Low-carbon industrial park optimal scheduling method based on enterprise flow shop flexible scheduling

Abstract

The application discloses a low-carbon industrial park optimization scheduling method based on enterprise flow shop flexible scheduling, which comprises the following steps: step 1, constructing an industrial park optimization scheduling model; constructing an objective function by taking carbon trading cost, green certificate trading cost and park operation cost as the basis; constraint conditions comprise power balance constraint, combined heat and power generator set constraint, energy storage device constraint, gas boiler constraint and photovoltaic output constraint; step 2, constructing an enterprise flow shop flexible scheduling model in the industrial park; step 3, taking the cost saved by enterprise scheduling as a subsidy, and adopting a genetic algorithm to calculate the optimization scheduling model, so that the scheduling scheme of the industrial park can be obtained; the application takes high-energy-consuming enterprises as flexible adjustable resources, and combines carbon trading and green certificate trading mechanism to carry out energy management and optimization scheduling of the industrial park; while reducing the production cost of enterprises and improving the production efficiency, the total carbon emission of the park can be reduced, and the economic efficiency of the operation of the industrial park is improved.

Description

Technical Field

[0001] This invention relates to the field of integrated energy optimization scheduling technology for industrial parks, specifically to a low-carbon industrial park optimization scheduling method based on flexible scheduling of enterprise assembly line workshops. Background Technology

[0002] With the introduction of the "dual carbon" target, we are currently at a critical stage of energy structure transformation, and sustainable economic development has placed urgent demands on adjusting the energy structure. Industrial parks are the core units for industrial agglomeration and development, and have become engines of China's economic growth. However, they are also important energy-consuming units and major carbon emitters. The rational utilization of multi-energy resources in industrial parks is an important way to achieve scientific and precise carbon emission reduction and alkaline carbon neutrality.

[0003] Currently, research on power dispatching in industrial parks lacks in-depth understanding of technologies related to coordinating power consumption plans for high-energy-consuming enterprises within these parks. Often, enterprises within industrial parks are simply treated as single, controllable loads without considering the specific processes and details of industrial production. This approach fails to reflect the actual production situation of these enterprises, making it difficult to implement proposed load adjustment schemes in real-world industrial production. Summary of the Invention

[0004] This invention addresses the problems existing in the prior art by providing an optimized scheduling method for low-carbon industrial parks based on flexible scheduling of enterprise assembly line workshops.

[0005] The technical solution adopted in this invention is: a method for optimizing the scheduling of low-carbon industrial parks based on flexible scheduling of enterprise assembly line workshops, comprising the following steps:

[0006] Step 1: Construct an optimized scheduling model for the industrial park; construct an objective function based on carbon trading costs, green certificate trading costs, and park operating costs; constraints include: power balance constraints, cogeneration generator unit constraints, energy storage equipment constraints, gas boiler constraints, and photovoltaic output constraints;

[0007] Step 2: Construct a flexible scheduling model for the assembly line workshops of high-energy-consuming enterprises within the industrial park;

[0008] minf2=μmax(C ijk )+(1-μ)[δ e (t)P ws (t)-ζ(C0-C op )]

[0009] In the formula: f2 is the objective function of the scheduling model, μ is the weight coefficient, and C ijk Let δ be the end time of workpiece i on machine k in process j. e (t) represents the real-time electricity price, P ws(t) represents the electricity consumed by high-energy-consuming enterprises, ζ represents the subsidy coefficient, C0 represents the initial operating cost of the industrial park, and C op For the park's operating costs;

[0010]

[0011] Where: M j Let N be the set of available machines for the j-th process, and n be the sum of the number of workpieces allocated to all available machines in each process; jk The number of workpieces processed on machine k in process j;

[0012] C ijk x ijk ≤S i(j+1)k x i(j+1)k i = 1, 2, ..., n; k ∈ M j+1

[0013] C ijk x ijk =S ijk x ijk +P ijk x ijk

[0014] C Rjkjk x Rjkjk ≤S (R+1)jkjk x (R+1)jkjk

[0015]

[0016]

[0017] In the formula: x ijk y ijkt z jkt A value of 0 or 1 indicates whether the workpiece is processed on machine k within a specific time and process; S i(j+1)k S is the start time of workpiece i on machine k in operation j+1. ijk P is the start time of workpiece i on machine k in process j. ijk R is the processing time of workpiece i on machine k in process j. jk For the Rth workpiece to be processed on machine k in process j, C Rjkjk For workpiece R jk The end time on machine k in process j, x Rjkjk For workpiece R jk Whether it is processed on machine k in process j, S (R+1)jkjk For workpiece (R+1) jk At the start time x on machine k in process j (R+1)jkjk For workpiece (R+1)jk Whether it is processed on machine k in process j; E ijk For the energy consumption of workpiece i in machine k during process j, IE jk I represents the standby power consumption per unit time of machine k in process j. jk Let m be the standby time of machine k for process j, and m be the number of processes.

[0018] Step 3: The park optimizes the scheduling based on the enterprise scheduling plan and returns the cost savings of the enterprise scheduling as a subsidy to the enterprise. By using an improved genetic algorithm to iteratively calculate the optimization model, a collaborative scheduling plan for the industrial park and high-energy-consuming enterprises can be obtained.

[0019] Furthermore, the objective function in step 1 is as follows:

[0020]

[0021] In the formula: f1 is the objective function, C op For the park's operating costs, For carbon trading costs, C ge Costs associated with green certificate transactions.

[0022] Furthermore, the operating costs of the park are as follows:

[0023]

[0024] In the formula: T is the scheduling period, P buy (t) represents the electricity purchased by the park, δ e,sell For electricity sales price, P sell (t) represents the electricity sold, δ g For natural gas prices, V g,buy (t) represents the amount of gas purchased;

[0025] Carbon trading costs are as follows:

[0026]

[0027] Where: ε c U is the carbon trading price coefficient. C For the carbon emissions of the park system, U P For the initial carbon allowance;

[0028] The transaction costs for green certificates are as follows:

[0029]

[0030] Where: δ ge For the market price of green certificates, ε ge P represents the green certificate quota coefficient for the park. Load (t) represents the electrical load, P ws(t) represents the electrical energy consumed by high-energy-consuming enterprises, P pv (t) represents the output of the photovoltaic unit.

[0031] Furthermore, the power balance constraint is as follows:

[0032] P buy (t)+P pv (t)+P CHP (t)=P sell (t)+P ES (t)+P Load (t)+P ws (t)

[0033] H CHP (t)+H GB (t)=H HS (t)+H Load (t)

[0034] V g,buy (t)L gas =M GT (t)+M GB (t)

[0035] In the formula: P CHP (t) represents the power generation capacity of the combined heat and power generator unit, P ES (t) represents the power of the battery, H CHP (t) represents the thermal power of the combined heat and power generator unit, H GB (t) represents the heat production efficiency of the gas-fired boiler, H HS (t) represents the power of the battery storage tank, H Load (t) represents the heat load of the park, V g,buy (t) represents the gas purchase volume, L gas M represents the calorific value of natural gas. GT (t) represents the natural gas consumed by the gas turbine, M GB (t) represents the natural gas consumed by the gas-fired boiler;

[0036] The constraints of combined heat and power (CHP) generator units are as follows:

[0037] P CHP (t)=θ GT M GT (t)

[0038] H GT (t)=P CHP (t)(1-θ GT -θ loss ) / θ GT

[0039] H CHP (t)=θ rec HGT (t)

[0040]

[0041] In the formula: θ GT For the power generation efficiency of a gas turbine, H GT (t) is, θ loss For the heat loss efficiency of the gas turbine, H CHP (t) is, θ rec This refers to the heat recovery efficiency of the waste heat boiler. This is the maximum generating capacity of the combined heat and power (CHP) generator unit.

[0042] The constraints of energy storage devices are as follows:

[0043]

[0044]

[0045]

[0046]

[0047] In the formula: S ES (t) represents the battery's energy storage capacity. This represents the lower limit of the battery's energy storage capacity. S represents the upper limit of the battery's energy storage capacity. HS (t) represents the capacity of the heat storage tank. This is the lower limit of the heat storage tank capacity. This is the upper limit of the heat storage tank capacity. The rated power of the battery, Rated power of the thermal storage tank;

[0048] The constraints for gas-fired boilers are as follows:

[0049] H GB (t)=θ GB M GB (t)

[0050]

[0051] In the formula: θ GB For the energy conversion efficiency of gas-fired boilers, This represents the lower limit of natural gas consumption for gas-fired boilers. This is the upper limit for the natural gas consumed by a gas-fired boiler;

[0052] The photovoltaic output constraints are as follows:

[0053] P wt (t)≤P f,wt (t)

[0054] In the formula: P f,wt (t) represents the predicted photovoltaic output, P wt (t) represents the photovoltaic power output.

[0055] Furthermore, the process of calculating and optimizing the scheduling model is as follows:

[0056] S1: Generate the initial population and calculate the maximum processing time, production cost, and electrical energy consumption of high-energy-consuming enterprises;

[0057] S2: Calculate the output and total operating cost of each piece of equipment in the industrial park based on the flexible scheduling model;

[0058] S3: Set the subsidy value to obtain the objective function value of the population and the fitness of individuals;

[0059] S4: Generate a new population through crossover, mutation, and selection;

[0060] S5: Determine if the termination condition is met. If not, proceed to step 1. If yes, exit and obtain the scheduling scheme.

[0061] The beneficial effects of this invention are:

[0062] (1) This invention treats high-energy-consuming enterprises as flexible and controllable resources, and combines carbon trading and green certificate trading mechanisms to carry out energy management and optimized scheduling of industrial parks.

[0063] (2) This invention effectively reduces the total processing time and energy consumption through reasonable workshop scheduling, thereby improving production efficiency;

[0064] (3) In this invention, different workshop processing schemes not only affect the production costs and time of enterprises, but also change the optimal scheduling results of the park; the industrial park and the high-energy-consuming enterprise's assembly line workshop coordinate their respective scheduling schemes to enable both parties to obtain higher benefits, reduce the carbon emissions of the park, and promote the low-carbon economic operation of the industrial park. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of the process of the present invention.

[0066] Figure 2 This is a schematic diagram of the genetic algorithm process in this invention.

[0067] Figure 3 This is the flexible scheduling result for a high-energy-consuming enterprise in Scenario 1 of this invention.

[0068] Figure 4 This is the power dispatch result for the industrial park in Scenario 1 of this invention.

[0069] Figure 5This is the thermal scheduling result of the industrial park in Scenario 1 of this invention. Detailed Implementation

[0070] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0071] like Figure 1 As shown, a method for optimizing the scheduling of low-carbon industrial parks based on flexible scheduling of enterprise assembly lines includes the following steps:

[0072] Step 1: Construct an optimized scheduling model for the industrial park; construct an objective function based on carbon trading costs, green certificate trading costs, and park operating costs; constraints include: power balance constraints, cogeneration generator unit constraints, energy storage equipment constraints, gas boiler constraints, and photovoltaic output constraints;

[0073] The objective function is as follows:

[0074]

[0075] In the formula: f1 is the objective function, C op For the park's operating costs, For carbon trading costs, C ge Costs associated with green certificate transactions.

[0076] The park's operating costs are as follows:

[0077]

[0078] In the formula: T is the scheduling period, P buy (t) represents the electricity purchased by the park, δ e,sell For electricity sales price, P sell (t) represents the electricity sold, δ g For natural gas prices, V g,buy (t) represents the amount of gas purchased.

[0079] Carbon trading costs are as follows:

[0080]

[0081] Where: ε c U is the carbon trading price coefficient. C For the carbon emissions of the park system, U P For the initial carbon allowance;

[0082] To limit carbon emissions from industrial parks, a carbon trading mechanism has been introduced. Industrial parks generate significant greenhouse gas emissions during operation, including carbon emissions from some energy supply equipment, purchased electricity, and the operation of production equipment in energy-intensive enterprises. The carbon emissions of the park system are calculated as follows:

[0083] U C=U B +U G +U E

[0084]

[0085]

[0086] P g,ctb (t)=P CHP (t)+H CHP (t)+H GB (t)

[0087] In the formula: U B For the carbon emissions generated by purchasing electricity from outside the industrial park, U G For the carbon emissions generated by functional equipment in industrial parks, U E Carbon emissions from production equipment of high-energy-consuming enterprises in the park area; a1 and b1 are emission characteristic coefficients for electricity purchased, a2 and b2 are carbon emission characteristic coefficients for gas-consuming equipment, P g,ctb (t) represents the equivalent natural gas utilization rate of gas-consuming equipment, and P CHP (t) represents the power generation of the combined heat and power (CHP) generator unit, H. CHP (t) represents the thermal power of the combined heat and power generator unit CHP, H GB (t) represents the heat output of the gas-fired boiler; m represents the total number of production processes in the high-energy-consuming enterprise; ξ j P is the additional carbon emission factor for process j. j,ws (t) represents the power consumption of process j during time period t.

[0088] Currently, the main method for initial carbon emission allowances in the carbon trading market is through free allowances, represented as follows:

[0089]

[0090] In the formula: U P For the initial carbon allowance, λ h and λ e These are the carbon quota coefficients for thermal power and electrical power, respectively.

[0091] The transaction amount of the industrial park participating in the carbon trading market is determined by the difference between the actual carbon emissions and the carbon emission allowance. Therefore, the carbon trading cost of the industrial park during the scheduling cycle is as shown in equation (3).

[0092] The costs of green certificate transactions are as follows: The government stipulates a minimum proportion of renewable energy in electricity consumption. Enterprises or users that do not meet the green electricity quota requirements in their electricity consumption need to purchase green certificates from the green certificate trading market.

[0093]

[0094] Where: δ ge For the market price of green certificates, ε ge P represents the green certificate quota coefficient for the park. Load (t) represents the electrical load, P ws (t) represents the electrical energy consumed by high-energy-consuming enterprises, P pv (t) represents the output of the photovoltaic unit.

[0095] The power balance constraints are as follows: the industrial park must meet the balance of electricity, heat and gas during operation.

[0096] P buy (t)+P pv (t)+P CHP (t)=P sell (t)+P ES (t)+P Load (t)+P ws (t) (5)

[0097] H CHP (t)+H GB (t)=H HS (t)+H Load (t) (6)

[0098] V g,buy (t)L gas =M GT (t)+M GB (t) (7)

[0099] In the formula: P CHP (t) represents the power generation capacity of the combined heat and power generator unit, P ES (t) represents the power of the battery, H CHP (t) represents the thermal power of the combined heat and power generator unit, H GB (t) represents the heat production efficiency of the gas-fired boiler, H HS (t) represents the power of the battery storage tank, H Load (t) represents the heat load of the park, V g,buy (t) represents the gas purchase volume, L gas M represents the calorific value of natural gas. GT (t) represents the natural gas consumed by the gas turbine, M GB (t) represents the natural gas consumed by the gas-fired boiler; M GT (t) and M GB (t) is converted into power based on the natural gas value.

[0100] The constraints of a combined heat and power (CHP) generator set are as follows: A CHP generator set includes a gas turbine and a waste heat boiler. The gas turbine generates electricity, and the waste heat generated is recovered and reused by the waste heat boiler to supply heat to users.

[0101] P CHP (t)=θ GT M GT (t) (8)

[0102] H GT (t)=P CHP (t)(1-θ GT -θ loss ) / θ GT (9)

[0103] H CHP (t)=θ rec H GT (t) (10)

[0104]

[0105] In the formula: θ GT For the power generation efficiency of a gas turbine, H GT (t) is, θ loss For the heat loss efficiency of the gas turbine, H CHP (t) is, θ rec This refers to the heat recovery efficiency of the waste heat boiler. This is the maximum generating capacity of the combined heat and power (CHP) generator unit.

[0106] The following constraints apply to energy storage equipment: the operation of batteries and thermal storage tanks in the park should meet the following constraints.

[0107]

[0108]

[0109]

[0110]

[0111] In the formula: S ES (t) represents the battery's energy storage capacity. This represents the lower limit of the battery's energy storage capacity. S represents the upper limit of the battery's energy storage capacity. HS (t) represents the capacity of the heat storage tank. This is the lower limit of the heat storage tank capacity. This is the upper limit of the heat storage tank capacity. The rated power of the battery, Rated power of the thermal storage tank;

[0112] The constraints for gas-fired boilers are as follows:

[0113] H GB (t)=θ GB MGB (t) (16)

[0114]

[0115] In the formula: θ GB For the energy conversion efficiency of gas-fired boilers, This represents the lower limit of natural gas consumption for gas-fired boilers. This is the upper limit for the natural gas consumed by a gas-fired boiler;

[0116] The photovoltaic output constraint is as follows: the actual photovoltaic output in the industrial park shall not exceed its predicted value.

[0117] P wt (t)≤P f,wt (t) (18)

[0118] In the formula: P f,wt (t) represents the predicted photovoltaic output, P wt (t) represents the photovoltaic power output.

[0119] To verify the effectiveness of the above method, peak shaving and valley filling indices for industrial parks under different scenarios were calculated. The peak shaving and valley filling index is characterized by the sum of squares of the rate of change of electrical load within adjacent time periods; the smaller the value, the better the effect of reducing the peak-valley difference in system load. Higher power supply reliability is also indicated by the following calculation method:

[0120]

[0121] In the formula: F EL P is a peak-shaving and valley-filling indicator. all,L (t) represents the total electrical load during time period t.

[0122] Step 2: Construct a flexible scheduling model for the flow workshops of high-energy-consuming enterprises within the industrial park; the flexible flow workshop scheduling problem of enterprises is a typical combinatorial optimization problem, which can generally be described as: n workpieces have m processing steps, and each step has M... j There are several different parallel machines. Each machine has a different processing time and energy consumption per unit time. Since industrial machines and electrical appliances typically cannot be completely shut down during processing, they consume energy even in standby mode. Therefore, the energy consumption of each machine consists of both processing energy consumption and standby energy consumption. The operational goal of a high-energy-consuming enterprise is to reduce processing time and lower production costs. Therefore, the enterprise's objective function and workshop operating constraints are as follows:

[0123] minf2=μmax(C ijk )+(1-μ)[δ e (t)P ws (t)-ζ(C0-C op (20)

[0124] In the formula: f2 is the objective function of the scheduling model, μ is the weight coefficient, and in this invention, μ = 1 is selected, that is, the operating cost when the cost weight is 0 is C0, C ijk Let δ be the end time of workpiece i on machine k in process j. e (t) represents the real-time electricity price, P ws (t) represents the electricity consumed by high-energy-consuming enterprises, ζ represents the subsidy coefficient, C0 represents the initial operating cost of the industrial park, and C op For the park's operating costs;

[0125]

[0126] Where: M j Let N be the set of available machines for the j-th process, and n be the sum of the number of workpieces allocated to all available machines in each process; jk The number of workpieces processed on machine k in process j;

[0127] C ijk x ijk ≤S i(j+1)k x i(j+1)k i = 1, 2, ..., n; k ∈ M j+1 (twenty two)

[0128] C ijk x ijk =S ijk x ijk +P ijk x ijk (twenty three)

[0129]

[0130]

[0131]

[0132] In the formula: x ijk y ijkt z jkt A value of 0 or 1 indicates whether the workpiece is processed on machine k within a specific time and process; x ijk A value of 1 indicates that workpiece i is processed on machine k in process j; y ijkt A value of 1 indicates that workpiece i is scheduled to be processed on machine k in process j during time interval t; z jkt A value of 1 indicates that workpiece i is not scheduled for processing on machine k in process j; S i(j+1)k S is the start time of workpiece i on machine k in operation j+1. ijk P is the start time of workpiece i on machine k in process j.ijk R is the processing time of workpiece i on machine k in process j. jk Let R be the R-th workpiece processed on machine k in process j. For workpiece R jk The end time on machine k in process j. For workpiece R jk Is it processed on machine k in process j? For workpiece (R+1) jk The start time on machine k in process j, For workpiece (R+1) jk Whether it is processed on machine k in process j; E ijk For the energy consumption of workpiece i in machine k during process j, IE jk I represents the standby power consumption per unit time of machine k in process j. jk Let m be the machine k standby time for process j, and m be the number of processes.

[0133] Equation (21) indicates that the sum of the number of workpieces allocated to all available machines in each process is n. Equation (22) indicates that the next stage of work for each workpiece must be completed before the previous stage can begin. Equation (23) indicates that the completion time of any workpiece depends on its processing time and start time on a particular machine. Equation (24) indicates that each machine can only process one workpiece at a time, that is, the start processing time of the R+1th workpiece processed on machine k in process j must be greater than or equal to the completion processing time of a workpiece on that machine. Equation (25) indicates that the processing of each workpiece in each process can only be allocated to one machine.

[0134] Step 3: The industrial park optimizes the scheduling based on the enterprises' scheduling plans and returns the cost savings from the scheduling to the enterprises as subsidies. A genetic algorithm is used to calculate the optimized scheduling model, thus obtaining the industrial park's scheduling plan. For example... Figure 2 As shown:

[0135] The process of calculating the optimal scheduling model is as follows:

[0136] S1: Generate the initial population and calculate the maximum processing time, production cost, and electrical energy consumption of high-energy-consuming enterprises;

[0137] S2: Calculate the output and total operating cost of each piece of equipment in the industrial park based on the flexible scheduling model; on the basis of the workshop scheduling, the park operation layer performs optimized scheduling and uses the cplex solver to calculate and obtain the output and total operating cost of each piece of equipment in the park layer.

[0138] S3: Set the subsidy value to obtain the objective function value of the population and the fitness of individuals;

[0139] S4: Generate a new population through crossover, mutation, and selection;

[0140] S5: Determine if the termination condition is met. If not, proceed to step 1. If yes, exit and obtain the scheduling scheme.

[0141] The termination condition is that the difference in fitness between the two generations of optimal solutions is less than the error radius.

[0142] The following simulation analysis uses data from an industrial park containing an automobile engine manufacturing company. The selected case has a scheduling cycle of 24 hours. The company processes 5 batches of workpieces, each batch having 3 processing steps. The processing machines and energy consumption per unit time are shown in Table 1, and the standby energy consumption per unit time of the machines is shown in Table 2.

[0143] Table 1. Processing parameters for high-energy-consuming enterprises

[0144]

[0145] Table 2. Standby power consumption of the machine per unit time

[0146]

[0147]

[0148] In addition, the subsidy coefficient ζ for high-energy-consuming enterprises in the park is 30%, the time-of-use electricity price is shown in Table 3, and the relevant system parameters in the park are shown in Table 4.

[0149] Table 3. Time-of-use electricity pricing

[0150]

[0151] Table 4. Relevant parameters of the industrial park

[0152]

[0153] To compare and analyze the impact of flexible scheduling, carbon trading, and green certificate trading in high-energy-consuming enterprises on the operation of industrial parks, the following scenarios are set up:

[0154] Scenario 1: Completion time weight μ = 0.5, considering carbon trading and green certificate trading;

[0155] Scenario 2: Completion time weight μ = 0.5, carbon trading and green certificate trading are not considered;

[0156] Scenario 3: Completion time weight μ = 1, considering carbon trading and green certificate trading;

[0157] Scenario 4: Completion time weight μ = 0, considering carbon trading and green certificate trading.

[0158] The flexible scheduling results for high-energy-consuming enterprises and the electricity and heat dispatching results for industrial parks in Scenario 1 are as follows: Figure 3-5 As shown.

[0159] Figure 3 The Gantt chart shows the firm's FFSP results with μ = 0.5, meaning the maximum processing time and electricity cost have the same weight in the optimization objective. Figure 3 In the process, each square represents a certain process arrangement for a batch of workpieces. The first process to start on machine 1 is process 1 for batch 1 workpieces, and the last process to finish in the scheduling is process 3 for batch 2 and batch 4 workpieces.

[0160] Figure 4 and Figure 5 The diagram illustrates the electricity and heat dispatching results for the industrial park based on this enterprise's dispatching scheme. As shown in the diagram, the heat load in the industrial park is provided by combined heat and power (CHP) units and gas-fired boilers. Since CHP units can simultaneously provide both heat and electricity, they are crucial energy suppliers during periods of high demand for both. In the morning, batteries are charged during the low electricity price periods of 0:00-2:00 and 5:00-6:00, and discharged during the high electricity price period of 7:00-10:00. Subsequently, batteries are charged again from 13:00-16:00 and discharged during the evening peak. This battery charging and discharging behavior meets the park's electricity load demand, effectively reducing the park's operating costs. Starting at 8:00, as photovoltaic output increases, the industrial park's purchased electricity decreases. During the peak photovoltaic output period of 12:00-16:00, the park's purchased electricity is zero, and there is surplus electricity available for battery charging while still meeting the heat load demand.

[0161] The scheduling results of high-energy-consuming enterprises and industrial parks under different target weights in this invention are shown in Table 5.

[0162] Table 5. Scheduling results for high-energy-consuming enterprises and industrial parks under different target weights

[0163]

[0164] As shown in Table 5, as the weight of completion time decreases, the completion time of enterprises increases, but the operating costs of the industrial park decrease significantly. High-energy-consuming enterprises receive more subsidies, thus effectively reducing production costs. Different FFSP schemes for enterprises can provide different peak shaving and valley filling effects for the park. Reasonable selection of processing machines can reduce the peak-valley difference of the park's load and improve the reliability of the park's power supply. The park can adjust the processing of enterprise assembly lines by selecting different completion time weights. Appropriate workshop scheduling schemes can improve production efficiency, reduce production costs, and reduce the peak-valley difference of the park's load.

[0165] By analyzing the results of Scenario 1 and Scenario 2, the impact of carbon trading and green certificate trading on the carbon emissions of industrial parks is compared. The operational results of industrial parks before and after considering carbon trading and green certificate trading are shown in Table 6.

[0166] Table 6. Industrial Park Operation Results Before and After Considering Carbon Trading and Green Certificate Trading

[0167]

[0168]

[0169] Table 6 shows the operating costs and carbon emissions of the industrial park before and after considering carbon trading and green certificate trading when μ = 0.5. It can be seen that after introducing carbon trading and green certificate trading mechanisms, the total operating cost of the industrial park increased by 9.75%. Carbon emissions from high-energy-consuming enterprises decreased by 6.01%, and the total carbon emissions of the park decreased by 11.35%. With the addition of carbon trading and green certificate trading mechanisms, since the subsidies received by enterprises are related to the total cost of the park, the workshop scheduling results of high-energy-consuming enterprises will also be affected by their carbon emissions. CHP units with lower carbon emissions per unit of energy supply in the park undertook more heating power, and their output power also increased accordingly. The frequency of battery charging and discharging behavior increased, reducing the total carbon emissions in the industrial park. Comparative analysis of the scheduling results shows that the scheduling method of this invention improves the park's enthusiasm for energy conservation and emission reduction, promotes the full utilization of energy in the park, and achieves low-carbon operation of the park.

[0170] This invention establishes a two-tiered optimized scheduling model for industrial parks with high-energy-consuming enterprises, combining carbon trading and green certificate trading mechanisms. This model enables multi-energy complementary operation and full utilization of energy within the park, reducing the overall operating costs. By treating high-energy-consuming enterprises as flexible and controllable resources for energy management and optimized scheduling, the model fully leverages the flexible adjustment capabilities of these enterprises. While reducing production costs and improving production efficiency, it effectively promotes peak shaving and valley filling of the park's load, reduces total carbon emissions, and enhances the economic efficiency of industrial park operations.

Claims

1. A method for optimizing the scheduling of low-carbon industrial parks based on flexible scheduling of enterprise assembly line workshops, characterized in that, Includes the following steps: Step 1: Construct an optimized scheduling model for the industrial park; construct an objective function based on carbon trading costs, green certificate trading costs, and park operating costs; constraints include: power balance constraints, cogeneration generator unit constraints, energy storage equipment constraints, gas boiler constraints, and photovoltaic output constraints; Step 2: Construct a flexible scheduling model for the assembly line workshops of high-energy-consuming enterprises within the industrial park; min f2=μmax(C ijk )+(1-μ)[d e (t)P ws (t)-ζ(C0-C op )] In the formula: f2 is the objective function of the scheduling model, μ is the weight coefficient, and C ijk Let δ be the end time of workpiece i on machine k in process j. e (t) represents the real-time electricity price, P ws (t) represents the electricity consumed by high-energy-consuming enterprises, ζ represents the subsidy coefficient, C0 represents the initial operating cost of the industrial park, and C op For the park's operating costs; Where: M j Let N be the set of available machines for the j-th process, and n be the sum of the number of workpieces allocated to all available machines in each process; jk The number of workpieces processed on machine k in process j; C ijk x ijk ≤S i(j+1)k x i(j+1)k ,i=1,2,L,n;k∈M j+1 C ijk x ijk =S ijk x ijk +P ijk x ijk In the formula: x ijk y ijkt z jkt A value of 0 or 1 indicates whether the workpiece is processed on machine k within a specific time and process; S i(j+1)k S is the start time of workpiece i on machine k in operation j+1. ijk P is the start time of workpiece i on machine k in process j. ijk R is the processing time of workpiece i on machine k in process j. jk Let R be the R-th workpiece processed on machine k in process j. For workpiece R jk The end time on machine k in process j. For workpiece R jk Is it processed on machine k in process j? For workpiece (R+1) jk The start time on machine k in process j, For workpiece (R+1) jk Whether it is processed on machine k in process j; E ijk For the processing energy consumption of workpiece i on machine k in process j, IE jk I represents the standby power consumption per unit time of machine k in process j. jk Let m be the machine k standby time for process j, and m be the number of processes. Step 3: The park optimizes the scheduling based on the enterprise scheduling plan and returns the cost savings from the enterprise scheduling to the enterprise as a subsidy. By using an improved genetic algorithm to iteratively calculate the optimization model, a collaborative scheduling plan for the industrial park and high-energy-consuming enterprises can be obtained.

2. The method for optimizing the scheduling of low-carbon industrial parks based on flexible scheduling of enterprise assembly line workshops according to claim 1, characterized in that, The objective function in step 1 is as follows: In the formula: f1 is the objective function, C op For the park's operating costs, For carbon trading costs, C ge Costs associated with green certificate transactions.

3. The method for optimizing the scheduling of low-carbon industrial parks based on flexible scheduling of enterprise assembly line workshops according to claim 2, characterized in that, The operating costs of the park are as follows: In the formula: T is the scheduling period, P buy (t) represents the electricity purchased by the park, δ e,sell For electricity sales price, P sell (t) represents the electricity sold, δ g For natural gas prices, V g,buy (t) represents the amount of gas purchased; The costs of carbon trading are as follows: Where: ε c U is the carbon trading price coefficient. C For the carbon emissions of the park system, U P This is the initial carbon allowance; The transaction costs for green certificates are as follows: Where: δ ge For the market price of green certificates, ε ge P represents the green certificate quota coefficient for the park. Load (t) represents the electrical load, P ws (t) represents the electrical energy consumed by high-energy-consuming enterprises, P pv (t) represents the output of the photovoltaic unit.

4. The method for optimizing the scheduling of low-carbon industrial parks based on flexible scheduling of enterprise assembly line workshops according to claim 3, characterized in that, The power balance constraints are as follows: P buy (t)+P pv (t)+P CHP (t)=P sell (t)+P ES (t)+P Load (t)+P ws (t) H CHP (t)+H GB (t)=H HS (t)+H Load (t) V g,buy (t)L gas =M GT (t)+M GB (t) In the formula: P CHP (t) represents the power generation capacity of the combined heat and power generator unit, P ES (t) represents the power of the battery, H CHP (t) represents the thermal power of the combined heat and power generator unit, H GB (t) represents the heat production efficiency of the gas-fired boiler, H HS (t) represents the power of the battery storage tank, H Load (t) represents the heat load of the park, V g,buy (t) represents the gas purchase volume, L gas M represents the calorific value of natural gas. GT (t) represents the natural gas consumed by the gas turbine, M GB (t) represents the natural gas consumed by the gas-fired boiler; The constraints of combined heat and power (CHP) generator units are as follows: P CHP (t)=θ GT M GT (t) H GT (t)=P CHP (t)(1-θ GT -θ loss ) / θ GT H CHP (t)=θ rec H GT (t) In the formula: θ GT For the power generation efficiency of a gas turbine, H GT (t) is, θ loss For the heat loss efficiency of the gas turbine, H CHP (t) is, θ rec This refers to the heat recovery efficiency of the waste heat boiler. This is the maximum generating capacity of the combined heat and power (CHP) generator unit. The constraints of energy storage devices are as follows: In the formula: S ES (t) represents the battery's energy storage capacity. This represents the lower limit of the battery's energy storage capacity. S represents the upper limit of the battery's energy storage capacity. HS (t) represents the capacity of the heat storage tank. This is the lower limit of the heat storage tank capacity. This is the upper limit of the heat storage tank capacity. The rated power of the battery, The rated power of the thermal storage tank; The constraints for gas-fired boilers are as follows: H GB (t)=θ GB M GB (t) In the formula: θ GB For the energy conversion efficiency of gas-fired boilers, This represents the lower limit of natural gas consumption for gas-fired boilers. This is the upper limit for the natural gas consumed by a gas-fired boiler; The photovoltaic output constraints are as follows: P wt (t)≤P f,wt (t) In the formula: P f,wt (t) represents the predicted photovoltaic output, P wt (t) represents the photovoltaic power output.

5. The method for optimizing the scheduling of low-carbon industrial parks based on flexible scheduling of enterprise assembly line workshops according to claim 4, characterized in that, The process of calculating and optimizing the scheduling model is as follows: S1: Generate the initial population and calculate the maximum processing time, production cost, and electrical energy consumption of high-energy-consuming enterprises; S2: Calculate the output and total operating cost of each piece of equipment in the industrial park based on the flexible scheduling model; S3: Set the subsidy value to obtain the objective function value of the population and the fitness of individuals; S4: Generate a new population through crossover, mutation, and selection; S5: Determine if the termination condition is met. If not, proceed to step 1. If yes, exit and obtain the scheduling scheme.

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