An industrial process microgrid planning method considering renewable energy supply

Abstract

The present invention discloses an industrial process microgrid planning method considering renewable energy power supply, belonging to the field of industrial process microgrid planning, including: The first stage: Under the condition of full power supply from the main grid, through optimization and solution, the energy consumption of processing machines and buffers in each time period is obtained when the energy consumption cost of processing workpieces in the industrial process microgrid is the lowest, so as to determine the energy consumption supply scale and energy consumption demand constraint in the second stage; The second stage: Design the power supply constraint of renewable energy, establish a power purchase mechanism from the main grid and related penalty costs for the time-of-use electricity price table, and construct a power storage system. Combining the energy consumption demand constraint, the renewable energy supply scale and the planning scheme of the power storage system, considering the demand-side response of the industrial microgrid, calculate the optimal economic planning scheme in the second stage. The present invention can simultaneously consider the construction cost of the power storage system and the utilization rate of renewable energy, and greatly reduce the electricity cost of the industrial process microgrid.

Description

Technical Field

[0001] The present invention belongs to the field of industrial process microgrid planning, and more specifically, relates to a method for planning an industrial process microgrid considering renewable energy supply. Background Art

[0002] In recent years, the electricity consumption of the industrial manufacturing industry has increased rapidly. This has led to high industrial manufacturing electricity costs, which have evolved into an important problem restricting development.

[0003] Many industrial manufacturing industries use renewable energy with lower costs for power supply, reducing electricity costs while achieving energy conservation and emission reduction. In fact, in response to the energy crisis and global warming, countries around the world have vigorously developed and utilized renewable energy in recent years. Among them, wind power and photovoltaic power generation have attracted much attention and developed rapidly globally, with installed capacities of up to 564 GW and 486 GW, and average annual growth rates of 35.5% and 42.8% in the past decade. Due to the high uncertainty of renewable energy, the phenomena of abandoning wind and light are occurring frequently. In this context, using a high proportion of renewable energy to supply power to the industrial process microgrid to increase industrial output and promote industrial development, and at the same time, through the demand-side response of the industrial process microgrid, to increase the consumption of new energy and solve the problem of energy waste, is of great significance. Therefore, it is of great practical significance to establish an industrial process microgrid that fully uses renewable energy and considers demand-side response.

[0004] However, in order to promote the large-scale consumption of new energy, in addition to relying on the demand-side response of the industrial process microgrid, it is also necessary to introduce equipment such as large-scale power storage systems to store renewable energy, reduce the impact of its uncertainty on the power grid, and improve the reliability of power grid operation. In contrast, in order to reduce the construction cost and operation cost of the industrial process microgrid, it is necessary to fully consider the construction cost of the power storage system and reduce the economic cost of microgrid construction. Therefore, how to balance the relationship between the construction cost of the microgrid and the utilization rate of renewable energy to minimize the electricity cost of the industrial process microgrid is a difficult problem. Summary of the Invention

[0005] In view of the deficiencies and improvement requirements of the prior art, the present invention provides a method for planning an industrial process microgrid considering renewable energy supply, aiming to simultaneously consider the construction cost of the power storage system and the utilization rate of renewable energy in the planning of the industrial process microgrid, and design an optimal planning scheme to significantly reduce the electricity cost of the industrial process microgrid.

[0006] To achieve the above object, according to one aspect of the present invention, a method for planning an industrial process microgrid considering renewable energy supply is provided, including: first-stage scheduling and second-stage scheduling;

[0007] The first-stage scheduling includes: when only purchasing electricity from the main power grid, taking the minimum energy consumption cost of processing workpieces within a single scheduling period as the optimization objective, taking the energy consumption of processing machines and buffers in each time period as decision variables, establishing a first-stage scheduling model, and performing optimization solution under preset first constraint conditions;

[0008] The second-stage scheduling includes:

[0009] According to the optimization solution results of the first-stage scheduling, calculate the energy consumption F M and F B of processing machines and buffers within a single scheduling period, and construct an energy consumption demand constraint F M+B ≤(1 + α)(F M + F N );

[0010] Under the consideration of renewable energy supply, taking the minimum workpiece processing cost and the maximum renewable energy access rate within a single scheduling period as the optimization objectives, taking the energy consumption of processing machines and buffers in each time period, the power purchase plan from the main power grid, the power storage and charge-discharge plan of the power storage system, and the power generation and consumption plan of renewable energy as decision variables, establishing a second-stage scheduling model, and performing optimization solution under second constraint conditions including the first constraint conditions and the energy consumption demand constraint, and calculating the optimal planning scheme of the industrial process microgrid according to the solution results;

[0011] Among them, F M+B represents the total energy consumption of processing workpieces within a single scheduling period in the second-stage scheduling; α represents the energy consumption margin, α > 0; the workpiece processing cost includes the construction cost of the power storage system, the power purchase penalty cost from the main power grid, and the energy consumption cost of processing workpieces; the renewable energy access rate is the proportion of renewable energy in the total energy consumption of processing workpieces.

[0012] Furthermore, the first constraint conditions include:

[0013]

[0014] Among them, T represents the total number of scheduling time periods within a single scheduling period; N M and N B respectively represent the numbers of processing machines and buffers in the industrial process network of the industrial process microgrid; N W represents the number of workpieces to be processed; and respectively represent the numbers of workpieces in the i-th processing machine in time period t, time period t - 1, the last time period and the first time period, represents the number of workpieces in the (i + 1)-th processing machine in time period t, Denotes the maximum number of workpieces that can be processed by the $i$-th processing machine during period $t$. Denotes the maximum number of workpieces that can be processed by the $(i - 1)$-th processing machine during period $t - 1$. And Denote the number of workpieces in the $i$-th buffer during periods $t$, $t - 1$, the last period, and the first period respectively. Denotes the number of workpieces in the $(i - 1)$-th buffer during period $t - 1$. Denotes the maximum buffer capacity of the $i$-th buffer during period $t$.

[0015] Furthermore, the objective function of the first-stage scheduling model is:

[0016]

[0017] Where, $F$ t M，1 And $F$ t B，1 Denote the energy consumption of the processing machine and the buffer during period $t$ in the first-stage scheduling respectively, and $\pi$ t Denotes the electricity price for purchasing electricity from the main power grid during period $t$.

[0018] Furthermore, the second constraint condition also includes: the electricity purchase constraint, and the expression is as follows:

[0019] $0\leq P$ t Grid $\leq P$ t GM $\cdot P$ t B

[0020]

[0021] Where, $P$ t Grid Denotes the electricity purchase quantity for purchasing electricity from the main power grid during period $t$, and $P$ t GM Denotes the maximum electricity purchase quantity for purchasing electricity from the main power grid during period $t$; $P$ t B Is a 0 / 1 variable, 0 means purchasing electricity from the main power grid during period $t$, and 1 means not purchasing electricity from the main power grid during period $t$; $P$ BM Denotes the maximum number of times allowed to purchase electricity from the main power grid within a single scheduling cycle.

[0022] Furthermore, the second constraint condition also includes: the construction constraint of the power storage system, and the expression is as follows:

[0023]

[0024] Where, $ESS$ CAP Table and $ESS$RAMP Both belong to the decision variables of the second-stage scheduling model, representing the energy storage capacity and charge-discharge capacity of the power storage system respectively; and represent the upper limit value of the energy storage capacity and the upper limit value of the charge-discharge capacity of the power storage system respectively.

[0025] Furthermore, the second constraint condition also includes: the operation constraint of the power storage system, and the expression is as follows:

[0026] ESS1 = ESS T+1

[0027] 0 ≤ ESS t ≤ ESS CAP

[0028] 0 ≤ P t char ≤ ESS RAMP

[0029] 0 ≤ P t dischar ≤ ESS RAMP

[0030] ESS t+1 = ESS t + P t char η ch - P t dischar / η dis

[0031] Among them, ESS t and ESS t+1 represent the electricity stored in the power storage system during time period t and time period t + 1 respectively; ESS1 and ESS T+1 represent the electricity stored in the power storage system during the first scheduling period of the current scheduling cycle and the next scheduling cycle respectively; P t char represents the charging amount of the power storage system during time period t; P t dischar represents the discharging amount of the power storage system during time period t.

[0032] Furthermore, the second constraint condition also includes: the consumption constraint of renewable energy, and the expression is as follows:

[0033] 0 ≤ RE t ≤ GE t

[0034] The second constraint condition also includes: the power supply-demand balance constraint, and the expression is as follows:

[0035] P tGrid +RE t +P t dischar =P t char +F t M +F t B

[0036] Among them, RE t represents the consumption of renewable energy within time period t, and GE t represents the power generation of renewable energy within time period t; F t M and F t B respectively represent the energy consumption of the processing machine and the buffer within time period t in the second-stage scheduling.

[0037] Furthermore, the construction cost of the power storage system is:

[0038] F1 = π ESS ·ESS CAP + π ESSR ·ESS RAMP

[0039] The cost of purchasing electricity from the main power grid as a penalty is:

[0040]

[0041] The amount of renewable energy consumed by the industrial process microgrid for processing workpieces is:

[0042]

[0043] The total energy consumption of the industrial process microgrid for processing workpieces is:

[0044]

[0045] And the objective function of the second-stage scheduling model is:

[0046] min J2 = F1 + F2 + J1

[0047] max J3 = F3 - F4

[0048] Among them, π ESS represents the cost price of the power storage system capacity, and π ESSR represents the cost price of the charge and discharge rate of the power storage system; π P represents the penalty factor.

[0049] Further, the optimal planning scheme of the industrial process microgrid includes the construction cost D1 of the power storage system and the renewable energy access rate D2, and the calculation formulas are as follows:

[0050] D1 = F1 *

[0051]

[0052] wherein, F1 * , F3 * and respectively represent the construction cost of the power storage system, the amount of renewable energy consumed by the industrial process microgrid for processing workpieces, and the total energy consumption of the industrial process microgrid for processing workpieces calculated according to the optimization solution results of the second stage.

[0053] 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 a processor, it controls the device where the computer-readable storage medium is located to execute the above-mentioned industrial process microgrid planning method considering renewable energy supply provided by the present invention.

[0054] Generally speaking, through the above technical solutions conceived by the present invention, the following beneficial effects can be achieved:

[0055] (1) The industrial process microgrid planning method considering renewable energy supply provided by the present invention includes two-stage scheduling. In the first-stage scheduling, through optimization solution, the most economical operation plan under the condition of full main grid power supply is obtained, thereby obtaining the energy consumption scale required for the industrial process microgrid to process parts, and constructing the energy consumption demand constraint for the second-stage scheduling according to this energy consumption scale, effectively avoiding excessive energy consumption in order to improve the consumption of renewable energy in the second-stage scheduling; in the second-stage scheduling, under the energy consumption demand constraint and other relevant constraints, the minimization of the workpiece processing cost and the maximization of the renewable energy access rate are used as the optimization objectives at the same time. Among them, the workpiece processing cost includes the construction cost of the power storage system, the purchase penalty cost from the main grid, and the energy consumption cost of processing workpieces. Therefore, through the second-stage scheduling, the comprehensive consideration of the construction cost of the power storage system and the utilization rate of renewable energy is realized. On the one hand, it reduces the construction cost for the operator of the industrial process microgrid and improves the economy, and on the other hand, it increases the proportion of the use of renewable energy, thereby reducing the operation cost of the industrial microgrid. Generally speaking, the present invention can greatly reduce the electricity cost of the industrial process microgrid.

[0056] (2) The industrial process microgrid planning method considering renewable energy supply provided by the present invention allows the industrial process microgrid to purchase electricity from the main grid, ensuring that the industrial process microgrid can obtain electricity through other means when the scale of renewable energy is relatively small, thereby minimizing the operating cost of the industrial process microgrid. At the same time, the penalty cost of purchasing electricity from the main grid will be considered in the second-stage scheduling. In its preferred scheme, specific power purchase constraints are also designed to limit the number of times the industrial process microgrid purchases electricity from the main grid. While ensuring the stable operation of the industrial process microgrid, the power purchase time is concentrated as much as possible, which is more convenient for the scheduling of the microgrid, and the power purchase from the main grid is reduced as much as possible, thereby increasing the absorption of renewable energy.

[0057] (3) The industrial process microgrid planning method considering renewable energy energy supply provided by the present invention establishes a power storage system for storing and releasing electric energy generated by renewable energy, thereby enhancing the flexible scheduling of the industrial process microgrid. At the same time, the storage capacity and charge and discharge capacity of the power storage system are used as decision variables in the second stage, and their boundary adjustments are provided, so that appropriate power storage system parameters can be selected in the optimal economic solution to calculate the construction cost of the power storage system. By minimizing the construction cost of the power storage system, the construction cost of the power grid is minimized.

[0058] (4) The industrial process microgrid planning method considering renewable energy energy supply provided by the present invention, in the operation constraints of the power storage system, is implemented by ESS1=ESS T+1 It ensures that the initial state of the power storage system is the same in each scheduling cycle, so as to carry out stable day-ahead scheduling. When the scale of renewable energy is high, it is converted into electrical energy storage through the power storage system, and then released in other time periods, thereby maximizing the absorption of renewable energy, reducing the power purchase of industrial microgrids, and maximizing the operating benefits of the grid.

[0059] (5) The industrial process microgrid planning method considering renewable energy supply provided by the present invention introduces a penalty factor π P , a penalty mechanism for purchasing electricity from the main grid is proposed. Through this penalty factor, the construction cost of the power storage system can be minimized without purchasing electricity from the main grid; by correcting the objective function related to the renewable energy access rate into a linear model, the efficiency and accuracy of the model solution are ensured.

[0060] (6) The industrial process microgrid planning method considering renewable energy supply provided by the present invention can effectively improve the renewable energy access rate under different scales of renewable energy, especially in the case of high proportion of renewable energy supply, and even achieve complete renewable energy supply. On this basis, it can effectively reduce the construction cost of the power storage system in the industrial process microgrid, and finally greatly reduce the electricity cost of the industrial process microgrid. Description of the Drawings

[0061] Figure 1 It is a schematic diagram of the processing network of the existing industrial process microgrid;

[0062] Figure 2 It is a schematic diagram of the industrial process microgrid planning method considering renewable energy supply provided by the embodiment of the present invention;

[0063] Figure 3 It is a schematic diagram of the Pareto curve of three Cases provided by the embodiment of the present invention; among them, (a) is the schematic diagram of the Pareto curve of Case1, and Case1 is slightly lower than the energy consumption scale, (b) is the schematic diagram of the Pareto curve of Case2, and Case2 is close to the energy consumption scale, (c) is the schematic diagram of the Pareto curve of Case3, and Case3 is higher than the energy consumption scale;

[0064] Figure 4 It is the decision-making scheme under various weights in Case1 provided by the embodiment of the present invention;

[0065] Figure 5 It is the part processing power dispatching diagram of 3 construction schemes in Case1 provided by the embodiment of the present invention; among them, (a) is the power dispatching diagram of the reconciliation solution in Case1, (b) is the power dispatching diagram with the highest renewable energy access rate in Case1, and (c) is the power dispatching diagram with the lowest construction cost in Case1;

[0066] Figure 6 It is the decision-making scheme under various weights in Case2 provided by the embodiment of the present invention;

[0067] Figure 7 It is the part processing power dispatching diagram of 2 construction schemes in Case2 provided by the embodiment of the present invention; among them, (a) is the power dispatching diagram of the reconciliation solution in Case2, and (b) is the power dispatching diagram with the highest renewable energy access rate in Case2;

[0068] Figure 8 It is the part processing Gantt chart and power dispatching diagram of 1 construction scheme in Case3 provided by the embodiment of the present invention;

[0069] Figure 9Relationship between the scale of renewable energy and the construction cost of the power storage system in Case 3 provided by the embodiments of the present invention

[0070] Figure 10 Part processing power scheduling diagrams of two construction schemes in Case 3 provided by the embodiments of the present invention; among them, (a) is the power scheduling diagram with a 1% increase rate of renewable energy scale, and (b) is the power scheduling diagram with a 30% increase rate of renewable energy scale. Detailed implementation manners

[0071] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0072] In the present invention, terms such as "first" and "second" in the present invention and the accompanying drawings (if any) are used to distinguish similar objects and do not have to be used to describe a specific order or sequence.

[0073] Before explaining the technical solution of the present invention in detail, the processing network in the industrial process microgrid involved in the present invention is briefly introduced as follows:

[0074] The processing network of the industrial process microgrid is composed of processing machines and buffers. A buffer is arranged between two adjacent processing machines, as Figure 1 shown. Therefore, the number N B of buffers in the processing network M and the number N B of processing machines M satisfy: N

[0075]

[0076] In order to effectively reduce the electricity cost of the industrial process microgrid, the present invention considers using renewable energy for power supply and builds a power storage system to store and release the electric energy generated by new energy, enhance the flexible scheduling of the industrial microgrid, and correspondingly provides a planning method combining two-stage scheduling to plan the industrial process microgrid, and simultaneously considers the construction cost of the power storage system and the utilization rate of renewable energy in the planning. The following are the embodiments.Example 1:

[0077] A method for planning an industrial process microgrid considering renewable energy power supply, as Figure 1 shown, including: the first-stage scheduling and the second-stage scheduling.

[0078] In this embodiment, the main function of the first-stage scheduling is to determine the energy consumption scale required for workpiece processing when the industrial process microgrid is powered by renewable energy. The first stage specifically includes: in the case of only purchasing electricity from the main grid, taking the minimum energy consumption cost of processing workpieces within a single scheduling period as the optimization objective, taking the energy consumption of processing machines and buffers in each time period as decision variables, establishing a first-stage scheduling model, and performing optimization solution under the preset first constraint conditions. In this embodiment, fully analyzing the operating characteristics of the processing network in the industrial process microgrid, the designed first constraint conditions include:

[0079] The capacity constraint of the processing machine, and the expression is as follows:

[0080]

[0081] The capacity constraint of the buffer, and the expression is as follows:

[0082]

[0083] The behavior constraint of the processing machine, that is, the processing quantity of the processing machine in the current time period is not greater than the sum of the product quantities in the upper-level processing machine and the upper-level buffer in the previous time period, and the expression is as follows:

[0084]

[0085] The balance constraint that the number of workpieces in the buffer satisfies the inflow and outflow, and the expression is as follows:

[0086]

[0087] At the beginning of production, the buffers in the processing network are all empty, and the production object must enter the production network from the first machine and complete processing on the last machine; when the system stops operating, in order to reduce unnecessary resource waste, all buffers should also be empty; the relevant expressions are as follows:

[0088]

[0089] Each processing machine should process each part once, and the expression is as follows:

[0090]

[0091] In the above expression, T represents the total number of scheduling periods within a single scheduling cycle. Optionally, in this embodiment, a scheduling cycle is one day, and one day is divided into T = 24 scheduling periods; N M and N B respectively represent the number of processing machines and buffers in the industrial process network of the industrial process microgrid; N W represents the number of workpieces to be processed; and respectively represent the number of workpieces in the i-th processing machine during period t, period t - 1, and the last and first periods, represents the number of workpieces in the (i + 1)-th processing machine during period t, represents the maximum number of workpieces that the i-th processing machine can process during period t, represents the maximum number of workpieces that the (i - 1)-th processing machine can process during period t - 1; and respectively represent the number of workpieces in the i-th buffer during period t, period t - 1, and the last and first periods, represents the number of workpieces in the (i - 1)-th buffer during period t - 1, represents the maximum buffer capacity of the i-th buffer during period t.

[0092] Correspondingly, the total energy consumption of machine operation and the total energy consumption of buffer operation during period t can be expressed by equations (8)-(9) as follows:

[0093]

[0094] where, and respectively represent the energy consumption when the i-th processing machine and the i-th buffer process one part; F t M，1 and F t B，1 respectively represent the energy consumption of the processing machine and the buffer during period t in the first-stage scheduling.

[0095] The objective function of the first-stage scheduling model is:

[0096]

[0097] where, π t represents the electricity price for purchasing electricity from the main power grid during period t in the time-of-use electricity price table provided by the main power grid.

[0098] In summary, in this embodiment, the first-stage scheduling model is:

[0099]

[0100] By solving the first-stage scheduling model, the number of workpieces processed by each processing machine in each time period, denoted as \(F\), can be obtained under the condition of minimizing the energy consumption cost of processing workpieces in a single day. t M，1 , and the number of workpieces buffered in each buffer, denoted as \(F\). t B，1 , and the processing plan of the industrial process microgrid under the condition of being completely powered by the main power grid by adjusting the load by changing the time period of processing workpieces in the industrial process network; based on the solution results of the first-stage scheduling model, the machine energy consumption and buffer energy consumption of processing workpieces in a single day under the optimal economic operation plan can be calculated as follows:

[0101]

[0102] The above energy consumption will be used as the basis for constructing the energy consumption demand constraint in the second-stage scheduling. The specific energy consumption demand constraint is:

[0103] \(F\) M+B ≤(1 + α)(\(F\) M +\(F\) N ) (10)

[0104] where, represents the total energy consumption of processing workpieces in a single scheduling period in the second-stage scheduling, \(F\) t M and \(F\) t B represent the energy consumption of the processing machine and the buffer in time period \(t\) respectively; α represents the energy consumption margin, α > 0; since the energy consumption calculated according to the solution results of the first-stage scheduling model is the optimal result, in constructing the energy consumption demand constraint in the second stage of this embodiment, the energy consumption margin is introduced on the basis of the optimal energy consumption, expanding the scheduling space of the second-stage scheduling and increasing the flexibility of the second-stage scheduling; in practical applications, the energy consumption margin can be set according to the actual scheduling requirements. Optionally, in this embodiment, the specific value of the energy consumption margin is α = 0.05.

[0105] In this embodiment, the main role of the second-stage scheduling is to design the optimal planning scheme of the industrial process microgrid by considering the construction cost of the power storage system and the utilization rate of renewable energy under the condition of considering the supply of renewable energy. The second-stage scheduling specifically includes:

[0106] Considering the supply of renewable energy, with the optimization goal of minimizing the workpiece processing cost within a single scheduling period and maximizing the renewable energy access rate, taking the energy consumption of processing machines and buffers in each time period, the power purchase plan from the main power grid, the power storage and charge-discharge plan of the power storage system, and the power generation and consumption plan of renewable energy as decision variables, a second-stage scheduling model is established, and it is optimized and solved under the second constraint conditions including the first constraint condition and the energy consumption demand constraint. According to the solution results, the optimal planning scheme of the industrial process microgrid is calculated;

[0107] Among them, the workpiece processing cost includes the construction cost of the power storage system, the power purchase penalty cost from the main power grid, and the energy consumption cost of processing workpieces; the renewable energy access rate is the proportion of renewable energy in the total energy consumption of processing workpieces.

[0108] For the second-stage scheduling model established in this embodiment, in addition to the above first constraint condition and energy consumption demand constraint, the constraint conditions also include the following:

[0109] The consumption constraint of renewable energy, the expression is as follows:

[0110] 0≤RE t ≤GE t (10)

[0111] Among them, RE t represents the consumption of renewable energy in time period t, and GE t represents the power generation of renewable energy in time period t; in this embodiment, renewable energy specifically includes wind power generation and photovoltaic power generation. Using Wind t and PV t to represent the wind power generation and photovoltaic power generation in time period t respectively, then GE t =Wind t +PV t ; Through the consumption constraint of renewable energy, the grid-connected quantity of renewable energy at each moment can be determined, which does not exceed the sum of wind and light processing. At the same time, the uncertainty of renewable energy is characterized and sampled.

[0112] Due to the uncertainty of renewable energy, to ensure the stable operation of the industrial microgrid when the scale of renewable energy is small, the mechanism of purchasing electricity from the main power grid is retained on the basis of the first-stage scheduling. The corresponding power purchase constraint is as follows:

[0113] 0≤P t Grid ≤P t GM ·P t B (12)

[0114]

[0115] Among them, P t Grid represents the electricity purchase volume from the main power grid during time period t, and P t GM represents the maximum electricity purchase volume from the main power grid during time period t; P t B is a 0 / 1 variable. Being 0 means purchasing electricity from the main power grid during time period t, and being 1 means not purchasing electricity from the main power grid during time period t; P BM represents the maximum number of times allowed to purchase electricity from the main power grid within a single scheduling cycle; through the above electricity purchase constraints, this embodiment can limit the number of times the industrial process microgrid purchases electricity from the main power grid, that is, the industrial process microgrid can purchase electricity at most during P BM time periods, thereby ensuring that when the scale of renewable energy is small, the industrial process microgrid has other sources of obtaining electricity to meet the workpiece processing requirements, minimizing the operation risk of the industrial process microgrid, while ensuring that the electricity purchase time is concentrated as much as possible, facilitating the scheduling of the industrial process microgrid, and reducing the electricity purchase from the main power grid, improving the consumption of renewable energy.

[0116] In order to enhance the flexible scheduling of the industrial microprocess grid, while considering the function of renewable energy in this embodiment, a power storage system will be built. When the scale of renewable energy is high, it will be converted into electrical energy and stored, and then released during other time periods, thereby maximizing the consumption of renewable energy as much as possible, reducing the electricity purchase volume of the industrial microgrid, and realizing the maximization of the grid operation revenue. In this embodiment, the scale constraint and the charge-discharge speed constraint of the power storage system are correspondingly designed in the constraint conditions of the second-stage scheduling model, and the expressions are as follows:

[0117]

[0118] Among them, ESS CAP table and ESS RAMP both belong to the decision variables of the second-stage scheduling model, and respectively represent the energy storage capacity and the charge-discharge capacity of the power storage system; and respectively represent the upper limit value of the energy storage capacity and the upper limit value of the charge-discharge capacity of the power storage system. In this embodiment, ESS CAP table and ESS RAMP are also used as the decision variables of the second-stage scheduling, and their boundary adjustments are provided, which can select appropriate power storage system parameters in the optimal economic plan for calculating the construction cost of the power storage system, and minimize the grid construction cost by minimizing the construction cost of the power storage system as much as possible.

[0119] To ensure the normal operation of the power storage system, corresponding operation constraints of the power storage system are designed in the constraint conditions of the second-stage scheduling model in this embodiment, which are specifically as follows:

[0120] ESS1 = ESS T+1 (16)

[0121] 0 ≤ ESS t ≤ ESS CAP (17)

[0122] 0 ≤ P t char ≤ ESS RAMP (18)

[0123] 0 ≤ P t dischar ≤ ESS RAMP (19)

[0124] ESS t+1 =ESS t +P t char η ch -P t dischar / η dis (20)

[0125] In the above expressions, ESS t and ESS t+1 represent the electricity stored in the power storage system during time period t and time period t + 1 respectively; ESS1 and ESS T+1 represent the electricity stored in the power storage system during the first scheduling period of the current scheduling cycle and the next scheduling cycle respectively; P t char represents the charging amount of the power storage system during time period t; P t dischar represents the discharging amount of the power storage system during time period t. Among them, 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; formulas (17) to (19) determine that the electricity stored in the power storage system and the charging and discharging speeds are within the set ranges, ensuring the safe operation of the power storage system; formula (20) reflects the relationship between the electricity stored in the power storage system in two adjacent time periods, that is, the electricity stored in the power storage system at the next moment is the sum of the electricity stored in the power system at the current moment and the electricity change caused by charging and discharging.

[0126] This embodiment also designs a power supply and demand balance constraint in the constraint conditions of the second-stage scheduling model, and the expression is as follows:

[0127] P t Grid+RE t +P t dischar =P t char +F t M +F t B (21)

[0128] wherein, F t M and F t B respectively represent the energy consumption of the processing machine and the buffer during time period t in the second-stage scheduling.

[0129] In this embodiment, the construction cost of the power storage system comprehensively considers the capacity and the charge-discharge rate. Accordingly, the construction cost of the power storage system is:

[0130] F1 = π ESS ·ESS CAP + π ESSR ·ESS RAMP (22)

[0131] wherein, π ESS represents the cost price of the power storage system capacity, and π ESSR represents the cost price of the charge-discharge rate of the power storage system.

[0132] In order to avoid purchasing electricity from the main power grid as much as possible, in this embodiment, a penalty cost is introduced for purchasing electricity from the main power grid, specifically:

[0133]

[0134] wherein, π P represents the penalty factor.

[0135] Based on the above formulas (22) and (23), in this embodiment, the first objective function of the second-stage scheduling is:

[0136] min J2 = F1 + F2 + J1

[0137] In this embodiment, the renewable energy access rate refers to the proportion of renewable energy in the energy consumption of the power grid for processing parts. During the operation of the power grid, the renewable energy used for processing parts can be expressed as:

[0138]

[0139] The total energy consumption of the industrial process microgrid for processing workpieces is:

[0140]

[0141] Then the second objective function can be expressed as:

[0142] max J3 = F3 / F4

[0143] Since there are many decision variables in the second-stage scheduling model, to ensure the efficiency and accuracy of model solution, in this embodiment, the second objective function is corrected, and the corrected objective function is:

[0144] max J3 = F3 - F4

[0145] Thus, the second-stage optimization model is obtained as:

[0146]

[0147] Solving the above model can obtain the energy consumption of processing machines and buffers in each time period, including F t M and F t M , the power purchase plan from the main power grid, including P t Grid and P t B , the power storage and charge / discharge plan of the power storage system, including: ESS CAP 、ESS RAMP 、ESS t 、P t char and P t dischar , as well as the power generation and consumption plan of renewable energy, including: RE t 、Wind t and PV t ; Based on the solution results, F1, F3 and F4 can be calculated, and thus the construction cost D1 of the power storage system and the renewable energy access rate D2 in the industrial process microgrid can be calculated as follows:

[0148] D1 = F1

[0149] D2 = F3 / F4

[0150] Through a large amount of experimental data analysis, it is found that the optimal planning method of the industrial microgrid considering demand-side response based on fully renewable energy supply can help guide the design of such industrial process microgrids, select an appropriate proportion of renewable energy supply according to the needs of managers, and minimize the construction cost of the power storage system as much as possible. This decision helps the safe and economic operation of the power grid and improves the economic benefits of managers.

[0151] Generally speaking, for the industrial process microgrid planning method considering renewable energy supply provided in this embodiment, in the first-stage scheduling, a time-of-use electricity price table is obtained to respond to the electricity price fluctuations on the demand side of the industrial process microgrid, and the day-ahead optimal economic scheduling plan of the industrial process microgrid is obtained; in the second stage, based on the optimal economic scheduling plan of the first stage, the energy consumption demand of the industrial microgrid is determined in the second-stage scheduling and the corresponding renewable energy supply scale is designed; for the supply scale of renewable energy, the uncertainty of renewable energy is characterized and sampled, and the power supply constraints of renewable energy are designed; for the time-of-use electricity price table, a power purchase mechanism from the main grid and related penalty costs are established to ensure the stable operation of the industrial process microgrid; a power storage system is constructed to store and release the electric energy generated by new energy, enhancing the flexible scheduling of the industrial process microgrid; combining the energy consumption demand of the industrial process microgrid, the supply quantity of renewable energy and the planning scheme of the power storage system, considering the demand-side response of the industrial microgrid in the second stage, the optimal economic planning scheme of the second stage is calculated; through the combination of the two-stage scheduling, while considering the construction cost of the power storage system and the utilization rate of renewable energy, an optimal planning scheme is designed, greatly reducing the electricity cost of the industrial process microgrid.

[0152] Embodiment 2:

[0153] A computer-readable storage medium includes: a stored computer program; when the computer program is executed by a processor, it controls the device where the computer-readable storage medium is located to execute the industrial process microgrid planning method considering renewable energy supply provided in the above Embodiment 1.

[0154] The following further illustrates the technical solutions of the present invention and the beneficial effects that can be achieved in combination with specific application examples.

[0155] The production and processing network schematic diagram of the industrial micro-process power grid is Figure 1 , and the network purchases electricity from the main grid to obtain the optimal economic scheduling plan in the first stage. The real-time electricity price is shown in Table 1.

[0156] Table 1 Time-of-Use Electricity Price Table

[0157]

[0158] After determining the energy consumption scale required for workpiece processing based on the optimization results of the first-stage scheduling, three Cases as shown in Table 2 are designed, corresponding to three renewable energy supply scales respectively. It should be noted that generally, when the proportion of renewable energy supply is greater than 30%, it can be called high-proportion energy supply. For the three Cases shown in Table 2, although the renewable energy scales are different, they all belong to the situation of high-proportion energy supply.

[0159] Table 2 Renewable Energy Scales of Three Cases

[0160]

[0161]

[0162] The Pareto curves of the three cases are as Figure 3 shown. When the scale of renewable energy is small, i.e., in Case 1, as Figure 3 (a) in it shows, the power consumption of the industrial process microgrid for processing all parts is slightly higher than the renewable energy that can be provided locally. Therefore, even if all the renewable energy is used, it can only account for 89.32%. Moreover, in order to fully utilize the renewable energy, the microgrid will build a larger-scale power storage system with a higher charge and discharge rate so as to store the excess renewable energy at each moment, which leads to an unnecessary increase in cost.

[0163] Similarly, when the scale of renewable energy is moderate, i.e., in Case 2, as Figure 3 (b) in it shows, if the renewable energy meets 99.4% of the power demand, the construction cost of the power storage system will reach about 3 times that when the renewable energy only meets 94.6% of the power demand. At the same time, compared with Figure 3 (a) in it, the cost of the power storage system with a renewable energy access rate of 94.6% is lower than that with a renewable energy access rate of 87.4. Therefore, it can be considered to construct a renewable energy environment that matches the scale of renewable energy supply and load energy consumption for the microgrid as much as possible.

[0164] As for when the scale of renewable energy is high, i.e., in Case 3, as Figure 3 (c) in it shows, the microgrid can adjust the processing time of the parts and change the load curve to adapt to the change curve of the renewable energy, so as to meet the demand without a power storage system and the main grid power supply.

[0165] Regarding the selection of the reconciliation solution for Case 1, since the weight needs to be set when searching for the optimal solution by double-objective optimization, and the setting of the weight is relatively subjective, the weight is traversed here, and the optimal solution selection under various weight conditions is considered. Figure 4 For the decision result.

[0166] Obviously, the more the operator focuses on the construction cost of the power storage system and the higher the weight assigned to it, the more inclined the operator is to choose a solution with a relatively low construction cost of the power storage system. At this time, the access rate of renewable energy also reaches the lowest. On the contrary, when the weight of the construction cost of the power storage system reaches the lowest, the operator will choose the solution with the highest access rate of renewable energy. Measured by the interval length of the weight, in most weight solutions, the solution with a construction cost of 21,125,000 yuan for the power storage system and an access rate of renewable energy of 89.13% will be selected. The specific results are shown in Table 3.

[0167] Table 3 Relationship between the change of the weight of the construction cost of the power storage system and the decision-making solution in Case1

[0168]

[0169] Select the solution with the widest weight range as the reconciled solution, and compare it with the solution with the lowest construction cost of the power storage system and the solution with the highest access rate of renewable energy. According to the number of processing machines / buffers used in the processing network in each time period, due to the insufficient scale of renewable energy itself, the industrial process microgrid rarely uses buffers to minimize unnecessary energy consumption. Especially in Case2, in order to maximize the access amount of renewable energy, the microgrid does not even use buffers. At the same time, the processing network will concentrate on processing during the noon period because the renewable energy supply is the largest at noon. Concentrating the load in this period can reduce the usage rate of the power storage system, reduce the construction cost, and the energy waste caused by the power storage system. The power scheduling diagrams for part processing in the three Cases are shown in (a), (b), and (c) in the figure respectively. According to Figure 5 It can be seen that in the three Cases, the microgrid needs to purchase electricity from the main grid, but the electricity purchase time periods are all in the low electricity price area, and the electricity purchase is concentrated in two time periods, ensuring that most of the power supply still adopts the plan of renewable energy. Compared with Figure 5 in (b), Figure 5 the charge-discharge speed in (c) is significantly lower. At the same time, with alternating charge and discharge and fewer continuous charging states, this can ensure the stable state of charge of the power storage system as much as possible and reduce the construction cost of the power storage system. And Figure 5 in (a) compared with Figure 5 in (c), the access rate of renewable energy has increased by 1.98%. Compared with Figure 5 in (b), the construction cost of the power storage system has decreased by 44.9%, and the indicators are more balanced.

[0170] For Case2, the results under different weights are as shown in Figure 6 and the specific results are shown in Table 4.

[0171] Table 4 Relationship between the weight change of the power storage system construction cost and the decision-making scheme in Case 2

[0172]

[0173] Clearly, the compromise solution matches the optimal solution when the operator prioritizes the cost of the power storage system. In this case, the cost is 13,750,000 yuan, and the renewable energy utilization rate is 94.59%. Within the weight space, there's a 43.76% probability of selecting this compromise solution. However, when the operator prioritizes increasing renewable energy utilization, there's a 12.32% probability of selecting the extreme solution, which achieves a renewable energy integration rate of 99.4%.

[0174] The power dispatch diagram for Case 2 and the power dispatch diagram for the highest renewable energy access rate are shown as follows: Figure 7 As shown in (a) and (b) in the figure. Obviously, due to Figure 7 (a) in the example needs to purchase electricity from the main grid, and the corresponding load energy consumption will be higher, so the access rate of renewable energy will be lower. In fact, when the grid wants to save the construction cost of the power storage system, it will adjust the processing time period to make the charging and discharging of the power storage system more balanced, such as Figure 7 As shown in (a) in the figure, the cost of the power storage system is reduced. For Case 2, in order to increase the access rate of renewable energy, since the total amount of renewable energy is capped, the power grid will try to reduce processing energy consumption as much as possible without using buffer zones, and adjust the load curve as much as possible to adapt to the changes in renewable energy, thereby making more reasonable use of the power storage system. However, its cost will still increase significantly. Figure 7 (b) relative to Figure 7 In case (a), the renewable energy integration rate increased by 5.09%, but the power storage system construction cost increased by 190.91%. This explains why the coordination solution favors reducing the power storage system construction cost. Comparing the dispatch results of the two different scenarios in Case 2, we conclude that when the industrial process microgrid determines to maximize the renewable energy integration rate, it can consider building a buffer or reducing the buffer capacity. This is because when the system is controlled, it will minimize the use of buffers to accommodate renewable energy.

[0175] For Case 3, when the scale of renewable energy is high, the microgrid can meet the load demand by relying solely on the power provided by renewable energy without purchasing electricity from the main grid. Considering the penalty cost of purchasing electricity from the main grid, for scenarios with a high scale of renewable energy, the microgrid will choose the solution with a 100% renewable energy access rate as the optimal solution and minimize the construction cost of the power storage system. Therefore, the Pareto front contains only one solution. The corresponding power dispatch diagrams for this solution are as follows:Figure 8 as shown

[0176] Obviously, for the scheduling of processing time, it is mainly adjusted according to the curve of renewable energy to reduce the use of the power storage system. For the power storage system, it mainly stores power when the renewable energy exceeds the load and releases power when the scale of renewable energy is relatively small, and the charging and discharging are carried out at intervals, which can meet the demand.

[0177] It is worth noting that as renewable energy gradually becomes abundant, the construction cost of the power storage system will also gradually decrease, as Figure 9 shown. When the growth rate of the scale of renewable energy is 1%, the construction cost of the power storage system still needs 37,875,000 yuan at this time. With the increase of renewable energy, when the growth rate reaches 20%, it is already possible to achieve full renewable energy coverage of the load without a power storage system.

[0178] The power scheduling diagrams with the growth rates of renewable energy scale of 1% and 30% are respectively as Figure 10 shown in (a) and (b) in the figure. At the same time, by comparing the usage quantities of processing machines and buffers in the processing network at each time period under different conditions, it can be seen that when the renewable energy is only larger in scale than the power consumption of the load, the power grid still needs to reduce the power consumption of the load as much as possible. As the scale of renewable energy increases, the use of buffers will also gradually increase. Moreover, when the scale of renewable energy increases to a certain extent, without the need for a power storage system to assist in charging and discharging, the microgrid can complete the matching of both supply and demand sides only by scheduling the processing time sequence and achieve full coverage of renewable energy.

[0179] In summary, the following conclusions can be obtained, which can provide certain reference value for the decision-makers of the industrial process microgrid to formulate the microgrid construction plan:

[0180] (1) If the industrial process microgrid is more inclined to use renewable energy with a smaller scale for power supply, it can be considered to purchase electricity from the main grid during periods with lower electricity prices and store it using power storage devices, especially to obtain higher benefits while ensuring system reliability; at the same time, since the decision-makers themselves are inclined to use renewable energy with a smaller scale for power supply, it can be considered to further not fully utilize renewable energy, thereby reducing the construction cost of the power storage system.

[0181] (2) If the industrial process microgrid is more inclined to use renewable energy with a moderate scale for power supply, it is still necessary to establish a mechanism for purchasing electricity from the main grid to ensure that the system can still operate stably when renewable energy is insufficient. At this stage, if the decision-makers are inclined to lower construction costs, the construction cost of the power storage system can be significantly reduced under the condition of sacrificing a very small renewable energy access rate.

[0182] (3) In the case where the renewable energy in the industrial microgrid is not sufficient on its own and it is necessary to consider purchasing electricity from the main grid centrally, if the industrial process microgrid is more inclined to make full use of renewable energy, a large-scale power storage system needs to be built at a high cost. However, correspondingly, under the influence of this result, the utilization rate of the buffer will decrease or even drop to 0. Therefore, the decision maker can consider reducing the number of buffers or even not setting up buffers to save the construction cost.

[0183] (4) If the industrial process microgrid is more inclined to use a relatively large scale of renewable energy for power supply, there is no need to consider the mechanism of purchasing electricity from the main grid anymore. And as the scale of renewable energy increases, the construction cost of the power storage system will also continue to decline. However, this will also cause a considerable amount of energy to be wasted. Therefore, it is recommended to still build a power storage system to improve the utilization rate of renewable energy.

[0184] Those skilled in the art can easily understand that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for planning an industrial process microgrid considering renewable energy supply, characterized in that, Including: The first-stage scheduling and the second-stage scheduling; The first-stage scheduling includes: in the case of only purchasing electricity from the main power grid, taking the minimum energy consumption cost of processing workpieces within a single scheduling cycle as the optimization goal, taking the energy consumption of processing machines and buffers in each time period as decision variables, establishing a first-stage scheduling model, and performing optimization solution under preset first constraint conditions; The second-stage scheduling includes: According to the optimal solution of the first-stage scheduling, calculate the energy consumption \(F\) of the processing machines and buffers within a single scheduling period M and \(F\) B , and construct the energy consumption requirement constraint \(F\) M+B \(\leq(1 + \alpha)(F\) M +F\) N ); In the case of considering renewable energy supply, taking the minimum workpiece processing cost and the maximum renewable energy access rate within a single scheduling cycle as the optimization goals, taking the energy consumption of processing machines and buffers in each time period, the power purchase plan from the main power grid, the power storage and charge-discharge plan of the power storage system, and the power generation and consumption plan of renewable energy as decision variables, establishing a second-stage scheduling model, and performing optimization solution under second constraint conditions including the first constraint conditions and the energy consumption demand constraint, and calculating the optimal planning scheme of the industrial process microgrid according to the solution results; Among them, F M+B represents the total energy consumption of the processed workpieces within a single scheduling period in the second-stage scheduling; α represents the energy consumption margin, where α > 0; the workpiece processing cost includes the construction cost of the power storage system, the penalty cost for purchasing electricity from the main power grid, and the energy consumption cost of processing the workpieces; the renewable energy access rate is the proportion of renewable energy in the total energy consumption of the processed workpieces; The first constraint conditions include: Among them, T represents the total number of scheduling time periods within a single scheduling cycle; N M and N B respectively represent the number of processing machines and buffers in the industrial process network of the industrial process microgrid; N W represents the number of workpieces to be processed; and respectively represent the number of workpieces in the i-th processing machine during time period t, time period t - 1, and the last and first time periods, represents the number of workpieces in the (i + 1)-th processing machine during time period t, represents the maximum number of workpieces that the i-th processing machine can process during time period t, represents the maximum number of workpieces that the (i - 1)-th processing machine can process during time period t - 1; and respectively represent the number of workpieces in the i-th buffer during time period t, time period t - 1, and the last and first time periods, represents the number of workpieces in the (i - 1)-th buffer during time period t - 1, represents the maximum buffer capacity of the i-th buffer during time period t.

2. The method for planning an industrial process microgrid considering renewable energy power supply according to claim 1, wherein The objective function of the first-stage scheduling model is: Among them, F t M，1 and F t B，1 respectively represent the energy consumption of the processing machine and the buffer during period t in the first-stage scheduling, and π t represents the electricity price for purchasing electricity from the main power grid during period t.

3. The industrial process microgrid planning method considering renewable energy power supply according to claim 2, characterized in that The second constraint conditions further include: power purchase constraint, and the expression is as follows: 0 ≤ P t Grid ≤ P t GM ·P t B Among them, P t Grid represents the electricity purchase quantity from the main power grid during time period t, and P t GM represents the maximum electricity purchase quantity from the main power grid during time period t; P t B is a 0 / 1 variable, where 0 means purchasing electricity from the main power grid during time period t, and 1 means purchasing electricity from the main power grid during time period t; P BM represents the maximum number of times allowed to purchase electricity from the main power grid within a single scheduling cycle.

4. The industrial process microgrid planning method considering renewable energy power supply according to claim 3, wherein The second constraint conditions further include: the construction constraint of the power storage system, and the expression is as follows: Among them, ESS CAP table and ESS RAMP both belong to the decision variables of the second-stage scheduling model, representing the energy storage capacity and charge-discharge capacity of the power storage system respectively; and represent the upper limit value of the energy storage capacity and the upper limit value of the charge-discharge capacity of the power storage system respectively.

5. The method for planning an industrial process microgrid considering renewable energy power supply according to claim 4, wherein The second constraint conditions further include: the operation constraint of the power storage system, and the expression is as follows: ESS1 = 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 Among them, ESS t and ESS t+1 are the amounts of electricity stored in the power storage system during time period t and time period t + 1 respectively; ESS1 and ESS T+1 represent the amounts of electricity stored in the power storage system during the first scheduling period of the current scheduling cycle and the next scheduling cycle respectively; P t char represents the charging amount of the power storage system during time period t; P t dischar represents the discharging amount of the power storage system during time period t.

6. The method for planning an industrial process microgrid considering renewable energy power supply according to claim 5, characterized in that The second constraint conditions further include: the consumption constraint of renewable energy, and the expression is as follows: 0 ≤ RE t ≤ GE t The second constraint conditions further include: the power supply-demand balance constraint, and the expression is as follows: P t Grid +RE t +P t dischar =P t char +F t M +F t B Among them, RE t represents the consumption of renewable energy within period t, and GE t represents the power generation of renewable energy within period t; F t M and F t B respectively represent the energy consumption of processing machines and buffers within period t in the second-stage scheduling.

7. The industrial process microgrid planning method considering renewable energy power supply according to claim 6, characterized in that The construction cost of the power storage system is: F1 = π ESS ·ESS CAP +π ESSR ·ESS RAMP The power purchase penalty cost from the main power grid is: The amount of renewable energy consumed by the industrial process microgrid for processing workpieces is: The total energy consumption of the industrial process microgrid for processing workpieces is: And, the objective function of the second-stage scheduling model is: minJ2 = F1 + F2 + J1 maxJ3 = F3 - F4 Among them, π ESS represents the cost price of the capacity of the power storage system, and π ESSR represents the cost price of the charge and discharge rate of the power storage system; π P represents the penalty factor.

8. The industrial process microgrid planning method considering renewable energy power supply according to claim 7, characterized in that, The optimal planning scheme of the industrial process microgrid includes: the construction cost D1 of the power storage system and the renewable energy access rate D2, and the calculation formulas are as follows: D1 = F1 * Among them, F1 * , F3 * and respectively represent the construction cost of the power storage system, the amount of renewable energy consumed by the industrial process microgrid for processing workpieces, and the total energy consumption of the industrial process microgrid for processing workpieces, which are calculated according to the optimization solution results of the second stage.

9. A computer-readable storage medium, characterized in that, Including: A stored computer program; when the computer program is executed by a processor, it controls the device where the computer-readable storage medium is located to execute the industrial process microgrid planning method considering renewable energy supply according to any one of claims 1 to 8.

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