CN113159567A - Industrial park off-grid scheduling method considering power failure time uncertainty - Google Patents

Industrial park off-grid scheduling method considering power failure time uncertainty Download PDF

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CN113159567A
CN113159567A CN202110419136.6A CN202110419136A CN113159567A CN 113159567 A CN113159567 A CN 113159567A CN 202110419136 A CN202110419136 A CN 202110419136A CN 113159567 A CN113159567 A CN 113159567A
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energy
grid
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power failure
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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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Beijing Jiaotong University
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    • G06—COMPUTING; CALCULATING OR COUNTING
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Abstract

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

Description

Industrial park off-grid scheduling method considering power failure time uncertainty
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 length.
Background
At present, the industrial park production is continuously streamlined and refined, the industrial park of high-level manufacturing industry usually comprises a plurality of complex production processes, and the energy consumption is huge, so that the industrial energy consumption accounts for a large amount in the energy consumption of the whole society, the reduction of the energy consumption of the industrial park is the primary target of an energy-saving task, and the reduction of the energy consumption cost is more important in the industrial park. In practical application, the power failure of an external power grid is divided into two conditions of planned power failure and unplanned power failure, for unplanned power failure, an industrial park operation control center cannot obtain the information of the power failure time length in advance, the uncertainty of the power failure time length directly influences the operation safety of an off-grid system, and therefore huge economic loss is caused, and the safe and economic operation of the off-grid park cannot be realized under the condition that the power failure time length is uncertain through the existing off-grid operation scheduling strategy.
In the prior art, a certain research is carried out on modeling and economic operation of the comprehensive energy system, and the modeling methods of the electric, gas and thermal comprehensive energy system are numerous, the Energy Hub (EH) model is widely applied, can effectively reflect Energy flow and conversion in the comprehensive Energy system, has simple modeling method and clear physical concept, however, in the prior art, most of the energy systems are only optimized for modeling of the comprehensive energy system of the industrial park, and only for the cooling, heating and power energy system of the industrial park, and for the actual industrial park, the main load of the method is industrial load, the method is a complete assembly line process, each production link has different coupling with an energy system, and the existing optimization method only takes the industrial production as fixed load to participate in optimization to obtain an optimization result which is not accurate and economical. And the coupling relation between the cold, hot and electric comprehensive energy and the industrial process energy is lack of uniform description, and the production process in an industrial production park is lack of uniform modeling. Meanwhile, a high-flow and fine production mode of modern industrial production puts higher requirements on the stability of energy system supply, and the accuracy of the method needs to be improved. On the other hand, in the existing method, uncertain variables in the model are processed by adopting random optimization and robust optimization methods, and both are researched aiming at a grid-connected operation scene. For an off-grid operation scene when an external power grid fails, an optimal scheduling method for an off-grid independent operation industrial park is lacked, uncertainty of off-grid operation time is not considered for operation of the off-grid industrial park at present, power failure time and other state quantities of an energy system are lacked in quantitative research on economic loss of an actual park, an effective method is lacked for describing influence of the uncertainty of the power failure time on an off-grid operation optimization decision result, and an off-grid operation scheduling strategy which gives consideration to risk and economy cannot be made for the park. .
Therefore, an industrial park off-grid scheduling method considering uncertainty of power outage duration is needed.
Disclosure of Invention
The invention provides an industrial park off-grid scheduling method considering uncertainty of power failure time length, and aims to solve the problems in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme.
An industrial park off-grid scheduling method considering uncertainty of power failure time length comprises the following steps:
establishing an industrial production process model according to the transmission of the material flow of the industrial park;
according to an energy hub of the industrial park and the industrial production process model, establishing a uniform model of the production process of the industrial park and the steady-state energy flow 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 the off-grid operation as an optimization target;
calculating the probability distribution of the external power grid power failure time length, and generating sample scenes with different power failure time lengths by adopting a Monte Carlo method according to the probability distribution of the external power grid power failure time length;
reducing the sample scenes with different power failure durations by adopting a K-means clustering algorithm to obtain a plurality of reduced sample scenes;
according to the plurality of reduced sample scenes, taking the occurrence probability of each scene in the plurality of reduced sample scenes as a weight coefficient, and performing collaborative optimization on the off-grid industrial park optimization scheduling model by adopting a random optimization method to obtain an off-grid scheduling strategy with the minimum power failure loss;
and performing off-grid scheduling on the industrial park according to the off-grid scheduling strategy under the condition of uncertainty of the power failure time length.
Preferably, the industrial process flow model is established based on the transfer of industrial park streams, comprising: the flow of the production material is used as a material flow, and an industrial production flow model is established according to the transmission of the material flow.
Preferably, the industrial process flow model comprises: the method comprises the steps of establishing a series-parallel system by taking materials as media, taking different production subtasks as nodes and taking a material transmission process as a branch, and establishing a production constraint mathematical model based on material production and transfer.
Preferably, the production constraint mathematical model based on material production and transfer comprises: an uninterruptible type subtask constraint, an interruptible type subtask constraint, and a warehouse subtask constraint.
Preferably, the uninterruptible-type subtask constraints include a relationship between a subtask state of the uninterruptible-type subtask and a start-stop variable, a limit of a minimum run time of the uninterruptible-type subtask and a minimum downtime of the uninterruptible-type subtask, and an output constraint of the subtask, as shown in the following equations (1) to (3), respectively:
Figure BDA0003027127510000031
Figure BDA0003027127510000032
Figure BDA0003027127510000033
interruptible subtask constraints include: the output of the interruptible subtask is shown in equation (4) below:
Figure BDA0003027127510000041
the warehousing subtask constraints include: the relationship between the real-time capacity of the warehousing sub-task and the input/output material, and the range limits of the real-time capacity of the warehousing sub-task and the input/output material capacity are shown in the following formulas (5) and (6), respectively:
Figure BDA0003027127510000042
Figure BDA0003027127510000043
wherein α represents a type of uninterruptible production task;
Figure BDA0003027127510000044
representing the running state of the ith workflow at the time t;
Figure BDA0003027127510000045
and
Figure BDA0003027127510000046
respectively representing action variables of starting and stopping the machine; h represents an arbitrary time;
Figure BDA0003027127510000047
and
Figure BDA0003027127510000048
minimum run time and minimum down time, respectively; n1 represents the number of lines for such an uninterruptible type task;
Figure BDA0003027127510000049
the output of each production line is fixed and is not adjustable and is not related to time;
Figure BDA00030271275100000410
is the total output of the whole subtask at time t;
beta represents a type of interruptible production task;
Figure BDA00030271275100000411
representing the running state of the ith workflow at the time t; n2 represents the number of production lines for such interruptible type tasks;
Figure BDA00030271275100000412
the actual output of each production line can be adjusted;
Figure BDA00030271275100000413
is the total output of the whole subtask at time t;
Si,tis the capacity of the warehouse at time t;
Figure BDA00030271275100000414
and
Figure BDA00030271275100000415
the yields of upstream and downstream processes, respectively;
Figure BDA00030271275100000416
and
Figure BDA00030271275100000417
respectively starting and stopping states of upstream and downstream production tasks;
Figure BDA00030271275100000418
representing the upper and lower limits of the warehousing capacity.
Preferably, the establishing a unified model of the production flow of the industrial park and the steady-state energy flow of the energy system according to the energy hub of the industrial park and the industrial production flow model comprises: and establishing a uniform model of the production flow of the industrial park and the steady-state energy flow of the energy system according to the energy hub model modeling method.
Preferably, the steady state energy flow unified model is shown in equation (7) below:
Figure BDA0003027127510000051
wherein, C1、C2Are all constant coefficient matrices, VoutRepresents the power output of the cold, heat and electricity; vinRepresents inputs to the system, including fuel, grid power, and production materials; v2Representing schedulable energy flows in an energy hub; x represents a correlation matrix of system inputs and fluence; y represents a correlation matrix of system output and fluence; z represents an efficiency incidence matrix of each energy device and energy flow in the system; i represents an identity matrix; r, Q is a coefficient matrix related to I, X, Z; c1、C2Representing a coefficient matrix associated with R, Q, Y; where subscript 1 denotes the coefficient matrix associated with the non-schedulable power flow and subscript 2 denotes the coefficient matrix associated with the schedulable power flow.
Preferably, the off-grid industrial park optimization scheduling model is shown in the following formulas (8) to (11):
F＝min(Cope+Closs_P+Closs_M) (8)
Vout＝C1Vin+C2V2 (9)
Figure BDA0003027127510000052
Figure BDA0003027127510000053
wherein, Cope＝Cgas+Cf+Con/off；
Figure BDA0003027127510000054
Closs_M＝∑c2S; f denotes the optimization target, CopeRepresenting the operating cost of the energy system, Closs_PRepresenting the cost of the production job stagnation, Closs_MRepresents the lost cost of the production raw material, CgasRepresenting the cost of gas purchase, CfRepresents the equipment operation and maintenance cost, Con/offIndicating the cost of starting and stopping the apparatus, C1、C2Representing the loss factor, F representing the planned production task,
Figure BDA0003027127510000055
representing the actual production quantity, and S representing the material loss quantity;
EES,trepresenting the charge capacity, Δ E, of the energy storage system at time tES,tIndicates the amount of change in the amount of stored electricity at time t, Δ EES,min、ΔEES,maxRespectively representing the upper and lower limits of the charge and discharge capacity, EES,min，ΔEES,maxRepresenting the upper and lower limits, eta, of the capacity of the energy storage systemESShows the charge-discharge efficiency, vES,tExternal power input representing time t;
Figure BDA0003027127510000061
respectively representing the output and the input of the energy conversion equipment at the time t, lambda represents the energy conversion efficiency,
Figure BDA0003027127510000062
to representUpper and lower limits of output power.
Preferably, the calculating the probability distribution of the blackout duration of the external power grid comprises: and acquiring and analyzing historical power failure time data of the external power grid to obtain the probability distribution of the power failure time of the external power grid.
Preferably, the specific number of the plurality of reduced 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 time length, the invention establishes a steady-state energy flow unified model based on the industrial production process and the comprehensive energy system through the 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 constraints and energy conversion equipment constraints as constraint conditions and taking minimum production loss during off-grid operation as an optimization target; based on an off-grid industrial park optimization 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, the optimization strategy is adopted to perform off-grid scheduling on the industrial park, and the purpose of minimizing 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.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic diagram of an off-grid scheduling method of an industrial park in consideration of uncertainty of blackout duration according to an embodiment;
FIG. 2 is a schematic diagram of a basic framework of a series-parallel system of an industrial process flow of an embodiment;
FIG. 3 is a schematic diagram of an energy junction structure of a steady-state energy flow unified model according to an embodiment;
FIG. 4 is a schematic diagram of an energy conversion node with a single output port;
FIG. 5 is a schematic diagram of a warehousing subtask;
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 battery production park;
FIG. 8 is a probability distribution plot of external grid blackout durations;
FIG. 9 is a schematic flow chart of the optimization procedure.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of 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 the context clearly indicates otherwise. 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. As used herein, the term "and/or" 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 convenience of understanding of the embodiments of the present invention, the following description will be further explained by taking specific embodiments as examples with reference to the drawings, and the embodiments of the present invention are not limited thereto.
Examples
Fig. 1 is a schematic diagram of an off-grid scheduling method of an industrial park in consideration of uncertainty of a power outage duration in this embodiment, and with reference to fig. 1, the method includes:
s1 an industrial process flow model is established based on the transfer of the industrial park streams.
The flow of the production material is used as a material flow, and an industrial production flow model is established according to the transmission of the material flow. The complete industrial production process comprises a plurality of processes of production, storage and reproduction from raw materials, semi-finished products to finished products, and the embodiment divides the production process into subtasks with different scheduling characteristics and respectively establishes a mathematical model according to the coupling among different production processes. The subtasks can contain a plurality of production devices and have strict time sequence constraint, and participate in the scheduling operation of the energy system as the minimum scheduling unit. Specifically, the industrial production process model comprises: the method comprises the steps of establishing a series-parallel system by taking materials as media, taking different production subtasks as nodes and taking a material transmission process as a branch, and establishing a production constraint mathematical model based on material production and transfer. Fig. 2 is a schematic diagram of a basic framework of the series-parallel system of the industrial production process of the embodiment.
The delivery of material is a physical link between different production processes. Since the yield of a production target is directly related to the consumption of energy, product constraints determine the energy requirements of industrial production. Based on this, the production constraint mathematical model based on material production and transfer in this embodiment includes three subtask forms of an uninterruptible subtask constraint, an interruptible subtask, and a warehousing subtask constraint, which are specifically as follows:
1) uninterruptible type subtask constraints:
an uninterruptible type subtask represents a set of consecutive steps with strict timing constraints that cannot be run independently until the previous step is generated. In the interaction of the energy system, the uninterruptible type subtask can be compared to a fixed power device, and the operating state can be controlled, and the operating state can be on or off. Typically a one-way continuous flow line production.
The uninterruptible subtask is characterized in that continuous adjustment cannot be performed, and only the on-off state of the workstation can be controlled. Furthermore, the minimum start-stop time constraint of each subtask must be considered; since the subtasks actually contain several constraints with strict timing constraints and require characterization of the entire process, the minimum run-time and minimum downtime limitations need to be considered, resulting in an uninterruptible type subtask constraint including:
the relationship between the subtask state and the start-stop variable of the uninterruptible type subtask, the minimum run time of the uninterruptible type subtask, and the limit of the minimum downtime of the uninterruptible type subtask, and the output constraint of the subtask are shown in the following equations (1) to (3), respectively:
Figure BDA0003027127510000091
Figure BDA0003027127510000092
Figure BDA0003027127510000093
wherein α represents a type of uninterruptible production task;
Figure BDA0003027127510000094
representing the running state of the ith workflow at the time t;
Figure BDA0003027127510000095
and
Figure BDA0003027127510000096
respectively representing action variables of starting and stopping the machine; h represents an arbitrary time;
Figure BDA0003027127510000097
and
Figure BDA0003027127510000098
minimum run time and minimum down time, respectively; n1 represents the number of lines for such an uninterruptible type task;
Figure BDA0003027127510000099
the output of each production line is fixed and is not adjustable and is not related to time;
Figure BDA00030271275100000910
is the total throughput of the entire subtask at time t.
2) Interruptible subtask constraints:
an interruptible subtask represents an accumulative task in which the semifinished product should be processed in several consecutive time periods. Thus, the operating state can be adjusted by controlling the amount of product without strict time constraints. Schematically, an interruptible subtask can be seen as a device with flexibly adjustable power in addition to the switching state. A typical representative is a battery charge and discharge test.
Compared with the uninterruptible subtask, the output of the interruptible subtask has more adjustable space, and the output of the subtask can be controlled besides the switch state. Compared with the uninterruptible type subtask, the output of the interruptible type subtask is adjustable, and the output of the interruptible type subtask is shown as the following formula (4):
Figure BDA0003027127510000101
beta represents a type of interruptible production task;
Figure BDA0003027127510000102
representing the running state of the ith workflow at the time t; n2 represents the number of production lines for such interruptible type tasks;
Figure BDA0003027127510000103
the actual output of each production line can be adjusted;
Figure BDA0003027127510000104
is the total throughput of the entire subtask at time t.
3) Constraint of storage subtask:
the storage subtask is used for describing a material storage. It is similar to stored energy in operating characteristics. Compared to energy storage, the same constraints exist between input, output and capacity.
The storage phase is a buffer area between two subtasks, and two connected processes are decoupled. Similar to energy storage, warehouses also have capacity limitations. The relationship between the real-time capacity of the warehousing sub-task and the input/output material, and the range limits of the real-time capacity of the warehousing sub-task and the input/output material capacity are shown in the following formulas (5) and (6), respectively:
Figure BDA0003027127510000105
Figure BDA0003027127510000106
wherein S isi,tIs the capacity of the warehouse at time t;
Figure BDA0003027127510000107
and
Figure BDA0003027127510000108
the yields of upstream and downstream processes, respectively;
Figure BDA0003027127510000109
and
Figure BDA00030271275100001010
respectively starting and stopping states of upstream and downstream production tasks;
Figure BDA00030271275100001011
representing the upper and lower limits of the warehousing capacity.
S2, according to the energy hub and the industrial production process model of the industrial park, a unified model of the production process of the industrial park and the steady-state energy flow of the energy system is established.
And establishing a uniform model of the production flow of the industrial park and the steady-state energy flow 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-substance in the energy system of the industrial park, and meets the power flow balance equation. The material flow is used as a generalized energy form, an industrial production process is added into the model to obtain a steady-state energy flow unified model, and fig. 3 is an energy hub structure schematic diagram of the steady-state energy flow unified model of the embodiment. Energy devices in an energy hub structure are considered nodes. There are generally two types of nodes in an energy hub structure, namely an energy conversion node and an energy storage node.
For a production type subtask, the energy hub structure can be viewed as an energy conversion node having multiple input ports and a single output port as shown in fig. 4. These two subtasks are similar to energy conversion elements, with inputs for energy and raw materials and outputs for semi-finished materials. Thus, the present embodiment is modeled for continuous and discrete subtasks as node types of energy coupling devices of the cogeneration set and the heat pump. In FIG. 4, vinInputting materials; w is ainIs the input of energy; v. ofoutIs the output of the semi-finished product, etaMAnd ηWAre respectively materialsConversion efficiency of flow and energy flow.
For the production subtask, the node balance equation is shown in equation (7) below:
Figure BDA0003027127510000111
wherein Z represents an efficiency correlation matrix of each energy device and energy flow in the system.
For the storage subtask, the model is relatively simple, since there is no coupling of the energy system. The Storage subtask is regarded as an Energy Storage System, and as shown in fig. 5, is a schematic structural diagram of the Storage subtask, and is similar to a Battery Energy Storage System (BESS) in form. v. ofstRefers to a virtual branch connected to a "State of Charge (SOC)".
To maintain the uniformity of the format with other components, virtual energy storage branches are added to the storage subtasks. From A'gRepresenting the stored original correlation matrix. Taking into account added branches, the node incidence matrix A of the storage componentgAs shown in the following formula (8):
Figure BDA0003027127510000121
the efficiency correlation matrix is:
Z＝[ηC-1/ηD-1] (9)
the resultant steady state energy flow unified model is shown in the following formula (10), wherein C1、C2Are constant coefficient matrices:
Figure BDA0003027127510000122
wherein, VoutRepresents the power output of the cold, heat and electricity; vinRepresents inputs to the system, including fuel, grid power, and production materials; v2Representing schedulable energy flows in an energy hub; x represents the relationship between system input and energy flowA joint matrix; y represents a correlation matrix of system output and fluence; i represents an identity matrix; r, Q denotes the coefficient matrix relating to I, X, Z; c1、C2Representing a coefficient matrix associated with R, Q, Y; where subscript 1 denotes the coefficient matrix associated with the non-schedulable power flow and subscript 2 denotes the coefficient matrix associated with the schedulable power flow.
S3 establishes an off-grid industrial park optimization scheduling model with the steady-state energy flow unified model, the storage system operation constraint and the energy conversion equipment constraint as constraint conditions and with the minimum production loss during the off-grid operation as an optimization target.
S31 energy flow balance constraint. The main constraints of system optimization are various energy balance constraints of cold, heat, electricity and the like and material balance constraints of industrial production. This part of the constraint is reflected by the steady state energy flow unified model.
S32 stores system operating constraints. The storage system has significant timing characteristics compared to the energy conversion device. The stored energy is coupled at the next moment. Therefore, it is necessary to set constraints on the SOC. The Storage devices in the extended EH include a Battery Energy Storage System (BESS), a Thermal Storage system (TS), a cold Storage system (CS), and two Storage subtasks. Equation (13) is the operation constraint of BESS, including the timing constraint of SOC, the range constraint of charge-discharge power, and the range constraint of SOC.
S33 energy conversion device constraints. Equation (14) collectively represents the constraint conditions of the energy conversion device, and respectively represents the rate of change and the output range of energy conversion.
Obtaining an off-grid industrial park optimization scheduling model as shown in the following formulas (11) to (14):
F＝min(Cope+Closs_P+Closs_M) (11)
Vout＝C1Vin+C2V2 (12)
Figure BDA0003027127510000131
Figure BDA0003027127510000132
wherein, Cope＝Cgas+Cf+Con/off；
Figure BDA0003027127510000133
Closs_M＝∑c2S; f denotes the optimization target, CopeRepresenting the operating cost of the energy system, Closs_PRepresenting the cost of the production job stagnation, Closs_MRepresents the lost cost of the production raw material, CgasRepresenting the cost of gas purchase, CfRepresents the equipment operation and maintenance cost, Con/offIndicating the cost of starting and stopping the apparatus, C1、C2Representing the loss factor, F representing the planned production task,
Figure BDA0003027127510000134
representing the actual production quantity, and S representing the material loss quantity;
EES,trepresenting the charge capacity, Δ E, of the energy storage system at time tES,tIndicates the amount of change in the amount of stored electricity at time t, Δ EES,min、ΔEES,maxRespectively representing the upper and lower limits of the charge and discharge capacity, EES,min，ΔEES,maxRepresenting the upper and lower limits, eta, of the capacity of the energy storage systemESShows the charge-discharge efficiency, vES,tExternal power input representing time t;
Figure BDA0003027127510000141
respectively representing the output and the input of the energy conversion equipment at the time t, lambda represents the energy conversion efficiency,
Figure BDA0003027127510000142
representing the upper and lower limits of the output power.
S4, calculating the probability distribution of the external power grid power failure time length, and generating sample scenes with different power failure time lengths by adopting a Monte Carlo method according to the probability distribution of the external power grid power failure time length.
And acquiring and analyzing historical power failure time data of the external power grid to obtain the probability distribution of the power failure time of the external power grid.
S5, reducing the sample scenes with different power failure durations by adopting a K-means clustering algorithm to obtain a plurality of reduced sample scenes.
Preferably, the specific number of the plurality of reduced sample scenes in this embodiment is 8.
S6, according to the plurality of reduced sample scenes, taking the occurrence probability of each scene in the plurality of reduced sample scenes as a weight coefficient, performing collaborative optimization on the off-grid industrial park optimization scheduling model by adopting a random optimization method, and obtaining an off-grid scheduling strategy with the minimum power failure loss through iterative processing;
and S7, performing off-grid scheduling on the industrial park according to the off-grid scheduling strategy under the condition that the power failure duration is uncertain.
The following is a specific application example of the industrial park off-grid scheduling method considering uncertainty of power outage duration according to the embodiment, taking an actual power battery located in the guangdong of china as an example, fig. 6 is a schematic diagram of a power battery production flow, and referring to fig. 6, firstly, an actual power battery production flow is analyzed, and production is divided into three subtasks based on a time sequence constraint and a storage link of the production flow. The method comprises the steps of Cell Production (CP) links (stirring, coating, drying, rolling and slicing), battery Packaging (PL) links (winding, welding, liquid injection and packaging) and final aging and testing (FG) links.
FIG. 7 is a schematic diagram of an energy hub structure of a steady-state energy flow unified model of a power battery production park. Referring to fig. 7, there are two stations in CP, two stations in PL, three stations in FG, and two Storage stations (MS). The energy system equipment comprises a Combined Heat and Power (CHP), a Heat Pump (HP), an Auxiliary gas Boiler (AB), an Absorption refrigerator (Absorption Chiller AC), a Centrifugal Chiller (CC), an electric energy storage (BESS), a thermal energy storage (TS) and a cold energy storage (CS) device, and cold and hot Power bus nodes are added to reduce the matrix scale, wherein v represents branch numbers and N represents node numbers.
Based on an energy pivot structure of a power battery production park steady-state energy flow unified model, the whole park steady-state energy flow unified model is established as shown in a formula (15).
Figure BDA0003027127510000151
Finally obtaining an output VoutAnd Vin，V2Relation of (a), C1And C2All the coefficient matrixes are constant coefficient matrixes which are only related to a system connection mode and equipment efficiency parameters, are unrelated to a system running state, do not participate in optimization calculation and are used for solving the problem of low efficiency of the system.
And establishing an optimization target of the off-grid operation of the industrial park. Aiming at the analysis and optimization target of the power battery production plant, the method 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 gas purchase cost and equipment operation and maintenance cost, and the operation and maintenance cost comprises startup/shutdown cost of cogeneration and air conditioning and maintenance cost of a power plant. The production task stasis cost is the lost revenue that is incurred by the difference between the planned production task and the production task completed during the actual blackout. The loss cost of the production principle is the raw material waste caused by the fact that the production materials cannot complete the production of the final product on the intermediate production process and an important production line and the power failure is interrupted midway, and the loss cost is also the most important loss of a high-tech production enterprise in the power failure.
And (3) comprehensively considering the cost of the three parts, and establishing an off-grid optimization scheduling objective function of the industrial park comprehensive energy system as shown in the formula (11).
And further obtaining an off-grid optimization scheduling model of the power battery production park considering industrial production according to the constraint conditions of the off-grid optimization problems of the formulas (12) to (14).
The product of the start-stop variable and the power variable exists in the off-grid optimization scheduling model, so that the model is a nonlinear problem. For the convenience of calculation, the large M method is adopted for linearizing the model.
Based on the off-grid optimized scheduling model, the probability distribution of the external power grid power failure duration is obtained by analyzing the historical power failure duration data of the external power grid, as shown in fig. 8. The off-grid scheduling policy with the minimum power outage loss is obtained by optimizing on the basis of the probability distribution, and the specific optimization steps are shown in fig. 9.
According to the probability distribution of the power failure time, sample scenes with different power failure times are produced by adopting a Monte Carlo method, and the possible state of the power failure time is simulated. And (3) reducing a large number of generated sample scenes with different power failure durations into 8 scenes by adopting a K-means clustering algorithm, wherein the sample scenes with different power failure durations and the occurrence probabilities are different.
And each individual scene has a determined power failure duration, the off-network optimization scheduling strategy is utilized for optimization to obtain the system power failure loss cost, then the 8 scenes are cooperatively optimized through a random optimization method, different weight coefficients are assigned according to the occurrence probability of each scene, and the optimal scheduling strategy which can meet the minimum comprehensive loss in the 8 scenes is obtained, namely the final optimal scheduling strategy. Table 1 below shows the power outage loss of the system at different power outage times obtained by using the final optimization strategy.
TABLE 1
Figure BDA0003027127510000161
When the power failure duration of the external power grid is determined, namely the off-grid running time of the known system, a power battery production park off-grid optimization scheduling model considering industrial production is adopted in a linearized manner. And (3) solving an off-grid optimization scheduling model by adopting Matlab and Cplex, setting different power-off durations and different energy storage state parameters by a single variable control method, and performing simulation verification on the power-off loss of the off-grid park in different scenes. Taking the CHP unit output of 75% and the energy storage system capacity of 50% as an example, the economic loss caused by the scheduling strategy corresponding to the 2-hour power failure duration (planned power failure duration) under the power failure duration of 1-5 hours is analyzed as shown in table 2 below.
TABLE 2
Figure BDA0003027127510000171
As can be seen from tables 1 and 2, compared with data in table 2, the industrial park off-grid scheduling method considering uncertainty of power failure time can effectively reduce loss in a long power failure time scene, and comprehensive risk is lower.
It will be appreciated by those skilled in the art that the foregoing types of applications are merely exemplary, and that other types of applications, whether presently existing or later to be developed, that may be suitable for use with the embodiments of the present invention, are also intended to be encompassed within the scope of the present invention and are hereby incorporated by reference.
It should be understood by those skilled in the art that the foregoing description of determining the invoking policy according to the user information is only for better illustrating the technical solutions of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention. Any method of determining the invoking policy based on the user attributes is included in the scope of embodiments of the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. An industrial park off-grid scheduling method considering uncertainty of power failure time length is characterized by comprising the following steps:
establishing an industrial production process model according to the transmission of the material flow of the industrial park;
according to an energy hub of the industrial park and the industrial production process model, establishing a uniform model of the production process of the industrial park and the steady-state energy flow 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 the off-grid operation as an optimization target;
calculating the probability distribution of the external power grid power failure time length, and generating sample scenes with different power failure time lengths by adopting a Monte Carlo method according to the probability distribution of the external power grid power failure time length;
reducing the sample scenes with different power failure durations by adopting a K-means clustering algorithm to obtain a plurality of reduced sample scenes;
according to the plurality of reduced sample scenes, taking the occurrence probability of each scene in the plurality of reduced sample scenes as a weight coefficient, and performing collaborative optimization on the off-grid industrial park optimization scheduling model by adopting a random optimization method to obtain an off-grid scheduling strategy with the minimum power failure loss;
and performing off-grid scheduling on the industrial park according to the off-grid scheduling strategy under the condition of uncertainty of the power failure time length.
2. The method of claim 1, wherein establishing an industrial process flow model based on the transfer of industrial park streams comprises: the flow of the production material is used as a material flow, and an industrial production flow model is established according to the transmission of the material flow.
3. The method of claim 1, wherein the industrial process model comprises: the method comprises the steps of establishing a series-parallel system by taking materials as media, taking different production subtasks as nodes and taking a material transmission process as a branch, and establishing a production constraint mathematical model based on material production and transfer.
4. The method of claim 3, wherein the material production and transfer based production constraint mathematical model comprises: an uninterruptible type subtask constraint, an interruptible type subtask constraint, and a warehouse subtask constraint.
5. The method according to claim 4, wherein the uninterruptible subtask constraints include a relationship between a subtask state of the uninterruptible subtask and a start stop variable, a limit on a minimum running time of the uninterruptible subtask and a minimum downtime of the uninterruptible subtask, and an output constraint of the subtask are respectively as shown in the following equations (1) to (3):
Figure FDA0003027127500000021
Figure FDA0003027127500000022
Figure FDA0003027127500000023
interruptible subtask constraints include: the output of the interruptible subtask is shown in equation (4) below:
Figure FDA0003027127500000024
the warehousing subtask constraints include: the relationship between the real-time capacity of the warehousing sub-task and the input/output material, and the range limits of the real-time capacity of the warehousing sub-task and the input/output material capacity are shown in the following formulas (5) and (6), respectively:
Figure FDA0003027127500000025
Figure FDA0003027127500000026
wherein α represents a type of uninterruptible production task;
Figure FDA0003027127500000027
representing the running state of the ith workflow at the time t;
Figure FDA0003027127500000028
and
Figure FDA0003027127500000029
respectively representing action variables of starting and stopping the machine; h represents an arbitrary time;
Figure FDA00030271275000000210
and
Figure FDA00030271275000000211
minimum run time and minimum down time, respectively; n1 represents the number of lines for such an uninterruptible type task;
Figure FDA0003027127500000031
the output of each production line is fixed and is not adjustable and is not related to time;
Figure FDA0003027127500000032
is the total output of the whole subtask at time t;
beta represents a type of interruptible production task;
Figure FDA0003027127500000033
represents the ith jobThe running state of the process at the time t; n2 represents the number of production lines for such interruptible type tasks;
Figure FDA0003027127500000034
the actual output of each production line can be adjusted;
Figure FDA0003027127500000035
is the total output of the whole subtask at time t;
Si,tis the capacity of the warehouse at time t;
Figure FDA0003027127500000036
and
Figure FDA0003027127500000037
the yields of upstream and downstream processes, respectively;
Figure FDA0003027127500000038
and
Figure FDA0003027127500000039
respectively starting and stopping states of upstream and downstream production tasks;
Figure FDA00030271275000000310
representing the upper and lower limits of the warehousing capacity.
6. The method of claim 1, wherein the modeling the industrial park production process unified with the steady state energy flow of the energy system based on the industrial park energy hub and the industrial production process model comprises: and establishing a uniform model of the production flow of the industrial park and the steady-state energy flow of the energy system according to the energy hub model modeling method.
7. The method of claim 1, wherein the steady state energy flow unified model is represented by the following equation (7):
Figure FDA00030271275000000311
wherein, C1、C2Are all constant coefficient matrices, VoutRepresents the power output of the cold, heat and electricity; vinRepresents inputs to the system, including fuel, grid power, and production materials; v2Representing schedulable energy flows in an energy hub; x represents a correlation matrix of system inputs and fluence; y represents a correlation matrix of system output and fluence; z represents an efficiency incidence matrix of each energy device and energy flow in the system; i represents an identity matrix; r, Q is a coefficient matrix related to I, X, Z; c1、C2Representing a coefficient matrix associated with R, Q, Y; where subscript 1 denotes the coefficient matrix associated with the non-schedulable power flow and subscript 2 denotes the coefficient matrix associated with the schedulable power flow.
8. The method of claim 1, wherein the off-grid industrial park optimization scheduling model is represented by the following equations (8) - (11):
F＝min(Cope+Closs_P+Closs_M) (8)
Vout＝C1Vin+C2V2 (9)
Figure FDA0003027127500000041
Figure FDA0003027127500000042
wherein, Cope＝Cgas+Cf+Con/off；
Figure FDA0003027127500000043
Closs_M＝∑c2*S；
F denotes the optimization target, CopeRepresenting the operating cost of the energy system, Closs_PRepresenting the cost of the production job stagnation, Closs_MRepresents the lost cost of the production raw material, CgasRepresenting the cost of gas purchase, CfRepresents the equipment operation and maintenance cost, Con/offIndicating the cost of starting and stopping the apparatus, C1、C2Representing the loss factor, F representing the planned production task,
Figure FDA0003027127500000044
representing the actual production quantity, and S representing the material loss quantity;
EES,trepresenting the charge capacity, Δ E, of the energy storage system at time tES,tIndicates the amount of change in the amount of stored electricity at time t, Δ EES,min、ΔEES,maxRespectively representing the upper and lower limits of the charge and discharge capacity, EES,min，ΔEES,maxRepresenting the upper and lower limits, eta, of the capacity of the energy storage systemESShows the charge-discharge efficiency, vES,tExternal power input representing time t;
Figure FDA0003027127500000045
respectively representing the output and the input of the energy conversion equipment at the time t, lambda represents the energy conversion efficiency,
Figure FDA0003027127500000046
representing the upper and lower limits of the output power.
9. The method of claim 1, wherein calculating the probability distribution of the blackout duration of the external power grid comprises: and acquiring and analyzing historical power failure time data of the external power grid to obtain the probability distribution of the power failure time of the external power grid.
10. The method of claim 1, wherein the specific number of the reduced sample scenes is 8.
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