CN113159567B - Industrial park off-grid scheduling method considering uncertainty of power outage duration - Google Patents

Industrial park off-grid scheduling method considering uncertainty of power outage duration Download PDF

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CN113159567B
CN113159567B CN202110419136.6A CN202110419136A CN113159567B CN 113159567 B CN113159567 B CN 113159567B CN 202110419136 A CN202110419136 A CN 202110419136A CN 113159567 B CN113159567 B CN 113159567B
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王小君
张义志
和敬涵
张沛
马元浩
张放
许寅
孙庆凯
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Abstract

The invention provides an industrial park off-grid scheduling method considering the uncertainty of power failure time length, which comprises the following steps: establishing an industrial production flow model according to the transmission of the material flow of the industrial park; establishing a steady-state energy flow unified model of the industrial park according to the energy hub and the industrial production flow model of the industrial park; establishing an off-grid industrial park optimal scheduling model; calculating probability distribution of the power failure time of the external power grid, and generating sample scenes with different power failure time by adopting a Monte Carlo method according to the probability distribution of the power failure time of the external power grid; adopting a K-means clustering algorithm to reduce sample scenes with different power failure time lengths; and according to the reduced multiple sample scenes, taking the occurrence probability of each scene in the reduced multiple sample scenes as a weight coefficient, and adopting a random optimization method to perform collaborative optimization on the off-grid industrial park optimal scheduling model to obtain an off-grid scheduling strategy with the minimum power failure loss.

Description

Industrial park off-grid scheduling method considering uncertainty of power outage duration
Technical Field
The invention relates to the technical field of electric power, in particular to an industrial park off-grid scheduling method considering uncertainty of power failure time.
Background
At present, the industrial park production is continuously processed and refined, and the industrial park of the high-level manufacturing industry often comprises a plurality of complex production procedures, so that the energy consumption is huge, the industrial energy consumption occupies a large scale in the whole society energy consumption, the reduction of the energy consumption of the industrial park is a primary target of energy saving tasks, and the reduction of the energy consumption cost is more important in the industrial park. In practical application, external power grid power failure is divided into two situations of planned power failure and non-planned power failure, for the non-planned power failure, an industrial park operation control center cannot acquire information of power failure time in advance, uncertainty of the power failure time directly influences safety of off-grid system operation, and therefore huge economic loss is caused, and the existing off-grid operation scheduling strategy cannot realize safe and economic operation of the off-grid park under the condition of uncertain power failure time.
In the prior art, a certain research is carried out on modeling and economic operation of a comprehensive Energy system, the modeling method of the electric, gas and heat comprehensive Energy system is numerous, wherein an Energy Hub (EH) model is widely applied, energy flow and conversion in the comprehensive Energy system can be effectively reflected, the modeling method is concise, and a physical concept is clear, but most of the prior art only carries out modeling optimization on the comprehensive Energy system of an industrial park, only carries out optimization on a cold and hot Energy system in the industrial park, and for an actual industrial park, the main load is industrial load, the modeling method is a complete pipeline process, each production link has different coupling with the Energy system, and the existing optimization method only takes industrial production as an optimization result obtained by the participation of fixed load, so that the optimization result is not accurate and economical enough. And lack of unified description of coupling relation between cold and hot electric comprehensive energy and industrial process energy, and lack of unified modeling of production processes in an industrial production park. Meanwhile, the high-flow and fine production mode of the modern industrial production puts higher requirements on the stability of energy system supply, and the accuracy of the method is required to be improved. On the other hand, in the existing method, uncertain variables in the model are processed by adopting a random optimization method and a robust optimization method, and the method is researched aiming at grid-connected operation scenes. For the off-grid operation scene when the external power grid fails, an optimal scheduling method for the off-grid independent operation industrial park is lacking, the uncertainty of the off-grid operation time is not considered for the operation of the off-grid industrial park at present, the economic losses of the actual park caused by the power failure time and other state quantities of the energy system are lack of quantitative research, and an effective method for describing the influence of the uncertainty of the power failure time on the off-grid operation optimal decision result is lacking, so that an off-grid operation scheduling strategy considering risks and economy cannot be made for the park. .
Therefore, there is a need for an industrial park off-grid scheduling method that takes into account the uncertainty of the outage duration.
Disclosure of Invention
The invention provides an industrial park off-grid scheduling method considering uncertainty of power failure duration, which aims to solve the problems in the prior art.
In order to achieve the above purpose, the present invention adopts the following technical scheme.
An industrial park off-grid scheduling method considering uncertainty of power outage duration, comprising:
establishing an industrial production flow model according to the transmission of the material flow of the industrial park;
According to the energy hub of the industrial park and the industrial production flow model, establishing a production flow of the industrial park and a steady-state energy flow unified model of an energy system;
Establishing an off-grid industrial park optimization scheduling model by taking the steady-state energy flow unified model, the storage system operation constraint and the energy conversion equipment constraint as constraint conditions and taking the minimum production loss during off-grid operation as an optimization target;
Calculating probability distribution of power failure time of an external power grid, and generating sample scenes with different power failure time by adopting a Monte Carlo method according to the probability distribution of the power failure time of the external power grid;
adopting a K-means clustering algorithm to reduce the sample scenes with different power failure time lengths to obtain a plurality of reduced sample scenes;
According to the reduced multiple sample scenes, taking the occurrence probability of each scene in the reduced multiple sample scenes as a weight coefficient, and adopting a random optimization method to perform collaborative optimization on the off-grid industrial park optimal scheduling model to obtain an off-grid scheduling strategy with minimum power failure loss;
and under the condition of uncertainty of the power failure duration, carrying out off-grid scheduling on the industrial park according to the off-grid scheduling strategy.
Preferably, establishing the industrial process flow model based on the delivery of the industrial park stream comprises: the flow of the production material is used as a material flow, and an industrial production flow model is built according to the transfer of the material flow.
Preferably, the industrial process flow model comprises: the material is used as a medium, different production subtasks are used as nodes, a series-parallel system established by taking a material transmission process as a branch and a production constraint mathematical model based on material production and transfer are used.
Preferably, the production constraint mathematical model based on material production and transfer comprises: uninterrupted subtask constraints, interruptible subtask constraints, and bin Chu Zi task constraints.
Preferably, the non-interruptible subtask constraint includes a relationship between a subtask state and a start-stop variable of the non-interruptible subtask, a limit of a minimum run time of the non-interruptible subtask and a minimum downtime of the non-interruptible subtask, and an output constraint of the subtask are respectively shown in the following formulas (1) to (3):
Interruptible subtask constraints include: the output of the interruptible subtask is shown in the following equation (4):
The warehouse subtask constraints include: the relation between the real-time capacity of the warehouse subtask and the input/output material, and the range limits of the real-time capacity of the warehouse subtask and the input/output material capacity are shown in the following formulas (5) and (6), respectively:
Wherein alpha represents a type of uninterrupted production task; Representing the running state of the ith workflow at the moment t; And Respectively representing the action variables of the start-stop machine; h represents any time; And Minimum run time and minimum downtime, respectively; n1 represents the number of production lines corresponding to such uninterruptible type tasks; The fixed output of each production line is not adjustable and is irrelevant to time; is the total yield of the whole subtask at the time t;
Beta represents a type of interruptible production task; representing the running state of the ith workflow at the moment t; n2 represents the number of production lines corresponding to such interruptible type tasks; is the actual yield of each production line and can be adjusted; is the total yield of the whole subtask at the time t;
S i,t is the capacity of the warehouse at the time t; And The yields of the upstream and downstream processes, respectively; And The start-stop states of the upstream and downstream production tasks are respectively; indicating the upper and lower limits of the warehouse capacity.
Preferably, establishing a steady state energy flow unified model of a production process and an energy system of the industrial park according to the energy hub of the industrial park and the industrial production process model comprises: and establishing a production flow of the industrial park and a steady-state energy flow unified model of the energy system according to the energy hub model modeling method.
Preferably, the steady state energy flow unification model is shown in the following formula (7):
Wherein, C 1、C2 is constant coefficient matrix, and V out represents the power output of cold and hot electricity; v in denotes inputs to the system, including fuel, grid power and production materials; v 2 denotes the schedulable energy flows in the energy hub; x represents an incidence matrix of system input and energy flow; y represents an incidence matrix of system output and energy flow; z represents the efficiency incidence matrix of each energy device and energy flow in the system; i represents an identity matrix; r, Q is a coefficient matrix associated with I, X, Z; c 1、C2 denotes a coefficient matrix related to R, Q, Y; where subscript 1 represents the coefficient matrix associated with the non-schedulable energy flow and subscript 2 represents the coefficient matrix associated with the schedulable energy flow.
Preferably, the off-grid industrial park optimization scheduling model is shown in the following formulas (8) - (11):
F＝min(Cope+Closs_P+Closs_M) (8)
Vout＝C1Vin+C2V2 (9)
Wherein, C ope＝Cgas+Cf+Con/off; C loss_M＝∑c2 S; f represents an optimization target, C ope represents an energy system operation cost, C loss_P represents a production task stagnation cost, C loss_M represents a production raw material loss cost, C gas represents a gas purchase cost, C f represents a device operation maintenance cost, C on/off represents a device start-stop cost, C 1、C2 represents a loss coefficient, H represents a planned production task, Representing actual throughput, S representing the amount of material lost;
E ES,t represents the storage capacity of the energy storage system at the time t, delta E ES,t represents the variation of the storage capacity at the time t, delta E ES,min、ΔEES,max represents the upper limit and the lower limit of the charge and discharge capacity of the energy storage system respectively, E ES,min,ΔEES,max represents the upper limit and the lower limit of the capacity of the energy storage system, eta ES represents the charge and discharge efficiency, and v ES,t represents the external electric energy input at the time t;
respectively represents the output and input of the energy conversion device at the time t, lambda represents the energy conversion efficiency, Indicating upper and lower limits of output power.
Preferably, calculating the probability distribution of the external grid outage duration comprises: and acquiring and analyzing historical power failure time length data of the external power grid to obtain probability distribution of the power failure time length of the external power grid.
Preferably, the specific number of the reduced multiple sample scenes is 8.
According to the technical scheme provided by the industrial park off-grid scheduling method considering the uncertainty of the power failure duration, the steady-state energy flow unified model based on the industrial production process and the comprehensive energy system is established through modeling of the industrial production process and based on the concept of the energy hub; establishing an off-grid industrial park optimization scheduling model by taking a steady-state energy flow unified model, storage system operation constraint and energy conversion equipment constraint as constraint conditions and taking minimum production loss during off-grid operation as an optimization target; based on an off-grid industrial park optimal scheduling model, a Monte Carlo method random sampling and K-means clustering scene reduction method is adopted to obtain a reduced scene sample, a random optimization method is adopted to obtain a final optimization strategy, and the off-grid industrial park is scheduled by adopting the optimization strategy, so that the purpose of minimum power failure loss of the industrial park under the condition of uncertain off-grid time is achieved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an industrial park off-grid scheduling method considering uncertainty of power outage duration according to an embodiment;
FIG. 2 is a schematic diagram of a basic framework of a serial-parallel system of an industrial process flow of an embodiment;
FIG. 3 is a schematic diagram of an energy hub structure of a steady-state energy flow unified model according to an embodiment;
FIG. 4 is a schematic diagram of an energy conversion node of a single output port;
FIG. 5 is a schematic diagram of a warehouse sub-task;
FIG. 6 is a schematic diagram of a power cell production process;
FIG. 7 is a schematic diagram of an energy hub structure of a steady state energy flow unified model of a power cell production park;
FIG. 8 is a probability distribution diagram of the outage duration of an external power grid;
FIG. 9 is a schematic flow chart of the optimization step.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the purpose of facilitating an understanding of the embodiments of the present invention, reference will now be made to the drawings, by way of example, and not to the limitation of the embodiments of the present invention.
Examples
Fig. 1 is a schematic diagram of an industrial park off-grid scheduling method considering uncertainty of power outage duration according to the embodiment, and referring to fig. 1, the method includes:
s1, establishing an industrial production flow model according to the transmission of the mass flow of the industrial park.
The flow of the production material is used as a material flow, and an industrial production flow model is built according to the transfer of the material flow. The complete industrial production process comprises a plurality of processes from raw materials, semi-finished products to finished products, wherein the processes of production, storage and reproduction are carried out, and the production process is divided into subtasks with different scheduling characteristics and mathematical models are respectively built according to the coupling among different production processes. The subtasks can contain a plurality of production devices and have strict time sequence constraint, and the subtasks are used as the minimum unit of scheduling to participate in the scheduling operation of the energy system. Specifically, the industrial process flow model includes: the material is used as a medium, different production subtasks are used as nodes, a series-parallel system established by taking a material transmission process as a branch and a production constraint mathematical model based on material production and transfer are used. Fig. 2 is a schematic diagram of a basic frame of a serial-parallel system of the industrial production process of the present embodiment.
Delivery of materials is a physical link between different production processes. Since the yield of a production target directly relates to the consumption of energy, the constraints of the product determine the energy requirements of industrial production. Based on the above, the production constraint mathematical model based on material production and transfer in this embodiment includes three subtasks including non-interruptible subtask constraint, interruptible subtask and warehouse subtask constraint, and specifically includes the following:
1) Non-interruptible subtask constraints:
The uninterrupted subtask represents a set of sequential steps with strict timing constraints that cannot be run independently until the previous step is generated. In the interaction of the energy system, schematically, the non-interruptible subtask may be compared to a power-fixed device, and the operating state may be controlled, which may be on or off. Typically, this is a unidirectional continuous line production.
The uninterrupted subtask is characterized in that continuous adjustment cannot be performed and the on-off state of the workstation can only be controlled. Furthermore, the minimum start-stop time constraint for each subtask must be considered; since the subtasks actually contain several constraints with strict timing constraints and require characterization of the overall process, the constraints of minimum run time and minimum downtime need to be considered, and thus the resulting non-interruptible subtask constraints include:
The relationship between the subtask state of the non-interruptible subtask and the start-stop variable, the limits of the minimum run time of the non-interruptible subtask and the minimum downtime of the non-interruptible subtask, and the output constraint of the subtask are shown in the following formulas (1) - (3), respectively:
Wherein alpha represents a type of uninterrupted production task; Representing the running state of the ith workflow at the moment t; And Respectively representing the action variables of the start-stop machine; h represents any time; And Minimum run time and minimum downtime, respectively; n1 represents the number of production lines corresponding to such uninterruptible type tasks; The fixed output of each production line is not adjustable and is irrelevant to time; Is the total yield of the entire subtask at time t.
2) Interruptible subtask constraints:
An interruptible subtask represents a cumulative task in which the semifinished product should be processed in several successive time periods. Thus, the operating state can be adjusted by controlling the quantity of products without strict time constraint. Illustratively, an interruptible subtask can be considered a device with flexible power regulation in addition to a switch state. Typical representatives are battery charge and discharge tests.
The output of the interruptible subtask is more adjustable than the non-interruptible subtask, and in addition to the switch state, the output of the subtask can be controlled. The output of the interruptible subtask is tunable compared to the non-interruptible subtask, the output of the interruptible subtask being represented by the following equation (4):
Beta represents a type of interruptible production task; representing the running state of the ith workflow at the moment t; n2 represents the number of production lines corresponding to such interruptible type tasks; is the actual yield of each production line and can be adjusted; Is the total yield of the entire subtask at time t.
3) Warehouse subtask constraints:
the warehouse sub-task is used to describe the material warehouse. It is similar in operational characteristics to stored energy. The same constraints exist between input, output and capacity compared to energy storage.
The storage phase is a buffer between two subtasks, decoupling two connected processes. Similar to energy storage, warehouses also have capacity limitations. The relation between the real-time capacity of the warehouse subtask and the input/output material, and the range limits of the real-time capacity of the warehouse subtask and the input/output material capacity are shown in the following formulas (5) and (6), respectively:
wherein S i,t is the capacity of the warehouse at the time t; And The yields of the upstream and downstream processes, respectively; And The start-stop states of the upstream and downstream production tasks are respectively; indicating the upper and lower limits of the warehouse capacity.
S2, establishing a steady-state energy flow unified model of the production flow and the energy system of the industrial park according to the energy hub and the industrial production flow model of the industrial park.
And establishing a production flow of the industrial park and a steady-state energy flow unified model of the energy system according to the energy hub model modeling method. The steady-state energy flow unified model of the embodiment represents the transfer and conversion relation of four energy forms of electricity-heat-cold-substances in an energy system of an industrial park, and satisfies a power balance equation. And adding the industrial production process into the model to obtain a steady-state energy flow unified model by taking the material flow as a generalized energy form, wherein fig. 3 is a schematic diagram of an energy hub structure of the steady-state energy flow unified model in the embodiment. The energy devices in the energy terminal structure are regarded as nodes. There are typically two types of nodes in an energy junction structure, namely an energy conversion node and an energy storage node.
For production subtasks, the energy hub structure may be regarded as an energy conversion node with multiple input ports and a single output port as shown in fig. 4. These two sub-tasks are similar to energy conversion elements, input as energy source and raw material, output as semi-finished material. Thus, the present embodiment models the node type of the energy coupling device as a cogeneration unit and a heat pump for continuous and discrete subtasks. In fig. 4, v in is the input of material; w in is the input of energy; v out is the output of the semifinished product, η M and η W are the conversion efficiencies of the material flow and the energy flow, respectively.
For the production subtasks, the node balance equation is shown as the following equation (7):
Wherein Z represents the efficiency correlation matrix of each energy device and energy flow in the system.
For the storage subtasks, the model is relatively simple, since there is no coupling of the energy system. The storage sub-task is considered as an energy storage system, and as shown in fig. 5, is a schematic structural diagram of a storage sub-task, which is similar to the battery energy storage system (Battery Energy Storage System, BESS). v st refers to a virtual branch connected to a "State of Charge (SOC)".
To maintain format uniformity with other components, virtual energy storage branches are added to the storage subtasks. The stored original correlation matrix is denoted with a' g. Considering the added branches, the node association matrix a g of the storage component is shown in the following equation (8):
The efficiency incidence matrix is:
Z＝[ηC-1/ηD-1] (9)
The final steady state energy flow unified model is shown in the following formula (10), wherein C 1、C2 is a constant coefficient matrix:
Wherein V out represents the power output of the cold and hot electricity; v in denotes inputs to the system, including fuel, grid power and production materials; v 2 denotes the schedulable energy flows in the energy hub; x represents an incidence matrix of system input and energy flow; y represents an incidence matrix of system output and energy flow; i represents an identity matrix; r, Q denotes a coefficient matrix relating to I, X, Z; c 1、C2 denotes a coefficient matrix related to R, Q, Y; where subscript 1 represents the coefficient matrix associated with the non-schedulable energy flow and subscript 2 represents the coefficient matrix associated with the schedulable energy flow.
S3, establishing an off-grid industrial park optimization scheduling model by taking a steady-state energy flow unified model, storage system operation constraint and energy conversion equipment constraint as constraint conditions and taking minimum production loss during off-grid operation as an optimization target.
S31 energy flow balance constraint. The main constraint of system optimization is various energy balance constraints such as cold, hot, electricity and the like and material balance constraint of industrial production. This part of the constraint is reflected by the steady state energy flow unified model.
S32 stores the system operation constraints. The memory system has significant timing characteristics compared to the energy conversion device. The stored energy is coupled at the next time. Therefore, it is necessary to set the constraint condition of the SOC. The Storage devices in the extended EH include a Battery Energy Storage System (BESS), a Thermal Storage system (TS), a Cooling Storage system (CS), and two Storage subtasks. Equation (13) is an operation constraint of the BESS, including a timing constraint of the SOC, a range constraint of the charge-discharge power, and a range constraint of the SOC.
S33, energy conversion equipment constraint. Equation (14) collectively represents the constraint conditions of the energy conversion device, and represents the rate of change and the output range of energy conversion, respectively.
The resulting off-grid industrial park optimization scheduling model is shown in the following formulas (11) - (14):
F＝min(Cope+Closs_P+Closs_M) (11)
Vout＝C1Vin+C2V2 (12)
Wherein, C ope＝Cgas+Cf+Con/off; C loss_M＝∑c2 S; f represents an optimization target, C ope represents an energy system operation cost, C loss_P represents a production task stagnation cost, C loss_M represents a production raw material loss cost, C gas represents a gas purchase cost, C f represents a device operation maintenance cost, C on/off represents a device start-stop cost, C 1、C2 represents a loss coefficient, H represents a planned production task, Representing actual throughput, S representing the amount of material lost;
E ES,t represents the storage capacity of the energy storage system at the time t, delta E ES,t represents the variation of the storage capacity at the time t, delta E ES,min、ΔEES,max represents the upper limit and the lower limit of the charge and discharge capacity of the energy storage system respectively, E ES,min,ΔEES,max represents the upper limit and the lower limit of the capacity of the energy storage system, eta ES represents the charge and discharge efficiency, and v ES,t represents the external electric energy input at the time t;
respectively represents the output and input of the energy conversion device at the time t, lambda represents the energy conversion efficiency, Indicating upper and lower limits of output power.
S4, calculating probability distribution of the power failure time of the external power grid, and generating sample scenes with different power failure time by adopting a Monte Carlo method according to the probability distribution of the power failure time of the external power grid.
And acquiring and analyzing historical power failure time length data of the external power grid to obtain probability distribution of the power failure time length of the external power grid.
S5, adopting a K-means clustering algorithm to reduce the sample scenes with different power failure time durations, and obtaining a plurality of reduced sample scenes.
Preferably, the specific number of the reduced multiple sample scenes in the embodiment is 8.
S6, according to the reduced multiple sample scenes, taking the occurrence probability of each scene in the reduced multiple sample scenes as a weight coefficient, adopting a random optimization method to carry out collaborative optimization on the off-grid industrial park optimal scheduling model, and obtaining an off-grid scheduling strategy with the minimum power failure loss through iterative processing;
And S7, carrying out off-grid scheduling on the industrial park according to an off-grid scheduling strategy under the condition of uncertainty of the power failure duration.
The following is a specific application example of the industrial park off-grid scheduling method taking the uncertainty of the power failure duration into consideration, taking an actual power battery located in Guangdong in China as an example, and fig. 6 is a schematic diagram of a power battery production flow, and referring to fig. 6, the actual power battery production flow is analyzed first, and the production is divided into three subtasks based on the time sequence constraint and the storage link of the production flow. The method comprises a Cell Production (CP) link (stirring, coating, drying, rolling and slicing), a battery Packaging (PL) link (winding, welding, liquid injection and packaging) and a final aging and testing (FG) link.
Fig. 7 is a schematic diagram of an energy hub structure of a steady state energy flow unified model of a power cell production park. Referring to fig. 7, there are two workstations in cp, two workstations in PL, three workstations in FG, and two Material Storage (MS). The energy system equipment comprises triple power supply units (Combined Heat and Power, CHP), heat pumps (Heat pumps, HP), auxiliary gas boilers (Auxiliary Boiler, AB), absorption refrigerators (Absorption CHILLER AC), centrifugal chillers (Centrifugal Chiller, CC), electric energy storage (BESS), thermal energy storage (TS) and cold energy storage (CS) equipment, and cold and hot bus nodes are added to reduce the matrix scale, wherein v represents branch numbers and N represents node numbers.
And (3) establishing the whole park steady-state energy flow unified model based on the energy hub structure of the steady-state energy flow unified model of the power battery production park, wherein the steady-state energy flow unified model of the whole park is shown as a formula (15).
And finally, obtaining a relational expression of the output V out and the output V in,V2, wherein C 1 and C 2 are coefficient matrixes, are only related to a system connection mode and equipment efficiency parameters, are irrelevant to a system running state, do not participate in optimization calculation, and are constant coefficient matrixes.
And (5) establishing an optimization target of off-grid operation of the industrial park. The analysis and optimization target for the power battery production plant comprises three parts of energy system operation cost, production task stagnation cost and most important production raw material loss cost. The energy system operation cost comprises the gas purchase cost and the equipment operation maintenance cost, and the operation and maintenance cost comprises the start-up/shut-down cost of the cogeneration and air conditioner and the maintenance cost of the power plant. The production task stagnation cost is a gain loss caused by the difference between the planned production task and the production task completed during the actual power outage. The loss cost of the production principle is the waste of raw materials caused by power failure in the middle of the production process and the production materials on the important production line when the production of the final product is not completed, and the cost is the most important loss of high-tech production enterprises during power failure.
And (3) comprehensively considering the three cost, and establishing an off-grid optimization scheduling objective function of the industrial park comprehensive energy system as shown in the above (11).
And further obtaining an off-grid optimization scheduling model of the power battery production park considering industrial production according to the constraint condition of the off-grid optimization problem of the formulas (12) - (14).
The off-grid optimization scheduling model is a nonlinear problem because of the product of start-stop variables and power variables. For the convenience of calculation, the model is linearized by adopting a large M method.
Based on the off-grid optimization scheduling model, the probability distribution of the power failure time of the external power grid is obtained through analysis of the historical power failure time data of the external power grid, as shown in fig. 8. The off-grid scheduling strategy with the minimum power failure loss is optimized on the basis of the probability distribution, and the specific optimization steps are shown in fig. 9.
And according to the probability distribution of the power failure time, adopting a Monte Carlo method to produce sample scenes with different power failure time, and simulating the possible states of the power failure time. And a K-means clustering algorithm is adopted to cut a large number of generated sample scenes with different power failure time lengths into 8 scenes, and the scenes have different power failure time lengths and occurrence probabilities respectively.
And for each single scene, the power failure time is determined, the optimization is performed by using the off-grid optimization scheduling strategy to obtain the system power failure loss cost, then the 8 scenes are subjected to collaborative optimization by using a random optimization method, different weight coefficients are assigned according to the occurrence probability of each scene, and the optimal scheduling strategy which meets the minimum comprehensive loss under 8 scenes is obtained, namely the final optimal scheduling strategy. Table 1 below shows the outage losses of the system at different outage times obtained using the final optimization strategy.
TABLE 1
When the power failure time of the external power grid is determined, namely the off-grid running time of the known system, the off-grid optimization scheduling model of the power battery production park taking industrial production into consideration is adopted. And adopting Matlab and Cplex to jointly solve the off-grid optimization scheduling model, setting different power outage time lengths and different energy storage state parameters by a single variable control method, and simulating and verifying the power outage loss of the off-grid park in different scenes. Taking the CHP unit output as 75% and the energy storage system capacity as 50%, the economic losses caused by the scheduling strategy corresponding to the 2-hour power outage duration (planned power outage duration) under the power outage duration of 1-5 hours are analyzed as shown in the following table 2.
TABLE 2
As can be seen from tables 1 and 2, compared with the data in table 2, the industrial park off-grid scheduling method taking the uncertainty of the power failure time into consideration can effectively reduce the loss in the scene of longer power failure time, and the comprehensive risk is lower.
It will be appreciated by those skilled in the art that the above application types are merely examples, and that other application types that may be present in the present invention or that may be present in the future are intended to be within the scope of the present invention as applicable thereto and are hereby incorporated by reference herein.
It should be understood by those skilled in the art that the above-mentioned decision to invoke a policy according to user information is merely a better illustration of the technical solution of the embodiments of the present invention, and is not a limitation of the embodiments of the present invention. Any method for determining a calling policy based on user attributes is included in the scope of the embodiments of the present invention.
From the above description of embodiments, it will be apparent to those skilled in the art that the present invention may be implemented in software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present invention.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims (5)

1. An industrial park off-grid scheduling method considering uncertainty of power outage duration is characterized by comprising the following steps:
establishing an industrial production flow model according to the transmission of the material flow of the industrial park; the industrial production flow model comprises: the material is used as a medium, different production subtasks are used as nodes, and a series-parallel system and a production constraint mathematical model based on material production and transfer are established by using a material transmission process as a branch; the production constraint mathematical model based on material production and transfer comprises the following steps: uninterrupted subtask constraints, interruptible subtask constraints and bin Chu Zi task constraints;
The non-interruptible subtask constraint comprises a relation between a subtask state and a start-stop variable of the non-interruptible subtask, a limit of a minimum running time of the non-interruptible subtask and a minimum downtime of the non-interruptible subtask, and an output constraint of the subtask, which are respectively shown in the following formulas (1) - (3):
Interruptible subtask constraints include: the output of the interruptible subtask is shown in the following equation (4):
The warehouse subtask constraints include: the relation between the real-time capacity of the warehouse subtask and the input/output material, and the range limits of the real-time capacity of the warehouse subtask and the input/output material capacity are shown in the following formulas (5) and (6), respectively:
Wherein alpha represents a type of uninterrupted production task; Representing the running state of the ith workflow at the moment t; And Respectively representing the action variables of the start-stop machine; h represents any time; And Minimum run time and minimum downtime, respectively; n1 represents the number of production lines corresponding to such uninterruptible type tasks; The fixed output of each production line is not adjustable and is irrelevant to time; is the total yield of the whole subtask at the time t;
Beta represents a type of interruptible production task; representing the running state of the ith workflow at the moment t; n2 represents the number of production lines corresponding to such interruptible type tasks; is the actual yield of each production line and can be adjusted; is the total yield of the whole subtask at the time t;
S i,t is the capacity of the warehouse at the time t; And The yields of the upstream and downstream processes, respectively; And The start-stop states of the upstream and downstream production tasks are respectively; Representing the upper and lower limits of the warehouse capacity;
according to the energy hub of the industrial park and the industrial production flow model, establishing a production flow of the industrial park and a steady-state energy flow unified model of an energy system; the steady state energy flow unified model is shown in the following formula (7):
Wherein, C 1、C2 is constant coefficient matrix, and V out represents the power output of cold and hot electricity; v in denotes inputs to the system, including fuel, grid power and production materials; v 2 denotes the schedulable energy flows in the energy hub; x represents an incidence matrix of system input and energy flow; y represents an incidence matrix of system output and energy flow; z represents the efficiency incidence matrix of each energy device and energy flow in the system; i represents an identity matrix; r, Q is a coefficient matrix associated with I, X, Z; c 1、C2 denotes a coefficient matrix related to R, Q, Y; wherein subscript 1 represents a coefficient matrix associated with an energy flow that cannot be scheduled and subscript 2 represents a coefficient matrix associated with an energy flow that can be scheduled;
establishing an off-grid industrial park optimization scheduling model by taking the steady-state energy flow unified model, the storage system operation constraint and the energy conversion equipment constraint as constraint conditions and taking the minimum production loss during off-grid operation as an optimization target; the off-grid industrial park optimization scheduling model is shown in the following formulas (8) - (11):
F＝min(Cope+Closs_P+Closs_M) (8)
Vout＝C1Vin+C2V2 (9)
Wherein, C ope＝Cgas+Cf+Con/off; C loss_M＝∑c2 S; f represents an optimization target, C ope represents an energy system operation cost, C loss_P represents a production task stagnation cost, C loss_M represents a production raw material loss cost, C gas represents a gas purchase cost, C f represents a device operation maintenance cost, C on/off represents a device start-stop cost, C 1、C2 represents a loss coefficient, H represents a planned production task, Representing actual throughput, S representing the amount of material lost;
E ES,t represents the storage capacity of the energy storage system at the time t, delta E ES,t represents the variation of the storage capacity at the time t, delta E ES,min、△EES,max represents the upper limit and the lower limit of the storage capacity respectively, E ES,min,EES,max represents the upper limit and the lower limit of the capacity of the energy storage system, eta ES represents the charge and discharge efficiency, and v ES,t represents the external electric energy input at the time t;
respectively represents the output and input of the energy conversion device at the time t, lambda represents the energy conversion efficiency, Representing upper and lower limits of output power;
Calculating probability distribution of power failure time of an external power grid, and generating sample scenes with different power failure time by adopting a Monte Carlo method according to the probability distribution of the power failure time of the external power grid;
adopting a K-means clustering algorithm to reduce the sample scenes with different power failure time lengths to obtain a plurality of reduced sample scenes;
According to the reduced multiple sample scenes, taking the occurrence probability of each scene in the reduced multiple sample scenes as a weight coefficient, and adopting a random optimization method to perform collaborative optimization on the off-grid industrial park optimal scheduling model to obtain an off-grid scheduling strategy with minimum power failure loss;
and under the condition of uncertainty of the power failure duration, carrying out off-grid scheduling on the industrial park according to the off-grid scheduling strategy.
2. The method of claim 1, wherein modeling the industrial process based on delivery of the industrial park stream comprises: the flow of the production material is used as a material flow, and an industrial production flow model is built according to the transfer of the material flow.
3. The method of claim 1, wherein establishing a steady state energy flow unification model of a production process and an energy system of the industrial park based on the energy hub of the industrial park and the industrial production process model comprises: and establishing a production flow of the industrial park and a steady-state energy flow unified model of the energy system according to the energy hub model modeling method.
4. The method of claim 1, wherein calculating the probability distribution of the outage duration of the external power grid comprises: and acquiring and analyzing historical power failure time length data of the external power grid to obtain probability distribution of the power failure time length of the external power grid.
5. The method of claim 1, wherein the specific number of the reduced plurality of sample scenes is 8.
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