Renewable energy bearing capacity determination method and device based on approximate dynamic programming

Abstract

The invention relates to a renewable energy bearing capacity determination method and device based on approximate dynamic programming, computer equipment, a storage medium and a computer program product. The method comprises the following steps: in response to a prediction request for the renewable energy bearing capacity of a target power system, obtaining constraint condition setting information input by the prediction request; according to the constraint condition setting information, constructing a power system planning problem for the target power system; solving the planning problem of the power system by adopting approximate dynamic planning to obtain bearing capacity information for the target power system; and displaying the display data generated based on the bearing capacity information. By adopting the method, the renewable energy bearing capacity of the power system can be efficiently calculated under the condition of considering various uncertainties of the power system.

Description

Technical Field

[0001] The present application relates to the field of renewable energy technology, and in particular to a method, device, computer equipment, storage medium and computer program product for determining renewable energy carrying capacity based on approximate dynamic programming. Background Art

[0002] The effective carrying capacity in a power system represents the additional load that can be provided in the power system at the same reliability level due to the installation of new generation units. It is a value that has long been established for traditional power plants. In recent years, wind power and solar power have also had similar effective carrying capacity values, and the calculation of effective carrying capacity has become an important part of the integration of renewable resources in power system planning.

[0003] Currently, in order to deal with a range of different forms of uncertainty that may occur in power systems, from hourly wind, solar and demand to daily and monthly changes in fuel supply for conventional units, existing stochastic programming frameworks are usually used to handle random conditions in optimization problems, and uncertain information stochastic programming is usually represented as a scenario tree. However, the size of the scenario tree grows exponentially with the increase in the duration considered, and when solving practical application problems in the real world, calculations to solve large-scale models are required, making it impossible to efficiently calculate the renewable energy carrying capacity of the power system while considering various uncertainties in the power system.

[0004] Therefore, traditional technologies have the problem of being unable to efficiently calculate the renewable energy carrying capacity of the power system while taking into account various uncertainties in the power system. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for determining renewable energy carrying capacity based on approximate dynamic programming, which can efficiently calculate the renewable energy carrying capacity of the power system while taking into account various uncertainties of the power system, in order to address the above-mentioned technical problems.

[0006] A method for determining renewable energy carrying capacity based on approximate dynamic programming, the method comprising:

[0007] In response to a forecast request for renewable energy carrying capacity of a target power system, obtaining constraint setting information inputted into the forecast request;

[0008] According to the constraint setting information, a power system planning problem for the target power system is constructed; wherein the operation cost of the target power system meets the preset operation cost as the solution target of the power system planning problem, and the load expected loss constraint condition and the random safety constraint condition are used as the constraint conditions of the power system planning problem;

[0009] Approximate dynamic programming is used to solve the power system planning problem and obtain the carrying capacity information of the target power system; the carrying capacity assessment information represents the renewable energy carrying capacity corresponding to the target power system;

[0010] Displays display data generated based on carrying capacity information.

[0011] In one embodiment, the solution target is composed of a first solution target and a second solution target; the first solution target includes solution targets corresponding to different operating states of the day; the second solution target includes a solution target corresponding to the load reduction situation of the day and a solution target corresponding to subsequent load reduction situations;

[0012] The first solution objective is expressed as:

[0013] ;

[0014] in, is a state variable, representing the current state of the target power system; Characterization about state variables The value function of Characterizing the initial state The value under uncertainty; E represents the expected value under uncertainty; Characterize uncertain parameters or random variables;

[0015] The second solution objective is expressed as:

[0016] ;

[0017] in, is a probability function, representing the situation The probability of the following; Characterization equipment Rated capacity; In the scenario ,time , Time period Next, equipment The current; Characterization equipment In mode Operating costs under Representation Scenario ,time , Time period and Mode Lower equipment The power flow; and Representation Scenario Next, equipment In time , Time period startup costs and downtime costs; Representation Scenario Down in time , Time period Load shedding on busbar b; is the load shedding penalty coefficient, which represents the economic loss of load shedding.

[0018] In one embodiment, the constraints include a first constraint, a second constraint, a third constraint, a fourth constraint, and a fifth constraint;

[0019] The first constraint is expressed as:

[0020] ;

[0021] in, Characterized in the The optimal value function of the stage corresponds to the optimal value function of the stage The optimal value obtained by minimizing the cost in all time periods; Characterization at stage ,time And the scenario Next, equipment The startup state; Characterization in Pattern Lower equipment operating costs; Characterization at stage ,time And the scenario Next, equipment In mode The power generation or load under Characterization at stage and time Next, equipment Start-up costs; Characterization Phase and time Next, equipment downtime costs; Characterizes the load shedding penalty coefficient, which is used to represent the economic loss of load shedding; Characterization at stage ,time And the scenario Next, the load removal amount on busbar b; Characterization at stage ,time And in scenario n-1, the device The coupling coefficient of Characterization equipment Rated capacity; Characterization at stage ,time And the scenario Next, equipment The estimated startup state of Characterization Phase ,time And the scenario Next, equipment In mode Estimated power generation or load under Characterizes the average load shedding penalty factor; Characterization at stage ,time And the scenario Load forecast for all units under

[0022] The second constraint is expressed as:

[0023] ;

[0024] in, Represents the power flow of unit i at time t and scenario n; Characterize the power flow prediction of unit i on bus m at time t and scenario n;

[0025] The third constraint is expressed as:

[0026] ;

[0027] in, represents the current of unit i at time t and scenario n; Represents the current prediction of unit i at time t and scenario n;

[0028] The fourth constraint is:

[0029] ;

[0030] in, Characterization equipment Minimum power generation capacity; Characterization equipment Maximum power generation capacity;

[0031] The fifth constraint is:

[0032] ;

[0033] in, Characterization at stage ,time And the scenario Load demand under Characterizes the safety margin, which is used to indicate the additional power generation required under load demand.

[0034] In one embodiment, approximate dynamic programming is used to solve the power system planning problem to obtain the carrying capacity information for the target power system, including:

[0035] Acquiring initialization state data of a target power system;

[0036] Based on approximate dynamic programming, the initialization state data is iteratively updated to obtain an iterative update result;

[0037] When the iterative update result satisfies a preset convergence condition, the carrying capacity information of the target power system is determined based on the iterative update result.

[0038] In one embodiment, based on approximate dynamic programming, the initialization state data is iteratively updated to obtain an iterative update result, including:

[0039] The initialization state data is processed by using an approximate value function to obtain the cost information of the expected remaining days; the expected remaining days are the remaining days that the target power system cannot meet the load demand; the cost information is the operating cost of the target power system in the expected remaining days;

[0040] Based on the cost information of the expected remaining days, the initialization state data is updated to obtain the updated state data;

[0041] The approximate value function is updated based on the updated state data to obtain an updated approximate value function as an iterative update result.

[0042] In one embodiment, the operating cost includes load reduction cost, no-load cost, power generation cost, startup cost, shutdown cost, and expected value cost of power shortage within the dispatch scope.

[0043] A device for determining renewable energy carrying capacity based on approximate dynamic programming, the device comprising:

[0044] A response module, configured to respond to a prediction request for renewable energy carrying capacity of a target power system and obtain constraint setting information inputted into the prediction request;

[0045] A construction module is used to construct a power system planning problem for a target power system according to constraint setting information; wherein the operation cost of the target power system meets the preset operation cost as the solution target of the power system planning problem, and the load expected loss constraint condition and the random safety constraint condition are the constraint conditions of the power system planning problem;

[0046] A solution module is used to solve the power system planning problem by using approximate dynamic programming to obtain the carrying capacity information for the target power system; the carrying capacity evaluation information represents the renewable energy carrying capacity corresponding to the target power system;

[0047] The display module is used to display display data generated based on the carrying capacity information.

[0048] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0049] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0050] A computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0051] The above-mentioned renewable energy carrying capacity determination method, device, computer equipment, storage medium and computer program product based on approximate dynamic programming obtain the constraint setting information of the prediction request input by responding to the prediction request of the renewable energy carrying capacity of the target power system; construct a power system planning problem for the target power system according to the constraint setting information; wherein, the operating cost of the target power system meets the preset operating cost as the solution target of the power system planning problem, and the load expected loss constraint and random safety constraint are used as the constraint conditions of the power system planning problem; the power system planning problem is solved by approximate dynamic programming to obtain the carrying capacity information for the target power system; the carrying capacity evaluation information represents the renewable energy carrying capacity corresponding to the target power system; and the display data generated based on the carrying capacity information is displayed; in this way, the operating cost of the baseline situation with the predicted value can be minimized while considering the variable cost under uncertain factors, which can effectively improve the evaluation accuracy of the effective carrying capacity of renewable energy under various uncertainties in power system planning and the accuracy of the prediction of the operating cost of the power system. Compared with traditional methods, it can assist dispatchers in making decisions based on current information and reduce the computational burden of predicting future states. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] FIG1 is an application environment diagram of a method for determining renewable energy carrying capacity based on approximate dynamic programming in one embodiment;

[0054] Figure 2 A schematic flow chart of a method for determining renewable energy carrying capacity based on approximate dynamic programming in one embodiment;

[0055] Figure 3 A schematic flow chart of a method for solving a random safety-constrained unit commitment model based on approximate dynamic programming in one embodiment;

[0056] Figure 4 A schematic flow chart of a method for determining renewable energy carrying capacity based on approximate dynamic programming in another embodiment;

[0057] Figure 5 It is a structural block diagram of a device for determining renewable energy carrying capacity based on approximate dynamic programming in one embodiment;

[0058] FIG. 6 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] The method for determining the renewable energy carrying capacity based on approximate dynamic programming provided in the embodiment of the present application can be applied in the application environment shown in Figure 1. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 responds to the prediction request for the renewable energy carrying capacity of the target power system, and obtains the constraint setting information input by the prediction request; the server 104 constructs the power system planning problem for the target power system according to the constraint setting information; wherein, the operating cost of the target power system meets the preset operating cost as the solution target of the power system planning problem, and the load expected loss constraint condition and the random safety constraint condition are used as the constraint condition of the power system planning problem; the server 104 uses approximate dynamic programming to solve the power system planning problem, and obtains the carrying capacity information for the target power system; the carrying capacity evaluation information represents the renewable energy carrying capacity corresponding to the target power system; the server 104 displays the display data generated based on the carrying capacity information. The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0061] In an exemplary embodiment, Figure 2 As shown, a method for determining renewable energy carrying capacity based on approximate dynamic programming is provided, and the method is applied to the server 104 in FIG. 1 as an example for description, including the following steps 202 to 208. Among them:

[0062] Step 202: In response to a prediction request for renewable energy carrying capacity of a target power system, constraint setting information inputted into the prediction request is obtained.

[0063] The target power system may be any power system in the field of energy management.

[0064] The prediction request may be a request for obtaining an evaluation value of a renewable energy carrying capacity for a target power system.

[0065] The constraint condition setting information may refer to the constraint conditions set by the user for evaluating the renewable energy carrying capacity of the target power system.

[0066] Optionally, the server obtains the constraint conditions set by the user in response to a prediction request initiated by the user for the renewable energy carrying capacity of the target power system.

[0067] Step 204, constructing a power system planning problem for the target power system according to the constraint setting information; wherein, the operating cost of the target power system meets the preset operating cost as the solution goal of the power system planning problem, and the load expected loss constraint and the random safety constraint are used as the constraint conditions of the power system planning problem.

[0068] The power system planning problem may refer to a planning task corresponding to energy planning for a target power system.

[0069] The preset operating cost may refer to the minimum operating cost.

[0070] Among them, the load loss expectation constraint condition can refer to a constraint condition determined based on a load loss expectation (LOLE) index, wherein the load loss expectation (LOLE) index is the expectation that the available power generation supply cannot meet the system load per hour, which can also be expressed as the expected number of days that the target power system cannot meet the load demand.

[0071] The random safety constraint condition may refer to a constraint condition corresponding to a random safety constraint unit commitment (SCUC).

[0072] Optionally, the server constructs a power system planning problem for the target power system based on the constraints set by the user, wherein minimizing the operating cost of the target power system is used as the goal of solving the power system planning problem, and expected load loss constraints and random safety constraints are used as constraints of the power system planning problem.

[0073] Step 206, using approximate dynamic programming to solve the power system planning problem, and obtain the carrying capacity information for the target power system; the carrying capacity evaluation information represents the renewable energy carrying capacity corresponding to the target power system.

[0074] Among them, Approximate Dynamic Programming can refer to ADP (Approximate Dynamic Programming, a nonlinear optimization method), which combines the ideas of Reinforcement Learning (RL) and Dynamic Programming (DP). Since the long-term SCUC formula is a large-scale, non-convex, non-deterministic polynomial time difficult (NP-hard) problem with uncertain parameters, approximate dynamic programming is used to decompose the original problem into sub-problems to evaluate the effective carrying capacity of wind and solar energy, while maintaining the same reliability level under the consideration of uncertain information. The one-year SCUC model can be solved iteratively through the following steps: Step 1: Starting from the initial state of each iteration, iteration 0 represents the basic situation with predicted values; Step 2: Solve the deterministic optimization for each day and use the approximate value function (VFA) to calculate the cost of the remaining days; Step 3: Update the value function approximation; Step 4: Get the Monte Carlo sample of the cost and calculate the initial state of the next day; Step 5: Update the iteration and return to step 1.

[0075] Optionally, the server uses approximate dynamic programming to solve the power system planning problem and obtain a carrying capacity value for the target power system.

[0076] Step 208: Display data generated based on the carrying capacity information.

[0077] The displayed data may refer to the numerical value represented by the bearing capacity information.

[0078] Optionally, the server displays display data generated based on the bearing capacity information.

[0079] In the above-mentioned method for determining renewable energy carrying capacity based on approximate dynamic programming, by responding to a prediction request for the renewable energy carrying capacity of the target power system, the constraint setting information of the prediction request input is obtained; according to the constraint setting information, a power system planning problem for the target power system is constructed; wherein, the operating cost of the target power system satisfies the preset operating cost as the solution target of the power system planning problem, and the load expected loss constraint and the random safety constraint are used as the constraint conditions of the power system planning problem; the power system planning problem is solved by approximate dynamic programming to obtain the carrying capacity information for the target power system; the carrying capacity evaluation information represents the renewable energy carrying capacity corresponding to the target power system; and the display data generated based on the carrying capacity information is displayed; in this way, the operating cost of the baseline situation with the predicted value can be minimized while considering the variable cost under uncertain factors, which can effectively improve the evaluation accuracy of the effective carrying capacity of renewable energy under various uncertainties in power system planning and the accuracy of the prediction of the operating cost of the power system. Compared with traditional methods, it can assist dispatchers in making decisions based on current information and reduce the computational burden of predicting future states.

[0080] In another embodiment, the solution target consists of a first solution target and a second solution target; the first solution target includes solution targets corresponding to different operating states of the day; the second solution target includes a solution target corresponding to the load reduction situation of the day and a solution target corresponding to subsequent load reduction situations.

[0081] Among them, the first solution goal corresponds to a multi-stage stochastic linear programming problem, and there are stages 1, ..., d, ..., ND every day. Then the first solution goal can be expressed as:

[0082] ;

[0083] In the above formula, is a state variable, representing the current state of the target power system; Characterization about state variables The value function of Characterizing the initial state The value under uncertainty; E represents the expected value under uncertainty; Characterize uncertain parameters or random variables.

[0084] Among them, the second solution goal corresponds to each node in each stage, and the minimum operating cost of each node is divided into a two-stage stochastic programming problem, including the first stage of the uncertainty independent solution for the load shedding time and the second stage of the scenario in the following days. Then the second solution goal can be expressed as:

[0085] ;

[0086] In the above formula, is a probability function, representing the situation The probability of the following; Characterization equipment Rated capacity; In the scenario ,time , Time period Next, equipment The current; Characterization equipment In mode Operating costs under Representation Scenario ,time , Time period and Mode Lower equipment The power flow; and Representation Scenario Next, equipment In time , Time period startup costs and downtime costs; Representation Scenario Down in time , Time period Load shedding on busbar b; is the load shedding penalty coefficient, which represents the economic loss of load shedding.

[0087] This embodiment is to add the load reduction cost to the objective function of the stochastic SCUC problem. The objective function of the stochastic SCUC problem is to minimize the operating costs of all generator sets, which generally include no-load costs, power generation costs, startup / shutdown costs, and expected energy shortage (EENS) costs within the entire dispatch range. The next embodiment is based on this embodiment, and adds the load loss expectation (LOLE) index as a restriction to the constraint set to represent the system reliability in the stochastic safety constrained unit combination (SCUC). The current stochastic safety constrained unit combination (SCUC) constraints include system power balance, actual power generation capacity limitations of thermal power generators, power generation limitations of solar and wind power generators, minimum startup and shutdown time limitations, system rotation and operation reserve requirements, unit climb and descent limitations, and fuel and emission limitations. The DC transmission network security constraints include node power balance, transmission flow equation, and transmission flow limitation. In addition, the present application also takes into account the daytime coupling of daily operation, that is, the unit The final commitment state and power dispatch of the next day are regarded as the initial state of the unit in the stochastic safety constrained unit commitment (SCUC) problem. In addition, the load shedding cost (also known as the value of lost load VOLL) can be determined, which is the load shedding multiplied by the load shedding price of the user, expressed in $ / kWh. In practical applications, the total load shedding power of each bus has an upper limit.

[0088] In another embodiment, the constraints include a first constraint, a second constraint, a third constraint, a fourth constraint, and a fifth constraint;

[0089] The first constraint is expressed as:

[0090] ;

[0091] in, Characterized in the The optimal value function of the stage corresponds to the optimal value function of the stage The optimal value obtained by minimizing the cost in all time periods; Characterization at stage ,time And the scenario Next, equipment The startup state; Characterization in Pattern Lower equipment operating costs; Characterization at stage ,time And the scenario Next, equipment In mode The power generation or load under Characterization at stage and time Next, equipment Start-up costs; Characterization Phase and time Next, equipment downtime costs; Characterizes the load shedding penalty coefficient, which is used to represent the economic loss of load shedding; Characterization at stage ,time And the scenario Next, the load removal amount on busbar b; Characterization at stage ,time And in scenario n-1, the device The coupling coefficient of Characterization equipment Rated capacity; Characterization at stage ,time And the scenario Next, equipment The estimated startup state of Characterization Phase ,time And the scenario Next, equipment In mode Estimated power generation or load under Characterizes the average load shedding penalty factor; Characterization at stage ,time And the scenario Load forecast for all units under

[0092] The second constraint is expressed as:

[0093] ;

[0094] in, Represents the power flow of unit i at time t and scenario n; Characterize the power flow prediction of unit i on bus m at time t and scenario n;

[0095] The third constraint is expressed as:

[0096] ;

[0097] in, represents the current of unit i at time t and scenario n; Represents the current prediction of unit i at time t and scenario n;

[0098] The fourth constraint is:

[0099] ;

[0100] in, Characterization equipment Minimum power generation capacity; Characterization equipment Maximum power generation capacity;

[0101] The fifth constraint is:

[0102] ;

[0103] in, Characterization at stage ,time And the scenario Load demand under Characterizes the safety margin, which is used to indicate the additional power generation required under load demand.

[0104] This application will state variables Deemed as crew commitment and power scheduling , and the decision variable Represents the start indicator , close indicator and load shedding (Load shedding of bus b at time t). The stochastic safety constrained unit commitment (SCUC) problem based on the approximate value function (VFA) can be decomposed into the terms in the first constraint. The first term of the first constraint corresponds to the actual cost of the day, and the second term of the first constraint corresponds to the state variable of the day (i.e. and )’s prediction cost ( ). In practical applications, the predicted cost can be described by future operating variables, which include future power dispatch and future unit commitment status.

[0105] This application establishes the relationship between the predicted cost and the state variables of the day (i.e. and ), corresponding to the second constraint and the third constraint.

[0106] The present application maintains the consistency of future power dispatch and future unit commitment status, corresponding to the fourth constraint, which is similar to the constraints of the traditional stochastic security constrained unit commitment (SCUC) problem.

[0107] This application considers future load shedding to ensure that the subproblem based on approximate dynamic programming is equivalent to the original problem. The load shedding cost in the previous iteration is used to approximate the future load shedding cost in the current iteration. is a predefined threshold used to indicate whether there is a predicted load reduction cost, corresponding to the fifth constraint.

[0108] In another embodiment, approximate dynamic programming is used to solve the power system planning problem to obtain the carrying capacity information for the target power system, including: obtaining the initialization state data of the target power system; based on the approximate dynamic programming, iteratively updating the initialization state data to obtain an iterative update result; when the iterative update result meets a preset convergence condition, determining the carrying capacity information of the target power system based on the iterative update result.

[0109] The initialization state data may refer to the initial state data of the target power system such as the current, power, and load on that day.

[0110] Optionally, the server obtains initialization state data of the target power system, and based on approximate dynamic programming, the server iteratively updates the initialization state data to obtain an iterative update result. When the iterative update result meets a preset convergence condition, the server determines the carrying capacity information of the target power system based on the iterative update result.

[0111] In this embodiment, the initialization state data of the target power system is obtained; based on approximate dynamic programming, the initialization state data is iteratively updated to obtain an iterative update result; when the iterative update result satisfies a preset convergence condition, the carrying capacity information of the target power system is determined based on the iterative update result, and approximate dynamic programming can be used to solve the objective function, thereby accurately determining the carrying capacity information of the target power system.

[0112] In another embodiment, based on approximate dynamic programming, the initialization state data is iteratively updated to obtain an iterative update result, including: using an approximate value function to process the initialization state data to obtain cost information of the expected remaining days; the expected remaining days are the remaining days that the target power system cannot meet the load demand; the cost information is the operating cost of the target power system in the expected remaining days; based on the cost information of the expected remaining days, the initialization state data is updated to obtain updated state data; based on the updated state data, the approximate value function is updated to obtain an updated approximate value function as the iterative update result.

[0113] Optionally, the server uses an approximate value function to process the initialization state data to obtain cost information of the expected remaining days. The server updates the initialization state data based on the cost information of the expected remaining days to obtain updated state data. The server updates the approximate value function based on the updated state data to obtain an updated approximate value function as an iterative update result.

[0114] In this embodiment, the cost information of the expected remaining days is obtained by processing the initialization state data using an approximate value function; the expected remaining days are the remaining days during which the target power system cannot meet the load demand; the cost information is the operating cost of the target power system in the expected remaining days; based on the cost information of the expected remaining days, the initialization state data is updated to obtain updated state data; based on the updated state data, the approximate value function is updated to obtain an updated approximate value function, which, as an iterative update result, can achieve optimization of the approximate value function.

[0115] The key to the stochastic safety-constrained unit commitment model based on approximate dynamic programming is to continuously update the approximate function, which can adaptively adjust the daily unit combination status and power dispatch by learning the history of the previous few days and the forecast information of the next few days. The approximate function advances forward in time without enumerating all states in the state space. Therefore, compared with the classical backward dynamic programming, the computational burden can be reduced. Since the approximate function contains integer variables, this patent uses differences to replace the derivatives of the approximate function. In addition, the variables can take minimum or maximum values, and in each iteration, the slope will be updated using the standard update equation, and the initial slope is defined by the ratio of the future load to the current day's load. In addition, when load shedding and no load shedding occur, the threshold is also updated between the current iteration and the previous iteration. Here, the iteration and The step size between is a decreasing function to maintain , and ensure that the value of the slope will converge to a normal level. The flowchart of the entire method is as follows Figure 3 As shown, based on the initialization state of the target power system, solve the deterministic optimization of each day (corresponding to the first solution target), use the approximate value function VFA to calculate the cost of the remaining days; update the value function approximation; obtain the Monte Carlo sample of the cost and calculate the initial state of the next day; update the approximate value function VFA; determine whether it has converged, and if it is determined to have converged, end the task, if it has not converged, update the initialization state, and return to the step of solving the deterministic optimization of each day (corresponding to the first solution target), and use the approximate value function VFA to calculate the cost of the remaining days until convergence.

[0116] In another embodiment, the operating cost includes load reduction cost, no-load cost, power generation cost, startup cost, shutdown cost, and expected value cost of insufficient power within the dispatch scope.

[0117] In another embodiment, Figure 4 As shown, a method for determining renewable energy carrying capacity based on approximate dynamic programming is provided, and the method is applied to the server 104 in FIG. 1 as an example for explanation, including the following steps:

[0118] Step S402: In response to a prediction request for renewable energy carrying capacity of a target power system, constraint setting information inputted into the prediction request is obtained.

[0119] Step S404, constructing a power system planning problem for the target power system according to the constraint setting information; wherein, the operating cost of the target power system meets the preset operating cost as the solution goal of the power system planning problem, and the load expected loss constraint and random safety constraint are used as the constraint conditions of the power system planning problem.

[0120] Step S406: acquiring initialization state data of the target power system.

[0121] Step S408: based on approximate dynamic programming, iteratively update the initialization state data to obtain an iterative update result.

[0122] Step S410, when the iterative update result meets the preset convergence condition, determine the carrying capacity information of the target power system based on the iterative update result; the carrying capacity evaluation information represents the renewable energy carrying capacity corresponding to the target power system.

[0123] Step S412: display data generated based on the bearing capacity information.

[0124] It should be noted that the specific definitions of the above steps can refer to the specific definitions of a method for determining renewable energy carrying capacity based on approximate dynamic programming above.

[0125] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed 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 part of the steps or stages in other steps.

[0126] Based on the same inventive concept, the embodiment of the present application also provides a device for determining renewable energy carrying capacity based on approximate dynamic programming for implementing the above-mentioned method for determining renewable energy carrying capacity based on approximate dynamic programming. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the device for determining renewable energy carrying capacity based on approximate dynamic programming provided below can refer to the limitations of the method for determining renewable energy carrying capacity based on approximate dynamic programming above, and will not be repeated here.

[0127] In an exemplary embodiment, Figure 5 As shown, a device for determining renewable energy carrying capacity based on approximate dynamic programming is provided, comprising: a response module 502, a construction module 504, a solution module 506 and a display module 508, wherein:

[0128] A response module 502, for responding to a prediction request for renewable energy carrying capacity of a target power system, and obtaining constraint setting information inputted in the prediction request;

[0129] A construction module 504 is used to construct a power system planning problem for a target power system according to constraint setting information; wherein the operation cost of the target power system meets the preset operation cost as the solution target of the power system planning problem, and the load expected loss constraint condition and the random safety constraint condition are the constraint conditions of the power system planning problem;

[0130] A solution module 506 is used to solve the power system planning problem by using approximate dynamic programming to obtain the carrying capacity information of the target power system; the carrying capacity evaluation information represents the renewable energy carrying capacity corresponding to the target power system;

[0131] The display module 508 is used to display display data generated based on the bearing capacity information.

[0132] In one embodiment, the solution target is composed of a first solution target and a second solution target; the first solution target includes solution targets corresponding to different operating states of the day; the second solution target includes a solution target corresponding to the load reduction situation of the day and a solution target corresponding to subsequent load reduction situations;

[0133] The first solution objective is expressed as:

[0134] ;

[0135] in, is a state variable, representing the current state of the target power system; Characterization about state variables The value function of Characterizing the initial state The value under uncertainty; E represents the expected value under uncertainty; Characterize uncertain parameters or random variables;

[0136] The second solution objective is expressed as:

[0137] ;

[0138] in, is a probability function, representing the situation The probability of the following; Characterization equipment Rated capacity; In the scenario ,time , Time period Next, equipment The current; Characterization equipment In mode Operating costs under Representation Scenario ,time , Time period and Mode Lower equipment The power flow; and Representation Scenario Next, equipment In time , Time period startup costs and downtime costs; Representation Scenario Down in time , Time period Load shedding on busbar b; is the load shedding penalty coefficient, which represents the economic loss of load shedding.

[0139] In one embodiment, the constraints include a first constraint, a second constraint, a third constraint, a fourth constraint, and a fifth constraint;

[0140] The first constraint is expressed as:

[0141] ;

[0142] in, Characterized in the The optimal value function of the stage corresponds to the optimal value function of the stage The optimal value obtained by minimizing the cost in all time periods; Characterization at stage ,time And the scenario Next, equipment The startup state; Characterization in Pattern Lower equipment operating costs; Characterization at stage ,time And the scenario Next, equipment In mode The power generation or load under Characterization at stage and time Next, equipment Start-up costs; Characterization Phase and time Next, equipment downtime costs; Characterizes the load shedding penalty coefficient, which is used to represent the economic loss of load shedding; Characterization at stage ,time And the scenario Next, the load removal amount on busbar b; Characterization at stage ,time And in scenario n-1, the device The coupling coefficient of Characterization equipment Rated capacity; Characterization at stage ,time And the scenario Next, equipment The estimated startup state of Characterization Phase ,time And the scenario Next, equipment In mode Estimated power generation or load under Characterizes the average load shedding penalty factor; Characterization at stage ,time And the scenario Load forecast for all units under

[0143] The second constraint is expressed as:

[0144] ;

[0145] in, Represents the power flow of unit i at time t and scenario n; Characterize the power flow prediction of unit i on bus m at time t and scenario n;

[0146] The third constraint is expressed as:

[0147] ;

[0148] in, represents the current of unit i at time t and scenario n; Represents the current prediction of unit i at time t and scenario n;

[0149] The fourth constraint is:

[0150] ;

[0151] in, Characterization equipment Minimum power generation capacity; Characterization equipment Maximum power generation capacity;

[0152] The fifth constraint is:

[0153] ;

[0154] in, Characterization at stage ,time And the scenario Load demand under Characterizes the safety margin, which is used to indicate the additional power generation required under load demand.

[0155] In one of the embodiments, the solution module 506 is specifically used to obtain the initialization state data of the target power system; based on approximate dynamic programming, iteratively update the initialization state data to obtain an iterative update result; when the iterative update result meets the preset convergence condition, determine the carrying capacity information of the target power system based on the iterative update result.

[0156] In one of the embodiments, the solution module 506 is specifically used to process the initialization state data using an approximate value function to obtain cost information of the expected remaining days; the expected remaining days are the remaining days that the target power system cannot meet the load demand; the cost information is the operating cost of the target power system in the expected remaining days; based on the cost information of the expected remaining days, the initialization state data is updated to obtain updated state data; based on the updated state data, the approximate value function is updated to obtain an updated approximate value function as an iterative update result.

[0157] In one embodiment, the operating cost includes load reduction cost, no-load cost, power generation cost, startup cost, shutdown cost, and expected value cost of power shortage within the dispatch scope.

[0158] Each module in the above-mentioned renewable energy carrying capacity determination device based on approximate dynamic programming can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0159] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG6. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the 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 computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data for determining the carrying capacity of renewable energy based on approximate dynamic programming. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining the carrying capacity of renewable energy based on approximate dynamic programming is implemented.

[0160] Those skilled in the art will understand that the structure shown in FIG. 6 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0161] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above-mentioned method for determining renewable energy carrying capacity based on approximate dynamic programming. Here, the steps of the method for determining renewable energy carrying capacity based on approximate dynamic programming can be the steps of the method for determining renewable energy carrying capacity based on approximate dynamic programming in each of the above-mentioned embodiments.

[0162] In one embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the processor executes the steps of the above-mentioned method for determining renewable energy carrying capacity based on approximate dynamic programming. Here, the steps of the method for determining renewable energy carrying capacity based on approximate dynamic programming can be the steps of the method for determining renewable energy carrying capacity based on approximate dynamic programming in each of the above-mentioned embodiments.

[0163] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the processor executes the steps of the above-mentioned method for determining renewable energy carrying capacity based on approximate dynamic programming. Here, the steps of the method for determining renewable energy carrying capacity based on approximate dynamic programming can be the steps of the method for determining renewable energy carrying capacity based on approximate dynamic programming in each of the above-mentioned embodiments.

[0164] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0165] The technical features of the above embodiments may be arbitrarily combined. 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 specification.

[0166] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining renewable energy carrying capacity based on approximate dynamic programming, characterized in that: The method comprises: In response to a prediction request for renewable energy carrying capacity of a target power system, obtaining constraint setting information inputted in the prediction request; According to the constraint setting information, a power system planning problem for the target power system is constructed; wherein the operating cost of the target power system satisfies the preset operating cost as the solution target of the power system planning problem, and the load expected loss constraint and the random safety constraint are used as the constraint conditions of the power system planning problem; The power system planning problem is solved by using approximate dynamic programming to obtain carrying capacity information for the target power system; the carrying capacity evaluation information represents the renewable energy carrying capacity corresponding to the target power system; Display data generated based on the bearing capacity information is displayed.

2. The method according to claim 1, characterized in that The solution target is composed of a first solution target and a second solution target; the first solution target includes solution targets corresponding to different operating states of the day; the second solution target includes a solution target corresponding to the load reduction situation of the day and a solution target corresponding to subsequent load reduction situations; The first solution objective is expressed as: ； in, is a state variable, representing the current state of the target power system; Characterization about state variables The value function of Characterizing the initial state The value under uncertainty; E represents the expected value under uncertainty; Characterize uncertain parameters or random variables; The second solution objective is expressed as: ； in, is a probability function, representing the situation The probability of the following; Characterization equipment Rated capacity; In the scenario ,time , Time period Next, equipment The current; Characterization equipment In mode Operating costs under Representation Scenario ,time , Time period and Mode Lower equipment The power flow; and Representation Scenario Next, equipment In time , Time period startup costs and downtime costs; Representation Scenario Down in time , Time period Load shedding on busbar b; is the load shedding penalty coefficient, which represents the economic loss of load shedding.

3. The method according to claim 1, characterized in that The constraint conditions include a first constraint condition, a second constraint condition, a third constraint condition, a fourth constraint condition and a fifth constraint condition; The first constraint is expressed as: ； in, Characterized in the The optimal value function of the stage corresponds to the optimal value function of the stage The optimal value obtained by minimizing the cost in all time periods; Characterization at stage ,time And the scenario Next, equipment The startup state; Characterization in Pattern Lower equipment operating costs; Characterization at stage ,time And the scenario Next, equipment In mode The power generation or load under Characterization at stage and time Next, equipment Start-up costs; Characterization Phase and time Next, equipment downtime costs; Characterizes the load shedding penalty coefficient, which is used to represent the economic loss of load shedding; Characterization at stage ,time And the scenario Next, the load removal amount on busbar b; Characterization at stage ,time And in scenario n-1, the device The coupling coefficient of Characterization equipment Rated capacity; Characterization at stage ,time And the scenario Next, equipment The estimated startup state of Characterization Phase ,time And the scenario Next, equipment In mode Estimated power generation or load under Characterizes the average load shedding penalty factor; Characterization at stage ,time And the scenario Load forecast for all units under ; The second constraint is expressed as: ； in, Represents the power flow of unit i at time t and scenario n; Characterize the power flow prediction of unit i on bus m at time t and scenario n; The third constraint is expressed as: ； in, represents the current of unit i at time t and scenario n; represents the current prediction of unit i at time t and scenario n; The fourth constraint condition is: ； in, Characterization equipment Minimum power generation capacity; Characterization equipment Maximum power generation capacity; The fifth constraint condition is: ； in, Characterization at stage ,time And the scenario Load demand under Characterizes the safety margin, which is used to indicate the additional power generation required under load demand.

4. The method according to claim 1, characterized in that The adopting of approximate dynamic programming to solve the power system planning problem to obtain the carrying capacity information for the target power system includes: Acquiring initialization state data of the target power system; Based on approximate dynamic programming, iteratively updating the initialization state data to obtain an iterative update result; In a case where the iterative update result satisfies a preset convergence condition, the carrying capacity information of the target power system is determined based on the iterative update result.

5. The method according to claim 1, characterized in that The iterative updating of the initialization state data based on approximate dynamic programming to obtain an iterative updating result includes: The initialization state data is processed by using an approximate value function to obtain cost information of the expected remaining days; the expected remaining days are the remaining days during which the target power system cannot meet the load demand; the cost information is the operating cost of the target power system during the expected remaining days; Based on the cost information of the expected remaining days, updating the initialization state data to obtain updated state data; The approximate value function is updated based on the updated state data to obtain an updated approximate value function as the iterative update result.

6. The method according to claim 1, characterized in that The operating costs include load reduction costs, no-load costs, power generation costs, startup costs, shutdown costs, and expected value costs of insufficient electricity within the dispatch range.

7. A device for determining renewable energy carrying capacity based on approximate dynamic programming, characterized in that: The device comprises: A response module, configured to respond to a prediction request for renewable energy carrying capacity of a target power system and obtain constraint setting information inputted in the prediction request; A construction module, used to construct a power system planning problem for the target power system according to the constraint setting information; wherein the operating cost of the target power system meets the preset operating cost as the solution target of the power system planning problem, and the load expected loss constraint and the random safety constraint are used as the constraint conditions of the power system planning problem; A solution module, used for solving the power system planning problem by using approximate dynamic programming to obtain carrying capacity information for the target power system; the carrying capacity evaluation information represents the renewable energy carrying capacity corresponding to the target power system; A display module is used to display display data generated based on the bearing capacity information.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. 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.

10. A computer program product, comprising a computer program, 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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