Energy storage system optimization method, optimization device, electronic equipment and storage medium

Abstract

The application relates to the technical field of power dispatch, in particular to an optimization method and device of an energy storage system, an electronic device and a storage medium. The optimization method of the energy storage system comprises the following steps: based on the topological structure and line impedance parameters of a target network, a power transmission distribution factor matrix and a voltage sensitivity factor matrix are calculated; by using the voltage sensitivity factor matrix, voltage amplitude constraints of each node in the target network are converted into time-varying power boundary constraints of the energy storage system at an access node; based on the power transmission distribution factor matrix and energy storage power of the energy storage system, branch transmission power of the energy storage system is calculated; based on the branch transmission power, branch power flow constraints are determined, and an energy storage optimization scheduling model is constructed based on the branch power flow constraints; the energy storage optimization scheduling model is modified through the time-varying power boundary constraints, and the energy storage system is optimized based on the modified energy storage optimization scheduling model.

Description

Technical Field

[0001] This application relates to the field of power dispatching technology, specifically to an optimization method, optimization device, electronic device, and storage medium for an energy storage system. Background Technology

[0002] With the construction of new power systems, energy storage systems, as an important regulating resource in distribution networks, can effectively improve the flexibility and operational efficiency of the grid. In practical applications, it is necessary to fully consider the physical characteristics of the distribution network and implement precise energy storage system scheduling strategies.

[0003] In related technologies, the mainstream energy storage dispatching scheme for distribution networks can be based on a complete AC power flow model. However, this method involves a complex nonlinear iterative solution process, resulting in excessive computation time and making it difficult to meet the requirements for rapid dispatching. Dispatch methods based on DC power flow models ignore resistance and reactive power components, thus failing to effectively constrain node voltages under high-resistance conditions in distribution networks, posing safety risks. While dispatching solutions based on mixed-integer linear programming linearize the nonlinear constraints, they rely on traditional commercial solvers, resulting in long computation times for large-scale or long-cycle dispatching evaluations, making it difficult to support rapid iteration of multiple schemes. Therefore, there is an urgent need for an energy storage system optimization method that can achieve both high efficiency in solution and economical dispatching while ensuring the safe operation of the distribution network. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes an optimization method, optimization device, electronic device, and storage medium for an energy storage system, which achieves accurate calculation of the energy storage system scheduling scheme while effectively ensuring the economy and efficiency of the calculation.

[0005] In a first aspect, embodiments of this application provide an optimization method for an energy storage system, comprising: calculating a power transmission distribution factor matrix and a voltage sensitivity factor matrix based on the topology and line impedance parameters of a target network; using the voltage sensitivity factor matrix to transform the voltage amplitude constraints of each node in the target network into time-varying power boundary constraints of the energy storage system at the access node; calculating the branch transmission power of the energy storage system based on the power transmission distribution factor matrix and the energy storage power of the energy storage system; determining the branch power flow constraints based on the branch power flow constraints, and constructing an energy storage optimization scheduling model based on the branch power flow constraints; correcting the energy storage optimization scheduling model through time-varying power boundary constraints, and optimizing the energy storage system based on the corrected energy storage optimization scheduling model.

[0006] In some embodiments, the power transmission distribution factor matrix is ​​calculated based on the topology and line impedance parameters of the target network, including: calculating the power transmission distribution factor matrix based on the node-branch correlation matrix, branch reactance vector, and node susceptance matrix of the target network; wherein, the calculated power transmission distribution factor matrix is ​​expressed as:

[0007] in, This represents the calculation of the power transfer distribution factor matrix; This represents the reciprocal diagonal matrix of branch reactance; Let be the branch reactance vector, with dimension ( ); This represents the node-branch association matrix, with dimension ( ); Represents the nodal susceptance matrix, with dimension ( ).

[0008] In some embodiments, the voltage sensitivity factor matrix is ​​calculated based on the topology and line impedance parameters of the target network, including: calculating the voltage sensitivity factor matrix based on the power transmission distribution factor matrix, the branch resistance of the target network, the power reference value, and the path from the slack node to each node; wherein, the voltage sensitivity factor matrix is ​​represented as:

[0009] in, Represents the voltage sensitivity factor matrix; Indicates the power reference value; This represents the calculation of the power transfer distribution factor matrix; Represented as from the balanced node to the node The set of all branches on the path; branch road The resistance.

[0010] In some embodiments, the voltage amplitude constraints of each node in the target network are transformed into time-varying power boundary constraints of the energy storage system at the access node, including: calculating the base voltage deviation of each node when the target network is not connected to the energy storage system; wherein the base voltage deviation is determined based on the voltage sensitivity factor matrix and the net injected power of each node; determining the upper and lower power limits of the energy storage system at the access node based on the base voltage deviation and the voltage amplitude constraints of each node; and calculating the time-varying power boundary constraints based on the upper and lower power limits.

[0011] In some embodiments, the branch transmission power of the energy storage system is calculated based on the power transmission distribution factor matrix and the energy storage power of the energy storage system, including: obtaining the first branch transmission power when the target network is not connected to the energy storage system; calculating the increment of the energy storage power on the first branch transmission power based on the power transmission distribution factor matrix and the energy storage power of the energy storage system; and adding the first branch transmission power to the increment to obtain the second branch transmission power after the energy storage system is connected.

[0012] In some embodiments, determining branch power flow constraints based on branch transmission power and constructing an energy storage optimization scheduling model based on branch power flow constraints includes: determining branch transmission capacity constraints based on the second branch transmission power and the upper limit of branch transmission capacity; constructing an objective function based on preset Lagrange multipliers and branch transmission capacity constraints; and generating an energy storage optimization scheduling model based on the objective function.

[0013] In some embodiments, the energy storage optimization scheduling model is modified by time-varying power boundary constraints, and the energy storage system is optimized based on the modified energy storage optimization scheduling model, including: solving the energy storage optimization scheduling model to obtain the initial energy storage power; modifying the initial energy storage power according to the time-varying power boundary constraints to obtain the modified energy storage power; and optimizing the scheduling of the energy storage system based on the modified energy storage power.

[0014] Secondly, embodiments of this application provide an optimization device for an energy storage system, comprising: a matrix calculation module configured to calculate a power transmission distribution factor matrix and a voltage sensitivity factor matrix based on the topology and line impedance parameters of a target network; a constraint generation module configured to use the voltage sensitivity factor matrix to convert the voltage amplitude constraints of each node in the target network into time-varying power boundary constraints of the energy storage system at the access node; a power calculation module configured to calculate the branch transmission power of the energy storage system based on the power transmission distribution factor matrix and the energy storage power of the energy storage system; a model generation module configured to determine branch power flow constraints based on the branch power flow constraints and construct an energy storage optimization scheduling model based on the branch power flow constraints; and an optimization processing module configured to correct the energy storage optimization scheduling model through time-varying power boundary constraints and perform optimization processing on the energy storage system based on the corrected energy storage optimization scheduling model.

[0015] Thirdly, embodiments of this application provide an electronic device, including: a processor and a memory, wherein the memory stores a program or instructions that can run on the processor, and when the program or instructions are executed by the processor, they implement the steps of the optimization method for the energy storage system as described in the first aspect.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium, including: storing a program or instructions on the readable storage medium, wherein when the program or instructions are executed by a processor, the program or instructions implement the steps of the optimization method for the energy storage system as described in the first aspect.

[0017] The technical solution provided in this application calculates the power transmission distribution factor matrix and voltage sensitivity factor matrix based on the topology and impedance parameters of the target network. The voltage sensitivity factor matrix is ​​used to decouple complex node voltage constraints, transforming them into time-varying power boundary constraints for the energy storage system. Then, combined with the branch power flow constraints determined by the power transmission distribution factor matrix, an energy storage optimization scheduling model is constructed. This model is then corrected based on the time-varying power boundary constraints, enabling rapid optimization of the energy storage system. The energy storage system optimization method provided in this application not only minimizes the full-cycle operating cost of the energy storage system at the economic level but also, at the physical level, ensures that the node voltages and line loads of the distribution network are within safe operating ranges through pre-emptive voltage boundary limiting and power flow penalty mechanisms. This achieves global optimization scheduling that balances extreme computational efficiency, economy, and grid physical security.

[0018] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0019] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 A flowchart illustrating the optimization method for an energy storage system provided in this application embodiment; Figure 2 A flowchart illustrating the conversion of voltage constraints into energy storage power boundaries is provided for embodiments of this application. Figure 3 A flowchart for calculating the branch transmission power of an energy storage system provided in this application embodiment; Figure 4 A flowchart for constructing an energy storage optimization scheduling model provided in this application embodiment; Figure 5 A flowchart for optimizing an energy storage system is provided as an embodiment of this application; Figure 6 The flowchart of the optimized scheduling using the Lagrange iterative algorithm is provided for the embodiments of this application; Figure 7 A flowchart illustrating the optimization method for an energy storage system provided in this application embodiment; Figure 8 A seven-day energy storage dispatch power comparison chart provided for embodiments of this application; Figure 9 A seven-day state of charge comparison chart provided for embodiments of this application; Figure 10 A seven-day power grid interaction comparison diagram provided for embodiments of this application; Figure 11 A comparison chart of power flow results for any two branches within seven days, provided as an embodiment of this application; Figure 12 A comparison chart of node voltage values ​​over seven days provided for embodiments of this application; Figure 13 A schematic diagram of an optimization device for an energy storage system provided in an embodiment of this application; Figure 14 This application provides a more specific schematic diagram of the hardware structure of an electronic device.

[0020] Reference numerals: 1310 - Matrix calculation module; 1320 - Constraint generation module; 1330 - Power calculation module; 1340 - Model generation module; 1350 - Optimization processing module; 1410 - Processor; 1420 - Memory; 1430 - Input / output interface; 1440 - Communication interface; 1450 - Bus. Detailed Implementation

[0021] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0022] It should be understood that the steps described in the method embodiments of this application may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this application is not limited in this respect.

[0023] As described in the background section, with the rapid development of new power systems and the continuous promotion of distributed energy, more and more distributed photovoltaic (PV) power is being connected to the distribution network. However, due to the inherent physical characteristics of PV power generation, such as strong randomness, significant intermittency, and the possibility of reverse power, as well as the relatively high line impedance ratio, greater voltage sensitivity to power changes, and complex network structure of the distribution network, large-scale PV power connection to the distribution network can easily lead to voltage exceeding limits and power flow congestion. At the same time, the reverse power flow brought by PV power generation can also increase the network loss of the distribution network system, and in severe cases, may even lead to line overload.

[0024] To achieve the coordinated application of photovoltaic (PV) power and distribution networks, those skilled in the art have proposed adding energy storage systems to power systems to mitigate PV fluctuations. However, in actual dispatching, it is necessary to simultaneously consider the economic efficiency and network security of the power system. Therefore, related technologies for energy storage dispatching in distribution networks mainly include: optimization methods based on the ACPF (Alternating Current Power Flow) model, which rely on iterative algorithms such as Newton-Raphson to solve nonlinear power flow equations to calculate dispatching methods. Although this method has high computational accuracy, it involves a large number of nonlinear operations, resulting in high overall computational complexity and long computation time, making it unsuitable for the actual dynamic dispatching of energy storage systems; optimization methods based on the DCPF (Direct Current Power Flow) model, which typically ignore resistance and reactive power factors, considering only branch power flow and not constraining node voltages. However, in high line impedance scenarios of distribution networks, ignoring resistance often leads to calculation errors, thus posing a risk to dispatching security; and methods using mixed-integer linear programming (Mixed-Integer Linear Programming). Although the mixed-integer linear programming method can solve scheduling schemes by linearizing nonlinear constraints and using commercial solvers, the computation time of this method is still relatively long. For example, when performing year-round optimization scheduling, commercial solvers need several seconds or minutes to solve the problem, which is difficult to meet the needs of rapid evaluation and iteration of multiple schemes in the planning stage.

[0025] To address the aforementioned problems, this application proposes an optimization method, optimization device, electronic device, and storage medium for an energy storage system. The technical solution of this application will be further described in detail below through specific embodiments.

[0026] refer to Figure 1 This is a flowchart of an optimization method for an energy storage system provided in an embodiment of this application.

[0027] Step S101: Based on the topology and line impedance parameters of the target network, calculate the power transmission distribution factor matrix and the voltage sensitivity factor matrix.

[0028] Specifically, in order to achieve the linearization of the nonlinear AC power flow equation, this application first calculates the power transmission distribution factor matrix based on the extracted branch correlation matrix and node susceptance matrix of the target network topology input by the user, and calculates the voltage sensitivity factor matrix based on the path matrix of the target network and combined with the line impedance parameters input by the user, so as to reflect the linear influence relationship between the active power injected by each node in the target network topology and the node voltage amplitude.

[0029] As an optional embodiment, based on the topology and line impedance parameters of the target network, the power transmission distribution factor matrix is ​​calculated, including: calculating the power transmission distribution factor matrix based on the node-branch correlation matrix, branch reactance vector, and node susceptance matrix of the target network; wherein, the calculated power transmission distribution factor matrix is ​​expressed as:

[0030] in, This represents the calculation of the power transfer distribution factor matrix; This represents the reciprocal diagonal matrix of branch reactance; Let be the branch reactance vector, with dimension ( ); This represents the node-branch association matrix, with dimension ( ); Represents the nodal susceptance matrix, with dimension ( ).

[0031] Specifically, in actual operation, the fluctuation of distributed photovoltaic power output is usually small relative to the total capacity of the power system, and the power system generally operates stably near a certain benchmark point. Therefore, in the method provided in this application, by ignoring the higher-order infinitesimal terms in the Taylor expansion, the nonlinear AC power flow equation is approximated as a linear relationship, so that the power transmission distribution factor (PTDF) matrix can accurately characterize the linear coupling relationship between the increment of node injected power and the corresponding branch power flow change.

[0032] Specifically, PTDF is based on the DC power flow assumption as a basis for physical simplification, including: ignoring branch resistance (i.e., assuming zero branch resistance) to simplify electrical parameters for network topology; keeping node voltages constant near a reference voltage to ignore the coupling effect of reactive power and voltage amplitude changes on active power flow; and using a small-angle approximation, i.e., approximating the voltage across two different nodes in the power network. and nodes Voltage phase angle between The sine value is approximately in radians, that is, at this time Based on the above DC power flow assumptions, the node can be obtained. and nodes Branch roads The meritorious trend The expression is:

[0033] in, For nodes The phase angle; For nodes The phase angle; branch road The reactance.

[0034] Based on the above branch active power flow expression, the node can be obtained. The power balance equation is:

[0035] in, For nodes Net injection power; In addition to nodes, the power system is represented by Any other node besides; Elements of the nodal susceptance matrix; For nodes The phase angle of the nodes.

[0036] To eliminate phase angle variables and directly establish the mapping between injected power and branch power flow, this application preferably extracts the branch reactance vector of the target network. And construct a node-branch association matrix that reflects the network topology. Furthermore, combining the nodal susceptance matrix Through matrix substitution, the expression for the power transfer distribution factor is obtained as follows:

[0037] Among them, the branch reactance vector The dimension is ( ),in, Represents the number of branches; Node-branch correlation matrix The dimension is ( ),in, Represents the number of nodes; node susceptance matrix The dimension is ( ); This represents the reciprocal diagonal matrix of the branch reactance.

[0038] It should be noted that the node branch association matrix A matrix used to describe network topology, representing the connection relationships between branches and nodes, in the node-branch association matrix. In the middle, for any row of branches Its starting node and the endpoint It is configured such that if the branch is from a node Starting from, then the location If the branch eventually connects to the node So, position Except for the starting and ending points, this branch path does not contact any other nodes in the power grid, so all remaining positions in this row should be filled with 0; Node susceptance matrix Used to describe the relationship between nodal injected power and nodal phase angle, when adding elements to the nodal susceptance matrix, the main diagonal elements are filled in. At that time, it is necessary to find the node All directly connected branches The reciprocal of the reactance of these branches (i.e., susceptance) Adding them all together, that is ; Fill in the non-diagonal elements At that time, only look at the nodes. and nodes Check if there are any directly connected branches. If so, fill in the node. and nodes The negative value of the reciprocal of the reactance of the branches connecting them, i.e. If there is no directly connected branch, enter 0.

[0039] Furthermore, based on the PTDF described above, any branch in the target network The active power flow can be represented as:

[0040] in, branch road The meritorious trend; For nodes When injecting unit power, the branch The amount of change in the trend.

[0041] As an optional embodiment, based on the topology and line impedance parameters of the target network, the voltage sensitivity factor matrix is ​​calculated, including: calculating the voltage sensitivity factor matrix based on the power transmission distribution factor matrix, the branch resistance of the target network, the power reference value, and the path from the slack node to each node; wherein, the voltage sensitivity factor matrix is ​​expressed as:

[0042] in, Represents the voltage sensitivity factor matrix; Indicates the power reference value; This represents the calculation of the power transfer distribution factor matrix; Represented as from the balanced node to the node The set of all branches on the path; branch road The resistance.

[0043] Specifically, in order to reflect the linear influence of node injected active power on node voltage amplitude, this application uses a calculation method based on the LinDistFlow model to obtain the voltage sensitivity factor matrix (VSF_P), realizing the physical scenario of high line impedance ratio of distribution network in the derivation of VSF_P.

[0044] The core equation of the LinDistFlow model is:

[0045] The left side of the equation represents the squared difference in voltage amplitude between the nodes at both ends of the branch. and These are the voltage amplitudes at the two ends of the branch; branch road The reactance; branch road The meritorious trend; For this branch road Unproductive trend.

[0046] To achieve dimensionality reduction solutions to the nonlinear physical equations, the above equations need to be approximated and linearized twice, including: When the system node voltage is close to the reference voltage, the approximate relationship of the squared voltage difference is used. The above equation can be simplified to:

[0047] in, This represents the system's reference voltage.

[0048] Considering the inherent characteristics of distribution networks, which have relatively high linear impedance, active power has a dominant influence on grid voltage fluctuations. Therefore, the voltage change of a single branch can be further simplified into a form that is only related to active power:

[0049] in, With the above formula The same as that used to represent the voltage change at the two ends of a branch.

[0050] Based on the simplified linear model of voltage change in the monotonic branch mentioned above, the voltage change from the distribution network slack node to the target node is analyzed. The voltage changes of all branches along the power supply path are accumulated, and combined with the PTDF matrix obtained in the above embodiment, the voltage sensitivity factor matrix VSF_P is calculated. The formula for calculating the elements in the VSF_P matrix is ​​as follows:

[0051] in, This indicates the slack from the balancing node to the target node. The set of all branches on the path; The power reference value is 10000 kVA, which is preferred in this embodiment of the application.

[0052] Furthermore, after obtaining the VSF_P matrix, the calculation of any node in the target network can be performed. The formula for calculating node voltage is:

[0053] in, For nodes The net injection power.

[0054] Step S102: Using the voltage sensitivity factor matrix, the voltage amplitude constraints of each node in the target network are transformed into time-varying power boundary constraints of the energy storage system at the access node.

[0055] Specifically, in traditional optimization scheduling models, node voltage constraints The problem involves multivariate coupled nonlinear constraints on the injected power of all nodes in the target network, which are extremely difficult to solve directly. Therefore, this embodiment of the application utilizes the linear mapping relationship of the VSF_P matrix obtained in step S101 above to decouple the voltage safety constraints of the nodes in the target network into single-variable time-varying power boundary constraints only for the energy storage system, thereby reducing the solution complexity of the subsequent optimization problem.

[0056] refer to Figure 2 This is a flowchart illustrating the conversion of voltage constraints into energy storage power boundaries, provided in an embodiment of this application.

[0057] Step S201: Calculate the base voltage deviation of each node when the target network is not connected to the energy storage system; wherein, the base voltage deviation is determined based on the voltage sensitivity factor matrix and the net injected power of each node; Step S202: Based on the base voltage deviation and the voltage amplitude constraints of each node, determine the upper and lower power limits of the energy storage system when it is connected to the node. Step S203: Calculate the time-varying power boundary constraints based on the upper and lower power limits.

[0058] Specifically, for any node, its total injected power includes the power provided by the energy storage system and the power provided by the infrastructure, during a specific time period. At that time, the node's node Voltage can be expanded as:

[0059] in, For energy storage system access node-to-node Voltage sensitivity factor; For energy storage systems during time periods The charging and discharging power; The nodes calculated in the above embodiments The net injection power.

[0060] Furthermore, the base voltage deviation caused by basic equipment such as photovoltaic output and conventional loads is defined as:

[0061] in, Based on the base voltage deviation.

[0062] Furthermore, the node voltage constraint can be rewritten as an inequality related to the energy storage power:

[0063] Based on the above inequalities, the power boundary of the energy storage system can be solved by algebraic rearrangement.

[0064] In this application, due to differences in the topology of different power distribution networks and the location of energy storage system installation, it will lead to... The mathematical sign of the inequality affects the direction of the inequality; therefore, this application also needs to derive the power boundary based on the sign characteristics of the sensitivity factor: when At this time, the energy storage system in the distribution network is usually located at the end node. When the energy storage system discharges, it causes a drop in node voltage. Therefore, when performing mathematical calculations, both sides of the inequality are divided by the negative sign. The direction of the inequality needs to be reversed. Regarding the upper limit constraint on voltage, the lower limit constraint on energy storage power can be obtained as follows:

[0065] Similarly, given the lower limit constraint on voltage, the upper limit constraint on energy storage power can be obtained as follows:

[0066] when At this time, the energy storage system in the distribution network is usually located at the upstream node. When the energy storage system discharges, it causes the node voltage to rise. Therefore, when performing mathematical calculations, both sides of the inequality are divided by the positive sign. If the direction of the inequality remains unchanged, then the upper voltage limit constraint is directly converted into the upper limit of the energy storage power, and the lower voltage limit constraint is directly converted into the lower limit of the energy storage power.

[0067] Through the above steps, this application calculates within a specific time period At that time, the absolute numerical boundaries of the charging and discharging power of the energy storage system (i.e., dynamic upper and lower limits) are defined, so that in subsequent scheduling optimization, it is only necessary to strictly ensure that the power command of the energy storage system falls within this boundary range, so as to achieve the limit-free operation of the node voltage in the target network, which greatly improves the computational efficiency of the algorithm.

[0068] Step S103: Calculate the branch transmission power of the energy storage system based on the power transmission distribution factor matrix and the energy storage power of the energy storage system.

[0069] Specifically, based on the power transmission distribution factor (PTDF) matrix constructed in step S101, a linear relationship between node input power and branch active power flow is further established, so that the active power flow of any branch in the target network can be represented as a linear combination of the power injected by each node.

[0070] refer to Figure 3 This is a flowchart illustrating the calculation of branch transmission power in an energy storage system provided in an embodiment of this application.

[0071] Step S301: Obtain the transmission power of the first branch when the target network is not connected to the energy storage system; Step S302: Based on the power transmission distribution factor matrix and the energy storage power of the energy storage system, calculate the increment of the energy storage power on the transmission power of the first branch. Step S303: Add the first branch transmission power to the increment to obtain the second branch transmission power after the energy storage system is connected.

[0072] Specifically, this involves placing other equipment (infrastructure) in the target network, excluding the energy storage system, during specific time periods. Using the net injected power as an input parameter, and employing the PTDE matrix constructed in step S101, the initial active power flow distribution of each branch in the target network is calculated without considering the influence of energy storage system charging and discharging, and denoted as the transmission power of the first branch. .

[0073] Furthermore, the planned charging and discharging power of the energy storage system at the access node is extracted. And extract the sensitivity coefficient from the access node to the target branch from the PTDE matrix. The planned charging and discharging power With sensitivity coefficient Multiplying them together gives the increment of the energy storage power to the power transmitted in the first branch.

[0074] Furthermore, based on the principle of linear power flow superposition, the transmission power of the first branch is added to the increment to calculate the final total active power flow of the branch after the energy storage system is actually connected and put into operation, which is the transmission power of the second branch.

[0075] Step S104: Determine branch power flow constraints based on branch transmission power, and construct an energy storage optimization scheduling model based on branch power flow constraints.

[0076] Specifically, when generating the optimal scheduling model, this application uses the Lagrange relaxation method to transform whether the branch power flow exceeds the limit and the degree of the branch power flow exceeding the limit into an economic penalty term in the optimal model, thereby enabling the energy storage system to actively avoid scheduling behaviors that cause power flow to exceed the limit in the optimal scheduling model.

[0077] refer to Figure 4 This is a flowchart of constructing an energy storage optimization scheduling model provided in an embodiment of this application.

[0078] Step S401: Determine the branch transmission capacity constraint based on the second branch transmission power and the branch transmission capacity upper limit.

[0079] Specifically, the upper limit of the transmission capacity of each branch in the target network is obtained, and combined with the transmission power of the second branch obtained in the above embodiment, the safe operation constraints of the branch power flow are established as follows:

[0080] in, This represents the maximum transmission capacity value. This is the power transmitted by the second branch.

[0081] Step S402: Construct an objective function based on preset Lagrange multipliers and branch transmission capacity constraints.

[0082] Specifically, the original economic objective function of the energy storage system is first constructed. The optimization objective of the original economic objective function is to minimize the total cost of the energy storage operation cycle. The expression of the original economic objective function is as follows:

[0083] in, This represents the total number of scheduling periods; For time period The electricity purchase price; For time period The electricity price; The charging power for the energy storage system; This represents the discharge power of the energy storage system.

[0084] Furthermore, the Lagrange multipliers Specifically, non-negative Lagrange multipliers are assigned to the upper and lower limits of the branch flow rates, respectively, to construct a Lagrange objective function that includes a penalty term. :

[0085] in, To represent the time of a unit scheduling period; To indicate a branch During the period The transmission power of the second branch (actual total transmission power of the branch). branch road During the period The upper limit of the current flow constrains the Lagrange multiplier; branch road During the period The lower bound of the trend constraint Lagrange multiplier.

[0086] It should be noted that the aforementioned Lagrange multipliers act as "virtual penalty prices" in the model to guide power flow exceeding limits. Specifically, when the constraints are satisfied, i.e., the branch power flow does not exceed the limits, the corresponding multipliers tend to 0 during iteration, resulting in a penalty term of 0, meaning it has no impact on the economic objectives of the energy storage system. When the constraints are violated, i.e., the branch power flow exceeds the limits, the corresponding multipliers and their product terms become positive, thus drastically increasing the value of the objective function.

[0087] The system can use the algorithm to adaptively adjust the charging and discharging power of the energy storage system according to the Lagrange objective function mentioned above to reduce the penalty term, that is, to find the minimum of the total cost, thereby guiding the solution direction of the optimization model and ensuring that the final output energy storage charging and discharging strategy strictly falls within the boundary range allowed by grid safety.

[0088] Step S403: Generate an energy storage optimization scheduling model based on the objective function.

[0089] Specifically, the Lagrange objective function constructed above... As the optimization objective of the model, the conventional physical operation constraints of the energy storage system itself (including but not limited to charge capacity limit and maximum charge / discharge power limit) and the "single-variable time-varying boundary constraints on energy storage power" obtained in step S102 above are combined to finally generate a complete energy storage optimization scheduling model.

[0090] Step S105: The energy storage optimization scheduling model is modified by time-varying power boundary constraints, and the energy storage system is optimized based on the modified energy storage optimization scheduling model.

[0091] Specifically, the voltage constraints and power flow constraints obtained from the above steps are further fused and solved. In this embodiment, a hybrid solution strategy of analytical solution and subgradient multiplier update is preferably adopted to output the final energy storage system optimization scheduling strategy.

[0092] refer to Figure 5 This is a flowchart of the optimization process for the energy storage system provided in the embodiments of this application.

[0093] Step S501: Solve the energy storage optimization scheduling model to obtain the initial energy storage power.

[0094] Specifically, in the embodiments of this application, the Lagrange objective function constructed above is first... Decoupling is performed, and a preliminary energy storage charging and discharging plan is derived using price signals. Specifically, the branch circuits are decoupled. During the period Second branch transmission power This represents the power transmitted through the first branch (basic power flow) without energy storage. The linear superposition of the power increment caused by the energy storage system:

[0095] in, branch road During the period The transmission power of the first branch; branch road During the period nodes Net injection power; For the time period The power of the energy storage system.

[0096] Substituting the above formula into the established Lagrange objective function In the numbers, and further with the power of energy storage systems The relevant penalty terms are extracted to construct the objective function for the energy storage system problem. :

[0097] Furthermore, to enable energy storage systems to adaptively avoid grid congestion, this application also defines a price adjustment amount to reflect the risk of line over-limit operations, the expression of which is:

[0098] in, This is for price adjustment.

[0099] Using price adjustments, the objective function for the energy storage system problem can be rewritten as a cost-only problem involving only charging and discharging actions, expressed as follows:

[0100] This allows us to obtain the adjusted electricity purchase price for charging the energy storage system and the electricity sales price for discharging the energy storage system. The electricity purchase price for charging the energy storage system is... It can be represented as:

[0101] The electricity price for energy storage systems can be expressed as:

[0102] Based on the aforementioned electricity purchase price for charging and electricity sales price for discharging energy storage systems, when there is a risk of exceeding the limit on a certain line in the distribution network, the penalty multiplier increases, which increases the charging cost or reduces the electricity sales revenue during this period, thereby avoiding charging and discharging behaviors that would exacerbate congestion.

[0103] Based on the adjusted prices mentioned above, this application also employs an Adaptive Sorting Algorithm (ASA) to quickly solve the energy storage optimization problem by sorting marginal values. This enables discharging during periods of high marginal value and charging during periods of low marginal value. Specifically, the marginal value for each period is first calculated. Marginal value is defined as the discharge revenue minus the charging cost, and is a core indicator used to measure the economic benefits of the energy storage system's charging and discharging operations in any given period. The larger the value, the more beneficial it is for the system to discharge (or avoid charging) during that period. The specific formula is as follows:

[0104] in, For energy storage systems during time periods The marginal value; The discharge efficiency of the energy storage system; The charging efficiency of the energy storage system.

[0105] Furthermore, the actual tag values ​​for the entire scheduling cycle are sorted in descending order, and the sorting logic is expressed as follows:

[0106] in, This is the time period index sequence output after being sorted in descending order; This is a sorting function.

[0107] Based on the index sequence obtained from the sorting, discharge power is allocated preferentially in the time period with high marginal value, and charging power is allocated in the time period with low marginal value, until the charge capacity limit is met, thereby achieving the rapid determination of the initial energy storage power for the current iteration.

[0108] Step S502: Correct the initial energy storage power according to the time-varying power boundary constraints to obtain the corrected energy storage power.

[0109] Specifically, based on the initial energy storage power obtained above, it is input into the time-varying power boundary constraint of the energy storage system at the access node calculated in step S102, so as to limit the initial energy storage power to a safe voltage range, thereby correcting the initial energy storage power.

[0110] Step S503: Optimize the scheduling of the energy storage system based on the corrected energy storage power.

[0111] Specifically, using the corrected energy storage power obtained above, the actual power flow of each branch is recalculated in the network, and the violation of power flow constraints is checked. In this embodiment, the subgradient method is first used to iteratively update the Lagrange multipliers, and the update rule is as follows:

[0112] in, Constrain the Lagrange multipliers for the updated trend limit; To constrain the Lagrange multipliers for the updated trend lower bound; branch road During the period The upper limit of the trend is exceeded, which violates the limit; branch road During the period The lower limit of the trend is exceeded, violating the limit in quantity; This is the iteration step size.

[0113] It should be noted that, to ensure the algorithm avoids oscillations and converges quickly, the iteration step size is preferably dynamically updated using the following decay strategy:

[0114] in, This is the initial step size, typically taken as 0.1-0.5; The attenuation rate is typically set to 0.01-0.05.

[0115] When the branch power flow violation amount approaches 0, or the system reaches the preset maximum number of iterations, the Lagrange relaxation algorithm is determined to have converged. At this point, the iteration terminates, and the corrected energy storage power sequence generated in the last iteration is taken as the globally optimal scheduling control command.

[0116] As a specific embodiment, the energy storage system optimization method of this application, in terms of solution efficiency, for an annual optimization scheduling task lasting 8760 hours, has a solution time of 4267 seconds for traditional commercial solvers. The solution time of the optimization method provided in this application is only 100 milliseconds, while the solution time of the traditional commercial solver is only 100 milliseconds. Compared with the traditional commercial solver, the speedup of the method in this application is 43 times, and it has a faster scheduling capability. In terms of economic optimization, the traditional commercial solver relies on exhaustive precise branch and bound to find the global optimum, and its objective function value is -701658 yuan. The objective function value calculated by the optimization method in this application is -687710 yuan. It can be seen that the objective function difference is only 1.99% while the calculation speed is increased by 43 times. This shows that the method provided by the embodiments of this application can guide the solver to find a high-quality near-optimal solution in the feasible region with extremely high accuracy, and achieve a perfect balance between calculation speed and economic benefits. In addition, in terms of the bottom line of grid security, the optimization algorithm of this application can satisfy the "voltage security constraint" and "power flow security constraint" of the entire network after outputting the scheduling strategy. Therefore, the scheduling optimization method of energy storage system provided by this application is also guaranteed in terms of security.

[0117] refer to Figure 6 This is a flowchart of the optimized scheduling process using the Lagrange iteration algorithm provided in an embodiment of this application.

[0118] Specifically, when performing actual optimization scheduling calculations using the energy storage system optimization method provided in this application, the power flow upper limit constraint Lagrange multipliers, power flow lower limit constraint Lagrange multipliers, and initial step size are first initialized. Simultaneously, the current iteration round is set to 0, and the maximum number of iterations is configured. In this embodiment, the maximum number of iterations is preferably 50. Further, the price adjustment amount is defined according to the current grid state, and an adaptive sorting greedy algorithm is used to quickly solve the energy storage optimization problem by sorting marginal values. That is, the marginal value is calculated sequentially, the time periods are arranged according to the marginal value, and the charging and discharging power is allocated to calculate the initial energy storage... The initial energy storage power is further corrected by applying voltage constraint boundaries to obtain the corrected energy storage power. Furthermore, the power flow constraint overrun is checked, and the Lagrange multiplier is used for iteration, i.e., calculating branch power flow, calculating power flow constraint overruns, and updating the Lagrange multiplier sequentially. Finally, the convergence of the objective function is determined. If the objective function converges, the current energy storage power sequence is output. If the Lagrange objective function does not converge, the price adjustment is redefined, and the above operations are repeated until the Lagrange objective function converges or the number of iterations reaches the preset maximum number of iterations.

[0119] refer to Figure 7 This is a flowchart of an optimization method for an energy storage system provided in an embodiment of this application.

[0120] Specifically, the basic parameters of the distribution network are first obtained, including network topology parameters, energy storage system parameters, photovoltaic load data, and electricity prices. These basic parameters serve as the global initialization parameters for subsequent optimized scheduling. Based on these parameters, the power transmission distribution factor matrix and voltage sensitivity factor matrix are calculated, reducing the complex nonlinear AC network to a linear matrix. Furthermore, the voltage constraint is converted into an energy storage power boundary. Then, the scheduling optimization strategy for the energy storage system is solved using a Lagrange iterative algorithm, with the specific solution process being the same as described above. Figure 6 The optimized scheduling process described in the corresponding specific embodiment ultimately converts the calculated mathematical solution into control commands and sends them to the control unit of the energy storage system to control the energy storage system to perform actual physical charging and discharging actions.

[0121] refer to Figure 8 This is a seven-day energy storage dispatch power comparison chart provided in the embodiments of this application.

[0122] Specifically, Figure 8 The diagram shows the energy storage dispatch power comparison curves of three different algorithms over a continuous 168-hour period. Curve 1 is the energy storage dispatch curve using a traditional commercial solver; Curve 2 represents the energy storage dispatch curve of a greedy algorithm that only considers DC power flow; and Curve 3 is the curve of the ultra-fast algorithm proposed in this application that combines AC power flow and voltage constraints. It can be seen that the energy storage dispatch power reflected by Curve 1 is smooth and dispersed, while the limit value of the energy storage dispatch power reflected by Curve 2 is larger than the limit value of the energy storage power reflected by Curve 3. This indicates that the method provided in this application forcibly limits the charging action by triggering the "safe power boundary," thereby effectively avoiding grid voltage exceeding the limit due to overcharging.

[0123] refer to Figure 9 This is a seven-day state of charge comparison diagram provided in the embodiments of this application.

[0124] Specifically, Figure 9 The diagram shows the state of charge (SOC) curves of three different algorithms over a continuous 168-hour period. Curve 4 represents the SOC curve using a traditional commercial solver; curve 5 represents the SOC curve of a greedy algorithm that only considers DC power flow; and curve 6 represents the SOC curve proposed in this application that combines AC power flow and voltage constraints. It can be seen that although curve 4 can smoothly reach the maximum SOC of 8 MWh, the charging time is extremely long. While curve 5 can quickly reach the maximum SOC, in actual distribution networks, this charging method can lead to voltage overshooting at the energy storage access point, posing a safety hazard. The method provided in this application, while ensuring efficiency, can also limit the maximum SOC to 6.5-7 MWh, thereby eliminating the safety hazard of the distribution network.

[0125] refer to Figure 10 This is a seven-day power grid interaction comparison diagram provided in the embodiments of this application.

[0126] Specifically, Figure 10 The graphs show the grid interaction curves of three different algorithms over a continuous 168-hour period. Curve 7 is the grid interaction curve of the traditional commercial solver solution method; curve 8 is used to characterize the grid interaction curve of the greedy algorithm that only considers DC power flow; curve 9 is the grid interaction curve proposed in this application that combines AC power flow and voltage constraints. It can be seen that curve 8 forms a sharp power purchase peak of more than 6MWh on the positive half-axis of the coordinate system, which can easily cause serious over-limits of the upstream transmission lines of the distribution network or transformer overload. Curve 7, due to its complex global time-series planning, has a relatively smooth power purchase and sale action, but requires a lot of computing power. Curve 9 provided by this application can not only achieve the "steep peak" shape of curve 8, that is, fast response power purchase during the period of optimal price, but its highest power purchase peak is significantly lower than that of curve 8, further verifying that the "safe power boundary" provided by this application can avoid the grid over-limits.

[0127] refer to Figure 11 This is a comparison chart of power flow results for any two branches within seven days, provided in an embodiment of this application.

[0128] Specifically, Figure 11 The figure shows the active power flow time-series variation curves of any two transmission lines over 168 hours. The dashed lines in the figure represent the physical thermal stability limit of the power flow. Curves 10 and 13 are branch power flow curves solved by traditional commercial solvers. Curves 11 and 14 are branch power flow curves using a greedy algorithm that only considers DC power flow. Curves 12 and 15 are branch power flow curves proposed in this application that combine AC power flow and voltage constraints. It can be seen that within the continuous period shown in the figure, the waveforms of curves 10 and 13 are relatively flat and the peak power flow of the branches is lower. Because curves 11 and 14 use a DC power flow approximation algorithm, they ignore the significant reactive power distribution and node voltage drop in the distribution network, resulting in the estimated peak power flow of the branches being significantly higher than that of curves 12 and 15. Therefore, the method provided in this application can perform high power throughput during the period with the highest marginal value while keeping the peak value below the power flow limit, thus achieving rapid optimization while ensuring the physical safety of the power grid.

[0129] refer to Figure 12 This is a comparison chart of node voltage values ​​over seven days provided in the embodiments of this application.

[0130] Specifically, Figure 12The figure shows the time-series changes of the base voltage values ​​of any node and energy storage node in the network over a continuous 168-hour period. The dashed lines in the figure represent the voltage limits of the nodes. Curves 16 and 19 are the branch power flow curves solved by the traditional commercial solver method. Curves 17 and 20 are the branch power flow curves of the greedy algorithm that only considers DC power flow. Curves 18 and 21 are the branch power flow curves proposed in this application that combine AC power flow and voltage constraints. It can be seen that curves 17 and 20 always appear as a straight line because they ignore the significant reactive power distribution and node voltage drop in the distribution network. The peak values ​​of curves 16 and 19 solved by the traditional commercial solver method, as well as curves 18 and 21 of the method provided in this application, are all within the voltage limit. Moreover, the node voltage value of the method provided in this application will immediately stop falling further when it reaches the lower voltage limit, which further verifies the effective execution of the "safe power boundary" provided in this application and effectively achieves precise voltage control.

[0131] In summary, the energy storage system optimization method provided in this application calculates the power transmission distribution factor matrix and voltage sensitivity factor matrix based on the topology and impedance parameters of the target network. The voltage sensitivity factor matrix is ​​used to decouple complex node voltage constraints, transforming them into time-varying power boundary constraints for the energy storage system. Then, combined with the branch power flow constraints determined by the power transmission distribution factor matrix, an energy storage optimization scheduling model is constructed. This model is then corrected based on the time-varying power boundary constraints, enabling rapid optimization of the energy storage system. The energy storage system optimization method provided in this application not only minimizes the full-cycle operating cost of the energy storage system at the economic level but also, at the physical level, ensures that the node voltages and line loads of the distribution network are within safe operating ranges through pre-emptive voltage boundary limiting and power flow penalty mechanisms. This achieves global optimization scheduling that balances extreme computational efficiency, economy, and grid physical security.

[0132] refer to Figure 13 This is a schematic diagram of an optimization device for an energy storage system provided in an embodiment of this application.

[0133] Based on the same concept, corresponding to the energy storage system optimization method provided in the above embodiments, this application also provides an energy storage system optimization device 1300, including a matrix calculation module 1310, a constraint generation module 1320, a power calculation module 1330, a model generation module 1340, and an optimization processing module 1350.

[0134] The matrix calculation module 1310 is configured to calculate the power transmission distribution factor matrix and the voltage sensitivity factor matrix based on the topology and line impedance parameters of the target network; the constraint generation module 1320 is configured to use the voltage sensitivity factor matrix to transform the voltage amplitude constraints of each node in the target network into time-varying power boundary constraints of the energy storage system at the access node; the power calculation module 1330 is configured to calculate the branch transmission power of the energy storage system based on the power transmission distribution factor matrix and the energy storage power of the energy storage system; the model generation module 1340 is configured to determine the branch power flow constraints based on the branch power flow constraints and construct an energy storage optimization scheduling model based on the branch power flow constraints; the optimization processing module 1350 is configured to correct the energy storage optimization scheduling model through time-varying power boundary constraints and perform optimization processing on the energy storage system based on the corrected energy storage optimization scheduling model.

[0135] According to the energy storage system optimization device of this application embodiment, based on the topology and line impedance parameters of the target network, the power transmission distribution factor matrix is ​​calculated. The matrix calculation module 1310 is further configured to: calculate the power transmission distribution factor matrix based on the node branch correlation matrix, branch reactance vector, and node susceptance matrix of the target network; wherein, the calculated power transmission distribution factor matrix is ​​expressed as:

[0136] in, This represents the calculation of the power transfer distribution factor matrix; This represents the reciprocal diagonal matrix of branch reactance; Let be the branch reactance vector, with dimension ( ); This represents the node-branch association matrix, with dimension ( ); Represents the nodal susceptance matrix, with dimension ( ).

[0137] According to the energy storage system optimization device of this application embodiment, a voltage sensitivity factor matrix is ​​calculated based on the topology and line impedance parameters of the target network. The matrix calculation module 1310 is further configured to: calculate the voltage sensitivity factor matrix based on the power transmission distribution factor matrix, the branch resistance of the target network, the power reference value, and the path from the slack node to each node; wherein, the voltage sensitivity factor matrix is ​​expressed as:

[0138] in, Represents the voltage sensitivity factor matrix; Indicates the power reference value; This represents the calculation of the power transfer distribution factor matrix; Represented as from the balanced node to the node The set of all branches on the path; branch road The resistance.

[0139] According to the energy storage system optimization device of the present application embodiment, the voltage amplitude constraints of each node in the target network are transformed into time-varying power boundary constraints of the energy storage system at the access node. The constraint generation module 1320 is further configured to: calculate the base voltage deviation of each node when the target network is not connected to the energy storage system; wherein, the base voltage deviation is determined based on the voltage sensitivity factor matrix and the net injected power of each node; determine the upper and lower power limits of the energy storage system at the access node based on the base voltage deviation and the voltage amplitude constraints of each node; and calculate the time-varying power boundary constraints based on the upper and lower power limits.

[0140] According to the energy storage system optimization device of the present application embodiment, the branch transmission power of the energy storage system is calculated based on the power transmission distribution factor matrix and the energy storage power of the energy storage system. The power calculation module 1330 is further configured to: obtain the first branch transmission power when the target network is not connected to the energy storage system; calculate the increment of the energy storage power to the first branch transmission power based on the power transmission distribution factor matrix and the energy storage power of the energy storage system; and add the first branch transmission power to the increment to obtain the second branch transmission power after the energy storage system is connected.

[0141] According to the energy storage system optimization device of the present application embodiment, the branch power flow constraint is determined based on the branch transmission power, and the energy storage optimization scheduling model is constructed based on the branch power flow constraint. The model generation module 1340 is further configured to: determine the branch transmission capacity constraint based on the second branch transmission power and the upper limit of the branch transmission capacity; construct an objective function based on the preset Lagrange multiplier and the branch transmission capacity constraint; and generate the energy storage optimization scheduling model based on the objective function.

[0142] According to the energy storage system optimization device of the present application embodiment, the energy storage optimization scheduling model is modified by time-varying power boundary constraints, and the energy storage system is optimized based on the modified energy storage optimization scheduling model. The optimization processing module 1350 is configured to: solve the energy storage optimization scheduling model to obtain the initial energy storage power; modify the initial energy storage power according to the time-varying power boundary constraints to obtain the modified energy storage power; and optimize the scheduling of the energy storage system based on the modified energy storage power.

[0143] The energy storage system optimization device of the above embodiments is used to implement the corresponding energy storage system optimization method in any of the foregoing embodiments, and has the beneficial effects of the corresponding energy storage system optimization method embodiments, which will not be repeated here.

[0144] Based on the same concept, corresponding to the energy storage system optimization method provided in any of the above embodiments, this application also provides an electronic device, including a processor and a memory, wherein the memory stores a program or instructions that can run on the processor, and when the program or instructions are executed by the processor, the above-described energy storage system optimization method is implemented.

[0145] Figure 14 This illustration shows a more specific hardware structure diagram of an electronic device according to an embodiment of this application. The device may include: a processor 1410, a memory 1420, an input / output interface 1430, a communication interface 1440, and a bus 1450. The processor 1410, memory 1420, input / output interface 1430, and communication interface 1440 are interconnected internally via the bus 1450.

[0146] The processor 1410 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0147] The memory 1420 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1420 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1420 and is called and executed by the processor 1410.

[0148] The input / output interface 1430 is used to connect input / output modules to realize information input and output. The input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0149] The communication interface 1440 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0150] Bus 1450 includes a pathway for transmitting information between various components of the device, such as processor 1410, memory 1420, input / output interface 1430, and communication interface 1440.

[0151] It should be noted that although the above-described device only shows the processor 1410, memory 1420, input / output interface 1430, communication interface 1440, and bus 1450, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0152] The electronic devices described above are used to implement the optimization method of the corresponding energy storage system in any of the foregoing embodiments, and have the beneficial effects of the corresponding energy storage system optimization method embodiments, which will not be repeated here.

[0153] Based on the same concept, corresponding to the energy storage system optimization method provided in any of the above embodiments, this application also provides a computer-readable storage medium, on which a program or instructions are stored, and when the program or instructions are executed by a processor, the steps of the energy storage system optimization method described above are implemented.

[0154] The aforementioned computer-readable storage medium can be any available medium or data storage device that a computer can access, including but not limited to magnetic storage (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MOs), etc.), optical storage (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs)).

[0155] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the optimization method of the corresponding energy storage system in any of the foregoing embodiments, and have the beneficial effects of the corresponding energy storage system optimization method embodiments, which will not be repeated here.

[0156] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0157] From the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of computer software products plus necessary general-purpose hardware platforms, and of course, they can also be implemented by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and includes several instructions to cause the terminal or network-side device to execute the methods described in the various embodiments of this application.

[0158] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other implementations under the guidance of this application without departing from the spirit and scope of the claims. All of these implementations are within the protection scope of this application.

Claims

1. An optimization method for an energy storage system, characterized in that, include: Based on the topology and line impedance parameters of the target network, the power transmission distribution factor matrix and voltage sensitivity factor matrix are calculated, including: The power transmission distribution factor matrix is ​​calculated based on the node branch correlation matrix, branch reactance vector, and node susceptance matrix of the target network. The calculated power transfer distribution factor matrix is ​​represented as follows: in, This represents the calculation of the power transfer distribution factor matrix; Let be the branch reactance vector, with dimension ( ); This represents the reciprocal diagonal matrix of branch reactance; This represents the node-branch association matrix, with dimension ( ); Represents the nodal susceptance matrix, with dimension ( ); Based on the power transfer distribution factor matrix, the branch resistance of the target network, the power reference value, and the path from the slack node to each node, calculate the voltage sensitivity factor matrix. The voltage sensitivity factor matrix is ​​represented as follows: in, Represents the voltage sensitivity factor matrix; Indicates the power reference value; This represents the calculation of the power transfer distribution factor matrix; Represented as from the balanced node to the node The set of all branches on the path; branch road The resistance; Using the voltage sensitivity factor matrix, the voltage amplitude constraints of each node in the target network are transformed into time-varying power boundary constraints of the energy storage system at the access node, including: Calculate the base voltage deviation of each node when the target network is not connected to the energy storage system; wherein, the base voltage deviation is determined based on the voltage sensitivity factor matrix and the net injected power of each node; Based on the base voltage deviation and the voltage amplitude constraints of each node, the upper and lower power limits of the energy storage system at the access node are determined. Calculate time-varying power boundary constraints based on the power upper limit and the power lower limit; Based on the power transmission distribution factor matrix and the energy storage power of the energy storage system, calculate the branch transmission power of the energy storage system. The branch power flow constraints are determined based on the branch transmission power, and an energy storage optimization scheduling model is constructed based on the branch power flow constraints. The energy storage optimization scheduling model is modified by applying time-varying power boundary constraints, and the energy storage system is then optimized based on the modified energy storage optimization scheduling model, including: The initial energy storage power is obtained by solving the energy storage optimization scheduling model. The initial energy storage power is corrected according to the time-varying power boundary constraints to obtain the corrected energy storage power; The energy storage system is optimized and scheduled based on the corrected energy storage power.

2. The optimization method for the energy storage system according to claim 1, characterized in that, The calculation of the branch transmission power of the energy storage system based on the power transmission distribution factor matrix and the energy storage power of the energy storage system includes: Obtain the transmission power of the first branch when the target network is not connected to the energy storage system; Based on the power transmission distribution factor matrix and the energy storage power of the energy storage system, calculate the increment of the energy storage power on the transmission power of the first branch. The transmission power of the first branch is added to the increment to obtain the transmission power of the second branch after the energy storage system is connected.

3. The optimization method for the energy storage system according to claim 2, characterized in that, The step of determining branch power flow constraints based on the branch transmission power and constructing an energy storage optimization scheduling model based on the branch power flow constraints includes: The branch transmission capacity constraint is determined based on the transmission power of the second branch and the upper limit of the branch transmission capacity. The objective function is constructed based on the preset Lagrange multipliers and the branch transmission capacity constraints; The energy storage optimization scheduling model is generated based on the objective function.

4. An optimization device for an energy storage system, characterized in that, include: The matrix calculation module is configured to calculate the power transfer distribution factor matrix and voltage sensitivity factor matrix based on the topology and line impedance parameters of the target network, including: The power transmission distribution factor matrix is ​​calculated based on the node branch correlation matrix, branch reactance vector, and node susceptance matrix of the target network. The calculated power transfer distribution factor matrix is ​​represented as follows: in, This represents the calculation of the power transfer distribution factor matrix; Let be the branch reactance vector, with dimension ( ); This represents the reciprocal diagonal matrix of branch reactance; This represents the node-branch association matrix, with dimension ( ); Represents the nodal susceptance matrix, with dimension ( ); Based on the power transfer distribution factor matrix, the branch resistance of the target network, the power reference value, and the path from the slack node to each node, calculate the voltage sensitivity factor matrix. The voltage sensitivity factor matrix is ​​represented as follows: in, Represents the voltage sensitivity factor matrix; Indicates the power reference value; This represents the calculation of the power transfer distribution factor matrix; Represented as from the balanced node to the node The set of all branches on the path; branch road The resistance; The constraint generation module is configured to use the voltage sensitivity factor matrix to transform the voltage amplitude constraints of each node in the target network into time-varying power boundary constraints of the energy storage system at the access node, including: Calculate the base voltage deviation of each node when the target network is not connected to the energy storage system; wherein, the base voltage deviation is determined based on the voltage sensitivity factor matrix and the net injected power of each node; Based on the base voltage deviation and the voltage amplitude constraints of each node, the upper and lower power limits of the energy storage system at the access node are determined. Calculate time-varying power boundary constraints based on the power upper limit and the power lower limit; The power calculation module is configured to calculate the branch transmission power of the energy storage system based on the power transmission distribution factor matrix and the energy storage power of the energy storage system. The model generation module is configured to determine branch power flow constraints based on the branch transmission power and to construct an energy storage optimization scheduling model based on the branch power flow constraints. The optimization processing module is configured to modify the energy storage optimization scheduling model using time-varying power boundary constraints, and to optimize the energy storage system based on the modified energy storage optimization scheduling model, including: The initial energy storage power is obtained by solving the energy storage optimization scheduling model. The initial energy storage power is corrected according to the time-varying power boundary constraints to obtain the corrected energy storage power; The energy storage system is optimized and scheduled based on the corrected energy storage power.

5. An electronic device, characterized in that, include: A processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the optimization method for the energy storage system as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the optimization method for the energy storage system as described in any one of claims 1 to 3.

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