Energy storage capacity optimal configuration method and device in low-carbon power grid and electronic equipment

Abstract

The invention provides an energy storage capacity optimal configuration method and device in a low-carbon power grid and electronic equipment, and relates to the technical field of electric power. The energy storage capacity optimal configuration method in the low-carbon power grid comprises the following steps: constructing a first operation optimization target and a second operation optimization target of a target low-carbon power grid based on operation constraint conditions; 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 risk measurement constraint, the worst carbon emission expected target and the total investment cost function of the low-carbon power grid, constructing a capacity optimization configuration model of the distribution robust constraint; and obtaining a target capacity configuration parameter of the target low-carbon power grid according to the capacity optimization configuration model. According to the method, the new energy power generation capacity can be scientifically and reasonably determined, and the extension scale of the transmission line and the capacity configuration of the energy storage system are synchronously planned.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method, device and electronic equipment for optimizing configuration of energy storage capacity in a low-carbon power grid. Background Art

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

[0003] In order to cope with the instability of renewable energy generation, the low-carbon power grid needs to increase the flexibility of dispatchable units and introduce energy storage systems to smooth power fluctuations and maintain supply and demand balance. Pumped storage is a mature and widely used energy storage method, but its application is limited by geographical conditions; and although electrochemical energy storage has attracted much attention in recent years, cost issues are still an obstacle to its large-scale promotion. Therefore, seeking a reasonable balance between investment costs, carbon emission reduction and system reliability has become an important issue in the optimal configuration of low-carbon power grid capacity.

[0004] Therefore, how to scientifically and reasonably determine the capacity of renewable energy power generation, 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 low-carbon power grids, is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In view of the above problems existing in the prior art, the present invention provides a method, device and electronic equipment for optimizing the configuration of energy storage capacity in a low-carbon power grid, so as to scientifically and rationally determine the capacity of new energy power generation, and simultaneously plan the expansion scale of the transmission line and the capacity configuration of the energy storage system, so as to ensure the stable operation and efficient development of the low-carbon power grid.

[0006] The present invention 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 constraints, the first operation optimization target and the second operation optimization target of the target low-carbon power grid are constructed; wherein the first operation optimization target is to minimize the load loss of the target low-carbon power grid under extreme weather conditions, and the second operation optimization target is to minimize the carbon emissions of the target low-carbon power grid under non-extreme weather conditions, and both the first operation optimization target and the second operation optimization target include uncertainty parameters; 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 the Shortfall risk and the first uncertainty fuzzy set, the first operation optimization target is used as a variable of an increasing convex function of the Shortfall risk, and a distributed robustness is established. Risk measurement constraint; based on Shortfall risk and the second uncertainty fuzzy set, the second operation optimization target is used as the variable of the increasing convex function of Shortfall risk to establish a distributed robust worst-case carbon emission target; based on the risk measurement constraint and the worst-case carbon emission target, as well as the total investment cost function of the low-carbon power grid, a distributed robust capacity optimization configuration model 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, the 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.

[0008] According to a method for optimizing energy storage capacity configuration in a low-carbon power grid provided by the present invention, the first uncertainty fuzzy set corresponding to the uncertainty parameter of the first operation optimization objective is constructed, including: Acquire a first data sample indicator set; wherein the first data sample indicator set includes the values ​​of the uncertainty parameters under historical extreme weather conditions; construct the first uncertainty fuzzy set based on the first 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 sample indicator set is less than a first preset threshold; constructing a second uncertainty fuzzy set corresponding to the uncertainty parameter of the second operation optimization objective, including: acquiring a second data sample indicator set; wherein the second data sample indicator set includes the values ​​of the uncertainty parameters under historical non-extreme weather conditions; constructing the 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.

[0009] According to a method for optimizing energy storage capacity configuration in a low-carbon power grid provided by the present invention, the operation constraint condition includes 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, including: The nonlinear product term is described by using Boolean auxiliary variables and the big-M method to obtain the linearized capacity optimization configuration model; based on the linearized capacity optimization configuration model, the target capacity configuration parameters of the target low-carbon power grid are obtained.

[0010] According to a method for optimizing energy storage capacity configuration in a low-carbon power grid provided by the present invention, the target capacity configuration parameters of the target low-carbon power grid are obtained according to the linearized capacity optimization configuration model, including: by considering 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, introducing a first Lipschitz constant in the risk measurement constraint, and obtaining a quantized risk measurement constraint; using a sample average approximation algorithm, converting the quantized risk measurement 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 carbon emission expected target as a function of the uncertainty parameter, introducing a second Lipschitz constant in the worst carbon emission expected target, and obtaining a quantized worst carbon emission expected target; using a sample average approximation algorithm, converting the quantized worst carbon emission expected target 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, the target capacity configuration parameters of the target low-carbon power grid are obtained.

[0011] According to a method for optimizing configuration of energy storage capacity in a low-carbon power grid provided by the present invention, the target capacity configuration parameters of the target low-carbon power grid are obtained based on the first mixed integer linear programming equation and the second mixed integer linear programming equation, including: using a maximization dual variable algorithm to solve the first mixed integer linear programming equation to obtain the value of the first Lipschitz constant; using a maximization dual variable algorithm to solve the second mixed integer linear programming equation to obtain the 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; solving the third mixed integer linear programming equation and the fourth mixed integer linear programming equation to obtain the target capacity configuration parameters of the target low-carbon power grid.

[0012] According to a method for optimizing the configuration of energy storage capacity in a low-carbon power grid provided by the present invention, the solving the third mixed integer linear programming equation and the fourth mixed integer linear programming equation to obtain the target capacity configuration parameters of the target low-carbon power grid includes: using the 𝜀-constraint method to solve the third mixed integer linear programming equation and the fourth mixed integer linear programming equation within the investment cost budget to obtain the Pareto front of the target capacity configuration parameters.

[0013] The present invention also provides a device for optimizing energy storage capacity configuration in a low-carbon power grid, comprising the following modules: The first construction module is used to construct the first operation optimization target and the second operation optimization target of the target low-carbon power grid based on the operation constraints; wherein the first operation optimization target is to minimize the load loss of the target low-carbon power grid under extreme weather conditions, and the second operation optimization target is to minimize the carbon emissions of the target low-carbon power grid under non-extreme weather conditions, and both the first operation optimization target and the second operation optimization target include uncertainty parameters; the second construction module is used to construct a first uncertainty fuzzy set corresponding to the uncertainty parameters of the first operation optimization target, and to construct a second uncertainty fuzzy set corresponding to the uncertainty parameters of the second operation optimization target; the third construction module is used to establish a distributed robustness 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. risk measurement constraint; a fourth construction module, used to establish a distributed robust worst-case carbon emission expected target based on the Shortfall risk and the second uncertainty fuzzy set, taking the second operation optimization target as the variable of the increasing convex function of the Shortfall risk; a fifth construction module, used to construct a capacity optimization configuration model with distributed robustness constraints based on the risk measurement constraints and the worst-case carbon emission expected target, as well as the total investment cost function of the low-carbon power grid; wherein the capacity optimization configuration model minimizes the total investment cost function of the low-carbon power grid and the worst-case carbon emission expected target while satisfying the risk measurement constraints; an acquisition module, used to obtain the 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.

[0014] The present invention also provides 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 configuration of energy storage capacity in a low-carbon power grid as described above is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for optimizing configuration of energy storage capacity in a low-carbon power grid as described in any one of the above is implemented.

[0016] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for optimizing configuration of energy storage capacity in a low-carbon power grid as described in any one of the above is implemented.

[0017] The method, device and electronic device for optimizing energy storage capacity configuration in a low-carbon power grid provided by the present invention construct the first operation optimization target and the second operation optimization target of the target low-carbon power grid based on the operation constraint conditions; construct the first uncertainty fuzzy set corresponding to the uncertainty parameter of the first operation optimization target, and based on the Shortfall risk and the first uncertainty fuzzy set, take the first operation optimization target as the variable of the increasing convex function of the Shortfall risk to establish a distributed robust risk measurement constraint; based on the Shortfall risk and the second uncertainty fuzzy set, take the second operation optimization target as the variable of the increasing convex function of the Shortfall risk to establish a distributed robust worst carbon emission expected target; based on the risk measurement constraint and the worst carbon emission expected target, and the total investment cost function of the low-carbon power grid, construct a distributed robust constraint capacity optimization configuration model. Since the three requirements of load loss risk, carbon emissions and investment cost are considered in the capacity optimization configuration model. Taking the load loss risk as the first constraint to be guaranteed, carbon emissions and investment costs become two goals that need to be weighed, and a planning scheme that enables the stable operation and efficient development of the target low-carbon power grid can be finally obtained. Therefore, the renewable energy power generation capacity can be determined scientifically and rationally, and the expansion scale of the transmission lines and the capacity configuration of the energy storage system can be planned simultaneously to ensure the stable operation and efficient development of the low-carbon power grid. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0019] Figure 1 It is a flow chart of the method for optimizing configuration of energy storage capacity in a low-carbon power grid provided by the present invention.

[0020] Figure 2 It is a flow chart of a method for obtaining target capacity configuration parameters of a target low-carbon power grid according to a capacity optimization configuration model provided by the present invention.

[0021] Figure 3It is a structural schematic diagram of the energy storage capacity optimization configuration device in the low-carbon power grid provided by the present invention.

[0022] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] Combine the following Figure 1-Figure 2 The present invention describes a method for optimizing configuration of energy storage capacity in a low-carbon power grid.

[0025] Figure 1 is a flow chart of the method for optimizing energy storage capacity configuration in a low-carbon power grid provided by the present invention, such as Figure 1 As shown, the method includes the following: Step 101: construct a first operation optimization target and a second operation optimization target of a target low-carbon power grid based on operation constraints.

[0026] The first operation optimization target is to minimize the load loss of the target low-carbon power grid under extreme weather conditions. The second operation optimization target is to minimize the carbon emissions 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 uncertainty parameters.

[0027] The target low-carbon power grid is a low-carbon power grid to be planned under the premise of a given site selection, whose renewable energy power generation capacity, number of transmission line expansions and energy storage capacity need to be determined.

[0028] Operational constraints are used to describe the operating characteristics and limitations of different components in the target low-carbon power grid.

[0029] As an example only, the operating constraints of the target low-carbon power grid may be constructed in the following manner.

[0030] In the target low-carbon power grid, the node set is Indicates that the time period collection is Indicates. For the node Conventional fossil fuel generators, Output power at all times The following constraints are met.

[0031]

[0032] Where (1a) represents the power generation capacity constraint; is the duration of each moment, (1b) restricts the incremental change of generator output between two consecutive time periods, which is called the ramp constraint; For Node The emission coefficient of the generator at (1c) is used to calculate the carbon emissions .

[0033] node The installed capacity of renewable power plants is , the uncertainty factor of renewable energy generation is It indicates that there is uncertainty. Then the power generation of renewable energy is , satisfying the following constraints

[0034] The operating constraints of energy storage include

[0035] In the formula For Node The power capacity of the energy storage depends on the capacity of the power electronic converter, (3a) limiting the charging power and discharge power , Indicates time The stored energy, 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 SoC; constraint (3d) stipulates that the final state and initial state of the cycle operation are equal. In the optimal operation problem of minimizing load shedding or carbon emissions, the constraint prohibiting simultaneous charging and discharging can be omitted.

[0036] During the specific implementation process, DC power flow can be used to model the target low-carbon power grid. is the branch set of the target low-carbon power grid. In the example, a slave node To Node The active power flow in the transmission line is recorded as ,node The voltage angle at . Assume the line reactance is , then the node To Node The DC power flow model between

[0037] Where (4a) is the active power flow of the line, and (4b) is the capacity limit of the transmitted power.

[0038] 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. To Node The number of existing transmission lines and the number of newly built transmission lines are and , then from the node To Node The active power is , which means that the power flow in each line is equal. This is a natural result because each line has the same parameters.

[0039] Assume Node New energy in Allow power abandonment at all times , then it must be non-negative, as follows

[0040] Set Node exist The load demand at the moment is , load abandonment Must be non-negative and no greater than the requirement, i.e.

[0041] The node power balance equation is as follows

[0042] because and is a variable, constraint (7) is nonlinear, and this term will be linearized in the following step. In some countries, distributed renewable energy generation is the main source of supply system demand. Although distributed generation is not explicitly considered in this paper, it can be incorporated into the total demand in the proposed model. Parameters and The uncertainties will be dealt with in subsequent steps.

[0043] One of the primary requirements for power system operation is to meet load demand. Under extreme weather conditions, the target low-carbon power grid based on renewable energy may inevitably experience load loss. Therefore, the first operation optimization goal is to minimize the load loss of the target low-carbon power grid under extreme weather conditions. As an example only, the establishment process of the first operation optimization goal is as follows.

[0044] Since extreme weather conditions may not last for a long time, their impact on carbon emissions can be neglected. The first operational optimization target under extreme weather conditions is shown below.

[0045]

[0046] Among them, capacity planning strategy and uncertain parameters In the first run the optimization objective is fixed and 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 , so it is expressed as a function , which means that given and The minimum load loss under

[0047] The second operational optimization objective is to minimize the carbon emissions of the target low-carbon power grid under non-extreme weather conditions.

[0048] Under normal weather conditions, load loss is not allowed in the target low-carbon power grid, and the goal of the target low-carbon power grid operation is to minimize carbon emissions, as follows.

[0049]

[0050] Similar to the first operation optimization objective, the minimum carbon emission as the optimal value of the second operation optimization objective is still and Function Since the operating costs of renewable energy generation are generally low, the operating costs of a low-carbon electricity system based mainly on renewable energy are not considered.

[0051] Since both the first operation optimization target and the second operation optimization target include uncertainty parameters: the uncertainty factor of renewable energy generation at each node of the target low-carbon power grid at different times and load demand .

[0052] Step 102: construct a first uncertainty fuzzy set corresponding to the uncertainty parameters of the first operation optimization objective, and construct a second uncertainty fuzzy set corresponding to the uncertainty parameters of the second operation optimization objective.

[0053] In a specific implementation process, the first uncertainty fuzzy set can be constructed in the following manner: obtaining a first data sample indicator set; wherein the first data sample indicator set includes the value of the uncertainty parameter under historical extreme weather conditions; constructing a first uncertainty fuzzy set according to the first 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 sample indicator set is less than a first preset threshold. As an example only, the above construction method is as follows.

[0054] For the definition in Two parameter distributions on and For example, their 1-norm Wasserstein-metric is defined as

[0055] It is a marginal and The joint distribution of represents the 1-norm.

[0056] Set as the first data sample indicator set. , Uncertain parameter representing one of the extreme weather conditions (Including uncertainty factors of renewable energy generation and load demand ) is implemented. Based on the data samples in the first data sample indicator set, an empirical probability distribution of extreme weather conditions is established as follows.

[0057]

[0058] in for The number of elements of Represents the indicator function. When the uncertain distribution is , the value is 1. Therefore, In each data sample Assigned with probability .

[0059] The first uncertainty fuzzy set Contains close to empirical distribution More precisely, and the empirical probability distribution The distance between them is not greater than the first preset threshold , the definition of the first uncertainty fuzzy set is as follows.

[0060] (12)

[0061] For definition in The distribution set on .

[0062]

[0063] For Node 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.

[0064] In a specific implementation process, the second uncertainty fuzzy set can be constructed in the following manner: obtaining a second data sample indicator set; wherein the second data sample indicator set includes the value of the uncertainty parameter under historical non-extreme weather conditions; constructing a second uncertainty fuzzy set according to 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 construction process of the second uncertainty fuzzy set participates in the construction process of the first uncertainty fuzzy set.

[0065] In the specific implementation process, the feasible set of uncertainty parameters under historical non-extreme weather conditions is defined as :

[0066] The feasible set is the sampled data The convex hull of is obtained in order to restrict it to normal conditions.

[0067] 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 distributed robust risk measurement constraint.

[0068] Shortfall risk is a convex risk measure with two parameters, where the constant represents the maximum acceptable loss, function It is an increasing convex function of the extreme value function, which is used to reflect the degree of loss. The random variable associated with the loss of load , the Shortfall risk with parameters 𝜆 and l is defined as

[0069] in Indicates the distribution of Expected operation.

[0070] Since the shortfall risk depends on the distribution and the load loss risk must be small enough for all possible distributions in the first uncertainty fuzzy set, the risk measure constraint of distribution robustness can be expressed as

[0071] in For acceptable load loss under extreme weather conditions, For the maximum load loss risk, select Function as a convex function to measure the loss when load loss occurs The risk metric constraint specifies an upper bound on the worst-case risk under extreme weather conditions.

[0072] Step 104: Based on the Shortfall risk and the second uncertainty fuzzy set, the second operation optimization target is used as a variable of the increasing convex function of the Shortfall risk to establish the worst carbon emission expected target with robust distribution.

[0073] The process of establishing a worst-case carbon emission target with high reliability is shown in step 103.

[0074] The worst-case carbon emission targets for the Blue Rift are shown below.

[0075]

[0076] Step 105: Based on the risk measurement constraints and the worst carbon emission expected target, as well as the total investment cost function of the low-carbon power grid, a capacity optimization configuration model with distributed robustness constraints is constructed.

[0077] The capacity optimization configuration model is to minimize the total investment cost function of the low-carbon power grid and the worst carbon emission expected target while satisfying the risk measurement constraints.

[0078] The total investment cost function of low-carbon power grid is as follows:

[0079] in, , , and is the cost coefficient.

[0080] In the total investment cost function of low-carbon power grid, the capacity variable The domain of

[0081] The capacity of each variable must be bounded, taking into account natural resource conditions and the availability of building sites, so , , and As the upper bound of the variable, a capacity optimization configuration model with distributed robust constraints is constructed as shown below.

[0082]

[0083] The capacity optimization configuration model compromises the total investment cost of the target low-carbon grid and the worst carbon emission expectations under normal conditions, and is subject to risk measurement constraints under extreme weather conditions. After all facilities of the target low-carbon grid are built, the investment cost is fixed, so the first goal is deterministic. Worst carbon emission expectation target and risk measurement constraints Affected by the uncertainty of renewable energy generation and demand. Therefore, the capacity optimization configuration model is a dual-objective distributed robust mixed integer nonlinear programming and cannot be solved directly. For specific solution methods, see Figure 2 The relevant content in will not be repeated here.

[0084] Step 106: Obtain target capacity configuration parameters 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 parameters.

[0085] For a detailed description of the target capacity configuration parameters of the target low-carbon power grid obtained according to the capacity optimization configuration model, see Figure 2 The relevant content in will not be repeated here.

[0086] Figure 2 is a flow chart of a method for obtaining target capacity configuration parameters of a target low-carbon power grid according to a capacity optimization configuration model provided by the present invention, such as Figure 2 As shown, the method includes the following: Step 201: Use Boolean auxiliary variables and the Big M method to express the nonlinear product term and obtain a linearized capacity optimization configuration model.

[0087] The only nonlinear part in the capacity optimization model comes from the product term in the operating constraint formula (7): .because and , so you can use Boolean auxiliary variables The Big M method rephrases this term in the following way.

[0088]

[0089] Then, the expression

[0090] Become about Linear. Non-linear relationship is equivalent to the following linear constraint

[0091] Among them is is a sufficiently large constant. If , then according to the second constraint we have , and the first constraint does not work; if , then according to the first constraint we have , and the second constraint has no effect.

[0092] 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.

[0093] The target capacity configuration parameters of the target low-carbon power grid include the renewable 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.

[0094] In the following steps, the target capacity configuration parameters of the target low-carbon power grid are obtained according to the linearized capacity optimization configuration model.

[0095] Step 202: By considering 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, the first Lipschitz constant is introduced into the risk measurement constraint to obtain a quantized risk measurement constraint.

[0096] Step 203: Using a sample average approximation algorithm, the quantified risk measurement constraint is converted into a first mixed integer linear programming equation.

[0097] Step 204: By considering the increasing convex function of the Shortfall risk in the upper bound expression of the worst-case expectation of the worst carbon emission target as a function of the uncertainty parameter, a second Lipschitz constant is introduced into the worst carbon emission target to obtain a quantized worst carbon emission target.

[0098] Step 205: Using a sample average approximation algorithm, the quantified worst carbon emission target is converted into a second mixed integer linear programming equation.

[0099] In a specific implementation process, steps 202 to 205 may be implemented using the following example process.

[0100] Considering the risk measurement constraints of distributed robustness

[0101] Therefore, the worst-case expectation in constraint (23) should be calculated. The upper bound of the worst-case expectation is

[0102] Because of the function ,so for See as about The Lipschitz constant for a function of

[0103] By formula (24), the worst-case expectation on the fuzzy set (which is an infinite-dimensional optimization problem) is simplified to the empirical probability distribution on the right-hand side The expectations below.

[0104] Using the sample average approximation, the distributionally robust risk measure constraint (16) can be conservatively approximated by the following constraint.

[0105]

[0106] in is an auxiliary variable, indicating Then by definition , (26) can be equivalently transformed into linear constraints, resulting in the first mixed integer linear programming equation as shown below.

[0107]

[0108] By the same argument, the worst-case carbon emission target (17) meets

[0109] The Lipschitz constant Has such a nature

[0110] Then, the worst carbon emission target (17) can be conservatively approximated by the second mixed integer linear programming equation shown below:

[0111] Step 206: Based on the first mixed integer linear programming equation and the second mixed integer linear programming equation, obtain the target capacity configuration parameters of the target low-carbon power grid.

[0112] Before solving the capacity optimization configuration model, it is necessary to determine the value of the first Lipschitz constant and the value of the second Lipschitz constant.

[0113] In some embodiments, the first mixed integer linear programming equation can be solved by maximizing the dual variable algorithm to obtain the value of the first Lipschitz constant; the second mixed integer linear programming equation can be solved by maximizing the dual variable algorithm to obtain the value of the second Lipschitz constant. As an example only, the specific solution process is as follows.

[0114] In formula (8) The definition of can be written in a compact form as follows

[0115] (31)

[0116] Among them express . Define a new auxiliary variable , Can be written about A linear function of .because For any and are all bounded. According to the strong duality of LP, we have the following constraints

[0117] And the KKT optimality condition is

[0118] set up For the matrix In the Line The elements of the column, similarly, For vector No. elements. Using the domain , and , and the big-M method, the first mixed integer linear programming equation for finding the maximum dual variable is as follows.

[0119]

[0120] Through the interpretation of the dual variables,

[0121] By solving (34), the optimal value is Relative to No. The maximum sensitivity of the component is called .because

[0122] about The sensitivity can be used

[0123] make

[0124] Then

[0125] 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.

[0126] 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

[0127] And find the dual optimal solution Then let

[0128] Then we get the value of the second Lipschitz constant.

[0129] 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; the third mixed integer linear programming equation and the fourth mixed integer linear programming equation are solved to obtain the target capacity configuration parameters of the target low-carbon power grid.

[0130] In the specific implementation process, the 𝜀-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 to obtain the Pareto frontier of the target capacity configuration parameters.

[0131] As an example only, the process of obtaining the Pareto frontier of the target capacity configuration parameter is as follows.

[0132] For a given total investment cost budget 𝐹, the capacity optimization configuration model is as follows, which includes the risk measurement constraint expressed by the third mixed integer linear programming equation and the worst carbon emission expected target expressed by the fourth mixed integer linear programming equation:

[0133] The optimal solution of the capacity optimization configuration model is , the worst carbon emission target is expressed as . Click to On the Pareto frontier of the capacity optimization configuration problem (20) (the capacity optimization configuration model shown in formula (20)). Next, we solve problem (41) (the capacity optimization configuration model shown in formula (41)) and obtain a set of Pareto points. The algorithm for solving problem (20) is summarized as follows.

[0134] Input: Parameters of question (20), investment cost budget range , and the step size parameter

[0135] Output: Pareto solution and points on the Pareto front .

[0136] Step 1: Initialization, solve MILP (34) (the first mixed integer linear programming equation), and obtain from (37) (The first Lipschitz constant). Let .

[0137] Step 2: Solve MILP (41) (the second mixed integer linear programming equation) and get; fixed , solving (39) and (40) yields (the second Lipschitz constant), update the optimal value of (41) and obtain

[0138] Step 3: If , terminate. Otherwise, update , go to step 2.

[0139] In the embodiments provided by the invention, the two conflicting goals of carbon emissions and investment costs are successfully handled by using the ε-constraint method and dictionary optimization technology, and a one-dimensional Pareto frontier is obtained, which provides a clear optimization path for decision makers. With the Pareto frontier, decision makers can imagine how much one goal will be damaged if the other goal is improved. Then, they can compromise the two goals based on their specific preferences. If the decision maker has no particular preference, when the Pareto frontier exists, the Nash negotiation criterion can make fair trade-offs between multiple goals.

[0140] The following is a description of the energy storage capacity optimization configuration device in the low-carbon power grid provided by the present invention. The energy storage capacity optimization configuration device in the low-carbon power grid described below and the energy storage capacity optimization configuration method in the low-carbon power grid described above can be referenced to each other.

[0141] Figure 3 Schematic diagram of the structure of the energy storage capacity optimization configuration device in the low-carbon power grid provided by the present invention. Figure 3 As shown, the device 300 includes the following modules.

[0142] The first construction module 310 is used to construct a first operation optimization target and a second operation optimization target of the target low-carbon power grid based on operation constraints; wherein the first operation optimization target is to minimize the load loss of the target low-carbon power grid under extreme weather conditions, and the second operation optimization target is to minimize the carbon emissions of the target low-carbon power grid under non-extreme weather conditions, and both the first operation optimization target and the second operation optimization target include uncertainty parameters.

[0143] The second construction module 320 is used to construct a first uncertainty fuzzy set corresponding to the uncertainty parameters of the first operation optimization objective, and to construct a second uncertainty fuzzy set corresponding to the uncertainty parameters of the second operation optimization objective.

[0144] The third building module 330 is used to establish a risk measurement constraint of distributional robustness based on the Shortfall risk and the first uncertainty fuzzy set by taking the first operation optimization objective as a variable of an increasing convex function of the Shortfall risk.

[0145] The fourth construction module 340 is used to establish a worst-case carbon emission expected target with robust distribution 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.

[0146] The fifth construction module 350 is used to construct a capacity optimization configuration model with distributed robustness constraints based on the risk measurement constraints, the worst carbon emission expected target, and the total investment cost function of the low-carbon power grid; 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 expected target while satisfying the risk measurement constraints.

[0147] The acquisition module 360 ​​is used to obtain the 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.

[0148] Figure 4 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 4As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the method for optimizing the configuration of energy storage capacity in the low-carbon power grid, the method comprising: constructing a first operation optimization target and a second operation optimization target of the target low-carbon power grid based on the operation constraint conditions; wherein the first operation optimization target is to minimize the load loss of the target low-carbon power grid under extreme weather conditions, and the second operation optimization target is to minimize the carbon emissions 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 the Shortfall risk. The variable of the increasing convex function of the Shortfall risk is used to establish a risk measurement constraint of distributed robustness; based on the Shortfall risk and the second uncertainty fuzzy set, the second operation optimization target is used as the variable of the increasing convex function of the Shortfall risk to establish a worst-case carbon emission expected target of distributed robustness; based on the risk measurement constraint and the worst-case carbon emission expected target, as well as the total investment cost function of the low-carbon power grid, a capacity optimization configuration model with distributed robustness 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-case carbon emission expected target while satisfying the risk measurement constraint; according to the capacity optimization configuration model, the 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.

[0149] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0150] On the other hand, the present invention further provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the method for optimizing the configuration of energy storage capacity in a low-carbon power grid provided by the above methods, the method comprising: based on operating constraints, constructing a first operating optimization target and a second operating optimization target of a target low-carbon power grid; wherein the first operating optimization target is to minimize the load loss of the target low-carbon power grid under extreme weather conditions, and the second operating optimization target is to minimize the carbon emissions of the target low-carbon power grid under non-extreme weather conditions, and the first operating optimization target and the second operating optimization target both include uncertainty parameters; constructing a first uncertainty fuzzy set corresponding to the uncertainty parameters of the first operating optimization target, and constructing a second uncertainty fuzzy set corresponding to the uncertainty parameters of the second operating optimization target; based on Shortfal l risk and the first uncertainty fuzzy set, taking the first operation optimization target as the variable of the increasing convex function of Shortfall risk, and establishing a risk measurement constraint of distributional robustness; based on the Shortfall risk and the second uncertainty fuzzy set, taking the second operation optimization target as the variable of the increasing convex function of Shortfall risk, and establishing a worst-case carbon emission expected target of distributional robustness; based on the risk measurement constraint and the worst-case carbon emission expected target, as well as the total investment cost function of the low-carbon power grid, constructing a capacity optimization configuration model with distributional robustness constraint; 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 expected target while satisfying the risk measurement constraint; according to the capacity optimization configuration model, the 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.

[0151] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the method for optimizing the configuration of energy storage capacity in a low-carbon power grid provided by the above-mentioned methods, the method comprising: constructing a first operation optimization target and a second operation optimization target of a target low-carbon power grid based on operation constraints; wherein the first operation optimization target is to minimize the amount of load loss of the target low-carbon power grid under extreme weather conditions, and the second operation optimization target is to minimize the amount of carbon emissions of the target low-carbon power grid under non-extreme weather conditions, and both the first operation optimization target and the second operation optimization target 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 Shortfall risk and the first uncertainty fuzzy set set, taking the first operation optimization objective as the variable of the increasing convex function of Shortfall risk, and establishing a risk measurement constraint of distributional robustness; based on the Shortfall risk and the second uncertainty fuzzy set, taking the second operation optimization objective as the variable of the increasing convex function of Shortfall risk, and establishing a worst-case carbon emission expected target of distributional robustness; based on the risk measurement constraint and the worst-case carbon emission expected target, and the total investment cost function of the low-carbon power grid, constructing a capacity optimization configuration model with distributional robustness constraint; 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 expected target while satisfying the risk measurement constraint; according to the capacity optimization configuration model, the 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.

[0152] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0153] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing energy storage capacity configuration in a low-carbon power grid, characterized in that: include: Based on the operation constraints, 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 load loss of the target low-carbon power grid under extreme weather conditions, and the second operation optimization target is to minimize the carbon emissions of the target low-carbon power grid under non-extreme weather conditions, and both the first operation optimization target and the second operation optimization target include uncertainty parameters; Constructing a first uncertainty fuzzy set corresponding to the uncertainty parameter of the first operation optimization objective, and constructing a second uncertainty fuzzy set corresponding to the uncertainty parameter 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 distributed robust risk measurement constraint; Based on the Shortfall risk and the second uncertainty fuzzy set, the second operation optimization target is used as a variable of the increasing convex function of the Shortfall risk to establish the worst carbon emission expected target with distributed robustness; Based on the risk measurement constraint, the worst carbon emission expected 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 carbon emission expected target while satisfying the risk measurement constraint; According to the capacity optimization configuration model, the 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: Acquire a first data sample indicator set; wherein the first data sample indicator set includes the value of the uncertainty parameter under historical extreme weather conditions; According to the first sample indicator set, the first uncertainty fuzzy set is constructed; wherein the distance between the empirical probability distribution of the first uncertainty fuzzy set and the empirical probability distribution of the first 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: Acquire a second data sample indicator set; wherein the second data sample indicator set includes the value of the uncertainty parameter under historical non-extreme weather conditions; The second uncertainty fuzzy set is constructed according to 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.

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 the target capacity configuration parameter of the target low-carbon power grid is obtained according to the capacity optimization configuration model, including: 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, the 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: The target capacity configuration parameters of the target low-carbon power grid are obtained according to the linearized capacity optimization configuration model, including: By considering 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 into the risk measurement constraint to obtain the quantized risk measurement 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 worst-case expected upper bound expression of the worst carbon emission expected target as a function of the uncertainty parameter, a second Lipschitz constant is introduced into the worst carbon emission expected target to obtain a quantified worst carbon emission expected target; Using a sample average approximation algorithm, the quantified worst 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 step of obtaining the 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: The step of solving the third mixed integer linear programming equation and the fourth mixed integer linear programming equation to obtain the target capacity configuration parameters of the target low-carbon power grid includes: By using the 𝜀-constraint method, within the investment cost budget, the third mixed integer linear programming equation and the fourth mixed integer linear programming equation are solved 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 used to construct a first operation optimization target and a second operation optimization target of a target low-carbon power grid based on operation constraints; wherein the first operation optimization target is to minimize the load loss of the target low-carbon power grid under extreme weather conditions, and the second operation optimization target is to minimize the carbon emissions of the target low-carbon power grid under non-extreme weather conditions, and both the first operation optimization target and the second operation optimization target include uncertainty parameters; A second construction module is used 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 used to establish a risk measurement constraint of distributed robustness by taking the first operation optimization objective as a variable of an increasing convex function of Shortfall risk based on Shortfall risk and the first uncertainty fuzzy set; A fourth building module is used to establish a worst-case carbon emission expected target with a distributed robustness 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; A fifth construction module is used to construct a capacity optimization configuration model with distributed robustness constraints based on the risk measurement constraint, the worst carbon emission expected target, and the total investment cost function of the low-carbon power grid; 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 expected target while satisfying the risk measurement constraint; The acquisition module is used to obtain the 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 configuration of energy storage capacity in a low-carbon power grid 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 configuration of energy storage capacity 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 configuration of energy storage capacity in a low-carbon power grid as claimed in any one of claims 1 to 6 is implemented.

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