A network-configuration-type energy storage site selection and capacity optimization method considering short-circuit ratio improvement of new energy multi-stations

Abstract

The application discloses a network type energy storage site selection and capacity optimization method considering short-circuit ratio improvement of new energy multi-station, relates to the technical field of power system and energy storage system planning, and comprises the following steps: based on the complex matrix expansion of the rectangular coordinate system under the accompanying impedance network, an accurate modeling method of the new energy multi-station short-circuit ratio considering the influence of resistance is proposed; based on the independent voltage source characteristics and short-time high-multiple overload capacity characteristics of the network type energy storage, a relationship model between the short-circuit ratio improvement of the new energy multi-station and the site selection and capacity of the network type energy storage is proposed; and based on the convex optimization theory and the mixed integer quadratic programming method, a network type energy storage site selection and capacity optimization model considering the short-circuit ratio improvement of the new energy multi-station is constructed. The method improves the short-circuit ratio of the new energy multi-station under a weak power grid, realizes the optimization of the site selection and capacity scheme of the network type energy storage, and improves the operation safety of the new energy unit and the reliability of the new energy power transmission under the weak power grid region.

Description

Technical Field

[0001] This 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 of grid-type energy storage that takes into account the improvement of the short-circuit ratio of multiple new energy power stations. Background Technology

[0002] Against the backdrop of global warming and fossil fuel shortages, new energy sources are developing rapidly, with installed capacity and penetration rates increasing year by year. New energy units are largely replacing traditional thermal power units and being connected to the grid. Unlike traditional thermal power units, new energy units connected to the grid via power electronic converters have low inertia and poor disturbance rejection capabilities. This weakens the grid's inertia and voltage support capabilities, creating regional weak grids. Under weak grid conditions, the reliable transmission of new energy power and the stable operation of new energy units face significant risks.

[0003] The maximum short-circuit ratio (MRSCR) of renewable energy power plants is an indicator defining the grid strength at the point of connection of renewable energy power plants, reflecting the plant's ability to withstand external disturbances. Grid-based energy storage possesses active active and reactive power support capabilities, enhancing grid strength, improving voltage support in weak grid areas, and increasing MRSCR. Compared to synchronous condensers, grid-based energy storage offers advantages such as flexible site selection, short construction periods, and diverse operating modes; however, it also faces the challenge of high construction costs.

[0004] Therefore, given the target of improving MRSCR, optimizing the construction location and capacity of grid-based energy storage, i.e., optimizing the site selection and capacity of grid-based energy storage, has become a key issue that urgently needs to be addressed in the fields of power system planning and energy storage system planning. Summary of the Invention

[0005] The purpose of this application is to provide an optimization method for the site selection and capacity determination of grid-connected energy storage that takes into account the improvement of the short-circuit ratio of multiple new energy power stations. This method aims to solve the problems in existing technologies, such as the complexity of the MRSCR index, which makes it difficult to embed into the optimization model of the planning problem for the site selection and capacity determination of grid-connected energy storage; the lack of accuracy caused by the power network resistance in existing MRSCR linearization methods; and the numerous nonlinear terms, low computational efficiency, and difficulty in applying existing optimization models for planning problems to improve MRSCR to large-scale power grids.

[0006] This application constructs a site selection and capacity optimization method for grid-based energy storage that considers improving the short-circuit ratio (MRR) of multiple renewable energy power plants. This method employs a complex-domain Cartesian coordinate expansion method, a node impedance element modeling method under adjoint networks, a mixed-integer quadratic programming model, convex optimization methods, and a model feasible region reduction method based on node electrical distance. This method achieves optimal site selection and capacity allocation for grid-based energy storage while improving the MRSCR of multiple renewable energy power plants and optimizing the construction cost. It can reduce the construction cost of grid-based energy storage while ensuring that the MRSCR meets given requirements, providing an optimal solution for the site selection and capacity configuration of grid-based energy storage.

[0007] The optimization method described in this application constructs a mixed-integer quadratic programming model. The construction of this model mainly includes the following core components:

[0008] First, we construct a linearized expression for the nodal impedance matrix elements that takes into account the influence of resistance.

[0009] To address the impact of resistance in power networks, the resistance of the power network... and reactance Perform an expansion similar to admittance. Let i be the electrical conductance between nodes i and j. Let be the negative of the susceptance between nodes i and j, as shown in the following formula:

[0010]

[0011] Based on Ohm's law and Kirchhoff's current law, the fundamental constraint formulas for constructing the model include:

[0012] (1) Formula for network branch current

[0013] According to Ohm's law, expand the real part of the branch current in a rectangular coordinate system. and the virtual part As shown in the following formula:

[0014]

[0015] in, and These are the real and imaginary parts of the voltage at node i, respectively, and L is the set of lines in the power network.

[0016] (2) Formula for unit-to-ground current

[0017] For a unit node g, the set of units (g, i) on node i is: By treating the unit node as an equivalent transient reactance grounding node with zero voltage at the ground node, the real part of the unit's ground current can be obtained. and the virtual part As shown below:

[0018]

[0019] (3) Nodal current balance formula

[0020] Based on the expressions for line current and generator-to-ground current, and using Kirchhoff's current law, the nodal current balance constraint equation is obtained as follows:

[0021]

[0022] in, and the virtual part Let be the real and imaginary parts of the current injected at node i. In the scenario of simply calculating impedance elements, let be the injected current of the node where impedance is calculated. The current injected into each node is set to 1, while the current injected into all other nodes is 0. In the MRSCR calculation, the current injected into the nodes of the renewable energy power plant is set to the per-unit value of its rated active and reactive power, where... Corresponding per-unit value of active power, This corresponds to the per-unit value of reactive power. N is the set of nodes in the power network.

[0023] Second, construct the second-order cone constraint of MRSCR.

[0024] By establishing the relationship between the real and imaginary parts of the voltage in the adjoint network and the MRSCR, a quadratic inequality constraint for the MRSCR is established:

[0025] The formula for calculating the MRSCR of node i in a new energy power station is as follows:

[0026]

[0027] in, Let i be the self-impedance of node i in the impedance matrix. Let n be the mutual impedance between node i and node j in the impedance matrix, and n be the number of renewable energy power stations in the regional power grid. This refers to the rated capacity of the new energy power station i.

[0028] Let the target MRSCR of the new energy power station at node i be . The set of new energy power station nodes in the system is K. Considering the relationship between the real and imaginary parts of the voltage and the MRSCR in the adjoint network, the following quadratic inequality constraint for MRSCR is obtained:

[0029]

[0030] The relationship between MRSCR and the square root of the accompanying network voltage is transformed into the relationship between the sum of squares of the voltage and the negative square of MRSCR by taking the square root.

[0031] The embedding logic of this method and optimization model involves coupling the location and capacity determination of grid-type energy storage with the MRSCR index through the adjoint network matrix. The MRSCR index is constrained by the node voltages of the adjoint network, which are fixed in the initial system parameters. Changing the node voltages requires configuring grid-type energy storage, i.e., utilizing the influence of the incorporation of the equivalent branch current of the energy storage on the node current balance constraint. This couples the configuration location and capacity of the grid-type energy storage with the MRSCR index, realizing the embedding of the second-order cone constraint modeling method of MRSCR with the location and capacity determination optimization model of grid-type energy storage.

[0032] Third, establish the coupling relationship between the short-circuit capacity of grid-type energy storage and MRSCR.

[0033] This application proposes an equivalent method for the short-circuit capacity of grid-connected energy storage in an impedance network, which represents the short-circuit capacity contributed by grid-connected energy storage during external grid faults. Equivalent to grounding impedance in an impedance network After its modulus is normalized, its linear mapping relationship with the short-circuit capacity is as follows:

[0034]

[0035] Considering that grid-type energy storage has the ability to withstand short-term high-rate overloads, its actual maximum short-circuit capacity is the rated capacity. η times, while the equivalent impedance to ground This directly alters the original power grid's impedance network, thus affecting the MRSCR (Medium-Range Sequence Ratio), and consequently establishing a coupling relationship between the short-circuit capacity of grid-connected energy storage and the short-circuit ratio of multiple renewable energy power plants. Based on the MRSCR, the following is determined: Then, based on the η-fold calculation relationship, the actual configured grid-type energy storage capacity can be obtained.

[0036] Fourth, construct and solve a mixed integer quadratic programming model 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 sites.

[0037] The quadratic programming model mainly consists of three parts: the MRSCR constraint set considering the location and capacity of grid-type energy storage, the configuration parameter constraint set for grid-type energy storage, and the objective function set.

[0038] (1) MRSCR constraints considering the location and capacity of grid-type energy storage

[0039] The MRSCR constraint set, also known as the MRSCR second-order cone constraint set, includes branch current constraints based on Ohm's law and node current balance constraints based on Kirchhoff's current law. The branch current constraints are given by the aforementioned network branch current formula.

[0040] The real part of the current in the adjoint network of the grid-type energy storage configured on node i and the virtual part The calculation is shown in the following formula:

[0041]

[0042] in, The short-circuit capacity that the grid-type energy storage on node i can provide. This is the system reference power correction factor, and its value is the reciprocal of the system reference power. For example, if the system reference power is 100 MVA, It is 0.01.

[0043] Will and Incorporating this into the nodal current balance formula updates the expression of the nodal current constraint set in the adjoint network, as shown in the following equation:

[0044]

[0045] (2) Parameter constraints for grid-type energy storage configuration

[0046] In the optimization model, the number of grid-type energy storage configurations is finite. Let... It is a Boolean variable; when it is equal to 1, energy storage is configured on node i, and when it is equal to 0, it is not configured. This represents the maximum number of energy storage configurations allowed. The constraint on the number of energy storage configurations is shown in the following formula:

[0047]

[0048] The capacity of a single energy storage unit must be less than the maximum configured capacity of the energy storage system. , It refers to capacity in terms of power, not capacity in terms of energy.

[0049]

[0050] (3) Objective function set

[0051] The objective function of the mixed-integer quadratic programming model is shown in the following equation:

[0052]

[0053] The objective function f consists of three parts, namely the capacity construction cost coefficient. Multiplication term, converter construction cost coefficient Multiplication terms and supporting facility construction cost coefficients Multiplication term. This includes the converter construction cost. It concerns the capacity of energy storage construction. The piecewise function is used to consider the converter construction costs corresponding to different scales of energy storage. The sum of the three parts is used as the final objective function.

[0054] Fifthly, this application provides a method for improving the solution efficiency of a grid-based energy storage location and capacity optimization model applicable to large-scale power grids. This method includes three model solution efficiency optimization methods:

[0055] (1) Remove unnecessary MRSCR constraints for renewable energy power plants to reduce the model decision space. Renewable energy power plants that meet the target MRSCR requirements are excluded from the MRSCR constraint group, but their impact on the mutual impedance of other renewable energy power plants is still considered.

[0056] (2) Based on the electrical distance between nodes, nodes that are far from all energy stations are eliminated to reduce the feasible region of the model. The electrical distance between nodes reflects the electrical connection between nodes. Even if a large-capacity grid-type energy storage is configured, a node that is far from a new energy station will find it difficult to effectively exert its voltage support capability. Therefore, candidate nodes for grid-type energy storage configuration are selected based on the comprehensive electrical distance between each node in the system and the new energy station nodes.

[0057] Sixthly, this application provides a process for formulating a site selection and capacity optimization scheme for grid-based energy storage in response to MRSCR optimization of new energy power plants under weak power grids. The process mainly consists of three parts:

[0058] (1) Input power network information and new energy power station information, calculate the initial MRSCR value, set the target MRSCR value, and judge and screen the new energy power stations that need to be optimized;

[0059] (2) Configure and optimize model parameters, including the maximum configuration capacity and number of grid-type energy storage and the construction cost of energy storage, and generate a mixed integer quadratic programming model to be solved;

[0060] (3) Execute the optimization model and check whether the result scheme meets the MRSCR improvement requirements. If it does not meet the requirements, return to the configuration optimization model parameter section to change the parameter boundary and re-optimize. If the model is solved successfully and the scheme meets the MRSCR improvement requirements, output the grid-type energy storage location and capacity scheme.

[0061] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0062] This application proposes a site selection and capacity optimization method for grid-based energy storage that considers improving the short-circuit ratio of multiple renewable energy power plants. This method effectively improves the short-circuit ratio of multiple renewable energy power plants while minimizing the construction cost of grid-based energy storage. First, a second-order cone constraint set of MRSCR (Medium Reduction Regulator) considering the influence of power network resistance is designed. Second, the relationship between the equivalent impedance of grid-based energy storage and MRSCR is proposed. Finally, a site selection and capacity optimization model for grid-based energy storage considering the improvement of the short-circuit ratio of multiple renewable energy power plants is constructed. Through the above technical solutions, this application achieves optimal site selection and capacity optimization schemes for grid-based energy storage in weak grid areas to improve the short-circuit ratio of multiple renewable energy power plants, demonstrating broad application prospects and significant technical advantages. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a schematic flowchart of a method for optimizing the site selection and capacity of grid-type energy storage that takes into account the improvement of the short-circuit ratio of multiple new energy power stations, as provided in an embodiment of this application. Detailed Implementation

[0065] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0066] The purpose of this application is to provide a method for optimizing the site selection and capacity determination of grid-type energy storage to improve the short-circuit ratio of multiple new energy power plants. This method aims to enhance the grid strength at the grid connection points of new energy power plants in weak grid areas and to provide a method for optimizing the site selection and capacity determination of grid-type energy storage with low construction costs.

[0067] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0068] In one exemplary embodiment, such as Figure 1 :

[0069] This embodiment provides a method for optimizing the location and capacity of grid-type energy storage based on the short-circuit ratio of multiple new energy sites. It incorporates the second-order cone constraint of MRSCR that considers the influence of resistance, and realizes the optimization of the location and capacity of grid-type energy storage and the generation of a scheme library.

[0070] Step 1: Input the power network parameters and the parameters of the renewable energy power plants, calculate the initial MRSCR considering the influence of power grid resistance, and screen the renewable energy power plants to be optimized.

[0071] Power network parameters include the impedance parameters of transmission lines, transformers, and generator sets, with the generator set impedance parameters being transient impedance parameters; new energy power station parameters include the rated capacity of new energy sources, the voltage level at the grid connection point, typical daily wind speed, and power curves.

[0072] Step 2: Configure the energy storage parameters and MRSCR improvement target of the hybrid integer quadratic programming model, and reduce the feasible energy storage site selection region based on the electrical distance.

[0073] Energy storage parameters include maximum energy storage power capacity and maximum number of energy storage units; the MRSCR improvement target is the target MRSCR value of the new energy power station. Nodes with large electrical distances to each new energy power station are excluded, and the remaining nodes are considered as nodes in the feasible site selection region for grid-type energy storage.

[0074] Step 3: Solve the model using a commercial solver and output the model solution status and energy storage configuration scheme. Store the scheme in the scheme library and print out the current grid-type energy storage configuration scheme.

[0075] The commercial solver used can be GUROBI 11.0.3 or above. The solver will output the model solution status. If the solution status is successful and the energy storage configuration scheme meets the actual requirements, the scheme will be stored in the scheme library and the current scheme will be printed out.

[0076] Step 4: Determine if the number of solutions in the solution library meets the given requirement.

[0077] If the number of solutions in the solution library reaches the given requirement, a solution library completion signal is sent and the solution library is backed up; otherwise, an infeasible cut-off model of an existing solution is added, and the model is solved again to generate a new solution.

[0078] Through the above steps, this application realizes the optimization of site selection and capacity determination schemes and the formulation of a scheme library for grid-type energy storage to improve the short-circuit ratio of multiple new energy power stations in weak grid areas, providing a theoretical reference for improving the grid strength in weak grid areas with dense new energy sources.

Claims

1. A network configuration type energy storage siting and sizing optimization method considering short-circuit ratio improvement of new energy multi-station, characterized in that, The optimization method can reduce the construction cost of grid-type energy storage while ensuring that the short-circuit ratio (MRSCR) of multiple new energy sites meets the given requirements, and provides an optimal solution for the site selection and capacity configuration of grid-type energy storage. The proposed optimization method for site selection and capacity determination of grid-type energy storage, which takes into account the improvement of the short-circuit ratio of multiple new energy sites, proposes an equivalent method for the short-circuit capacity of grid-type energy storage in the impedance network. The key feature is that the short-circuit capacity contributed by grid-type energy storage during external grid faults is equivalent to the grounding impedance in the impedance network. The short-circuit capacity is linked to the overload capacity of grid-type energy storage and the actual configured capacity, thus constructing a linear mapping relationship between the short-circuit capacity demand and the actual configured capacity. The proposed optimization method for site selection and capacity determination of grid-type energy storage, which considers the improvement of short-circuit ratio at multiple new energy sites, constructs a mixed-integer quadratic programming model. Its objective function is to minimize the sum of the construction costs of the grid-type energy storage capacity, the construction costs of the converter, and the supporting construction costs such as cables and relay protection devices. The model's constraints are achieved by incorporating the real and imaginary parts of the equivalent branch currents of the grid-type energy storage into the node current balance formula of the accompanying network to constrain the improvement of the MRSCR. Except for the terms involving the multiplication of integer and continuous variables and the terms containing the L2 norm constraint, the rest of the model is linearly expressed. The MRSCR obtained from the model solution 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.

2. The method of claim 1, wherein the method is characterized by The short-circuit capacity of grid-type energy storage The per-unit value is related to the impedance matrix Z, and further changes the MRSCR; the short-circuit capacity of the grid-type energy storage After the modulus is normalized, its reciprocal can be equivalent to the ground impedance of grid-type energy storage during grid-side faults. With the converter overload capacity being η times the rated capacity of the grid-type energy storage In the case of [the specific situation], the equivalent relationship is as follows:

3. The method of claim 1, wherein the method is characterized by, The hybrid integer quadratic programming model constructed in the method utilizes the MRSCR second-order cone constraint set, which includes branch current constraints based on Ohm's law and node current balance constraints based on Kirchhoff's current law. The model extends the branch current constraints in the constraint set to include the current constraints of the equivalent branches of the grid-type energy storage. The node current balance constraints in the MRSCR second-order cone constraint set are modified to incorporate the currents of the equivalent branches of the grid-type energy storage. The quadratic programming model improves the solution efficiency by excluding the site node optimization constraints in the power grid that already satisfy the MRSCR and by reducing the feasible region of energy storage site selection based on the distance between electrical nodes.

Images