A high-energy-consumption industrial load optimal scheduling method considering electricity-carbon coordination

Abstract

The application provides a high-energy-consumption industrial load optimal scheduling method considering electric-carbon cooperation, and relates to the field of power system operation and control; the method comprises the following steps: constructing an electric-carbon cooperation optimization model with the minimum total cost as an optimization target based on power market data and carbon emission data; constructing a production scheduling optimization model with the minimum production cost as an optimization target based on production cost data; performing hierarchical optimization and solving the electric-carbon cooperation optimization model and the production scheduling optimization model to obtain an optimal demand response bidding scheme and an optimal production scheduling scheme of a target user in a preset scheduling period; the application can reduce the power consumption cost of an enterprise and realize deep carbon reduction; the hierarchical optimization and solving and the Lagrange relaxation method for processing the coupling constraints between upper and lower layers can balance the solving efficiency and the model accuracy, the calculation explosion of directly solving a large-scale mixed integer nonlinear programming problem is avoided, and the application has good engineering practicability.

Description

Technical Field

[0001] This invention relates to the field of power system operation and control, and in particular to a method for optimizing the scheduling of high-energy-consuming industrial loads that takes into account the coordination of electricity and carbon emissions. Background Technology

[0002] As my country's "dual carbon" goals are being implemented more deeply, a new power system based on new energy sources is being built at an accelerated pace. The proportion of renewable energy generation such as wind power and photovoltaic power is constantly increasing, but their inherent volatility and intermittency pose a severe challenge to the real-time balance of the power system, and the system's "flexibility gap" is widening.

[0003] High-energy-consuming industries (such as electrolytic aluminum, polysilicon, and steel) account for more than 60% of the total electricity consumption of the whole society. Their production processes have significant "thermal energy storage" characteristics. The core production units (such as electrolytic cells and CVD deposition furnaces) store huge amounts of thermal energy, forming natural "thermal batteries", which are highly potential flexible resources on the demand side of the power grid.

[0004] However, existing scheduling models often separate production planning from electricity consumption planning, failing to fully consider the complex constraints of production processes (such as temperature rigidity, continuous production, and material balance), making load regulation schemes infeasible in actual production. Furthermore, there is insufficient coordination between price-based and incentive-based demand responses, making it difficult to minimize enterprise electricity costs. In addition, there are significant shortcomings in solution efficiency and practicality. In recent years, some studies have begun to focus on the potential of industrial loads participating in grid interaction, but most simplify the load as a switchable "black box" unit, ignoring the physicochemical processes and energy coupling relationships within the production process.

[0005] Therefore, there is a need for a method for optimizing the scheduling of high-energy-consuming industrial loads that can deeply integrate production processes and take into account the electricity market and carbon trading mechanisms, so as to achieve the dual goals of minimizing enterprise electricity costs and deeply reducing carbon emissions while ensuring production safety. Summary of the Invention

[0006] This invention provides a method for optimizing the scheduling of high-energy-consuming industrial loads that takes into account the synergy between electricity and carbon emissions, in order to overcome the shortcomings of existing technologies. It achieves deep carbon reduction while reducing the electricity costs of enterprises, and balances solution efficiency and model accuracy. It avoids the computational explosion of directly solving large-scale mixed integer nonlinear programming problems and has good engineering applicability.

[0007] In a first aspect, the present invention provides a method for optimizing the scheduling of high-energy-consuming industrial loads that takes into account the synergy between electricity and carbon emissions, comprising: Acquire target users' electricity market data, carbon emission data, production cost data, and production process data within a preset scheduling cycle; Based on the electricity market data and the carbon emission data, an electricity-carbon co-optimization model is constructed with the goal of minimizing total cost. The electricity-carbon co-optimization model uses the demand response bid amount as the decision variable. Based on the production cost data, a production scheduling optimization model is constructed with the goal of minimizing production costs. The production scheduling optimization model uses production scheduling schemes as decision variables and production process constraints constructed based on the generated process data as constraints. Using the difference between the demand response bid amount and the demand response amount achieved by the production scheduling scheme in each time period within the preset scheduling period as a coupling constraint, the electric carbon co-optimization model and the production scheduling optimization model are solved hierarchically based on the coupling constraint to obtain the optimal demand response bid scheme and the optimal production scheduling scheme for the target user within the preset scheduling period; wherein, the demand response bid scheme represents the set of demand response bid amounts in each time period within the preset scheduling period.

[0008] Secondly, the present invention also provides a high-energy-consuming industrial load optimization scheduling system that takes into account the synergy between electricity and carbon emissions, comprising: The data acquisition module is used to acquire electricity market data, carbon emission data, production cost data, and production process data of the target user within a preset scheduling cycle; The model building module constructs an electricity-carbon co-optimization model with the goal of minimizing total cost based on the electricity market data and the carbon emission data, and the electricity-carbon co-optimization model uses the demand response bid amount as the decision variable; and constructs a production scheduling optimization model with the goal of minimizing production cost based on the production cost data, and the production scheduling optimization model uses the production scheduling scheme as the decision variable and the production process constraints constructed based on the generated process data as the constraint conditions. The model solving module is used to perform hierarchical optimization solutions for the electric carbon co-optimization model and the production scheduling optimization model based on the difference between the demand response bid amount and the demand response amount realized by the production scheduling scheme in each time period within the preset scheduling period as a coupling constraint; wherein, the demand response bid scheme represents the set of demand response bid amounts in each time period within the preset scheduling period.

[0009] Thirdly, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the high-energy-consuming industrial load optimization scheduling method considering the coordination of electricity and carbon as described above.

[0010] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the high-energy-consuming industrial load optimization scheduling method considering the coordination of electricity and carbon as described above.

[0011] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the high-energy-consuming industrial load optimization scheduling method considering the coordination of electricity and carbon as described above.

[0012] According to the specific embodiments provided in this application, the following technical effects are disclosed: This invention provides a method for optimizing the scheduling of high-energy-consuming industrial loads that considers the synergy between electricity and carbon emissions. Based on electricity market data and carbon emission data, this invention constructs a synergistic optimization model with the goal of minimizing total cost. It integrates electricity purchase cost, production adjustment cost, and carbon trading cost into the objective function, achieving deep carbon reduction while lowering enterprise electricity costs. This invention solves the synergistic optimization model and the production scheduling optimization model in a hierarchical manner, and uses the Lagrange relaxation method to handle the coupling constraints between the upper and lower layers. This balances solution efficiency and model accuracy, avoiding the computational explosion of directly solving large-scale mixed-integer nonlinear programming problems, and has good engineering applicability. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0014] Figure 1 This is a flowchart illustrating a method for optimizing the scheduling of high-energy-consuming industrial loads that takes into account the synergy between electricity and carbon emissions, provided by this invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0016] Before providing a further detailed description of the embodiments of this application, the nouns and terms involved in the embodiments of this application will be explained, and the nouns and terms involved in the embodiments of this application shall be interpreted as follows.

[0017] (1) High-energy-consuming industrial loads: These refer to industrial users that consume a large amount of electrical energy during the production process and whose production processes have significant thermal inertia or energy storage characteristics, thus possessing demand-side response potential. Examples include production facilities in industries such as electrolytic aluminum, polysilicon, and steel. These loads are not simple interruptible loads, and their power regulation must strictly follow their complex internal physicochemical laws.

[0018] (2) Electricity-Carbon Co-optimization Model: This refers to a comprehensive optimization model that aims to minimize total operating costs and incorporates various economic and environmental factors, such as electricity purchase costs in multi-stage electricity markets (e.g., medium- and long-term, day-ahead, and real-time), carbon emission costs related to electricity consumption, production adjustment costs, and benefits from participating in demand response. This model seeks to find the optimal balance between economic and environmental benefits.

[0019] (3) Lagrange relaxation method: This is a mathematical method for dealing with optimization problems with coupled constraints. By introducing Lagrange multipliers, the coupled constraints are transformed into penalty terms in the objective function, thereby decomposing the original problem into multiple subproblems that can be solved independently. In this application, this method is used to decouple the complex relationship between upper-level economic decision-making and lower-level physical production scheduling.

[0020] (4) Subgradient method: This is an iterative algorithm for solving non-smooth convex optimization problems. In this application, it specifically refers to a method used to iteratively update the Lagrange multipliers based on the violation of coupling constraints, thereby driving the hierarchical optimization problem to converge to the optimal solution. Through this method, the upper and lower sub-problems can coordinate with each other during the iteration process and jointly approach the global optimal solution.

[0021] In one exemplary embodiment, such as Figure 1 As shown, this application provides a method for optimizing the scheduling of high-energy-consuming industrial loads that considers the synergy between electricity and carbon emissions. This method is executed by computer equipment, specifically by a terminal or server alone, or by both. In this embodiment, the method for optimizing the scheduling of high-energy-consuming industrial loads that considers the synergy between electricity and carbon emissions includes the following steps S101 to S104. Wherein: Step S101: Obtain the target user's electricity market data, carbon emission data, production cost data, and production process data within the preset scheduling cycle, and normalize the obtained data to eliminate the influence of dimensions, and use it as the input variables for the subsequent model.

[0022] Step S102: Based on the electricity market data and the carbon emission data, construct an electricity-carbon co-optimization model with the goal of minimizing total cost. The electricity-carbon co-optimization model uses the demand response bid amount as the decision variable. Step S103: Construct a production scheduling optimization model based on the production cost data with the goal of minimizing production costs. The production scheduling optimization model uses production scheduling schemes as decision variables and production process constraints constructed based on the generated process data as constraints. The production scheduling scheme includes variables such as equipment start-up and shutdown status, production power, temperature status, and material inventory.

[0023] Step S104 uses the difference between the demand response bid amount and the demand response amount achieved by the production scheduling scheme in each time period within the preset scheduling period as a coupling constraint. Based on the coupling constraint, the electric carbon co-optimization model and the production scheduling optimization model are solved in a hierarchical manner to obtain the optimal demand response bid scheme and the optimal production scheduling scheme for the target user within the preset scheduling period.

[0024] In summary, by implementing steps S101 to S104, this invention constructs an electricity-carbon co-optimization model with the goal of minimizing total cost based on electricity market data and carbon emission data. It integrates electricity purchase cost, production adjustment cost, and carbon trading cost into the objective function, achieving deep carbon reduction while lowering enterprise electricity costs. Furthermore, this invention balances solution efficiency and model accuracy by performing hierarchical optimization on the electricity-carbon co-optimization model and the production scheduling optimization model, and by using the Lagrange relaxation method to handle coupling constraints between the upper and lower layers. This avoids the computational explosion of directly solving large-scale mixed-integer nonlinear programming problems, demonstrating good engineering applicability.

[0025] In one embodiment of this application, in step S101, the electricity market data includes the electricity purchase cost of medium- and long-term CFDs, the day-ahead market electricity purchase cost, the real-time deviation cost, the production adjustment cost, and the demand response revenue; the expression of the electricity-carbon co-optimization model is: ; In the formula, Indicates the total cost; This represents the cost of electricity purchased under medium- to long-term contracts for difference (CFDs). This indicates the current day's market electricity purchase cost; Indicates real-time deviation cost; Indicates production adjustment costs; This represents the carbon emission cost calculated based on carbon emission data; This indicates the revenue generated from demand response.

[0026] The formula for calculating the day-ahead market electricity purchase cost is as follows: ; In the formula, This indicates the current market electricity purchase cost. This represents the day-ahead market electricity price for time period t; This indicates the amount of electricity purchased in the day-ahead market during time period t; The real-time deviation cost is used to characterize the deviation between the actual load and the day-ahead purchased electricity. The formula for calculating the real-time deviation cost is as follows: ; In the formula, Indicates real-time deviation cost; This represents the real-time market electricity price for time period t; This represents the actual electricity consumption during time period t; This represents the day-ahead electricity purchases for time period t; The carbon emission data includes carbon trading prices and carbon emission factors, and the formula for calculating the carbon emission cost is as follows: ; In the formula, Indicates carbon emission costs, This represents the carbon trading price for time period t. This represents the carbon emission factor for time period t.

[0027] In one embodiment of this application, step S103, the production process data includes: equipment power, equipment operating status, equipment temperature status, material inventory status, and production task data of the target industrial production equipment at various times within a preset scheduling cycle. The production process constraints constructed based on the above-generated process data include: equipment power constraints, rigid temperature constraints, start-stop constraints, material balance constraints, energy balance constraints, and production task constraints, wherein: The device power constraint is an inherent capability limitation of the device, such as the rated power of a motor; in this embodiment, the device power constraint can be a power balance constraint and a power boundary, where the power balance constraint is... The power boundary is In the formula, This represents the actual electricity consumption during time period t; This represents the power generation capacity during time period t.

[0028] The temperature rigidity constraint refers to the safe range that the equipment temperature must maintain. For example, the temperature of the electrolytic cell must be maintained in the range of 950-970℃, and the temperature of the polysilicon deposition furnace must be maintained in the range of 1100-1200℃. The temperature deviation between the electrolytic cell temperature and the polysilicon deposition furnace temperature should be within the allowable range. Temperature deviation from the allowable range will lead to a decrease in product quality or damage to the equipment.

[0029] The start-up and shutdown constraints include the start-up and shutdown sequence of the equipment and the minimum operating / downtime. Once the equipment is started, it must run continuously for at least tens of hours (the specific duration depends on process requirements); once the core equipment is shut down, it must remain in a stopped state for a sufficiently long time. The minimum operating / downtime is determined by the thermal inertia of the equipment; frequent start-ups and shutdowns will lead to increased energy consumption and shortened equipment lifespan.

[0030] The material balance refers to the strict upstream and downstream supply and demand relationship between each process, and the intermediate product inventory must be maintained within a reasonable range; the energy balance constraint refers to the need to meet the real-time supply and demand balance of different forms of energy such as electricity, heat, and steam.

[0031] The above constraints together constitute the "rigid boundary" of high-energy-consuming industrial loads, and any feasible scheduling scheme must be formulated within this boundary.

[0032] It should be noted that the aforementioned electrocarbon synergistic optimization model has the following constraints: 1. Daily reporting and deviation constraints: In the formula, This represents the amount of electricity purchased in the market during the period t up to the previous day; This indicates the maximum allowable deviation in electrical charge.

[0033] 2. Constraints on demand response / auxiliary service capabilities: In the formula, This represents the actual amount of demand response reduction (the amount of power / output reduction) completed by the production scheduling plan during time period t. These represent the device status during time period t. The maximum adjustable response capability is as follows.

[0034] The reduced demand response amount can be expressed as: ; In the formula, This represents the baseline production capacity when not participating in demand response; This represents the actual power of the equipment during time period t.

[0035] In one embodiment of this application, in step S102, the demand response bidding scheme is the set of demand response bids for each time period within the preset scheduling period; the demand response revenue is the product of the demand response bids within the preset scheduling period and the demand response compensation price. The formula for calculating the demand response revenue is as follows: ; In the formula, T represents the preset scheduling period; This represents the demand response compensation price for time period t; This indicates the demand response bid volume submitted by the target user during time period t. The demand response bid volume refers to the adjustable load capacity submitted by high-energy-consuming industrial enterprises to the power grid or demand response market during each dispatch period.

[0036] In one embodiment of this application, in step S103, the production cost data includes production costs incurred during equipment operation and start-up / shutdown, costs incurred during the production process, and additional costs incurred by adjusting production power or production plans to meet demand response bids; the production scheduling scheme includes variables such as production power, equipment start-up / shutdown status, temperature status, and material inventory. The production scheduling optimization model can be expressed as: ; In the formula, This indicates the costs incurred during equipment operation, start-up, shutdown, and the production process. This refers to the additional costs incurred in adjusting production capacity or production plans to fulfill demand response bids.

[0037] As one embodiment of this application, step S104 involves performing hierarchical optimization solutions for the electro-carbon co-optimization model and the production scheduling optimization model based on the coupling constraints, including steps S201 to S205. Wherein: Step S201: Based on the electric carbon co-optimization model and the production scheduling optimization model, construct a hierarchical architecture that combines upper-level optimization and lower-level optimization.

[0038] Step S202: Construct a penalty term based on the Lagrange multipliers and the coupling constraints, and construct a Lagrange relaxation function that includes the penalty term and the electrocarbon co-optimization model.

[0039] In step S202 of this application embodiment, the coupling constraint can be expressed as: ; In the formula, Let t represent the coupling constraint for time period t, x represent the production scheduling scheme determined by the lower-level production scheduling optimization model, and y represent the demand response bidding scheme determined by the upper-level electricity-carbon collaborative optimization model. This represents the demand response bid amount for time period t determined by the upper-level electricity-carbon co-optimization model. The demand response bid amount serves as a constraint for the lower-level optimization. This constraint indicates that the actual load scheduling capacity provided by the lower-level production scheduling scheme must not be lower than the demand response bid amount declared by the upper level in the corresponding time period. This represents the maximum demand response capability that the lower-level production scheduling scheme x can actually provide under the condition that the production process constraints are met.

[0040] Furthermore, the coupling constraints are multiplied by the corresponding Lagrange multipliers and summed to obtain the Lagrange penalty term, which can be expressed as: ; In the formula, Represents the Lagrange multiplier for time interval t.

[0041] To quantify coupling constraints, this application introduces nonnegative relaxation variables. Represents the amount of unfulfilled demand response during time period t, a non-negative slack variable. The calculation formula is: ; When the production scheduling plan can meet the maximum demand response capacity ,at this time, When the production scheduling scheme cannot fully meet the maximum demand response capacity, Its value This indicates the amount of unfulfilled demand responses that could not be completed.

[0042] Based on the above nonnegative relaxation variables The Lagrange penalty term can be expressed as .

[0043] Let the target user's demand response bid amount in time period t be... Specifically, it includes load capacity that can be reduced and load capacity that can be transferred, which consists of the demand response bid volume for each time period; whereby the demand response bid volume represents the application volume for a single time period, and the demand response bid scheme represents the collection of bid volumes for each time period throughout the entire scheduling cycle.

[0044] Demand response bid volume through demand response benefit items Enter the electro-carbon co-optimization model. Let... Let t be the actual amount of demand response completed by the target user during time period t. Then, the actual demand response revenue is: Among them, actual demand response volume satisfy: Assuming that all demand response capacities submitted by the target users are successfully bid on and can be fully fulfilled, then... .

[0045] According to the aforementioned electricity-carbon co-optimization model, while increasing the demand response bid quantity can improve demand response benefits, it may also lead to increased production power adjustment range, equipment start-up and shutdown costs, actual deviation costs, and penalty costs for failing to fulfill response commitments. Therefore, the demand vector bid quantity must also meet the constraints of the actual response capacity provided by the production scheduling scheme. ; in, This represents the maximum demand response capability that an enterprise can actually provide under the production scheduling scheme, provided that production process constraints such as equipment operation, temperature safety, material balance, and energy are met.

[0046] Because there is a discrepancy between the upper-level demand response bid quantity and the actual demand response capacity that the lower-level production scheduling scheme can provide, this application uses a Lagrange penalty term as a penalty cost to guide the upper and lower level decisions to gradually satisfy the coupling constraints in order to measure the discrepancy between the two levels. Therefore, based on the above Lagrange penalty term, the Lagrange relaxation function constructed by adding the penalty term to the electric carbon co-optimization model can be expressed as: ; in, , Let represent the production scheduling scheme determined based on the demand response bidding scheme y; therefore, the upper-level optimization problem can be expressed as: .therefore, Let y represent the total enterprise cost corresponding to demand response bidding scheme y, where y is the upper-level demand response bidding scheme. This represents the optimal production scheduling scheme obtained under this bidding proposal. The upper-level particle swarm optimization algorithm searches for different demand response bidding schemes y and calls the lower-level model to solve for the corresponding schemes. Calculate the corresponding bid proposals. Finally, the demand response bidding scheme that minimizes the company's total cost was determined.

[0047] In summary, the steps for determining the demand response bidding scheme and production scheduling scheme are as follows: First, the upper-level optimization provides a set of demand response bidding schemes (including the demand response bidding quantities for each time period); second, this demand response bidding scheme is input as a fixed parameter into the lower-level model, and a lower-level mixed-integer linear programming model is established under constraints such as equipment power boundaries, start-stop logic, minimum running time, temperature safety boundaries, material balance, and energy balance. Then, the branch and bound method is used to solve the lower-level model to obtain the optimal production scheduling scheme under the given demand response bidding scheme. Finally, based on the optimal production scheduling scheme, the actual electricity load, available demand response capacity, production scheduling cost, and coupling constraint violation amount for each time period of the enterprise are calculated. The comprehensive cost obtained from the lower-level solution is fed back to the upper level as the fitness evaluation result of the demand response bidding scheme. The upper level then adjusts the demand response bidding amount for each time period based on the feedback results until the demand response bidding scheme with the minimum comprehensive cost and the corresponding production scheduling scheme are obtained. Therefore, the demand response bidding scheme, on the one hand, limits the feasible domain of the lower-level production scheduling scheme through demand response performance constraints, and on the other hand, affects the lower-level optimization results through production adjustment costs and non-performance penalty costs, thereby achieving collaborative optimization between the upper-level demand response bidding scheme and the lower-level production scheduling decision.

[0048] Step S203: Taking into account the penalty term, solve the production scheduling optimization model to obtain the production scheduling scheme. The specific steps include: First, the penalty term is added to the production scheduling optimization model to obtain the lower-level objective function of the hierarchical architecture, which can be expressed as: ; Secondly, the lower-level objective function is solved using a mixed-integer linear programming solver (MILP solver) with branch and bound to obtain the production scheduling scheme.

[0049] Step S204: Solve the Lagrange relaxation function according to the production scheduling scheme to obtain the demand response bidding scheme; this embodiment uses the particle swarm optimization algorithm to solve the Lagrange relaxation function, specifically including the following steps S301 to S303. Wherein: Step S301: Set the particle swarm size, where the particle swarm size represents the number of particles in the particle swarm; in the particle swarm, each particle represents a set of demand response bidding schemes, the position of the particle represents the demand response bidding amount in each time period, and the velocity of the particle represents the adjustment direction of the demand response bidding scheme. Step S302: Perform a loop optimization step based on the particle swarm optimization; wherein the loop optimization step includes: The Lagrange relaxation function is solved based on the demand response bid volume.

[0050] The position and velocity of the particle are updated.

[0051] The position update formula for the particle is as follows: ; The velocity update formula for the particle is: ; In the formula, , Let represent the velocity and position of the i-th particle in the particle swarm at the k-th iteration, respectively; This represents the inertia weight, used to balance global search capability and local search capability; , where represents the learning factor, and represents the step size of an individual towards the local optimal particle and the global optimal particle, respectively; Use random numbers in the range [0,1] to increase the randomness of the search; , Let represent the individual optimal position and the global optimal position of the i-th particle, respectively.

[0052] Step S303: Repeat the loop optimization steps until the number of loops reaches the preset condition.

[0053] Step S205: Repeat the steps of generating the scheduling optimization model and solving the Lagrange relaxation function to achieve coordinated convergence of the upper-level and lower-level optimizations, thereby obtaining the optimal demand response bidding scheme and the optimal production scheduling scheme. The specific steps are as follows: Step 1: Initialize the particle swarm and Lagrange multipliers. Each particle in the particle swarm represents a set of demand response bidding schemes. Step 2: For each particle in the particle swarm, call the MILP solver to solve the lower-level scheduling optimization model to obtain the production plan and the corresponding production cost; Step 3: Substitute the demand response bidding scheme corresponding to each particle into the upper-level Lagrange relaxation function to calculate the fitness value represented by each particle, where the fitness value is the total cost of the enterprise. In steps two and three, in the k-th iteration, the demand response bidding scheme for particle i in the k-th iteration is... First, the lower-level production scheduling optimization model is invoked to solve for the demand response bidding scheme. Corresponding production scheduling scheme Then, the fitness value is calculated based on the upper-level Lagrange relaxation function. The calculation formula is as follows: ; In the formula, Let i represent the total cost of the enterprise corresponding to the current demand response bidding proposal represented by particle i; Indicates the penalty item, where: Denotes the Lagrange multipliers in the k-th iteration; This represents the demand response bidding scheme for particle i during time period t in the k-th iteration; Indicates production scheduling scheme The actual completed demand vector. Since this invention aims to minimize the total enterprise cost, the smaller the fitness value, the better the demand response bidding scheme represented by the particle.

[0054] Step 4: Update the individual optimal position and global optimal position of particle i. The formula for updating the individual optimal position of particle i is: ; In the above update formula, when particle i obtains its fitness value in the k-th iteration... The fitness value is less than the value obtained in the previous (k-1) iteration. season Otherwise, let Next, after updating the individual optimal positions of all particles, the position of the particle with the smallest fitness value among the individual optimal positions is selected as the current global optimal position. Then the global optimal position is .

[0055] It should also be noted that the particle swarm optimization algorithm determines whether to end the iteration based on the following conditions: The current iteration count m has reached the preset maximum iteration count M; Alternatively, the change in the global optimal fitness over L consecutive iterations is less than a preset threshold.

[0056] It is understandable that when any of the above conditions are met, the particle swarm iteration stops and the current global optimal position is taken as the optimal demand response bidding scheme. When all of the above conditions are met, a new demand response bidding scheme is generated according to the velocity update formula and the position update formula, and the next iteration begins.

[0057] Step 5: Update the Lagrange multipliers according to the coupling constraints. It should be noted that this embodiment updates the Lagrange multipliers using the subgradient method; the update formula for the Lagrange multipliers is: ; In the formula, This represents the step size of the k-th iteration; This represents a coupling constraint. The update formula above indicates that if the coupling constraint... If the value is greater than 0 (coupling constraints are not satisfied), then the Lagrange multiplier is greater than 0. Increase the penalty for violating the rules; conversely, decrease the penalty.

[0058] Step 6: Repeat steps 2 to 5 until the amount of coupling constraint violation is less than the preset tolerance and the change in total enterprise cost for a preset number of consecutive iterations is less than the preset threshold; or the number of iterations reaches the maximum number of iterations; output the optimal demand response bidding scheme and the optimal production scheduling scheme that satisfy the coupling constraint tolerance and have the lowest total enterprise cost during the historical iteration process.

[0059] Based on the same inventive concept, this application provides a high-energy-consuming industrial load optimization scheduling system that takes into account the synergy between electricity and carbon emissions. The high-energy-consuming industrial load optimization scheduling system that takes into account the synergy between electricity and carbon emissions provided by this invention is described below. The high-energy-consuming industrial load optimization scheduling system that takes into account the synergy between electricity and carbon emissions described below can be referred to in correspondence with the high-energy-consuming industrial load optimization scheduling method that takes into account the synergy between electricity and carbon emissions described above.

[0060] A high-energy-consuming industrial load optimization scheduling system considering electricity-carbon synergy includes a data acquisition module, a model building module, and a model solving module. Wherein: The data acquisition module is used to acquire electricity market data, carbon emission data, production cost data, and production process data of the target user within a preset scheduling cycle; The model building module constructs an electricity-carbon co-optimization model with the goal of minimizing total cost based on the electricity market data and the carbon emission data, and the electricity-carbon co-optimization model uses the demand response bid quantity as the decision variable; and constructs a production scheduling optimization model with the goal of minimizing production cost based on the production cost data, and the production scheduling optimization model uses the production scheduling scheme as the decision variable and the production process constraints constructed based on the generated process data as the constraint conditions. The model solving module is used to perform hierarchical optimization solutions for the electric carbon co-optimization model and the production scheduling optimization model based on the difference between the demand response bid amount and the demand response amount realized by the production scheduling scheme in each time period within the preset scheduling period as a coupling constraint; wherein, the demand response bid scheme represents the set of demand response bid amounts in each time period within the preset scheduling period.

[0061] In one exemplary embodiment, this application also provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0062] In one exemplary embodiment, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0063] In one exemplary embodiment, this application provides a computer program product including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0064] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0065] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0066] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0067] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0068] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for optimizing the scheduling of high-energy-consuming industrial loads, taking into account the synergy between electricity and carbon emissions, characterized in that, include: Acquire target users' electricity market data, carbon emission data, production cost data, and production process data within a preset scheduling cycle; Based on the electricity market data and the carbon emission data, an electricity-carbon co-optimization model is constructed with the goal of minimizing total cost. The electricity-carbon co-optimization model uses the demand response bid amount as the decision variable. Based on the production cost data, a production scheduling optimization model is constructed with the goal of minimizing production costs. The production scheduling optimization model uses production scheduling schemes as decision variables and production process constraints constructed based on the generated process data as constraints. Using the difference between the demand response bid amount and the demand response amount achieved by the production scheduling scheme in each time period within the preset scheduling cycle as a coupling constraint, the electric carbon co-optimization model and the production scheduling optimization model are solved hierarchically based on the coupling constraint to obtain the optimal demand response bid scheme and the optimal production scheduling scheme for the target user within the preset scheduling cycle. Among them, the demand response bidding scheme represents the set of demand response bidding quantities for each time period within the preset scheduling cycle.

2. The method for optimizing the scheduling of high-energy-consuming industrial loads considering electricity-carbon synergy as described in claim 1, characterized in that, The electricity market data includes the purchase cost of electricity under medium- and long-term contracts for difference (CFDs), the purchase cost of electricity in the day-ahead market, the real-time deviation cost, the production adjustment cost, and the demand response revenue; the expression of the electricity-carbon co-optimization model is: ； In the formula, Indicates the total cost; This represents the cost of electricity purchased under medium- to long-term contracts for difference (CFDs). This indicates the current day's market electricity purchase cost; Indicates real-time deviation cost; Indicates production adjustment costs; This represents the carbon emission cost calculated based on carbon emission data; This indicates the revenue generated from demand response.

3. The method for optimizing the scheduling of high-energy-consuming industrial loads considering electricity-carbon synergy as described in claim 2, characterized in that, The demand response revenue is the product of the demand response bid volume and the demand response compensation price within the preset scheduling period.

4. The method for optimizing the scheduling of high-energy-consuming industrial loads considering electricity-carbon synergy as described in claim 1, characterized in that, The production cost data includes production costs incurred during equipment operation, start-up and shutdown, costs incurred during the production process, and additional costs incurred by adjusting production power or production plans to meet demand response bids; the production scheduling scheme includes production power and equipment start-up and shutdown status.

5. The method for optimizing the scheduling of high-energy-consuming industrial loads considering electricity-carbon synergy as described in claim 1, characterized in that, Based on the aforementioned coupling constraints, a hierarchical optimization solution is performed on the electro-carbon co-optimization model and the production scheduling optimization model, including: Based on the aforementioned electricity-carbon co-optimization model and the aforementioned production scheduling optimization model, a hierarchical architecture combining upper-level optimization and lower-level optimization is constructed. Based on the Lagrange multipliers and the coupling constraints, a penalty term is constructed, and a Lagrange relaxation function including the penalty term and the electric carbon co-optimization model is constructed. Taking into account the penalty term, the production scheduling optimization model is solved to obtain the production scheduling scheme; Based on the production scheduling scheme, the Lagrange relaxation function is solved to obtain the demand response bidding scheme; The generation and scheduling optimization model and the solution steps of the Lagrange relaxation function are repeated cyclically to achieve coordinated convergence of the upper-level optimization and the lower-level optimization, so as to obtain the optimal demand response bidding scheme and the optimal production scheduling scheme.

6. The method for optimizing the scheduling of high-energy-consuming industrial loads considering electricity-carbon synergy as described in claim 5, characterized in that, The step of solving the Lagrange relaxation function according to the production scheduling scheme includes: A particle swarm size is defined, which represents the number of particles in the particle swarm; in the particle swarm, each particle represents a set of demand response bidding schemes, the position of the particle represents the demand response bidding amount in each time period, and the velocity of the particle represents the adjustment direction of the demand response bidding scheme. A cyclic optimization step is performed based on the particle swarm optimization; wherein the cyclic optimization step includes: Solve for the Lagrange relaxation function based on the demand response bid volume; Update the position and velocity of the particle; The loop optimization steps are executed repeatedly until the number of iterations reaches the preset condition.

7. The method for optimizing the scheduling of high-energy-consuming industrial loads considering electricity-carbon synergy according to any one of claims 1-6, characterized in that, The production process data includes: equipment power, equipment operating status, equipment temperature status, material inventory status, and production task data of the target industrial production equipment at each time period within the preset scheduling cycle; the production process constraints include: equipment power constraints, temperature rigid constraints, start-stop constraints, material balance constraints, energy balance constraints, and production task constraints.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the high-energy-consuming industrial load optimization scheduling method that takes into account the coordination of electricity and carbon as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the high-energy-consuming industrial load optimization scheduling method that takes into account the coordination of electricity and carbon as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the high-energy-consuming industrial load optimization scheduling method that takes into account the coordination of electricity and carbon as described in any one of claims 1 to 6.

Images