Power consumption scheduling method and device based on state task network, equipment and medium

Abstract

The invention discloses a power consumption scheduling method, device and equipment based on a state task network, and a medium, and the method comprises the steps: dividing the production process data of a target user into state nodes and task nodes through the state task network, obtaining operation data of different working conditions under each task node and material data under each state node; obtaining time-of-use electricity price data and power generation data; and based on the operation data, the material data, the time-of-use electricity price data and the power generation data, constructing a power utilization scheduling model according to a preset rule, and solving the power utilization scheduling model to obtain a power utilization scheme. The state task network gives consideration to the complexity of the actual production process of the user and the diversity of production targets, and comprehensively considers the operation data, the material data, the time-of-use electricity price data and the power generation data, thereby enabling the target user to reasonably adjust the power load, reducing the power cost, and improving the user experience. And photovoltaic consumption and optimal configuration of electric power resources are promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching, and in particular to a power dispatching method, device, equipment and medium based on a state task network. Background Art

[0002] Driven by the goal of building a new power system dominated by new energy, a large number of new energy units have been connected to the power grid. However, new energy generators have problems such as large volatility and difficult regulation and control, which weakens the flexible regulation ability of the system supply side. Compared with commercial and residential users, industrial users have advantages such as large response capacity, stable load and high level of automation, and are the most important demand-side resources in the power system.

[0003] However, in the prior art, in the optimization scheduling for industrial users, due to the complexity of the actual production process of industrial users and the diversity of production goals, the specific electricity consumption behavior of industrial users in the production process is not analyzed, resulting in inaccurate optimization scheduling for industrial users, and unable to provide industrial users with better electricity scheduling strategies, resulting in high production costs for industrial users. Summary of the invention

[0004] In view of this, the present invention provides a method, device, equipment and medium for power scheduling based on a state task network, which are used to solve the problem of inaccurate optimization scheduling for industrial users in the prior art.

[0005] To achieve one or part or all of the above purposes or other purposes, the present invention proposes a power dispatching method based on a state task network, comprising:

[0006] The target user's production process data is divided into state nodes and task nodes through the state-task network, and the operation data of each task node and the material data under each state node are obtained;

[0007] Obtaining time-of-use electricity price data of the target user's area and power generation data of the target user;

[0008] Based on the operation data, the material data, the time-of-use electricity price data and the power generation data, a power dispatching model is constructed according to preset rules, and the power dispatching model is solved by a mixed integer linear programming model to obtain a power consumption plan for the target user.

[0009] Optionally, the step of dividing the production process data of the target user into state nodes and task nodes through the state-task network includes:

[0010] Dividing the product data in the production process data into the state nodes through the state task network;

[0011] The production processes in the production process data are divided into the task nodes through the state task network.

[0012] Optionally, the step of obtaining the operation data of each task node and the material data of each status node includes:

[0013] Acquire storage data corresponding to the product data, and use the storage data as material data corresponding to the state node;

[0014] Obtaining the working condition type data of the task node;

[0015] If the operating condition type data is not one, the operating data of the task node under different operating conditions is obtained.

[0016] Optionally, the step of constructing a power dispatching model according to preset rules based on the operation data, the material data, the time-of-use electricity price data and the power generation data includes:

[0017] Based on the purchased power in the operation data, the time-of-use electricity price data, the sold power and the sold electricity price data in the power generation data, constructing the objective function in the power dispatch model in a discrete time manner according to the function rules in the preset rules;

[0018] Constructing constraint conditions in the power dispatch model based on the material data and the constraint rules in the preset rules;

[0019] The objective function and the constraint conditions are combined to obtain the electricity dispatching model.

[0020] Optionally, the step of constructing the objective function in the power dispatching model in a discrete time manner according to the function rules in the preset rules based on the power purchase power in the operation data, the time-of-use electricity price data, the power sale power and the power sale price data in the power generation data includes:

[0021] Acquire production cycle data of a target user, and determine discrete durations according to the production cycle data;

[0022] According to the discrete duration, in a discrete time manner, through the function rules in the preset rules, a function representing the minimization of electricity cost is constructed according to the purchased electricity power in the operating data, the time-of-use electricity price data, the sold electricity power and the sold electricity price data in the power generation data, and the function representing the minimization of electricity cost is used as the objective function.

[0023] Optionally, the power generation data includes first power generation data under a first preset scenario and second power generation data under a second preset scenario, and both the first power generation data and the second power generation data are used to construct the power dispatching model.

[0024] Optionally, the constraints include at least electric power balance constraints, power purchase and sales status constraints, power purchase and sales balance constraints, material balance constraints, material storage constraints, finished product output constraints and operating condition continuity constraints.

[0025] On the other hand, an embodiment of the present application provides a power scheduling device based on a state task network, the device comprising:

[0026] The node division module is used to divide the production process data of the target user into state nodes and task nodes through the state task network, and obtain the operation data of each task node and the material data under each state node;

[0027] A data acquisition module, used to obtain the time-of-use electricity price data of the target user's area and the power generation data of the target user;

[0028] The model building and solving module is used to build a power dispatching model according to preset rules based on the operating data, the material data, the time-of-use electricity price data and the power generation data, and solve the power dispatching model by solving the mixed integer linear programming model to obtain the power consumption plan of the target user.

[0029] On the other hand, an embodiment of the present application provides an electronic device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the steps of the power scheduling method based on the state task network as described above are performed.

[0030] On the other hand, an embodiment of the present application provides a computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the power scheduling method based on the state task network as described above are executed.

[0031] Implementing the embodiments of the present invention will have the following beneficial effects:

[0032] The production process data of the target user is divided into state nodes and task nodes through the state task network, and the operation data of each task node and the material data under each state node are obtained; the time-of-use electricity price data of the target user's area and the power generation data of the target user are obtained; based on the operation data, the material data, the time-of-use electricity price data and the power generation data, a power dispatching model is constructed according to preset rules, and the power dispatching model is solved by the solution method of the mixed integer linear programming model to obtain the power consumption plan of the target user. While taking into account the complexity of the user's actual production process and the diversity of production goals through the state task network, the operation data, the material data, the time-of-use electricity price data and the power generation data are comprehensively considered, so that the target user can reasonably adjust the power load, reduce the power cost, and promote the optimal configuration of photovoltaic consumption and power resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0034] in:

[0035] Figure 1 It is a flow chart of a power dispatching method based on a state task network provided in an embodiment of the present application;

[0036] Figure 2 It is a state task network diagram in a power dispatching method based on a state task network provided in an embodiment of the present application;

[0037] Figure 3 It is a schematic diagram of power generation output of a target user in a power dispatching method based on a state task network provided in an embodiment of the present application;

[0038] Figure 4 It is a schematic diagram of power purchase power and power sale power in a power dispatching method based on a state task network provided in an embodiment of the present application;

[0039] Figure 5 It is a structural schematic diagram of a power dispatching device based on a state task network provided in an embodiment of the present application;

[0040] Figure 6 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application;

[0041] Figure 7 It is a structural schematic diagram of a computer-readable storage medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] like Figure 1 As shown, the embodiment of the present application provides a power scheduling method based on a state task network, including:

[0044] S101, dividing the production process data of the target user into state nodes and task nodes through the state task network, and obtaining the operation data of each task node and the material data under each state node;

[0045] For example, in the State-Task Network (STN), there are two basic node types in the STN method: state nodes and task nodes. The state node encompasses the material inventory of the entire production process, including entities such as raw materials, intermediate products or finished products. The task node is the feature (process) of the production activity, which can represent a single task or a collection of multiple tasks. The task node can be further divided into unschedulable tasks and schedulable tasks, that is, the products (finished products, semi-finished products) and processing procedures in the production process data are state nodes and task nodes.

[0046] Exemplarily, the operating data include but are not limited to operating parameters such as raw material consumption rate, product yield, and power consumption; the material data include but are not limited to the initial value of production raw materials, the upper limit and lower limit of material storage, and other material (finished products, semi-finished products) storage quantities.

[0047] S102, obtaining time-of-use electricity price data of the target user's area and power generation data of the target user;

[0048] Exemplarily, the time-of-use electricity price policy for industrial users in the local area of ​​the target user is used to obtain the electricity purchase prices (time-of-use electricity price data) of the target user from the power grid at different times, where the different times include at least the peak electricity consumption period, the low electricity consumption period and the flat electricity consumption period.

[0049] Exemplarily, the power generation data includes the power generation power of the target user's self-built photovoltaic power generation device and the electricity price when the power generated by the self-built photovoltaic power generation device is sold.

[0050] In a possible implementation, the power generation data includes first power generation data under a first preset scenario and second power generation data under a second preset scenario, and both the first power generation data and the second power generation data are used to construct the power scheduling model.

[0051] Exemplarily, since photovoltaic power generation is affected by the weather, the power generation data includes first power generation data in a sunny scene (first preset scene) and second power generation data in a cloudy scene (second preset scene). That is, the power generation data of the target user needs to be acquired considering weather factors. When the weather factor is sunny, the first power generation data is used for the subsequent process of constructing a power scheduling model. Similarly, when the weather factor is cloudy, the second power generation data is used for the subsequent process of constructing a power scheduling model.

[0052] For example, the first power generation data in a sunny scene (first preset scene) is different from the second power generation data in a cloudy scene (second preset scene), and is expressed in the form of power generation output, such as Figure 3 shown.

[0053] S103, constructing a power dispatching model according to preset rules based on the operation data, the material data, the time-of-use electricity price data and the power generation data, and solving the power dispatching model by means of a mixed integer linear programming model to obtain a power consumption plan for the target user.

[0054] Exemplarily, under the combined influence of the time-of-use electricity price scenario and the target user's self-built photovoltaic output scenario, the industrial production process, material quantity, equipment power consumption and production constraints are taken into consideration, and an intelligent electricity scheduling model is constructed with the goal of minimizing the electricity cost of industrial users. The electricity load of each link in the industrial production of the target user is optimized and adjusted, and the electricity scheduling model is solved by solving the mixed integer linear programming model to obtain the electricity plan of the target user.

[0055] For example, the solution of the mixed integer linear programming model is that the objective function (electricity dispatch model) is linear, all constraints are also linear, and finally, the decision variables can take any real number. If some decision variables in the linear programming problem must be integers, then the planning problem is transformed into a mixed integer linear programming problem. The optimization problem has not only conditional constraints, but also integer constraints.

[0056] The production process data of the target user is divided into state nodes and task nodes through the state task network, and the operation data of each task node and the material data under each state node are obtained; the time-of-use electricity price data of the target user's area and the power generation data of the target user are obtained; based on the operation data, the material data, the time-of-use electricity price data and the power generation data, a power dispatching model is constructed according to preset rules, and the power dispatching model is solved by the solution method of the mixed integer linear programming model to obtain the power consumption plan of the target user. While taking into account the complexity of the user's actual production process and the diversity of production goals through the state task network, the operation data, the material data, the time-of-use electricity price data and the power generation data are comprehensively considered, so that the target user can reasonably adjust the power load, reduce the power cost, and promote the optimal configuration of photovoltaic consumption and power resources.

[0057] In a possible implementation, the step of dividing the production process data of the target user into state nodes and task nodes through the state-task network includes:

[0058] Dividing the product data in the production process data into the state nodes through the state task network;

[0059] The production processes in the production process data are divided into the task nodes through the state task network.

[0060] For example, Figure 2 As shown in the figure, taking the target users in the cement industry as an example, the cement manufacturing process (production process data) is as follows: the raw materials are ground into cement raw materials through raw meal mills, the raw materials are calcined in vertical kilns and rotary kilns to obtain cement clinkers, and the clinkers are ground into cement finished products through cement mills. Among them, the grinding of cement raw materials and the grinding of clinkers are schedulable tasks in the task nodes, and the cement calcination link is a non-schedulable task in the task nodes. Raw materials, raw materials, clinkers and finished products are status nodes.

[0061] In a possible implementation, the step of acquiring the operation data of each task node and the material data of each status node includes:

[0062] Acquire storage data corresponding to the product data, and use the storage data as material data corresponding to the state node;

[0063] Obtaining the working condition type data of the task node;

[0064] If the operating condition type data is not one, the operating data of the task node under different operating conditions is obtained.

[0065] For example, since the daily cement output of the target users of the cement industry must reach a minimum of 4,000 tons, the initial value of the raw materials invested in production by the target users of the cement industry is set to 8,000 tons per day. In the case of a storage warehouse, the capacity of the raw material storage warehouse is less than that of the clinker storage warehouse, and the finished cement product is also equipped with a storage warehouse. The upper and lower limits of material storage at the state node i (i = 1, 2, 3, 4) during the production process are determined by the storage warehouse capacity of the intermediate products cement raw materials, clinker, and the finished cement product. The capacity of each storage warehouse, that is, the storage data is shown in Table 1:

[0066] Table 1 Storage data

[0067] Status Number Status Name Initial value (tons) Storage lower limit (tons) Storage limit (tons) S1 raw material 8000 0 inf S2 Raw Materials 0 0 2000 S3 Clinker 0 0 5000 S4 cement 0 0 10000

[0068] For example, the raw material consumption rate, product yield, and electric power consumption (operation data) corresponding to the operating conditions of each task node are shown in Table 2.

[0069] Table 2 Operation data

[0070]

[0071] There are three operating conditions for raw meal grinding (task node), namely operating conditions 0, 1, and 2, where condition 0 indicates that the raw meal mill is in the closed state, condition 1 indicates that the raw meal mill is in the low-power operating state, and condition 2 indicates that the raw meal mill is in the high-power operating state; considering that the start-up and shutdown preparation time of the vertical kiln and rotary kiln calcining equipment is long, and the large heat loss during the start-up and shutdown process damages the production economy of the enterprise, the cement calcination link is generally in the full-load start-up state and belongs to the unschedulable task node in the state task network; similar to the raw meal grinding link, the cement clinker mill (task node) also has three operating conditions, where condition 0 indicates that the clinker mill is in the closed state, condition 1 indicates that the clinker mill is in the low-power operating state, and condition 2 indicates that the clinker mill is in the high-power operating state.

[0072] In a possible implementation, the step of constructing a power dispatching model according to preset rules based on the operation data, the material data, the time-of-use electricity price data, and the power generation data includes:

[0073] Based on the purchased power in the operation data, the time-of-use electricity price data, the sold power and the sold electricity price data in the power generation data, constructing the objective function in the power dispatch model in a discrete time manner according to the function rules in the preset rules;

[0074] Constructing constraint conditions in the power dispatch model based on the material data and the constraint rules in the preset rules;

[0075] The objective function and the constraint conditions are combined to obtain the electricity dispatching model.

[0076] For example, taking the target users in the cement industry as an example, considering the target users’ business model, a model based on the electricity cost V of a certain industrial user is constructed. cost The intelligent power dispatching model with minimization (min) as the objective function has the following expression:

[0077]

[0078] where λ t is the electricity purchase price at time t; γ is the PV (photovoltaic power generation) surplus power on-grid electricity price; P t b and P t s are the purchased power and sold power at time t, respectively. The objective function described in the above formula takes into account the income of industrial users from selling the excess photovoltaic power that cannot be consumed to the power company, and the power company purchases this part of electricity at a fixed price γ.

[0079] For example, taking the business model of self-generation and self-use and selling the surplus electricity as an example, the changes in the purchased power and sold power are as follows: Figure 4 shown.

[0080] Exemplarily, the constraints include at least electric power balance constraints, power purchase and sales status constraints, power purchase and sales balance constraints, material balance constraints, material storage constraints, finished product output constraints and operating condition continuity constraints.

[0081] Specifically, the electric power balance constraint is expressed as follows:

[0082]

[0083] Where: P t g is the output of the photovoltaic power generation device at time t; P t is the productive load power. This constraint ensures that the power obtained by the cement plant at time t is equal to the power consumed.

[0084] The power purchase and sales status constraints are as follows:

[0085]

[0086] Where: and are the 0-1 state variables of the cement plant at time t in the state of purchasing electricity and selling electricity. When industrial users purchase electricity from power companies; when When , the cement plant sells electricity to the power company. This constraint prevents the cement plant from purchasing electricity from the power company and selling electricity to the power company at the same time.

[0087] The balance constraint of electricity purchase and sale is expressed as follows:

[0088]

[0089]

[0090] Where: P b,max and P s,max They are the upper limits of the power that a cement plant can purchase from power companies and the upper limits of the power that it can sell to power companies.

[0091] Material balance constraints are expressed as follows:

[0092]

[0093] Where: S i,t is the material inventory of state node i at time t; g i,j,t and c i,j,t are the production rate and consumption rate of node i in task j at time t; G i and C i They are respectively the set of task nodes associated with state node i for producing and consuming the material of the node.

[0094] Material storage constraints, the expression is as follows:

[0095]

[0096] Where: and are the lower and upper limits of the material storage quantity of state node i respectively.

[0097] Finished product output constraint, the expression is as follows:

[0098]

[0099] Where: is the daily output required for state node i. In this example, the daily output of cement is required to reach 4,000 tons.

[0100] The operating condition continuity constraint is expressed as follows:

[0101]

[0102] Where: Ω i is the adjustable time set of state node i, M iis the number of adjustable operating conditions for state node i. In the example, the duration of a single cement raw meal grinding stage is 2 hours, so the set of schedulable moments for cement raw meal grinding is the set when t is an even number, and when t is an odd number, its operating condition remains consistent with the operating condition at t-1 and cannot be adjusted.

[0103] In a possible implementation, the step of constructing the objective function in the power dispatching model in a discrete time manner according to the function rules in the preset rules based on the purchased power in the operation data, the time-of-use electricity price data, the sold power and the sold electricity price data in the power generation data includes:

[0104] Acquire production cycle data of a target user, and determine discrete durations according to the production cycle data;

[0105] According to the discrete duration, in a discrete time manner, through the function rules in the preset rules, a function representing the minimization of electricity cost is constructed according to the purchased electricity power in the operating data, the time-of-use electricity price data, the sold electricity power and the sold electricity price data in the power generation data, and the function representing the minimization of electricity cost is used as the objective function.

[0106] Exemplarily, a discrete time system model is adopted to divide a day into n time periods, each time period is h long. In this example, considering the cement production characteristics (production cycle data), a day is divided into 24 time periods, each time period is 1 hour long.

[0107] In a possible implementation, Figure 5 As shown, an embodiment of the present application provides a power scheduling device based on a state task network, the device comprising:

[0108] The node division module 201 is used to divide the production process data of the target user into state nodes and task nodes through the state task network, and obtain the operation data of each task node and the material data under each state node;

[0109] The data acquisition module 202 is used to obtain the time-of-use electricity price data of the target user's area and the power generation data of the target user;

[0110] The model building and solving module 203 is used to build a power dispatching model according to preset rules based on the operating data, the material data, the time-of-use electricity price data and the power generation data, and solve the power dispatching model by solving the mixed integer linear programming model to obtain the power consumption plan of the target user.

[0111] In a possible implementation manner, the node partitioning module 201 is used to:

[0112] Dividing the product data in the production process data into the state nodes through the state task network;

[0113] The production processes in the production process data are divided into the task nodes through the state task network.

[0114] In a possible implementation manner, the node partitioning module 201 is used to:

[0115] Acquire storage data corresponding to the product data, and use the storage data as material data corresponding to the state node;

[0116] Obtaining the working condition type data of the task node;

[0117] If the operating condition type data is not one, the operating data of the task node under different operating conditions is obtained.

[0118] In a possible implementation, the model building and solving module 203 is used to:

[0119] Based on the purchased power in the operation data, the time-of-use electricity price data, the sold power and the sold electricity price data in the power generation data, constructing the objective function in the power dispatch model in a discrete time manner according to the function rules in the preset rules;

[0120] Constructing constraint conditions in the power dispatch model based on the material data and the constraint rules in the preset rules;

[0121] The objective function and the constraint conditions are combined to obtain the electricity dispatching model.

[0122] In a possible implementation, the model building and solving module 203 is used to:

[0123] Acquire production cycle data of a target user, and determine discrete durations according to the production cycle data;

[0124] According to the discrete duration, in a discrete time manner, through the function rules in the preset rules, a function representing the minimization of electricity cost is constructed according to the purchased electricity power in the operating data, the time-of-use electricity price data, the sold electricity power and the sold electricity price data in the power generation data, and the function representing the minimization of electricity cost is used as the objective function.

[0125] In a possible implementation, the power generation data includes first power generation data under a first preset scenario and second power generation data under a second preset scenario, and both the first power generation data and the second power generation data are used to construct the power scheduling model.

[0126] In a possible implementation, the constraints include at least electric power balance constraints, power purchase and sales status constraints, power purchase and sales balance constraints, material balance constraints, material storage constraints, finished product output constraints and operating condition continuity constraints.

[0127] The production process data of the target user is divided into state nodes and task nodes through the state task network, and the operation data of each task node and the material data under each state node are obtained; the time-of-use electricity price data of the target user's area and the power generation data of the target user are obtained; based on the operation data, the material data, the time-of-use electricity price data and the power generation data, a power dispatching model is constructed according to preset rules, and the power dispatching model is solved by the solution method of the mixed integer linear programming model to obtain the power consumption plan of the target user. While taking into account the complexity of the user's actual production process and the diversity of production goals through the state task network, the operation data, the material data, the time-of-use electricity price data and the power generation data are comprehensively considered, so that the target user can reasonably adjust the power load, reduce the power cost, and promote the optimal configuration of photovoltaic consumption and power resources.

[0128] In a possible implementation, Figure 6 As shown, an embodiment of the present application provides an electronic device 300, including: a memory 310, a processor 320 and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, it implements: dividing the production process data of the target user into state nodes and task nodes through a state-task network, and obtaining the operation data of each task node and the material data under each state node; obtaining the time-of-use electricity price data of the area where the target user is located and the power generation data of the target user; constructing a power scheduling model according to preset rules based on the operation data, the material data, the time-of-use electricity price data and the power generation data, and solving the power scheduling model by a solution method of a mixed integer linear programming model to obtain the power consumption plan of the target user.

[0129] The production process data of the target user is divided into state nodes and task nodes through the state task network, and the operation data of each task node and the material data under each state node are obtained; the time-of-use electricity price data of the target user's area and the power generation data of the target user are obtained; based on the operation data, the material data, the time-of-use electricity price data and the power generation data, a power dispatching model is constructed according to preset rules, and the power dispatching model is solved by the solution method of the mixed integer linear programming model to obtain the power consumption plan of the target user. While taking into account the complexity of the user's actual production process and the diversity of production goals through the state task network, the operation data, the material data, the time-of-use electricity price data and the power generation data are comprehensively considered, so that the target user can reasonably adjust the power load, reduce the power cost, and promote the optimal configuration of photovoltaic consumption and power resources.

[0130] In a possible implementation, Figure 7 As shown, an embodiment of the present application provides a computer-readable storage medium 400, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, it implements: dividing the production process data of the target user into state nodes and task nodes through a state-task network, and obtaining the operation data of each task node and the material data under each state node; obtaining the time-of-use electricity price data of the area where the target user is located and the power generation data of the target user; constructing a power scheduling model according to preset rules based on the operation data, the material data, the time-of-use electricity price data and the power generation data, and solving the power scheduling model by solving the mixed integer linear programming model to obtain the power consumption plan of the target user.

[0131] The production process data of the target user is divided into state nodes and task nodes through the state task network, and the operation data of each task node and the material data under each state node are obtained; the time-of-use electricity price data of the target user's area and the power generation data of the target user are obtained; based on the operation data, the material data, the time-of-use electricity price data and the power generation data, a power dispatching model is constructed according to preset rules, and the power dispatching model is solved by the solution method of the mixed integer linear programming model to obtain the power consumption plan of the target user. While taking into account the complexity of the user's actual production process and the diversity of production goals through the state task network, the operation data, the material data, the time-of-use electricity price data and the power generation data are comprehensively considered, so that the target user can reasonably adjust the power load, reduce the power cost, and promote the optimal configuration of photovoltaic consumption and power resources.

[0132] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0133] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0134] The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, etc., or any suitable combination of the foregoing.

[0135] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0136] It should be understood by those skilled in the art that the modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented by a program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0137] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described above, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

[0138] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A power dispatching method based on a state task network, characterized in that: The method comprises: The target user's production process data is divided into state nodes and task nodes through the state-task network, and the operation data of each task node and the material data under each state node are obtained; Obtaining time-of-use electricity price data of the target user's area and power generation data of the target user; Based on the operation data, the material data, the time-of-use electricity price data and the power generation data, a power dispatching model is constructed according to preset rules, and the power dispatching model is solved by a mixed integer linear programming model to obtain a power consumption plan for the target user.

2. The power dispatching method based on state task network according to claim 1 is characterized in that: The step of dividing the production process data of the target user into state nodes and task nodes through the state-task network includes: Dividing the product data in the production process data into the state nodes through the state task network; The production processes in the production process data are divided into the task nodes through the state task network.

3. The power dispatching method based on state task network according to claim 2 is characterized in that: The step of obtaining the operation data of each task node and the material data of each status node includes: Acquire storage data corresponding to the product data, and use the storage data as material data corresponding to the state node; Obtaining the working condition type data of the task node; If the operating condition type data is not one, the operating data of the task node under different operating conditions is obtained.

4. The power dispatching method based on state task network according to claim 1 is characterized in that: The step of constructing a power dispatching model according to preset rules based on the operation data, the material data, the time-of-use electricity price data and the power generation data comprises: Based on the purchased power in the operation data, the time-of-use electricity price data, the sold power and the sold electricity price data in the power generation data, constructing the objective function in the power dispatch model in a discrete time manner according to the function rules in the preset rules; Constructing constraint conditions in the power dispatch model based on the material data and the constraint rules in the preset rules; The objective function and the constraint conditions are combined to obtain the electricity dispatching model.

5. The power dispatching method based on state task network according to claim 4 is characterized in that: The step of constructing the objective function in the power dispatching model in a discrete time manner according to the function rules in the preset rules based on the power purchase power in the operation data, the time-of-use electricity price data, the power sale power and the power sale price data in the power generation data includes: Acquire production cycle data of a target user, and determine discrete durations according to the production cycle data; According to the discrete duration, in a discrete time manner, through the function rules in the preset rules, a function representing the minimization of electricity cost is constructed according to the purchased electricity power in the operating data, the time-of-use electricity price data, the sold electricity power and the sold electricity price data in the power generation data, and the function representing the minimization of electricity cost is used as the objective function.

6. The power dispatching method based on state task network according to claim 1 is characterized in that: The power generation data includes first power generation data under a first preset scenario and second power generation data under a second preset scenario, and both the first power generation data and the second power generation data are used to construct the power dispatching model.

7. The power dispatching method based on state task network according to claim 4 is characterized in that: The constraints include at least electric power balance constraints, power purchase and sales status constraints, power purchase and sales balance constraints, material balance constraints, material storage constraints, finished product output constraints and operating condition continuity constraints.

8. A power dispatching device based on a state task network, characterized in that: The device comprises: The node division module is used to divide the production process data of the target user into state nodes and task nodes through the state task network, and obtain the operation data of each task node and the material data under each state node; A data acquisition module, used to obtain the time-of-use electricity price data of the target user's area and the power generation data of the target user; The model building and solving module is used to build a power dispatching model according to preset rules based on the operating data, the material data, the time-of-use electricity price data and the power generation data, and solve the power dispatching model by solving the mixed integer linear programming model to obtain the power consumption plan of the target user.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the power scheduling method based on the state task network as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the power dispatching method based on the state task network as described in any one of claims 1 to 7 are implemented.

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