Networking type energy storage locating and sizing optimization method considering new energy multi-station short circuit ratio improvement

Abstract

The invention discloses a network construction type energy storage locating and sizing optimization method considering new energy multi-station short circuit ratio improvement, and relates to the technical field of power system and energy storage system planning. Proposing an accurate modeling method of the new energy multi-station short-circuit ratio considering the resistance influence; based on the characteristics of a network construction type energy storage independent voltage source and the characteristics of short-time high-rate overload capacity, a relation model between new energy multi-station short-circuit ratio improvement and network construction type energy storage site selection capacity is provided; based on a convex optimization theory and a mixed integer quadratic programming method, a network construction type energy storage locating and sizing optimization model considering new energy multi-station short-circuit ratio improvement is constructed. The new energy multi-station short-circuit ratio under the weak power grid is improved, the addressing and sizing scheme optimization of network-building type energy storage is realized, and the operation safety of a new energy unit and the reliability of new energy power transmission under the weak power grid area are improved.

Description

Technical Field

[0001] The present invention relates to the field of power system and energy storage system planning, and in particular to a method for optimizing the site selection and capacity determination of grid-type energy storage taking into account the improvement of the short-circuit ratio of multiple new energy stations. Background Art

[0002] Against the backdrop of global warming and fossil fuel shortages, my country's new energy is developing rapidly, with new energy installed capacity and penetration increasing year by year. New energy units are replacing traditional thermal power units on a large scale and connected to the grid. Unlike traditional thermal power units, new energy units connected to the grid through power electronic converters have low inertia and poor anti-interference capabilities. This weakens the inertia and voltage support capabilities of the grid, forming a regional weak grid. Under a weak grid, there are huge risks in the reliable delivery of new energy power and the stable operation of new energy units.

[0003] The new energy multi-station short-circuit ratio (MRSCR) is an indicator that defines the strength of the power grid at the connection point of the new energy station, reflecting the ability of the new energy station to resist external disturbances. Grid-type energy storage has the ability to actively support active power and reactive power, which can improve the strength of the power grid, improve the voltage support capacity in weak power grid areas, and increase MRSCR. Compared with synchronous phase regulators, grid-type energy storage has the advantages of flexible site selection, short construction period and diverse operation modes, but grid-type energy storage also faces the problem of high construction costs.

[0004] Therefore, under a given MRSCR improvement target, optimizing the construction location and capacity of grid-type energy storage, that is, optimizing the site selection and sizing of grid-type energy storage, has become a key issue that needs to be urgently addressed in the field of power system planning and energy storage system planning. Summary of the invention

[0005] The purpose of this application is to provide a method for optimizing the siting and sizing of grid-type energy storage taking into account the improvement of the short-circuit ratio of multiple new energy stations, so as to solve the problems in the prior art that the MRSCR index is complex and difficult to embed into the optimization model of the planning problem of the siting and sizing of grid-type energy storage; the existing MRSCR linearization method does not consider the problem of insufficient accuracy caused by the resistance of the power network; the existing optimization model for the planning problem of improving MRSCR has many nonlinear terms, low computational efficiency, and is difficult to apply to large-scale power grids.

[0006] The present application constructs a method for optimizing the site selection and sizing of grid-type energy storage taking into account the improvement of the short-circuit ratio of multiple new energy stations through a complex domain rectangular coordinate expansion method, a node impedance element modeling method under an adjoint network, a mixed integer quadratic programming model, a convex optimization method, and a model feasible domain reduction method based on node electrical distance, so as to achieve the improvement of the MRSCR of multiple new energy stations and the site selection and sizing of the optimal construction cost of grid-type energy storage.

[0007] To achieve the above objectives, this application provides the following solutions:

[0008] On the first aspect, the present application provides a linear modeling method for node impedance matrix elements based on complex domain rectangular coordinate expansion, which can take into account the influence of resistance in the power network. The model is divided into three groups of formulas: network branch current formula, unit ground current formula, and node current balance formula.

[0009] The resistance of the power grid r ij and reactance x ij Perform an expansion similar to admittance, Y ij,r is the conductance between nodes i and j, Y ij,m is the inverse of the susceptance between nodes i and j, as follows:

[0010]

[0011] (1) Network branch current formula

[0012] According to Ohm's law, the real part of the branch current I is expanded in the rectangular coordinate system ij,r and the imaginary part I ij,m As shown below:

[0013]

[0014] Among them, V i,r and V i,m are the real and imaginary parts of the voltage at node i, respectively, and L is the set of lines in the power network.

[0015] (2) Generator-to-ground current formula

[0016] For a unit node g, the set of units (g,i) on node i is N G The unit node is equivalent to a transient reactance grounding node, the voltage on the ground node is 0, and the real part of the unit-to-ground current is obtained and the imaginary part As shown below:

[0017]

[0018] (3) Node current balance formula

[0019] Based on the expression of line current and unit ground current, using Kirchhoff's current law, the node current balance constraint is obtained as shown below:

[0020]

[0021] in, and the imaginary part is the real and imaginary part of the current injected at node i. In the scenario of simply calculating the impedance element, the injected current of the impedance node is also calculated is 1, and the injected current of other nodes is 0; in the MRSCR calculation, the injected current of the new energy station node is the per-unit value of its rated active power and reactive power, where Corresponding to the per unit value of active power, Corresponding to the reactive power per unit value. N is the node set in the power network.

[0022] In the second aspect, the present application provides a second-order cone constraint modeling method for MRSCR. Assume that the target MRSCR of the new energy station at node i is MRSCR i,obj , the set of new energy station nodes in the system is K. Considering the relationship between the real part and imaginary part of the voltage in the adjoint network and the MRSCR, the MRSCR quadratic inequality constraint is obtained as follows:

[0023]

[0024] The square root relationship between MRSCR and the accompanying network voltage is converted into the relationship between the sum of the squares of the voltage and the negative square of MRSCR by taking the square root.

[0025] In the third aspect, the present application provides a coupling relationship between the short-circuit capacity of a grid-type energy storage and the short-circuit ratio of multiple new energy stations. ac,GFM In the equivalent impedance network analysis under power grid fault, it can be equivalent to an impedance to ground Z eq,GFM , whose modulus is equal to the inverse of the short-circuit capacity, as shown in the following formula:

[0026]

[0027] Considering that the grid-type energy storage has a short-term high-rate overload capacity, its actual maximum short-circuit capacity that can be provided is the rated capacity S N,GFM η times, and the equivalent impedance to ground Z eq,GFM It will directly change the impedance network of the original power grid, thus affecting the MRSCR. eq,GFM The MRSCR will increase with the size of Z, thus establishing a coupling relationship between the short-circuit capacity of the grid-type energy storage and the short-circuit ratio of the new energy multi-station. eq,GFM Afterwards, the actual configured grid-type energy storage capacity can be obtained according to the calculation relationship of η times.

[0028] Fourthly, the present application provides a mixed integer quadratic programming model for optimizing the site selection and sizing of grid-type energy storage taking into account the improvement of the short-circuit ratio of multiple new energy stations. The quadratic programming model mainly includes three parts: the MRSCR constraint group considering the site selection and sizing of grid-type energy storage, the grid-type energy storage configuration parameter constraint group, and the objective function group.

[0029] (1) MRSCR constraint group considering grid-type energy storage site selection and capacity determination

[0030] The real part of the current in the accompanying network of the grid-connected energy storage configured at node i and the imaginary part The calculation is as follows:

[0031]

[0032] Among them, S i is the short-circuit capacity that the grid-connected energy storage can provide at node i, α pu is the system base power correction factor, and its value is the reciprocal of the system base power. For example, if the system base power is 100MVA, α pu is 0.01.

[0033] Will and Incorporate it into the node current balance formula and update the expression of the node current constraint group in the adjoint network as shown below:

[0034]

[0035] (2) Grid-connected energy storage configuration parameter constraint group

[0036] In the optimization model, the number of grid-type energy storage configurations is limited. i is a Boolean variable. When it is equal to 1, energy storage is configured on node i. When it is equal to 0, no energy storage is configured. con,max is the maximum number of energy storage configurations allowed. The energy storage configuration number constraint is constructed as follows:

[0037]

[0038] The capacity of a single energy storage system needs to be less than the set maximum energy storage system configuration capacity S max , S max It is the capacity in terms of power, not energy.

[0039]

[0040] (3) Objective function group

[0041] The objective function of the mixed integer quadratic programming model is shown below:

[0042]

[0043] The objective function f consists of three parts: ol Multiplication term, converter construction cost coefficient k pc Multiplication item and supporting facilities construction cost coefficient kcn The multiplication term. The converter construction cost Q i It is about energy storage construction capacity S i The piecewise function is used to consider the converter construction costs corresponding to different scales of energy storage.

[0044] In the fifth aspect, the present application provides a method for improving the efficiency of solving a grid-type energy storage site selection and capacity optimization model applicable to large-scale power grids. The method includes three model solving efficiency optimization methods:

[0045] (1) Remove unnecessary MRSCR constraints of new energy stations to reduce the model decision space. New energy stations that meet the target MRSCR requirements are excluded from the MRSCR constraint group, but their mutual impedance impact on other new energy stations is still considered.

[0046] (2) Based on the node electrical distance, nodes that are far away from each energy station are eliminated to reduce the feasible domain of the model. The node electrical distance reflects the electrical connection between nodes. Even if a node that is far away from a new energy station is equipped with a large-capacity grid-type energy storage, it is difficult to effectively exert its voltage support capacity. Therefore, candidate nodes for grid-type energy storage configuration are selected based on the comprehensive electrical distance of each node in the system to the new energy station node.

[0047] In the sixth aspect, this application provides a process for formulating a grid-type energy storage site selection and capacity optimization plan for MRSCR optimization of new energy stations under weak power grids. The process is mainly divided into three parts:

[0048] (1) Input power network information and new energy station information, calculate the initial MRSCR value, set the target MRSCR value, and determine and select the new energy stations that need to be optimized;

[0049] (2) configuring the optimization model parameters, including the maximum configuration capacity and number of grid-type energy storage and the energy storage construction cost, and generating a mixed integer quadratic programming model to be solved;

[0050] (3) Execute the optimization model and check whether the resulting solution meets the MRSCR improvement requirements. If not, return to the configuration optimization model parameter part to change the parameter boundaries and re-optimize. If the model is successfully solved and the solution meets the MRSCR improvement requirements, output the grid-type energy storage site selection and sizing solution.

[0051] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0052] This application proposes a method for optimizing the site selection and sizing of grid-type energy storage that takes into account the improvement of the short-circuit ratio of multiple renewable energy stations, which effectively improves the short-circuit ratio of multiple renewable energy stations and minimizes the construction cost of grid-type energy storage. First, a second-order cone constraint group of MRSCR is designed that considers the influence of power network resistance. Secondly, the relationship between the equivalent impedance of grid-type energy storage and MRSCR is proposed. Finally, a site selection and sizing optimization model for grid-type energy storage that takes into account the improvement of the short-circuit ratio of multiple renewable energy stations is constructed. Through the above-mentioned technical scheme, this application realizes the optimization of the site selection and sizing scheme for grid-type energy storage that improves the short-circuit ratio of multiple renewable energy stations in weak power grid areas, which has broad application prospects and significant technical advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 A schematic flow chart of a method for optimizing the siting and sizing of grid-type energy storage taking into account the improvement of the short-circuit ratio of multiple renewable energy stations provided in one embodiment of the present application. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0056] The purpose of this application is to provide a method for optimizing the site selection and sizing of grid-type energy storage with improved short-circuit ratio of multiple renewable energy stations, aiming to improve the grid strength of renewable energy station connection points in weak power grid areas, and to provide a method for optimizing the site selection and sizing of grid-type energy storage with low construction cost.

[0057] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0058] In an exemplary embodiment, Figure 1 :

[0059] This embodiment provides a method for optimizing the site selection and sizing of grid-type energy storage taking into account the short-circuit ratio of multiple renewable energy stations, combined with the MRSCR second-order cone constraint considering the influence of resistance, to achieve the optimization of the site selection and sizing schemes for grid-type energy storage and the generation of a scheme library.

[0060] Step 1: Input the power network parameters and new energy station parameters, calculate the initial MRSCR considering the influence of power grid resistance and select the new energy station to be optimized.

[0061] The power network parameters include the impedance parameters of transmission lines, transformers, and generator sets. The impedance parameters of generator sets are transient impedance parameters. The new energy site parameters include the rated capacity of new energy, grid connection point voltage level, typical daily wind speed, and power curve.

[0062] Step 2: Configure the energy storage parameters and MRSCR improvement target of the mixed integer quadratic programming model, and reduce the feasible domain of energy storage site selection according to the electrical distance.

[0063] Energy storage parameters include the maximum power capacity of energy storage and the maximum amount of configured energy storage; the MRSCR improvement target is the target MRSCR value of the new energy station. Excluding nodes with large electrical distances to each new energy station, the remaining nodes are used as feasible domain nodes for network-based energy storage site selection.

[0064] Step 3: Use a commercial solver to solve the model and output the model solution status and energy storage configuration plan, store the plan in the plan library, and print out the current grid-type energy storage configuration plan.

[0065] The commercial solver used can be GUROBI 11.0.3 and above for solving. The solver will output the model solution status. If the solution status is successful and the energy storage configuration plan meets the actual requirements, the plan will be stored in the plan library and the current plan will be printed out.

[0066] Step 4: Determine whether the number of solutions in the solution library meets the given requirements

[0067] If the number of solutions in the solution library reaches the given requirement, a signal that the solution library has been prepared is sent and the solution library is backed up; otherwise, infeasible cuts of existing solutions are added to the model, and the model is solved again to generate new solutions.

[0068] Through the above steps, this application realizes the optimization of network-type energy storage site selection and sizing schemes and the formulation of a scheme library for improving the short-circuit ratio of multiple new energy stations in weak power grid areas, providing a theoretical reference for improving the power grid strength in weak power grid areas with dense new energy sources.

Claims

1. An accurate second-order cone constraint modeling method for renewable energy multi-station short circuit ratio (MRSCR), characterized by Avoiding the inversion of the complex admittance matrix and directly obtaining the relevant elements of the impedance matrix reflecting the MRSCR, and the impedance matrix elements take into account the influence of the resistance in the power network on the MRSCR; The exact second-order cone constraint of the MRSCR is an inequality constraint consisting of the second norm of the impedance matrix element and the negative quadratic power of the target MRSCR value; The complex admittance matrix is ​​constructed by the parameters of power equipment including lines, transformers, and units in the regional power grid, including: the resistance and reactance values ​​of the transmission lines, the resistance and reactance values ​​of the transformers, and the transient reactance values ​​of the generator sets to the ground; The MRSCR indicator takes into account the impact of the rated capacity of new energy stations. The precise second-order cone constraint modeling method of the MRSCR allows the input of complex rated capacity of new energy stations, including active power capacity and reactive power capacity, and parameters can be set according to the actual capacity of the new energy stations.

2. A method for optimizing the site selection and capacity of grid-connected energy storage taking into account the improvement of the short-circuit ratio of multiple renewable energy stations, characterized in that: The optimization method can reduce the construction cost of grid-type energy storage and provide an optimal solution for site selection and capacity configuration of grid-type energy storage while ensuring that MRSCR meets given requirements. The method for optimizing the location and capacity of grid-type energy storage taking into account the improvement of the short-circuit ratio of multiple new energy stations proposes an equivalent method for the short-circuit capacity of grid-type energy storage in an impedance network, which is characterized in that the short-circuit capacity contributed by the grid-type energy storage when an external power grid fails is equivalent to the grounding impedance in the impedance network; the short-circuit capacity is linked to the actual configuration capacity through the overload capacity of the grid-type energy storage, and a linear mapping relationship between the short-circuit capacity demand and the actual configuration capacity is constructed; The grid-type energy storage site selection and sizing optimization method taking into account the improvement of the short-circuit ratio of multiple new energy stations constructs a mixed integer quadratic programming model, which is characterized by the objective function taking into account the construction cost of the grid-type energy storage capacity, the converter construction cost and the supporting construction cost such as cables and relay protection devices; the constraints of the model take into account the improvement of MRSCR; except for the multiplication terms of integer variables and continuous variables and the terms containing the second norm constraints of variables, the rest of the model is linear expression; the MRSCR obtained by solving the model is completely consistent with the result obtained by inverting the admittance matrix; the model uses convex optimization theory and the big M method to linearize the logical constraints.

3. The accurate second-order cone constraint modeling method of short-circuit ratio of multiple stations of new energy according to claim 1 is characterized in that At the level of grid-connected energy storage equipment planning, MRSCR does not take into account the impact of voltage fluctuations at the actual grid connection point of the new energy station, and uses a per-unit value of 1 instead. The calculation formula for MRSCR at the new energy station node i is as follows: in, is the self-impedance of node i in the impedance matrix, is the mutual impedance between node i and node j in the impedance matrix, n is the number of new energy stations in the regional power grid, is the rated capacity of the new energy station i.

4. According to the short-circuit ratio formula of new energy multiple stations in claim 3, the new energy station i is injected with Equivalent current with the same per-unit value The complex voltage generated at node i The modulus of is the inverse of the MRSCR on node i. The transformation matrix of the impedance network Z is as follows:

5. According to the transformation matrix of claim 4, a companion network considering resistance is constructed. The companion network is similar to the regional power grid structure in topology, with the same line resistance and reactance values, and the generator set nodes are connected with their equivalent transient reactance to ground x d On the adjoint network, based on Ohm's law and Kirchhoff's current law, the branch current formula and the node current balance formula are listed to form the MRSCR second-order cone constraint group. The MRSCR second-order cone constraint group includes two sets of branch current constraints corresponding to the real and imaginary parts of complex numbers, two sets of node current balance constraints, two sets of unit ground current constraints and a set of inequality constraints between the MRSCR expression based on node voltage and the target MRSCR value. The MRSCR second-order cone constraint group is characterized by For a certain power grid structure, including the power grid structure, power distribution within the system and certain network parameters, the number of equations and variables in the MRSCR second-order cone constraint group are equal, and the solved MRSCR is uniquely determined.

6. A method for optimizing the location and capacity of grid-connected energy storage taking into account the improvement of the short-circuit ratio of multiple renewable energy stations according to claim 2, characterized in that The short-circuit capacity of grid-type energy storage The per unit value is associated with the impedance matrix Z, and further changes the MRSCR; the short-circuit capacity of the grid-type energy storage After the modulus value is normalized, its reciprocal can be equivalent to the ground impedance of the grid-type energy storage when the grid side fails. When the converter overload capacity is η times the rated capacity of the grid-type energy storage In the case of , the equivalent relationship is as follows:

7. The mixed integer quadratic programming model of claim 2 is characterized in that Combined with the MRSCR second-order cone constraint group in claim 4, the branch current constraint group in the constraint group is expanded, and the expanded constraint is the current constraint of the equivalent branch of the grid-type energy storage; the node current balance constraint group in the MRSCR second-order cone constraint group is modified to incorporate the current of the equivalent branch of the grid-type energy storage. The quadratic programming model improves the solution efficiency by excluding the optimization constraints of the station nodes that have met the MRSCR in the power grid and reducing the feasible domain of the energy storage site selection point based on the distance of the electrical nodes.

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