Asynchronous interconnected power grid frequency security constraint cooperative scheduling method and system

Abstract

The invention discloses an asynchronous interconnected power grid frequency security constraint cooperative scheduling method and system. The method comprises the following steps: constructing a DR-FCCS model comprising a generator response module, a sending end power grid operation cost limiting module, a receiving end power grid operation limiting module and an HVDC link constraint module under uncertainty; the uncertainty of the constructed DR-FCCS model is described on the basis of a Wasserstein measurement fuzzy set; solving DR joint opportunity constraints based on conditional value-at-risk approximation, converting joint probability constraints into individual probability constraints, and optimizing a DR-FCCS model; and solving the optimized DR-FCCS model by using a sequential solution algorithm to obtain an asynchronous interconnected power grid collaborative optimization scheduling scheme so as to realize system collaboration. According to the invention, the frequency resources from the'source-network-load-storage 'side can be collaboratively optimized, so that the frequency security is realized.

Description

Background Art

[0002] High Voltage Direct Current (HVDC) is an effective and popular power transmission technology that enables long-distance transmission between remote renewable energy bases and load centers. Due to its technical and economic advantages, HVDC is often used to support asynchronous AC grid interconnection. An asynchronous grid with multiple HVDC links supports a Sending-End Grid (SEG) and a Receiving-End Grid (REG) operating at independent frequencies and phase angles. An asynchronous grid interconnected by multiple HVDC links is as shown in Figure 4 shown.

[0003] Renewable energy generation is vulnerable to weather conditions, with significant volatility and randomness. Its output characteristics cause grid frequency fluctuations, reducing the overall inertia level of the system. In addition, new energy generation equipment is connected to the grid through a fast-response power electronic interface, lacking the inertia response and primary frequency regulation capabilities of traditional synchronous generators. Moreover, HVDC links contribute to zero inertia and frequency support, so the asynchronous interconnection of HVDC links also reduces the synchronous area and limits the frequency resource sharing between the SEG and the REG. Therefore, asynchronous grids face the risk of frequency security in emergencies.

[0004] To improve system frequency security, a natural idea is to incorporate frequency constraints into the operation scheduling model, called frequency-constrained scheduling, that is, the restrictions on the Rate-of-Change-of-Frequency (RoCoF), Maximum Frequency Deviation (MFD), and Quasi-Steady-State (QSS) frequency deviation. Existing research has focused on synchronous systems, and the corresponding modeling and methods are difficult to directly apply to the frequency-constrained HVDC problems of asynchronous grids.

[0005] Existing work has mainly focused on the collaborative optimization of frequency support resources of generators, renewable energy, and HVDC links. We have noticed the following two limitations in current research:

[0006] First, in addition to generators, renewable energy, and HVDC links with frequency support capabilities, non-critical loads in the SEG and the REG can be utilized to provide support through appropriate load shedding. In addition, energy storage systems commonly deployed in power systems have flexible and fast regulation characteristics to achieve frequency support. At the same time, none of the above work has explored the coordination of multiple types of frequency resources from the perspective of the "source-grid-load-storage" side of the entire asynchronous grid to achieve frequency security;

[0007] Second, little attention has been paid to the uncertainty management in the frequency-constrained operation of asynchronous power grids. How to use the Distributionally Robust (DR) joint chance-constrained model to solve the uncertainty in the frequency-constrained scheduling problem of asynchronous power grids while maintaining a balance among cost, reliability, computational tractability, and scalability is a crucial issue that has not been studied so far. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to provide a method and system for coordinated scheduling of frequency security constraints in an asynchronous interconnected power grid, which can coordinately optimize the frequency resources from the "source-grid-load-storage" side to achieve frequency security, and is used to solve the technical problem of safe and economic scheduling of a low-inertia asynchronous interconnected power grid under uncertain environments.

[0009] The present invention adopts the following technical solutions:

[0010] A method for coordinated scheduling of frequency security constraints in an asynchronous interconnected power grid includes the following steps:

[0011] Construct a DR-FCCS model including a generator response module under uncertainty, a sending-end power grid operation cost limitation module, a receiving-end power grid operation limitation module, and an HVDC link constraint module;

[0012] Describe the uncertainty of the DR-FCCS model constructed based on the Wasserstein metric fuzzy set;

[0013] Approximately solve the DR joint chance constraint based on conditional value-at-risk, convert the joint probability constraint into an individual probability constraint, and optimize the DR-FCCS model;

[0014] Use the sequential solution algorithm to solve the optimized DR-FCCS model to obtain a coordinated optimization scheduling scheme for the asynchronous interconnected power grid and achieve system coordination.

[0015] Preferably, the generator response module is specifically:

[0016] The wind farm in the sending-end power grid The available wind power is modeled as follows:

[0017]

[0018] Where and are the available wind power, the wind power prediction value, and the uncertainty prediction error of wind farm w at the SEG, respectively;

[0019] The output power of the wind farm in the load shedding mode is expressed as:

[0020]

[0021] Among them, and are the unused power reserved under the actual wind power injection and load shedding modes, is the load shedding ratio to be optimized, is the actual output of wind power, is the predicted value of wind power, is the wind power prediction error; The response of each generator to the uncertain prediction error of the wind farm in the sending-end power grid is:

[0022]

[0023] Among them, and are the actual output of generator g in SEG, is the day-ahead planned output of generator g in SEG, is the adjustment factor vector, is the wind power output prediction error vector in SEG.

[0024] Preferably, the sending-end power grid operation cost limitation module is specifically:

[0025] The overall system power balance of SEG under uncertain conditions is:

[0026]

[0027] Among them, g is the generator index, is the set of generators in SEG, is the day-ahead planned output of generator g in SEG at time t, w is the wind farm index, is the set of wind farms in SEG, is the load shedding rate of wind farm w in SEG at time t, is the predicted value of wind farm w in SEG at time t, e is the energy storage system index, ε S is the set of energy storage systems in SEG, is the discharge power of energy storage system e in SEG at time t, is the charging power of energy storage system e in SEG at time t, d is the load index, is the set of loads in SEG, is the power demand of load d in SEG at time t, is the transmission power of HVDC transmission line l at time t, l is the HVDC transmission line index, is the HVDC transmission line index, is the adjustment factor of generator g in SEG for the prediction deviation of wind farm w at time t, The load shedding rate of the SEG wind farm w at time t;

[0028] Constraints on the SEG line power flow:

[0029]

[0030] where A S,G , A S,W , A S,E and A S,DC are the incident matrices of the bus generator, bus wind farm, bus HVDC link, and bus ESS in the SEG respectively, P t S , P t S,de , P t S,ch (P t S,dch ) and are the output power of the generator, the actual wind power injection, the charge (discharge) power of the energy storage system, and the load vector in the SEG respectively, Φ S is the phase shift factor matrix of the SEG, is the row of the Φ S matrix related to the line ij in the SEG;

[0031] Using DR joint chance constraints to constrain the line power flow:

[0032]

[0033] where is the distribution fuzzy set of the random vector ξ t , is the risk parameter, is the transmission capacity of the SEG line ij, is the probability distribution, is the set of transmission lines in the SEG.

[0034] Constraints on the SEG generator:

[0035] Constraining the start stop and on / off state logical consistency between:

[0036]

[0037] The minimum on-time and minimum off-time constraints are as follows:

[0038]

[0039] where t sis the time period index, is the minimum running time after the generator g in SEG is started, is the start - up state variable of the generator g in SEG at time t, is the state variable of the generator g in SEG at time ts, is the minimum shutdown time after the generator g in SEG is shut down, is the shutdown state variable of the generator g in SEG at time t;

[0040] Ensure that the generated electricity is combined with PFR reserve, SFR reserve and up - down regulation reserve and within the generation limit:

[0041]

[0042] where, is the day - ahead planned output of the generator g in SEG at time t, is the primary frequency regulation reserve of the generator g in SEG at time t, is the secondary frequency regulation reserve of the generator g in SEG at time t, is the upward regulation reserve of the generator g in SEG at time t, is the upper limit value of the output of the generator g in SEG, is the downward regulation reserve of the generator g in SEG at time t, is the lower limit value of the output of the generator g in SEG;

[0043] The DR joint chance constraint ensures that the reserve activation is within the up - down reserve capacity for all distributions in the fuzzy set and is at least 1 - ∈ R :

[0044]

[0045] where, is the adjustment factor vector of the generator g in SEG at time t, is the power prediction deviation vector of the wind farm in SEG at time t, is the set of generators in SEG, is the risk of allowing the generator reserve constraint in SEG to exceed the limit;

[0046] Apply restrictions to the PFR reserve and up - down reserve capacity respectively:

[0047]

[0048]

[0049] where, is the maximum primary frequency regulation reserve capacity of generator g in SEG, is the maximum upward regulation reserve capacity of generator g in SEG, is the maximum downward regulation reserve capacity of generator g in SEG;

[0050] Restrict the maximum rate at which the power output of generator g in the sending - end power grid can increase or decrease within a specific time:

[0051]

[0052] where, is the day - ahead planned output of generator g in SEG at time t - 1, is the upward ramp - rate capacity of generator g in SEG, is the state variable of generator g in SEG at time t - 1, is the start - up state variable of generator g in SEG at time t, is the maximum upward ramp - rate capacity at the start - up moment of generator g in SEG, is the shut - down state variable of generator g in SEG at time t, is the maximum downward ramp - rate capacity at the shut - down moment of generator g in SEG;

[0053] Sending - end power grid non - critical load constraint:

[0054]

[0055] where, is the curtailment of non - critical load d in SEG at time t, is the demand power of non - critical load d in SEG at time t, is the set of non - critical loads in SEG;

[0056] Constraints of wind farms in the sending - end power grid:

[0057]

[0058] where, is the virtual inertia of wind farm w in SEG at time t, is the flag variable for providing virtual inertia of wind farm w in SEG at time t, RoCoF max is the maximum value of the allowable system frequency change rate, is the virtual damping of the wind farm in SEG, is the flag variable for providing virtual damping of wind farm w in SEG, Δf max is the maximum allowable system frequency deviation, is the set of wind farms in SEG, For the SEG wind farm, the chance-constrained allows the risk of over-limit, is the prediction deviation of wind farm w in the SEG at time t;

[0059] Constraints of the energy storage system in the sending-end power grid:

[0060] Ensure that the discharge power and the sum of the additional output power for frequency support are within the maximum discharge power range, specifically as follows:

[0061]

[0062] Among them, is the virtual inertia of energy storage device e in the SEG at time t, is the flag variable for providing virtual inertia of energy storage device e in the SEG at time t, is the virtual damping of energy storage system e in the SEG, is the flag variable for providing virtual damping of energy storage system e in the SEG, is the flag variable for the discharge behavior of energy storage system e at time t;

[0063] Constrain the charging power as follows:

[0064]

[0065] Among them, is the flag variable for the charging behavior of energy storage system e at time t, is the charging capacity of energy storage system e; variables related to the energy storage system are non-negative:

[0066]

[0067] Among them, is the discharge capacity of energy storage system e;

[0068] At the same time, simultaneous discharge and charging behaviors are prohibited:

[0069]

[0070] Time conversion of the state of charge level:

[0071]

[0072] Among them, and are the coefficients of self-discharge rate, charging efficiency, and discharge efficiency, is the state of charge level of energy storage system e at time t;

[0073] Level limit of the state of charge SoC:

[0074]

[0075] Among them, is the lower limit value of the energy storage system e, is the upper limit value of the energy storage system e;

[0076] The SoC level of the cycle returns to its initial level

[0077]

[0078] Among them, is the charge level of the energy storage system e at the last moment of scheduling.

[0079] Preferably, the receiving - end power grid operation restriction module includes:

[0080] The operation constraints of the system - wide power balance of the receiving - end power grid, line power flow, generators, wind farms, and energy storage systems are as follows:

[0081]

[0082]

[0083]

[0084] Among them, is the day - ahead planned output of generator g in REG at time t, is the load - shedding rate of wind farm w in REG at time t, is the predicted value of wind farm w in REG at time t, g is the generator index, is the set of generators in REG, w is the wind farm index, is the set of wind farms in REG, is the discharge power of energy storage system e in REG at time t, is the charging power of energy storage system e in REG at time t, e is the energy storage system index, ε S is the set of energy storage systems in SEG, l is the HVDC transmission line index, is the HVDC transmission line index, is the transmission power of HVDC transmission line l at time t, is the power demand of load d in REG at time t, d is the load index, is the set of loads in REG, is REG, is the load - shedding rate of wind farm w in REG at time t, is REG, Φ related to line ij in REG S Row of the matrix, A R,G Bus generator in REG, P t R Output power of the generator in REG, A R,W Bus wind farm in REG, P t R,de Actual wind power injection in REG, A R,DC Bus ESS incidence matrix in REG, A R,E Bus HVDC link in REG, P t R,dch Discharge power of the energy storage system in REG, P t R,ch Charging power of the energy storage system in REG Load vector in REG Probability distribution Constraint on the line power flow in REG Set of transmission lines in REG Risk parameter in REG In REG Shutdown status variable of generator g in REG at time t Status variable of generator g in REG at time t Status variable of generator g in REG at time t - 1 Startup status variable of generator g in REG at time tr Status variable of generator g in REG at time tr Minimum running time after startup of generator g in REG, t r Time period index Minimum shutdown time after shutdown of generator g in REG Shutdown status variable of generator g in REG at time t Status variable of generator g at time tr Day-ahead scheduled output of generator g in REG at time t In REG Secondary frequency regulation reserve of generator g in REG at time t Upward regulation reserve of generator g in REG at time t Output upper limit value of generator g in REG Downward regulation reserve of generator g in REG at time t In REG Adjustment factor vector of generator g in REG at time t is the power prediction deviation vector of the REG wind farm at time t, is the upward regulation reserve of generator g in REG at time t, is the downward regulation reserve of generator g in REG at time t, in REG, in REG, in REG, in REG, in REG, in REG, is the maximum upward ramp rate of generator g in REG at the start-up moment, is the shutdown state variable of generator g in REG at time t, in REG, in REG, is the set of non-critical loads in REG, is the virtual inertia of wind farm w in REG at time t, is the flag variable for wind farm w in REG to provide virtual inertia, RoCoF max is the maximum allowable value of the system frequency change rate, is the virtual damping of the wind farm in REG, is the flag variable for wind farm w in REG to provide virtual damping, Δf max is the maximum allowable frequency deviation of the system, is the prediction deviation of wind farm w in REG at time t, in REG, is the coefficient of the self-discharge rate in REG, is the coefficient of the charging efficiency in REG, is the coefficient of the discharging efficiency in REG, is the state of charge of the energy storage system e at the last moment of scheduling in REG, is the lower limit value of the energy storage system e in REG, is the upper limit value of the energy storage system e in REG.

[0085] Preferably, the HVDC link constraint module is specifically:

[0086] The operation constraints of the HVDC link are:

[0087]

[0088] Among them, is the transmission power of the high-voltage direct current transmission line l at time t, is the emergency power increase of the high-voltage direct current transmission line l at time t, α DCis the overload rate of the high - voltage direct - current (HVDC) transmission line l, is the transmission capacity of the HVDC transmission line l.

[0089] Preferably, it is characterized in that the objective function of the DR - FCCS model is:

[0090]

[0091] Wherein, is the start - up cost of generator g in SEG, is the start - up flag variable of generator g in SEG at time t, is the shutdown cost of generator g in SEG, is the shutdown flag variable of generator g in SEG at time t, is the no - load cost of generator g in SEG, is the status variable of generator g in SEG at time t, is the power - generation cost of generator g in SEG, is the day - ahead planned value of generator g in SEG at time t, is the primary frequency - regulation reserve cost of generator g in SEG, is the primary frequency - regulation reserve capacity of generator g in SEG at time t, is the secondary frequency - regulation reserve cost of generator g in SEG, is the secondary frequency - regulation reserve capacity of generator g in SEG at time t, is the upward regulation cost of generator g in SEG, is the upward regulation reserve capacity of generator g in SEG at time t, is the downward regulation cost of generator g in SEG, is the downward regulation reserve capacity of generator g in SEG at time t, is the reduction amount of non - critical load d in SEG at time t; is the start - up cost of generator g in REG, is the start - up flag variable of generator g in REG at time t, is the shutdown cost of generator g in REG, is the shutdown flag variable of generator g in REG at time t, is the no - load cost of generator g in REG, is the status variable of generator g in REG at time t, is the power - generation cost of generator g in REG, is the day - ahead planned value of generator g in REG at time t, is the primary frequency - regulation reserve cost of generator g in REG, is the primary frequency regulation reserve capacity of generator g in REG at time t, is the secondary frequency regulation reserve cost of generator g in REG, is the secondary frequency regulation reserve capacity of generator g in REG at time t, is the upward regulation cost of generator g in REG, is the upward regulation reserve capacity of generator g in REG at time t, is the downward regulation cost of generator g in REG, is the downward regulation reserve capacity of generator g in REG at time t, is the curtailment of non-critical load d in REG at time t, t is the time period index, T is the set of time periods, d is the load index, is the set of non-critical loads in REG, is the cost of load curtailment of non-critical load d in REG at time t, is the cost of load curtailment of non-critical load d in SEG at time t.

[0092] Preferably, the uncertainty of the DR-FCCS model constructed based on the Wasserstein metric fuzzy set description in step S1 is specifically:

[0093] Use the type-1 Wasserstein distance to quantify the distance between potential probability distributions and The corresponding Wasserstein metric fuzzy set is defined as:

[0094]

[0095] where, represents the set of all underlying distributions supporting Ξ, and Ξ is set to δ is the radius of the Wasserstein ball, is the fuzzy set;

[0096] Define the type-1 Wasserstein distance as:

[0097]

[0098] where, are the marginal values of ξ and respectively and is the joint distribution of ξ and, ξ is a random variable, is the reference random variable.

[0099] Preferably, the conversion of the joint probability constraint to an individual probability constraint is specifically:

[0100]

[0101] Among them, λ is the optimization variable, δ is the radius of the Wasserstein ball, and z i is the optimization variable, N is the sample size, β is the optimization variable, is an affine function of the optimization variable x, is the auxiliary parameter, ξ i is the sample i, b k (x) is an affine function of the optimization variable x, ∈ is the risk coefficient, K is the dimension of the random variable, and λ is the optimization variable, represents the real number field.

[0102] Preferably, the sequential solution algorithm is used to solve the optimized DR-FCCS model as follows:

[0103]

[0104] (X,Z) ∈ Ψ

[0105] Among them, X and Z are vectors of continuous variables and binary variables respectively, Ψ is the constraint set, and f(X,Z) is the objective function, is the reconstruction of the worst-case CVaR constraint.

[0106] In a second aspect, an embodiment of the present invention provides an asynchronous interconnected power grid frequency security constraint coordinated scheduling system, including:

[0107] A construction module that constructs a DR-FCCS model including a generator response module under uncertainty, a sending-end power grid operation cost limit module, a receiving-end power grid operation limit module, and an HVDC link constraint module;

[0108] A metric module that describes the uncertainty of the DR-FCCS model constructed in step S1 based on the Wasserstein metric fuzzy set;

[0109] A constraint module that approximately solves the DR joint chance constraint based on conditional value at risk, converts the joint probability constraint into an individual probability constraint, and optimizes the DR-FCCS model;

[0110] An output module that uses the sequential solution algorithm to solve the optimized DR-FCCS model to obtain a coordinated scheduling scheme for the asynchronous interconnected system.

[0111] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-mentioned asynchronous interconnected power grid frequency security constraint coordinated scheduling method.

[0112] Fourthly, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, and when the computer program is executed by a processor, the steps of the above asynchronous interconnected power grid frequency security constraint collaborative scheduling method are implemented.

[0113] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above asynchronous interconnected power grid frequency security constraint collaborative scheduling method are implemented.

[0114] Sixthly, an embodiment of the present invention provides an electronic device, including a computer program, and when the computer program is executed by the electronic device, the steps of the above asynchronous interconnected power grid frequency security constraint collaborative scheduling method are implemented.

[0115] Compared with the prior art, the present invention has at least the following beneficial effects:

[0116] An asynchronous interconnected power grid frequency security constraint collaborative scheduling method. The model fully utilizes and collaboratively optimizes the frequency modulation resources of the entire source-network-load-storage link to ensure the system frequency security after a fault. The model designs a Wasserstein distribution robust joint chance constraint to handle the uncertainty of wind power. The designed Wasserstein distribution robust joint chance constraint method does not need to rely on probability distribution assumptions, can make full use of the existing historical data. Compared with the existing research on individual chance constraint modeling, the joint constraint probability modeling can provide stronger probability guarantee for the security of the entire system. The sequential iteration algorithm can efficiently solve the complex DR-FCCS model.

[0117] Furthermore, the HVDC line modeling considers two states of normal power transmission and emergency power support, can provide support for the system frequency security, and realize the mutual assistance of SEG and REG frequency resources.

[0118] Furthermore, the Wasserstein distribution robust joint chance constraint method does not need to rely on probability distribution assumptions, can make full use of the existing historical data, and realizes effective decision-making under uncertain environments.

[0119] Furthermore, compared with the existing research on individual chance constraint modeling, the joint constraint probability modeling can provide stronger probability guarantee for the security of the entire system.

[0120] Furthermore, it can efficiently solve the complex DR-FCCS model.

[0121] It can be understood that the beneficial effects of the second to sixth aspects above can refer to the relevant descriptions in the first aspect above, and will not be elaborated here.

[0122] In summary, the present invention realizes the optimization and integration of the cross-system frequency regulation ability through the collaborative scheduling of resources in the whole process of source-network-load-storage; adopts the Wasserstein distributionally robust joint chance constraint to handle the uncertainty of wind power, breaks through the limitations of traditional probability distribution assumptions, constructs a joint constraint framework based on historical data, and improves the system safety probability guarantee intensity compared with the separate chance constraint; develops a sequential iteration algorithm to effectively solve the problem of solving complex non-linear models.

[0123] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0124] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the accompanying drawings required to be used in the embodiments of the present application will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present application, and those of ordinary skill in the art can also obtain other accompanying drawings based on these drawings without creative efforts.

[0125] Figure 1 It is a flowchart of a frequency constraint coordinated scheduling method for an asynchronous AC network under uncertainty based on distributional robustness;

[0126] Figure 2 It is a schematic diagram of the DR-FCCS model module;

[0127] Figure 3 It is a flowchart of the sequential solution algorithm;

[0128] Figure 4 It is a schematic diagram of an asynchronous power grid interconnected with multiple HVDC links;

[0129] Figure 5 It is a schematic diagram of an asynchronous power grid test system;

[0130] Figure 6 It is a schematic diagram of the wind power prediction value and total load profile of the sending-end power grid;

[0131] Figure 7 It is a schematic diagram of the wind power prediction value and total load profile of the receiving-end power grid;

[0132] Figure 8 It is a comparison chart of the total cost under three models;

[0133] Figure 9 It is a schematic diagram of a computer device provided by an embodiment of the present invention;

[0134] Figure 10 It is a block diagram of an electronic device provided by an embodiment of the present invention.

[0135] Among them, 60. computer device; 61. processor; 62. memory; 63. computer program; 600. electronic device; 610. processing unit; 620. storage unit; 6201. random access storage unit; 6202. cache storage unit; 6203. read-only storage unit; 6204. program / utilities; 6205. program module; 630. bus; 640. display unit; 650. input / output interface; 660. network adapter; 700. external device. Detailed implementation manners

[0136] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0137] In the description of the present invention, it should be understood that the terms "include" and "comprise" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0138] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0139] It should be further understood that the term " / and" as used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations. For example, A and / or B can represent three cases: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present invention generally represents an "or" relationship between the related objects before and after.

[0140] It should be understood that although the terms first, second, third, etc. may be used in the embodiments of the present invention to describe preset ranges, etc., these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0141] Depending on the context, as used herein, the word "if" can be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detecting (stated condition or event)" can be interpreted as "when determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)".

[0142] Various structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the accompanying drawings. These figures are not drawn to scale, where for the purpose of clear expression, some details are enlarged and some details may be omitted. The shapes of various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. And those skilled in the art can design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0143] The present invention provides an asynchronous interconnected power grid frequency security constraint collaborative scheduling method, constructs a source-network-load-storage collaborative scheduling framework, and integrates cross-regional frequency regulation resources; adopts Wasserstein distributionally robust joint chance constraints, and transforms the joint probability constraint into a solvable form through CVaR approximation, enhancing the system security probability guarantee while avoiding traditional probability distribution assumptions; designs a sequential solution strategy, and breaks through the calculation bottleneck of high-dimensional non-linear models through modular iterative solution. This method realizes the coordinated optimization of power grid frequency security and operation economy under uncertain environments.

[0144] Embodiment 1

[0145] Please refer to Figure 1 , an asynchronous interconnected power grid frequency security constraint collaborative scheduling method of the present invention includes the following steps:

[0146] S1. Construct a DR-FCCS model;

[0147] Please refer to Figure 2 , constructing a DR-FCCS model mainly includes five constraint modules: a generator response module under uncertainty, an objective function establishment module, a sending-end power grid operation cost limitation module, a receiving-end power grid operation limitation module, and an HVDC link constraint module, which are specifically as follows:

[0148] 1. Generator response module under uncertainty:

[0149] Taking the sending-end power grid as an example for illustration.

[0150] 1) Wind power uncertainty modeling:

[0151] The wind farms in the sending-end power grid The available wind power of the wind farm is modeled as follows:

[0152]

[0153] Wherein, and are the available wind power, the wind power prediction value, and the uncertainty prediction error of the wind farm w at the SEG, respectively.

[0154] To provide frequency support in case of an accident, it is considered to operate the wind farm in a load shedding mode so as to store energy for virtual inertia and damping. The output power of the wind farm in the load shedding mode is expressed as:

[0155]

[0156] Wherein, and are the actual wind power injection and the unused power reserved in the load shedding mode, is the load shedding ratio to be optimized.

[0157] 2) Modeling the response of the generator to the uncertain prediction error:

[0158] Following the affine decision rule, the response of each generator to the uncertain prediction error of the wind farm in the sending-end power grid is:

[0159]

[0160] Wherein, and are the actual power and the rated power of the generator g in the SEG, corresponding to ξ t ≠0 and ξ t =0. The vector is affected by the optimization, describing the response of the generator to the prediction error of the wind farm w.

[0161] Similarly, for the receiving-end power grid, a similar modeling method is used to describe the uncertainty of the wind power and the response of the generator to the uncertain prediction error.

[0162] 2. Objective function:

[0163] The objective function of the DR-FCCS model is to minimize the total operating cost of the asynchronous grid:

[0164] minC S +C R

[0165]

[0166] The first eight items respectively represent the start-up cost, shut-down cost, no-load cost, operation cost, PFR reserve cost, SFR reserve cost, up and down regulation reserve cost, and load shedding cost in the sending-end power grid. It is the value of lost load (VOLL) of the sending-end power grid load d. The same applies to the receiving-end power grid.

[0167] 3. Sending-end power grid operation cost limitation module

[0168] 1) Total system power balance constraint of SEG:

[0169] The total system power balance of SEG under uncertain conditions is:

[0170]

[0171] Substitute and into the above formula, and equivalently obtain:

[0172]

[0173] 2) Constraint on the power flow of SEG lines:

[0174] Based on the shifted DC power flow model, the power flow calculation on each line of SEG is as follows:

[0175]

[0176] Among them, and are the incident matrices of the bus generator, bus wind farm, bus HVDC link, and bus ESS respectively. P t S , P t S,de , P t S,ch (P t S,dch ) and are the output power of the generator, the actual wind power injection, the charging (discharging) power of the energy storage system, and the load vector respectively. Φ S is the phase shift factor matrix of SEG, which relates the line power flow to the node power injection. is the row of the Φ S matrix related to line ij.

[0177] The line power flow involves uncertainty. To manage uncertainty and limit the risk of line overload, we use DR joint chance constraints to constrain the line power flow:

[0178]

[0179] Among them, is the random vector ξ t 's fuzzy set. The risk parameter is the combined penalty probability predefined for line overload, is the transmission capacity of the SEG line ij.

[0180] 3) Constraints on the SEG generator:

[0181] Constrain the startup shutdown and on / off state to strengthen the logical consistency between them:

[0182]

[0183] Limit the minimum on-time and minimum off-time:

[0184]

[0185] Ensure that the power generation is combined with the PFR reserve, SFR reserve and up / down regulation reserve and within the generation limit:

[0186]

[0187] The DR combined chance constraint ensures that the reserve activation is within the up / down reserve capacity for all distributions in the fuzzy set and is at least 1 - ∈ R :

[0188]

[0189] Apply restrictions to the PF reserve and up / down reserve capacity respectively:

[0190]

[0191] Limit the maximum rate at which the power output of the generator g in the sending-end power grid can increase or decrease within a specific time:

[0192]

[0193] 4) Constraints on non-critical loads in the sending-end power grid:

[0194] The reduction amount of non-critical loads should not exceed the loads themselves.

[0195]

[0196] 5) Constraints on wind farms in the sending-end power grid:

[0197] Unused power reserved by the wind farm to support virtual inertia and damping configuration There is uncertainty. In this paper, a DR joint chance constraint is designed, which ensures that the probability that all wind farms can provide the promised virtual inertia and damping is at least the probability level for all distributions within the fuzzy set

[0198]

[0199] is related to Δf(τ), making the above formula very complex. Approximate this formula. Obviously, can be relaxed to:

[0200]

[0201] The DR joint chance constraint is approximated as:

[0202]

[0203] 6) Constraints of the sending-end power grid on the energy storage system:

[0204] Ensure that the sum of the discharge power and the additional output power for frequency support is within the maximum discharge power range.

[0205]

[0206] Contains and Δf(τ). Similarly, approximate the above formula to get:

[0207]

[0208] Constrain the charging power Apply constraints.

[0209]

[0210] Variables related to the energy storage system are non-negative.

[0211]

[0212] Simultaneous discharging and charging behaviors are prohibited.

[0213]

[0214] Time conversion of the state of charge (SoC) level. Where and It is the coefficient of self-discharge rate, charging efficiency, and discharging efficiency.

[0215]

[0216] The level limit of SoC.

[0217]

[0218] The SoC level of the last cycle is restored to its initial level

[0219]

[0220] 4. Receiver-side power grid operation constraint module:

[0221] Similarly to the sender-side power grid, the operation constraints of system-wide power balance, line power flow, generators, wind farms, and energy storage systems in the receiver-side power grid are similar to those in the sender-side power grid, and the constraints are as follows:

[0222]

[0223]

[0224] 5. HVDC link constraint module

[0225] The operation constraints of the HVDC link are:

[0226]

[0227] Ensures the emergency power setpoint of the HVDC link, i.e., the base-case power setpoint and the emergency power increase The sum should be within its short-term emergency limit, α DC is the emergency overload rate.

[0228]

[0229] Apply a ramp constraint to the HVDC constraint within two consecutive hours.

[0230] S2. The Wasserstein metric fuzzy set describes uncertainty;

[0231] The uncertainty of the DR-FCCS model lies in the wind power prediction errors of SEG and REG. Selecting the type of fuzzy set to describe uncertainty is a key issue before the DR joint chance-constrained reconstruction. The present invention selects the Wasserstein metric fuzzy set, and its advantages are:

[0232] 1) It makes good use of the available data samples

[0233] 2) It can promote the refactoring of easily handled constraints

[0234] 3) It has an asymptotic consistency guarantee.

[0235] Taking n as a random vector as an example, this paper introduces the use of the Wasserstein metric fuzzy set to describe uncertainty. Given a series of samples of the uncertainty ξ The empirical distribution based on N samples is:

[0236]

[0237] where Ι{·} is the indicator function.

[0238] This invention uses the type-1 Wasserstein distance to quantify the potential probability distribution and the distance between them. The corresponding Wasserstein metric fuzzy set is defined as:

[0239]

[0240] where represents the set of all underlying distributions that support Ξ. Using can significantly improve the computational efficiency of the optimization model and has no significant impact on its results. Therefore, Ξ is set to The parameter δ is the radius of the Wasserstein ball, which controls the size of the fuzzy set and enables the adjustment of the conservatism degree of the DR-FCCS model.

[0241] Type-1 Wasserstein distance is defined as:

[0242]

[0243] where are the marginal values of ξ and respectively and the joint distribution of.

[0244] For the uncertainties and in the DR-HCCS problem, this invention uses the Wasserstein-metric fuzzy set to model them.

[0245] S3. Approximately solve the DR joint chance constraint by CVaR and convert the joint probability constraint into an individual probability constraint;

[0246] Consider a general DR joint constraint:

[0247]

[0248] Among them, is an affine mapping of the optimization variable , and ξ is an n-dimensional random vector

[0249] A simple method for approximating joint chance constraints is to decompose them into multiple individual chance constraints, called Bonferroni approximation; however, this approximation that lacks consideration of the correlation between uncertain constraints may be too conservative. CVaR approximation performs more reasonably in the case where multiple uncertain constraints are correlated. Therefore, this paper uses CVaR approximation to solve the DR joint chance constraint.

[0250] First, equivalently transform the joint probability constraint into an individual probability constraint:

[0251]

[0252] Among them, the choice of α does not affect the feasible set defined by Equation (61).

[0253] The present invention selects the worst-case CvaR approximation constraint to approximately obtain:

[0254]

[0255] Define Proposition 1:

[0256] For any fixed the worst-case CvaR constraint is equivalent to the following linear programming:

[0257]

[0258] S4. Use the sequential solution algorithm to solve the optimized DR-FCCS model to obtain the coordinated optimization dispatch scheme for the asynchronous interconnected power grid and achieve system coordination.

[0259] The DR-FCCS model can be converted into a Mixed-Integer Second-Order Cone Program (MISOCP) using Proposition 1. The present invention represents the obtained MISOCP in a compact form:

[0260]

[0261] (X, Z) ∈ Ψ

[0262] Among them, X and Z are vectors of continuous variables and binary variables respectively, Ψ represents the constraint set defined above, and f(X, Z) represents the objective function defined above, represents the reconstruction of the worst-case CVaR constraint defined above.

[0263] According to Proposition 1, when the scaling parameter is fixed, the optimization problem is a MISOCP. The choice of α will affect the quality of the CVaR approximation of the DR joint chance constraint. Therefore, the choice of α is a key issue.

[0264] The present invention proposes a sequential solution method that sequentially optimizes (X, Z) and α to solve the variables in the optimization problem, namely the scaling parameter α. For an optimization model with bi-convex constraints, the convergence analysis of this sequential solution algorithm has been guaranteed by literature calculations, and the method of the present invention will converge to a finite limit.

[0265] Please refer to Figure 3 , and the steps of the sequential solution algorithm are as follows:

[0266] S401. Initialize δ gap ← 0.01, f 0 ← +∞, i ← 1, i max ← 100,

[0267] S402. Set (X i , Z i ) as When |(f i - f i-1 )| / f i <δ gap or i ≥ i max , jump to step S405; otherwise, jump to step S403;

[0268] S403. Set as

[0269] S404. Update i ← i + 1 and return to step S402;

[0270] S405. Output (X i , Z i ) and f i .

[0271] Those skilled in the art can understand that various aspects of the present invention can be implemented as a system, a method, or a program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuitry", "module", or "platform" here.

[0272] Example 2

[0273] The present invention provides an asynchronous interconnected power grid frequency security-constrained coordinated scheduling system, which can be used to implement the above-mentioned asynchronous interconnected power grid frequency security-constrained coordinated scheduling method. Specifically, the asynchronous interconnected power grid frequency security-constrained coordinated scheduling system includes a construction module, a measurement module, a constraint module, and an output module.

[0274] Among them, the construction module constructs a DR-FCCS model including a generator response module under uncertainty, a sending-end power grid operation cost limitation module, a receiving-end power grid operation limitation module, and an HVDC link constraint module.

[0275] The measurement module describes the uncertainty of the DR-FCCS model constructed in step S1 based on the Wasserstein metric fuzzy set.

[0276] The constraint module approximately solves the DR joint chance constraint based on conditional value at risk, converts the joint probability constraint into an individual probability constraint, and optimizes the DR-FCCS model.

[0277] The output module uses the sequential solution algorithm to solve the optimized DR-FCCS model and obtains the coordinated scheduling scheme of the asynchronous interconnected system.

[0278] Embodiment 3

[0279] The present invention provides a terminal device, which includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may also be other general-purpose processors, a graphics processing unit (GPU), a tensor processing unit (TPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function. The processor described in the embodiment of the present invention can be used for the operations of the asynchronous interconnected power grid frequency security-constrained coordinated scheduling method, including:

[0280] Construct a DR-FCCS model that includes a generator response module under uncertainty, a sending-end power grid operation cost limit module, a receiving-end power grid operation limit module, and an HVDC link constraint module; describe the uncertainty of the DR-FCCS model constructed based on the Wasserstein metric fuzzy set; approximately solve the DR joint chance constraint based on conditional value at risk, convert the joint probability constraint into an individual probability constraint, and optimize the DR-FCCS model; use the sequential solution algorithm to solve the optimized DR-FCCS model to obtain a coordinated optimal scheduling scheme for the asynchronous interconnected power grid and achieve system coordination.

[0281] Please refer to Figure 9 , the terminal device is a computer device. The computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, it implements the asynchronous interconnected power grid frequency security constraint coordinated scheduling method in the embodiment. To avoid repetition, it will not be elaborated here one by one. Alternatively, when the computer program 63 is executed by the processor 61, it implements the functions of each model / unit in the asynchronous interconnected power grid frequency security constraint coordinated scheduling system in the embodiment. To avoid repetition, it will not be elaborated here one by one.

[0282] The computer device 60 can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art can understand that Figure 9 merely examples of the computer device 60, which do not constitute a limitation on the computer device 60. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0283] The so-called processor 61 may be a central processing unit (CPU), or may also be other general-purpose processors, a graphics processing unit (GPU), a tensor processing unit (TPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0284] The memory 62 can be an internal storage unit of the computer device 60, such as the hard disk or memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk equipped on the computer device 60, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc.

[0285] Furthermore, the memory 62 can also include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0286] Please refer to Figure 10 , the terminal device is the electronic device 600, and the electronic device 600 is presented in the form of a general computing device. The components of the electronic device can include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0287] Among them, the storage unit stores program codes, and the program codes can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present invention described in the method part of this specification. For example, the processing unit 610 can execute as Figure 1 shown in the steps.

[0288] The storage unit 620 can include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 6201 and / or a cache storage unit 6202, and can further include a read-only storage unit (ROM) 6203.

[0289] The storage unit 620 can also include a program / utilities 6204 having a set (at least one) of program modules 6205. Such program modules 6205 include but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0290] The bus 630 can represent one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0291] The electronic device 600 can also communicate with one or more external devices 700 (such as a keyboard, a pointing device, a Bluetooth device, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (such as a router, a modem). Such communication can be carried out through the input / output interface 650. Moreover, the electronic device 600 can also communicate with one or more networks (such as a local area network, a wide area network, and / or a public network, such as the Internet) through the network adapter 660. The network adapter 660 can communicate with other modules of the electronic device 600 through the bus 630. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0292] Embodiment 4

[0293] The present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device and is used for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. It can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. The computer-readable storage medium provides a storage space, and this storage space stores the operating system of the terminal. And, in this storage space, one or more instructions suitable for being loaded and executed by a processor are also stored, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples of the computer-readable storage medium here include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0294] The computer-readable storage medium also includes a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, radio frequency, etc., or any suitable combination of the above.

[0295] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network or a wide area network, or can be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0296] One or more instructions stored in the computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the asynchronous interconnected power grid frequency security constraint coordinated scheduling method in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor as follows:

[0297] Construct a DR-FCCS model that includes a generator response module under uncertainty, a sending-end power grid operation cost limit module, a receiving-end power grid operation limit module, and an HVDC link constraint module; describe the uncertainty of the constructed DR-FCCS model based on the Wasserstein metric fuzzy set; approximately solve the DR joint chance constraint based on conditional value at risk, convert the joint probability constraint into an individual probability constraint, and optimize the DR-FCCS model; use a sequential solution algorithm to solve the optimized DR-FCCS model to obtain an asynchronous interconnected power grid coordinated optimization scheduling scheme and achieve system coordination.

[0298] In each of the embodiments provided in the present application, the databases involved may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on a blockchain, etc., and is not limited thereto. In each of the embodiments provided in the present application, the processors involved may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., and is not limited thereto.

[0299] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0300] Please refer to Figure 5 , which proves the effectiveness of the DR-FCCS method proposed by the present invention on an asynchronous power grid through a case. The method of the present invention is obtained by interconnecting two improved IEEE 24-bus reliability systems and three HVDC links; 5 wind farms and 2 energy storage systems are added to the sending-end power grid, and 2 wind farms and 2 energy storage systems are added to the receiving-end power grid. The wind farm capacity in the sending-end power grid accounts for 37.6% of the total power generation capacity, which is higher than 19% of the REG, while the load demand in the receiving-end power grid is greater than that in the sending-end power grid. The wind power prediction values and total load profiles in the sending-end power grid and the receiving-end are as Figure 6 and Figure 7 shown.

[0301] The frequency-related parameters are set as follows:

[0302] f0 = 50Hz, RoCoF max = 0.5Hz / s, Δf max = 0.5Hz,

[0303] The generator with a maximum capacity of 600MW located at R23 trips. 25 wind power prediction error training samples and 5000 wind power prediction error test samples are generated from the Gaussian distribution, and their means are set to 0 for all t ∈ τ, and the standard deviation is set to 5% of the wind power prediction value.

[0304] Solve the DR-FCCS using training samples and evaluate the out-of-sample performance of the solution using test samples. Set

[0305] The present invention compares the following three FCCS methods to demonstrate the performance of the model proposed by the present invention.

[0306] M1: FCCS model without the support of HVDC emergency power supply;

[0307] M2: FCCS model with the support of HVDC emergency power supply, but without utilizing the frequency support of wind farms, ESSs, and non-critical load shedding;

[0308] M3: The model proposed by the present invention.

[0309] In this case study, the magnitude of the generated power disturbance is increased from 500 MW to 900 MW in steps of 100 MW, and its impact on the three FCCS methods is observed. The results are as Figure 8 shown. The total operating cost effect of the model proposed by the method of the present invention is significantly lower than that of M1 and M2.

[0310] In summary, an asynchronous interconnected power grid frequency security constraint coordinated scheduling method and system according to the present invention integrates frequency modulation resources in all links of the source-network-load-storage to achieve complementary cross-regional frequency modulation capabilities and improve the frequency dynamic response efficiency. Based on the Wasserstein distributionally robust joint chance constraint, historical data is directly used to model the uncertainty of wind power, avoiding the deviation of traditional probability distribution assumptions, and the joint constraint provides a more stringent system security probability guarantee than individual constraints. The proposed sequential iterative algorithm decomposes the high-dimensional non-linear optimization problem into modules that can be solved in parallel, significantly reducing the computational complexity and improving the practicality of the model. While ensuring frequency security under extreme faults, the system operating cost is optimized, providing a scheduling scheme that takes into account both reliability and economy for a high-proportion new energy power grid.

[0311] The above content is only to illustrate the technical idea of the present invention and cannot be used to limit the protection scope of the present invention. Any modification made on the basis of the technical solution according to the technical idea proposed by the present invention falls within the protection scope of the claims of the present invention.

Claims

1. A frequency security-constrained collaborative scheduling method for an asynchronous interconnected power grid, characterized in that It includes the following steps: Construct a DR-FCCS model that includes a generator response module under uncertainty, a sending-end power grid operation cost limit module, a receiving-end power grid operation limit module, and an HVDC link constraint module; Describe the uncertainty of the DR-FCCS model constructed based on the Wasserstein metric fuzzy set; Approximately solve the DR joint chance constraint based on conditional value-at-risk, convert the joint probability constraint into an individual probability constraint, and optimize the DR-FCCS model; Use the sequential solution algorithm to solve the optimized DR-FCCS model to obtain a coordinated optimization scheduling scheme for the asynchronous interconnected power grid and achieve system coordination.

2. The frequency security-constrained coordinated dispatching method for an asynchronous interconnected power grid according to claim 1, wherein The generator response module is specifically as follows: Wind farms in the power grid at the sending end The available wind power in the wind farms is modeled as follows: Among them, and are respectively the available wind power, the predicted wind power, and the uncertainty prediction error of wind farm w at SEG. The output power of the wind farm in the load shedding mode is expressed as: Among them, and are the unused power reserved under the actual wind power injection and load shedding modes, is the load shedding ratio to be optimized, is the actual wind power output, is the wind power prediction value, is the wind power prediction error; the response of each generator to the uncertain prediction error of the wind farm in the sending-end power grid is: Among them, is the actual output of generator g in SEG, is the day-ahead planned output of generator g in SEG, is the adjustment factor vector, is the prediction error vector of wind power output in SEG.

3. The frequency security-constrained coordinated dispatching method for an asynchronous interconnected power grid according to claim 1, characterized in that The sending-end power grid operation cost limit module is specifically as follows: The overall system power balance of the SEG under uncertain conditions is: where g is the generator index, is the set of generators in SEG, is the day-ahead scheduled output of generator g in SEG at time t, w is the wind farm index, is the set of wind farms in SEG, is the load shedding rate of wind farm w in SEG at time t, is the predicted value of wind farm w in SEG at time t, e is the energy storage system index, ε S is the set of energy storage systems in SEG, is the discharging power of energy storage system e in SEG at time t, is the charging power of energy storage system e in SEG at time t, d is the load index, is the set of loads in SEG, is the power demand of load d in SEG at time t, is the transmission power of HVDC transmission line l at time t, l is the HVDC transmission line index, is the HVDC transmission line index, is the adjustment factor of generator g in SEG for the prediction deviation of wind farm w at time t, is the load shedding rate of wind farm w in SEG at time t; Constraints on the SEG line power flow: Among them, A S,G , A S,W , A S,E and A S,DC are the incident matrices of the bus generator, bus wind farm, bus HVDC link, and bus ESS in the SEG, respectively. and are the output power of the generator, actual wind power injection, charge (discharge) power of the energy storage system, and load vector in the SEG, respectively. Φ S is the phase-shifting factor matrix of the SEG. is the row of the Φ S matrix related to line ij in the SEG. Use the DR joint chance constraint to constrain the line power flow: Among them, is the distribution fuzzy set of the random vector ξ t , is the risk parameter, is the transmission capacity of the SEG line ij, is the probability distribution, is the set of transmission lines in the SEG; Constraints on the SEG generator: Restricting the startup of the generator Shutdown and the on / off state Logical consistency between: The minimum on-time and minimum off-time constraints are as follows: where t s is the time period index, is the minimum running time after the generator g in the SEG is started, is the start status variable of the generator g in the SEG at time t, is the status variable of the generator g in the SEG at time ts, is the minimum shutdown time after the generator g in the SEG is shut down, is the shutdown status variable of the generator g in the SEG at time t; Ensure that the power generation is combined with PFR reserve, SFR reserve, and up and down regulation reserves and within the generation limit: Among them, is the day-ahead planned output of generator g in SEG at time t, is the primary frequency regulation reserve of generator g in SEG at time t, is the secondary frequency regulation reserve of generator g in SEG at time t, is the upward regulation reserve of generator g in SEG at time t, is the upper limit of the output of generator g in SEG, is the downward regulation reserve of generator g in SEG at time t, is the lower limit of the output of generator g in SEG; DR combined with chance constraints ensures that the reserve activation is within the upper and lower reserve capacities for all distributions in the fuzzy set and is at least 1 - ∈ R : Among them, is the adjustment factor vector of generator g in SEG at time t, is the power prediction deviation vector of the wind farm in SEG at time t, is the generator set in SEG, is the risk of allowing the violation of the generator reserve constraint in SEG; Apply restrictions on the PFR reserve and the up and down reserve capacities respectively: Among them, is the maximum primary frequency regulation reserve capacity of generator g in SEG, is the maximum upward regulation reserve capacity of generator g in SEG, is the maximum downward regulation reserve capacity of generator g in SEG; Restrict the maximum rate at which the power output of generator g in the sending-end power grid can increase or decrease within a specific time: wherein, is the day-ahead scheduled output of generator g in SEG at time t-1, is the upward ramp rate of generator g in SEG, is the state variable of generator g in SEG at time t-1, is the start-up state variable of generator g in SEG at time t, is the maximum upward ramp rate at the start-up time of generator g in SEG, is the shutdown state variable of generator g in SEG at time t, is the maximum downward ramp rate at the shutdown time of generator g in SEG; Sending-end power grid non-critical load constraint: Among them, is the reduction amount of non-critical load d in the SEG at time t, is the demand power of non-critical load d in the SEG at time t, is the set of non-critical loads in the SEG; Constraints on the wind farms in the sending-end power grid: Among them, is the virtual inertia of wind farm w in SEG at time t, is the flag variable for wind farm w in SEG to provide virtual inertia at time t, RoCoF max is the maximum value of the frequency change rate allowed by the system, is the virtual damping of the wind farm in SEG, is the flag variable for wind farm w in SEG to provide virtual damping at time t, Δf max is the maximum frequency deviation allowed by the system, is the set of wind farms in SEG, is the risk of opportunity constraint violation allowed for the wind farm in SEG, is the prediction deviation of wind farm w in SEG at time t; Constraints on the energy storage system in the sending-end power grid: Ensure the discharge power and the sum of the additional output power for frequency support is within the maximum discharge power range, as follows: Among them, is the virtual inertia of the energy storage device e in the SEG at time t, is the flag variable for the energy storage device e in the SEG to provide virtual inertia at time t, is the virtual damping of the energy storage system e in the SEG, is the flag variable for the energy storage system e in the SEG to provide virtual damping, is the flag variable for the discharging behavior of the energy storage system e at time t; Restrict the charging power as follows: wherein, is a flag variable for the charging behavior of the energy storage system e at time t, is the charging capacity of the energy storage system e; The variables related to the energy storage system are non-negative: Among them, is the discharge capacity of the energy storage system e; Simultaneous discharging and charging behaviors are prohibited: Time conversion of the state of charge level: Among them, and are the coefficients of the self-discharge rate, charging efficiency, and discharging efficiency, is the state of charge of the energy storage system e at time t; Level limit of the state of charge SoC: Among them, is the lower limit value of the energy storage system e, is the upper limit value of the energy storage system e; The periodic SoC level returns to its initial level Among them, is the state of charge at the last moment of the energy storage system e scheduling.

4. The frequency security-constrained coordinated dispatching method for an asynchronous interconnected power grid according to claim 1, wherein The receiving-end power grid operation limit module includes: The operation constraints, line power flow, generators, wind farms, and energy storage systems of the system-wide power balance of the receiving-end power grid are constrained as follows: Among them, is the day-ahead scheduled output of generator g in REG at time t, is the load shedding rate of wind farm w in REG at time t, is the predicted value of wind farm w in REG at time t, g is the generator index, is the set of generators in REG, w is the wind farm index, is the set of wind farms in REG, is the discharging power of energy storage system e in REG at time t, is the charging power of energy storage system e in REG at time t, e is the energy storage system index, ε S is the set of energy storage systems in SEG, l is the HVDC transmission line index, is the HVDC transmission line index, is the transmission power of HVDC transmission line l at time t, is the power demand of load d in REG at time t, d is the load index, is the set of loads in REG, is REG, is the load shedding rate of wind farm w in REG at time t, is REG, is Φ related to line ij in REG S row of the matrix, A R,G is the bus generator in REG, is the output power of the generator in REG, A R,W is the bus wind farm in REG, is the actual wind power injection in REG, A R,DC is the bus ESS incidence matrix in REG, A R,E is the bus HVDC link in REG, is the discharging power of the energy storage system in REG, is the charging power of the energy storage system in REG, is the load vector in REG, is the probability distribution, is the constraint on the line power flow in REG, is the set of transmission lines in REG, is the risk parameter in REG, is in REG, is the shutdown status variable of generator g in REG at time t, is the status variable of generator g in REG at time t, is the status variable of generator g at time t - 1 in REG, is the startup status variable of generator g at time tr in REG, is the state variable of generator g in REG at time tr is the minimum running time after startup of generator g in REG, t r is the time period index is the minimum shutdown time after shutdown of generator g in REG is the shutdown state variable of generator g in REG at time t is the state variable of generator g at time tr is the day-ahead scheduled output of generator g in REG at time t in REG is the secondary frequency regulation reserve of generator g in REG at time t is the upward regulation reserve of generator g in REG at time t is the upper limit value of the output of generator g in REG is the downward regulation reserve of generator g in REG at time t in REG is the adjustment factor vector of generator g in REG at time t is the power prediction deviation vector of the wind farm in REG at time t is the upward regulation reserve of generator g in REG at time t is the downward regulation reserve of generator g in REG at time t in REG in REG in REG in REG in REG in REG is the maximum upward ramp rate at the startup moment of generator g in REG is the shutdown state variable of generator g in REG at time t in REG in REG is the set of non-critical loads in REG is the virtual inertia of wind farm w in REG at time t is the flag variable for providing virtual inertia of wind farm w in REG at time t, RoCoF max is the maximum value of the allowable system frequency change rate is the virtual damping of the wind farm in REG is the flag variable for providing virtual damping of wind farm w in REG, Δf max is the maximum allowable frequency deviation of the system is the prediction deviation of wind farm w in REG at time t is in REG, is the coefficient of the self-discharge rate in REG, is the coefficient of the charging efficiency in REG, is the coefficient of the discharging efficiency in REG, is the state of charge at the last moment of the energy storage system e scheduling in REG, is the lower limit value of the energy storage system e in REG, is the upper limit value of the energy storage system e in REG.

5. The frequency security-constrained coordinated dispatching method for an asynchronous interconnected power grid according to claim 1, wherein The HVDC link constraint module is specifically as follows: The operation constraints of the HVDC link are: Among them, is the transmission power of the high-voltage direct current (HVDC) transmission line l at time t, is the emergency power increase of the HVDC transmission line l at time t, and α DC is the overload rate of the HVDC transmission line l, is the transmission capacity of the HVDC transmission line l.

6. The frequency security-constrained collaborative scheduling method for an asynchronous interconnected power grid according to any one of claims 1 to 5, characterized in that The objective function of the DR-FCCS model is: Among them, is the start-up cost of generator g in SEG, is the start-up flag variable of generator g at time t in SEG, is the shut-down cost of generator g in SEG, is the shut-down flag variable of generator g at time t in SEG, is the no-load cost of generator g in SEG, is the status variable of generator g at time t in SEG, is the power generation cost of generator g in SEG, is the day-ahead planned value of generator g at time t in SEG, is the primary frequency regulation reserve cost of generator g in SEG, is the primary frequency regulation reserve capacity of generator g at time t in SEG, is the secondary frequency regulation reserve cost of generator g in SEG, is the secondary frequency regulation reserve capacity of generator g at time t in SEG, is the upward regulation cost of generator g in SEG, is the upward regulation reserve capacity of generator g at time t in SEG, is the downward regulation cost of generator g in SEG, is the downward regulation reserve capacity of generator g at time t in SEG, is the curtailment amount of non-critical load d in SEG at time t; is the start-up cost of generator g in REG, is the start-up flag variable of generator g at time t in REG, is the shut-down cost of generator g in REG, is the shut-down flag variable of generator g at time t in REG, is the no-load cost of generator g in REG, is the status variable of generator g at time t in REG, is the power generation cost of generator g in REG, is the day-ahead planned value of generator g at time t in REG, is the primary frequency regulation reserve cost of generator g in REG, is the primary frequency regulation reserve capacity of generator g at time t in REG, is the secondary frequency regulation reserve cost of generator g in REG, is the secondary frequency regulation reserve capacity of generator g at time t in REG, is the upward regulation cost of generator g in REG, is the upward regulation reserve capacity of generator g at time t in REG, For the downward regulation cost of generator g in REG, For the downward regulation reserve capacity of generator g at time t in REG, For the curtailment of non-critical load d at time t in REG, where t is the time period index, T is the set of time periods, and d is the load index, For the set of non-critical loads in REG, For the cost of load curtailment of non-critical load d at time t in REG, For the cost of load curtailment of non-critical load d at time t in SEG.

7. The method for coordinated dispatching of frequency security constraints in an asynchronous interconnected power grid according to claim 1, characterized in that, The uncertainty of the DR-FCCS model constructed based on the Wasserstein metric fuzzy set description in step S1 is specifically: Using the type-1 Wasserstein distance to quantify the distance between potential probability distributions and The corresponding Wasserstein metric fuzzy set is defined as follows: Among them, represents the set of all underlying distributions that support Ξ, where Ξ is set to δ is the radius of the Wasserstein ball, is a fuzzy set; Define the Wasserstein distance of type 1 as follows: Among them, are the marginal values of ξ and respectively and is the joint distribution of, ξ is a random variable, is a reference random variable.

8. The method for collaborative dispatching of asynchronous interconnected power grid frequency security constraints according to claim 1, wherein Converting the joint probability constraint into an individual probability constraint is specifically: where λ is the optimization variable, δ is the radius of the Wasserstein ball, z i is the optimization variable, N is the sample size, β is the optimization variable, is the affine function of the optimization variable x, is the auxiliary parameter, ξ i is the sample i, b k (x) is the affine function of the optimization variable x, ∈ is the risk coefficient, K is the dimension of the random variable, λ is the optimization variable, represents the real number field.

9. The frequency security constraint collaborative dispatching method for an asynchronous interconnected power grid according to claim 1, characterized in that Using the sequential solution algorithm to solve the optimized DR-FCCS model is as follows: where X and Z are vectors of continuous and binary variables respectively, Ψ is a set of constraints, and f(X, Z) is an objective function, is the reformulation of the worst-case CVaR constraint.

10. An asynchronous interconnected power grid frequency security constraint collaborative dispatching system, characterized in that, It includes: A construction module that constructs a DR-FCCS model including a generator response module under uncertainty, a sending-end power grid operation cost limit module, a receiving-end power grid operation limit module, and an HVDC link constraint module; A metric module that describes the uncertainty of the DR-FCCS model constructed in step S1 based on the Wasserstein metric fuzzy set; A constraint module that approximately solves the DR joint chance constraint based on conditional value-at-risk, converts the joint probability constraint into an individual probability constraint, and optimizes the DR-FCCS model; An output module that uses the sequential solution algorithm to solve the optimized DR-FCCS model to obtain a coordinated scheduling scheme for the asynchronous interconnected system.

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