CN115829091A - Industrial process microgrid planning method considering renewable energy supply - Google Patents
Industrial process microgrid planning method considering renewable energy supply Download PDFInfo
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Abstract
The invention discloses an industrial process microgrid planning method considering renewable energy supply, belonging to the field of industrial process microgrid planning and comprising the following steps: the first stage is as follows: under the condition that the main power grid is completely powered, when the energy consumption cost of processing the workpiece by the industrial process microgrid is the lowest, the energy consumption of the processing machine and the buffer in each time period is obtained through optimization solution, so that the energy consumption supply scale and the energy consumption requirement constraint of the second stage are determined; and a second stage: designing power supply constraint of renewable energy sources, establishing a power purchasing mechanism and related penalty cost from a main power grid aiming at a time-of-use electricity price table, constructing a power storage system, combining energy consumption demand constraint, renewable energy source supply scale and a planning scheme of the power storage system, considering the demand side response of an industrial micro-grid, and calculating an optimal economic planning scheme of a second stage. The invention can simultaneously consider the construction cost of the power storage system and the utilization rate of renewable energy sources, and greatly reduce the power consumption cost of the industrial process microgrid.
Description
Technical Field
The invention belongs to the field of industrial process microgrid planning, and particularly relates to an industrial process microgrid planning method considering renewable energy.
Background
In order to support the strengthening of economic development and construction and manufacture of China, the power consumption of the industrial manufacturing industry is rapidly increased in recent years. According to statistics, in the first half of 2021 year, the electricity consumption of industries such as steel, building materials, nonferrous metals, chemical industry, petrochemical industry and the like in China accounts for nearly half of the electricity consumption of the society, wherein the increment accounts for 42% of the total increment of the electricity demand of the whole society. This leads to ever higher electricity costs for industrial manufacturing and evolves as an important issue restricting development.
With the proposal and implementation of the national 'double-carbon' major strategy, many industrial manufacturing industries adopt renewable energy sources with lower cost for power supply, thereby realizing energy conservation and emission reduction and simultaneously reducing the power consumption cost. In fact, in order to cope with energy crisis and global warming, countries around the world have been vigorously developing and utilizing renewable energy in recent years. Wind power and photovoltaic power generation are concerned and rapidly develop in the global range, the installed capacity of the wind power and photovoltaic power generation is as high as 564GW and 486GW, and the speed increase is 35.5 percent and 42.8 percent in ten years. According to statistics, the wind power accumulated grid-connected installed capacity in 2018 of China is up to 1.84 hundred million kilowatts and accounts for 9.7 percent of the total power generation installed capacity. However, due to the high degree of uncertainty in renewable energy sources, the phenomenon of wind and light abandonment is frequent. In the first quarter of 2019, the air volume of China is abandoned to be 128.3 hundred million kilowatt-hours. Under the background, a high proportion of renewable energy is adopted to supply power to the industrial process microgrid so as to improve the industrial yield and promote the industrial development, and meanwhile, the demand side response of the industrial process microgrid is adopted so as to improve the consumption of new energy and solve the problem of energy waste, so that the method has very important significance. Therefore, the establishment of the industrial process microgrid which completely adopts renewable energy and considers demand side response has very important practical significance.
However, in order to promote large-scale new energy consumption, in addition to the demand-side response depending on the industrial process microgrid, it is necessary to introduce equipment such as a large-scale power storage system for storing renewable energy, so as to reduce the influence of uncertainty on the power grid and improve the reliability of power grid operation. In contrast, in order to reduce the construction cost and the operation cost of the industrial process microgrid, the construction cost of the power storage system needs to be fully considered, and the economic cost for constructing the microgrid needs to be reduced. Therefore, how to balance the relationship between the construction cost of the microgrid and the utilization rate of renewable energy sources and minimize the power consumption cost of the microgrid in the industrial process is a difficult problem.
Disclosure of Invention
Aiming at the defects and the improvement requirements of the prior art, the invention provides an industrial process microgrid planning method considering renewable energy supply, and aims to design an optimal planning scheme by considering the construction cost of a power storage system and the utilization rate of renewable energy sources in the planning of an industrial process microgrid so as to greatly reduce the power consumption cost of the industrial process microgrid.
To achieve the above object, according to one aspect of the present invention, there is provided an industrial process microgrid planning method considering renewable energy, comprising: first-stage scheduling and second-stage scheduling;
the first stage scheduling comprises: under the condition of purchasing power only from a main power grid, establishing a first-stage scheduling model by taking the lowest energy consumption cost of processing workpieces in a single scheduling period as an optimization target and taking the energy consumption of processing machines and a buffer as decision variables in each period of time, and performing optimization solution under a preset first constraint condition;
the second stage of scheduling comprises:
calculating the energy consumption F of the processing machine and the buffer in a single scheduling period according to the optimized solving result of the first-stage scheduling M And F B And constructing an energy consumption requirement constraint F M+B ≤(1+α)(F M +F N )；
Under the condition of considering energy supply of renewable energy sources, so that the workpiece processing cost is the lowest in a single scheduling period and the access rate of the renewable energy sources is the maximum optimization target, energy consumption of processing machines and buffers in each time period, a plan of purchasing electricity to a main power grid, an electricity storage and charging and discharging plan of an electricity storage system and a power generation and consumption plan of the renewable energy sources are used as decision variables, a second-stage scheduling model is established, optimization solution is carried out under a second constraint condition comprising a first constraint condition and an energy consumption demand constraint, and an optimal planning scheme of the industrial process microgrid is calculated according to a solution result;
wherein, F M+B Representing the total energy consumption of processing the workpieces in a single scheduling period in the second-stage scheduling; α represents the energy consumption margin, α>0; the workpiece processing cost comprises the construction cost of the power storage system, the electricity purchasing penalty cost to the main power grid and the energy consumption cost for processing the workpiece; the access rate of the renewable energy sources is the proportion of the renewable energy sources in the total energy consumption of the processed workpiece.
Further, the first constraint includes:
wherein T represents the total number of scheduling periods within a single scheduling cycle; n is a radical of M And N B Respectively representing the number of processing machines and buffers in an industrial process network of the industrial process microgrid; n is a radical of W Representing the number of workpieces to be processed;
and
respectively representing the time period t, the time period t-1 and the number of workpieces in the ith processing machine in the last time period and the first time period,
indicating the number of workpieces in the (i + 1) th processing machine during time period t,
representing the maximum number of workpieces that can be processed by the ith processing machine during time period t,
represents the maximum number of workpieces which can be processed by the i-1 st processing machine in the time period t-1;
and
respectively representing the number of workpieces in the ith buffer in the time period t, the time period t-1, the last time period and the first time period,
indicating the number of workpieces in the i-1 th buffer in the time period t-1,
representing the maximum buffer capacity of the ith buffer during time period t.
Further, the objective function of the first stage scheduling model is:
wherein, F t M，1 And F t B，1 Respectively representing the energy consumption of the machine and the buffer during the time period t in the first-stage scheduling, pi t Representing the electricity rate for purchasing electricity from the main grid during the time period t.
Further, the second constraint condition further includes: and (3) power purchase constraint, wherein the expression is as follows:
0≤P t Grid ≤P t GM ·P t B
wherein, P t Grid Representing the amount of electricity purchased to the main grid, P, during a time period t t GM Representing the maximum electricity purchasing quantity for purchasing electricity from the main power grid within the time period t; p t B The variable is 0/1, wherein 0 represents that electricity is purchased from the main power grid within a time period t, and 1 represents that electricity is purchased from the main power grid within the time period t; p BM Representing the maximum number of allowed purchases of electricity to the main grid during a single dispatch period.
Further, the second constraint condition further includes: the construction constraints of the power storage system are expressed as follows:
wherein the ESS CAP Watch and ESS RAMP Decision variables belonging to the second-stage scheduling model respectively represent the energy storage capacity and the charge-discharge capacity of the power storage system;
and
the upper limit of the energy storage capacity and the upper limit of the charge/discharge capacity of the power storage system are respectively indicated.
Further, the second constraint condition further includes: the operation constraint of the power storage system is as follows:
ESS 1 ＝ESS T+1
0≤ESS t ≤ESS CAP
0≤P t char ≤ESS RAMP
0≤P t dischar ≤ESS RAMP
ESS t+1 ＝ESS t +P t char η ch -P t dischar /η dis
wherein, ESS t And ESS t+1 The electric quantity stored by the electric power storage system in the time period t and the time period t +1 respectively; ESS 1 And ESS T+1 Respectively representing the electric quantity stored by the electric power storage system in the first scheduling period of the current scheduling cycle and the next scheduling cycle; p t char Represents the amount of charge of the power storage system over time period t; p t dischar Representing the amount of discharge of the power storage system over time period t.
Further, the second constraint condition further includes: the consumption constraint of renewable energy sources is expressed as follows:
0≤RE t ≤GE t
the second constraint further comprises: and (3) power supply and demand balance constraint, wherein the expression is as follows:
P t Grid +RE t +P t dischar ＝P t char +F t M +F t B
wherein RE t Representing consumption of renewable energy, GE, over a period of time t t Representing the power generation amount of the renewable energy source in a time period t; f t M And F t B Respectively, representing the energy consumption of the processor and the buffer during the time period t in the second stage scheduling.
Further, the construction cost of the power storage system is:
F 1 ＝π ESS ·ESS CAP +π ESSR ·ESS RAMP
the penalty cost for purchasing electricity to the main power grid is:
the renewable energy amount consumed by the industrial process micro-grid processing workpiece is as follows:
the total energy consumption of the industrial process microgrid for processing the workpiece is as follows:
and, the objective function of the second stage scheduling model is:
minJ 2 ＝F 1 +F 2 +J 1
maxJ 3 ＝F 3 -F 4
wherein, pi ESS Cost price, pi, representing the capacity of an electric power storage system ESSR A cost price representing a charging and discharging speed of the power storage system; pi P A penalty factor is indicated.
Further, the optimal planning scheme of the industrial process microgrid comprises the following steps: construction cost D of electric power storage system 1 And renewable energy access rate D 2 The calculation formulas are respectively as follows:
D 1 ＝F 1 *
wherein, F 1 * 、
And
and respectively calculating the construction cost of the power storage system, the renewable energy consumption consumed by the industrial process micro-grid for processing the workpiece and the total energy consumption of the industrial process micro-grid for processing the workpiece according to the optimization solving result of the second stage.
According to another aspect of the present invention, there is provided a computer-readable storage medium including: a stored computer program; when the computer program is executed by the processor, the device of the computer readable storage medium is controlled to execute the industrial process microgrid planning method considering renewable energy supply provided by the invention.
Generally, by the above technical solution conceived by the present invention, the following beneficial effects can be obtained:
(1) The industrial process microgrid planning method considering renewable energy supply provided by the invention comprises two-stage scheduling, wherein in the first-stage scheduling, the most economic operation scheme under the condition of completely supplying power to a main power grid is obtained through optimized solution, so that the energy consumption scale required by processing parts of the industrial process microgrid is obtained, the energy consumption demand constraint of the second-stage scheduling is constructed according to the energy consumption scale, and the excessive increase of energy consumption for improving renewable energy consumption in the second-stage scheduling is effectively avoided; in the second stage scheduling, under the constraint of energy consumption demand and other related constraints, the minimization of workpiece processing cost and the maximization of the access rate of renewable energy resources are simultaneously taken as optimization targets, wherein the workpiece processing cost comprises the construction cost of the power storage system, the electricity purchasing penalty cost to the main power grid and the energy consumption cost for processing workpieces, so that the comprehensive consideration of the construction cost of the power storage system and the utilization rate of the renewable energy resources is realized through the second stage scheduling, on one hand, the construction cost is reduced for an operator of an industrial process microgrid, the economy is improved, on the other hand, the use proportion of the renewable energy resources is improved, and the operation cost of the industrial microgrid is reduced. In general, the invention can greatly reduce the power consumption cost of the industrial process microgrid.
(2) The industrial process microgrid planning method considering renewable energy supply provided by the invention allows an industrial process microgrid to purchase electricity to a main grid, ensures that when the scale of renewable energy is smaller, the industrial process microgrid can obtain electric energy in other modes, minimizes the operation cost of the industrial process microgrid, and simultaneously considers the punishment cost of purchasing electricity to the main grid in the second stage scheduling.
(3) According to the industrial process microgrid planning method considering renewable energy supply, provided by the invention, an electric power storage system is established for storing and releasing electric energy generated by renewable energy, the flexible scheduling of the industrial process microgrid is enhanced, meanwhile, the storage capacity and the charging and discharging capacity of the electric power storage system are used as decision variable variables of the second stage, and the boundary adjustment is provided, so that appropriate electric power storage system parameters can be selected in an optimal economic scheme for calculating the construction cost of the electric power storage system, and the construction cost of the electric power storage system is reduced as much as possible, and the minimization of the construction cost of the power grid is realized.
(4) The industrial process microgrid planning method considering renewable energy supply provided by the invention is implemented in the operation constraint of a power storage system through an ESS 1 ＝ESS T+1 The initial state of the power storage system in each scheduling period is guaranteed to be the same, so that stable day-ahead scheduling is carried out, the power storage system is converted into electric energy storage when the renewable energy scale is high, the electric energy storage system is released in other time periods, the consumption of the renewable energy is improved as far as possible, the electric quantity purchased by an industrial micro-grid is reduced, and the maximization of the power grid operation income is realized.
(5) The industrial process microgrid planning method considering renewable energy supply provided by the invention introduces a penalty factor pi P A penalty mechanism for purchasing electricity from the main grid is proposed, by which it is possible to minimize the construction cost of the electric power storage system without purchasing electricity from the main grid; by modifying the objective function related to the renewable energy access rate into a linear model, the efficiency and accuracy of model solving are ensured.
(6) According to the industrial process microgrid planning method considering renewable energy supply, provided by the invention, under the condition of different renewable energy scales, especially under the condition of high-proportion renewable energy supply, the renewable energy access rate can be effectively improved, even the renewable energy can be completely supplied, on the basis, the construction cost of an electric power storage system in an industrial process microgrid can be effectively reduced, and finally the power consumption cost of the industrial process microgrid is greatly reduced.
Drawings
FIG. 1 is a schematic diagram of a conventional industrial process microgrid processing network;
fig. 2 is a schematic diagram of a microgrid planning method of an industrial process considering renewable energy supply according to an embodiment of the present invention;
FIG. 3 is a graph showing Pareto curves for three cases according to an embodiment of the present invention; wherein (a) is a Pareto curve diagram of Case1, case1 is slightly lower than the energy consumption scale, (b) is a Pareto curve diagram of Case2, case2 is close to the energy consumption scale, and (c) is a Pareto curve diagram of Case3, case3 is higher than the energy consumption scale;
FIG. 4 is a decision scheme under weights in Case1 according to an embodiment of the present invention;
FIG. 5 is a diagram of part processing power schedules for 3 build scenarios in Case1 provided by an embodiment of the present invention; wherein, (a) is a demodulation power scheduling graph of Case1, (b) is a highest renewable energy access rate power scheduling graph of Case1, and (c) is a lowest construction cost power scheduling graph of Case 1;
FIG. 6 is a decision scheme under various weights of Case2 according to an embodiment of the present invention;
FIG. 7 is a diagram of part processing power schedules for 2 build scenarios in Case2 provided by an embodiment of the present invention; wherein, (a) is a demodulation and demodulation power scheduling graph of Case2, and (b) is a highest renewable energy access rate power scheduling graph of Case 2;
FIG. 8 is a Gantt chart of part processing and a power dispatching chart for 1 build scenario in Case3 provided by an embodiment of the present invention;
fig. 9 is a relationship between the renewable energy scale in Case3 and the construction cost of the electric power storage system provided by the embodiment of the present invention;
FIG. 10 is a part fabrication power schedule for the build scenario 2 in Case3 according to an embodiment of the present invention; the power schedule map includes (a) a power schedule map in which the renewable energy scale increase rate is 1%, and (b) a power schedule map in which the renewable energy scale increase rate is 30%.
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. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
In the present application, the terms "first," "second," and the like (if any) in the description and the drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Before explaining the technical scheme of the invention in detail, the processing network in the industrial process microgrid related to the invention is briefly introduced as follows:
the processing network of the industrial process microgrid consists of processing machines and buffers, one buffer is arranged between two adjacent processing machines, as shown in figure 1, so that the number N of buffers in the processing network B Number N of machining machines M Satisfies the following conditions: n is a radical of B ＝N M -1. After entering the processing network, the raw materials sequentially enter each processing machine for processing until the processing is finished on the last processing machine; the buffers provided between the processing machines can adjust the time for which the workpiece flows from the upper-stage processing machine to the lower-stage processing machine.
In order to effectively reduce the power consumption cost of the industrial process microgrid, renewable energy sources are adopted for power supply, an electric power storage system is built for storing and releasing electric energy generated by new energy sources and enhancing flexible scheduling of the industrial microgrid, accordingly, a planning method combining two-stage scheduling is provided for planning the industrial process microgrid, and the construction cost of the electric power storage system and the utilization rate of the renewable energy sources are simultaneously considered in the planning. The following are examples.
Example 1:
an industrial process microgrid planning method considering renewable energy supply, as shown in fig. 1, comprises: first phase scheduling and second phase scheduling.
In this embodiment, the primary role of the first-stage scheduling is to determine the scale of energy consumption required for processing the workpiece in the case of the renewable energy function of the industrial process microgrid. The first stage specifically comprises: under the condition of purchasing power only from a main power grid, a first-stage scheduling model is established by taking the energy consumption of a processing machine and a buffer as decision variables and taking the energy consumption of the processing machine and the buffer in each period as an optimization target, and the optimization solution is carried out under a preset first constraint condition. In this embodiment, the operation characteristics of the processing network in the industrial process microgrid are fully analyzed, and the designed first constraint condition includes:
capacity constraints of the processing machine, the expression is as follows:
the capacity of the buffer is restricted, and the expression is as follows:
the action constraint of the processing machine, namely the processing quantity of the processing machine in the current time period is not more than the sum of the product quantity in the previous-level processing machine and the previous-level buffer in the previous time period, and the expression is as follows:
the number of workpieces in the buffer satisfies a balance constraint with the incoming and outgoing flows, and the expression is as follows:
when the production starts, all the buffers in the processing network are empty, and the production pair object must enter the production network from the first machine and complete the processing on the last machine; when the system stops operating, all the buffers should be empty in order to reduce unnecessary resource waste; the correlation expression is as follows:
each machining machine should perform one machining for each part, and the expression is as follows:
in the above expression, T represents the total number of scheduling periods in a single scheduling cycle, optionally, in this embodiment, one scheduling cycle is one day, and one day is divided into T =24 scheduling periods; n is a radical of M And N B Respectively representing the number of processing machines and buffers in an industrial process network of the industrial process microgrid; n is a radical of W Representing the number of workpieces to be processed;
and
respectively representing the number of workpieces in the ith processing machine during time period t, time period t-1 and the last time period and the first time period,
representing the number of workpieces in the (i + 1) th processing machine during time t,
representing the maximum number of workpieces that can be processed by the ith processing machine during time period t,
represents the maximum number of workpieces which can be processed by the i-1 st processing machine in the time period t-1;
and
respectively representing the number of workpieces in the ith buffer in the time period t, the time period t-1, the last time period and the first time period,
indicating the number of workpieces in the i-1 th buffer in the time period t-1,
representing the maximum buffer capacity of the ith buffer during time period t.
Accordingly, the total energy consumption for machine operation and the total energy consumption for buffer operation for time period t can be expressed by equations (8) - (9) as follows:
wherein the content of the first and second substances,
and
respectively representing the energy consumption of the ith processing machine and the ith buffer when processing a part; f t M，1 And F t B，1 Representing the power consumption of the processor and the buffer during the time period t in the first stage scheduling, respectively.
The objective function of the first stage scheduling model is:
wherein, pi t And represents the electricity price for purchasing electricity from the main grid in the time period t in the time-of-use electricity price table provided by the main grid.
In summary, in this embodiment, the first-stage scheduling model is:
by solving the first-stage scheduling model, the number F of the workpieces processed by each processing machine in each time period can be obtained under the condition that the energy consumption cost of processing the workpieces on a single day is minimum t M，1 And the number F of buffered workpieces per buffer t B，1 Changing the load by adjusting the time period of the industrial process network for processing the workpiece, and adopting the processing scheme of the industrial process microgrid completely under the condition of power supply by the main power grid; based on the solving result of the first-stage scheduling model, the machine energy consumption and the buffer energy consumption of the single-day processing workpiece under the optimal economic operation scheme can be calculated as follows:
the above energy consumption will be used as the basis for constructing the energy consumption demand constraint in the second stage scheduling, and the specific energy consumption demand constraint is as follows:
F M+B ≤(1+α)(F M +F N ) (10)
wherein the content of the first and second substances,
representing the total energy consumption, F, of the workpieces processed in a single scheduling cycle in the second stage of scheduling t M And F t B Respectively representing the energy consumption of the processing machine and the buffer in the time period t; α represents the energy consumption margin, α>0; because the energy consumption calculated according to the solution result of the first-stage scheduling model is the optimal result, when the energy consumption demand constraint of the second stage is constructed, the energy consumption margin is introduced on the basis of the optimal energy consumption, and the second-stage scheduling is expandedThe scheduling space of the second stage is increased, so that the flexibility of the second stage scheduling is improved; in practical application, the energy consumption margin may be set according to an actual scheduling requirement, and optionally, in this embodiment, a specific value of the energy consumption margin is α =0.05.
In this embodiment, the main effect of the second-stage scheduling is to design an optimal planning scheme of the industrial process microgrid in consideration of the construction cost of the power storage system and the utilization rate of renewable energy under the condition of considering the renewable energy supply. The second stage scheduling specifically includes:
under the condition of considering energy supply of renewable energy sources, so that the workpiece processing cost is the lowest in a single scheduling period and the access rate of the renewable energy sources is the maximum optimization target, energy consumption of processing machines and buffers in each time period, a plan of purchasing electricity to a main power grid, an electricity storage and charging and discharging plan of an electricity storage system and a power generation and consumption plan of the renewable energy sources are used as decision variables, a second-stage scheduling model is established, optimization solution is carried out under a second constraint condition comprising a first constraint condition and an energy consumption demand constraint, and an optimal planning scheme of the industrial process microgrid is calculated according to a solution result;
the workpiece processing cost comprises the construction cost of the power storage system, the electricity purchasing penalty cost to the main power grid and the energy consumption cost for processing the workpiece; the access rate of the renewable energy sources is the proportion of the renewable energy sources in the total energy consumption of the processed workpiece.
In the second-stage scheduling model established in this embodiment, the constraint conditions include the following constraint conditions in addition to the first constraint condition and the energy consumption requirement constraint condition:
the consumption constraint of renewable energy sources is expressed as follows:
0≤RE t ≤GE t (10)
wherein RE t Representing consumption of renewable energy, GE, over a period of time t t Representing the generated energy of the renewable energy source in a time period t; in this embodiment, the renewable energy specifically includes Wind power generation and photovoltaic power generation, in Wind t And PV t Respectively representing wind power generation amount and photovoltaic power generation amount in time period t, then GE t ＝Wind t +PV t (ii) a The grid-connected quantity of the renewable energy sources at each moment can be determined through the consumption constraint of the renewable energy sources, the grid-connected quantity does not exceed the sum of wind and light processing, and meanwhile, the uncertainty characterization and sampling of the renewable energy sources are achieved.
Because renewable energy has uncertainty, in order to ensure that the industrial micro-grid can stably operate when the scale of the renewable energy is small, a mechanism for purchasing electricity from a main grid is reserved on the basis of first-stage scheduling, and corresponding electricity purchasing constraints are as follows:
0≤P t Grid ≤P t GM ·P t B (12)
wherein, P t Grid Represents the purchase amount of electricity, P, to the main grid during the time period t t GM Representing the maximum electricity purchasing quantity for purchasing electricity from the main power grid within the time period t; p t B The variable is 0/1, wherein 0 represents that the power is purchased from the main power grid within the time period t, and 1 represents that the power is purchased from the main power grid within the time period t; p BM Representing the maximum number of times of electricity purchasing allowed to the main power grid in a single scheduling period; through the electricity purchasing constraint, the embodiment can limit the times of electricity purchasing from the industrial process microgrid to the main power grid, namely the industrial process microgrid can be at most P BM The electricity is purchased in each time period, so that the industrial process micro-grid is guaranteed to have other sources for acquiring electricity to meet the workpiece processing requirements when the renewable energy scale is small, the operation risk of the industrial process micro-grid is minimized, the electricity purchasing time is guaranteed to be concentrated as far as possible, the scheduling of the industrial process micro-grid is facilitated, the electricity purchasing from a main grid is reduced, and the consumption of the renewable energy is increased.
In order to enhance the flexible scheduling of the industrial micro-flow grid, the power storage system is built while considering the function of renewable energy sources, the renewable energy sources are converted into electric energy to be stored through the power storage system when the scale of the renewable energy sources is high, and then the electric energy is released in other time periods, so that the consumption of the renewable energy sources is improved as much as possible, the electric energy purchasing quantity of the industrial micro-flow grid is reduced, and the maximization of the operation benefit of the grid is realized. In the embodiment, the scale constraint and the charge-discharge speed constraint of the power storage system are designed correspondingly in the constraint conditions of the second-stage scheduling model, and the expression is as follows:
wherein, ESS CAP Watch and ESS RAMP Decision variables belonging to the second-stage scheduling model respectively represent the energy storage capacity and the charge-discharge capacity of the power storage system;
and
the energy storage capacity upper limit value and the charge/discharge capacity upper limit value of the power storage system are respectively represented. In this embodiment, the ESS CAP Watch and ESS RAMP The method is also used as a decision variable of the second-stage scheduling, provides boundary adjustment of the second-stage scheduling, can select proper parameters of the power storage system in an optimal economic scheme to be used for calculating the construction cost of the power storage system, and achieves the minimization of the construction cost of the power grid by reducing the construction cost of the power storage system as much as possible.
In order to ensure the normal operation of the power storage system, the present embodiment designs the operation constraint of the power storage system in the constraint condition of the second-stage scheduling model, which is specifically as follows:
ESS 1 ＝ESS T+1 (16)
0≤ESS t ≤ESS CAP (17)
0≤P t char ≤ESS RAMP (18)
0≤P t dischar ≤ESS RAMP (19)
ESS t+1 ＝ESS t +P t char η ch -P t dischar /η dis (20)
in the above expression, ESS t And ESS t+1 The electric quantity stored by the electric power storage system in the time period t and the time period t +1 respectively; ESS 1 And ESS T+1 Respectively representing the electric quantity stored by the electric power storage system in the first scheduling period of the current scheduling cycle and the next scheduling cycle; p is t char Represents the amount of charge of the power storage system over time period t; p t dischar Representing the amount of discharge of the power storage system over time period t. Wherein, the formula (16) ensures that the initial state of the power storage system is the same every day, so as to perform stable day-ahead scheduling; the formulas (17) to (19) determine that the electric quantity and the charging and discharging speed stored in the electric power storage system are both in the set range, so that the safe operation of the electric power storage system is ensured; the formula (20) reflects the relationship between the stored electric quantity of the electric power storage system in two adjacent time intervals, that is, the electric quantity stored by the electric power storage system at the next moment is the sum of the electric quantity stored by the electric power system at the current moment and the electric quantity change caused by charging and discharging.
In this embodiment, an electric power supply and demand balance constraint is further designed in the constraint conditions of the second-stage scheduling model, and the expression is as follows:
P t Grid +RE t +P t dischar ＝P t char +F t M +F t B (21)
wherein, F t M And F t B Respectively representing the energy consumption of the processor and the buffer in the time period t in the second stage scheduling.
In this embodiment, the construction cost of the power storage system comprehensively considers the capacity and the charging and discharging speed, and accordingly, the construction cost of the power storage system is:
F 1 ＝π ESS ·ESS CAP +π ESSR ·ESS RAMP (22)
wherein, pi ESS Cost price, pi, representing the capacity of an electric power storage system ESSR Represents the cost price of the charging and discharging speed of the power storage system.
In order to avoid purchasing electricity from the main grid as much as possible, in this embodiment, a penalty cost is introduced for purchasing electricity from the main grid, specifically:
wherein, pi P A penalty factor is indicated.
Based on the above equations (22) and (23), in the present embodiment, the first objective function of the second stage scheduling is:
minJ 2 ＝F 1 +F 2 +J 1
in this embodiment, the access rate of the renewable energy source refers to the proportion of the renewable energy source in the energy consumption of the power grid processing part. In grid operation, the renewable energy source used to process parts can be expressed as:
the total energy consumption of the industrial process microgrid for processing workpieces is as follows:
the second objective function can be expressed as:
maxJ 3 ＝F 3 /F 4
since the second-stage scheduling model has more decision variables, in order to ensure the efficiency and accuracy of the model solution, in this embodiment, the second objective function is modified, and the modified objective function is:
maxJ 3 ＝F 3 -F 4
thus, the second stage optimization model is obtained as:
solving the model to obtain the energy consumption of the processor and the buffer in each time interval, including F t M And F t M Plan to purchase electricity to the main grid, including P t Grid And P t B An electrical storage and charging and discharging plan for an electrical storage system, comprising: ESS CAP 、ESS RAMP 、ESS t 、P t char And P t dischar And a power generation and consumption plan for renewable energy sources, comprising: RE t 、Wind t And PV t (ii) a Based on the solution result, F can be calculated 1 、F 3 And F 4 And therefore, the construction cost D of the power storage system in the industrial process microgrid can be calculated 1 And renewable energy access rate D 2 Respectively as follows:
D 1 ＝F 1
D 2 ＝F 3 /F 4
through analysis of a large amount of experimental data, the optimal planning method of the industrial microgrid considering the response of the demand side based on complete renewable energy supply is found to help guide the design of the microgrid of the industrial process, a proper renewable energy supply proportion is selected according to the requirements of a manager, and the construction cost of the power storage system is reduced as much as possible. The decision is helpful for safe and economic operation of the power grid, and the economic benefit of a manager is improved.
In summary, according to the method for planning the industrial process microgrid considering renewable energy supply provided by this embodiment, in the first-stage scheduling, a time-of-use electricity price table is obtained, and a day-ahead optimal economic scheduling scheme of the industrial process microgrid is obtained by responding to electricity price fluctuation on the demand side of the industrial process microgrid; in the second stage, according to the optimal economic dispatching scheme in the first stage, the energy consumption requirement of the industrial micro-grid is established in the second stage dispatching, and the corresponding renewable energy supply scale is designed; carrying out uncertainty characterization and sampling of renewable energy sources according to the supply scale of the renewable energy sources, and designing power supply constraints of the renewable energy sources; aiming at a time-of-use electricity price table, establishing a power purchasing mechanism from a main power grid and related punishment cost so as to ensure the stable operation of an industrial process microgrid; constructing an electric power storage system for storing and releasing electric energy generated by new energy, and enhancing flexible scheduling of the industrial process microgrid; calculating an optimal economic planning scheme of the second stage by considering the response of the industrial microgrid demand side of the second stage in combination with the energy consumption demand of the industrial process microgrid, the supply amount of renewable energy sources and the planning scheme of the power storage system; by combining the two-stage scheduling, the construction cost of the power storage system and the utilization rate of renewable energy sources are considered at the same time, an optimal planning scheme is designed, and the power consumption cost of the industrial process microgrid is greatly reduced.
Example 2:
a computer-readable storage medium, comprising: a stored computer program; when the computer program is executed by the processor, the apparatus on which the computer readable storage medium is stored is controlled to execute the industrial process microgrid planning method considering renewable energy supply provided in the above embodiment 1.
The technical solution and the obtained beneficial effects of the present invention are further described below with reference to specific application examples.
The schematic diagram of the production and processing network of the industrial micro-flow power grid is shown in fig. 1, and the network acquires the optimal economic dispatching scheme of the first stage by purchasing power from the main power grid. The real-time electricity rates are shown in table 1.
TABLE 1 time-of-use electricity price table
After the energy consumption scale required for workpiece processing is determined based on the optimization result of the first-stage scheduling, three cases as shown in table 2 are designed, corresponding to the supply scales of three renewable energy sources, respectively. It should be noted that, generally, the renewable energy supply ratio is greater than 30%, which is called high-ratio energy supply, and the renewable energy scales of the three cases shown in table 2 are different, but they are all cases of high-ratio functions.
TABLE 2 renewable energy Scale for three Case cases
Pareto curves for three cases are shown in FIG. 3. When the renewable energy source is small in scale, that is, in Case1, as shown in (a) of fig. 3, the power consumption of the industrial process microgrid processing all parts is slightly higher than that of the renewable energy source which can be provided locally, and therefore, even if the renewable energy source is used in its entirety, it is only 89.32%. Furthermore, in order to fully utilize renewable energy, the micro-grid is equipped with a larger-scale power storage system and a higher charging/discharging speed so as to store the surplus renewable energy at each moment, thereby causing an unnecessary increase in cost.
Similarly, when the renewable energy source is moderate in size, i.e., in Case2, as shown in (b) in fig. 3, if 99.4% of the power demand is satisfied by the renewable energy source, the construction cost of the power storage system may reach about 3 times as much as when the renewable energy source satisfies only 94.6% of the power demand. Meanwhile, compared to (a) in fig. 3, the cost of the power storage system with the renewable energy access rate of 94.6% is lower than that when the renewable energy access rate is 87.4. Therefore, the renewable energy environment which is matched with the scale of the energy consumption of the load can be constructed for the micro-grid as far as possible.
As for the Case of a large scale of renewable energy, that is, in Case3, as shown in (c) of fig. 3, the microgrid can change the load curve to adapt to the change curve of the renewable energy by adjusting the time for processing parts, thereby realizing the purpose of satisfying the demand without the need for a power storage system and a main grid power supply.
Considering the selection of the demodulation and the solution of Case1, because the weight needs to be set when the optimal solution is searched by the double-target optimization, and the setting of the weight is subjective, the weight is traversed here, and the optimal solution selection under the condition of various weights is considered. Fig. 4 shows the decision result.
Obviously, the more concerned the operator is about the construction cost of the power storage system, the higher the weight value set for the operator is, the more the operator tends to select the scheme with the lower construction cost of the power storage system, and the access rate of the renewable energy source reaches the lowest. On the contrary, when the construction cost of the power storage system reaches the lowest weight, the operator selects the scheme with the highest access rate of the renewable energy sources. And by taking the interval length of the weight as a measure, in most weight schemes, a scheme with the construction cost of the power storage system of 21125000 yuan and the access rate of the renewable energy source of 89.13% is selected, and specific results are shown in table 3.
TABLE 3 relationship between weight change of construction cost of power storage system and decision scheme in Case1
The solution with the widest weight range is selected as the demodulation solution and compared with the solution with the lowest construction cost of the power storage system and the solution with the highest access rate of the renewable energy, the number of processing machines/buffers used by the processing network in each time period can be known, and due to the fact that the scale of the renewable energy is insufficient, the buffers are rarely used by the industrial process microgrid, unnecessary energy consumption is reduced as far as possible, and particularly in Case2, in order to maximize the access amount of the renewable energy, the microgrid even does not use the buffers. Meanwhile, the processing network will concentrate on processing during noon, because renewable energy sources supply most energy during noon, and the concentration of loads during this period can reduce the utilization rate of the power storage system, reduce the construction cost, and reduce energy waste caused by the power storage system. Under three cases, the power scheduling diagrams of part processing are respectively shown in (a), (b) and (c) in the diagrams, and as can be seen from fig. 5, under the three cases, the micro-grid needs to purchase power from the main grid, but the power purchasing time periods are all in low-price areas, and the power purchasing is concentrated in two time periods, so that the planning that most power supply still adopts renewable energy is guaranteed. Compared with (b) in fig. 5, the charging and discharging speed in (c) in fig. 5 is significantly lower, and at the same time, the charging and discharging are alternated, so that the continuous charging state is less, the state of charge of the power storage system can be ensured to be stable as much as possible, and the construction cost of the power storage system is reduced. And (a) in fig. 5 is compared with (c) in fig. 5, the access rate of renewable energy sources is improved by 1.98%, and compared with (b) in fig. 5, the construction cost of the power storage system is reduced by 44.9%, and the indexes are more balanced.
For Case2, the results under different weights are shown in fig. 6, and the specific results are shown in table 4.
TABLE 4 relationship between weight change of construction cost of power storage system and decision scheme in Case2
Obviously, the reconciliation is consistent with the optimal solution when the operator is most concerned about the construction cost of the electric power storage system, which is 13750000 yuan and 94.59% of the renewable energy utilization. In the weight space, there is a 43.76% probability of selecting the demodulation solution. When the operator wants to improve the utilization rate of the renewable energy, the operator has a 12.32% probability of selecting an extreme solution, and the access rate of the renewable energy can reach 99.4%.
The demodulation and demodulation power schedule map and the highest renewable energy access rate power schedule map of Case2 are shown as (a) and (b) in fig. 7, respectively. Obviously, since (a) in fig. 7 requires power purchase from the main grid, the energy consumption of the load for simultaneously demodulating and demodulating will be more, and therefore the access rate of the renewable energy source will be lower. In fact, when the power grid is used to save the construction cost of the power storage system, the time period is obtained by adjusting the processing, so that the charging and discharging of the power storage system are more balanced, as shown in (a) of fig. 7, thereby reducing the cost of the power storage system. In Case2, in order to improve the access rate of the renewable energy, since the total amount of the renewable energy has an upper limit, the power grid reduces the processing energy consumption as much as possible without using a buffer zone, and simultaneously adjusts the load curve as much as possible to adapt to the change of the renewable energy, so that the power storage system is used more reasonably, but the cost of the power storage system is still not increased a little, and (b) in fig. 7 is improved by 5.09% compared with (a) in fig. 7, and the construction cost of the power storage system is increased by 190.91%, which is also the reason why the adjustment and the release are more inclined to select to reduce the construction cost of the power storage system. Comparing the scheduling results of two different cases of Case2 can lead to the conclusion that: when the industrial process microgrid decides to maximize the access rate of renewable energy sources, the construction of the buffer can be considered, or the capacity of the buffer can be reduced. Because the system can be adjusted to accommodate renewable energy sources, the use of buffers is reduced as much as possible.
For Case3, when the renewable energy source is high in scale, the micro-grid can meet the load demand only depending on the electric energy provided by the renewable energy source without purchasing electricity from the main grid. Considering the penalty cost of purchasing electricity from the main grid, for a scene with a large renewable energy scale, the micro-grid selects a solution with the renewable energy access rate of 100% as the optimum, and the construction cost of the power storage system is minimized, so that the Pareto frontier only contains one solution. The solution-corresponding power schedule diagrams are shown in fig. 8, respectively.
Obviously, for the scheduling of the processing time, it is mainly adjusted according to the curve of the renewable energy source, reducing the use of the power storage system, and for the power storage system, it is mainly to store the power when the renewable energy source exceeds the load, release the power when the renewable energy source is small in scale, and the charging and discharging interval is performed, that is, it can meet the demand.
It is noted that as renewable energy resources become richer, the construction cost of the power storage system also gradually decreases, as shown in fig. 9. When the increase rate of the renewable energy scale is 1%, the construction cost of the power storage system still needs 37875000 yuan, and with the increase of the renewable energy, when the increase rate reaches 20%, the full renewable energy coverage of the load can be realized without the power storage system.
The power schedule charts comparing the renewable energy scale increase rates of 1% and 30% are shown in (a) and (b) in fig. 10, respectively. Meanwhile, comparing the usage amounts of the processing machines and the buffers of the processing network in each time interval under different conditions, when the renewable energy is only larger than the power consumption of the load, the power grid still needs to reduce the power consumption of the load as much as possible, and as the scale of the renewable energy increases, the usage amount of the buffers also gradually increases. Moreover, after the renewable energy scale is increased to a certain degree, the charging and discharging assistance of the power storage system is not needed, the micro-grid can complete the matching of the supply and demand sides only by scheduling the processing time sequence, and the full coverage of the renewable energy is realized.
In conclusion, the following conclusions can be obtained, and certain reference values are provided for a decision maker of the industrial process microgrid to make a microgrid construction scheme:
(1) If the industrial process microgrid is more biased to renewable energy sources with smaller scale for power supply, the method can be considered to concentrate on purchasing electricity from the main power grid in a period with lower electricity price and utilize an electric power storage device for storage, and particularly obtain higher benefits while ensuring the reliability of the system; meanwhile, since the decider prefers the renewable energy of a smaller scale for power supply by itself, it is possible to consider further incomplete utilization of the renewable energy, thereby reducing the construction cost of the power storage system.
(2) If the industrial process microgrid is more biased towards renewable energy sources with moderate scale for power supply, a power purchasing mechanism from a main power grid still needs to be established for ensuring that the system can still stably operate when the renewable energy sources are insufficient. At this stage, if the decision maker is biased towards lower construction costs, the construction costs of the power storage system can be reduced in large proportion at the expense of a small renewable energy access rate.
(3) The situation that renewable energy of an industrial micro-grid is insufficient, and electricity is purchased from a main grid in a centralized mode needs to be considered, and if the industrial process micro-grid is more biased to fully utilize the renewable energy, a large-scale power storage system needs to be built at a high price. Accordingly, the usage rate of the buffer is reduced to 0, so that the decision maker can consider reducing the number of the buffers even without the buffers, thereby saving the construction cost.
(4) In the case of industrial process micro-grids, which are more biased toward higher-scale renewable energy sources for power supply, there is no need to consider the mechanism of purchasing electricity from the main grid, and as the scale of renewable energy sources increases, the construction cost of the power storage system will also continuously decrease, but this will also cause a considerable portion of energy to be wasted, so it is suggested that the power storage system is still constructed for improving the utilization rate of renewable energy sources.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. An industrial process microgrid planning method considering renewable energy supply is characterized by comprising the following steps: first-stage scheduling and second-stage scheduling;
the first stage scheduling comprises: under the condition of purchasing power only from a main power grid, establishing a first-stage scheduling model by taking the lowest energy consumption cost of processing workpieces in a single scheduling period as an optimization target and taking the energy consumption of processing machines and a buffer as decision variables in each period of time, and performing optimization solution under a preset first constraint condition;
the second stage scheduling comprises:
calculating the energy consumption F of the processing machine and the buffer in a single scheduling period according to the optimized solving result of the first-stage scheduling M And F B And constructing a power consumption requirement constraint F M+B ≤(1+α)(F M +F N )；
Under the condition of considering energy supply of renewable energy sources, so that the workpiece processing cost is the lowest in a single scheduling period and the access rate of the renewable energy sources is the maximum optimization target, a second-stage scheduling model is established by taking the energy consumption of processing machines and buffers in each time period, a plan of purchasing electricity to a main power grid, an electricity storage and charge-discharge plan of an electricity storage system and a power generation and consumption plan of the renewable energy sources as decision variables, optimization solution is carried out under a second constraint condition comprising the first constraint condition and the energy consumption demand constraint, and an optimal planning scheme of the industrial process microgrid is calculated according to a solution result;
wherein, F M+B Representing the total energy consumption of processing the workpieces in a single scheduling period in the second-stage scheduling; α represents the energy consumption margin, α>0; the workpiece processing cost comprises the construction cost of the power storage system, the electricity purchasing penalty cost to the main power grid and the energy consumption cost for processing the workpiece; the access rate of the renewable energy sources is the proportion of the renewable energy sources in the total energy consumption of the processed workpiece.
2. The method of claim 1, wherein the first constraint comprises:
wherein T represents the total number of scheduling periods within a single scheduling cycle; n is a radical of hydrogen M And N B Respectively representing the number of processing machines and buffers in an industrial process network of the industrial process microgrid; n is a radical of W Indicating the number of workpieces needing to be processed;
and
respectively representing the number of workpieces in the ith processing machine during time period t, time period t-1 and the last time period and the first time period,
indicating the number of workpieces in the (i + 1) th processing machine during time period t,
representing the maximum number of workpieces that can be processed by the ith processing machine during time period t,
represents the maximum number of workpieces which can be processed by the i-1 st processing machine in the time period t-1;
and
respectively representing the number of workpieces in the ith buffer in the time period t, the time period t-1, the last time period and the first time period,
indicating the number of workpieces in the i-1 th buffer in the time period t-1,
representing the maximum buffer capacity of the ith buffer during time period t.
3. The method of claim 2, wherein the objective function of the first stage scheduling model is:
wherein, F t M，1 And F t B，1 Respectively representing the energy consumption of the machine and the buffer during the time period t in the first-stage scheduling, pi t Representing the electricity rate for purchasing electricity from the main grid during the time period t.
4. The method of claim 3, wherein the second constraint further comprises: and (3) power purchase constraint, wherein the expression is as follows:
0≤P t Grid ≤P t GM ·P t B
wherein, P t Grid Representing the amount of electricity purchased to the main grid, P, during a time period t t GM Representing the maximum electricity purchasing quantity for purchasing electricity from the main power grid within the time period t; p t B The variable is 0/1, wherein 0 represents that the power is purchased from the main power grid within the time period t, and 1 represents that the power is purchased from the main power grid within the time period t; p BM Representing the maximum number of allowed purchases of electricity to the main grid during a single dispatch period.
5. The method of claim 4, wherein the second constraint further comprises: the construction constraints of the power storage system are expressed as follows:
wherein, ESS CAP Watch and ESS RAMP Decision variables belonging to the second-stage scheduling model respectively represent the energy storage capacity and the charge-discharge capacity of the power storage system;
and
the upper limit of the energy storage capacity and the upper limit of the charge/discharge capacity of the power storage system are respectively indicated.
6. The method of claim 5, wherein the second constraint further comprises: the operation constraint of the power storage system is as follows:
ESS 1 ＝ESS T+1
0≤ESS t ≤ESS CAP
0≤P t char ≤ESS RAMP
0≤P t dischar ≤ESS RAMP
ESS t+1 ＝ESS t +P t char η ch -P t dischar /η dis
wherein, ESS t And ESS t+1 The electric quantity stored by the electric power storage system in the time period t and the time period t +1 respectively; ESS 1 And ESS T+1 Respectively representing the current scheduling period and the next schedulingThe amount of electricity stored by the electricity storage system during a first scheduling period of the degree cycle; p t char Represents the amount of charge of the power storage system over time period t; p t dischar Representing the amount of discharge of the power storage system over time period t.
7. The method of claim 6, wherein the second constraint further comprises: the consumption constraint of renewable energy sources is expressed as follows:
0≤RE t ≤GE t
the second constraint further comprises: and (3) power supply and demand balance constraint, wherein the expression is as follows:
P t Grid +RE t +P t dischar ＝P t char +F t M +F t B
wherein RE t Representing consumption of renewable energy, GE, over a period of time t t Representing the power generation amount of the renewable energy source in a time period t; f t M And F t B Respectively, representing the energy consumption of the processor and the buffer during the time period t in the second stage scheduling.
8. The method of claim 7 for planning an industrial process microgrid considering renewable energy sources, wherein the construction cost of the power storage system is:
F 1 ＝π ESS ·ESS CAP +π ESSR ·ESS RAMP
the penalty cost for purchasing electricity to the main power grid is:
the renewable energy amount consumed by the industrial process micro-grid processing workpiece is as follows:
the total energy consumption of the industrial process microgrid for processing the workpiece is as follows:
and, the objective function of the second stage scheduling model is:
min J 2 ＝F 1 +F 2 +J 1
max J 3 ＝F 3 -F 4
wherein, pi ESS Cost price, pi, representing the capacity of an electric power storage system ESSR A cost price representing a charging and discharging speed of the power storage system; pi P A penalty factor is indicated.
9. The method of claim 8, wherein the optimal planning for the industrial process microgrid comprises: construction cost D of electric power storage system 1 And renewable energy access rate D 2 The calculation formulas are respectively as follows:
D 1 ＝F 1 *
wherein, F 1 * 、F 3 * And
and respectively calculating the construction cost of the power storage system, the renewable energy consumption consumed by the industrial process micro-grid for processing the workpiece and the total energy consumption of the industrial process micro-grid for processing the workpiece according to the optimization solving result of the second stage.
10. A computer-readable storage medium, comprising: a stored computer program; when being executed by a processor, the computer program controls a device on the computer readable storage medium to execute the industrial process microgrid planning method considering renewable energy supply of any one of claims 1 to 9.
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