CN115964834A - Enterprise comprehensive energy planning method and system based on virtual energy storage of production process - Google Patents
Enterprise comprehensive energy planning method and system based on virtual energy storage of production process Download PDFInfo
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Abstract
The invention discloses an enterprise comprehensive energy planning method and system based on virtual energy storage of a production process, and belongs to the technical field of comprehensive energy planning. The existing optimization scheme does not consider the influence of the actual production process flow of the industrial park and the virtual energy storage possibly generated in the production process, has poor economical efficiency and is not beneficial to popularization and use. The comprehensive energy planning method based on the virtual energy storage of the production process provided by the invention has the advantages that the virtual energy storage characteristic shown in the schedulable process link of the battery production enterprise is considered while the planning optimization model is constructed, the production orders of the battery production enterprise are taken as constraints to carry out reasonable planning, the schedulable space of the planning model is further excavated, the total cost in the whole production life cycle can be effectively reduced, the economy is good, and the popularization and the use are facilitated.
Description
Technical Field
The invention relates to a comprehensive energy planning method and a comprehensive energy planning system based on virtual energy storage of a production process, and belongs to the technical field of comprehensive energy planning.
Background
The comprehensive energy system can realize multi-energy complementation and cascade utilization of energy, and can effectively solve the double challenges brought by fossil energy exhaustion and environmental pollution. The method has very important significance in effective configuration and rationalization planning of the comprehensive energy system, and becomes a focus of attention in the current field. The current research on the planning of the park integrated energy system mainly focuses on the configuration of equipment capacity.
Document [1] proposes a comprehensive energy microgrid (multi-energy microgrid) device capacity configuration method considering economy and energy supply reliability. In the literature [2], a microgrid is used as a research object, and the influence of different equipment configuration schemes on the reliability and the economy of a microgrid system is deeply analyzed. In addition, there have been studies at present that also take into account the effects of virtual energy storage when planning a campus integrated energy system. Document [3] proposes a comprehensive energy station operation scheduling and planning configuration method considering building virtual energy storage under a demand response mechanism, and carefully considers building virtual energy storage characteristics based on building heat balance and considers demand response capability of users.
However, the currently constructed virtual energy storage model mainly focuses on building heat balance and flexibility loads, and does not deeply excavate virtual energy storage possibly generated by the production process flow of the industrial park. The industrial park comprehensive energy system is used as a specific scene of the comprehensive energy system, can show the virtual energy storage characteristic by matching with the production process flow, and has a potential schedulable space. In order to further consider virtual energy storage possibly generated by the production process flow of the industrial park, the influence of the production process flow on the economical efficiency and the safety of system operation is analyzed in the document [4 ]. Document [5] constructs a distributed cyber-physical (cyber-physical) system by taking a typical industrial park as an example, and analyzes the influence of a production process and order arrangement on the operation of the system. However, the current deep mining on the characteristics of the industrial production process flow mainly focuses on the operation scheduling aspect of the system, and no corresponding research is carried out on the planning aspect.
In summary, the above research does not consider the influence of the actual production process flow of the industrial park and the virtual energy storage possibly generated in the production process on the planning method of the industrial park integrated energy system.
Reference:
[1]Ge Shaoyun,Li Jifeng,Liu Hong,Sun Hao,and Wang Yiran,“Research on operation-planning double layer optimization design method for multi-energy microgrid considering reliability,”Applied Sciences,vol.8,no.11,pp.2062:1-21,Oct 2018.
[2]Adefarati T,and Bansal R C,“Reliability and economic assessment of a microgrid power system with the integration of renewable energy resources,”Applied Energy,vol.206,pp.911-933,Nov 2017.
[3]Ge Shaoyun,Li Jifeng,Liu Hong,and He Xingtang,“Demand-side energy management method for building clusters based on reinforcement learning,”the 4th IEEE Conference on Energy Internet and Energy system Integration(EI2 2020),pp.1-6,Oct 2020.
[4]Yizhi Zhang,Xiaojun Wang,Jinghan He,Yin Xu,and Wei Pei,“Optimization of distributed integrated multi-energy system considering industrial process based on energy hub,”Journal of Modern Power Systems and Clean Energy,vol.8,no.5,pp.863-873,Sep 2020.
[5]Wei Pei,Xin Ma,Wei Deng,Xinhe Chen,Hongjian Sun,and Dan Li,“Industrial multi-energy and production management scheme in cyber-physical environments:a case study in a battery manufacturing plant,”IET Cyber-Physical Systems:Theory&Applications,vol.4,no.1,pp.13-21,Oct 2018。
disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a method which fully considers the virtual energy storage characteristic of the production process, constructs a planning optimization model while constructing an operation scheduling model, optimizes the operation condition by taking the type and the number of equipment as optimization variables, optimizes the energy supply in a system and the capacity and the number of storage equipment, can effectively reduce the total cost in the whole production life cycle, has good economy and is beneficial to popularization and use;
meanwhile, the dispatching model is operated, the output of energy supply equipment in the system and the operation of energy storage equipment are optimized by combining an equipment scheme set by the planning optimization model, the total cost in the whole life cycle of production is further reduced, and the comprehensive energy planning method and the comprehensive energy planning system based on the virtual energy storage of the production process are particularly suitable for battery production enterprises and have wide application range.
In order to achieve the above object, a first technical solution of the present invention is:
a comprehensive energy planning method based on virtual energy storage of a production process,
the method comprises the steps of firstly, acquiring integral load and time-by-time load demand data in a system;
secondly, setting constraint conditions according to the load in the first step and the time-by-time load demand data;
the constraint conditions at least comprise a maximum load constraint or/and a self-sufficient probability constraint or/and an energy supply equipment operation constraint or/and an energy storage equipment operation constraint or/and a power balance constraint or/and a production order constraint;
thirdly, establishing an objective function by taking the lowest total cost in the whole life cycle as a target according to the constraint conditions and the virtual energy storage characteristics of the production process in the second step;
fourthly, according to the objective function in the third step, a planning optimization model and an operation scheduling model are constructed, and the comprehensive energy of virtual energy storage of the production process is effectively optimized;
the planning optimization model takes the type and the number of the equipment as optimization variables, optimizes the operation condition and optimizes the capacity and the number of the energy supply and storage equipment in the system;
the operation scheduling model is combined with an equipment scheme set by the planning optimization model to optimize the output of energy supply equipment and the operation of energy storage equipment in the system.
Through continuous exploration and test, the virtual energy storage characteristics of an enterprise in a schedulable process link are fully considered, the virtual energy storage characteristics are mapped into a planning period of a long time scale, equipment resources are reasonably planned and configured, the production process flow of a production enterprise can be effectively scheduled, and virtual energy storage excavation in the production process of the enterprise is realized, so that the virtual energy storage in the production process can be reasonably utilized, the planning and configuration cost of a system is reduced, and the reasonable configuration of equipment and the accurate investment of a park energy supply system are guided.
The method has the advantages that the planning optimization model is built while the operation scheduling model is built, the operation working condition is optimized by taking the type and the number of the equipment as optimization variables, the energy supply in the system and the capacity and the number of the storage equipment are optimized, the total cost in the whole production life cycle can be effectively reduced, the economy is good, and the popularization and the use are facilitated.
Meanwhile, the scheduling model is operated, the equipment scheme set by the planning optimization model is combined, the output of energy supply equipment in the system and the operation of energy storage equipment are optimized, the total cost in the whole life cycle of production is further reduced, and the method is particularly suitable for battery production enterprises and wide in application range.
Furthermore, the method can be applied to the comprehensive energy system planning of the battery production enterprise, the virtual energy storage characteristic expressed by the schedulable process link of the battery production enterprise can be fully considered, and the virtual energy storage characteristic is mapped into the planning period of a long time scale; and then, equipment resources can be reasonably planned and configured according to production orders of battery production enterprises, and the reasonable configuration of the equipment and the accurate investment of a park energy supply system are guided, so that the planning analysis of a battery factory is realized, and the efficient planning configuration of an industrial park comprehensive energy system is supported. The method has very important significance for the friendly interaction between the user and the superior power grid and the realization of the target of energy conservation and emission reduction of the user. As a preferable technical measure:
the maximum load constraint in the second step is used for enabling the maximum output power of the energy supply equipment planned and configured by the comprehensive energy system to meet the maximum requirement of the electric/thermal terminal load of the system;
the self-sufficient probability constraint is used for enabling the industrial park to meet a certain amount of load requirements in the system in an electric energy island mode and ensuring the stable operation of the system;
the energy supply equipment operation constraints are used for enabling the energy supply equipment configured by the comprehensive energy system planning to meet the rated power and the climbing constraints of the equipment in the operation process;
the energy storage equipment operation constraint is used for enabling the energy storage equipment configured by the system planning to meet the charging and discharging energy power and capacity constraint of the equipment in the operation process;
the power balance constraint is used for guaranteeing that the energy load supply and demand in the industrial park reach real-time balance under the condition of considering the virtual energy storage effect of energy storage equipment in the system and the production process:
the production order constraints are used for considering energy consumption loads of the production orders.
As a preferable technical measure:
the maximum load constraint is calculated as follows:
the self-sufficient probability constraint guides the planning configuration in the system through the probability that the self-sufficient system meets the load requirement in the planning period, and the calculation formula is as follows:
in the formula, P st The probability of self-sufficiency of a certain type of terminal load in the system;
the calculation formula of the operation constraint of the energy supply equipment is as follows:
in the formula (I), the compound is shown in the specification,
respectively the maximum/minimum output of the energy supply device m; />
Reducing the climbing speed of the output force and increasing the climbing speed of the output force for the energy supply equipment m respectively;
the calculation formula of the energy storage device operation constraint is as follows:
in the formula, M m (t) capacity of the energy storage device M at time t;
maximum/minimum capacity of the energy storage device M, respectively; />
The maximum charging/discharging power of the energy storage device M;
the calculation formula of the power balance constraint is as follows:
in the formula (I), the compound is shown in the specification,
the output of the energy supply equipment m at the moment t is provided; />
The output of the energy storage device M at the moment t is obtained;
the output of the virtual energy storage at the moment t in the production process is provided; p Load (t) is the demand of the load at time t; />
The energy stored at the moment t of the energy storage device is used; />
Energy stored at the moment t for virtual energy storage in the production process;
the production order constraints comprise continuous production load constraints and discrete production electricity load constraints;
the continuous production load constraint is a part which does not allow power failure in the production process flow, and the state of the continuous production load constraint only depends on the start and the end of an order;
the discrete production power load constraint is the energy consumption load required by the sudden production task, and the state of the discrete production power load constraint is directly related to the order and the production task.
As a preferable technical measure:
the calculation formula of the continuous production load constraint is as follows:
in the formula, P Load,c (t) total energy consumption for continuous production load for time period t; n is a radical of hydrogen c The total number of production workshops; m c,i For the number of outputs of the production plant i, M for the continuous production of the electrical loads c,i Is a fixed value; p is c,i (t) is the energy consumption of the production shop i to produce a unit quantity of product at time t; u. u c,i (t) is a Boolean variable, which represents the working state of the production workshop i in the period of t, and the following constraint conditions are specifically required to be met:
wherein, the first and the second end of the pipe are connected with each other,
respectively representing the starting state and the ending state of the production workshop i;
the calculation formula of the discrete production electrical load constraint is as follows:
in the formula, P Load,d (t) total power consumption of the discrete production load at the time period t; n is a radical of d The total number of production workshops; m is a group of d,j For the number of outputs of production shop j, different from the continuous production load, M d,j Not a fixed value, but rather a correlation with the number of orders and the time of completion; p d,j (t) is the energy consumption of the production shop i to produce a unit quantity of product at time t; u. u d,j (t) is also a boolean variable;
thus, the load of the battery factory over the statistical period is calculated as:
in the formula, N T A statistical time period is scheduled; p Load,o When (t) is tLoad requirements other than production orders.
As a preferable technical measure:
the calculation formula of the objective function in the third step is as follows:
min{C in +C op }
in the formula, C in The cost of equipment construction; c op The economic cost under the condition of normal operation of the system is reflected as the operation cost of the system;
the equipment construction costs and system operating costs constitute the total cost of the full life cycle.
As a preferable technical measure:
the calculation formula of the equipment construction cost is as follows:
in the formula, C in The cost of equipment construction; n is a radical of m The construction quantity of the mth type equipment; c inst,m Investment and construction cost of the mth type equipment per unit capacity; m m The installation capacity of the m-th equipment; c scra,m The residual value of the m-th equipment;
the residual value of the equipment is selected to be 5% of the initial investment; y is the planned life cycle; r is the actual interest rate, 5%.
As a preferable technical measure:
the calculation formula of the system operation cost is as follows:
in the formula, C op The economic cost under the condition of normal operation of the system is reflected as the operation cost of the system;
fuel cost for class m devices; />
The operation and maintenance cost of the m-th type equipment is different from the fuel and operation cost of different unit equipment, and the annual maintenance cost is about 3 percent of the initial investment.
In order to achieve the above object, a second technical solution of the present invention is:
based on a production process virtual energy storage comprehensive energy planning method,
establishing a planning optimization model and an operation scheduling model according to the virtual energy storage of the production process;
the planning optimization model is used for adjusting the type and the number of the equipment, and the construction method comprises the following steps:
firstly, acquiring integral load data in a system;
secondly, setting load constraints of cost and load data according to the load data in the first step;
thirdly, establishing a planning objective function for optimizing the equipment construction cost in the whole life cycle according to the constraint in the second step;
fourthly, according to a planning objective function, optimizing the operation condition by taking the type and the number of the equipment as optimization variables, and establishing a planning optimization model of energy supply and energy storage equipment in the system;
the operation scheduling model is used for providing a time-by-time optimization scheduling scheme of each device, and the construction method comprises the following steps:
step one, acquiring a time-by-time load demand curve in a planning time interval;
step two, setting operation constraints in the aspects of system supply and demand balance and supply/energy storage equipment operation according to the time-by-time load demand curve in the step one;
step three, establishing an operation objective function according to the constraint in the step two, wherein the operation objective function is used for optimizing the system operation cost in the whole life cycle;
step four, establishing an operation scheduling model capable of performing time-by-time optimal scheduling on each device by taking the start-stop and output of the energy storage device as optimization variables according to the operation objective function;
optimizing the capacity and the quantity of energy supply and storage equipment in the system through a planning optimization model; and then, operating the scheduling model, combining the equipment scheme set by the planning optimization model, establishing an objective function by aiming at the minimization of a planning objective function and an operating objective function, and optimizing the output of energy supply equipment in the system and the operation of energy storage equipment.
Through continuous exploration and tests, the planning optimization model is constructed while the operation scheduling model is constructed, the operation working condition is optimized by taking the type and the number of the equipment as optimization variables, and the energy supply and the capacity and the number of the storage equipment in the system are optimized, so that the total cost in the whole life cycle of production can be effectively reduced, the economy is good, and the popularization and the use are facilitated.
Meanwhile, the scheduling model is operated, the equipment scheme set by the planning optimization model is combined, the output of energy supply equipment in the system and the operation of energy storage equipment are optimized, the total cost in the whole production life cycle is further reduced, and the method is particularly suitable for battery production enterprises and wide in application range.
As a preferable technical measure:
the calculation formula of the planning objective function in the third step is as follows:
in the formula, C in Cost for equipment construction; n is a radical of m The number of the m-th equipment; c inst,m Investment and construction cost of the mth type equipment per unit capacity; m m Is the installation capacity of the m-th class of equipment; c scra,m The residual value of the m-th equipment;
the residual value of the equipment is selected to be 5% of the initial investment; y is the planned life cycle; r is the actual interest rate, 5%.
The calculation formula of the running objective function in the third step is as follows:
in the formula, C op The economic cost under the condition of normal operation of the system is reflected as the operation cost of the system;
fuel cost for class m devices; />
The operation and maintenance cost of the mth equipment is different from the fuel and operation cost of different unit equipment, and the annual maintenance cost is about 3% of the initial investment;
the calculation formula of the objective function is as follows:
min{C in +C op }
in the formula, C in The cost of equipment construction; c op The economic cost under the condition of normal operation of the system is reflected as the operation cost of the system;
the equipment construction costs and system operating costs constitute the total cost over the life cycle.
In order to achieve the above object, a third technical solution of the present invention is:
a method for planning comprehensive energy sources of battery production enterprises based on virtual energy storage of production process,
by applying the comprehensive energy planning method based on the production process virtual energy storage, the production process flow of a battery production enterprise is optimized and effectively scheduled, the virtual energy storage in the production process is excavated, and the virtual energy storage in the production process is reasonably utilized, so that the planning configuration cost of producing power cells and 3C cells is reduced;
the production process flow of the battery production enterprise comprises a drying process, a formation process and a capacity grading process;
the drying process reasonably allocates drying time and product quantity by utilizing the virtual energy storage characteristic, so as to generate virtual heat storage;
the formation process is used for charging the battery to 50% of electric quantity, and generates virtual electricity storage by reasonably arranging the battery charging time;
the capacity grading process is used for charging the battery to 100% of electric quantity, then completely discharging the electric energy, and then charging the battery to 50% of electric quantity, and the virtual electricity storage is generated by reasonably arranging the time for storing and discharging the energy of the battery;
the invention can effectively schedule the production process flow of the battery production enterprise, further effectively excavate the virtual energy storage in the production process, and is convenient for reasonably utilizing the virtual energy storage in the production process, thereby reducing the planning and configuration cost of the system.
The comprehensive energy system of the battery production enterprise is effectively planned, the virtual energy storage characteristic shown by the schedulable process link of the battery production enterprise is fully considered, and the virtual energy storage characteristic is mapped into the planning period of a long time scale; furthermore, the equipment resources can be reasonably planned and configured according to the production orders of the battery production enterprises, and the reasonable configuration of the equipment and the accurate investment of the park energy supply system are guided, so that the planning analysis of the battery factory is realized, and the efficient planning configuration of the industrial park comprehensive energy system is supported.
The method has very important significance for the friendly interaction between the user and the superior power grid and the realization of the target of energy conservation and emission reduction of the user.
In order to achieve the above object, a fourth technical solution of the present invention is:
a battery production enterprise comprehensive energy planning system based on virtual energy storage of a production process,
the comprehensive energy planning method is provided with an energy station and applied to the comprehensive energy planning method based on the production process virtual energy storage;
the energy station comprises energy management equipment, a distributed power supply, energy storage equipment, energy conversion equipment and energy load equipment;
the energy storage equipment IS divided into two terminal energy sources of electricity storage ES equipment, ice storage IS equipment, water storage WS equipment and electricity/cold;
the energy conversion and energy load equipment comprises a gas internal combustion engine GT, an electric refrigerator ER, a lithium bromide refrigerator LBR and a double-working-condition refrigerator DSC.
Through continuous exploration and test, the invention provides a method for planning a comprehensive energy system of a battery production enterprise, which considers virtual energy storage of a production process, constructs a planning optimization model and an operation scheduling model by using the virtual energy storage characteristic shown by a schedulable process link of the battery production enterprise, and maps the planning optimization model and the operation scheduling model into a planning period of a long time scale.
Therefore, the equipment resources can be reasonably planned and configured according to the production orders of the battery production enterprises, and the reasonable configuration of the equipment is guided to be accurately associated with the park energy supply system, so that the planning analysis of the battery factory is realized, and the efficient planning configuration of the industrial park comprehensive energy system is supported.
Furthermore, the method has very important significance for the friendly interaction between the user and the superior power grid and the realization of the target of energy conservation and emission reduction of the user.
Compared with the prior art, the invention has the following beneficial effects:
through continuous exploration and test, the planning optimization model is constructed while the operation scheduling model is constructed, the operation condition is optimized by taking the type and the number of the equipment as optimization variables, the energy supply in the system and the capacity and the number of the storage equipment are optimized, the total cost in the whole life cycle of production can be effectively reduced, the economy is good, and the popularization and the use are facilitated.
Meanwhile, the scheduling model is operated, the output of energy supply equipment in the system and the operation of energy storage equipment are optimized by combining an equipment scheme set by the planning optimization model, and further, the production process flow of a production enterprise can be effectively scheduled, so that the virtual energy storage in the production process of the enterprise is conveniently excavated, and the virtual energy storage in the production process is reasonably utilized, thereby reducing the planning configuration cost of the system, being particularly suitable for battery production enterprises, and having wide application range.
Drawings
FIG. 1 is a diagram of an integrated energy system architecture of the present invention;
FIG. 2 is a block diagram of an integrated energy planning model according to the present invention;
fig. 3 is a flow chart of an integrated energy planning method according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
On the contrary, the invention is intended to cover alternatives, modifications, equivalents and alternatives which may be included within the spirit and scope of the invention as defined by the appended claims. Furthermore, in the following detailed description of the present invention, certain specific details are set forth in order to provide a better understanding of the present invention. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details.
Unless defined otherwise, all 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. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "or/and" includes any and all combinations of one or more of the associated listed items.
Based on a production process virtual energy storage comprehensive energy planning method,
the method comprises the steps of firstly, acquiring the integral load in a system and time-by-time load demand data;
secondly, setting constraint conditions according to the load in the first step and the time-by-time load demand data;
the constraint conditions at least comprise a maximum load constraint or/and a self-sufficient probability constraint or/and an energy supply equipment operation constraint or/and an energy storage equipment operation constraint or/and a power balance constraint or/and a production order constraint;
thirdly, establishing an objective function by taking the lowest total cost in the whole life cycle as a target according to the constraint conditions and the virtual energy storage characteristics of the production process in the second step;
fourthly, according to the objective function in the third step, a planning optimization model and an operation scheduling model are constructed, and the comprehensive energy of virtual energy storage of the production process is effectively optimized;
the planning optimization model takes the type and the number of the equipment as optimization variables, optimizes the operation condition and optimizes the capacity and the number of the energy supply and storage equipment in the system;
and the operation scheduling model optimizes the output of the energy supply equipment and the operation of the energy storage equipment in the system by combining the equipment scheme set by the planning optimization model.
Through continuous exploration and tests, the planning optimization model is constructed while the operation scheduling model is constructed, the operation working condition is optimized by taking the type and the number of the equipment as optimization variables, and the energy supply and the capacity and the number of the storage equipment in the system are optimized, so that the total cost in the whole life cycle of production can be effectively reduced, the economy is good, and the popularization and the use are facilitated.
Meanwhile, the scheduling model is operated, the equipment scheme set by the planning optimization model is combined, the output of energy supply equipment in the system and the operation of energy storage equipment are optimized, the total cost in the whole production life cycle is further reduced, and the method is particularly suitable for battery production enterprises and wide in application range.
The invention is applied to a specific embodiment of a battery production enterprise:
firstly, an industrial park comprehensive energy system architecture taking a battery production enterprise as an example is given, and the production process flow of the battery production enterprise is analyzed in detail to obtain the virtual energy storage characteristics generated by the production process flow.
Secondly, considering the virtual energy storage characteristic, the invention constructs a park comprehensive energy system planning model of the battery production enterprise considering the virtual energy storage of the production process; finally, a corresponding planning flow and a solving method are provided.
1. Analyzing the virtual energy storage characteristics of the production process flow of the comprehensive energy planning system of the battery production enterprise
The architecture of the comprehensive energy planning system for the battery production enterprise is shown in fig. 1, and the system realizes the production inside a park and the energy supply in other aspects by configuring energy stations with autonomous dispatching capacity.
The energy station consists of energy management equipment, a distributed power supply, energy storage equipment, energy conversion equipment and energy loads, and comprises equipment such as a gas internal combustion engine (GT), an Electric Refrigerator (ER), a Lithium Bromide Refrigerator (LBR), a double-working-condition refrigerator (DSC), an Electricity Storage (ES), an Ice Storage (IS), a Water Storage (WS) and the like, and two kinds of terminal energy of electricity/cold.
The production process flow of the power battery cell and the 3C battery cell mainly comprises the steps of battery cell production, packaging processing, constant volume, partial volume and the like. The process flows of coating, stirring, liquid injection and the like do not allow power failure, the energy demand characteristics are relatively clear, and the process links of drying, formation, capacity grading and the like can dig virtual energy storage in the production process through effective scheduling plans.
Specifically, the drying time and the product quantity can be reasonably allocated in the drying process, so that virtual heat storage is generated; the formation process refers to a process of charging the battery to 50% of electric quantity, the capacity grading process refers to a process of charging the battery to 100% of electric quantity, then completely discharging the electric energy, and then charging the battery to 50% of electric quantity, and the two process links can generate virtual electricity storage by reasonably arranging the time of energy storage and energy discharge of the battery. Such schedulable process links can be considered in the planning of the comprehensive energy system of the industrial park, and the virtual energy storage in the production process is reasonably utilized, so that the planning and configuration cost of the system is reduced.
2. Industrial park comprehensive energy system planning model applying the invention
The industrial park integrated energy system planning model comprises two layers. The first is optimization at the planning level, i.e. optimizing the capacity and number of energy supply and storage devices within the system. And the second is the optimization of a scheduling level, namely, the output of energy supply equipment in the system and the operation of energy storage equipment are optimized. Both aspects aim at economy and further consider the influence of virtual energy storage in the production process flow on the basis of the economy.
The industrial park integrated energy system planning model takes the total cost in the whole life cycle as an objective function.
The total cost mainly relates to the equipment construction cost and the system operation cost.
The target function expression is:
min{C in +C op } (1)
in the formula, C in The cost of equipment construction; c op The economic cost under the condition of normal operation of the system is reflected for the operation cost of the system.
The system has the following equipment construction cost:
in the formula, N m The construction quantity of the mth type equipment; c inst,i Investment and construction cost of the mth type equipment per unit capacity; m is a group of m The installation capacity of the m-th equipment; c scra,m Selecting the residual value of the equipment in the text as 5% of the initial investment for the residual value of the mth equipment; y is the planned life cycle; r is the actual interest rate, 5%.
The annual operating cost of the system is as follows:
in the formula (I), the compound is shown in the specification,
fuel cost for class m devices; />
The operation and maintenance cost of the m-th equipment is different from the fuel and operation cost of different unit equipment, and the annual maintenance cost is about 3% of the initial investment.
The main constraints are as follows:
(1) And (4) maximum load constraint. The integrated energy system plan configures the maximum output power of the energy supply equipment to meet the maximum demand of the electric/thermal terminal loads of the system.
(2) Self-sufficient probabilistic constraints. The industrial park meets a certain amount of load requirements in the system in an electric energy isolated island mode, and the stable operation of the system is guaranteed to be very important. Thus, the planning configuration within the system is guided by the probability that the system is self-sufficient to meet the load demand within the planning cycle.
In the formula, P st The probability of self-sufficiency of a certain class of terminal loads within the system.
(3) And (5) energy supply equipment operation constraint. The energy supply equipment planned and configured by the integrated energy system needs to meet the rated power and the climbing constraint of the equipment in the operation process.
In the formula (I), the compound is shown in the specification,
respectively the maximum/minimum output of the energy supply device m; />
The ramp speeds of the output force are reduced and increased for the energy supply device m, respectively.
(4) And (5) operating and restricting the energy storage equipment. The energy storage equipment configured by system planning needs to meet the charging and discharging power and capacity constraint of the equipment in the operation process.
In the formula (I), the compound is shown in the specification,
maximum/minimum capacity of the energy storage device M, respectively; />
The maximum charging/discharging power of the energy storage device M.
(5) And (4) power balance constraint. Under the condition of considering the virtual energy storage effect of energy storage equipment in the system and the production process, the energy load supply and demand in the industrial park can be ensured to reach real-time balance:
in the formula (I), the compound is shown in the specification,
the output of the energy supply equipment m at the moment t is provided; />
The output of the energy storage device M at the moment t is obtained; />
The output of the virtual energy storage at the moment t in the production process is provided; p Load (t) is the demand of the load at time t; />
The energy stored at the moment t of the energy storage device is used; />
Is a production processAnd (4) the energy stored at the time t in the virtual energy storage.
(6) Production order constraints.
The production order load in the battery factory can be mainly divided into two categories, the first category is continuous production load, specifically refers to a part which is not allowed to be powered off in the production process flow, the state of the part only depends on the start and the end of the order, and the state can be specifically expressed as follows:
in the formula, P Load,c (t) total energy consumption for continuous production load for t periods; n is a radical of c The total number of production workshops; m is a group of c,i For the number of outputs of the production plant i, M for continuous production of the electrical loads c,i Is a fixed value; p is c,i (t) is the energy consumption of the production shop i to produce a unit quantity of product at time t; u. u c,i (t) is a Boolean variable, which represents the working state of the production workshop i in the period of t, and the following constraint conditions are specifically required to be met:
wherein, the first and the second end of the pipe are connected with each other,
respectively representing the start and end states of the production shop i. />
The second type is discrete production electrical load, specifically, energy consumption load required by a sudden production task, the state of which is directly related to the order and the production task, and the load can be specifically expressed as:
in the formula, P Load,d (t) total power consumption of the discrete production load at the time period t; n is a radical of hydrogen d The total number of production workshops; m is a group of d,j To produce forNumber of outputs of plant j, different from the continuous production load, M d,j Not a fixed value, but rather a correlation with the number of orders and the time of completion; p d,j (t) is the energy consumption of the production shop i to produce a unit quantity of product at time t; u. u d,j (t) is also a Boolean variable.
Thus, the calculation formula for the load of the battery factory over the statistical time period is:
in the formula, N T A statistical time period is scheduled; p is Load,o (t) load demands other than production orders for time period t.
In summary, the model framework of the integrated energy system planning for the industrial park is shown in fig. 2, in which the red portion is a dispatchable portion.
3 the invention is applied to the planning process and the solving method of the comprehensive energy system of the industrial park
The optimization planning model of the industrial park comprehensive energy system comprises two layers of planning and operation scheduling.
Firstly, optimization of a planning level is carried out, constraints in cost and load aspects are considered on the basis of the overall load level in the system, the formula (2) is used as an optimization target, the optimized operation working condition of the whole life cycle is considered, the type and the number of equipment are used as optimization variables, and an optimized combination scheme of energy supply and energy storage equipment in the system is provided.
In the aspect of algorithm, in order to effectively generate different planning schemes, the invention adopts the quantum particle swarm algorithm to solve the optimization problem of the planning layer model.
The second is running schedule level optimization. And aiming at a time-by-time load demand curve in a planning time interval, considering system supply and demand balance and constraints on the operation of the energy supply/storage equipment, and combining an equipment scheme provided by a planning layer to provide a time-by-time optimized scheduling scheme of each equipment by taking the start, stop and output of the energy storage equipment as optimized variables.
In the aspect of algorithm, the optimization problem of the scheduling level is a mixed linear optimization problem, and a mature commercial solver CPLEX is called to solve in an MATLAB environment based on a YALMIP platform. The optimization of the scheduling layer serves the optimization of the planning layer, and meanwhile, the optimization of the running scheduling layer is a sub-optimization process of the optimization of the planning layer. The specific optimization procedure is shown in fig. 3.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A comprehensive energy planning method based on production process virtual energy storage is characterized in that,
the method comprises the steps of firstly, acquiring the integral load in a system and time-by-time load demand data;
secondly, setting constraint conditions according to the load in the first step and the time-by-time load demand data;
the constraint conditions at least comprise a maximum load constraint or/and a self-sufficient probability constraint or/and an energy supply equipment operation constraint or/and an energy storage equipment operation constraint or/and a power balance constraint or/and a production order constraint;
thirdly, establishing a target function by taking the lowest total cost in the whole life cycle as a target according to the constraint conditions and the virtual energy storage characteristics of the production process in the second step;
fourthly, according to the objective function in the third step, a planning optimization model and an operation scheduling model are constructed, and the comprehensive energy of the virtual energy storage of the production process is effectively optimized;
the planning optimization model takes the type and the number of the equipment as optimization variables, optimizes the operation condition and optimizes the capacity and the number of the energy supply and storage equipment in the system;
the operation scheduling model is combined with an equipment scheme set by the planning optimization model to optimize the output of energy supply equipment and the operation of energy storage equipment in the system.
2. The method of claim 1, wherein the energy storage system is a virtual energy storage system,
the maximum load constraint in the second step is used for enabling the maximum output power of the energy supply equipment planned and configured by the comprehensive energy system to meet the maximum requirement of the electric/thermal terminal load of the system;
the self-sufficient probability constraint is used for enabling the industrial park to meet a certain amount of load requirements in the system in an electric energy island mode and ensuring the stable operation of the system;
the energy supply equipment operation constraints are used for enabling the energy supply equipment configured by the comprehensive energy system planning to meet the rated power and the climbing constraints of the equipment in the operation process;
the energy storage equipment operation constraint is used for enabling the energy storage equipment configured by the system planning to meet the charging and discharging energy power and capacity constraint of the equipment in the operation process;
the power balance constraint is used for guaranteeing that the energy load supply and demand in the industrial park reach real-time balance under the condition of considering the virtual energy storage effect of energy storage equipment in the system and the production process:
the production order constraints are used for considering energy consumption loads of the production orders.
3. The method of claim 2, wherein the step of generating the virtual energy storage comprises generating a virtual energy storage space,
the maximum load constraint is calculated as follows:
the self-sufficient probability constraint guides the planning configuration in the system through the probability that the self-sufficient system meets the load requirement in the planning period, and the calculation formula is as follows:
in the formula, P st The probability of self-sufficiency of a certain type of terminal load in the system;
the calculation formula of the operation constraint of the energy supply equipment is as follows:
in the formula (I), the compound is shown in the specification,
respectively the maximum/minimum output of the energy supply device m; />
Reducing the climbing speed of the output and increasing the climbing speed of the output for the energy supply equipment m respectively; />
The calculation formula of the energy storage device operation constraint is as follows:
in the formula, M m (t) capacity of the energy storage device M at time t;
maximum/minimum capacity of the energy storage device M, respectively; />
The maximum charging/discharging power of the energy storage device M;
the calculation formula of the power balance constraint is as follows:
in the formula (I), the compound is shown in the specification,
the output of the energy supply equipment m at the moment t is provided; />
The output of the energy storage equipment M at the moment t is obtained; />
The output of the virtual energy storage at the time t in the production process is realized; p is Load (t) is the demand of the load at time t; />
The energy stored at the moment t of the energy storage device is used; />
Energy stored at the time t for virtual energy storage in the production process;
the production order constraints comprise continuous production load constraints and discrete production electricity load constraints;
the continuous production load constraint is a part which does not allow power failure in the production process flow, and the state of the continuous production load constraint only depends on the start and the end of an order;
the discrete production power load constraint is the energy consumption load required by the sudden production task, and the state of the discrete production power load constraint is directly related to the order and the production task.
4. The method of claim 3, wherein the step of generating the virtual energy storage comprises generating a virtual energy storage system,
the calculation formula of the continuous production load constraint is as follows:
in the formula, P Load,c (t) total energy consumption for continuous production load for t periods; n is a radical of c The total number of production workshops; m c,i For the number of outputs of the production plant i, M for continuous production of the electrical loads c,i Is a fixed value; p c,i (t) production in production shop i for t time periodEnergy consumption per unit quantity of product; u. of c,i (t) is a Boolean variable, which represents the working state of the production workshop i in the period of t, and the following constraint conditions are specifically required to be met:
wherein the content of the first and second substances,
respectively representing the initial state and the end state of the production workshop i;
the calculation formula of the discrete production electrical load constraint is as follows:
in the formula, P Load,d (t) total power consumption of the discrete production load at the time period t; n is a radical of d The total number of production workshops; m is a group of d,j For the number of outputs of production shop j, different from the continuous production load, M d,j Not a fixed value, but rather a correlation with the number of orders and the time of completion; p d,j (t) is the energy consumption of the production shop i to produce a unit quantity of product at time t; u. of d,j (t) is also a Boolean variable;
thus, the calculation formula for the load of the battery factory over the statistical time period is:
in the formula, N T A statistical time period is scheduled; p is Load,o (t) load demands other than production orders for time period t.
5. The method of claim 1, wherein the energy storage system is a virtual energy storage system,
the calculation formula of the objective function in the third step is as follows:
min{C in +C op }
in the formula, C in Cost for equipment construction; c op The economic cost under the condition of normal operation of the system is reflected as the system operation cost;
the equipment construction costs and system operating costs constitute the total cost of the full life cycle.
6. The method of claim 5, wherein the step of generating the virtual energy storage comprises generating a virtual energy storage system,
the calculation formula of the equipment construction cost is as follows:
in the formula, C in The cost of equipment construction; n is a radical of m The construction quantity of the mth type equipment; c inst,m Investment and construction cost of the mth type equipment per unit capacity; m m The installation capacity of the m-th equipment; c scra,m The residual value of the m-th equipment; y is the planned life cycle; r is the actual interest rate;
the calculation formula of the system operation cost is as follows:
in the formula, C op The economic cost under the condition of normal operation of the system is reflected as the operation cost of the system;
fuel cost for class m devices; />
For the maintenance and operation of the m-th equipment, the fuel produced by different unitsUnlike operational costs.
7. A comprehensive energy planning method based on production process virtual energy storage is characterized in that,
according to the production process virtual energy storage, a planning optimization model and an operation scheduling model are constructed;
the planning optimization model is used for adjusting the type and the number of the equipment, and the construction method comprises the following steps:
firstly, acquiring integral load data in a system;
secondly, setting load constraints of cost and load data according to the load data in the first step;
thirdly, establishing a planning objective function for optimizing the equipment construction cost in the whole life cycle according to the constraint in the second step;
fourthly, according to a planning objective function, optimizing the operation condition by taking the type and the number of the equipment as optimization variables, and establishing a planning optimization model of energy supply and energy storage equipment in the system;
the operation scheduling model is used for providing a time-by-time optimization scheduling scheme of each device, and the construction method comprises the following steps:
step one, acquiring a time-by-time load demand curve in a planning time interval;
step two, setting operation constraints in the aspects of system supply and demand balance and supply/energy storage equipment operation according to the time-by-time load demand curve in the step one;
step three, establishing an operation objective function according to the constraint in the step two, wherein the operation objective function is used for optimizing the system operation cost in the whole life cycle;
step four, establishing an operation scheduling model capable of performing time-by-time optimal scheduling on each device by taking the starting, stopping and output of the energy storage device as optimization variables according to the operation objective function;
optimizing the capacity and the quantity of energy supply and storage equipment in the system through a planning optimization model; and then, operating the scheduling model, combining the equipment scheme set by the planning optimization model, establishing an objective function by aiming at the minimization of a planning objective function and an operating objective function, and optimizing the output of energy supply equipment in the system and the operation of energy storage equipment.
8. The method of claim 7, wherein the step of generating the virtual energy storage comprises generating a virtual energy storage system,
the calculation formula of the planning objective function in the third step is as follows:
in the formula, C in The cost of equipment construction; n is a radical of m The construction quantity of the mth type equipment; c inst,m Investment construction cost per unit capacity for the mth type of equipment; m m Is the installation capacity of the m-th class of equipment; c scra,m The residual value of the m-th equipment; y is the planned life cycle; r is the actual interest rate;
the calculation formula of the running objective function in the third step is as follows:
in the formula, C op The economic cost under the condition of normal operation of the system is reflected as the operation cost of the system;
fuel cost for class m devices; />
The operation and maintenance cost of the mth equipment is different from the operation and maintenance cost of the fuel generated by different unit equipment;
the calculation formula of the objective function is as follows:
min{C in +C op }
in the formula, C in The cost of equipment construction; c op To transport to the systemThe running cost reflects the economic cost under the condition of normal operation of the system;
the equipment construction costs and system operating costs constitute the total cost over the life cycle.
9. A battery production enterprise comprehensive energy planning method based on virtual energy storage of a production process is characterized in that,
the comprehensive energy planning method based on the virtual energy storage of the production process is applied to optimize and effectively schedule the production process flow of the battery production enterprise, excavate the virtual energy storage in the production process, and reasonably utilize the virtual energy storage in the production process so as to reduce the planning configuration cost for producing power cells and 3C cells;
the production process flow of the battery production enterprise comprises a drying process, a formation process and a capacity grading process;
the drying process reasonably allocates drying time and product quantity by utilizing the virtual energy storage characteristic, so as to generate virtual heat storage;
the formation process is used for generating virtual electricity storage by reasonably arranging the battery charging time in the process of charging the battery to 50% of electric quantity;
the capacity grading process is used for charging the battery to 100% of electric quantity, then completely discharging the electric energy, and then charging the battery to 50% of electric quantity, and the time for storing and discharging the energy of the battery is reasonably arranged, so that virtual electricity storage is generated.
10. A battery production enterprise comprehensive energy planning system based on virtual energy storage of a production process is characterized in that,
it is provided with an energy station; and applying the battery production enterprise integrated energy planning method based on virtual energy storage of the production process according to claim 9;
the energy station comprises energy management equipment, a distributed power supply, energy storage equipment, energy conversion equipment and energy load equipment;
the energy storage equipment IS divided into two terminal energy sources of electricity storage ES equipment, ice storage IS equipment, water storage WS equipment and electricity/cold;
the energy conversion and energy load equipment comprises a gas internal combustion engine GT, an electric refrigerator ER, a lithium bromide refrigerator LBR and a double-working-condition refrigerator DSC.
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