Energy storage distribution robust planning method considering GCT-CET mechanism and wind power uncertainty

Abstract

The invention belongs to the technical field of power systems, and discloses an energy storage distribution robust planning method considering a GCT-CET mechanism and wind power uncertainty, and the method comprises the steps: S1, building a regional power grid model containing wind power and energy storage; s2, establishing an energy storage distribution robust planning model based on data driving, and linearizing constraint conditions and a wind power uncertainty set; and S3, solving by adopting a parallel calculation column and a constraint generation algorithm. According to the method, probability information contained in a large amount of wind power output historical scenes is effectively utilized, the advantages of a robust optimization method and a stochastic programming method are fused, and an energy storage optimization configuration scheme is given to guarantee ordered grid connection and safe consumption of large-scale wind power.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a robust planning method for energy storage distribution considering a GCT-CET mechanism and wind power uncertainty. Background Art

[0002] With the rapid development of wind power generation and the increasing installed capacity, the impact of its volatility, randomness and reverse peak-shaving characteristics on the system is also increasing. As a hub connecting the power generation, transmission, distribution and consumption of the power system, energy storage plays an important role in power grid construction. At the same time, based on the current status of new energy consumption and the current low-carbon emission reduction strategic goals, it is imperative to give full play to the role of energy storage in achieving optimal resource allocation, promoting low-carbon emission reduction in the power system, curbing the uncertainty of new energy and improving the quality of power.

[0003] However, due to its high investment and operating costs, the large-scale use of energy storage is limited to a certain extent. Currently, power departments and energy storage operators need to face the following problems:

[0004] 1. The stochastic planning method in the optimal configuration of energy storage under the background of high proportion of wind power relies on the precise probability distribution of uncertainties, but the precise probability distribution of uncertainties is usually difficult to obtain accurately in practice, which makes it difficult to reliably show the expected utility of the decision-making results in reality;

[0005] 2. The probability of the worst scenario of actual output of new energy is often low, while the probability information contained in a large number of historical scenarios of wind power output is wasted, resulting in the results of traditional robust optimization schemes being too conservative and uneconomical. The decision results are usually too conservative, which is not conducive to improving the economic efficiency of power system operation and the precise configuration of energy storage.

[0006] 3. In the process of optimizing the configuration of energy storage in the power system, it is difficult to take into account the needs of wind power disturbance suppression and low-carbon economic operation of the system. It is necessary to consider the uncertainty of wind power output in the planning model, and improve the economic efficiency of system operation while ensuring the new energy consumption and control capabilities.

[0007] How to effectively utilize the probabilistic information contained in a large number of historical wind power output scenarios, integrate the advantages of robust optimization methods and stochastic programming methods, and provide an optimal configuration plan for energy storage to ensure the orderly grid connection and safe absorption of large-scale wind power is a current problem to be solved. Summary of the invention

[0008] To solve the above technical problems, an embodiment of the present invention provides a robust planning method for energy storage distribution considering the GCT-CET mechanism and wind power uncertainty. In order to have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary is not a general review, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0009] According to a first aspect of an embodiment of the present invention, a robust planning method for energy storage distribution considering a GCT-CET mechanism and wind power uncertainty is provided, comprising:

[0010] Step S1, establishing a regional power grid model including wind power and energy storage;

[0011] Step S2, establishing a data-driven energy storage distribution robust planning model, and linearizing the constraints and wind power uncertainty set;

[0012] Step S3, solving by using parallel computing column and constraint generation algorithm.

[0013] Optionally, the energy storage distributed robust configuration model includes a GCT-CET joint transaction model, wherein the GCT-CET joint transaction cost is expressed by the following formula:

[0014] C CET-GCT =C CET +C GCT (1)

[0015] C CET is the tiered CET transaction cost, C GCT is the GCT transaction cost.

[0016] Optionally, the GCT transaction cost is expressed by the following formula:

[0017] C GCT =λ green (Q s -Q d ) (2)

[0018] Among them, λ green Indicates the transaction price of green certificates; Q s Indicates the quota indicator of the number of green certificates that the system needs to hold; Q d Indicates the number of green certificates obtained by the system's renewable energy power generation;

[0019]

[0020] Among them, α green Indicates the system green certificate quantity quota coefficient; ∑P load,tRepresents the total power demand of the system during the dispatch period; κ green It represents the quantitative coefficient of converting wind power generation into the number of green certificates. One green certificate corresponds to 1MWh of wind power output. ∑P wind,t Indicates the system wind power output.

[0021] Optionally, the green certificate transaction price is established through a quantity competition Cournot transaction model. According to the Cournot model formula, the green certificate transaction price model can be expressed as:

[0022]

[0023] in, and are the two positive parameters of the inverse price function of the Cournot model; Q c Indicates the number of green certificates sold by the system; is the GCT transaction price ratio calculated based on historical data; green,0 Indicates the basic transaction price of green certificates.

[0024] Optionally, the objective function of the energy storage distribution robust planning model is to minimize the sum of the equal annual investment cost and the annual operating cost of the energy storage, as shown in the following formula:

[0025] min C=C ess +σC fy (6)

[0026] Among them, C is the annual comprehensive cost of the system; C ess and C fy are the equal annual investment cost and annual operating cost of energy storage respectively; σ=365 is the equivalence factor of the equal annual investment cost and operating cost.

[0027] Optionally, the annual investment cost of energy storage is C ess It includes fixed costs and variable costs. Fixed costs include the device costs and installation costs of the energy storage device itself, and variable costs include maintenance and disposal costs, which are reflected through power costs and capacity costs, as shown in the following formula:

[0028]

[0029] Where r is the investment discount rate; T ess is the economic service life of energy storage; c ess,a is the investment cost of the energy storage device at node a; x a 0-1 decision variable for configuring energy storage device for node a; k tc k is the maintenance and disposal cost coefficient of the energy storage device; de is the device cost and installation cost coefficient of the energy storage device itself; c p is the unit power cost of the energy storage device; ce is the unit capacity cost of the energy storage device; P ess,a The power of the energy storage device configured for node a; The capacity of the energy storage device configured for node a; Ω ess It is the node set to be configured for the energy storage device.

[0030] Optionally, the annual operating cost of the energy storage is as follows:

[0031]

[0032] Among them, T, N g 、N B They are the number of scheduling periods, the number of thermal power units, and the number of system nodes; and are the penalty costs for unit wind curtailment and unit load shedding of node i respectively; and are the wind abandonment and load shedding of node i in period t respectively; P g,t is the active power output of thermal power unit g in period t; a g 、b g 、c g They are respectively the quadratic coefficient, linear coefficient and constant coefficient of the quadratic relationship between the fuel cost g of the thermal power unit and the generated power.

[0033] Optionally, the constraints of the energy storage distribution robust planning model are divided into two parts: the first stage is the investment decision constraints of energy storage, and the second stage is the system operation constraints under a given investment decision.

[0034] Optionally, the investment decision constraint is as follows:

[0035]

[0036] Among them, x i represents the investment decision variable for the energy storage system to be installed at node i; if installed, the value is 1, otherwise the value is 0; Γ ess The maximum number of energy storage system nodes allowed to be installed;

[0037] Optionally, based on the investment decision constraints, energy storage capacity and power constraints are as follows:

[0038]

[0039] in, and They represent the maximum values ​​of the rated capacity and rated power of the energy storage system at node i respectively; σ i,max and σ i,min are the upper and lower limits of the state of charge of the energy storage system at node i; E i,max and Ei,min are the upper and lower limits of the energy storage capacity of the energy storage system at node i respectively.

[0040] Optionally, based on the investment decision constraints, the output constraints of wind turbines and thermal power plants are as follows:

[0041]

[0042] in, They are the upper and lower limits of the output of the thermal power unit at node a respectively; and is the downward and upward climbing rate; P w,a,t Powering wind turbines.

[0043] Optionally, based on the investment decision constraint, the energy storage output constraint is as follows:

[0044]

[0045] in, is the charging and discharging power of the energy storage system at node i in time period t; E i,t E is the storage capacity of the energy storage system at node i in time period t; i,0 is the original storage capacity of the energy storage system at node i.

[0046] Optionally, based on the investment decision constraint, the power flow constraint condition is as follows:

[0047]

[0048] Among them, P g,t is the active power output vector of the thermal power plant at time t; P w,t is the wind power active output vector at time t; P ess,t is the output power vector of the energy storage device at time t; P l,t is the load vector of each node at time t; A is the system node branch association matrix; P LI,t is the branch power vector at time t; P ab,t is the power of line ab at time t, and P LI,t Middle element; B ab is the line susceptance between nodes a and b; θ ab,t is the phase angle difference between nodes a and b at time t; θ a,t is the phase angle of node a at time t; θ min and θ max are the upper and lower limits of the phase angle respectively.

[0049] Optionally, based on the investment decision constraint, the node wind abandonment constraint is as follows:

[0050]

[0051] in, Indicates the maximum proportion of wind power abandonment at node i.

[0052] Optionally, based on the investment decision constraint, the node load shedding constraint is as follows:

[0053]

[0054] in, It indicates the maximum proportion of load shedding of node i, and Pl,a,t indicates the load shedding of node a at time t.

[0055] Optionally, step S3 includes: solving the model using a parallel computing column and constraint generation algorithm, converting the min-max-min robust optimization problem into a mixed integer programming main problem and a linear programming sub-problem that can be calculated in parallel, the main problem provides an upper bound value for the original problem, and after the sub-problem obtains the optimal energy storage planning solution, a system operation simulation is performed to obtain a lower bound value; including:

[0056] Step S31, solving the main problem of the model to obtain investment decision variables for energy storage planning;

[0057] Step S32, decomposing the lower layer problem into S independent linear programming problems, and solving them simultaneously by parallel computing;

[0058] Step S33, according to the optimal solution of the linear programming problem corresponding to the S scenarios in the mth iteration, further solve the intermediate layer problem to obtain the solution corresponding to the worst probability distribution;

[0059] Step S34, if there is an optimal solution to the sub-problem, add the optimal set to the main problem;

[0060] Step S35, if the subproblem is unbounded or there is no feasible solution, then add the optimal set to the main problem;

[0061] Step S36, the main problem and the sub-problem are solved alternately and iteratively, and the gap between the upper bound and the lower bound is continuously narrowed; when the gap between the upper bound and the lower bound is less than a given convergence threshold, the algorithm iteration process ends and the planning result is output.

[0062] According to a second aspect of an embodiment of the present invention, a computer device is provided.

[0063] In some embodiments, 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.

[0064] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0065] The present invention studies the energy storage configuration planning method and proposes an energy storage planning model that introduces the GCT-CET joint trading mechanism. The model can integrate the effectiveness of the GCT mechanism and the CET mechanism in promoting the new energy consumption and reducing carbon emissions, respectively, to achieve the comprehensive optimization of the system's economy, low carbon and new energy consumption capacity. The planning model integrates the historical information of wind power output to make investment decisions. The obtained energy storage planning scheme is less conservative than the traditional robust planning, and takes into account the random fluctuations in the probability of wind power output scenarios. It integrates the characteristics of stochastic planning and robust optimization, does not require complex conversions and calculations of bilinear terms, and helps to improve the solution efficiency.

[0066] 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

[0067] 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.

[0068] Figure 1 It is a flow chart of a robust planning method for energy storage distribution considering GCT-CET mechanism and wind power uncertainty according to an exemplary embodiment;

[0069] Figure 2 is a schematic diagram of a Garver 6-node system according to an exemplary embodiment;

[0070] Figure 3 It is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0071] 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.

[0072] 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.

[0073] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0079] In order to meet the needs of balancing wind power disturbance and system low-carbon economic operation in the process of energy storage optimization configuration, the present invention proposes a robust planning method for energy storage distribution that considers the GCT (Green Certificate Trading)-CET (Carbon Emission Trading) mechanism and wind power uncertainty. Figure 1 As shown, it includes: step S1, establishing a regional power grid model including wind power and energy storage; step S2, establishing a data-driven energy storage distribution robust planning model, linearizing the constraints and wind power uncertainty sets, and solving them using a parallel computing column and constraint generation algorithm.

[0080] The impact of GCT and CET on system operation is taken into consideration during planning, and priority is given to the output of green electricity and low-carbon units to maximize the system's profits in GCT and CET and achieve low-carbon economic planning and operation of the system.

[0081] The distributed robust configuration model of energy storage includes the GCT-CET joint transaction model, where the GCT-CET joint transaction cost is expressed by the following formula:

[0082] C CET-GCT =C CET +C GCT (1)

[0083] C CET is the CET transaction cost, C GCTis the GCT transaction cost.

[0084] GCT transaction costs are expressed as follows:

[0085] C GCT =λ green (Q s -Q d ) (2)

[0086] Among them, λ green Indicates the transaction price of green certificates; Q s Indicates the quota indicator of the number of green certificates that the system needs to hold; Q d Indicates the number of green certificates obtained by the system's renewable energy power generation.

[0087]

[0088] Among them, α green Indicates the system green certificate quantity quota coefficient; ∑P load,t Represents the total power demand of the system during the dispatch period; κ green It represents the quantitative coefficient of converting wind power generation into the number of green certificates. One green certificate corresponds to 1MWh of wind power output. ∑P wind,t Indicates the system wind power output.

[0089] Influenced by market rules, the transaction price of green certificates varies with their quantity. The transaction price of green certificates is established through the Cournot trading model of quantity competition. According to the Cournot model formula, the green certificate transaction price model can be expressed as:

[0090]

[0091] in, and are the two positive parameters of the inverse price function of the Cournot model; Q c Indicates the number of green certificates sold by the system; is the GCT transaction price ratio calculated based on historical data; green,0 It indicates the basic transaction price of green certificates, which is usually set in combination with the current actual market transaction price of green certificates. For example, the basic transaction price of green certificates in this article is set to RMB 220 per copy.

[0092] Tiered carbon trading costs C CET It is expressed by the following formula:

[0093]

[0094] Among them, E q is the carbon emission quota of each power generation unit in the system; σ is the basic price of carbon trading; E allis the actual carbon emissions of the system; x is the penalty factor of the step-by-step carbon trading, which is 0.25; y is the compensation coefficient of the step-by-step carbon trading, which is 0.2; d is the interval length of carbon emissions, which is 2000t.

[0095] The objective function of the energy storage distributed robust planning model is to minimize the sum of the equal annual investment cost and annual operating cost of energy storage (i.e., the annual comprehensive cost).

[0096] minC=C ess +ξ y C fy (6)

[0097] Among them, C is the annual comprehensive cost of the system; C ess and C fy are the annual investment cost and annual operating cost of energy storage respectively; y =365 is the equivalence factor between the annual investment cost and the operating cost.

[0098] Equivalent annual investment cost of energy storage C ess It includes fixed costs and variable costs. Fixed costs include the cost of the energy storage device itself, installation costs, etc. Variable costs mainly include maintenance, disposal and other costs, which are reflected in power costs and capacity costs.

[0099]

[0100] Where r is the investment discount rate; T ess is the economic service life of energy storage; c ess,a is the investment cost of the energy storage device at node a; x a 0-1 decision variable for configuring energy storage device for node a; k tc k is the maintenance and disposal cost coefficient of the energy storage device; de is the device cost and installation cost coefficient of the energy storage device itself; c p is the unit power cost of the energy storage device; c e is the unit capacity cost of the energy storage device; P ess,a The power of the energy storage device configured for node a; The capacity of the energy storage device configured for node a; Ω ess It is the node set to be configured for the energy storage device.

[0101] The operating costs of energy storage include the fuel costs of thermal power units, GCT-CET joint transaction costs, and penalty costs for wind curtailment and load shedding.

[0102]

[0103] Among them, T, N g 、N B They are the number of scheduling periods, the number of thermal power units, and the number of system nodes; and are the penalty costs for unit wind curtailment and unit load shedding of node i respectively; and are the wind abandonment and load shedding of node i in period t respectively; P g,t is the active power output of thermal power unit g in period t; a g 、b g 、c g They are respectively the quadratic coefficient, linear coefficient and constant coefficient of the quadratic relationship between the fuel cost g of the thermal power unit and the generated power.

[0104] The constraints of the energy storage distributed robust planning model are divided into two parts: the first stage is the investment decision constraints for energy storage, and the second stage is the system operation constraints under a given investment decision.

[0105] The investment decision constraints are as follows:

[0106]

[0107] Among them, x i represents the investment decision variable for the energy storage system to be installed at node i; if installed, the value is 1, otherwise the value is 0; Γ ess The maximum number of energy storage system nodes allowed to be installed;

[0108] Based on the investment decision constraints, the energy storage capacity and power constraints are as follows:

[0109]

[0110] in, and They represent the maximum values ​​of the rated capacity and rated power of the energy storage system at node i respectively; σ i,max and σ i,min are the upper and lower limits of the state of charge of the energy storage system at node i; E i,max and E i,min are the upper and lower limits of the energy storage capacity of the energy storage system at node i respectively.

[0111] The output constraints of wind turbines and thermal power units are as follows:

[0112]

[0113] in, They are the upper and lower limits of the output of the thermal power unit at node a respectively; and is the downward and upward climbing rate; P w,a,t Powering wind turbines.

[0114] The energy storage output constraints are as follows:

[0115]

[0116] in, is the charging and discharging power of the energy storage system at node i in time period t; E i,t E is the storage capacity of the energy storage system at node i in time period t; i,0 is the original storage capacity of the energy storage system at node i.

[0117] The power flow constraints are as follows:

[0118]

[0119] Among them, P g,t is the active power output vector of the thermal power plant at time t; P w,t is the wind power active output vector at time t; P ess,t is the output power vector of the energy storage device at time t; P l,t is the load vector of each node at time t; A is the system node branch association matrix; P LI,t is the branch power vector at time t; P ab,t is the power of line ab at time t, and P LI,t Middle element; B ab is the line susceptance between nodes a and b; θ ab,t is the phase difference between nodes a and b at time t; θ a,t is the phase angle of node a at time t; θ min and θ max are the upper and lower limits of the phase angle respectively.

[0120] The node wind abandonment constraints are as follows:

[0121]

[0122] in, Indicates the maximum proportion of wind power abandonment at node i.

[0123] The node load shedding constraints are as follows:

[0124]

[0125] in, It indicates the maximum proportion of load shedding of node i, and Pl,a,t indicates the load shedding of node a at time t.

[0126] Next, the energy storage distribution robust planning model is solved, including:

[0127] A two-stage distributed robust planning model for energy storage configuration based on data-driven is established. In the first stage, the energy storage investment decision with the minimum annual comprehensive cost is sought. In the second stage, the operation simulation of the system is carried out under a given energy storage planning scheme, with the goal of minimizing the operating cost. A, b, G, g, E, F, d, U, V and w represent the corresponding matrix or vector form of the variables in the model. The matrix form of the distributed robust planning model for energy storage is:

[0128]

[0129] Among them, x is the decision variable of the first stage, which determines the corridor channel and number of new lines, the location of the energy storage system, and the configured capacity and power; Ω is the probability distribution uncertainty set of wind power output scenarios; X is the investment decision variable set of transmission network and energy storage; Y is the system operation decision under the given energy storage planning scheme and wind power output scenario probability distribution, including the output plan of thermal power units, the charging and discharging power of energy storage systems, etc.

[0130] The first type of constraint cost is as follows, including investment and construction constraints: Equations (9) and (10)

[0131] stAx≥b (19)

[0132] The second type of constraints is as follows, including operation constraints: Equations (11), (13), (15)-(17)

[0133]

[0134] Among them, ξ s is the sth typical wind power output scenario obtained after clustering the historical wind power output scenarios;

[0135] The third type of constraint is as follows, including equations (12) and (14):

[0136] Ex+Fy s ≥d,s=1,2,…,K (21)

[0137] The probability uncertainty set of wind power output scenarios is as follows:

[0138]

[0139] The data-driven energy storage configuration planning model is a min-max-min three-layer robust optimization problem. The outer layer makes the energy storage investment decision with the lowest annual comprehensive cost. The middle layer finds the worst probability distribution of K typical scenarios. The inner layer model finds the operating decision variable with the smallest expected operating cost under given investment decisions and the worst probability distribution.

[0140] Next, the planning model is solved.

[0141] The model is solved by a parallel computing column and constraint generation algorithm, which transforms the min-max-min robust optimization problem into a mixed integer programming main problem and a linear programming subproblem that can be computed in parallel. The main problem provides an upper bound for the original problem. After the subproblem obtains the optimal energy storage planning solution, a system operation simulation is performed to obtain the lower bound.

[0142] Specifically, in step S31, the main problem of the model is solved to obtain investment decision variables for energy storage planning.

[0143] Step S32, decompose the lower-level problem into S independent linear programming problems, and solve them simultaneously by parallel computing.

[0144] Step S33, based on the optimal solution of the linear programming problem corresponding to the S scenarios in the mth iteration, further solve the intermediate layer problem to obtain the solution corresponding to the worst probability distribution.

[0145] Step S34: If there is an optimal solution to the sub-problem, add the optimal set to the main problem.

[0146]

[0147] Step S35: If the subproblem is unbounded or does not have a feasible solution, add the optimal set to the main problem.

[0148]

[0149] Step S36, the main problem and the sub-problem are solved alternately and iteratively, and the gap between the upper bound and the lower bound is continuously narrowed. When the gap between the upper bound and the lower bound is less than a given convergence threshold, the algorithm iteration process ends and the planning result is output.

[0150] The following is a specific embodiment of the method according to the present invention. Figure 2 The simulation of the Garver6 node system shown in the figure verifies that the investment discount rate is set to 10%, the cycle life is 20 years, and the penalty costs for wind abandonment and load shedding are 1000 yuan / MWh and 40000 yuan / MWh respectively. Wind farms with installed capacities of 250MW and 200MW are connected to nodes 2 and 6 respectively, with an annual maximum load of 700MW and a total installed capacity of 1250MW. The confidence level of the 1-norm and ∞-norm constraints is α1=α ∞ =99.99%.

[0151] (1) Comparison of the impact of CET and GCT mechanisms

[0152] Table 1 Calculation results of each scheme

[0153] plan CET cost / 10,000 yuan GCT cost / 10,000 yuan Operating cost / 10,000 yuan CET and GCT mechanisms are not considered / / 30954 CET mechanism only 1545 / 28663 GCT mechanism only / 2927 25632 GCT-CET joint transaction mechanism 1918 3082 22932

[0154] It can be seen that the introduction of the GCT-CET joint trading mechanism in energy storage configuration planning can combine the different advantages of the two mechanisms to achieve the best overall economic and low-carbon performance of the system.

[0155] (2) Comparison of optimization methods

[0156]

[0157]

[0158] It can be seen that: (1) the planning result obtained by random planning is the most economical. This is because random planning makes optimization decisions based on a given wind power output scenario, without taking into account the deviation between the actual probability of the wind power output scenario and the initial probability. Therefore, the decision result is more economical but less conservative.

[0159] (2) Traditional robust planning methods only make decisions under the worst scenario of wind power output fluctuations, which has poor economic efficiency.

[0160] (3) The proposed data-driven energy storage configuration planning model can achieve a good compromise between the economy and conservatism of investment decisions.

[0161] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, 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 through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0162] 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 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.

[0163] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0164] In one embodiment, a computer-readable storage medium is provided, 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.

[0165] 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).

[0166] 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 robust planning method for energy storage distribution considering GCT-CET mechanism and wind power uncertainty, characterized in that: include: Step S1, establishing a regional power grid model including wind power and energy storage; Step S2, establishing a data-driven energy storage distribution robust planning model, and linearizing the constraints and wind power uncertainty set; Step S3, solving by using parallel computing column and constraint generation algorithm.

2. The method for robust planning of energy storage distribution considering the GCT-CET mechanism and wind power uncertainty as claimed in claim 1, characterized in that: The energy storage distributed robust configuration model includes a GCT-CET joint transaction model, wherein the GCT-CET joint transaction cost is expressed by the following formula: C CET-GCT =C CET +C GCT (1) C CET is the tiered CET transaction cost, C GCT is the GCT transaction cost.

3. The method for robust planning of energy storage distribution considering the GCT-CET mechanism and wind power uncertainty as claimed in claim 2, characterized in that: The GCT transaction cost is expressed by the following formula: C GCT =λ green (Q s -Q d ) (2) Among them, λ green Indicates the transaction price of green certificates; Q s Indicates the quota indicator of the number of green certificates that the system needs to hold; Q d Indicates the number of green certificates obtained by the system's renewable energy power generation; Among them, α green Indicates the system green certificate quantity quota coefficient; ∑P load,t Represents the total power demand of the system during the dispatch period; κ green It represents the quantitative coefficient of converting wind power generation into the number of green certificates. One green certificate corresponds to 1MWh of wind power output. ∑P wind,t Indicates the system wind power output.

4. The method for robust planning of energy storage distribution considering the GCT-CET mechanism and wind power uncertainty as claimed in claim 3 is characterized in that: The green certificate transaction price is established through the Cournot transaction model of quantity competition. According to the Cournot model formula, the green certificate transaction price model can be expressed as: in, and are the two positive parameters of the inverse price function of the Cournot model; Q c Indicates the number of green certificates sold by the system; is the GCT transaction price ratio calculated based on historical data; green,0 Indicates the basic transaction price of green certificates.

5. The method for robust planning of energy storage distribution considering the GCT-CET mechanism and wind power uncertainty as claimed in claim 4 is characterized in that: The objective function of the energy storage distribution robust planning model is to minimize the sum of the equal annual investment cost and annual operating cost of energy storage, as shown in the following formula: minC=C ess +σC fy (6) Among them, C is the annual comprehensive cost of the system; C ess and C fy are the equal annual investment cost and annual operating cost of energy storage respectively; σ=365 is the equivalence factor of the equal annual investment cost and operating cost.

6. The method for robust planning of energy storage distribution considering the GCT-CET mechanism and wind power uncertainty as claimed in claim 5, characterized in that: The annual investment cost of energy storage is C ess It includes fixed costs and variable costs. Fixed costs include the device costs and installation costs of the energy storage device itself, and variable costs include maintenance and disposal costs, which are reflected through power costs and capacity costs, as shown in the following formula: Where r is the investment discount rate; T ess is the economic service life of energy storage; c ess,a is the investment cost of the energy storage device at node a; x a 0-1 decision variable for configuring energy storage device for node a; k tc k is the maintenance and disposal cost coefficient of the energy storage device; de is the device cost and installation cost coefficient of the energy storage device itself; c p is the unit power cost of the energy storage device; c e is the unit capacity cost of the energy storage device; P ess,a The power of the energy storage device configured for node a; The capacity of the energy storage device configured for node a; Ω ess It is the node set to be configured for the energy storage device.

7. The method for robust planning of energy storage distribution considering the GCT-CET mechanism and wind power uncertainty as claimed in claim 6, characterized in that: The annual operating cost of energy storage is as follows: Among them, T, N g 、N B They are the number of scheduling periods, the number of thermal power units, and the number of system nodes; and are the penalty costs for unit wind curtailment and unit load shedding of node i respectively; and are the wind abandonment and load shedding of node i in period t respectively; P g,t is the active power output of thermal power unit g in period t; a g , b g 、c g They are respectively the quadratic coefficient, linear coefficient and constant coefficient of the quadratic relationship between the fuel cost g of the thermal power unit and the generated power.

8. The method for robust planning of energy storage distribution considering the GCT-CET mechanism and wind power uncertainty as claimed in claim 7, characterized in that: The constraints of the energy storage distribution robust planning model are divided into two parts: the first stage is the investment decision constraints of energy storage, and the second stage is the system operation constraints under a given investment decision.

9. The method for robust planning of energy storage distribution considering the GCT-CET mechanism and wind power uncertainty as claimed in claim 8, characterized in that: The investment decision constraint is as follows: Among them, x i represents the investment decision variable for the energy storage system to be installed at node i; if installed, the value is 1, otherwise the value is 0; Γ ess The maximum number of energy storage system nodes allowed to be installed.

10. The method for robust planning of energy storage distribution considering the GCT-CET mechanism and wind power uncertainty as claimed in claim 9, characterized in that: Based on the investment decision constraints, the energy storage capacity and power constraints are as follows: in, and They represent the maximum values ​​of the rated capacity and rated power of the energy storage system at node i respectively; σ i,max and σ i,min are the upper and lower limits of the state of charge of the energy storage system at node i; E i,max and E i,min are the upper and lower limits of the energy storage capacity of the energy storage system at node i respectively.

11. The method for robust planning of energy storage distribution considering GCT-CET mechanism and wind power uncertainty as claimed in claim 10, characterized in that: Based on the investment decision constraints, the output constraints of wind turbines and thermal power units are as follows: in, They are the upper and lower limits of the output of the thermal power unit at node a respectively; and is the downward and upward climbing rate; P w,a,t Powering wind turbines.

12. The method for robust planning of energy storage distribution considering the GCT-CET mechanism and wind power uncertainty as claimed in claim 11, characterized in that: Based on the investment decision constraints, the energy storage output constraints are as follows: AND i,min ≤E i,t ≤E i,max (14) in, is the charging and discharging power of the energy storage system at node i in time period t; E i,t E is the storage capacity of the energy storage system at node i in time period t; i,0 is the original storage capacity of the energy storage system at node i.

13. The method for robust planning of energy storage distribution considering GCT-CET mechanism and wind power uncertainty as claimed in claim 12, characterized in that: Based on the investment decision constraints, the power flow constraints are as follows: Among them, P g,t is the active power output vector of the thermal power plant at time t; P w,t is the wind power active output vector at time t; P ess,t is the output power vector of the energy storage device at time t; P l,t is the load vector of each node at time t; A is the system node branch association matrix; P LI,t is the branch power vector at time t; P ab,t is the power of line ab at time t, and P LI,t Middle element; B ab is the line susceptance between nodes a and b; θ ab,t is the phase difference between nodes a and b at time t; θ a,t is the phase angle of node a at time t; θ min and θ max are the upper and lower limits of the phase angle respectively.

14. The method for robust planning of energy storage distribution considering GCT-CET mechanism and wind power uncertainty as claimed in claim 13, characterized in that: Based on the investment decision constraints, the node wind curtailment constraints are as follows: 0≤ΔP w,a,t ≤r i sw P w,a,t ,a∈Ω w (16) Among them, r i sw Indicates the maximum proportion of wind abandonment at node i.

15. The method for robust planning of energy storage distribution considering GCT-CET mechanism and wind power uncertainty as claimed in claim 14, characterized in that: Based on the investment decision constraints, the node load shedding constraints are as follows: 0≤ΔP 1,a,t ≤r i sl P 1,a,t ,a∈Ω1 (17) Among them, r i sl It indicates the maximum proportion of load shedding of node i, and Pl,a,t indicates the load shedding of node a at time t.

16. The method for robust planning of energy storage distribution considering GCT-CET mechanism and wind power uncertainty as claimed in claim 15, characterized in that: The step S3 includes: solving the model by using a parallel computing column and constraint generation algorithm, converting the min-max-min robust optimization problem into a mixed integer programming main problem and a linear programming sub-problem that can be calculated in parallel, the main problem provides an upper bound value for the original problem, and after the sub-problem obtains the optimal energy storage planning solution, a system operation simulation is performed to obtain a lower bound value; including: Step S31, solving the main problem of the model to obtain investment decision variables for energy storage planning; Step S32, decomposing the lower layer problem into S independent linear programming problems, and solving them simultaneously by parallel computing; Step S33, according to the optimal solution of the linear programming problem corresponding to the S scenarios in the mth iteration, further solve the intermediate layer problem to obtain the solution corresponding to the worst probability distribution; Step S34, if there is an optimal solution to the sub-problem, add the optimal set to the main problem; Step S35, if the subproblem is unbounded or there is no feasible solution, then add the optimal set to the main problem; Step S36, the main problem and the sub-problem are solved alternately and iteratively, and the gap between the upper bound and the lower bound is continuously narrowed; when the gap between the upper bound and the lower bound is less than a given convergence threshold, the algorithm iteration process ends and the planning result is output.

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