Shared energy storage and power transmission line cooperative configuration method, system, equipment and medium

Abstract

The invention relates to the technical field of power systems, and discloses a shared energy storage and power transmission line cooperative configuration method, system, device and medium, and the method comprises the steps: analyzing a high-proportion new energy power system, and building an uncertain demand scene generation function; generating an uncertain demand scene of the high-proportion new energy power system according to the uncertain demand scene generation function; based on the uncertain demand scene, establishing a shared energy storage and power transmission line double-layer collaborative optimization configuration model; and solving the double-layer collaborative optimization configuration model to obtain a shared energy storage and power transmission line configuration scheme. Compared with a traditional single configuration scheme, the method has better economical efficiency, meanwhile, the influence of the limited constraint of the power transmission line of the high-proportion new energy power system on new energy consumption can be effectively relieved, and the wind curtailment and light curtailment amount of the high-proportion new energy power system is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, system, device and medium for collaboratively configuring shared energy storage and transmission lines. Background Art

[0002] Driven by the "dual carbon" goal, the traditional power system has gradually evolved into a high-proportion new energy power system with new energy as the main body. While the high-proportion new energy power system has the advantages of being green and low-carbon, it also faces many new challenges.

[0003] Net load refers to the difference between the total user load demand and the output of renewable energy. The uncertainty of renewable energy output and the randomness of user electricity consumption behavior have increased the demand for regulating the net load of power systems with a high proportion of renewable energy. The random volatility of net load leads to a certain deviation between its predicted value and the actual value. The scenario generation model cannot accurately describe the demand scenario and cannot solve the problem of uncertainty in demand for power systems with a high proportion of renewable energy.

[0004] Shared energy storage can promote the grid connection and consumption of new energy, meet the uncertain demand caused by the deviation between the net load forecast and the actual value, and plays an important regulatory role in the power system with a high proportion of new energy.

[0005] Therefore, how to provide coordinated optimization configuration of shared energy storage and transmission lines for high-proportion new energy power systems has become an urgent problem to be solved. Summary of the invention

[0006] The embodiments of the present invention provide a method, system, device and medium for collaboratively configuring shared energy storage and transmission lines to solve the above-mentioned technical problems in the prior art.

[0007] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0008] According to a first aspect of an embodiment of the present invention, a method for collaboratively configuring shared energy storage and transmission lines is provided.

[0009] In one embodiment, the method for collaboratively configuring shared energy storage and transmission lines includes:

[0010] Analyze the high-proportion renewable energy power system and establish the uncertain demand scenario generation function;

[0011] Generate an uncertain demand scenario for a high-proportion new energy power system according to the uncertain demand scenario generation function;

[0012] Based on the uncertain demand scenario, a two-layer collaborative optimization configuration model of shared energy storage and transmission lines is established;

[0013] The two-layer collaborative optimization configuration model is solved to obtain a shared energy storage and transmission line configuration plan.

[0014] In one embodiment, analyzing a high-proportion new energy power system and establishing an uncertain demand scenario generation function includes:

[0015] Obtain historical net load data of high-proportion renewable energy power systems;

[0016] According to the deviation between the actual value and the predicted value of the net load in each time span, the generalized Pareto distribution function is fitted;

[0017] Using the generalized Pareto distribution function to fit the net load historical data, to obtain fitted net load historical data;

[0018] The fitted net load historical data is transformed by using probability integral transformation to obtain Gaussian data that satisfies the Gaussian distribution on [0, 1].

[0019] The obtained Gaussian data are processed using the GEMINI improved graph LASSO method to obtain a multivariate Gaussian distribution function for generating uncertain demand scenarios, and the multivariate Gaussian distribution function is used as the uncertain demand scenario generation function.

[0020] In one embodiment, generating an uncertain demand scenario for a high proportion of new energy power system according to the uncertain demand scenario generation function includes:

[0021] Monte Carlo samples are generated according to the multivariate Gaussian distribution function, and the predicted value of the net load is added to the Monte Carlo samples to obtain uncertain demand scenarios for site selection and capacity determination of shared energy storage and expansion of transmission lines in a high-proportion new energy power system.

[0022] In one embodiment, the two-layer collaborative optimization configuration model includes an upper-layer investment decision model and a lower-layer optimization scheduling model; wherein:

[0023] The upper-level investment decision model is used to generate investment decisions for shared energy storage and transmission lines based on the synergistic relationship between shared energy storage and transmission line planning, with the goal of minimizing annual comprehensive costs;

[0024] The lower-level optimization scheduling model is used to generate a shared energy storage and transmission line configuration plan based on the investment decisions of the shared energy storage and transmission lines transmitted by the upper-level investment decision model, with the goal of minimizing the overall system operating cost.

[0025] In one embodiment, the annual comprehensive cost includes: the annual investment cost of shared energy storage and the annual investment cost of transmission lines; the comprehensive operating cost of the system includes: the income of conventional units, the income of wind and solar grid connection, the income of shared energy storage participating in multi-market transactions, the operating costs of conventional units and shared energy storage, the penalty costs for wind and solar abandonment, and the network loss costs.

[0026] In one embodiment, the constraint conditions of the upper-level investment decision model include: new line constraints and buildable capacity constraints of energy storage.

[0027] In one embodiment, the constraints of the lower-level optimization scheduling model include: power balance constraints of each node in the system, branch flow constraints of the system, power limit constraints of each branch, node voltage constraints, pumped storage constraints, electrochemical energy storage operation constraints, conventional unit output constraints, conventional unit climbing constraints, wind farm output constraints and photovoltaic power station operation constraints.

[0028] According to a second aspect of an embodiment of the present invention, a system for collaboratively configuring shared energy storage and transmission lines is provided.

[0029] In one embodiment, the shared energy storage and transmission line coordinated configuration system includes:

[0030] Scenario function building module, used to analyze high-proportion renewable energy power systems and build uncertain demand scenario generation functions;

[0031] A demand scenario generation module, used to generate an uncertain demand scenario for a high-proportion new energy power system according to the uncertain demand scenario generation function;

[0032] An optimization model configuration module, used to establish a two-layer collaborative optimization configuration model of shared energy storage and transmission lines based on the uncertain demand scenario;

[0033] The optimization model solving module is used to solve the double-layer collaborative optimization configuration model to obtain a shared energy storage and transmission line configuration plan.

[0034] In one embodiment, when the scenario function establishment module analyzes the high-proportion renewable energy power system and establishes the uncertain demand scenario generation function, the module obtains the net load historical data of the high-proportion renewable energy power system; fits the generalized Pareto distribution function according to the deviation between the actual value and the predicted value of the net load in each time span; fits the net load historical data using the generalized Pareto distribution function to obtain the fitted net load historical data; transforms the fitted net load historical data using the probability integral transformation to obtain Gaussian data that satisfies the Gaussian distribution on [0, 1]; processes the obtained Gaussian data using the GEMINI improved graph LASSO method to obtain a multivariate Gaussian distribution function for generating uncertain demand scenarios, and uses the multivariate Gaussian distribution function as the uncertain demand scenario generation function.

[0035] In one embodiment, when the demand scenario generation module generates an uncertain demand scenario for a high-proportion new energy power system based on the uncertain demand scenario generation function, it generates a Monte Carlo sample based on a multivariate Gaussian distribution function, and adds a predicted value of the net load to the Monte Carlo sample to obtain an uncertain demand scenario for the site selection and sizing of shared energy storage and the expansion of transmission lines for a high-proportion new energy power system.

[0036] In one embodiment, the two-layer collaborative optimization configuration model includes an upper-layer investment decision model and a lower-layer optimization scheduling model; wherein:

[0037] The upper-level investment decision model is used to generate investment decisions for shared energy storage and transmission lines based on the synergistic relationship between shared energy storage and transmission line planning, with the goal of minimizing annual comprehensive costs;

[0038] The lower-level optimization scheduling model is used to generate a shared energy storage and transmission line configuration plan based on the investment decisions of the shared energy storage and transmission lines transmitted by the upper-level investment decision model, with the goal of minimizing the overall system operating cost.

[0039] In one embodiment, the annual comprehensive cost includes: the annual investment cost of shared energy storage and the annual investment cost of transmission lines; the comprehensive operating cost of the system includes: the income of conventional units, the income of wind and solar grid connection, the income of shared energy storage participating in multi-market transactions, the operating costs of conventional units and shared energy storage, the penalty costs for wind and solar abandonment, and the network loss costs.

[0040] In one embodiment, the constraints of the upper-level investment decision model are: new line constraints and buildable capacity constraints of energy storage.

[0041] In one embodiment, the constraints of the lower-level optimization scheduling model are: power balance constraints of each node in the system, branch flow constraints of the system, power limit constraints of each branch, node voltage constraints, pumped storage constraints, electrochemical energy storage operation constraints, conventional unit output constraints, conventional unit climbing constraints, wind farm output constraints and photovoltaic power station operation constraints.

[0042] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0043] In one embodiment, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0044] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0045] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0046] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0047] The present invention analyzes the high-proportion new energy power system to obtain the uncertain demand scenario of the high-proportion new energy power system, and then establishes a two-layer collaborative optimization configuration model according to the uncertain demand scenario; wherein, the two-layer collaborative optimization configuration model includes an upper-level investment decision model and a lower-level optimization scheduling model, the upper-level investment decision model simultaneously considers the location and capacity of shared energy storage and the investment plan for the expansion of transmission lines, reflects the synergistic relationship between shared energy storage and transmission line planning, and minimizes the total investment cost of shared energy storage and transmission lines; the lower-level optimization scheduling model satisfies the uncertainty of demand while comprehensively considering the output of conventional units, the charging and discharging capacity of shared energy storage and the line flow, so as to minimize the comprehensive operation cost of the system. Compared with the single transmission line and shared energy storage configuration, the joint configuration of shared energy storage and transmission lines has better economy, and can effectively alleviate the impact of transmission line constraints on the new energy consumption of high-proportion new energy power systems, and reduce the amount of wind and solar power abandonment in high-proportion new energy power systems.

[0048] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0050] Figure 1is a flow chart of a method for collaboratively configuring shared energy storage and transmission lines according to an exemplary embodiment;

[0051] Figure 2 is a structural block diagram of a system for collaboratively configuring a shared energy storage and a transmission line according to an exemplary embodiment;

[0052] Figure 3 is a schematic diagram of a solution process of a double-layer collaborative optimization configuration model according to an exemplary embodiment;

[0053] Figure 4 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0054] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0055] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0056] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0057] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0058] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0059] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0060] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0061] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0062] Figure 1An embodiment of a method for collaboratively configuring shared energy storage and transmission lines of the present invention is shown.

[0063] In this optional embodiment, the method for collaboratively configuring shared energy storage and transmission lines includes:

[0064] Step S101, analyzing a high proportion of new energy power system and establishing an uncertain demand scenario generation function;

[0065] Step S103, generating an uncertain demand scenario for a high-proportion new energy power system according to the uncertain demand scenario generation function;

[0066] Step S105, establishing a two-layer collaborative optimization configuration model of shared energy storage and transmission lines based on the uncertain demand scenario;

[0067] Step S107, solving the double-layer collaborative optimization configuration model to obtain a shared energy storage and transmission line configuration plan.

[0068] Figure 2 An embodiment of a shared energy storage and transmission line coordinated configuration system of the present invention is shown.

[0069] In this optional embodiment, the shared energy storage and transmission line coordinated configuration system includes:

[0070] The scenario function establishment module 201 is used to analyze the high-proportion new energy power system and establish an uncertain demand scenario generation function;

[0071] A demand scenario generation module 203, configured to generate an uncertain demand scenario for a high proportion of new energy power system according to the uncertain demand scenario generation function;

[0072] An optimization model configuration module 205 is used to establish a two-layer collaborative optimization configuration model of shared energy storage and transmission lines based on the uncertain demand scenario;

[0073] The optimization model solving module 207 is used to solve the double-layer collaborative optimization configuration model to obtain a shared energy storage and transmission line configuration solution.

[0074] In the above embodiment, when analyzing a high-proportion new energy power system and establishing an uncertain demand scenario generation function, the net load historical data of the high-proportion new energy power system is obtained; according to the deviation between the actual value and the predicted value of the net load in each time span, a generalized Pareto distribution function is fitted; the net load historical data is fitted using the generalized Pareto distribution function to obtain the fitted net load historical data; the fitted net load historical data is transformed using a probability integral transform to obtain Gaussian data that satisfies a Gaussian distribution on [0, 1]; the obtained Gaussian data is processed using the GEMINI improved graph LASSO method to obtain a multivariate Gaussian distribution function for generating uncertain demand scenarios, and the multivariate Gaussian distribution function is used as the uncertain demand scenario generation function.

[0075] Among them, the generalized Pareto distribution function takes into account the heavy-tail effect caused by the repeated excessive deviation between the net load forecast value and the actual value compared to the empirical distribution function. If the heavy-tail effect is not considered, heavy-tail data that cannot be explained by the Gaussian distribution will be generated, so that rare extreme uncertain demand scenarios will not be covered by the scenarios generated by Monte Carlo simulation; therefore, the uncertain demand scenarios finally obtained by processing the data using the generalized Pareto distribution function can cover the relatively rare extreme uncertain demand scenarios. The GEMINI improved Graph LASSO method processes the transformed Gaussian data and only needs to use one matrix in the matrix variable multivariate Gaussian distribution to estimate the precision matrix and covariance matrix very well (for example, the GEMINI improved Graph Lasso method regards the NZ×Nh×NZ×Nh order covariance matrix and precision matrix as the tensor product of the NZ×NZ order and Nh×Nh order covariance matrix and precision matrix (also called Kronecker product), where the NZ×NZ covariance matrix and precision matrix are used to capture the dependencies between spatial components (net load partitions), and the Nh×Nh covariance matrix and precision matrix are used to capture the dependencies between temporal components (time ranges)). This form of precision matrix and covariance matrix can greatly reduce the number of parameters that need to be estimated.

[0076] When generating an uncertain demand scenario for a high-proportion new energy power system based on the uncertain demand scenario generation function, a Monte Carlo sample is generated based on the multivariate Gaussian distribution function (the step of inverting the Gaussian Copula function to obtain a sample with an appropriate marginal distribution), and the predicted value of the net load is added to the Monte Carlo sample to obtain an uncertain demand scenario for the site selection, sizing, and transmission line expansion of shared energy storage in a high-proportion new energy power system.

[0077] In addition, in the above embodiment, the two-layer collaborative optimization configuration model includes an upper-layer investment decision model and a lower-layer optimization scheduling model; wherein the upper-layer investment decision model is used to generate investment decisions for shared energy storage and transmission lines based on the collaborative relationship between shared energy storage and transmission line planning, with the goal of minimizing the annual comprehensive cost; the lower-layer optimization scheduling model is used to generate a shared energy storage and transmission line configuration plan based on the investment decisions for shared energy storage and transmission lines transmitted by the upper-layer investment decision model, with the goal of minimizing the comprehensive operating cost of the system.

[0078] The annual comprehensive cost includes: the annual investment cost of shared energy storage and the annual investment cost of transmission lines; the comprehensive operating cost of the system includes: the income of conventional units, the income of wind and solar grid connection, the income of shared energy storage participating in multiple market transactions, the operating costs of conventional units and shared energy storage, the penalty costs for wind and solar abandonment, and network loss costs.

[0079] The constraints of the upper investment decision model include: new line constraints and buildable capacity constraints of energy storage. The constraints of the lower optimization scheduling model include: power balance constraints of each node in the system, branch flow constraints of the system, power limit constraints of each branch, node voltage constraints, pumped storage constraints, electrochemical energy storage operation constraints, conventional unit output constraints, conventional unit ramp constraints, wind farm output constraints and photovoltaic power station operation constraints.

[0080] Specifically, for the upper-level investment decision model, the objective function formula of the upper-level investment decision model is:

[0081] min F 1 =f es +f line +F 2 (X,Y)

[0082]

[0083] Where, min F 1 is the minimum annual comprehensive cost; f es is the annual investment cost of shared energy storage; f line is the annual investment cost of the transmission line; F 2 (X,Y) is the target value returned by the lower-level optimization scheduling model; X is the decision variable of the upper-level investment decision model, including the access location, power and capacity of shared energy storage and the number of new transmission lines; Y is the objective function of the lower-level optimization scheduling model; m is the discount rate of shared energy storage; n is the discount rate of transmission lines; i is the planning period of energy storage and transmission lines; c is the planning period of energy storage and transmission lines; ess,inv is the unit investment cost of electrochemical energy storage; c ph,inv Cap is the unit investment cost of pumped storage; essCap is the planned capacity of electrochemical energy storage; ph The planned capacity of pumped storage; line.ij is the unit investment cost of the new line of branch ij; Is the qth line of the ijth branch expanded, the value 1 indicates expansion, the value 0 indicates no expansion; Ω is the set of lines to be selected for expansion;

[0084] For the upper-level investment decision model, the constraint condition formula of the upper-level investment decision model is as follows:

[0085] 1) The constraint formula for the new line is:

[0086]

[0087] In the formula, z l is the number of newly added candidate routes in the corridor where route l is located; The maximum number of new lines allowed;

[0088] 2) The constrained formula for the buildable capacity of energy storage is:

[0089] Cap ph,min ≤Cap ph ≤Cap ph,max

[0090] Cap ess,min ≤Cap ess ≤Cap ess,max

[0091] In the formula, Cap ph Cap is the installed capacity of pumped storage; ph,max Cap is the upper limit of the installed capacity of pumped storage; ph,min Cap is the lower limit of installed capacity of pumped storage; ess Cap is the installed capacity of electrochemical energy storage; ess,max Cap is the upper limit of the installed capacity of electrochemical energy storage; ess,min It is the lower limit of the installed capacity of electrochemical energy storage.

[0092] For the lower-level optimization scheduling model, the objective function formula of the lower-level optimization scheduling model is:

[0093] f 2 =C g,oper +C es,oper +C wind +C PV +C loss -R g -R wind -R pv -R es

[0094]

[0095]

[0096] Where, min F 2 is the comprehensive operating cost of the system; C g,oper is the operating cost of conventional units; C es,oper is the energy storage operation cost; C wind , C PV are the wind power and photovoltaic power abandonment costs respectively; C loss is the network loss cost; R g is the revenue of conventional units; R wind is the grid-connected income of wind turbines; R pv is the income from the photovoltaic unit connected to the grid; R es To share the benefits of energy storage; g is the on-grid electricity price of conventional units; P g,s,t is the output of the conventional unit at time t in scenario s; e wind is the on-grid electricity price of wind turbines; P wind,s,t is the output of the wind turbine at time t in scenario s; e pv is the on-grid electricity price of photovoltaic units; P pv,s,t is the output of the photovoltaic unit at time t in scenario s; b e,s,t is the revenue coefficient of shared energy storage auxiliary services; P dis,s,t is the charge amount of shared energy storage at time t in scenario s; P cha,s,t is the discharge amount of shared energy storage at time t in scenario s; b f,s,t P is the profit coefficient of shared energy storage participating in the electric energy market; cap,s,t b is the electricity cleared by the shared energy storage at time t in scenario s; ren,s,t P is the shared energy storage capacity leasing income coefficient; ren,s,t To share energy storage, lease capacity at time t in scenario s; a i is the quadratic coefficient of energy consumption cost of conventional units; b i is the first-term coefficient of energy consumption cost of conventional units; c i is the energy consumption cost constant of conventional units; c ess,oper is the unit operating cost of electrochemical energy storage; P ess,s,t is the charge and discharge power at time t in electrochemical energy storage scenario s; c ph,oper is the unit operating cost of pumped storage; P ph,s,t is the output at time t in pumped storage scenario s; α and β are the unit wind and solar curtailment costs in wind power and photovoltaic scenarios respectively; are the predicted outputs of wind power and photovoltaic power at time t under scenario s; c loss is the unit network loss cost; P loss,s,ij,tis the network loss of transmission line ij at time t under scenario s.

[0097] For the lower-level optimization scheduling model, the constraint condition formula of the lower-level optimization scheduling model is as follows:

[0098] 1) The power balance constraint formula of each node in the system is:

[0099] P netload,s,t =P ess,s,t -P ph,s,t +P wind,s,t +P pv,s,t +P g,s,t +AP ij,t,s

[0100] Where P netload,s,t is the system net load at time t under scenario s; P ij,t,s is the active power of the line; A is the node-branch association matrix of the line; P ess,s,t is the charge and discharge power at time t in electrochemical energy storage scenario s; P ph,s,t is the output at time t in pumped storage scenario s; P pv,s,t is the output P of the photovoltaic unit at time t in scenario s wind,s,t is the output of the wind turbine at time t in scenario s; P g,s,t is the output at time t in the conventional unit scenario s.

[0101] 2) The branch flow constraint formula of the system is:

[0102] P ij,t,s -b ij n line,ij (α i,t,s -α j,t,s )=0

[0103]

[0104] Where P ij,t,s is the power of line ij at time t in scenario s; b ij is the susceptance of branch ij; n line,ij is the total number of lines; α i,t,s , α j,t,s are the voltage phase angles of node i and node j at time t in scenario s respectively.

[0105] 3) The power limit constraint formula of each branch is:

[0106]

[0107] In the formula, is the maximum output power of line ij.

[0108] 4) The node voltage constraint formula is:

[0109] 0.95V N ≤V i,t ≤1.05V N

[0110] Where V N is the rated voltage of each node; V i,t is the actual voltage of node i at time t.

[0111] 5) Pumped storage constraint formulas include:

[0112] A. The storage capacity constraint formula of the pumped storage power station is:

[0113]

[0114] In the formula, E hy,s,t+1 、E hy,s,t are the storage capacities at time t+1 and time t under the pumped storage power station scenario s; η c , η f They are the pumping and power generation efficiencies of pumped storage power stations, respectively; are respectively the power generation and pumping power at time t in the pumped-storage power station scenario s; Δt is the time interval.

[0115] B. The formula for conservation constraint of pumped storage power station capacity is:

[0116] E p,s,t＝0 =E hy,t＝T

[0117] In the formula, E p,s,t＝0 is the initial storage capacity of the pumped storage power station scenario s, E hy,t＝T is the final storage capacity at time t under the pumped storage power station scenario s, and T is the final time.

[0118] C. The formula for the storage capacity constraint of the pumped storage power station is:

[0119] E hy,min ≤E hy,s,t ≤E hy,max

[0120] In the formula, E hy,s,t is the storage capacity at time t in the pumped storage power station scenario s; E hy,min 、E hy,max They are the upper and lower limits of the storage capacity of pumped storage power stations respectively.

[0121] D. The formula for power generation constraints of pumped storage power stations is:

[0122]

[0123] In the formula, P is the power generation capacity of the pumped storage power station at time t in scenario s; hy,up,max The upper limit of the power generation capacity of a pumped storage power station.

[0124] E. The formula for the pumping constraint condition of a pumped storage power station is:

[0125]

[0126] In the formula, is the pumping power at time t in the pumped storage power station scenario s; P hy,down,max It is the upper limit of pumping power of pumped storage power station.

[0127] F. The constraint formula of the pumped storage power station power (greater than zero means it is in power generation condition, less than zero means it is in pumping condition) is:

[0128]

[0129] Where P hy,s,t The output of the pumped storage power station at time t in scenario s; They are respectively the power generation and pumping power at time t in the pumped-storage power station scenario s.

[0130] J. The formula for the operating condition constraints of the pumped storage power station is:

[0131]

[0132] In the formula, They are respectively the power generation and pumping power at time t in the pumped-storage power station scenario s.

[0133] 6) The operating constraint formula of electrochemical energy storage is:

[0134] P ess,s,t =Temp dis,s,t P dis,s,t +Temp cha,s,t P cha,s,t

[0135]

[0136] Where P ess,s,t Temp is the charge and discharge power at time t in electrochemical energy storage scenario s; dis,s,t Temp is the discharge 0-1 variable at time t in the electrochemical energy storage scenario s; cha,s,t P is the charging 0-1 variable at time t in the electrochemical energy storage scenario s; dis,s,t , P cha,s,t are the charge and discharge amounts of electrochemical energy storage at time t under scenario s; P dis,max , P cha,maxThey are the upper limits of electrochemical energy storage charge and discharge capacity.

[0137] The energy storage capacity should satisfy the charge constraints under its energy capacity, and the initial and final charge states should be equal:

[0138] SOC min ≤SOC s,t ≤SOC max

[0139]

[0140] SOC s,t =SOC s,0

[0141] In the formula, SOC s,t SOC is the state of charge at the end of time t under scenario s; s,0 is the initial state of charge of energy storage, which is 0.4; η is the charge and discharge efficiency of electrochemical energy storage; SOC s,t-1 is the state of charge at the end of time t-1 under scenario s; SOC min , SOC max are the upper and lower limits of energy storage charge state respectively; P dis,s,t , P cha,s,t are the charge and discharge amounts of electrochemical energy storage at time t under scenario s; Δt is the time interval; Cap ess is the electrochemical energy storage capacity.

[0142] 7) The output constraint formula of conventional units is:

[0143]

[0144] Where P gen,i,t represents the active output of thermal power unit i; and They respectively represent the upper and lower limits of the active output of thermal power unit i.

[0145] 8) The formula for the climbing constraint condition of conventional units is:

[0146]

[0147] In the formula, and Respectively represent the up and down climbing limits of thermal power unit i; P gen,i,t represents the active output of thermal power unit i; Δt is the time interval.

[0148] 9) The formula for wind farm output constraint is:

[0149]

[0150] Where P wind,s,t is the output of the wind turbine at time t in scenario s, is the maximum output of the wind turbine at time t in scenario s.

[0151] 10) The formula for the operation constraints of a photovoltaic power station is:

[0152]

[0153] Where P pv,s,t is the output of the photovoltaic unit at time t in scenario s; is the maximum output of the photovoltaic unit at time t in scenario s

[0154] In the present invention, the upper investment decision model considers the location and capacity of shared energy storage and the investment plan for the expansion of transmission lines at the same time, reflecting the synergistic relationship between shared energy storage and transmission line planning, so as to minimize the total investment cost of shared energy storage and transmission lines; the lower optimization scheduling model satisfies the uncertainty of demand while comprehensively considering the output of conventional units, the charging and discharging capacity of shared energy storage and line flow, so as to minimize the comprehensive operating cost of the system. Compared with the single transmission line and shared energy storage configuration, the joint configuration of shared energy storage and transmission lines has better economy, and can effectively alleviate the impact of transmission line constraints on the new energy consumption of high-proportion new energy power systems, and reduce the amount of wind and solar power abandoned in high-proportion new energy power systems.

[0155] Figure 3 The solution process of the double-layer collaborative optimization configuration model of the present invention is shown. When solving the double-layer collaborative optimization configuration model, the upper-layer investment decision model passes the investment planning scheme with the lowest comprehensive cost to the lower layer. The lower-layer optimization scheduling model uses multivariate Gaussian distribution and Monte Carlo simulation to generate uncertain demand scenarios, and based on the shared energy storage and transmission line planning scheme transmitted by the upper-layer investment decision model, the system operation results are obtained through optimization scheduling, and then the optimal system operation cost is passed to the upper-layer investment decision model. The upper-layer investment decision model and the lower-layer optimization scheduling model iterate each other, and finally obtain the most economically optimal shared energy storage access location and capacity, and transmission line expansion configuration scheme.

[0156] The present invention can be used to solve the problem of optimal configuration of shared energy storage and transmission lines in a high-proportion new energy power system under uncertain demand scenarios. On the one hand, the generalized Pareto distribution function and the GEMINI improved graph LASSO method are used to process the net load historical data to generate Monte Carlo scenarios for uncertain demand. On the other hand, a two-layer comprehensive optimization configuration model for shared energy storage and transmission lines is established, and the shared energy storage site selection, sizing, and transmission line expansion configuration scheme that meets the objective function of the two-layer model and is economically optimal is obtained through solution. The influence of the heavy-tail effect on the coverage of uncertain demand scenarios is overcome, the efficiency of scenario generation is improved, the phenomenon of wind and solar abandonment in high-proportion new energy power systems is significantly reduced, the system's wind and solar acceptance capacity is improved, and the economy of the transmission and storage joint configuration system is significantly improved.

[0157] Figure 4 An embodiment of a computer device of the present invention is shown. The computer device may be a server, and the computer device includes a processor, a memory, and a network interface connected via a system bus. 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 static information and dynamic information data. The network 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, the steps in the above method embodiment are implemented.

[0158] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0159] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0160] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed 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, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0161] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for collaboratively configuring shared energy storage and transmission lines, characterized in that: include: Analyze the high-proportion renewable energy power system and establish the uncertain demand scenario generation function; Generate an uncertain demand scenario for a high-proportion new energy power system according to the uncertain demand scenario generation function; Based on the uncertain demand scenario, a two-layer collaborative optimization configuration model of shared energy storage and transmission lines is established; The two-layer collaborative optimization configuration model is solved to obtain a shared energy storage and transmission line configuration plan.

2. The method for collaboratively configuring shared energy storage and transmission lines according to claim 1, characterized in that: Analyze the high-proportion renewable energy power system and establish the uncertain demand scenario generation function including: Obtain historical net load data of high-proportion renewable energy power systems; According to the deviation between the actual value and the predicted value of the net load in each time span, the generalized Pareto distribution function is fitted; Using the generalized Pareto distribution function to fit the net load historical data, to obtain fitted net load historical data; The fitted net load historical data is transformed by using probability integral transformation to obtain Gaussian data that satisfies the Gaussian distribution on [0, 1]. The obtained Gaussian data are processed using the GEMINI improved graph LASSO method to obtain a multivariate Gaussian distribution function for generating uncertain demand scenarios, and the multivariate Gaussian distribution function is used as the uncertain demand scenario generation function.

3. The method for collaboratively configuring shared energy storage and transmission lines according to claim 2, characterized in that: According to the uncertain demand scenario generation function, generating an uncertain demand scenario for a high proportion of new energy power system includes: Monte Carlo samples are generated according to the multivariate Gaussian distribution function, and the predicted value of the net load is added to the Monte Carlo samples to obtain uncertain demand scenarios for site selection and capacity determination of shared energy storage and expansion of transmission lines in a high-proportion new energy power system.

4. The method for collaboratively configuring shared energy storage and transmission lines according to claim 3, characterized in that: The two-layer collaborative optimization configuration model includes an upper-layer investment decision model and a lower-layer optimization scheduling model; wherein, The upper-level investment decision model is used to generate investment decisions for shared energy storage and transmission lines based on the synergistic relationship between shared energy storage and transmission line planning, with the goal of minimizing annual comprehensive costs; The lower-level optimization scheduling model is used to generate a shared energy storage and transmission line configuration plan based on the investment decisions of the shared energy storage and transmission lines transmitted by the upper-level investment decision model, with the goal of minimizing the overall system operating cost.

5. The method for collaboratively configuring shared energy storage and transmission lines according to claim 4, characterized in that: The annual comprehensive cost includes: the annual investment cost of shared energy storage and the annual investment cost of transmission lines; the comprehensive operating cost of the system includes: the income of conventional units, the income of wind and solar grid connection, the income of shared energy storage participating in multiple market transactions, the operating costs of conventional units and shared energy storage, the penalty costs for wind and solar abandonment, and the network loss costs.

6. The method for collaboratively configuring shared energy storage and transmission lines according to claim 5, characterized in that: The constraints of the upper-level investment decision model include: new line constraints and buildable capacity constraints of energy storage.

7. The method for collaboratively configuring shared energy storage and transmission lines according to claim 6, characterized in that: The constraints of the lower-level optimization scheduling model include: power balance constraints of each node in the system, branch flow constraints of the system, power limit constraints of each branch, node voltage constraints, pumped storage constraints, electrochemical energy storage operation constraints, conventional unit output constraints, conventional unit climbing constraints, wind farm output constraints and photovoltaic power station operation constraints.

8. A shared energy storage and transmission line coordinated configuration system, characterized in that: include: Scenario function building module, used to analyze high-proportion renewable energy power systems and build uncertain demand scenario generation functions; A demand scenario generation module, used to generate an uncertain demand scenario for a high-proportion new energy power system according to the uncertain demand scenario generation function; An optimization model configuration module, used to establish a two-layer collaborative optimization configuration model of shared energy storage and transmission lines based on the uncertain demand scenario; The optimization model solving module is used to solve the double-layer collaborative optimization configuration model to obtain a shared energy storage and transmission line configuration plan.

9. The shared energy storage and transmission line coordinated configuration system according to claim 8, characterized in that: When analyzing a high-proportion renewable energy power system and establishing an uncertain demand scenario generation function, the scenario function establishment module obtains the net load historical data of the high-proportion renewable energy power system; fits a generalized Pareto distribution function according to the deviation between the actual value and the predicted value of the net load in each time span; fits the net load historical data using the generalized Pareto distribution function to obtain the fitted net load historical data; transforms the fitted net load historical data using probability integral transformation to obtain Gaussian data that satisfies the Gaussian distribution on [0, 1]; processes the obtained Gaussian data using the GEMINI improved graph LASSO method to obtain a multivariate Gaussian distribution function for generating uncertain demand scenarios, and uses the multivariate Gaussian distribution function as the uncertain demand scenario generation function.

10. The shared energy storage and transmission line coordinated configuration system according to claim 9, characterized in that: When the demand scenario generation module generates an uncertain demand scenario for a high-proportion new energy power system based on the uncertain demand scenario generation function, it generates a Monte Carlo sample based on a multivariate Gaussian distribution function, and adds a predicted value of the net load to the Monte Carlo sample to obtain an uncertain demand scenario for site selection, sizing, and transmission line expansion of a shared energy storage system with a high proportion of new energy power.

11. The shared energy storage and transmission line coordinated configuration system according to claim 10, characterized in that: The two-layer collaborative optimization configuration model includes an upper-layer investment decision model and a lower-layer optimization scheduling model; wherein, The upper-level investment decision model is used to generate investment decisions for shared energy storage and transmission lines based on the synergistic relationship between shared energy storage and transmission line planning, with the goal of minimizing annual comprehensive costs; The lower-level optimization scheduling model is used to generate a shared energy storage and transmission line configuration plan based on the investment decisions of the shared energy storage and transmission lines transmitted by the upper-level investment decision model, with the goal of minimizing the overall system operating cost.

12. The shared energy storage and transmission line coordinated configuration system according to claim 11, characterized in that: The annual comprehensive cost includes: the annual investment cost of shared energy storage and the annual investment cost of transmission lines; the comprehensive operating cost of the system includes: the income of conventional units, the income of wind and solar grid connection, the income of shared energy storage participating in multiple market transactions, the operating costs of conventional units and shared energy storage, the penalty costs for wind and solar abandonment, and the network loss costs.

13. The shared energy storage and transmission line coordinated configuration system according to claim 12, characterized in that: The constraints of the upper-level investment decision model are: new line constraints and buildable capacity constraints of energy storage.

14. The shared energy storage and transmission line coordinated configuration system according to claim 13, characterized in that: The constraints of the lower-level optimization scheduling model are: power balance constraints of each node in the system, branch flow constraints of the system, power limit constraints of each branch, node voltage constraints, pumped storage constraints, electrochemical energy storage operation constraints, conventional unit output constraints, conventional unit climbing constraints, wind farm output constraints and photovoltaic power station operation constraints.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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