Method and device for optimal configuration of energy storage capacity in low-carbon power grid and electronic equipment

Abstract

The application provides a low-carbon power grid energy storage capacity optimization configuration method and device and electronic equipment, and relates to the technical field of electric power. The low-carbon power grid energy storage capacity optimization configuration method comprises the following steps: based on operation constraint conditions, a first operation optimization target and a second operation optimization target of a target low-carbon power grid are constructed; a first uncertainty fuzzy set corresponding to the uncertainty parameters of the first operation optimization target is constructed, and a second uncertainty fuzzy set corresponding to the uncertainty parameters of the second operation optimization target is constructed; based on a risk measurement constraint and a worst carbon emission expectation target, and a total investment cost function of the low-carbon power grid, a capacity optimization configuration model with distribution robustness constraint is constructed; and target capacity configuration parameters of the target low-carbon power grid are obtained according to the capacity optimization configuration model. The application can scientifically and reasonably determine the new energy power generation capacity, and simultaneously plan the expansion scale of the transmission line and the capacity configuration of the energy storage system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power, and particularly relates to a method and device for optimizing configuration of energy storage capacity in a low-carbon power grid and electronic equipment. BACKGROUND

[0002] The output characteristics of new energy generation are highly dependent on weather conditions, showing strong volatility and randomness, which poses new challenges to the stable power supply of the power system.

[0003] In order to cope with the instability of new energy generation, the flexibility of dispatchable units needs to be increased in the low-carbon power grid, and energy storage systems are introduced to suppress power fluctuations and maintain supply and demand balance. Pumped storage as a mature and widely used energy storage method is limited by geographical conditions, and although electrochemical energy storage has attracted much attention in recent years, the cost problem is still an obstacle to its large-scale promotion. Therefore, seeking a reasonable balance between investment cost, carbon emission reduction and system reliability has become an important issue for capacity optimization configuration in the low-carbon power grid.

[0004] Therefore, how to scientifically and reasonably determine the new energy generation capacity, and simultaneously plan the expansion scale of transmission lines and the capacity configuration of energy storage systems to ensure the stable operation and efficient development of the low-carbon power grid is an urgent technical problem to be solved. SUMMARY

[0005] In view of the above problems in the prior art, the present application provides a method and device for optimizing configuration of energy storage capacity in a low-carbon power grid, to scientifically and reasonably determine the new energy generation capacity, and simultaneously plan the expansion scale of transmission lines and the capacity configuration of energy storage systems to ensure the stable operation and efficient development of the low-carbon power grid.

[0006] The present application provides a method for optimizing configuration of energy storage capacity in a low-carbon power grid, comprising the following steps.

[0007] Based on the operation constraint condition, a first operation optimization target and a second operation optimization target of the target low-carbon power grid are constructed; wherein the first operation optimization target is to minimize the loss of load of the target low-carbon power grid under extreme weather conditions, and the second operation optimization target is to minimize the carbon emission of the target low-carbon power grid under non-extreme weather conditions, and the first operation optimization target and the second operation optimization target both include uncertain parameters; a first uncertainty fuzzy set corresponding to the uncertain parameters of the first operation optimization target is constructed, and a second uncertainty fuzzy set corresponding to the uncertain parameters of the second operation optimization target is constructed; based on the Shortfall risk and the first uncertainty fuzzy set, the first operation optimization target is taken as a variable of an increasing convex function of the Shortfall risk, and a distribution-robust risk measurement constraint is established; based on the Shortfall risk and the second uncertainty fuzzy set, the second operation optimization target is taken as a variable of an increasing convex function of the Shortfall risk, and a distribution-robust worst carbon emission expectation target is established; based on the risk measurement constraint and the worst carbon emission expectation target, and a total investment cost function of the low-carbon power grid, a capacity optimization configuration model of the distribution-robust constraint is constructed; wherein the capacity optimization configuration model is to minimize the total investment cost function of the low-carbon power grid and the worst carbon emission expectation target under the condition of meeting the risk measurement constraint; target capacity configuration parameters of the target low-carbon power grid are obtained according to the capacity optimization configuration model, so as to plan new energy power generation equipment, transmission lines and energy storage equipment in the target low-carbon power grid based on the target capacity configuration parameters.

[0008] According to the low-carbon power grid energy storage capacity optimization configuration method provided by the application, the first uncertainty fuzzy set corresponding to the uncertain parameters of the first operation optimization target is constructed, which comprises:

[0009] A first data sample index set is obtained; wherein the first data sample index set includes values of the uncertain parameters under historical extreme weather conditions; the first uncertainty fuzzy set is constructed according to the first sample index set; wherein the distance between the empirical probability distribution of the first uncertainty fuzzy set and the empirical probability distribution of the first sample index set is less than a first preset threshold; the second uncertainty fuzzy set corresponding to the uncertain parameters of the second operation optimization target is constructed, which comprises: obtaining a second data sample index set; wherein the second data sample index set includes values of the uncertain parameters under historical non-extreme weather conditions; the second uncertainty fuzzy set is constructed according to the second sample index set; wherein the distance between the empirical probability distribution of the second uncertainty fuzzy set and the empirical probability distribution of the second sample index set is less than a second preset threshold.

[0010] According to the low-carbon power grid energy storage capacity optimization configuration method provided by the application, the operation constraint condition comprises a nonlinear product term, and the target capacity configuration parameter of the target low-carbon power grid is obtained according to the capacity optimization configuration model, comprising:

[0011] The nonlinear product term is expressed by using a Boolean auxiliary variable and a large M method, and the capacity optimization configuration model after linearization is obtained; and the target capacity configuration parameter of the target low-carbon power grid is obtained according to the capacity optimization configuration model after linearization.

[0012] According to the low-carbon power grid energy storage capacity optimization configuration method provided by the application, the target capacity configuration parameter of the target low-carbon power grid is obtained according to the capacity optimization configuration model after linearization, comprising: introducing a first Lipschitz constant in the risk measurement constraint by regarding an increasing convex function of a Shortfall risk in an upper bound expression of a worst-case expectation of the risk measurement constraint as a function of the uncertainty parameter, to obtain a quantized risk measurement constraint; converting the quantized risk measurement constraint into a first mixed integer linear programming equation by using a sample average approximation algorithm; introducing a second Lipschitz constant in the worst-case carbon emission expectation target by regarding an increasing convex function of a Shortfall risk in an upper bound expression of a worst-case expectation of the worst-case carbon emission expectation target as a function of the uncertainty parameter, to obtain a quantized worst-case carbon emission expectation target; converting the quantized worst-case carbon emission expectation target into a second mixed integer linear programming equation by using a sample average approximation algorithm; and obtaining the target capacity configuration parameter of the target low-carbon power grid based on the first mixed integer linear programming equation and the second mixed integer linear programming equation.

[0013] According to the low-carbon power grid energy storage capacity optimization configuration method provided by the application, the target capacity configuration parameter of the target low-carbon power grid is obtained based on the first mixed integer linear programming equation and the second mixed integer linear programming equation, comprising: obtaining the value of the first Lipschitz constant by solving the first mixed integer linear programming equation by using a maximization dual variable algorithm; obtaining the value of the second Lipschitz constant by solving the second mixed integer linear programming equation by using a maximization dual variable algorithm; obtaining a third mixed integer linear programming equation by substituting the value of the first Lipschitz constant into the first mixed integer linear programming equation; obtaining a fourth mixed integer linear programming equation by substituting the value of the second Lipschitz constant into the second mixed integer linear programming equation; and obtaining the target capacity configuration parameter of the target low-carbon power grid by solving the third mixed integer linear programming equation and the fourth mixed integer linear programming equation.

[0014] According to the low-carbon power grid energy storage capacity optimization configuration method provided by the application, the third mixed integer linear programming equation and the fourth mixed integer linear programming equation are solved to obtain the target capacity configuration parameter of the target low-carbon power grid, including: using the epsilon-constraint method, solving the third mixed integer linear programming equation and the fourth mixed integer linear programming equation within the investment cost budget range to obtain the Pareto frontier of the target capacity configuration parameter.

[0015] The application further provides a low-carbon power grid energy storage capacity optimization configuration device, comprising the following modules:

[0016] The first construction module is used for constructing a first operation optimization target and a second operation optimization target of the target low-carbon power grid based on operation constraint conditions; wherein the first operation optimization target is to minimize the loss of load of the target low-carbon power grid under extreme weather conditions, and the second operation optimization target is to minimize the carbon emission of the target low-carbon power grid under non-extreme weather conditions; the first operation optimization target and the second operation optimization target both comprise an uncertainty parameter; the second construction module is used for constructing a first uncertainty fuzzy set corresponding to the uncertainty parameter of the first operation optimization target, and constructing a second uncertainty fuzzy set corresponding to the uncertainty parameter of the second operation optimization target; the third construction module is used for taking the first operation optimization target as a variable of an increasing convex function of a shortfall risk based on the shortfall risk and the first uncertainty fuzzy set, and establishing a distribution-robust risk measurement constraint; the fourth construction module is used for taking the second operation optimization target as a variable of an increasing convex function of a shortfall risk based on the shortfall risk and the second uncertainty fuzzy set, and establishing a distribution-robust worst carbon emission expectation target; the fifth construction module is used for constructing a distribution-robust constraint capacity optimization configuration model based on the risk measurement constraint, the worst carbon emission expectation target, and a low-carbon power grid total investment cost function; wherein the capacity optimization configuration model is to minimize the low-carbon power grid total investment cost function and the worst carbon emission expectation target under the condition of meeting the risk measurement constraint; and the acquisition module is used for obtaining a target capacity configuration parameter of the target low-carbon power grid according to the capacity optimization configuration model, so as to plan new energy power generation equipment, transmission lines and energy storage equipment in the target low-carbon power grid based on the target capacity configuration parameter.

[0017] The application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the low-carbon power grid energy storage capacity optimization configuration method according to any one of the above-mentioned methods when executing the computer program.

[0018] The application further provides a non-transitory computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the low-carbon power grid energy storage capacity optimization configuration method.

[0019] The application further provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the low-carbon power grid energy storage capacity optimization configuration method.

[0020] The low-carbon power grid energy storage capacity optimization configuration method, device and electronic equipment provided by the application are based on operation constraints, a first operation optimization target and a second operation optimization target of a target low-carbon power grid are constructed, a first uncertainty fuzzy set corresponding to an uncertainty parameter of the first operation optimization target is constructed, the first operation optimization target is taken as a variable of an increasing convex function of a Shortfall risk based on the Shortfall risk and the first uncertainty fuzzy set, a distribution-robust risk measurement constraint is established, the second operation optimization target is taken as a variable of an increasing convex function of the Shortfall risk based on the Shortfall risk and a second uncertainty fuzzy set, a distribution-robust worst carbon emission expectation target is established, and a capacity optimization configuration model of a distribution-robust constraint is constructed based on the risk measurement constraint and the worst carbon emission expectation target and a total investment cost function of the low-carbon power grid. Since the load loss risk, carbon emission and investment cost are considered in the capacity optimization configuration model, the load loss risk is taken as a constraint condition that needs to be ensured first, and the carbon emission and the investment cost become two targets that need to be balanced, so that a planning scheme that enables the target low-carbon power grid to stably operate and efficiently develop can be finally obtained. Therefore, the new energy generation capacity can be scientifically and reasonably determined, and the expansion scale of a transmission line and the capacity configuration of an energy storage system can be simultaneously planned, so as to ensure the stable operation and efficient development of the low-carbon power grid. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0022] Figure 1 is a flowchart of the low-carbon power grid energy storage capacity optimization configuration method provided by the application.

[0023] Figure 2 is a flowchart of the method for obtaining the target capacity configuration parameters of the target low-carbon power grid according to the capacity optimization configuration model.

[0024] Figure 3It is a structural schematic view of the low-carbon power grid energy storage capacity optimization configuration device provided by the application.

[0025] Figure 4 It is a structural schematic view of the electronic device provided by the application. DETAILED DESCRIPTION

[0026] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0027] The low-carbon power grid energy storage capacity optimization configuration method of the present application will be described below. Figures 1-2 The low-carbon power grid energy storage capacity optimization configuration method of the present application will be described below.

[0028] Figure 1 It is a flowchart of the low-carbon power grid energy storage capacity optimization configuration method provided by the application, as shown in the figure, the method comprises the following: Figure 1

[0029] Step 101, based on the operation constraint condition, a first operation optimization target and a second operation optimization target of the target low-carbon power grid are constructed.

[0030] The first operation optimization target is to minimize the loss of load of the target low-carbon power grid under extreme weather conditions. The second operation optimization target is to minimize the carbon emission of the target low-carbon power grid under non-extreme weather conditions. Both the first operation optimization target and the second operation optimization target include uncertain parameters.

[0031] The target low-carbon power grid is a low-carbon power grid to be planned, which needs to determine the new energy generation capacity, the number of transmission line expansion and the energy storage capacity under the premise of given site selection.

[0032] The operation constraint condition is used to describe the operation characteristics and limitation conditions of different components in the target low-carbon power grid.

[0033] Only as an example, the operation constraint condition of the target low-carbon power grid can be constructed in the following way.

[0034] In the target low-carbon power grid, the node set is represented by , and the time period set is represented by . For the conventional fossil fuel generator at node , the output power at time satisfies the following constraint condition.

[0035] ​

[0036] (1a) represents the generation capability constraint; (1b) limits the incremental change of generator output in two consecutive time periods, referred to as ramping constraint, for the duration of each time instant; is the emission factor of the generator at node (1c) calculates the carbon emission .

[0037] is the renewable power plant installed capacity at node is the renewable energy generation uncertainty factor, which is uncertain. Then the renewable energy generation is , satisfying the following constraints

[0038]

[0039] The operation constraints of the energy storage include

[0040]

[0041] where is the power capacity of the energy storage at node , which depends on the capacity of the power electronic converter, (3a) limits the charging power and the discharging power , represents the stored energy at time , is the initial stored energy, (3b) is the dynamic model of the state of charge (SoC); represents the energy capacity of the energy storage, which depends on the capacity of the battery array, (3c) represents the feasible range of the SoC; constraint (3d) specifies that the final state and the initial state of the periodic operation are equal. In the optimal operation problem of minimizing the load loss or carbon emission, the constraint of prohibiting simultaneous charging and discharging can be omitted.

[0042] In the specific implementation process, the target low-carbon power grid can be modeled by using the direct current flow. is the branch set of the target low-carbon power grid. At time , the active power flow in a transmission line from node to node is denoted as , and the voltage angle at node is denoted as . Assuming that the line reactance is , then the direct current flow model between node and node is

[0043] ​​

[0044] where (4a) is the active power flow of the line, and (4b) is the capacity limit of the transmission power.

[0045] In the expansion of transmission lines, it is assumed that new transmission lines with the same parameters as the existing transmission lines can be built. It is assumed that the number of existing transmission lines from node to node and the number of new transmission lines are and respectively, then the active power from node to node is , which means that the power flow of each line is equal. This is a natural result because each line has the same parameters.

[0046] Assuming that the new energy at node allows for power curtailment at time , it must be non-negative, as follows

[0047]

[0048] Let the load demand at node at time be , and the amount of curtailed load must be non-negative and not greater than the demand, i.e.

[0049]

[0050] The node power balance equation is as follows

[0051]

[0052] Since and are variables, the constraint (7) is nonlinear, which will be linearized in the following steps. In some countries, distributed renewable energy generation is the main source of supply system demand. Although the present invention does not explicitly consider distributed generation, it can be incorporated into the total demand in the proposed model. The uncertainty of parameters and will be handled together in the subsequent steps.

[0053] ​One of the primary requirements for power system operation is to meet the load demand. Under extreme weather conditions, the target low-carbon power grid dominated by renewable energy sources can inevitably experience load loss. Therefore, the first operation optimization objective is to minimize the amount of load loss of the target low-carbon power grid under extreme weather conditions. By way of example only, the establishment process of the first operation optimization objective is shown as follows.

[0054] Since extreme weather conditions can not last for a long time, their impact on carbon emissions can be negligible. The first operation optimization objective under extreme weather conditions is shown as follows.

[0055]

[0056] where the capacity planning strategy and the uncertain parameters are fixed, the decision variables include , , , , , , , , . The first operation optimization objective is to minimize the amount of load loss within a certain period of time. The first operation optimization objective belongs to linear programming (LP), and its optimal value depends on and , and is therefore expressed as a function , which represents the minimum load loss under given and .

[0057] The second operation optimization objective is to minimize the amount of carbon emissions of the target low-carbon power grid under non-extreme weather conditions.

[0058] Under normal weather conditions, the target low-carbon power grid is not allowed to experience load loss, and the goal of the target low-carbon power grid operation is to minimize carbon emissions, as follows.

[0059]

[0060] Similar to the first operation optimization objective, the optimal value of the minimum carbon emissions as the second operation optimization objective is still a function and of . Since the operating cost of renewable energy generation is usually very low, the operating cost of the low-carbon power system dominated by renewable energy sources is not considered.

[0061] ​Since both the first operation optimization objective and the second operation optimization objective include uncertain parameters: the uncertain factors of renewable energy generation of each node of the target low-carbon power grid at different time points and load demand .

[0062] Step 102, constructing a first uncertainty fuzzy set corresponding to the uncertain parameters of the first operation optimization objective, and constructing a second uncertainty fuzzy set corresponding to the uncertain parameters of the second operation optimization objective.

[0063] In the specific implementation process, the first uncertainty fuzzy set can be constructed by the following method: obtaining a first data sample index set; wherein the first data sample index set includes the values of the uncertain parameters under the historical extreme weather conditions; constructing the first uncertainty fuzzy set according to the first sample index set; wherein the distance between the empirical probability distribution of the first uncertainty fuzzy set and the empirical probability distribution of the first sample index set is less than a first preset threshold. Only as an example, the above construction method is as follows.

[0064] For two parameter distributions and defined on , their 1-norm Wasserstein-metric is defined as

[0065]

[0066] is a joint distribution with marginal and , where denotes the 1-norm.

[0067] Let the first data sample index set be. For any , denotes the realization of the uncertain parameter (including the uncertain factors of renewable energy generation and load demand ) of one of the extreme weather conditions. According to the data samples in the first data sample index set, the empirical probability distribution of the extreme weather condition is established as follows.

[0068]

[0069] where is the number of elements of , denotes the index function, which is 1 when the uncertain distribution is . Therefore, in In each data sample Assigned with probability .

[0070] The first uncertainty fuzzy set Contains close to empirical distribution More precisely, and empirical probability distribution The distance between them is not greater than the first preset threshold , the first uncertainty fuzzy set is defined as follows.

[0071] (12)

[0072] For the definition The distribution set on .

[0073]

[0074] For nodes The upper bound of the load demand at . Including extreme weather conditions All possible realizations of . Radius The choice should depend on the amount of data and the risk appetite of the decision maker.

[0075] In a specific implementation, the second uncertainty fuzzy set can be constructed by: obtaining a second data sample indicator set; wherein the second data sample indicator set includes values ​​of uncertainty parameters under historical non-extreme weather conditions; and constructing a second uncertainty fuzzy set based on the second sample indicator set; wherein the distance between the empirical probability distribution of the second uncertainty fuzzy set and the empirical probability distribution of the second sample indicator set is less than a second preset threshold. The process of constructing the second uncertainty fuzzy set is similar to the process of constructing the first uncertainty fuzzy set.

[0076] In the specific implementation process, the feasible set of uncertainty parameters under historical non-extreme weather conditions is defined as :

[0077]

[0078] The feasible set is the sample data The convex hull of is obtained in order to restrict it to normal conditions.

[0079] Step 103: Based on the Shortfall risk and the first uncertainty fuzzy set, the first operation optimization objective is used as a variable of the increasing convex function of the Shortfall risk to establish a distributionally robust risk measurement constraint.

[0080] Shortfall risk is a risk measure with convexity property with two parameters, where the constant represents the maximum acceptable loss, and the function is an increasing convex function of the value function, which is used to reflect the degree of loss damage. For a specific distribution with a random variable associated with the loss, the Shortfall risk with parameters λ and l is defined as

[0081]

[0082] where represents the expectation operation with distribution .

[0083] Since the Shortfall risk depends on the distribution, and the loss risk must be small enough for all possible distributions in the first uncertainty fuzzy set, the distribution-robust risk measure constraint can be expressed as

[0084]

[0085] where is the acceptable loss under extreme weather conditions, is the maximum loss risk, and the function is chosen as the convex function to measure the loss when the loss occurs . The risk measure constraint specifies the upper bound of the worst-case risk under extreme weather conditions.

[0086] Step 104, based on the Shortfall risk and the second uncertainty fuzzy set, the second operation optimization target is established as a variable of the increasing convex function of the Shortfall risk, and the distribution-robust worst-case carbon emission expectation target is established.

[0087] The process of establishing the distribution-robust worst-case carbon emission expectation target is described in step 103.

[0088] The distribution-robust worst-case carbon emission expectation target is shown as follows.

[0089]

[0090] Step 105, based on the risk measure constraint and the worst-case carbon emission expectation target, and the total investment cost function of the low-carbon power grid, a capacity optimization configuration model with distribution-robust constraints is constructed.

[0091] The capacity optimization configuration model minimizes the total investment cost function of the low-carbon power grid and the worst-case carbon emission expectation target under the condition of meeting the risk measure constraint.

[0092] The total investment cost function of the low-carbon power grid is as follows:

[0093]

[0094] wherein, , , and are cost coefficients.

[0095] In the total investment cost function of the low-carbon power grid, the capacity variable has a domain as follows:

[0096]

[0097] Considering the natural resource conditions and the availability of construction sites, the capacity of each variable must be bounded, and therefore, , , and are the upper bounds of the variables. A capacity optimization configuration model with distributional robustness constraints is constructed as follows.

[0098]

[0099] The capacity optimization configuration model compromises the total investment cost of the target low-carbon power grid and the worst-case carbon emission expectation under normal conditions, and is subject to risk measurement constraints under extreme weather conditions. After all facilities of the target low-carbon power grid are built, the investment cost is fixed, and therefore the first objective is deterministic. The worst-case carbon emission expectation objective and the risk measurement constraint are affected by the uncertainty of renewable energy generation and demand. Therefore, the capacity optimization configuration model is a bi-objective distributional robust mixed-integer nonlinear programming, which cannot be directly solved. For specific solving methods, refer to the related content in Figure 2 , which will not be repeated here.

[0100] Step 106, obtaining target capacity configuration parameters of the target low-carbon power grid according to the capacity optimization configuration model, to plan new energy power generation equipment, transmission lines and energy storage equipment in the target low-carbon power grid based on the target capacity configuration parameters.

[0101] For detailed description of obtaining target capacity configuration parameters of the target low-carbon power grid according to the capacity optimization configuration model, refer to the related content in Figure 2 , which will not be repeated here.

[0102] Figure 2 is a flowchart of the method for obtaining target capacity configuration parameters of the target low-carbon power grid according to the capacity optimization configuration model provided by the present application, as shown in Figure 2 , which includes the following:

[0103] Step 201, using Boolean auxiliary variables and the big M method, the nonlinear product term is represented, and the linearized capacity optimization configuration model is obtained.

[0104] The only nonlinear part in the capacity optimization configuration model comes from the product term in the operation constraint formula (7) . Since and , this term can be re-expressed using Boolean auxiliary variables and the big M method in the following way.

[0105]

[0106] Then, the expression

[0107]

[0108] becomes linear with respect to . The nonlinear relationship is equivalent to the following linear constraints

[0109]

[0110] where is a large enough constant. If , then according to the second constraint , and the first constraint does not work; if , then according to the first constraint , and the second constraint does not work.

[0111] In summary, after adding (21) and (22) to the constraints, the nonlinear term in formula (7) can be equivalently replaced by , thus obtaining the linearized capacity optimization configuration model.

[0112] The target capacity configuration parameters of the target low-carbon power grid include the new energy power generation capacity of each node in the target low-carbon power grid, the number of transmission line expansions, and the energy storage capacity.

[0113] In the following steps, according to the linearized capacity optimization configuration model, the target capacity configuration parameters of the target low-carbon power grid are obtained.

[0114] Step 202, by regarding the increasing convex function of the Shortfall risk in the upper bound expression of the worst-case expectation of the risk measurement constraint as a function of the uncertainty parameter, a first Lipschitz constant is introduced in the risk measurement constraint, and a quantized risk measurement constraint is obtained.

[0115] Step 203, transform the quantized risk measure constraint into a first mixed integer linear programming equation using sample average approximation algorithm.

[0116] Step 204, introduce a second Lipschitz constant in the worst-case carbon emission expectation target by treating the increasing convex function of Shortfall risk in the upper bound expression of the worst-case carbon emission expectation target as a function of the uncertainty parameter, obtaining the quantized worst-case carbon emission expectation target.

[0117] Step 205, transform the quantized worst-case carbon emission expectation target into a second mixed integer linear programming equation using sample average approximation algorithm.

[0118] In the implementation process, steps 202-205 can be implemented using the following example process.

[0119] Considering distributionally robust risk measure constraints

[0120]

[0121] Therefore, the worst-case expectation in constraint (23) should be calculated. The upper bound of the worst-case expectation is

[0122]

[0123] Since the function , is , the Lipschitz constant when

[0124]

[0125] By formula (24), the worst-case expectation over the fuzzy set, which is an infinite-dimensional optimization problem, is simplified to the expectation under the empirical probability distribution on the right side.

[0126] Using sample average approximation, the distributionally robust risk measure constraint (16) can be conservatively approximated by the following constraint.

[0127]

[0128] where is an auxiliary variable representing . Then by defining , (26) can be equivalently transformed into a linear constraint, obtaining the first mixed integer linear programming equation as shown below.

[0129]

[0130] By the same argument, the worst-case carbon emission expectation target (17) satisfies

[0131]

[0132] where L is the Lipschitz constant has the property that

[0133]

[0134] Then, the worst-case carbon emission expectation target (17) can be conservatively approximated by the second mixed-integer linear programming equation shown below

[0135]

[0136] Step 206, obtaining the target capacity configuration parameter of the target low-carbon power grid based on the first mixed-integer linear programming equation and the second mixed-integer linear programming equation.

[0137] Before solving the capacity optimization configuration model, the value of the first Lipschitz constant and the value of the second Lipschitz constant need to be determined.

[0138] In some embodiments, the value of the first Lipschitz constant can be obtained by solving the first mixed-integer linear programming equation using a maximization dual variable algorithm, and the value of the second Lipschitz constant can be obtained by solving the second mixed-integer linear programming equation using a maximization dual variable algorithm. As an example, the specific solving process is shown below.

[0139] In formula (8), the definition of can be written in a compact form as shown below

[0140] (31)

[0141] where the term represents . A new auxiliary variable is defined as can be written as a linear function of , that is, . Since is bounded for any and , according to the strong duality of LP, there are the following constraints

[0142]

[0143] and the KKT optimality condition is

[0144]

[0145] set up is a matrix In the Rank The elements of the column, similarly, is a vector No. elements. Use the domain , and , and the big-M method, the first mixed integer linear programming equation for finding the maximum dual variable is as follows.

[0146]

[0147] By interpreting the dual variables,

[0148]

[0149] By solving (34), the optimal value is Relative to No. The maximum sensitivity of the component is called .because

[0150]

[0151] about The sensitivity can be used

[0152]

[0153] make

[0154]

[0155] Then

[0156]

[0157] Notice, is linear, so (37) can be obtained by solving the first mixed integer linear programming equation. , in formula (37) It also satisfies equation (25), which is the value of the first Lipschitz constant.

[0158] Normally, if is fixed, according to the properties of Lipschitz continuity and local Lipschitz continuity

[44] , the point The maximum local Lipschitz constant is in The Lipschitz constant on . Therefore, for every point , solve

[0159]

[0160] and find the dual optimal solution . Then let

[0161]

[0162] and get the value of the second Lipschitz constant.

[0163] In some embodiments, the value of the first Lipschitz constant can be substituted into the first mixed integer linear programming equation to obtain a third mixed integer linear programming equation; the value of the second Lipschitz constant can be substituted into the second mixed integer linear programming equation to obtain a fourth mixed integer linear programming equation; and the target capacity configuration parameters of the target low-carbon power grid are obtained by solving the third mixed integer linear programming equation and the fourth mixed integer linear programming equation.

[0164] In the specific implementation process, the e-constraint method can be used to solve the third mixed integer linear programming equation and the fourth mixed integer linear programming equation within the investment cost budget range to obtain the Pareto frontier of the target capacity configuration parameters.

[0165] For example only, the process of obtaining the Pareto frontier of the target capacity configuration parameters is as follows.

[0166] For a given total investment cost budget F, the capacity optimization configuration model is as follows, which includes a risk measurement constraint represented by the third mixed integer linear programming equation and a worst carbon emission expectation target represented by the fourth mixed integer linear programming equation:

[0167]

[0168] The optimal solution of the capacity optimization configuration model is , and the worst carbon emission expectation target is represented as . The point pair is on the Pareto frontier of the capacity optimization configuration problem of problem (20) (the capacity optimization configuration model shown in formula (20)). Different solutions of problem (41) (the capacity optimization configuration model shown in formula (41)) are obtained to obtain a set of Pareto points. The problem (20) solving algorithm is summarized as follows.

[0169] Input: parameters of problem (20), investment cost budget range , and step parameter

[0170] Output: Pareto solution and points on the Pareto frontier .

[0171] Step 1: Initialization, solve MILP (34) (the first mixed integer linear programming equation), and get from (37) (the first Lipschitz constant). Let .

[0172] Step 2: Solve MILP (41) (the second mixed integer linear programming equation), get; fix , solve (39) and (40) to get (the second Lipschitz constant), update the optimal value of (41) and get

[0173] Step 3: If , terminate. Otherwise, update , go to Step 2.

[0174] The embodiments provided by the application successfully handle the two conflicting goals of carbon emissions and investment costs by using the epsilon-constraint method and dictionary optimization technology, and obtain a one-dimensional Pareto boundary, which provides a clear optimization path for decision makers. With the Pareto boundary, decision makers can imagine how much one goal will be destroyed if another goal is improved. Then, they can compromise between the two goals according to their specific preferences. If the decision maker has no special preference, when the Pareto boundary exists, the Nash negotiation criterion can make a fair trade-off between multiple goals.

[0175] The low-carbon power grid energy storage capacity optimization configuration device provided by the application is described below, and the low-carbon power grid energy storage capacity optimization configuration device described below can be correspondingly referred to the low-carbon power grid energy storage capacity optimization configuration method described above.

[0176] Figure 3 is a structural schematic diagram of the low-carbon power grid energy storage capacity optimization configuration device provided by the application. As shown in Figure 3 , the device 300 includes the following modules.

[0177] The first construction module 310 is configured to construct a first operation optimization target and a second operation optimization target of a target low-carbon power grid based on operation constraint conditions; wherein the first operation optimization target is to minimize the loss of load of the target low-carbon power grid under extreme weather conditions, and the second operation optimization target is to minimize the carbon emission of the target low-carbon power grid under non-extreme weather conditions, and the first operation optimization target and the second operation optimization target both include uncertain parameters.

[0178] The second construction module 320 is configured to construct a first uncertainty fuzzy set corresponding to the uncertainty parameter of the first operation optimization target, and construct a second uncertainty fuzzy set corresponding to the uncertainty parameter of the second operation optimization target.

[0179] The third construction module 330 is configured to, based on the shortfall risk and the first uncertainty fuzzy set, establish a distribution-robust risk measurement constraint by taking the first operation optimization target as a variable of an increasing convex function of the shortfall risk.

[0180] The fourth construction module 340 is configured to, based on the shortfall risk and the second uncertainty fuzzy set, establish a distribution-robust worst-case carbon emission expectation target by taking the second operation optimization target as a variable of an increasing convex function of the shortfall risk.

[0181] The fifth construction module 350 is configured to construct a distribution-robust constraint capacity optimization configuration model based on the risk measurement constraint, the worst-case carbon emission expectation target, and a total investment cost function of the low-carbon power grid, wherein the capacity optimization configuration model is configured to minimize the total investment cost function of the low-carbon power grid and the worst-case carbon emission expectation target under the condition of meeting the risk measurement constraint.

[0182] The acquisition module 360 is configured to obtain target capacity configuration parameters of the target low-carbon power grid according to the capacity optimization configuration model, and plan new energy power generation equipment, transmission lines and energy storage equipment in the target low-carbon power grid based on the target capacity configuration parameters.

[0183] Figure 4 An example of an entity structure diagram of an electronic device is shown in FIG. 1. Figure 4As shown, the electronic device can include a processor 410, a communications interface 420, a memory 430, and a communications bus 440, wherein the processor 410, the communications interface 420, and the memory 430 complete communication with each other through the communications bus 440. The processor 410 can invoke a logic instruction in the memory 430 to execute a low-carbon power grid energy storage capacity optimization configuration method, which includes: based on the operation constraint condition, constructing a first operation optimization target and a second operation optimization target of a target low-carbon power grid; wherein the first operation optimization target is to minimize the loss of load of the target low-carbon power grid under extreme weather conditions, and the second operation optimization target is to minimize the carbon emission of the target low-carbon power grid under non-extreme weather conditions, and the first operation optimization target and the second operation optimization target both include uncertainty parameters; constructing a first uncertainty fuzzy set corresponding to the uncertainty parameters of the first operation optimization target, and constructing a second uncertainty fuzzy set corresponding to the uncertainty parameters of the second operation optimization target; based on the Shortfall risk and the first uncertainty fuzzy set, taking the first operation optimization target as a variable of an increasing convex function of the Shortfall risk, establishing a distribution-robust risk measurement constraint; based on the Shortfall risk and the second uncertainty fuzzy set, taking the second operation optimization target as a variable of an increasing convex function of the Shortfall risk, establishing a distribution-robust worst-case carbon emission expectation target; based on the risk measurement constraint and the worst-case carbon emission expectation target, and a low-carbon power grid total investment cost function, constructing a capacity optimization configuration model with distribution-robust constraints; wherein the capacity optimization configuration model is to minimize the low-carbon power grid total investment cost function and the worst-case carbon emission expectation target under the condition of meeting the risk measurement constraint; obtaining a target capacity configuration parameter of the target low-carbon power grid according to the capacity optimization configuration model, to plan new energy power generation equipment, transmission lines, and energy storage equipment in the target low-carbon power grid based on the target capacity configuration parameter.

[0184] In addition, the logic instructions in the memory 430 described above can be implemented in the form of a software function unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0185] In another aspect, the present application also provides a computer program product, the computer program product comprising a computer program, the computer program being stored in a non-transitory computer readable storage medium, and the computer program being executable by a processor, so that a computer can execute a low-carbon power grid energy storage capacity optimization configuration method provided by the above-mentioned method, the method comprising: based on the operation constraint condition, constructing a first operation optimization target and a second operation optimization target of a target low-carbon power grid; wherein the first operation optimization target is to minimize the loss of load of the target low-carbon power grid under extreme weather conditions, and the second operation optimization target is to minimize the carbon emission of the target low-carbon power grid under non-extreme weather conditions, and the first operation optimization target and the second operation optimization target both include uncertainty parameters; constructing a first uncertainty fuzzy set corresponding to the uncertainty parameters of the first operation optimization target, and constructing a second uncertainty fuzzy set corresponding to the uncertainty parameters of the second operation optimization target; based on the Shortfall risk and the first uncertainty fuzzy set, taking the first operation optimization target as a variable of an increasing convex function of the Shortfall risk, establishing a distribution-robust risk measurement constraint; based on the Shortfall risk and the second uncertainty fuzzy set, taking the second operation optimization target as a variable of an increasing convex function of the Shortfall risk, establishing a distribution-robust worst carbon emission expectation target; based on the risk measurement constraint and the worst carbon emission expectation target, and a low-carbon power grid total investment cost function, constructing a capacity optimization configuration model with distribution-robust constraints; wherein the capacity optimization configuration model is to minimize the low-carbon power grid total investment cost function and the worst carbon emission expectation target under the condition of meeting the risk measurement constraint; obtaining a target capacity configuration parameter of the target low-carbon power grid according to the capacity optimization configuration model, so as to plan new energy power generation equipment, transmission lines and energy storage equipment in the target low-carbon power grid based on the target capacity configuration parameter.

[0186] In another aspect, the present application also provides a non-transitory computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements a low-carbon power grid energy storage capacity optimization configuration method provided by each of the above methods. The method comprises: based on operation constraints, constructing a first operation optimization target and a second operation optimization target of a target low-carbon power grid; wherein the first operation optimization target is to minimize the loss of load of the target low-carbon power grid under extreme weather conditions, and the second operation optimization target is to minimize the carbon emission of the target low-carbon power grid under non-extreme weather conditions, and the first operation optimization target and the second operation optimization target both include uncertainty parameters; constructing a first uncertainty fuzzy set corresponding to the uncertainty parameters of the first operation optimization target, and constructing a second uncertainty fuzzy set corresponding to the uncertainty parameters of the second operation optimization target; based on the Shortfall risk and the first uncertainty fuzzy set, taking the first operation optimization target as a variable of an increasing convex function of the Shortfall risk, establishing a distribution-robust risk measurement constraint; based on the Shortfall risk and the second uncertainty fuzzy set, taking the second operation optimization target as a variable of an increasing convex function of the Shortfall risk, establishing a distribution-robust worst-case carbon emission expectation target; based on the risk measurement constraint and the worst-case carbon emission expectation target, and a low-carbon power grid total investment cost function, constructing a distribution-robust constraint capacity optimization configuration model; wherein the capacity optimization configuration model is to minimize the low-carbon power grid total investment cost function and the worst-case carbon emission expectation target under the condition of meeting the risk measurement constraint; obtaining a target capacity configuration parameter of the target low-carbon power grid according to the capacity optimization configuration model, so as to plan new energy power generation equipment, transmission lines and energy storage equipment in the target low-carbon power grid based on the target capacity configuration parameter.

[0187] The device embodiments described above are only schematic, wherein the units shown as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment scheme. Those skilled in the art can understand and implement without creative labor.

[0188] Those skilled in the art can clearly understand the technical solutions of the various embodiments from the above description of the embodiments, and the various embodiments can be implemented by means of software with the necessary general hardware platforms, and of course, can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that makes a contribution, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0189] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing energy storage capacity configuration in a low-carbon power grid, characterized in that: include: Based on the operational constraints, a first operational optimization objective and a second operational optimization objective of the target low-carbon power grid are constructed; wherein the first operational optimization objective is to minimize the load loss of the target low-carbon power grid under extreme weather conditions, and the second operational optimization objective is to minimize the carbon emissions of the target low-carbon power grid under non-extreme weather conditions, and both the first operational optimization objective and the second operational optimization objective include uncertainty parameters; Constructing a first uncertainty fuzzy set corresponding to the uncertainty parameters of the first operation optimization objective, and constructing a second uncertainty fuzzy set corresponding to the uncertainty parameters of the second operation optimization objective; Based on the Shortfall risk and the first uncertainty fuzzy set, the first operation optimization objective is used as a variable of the increasing convex function of the Shortfall risk to establish a risk measurement constraint of distributional robustness; Based on the Shortfall risk and the second uncertainty fuzzy set, the second operation optimization objective is used as a variable of the increasing convex function of the Shortfall risk to establish a worst-case carbon emission target with distributed robustness; Based on the risk measurement constraint, the worst-case carbon emission target, and the total investment cost function of the low-carbon power grid, a capacity optimization configuration model with distributed robustness constraints is constructed; wherein the capacity optimization configuration model is to minimize the total investment cost function of the low-carbon power grid and the worst-case carbon emission target while satisfying the risk measurement constraint; According to the capacity optimization configuration model, target capacity configuration parameters of the target low-carbon power grid are obtained, so as to plan the new energy power generation equipment, transmission lines and energy storage equipment in the target low-carbon power grid based on the target capacity configuration parameters.

2. The method for optimizing energy storage capacity configuration in a low-carbon power grid according to claim 1, characterized in that: The step of constructing a first uncertainty fuzzy set corresponding to the uncertainty parameter of the first operation optimization objective includes: Obtaining a first data sample indicator set; wherein the first data sample indicator set includes values ​​of the uncertainty parameters under historical extreme weather conditions; Constructing the first uncertainty fuzzy set based on the first data sample indicator set; wherein the distance between the empirical probability distribution of the first uncertainty fuzzy set and the empirical probability distribution of the first data sample indicator set is less than a first preset threshold; The step of constructing a second uncertainty fuzzy set corresponding to the uncertainty parameter of the second operation optimization objective includes: Obtaining a second data sample indicator set; wherein the second data sample indicator set includes values ​​of the uncertainty parameter under historical non-extreme weather conditions; Based on the second data sample indicator set, the second uncertainty fuzzy set is constructed; wherein the distance between the empirical probability distribution of the second uncertainty fuzzy set and the empirical probability distribution of the second data sample indicator set is less than a second preset threshold.

3. The method for optimizing energy storage capacity configuration in a low-carbon power grid according to claim 2, characterized in that: The operation constraint condition includes a nonlinear product term, and obtaining the target capacity configuration parameters of the target low-carbon power grid according to the capacity optimization configuration model includes: The nonlinear product term is expressed by using Boolean auxiliary variables and the big-M method to obtain the linearized capacity optimization configuration model; According to the linearized capacity optimization configuration model, target capacity configuration parameters of the target low-carbon power grid are obtained.

4. The method for optimizing energy storage capacity configuration in a low-carbon power grid according to claim 3, characterized in that: Obtaining target capacity configuration parameters of the target low-carbon power grid according to the linearized capacity optimization configuration model includes: By considering an increasing convex function of the Shortfall risk in the upper bound expression of the worst-case expectation of the risk metric constraint as a function of the uncertainty parameter, a first Lipschitz constant is introduced into the risk metric constraint to obtain a quantized risk metric constraint; Using a sample average approximation algorithm, converting the quantified risk metric constraint into a first mixed integer linear programming equation; By considering the increasing convex function of the Shortfall risk in the upper bound expression of the worst-case expectation of the worst-case carbon emission target as a function of the uncertainty parameter, a second Lipschitz constant is introduced into the worst-case carbon emission target to obtain a quantified worst-case carbon emission target; Using a sample average approximation algorithm, the quantified worst-case carbon emission target is converted into a second mixed integer linear programming equation; Based on the first mixed integer linear programming equation and the second mixed integer linear programming equation, target capacity configuration parameters of the target low-carbon power grid are obtained.

5. The method for optimizing energy storage capacity configuration in a low-carbon power grid according to claim 4, characterized in that: The obtaining of target capacity configuration parameters of the target low-carbon power grid based on the first mixed integer linear programming equation and the second mixed integer linear programming equation includes: solving the first mixed integer linear programming equation using a maximization dual variable algorithm to obtain a value of the first Lipschitz constant; solving the second mixed integer linear programming equation using a maximization dual variable algorithm to obtain a value of the second Lipschitz constant; Substituting the value of the first Lipschitz constant into the first mixed integer linear programming equation to obtain a third mixed integer linear programming equation; Substituting the value of the second Lipschitz constant into the second mixed integer linear programming equation to obtain a fourth mixed integer linear programming equation; The third mixed integer linear programming equation and the fourth mixed integer linear programming equation are solved to obtain target capacity configuration parameters of the target low-carbon power grid.

6. The method for optimizing energy storage capacity configuration in a low-carbon power grid according to claim 5, characterized in that: Solving the third mixed integer linear programming equation and the fourth mixed integer linear programming equation to obtain target capacity configuration parameters of the target low-carbon power grid includes: The third mixed integer linear programming equation and the fourth mixed integer linear programming equation are solved within the investment cost budget using the 𝜀-constraint method to obtain the Pareto front of the target capacity configuration parameters.

7. A device for optimizing energy storage capacity configuration in a low-carbon power grid, characterized in that: include: A first construction module is configured to construct a first operation optimization objective and a second operation optimization objective of a target low-carbon power grid based on operation constraints; wherein the first operation optimization objective is to minimize the load loss of the target low-carbon power grid under extreme weather conditions, and the second operation optimization objective is to minimize the carbon emissions of the target low-carbon power grid under non-extreme weather conditions, and both the first operation optimization objective and the second operation optimization objective include uncertainty parameters; A second construction module is configured to construct a first uncertainty fuzzy set corresponding to the uncertainty parameter of the first operation optimization objective, and to construct a second uncertainty fuzzy set corresponding to the uncertainty parameter of the second operation optimization objective; A third building module is configured to establish a risk measurement constraint of distributional robustness based on the Shortfall risk and the first uncertainty fuzzy set, taking the first operation optimization objective as a variable of an increasing convex function of the Shortfall risk; A fourth building module is configured to establish a worst-case carbon emission target with distributed robustness based on the Shortfall risk and the second uncertainty fuzzy set, using the second operation optimization objective as a variable of an increasing convex function of the Shortfall risk; A fifth construction module is configured to construct a capacity optimization configuration model with distributed robustness constraints based on the risk measurement constraint, the worst-case carbon emission target, and the total investment cost function of the low-carbon power grid; wherein the capacity optimization configuration model is configured to minimize the total investment cost function of the low-carbon power grid and the worst-case carbon emission target while satisfying the risk measurement constraint; An acquisition module is used to obtain target capacity configuration parameters of the target low-carbon power grid according to the capacity optimization configuration model, so as to plan the new energy power generation equipment, transmission lines and energy storage equipment in the target low-carbon power grid based on the target capacity configuration parameters.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for optimizing energy storage capacity configuration in a low-carbon power grid as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing energy storage capacity configuration in a low-carbon power grid as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for optimizing energy storage capacity configuration in a low-carbon power grid as claimed in any one of claims 1 to 6 is implemented.

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