modulation demand generated by the recovery effect and the virtual inertia time constant, where t represents time.

[0066] It should be noted that after being modified with a virtual synchronous machine, the wind turbine can provide inertial response and frequency support. The modified wind turbine adjusts its power output by detecting the rate of change of frequency (RoCoF) to provide system frequency support; therefore, this embodiment models its virtual inertial support power. Furthermore, the wind turbine achieves primary frequency regulation support by adding an adjustment term proportional to the frequency deviation to the original active power reference value. This frequency regulation method requires the wind turbine to maintain a certain power reserve capacity, but the reserve capacity must not exceed the wind turbine's operating power limit; this constraint is given. In addition, the recovery effect of virtual inertia may increase the demand for frequency regulation capacity to maintain system safety. However, it is difficult to accurately determine the additional demand for wind turbine frequency regulation capacity. This demand should be set as the energy required to restore the wind turbine's acceleration, expressed as the energy released to compensate for the virtual inertial support provided by the wind turbine. Therefore, this embodiment models the recovery effect of compensating the wind turbine.

[0067] In practice, the frequency support model for the inertia resources of the photovoltaic power station is expressed as follows:

[0068]

[0069] in,

[0070]

[0071] In the formula, P represents the virtual inertia-supported power and virtual inertia time constant of the photovoltaic power station, respectively. v , and These represent the output power, backup power, and maximum output power of the photovoltaic power station, respectively.

[0072] It should be noted that, unlike wind turbines which contain rotating parts, photovoltaic power plants are composed of static components and do not have inertial support capabilities. Photovoltaic power plants can provide inertial response and primary frequency regulation support by working in conjunction with energy storage equipment and by adopting virtual synchronous machine technology.

[0073] In practice, the frequency support model for the inertia resources of the energy storage is expressed as follows:

[0074]

[0075] in,

[0076]

[0077] Among them, the primary frequency regulation power of energy storage meets the following requirements:

[0078]

[0079] In the formula, Let f0 represent the virtual inertia support power and virtual inertia time constant of the energy storage, respectively; f0 represent the rated frequency of the system; and RoCoF(t) represent the rate of frequency change of the energy storage bus node at time t. RoCoF represents the maximum inertial support power of the energy storage output. max This represents the maximum frequency change rate of the energy storage bus node. Δf max H represents the energy storage capacity and the maximum deviation from the rated frequency during the system frequency response process, respectively. b,max This represents the maximum virtual inertial time constant provided by energy storage. P represents the primary frequency regulation power of energy storage. B This indicates the output power of the energy storage system.

[0080] It should be noted that energy storage can also provide inertial response and primary frequency regulation support through a virtual synchronous machine control strategy. This embodiment describes the energy storage inertial response output power. At the initial moment of a disturbance, due to the inherent delay of the virtual inertia, only the synchronous generator can provide an instantaneous inertial response. Therefore, the corresponding power reserve capacity must be configured based on the inertial response power provided by the energy storage during the instantaneous disturbance. The inertial resource frequency support model of the energy storage in this embodiment gives the maximum inertial support power output by the energy storage. The virtual inertial response power provided by the energy storage system stops when the system frequency drops to its lowest point. Therefore, the required energy storage capacity configuration can be determined by calculating the inertial response power through integration. Since there is an upper limit to the virtual inertial time constant of the energy storage system, its value must also meet certain requirements.

[0081] It should be noted that the virtual inertial response power provided by the energy storage system stops when the system frequency drops to its lowest point. Therefore, in this embodiment, the required energy storage capacity configuration is determined by integrating the inertial response power.

[0082]

[0083] In the formula, t nadir t0 represents the time when the system reaches its lowest frequency, and t0 represents the initial time of the system.

[0084] S2. Based on the current structure of the new energy power system, the computational inertia of each node in the power system is obtained. Then, based on the constructed energy storage location model with the objective function of minimizing the variance of the node computational inertia and the constraint of the number of energy storage locations, the optimal energy storage location in the current power system is obtained.

[0085] In implementation, the calculated inertia of each node in the power system is expressed as follows:

[0086]

[0087] In the formula, H m B represents the calculated inertia of node m in a power system. i,m r represents the susceptance between the potential node i and node m of the synchronous generator. m,i H represents the element in the correlation matrix between network node voltages and synchronous generator internal potentials and node voltages, corresponding to the voltage relationship between node m and synchronous generator i. i I represents the inertial time constant of synchronous generator i. m This represents the set of generators associated with node m in the power system.

[0088] In practice, the objective function F1 of the energy storage location model is expressed as:

[0089]

[0090] in,

[0091]

[0092] In the formula, H′ m This represents the calculated inertia of node m after taking into account the energy storage configuration; ξ represents the average nodal inertia taking into account energy storage configuration; m This indicates whether energy storage should be deployed at node m. A value of 1 indicates deployment, and a value of 0 indicates no deployment. N represents the number of nodes in the system.

[0093] In practical implementation, the node inertia H′ after energy storage configuration should be taken into account. m, represented as:

[0094]

[0095] In the formula, This represents the equivalent inertial time constant that the energy storage deployed at node m can provide.

[0096] In practical implementation, the constraints of the energy storage site selection model are expressed as follows:

[0097]

[0098] In the formula, This indicates the upper limit of the number of energy storage configurations.

[0099] It should be noted that the calculated inertia of each node in the power system is obtained through the following derivation:

[0100] The inertia of node m is defined as the ratio of the disturbance power to the rate of change of the initial frequency at node m, i.e.:

[0101]

[0102] In the formula, ΔP represents the disturbance power at node m, and Δf m This represents the frequency deviation at node m.

[0103] Ignoring the influence of conductance, considering the transient reactance of the synchronous generator, and simplifying the load to its equivalent admittance, we obtain the augmented admittance matrix of the system, namely:

[0104]

[0105] In the formula, Y s Y represents the augmented matrix of the system. nn Y mm Y represents the self-admittance parameters of the synchronous generator node and other nodes, respectively. nm Y mn Y represents the mutual admittance relationship between the synchronous generator node and other nodes. n Y represents a diagonal matrix composed of the transient reactances of a synchronous generator. ln Y lm These represent the equivalent load admittances for the synchronous generator node and other nodes, respectively.

[0106] The corresponding network equation:

[0107]

[0108] In the formula, I n U represents the current vector injected into the generator system. e and U netRepresent the voltage vectors of the generator's internal potential nodes and network nodes, respectively; augmented admittance matrix Y s It is divided into four parts: Y1, Y2, Y3, and Y4, where Y1 = Y n Y2 = [-Y n ,0],Y3=[-Y n ,0] T Y4 = [Y nn +Y n +Y ln Y nm ;Y mn Y mm +Y lm ].

[0109] The relationship between node frequencies and synchronous generator terminal frequencies is derived based on the power system network equations, namely:

[0110]

[0111] In the formula, f m f represents the node frequency of node m. i V i Let R represent the frequency and internal potential amplitude of synchronous generator i, respectively; where the correlation matrix R between the network node voltage and the internal potential node voltage of the synchronous generator is expressed as R = -Y4. -1 Y3.

[0112] When a disturbance power ΔP occurs at node m, the unbalanced power is distributed to each synchronous generator node according to the synchronization power coefficient. The frequency change rate of synchronous generator i is obtained based on the synchronous generator rotor motion equation and the unbalanced power borne by synchronous generator i. for:

[0113]

[0114] In the formula, D i,m and ΔP i Let D represent the synchronization power coefficient and unbalanced power corresponding to synchronous generator i, respectively. i,m Represented as:

[0115]

[0116] In the formula, V m V represents the potential amplitude at node m. i δ represents the internal electromotive force of synchronous generator i. i,m0 This represents the initial phase angle difference between the voltages of synchronous generator i and node m.

[0117] Assuming the system voltage is near its rated value, the calculated inertia of node m is obtained, i.e.:

[0118]

[0119] It is understood that in this embodiment, the spatial distribution characteristics of the system's inertia can be measured by calculating the inertia at each node.

[0120] S3. Based on the frequency support model of multiple types of inertia resources and the optimal location of energy storage in the current power system, a collaborative planning model is constructed with the goal of minimizing annual investment cost and operating cost, and with typical daily operation constraints and frequency security constraints. The model is solved to obtain the optimal collaborative planning scheme and it is then implemented in the power system. The collaborative planning scheme includes the retrofit capacity of wind power and photovoltaic units and the configuration capacity of energy storage at the optimal location.

[0121] In practice, the objective function F2 of the collaborative programming model is expressed as:

[0122]

[0123] In the formula, C Inv C represents the investment cost. Ope This indicates operating costs.

[0124] In practice, the investment cost includes the cost of retrofitting wind and solar power units with virtual synchronous machines and the cost of configuring energy storage; investment cost C Inv , represented as:

[0125]

[0126] In the formula, c W c V c B c E These represent the unit cost coefficients for wind turbine retrofitting, photovoltaic power station retrofitting, and energy storage system power capacity and energy capacity, respectively. This indicates the retrofit capacity of the wth wind turbine unit. This represents the retrofit capacity of the v-th photovoltaic power station. Let represent the power capacity and energy capacity of the b-th energy storage system, respectively, and α1 represent the discount rate for wind power and photovoltaic retrofitting. α represents the lifespan of wind and solar power, and α2 represents the discount rate of the energy storage system. N represents the lifespan of the energy storage system. w N v N b These represent the total number of wind turbines, photovoltaic power plants, and energy storage units in the power system, respectively.

[0127] In practice, operating costs include power generation costs, carbon emission costs, and renewable energy curtailment costs under typical daily scenarios.

[0128] Specifically, operating cost C Ope , represented as:

[0129]

[0130] In the formula, c g c e c represents the generation cost and carbon emission cost coefficients of a synchronous generator, respectively. cur P represents the penalty coefficient for curtailment of wind and solar power, a renewable energy source. i,t,r E i,t,r Let represent the active power output and CO2 emissions of synchronous generator i at time t under scenario r, respectively. Let P be the maximum output power of the wind turbine and the photovoltaic power station at time t under scenario r. w,t,r P v,t,r Let U represent the actual output power of the wind turbine and photovoltaic power station, U represent the energy storage system configuration scheme to be determined during the planning stage, Ξ represent the selected typical day set, ψ represent the time period set within each typical day, p represent the time period within a typical day, and π represent the actual output power of the wind turbine and photovoltaic power station. r This represents the probability weight corresponding to scenario r; where scenario r represents different wind power and photovoltaic processing scenarios.

[0131] It should be noted that the operating cost is to seek an optimal energy storage planning scheme, which aims to maximize the minimum performance that the system can achieve under the most unfavorable operating scenario (i.e., minimize operating cost), thereby ensuring the robustness of investment decisions.

[0132] In implementation, the frequency security constraints include the system frequency minimum point constraint and the system frequency change rate constraint; wherein, the system frequency minimum point constraint and the system frequency change rate constraint are constructed based on a frequency support model for multiple types of inertia resources.

[0133] In specific implementation, the constraint on the lowest system frequency point is expressed as follows:

[0134] fnadir≥f0-Δf max ,

[0135] in,

[0136]

[0137] in,

[0138] In the formula, fnadir represents the lowest point of the system frequency, H syn H vir These represent the overall inertia levels of the synchronous generator and the virtual inertial resources, respectively. P represents the primary frequency regulation power of energy storage. loss Indicates the system's disturbance power. T represents the total primary frequency regulation power provided by the synchronous generator, and T1, T2, and T3 represent the primary frequency regulation response times of the photovoltaic, synchronous generator, and wind power generation, respectively. del1 T del2 T del3 These represent the delivery time of the regulated power for photovoltaic, synchronous generator, and wind power generation, respectively.

[0139] Specifically, the overall inertia level of the synchronous generator is obtained by calculating the sum of the inertia of all synchronous generators.

[0140] In specific implementation, the system frequency change rate constraint is expressed as:

[0141]

[0142] In the formula, RoCoF lim ΔP represents the limit of the system's rate of change of frequency. loss This indicates the power loss of the system.

[0143] During implementation, typical daily operating constraints include node balance constraints, DC constraints, synchronous generator unit operating constraints, energy storage charging / discharging power constraints, primary frequency regulation power constraints, and energy storage energy change constraints; typical daily operating constraints are expressed as:

[0144]

[0145]

[0146] In the formula, P l,t Let P be the transmission power of line l at time t. d,t Let P be the magnitude of the node load d at time t. i,t P w,t P v,t Let i be the output of the synchronous generator unit i, the wind turbine unit w, and the photovoltaic unit v at time t, respectively. Let G(m), W(m), V(m), E(m), L(m), and D(m) represent the energy storage charging and discharging power at time t, respectively. G(m), W(m), V(m), E(m), L(m), and D(m) represent the sets of all synchronous generators connected to node m, all wind turbine sets, all photovoltaic power stations, all energy storage systems, all transmission lines, and all loads, respectively. and Let be the rated power of photovoltaic and wind power at time t, respectively. and These represent the primary frequency regulation reserve power of the wind turbine, photovoltaic unit, and energy storage at time t, respectively. This refers to the rated power of the energy storage system. Let Δe be a 0-1 variable representing the energy storage charging and discharging operating states at time t, where 0 represents the charging state and 1 represents the discharging state; n,t To provide the change in energy quantity for primary frequency regulation at time t, Δt1, Δt2, and Δt3 represent the rapid power support period, power hold-up period, and power recovery period, respectively, and Δt represents the total effective time of the primary frequency regulation response. n,t Let be the amount of energy stored at time t. η represents the stored energy quantity at initial time t0; c η d These represent the energy conversion efficiency during the charging and discharging processes of energy storage, P. c,t Let P be the charging power of the energy storage system at time t. d,t Let be the discharge power of the energy storage system at time t. This represents the lower limit of the energy of the nth energy storage system. Let be the rated capacity of the nth energy storage system. The values ​​for the rapid power support period Δt1, power hold-up period Δt2, and power recovery period Δt3 are 5, 25, and 300 seconds, respectively.

[0147] Compared with existing technologies, this embodiment provides a collaborative planning method for multiple types of inertia resources in a new energy power system. It realizes collaborative planning of multiple types of inertia resources in a new energy power system, comprehensively considers the economic synergistic optimization of wind power and photovoltaic virtual synchronous machine transformation and energy storage deployment, and makes full use of the influence of the spatial distribution characteristics of grid inertia on frequency stability. It effectively improves the spatial distribution characteristics of system inertia, rationally allocates multiple types of inertia resources, and effectively improves the economy, frequency stability and security of system operation.

[0148] Example 2

[0149] A specific embodiment of the present invention discloses a collaborative planning system for multiple types of inertia resources in a new energy power system, comprising:

[0150] The frequency support model construction module is used to construct frequency support models for various types of inertia resources in the current new energy power system. The types of inertia resources include wind turbines, photovoltaic power plants, and energy storage.

[0151] The energy storage location module is used to obtain the computational inertia of each node in the power system based on the current new energy power system structure, and then obtain the optimal energy storage location in the current power system based on the constructed energy storage location model with minimizing the variance of node computational inertia as the objective function and the number of energy storage locations as the constraint.

[0152] The collaborative planning module is used to construct a collaborative planning model based on the frequency support model of multiple types of inertia resources and the optimal location of energy storage in the current power system. The model has the objective function of minimizing annual investment cost and operating cost, and is subject to typical daily operation constraints and frequency security constraints. The model is solved to obtain the optimal collaborative planning scheme and put it into operation in the power system. The collaborative planning scheme includes the retrofit capacity of wind power and photovoltaic units and the configuration capacity of energy storage at the optimal location.

[0153] The specific implementation process of this invention can be found in the above method embodiments, and will not be repeated here.

[0154] Since this embodiment is based on the same principle as the above method embodiments, this system also has the corresponding technical effects of the above method embodiments.

[0155] Example 3

[0156] To verify the correctness and effectiveness of Embodiments 1 and 2 of the present invention, computational examples were conducted on the improved IEEE-39 node system to verify the schemes in the above embodiments, such as... Figure 2 As shown, two wind turbines are connected to bus nodes 32 and 33 respectively, with a total capacity of 2500MW; two photovoltaic power stations are connected to bus nodes 34 and 36 respectively, with a total capacity of 9000MW. In the verification example, the minimum frequency point and frequency change rate limits are set to 1Hz and 0.5Hz / s respectively. Based on historical data clustering analysis, wind power, photovoltaic, and load data for four typical days were generated. Considering different combinations of three virtual inertial resources—energy storage, wind power, and photovoltaic—five planning scenarios were set up (as shown in Table 1) to construct a multi-type inertial resource collaborative planning model embedded in the typical day's operation simulation. By comprehensively considering the range of cost variations, the energy storage configuration cost and the virtual synchronous machine modification cost for wind power and photovoltaic were determined. Specific system operating parameters are shown in Table 2.

[0157] Table 1. Scenario Settings for Multi-Type Resource Collaborative Planning

[0158]

[0159] Table 2. Planning parameter settings for the verification case.

[0160]

[0161] Table 3 presents the results of the collaborative planning. Figure 3 It illustrates the system cost composition in different scenarios. Figure 3It can be seen that the comparison between Scenario 1 and Scenario 2 reflects the impact of frequency security constraints on system operation. Because Scenario 2 considers frequency security constraints, renewable energy sources lack inertial response and frequency regulation capabilities, requiring additional synchronous generators to meet the requirements, directly leading to increased operating costs. The comparative analysis of Scenario 3, 4, and 5 examines collaborative planning strategies under different combinations of various inertial resources. It is evident that while the introduction of more virtual inertial resources increases planning costs, it also improves renewable energy utilization and reduces primary frequency regulation costs, ultimately driving a downward trend in total costs. Compared to Scenario 2, which relies solely on synchronous generators for frequency support, Scenario 5, through optimized configuration of various inertial resources, reduces total costs by 16.17% and increases renewable energy utilization by 10.98% while ensuring frequency support. The results demonstrate that rational planning of various types of inertial resources can significantly improve system economics.

[0162] Table 3 Results of Collaborative Planning

[0163]

[0164] like Figure 4 and Figure 5 The figure shows a comparison of the minimum frequency point and RoCoF under typical daily operation scenarios for Scenario 1 and Scenario 5, respectively. As can be seen from the figure, Scenario 1 exhibits severe exceedances in both RoCoF and the minimum frequency point, reflecting that frequency instability poses a significant threat to operational safety. In contrast, Scenario 5, by configuring multiple types of virtual inertial resources, significantly improves the system's inertial support and frequency regulation capabilities. Under the same disturbance conditions, Scenario 5's RoCoF and minimum frequency point strictly meet the allowable limits of the frequency safety criteria throughout the entire process, fully verifying that the optimized configuration of multiple types of inertial resources can effectively improve system frequency stability.

[0165] Spatial smoothing of discrete data is performed using cubic spline interpolation. The impact of various inertial resource configurations on the spatial distribution of system inertia is analyzed through system inertial heatmaps. Figure 6 and Figure 7 As shown, a comparison illustrates the spatial distribution characteristics of the system's inertial space before and after configuring multiple types of inertial resources: Figure 6 The area around node 3 shows a clear low inertia characteristic, which is mainly due to the lack of inertial support capacity of renewable energy units at nodes 32, 33, 34 and 36 that have not undergone virtual synchronization machine transformation. Figure 7 This indicates that by implementing virtual synchronous machine retrofits for wind turbines and photovoltaic power plants, and optimizing the configuration of energy storage systems, the overall inertia level of the system is significantly improved and the spatial distribution becomes more balanced.

[0166] In summary, the comparative analysis verifies the effectiveness of the proposed collaborative planning model, demonstrating that optimizing the configuration of multiple types of virtual inertial resources can effectively improve the spatial distribution characteristics of system inertia, thereby ensuring the safe and stable operation of the power grid.

[0167] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0168] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A collaborative planning method for multiple types of inertia resources in a new energy power system, characterized in that, Includes the following steps: Construct a frequency support model for multiple types of inertia resources in the current new energy power system, where the inertia resource types include wind turbines, photovoltaic power plants and energy storage; Based on the current structure of the new energy power system, the computational inertia of each node in the power system is obtained. Then, based on the constructed energy storage location model with the objective function of minimizing the variance of the node computational inertia and the constraint of the number of energy storage locations, the optimal energy storage location in the current power system is obtained. Based on the frequency support model of multiple types of inertia resources and the optimal location of energy storage in the current power system, a collaborative planning model is constructed with the goal of minimizing annual investment cost and operating cost, and with typical daily operation constraints and frequency security constraints. The model is solved to obtain the optimal collaborative planning scheme and put it into operation in the power system. The collaborative planning scheme includes the retrofit capacity of wind power and photovoltaic units and the configuration capacity of energy storage at the optimal location.

2. The collaborative planning method for multiple types of inertia resources in a new energy power system according to claim 1, characterized in that, The calculated inertia of each node in the power system is expressed as follows: In the formula, H m B represents the calculated inertia of node m in a power system. i,m r represents the susceptance between the potential node i and node m of the synchronous generator. m,i H represents the element in the correlation matrix between network node voltages and synchronous generator internal potentials and node voltages, corresponding to the voltage relationship between node m and synchronous generator i. i I represents the inertial time constant of synchronous generator i. m This represents the set of generators associated with node m in the power system.

3. The collaborative planning method for multiple types of inertia resources in a new energy power system according to claim 2, characterized in that, The objective function F1 of the energy storage location model is expressed as: in, In the formula, H′ m This represents the calculated inertia of node m after taking into account the energy storage configuration; ξ represents the average nodal inertia taking into account energy storage configuration; m This indicates whether energy storage should be deployed at node m. A value of 1 indicates deployment, and a value of 0 indicates no deployment. N represents the number of nodes in the system.

4. The collaborative planning method for multiple types of inertia resources in a new energy power system according to claim 3, characterized in that, The node inertia H′ after taking energy storage configuration into account m , represented as: In the formula, This represents the equivalent inertial time constant that the energy storage deployed at node m can provide.

5. The collaborative planning method for multiple types of inertia resources in a new energy power system according to claim 1, characterized in that, The frequency security constraints include the system frequency minimum point constraint and the system frequency change rate constraint; wherein, the system frequency minimum point constraint and the system frequency change rate constraint are constructed based on a frequency support model of multiple types of inertia resources.

6. The collaborative planning method for multiple types of inertia resources in a new energy power system according to claim 5, characterized in that, The constraint on the lowest frequency point of the system is expressed as follows: fnadir≥f0-Δf max , in, in, In the formula, fnadir represents the lowest point of the system frequency, H syn H vir These represent the overall inertia levels of the synchronous generator and the virtual inertial resources, respectively. P represents the primary frequency regulation power of energy storage. loss Indicates the system's disturbance power. T represents the total primary frequency regulation power provided by the synchronous generator, and T1, T2, and T3 represent the primary frequency regulation response times of the photovoltaic, synchronous generator, and wind power generation, respectively. del1 T del2 T del3 These represent the delivery times for the regulating power generated by photovoltaic, synchronous generators, and wind power, respectively. This indicates the standby power of the wind turbine. This indicates the reserve power of the photovoltaic power station.

7. The collaborative planning method for multiple types of inertia resources in a new energy power system according to claim 6, characterized in that, The system frequency change rate constraint is expressed as: In the formula, RoCoF lim ΔP represents the limit of the system's rate of change of frequency. loss This indicates the power loss of the system.

8. The collaborative planning method for multiple types of inertia resources in a new energy power system according to claim 1, characterized in that, The frequency support model for the inertia resources of the wind turbine is expressed as follows: in, In the formula, Indicates virtual inertia support power. Represents the virtual inertial time constant. Represents the rate of change of system frequency. P represents the maximum inertial time constant that a wind turbine can provide. w , These represent the wind turbine's output power, reserve power, and maximum output power, respectively. This represents the power used to compensate for the released virtual inertia response and primary frequency regulation in the wind turbine recovery effect. k represents the response dead time of the virtual inertia. rec This represents the proportional relationship between the frequency modulation demand generated by the recovery effect and the virtual inertia time constant, where t represents time.

9. The collaborative planning method for multiple types of inertia resources in a new energy power system according to claim 8, characterized in that, The frequency support model for the inertia resources of the energy storage is expressed as follows: in, Among them, the primary frequency regulation power of energy storage meets the following requirements: In the formula, Let f0 represent the virtual inertia support power and virtual inertia time constant of the energy storage, respectively; let f0 represent the rated frequency of the system; and let RoCoF(t) represent the rate of frequency change of the energy storage bus node at time t. RoCoF represents the maximum inertial support power of the energy storage output. max This represents the maximum frequency change rate of the energy storage bus node. Δf max H represents the energy storage capacity and the maximum deviation from the rated frequency during the system frequency response process, respectively. b,max This represents the maximum virtual inertial time constant provided by energy storage. P represents the primary frequency regulation power of energy storage. B This indicates the output power of the energy storage system.

10. A collaborative planning system for multiple types of inertia resources in a new energy power system, characterized in that, include: The frequency support model construction module is used to construct frequency support models for various types of inertia resources in the current new energy power system. The types of inertia resources include wind turbines, photovoltaic power plants, and energy storage. The energy storage location module is used to obtain the computational inertia of each node in the power system based on the current new energy power system structure, and then obtain the optimal energy storage location in the current power system based on the constructed energy storage location model with minimizing the variance of node computational inertia as the objective function and the number of energy storage locations as the constraint. The collaborative planning module is used to construct a collaborative planning model based on the frequency support model of multiple types of inertia resources and the optimal location of energy storage in the current power system. The model has the objective function of minimizing annual investment cost and operating cost, and is subject to typical daily operation constraints and frequency security constraints. The model is solved to obtain the optimal collaborative planning scheme and put it into operation in the power system. The collaborative planning scheme includes the retrofit capacity of wind power and photovoltaic units and the configuration capacity of energy storage at the optimal location.

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