Offshore wind power networking unit configuration method considering economy and stability

Abstract

The invention relates to the technical field of new energy power generation and electric power system planning and operation, and particularly provides an offshore wind power networking unit configuration method considering economy and stability. The method comprises the following steps: constructing a wake flow matrix between units; establishing an economic model containing wake effect differentiation opportunity cost; constructing a bimodal network based on the superposition theorem to calculate a generalized system strength index, and deducing a critical short-circuit ratio threshold value; and constructing a collaborative optimization model taking net income maximization as a target, and solving an optimal locating and sizing scheme of the deconstruction network unit under the constraint of system strength. According to the method, the idle capacity of the wake flow area is utilized, stability and economy cooperation is achieved, and the full-life-cycle income is improved.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation and power system planning and operation technology, and specifically to a method for configuring offshore wind power grid units that takes into account both economic efficiency and stability. Background Technology

[0002] With the acceleration of the global energy transition, offshore wind power is developing towards deep-sea, large-scale, and clustered development. Because offshore wind farms are far from the onshore main grid, they are usually connected to the grid through long-distance high-voltage AC submarine cables or flexible DC transmission systems. This makes the sending-end grid exhibit obvious "weak connectivity" characteristics, that is, a low short-circuit ratio and weak system immunity to disturbances.

[0003] Traditional wind turbines often employ phase-locked loop (PLL)-based control strategies, treating them as equivalent current sources. In weak grid environments, grid-connected turbines are highly susceptible to broadband oscillations or grid disconnection due to PLL instability. Grid-connected turbines, by simulating the voltage source characteristics of synchronous generators, can provide the system with virtual inertia, voltage support, and primary frequency regulation, significantly improving the stability of weak grids.

[0004] However, in actual engineering planning, the optimal configuration of grid-connected generating units faces the following severe challenges:

[0005] Grid-connected wind turbines must reserve a certain amount of active power to maintain voltage source characteristics and provide frequency support. This means that the turbines must operate at reduced load below their maximum power point, resulting in opportunity costs due to the forfeiture of some generating capacity. Furthermore, the internal characteristics of wind farms are not fully utilized: Offshore wind farms have a significant wake effect. Turbines downstream of the prevailing wind direction are blocked by upstream turbines, reducing input wind speed and limiting their theoretical maximum usable power. Existing technologies typically assume uniform or random distribution of turbines within the wind farm, failing to utilize the "idle capacity" of downstream turbines for grid backup tasks, thus wasting resources.

[0006] With the introduction of grid-connected generating units, the system exhibits complex characteristics of mixed voltage and current source devices. The traditional short-circuit ratio index is derived based on the current source assumption, which cannot accurately quantify the improvement effect of grid-connected generating units on system strength, resulting in blurred stability constraint boundaries and difficulty in guiding precise capacity configuration. Summary of the Invention

[0007] In view of this, the present invention provides a method for configuring offshore wind power grid-connected units that takes into account both economy and stability, so as to accurately quantify the stability of the hybrid system and to calculate the standby opportunity cost in combination with the wake effect, thereby achieving optimal grid-connected unit site selection and capacity determination in terms of both economy and stability.

[0008] In a first aspect, the present invention provides a method for configuring offshore wind turbines in a grid-connected system that takes into account both economic efficiency and stability, the method comprising: Step 1: Obtain the network topology parameters, turbine rated parameters, wind resource distribution data, and wake matrix describing the mutual shading relationship between the wakes of the offshore wind farm cluster; Step 2: Construct a full life cycle economic cost model that includes grid-connected units and grid-linked units; the full life cycle economic cost model includes initial investment cost, operation and maintenance cost, and active power reserve opportunity cost arising from the active power reserve reserve ratio set for grid-connected units; Step 3: Construct a dual-modal hybrid equivalent network for offshore wind farm clusters based on the superposition theorem, quantify the equivalent short-circuit response components of grid-connected and grid-following units at the evaluation nodes, and calculate the generalized hybrid source short-circuit ratio index by superposition. Step 4: Derive the critical short-circuit ratio threshold of the system based on the static voltage stability limit of the system; Step 5: Construct a collaborative optimization configuration model for grid-connected generating units; the model sets the installation location status of each unit and the active power reserve ratio of the grid-connected generating units as optimization decision variables, takes maximizing the net benefit over the entire life cycle as the objective function, and takes the improved multi-infeed short-circuit ratio index being greater than the critical short-circuit ratio threshold and the active power reserve ratio meeting the minimum requirements for grid-connected system operation as constraints. Step 6: Solve the collaborative optimization configuration model of the grid-type units, and output the optimal location, number of units, and active power reserve ratio of the grid-type units that maximize the objective function.

[0009] Furthermore, for the wake matrix obtained in step one, the theoretical maximum available power considering the wake effect is calculated. The process is as follows: (1) Analyzing the wake matrix Calculate the comprehensive wake velocity deficit coefficient of the i-th unit in the t-th time period. : ; Where t is the discrete-time section index within the entire lifecycle; This refers to the set of wind turbine units located upstream of turbine unit i. Wake matrix The element in represents the single-unit wake velocity deficit contribution of upstream unit j to downstream unit i at the t-th time section. (2) Correct the actual input wind speed of unit i at the t-th time segment. And calculate the theoretical maximum available power: ; ; in, Here is the free-flow wind speed data at the t-th time section; This is the wind speed-power characteristic curve function of the unit.

[0010] Furthermore, regarding step two, the opportunity cost of active power reserve... The calculation formula is: ; Where T is the total number of time segments throughout the entire life cycle; The time step is the interval between adjacent time sections; N is the total number of wind turbine units. For the 0-1 decision variables that characterize the installation location status, the value is 1 when the optimization model decides to configure the i-th unit as a grid-type unit, and 0 otherwise. Let be the on-grid electricity price for the t-th time segment; The theoretical maximum available power at the t-th time segment calculated in claim 2; This is the reference value of the actual active power of the i-th grid-type unit at the t-th time segment, and its value is determined by the continuous decision variable active power reserve ratio. The decision satisfies the following relation: ; Furthermore, regarding step three, the calculation process for the generalized hybrid source short-circuit ratio index includes: Based on differences in control strategies, grid-connected units are characterized as voltage source models with internal impedance, while grid-linked units are characterized as controlled current source models. Using the superposition principle, the equivalent short-circuit current response of each unit at evaluation node p is calculated. The generalized hybrid source short-circuit ratio of node p is calculated according to the following formula. ; in, Rated voltage; This is the system's equivalent rated power; The output current for the j-th grid-connected unit; Let be the impedance transfer coefficient of the j-th grid-type unit to node p; The output current of the kth grid-connected unit; Let be the impedance transfer coefficient of the k-th grid-connected unit to node p.

[0011] Furthermore, regarding step four, the calculation method for the critical short-circuit ratio threshold is as follows: Calculate the active power limit of the system under static voltage stability critical state. The critical short-circuit ratio threshold is calculated according to the following formula. : ; in, For system short-circuit capacity; The current reactive power operating point of the point of common coupling; the active power limit value. The calculation formula is: P l i m i t = U t h 2 2 X s y s ⋅ [ R s y s X s y s ( 1 + 2 Q o p X s y s U t h 2 ) + Z s y s X s y s ⋅ Oh s t a b ] ; in, The amplitude of the Thevenin equivalent voltage source on the system side; These represent the magnitude, resistive component, and reactance component of the Thevenin equivalent impedance on the system side, respectively. The voltage stability coefficient is calculated as follows: .

[0012] Furthermore, regarding step five, the objective function F of the optimized configuration model is expressed as: ; Where F represents the net income over the entire life cycle; The theoretical electricity sales revenue over the entire life cycle is the sum of electricity sales revenue corresponding to the theoretical maximum available power at all time points t. ; Where T is the total number of time segments throughout the entire life cycle; The time step is the interval between adjacent time sections; N is the total number of wind turbine units. Let be the unit electricity price at the t-th time segment; This represents the theoretical maximum usable power after taking into account the wake effect.

[0013] The incremental investment cost of grid-connected units relative to grid-following units is based on decision variables. calculate; ; in, These are the unit capacity costs for grid-connected and grid-linked generating units, respectively.

[0014] For the total lifecycle operation and maintenance cost, based on decision variables calculate; C m a i n t = ∑ t = 1 T 1 ( 1 + r ) t ∑ i = 1 N S i ⋅ [ x i m G F M + ( 1 − x i ) m G F L ] ; in, , respectively, represent the unit capacity operation and maintenance costs of grid-connected and grid-linked generating units, and r is the discount rate.

[0015] Furthermore, regarding step six, the decision variables and constraints involved in solving the collaborative optimization configuration model include: (1) Discrete decision variable vector X = [ C 1 , C 2 , … , C N ] This indicates whether each unit is selected as a grid-type unit, corresponding to the installation location status in step five. (2) Vector of continuous decision variables R = [ r 1 , r 2 , … , r N ] This characterizes the active power reserve ratio of each grid-type unit; The constraints of the optimized configuration model specifically include: (1) System stability constraints: That is, the generalized mixed-source short-circuit ratio index of all evaluation nodes must be greater than the critical short-circuit ratio threshold; (2) Active power reserve constraint: The active power reserve ratio of grid-type units must be greater than the minimum reserve rate required for the system to maintain its grid-type function.

[0016] Based on the above technical solution, the method of the present invention has the following beneficial technical effects:

[0017] 1. This invention constructs a differentiated active power reserve opportunity cost model based on wake effect, which can significantly improve the economic benefits throughout the entire life cycle. By quantifying the wake obstruction relationship within the wind farm, this invention accurately identifies downstream turbine locations with idle capacity due to environmental constraints. Without encroaching on the power generation space of upstream high-yield units, it effectively utilizes resources in low-wind-speed areas to undertake system backup tasks, significantly reducing wind curtailment losses caused by reserved reserves.

[0018] 2. This invention enhances the engineering practicality of the planning scheme through deep collaborative optimization of spatial site selection and parameter capacity determination for grid-connected wind turbines. By incorporating discrete installation location states and continuous active power reserve ratios into a unified optimization framework, it achieves the lowest-cost capacity configuration while ensuring system stability, effectively improving the support capability of offshore wind farms for new power systems. Attached Figure Description

[0019] Figure 1 This is a flowchart of an offshore wind power grid configuration method that takes into account both economy and stability, provided by an embodiment of the present invention. Detailed Implementation

[0020] To describe the present invention in more detail, the technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] Figure 1 This is a flowchart of a method for configuring offshore wind turbines that takes into account both economy and stability, as provided in an embodiment of the present invention. Figure 1 As shown, the method may include the following steps: Step 1: Obtain the network topology parameters, turbine rated parameters, wind resource distribution data, and wake matrix describing the mutual shading relationship between the wakes of the offshore wind farm cluster; Step 2: Construct a full life cycle economic cost model that includes grid-connected units and grid-linked units; the full life cycle economic cost model includes initial investment cost, operation and maintenance cost, and active power reserve opportunity cost arising from the active power reserve reserve ratio set for grid-connected units; Step 3: Construct a dual-modal hybrid equivalent network for offshore wind farm clusters based on the superposition theorem, quantify the equivalent short-circuit response components of grid-connected and grid-following units at the evaluation nodes, and calculate the generalized hybrid source short-circuit ratio index by superposition. Step 4: Derive the critical short-circuit ratio threshold of the system based on the static voltage stability limit of the system; Step 5: Construct a collaborative optimization configuration model for grid-connected generating units; the model sets the installation location status of each unit and the active power reserve ratio of the grid-connected generating units as optimization decision variables, takes maximizing the net benefit over the entire life cycle as the objective function, and takes the improved multi-infeed short-circuit ratio index being greater than the critical short-circuit ratio threshold and the active power reserve ratio needing to meet the minimum requirements for grid-connected system operation as constraints. Step 6: Solve the collaborative optimization configuration model of the grid-type units, and output the optimal location, number of units, and active power reserve ratio of the grid-type units that maximize the objective function.

[0022] In this embodiment of the invention, the wake matrix obtained in step one is used to calculate the theoretical maximum available power after considering the wake effect. The specific process is as follows: (1) Analyzing the wake matrix Calculate the comprehensive wake velocity deficit coefficient of the i-th unit in the t-th time period. : ; Where t is the discrete-time section index within the entire lifecycle; This refers to the set of wind turbine units located upstream of turbine unit i. Wake matrix The element in represents the single-unit wake velocity deficit contribution of upstream unit j to downstream unit i at the t-th time section. (2) Correct the actual input wind speed of unit i at the t-th time segment. And calculate the theoretical maximum available power: ; ; in, Here is the free-flow wind speed data at the t-th time section; This is the wind speed-power characteristic curve function of the unit.

[0023] In this embodiment of the invention, the opportunity cost of active power reserve is involved in step two. The calculation formula is: ; Where T is the total number of time segments throughout the entire life cycle; The time step is the interval between adjacent time sections; N is the total number of wind turbine units. For the 0-1 decision variables that characterize the installation location status, the value is 1 when the optimization model decides to configure the i-th unit as a grid-type unit, and 0 otherwise. Let be the on-grid electricity price for the t-th time segment; The theoretical maximum available power at the t-th time segment calculated in claim 2; This is the reference value of the actual active power of the i-th grid-type unit at the t-th time segment, and its value is determined by the continuous decision variable active power reserve ratio. The decision satisfies the following relation: ; In this embodiment of the invention, the calculation process of the generalized hybrid source short-circuit ratio index in step three includes: Based on differences in control strategies, grid-connected units are characterized as voltage source models with internal impedance, while grid-linked units are characterized as controlled current source models. Using the superposition principle, the equivalent short-circuit current response of each unit at evaluation node p is calculated. The generalized hybrid source short-circuit ratio of node p is calculated according to the following formula. ; in, Rated voltage; This is the system's equivalent rated power; The output current for the j-th grid-connected unit; Let be the impedance transfer coefficient of the j-th grid-type unit to node p; The output current of the kth grid-connected unit; Let be the impedance transfer coefficient of the k-th grid-connected unit to node p.

[0024] In this embodiment of the invention, the method for calculating the critical short-circuit ratio threshold in step four is as follows: Calculate the active power limit of the system under static voltage stability critical state. The critical short-circuit ratio threshold is calculated according to the following formula. : ; in, For system short-circuit capacity; The current reactive power operating point of the point of common coupling; the active power limit value. The calculation formula is: P l i m i t = U t h 2 2 X s y s ⋅ [ R s y s X s y s ( 1 + 2 Q o p X s y s U t h 2 ) + Z s y s X s y s ⋅ Oh s t a b ] ; in, The amplitude of the Thevenin equivalent voltage source on the system side; These represent the magnitude, resistive component, and reactance component of the Thevenin equivalent impedance on the system side, respectively. The voltage stability coefficient is calculated as follows: .

[0025] In this embodiment of the invention, the objective function F of the optimized configuration model in step five is expressed as: ; Where F represents the net income over the entire life cycle; The theoretical electricity sales revenue over the entire life cycle is the sum of electricity sales revenue corresponding to the theoretical maximum available power at all time points t. ; Where T is the total number of time segments throughout the entire life cycle; The time step is the interval between adjacent time sections; N is the total number of wind turbine units. Let be the unit electricity price at the t-th time segment; This represents the theoretical maximum usable power after taking into account the wake effect.

[0026] The incremental investment cost of grid-connected units relative to grid-following units is based on decision variables. calculate; ; in, These are the unit capacity costs for grid-connected and grid-linked generating units, respectively.

[0027] For the total lifecycle operation and maintenance cost, based on decision variables calculate; C m a i n t = ∑ t = 1 T 1 ( 1 + r ) t ∑ i = 1 N S i ⋅ [ x i m G F M + ( 1 − x i ) m G F L ] ; in, , respectively, represent the unit capacity operation and maintenance costs of grid-connected and grid-linked generating units, and r is the discount rate.

[0028] In this embodiment of the invention, the decision variables involved in solving the collaborative optimization configuration model in step six include: (1) Discrete decision variable vector X = [ C 1 , C 2 , … , C N ] This indicates whether each unit is selected as a grid-type unit, corresponding to the installation location status in step five. (2) Vector of continuous decision variables R = [ r 1 , r 2 , … , r N ] This characterizes the active power reserve ratio of each grid-type unit; The constraints of the optimized configuration model specifically include: (1) System stability constraints: That is, the generalized mixed-source short-circuit ratio index of all evaluation nodes must be greater than the critical short-circuit ratio threshold; (2) Active power reserve constraint: The active power reserve ratio of grid-type units must be greater than the minimum reserve rate required for the system to maintain its grid-type function.

Claims

1. A method for offshore wind power grid integration unit configuration considering economy and stability, characterized in that, The method comprises the following steps: Step 1: obtaining network topology parameters of the offshore wind farm group, unit rated parameters, wind resource distribution data and a wake matrix describing the mutual shielding relationship between units; Step 2: constructing a full life cycle economic cost model including network-forming units and network-following units; the full life cycle economic cost model includes initial investment cost, operation and maintenance cost and active reserve opportunity cost caused by setting an active reserve reservation ratio of the network-forming units; Step 3: constructing a double-mode hybrid equivalent network of the offshore wind farm group based on the superposition theorem, quantifying equivalent short-circuit response components of the network-forming units and the network-following units at the evaluation node respectively, and calculating a generalized hybrid source short-circuit ratio index through superposition; Step 4: deriving a critical short-circuit ratio threshold of the system based on a system static voltage stability limit; Step 5: constructing a network-forming unit collaborative optimization configuration model; the model sets installation position states of each unit and an active reserve reservation ratio of the network-forming units as optimization decision variables, sets maximum full life cycle net income as an objective function, and sets an improved multi-infeed short-circuit ratio index greater than the critical short-circuit ratio threshold and an active reserve reservation ratio meeting a minimum requirement of system network operation as constraint conditions; Step 6: solving the network-forming unit collaborative optimization configuration model, and outputting optimal network-forming unit arrangement positions, configuration quantities and active reserve reservation ratios that maximize the objective function.

2. The method of claim 1, wherein, The wake matrix obtained in step one, calculate the theoretical maximum available power after considering the wake effect The process is: (1) Resolving the wake matrix , calculate the comprehensive wake velocity loss coefficient of the i th unit in the t th time period : ； where t is the discrete time section index within the full life cycle; is the set of wind turbines located upstream of the wind turbine i; is the wake matrix is the element in the matrix, representing the single wake velocity deficit contribution value of the upstream turbine j to the downstream turbine i at the tth time section. (2) Correct the actual input wind speed of the unit i at the tth time section And calculate the theoretical maximum available power: ； ； wherein, is the free stream wind speed data for the tth time slice; is the wind speed-power characteristic curve function of the unit.

3. The method of claim 1, wherein, The step two active reserve opportunity cost The calculation formula is: ； wherein T is the total number of time sections in the whole life cycle; is the time step between adjacent time sections; N is the total number of wind turbines; is a 0-1 decision variable representing the installation location state, taking 1 when the optimization model decides to configure the ith turbine as a grid-forming type, otherwise taking 0; is the on-grid electricity price of the tth time section; is the theoretical maximum available power of the tth time section calculated in claim 2; is the actual active power reference value of the ith grid-forming type turbine in the tth time section, the value of which is determined by the continuous decision variable active reserve reservation proportion , which satisfies the relationship: 。 4. The method of claim 1, wherein, The calculation process of the generalized hybrid source short-circuit ratio index in step 3 comprises: According to the difference of control strategy, the network-constructing type unit is characterized as a voltage source model with internal impedance, and the network-following type unit is characterized as a controlled current source model; the superposition principle is used to calculate the equivalent short-circuit current response generated by each unit at the evaluation node p; the generalized mixed source short-circuit ratio of the node p is calculated according to the following formula : ； wherein, is the rated voltage; is the system equivalent rated power; is the output current of the jth grid-forming unit; is the impedance transfer coefficient of the jth grid-forming unit to node p; is the output current of the kth grid-following unit; is the impedance transfer coefficient of the kth grid-following unit to node p.

5. The method of claim 1, wherein, The calculation method of the critical short-circuit ratio threshold in step 4 is: Active power limit value at which a computing system is in a static voltage stability critical state ; the critical short circuit ratio threshold value is calculated in accordance with the formula : ； wherein is the system short circuit capacity; is the current reactive power operating point of the point of common coupling; said active power limit value is calculated as: ； wherein, is the amplitude of the system-side Thevenin equivalent voltage source; are the modulus, resistance component and reactance component of the system-side Thevenin equivalent impedance, respectively; is the voltage stability state coefficient, whose calculation formula is .

6. The method of claim 1, wherein, The objective function F of the optimization configuration model in step 5 is expressed as: ； F is the net benefit of the whole life cycle; F is the net benefit of the whole life cycle; F is the net benefit of the whole life cycle; ； Wherein, T is the total number of time sections in the whole life cycle; is the time step between adjacent time sections; N is the total number of wind turbines; is the unit electricity price of the tth time section; is the theoretical maximum available power after considering the wake effect; For the incremental investment cost of grid-forming units relative to grid-following units, the decision variable is calculated; ； wherein, respectively, the unit capacity cost of the network-constructing unit and the network-following unit. For full life cycle operating and maintenance costs, based on decision variables Calculations; ； wherein, are the unit capacity operation and maintenance costs of network-forming and network-following units, respectively, and r is the discount rate.

7. The method of claim 1, wherein, The decision variables involved in the solving process of the collaborative optimization configuration model in step 6 comprise: (1) a discrete decision variable vector , representing whether each unit is selected as a grid-forming unit, corresponding to the installation location state in step five; (2) a continuous decision variable vector , representing the active reserve reservation proportion of each network-forming unit; The constraint conditions of the collaborative optimization configuration model comprise: (1) System stability constraint: i.e. the generalized mixed source short circuit ratio index of all evaluation nodes needs to be greater than the critical short circuit ratio threshold; (2) Active reserve constraint: That is, the active reserve reservation ratio of the grid-forming unit needs to be greater than the minimum reserve rate required for the system to maintain the grid-forming function.

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