Distributed phase modifier optimal configuration method and device, terminal and medium

Abstract

The invention discloses a distributed phase modifier optimal configuration method and device, a terminal and a medium, and the method comprises the steps: formulating operation constraint conditions of a new energy station according to the obtained network topology and operation parameters of a new energy power system; with the purpose of minimizing the comprehensive cost, a phase modifier configuration optimization model considering the dynamic electricity price and the station thermal power generating unit decommissioning transformation is constructed; and solving the phase modifier configuration optimization model in combination with the operation constraint condition and a set station benefit constraint condition, and outputting an optimal scheme of the installation position and the capacity configuration of the phase modifier. According to the method, on the basis of comprehensively considering the problems of thermal power generating unit retirement transformation and dynamic electricity price economic benefits, the configuration optimization scheme of the distributed phase modifier is determined; the problems of unstable voltage, harmonic current increase, poor transient stability, poor economical efficiency and the like caused by improper site selection capacity of the distributed phase modifier are effectively avoided, and the overall stability and operation benefits of a power grid are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a distributed phase regulator optimization configuration method, device, terminal and medium. Background Art

[0002] In response to the national "dual carbon" energy transformation strategic goal and to build a new power system with a high proportion of green, clean and sustainable development, the widespread use of renewable energy such as wind power and photovoltaics is gradually replacing traditional thermal power units; this change has brought about a profound change in the structure of the power system. The proportion of conventional generating units in the system has gradually decreased, which has significantly weakened the strength of the power grid dominated by traditional units. The new power system as a whole presents weaker power grid characteristics, which poses new challenges to the stability and security of the power system.

[0003] With the increase in the penetration rate of renewable energy, the frequency and voltage stability of the power grid are unprecedentedly affected; the intermittent and uncertain nature of renewable energy power generation equipment leads to voltage fluctuations in the operation of the power grid, which in turn affects the normal operation of the power system. In order to deal with this problem, multiple solutions have been proposed. One of the main solutions is to introduce reactive compensation equipment such as static VAR compensation devices (SVC), static VAR generators (SVG) and synchronous condensers (STATCOM). These devices can effectively improve the reactive power support of the power grid and improve voltage stability. However, compared with synchronous condensers, SVC and SVG have a certain hysteresis effect in reactive power compensation, which easily leads to the phenomenon of "misaligned compensation" of reactive voltage. This phenomenon will lead to instability in the operation of the power grid, especially when renewable energy power generation fluctuates greatly, which may further aggravate the risk of the system. In addition, SVC and SVG cannot be effectively decoupled from renewable energy equipment, which may induce co-frequency oscillation and affect the safe operation of the power system. As a rotating synchronous motor, the condenser has unique advantages. It can not only provide reactive power support, but also effectively enhance the short-circuit capacity and steady-state performance of the power grid through its rotation characteristics. This feature enables the phase regulator to better support the sending-end power grid with renewable energy feed-in from multiple stations, and solve the challenges faced by the power grid during the penetration of renewable energy. The advantages of the phase regulator in transient response and steady-state performance make it an important device to solve the problem of declining power grid strength.

[0004] However, most of the current configuration schemes for phase-shifting devices adopt the method of centralized configuration of large-capacity phase-shifting devices, which are usually electrically far away from new energy sites, resulting in limited ability to provide short-circuit capacity and improve grid strength. In some related technologies, specific optimization problems are considered and specific configuration schemes for phase-shifting devices are optimized, but the long-term benefits of the sites and the mutual influence between the short-circuit ratios of multiple new energy sites are not considered, and the optimized capacity is a fixed capacity, which cannot accurately determine the deployment capacity when the grid strength is controlled at a certain level. In other related technologies, the two-stage optimization of phase-shifting devices is considered using steady-state and transient indicators for the power grid, but the benefits of the sites are not considered, and the step-by-step optimization of capacity and site selection makes it difficult to ensure the decoupling of the two-stage optimization problems. Therefore, the current configuration schemes for phase-shifting devices need to be further optimized. Summary of the invention

[0005] In order to solve the deficiencies in the prior art, the present invention provides a distributed phase-shifting phase optimization configuration method, device, terminal and medium. According to the structure of the current high-proportion new energy power grid, and taking into account the decommissioning and transformation of thermal power units and the economic benefits of dynamic electricity prices, the configuration optimization scheme of the distributed phase-shifting phase is determined, which effectively avoids the problems of voltage instability, increased harmonic current, poor transient stability and poor economy caused by inappropriate siting and capacity of the distributed phase-shifting phase, thereby ensuring the overall stability and operating efficiency of the power grid.

[0006] The present invention adopts the following technical solution.

[0007] In a first aspect, the present invention provides a distributed phase regulator optimization configuration method, the method comprising:

[0008] Step 1: According to the network topology and operation parameters of the new energy power system, formulate the operation constraints of the new energy station;

[0009] Step 2: With the goal of minimizing the comprehensive cost, a phase-shifting configuration optimization model is constructed that takes into account dynamic electricity prices and the decommissioning and transformation of thermal power units at the site;

[0010] Step 3: Solve the phase-shifting phase configuration optimization model in combination with the operation constraints and the set site benefit constraints, and output the optimal solution for the phase-shifting phase installation location and capacity configuration.

[0011] In combination with the first aspect, optionally, in step 1, the operating constraints of the new energy station include short-circuit ratio constraints of the new energy station, active power constraints and reactive power constraints of the grid nodes, active balance constraints and reactive balance constraints of the inflow and outflow of the grid nodes, phase-shifting configuration capacity constraints of the new energy station and / or grid node voltage constraints.

[0012] In combination with the first aspect, optionally, the active power balance constraint and reactive power balance constraint flowing into and out of the power grid node are expressed as follows:

[0013]

[0014] Where P i and Q i Respectively represent the active power and reactive power of grid node i, i = 1, 2...N, N is the number of new energy stations in the new energy power system where phase regulators can be installed; V i and V j Represent the voltage of grid node i and node j respectively; G ij and B ij are the real and imaginary parts corresponding to the elements in the i-th row and j-th column of the node admittance matrix of the network topology; θ ij is the phase difference between node i and node j.

[0015] In combination with the first aspect, optionally, in step 2, the objective function of the phase-shifting configuration optimization model is expressed as follows:

[0016] F=min(I+G)

[0017]

[0018] In the formula, F is the comprehensive cost, I represents the total cost of the funded station, G is the total cost of the power grid's electricity price subsidy for the funded station; N is the number of new energy stations in the new energy power system that can be equipped with phase regulators; c 1,i represents the cost per megawatt required to install a phase regulator at the i-th new energy station or to modify the phase regulator according to the existing thermal power units at the station; c 2,i (t) represents the cost per megawatt required to maintain the condenser at the i-th renewable energy station in the t-th year; T represents the design life of the condenser; x i is an indicator variable indicating whether a condenser is installed at station i, 0 means not installed, 1 means installed; i is the capacity of the phase regulator installed at station i; m i (c 1,i y i ) is the amount of electricity purchased by the power grid from station i, and the amount of electricity purchased is equal to the investment cost c of the station. 1,i y i Positively correlated; p i (c 1,i y i ) is the unit electricity price of the subsidy policy for the power grid to purchase electricity from station i, and the unit electricity price of the subsidy policy is equal to the investment cost c of the station 1,i y i There is a positive correlation.

[0019] In combination with the first aspect, optionally, in step 3, the expression of the station benefit constraint condition set is as follows:

[0020]

[0021] In combination with the first aspect, optionally, in step 3, the algorithm used to solve the phase regulator configuration optimization model is an adaptive particle swarm optimization (PSO) algorithm;

[0022] The adaptive particle swarm PSO algorithm includes: generating an initial population according to the renewable energy station nodes accessible to the phase regulator and the configurable capacity range of the phase regulator, updating the algorithm parameters within the operating constraints and the station benefit constraints in combination with the penalty function method when optimizing the population, and calculating the feasible solution of the power flow using the established new power system data model, so as to realize the entire search process towards the optimal, thereby obtaining the optimal solution.

[0023] In combination with the first aspect, optionally, the specific steps of using an adaptive particle swarm PSO algorithm to solve the phase-shifting configuration optimization model include:

[0024] Step 3.1: Establish a new power system data model based on the network topology and operation parameters of the new energy power system;

[0025] Step 3.2: Initialize algorithm parameters: set the population size, number of iterations, learning factor and inertia factor, and randomly generate a particle population with initial position and initial velocity within the set range to represent the initial configuration of the phase regulator. Each particle represents a possible phase regulator configuration scheme.

[0026] Step 3.3: Based on the current fitness value of each particle, the algorithm parameters are updated within the operation constraints and station benefit constraints in combination with the penalty function method to eliminate particles that exceed the constraints and form a new generation of population;

[0027] Step 3.4: Calculate the speed and position of each particle in the new generation population based on the set learning factor and inertia factor, and calculate the feasible solution of the power flow using the Newton-Raphson power flow method through the new power system data model. Under the feasible solution of the power flow, calculate the fitness value of the current phase-shifting configuration corresponding to each particle;

[0028] Step 3.5: Compare the calculated fitness value of each particle at the current time with its previous fitness value, and take the higher fitness value as the final fitness value after this update;

[0029] Step 3.6: Repeat steps 3.3-3.5 above until the set number of iterations is met, and select the individual with the highest fitness value in the final population as the optimal solution, that is, the optimal configuration solution of the distributed phase regulator is obtained.

[0030] In combination with the first aspect, optionally, the calculation formulas for the speed and position of each particle are as follows:

[0031] v i l+1 =ωv i l +c1·rand1·(pb i l -z i l )+c2·rand2·(gb i l -z i l )

[0032] z i l+1 =z i l +v i l+1

[0033] In the formula, v i l+1 and v i l are the updated velocities of particle i after the lth iteration; z i l+1 and z i l are the positions of particle i after the lth iteration update; ω is the inertia factor; c1 and c2 are the first learning factor and the second learning factor respectively; rand1 and rand2 are random numbers between 0 and 1; pb i l is the optimal position of particle i in the lth iteration, gb i l is the optimal population position of particle i in the lth iteration.

[0034] In a second aspect, the present invention provides a distributed phase condenser optimization configuration device, which runs the steps of any method described in the first aspect of the present invention, and the device comprises:

[0035] A formulation module is used to formulate the operation constraints of the new energy stations according to the network topology and operation parameters of the new energy power system obtained;

[0036] A construction module is used to build a phase-converter configuration optimization model taking into account dynamic electricity prices and the decommissioning and transformation of thermal power units at the site with the goal of minimizing the comprehensive cost;

[0037] The optimization configuration module is used to solve the phase-shifting phase configuration optimization model in combination with the operation constraints and the set site benefit constraints, and output the optimal solution for the installation location of the phase-shifting phase and its capacity configuration.

[0038] In conjunction with the second aspect, optionally, in the construction module, the objective function of the phase-shifting configuration optimization model is expressed as follows:

[0039] F=min(I+G)

[0040]

[0041] In the formula, F is the comprehensive cost, I represents the total cost of the funded station, G is the total cost of the power grid's electricity price subsidy for the funded station; N is the number of new energy stations in the new energy power system that can be equipped with phase regulators; c 1,i represents the cost per megawatt required to install a phase regulator at the i-th new energy station or to modify the phase regulator according to the existing thermal power units at the station; c 2,i (t) represents the cost per megawatt required to maintain the condenser at the i-th renewable energy station in the t-th year; T represents the design life of the condenser; x i is an indicator variable indicating whether a condenser is installed at station i, 0 means not installed, 1 means installed; i is the capacity of the phase regulator installed at station i; m i (c 1,i y i ) is the amount of electricity purchased by the power grid from station i, and the amount of electricity purchased is equal to the investment cost c of the station. 1,i y i Positively correlated; p i (c 1,i y i ) is the unit electricity price of the subsidy policy for the power grid to purchase electricity from station i, and the unit electricity price of the subsidy policy is equal to the investment cost c of the station 1,i y i There is a positive correlation.

[0042] In conjunction with the second aspect, optionally, the expression of the station benefit constraint condition set in the optimization configuration module is as follows:

[0043]

[0044] In a third aspect, the present invention provides a terminal, including a processor and a storage medium;

[0045] The storage medium is used to store instructions;

[0046] The processor is used to operate according to the instructions to execute the steps of any one of the methods described in the first aspect of the present invention.

[0047] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect of the present invention.

[0048] The beneficial effects of the present invention are as follows:

[0049] At present, in the research on distributed phase-shifting configuration schemes for the improvement of power grid strength after the connection of new energy to the grid, no research has considered the benefits of phase-shifting for the retirement and transformation of thermal power units by new energy stations and the economic benefits of dynamic electricity prices after the installation of phase-shifting. In view of the shortcomings of the existing technology, the present invention proposes a distributed phase-shifting configuration optimization scheme that takes into account the dynamic electricity prices of the stations and the retirement and transformation of units. The phase-shifting configuration scheme is obtained by selecting the location and sizing the phase-shifting with the goal of maximizing economic benefits, and is optimized and solved by the particle swarm algorithm. The phase-shifting configuration scheme can effectively improve the support strength of the power grid under the optimal economic conditions. Its specific beneficial effects include:

[0050] (1) Enhanced voltage regulation capability: The present invention can effectively suppress grid voltage fluctuations under variable load and new energy output conditions by rationally adjusting the camera, thereby maintaining the voltage level within the specified range and ensuring the safe and stable operation of the power system.

[0051] (2) Improved dynamic responsiveness: The optimized configuration enables the phase regulator to respond quickly to transient load changes and system disturbances, improving the grid’s ability to adapt to dynamic loads and renewable energy fluctuations, thereby enhancing the transient stability of the overall system.

[0052] (3) Power factor optimization: By scientifically configuring the phase regulator, the present invention can significantly improve the power factor of the power grid, reduce reactive power loss, and improve power transmission efficiency, thereby reducing power loss and equipment heating and extending the service life of the equipment.

[0053] (4) Improved economy and sustainability: The present invention reduces the reliance on large-scale centralized phase-shifting equipment and proposes a distributed phase-shifting configuration scheme that takes into account dynamic electricity prices and unit decommissioning and transformation, thereby maximizing economic benefits. In traditional schemes, grid optimization focuses more on technical feasibility while ignoring the impact of electricity price fluctuations on the electricity market. The present invention takes dynamic electricity prices into consideration and studies economic driving factors that are more in line with actual electricity market operations, making the phase-shifting deployment scheme more valuable for practical application, reducing investment and maintenance costs, and improving the economic efficiency of the power system operation. This creates favorable conditions for the efficient integration and utilization of new energy and promotes the sustainable development of the power system.

[0054] (5) Improvement of the accuracy and efficiency of site selection optimization for distributed phase-shifters: The present invention adopts an adaptive particle swarm optimization (PSO) algorithm to optimize the site selection and sizing of phase-shifters, which can effectively solve complex multi-variable and multi-constraint optimization problems, and combines the penalty function method to update the algorithm parameters, thereby improving the computational efficiency and optimization accuracy. By simulating different grid configurations and actual scenarios, it is verified that the optimization scheme has good adaptability and economic benefits in grids with a high proportion of renewable energy access, further enhancing the application potential of phase-shifters in grid construction and transformation, and providing important theoretical support for improving the stability of the grid after renewable energy access. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A schematic diagram of a flow chart of a distributed phase condenser optimization configuration method according to an embodiment of the present invention;

[0056] Figure 2 This is an equivalent schematic diagram of a renewable energy multi-feed AC system in an embodiment of the present invention;

[0057] Figure 3 This is an equivalent schematic diagram of a single-node model of a phase regulator connected to an AC system in an embodiment of the present invention;

[0058] Figure 4 A schematic diagram of a process for solving the optimal configuration scheme of a distributed phase regulator in an embodiment of the present invention;

[0059] Figure 5 A topological structure diagram of an AC receiving-end power grid with a high proportion of new energy input in an embodiment of the present invention;

[0060] Figure 6 It is a schematic diagram of the short-circuit ratio of each station in the receiving-end power grid when the phase regulator is not added in the embodiment of the present invention;

[0061] Figure 7 It is a schematic diagram for comparing the short-circuit ratios of various stations in the receiving-end power grid after adding a phase regulator in an embodiment of the present invention;

[0062] Figure 8 It is a schematic diagram for comparing the short-circuit ratios before and after adding the phase regulator when the dynamic electricity price is considered alone in an embodiment of the present invention;

[0063] Fig. 9 It is a schematic diagram for comparing the short-circuit ratios before and after adding a phase regulator when considering the retirement of a thermal power unit alone in an embodiment of the present invention;

[0064] Fig.10 It is a schematic diagram of short-circuit ratio comparison before and after adding phase regulator when considering dynamic electricity price and retirement of thermal power units in an embodiment of the present invention;

[0065] Fig.11 This is a structural principle block diagram of the distributed phase regulator optimization configuration device in the present invention. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in the present invention are only part of the embodiments of the present invention, not all of the embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0067] Embodiment 1:

[0068] Reference Figure 1 The embodiment of the present invention provides a distributed phase regulator optimization configuration method, which specifically includes the following steps:

[0069] Step 1: According to the network topology and operation parameters of the new energy power system, formulate the operation constraints of the new energy station;

[0070] Step 2: With the goal of minimizing the comprehensive cost, a phase-shifting configuration optimization model is constructed that takes into account dynamic electricity prices and the decommissioning and transformation of thermal power units at the site;

[0071] Step 3: Solve the phase-shifting phase configuration optimization model in combination with the operation constraints and the set site benefit constraints, and output the optimal solution for the phase-shifting phase installation location and capacity configuration.

[0072] It is further explained that, based on the definition of the short-circuit ratio MRSCR of new energy multiple stations, the present invention analyzes the impact of the access of phase-shifting devices on the access nodes and other nodes in the power grid from a mechanism perspective; in response to the issue of the benefits of new energy stations, a distributed phase-shifting device configuration optimization model that takes into account dynamic electricity prices and the decommissioning and transformation of thermal power units at the station is proposed, and the location and capacity of the phase-shifting devices are determined with the goal of maximizing economic benefits; a configuration optimization scheme for distributed phase-shifting devices that should be used for domestic power grids with a high proportion of new energy is proposed.

[0073] Among them, the changes in short-circuit ratio after multiple renewable energy stations are connected to the phase regulator and their analysis are as follows:

[0074] The short-circuit ratio SCR is an important indicator to measure the strength of the power grid at the power source or load grid connection point. The traditional short-circuit ratio applicable to single-input AC systems is characterized by the ratio of the AC short-circuit capacity to the capacity of the power electronic equipment. However, in the scenario of new energy cluster grid connection, the traditional short-circuit ratio cannot take into account the impact of adjacent branch equipment of new energy, resulting in poor evaluation results and cannot accurately reflect the true strength of the grid connection point of the new energy unit. Therefore, for the multi-input AC system such as the new energy cluster development model, considering the mutual influence of new energy between multiple stations, the new energy multi-station short-circuit ratio MRSCR can be used to measure the true strength of the system. The AC system with new energy access to multiple stations can be simplified by the Thevenin equivalence theorem and the superposition theorem as follows: Figure 2 The equivalent diagram of the AC system with multiple renewable energy inputs is shown in Figure 1. According to the Thevenin equivalence theorem, the renewable energy connected to the AC system grid point can be equivalent to a power disturbance of equivalent capacity, and then according to the superposition theorem, the renewable energy of multiple stations can be superimposed on the original AC system, resulting in changes in the node bus voltage and node impedance matrix of the original system.

[0075] As an indicator of static voltage stability analysis, the short-circuit ratio of new energy multi-stations is essentially calculated from the steady-state power flow of the equivalent circuit of the grid-connected AC system. Figure 1 The node voltage equation established by the system equivalent diagram shown using its node impedance matrix is ​​as follows:

[0076]

[0077] Where U PCCi is the bus voltage of the new energy grid-connected node at node i, I PCCi The current injected into the AC system by the new energy grid-connected bus at node i, is the equivalent node impedance matrix Z of the multi-station new energy AC system eq The element in row i and column j. It represents the impedance of the AC system to the ground when starting from node i in the equivalent node impedance matrix. Represents the mutual impedance between node i and node j. Characterizes the electrical distance between two points.

[0078] The MRSCR of renewable energy multi-station short-circuit ratio takes into account the mutual influence of renewable energy between stations and reflects the real strength of grid-connected units in renewable energy cluster scenarios. Referring to the definition of short-circuit ratio, the MRSCR of the i-th renewable energy station node in the system is defined as:

[0079]

[0080] Where S aci is the short-circuit capacity of the AC system at the front station i before the new energy is connected, S PCCi is the equivalent new energy capacity of station i after the new energy is connected. Assuming that the grid connection point is no-load before the new energy is connected to the AC system, the bus voltage is the open circuit voltage, then the short-circuit current I sci It can be expressed as the ratio of the bus voltage to the impedance of the point to ground:

[0081]

[0082] Voltage type converter I sci is the short-circuit current of the AC system before the station i is connected to the new energy source, is the open circuit voltage of the AC system at station i when it is running without load. Considering the interaction of nodes at different electrical distances, the equivalent new energy capacity S of the new energy grid connection point i is PCCi Defined as:

[0083]

[0084] Where P PCCi , P PCCj are the power capacity of the new energy source connected to station i and station j respectively. Therefore, the calculation method of the short-circuit ratio of multiple new energy stations is expressed as:

[0085]

[0086] Where U Ni is the nominal bus voltage at the point where the station i is connected to the renewable energy source. The equivalent diagram of the model of a single node of the AC system after the phase regulator is connected is as follows: Figure 3 As shown in the figure, the change of circuit structure leads to the change of self-impedance and mutual impedance in the node impedance matrix of the AC system, that is, and changes, which in turn leads to changes in the short-circuit current and equivalent new energy capacity in the short-circuit ratio calculation process of multiple stations.

[0087] In the single-node model, the connection of the phase regulator is equivalent to adding an additional ground branch on the grid side. The self-impedance of the new energy station i changes as follows:

[0088]

[0089] Where Z Ti Represents the total impedance of the line and transformer on the renewable energy side, Z sc It represents the equivalent impedance of the phase regulator and transformer to ground, Z i Indicates the equivalent impedance of the grid side to the ground.

[0090] When the phase regulator is connected to the new energy station k in the multi-node model, it is equivalent to adding an additional ground branch at the node k on the new energy side. The mutual impedance changes of the new energy station i and the new energy station j are as follows:

[0091]

[0092] Where Z′ eqij , Z eqij are the equivalent mutual impedances between station i and station j before and after the condenser is connected, Z eqik , Z eqjk is the equivalent impedance between the condenser access node i, the condenser access node j and the condenser access node k, Z eqkkis the equivalent self-impedance of the phase shifter access node. The MRSCR of the new energy station i after the phase shifter is connected is expressed as follows:

[0093]

[0094] Let (5) be multiplied both above and below by Formula (8) is multiplied by Z both above and below e ' qii , we can get:

[0095]

[0096] It can be seen from equations (6) and (7) that after adding the phase modulator, the self-impedance and mutual impedance of the AC system node impedance matrix are reduced, resulting in the MRSCR after the phase modulator is connected being always smaller than that before the phase modulator is connected.

[0097] In summary, configuring phase regulators in renewable energy stations can improve the short-circuit ratio of renewable energy multi-stations by changing the self-impedance and mutual impedance in the node impedance matrix, and effectively enhance the strength of the multi-input AC system.

[0098] Therefore, the present invention proposes that it is a feasible solution to gradually configure distributed phase regulators to each new energy station; by configuring distributed phase regulators at each new energy station, real-time monitoring and regulation of the power grid can be realized, and the strength and stability of the power grid can be effectively improved; distributed phase regulators can quickly respond to changes in the power grid according to the real-time status of the power grid, and realize dynamic adjustment of reactive power compensation and short-circuit capacity; this configuration method can significantly improve the overall stability of the power grid and reduce the risks caused by fluctuations in new energy power generation; in addition, distributed phase regulators can also effectively reduce the losses in the transmission process of the power grid and improve the efficiency of power transmission. This is of great significance for further promoting the utilization of new energy and optimizing the operation of the power grid; in practical applications, the configuration of distributed phase regulators needs to comprehensively consider the operating characteristics of the power grid, load distribution, and the characteristics of new energy power generation, and formulate a reasonable layout plan; through an intelligent control system, centralized monitoring and scheduling of distributed phase regulators can be realized to ensure the safe and stable operation of the power grid in the face of fluctuations in new energy power generation. Therefore, with the continuous increase in the proportion of new energy in the power system, it is particularly important to build a powerful power grid support system; the introduction of distributed phase regulators in the present invention can not only solve the voltage stability problem of the power grid caused by fluctuations in new energy, but also effectively improve the strength of the power grid and ensure the safe and efficient operation of the power system.

[0099] Based on the above analysis, the present invention uses the impact of the access of phase-shifting devices on the access nodes and other nodes in the power grid, and proposes a distributed phase-shifting device configuration optimization model that takes into account dynamic electricity prices and the decommissioning and transformation of thermal power units in the field to address the problem of the benefits of new energy fields and stations, and selects the location and capacity of the phase-shifting devices with the goal of maximizing economic benefits. The following will specifically describe the optimization model for the configuration and deployment of distributed phase-shifting devices.

[0100] The distributed phase-converter configuration strategy taking into account dynamic electricity prices proposed in the present invention mainly considers the interests of both the station responsible for installing and maintaining the phase-converter and the power grid that provides electricity price subsidies to the funding station; that is, the objective function of the optimization problem is the sum of the cost required for the funding station to install and maintain the phase-converter and the cost required for the power grid to purchase a certain amount of electricity from the station at a subsidized electricity price; assuming that the installation price and maintenance price of the phase-converter are both proportional to the capacity, the installation cost and maintenance cost per unit capacity of the phase-converter depends on whether the station has retired units, where the installation cost per megawatt when the station has retired units converted into phase-converters is less than the installation cost per megawatt when the phase-converter is directly installed, and the annual maintenance cost per unit capacity of the phase-converter converted from retired units is greater than the annual maintenance cost per unit capacity when the phase-converter is directly installed, and the maintenance cost per unit capacity of the phase-converter at each station increases year by year. The electricity price subsidy policy given by the power grid to the funding station mainly considers subsidizing the station by purchasing a certain amount of electricity from the station that invests in installing the phase-converter at a subsidized electricity price every year within a certain period of time. The price and quantity of electricity purchased at each station depend on the cost of installing the phase-converter at the station. The decision variables of the optimization problem mainly consider the installation location of the phase-shifting device and the capacity of the phase-shifting device. The constraints mainly consider controlling the grid strength above a certain index and ensuring that the station does not lose money. The grid strength constraint mainly considers controlling the short-circuit ratio of multiple new energy stations above the minimum value at which the grid can operate stably, as well as other constraints to ensure the normal operation of the grid, such as the short-circuit ratio constraint of the new energy station, the active power constraint and reactive power constraint of the grid node, the active balance constraint and reactive balance constraint of the inflow and outflow of the grid node, the configuration capacity constraint of the phase-shifting device of the new energy station, and the voltage constraint of the grid node. The constraint of not losing money on the station profit mainly considers that the economic benefits obtained by each station through the electricity price subsidy policy within a certain period of time are greater than the cost of installing and maintaining the phase-shifting device.

[0101] The decision variables of the phase-converter optimization configuration model consider the installation location of the phase-converter and the capacity of the phase-converter. In order to accurately control the reactive power compensation required for a certain level of high-proportion renewable energy grid strength, the phase-converter capacity is changed from a fixed capacity per unit to a continuous change within a certain range of values. The decision variable x is expressed as follows:

[0102] x=[x1,x2...x N ,y1,y2...y N ] T (10)

[0103] Where N is the number of renewable energy stations in the power grid that can be equipped with phase regulators; x i is an indicator variable indicating whether a condenser is installed at station i, 0 means not installed, 1 means installed; i The capacity of the phase regulator installed at station i.

[0104] As an embodiment of the present invention, in step 1, the operation constraints of the new energy station are formulated according to the network topology and operation parameters of the new energy power system as follows:

[0105] ① Short-circuit ratio constraints of new energy stations:

[0106] The short-circuit ratio of multiple new energy stations is between 1.7 and 2.1, which can meet the normal operation requirements of new energy stations connected to new energy power generation equipment. Considering that the critical short-circuit ratio of new energy stations connected to the grid is usually 2.0 to 2.5, a certain stability margin is retained while considering economic factors. min Take 2.0.

[0107] MRSCR i >MRSCR min (11)

[0108] Where, MRSCR i is the short-circuit ratio of the i-th new energy station, and its value is obtained by calculating formula (8).

[0109] ② New energy station phase regulator configuration capacity constraints:

[0110] 0≤y i ≤y max i=1,2...N (12)

[0111] In the formula, y i is the capacity of the condenser installed at station i, and the condenser capacity y of the new energy station i There is a certain upper limit, and this embodiment takes the upper limit capacity y max It is 300MW.

[0112] ③Grid node voltage constraints:

[0113] The node voltage of the new energy station has upper and lower limits. i Should be less than the maximum rated voltage V i max and greater than the minimum rated voltage V i min :

[0114] V i min ≤V i ≤V imax i=1,2...N (13)

[0115] ④ Active power constraints and reactive power constraints of power grid nodes:

[0116] The reactive power of conventional generator sets also has upper and lower limits. Its reactive power Q i Should be less than the maximum rated power and greater than the minimum rated power Its active power P i Should be less than the maximum rated power P i min and greater than the minimum rated power P i max

[0117]

[0118] Where P i and Q i They represent the active power and reactive power of grid node i respectively,

[0119] ⑤ Active power balance constraints and reactive power balance constraints flowing into and out of power grid nodes:

[0120]

[0121] Where, P i and Q i Respectively represent the active power and reactive power of grid node i, i = 1, 2...N, N is the number of new energy stations in the new energy power system where phase regulators can be installed; V i and V j Represent the voltage of grid node i and node j respectively; G ij and B ij are the real and imaginary parts corresponding to the elements in the i-th row and j-th column of the node admittance matrix of the network topology; θ ij is the phase difference between node i and node j.

[0122] As an embodiment of the present invention, the specific construction process of the objective function of the phase-shifting phase configuration optimization model taking into account the dynamic electricity price and the decommissioning and transformation of the thermal power units at the site in step 2 is as follows.

[0123] The objective function considers the costs of both the funding station and the power grid that provides electricity price subsidies to the station. The cost considered by the funding station is the installation cost of the phase shifter in the first year and the subsequent annual maintenance cost. When the design life is T, the total cost I of the funding station is expressed as follows:

[0124]

[0125] In the formula, c1,i represents the cost per megawatt required to install a phase condenser at the i-th new energy station or to retrofit a phase condenser based on the existing thermal power units at the station, where the cost of retrofitting a phase condenser to a thermal power unit is much lower than directly installing a phase condenser; c 2,i (t) represents the cost required for maintaining the phase-converter per megawatt at the i-th new energy station in the t-th year; T represents the design life of the phase-converter; from the first year to the T-th year, the maintenance cost per megawatt of the phase-converter increases year by year with the age t, among which the annual maintenance cost of the phase-converter modified from the thermal power unit is greater than the annual maintenance cost of the phase-converter directly installed.

[0126] The cost considered by the power grid is the electricity price subsidy policy for the contributing station for a total of T years starting from the installation of the phase-shifting machine. The subsidy policy mainly considers the dynamic changes in electricity prices and the amount of electricity purchased by the power grid from the station. The rise and fall of dynamic electricity prices and the amount of electricity purchased are based on the amount of investment of the contributing station. Since the installation cost of the phase-shifting machine is much greater than the subsequent annual maintenance cost, the investment cost of the contributing station is mainly positively correlated with the installation cost of the phase-shifting machine. Therefore, the total cost G of the power grid's electricity price subsidy for the contributing station can be expressed as follows:

[0127]

[0128] In the formula, m i (c 1,i y i ) is the amount of electricity purchased by the power grid from station i, and the relationship between the purchased electricity and the investment of the station c 1,i y i Positively correlated; p i (c 1,i y i ) is the unit electricity price of the subsidy policy for the power grid to purchase electricity from station i. The unit electricity price in the subsidy policy is greater than the original unit electricity price p0. The unit electricity price of the subsidy policy is also related to the capital contribution of the station c 1,i y i There is a positive correlation.

[0129] Therefore, the total cost of both parties is used as the objective function F and is expressed as:

[0130]

[0131] In the formula, F is the comprehensive cost, I represents the total cost of the funded station, G is the total cost of the power grid's electricity price subsidy for the funded station; N is the number of new energy stations in the new energy power system that can be equipped with phase regulators; c 1,i represents the cost per megawatt required to install a phase regulator at the i-th new energy station or to modify the phase regulator according to the existing thermal power units at the station; c 2,i (t) represents the cost per megawatt required to maintain the condenser at the i-th renewable energy station in the t-th year; T represents the design life of the condenser; xi is an indicator variable indicating whether a condenser is installed at station i, 0 means not installed, 1 means installed; i is the capacity of the phase regulator installed at station i; m i (c 1,i y i ) is the amount of electricity purchased by the power grid from station i, and the amount of electricity purchased is equal to the investment cost c of the station. 1,i y i Positively correlated; p i (c 1,i y i ) is the unit electricity price of the subsidy policy for the power grid to purchase electricity from station i, and the unit electricity price of the subsidy policy is equal to the investment cost c of the station 1,i y i There is a positive correlation.

[0132] A preferred but non-limiting embodiment of the present invention is to enable the phase-converter deployment scheme to improve the operating efficiency while ensuring the overall stability of the power grid; when solving and optimizing the phase-converter configuration optimization model in step 3, a station benefit constraint condition is also set: the economic benefit obtained by each station through the subsidy policy within a certain number of years is greater than the cost paid by the station to install and maintain the phase-converter; the benefit of the station is the unit electricity price profit of the subsidy policy (p i (c 1,i y i )-p0) and the phase shifter capacity m installed at the station i (c 1,i y i ), the installation and maintenance cost of the condenser is the first year's installation cost c 1,i y i and maintenance costs within T years Therefore, the station benefit constraint condition is expressed as follows:

[0133]

[0134] Furthermore, the present invention mainly targets distributed phase regulators and proposes a distributed phase regulator optimization configuration method for a high-proportion renewable energy power grid. According to the distribution of renewable energy stations and the parameters of the power grid model, a data simulation model of the AC receiving power grid with a high proportion of renewable energy input is established based on the power grid motor load matrix, line impedance matrix and station power matrix; for the data simulation model and optimization model established as above, in step 3 of the present invention, the process of using an adaptive particle swarm PSO algorithm to solve the camera configuration optimization model is as follows: Figure 4 The specific steps are as follows:

[0135] Step 3.1: Establish a new power system data model based on the network topology and operation parameters of the new energy power system;

[0136] Step 3.2: Initialize algorithm parameters: set the population size, number of iterations, learning factor and inertia factor, and randomly generate a population of particles with initial positions and initial velocities within the set range to represent the initial configuration of the condenser. Each particle represents a possible condenser configuration scheme, where the first N variables x i Indicates whether the new energy station is equipped with a phase regulator, and the last N variables y i Represents the capacity of the camera.

[0137] Step 3.3: Based on the current fitness value of each particle, the algorithm parameters are updated within the operation constraints and station benefit constraints in combination with the penalty function method to eliminate particles that exceed the constraints and form a new generation of population;

[0138] In each iteration, the algorithm parameters are updated within the boundary conditions. If the boundary conditions are exceeded, a penalty term is introduced into the objective function, that is, a larger value (penalty factor) is added to the current fitness, so that the result of this iteration is automatically eliminated in the optimization result.

[0139] Step 3.4: Calculate the speed and position of each particle in the new generation population based on the set learning factor and inertia factor, and calculate the feasible solution of the power flow using the Newton-Raphson power flow method through the new power system data model. Under the feasible solution of the power flow, calculate the fitness value of the current phase-shifting configuration corresponding to each particle;

[0140] In this embodiment, the calculation formulas for the speed and position of each particle are as follows:

[0141] v i l+1 =ωv i l +c1·rand1·(pb i l -z i l )+c2·rand2·(gb i l -z i l )

[0142] z i l+1 =z i l +v i l+1

[0143] In the formula, v i l+1 and v i l are the updated velocities of particle i after the lth iteration; zi l+1 and z i l are the positions of particle i after the lth iteration update; ω is the inertia factor; c1 and c2 are the first learning factor and the second learning factor respectively; rand1 and rand2 are random numbers between 0 and 1; pb i l is the optimal position of particle i in the lth iteration, gb i l is the optimal population position of particle i in the lth iteration.

[0144] Step 3.5: Compare the calculated fitness value of each particle at the current time with its previous fitness value, and take the higher fitness value as the final fitness value after this update;

[0145] Specifically, compare the fitness value under the current phase-shifting configuration with the fitness value under the optimal point pb of a single particle, record the phase-shifting configuration with the best fitness and update it to pb of the next iteration; compare the fitness value under the current phase-shifting configuration with the fitness value under the optimal point gb of the group of particles, record the phase-shifting configuration with the best fitness and update it to gb of the next iteration. Phase-shifting configuration solutions that do not meet the constraints of short-circuit ratio, economic benefits of investment stations, etc. will be automatically eliminated during the comparison process. Among the phase-shifting configurations that meet the constraints, continuously update and record the optimal fitness and corresponding configuration solutions so far.

[0146] Step 3.6: Repeat steps 3.3-3.5 above until the set number of iterations is met, and select the individual with the highest fitness value in the final population as the optimal solution, that is, the optimal configuration solution of the distributed phase regulator is obtained.

[0147] Among them, the simulations corresponding to all corresponding phase-shifting configuration schemes are completed, and the most economically optimal phase-shifting optimization configuration is obtained based on the distributed phase-shifting configuration scheme of the AC receiving-end power grid that meets the constraint conditions.

[0148] The effectiveness of the distributed phase condenser configuration optimization method proposed in the embodiment of the present invention is further illustrated below with reference to experimental examples.

[0149] The simulation example of the present invention adopts an IEEE39 node distribution system including 4 new energy stations. The rated capacity of a single new energy device is set to 1.5MW, and wind power new energy devices with capacities of 75MW, 75MW, 150MW, and 150MW are added to stations 9, 32, 38, and 39 respectively. The total external capacity is 600MW, which is connected to the receiving end AC grid after being stepped up and collected to the busbar by a transformer to form a multi-feed AC system. The phase-shifting phase access node and capacity size are to be determined. The AC receiving end grid topology structure with a high proportion of new energy feed-in is shown in the figure below. Figure 5 As shown. The other parameters of the network topology refer to the IEEE39 node standard system. The AC system capacity reference value is 100MVA, and the voltage reference value is 500KV. Node 31 is taken as a relaxed node. Except for the relaxed node, the voltage level of the bus nodes where each motor is located is 20KV, node 12 is 25KV, and the remaining bus voltages are all 500KV. According to the above economic constraint indicators and power grid strength constraint indicators, the distributed phase-shifting device of the AC receiving-end power grid is optimized and deployed in the following situations. The power grid data model is constructed through the MATPOWER simulation platform to verify the rationality and effectiveness of the phase-shifting device deployment scheme proposed in the present invention.

[0150] (1) Before the condenser is connected, when the condenser is not configured, multiple stations at each node of the AC receiving power grid are short-circuited. For example, Figure 6 As shown. The MRSCR of each station is below 2, and the overall receiving-end power grid strength is low, showing a weak power grid trend. The grid nodes that do not meet MRSCR>1.5 are node 9, node 15, and node 25, making it difficult to maintain normal access and stable operation of new energy equipment. When phase regulators are not configured, the receiving-end power grids do not meet the standard requirements. Without considering the retirement of new energy station units and the power grid's electricity price subsidy policy, the phase regulator deployment of the receiving-end power grid is optimized. According to Figure 3 The deployment scheme flow chart is used to optimize the phase condenser. The phase condenser deployment scheme is shown in Table 1. The optimized deployment scheme is close to the station with low short circuit. When the short circuit of the station is low, the capacity of the nearby phase condenser is configured larger. After adding the phase condenser, the MRSCR of each station is as follows Figure 7 As shown, at this time, the short-circuit ratios of the stations are all greater than 2, with the lowest short-circuit ratio being 2.139, which meets the safety constraints given by the receiving power grid.

[0151] Table 1 Phase-shifting configuration scheme without considering the retirement of new energy station units and the power grid’s electricity price subsidy policy

[0152]

[0153] (2) When considering dynamic electricity prices separately, a dynamic electricity price subsidy is added to each station. The subsidy amount is the amount of electricity purchased by the grid m i (c 1,i yi ) and electricity price p i (c 1,i y i The dynamic electricity price subsidy set at station 19 and station 22 is twice that of other stations, according to Figure 3 The deployment scheme flow chart is used to optimize the phase-shifting system. The phase-shifting system deployment scheme is shown in Table 2. Due to the increase in subsidy policies, the stations are more willing to install phase-shifting systems due to increased profits. The optimization results show that the phase-shifting system is installed in stations with larger subsidy policies, which minimizes the costs of both parties while ensuring the strength of the power grid. At this time, the MRSCR of each station is as follows: Figure 8 As shown, at this time, the short-circuit ratios of the stations are all greater than 2, among which the lowest short-circuit ratio is 2.144, which meets the safety constraints given by the receiving power grid.

[0154] Table 2 Phase-shifting configuration scheme when dynamic electricity price is considered alone

[0155]

[0156]

[0157] (3) When considering the phase-shifting conversion of the decommissioned thermal power units at the site separately, Figure 4 The phase regulator is optimized according to the process. Phase regulators are considered to be retired and renovated in stations 7, 25 and 31, and the cost is much lower than directly installing phase regulators. The phase regulator optimization deployment plan is shown in Table 3. Since the station is renovated according to the original thermal power units, the price is much lower than directly installing phase regulators. Therefore, the overall optimization result tends to install phase regulators when the station has thermal power units to be renovated, and the overall cost is lower. At this time, the MRSCR of each station is as follows Fig. 9 As shown, at this time, the short-circuit ratios of the stations are all greater than 2, with the lowest short-circuit ratio being 2.049, which meets the safety constraints given by the receiving power grid.

[0158] Table 3 Phase-shifting configuration scheme when considering the retirement of thermal power units alone

[0159]

[0160] (4) When considering dynamic electricity prices and the retirement of thermal power units at the site, Figure 4 The phase regulator is optimized according to the process. Phase regulators with retired thermal power units are considered in stations 18 and 31, and dynamic electricity price subsidies are considered in stations 7 and 18. The deployment plan of phase regulators is shown in Table 4. The overall deployment plan tends to be installed in stations with retired thermal power units or in stations with larger electricity price subsidy policies, with the lowest overall cost and the best grid strength control. At this time, the MRSCR of each station is as follows Fig.10As shown, at this time, the short-circuit ratios of the stations are all greater than 2, with the lowest short-circuit ratio being 2.133, which meets the safety constraints given by the receiving power grid.

[0161] Table 4 Phase-shifting configuration scheme considering both dynamic electricity prices and retirement of thermal power units

[0162]

[0163] This paper proposes a distributed phase-shifting optimization scheme for power grids with a high proportion of new energy sources, taking into account the dynamic electricity price of the station and the retirement of the unit; the proposed phase-shifting configuration scheme is verified by the MATPOWER data model simulation, which ensures the strength of the power grid while taking into account the optimal economic performance of both the station and the power grid. The experimental results show that:

[0164] 1. When there is an electricity price compensation policy, while ensuring the strength of the power grid, new energy stations with higher electricity price compensation policies are more willing to install phase-shifting devices, and the installed phase-shifting devices have a larger capacity.

[0165] 2. When considering the retirement of thermal power units, while ensuring the strength of the power grid, the retirement of thermal power units will lead to a greater willingness for stations to install phase-shifting devices with lower costs, and the installed capacity of phase-shifting devices will be larger.

[0166] 3. When considering both at the same time, the overall deployment plan tends to be installed at sites with retired thermal power units or at sites with larger electricity price subsidy policies, so that the overall cost is minimized and the grid strength control is best.

[0167] The beneficial effects of the present invention are as follows:

[0168] At present, in the research on distributed phase-shifting configuration schemes for the problem of improving the strength of the power grid after the connection of new energy to the grid, no research has considered the benefits of phase-shifting for the retirement and transformation of thermal power units by new energy stations and the economic benefits of dynamic electricity prices after the installation of phase-shifting. In view of the shortcomings of the existing technology, the present invention proposes a distributed phase-shifting configuration optimization scheme that takes into account the dynamic electricity prices of the stations and the retirement and transformation of units. The phase-shifting configuration scheme is obtained by selecting the location and sizing the phase-shifting with the goal of maximizing economic benefits, and is optimized and solved by the particle swarm algorithm. The phase-shifting configuration scheme can effectively improve the support strength of the power grid under the optimal economic conditions. Its specific beneficial effects include:

[0169] (1) Enhanced voltage regulation capability: The present invention can effectively suppress grid voltage fluctuations under variable load and new energy output conditions by rationally adjusting the camera, thereby maintaining the voltage level within the specified range and ensuring the safe and stable operation of the power system.

[0170] (2) Improved dynamic responsiveness: The optimized configuration enables the phase regulator to respond quickly to transient load changes and system disturbances, improving the grid’s ability to adapt to dynamic loads and renewable energy fluctuations, thereby enhancing the transient stability of the overall system.

[0171] (3) Power factor optimization: By scientifically configuring the phase regulator, the present invention can significantly improve the power factor of the power grid, reduce reactive power loss, and improve power transmission efficiency, thereby reducing power loss and equipment heating and extending the service life of the equipment.

[0172] (4) Improved economy and sustainability: The present invention reduces the reliance on large-scale centralized phase-shifting equipment and proposes a distributed phase-shifting configuration scheme that takes into account dynamic electricity prices and unit decommissioning and transformation, thereby maximizing economic benefits. In traditional schemes, grid optimization focuses more on technical feasibility while ignoring the impact of electricity price fluctuations on the electricity market. The present invention takes dynamic electricity prices into consideration and studies economic driving factors that are more in line with actual electricity market operations, making the phase-shifting deployment scheme more valuable for practical application, reducing investment and maintenance costs, and improving the economic efficiency of the power system operation. This creates favorable conditions for the efficient integration and utilization of new energy and promotes the sustainable development of the power system.

[0173] (5) Improvement of the accuracy and efficiency of site selection optimization for distributed phase-shifters: The present invention adopts an adaptive particle swarm optimization (PSO) algorithm to optimize the site selection and sizing of phase-shifters, which can effectively solve complex multi-variable and multi-constraint optimization problems, and combines the penalty function method to update the algorithm parameters, thereby improving the computational efficiency and optimization accuracy. By simulating different grid configurations and actual scenarios, it is verified that the optimization scheme has good adaptability and economic benefits in grids with a high proportion of renewable energy access, further enhancing the application potential of phase-shifters in grid construction and transformation, and providing important theoretical support for improving the stability of the grid after renewable energy access.

[0174] Embodiment 2:

[0175] like Fig.11 As shown, the present invention provides a distributed phase regulator optimization configuration device, which is used to implement the steps of the method in the above embodiment 1, and the device specifically includes:

[0176] A formulation module is used to formulate the operation constraints of the new energy stations according to the network topology and operation parameters of the new energy power system obtained;

[0177] A construction module is used to build a phase-converter configuration optimization model taking into account dynamic electricity prices and the decommissioning and transformation of thermal power units at the site with the goal of minimizing the comprehensive cost;

[0178] The optimization configuration module is used to solve the phase-shifting phase configuration optimization model in combination with the operation constraints and the set site benefit constraints, and output the optimal solution for the installation location of the phase-shifting phase and its capacity configuration.

[0179] As an embodiment of the present invention, in the construction module, the objective function of the phase-shifting configuration optimization model is expressed as follows:

[0180] F=min(I+G)

[0181]

[0182] In the formula, F is the comprehensive cost, I represents the total cost of the funded station, G is the total cost of the power grid's electricity price subsidy for the funded station; N is the number of new energy stations in the new energy power system that can be equipped with phase regulators; c 1,i represents the cost per megawatt required to install a phase regulator at the i-th new energy station or to modify the phase regulator according to the existing thermal power units at the station; c 2,i (t) represents the cost per megawatt required to maintain the condenser at the i-th renewable energy station in the t-th year; T represents the design life of the condenser; x i is an indicator variable indicating whether a condenser is installed at station i, 0 means not installed, 1 means installed; i is the capacity of the phase regulator installed at station i; m i (c 1,i y i ) is the amount of electricity purchased by the power grid from station i, and the amount of electricity purchased is equal to the investment cost c of the station. 1,i y i Positively correlated; p i (c 1,i y i ) is the unit electricity price of the subsidy policy for the power grid to purchase electricity from station i, and the unit electricity price of the subsidy policy is equal to the investment cost c of the station 1,i y i There is a positive correlation.

[0183] Furthermore, the expression of the station benefit constraint condition set in the optimization configuration module is as follows:

[0184]

[0185] The distributed phase condenser optimization configuration device provided in the embodiment of the present invention and the distributed phase condenser optimization configuration method provided in Example 1 are based on the same technical concept, and can produce the beneficial effects as described in Example 1. For the contents not described in detail in this embodiment, please refer to Example 1.

[0186] Embodiment three:

[0187] A terminal provided by an embodiment of the present invention includes a processor and a storage medium;

[0188] The storage medium is used to store instructions;

[0189] The processor is used to operate according to the instruction to execute the steps of the method according to any one of the first embodiments.

[0190] Embodiment 4:

[0191] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of any method described in Embodiment 1 are implemented.

[0192] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0193] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0194] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0195] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0196] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A distributed phase condenser optimization configuration method, characterized in that: Methods include: Step 1: According to the network topology and operation parameters of the new energy power system, formulate the operation constraints of the new energy station; Step 2: With the goal of minimizing the comprehensive cost, a phase-shifting configuration optimization model is constructed that takes into account dynamic electricity prices and the decommissioning and transformation of thermal power units at the site; Step 3: Solve the phase-shifting phase configuration optimization model in combination with the operation constraints and the set site benefit constraints, and output the optimal solution for the phase-shifting phase installation location and capacity configuration.

2. The distributed phase condenser optimization configuration method according to claim 1 is characterized in that: In step 1, the operating constraints of the new energy station include the short-circuit ratio constraint of the new energy station, the active power constraint and reactive power constraint of the grid node, the active balance constraint and reactive balance constraint of the inflow and outflow of the grid node, the phase-shifting configuration capacity constraint of the new energy station and / or the voltage constraint of the grid node.

3. The distributed phase condenser optimization configuration method according to claim 2 is characterized in that: The active power balance constraint and reactive power balance constraint flowing into and out of the power grid node are expressed as follows: Where P i and Q i Respectively represent the active power and reactive power of grid node i, i = 1, 2...N, N is the number of new energy stations in the new energy power system where phase regulators can be installed; V i and V j Represent the voltage of grid node i and node j respectively; G ij and B ij are the real and imaginary parts corresponding to the elements in the i-th row and j-th column of the node admittance matrix of the network topology; θ ij is the phase difference between node i and node j.

4. The distributed phase condenser optimization configuration method according to claim 1, characterized in that: In step 2, the objective function of the phase-shifting configuration optimization model is expressed as follows: F=min(I+G) In the formula, F is the comprehensive cost, I represents the total cost of the funded station, G is the total cost of the power grid's electricity price subsidy for the funded station; N is the number of new energy stations in the new energy power system that can be equipped with phase regulators; c 1,i represents the cost per megawatt required to install a phase regulator at the i-th new energy station or to modify the phase regulator according to the existing thermal power units at the station; c 2,i (t) represents the cost per megawatt required to maintain the condenser at the i-th renewable energy station in the t-th year; T represents the design life of the condenser; x i is an indicator variable indicating whether a condenser is installed at station i, 0 means not installed, 1 means installed; i is the capacity of the phase regulator installed at station i; m i (c 1,i y i ) is the amount of electricity purchased by the power grid from station i, and the amount of electricity purchased is equal to the investment cost c of the station. 1, i y i Positively correlated; p i (c 1,i y i ) is the unit electricity price of the subsidy policy for the power grid to purchase electricity from station i, and the unit electricity price of the subsidy policy is equal to the investment cost c of the station 1,i y i There is a positive correlation.

5. The distributed phase condenser optimization configuration method according to claim 4 is characterized in that: In step 3, the expression of the station benefit constraint condition is set as follows:

6. The distributed phase condenser optimization configuration method according to claim 1, characterized in that: In step 3, the algorithm used to solve the phase-shifting configuration optimization model is an adaptive particle swarm optimization (PSO) algorithm; The adaptive particle swarm PSO algorithm includes: generating an initial population according to the renewable energy station nodes accessible to the phase regulator and the configurable capacity range of the phase regulator, updating the algorithm parameters within the operating constraints and the station benefit constraints in combination with the penalty function method when optimizing the population, and calculating the feasible solution of the power flow using the established new power system data model, so as to realize the entire search process towards the optimal, thereby obtaining the optimal solution.

7. The distributed phase condenser optimization configuration method according to claim 6, characterized in that: The specific steps of solving the phase condenser configuration optimization model using the adaptive particle swarm PSO algorithm include: Step 3.1: Establish a new power system data model based on the network topology and operation parameters of the new energy power system; Step 3.2: Initialize algorithm parameters: set the population size, number of iterations, learning factor and inertia factor, and randomly generate a particle population with initial position and initial velocity within the set range to represent the initial configuration of the phase regulator. Each particle represents a possible phase regulator configuration scheme. Step 3.3: Based on the current fitness value of each particle, the algorithm parameters are updated within the operation constraints and station benefit constraints in combination with the penalty function method to eliminate particles that exceed the constraints and form a new generation of population; Step 3.4: Calculate the speed and position of each particle in the new generation population based on the set learning factor and inertia factor, and calculate the feasible solution of the power flow using the Newton-Raphson power flow method through the new power system data model. Under the feasible solution of the power flow, calculate the fitness value of the current phase-shifting configuration corresponding to each particle; Step 3.5: Compare the calculated fitness value of each particle at the current time with its previous fitness value, and take the higher fitness value as the final fitness value after this update; Step 3.6: Repeat steps 3.3-3.5 above until the set number of iterations is met, and select the individual with the highest fitness value in the final population as the optimal solution, that is, the optimal configuration solution of the distributed phase regulator is obtained.

8. The distributed phase condenser optimization configuration method according to claim 7, characterized in that: In step 3.4, the calculation formulas for the speed and position of each particle are as follows: in i l+1 =ω v i l +c1 rand1 (pb i l -With i l )+c2 rand2 (gb i l -With i l ) with i l+1 =with i l +v i l+1 In the formula, v i l+1 and v i l are the updated velocities of particle i after the lth iteration; z i l+1 and z i l are the positions of particle i after the lth iteration update; ω is the inertia factor; c1 and c2 are the first learning factor and the second learning factor respectively; rand1 and rand2 are random numbers between 0 and 1; pb i l is the optimal position of particle i in the lth iteration, gb i l is the optimal population position of particle i in the lth iteration.

9. A distributed phase condenser optimization configuration device, running the distributed phase condenser optimization configuration method according to any one of claims 1 to 8, characterized in that: The device includes: A formulation module is used to formulate the operation constraints of the new energy stations according to the network topology and operation parameters of the new energy power system obtained; A construction module is used to build a phase-converter configuration optimization model taking into account dynamic electricity prices and the decommissioning and transformation of thermal power units at the site with the goal of minimizing the comprehensive cost; The optimization configuration module is used to solve the phase-shifting phase configuration optimization model in combination with the operation constraints and the set site benefit constraints, and output the optimal solution for the installation location of the phase-shifting phase and its capacity configuration.

10. The distributed phase condenser optimization configuration device according to claim 9, characterized in that: In the building block, the objective function of the phase-shifting configuration optimization model is expressed as follows: F=min(I+G) In the formula, F is the comprehensive cost, I represents the total cost of the funded station, G is the total cost of the power grid's electricity price subsidy for the funded station; N is the number of new energy stations in the new energy power system that can be equipped with phase regulators; c 1,i represents the cost per megawatt required to install a phase regulator at the i-th new energy station or to modify the phase regulator according to the existing thermal power units at the station; c 2,i (t) represents the cost per megawatt required to maintain the condenser at the i-th renewable energy station in the t-th year; T represents the design life of the condenser; x i is an indicator variable indicating whether a condenser is installed at station i, 0 means not installed, 1 means installed; i is the capacity of the phase regulator installed at station i; m i (c 1,i y i ) is the amount of electricity purchased by the power grid from station i, and the amount of electricity purchased is equal to the investment cost c of the station. 1, i y i Positively correlated; p i (c 1,i y i ) is the unit electricity price of the subsidy policy for the power grid to purchase electricity from station i, and the unit electricity price of the subsidy policy is equal to the investment cost c of the station 1,i y i There is a positive correlation.

11. The distributed phase condenser optimization configuration device according to claim 10, characterized in that: The expression of the station benefit constraint condition set in the optimization configuration module is as follows:

12. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-8.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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