Energy storage and new energy optimization method and device considering dynamic section limits

Abstract

The present application discloses a method and device for optimizing energy storage and new energy taking into account dynamic section limits, and relates to the technical field of power system optimization and scheduling. The method improves the traditional fixed-value limit section constraint into a section limit time-varying constraint during the coordinated optimization and scheduling of energy storage and new energy, and adjusts the section limit power in the section limit time-varying constraint in real time according to the operating status and load changes of the power system, so that the section limit power can adapt to the actual operating conditions more flexibly, thereby improving the operating safety and wind and solar power output of the power system, promoting the new energy consumption of the power system, increasing the new energy output, and reducing the amount of wind and solar power abandoned.

Description

Technical Field

[0001] The present application relates to the technical field of power system optimization and dispatching, and in particular to a method and device for optimizing energy storage and new energy by considering dynamic section limits. Background Art

[0002] In recent years, new energy sources, represented by distributed power generation (DGs), have rapidly developed globally. These renewable energy sources offer advantages such as being renewable, clean, and pollution-free. However, renewable energy generation is characterized by volatility, randomness, and intermittency. The uncertainty in their output significantly impacts the safe and stable operation of the power grid.

[0003] To address the uncertainties of renewable energy generation, energy storage technology has garnered widespread attention and application. Energy storage power stations can store energy when renewable energy generation is in excess and release it when it's insufficient, thereby smoothing fluctuations in renewable energy output and improving the reliability and stability of the power grid.

[0004] Therefore, in the context of large-scale access to new energy, it is particularly important to achieve coordinated optimization of new energy sites and energy storage power stations. The coordinated optimization of new energy and energy storage is the key to achieving safe, economical and efficient operation of the power system.

[0005] In the coordinated optimization of renewable energy and energy storage, section time limits are a key consideration. Existing methods tend to be conservative in selecting section limits. These limits are typically fixed based on historical data and experience, failing to reflect dynamic changes in actual operation. While setting these limits ensures power system security, it cannot guarantee power consumption. Summary of the Invention

[0006] The purpose of this application is to provide a method and device for optimizing energy storage and new energy that takes into account dynamic section limits, which can dynamically adjust the time-varying power of section limits, thereby improving the output power of new energy while ensuring the safety of the power system, effectively improving the flexibility of the system, and improving the efficiency of new energy utilization.

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

[0008] In a first aspect, the present application provides an energy storage and new energy optimization method considering dynamic cross-section limits, including:

[0009] Obtain real-time energy storage discharge power, real-time energy storage charging power, real-time wind power generation power, real-time photovoltaic power generation power, and real-time wind and solar power curtailment power;

[0010] Inputting the real-time energy storage discharge power and the real-time energy storage charging power into an energy storage optimization operation model to obtain an energy storage optimization amount, wherein the energy storage optimization operation model includes an energy storage optimization objective function and an energy storage constraint set, the energy storage optimization objective function is a function constructed with the goal of maximizing a first difference, the first difference is a value obtained by performing a difference calculation based on the energy storage end's balance optimization amount for the power system, the energy storage end operation and maintenance coefficient, and the energy storage end life loss coefficient, and the energy storage constraint set includes an energy storage charging and discharging state constraint, an energy storage charging and discharging power constraint, and an energy storage charge constraint;

[0011] The real-time wind power generation power, the real-time photovoltaic power generation power, and the real-time wind and solar power abandonment power are input into a new energy optimization operation model to obtain a new energy optimization amount, wherein the new energy optimization objective function and the new energy constraint set are provided, the new energy optimization objective function is a function constructed with the goal of maximizing a second difference, the second difference is a value obtained by performing a difference calculation based on the new energy absorption amount and the wind and solar power abandonment penalty amount, and the new energy constraint set includes a photovoltaic fluctuation penalty constraint and a wind power fluctuation penalty constraint;

[0012] The energy storage optimization amount and the new energy optimization amount are input into the energy storage and new energy day-ahead collaborative optimization model to obtain the scheduling results of energy storage and new energy, wherein the energy storage and new energy day-ahead collaborative optimization model includes a collaborative optimization objective function and a power system operation constraint set, the collaborative optimization objective function is a function calculated based on the energy storage optimization amount and the new energy optimization amount, the power system operation constraint set includes the interconnection line transmission power constraint, phase angle constraint and section limit power time-varying constraint between each node in the power system, and the section limit power time-varying constraint in the section limit power time-varying constraint. The section limit power is a value obtained by substituting the real-time node active power into the section limit time-varying linear relationship. The section limit time-varying linear relationship is a relationship obtained by fitting the historical node active power and the historical section limit power. The historical node active power and the historical section limit power are values ​​obtained by processing the injected power and load of the target node on the typical day of the historical year using the continuous power flow method. The typical day of the historical year refers to the historical date when the section limit power in the power system exceeds the section constant threshold. The target node refers to the node corresponding to the transmission line at the section.

[0013] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned energy storage and new energy optimization method considering dynamic section limits in the first aspect.

[0014] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned energy storage and new energy optimization method considering dynamic section limits in the first aspect.

[0015] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned energy storage and new energy optimization method considering dynamic section limits in the first aspect.

[0016] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0017] The present application provides a method and device for optimizing energy storage and new energy taking into account dynamic section limits. By inputting real-time energy storage discharge power, real-time energy storage charging power, real-time wind power generation power, real-time photovoltaic power generation power, and real-time wind and solar power curtailment into separately constructed energy storage optimization operation models and new energy optimization operation models, energy storage optimization quantities and new energy optimization quantities are obtained. A collaborative optimization objective function is constructed based on the energy storage optimization quantities and new energy optimization quantities, and based on a set of power system operation constraints, the optimal scheduling results of energy storage and new energy are determined. In particular, the traditional fixed-value limit section constraints in the power system operation constraint set are improved into time-varying section limit constraints. By adjusting the section limit power in the time-varying section limit constraints in real time according to the operating status and load changes of the power system, the section limit power can be more flexibly adapted to the actual operating conditions, thereby improving the operating safety and wind and solar power output of the power system, promoting the new energy consumption of the power system, increasing the external transmission of new energy, and reducing the amount of wind and solar power curtailment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 This is an application environment diagram of an energy storage and new energy optimization method considering dynamic cross-section limits in Example 1 of the present application;

[0020] Figure 2 A schematic flow chart of an energy storage and new energy optimization method considering dynamic cross-section limits provided in Example 1 of the present application;

[0021] Figure 3 A schematic diagram of the structure of a computer device provided in Example 2 of the present application. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0023] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0024] Example 1

[0025] The embodiment of the present application provides an energy storage and new energy optimization method considering dynamic section limits, which can be applied to Figure 1In the application environment shown, the terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the real-time energy storage discharge power, real-time energy storage charging power, real-time wind power generation power, real-time photovoltaic power generation power, and real-time wind and solar power curtailment to the server 104. The server 104 uses the energy storage optimization operation model, the new energy optimization operation model, and the energy storage and new energy day-ahead collaborative optimization model to obtain the scheduling results of energy storage and new energy based on the received real-time energy storage discharge power, real-time energy storage charging power, real-time wind power generation power, real-time photovoltaic power generation power, and real-time wind and solar power curtailment. The energy storage and new energy day-ahead collaborative optimization model includes a collaborative optimization objective function and a power system operation constraint set. The section limit power time-varying constraint in the power system operation constraint set is a time-varying constraint. The section limit power is a value obtained by substituting the real-time node active power into the section limit time-varying linear relationship. The section limit time-varying linear relationship is a relationship obtained by fitting the historical node active power and the historical section limit power. The historical node active power and the historical section limit power are values ​​obtained by processing the injection power and load of the target node on typical days of the historical year using the continuous power flow method. The server 104 can feed back the obtained scheduling results of energy storage and renewable energy to the terminal 102. In addition, in some embodiments, an energy storage and renewable energy optimization method considering dynamic section limits can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly process the real-time energy storage discharge power, real-time energy storage charging power, real-time wind power generation power, real-time photovoltaic power generation power, and real-time wind and solar power curtailment using the energy storage and renewable energy optimization method considering dynamic section limits. Alternatively, the server 104 can obtain the real-time energy storage discharge power, real-time energy storage charging power, real-time wind power generation power, real-time photovoltaic power generation power, and real-time wind and solar power curtailment from a data storage system and process them using the energy storage and renewable energy optimization method considering dynamic section limits.

[0026] Terminal 102 may include, but is not limited to, various desktop computers, laptops, smartphones, tablet computers, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers, or may be a cloud server.

[0027] In an exemplary embodiment, a method for optimizing energy storage and new energy considering dynamic section limits is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the process, including the following steps 201 to 208.

[0028] Step 201: Acquire real-time energy storage discharge power, real-time energy storage charging power, real-time wind power generation power, real-time photovoltaic power generation power, and real-time wind and solar power curtailment power.

[0029] Step 202: Input the real-time energy storage discharge power and the real-time energy storage charging power into an energy storage optimization operation model to obtain an energy storage optimization amount. The energy storage optimization operation model includes an energy storage optimization objective function and an energy storage constraint set. The energy storage optimization objective function is a function constructed with the goal of maximizing a first difference. The first difference is a value obtained by performing a difference calculation based on the energy storage end's balance optimization amount for the power system, the energy storage end operation and maintenance coefficient, and the energy storage end life loss coefficient. The energy storage constraint set includes an energy storage charging and discharging state constraint, an energy storage charging and discharging power constraint, and an energy storage charge constraint.

[0030] Step 203: Input the real-time wind power generation power, the real-time photovoltaic power generation power, and the real-time wind and solar power abandonment power into the new energy optimization operation model to obtain the new energy optimization amount, wherein the new energy optimization objective function and the new energy constraint set, the new energy optimization objective function is a function constructed with the goal of maximizing the second difference, the second difference is a value obtained by performing a difference calculation based on the new energy absorption amount and the wind and solar power abandonment penalty amount, and the new energy constraint set includes a photovoltaic fluctuation penalty constraint and a wind power fluctuation penalty constraint.

[0031] Step 204: Input the energy storage optimization amount and the new energy optimization amount into the energy storage and new energy day-ahead collaborative optimization model to obtain the scheduling results of energy storage and new energy, wherein the energy storage and new energy day-ahead collaborative optimization model includes a collaborative optimization objective function and a power system operation constraint set, wherein the collaborative optimization objective function is a function calculated based on the energy storage optimization amount and the new energy optimization amount, and the power system operation constraint set includes the tie line transmission power constraint, phase angle constraint and section limit power time-varying constraint between each node in the power system, and the section limit power time-varying constraint between each node at the section. The section limit power in the beam is a value obtained by substituting the real-time node active power into the section limit time-varying linear relationship. The section limit time-varying linear relationship is a relationship obtained by fitting the historical node active power and the historical section limit power. The historical node active power and the historical section limit power are values ​​obtained by processing the injected power and load of the target node on the typical day of the historical year using the continuous power flow method. The typical day of the historical year refers to the historical date when the section limit power in the power system exceeds the section constant threshold. The target node refers to the node corresponding to the transmission line at the section.

[0032] This embodiment adopts the above steps 201 to 204 to improve the fixed-value limit section constraint in the prior art into a time-varying section limit constraint. By adjusting the section limit (i.e., the section limit power) in real time according to the operating status and load changes of the power system (i.e., the injected power and load of the node in the power system), the section limit can be more flexibly adapted to the actual operating conditions, thereby improving the operating safety and wind and solar power output of the power system and promoting the new energy consumption of the power system. At the same time, the time-varying section limit constraint of this embodiment can increase the new energy output and reduce the amount of wind and solar power curtailment. It overcomes the defect that "fixed-value limit section constraints are usually fixed limits set based on historical data and experience, and cannot timely reflect the dynamic changes in actual operation. Such static limit constraints may lead to insufficient safety margins or excessive conservatism in system operation in actual applications, thereby adversely affecting the new energy consumption of the power system, especially limiting power transmission within the line." The dynamic section limit constraint adopted in this embodiment can adjust the limit in real time according to the actual operating conditions, making the power system operation more flexible and efficient, and dynamically increasing the new energy output.

[0033] At the same time, in the coordinated optimization process of energy storage and new energy, this embodiment models the energy storage end and the new energy end respectively, comprehensively considers the energy storage end life loss coefficient and the energy storage end operation and maintenance coefficient, and uses the energy storage end equipment to promote the consumption of new energy, thereby improving the consumption capacity of new energy, thereby improving the new energy transmission efficiency of the power system, and overcoming the defect that "the joint modeling of energy storage and new energy in the existing methods is usually relatively simplified, ignoring the life loss cost and operation and maintenance cost of energy storage equipment. This simplified model may lead to low operating efficiency and shortened life of energy storage equipment in actual application, affecting the economy and stability of the overall system". The modeling method of this embodiment can more accurately describe the operating characteristics of energy storage and new energy, consider the energy storage life loss and energy storage operation and maintenance coefficient, thereby optimizing the operation strategy of the power system, and improving the economic benefits and stability of the power system.

[0034] This embodiment takes into account that existing multi-objective optimization problem-solving methods typically employ weighted summation methods, Pareto optimal solutions, and goal programming methods. In practical applications, these methods often struggle to determine the appropriate weights for each objective, potentially leading to a solution that favors a particular objective and fails to adequately balance the interests of each objective. Existing objective weight assignment methods typically employ expert scoring methods, entropy weighting methods, and other methods to determine the weights of each objective. These methods often rely on subjective experience or statistical data, making them difficult to dynamically adjust. Furthermore, in complex and volatile power systems, the rationality and accuracy of weight assignment cannot be guaranteed.

[0035] In order to overcome this technical defect, as an optional implementation method, this embodiment adopts the target hierarchy weight method to determine the energy storage optimization amount weight and the new energy optimization amount weight in the collaborative optimization objective function.

[0036] This embodiment uses a target-level weighting method to reasonably allocate the importance of each target in the system, achieving a solution to the day-ahead collaborative optimization scheduling problem of the power system that is in line with reality. By reasonably dynamically allocating the weight values ​​of the two models (i.e., the energy storage optimization quantity weight corresponding to the energy storage optimization operation model and the new energy optimization quantity weight corresponding to the new energy optimization operation model) and then solving the problem, the weights of each target are dynamically adjusted according to actual conditions, avoiding a single target bias and ensuring that the optimization results are more in line with actual needs. While ensuring the safe and stable operation of the power system, the benefits of each entity, including distributed energy operators, energy storage system operators, and grid operators, are maximized.

[0037] In this embodiment, the above steps 201 to 204 involve the calculation process of the cross-section limit power in the cross-section limit time-varying constraint, the establishment and solution process of the energy storage operation model, the new energy operation model, and the energy storage and new energy day-ahead collaborative optimization model. In order to make the implementation steps of these specific processes more clear to those skilled in the art, the following combined Figure 2 Further explanation.

[0038] (1) Convert the fixed limit section constraint into a time-varying section limit constraint.

[0039] Northern Hebei currently boasts abundant renewable energy resources and a large installed capacity, but the region is relatively sparsely populated with industrial and power-hungry users. Consuming renewable energy power in the region presents a significant challenge. In particular, the region's complex terrain makes it difficult to lay transmission lines, resulting in multiple restrictions on renewable energy power transmission. The most severe transmission restrictions are located along sections, where the maximum power output is defined as the maximum transmission capacity. To increase renewable energy absorption and achieve efficient clean energy utilization, improving the maximum power output at these sections is crucial. Current selection of maximum transmission capacity at these sections tends to be conservative, often using a fixed transmission capacity at the worst possible time as the maximum transmission capacity limit for all times. While this ensures safety, it fails to guarantee absorption.

[0040] This embodiment changes the fixed section limit power into a dynamic section limit time-varying power (section limit power), thereby increasing the output power of renewable energy while ensuring safety, effectively improving system flexibility, and improving the utilization efficiency of renewable energy.

[0041] The fixed limit section constraint is converted into a time-varying section limit constraint. The specific process includes:

[0042] Step 1: Screen based on the section constant limit, select the date that exceeds the section limit threshold, calculate the corresponding section limit under different dates according to the continuous power flow method, and then build a section limit database.

[0043] In the collaborative optimization of energy storage and new energy in this embodiment, the calculation of the cross-sectional limit value of the power flow is transformed into a time-varying calculation method of the cross-sectional limit that dynamically changes according to the node injection power. While satisfying the constraint that the power flow does not exceed the limit, it greatly improves the regional new energy transmission power, providing an effective solution for the transmission of new energy in areas with large power generation.

[0044] As the injected power of distributed energy resources connected to the distribution network changes, the system flow will also change accordingly. Fixed-value section limits can lead to a series of problems, such as reduced system transmission capacity and exceeding line carrying capacity limits. By building a section limit database, we can provide a foundation for establishing a time-varying linear relationship for section limits.

[0045] Step 1-1: Obtain the injected power and load of renewable energy at each node on a typical day in the past year. The typical day in the past year refers to the historical date when the section limit power in the power system exceeds the section fixed value threshold. The injected power includes active power P and reactive power Q, and the load includes active load P. L and reactive load Q LAssume that the line set at the section is c, the total number of new energy nodes connected to the power system is m, and any transmission line (i.e. branch) between node a and node b is l ab , all l ab The line set c at the section is formed.

[0046] The section limit power objective function is constructed with the goal of maximizing the section output power. The expression of the constraint function of the section limit power objective function is:

[0047]

[0048] In the above formula, U a and U b represents the voltage between nodes a and b, G ab represents the line conductance between node a and node b, δ ab represents the phase angle between node a and node b, B ab Represents the susceptance between nodes a and b.

[0049] Step 1-2: The above-mentioned section limit power objective function is subject to the following constraints (i.e., section limit power objective function constraint function):

[0050] P l -H ab P ab ≤P l,max ;

[0051]

[0052] P a,min ≤P a ≤P a,max ;

[0053] Q a,min ≤Q a ≤Q a,max ;

[0054] V a,min ≤V a ≤V a,max ;

[0055] Among them, H ab is the branch disconnection distribution factor between node a and node b; P ab is the branch power flow between node a and node b; P l,max is the maximum active power limit of line l; P a is the active power of the new energy output of node a; P a,min 、P a,max are the minimum active power and maximum active power of the renewable energy output of node a respectively; Q ais the reactive power of the renewable energy output of node a; Q a,min , Q a,max are the minimum reactive power and maximum reactive power of the renewable energy output of node a respectively; V a is the voltage amplitude of node a; V a,min 、V a,max are the minimum and maximum voltage amplitudes of node a respectively.

[0056] Step 1-3: Introduce the continuous variable α to adjust the active power output of the renewable energy output and calculate the cross-section output power. The iterative formula is as follows:

[0057]

[0058] in, is the initial value of active power of node a; is the first iteration value of the active power of node a; and so on, is the nth iteration value of the active power of node a.

[0059] Based on the above formula, incorporating the load change of node a can obtain the overall injection power change value of node a. The calculation formulas for the active power change ΔP and reactive power change ΔQ are as follows:

[0060]

[0061] in, and are the active load and reactive load values ​​of node a respectively; listed here is the nth iteration formula.

[0062] Step 1-4: Iterate according to the continuous power flow method and combine it with the Newton-Raphson method to obtain the accurate power flow solution for the new state point. The iterative model of the continuous power flow method is as follows:

[0063]

[0064] Among them, J Pδ 、J PU 、J Qδ With J PU It is a known quantity of Jacobian submatrix in the conventional power flow calculation process and can be directly substituted for use;

[0065] The dδ obtained from the above formula (n+1) 、dU (n+1) with da (n+1) Substitute the continuous power flow method state point prediction model formula to obtain the result after a new round of iteration. The continuous power flow method state point prediction model formula is as follows:

[0066]

[0067] Among them, h represents the prediction step size, which can be set manually.

[0068] Step 1-5: After each iteration, the iterative results U, P, Q and δ are substituted into the section limit power objective function and the section limit power objective function constraint function to solve. When the objective function meets the constraint conditions and converges (i.e., the section output power reaches a stable state), and when the set step size meets the given accuracy requirements, the output section limit power P is cs Based on this, the active power P of the renewable energy output of all other nodes in the power system is calculated, and the section limit power corresponding to each scheduling time interval and the active power of all nodes (i.e., the m different nodes mentioned above) are stored in the section limit database.

[0069] The above process is repeated according to the data obtained on different typical days to form the final section limit database.

[0070] Step 2: Based on the formed section limit database, typical samples are screened, and the active power of each node is fitted with the section limit using multiple linear regression to form a time-varying linear relationship of the section limit.

[0071] The data input in step 1 is preprocessed. The preprocessing content includes supplementing missing values, removing outliers, etc. to ensure the quality and identity of the data.

[0072] Multiple linear regression models are often used to describe the relationship between multiple independent variables and a dependent variable, and to fit the data through a linear regression equation. The dependent variable y here is P cs , is the section limit power at the two-node section of the power system, and the independent variable x is the active power P of the new energy output at m different nodes.

[0073] Step 2-1: You can establish a multiple linear regression equation as shown in the following formula:

[0074] y=l0+l1x1+l2x2+...+l m x m ;

[0075] Among them, λ0, λ1 to λ m is the regression coefficient, which represents the effect of each independent variable on the dependent variable.

[0076] Step 2-2: Assume that there are a total of c groups of data in the cross-section limit database for training, which can be further expressed in matrix form as follows:

[0077]

[0078] The above formula can also be written as:

[0079] y = xλ;

[0080] Multiply both sides of the above equation by x on the left T , and then put λ alone on the side of the equal sign for transformation, the regression coefficient after training can be expressed as follows:

[0081]

[0082] Finally, the regression equation can be calculated as:

[0083]

[0084] For the data after training with the linear regression equation, the above formula can also be written as:

[0085]

[0086] Step 2-3: Use the existing data to test, and use the data trained by the linear regression equation to Indicates that the original data is expressed as P cs Indicates that by screening the residual square R 2 The optimal regression coefficient and residual square R can be obtained 2 The following formula is used for calculation:

[0087]

[0088] After manually screening out samples with larger residual squares and retraining the multiple linear regression equation, the final cross-section limit time-varying formula can be obtained:

[0089]

[0090] in, to is the regression coefficient after retraining, P1 to P m is the active power corresponding to the 1st node to the mth node.

[0091] (2) Evaluation of the system peak load support capability of energy storage and modeling of energy storage operation model, evaluation of the system dispatch support capability of distributed energy (i.e. new energy) and modeling of new energy operation model.

[0092] In the power system, energy storage is an important component of new energy power stations. To increase the output power of new energy, it is necessary to accurately model the energy storage.

[0093] Existing methods often use a joint modeling approach for energy storage and renewable energy. However, this modeling approach is often simplistic and ignores the lifespan and operation and maintenance costs of energy storage equipment. In practical applications, this simplified modeling can lead to low efficiency and shortened lifespans of energy storage equipment, impacting the economics and stability of the overall system.

[0094] To overcome such technical deficiencies, this embodiment separately models the energy storage operation model and the new energy operation model, which can more accurately describe the operating characteristics of the energy storage end and the new energy end, consider the energy storage life loss and operation and maintenance coefficient, use energy storage equipment to promote the consumption of new energy, optimize the operation strategy of the power system, thereby improving the efficiency of the power system's new energy transmission, and enhancing economic benefits and system stability.

[0095] The energy storage side entity plays an important role in smoothing out the fluctuations of renewable energy during the operation of the power system and in peak shaving and valley filling. The energy storage optimization operation model includes an energy storage optimization objective function and an energy storage constraint set. The energy storage optimization objective function is a function constructed with the goal of maximizing the first difference. The first difference is the value obtained by calculating the difference between the energy storage end's balance optimization amount for the power system, the energy storage end operation and maintenance coefficient, and the energy storage end life loss coefficient. The energy storage optimization objective function comprehensively considers energy storage charging and discharging, operation and maintenance, and life loss, and is expressed as follows:

[0096]

[0097] Where f1 is the first difference; t is the tth scheduling time interval. There are 24 hours in a day, and every 15 minutes is the minimum scheduling interval. There are 96 points in a day, that is, 96 scheduling time intervals. is the energy storage discharge power (i.e., energy storage discharge amount) at the tth scheduling time interval; is the energy storage charging power (i.e., energy storage charging capacity) at the tth scheduling time interval; μ t is the energy storage charge and discharge coefficient at the tth scheduling time interval; Δt e k is the energy storage time; t h is the unit coefficient for operation and maintenance of energy storage terminal; dis h is the life loss coefficient of energy storage end discharge; ch The life loss coefficient of charging the energy storage end; γ t It is the unit coefficient of life loss during charging and discharging of the energy storage end.

[0098] The expression of the energy storage constraint set is:

[0099]

[0100] in, and They represent the energy storage discharge state and energy storage charge state respectively, both are 0-1 variables; E ch,max The maximum power of energy storage charging (i.e. the maximum energy storage charging capacity); E dis,max is the maximum power of energy storage discharge (i.e., maximum energy storage discharge capacity); is the energy storage charge at the tth scheduling time interval on the dth day; S oc,max is the maximum charge of energy storage; S oc,min is the minimum charge of energy storage; is the energy storage charge at the (t+1)th scheduling time interval on the dth day; is the energy storage charge at the 96th scheduling interval on the dth day (i.e., the energy storage charge at the end of the dth day); is the energy storage charge at the 0th scheduling time interval on the (d+1)th day (i.e., the energy storage charge at the initial moment of the (d+1)th day).

[0101] The access of distributed energy to the power system will bring power support and performance improvement to the power system. While bringing new energy output, it also brings fluctuations in wind and solar power. Therefore, while it involves the support of distributed energy for the dispatching capacity of the power system, it is also necessary to consider the risks brought by distributed energy fluctuations to the system.

[0102] The new energy optimization operation model includes a new energy optimization objective function and a new energy constraint set. The new energy optimization objective function is a function constructed with the goal of maximizing the second difference. The second difference is a value obtained by performing a difference calculation based on the new energy consumption amount and the wind and solar power abandonment penalty amount. The new energy constraint set includes a photovoltaic fluctuation penalty constraint and a wind power fluctuation penalty constraint. The expression of the new energy optimization objective function is:

[0103]

[0104] Wherein, f2 is the second difference; t is the tth scheduling time interval; is the wind power generation power (i.e. wind power generation) in the tth scheduling time interval; is the photovoltaic power generation power (i.e. photovoltaic power generation) in the tth scheduling time interval; is the wind and solar power curtailment (i.e., the amount of curtailed wind and solar power) in the tth scheduling time interval; is the renewable energy transmission power (i.e., renewable energy transmission volume) in the tth scheduling time interval; z t is the power system performance improvement coefficient at the tth dispatch time interval; is the wind power fluctuation penalty fluctuation amount in the tth scheduling time interval; is the PV fluctuation penalty fluctuation amount in the tth scheduling time interval; f is the new energy penalty fluctuation coefficient.

[0105] After energy storage compensation, if the difference between the wind power transmission power (i.e., wind and solar power output) in the current scheduling time interval and the wind and solar power output in the previous scheduling time interval is still greater than 25% of the rated wind and solar power output power, it will affect the stability of the power system. Therefore, it will be subject to a wind and solar power output fluctuation penalty, which will be reflected in the objective function with a certain coefficient.

[0106] The expression of the new energy constraint set is:

[0107]

[0108]

[0109] in, is the wind power transmission power in the tth scheduling time interval; is the discharge power of energy storage on wind power in the tth scheduling time interval; is the charging power of energy storage on wind power in the tth scheduling time interval; is the photovoltaic power delivered during the t-th scheduling time interval; is the discharge power of energy storage on photovoltaic power generation in the tth scheduling time interval; is the charging power of energy storage on photovoltaic power generation in the tth scheduling time interval; Δt wind is the wind power fluctuation time; is the minimum limit of wind power fluctuation; N wind,N is the rated power of wind power output; Δt pv is the photovoltaic fluctuation time; is the minimum limit of photovoltaic fluctuation; N pv,N is the rated power of photovoltaic output; is the wind power transmission power in the (t-1)th scheduling time interval; is the photovoltaic power delivered during the (t-1)th scheduling time interval.

[0110] (3) Construct a day-ahead collaborative optimization model for energy storage and new energy, and use the target hierarchy weighting method to reasonably allocate the importance of each target in the system, so as to achieve a solution to the system day-ahead collaborative optimization scheduling problem that fits the reality.

[0111] Step 3-1: Build a collaborative optimization model for energy storage and new energy.

[0112] The day-ahead collaborative optimization model of energy storage and new energy includes a collaborative optimization objective function and a set of power system operation constraints. The collaborative optimization objective function is a function calculated based on the energy storage optimization amount and the new energy optimization amount. The power system operation constraint set includes the interconnection line transmission power constraint, phase angle constraint, and section limit power time-varying constraint between nodes in the power system.

[0113] The expression of the collaborative optimization objective function is:

[0114] f=w1·f1+w2·f2;

[0115] Wherein, f is the scheduling result of energy storage and new energy; w1 is the weight vector corresponding to f1; f1 is the energy storage optimization amount; f2 is the new energy optimization amount; w2 is the weight vector corresponding to f2;

[0116] The section limit power obtained in step (1) is incorporated into the system network operation constraint set. The expression of the power system operation constraint set is:

[0117]

[0118] Among them, G t,i is the power plant output of the i-th node in the t-th scheduling time interval; is the wind power generation of the ith node in the tth scheduling time interval; is the photovoltaic power generation of the i-th node in the t-th scheduling time interval; is the energy storage discharge of the i-th node in the t-th scheduling time interval; is the energy storage charge of the i-th node in the t-th scheduling time interval; is the amount of renewable energy curtailment at the i-th node in the t-th scheduling time interval; L load,i is the load size of the i-th node in the t-th scheduling time interval; is the transmission power of the tie line between the i-th node and the j-th node in the t-th scheduling time interval; is the phase angle of the i-th node in the t-th scheduling time interval; x i,j is the reactance value between the i-th node and the j-th node; B is the number of nodes in the power system; q min is the minimum phase angle allowed by the power system; q max is the maximum phase angle allowed by the power system; C min is the minimum transmission value of the tie line allowed by the power system; C max The maximum transmission value of the tie line allowed by the power system; is the transmission value of the tie line between the i-th node and the j-th node at the section; is the section limit power of the i-th node and the j-th node at the section, (Right now ) is to substitute the output of m nodes in this example into The formula is obtained.

[0119] Step 3-2: Determine the weight coefficients of each part in the day-ahead coordinated optimization model of energy storage and new energy based on the target hierarchical weighting method, analyze and solve the day-ahead optimized coordinated scheduling of distributed energy planning and energy storage configuration, and determine the day-ahead optimal scheduling result of the power system.

[0120] In the power system, models usually need to consider multiple factors. Especially after the "dual carbon" goal was proposed, the safe operation of the system and the efficient access and absorption of new energy are both important influencing factors.

[0121] Existing technologies typically employ weighted summation, Pareto optimality, and goal programming to optimize the operation of renewable energy consumption and energy storage. In practical applications, these methods often struggle to determine the appropriate weights for each objective, potentially biasing the solution toward one objective and failing to fully balance the interests of all. Existing objective weighting methods typically employ expert scoring and entropy weighting to determine the weights for each objective. These methods often rely on subjective experience or statistical data, making them difficult to dynamically adjust. In complex and volatile power systems, they cannot guarantee the rationality and accuracy of the weightings for renewable energy consumption, energy storage operation, and system security.

[0122] This embodiment dynamically distributes and solves the weight values ​​of f1 and f2 in a reasonable manner, and dynamically adjusts the weights of each target according to actual conditions, thereby avoiding a single target bias and ensuring that the optimization results are more in line with actual needs. It maximizes the new energy transmission volume and absorption rate while ensuring the safe and stable operation of the system.

[0123] The specific process of the multi-objective optimization method for energy storage and new energy based on the target hierarchical weighting method proposed in this embodiment includes:

[0124] Step 3-2-1: Determine the hierarchical structure.

[0125] First, determine the hierarchical structure of the comprehensive optimization problem. The optimization structure can be divided into the following three parts:

[0126] Objective layer: overall goal (maximization of comprehensive benefits); criterion layer: f1, f2; solution layer: final optimization solution.

[0127] Step3-2-2: Use the analytic hierarchy process (AHP) to determine the weight of each goal.

[0128] First, based on prior experience or expert opinion, a pairwise comparison matrix is ​​constructed for the two objectives, f1 and f2, to determine the relative importance of the three objectives. Next, the pairwise comparison matrix is ​​normalized to obtain a normalized matrix. The average value of each row is then calculated to obtain a weight vector. Finally, the consistency of the comparison matrix is ​​tested using the consistency ratio (CR). If the CR is less than 0.1, the consistency of the comparison matrix is ​​considered acceptable. Otherwise, the pairwise comparison matrix needs to be readjusted to determine w1 and w2.

[0129] The collaborative optimization objective function is solved using a single-objective optimization algorithm, and the multi-objective optimization scheduling results of energy storage and new energy are obtained considering the time-varying section limits.

[0130] The present application also provides an application scenario, which applies the above-mentioned energy storage and new energy optimization method considering dynamic section limits. Specifically: the energy storage and new energy optimization method considering dynamic section limits provided in this embodiment can be applied in the energy management and scheduling scenario of the smart grid. The energy management and scheduling scenario of the smart grid includes links such as energy production scheduling, energy storage system management and load forecasting. The energy storage and new energy optimization method considering dynamic section limits provided in this embodiment belongs to the comprehensive optimization link in energy production scheduling. The energy storage and new energy optimization method considering dynamic section limits provided in this embodiment provides an advanced solution for the efficient operation of the smart grid by comprehensively optimizing energy storage and new energy, and supports the development and utilization of sustainable energy.

[0131] Example 2

[0132] This embodiment provides a computer device, which can be a server or a terminal. Its internal structure diagram can be as follows: Figure 3 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store any data involved in the energy storage and new energy optimization method considering dynamic section limits. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements the energy storage and new energy optimization method considering dynamic section limits in Example 1.

[0133] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0134] Example 3

[0135] This embodiment provides a computer device including a memory and a processor. The memory stores a computer program. When the processor executes the computer program, it implements the energy storage and new energy optimization method considering dynamic section limits in Example 1.

[0136] Example 4

[0137] This embodiment provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the energy storage and new energy optimization method considering dynamic section limits in embodiment 1 is implemented.

[0138] Example 5

[0139] This embodiment provides a computer program product, including a computer program, which, when executed by a processor, implements the energy storage and new energy optimization method considering dynamic section limits in Example 1.

[0140] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0141] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0142] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0143] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0144] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for optimizing energy storage and new energy considering dynamic cross-section limits, characterized in that: The energy storage and new energy optimization method considering dynamic section limits includes: Obtain real-time energy storage discharge power, real-time energy storage charging power, real-time wind power generation power, real-time photovoltaic power generation power, and real-time wind and solar power curtailment power; Inputting the real-time energy storage discharge power and the real-time energy storage charging power into an energy storage optimization operation model to obtain an energy storage optimization amount, wherein the energy storage optimization operation model includes an energy storage optimization objective function and an energy storage constraint set, the energy storage optimization objective function is a function constructed with the goal of maximizing a first difference, the first difference is a value obtained by performing a difference calculation based on the energy storage end's balance optimization amount for the power system, the energy storage end operation and maintenance coefficient, and the energy storage end life loss coefficient, and the energy storage constraint set includes an energy storage charging and discharging state constraint, an energy storage charging and discharging power constraint, and an energy storage charge constraint; The real-time wind power generation power, the real-time photovoltaic power generation power, and the real-time wind and solar power abandonment power are input into a new energy optimization operation model to obtain a new energy optimization amount, wherein the new energy optimization operation model includes a new energy optimization objective function and a new energy constraint set, the new energy optimization objective function is a function constructed with the goal of maximizing a second difference, the second difference is a value obtained by performing a difference calculation based on the new energy absorption amount and the wind and solar power abandonment penalty amount, and the new energy constraint set includes a photovoltaic fluctuation penalty constraint and a wind power fluctuation penalty constraint; The energy storage optimization amount and the new energy optimization amount are input into the energy storage and new energy day-ahead collaborative optimization model to obtain the scheduling results of energy storage and new energy, wherein the energy storage and new energy day-ahead collaborative optimization model includes a collaborative optimization objective function and a power system operation constraint set, the collaborative optimization objective function is a function calculated based on the energy storage optimization amount and the new energy optimization amount, the power system operation constraint set includes the interconnection line transmission power constraint, phase angle constraint and section limit power time-varying constraint between each node in the power system, and the section limit power time-varying constraint in the section limit power time-varying constraint. The section limit power is a value obtained by substituting the real-time node active power into the section limit time-varying linear relationship. The section limit time-varying linear relationship is a relationship obtained by fitting the historical node active power and the historical section limit power. The historical node active power and the historical section limit power are values ​​obtained by processing the injected power and load of the target node on a typical day of a historical year using the continuous power flow method. The typical day of a historical year refers to a historical date on which the section limit power in the power system exceeds the section fixed value threshold. The target node refers to the node corresponding to the transmission line located at the section. The time-varying linear relationship of the cross-section limit is: in, is the section limit power; to are the regression coefficients after training; P1 is the active power of the first node in real time; P m is the real-time active power of the mth node.

2. The energy storage and new energy optimization method considering dynamic section limits according to claim 1, characterized in that: The target hierarchy weight method is adopted to determine the energy storage optimization amount weight and the new energy optimization amount weight in the collaborative optimization objective function.

3. The energy storage and new energy optimization method considering dynamic section limits according to claim 1, characterized in that: The continuous power flow method is used to process the injected power and load of the target node on typical days in historical years, including: Determining an initial value of injected power, an initial value of load, and an initial value of voltage of the target node; Calculating the injected power, load and voltage of the target node after the next iteration using a continuous power flow method according to the injected power initial value, the load initial value and the voltage initial value; Input the injected power, load, and voltage of the target node after the next iteration into the section limit power objective function and the section limit power objective function constraint function to obtain the next section delivery power, wherein the section limit power objective function is a function constructed with the section delivery power as the goal, and the section limit power objective function constraint function includes the active power constraint of the transmission line at the section, the new energy active power constraint of the target node, the new energy reactive power constraint of the target node, and the voltage constraint of the target node; Determining whether the next section power delivery reaches a stable state, and determining whether the prediction step length in the continuous power flow method is greater than a step length threshold, to obtain a first determination result; When the first judgment result is yes, the next section delivery power is used as the section limit power, and the active power of all nodes in the power system is calculated based on the section limit power; When the first judgment result is no, the injected power, load and voltage of the target node after the next iteration are used as the new initial injected power value, initial load value and initial voltage value, and the process returns to step "calculating the injected power, load and voltage of the target node after the next iteration using the continuous power flow method based on the initial injected power value, the initial load value and the initial voltage value".

4. The energy storage and new energy optimization method considering dynamic section limits according to claim 1, characterized in that: The expression of the energy storage optimization operation model includes: The expression of the energy storage optimization objective function is: Wherein, f1 is the first difference; t is the tth scheduling time interval; is the energy storage discharge power at the tth scheduling time interval; is the energy storage charging power in the tth scheduling time interval; μ t is the energy storage charge and discharge coefficient at the tth scheduling time interval; Δt e k is the energy storage time; t The unit coefficient of energy storage operation and maintenance; η dis is the life loss coefficient of energy storage end discharge; η ch The life loss coefficient of charging the energy storage end; γ t The unit coefficient of life loss of energy storage terminal charging and discharging; The expression of the energy storage constraint set is: in, and Respectively represent the energy storage discharge state and energy storage charging state; E ch,max Maximum power for charging energy storage; E dis,max is the maximum power of energy storage discharge; is the energy storage charge at the tth scheduling time interval on the dth day; S oc,max is the maximum charge of energy storage; S oc,min is the minimum charge of energy storage; is the energy storage charge at the (t+1)th scheduling time interval on the dth day; is the energy storage charge at the 96th dispatch time interval on day d; is the energy storage charge at the 0th scheduling time interval on the (d+1)th day.

5. The energy storage and new energy optimization method considering dynamic section limits according to claim 1, characterized in that: The expression of the new energy optimization operation model includes: The expression of the new energy optimization objective function is: Wherein, f2 is the second difference; t is the tth scheduling time interval; is the wind power generation power in the tth scheduling time interval; is the photovoltaic power generation power in the tth scheduling time interval; is the curtailed wind and solar power in the tth scheduling time interval; is the renewable energy transmission power in the tth scheduling time interval; t is the power system performance improvement coefficient at the tth dispatch time interval; is the wind power fluctuation penalty fluctuation amount in the tth scheduling time interval; is the PV fluctuation penalty fluctuation amount at the t-th scheduling time interval; φ is the new energy penalty fluctuation coefficient; The expression of the new energy constraint set is: in, is the wind power transmission power in the tth scheduling time interval; is the discharge power of energy storage on wind power in the tth scheduling time interval; is the charging power of energy storage on wind power in the tth scheduling time interval; is the photovoltaic power delivered during the t-th scheduling time interval; is the discharge power of energy storage on photovoltaic power generation in the tth scheduling time interval; is the charging power of energy storage on photovoltaic power generation in the tth scheduling time interval; Δt wind is the wind power fluctuation time; is the minimum limit of wind power fluctuation; N wind,N is the rated power of wind power output; Δt pv is the photovoltaic fluctuation time; is the minimum limit of photovoltaic fluctuation; N pv,N is the rated power of photovoltaic output; is the wind power transmission power in the (t-1)th scheduling time interval; is the photovoltaic power delivered during the (t-1)th scheduling time interval.

6. The energy storage and new energy optimization method considering dynamic section limits according to claim 1, characterized in that: The expression of the energy storage and new energy day-ahead coordinated optimization model includes: The expression of the collaborative optimization objective function is: f=w1·f1+w2·f2; Wherein, f is the scheduling result of energy storage and new energy; w1 is the weight vector corresponding to f1; f1 is the energy storage optimization amount; f2 is the new energy optimization amount; w2 is the weight vector corresponding to f2; The expression of the power system operation constraint set is: Among them, G t,i is the power plant output of the i-th node in the t-th scheduling time interval; is the wind power generation of the ith node in the tth scheduling time interval; is the photovoltaic power generation of the i-th node in the t-th scheduling time interval; is the energy storage discharge of the i-th node in the t-th scheduling time interval; is the energy storage charge of the i-th node in the t-th scheduling time interval; is the amount of renewable energy curtailment at the i-th node in the t-th scheduling time interval; L load,i is the load size of the i-th node in the t-th scheduling time interval; is the transmission power of the tie line between the i-th node and the j-th node in the t-th scheduling time interval; is the phase angle of the i-th node in the t-th scheduling time interval; x i,j is the reactance value between the i-th node and the j-th node; B is the number of nodes in the power system; θ min is the minimum phase angle allowed by the power system; θ max is the maximum phase angle allowed by the power system; C min is the minimum transmission value of the tie line allowed by the power system; C max The maximum transmission value of the tie line allowed by the power system; is the transmission value of the tie line between the i-th node and the j-th node at the section; is the section limit power of the i-th node and the j-th node at the section.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the energy storage and new energy optimization method considering dynamic section limits as described in any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the energy storage and new energy optimization method considering dynamic section limits described in any one of claims 1 to 6 is implemented.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the energy storage and new energy optimization method considering dynamic section limits described in any one of claims 1 to 6 is implemented.

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