Capacity configuration and scheduling strategy optimization method and device and readable storage medium

Abstract

The invention relates to a capacity configuration and scheduling strategy optimization method and device and a readable storage medium. The method comprises the following steps: obtaining an uncertainty description set according to a prediction characteristic curve of an uncertainty parameter; wherein the uncertainty description set represents an uncertainty set of source-load double-side photovoltaic output and an uncertainty set of load demand; constructing a two-stage robust optimization model based on the uncertainty description set; converting the two-stage robust optimization model into a main problem and a sub-problem by using a decomposition algorithm; and iteratively solving the main problem and the sub-problems to obtain a target capacity configuration result and an operation scheduling strategy. By adopting the method, a configuration scheme and an operation scheduling scheme with robustness and economic low-carbon property can be formed under the uncertain condition, and the application range of the optimization method is effectively expanded.

Description

Technical Field

[0001] The present application relates to the technical field of power grid dispatching, and in particular to a method, device and readable storage medium for optimizing capacity configuration and dispatching strategy. Background Art

[0002] As a typical terminal energy system application form, the core of the construction and development of the photovoltaic storage and charging system lies in how to effectively utilize various forms of energy and optimize the system's capacity design and operation scheduling strategy so that the photovoltaic storage and charging system can achieve the goals of supply and demand balance, low-carbon operation and efficient operation.

[0003] However, with the gradual increase in the proportion of distributed renewable energy in photovoltaic storage and charging systems, the structure of photovoltaic storage and charging systems has become increasingly complex, and the energy load demand has shown dynamic changes. This has led to the difficulty of applying traditional optimization solutions based on system-specific scenarios to scenarios with strong uncertainty, low inertia, and high randomness on both the source and load sides. In other words, the optimization solutions of traditional capacity configuration and scheduling strategies lack universality. Summary of the Invention

[0004] Based on this, it is necessary to provide an optimization method, device and readable storage medium that can be universally adapted for capacity configuration and scheduling strategies of optical energy storage systems in order to address the above technical issues.

[0005] In a first aspect, in one embodiment, the present application provides a method for optimizing capacity configuration and scheduling strategy, which is applied to a control device of a solar-storage-charging system, and the method includes:

[0006] Based on the prediction characteristic curve of the uncertainty parameters, an uncertainty description set is obtained; wherein the uncertainty description set represents the uncertainty set of the photovoltaic output on both sides of the source and load and the uncertainty set of the load demand;

[0007] Based on the uncertainty description set, a two-stage robust optimization model is constructed;

[0008] Using decomposition algorithm, the two-stage robust optimization model is transformed into a main problem and sub-problems;

[0009] Iteratively solve the main problem and sub-problems to obtain the target capacity configuration result and operation scheduling strategy.

[0010] In one embodiment, the step of obtaining an uncertainty description set according to a prediction characteristic curve of uncertainty parameters includes:

[0011] According to the prediction characteristic curve of the uncertainty parameter, the photovoltaic output prediction characteristic data and the load prediction characteristic data are obtained;

[0012] Based on the photovoltaic output prediction characteristic data and load prediction characteristic data, the uncertainty description set is obtained using the polyhedron set.

[0013] In one embodiment, after the step of obtaining the uncertainty description set using the polyhedron set based on the photovoltaic output prediction characteristic data and the load prediction characteristic data, the method further includes:

[0014] Uncertainty is introduced into the uncertainty description set to obtain the uncertainty description set with introduced uncertainty.

[0015] In one embodiment, the step of constructing a two-stage robust optimization model based on the uncertainty description set includes:

[0016] Construct an objective function based on the system operation and maintenance parameters of the solar-storage-charging system;

[0017] Based on the uncertainty description set, determine the constraint conditions; wherein the constraint conditions are used to constrain the operating state of the photovoltaic storage and charging system equipment and to constrain the energy balance of the photovoltaic storage and charging system;

[0018] According to the objective function and constraints, a two-stage robust optimization model is constructed based on the min-max-min structure.

[0019] In one embodiment, the decomposition algorithm includes a C&CG method; the steps of converting the two-stage robust optimization model into a main problem and subproblems using the decomposition algorithm include:

[0020] According to the two-stage robust optimization model, the C&CG method is used to decompose the main problem and the original sub-problems; the original sub-problems include the inner minimization problem and the outer maximization problem;

[0021] Based on the inner minimization problem and the outer maximization problem, the subproblems of the two-stage robust optimization model are determined using strong duality theory.

[0022] In one embodiment, the steps of iteratively solving the main problem and the subproblems to obtain the target capacity configuration result and the operation scheduling strategy include:

[0023] Based on the uncertainty description set, determine the initial worst-case scenario and set the initial upper and lower cost bounds;

[0024] Perform the following iterative loop operations:

[0025] Solve the main problem according to the worst scenario, calculate the first-stage solution result, and use the main problem objective function value in the first-stage solution result as a new cost lower bound;

[0026] Substitute the solution of the first stage into the subproblem, calculate the solution of the second stage, and determine the new upper bound of the cost and the new worst-case scenario based on the solution of the second stage;

[0027] If the cost lower bound and the cost upper bound do not meet the preset accuracy conditions, the new uncertainty variables and new constraints of the main problem are determined according to the solution results of the second stage, and the next round of iterative cycle operations is performed according to the new cost upper bound, the new cost lower bound and the new worst scenario;

[0028] If the cost lower bound and the cost upper bound meet the preset accuracy conditions, the target capacity configuration result and the operation scheduling strategy are determined based on the solution results of the first stage and the second stage.

[0029] In a second aspect, in one embodiment, the present application provides a device for optimizing capacity configuration and scheduling strategy, which is applied to a solar-storage-charging system. The device includes:

[0030] An uncertainty description set acquisition module is used to obtain an uncertainty description set based on a prediction characteristic curve of uncertainty parameters; wherein the uncertainty description set represents the uncertainty set of photovoltaic output on both sides of the source and load and the uncertainty set of load demand;

[0031] The optimization model construction module is used to construct a two-stage robust optimization model based on the uncertainty description set;

[0032] Model decomposition module, which is used to transform the two-stage robust optimization model into a main problem and sub-problems using a decomposition algorithm;

[0033] The solution module is used to iteratively solve the main problem and sub-problems to obtain the target capacity configuration results and operation scheduling strategy.

[0034] In a third aspect, in one embodiment, the present application provides a control device, which includes a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the steps in the various method embodiments of the first aspect above.

[0035] In a fourth aspect, in one embodiment, the present application provides a photovoltaic storage and charging system, comprising a control device as described in the third aspect; the system also includes a photovoltaic device, an energy storage device, and an electrical load including a charging device, which are respectively connected to the control device; the photovoltaic device, the energy storage device, and the electrical load are connected to each other in pairs.

[0036] In a fifth aspect, in one embodiment, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps in the various method embodiments of the above-mentioned first aspect when the computer program is executed by a processor.

[0037] The above-mentioned optimization method, device and readable storage medium for capacity configuration and scheduling strategy obtain an uncertainty description set based on the prediction characteristic curve of the uncertainty parameter, and then further construct a corresponding two-stage robust optimization model based on the obtained uncertainty description set. The decomposition algorithm is then used to transform the two-stage robust optimization model into a main problem and sub-problems, and the main problem and sub-problems are iteratively solved to finally obtain the target capacity configuration result and operation scheduling strategy. Through the above-mentioned method, the present application can form a robust and economical low-carbon configuration scheme and operation scheduling scheme under uncertainty conditions, effectively expanding the application scope of the optimization method. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 FIG. 1 is an application environment diagram of a method for optimizing capacity configuration and scheduling strategy in one embodiment;

[0040] Figure 2 1 is a flow chart of a method for optimizing capacity configuration and scheduling strategy in one embodiment;

[0041] Figure 3 A schematic diagram of a process for obtaining an uncertainty description set in one embodiment;

[0042] Figure 4 A schematic diagram of a process for constructing a two-stage robust optimization model in one embodiment;

[0043] Figure 5 A schematic diagram of a process for decomposing a two-stage robust optimization model in one embodiment;

[0044] Figure 6 A schematic diagram of a process for iteratively solving a main problem and subproblems in one embodiment;

[0045] Figure 7 A structural block diagram of an apparatus for optimizing capacity configuration and scheduling strategy in one embodiment;

[0046] Figure 8 FIG. 4 is a diagram showing the internal structure of a control device in one embodiment. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0048] With the development of new power systems, building an energy system that coordinates power generation, grid loading, and storage, and supports the integration of distributed renewable energy, has become a key initiative in energy transformation. As a typical terminal energy system application, the core of the construction and development of photovoltaic storage and charging systems is to effectively utilize multiple energy sources and coordinately optimize system capacity design and operation scheduling to achieve supply and demand balance, low-carbon operation, and high efficiency.

[0049] However, as the proportion of distributed renewable energy in photovoltaic storage and charging systems gradually increases, the structure of photovoltaic storage and charging systems becomes increasingly complex, and energy load demand changes dynamically. This makes photovoltaic storage and charging systems face the problems of strong uncertainty, low inertia, and high randomness on both the source and load sides. These problems bring severe challenges to their operation optimization.

[0050] Traditional deterministic optimization methods optimize the design of solar-storage-charging systems based on specific scenarios. They largely ignore the uncertainty of system operation, models and variables, resulting in an inability to accurately reflect the system's status and capabilities. As a result, traditional optimization methods often obtain non-optimal technical and economic configuration operation optimization solutions.

[0051] To this end, this application proposes a universal optimization method for capacity configuration and scheduling strategy, which can meet the complex optimization needs of the solar-storage-charging system under the multi-dimensional uncertainty of source and load.

[0052] The optimization method of capacity configuration and scheduling strategy provided in the embodiment of the present application can be applied to Figure 1 In the illustrated solar-storage-charging system 100, the solar-storage-charging system 100 includes a control device 102; a photovoltaic device 104, an energy storage device 106, and an electrical load 108 including a charging device (e.g., a charging station), all connected to the control device 102; the photovoltaic device 104, the energy storage device 102, and the electrical load 108 are interconnected in pairs.

[0053] In an exemplary embodiment, Figure 2 As shown in the figure, a capacity configuration and scheduling strategy optimization method is provided, which is applied to Figure 1 The control device 100 in FIG. 1 is taken as an example to illustrate the method, which includes the following steps S202 to S206. In which:

[0054] Step S202: Obtain an uncertainty description set based on the prediction characteristic curve of the uncertainty parameter.

[0055] Among them, the uncertainty description set represents the uncertainty set of photovoltaic output on both sides of the source and load and the uncertainty set of load demand.

[0056] For example, the prediction characteristic curve of the uncertainty parameter can represent the prediction of the photovoltaic output and load demand on both the source and load sides of the solar-storage-charging system within a set time.

[0057] Specifically, the control device can construct an uncertainty description set that can describe the photovoltaic output on both sides of the source and load and the load demand prediction based on the prediction characteristic curve of the set uncertainty parameters.

[0058] Step S204: construct a two-stage robust optimization model based on the uncertainty description set.

[0059] Among them, the two-stage robust optimization model includes objective functions and constraints, which can be used to find the scheduling plan with the lowest system operating cost under the worst scenario of the solar-storage-charging system.

[0060] For example, the objective function of the two-stage robust optimization model can be constructed using system operation and maintenance parameters. In some examples, the system operation and maintenance parameters may include total system cost, system equipment investment cost, carbon emission penalty cost, system operation and maintenance cost, system power purchase cost, system power sales revenue, equipment installation capacity, unit installation cost, charging and discharging power of electric energy storage, power purchase price, power sales price, and power sales to the grid.

[0061] Specifically, the control device constructs a two-stage robust optimization model based on the obtained uncertainty description set. Through the two-stage robust optimization model, a scheduling plan that minimizes the system operation cost under the worst scenario can be obtained.

[0062] Step S206: using a decomposition algorithm, the two-stage robust optimization model is transformed into a main problem and sub-problems.

[0063] The decomposition algorithm can decompose the constructed two-stage robust optimization model to obtain corresponding main problems and subproblems. Optionally, the decomposition algorithm can include a C&CG method (Column-and-Constraint Generation).

[0064] Specifically, the control device can decompose the two-stage robust optimization model into corresponding main problems and sub-problems through a preset decomposition algorithm.

[0065] Step S208: Iteratively solve the main problem and sub-problems to obtain the target capacity configuration result and operation scheduling strategy.

[0066] Among them, the iterative solution process of the main problem and the sub-problem is a double-layer iterative solution.

[0067] Specifically, based on the decomposed main problem and sub-problems, the control device can set the relevant initial parameter environment and convergence conditions for the iterative solution, and then iteratively solve the main problem and sub-problems, and finally obtain the target capacity configuration result that optimizes the capacity configuration of the energy storage equipment and the operation scheduling strategy that makes the photovoltaic storage and charging system highly robust and economical and low-carbon.

[0068] The above-mentioned optimization method for capacity configuration and scheduling strategy obtains an uncertainty description set based on the prediction characteristic curve of the uncertainty parameter. Then, based on the obtained uncertainty description set, a corresponding two-stage robust optimization model is further constructed. Then, using the decomposition algorithm, the two-stage robust optimization model is converted into a main problem and sub-problems, and the main problem and sub-problems are iteratively solved to finally obtain the target capacity configuration result and operation scheduling strategy. Through the above-mentioned method, the present application can form a robust and economical low-carbon configuration scheme and operation scheduling scheme under uncertainty conditions, effectively expanding the application scope of the optimization method.

[0069] In one embodiment, Figure 3 As shown, the step of obtaining the uncertainty description set according to the prediction characteristic curve of the uncertainty parameter includes the following steps S302 to S304.

[0070] Step S302 : Obtain photovoltaic output prediction characteristic data and load prediction characteristic data according to the prediction characteristic curve of the uncertainty parameter.

[0071] The photovoltaic output prediction characteristic data can be used to represent the photovoltaic output of both the source and load sides of the photovoltaic storage and charging system within a set time period; the load prediction characteristic data can be used to represent the prediction of load operation conditions within a set time period. For example, the prediction characteristic curve of the uncertainty parameter can be directly input into the control device by the user through the terminal.

[0072] Specifically, the control device can obtain photovoltaic output prediction characteristic data and load prediction characteristic data based on the obtained prediction characteristic curve of the uncertainty parameter.

[0073] Step S304 : Based on the photovoltaic output prediction characteristic data and the load prediction characteristic data, an uncertainty description set is obtained using a polyhedron set.

[0074] Specifically, based on the obtained photovoltaic output prediction characteristic data and load prediction characteristic data, the control system can construct the corresponding uncertainty description set using a polyhedron set with linear structure, robustness and flexible control.

[0075] For example, as shown in the following formula 1, the uncertainty description set can be expressed as:

[0076] (Formula 1)

[0077] in, is the uncertainty description set; is a time set In the moment, there ; is the uncertainty variable of the uncertainty description set; and They are photovoltaic output and load power respectively. Photovoltaic output and load power can be obtained from photovoltaic output prediction characteristic data and load prediction characteristic data; and are the nominal values ​​of photovoltaic output and load power respectively; and are the maximum fluctuation deviations allowed for photovoltaic output and load power, respectively, and both are positive numbers.

[0078] In one embodiment, after the step of obtaining the uncertainty description set based on the photovoltaic output and the load power using the polyhedron set, the following step S306 is also included.

[0079] Step S306: introduce uncertainty into the uncertainty description set to obtain the uncertainty description set with introduced uncertainty.

[0080] Specifically, since the uncertainty description set may be overly conservative, the embodiment of the present application introduces dimensionless uncertainty into the uncertainty description set. (also called robust measure), using this uncertainty The degree of deviation between the uncertain scene set in the uncertainty description set and the predicted average scene set in the uncertainty description set can be constrained. It can be understood that the uncertainty The larger the value of , the greater the uncertainty of the uncertainty description set, which can make the final optimal solution more robust.

[0081] For example, combined with the setting of the worst-case scenario optimization in robust optimization, the uncertainty description set after introducing uncertainty is expressed as follows:

[0082] (Formula 2)

[0083] in, and are the uncertainty coefficients corresponding to the PV output and load power in the uncertainty variables, which are random variables with symmetrical distribution and can be used to reflect the degree of deviation of the uncertainty variables; and The uncertainties corresponding to PV output and load power, respectively, can be used to adjust the conservative level of each constraint, that is, the worst case scenario corresponding to all uncertainties.

[0084] Furthermore, the fluctuation deviation and uncertainty of the above-mentioned uncertainty variables can be adaptively adjusted according to the actual prediction data accuracy of the solar storage and charging system and the conservative level of the system planning and scheduling to deal with uncertainty fluctuations, so that the method of this application can provide a more flexible and adaptable decision-making solution.

[0085] In one embodiment, Figure 4 As shown, the steps of constructing a two-stage robust optimization model based on the uncertainty description set include the following steps S402 to S406.

[0086] Step S402: construct an objective function based on the system operation and maintenance parameters of the solar-storage-charging system.

[0087] Among them, the system operation and maintenance parameters can characterize the equipment operation cost, equipment maintenance cost, carbon emissions, electricity purchase and sales situation, and equipment operation status of the solar storage and charging system.

[0088] Specifically, the control device can construct the objective function in the two-stage robust optimization model based on the system operation and maintenance parameters of the solar-storage-charging system.

[0089] For example, according to Formula 3 to Formula 8, the objective function can be expressed as follows:

[0090] (Formula 3)

[0091] (Formula 4)

[0092] (Formula 5)

[0093] (Formula 6)

[0094] (Formula 7)

[0095] (Formula 8)

[0096] in, The total system cost of the solar-storage-charging system; Equipment investment cost for the solar-storage-charging system; Carbon emission penalty costs for solar-storage systems; System operation and maintenance costs for the solar-storage-charging system; The system power purchase cost for the solar-storage-charging system; The revenue from electricity sales for the solar-storage-charging system; and They are the devices in the optical storage and charging system installed capacity and installation costs; The interest rate of the system operation income of the solar-storage-charging system; For equipment in the solar storage and charging system Equipment life; Carbon taxes required to operate solar-storage-charging systems; CO2 emission factors for electricity generated for the grid; Power purchase for the solar-storage-charging system; is the unit maintenance cost of photovoltaic equipment; is the unit maintenance cost of energy storage equipment; 、 are the charging power and discharging power of the electrical energy storage in the energy storage device, respectively; The electricity purchase price for the solar-storage-charging system; The electricity selling price of the solar-storage-charging system; The amount of electricity sold by the solar-storage-charging system to the power grid.

[0097] Step S404: determining constraint conditions based on the uncertainty description set.

[0098] The constraints are used to constrain the operating state of the solar-storage-charging system equipment and the energy balance of the solar-storage-charging system. That is, the constraints of the two-stage robust optimization model can include the operating state constraints of the equipment and the energy balance constraints.

[0099] Specifically, the control device can determine the constraints of the two-stage robust optimization model according to the constructed uncertainty description set.

[0100] For example, according to the following formulas 9 to 20, the constraints of the two-stage robust optimization model can be expressed as:

[0101] (Formula 9)

[0102] (Formula 10)

[0103] (Formula 11)

[0104] (Formula 12)

[0105] (Formula 13)

[0106] (Formula 14)

[0107] (Formula 15)

[0108] (Formula 16)

[0109] (Formula 17)

[0110] (Formula 18)

[0111] (Formula 19)

[0112] (Formula 20)

[0113] in, express The electrical energy storage state of the energy storage device at any moment is the ratio of the remaining electrical energy of the energy storage device to its capacity; The electrical energy storage capacity of the energy storage device; 、 They are the energy storage charging efficiency and discharging efficiency of the energy storage device respectively; 、 are the minimum and maximum values ​​corresponding to the electrical energy storage SOC of the energy storage device; 、 Binary variables representing the energy storage charging state and discharging state of the energy storage device, which are 1 when it is on and 0 when it is off; 、 are the maximum charging rate and discharging rate of the electric energy storage, respectively; is the grid interaction power, is its maximum value, which constrains the maximum value of the network connection between the solar-storage-charging system and the grid regardless of the power purchase and sales status; 、 They are The maximum value of electricity purchase and sale at any moment; 、 They are The electricity purchasing status and electricity selling status at each moment are binary variable values, which are 0 or 1; in addition, the system's electricity purchasing and selling actions cannot be performed at the same time.

[0114] Step S405 : constructing a two-stage robust optimization model based on the min-max-min structure according to the objective function and the constraints.

[0115] It can be understood that the purpose of constructing the two-stage robust optimization model is to find the operation scheduling strategy that minimizes the operating cost of the solar-storage-charging system under the worst scenario.

[0116] Specifically, the two-stage robust optimization model contains two levels of decision variables, namely the min-max-min structure, that is, .

[0117] Exemplarily, as shown in the following formula 21, the constructed two-stage robust optimization model is as follows:

[0118] (Formula 21)

[0119] It is understandable that the two-stage robust optimization model The minimization of the outer layer of the structure is the first stage problem, and the optimization variables are discrete decision variables , which is used to indicate the capacity of energy storage equipment, the operating status of energy storage equipment, and the status of electricity purchase and sale; The maximum minimization of the inner layer of the structure is the second stage problem, and the optimization variables are the specific operating power of each device in the solar storage and charging system and the continuous decision variables of the purchase and sale power. and uncertainty variables ; Uncertain variables belong to the uncertainty set ; The second stage minimization problem in the inner layer is equal to the objective function, is the coefficient column vector corresponding to the objective function.

[0120] Among them, the feasible domain of the two-stage robust optimization model is Specifically expressed as:

[0121] (Formula 22)

[0122] in, Indicates that when given a set of first-stage decision variables and uncertainty variables When the continuous decision variable The value range of 、 、 、 and The decision variables and The coefficient matrix of the corresponding constraints; and are constant column vectors respectively; 、 、 and They are The dual multiplier of the constraints corresponding to the inner minimization problem of the structure.

[0123] It can be understood that for each given set of uncertain variables, there is a corresponding optimal objective function value, and the purpose of the max layer in the second stage optimization problem of the two-stage robust optimization model is to find the worst scenario that leads to the highest operating cost of the solar-storage-charging system.

[0124] In one embodiment, the decomposition algorithm includes the C&CG method; Figure 5 As shown, the steps of converting the two-stage robust optimization model into a main problem and sub-problems using a decomposition algorithm include the following steps S502 to S504.

[0125] Step S502: Based on the two-stage robust optimization model, the main problem and the original sub-problems are decomposed by the C&CG method.

[0126] Among them, the original sub-problems include the inner minimization problem and the outer maximization problem.

[0127] Specifically, the control device can use the C&CG method to decompose the main problem and the original sub-problems corresponding to the two-stage robust optimization model.

[0128] For example, as shown in the following formula 23, the decomposed main problem can be expressed as:

[0129] (Formula 23)

[0130] in, For the New decision variables related to the subproblem are added to the main problem during the first iteration; is the total number of iterations; For the In the iterative solution process, the worst scenario under the corresponding discrete decision variables is found by solving the subproblems.

[0131] Furthermore, as shown in the following formula 24, the decomposed original subproblem can be expressed as:

[0132] (Formula 24)

[0133] Step S504 : Based on the inner minimization problem and the outer maximization problem, the subproblems of the two-stage robust optimization model are determined using the strong duality theory.

[0134] Specifically, the control device can utilize strong duality theory to obtain subproblems of a two-stage robust optimization model based on an inner minimization problem and an outer maximization problem.

[0135] For example, as shown in the following formula 25, using the strong duality theory, the inner minimization problem of the original subproblem can be transformed into a maximization problem, and the transformed maximization problem is merged with the outer maximization problem to obtain the following original maximization problem:

[0136] (Formula 25)

[0137] in, is a bilinear term, then the uncertainty variable corresponding to the optimal solution of the dual problem in the above formula is At the boundary extreme of the uncertainty set, that is, when the photovoltaic output takes the minimum value of the uncertainty set interval and the load power takes the maximum value of the uncertainty set interval, the two-stage robust optimization model can achieve the "worst scenario - maximum operating cost" situation.

[0138] Furthermore, due to the existence of binary variables, continuous auxiliary variables are introduced into the original maximization problem through the big M method to linearize it, and finally a sub-problem of the two-stage robust optimization model is obtained, as shown in the following formula 26:

[0139] (Formula 26)

[0140] in, is the upper bound of the dual variable; is the maximum deviation value of the uncertainty variable; is the introduced continuous auxiliary variable.

[0141] In one embodiment, the step of iteratively solving the main problem and the sub-problems to obtain the target capacity configuration result and the operation scheduling strategy includes the following steps:

[0142] Step S602 : Based on the uncertainty description set, determine the initial worst scenario and set the initial cost upper bound and cost lower bound.

[0143] Specifically, the control device can obtain the initial uncertain variables of the iterative solution process based on the uncertainty description set, and set the corresponding cost upper bound, cost lower bound and number of iterations of the iterative solution process.

[0144] In some examples, the control device may give a set of values ​​of uncertain variables as the initial worst scenario when iteratively solving the decomposed main and subproblems, and set the cost lower bound LB=−∞, the cost upper bound UB=+∞, and the number of iterations i=1.

[0145] After step S602 is completed, as shown in steps S604 to S610, the following iterative loop operations are performed:

[0146] Step S604 , solving the main problem according to the worst scenario, calculating the first-stage solution result, and using the main problem objective function value in the first-stage solution result as a new cost lower bound.

[0147] Among them, the solution results of the first stage can include the objective function value of the main problem, the optimal solution of the current energy storage device configuration, and the optimal solution of the state variables of the solar-storage-charging system.

[0148] Specifically, based on the main problem shown in Formula 23 above, the control device can iteratively solve the problem according to the current worst scenario, calculate the optimal solution for the current energy storage device configuration and the optimal solution for the state variables, and update the cost lower bound LB based on the objective function value of the main problem obtained by the solution.

[0149] Step S606: Substitute the solution result of the first stage into the subproblem, calculate and obtain the solution result of the second stage, and determine a new upper bound of the cost and a new worst scenario based on the solution result of the second stage.

[0150] The solution results of the second stage may include the objective function value of the sub-problem, the variable scheduling results, and the uncertainty variable values.

[0151] Specifically, the control device can substitute the optimal solution of the energy storage device configuration and the optimal solution of the state variables obtained by solving the above main problem into the sub-problem (such as Formula 26), and obtain the objective function value of the sub-problem, the variable scheduling result and the uncertainty variable value. Then, the cost upper bound UB is updated according to the objective function value of the sub-problem, and the new worst scenario is determined according to the variable scheduling result and the uncertainty variable value of the second-stage solution result.

[0152] Step S608: If the cost lower bound and the cost upper bound do not meet the preset accuracy conditions, the new uncertainty variables and new constraints of the main problem are determined according to the solution results of the second stage, and the next round of iterative loop operation is performed according to the new cost upper bound, the new cost lower bound and the new worst scenario.

[0153] In some examples, when the absolute value of the difference between the lower cost bound and the upper cost bound is greater than a preset accuracy threshold, it is determined that the current lower cost bound and the upper cost bound do not meet the preset accuracy condition; when the absolute value of the difference between the lower cost bound and the upper cost bound is less than or equal to the preset accuracy threshold, it is determined that the current lower cost bound and the upper cost bound meet the preset accuracy condition.

[0154] Specifically, when the cost lower bound and the cost upper bound do not meet the preset accuracy conditions, the control device determines the new uncertainty variables and new constraints of the main problem in the next round of iterative solution based on the solution results of the second stage, and performs the next round of iterative cycle operations based on the updated cost upper bound, the new cost lower bound and the new worst scenario.

[0155] Step S610: If the new cost lower bound and the new cost upper bound meet the preset accuracy condition, the target capacity configuration result and the operation scheduling strategy are determined according to the solution results of the first stage and the solution results of the second stage.

[0156] Specifically, when the lower bound and upper bound of the cost meet the preset accuracy conditions, it means that the results of the iterative solution have converged. The control device can determine the target capacity configuration result that optimizes the energy storage capacity configuration of the current photovoltaic storage and charging system based on the currently obtained first-stage solution result, and then further solve the second-stage solution result based on the current first-stage solution result to obtain the operating cost of the current photovoltaic storage and charging system and the corresponding operation scheduling strategy.

[0157] It can be understood that the above-mentioned system optimization method of the present application fully considers the uncertainty of the photovoltaic storage and charging system and the related impacts of uncertainty. The robust optimization process of the present application does not require the probability distribution of the uncertainty parameters to be given in advance. As long as the value of the parameter is within the range of the uncertainty description set, the solution of the robust optimization model must be feasible, and the amount of calculation required to obtain the optimal solution in this way can be greatly reduced, which can effectively improve the system optimization efficiency and is more suitable for engineering application scenarios.

[0158] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0159] Based on the same inventive concept, the embodiments of the present application also provide a device for optimizing capacity configuration and scheduling strategies for implementing the aforementioned method for optimizing capacity configuration and scheduling strategies. The implementation solution provided by this device is similar to the implementation solution described in the aforementioned method. Therefore, the specific limitations in the embodiments of one or more device for optimizing capacity configuration and scheduling strategies provided below can be found in the limitations of the method for optimizing capacity configuration and scheduling strategies described above and will not be repeated here.

[0160] In an exemplary embodiment, Figure 7As shown, the present application provides a capacity configuration and scheduling strategy optimization device 700, which is applied to a solar storage and charging system. The device 700 includes:

[0161] The uncertainty description set acquisition module 702 is used to obtain an uncertainty description set based on the prediction characteristic curve of the uncertainty parameter; wherein the uncertainty description set represents the uncertainty set of the photovoltaic output on both sides of the source and the load and the uncertainty set of the load demand;

[0162] An optimization model construction module 704 is used to construct a two-stage robust optimization model based on the uncertainty description set;

[0163] A model decomposition module 706 is used to convert the two-stage robust optimization model into a main problem and sub-problems using a decomposition algorithm;

[0164] The solution module 708 is used to iteratively solve the main problem and sub-problems to obtain the target capacity configuration result and the operation scheduling strategy.

[0165] In one embodiment, the uncertainty description set acquisition module 702 is further configured to:

[0166] According to the prediction characteristic curve of the uncertainty parameter, the photovoltaic output prediction characteristic data and the load prediction characteristic data are obtained;

[0167] Based on the photovoltaic output prediction characteristic data and load prediction characteristic data, the uncertainty description set is obtained using the polyhedron set.

[0168] In one embodiment, the uncertainty description set acquisition module 702 is further configured to:

[0169] Uncertainty is introduced into the uncertainty description set to obtain the uncertainty description set with introduced uncertainty.

[0170] In one embodiment, the optimization model building module 704 is also used to

[0171] Construct an objective function based on the system operation and maintenance parameters of the solar-storage-charging system;

[0172] Based on the uncertainty description set, determine the constraint conditions; wherein the constraint conditions are used to constrain the operating state of the photovoltaic storage and charging system equipment and to constrain the energy balance of the photovoltaic storage and charging system;

[0173] According to the objective function and constraints, a two-stage robust optimization model is constructed based on the min-max-min structure.

[0174] In one embodiment, the decomposition algorithm includes a C&CG method; the model decomposition module 706 is further configured to:

[0175] According to the two-stage robust optimization model, the C&CG method is used to decompose the main problem and the original sub-problems; the original sub-problems include the inner minimization problem and the outer maximization problem;

[0176] Based on the inner minimization problem and the outer maximization problem, the subproblems of the two-stage robust optimization model are determined using strong duality theory.

[0177] In one embodiment, the solution module 708 is further configured to:

[0178] Based on the uncertainty description set, determine the initial worst-case scenario and set the initial upper and lower cost bounds;

[0179] Perform the following iterative loop operations:

[0180] Solve the main problem according to the worst scenario, calculate the first-stage solution result, and use the main problem objective function value in the first-stage solution result as a new cost lower bound;

[0181] Substitute the solution of the first stage into the subproblem, calculate the solution of the second stage, and determine the new upper bound of the cost and the new worst-case scenario based on the solution of the second stage;

[0182] If the cost lower bound and the cost upper bound do not meet the preset accuracy conditions, the new uncertainty variables and new constraints of the main problem are determined according to the solution results of the second stage, and the next round of iterative cycle operations is performed according to the new cost upper bound, the new cost lower bound and the new worst scenario;

[0183] If the cost lower bound and the cost upper bound meet the preset accuracy conditions, the target capacity configuration result and the operation scheduling strategy are determined based on the solution results of the first stage and the second stage.

[0184] Each module in the aforementioned capacity configuration and scheduling strategy optimization apparatus 700 may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0185] In an exemplary embodiment, a control device is provided. The control device may be a server, and its internal structure diagram may be as follows: Figure 8As shown. The control device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the control device is used to provide computing and control capabilities. The memory of the control device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the control device is used to store data such as prediction characteristic curves of uncertainty parameters. The input / output interface of the control device is used to exchange information between the processor and external devices. The communication interface of the control device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing capacity configuration and scheduling strategy is implemented.

[0186] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the control device to which the scheme of the present application is applied. The specific control device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0187] In one embodiment, Figure 1 As shown, a photovoltaic storage and charging system 100 is also provided, including a control device 102 as described in the above embodiment; the system 100 also includes a photovoltaic device 104, an energy storage device 106 and an electrical load 108 including a charging device, which are respectively connected to the control device 102; the photovoltaic device 104, the energy storage device 106 and the electrical load 108 are connected to each other in pairs.

[0188] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0189] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0190] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0191] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0192] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0193] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for optimizing capacity configuration and scheduling strategy, characterized in that: A control device applied to a solar storage and charging system, the method comprising: An uncertainty description set is obtained based on the prediction characteristic curve of the uncertainty parameter; wherein the uncertainty description set represents the uncertainty set of the photovoltaic output on both sides of the source and the load and the uncertainty set of the load demand; Based on the uncertainty description set, a two-stage robust optimization model is constructed; Using a decomposition algorithm, the two-stage robust optimization model is converted into a main problem and sub-problems; The main problem and the sub-problems are solved iteratively to obtain the target capacity configuration result and the operation scheduling strategy.

2. The method according to claim 1, characterized in that The steps of obtaining the uncertainty description set according to the prediction characteristic curve of the uncertainty parameter include: Obtaining photovoltaic output prediction characteristic data and load prediction characteristic data according to the prediction characteristic curve of the uncertainty parameter; The uncertainty description set is obtained by using a polyhedron set based on the photovoltaic output prediction characteristic data and the load prediction characteristic data.

3. The method according to claim 2, characterized in that After the step of obtaining the uncertainty description set based on the photovoltaic output prediction characteristic data and the load prediction characteristic data using a polyhedron set, the method further includes: Uncertainty is introduced into the uncertainty description set to obtain the uncertainty description set with the uncertainty introduced.

4. The method according to claim 1, wherein The steps of constructing a two-stage robust optimization model based on the uncertainty description set include: Constructing an objective function according to system operation and maintenance parameters of the solar-storage-charging system; Determining constraints based on the uncertainty description set; wherein the constraints are used to constrain the operating state of the photovoltaic storage and charging system equipment and to constrain the energy balance of the photovoltaic storage and charging system; According to the objective function and the constraints, the two-stage robust optimization model is constructed based on a min-max-min structure.

5. The method according to claim 1, wherein The decomposition algorithm includes the C&CG method; The steps of converting the two-stage robust optimization model into a main problem and subproblems using a decomposition algorithm include: According to the two-stage robust optimization model, the main problem and the original sub-problems are decomposed by the C&CG method; The original sub-problems include an inner minimization problem and an outer maximization problem; Based on the inner minimization problem and the outer maximization problem, the subproblems of the two-stage robust optimization model are determined using strong duality theory.

6. The method according to claim 1, wherein The step of iteratively solving the main problem and the subproblems to obtain a target capacity configuration result and an operation scheduling strategy includes: Based on the uncertainty description set, determine the initial worst-case scenario and set the initial upper and lower cost bounds; Perform the following iterative loop operations: Solve the main problem according to the worst scenario, calculate the first-stage solution result, and use the main problem objective function value in the first-stage solution result as a new cost lower bound; Substituting the solution of the first stage into the subproblem, calculating the solution of the second stage, and determining a new upper bound of the cost and a new worst-case scenario based on the solution of the second stage; If the cost lower bound and the cost upper bound do not meet the preset accuracy condition, then determining new uncertainty variables and new constraints of the main problem according to the solution result of the second stage, and performing the next round of the iterative loop operation according to the new cost upper bound, the new cost lower bound and the new worst scenario; If the cost lower bound and the cost upper bound meet the preset accuracy condition, the target capacity configuration result and the operation scheduling strategy are determined according to the first stage solution result and the second stage solution result.

7. A device for optimizing capacity configuration and scheduling strategy, characterized in that: A control device for a solar storage and charging system, comprising: An uncertainty description set acquisition module is used to obtain an uncertainty description set based on a prediction characteristic curve of uncertainty parameters; wherein the uncertainty description set represents the uncertainty set of photovoltaic output on both sides of the source and load and the uncertainty set of load demand; An optimization model construction module, configured to construct a two-stage robust optimization model based on the uncertainty description set; A model decomposition module, configured to convert the two-stage robust optimization model into a main problem and sub-problems using a decomposition algorithm; The solution module is used to iteratively solve the main problem and the sub-problems to obtain the target capacity configuration result and the operation scheduling strategy.

8. A control device, characterized in that: The control device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that the processor implements the steps of any one of the methods of claims 1 to 6 when executing the computer program.

9. A solar storage and charging system, characterized in that: The system comprises a control device as described in claim 8; the system further comprises a photovoltaic device, an energy storage device and an electrical load including a charging device respectively connected to the control device; the photovoltaic device, the energy storage device and the electrical load are connected to each other in pairs.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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