Reliable Dispatch Method and Device for Low-Carbon Economy in Industrial Parks Considering Electricity Carbon Trading

Abstract

This invention discloses a reliable scheduling method and apparatus for low-carbon economy in industrial parks, considering electricity carbon trading. The method acquires relevant data within the park; constructs a data coupling model of load, enterprises, and the electricity carbon market; constructs a flexible load demand response model and an enterprise operation mathematical model, including a cement plant operation model and a new energy power generation enterprise operation model; constructs a tiered carbon trading cost model for the park; presets power balance constraints and cost constraints; constructs a low-carbon economy scheduling model for the park based on the above models; solves for the scheduling scheme; and performs reliable low-carbon economy scheduling of the industrial park based on the scheduling scheme. This invention solves the technical problems in existing technologies for industrial park scheduling, such as neglecting the electricity carbon trading mechanism, the difficulty in effectively measuring, storing, and tracing enterprise operation and market transaction data, and the high difficulty in tracing and tracking the source of data.

Description

Technical Field

[0001] This invention relates to the field of low-carbon economic dispatching technology for industrial parks, and in particular to a method and apparatus for low-carbon economic dispatching of industrial parks that takes into account electricity carbon trading. Background Technology

[0002] The project aims to jointly regulate high-energy-consuming and high-carbon-emission industrial enterprises, new energy sources, and energy storage within the industrial park. Leveraging the decentralized, transparent, traceable, immutable, and autonomous characteristics of blockchain, it will establish a system for recording and monitoring information on industrial enterprises, loads, wind, solar, and energy storage operations, as well as electricity and carbon trading. This will promote the absorption of renewable energy and encourage high-energy-consuming and high-carbon-emission industrial enterprises to use fuels with high calorific value and low carbon content, thereby achieving energy conservation and emission reduction within the park. Currently, research on low-carbon economic dispatching in industrial parks mainly falls into two categories: the first focuses on the power generation side, primarily addressing the coordinated dispatching of traditional fossil fuel power plants and new energy power generation to promote the efficient absorption of new energy output, thereby reducing the output of traditional fossil fuel power plants and achieving the goal of reducing carbon emissions at the source; the second focuses on the load side, primarily addressing demand-side response by flexibly regulating flexible loads and mobilizing user-side resources to participate in grid dispatching and regulation, indirectly promoting the grid's ability to absorb new energy and contributing to carbon emission reduction.

[0003] Existing low-carbon dispatching methods for industrial parks primarily focus on economic efficiency and carbon emission reduction, incorporating carbon emission costs as part of the overall cost structure. This carbon emission model is relatively simplistic and neglects the impact of the carbon trading market. This is particularly problematic in scenarios where industrial parks contain industrial production enterprises, which, as high-energy-consuming and high-carbon-emission loads, significantly influence carbon emission management. Furthermore, most power dispatching methods focus on the electricity consumption of industrial production enterprises, ignoring carbon emissions from material handling and fossil fuel combustion during production. They fail to consider the impact of different types of fossil fuels on carbon emissions, taking into account their varying calorific values ​​and carbon content. In addition, traditional dispatching methods often rely on operational data reported by enterprises and estimate emissions based on coefficients such as carbon emission factors. This results in discrepancies between the obtained carbon emission data and actual emissions, and lacks mechanisms for real-time monitoring of park operations, transparent data storage, and source tracing. Consequently, the dispatching strategies developed using these methods are less flexible and may lead to significant discrepancies between anticipated and actual operations.

[0004] In the prior art, a comprehensive energy system optimization scheduling method based on tiered carbon trading in industrial parks has a broad definition of industrial users, failing to focus on the production process of specific industrial production units. It ignores the carbon emissions generated by material handling and fossil fuel combustion in specific production processes, and instead uses a rough estimate of the overall carbon emissions of industrial users based on carbon emission factors and other coefficient methods. The resulting carbon emission data has errors compared to the actual carbon emissions. In addition, this method fails to establish a real-time monitoring mechanism for park operation data and a transparent data storage mechanism. During the scheduling operation, it may face situations such as data tampering and data falsification, resulting in economic losses (Chen Peng, Qian Chen, Lan Li, Fu Chen, Lü Fei, Ren Hongbo. Comprehensive Energy System Optimization Scheduling Method Based on Tiered Carbon Trading in Industrial Parks: CN202310599902.0[P]. 2023-08-22.). Summary of the Invention

[0005] This invention provides a reliable scheduling method and apparatus for low-carbon economy in industrial parks that takes into account carbon trading. It is used to solve the technical problems of existing technologies that ignore the comprehensive impact of carbon trading mechanisms, data storage, and different fossil energy sources involved in production on park scheduling, resulting in weak new energy absorption capacity, untraceable data, and inaccurate carbon accounting.

[0006] The objective of this invention is achieved by at least one of the following technical solutions.

[0007] A reliable dispatch method for low-carbon economy in industrial parks considering electricity carbon trading includes the following steps:

[0008] S1. Obtain relevant data information within the park through smart meters and carbon emission meters, including load power, carbon emissions, enterprise electricity consumption and enterprise electricity purchase, as well as the amount of fossil energy purchased and the output of enterprise products uploaded by enterprises themselves.

[0009] S2. Based on the blockchain architecture, organize the on-chain data of load, enterprise production and market transactions, and build a data coupling model of load, enterprise and electricity carbon market;

[0010] S3. Based on load data, relevant on-chain data of enterprise production and market transactions, construct a flexible load demand response model that can be shifted and an enterprise operation mathematical model. The enterprise operation mathematical model includes a cement plant operation model and a new energy power generation enterprise operation model.

[0011] S4. Construct a tiered carbon trading cost model for the target park based on the carbon trading mechanism. The tiered carbon trading cost model includes a carbon emission quota sub-model, a carbon emission sub-model, and a tiered carbon emission trading sub-model.

[0012] S5. Based on preset power balance constraints and preset cost constraints, construct a low-carbon economic scheduling model for the target industrial park according to the data coupling model, the movable flexible load demand response model, the enterprise operation mathematical model, and the tiered carbon trading cost model, solve for the scheduling scheme, and perform reliable low-carbon economic scheduling of the industrial park according to the scheduling scheme.

[0013] Furthermore, the data coupling model is expressed as:

[0014]

[0015] in, These are the datasets uploaded to the blockchain at time t by the i-th cement plant, the k-th new energy power generation enterprise, the flexible load, and the electricity and carbon markets within the park. Let t be the cement output (t) of the i-th cement plant at time t; Let be the electricity consumption (MW) of the cement production of the i-th cement plant at time t; Let t be the amount of electricity (MW) that the i-th cement plant purchases from the upstream power grid at time t; Let T represent the carbon emissions (t) generated by the high-temperature decomposition of carbonates during the calcination of cement raw materials at the i-th cement plant at time t, and the carbon emissions (t) released by fuel combustion. Let t be the amount of carbon emissions transferred from the i-th cement plant when it purchases electricity from the grid at time t. Let t be the amount of coal consumed (t) during the cement production process of the i-th cement plant at time t; Let be the natural gas consumption (m³) during the cement production process of the i-th cement plant at time t. 3 ); Let i be the initial carbon emission allowance for the i-th cement plant; These represent the wind turbine output and wind curtailment power (MW) of the k-th new energy power generation enterprise at time t, respectively. These represent the photovoltaic output and curtailment power (MW) of the k-th renewable energy power generation enterprise at time t, respectively. These represent the charging and discharging power (MW) of the energy storage equipment of the k-th new energy power generation enterprise at time t; Let P be the energy storage capacity (MWh) of the energy storage device of the k-th new energy power generation enterprise at time t; t L P represents the predicted electrical load (MW) for the day before time t; t LDR P represents the electrical load (MW) after participating in demand response at time t; t DR_shift Let t be the transferable electrical load (MW). A positive value indicates that the transferable load is being moved out, and a negative value indicates that the transferable load is being moved in. Let t represent the buyer information, seller information, transaction volume, and transaction time of the a-th transaction occurring in the electricity market at time t, where a = 1, 2, ..., n, and n is the total transaction volume occurring in the electricity market within a trading cycle; Let t represent the buyer information, seller information, transaction volume, and transaction time of the b-th transaction occurring in the carbon market at time t, where b = 1, 2, ..., m, and m is the total transaction volume in the carbon market within a trading cycle.

[0016] Furthermore, based on the load data, a movable flexible load demand response model for the target park is constructed as follows:

[0017]

[0018] Where T is the production cycle; P t L P represents the predicted electrical load (MW) for the day before time t; t DR_shift ,P t LDR Let P be the load that can be shifted at time t, the load that participates in demand response (MW), and the load that participates in demand response. t DR_shift A positive value indicates the transfer of movable load, while a negative value indicates the transfer of electrical load (MW); ω is the demand response correlation coefficient.

[0019] Furthermore, a mathematical model for enterprise operation is constructed based on the enterprise's production and market transaction information. This mathematical model includes a cement plant operation model and a new energy power generation enterprise operation model.

[0020] Furthermore, the cement plant operation model is expressed as follows:

[0021]

[0022] Where T represents the production cycle; Let τ be the maximum cement output (t / h) of the i-th cement plant per unit time; τ be the continuous production time (h). Let t be the cement output (t) of the i-th cement plant at time t; Let be the target output (t) of the i-th cement plant in one production cycle; Let η be the clinker output (t) of the i-th cement plant at time t; i,5 Let be the supply ratio of the i-th cement plant in the cement grinding stage (i.e., the mass of raw materials required to produce one unit mass of product); Let a be the amount of electricity (MW) that the i-th cement plant purchases from the upstream power grid at time t; e ,b e ,c e Parameters for calculating carbon emissions from coal-fired power units; Let be the total carbon emissions (t) of the i-th cement plant during one production cycle; Let T represent the carbon emissions (t) generated by the high-temperature decomposition of carbonates during the calcination of cement raw materials at the i-th cement plant at time t, and the carbon emissions (t) released by fuel combustion. Let t be the amount of carbon emissions transferred from the i-th cement plant when it purchases electricity from the upper-level power grid.

[0023] Furthermore, the operating model of the new energy power generation enterprise is expressed as follows:

[0024]

[0025] in, For the k-th new energy power generation enterprise, the binary decision variables for charging and discharging energy storage equipment at time t; The upper limit of charging and discharging power (MW) for the energy storage equipment of the kth new energy power generation enterprise; e represents the loss coefficient of the energy storage equipment of the k-th new energy power generation enterprise. ES The charge / discharge coefficient of the energy storage device; This represents the upper and lower limits (MWh) of the energy storage capacity of the energy storage equipment of the kth new energy power generation enterprise; Let t be the energy storage capacity (MWh) of the k-th new energy power generation enterprise's energy storage device at time t; τ be the continuous production time (h); and T be the production cycle. Let MVA be the rated capacity of the photovoltaic and wind power generation equipment of the kth new energy power generation enterprise; These represent the photovoltaic output and curtailment power (MW) of the k-th renewable energy power generation enterprise at time t, respectively. These represent the wind turbine output and wind curtailment power (MW) of the k-th new energy power generation enterprise at time t, respectively. These represent the reactive power (Mvar) output by the photovoltaic and wind power generation enterprises at time t, respectively. These represent the charging and discharging power (MW) of the energy storage equipment of the k-th new energy power generation enterprise at time t; The new energy output (MW) of the k-th new energy power generation enterprise at time t.

[0026] Furthermore, a tiered carbon trading cost model for the target industrial park is constructed based on the carbon trading mechanism. The tiered carbon trading cost model includes a carbon emission quota sub-model, a carbon emission sub-model, and a tiered carbon emission trading sub-model.

[0027] Furthermore, the carbon emission quota sub-model is expressed as follows:

[0028]

[0029] Among them, P te_buy I represents the electricity (MW) purchased by the park from the higher-level power grid at time t; CE , i represents the set and index of cement plants, respectively; T represents the production cycle; These are the carbon emission allowances for electricity purchased from the upper-level power grid and for cement plants, respectively. The total carbon emission allowance within the park; Let i be the initial carbon emission allowance for the i-th cement plant; Let t be the cement output (t) of the i-th cement plant at time t; Let r be the clinker output (t) of the i-th cement plant at time t; e This serves as a benchmark for carbon emissions from coal-fired power units. These are the carbon emission benchmarks (tCO2 / t) for cement clinker production and cement grinding, respectively.

[0030] Furthermore, the carbon emission sub-model is expressed as:

[0031]

[0032] Among them, P t e_buy I represents the electricity (MW) purchased by the park from the higher-level power grid at time t; CE , i represents the set and index of cement plants, respectively; T represents the production cycle; These represent the electricity purchased from the upstream power grid and the actual carbon emissions (t) from the cement plant, respectively. Let T represent the carbon emissions (t) generated by the high-temperature decomposition of carbonates during the calcination of cement raw materials at the i-th cement plant at time t, and the carbon emissions (t) released by fuel combustion. The total actual carbon emissions (t) within the park; a e ,b e ,c e These are the parameters for calculating carbon emissions from coal-fired power units.

[0033] Furthermore, the tiered carbon emissions trading sub-model is expressed as follows:

[0034]

[0035]

[0036] in, The actual amount of carbon emission rights traded in the carbon trading market by the park, and the actual amount of carbon emissions. carbon emission allowances The difference in carbon emissions necessitates the adoption of a tiered pricing mechanism to further limit carbon emissions. For tiered carbon trading costs, if If the value is less than 0, it means that the actual carbon emissions are less than the carbon emission allowance, and carbon trading brings revenue to the park; d is the interval length (t); λ is the carbon trading base price (yuan / t); and α is the price growth rate.

[0037] Furthermore, the overall objective function C of the low-carbon economic dispatch model is determined by carbon trading costs. Electricity purchase cost Cement plant operating costs Operating costs of new energy power generation enterprises Composition, expressed as:

[0038]

[0039]

[0040]

[0041]

[0042] in, For tiered carbon trading costs; The cost of purchasing electricity, of which P represents the electricity purchase cost coefficient at time t. t e_buy The electricity purchased by the park from the upper-level power grid at time t (MW); The operating costs of a cement plant include the material costs of cement production and the cost of purchasing fossil fuels; coal_buy ,c gas_buy These are the cost coefficients for purchasing coal and gas, respectively. Let t be the amount of coal consumed (t) during the cement production process of the i-th cement plant at time t; Let be the natural gas consumption (m³) during the cement production process of the i-th cement plant at time t. 3 );c ma This is the material cost coefficient per unit output of cement. Let t be the cement output (t) of the i-th cement plant at time t; The operating costs for new energy power generation companies include the costs of wind and solar curtailment and the operating costs of energy storage equipment; CE , i represents the set and index of cement plants, respectively; T represents the production cycle; K RE k represents the set and index of new energy power generation companies, respectively; These are the penalty coefficients for wind curtailment and solar curtailment, respectively; c ESS This is the operating cost coefficient for energy storage equipment; These represent the wind curtailment power (MW) of the k-th renewable energy power generation enterprise at time t; Let be the curtailed solar power (MW) of the k-th renewable energy power generation enterprise at time t; These represent the charging and discharging power (MW) of the energy storage equipment of the k-th new energy power generation enterprise at time t;

[0043] The preset power balance constraint is an electric power balance constraint, specifically expressed as follows:

[0044]

[0045] Among them, I CE , i represents the set and index of the cement plants, respectively; K RE k represents the set and index of new energy power generation companies, respectively; P t LDR Let t be the electrical load (MW) after participating in demand response; These represent the predicted maximum photovoltaic output power and curtailment power (MW) of the kth renewable energy power generation enterprise at time t, respectively. P represents the predicted maximum output power and wind curtailment power (MW) of the k-th wind turbine unit of the new energy power generation enterprise at time t, respectively; t e_buy The amount of electricity (MW) that the park purchases from the higher-level power grid at time t; These represent the charging and discharging power (MW) of the energy storage equipment of the k-th new energy power generation enterprise at time t; Let be the power consumption (MW) of the i-th cement plant at time t.

[0046] A reliable dispatching system for low-carbon economy in industrial parks considering electricity carbon trading includes:

[0047] The sensing unit is used to acquire relevant data and information on load power, electricity purchases by enterprises, fossil energy purchases, carbon emissions, enterprise product output, and enterprise electricity consumption within the park.

[0048] The first model building unit, based on the blockchain architecture, sorts out the on-chain data of load, enterprise production and market transactions, and builds a data coupling model of load, enterprise and electricity carbon market;

[0049] The second model construction unit constructs a flexible load demand response model and an enterprise operation mathematical model based on load data, relevant on-chain data of enterprise production and market transactions. The enterprise operation mathematical model includes a cement plant operation model and a new energy power generation enterprise operation model.

[0050] The third model building unit is used to build a tiered carbon trading cost model for the target park based on the carbon trading mechanism. This model includes a carbon emission quota sub-model, a carbon emission sub-model, and a tiered carbon emission trading sub-model.

[0051] The park scheduling unit is used to preset power balance constraints and preset cost constraints, construct a low-carbon economic scheduling model for the target park based on the data coupling model, the enterprise operation model, the movable flexible load demand response model and the tiered carbon trading cost model, solve for the scheduling scheme, and perform reliable low-carbon economic scheduling of the industrial park based on the scheduling scheme.

[0052] As can be seen from the above technical solutions, the embodiments of the present invention have the following advantages:

[0053] This invention presents a reliable scheduling method for low-carbon economy in industrial parks, considering electricity carbon trading. The method includes: acquiring load power, carbon emissions, enterprise electricity consumption, and enterprise electricity purchases within the park through smart meters and carbon emission meters; enterprises independently uploading relevant data on fossil energy purchases and enterprise product output; based on a blockchain architecture, organizing the on-chain data of load, enterprise production, and market transactions to construct a data coupling model of load, enterprises, and the electricity carbon market; constructing a flexible load demand response model and an enterprise operation mathematical model based on the on-chain data of load, enterprise production, and market transactions. The enterprise operation mathematical model includes a cement plant operation model and a new energy power generation enterprise operation model. A tiered carbon trading cost model for the target park is constructed based on the carbon trading mechanism, including a carbon emission quota sub-model, a carbon emission sub-model, and a tiered carbon emission trading sub-model; pre-setting power balance constraints and pre-setting cost constraints; constructing a low-carbon economy scheduling model for the park based on the above models; solving for the scheduling scheme; and performing reliable low-carbon economy scheduling of the industrial park according to the scheduling scheme.

[0054] The present invention provides a reliable scheduling method and apparatus for low-carbon economy in industrial parks, considering carbon trading. By introducing a tiered carbon trading mechanism to participate in the coordinated scheduling of sources, loads, and storage, it can effectively limit carbon emissions in the park. Furthermore, by utilizing the decentralized, transparent, traceable, immutable, and autonomous characteristics of blockchain, it enables the storage and verification of information on industrial production enterprises, loads, wind, solar, and storage operations, as well as carbon trading information, within the park. This promotes the consumption of renewable energy and encourages high-energy-consuming and high-carbon-emission industrial production enterprises to use fuels with high calorific value and low carbon content, thereby achieving low-carbon economic operation of the park system. Therefore, the present invention solves the technical problems of existing technologies that neglect carbon trading mechanisms and face difficulties in effectively measuring, storing, and tracing enterprise operation and market transaction data, thus impacting the comprehensive effects on park scheduling. Attached Figure Description

[0055] Figure 1 A flowchart illustrating a reliable scheduling method for low-carbon economy in industrial parks that considers electricity carbon trading, provided in an embodiment of the present invention.

[0056] Figure 2 A schematic diagram of the structure of a reliable dispatching device for a low-carbon economy in an industrial park that considers electricity carbon trading, provided in an embodiment of the present invention.

[0057] Figure 3 This is a schematic diagram of the target park structure provided in an embodiment of the present invention;

[0058] Figure 4 This is a schematic diagram of the data block structure of the target park provided in an embodiment of the present invention;

[0059] Figure 5 This is a schematic diagram illustrating the coupling relationship between park data and blockchain provided in an embodiment of the present invention.

[0060] Figure 6 The target park wind and solar power output and electricity load curves are provided as an application example of the present invention.

[0061] Figure 7 The scheduling power curve of the target park is provided as an application example of the present invention;

[0062] Figure 8 A fossil fuel consumption curve of a target industrial park provided as an application example of the present invention. Detailed Implementation

[0063] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] Example:

[0065] For ease of understanding, such as Figure 1 As shown, an embodiment of the reliable scheduling method for low-carbon economy in industrial parks considering electricity carbon trading provided by the present invention includes:

[0066] Step 1: Obtain the load power, carbon emissions, enterprise electricity consumption and enterprise electricity purchase in the park through smart meters and carbon emission meters. Enterprises independently upload relevant data information on fossil energy purchases and enterprise product output.

[0067] In one embodiment, the structure of the target campus is as follows: Figure 3As shown, enterprise-related information data can include, but is not limited to, enterprise-related production and operation data and transaction data. Specific enterprise operation and transaction information includes cement plant output, average load in production processes, carbon emissions, and fossil fuel consumption. Furthermore, relevant parameter information may also include the upstream grid's electricity purchase price, coal price, natural gas price, carbon price, renewable energy output forecast curves, and electricity load forecast curves. In addition, other relevant data information can be obtained according to actual modeling and analysis needs; this is not limited here and is merely an example.

[0068] Step 2: Based on the blockchain architecture, organize the on-chain data of load, enterprise production and market transactions, and build a data coupling model of load, enterprise and electricity carbon market.

[0069] In one embodiment, the target park's data block structure and data coupling relationship with the blockchain are as follows: Figure 4 and Figure 5 As shown, the data coupling model is further expressed as:

[0070]

[0071] in, These are the datasets uploaded to the blockchain at time t by the i-th cement plant, the k-th new energy power generation enterprise, the flexible load, and the electricity and carbon markets within the park. Let t be the cement output (t) of the i-th cement plant at time t; Let be the electricity consumption (MW) of the cement production of the i-th cement plant at time t; Let t be the amount of electricity (MW) that the i-th cement plant purchases from the upstream power grid at time t; Let T represent the carbon emissions (t) generated by the high-temperature decomposition of carbonates during the calcination of cement raw materials at the i-th cement plant at time t, and the carbon emissions (t) released by fuel combustion. Let t be the amount of carbon emissions transferred from the i-th cement plant when it purchases electricity from the grid at time t. Let t be the amount of coal consumed (t) during the cement production process of the i-th cement plant at time t; Let be the natural gas consumption (m³) during the cement production process of the i-th cement plant at time t. 3 ); Let i be the initial carbon emission allowance for the i-th cement plant; These represent the wind turbine output and wind curtailment power (MW) of the k-th new energy power generation enterprise at time t, respectively. These represent the photovoltaic output and curtailment power (MW) of the k-th renewable energy power generation enterprise at time t, respectively. These represent the charging and discharging power (MW) of the energy storage equipment of the k-th new energy power generation enterprise at time t; Let P be the energy storage capacity (MWh) of the energy storage device of the k-th new energy power generation enterprise at time t;t L P represents the predicted electrical load (MW) for the day before time t; t LDR P represents the electrical load (MW) after participating in demand response at time t; t DR_shift Let t be the transferable electrical load (MW). A positive value indicates that the transferable load is being moved out, and a negative value indicates that the transferable load is being moved in. These represent the buyer information, seller information, transaction volume, and transaction time of the a-th transaction occurring in the electricity market at time t, respectively, and n is the total transaction volume occurring in the electricity market within a trading cycle; Let t represent the buyer information, seller information, transaction volume, and transaction time of the b-th transaction occurring in the carbon market at time t, and m represent the total transaction volume occurring in the carbon market within a trading cycle.

[0072] Step 3: Based on load data, relevant on-chain data of enterprise production and market transactions, construct a flexible load demand response model that can be shifted and an enterprise operation mathematical model. The enterprise operation mathematical model includes a cement plant operation model and a new energy power generation enterprise operation model.

[0073] In one embodiment, the cement plant operation model is expressed as:

[0074]

[0075] The main production processes in a cement plant include raw material crushing, raw meal grinding, fuel grinding, clinker calcination, and cement grinding (production process numbers are j = 1 to 5); J represents the total number of production processes; and T represents the production cycle. Let j be the operating state of production stage j at time t in the i-th cement plant (0-1 variable); Let be the average load (MW) of production stage j in the i-th cement plant; Let be the fixed load (MW) of the i-th cement plant; Let τ be the maximum cement output (t / h) of the i-th cement plant per unit time; τ be the continuous production time (h). Let t be the cement output (t) of the i-th cement plant at time t; Let be the target output (t) of the i-th cement plant in one production cycle; Let η be the clinker output (t) of the i-th cement plant at time t; i,5 Let F be the supply ratio of the i-th cement plant in the cement grinding stage (i.e., the mass of raw materials required to produce one unit mass of product); cc The carbon emission factor in the clinker production process is generally taken as 0.538; h sceThe standard coal consumption required for clinker production is taken as 110 kgce / t; ρ represents the molecular weight ratio of carbon dioxide to carbon, i.e., 44 / 12; Q sce The calorific value of standard coal is taken as 2.931 × 10⁻⁶. -2 TJ / t;Q gas The calorific value of natural gas combustion is taken as 3.559 × 10⁻⁶. -5 TJ / m 3 Q coal The calorific value of coal combustion is taken as 2.149 × 10⁻⁶. -2 TJ / t;U coal The carbon content per unit calorific value of coal is taken as 26.24 tC / TJ; σ coal The carbon oxidation rate of coal is taken as 93%; U gas σ represents the carbon content per unit calorific value of natural gas, taken as 15.3 tC / TJ; gas The carbon oxidation rate of coal is taken as 99%. Let a be the amount of electricity (MW) that the i-th cement plant purchases from the upstream power grid at time t; e ,b e ,c e Parameters for calculating carbon emissions from coal-fired power units; Let be the total carbon emissions (t) of the i-th cement plant during one production cycle; Let T represent the carbon emissions (t) generated by the high-temperature decomposition of carbonates during the calcination of cement raw materials at the i-th cement plant at time t, and the carbon emissions (t) released by fuel combustion. Let t be the amount of carbon emissions transferred from the i-th cement plant when it purchases electricity from the upper-level power grid.

[0076] The operating model of new energy power generation enterprises is expressed as follows:

[0077]

[0078] in, For the k-th new energy power generation enterprise, the binary decision variables for charging and discharging energy storage equipment at time t; The upper limit of charging and discharging power (MW) for the energy storage equipment of the kth new energy power generation enterprise; e represents the loss coefficient of the energy storage equipment of the k-th new energy power generation enterprise. ES The charge / discharge coefficient of the energy storage device; This represents the upper and lower limits (MWh) of the energy storage capacity of the energy storage equipment of the kth new energy power generation enterprise; Let t be the energy storage capacity (MWh) of the k-th new energy power generation enterprise's energy storage device at time t; τ be the continuous production time (h); and T be the production cycle. Let MVA be the rated capacity of the photovoltaic and wind power generation equipment of the kth new energy power generation enterprise; These represent the photovoltaic output and curtailment power (MW) of the k-th renewable energy power generation enterprise at time t, respectively. These represent the wind turbine output and wind curtailment power (MW) of the k-th new energy power generation enterprise at time t, respectively. These represent the reactive power (Mvar) output by the photovoltaic and wind power generation enterprises at time t, respectively. These represent the charging and discharging power (MW) of the energy storage equipment of the k-th new energy power generation enterprise at time t; The renewable energy output (MW) of the k-th renewable energy power generation enterprise at time t;

[0079] In one embodiment, a portion of the flexible electrical load within the target park is modeled as a time-shiftable load and participates in demand response regulation.

[0080] The model for a movable flexible load is expressed as follows:

[0081]

[0082] Where T is the production cycle; P t L P represents the predicted electrical load (MW) for the day before time t; t DR_shift ,P t LDR Let P be the load that can be shifted at time t, the load that participates in demand response (MW), and the load that participates in demand response. t DR_shift A positive value indicates the transfer of movable load, while a negative value indicates the transfer of electrical load (MW); ω is the demand response correlation coefficient.

[0083] Step 4: Construct a tiered carbon trading cost model for the target park based on the carbon trading mechanism. The tiered carbon trading cost model includes a carbon emission quota sub-model, a carbon emission sub-model, and a tiered carbon emission trading sub-model.

[0084] Considering that all the electricity purchased by the industrial park from the upper-level power grid comes from coal-fired power units, the carbon emissions of the industrial park mainly come from cement plants. Therefore, this embodiment uses the baseline method to determine the free carbon emission quota.

[0085] Furthermore, the carbon emission quota sub-model is expressed as:

[0086]

[0087] Among them, P t e_buy Let I be the amount of electricity (MW) that the park purchases from the upper-level power grid at time t. CE , i represents the set and index of cement plants, respectively; T represents the production cycle; These are the carbon emission allowances for electricity purchased from the upper-level power grid and for cement plants, respectively. The total carbon emission allowance within the park; Let i be the initial carbon emission allowance for the i-th cement plant; Let t be the cement output (t) of the i-th cement plant at time t; Let r be the clinker output (t) of the i-th cement plant at time t; e This serves as a benchmark for carbon emissions from coal-fired power units. These are the carbon emission benchmarks (tCO2 / t) for cement clinker production and cement grinding, respectively.

[0088] Furthermore, the carbon emission sub-model is expressed as:

[0089]

[0090] Among them, P t e_buy I represents the electricity (MW) purchased by the park from the higher-level power grid at time t; CE , i represents the set and index of cement plants, respectively; T represents the production cycle; These represent the electricity purchased from the upstream power grid and the actual carbon emissions (t) from the cement plant, respectively. Let T represent the carbon emissions (t) generated by the high-temperature decomposition of carbonates during the calcination of cement raw materials at the i-th cement plant at time t, and the carbon emissions (t) released by fuel combustion. The total actual carbon emissions (t) within the park; a e ,b e ,c e These are the parameters for calculating carbon emissions from coal-fired power units.

[0091] Furthermore, the tiered carbon emissions trading sub-model is expressed as:

[0092]

[0093]

[0094] in, The actual amount of carbon emission rights traded in the carbon trading market by the park, and the actual amount of carbon emissions. carbon emission allowances The difference in carbon emissions necessitates the adoption of a tiered pricing mechanism to further limit carbon emissions. For tiered carbon trading costs, if If the value is less than 0, it means that the actual carbon emissions are less than the carbon emission allowance, and carbon trading brings revenue to the park; d is the interval length (t); λ is the carbon trading base price (yuan / t); and α is the price growth rate.

[0095] Step 5: Set preset power balance constraints and preset cost constraints. Based on the data coupling model, the movable flexible load demand response model, the enterprise operation mathematical model, and the tiered carbon trading cost model, construct the low-carbon economic dispatch model for the target park and solve for the dispatch scheme.

[0096] Furthermore, the overall objective function C of the low-carbon economic dispatch model is determined by carbon trading costs. Electricity purchase cost Cement plant operating costs Operating costs of new energy power generation enterprises Composition, expressed as:

[0097]

[0098]

[0099]

[0100]

[0101] in, For tiered carbon trading costs; The cost of purchasing electricity, of which P represents the electricity purchase cost coefficient at time t. t e_buy The electricity purchased by the park from the upper-level power grid at time t (MW); The operating costs of a cement plant include the material costs of cement production and the cost of purchasing fossil fuels; coal_buy ,c gas_buy These are the cost coefficients for purchasing coal and gas, respectively. Let t be the amount of coal consumed (t) during the cement production process of the i-th cement plant at time t; Let be the natural gas consumption (m³) during the cement production process of the i-th cement plant at time t. 3 );c ma This is the material cost coefficient per unit output of cement. Let t be the cement output (t) of the i-th cement plant at time t; The operating costs for new energy power generation companies include the costs of wind and solar curtailment and the operating costs of energy storage equipment; CE , i represents the set and index of cement plants, respectively; T represents the production cycle; K RE k represents the set and index of new energy power generation companies, respectively; These are the penalty coefficients for wind curtailment and solar curtailment, respectively; c ESS This is the operating cost coefficient for energy storage equipment; These represent the wind curtailment power (MW) of the k-th renewable energy power generation enterprise at time t; Let be the curtailed solar power (MW) of the k-th renewable energy power generation enterprise at time t; Let denoted as , and represent the charging and discharging power (MW) of the energy storage equipment at time t for the k-th new energy power generation enterprise. The preset cost constraints are cost-related parameters calculated based on the tiered carbon trading cost model, used to influence the solution of the low-carbon economic dispatch model. By combining the preset power balance constraints and preset cost constraints to solve the low-carbon economic dispatch model, a dispatch scheme can be obtained. Furthermore, the dispatch scheme specifically includes, but is not limited to, the amount of electricity purchased by the park at each time point, the amount of fossil fuel purchased, the amount of fossil fuel consumed, the output of the energy storage equipment, the output of the cement plant, the demand response load, and the amount of wind and solar power curtailment. Based on the dispatch scheme, low-carbon economic dispatch of the target park can be achieved.

[0102] The preset power balance constraint is an electric power balance constraint, specifically expressed as follows:

[0103]

[0104] Among them, I CE , i represents the set and index of the cement plants, respectively; K RE k represents the set and index of new energy power generation companies, respectively; P t LDR The electrical load (MW) after participating in demand response at time t can be calculated based on the demand response model of a movable flexible load. These are the predicted maximum photovoltaic output power and curtailed power (MW) of the kth renewable energy power generation enterprise at time t, respectively, with curtailed power being an optimizable variable; P represents the predicted maximum output power of the wind turbine unit of the k-th renewable energy power generation enterprise at time t and the curtailment power (MW), respectively, where the curtailment power is an optimizable variable; t e_buy The electricity purchased by the park from the upper-level power grid at time t (MW); These are the charging and discharging power (MW) of the energy storage equipment of the kth new energy power generation enterprise at time t, which can be calculated based on the operation model of the new energy power generation enterprise; Let be the power consumption (MW) of the i-th cement plant at time t, which can be calculated based on the cement plant operation model.

[0105] For ease of understanding, one embodiment provides an application example of a reliable scheduling method for low-carbon economy in industrial parks that considers electricity carbon trading. The method obtains operational transaction information for each enterprise within the park. Relevant operational data for cement plants are shown in Table 1; relevant operational parameters for new energy power generation enterprises are shown in Table 2; electricity purchase and fossil fuel prices are shown in Table 3; carbon trading-related parameters are shown in Table 4; and wind power output, solar power output, electrical load, and heat load prediction curves are shown in Table 5. Figure 6 As shown.

[0106] Table 1. Examples of relevant operational data values ​​for cement plants

[0107]

[0108] Table 2. Examples of Relevant Operational Data Values ​​for New Energy Power Generation Enterprises

[0109]

[0110] Table 3 Electricity Purchase and Fossil Energy Prices

[0111]

[0112] Table 4. Carbon Trading Related Parameters

[0113]

[0114] To fully verify the advancement of the present invention's reliable scheduling method for low-carbon economy in industrial parks that considers electricity carbon trading, in one embodiment, the following three schemes are compared and analyzed:

[0115] Option 1: Without considering carbon trading mechanisms and flexible load demand response, optimize the scheduling of cement plant loads and renewable energy power generation enterprises in the target industrial park;

[0116] Option 2: Consider a fixed-price carbon trading mechanism, without considering flexible load demand response, and optimize the scheduling of cement plant load and renewable energy power generation enterprises in the target industrial park;

[0117] Option 3: Adopt the method provided in the embodiments of the present invention, that is, under the tiered carbon trading mechanism, consider flexible load demand response, and jointly and flexibly regulate the sources, loads and storage in the park to achieve low-carbon economic operation of the target park.

[0118] Table 5 compares the optimization results of Schemes 1, 2, and 3. Please refer to the optimized operation results of the method provided by this invention. Figure 7 and Figure 8 A comparison of the optimization results of the three schemes shows that Scheme 2, based on Scheme 1, adds a fixed carbon price trading mechanism, increasing the use of natural gas in the park and reducing carbon emissions. Scheme 3, based on Scheme 2, improves the fixed-price carbon trading mechanism into a tiered carbon trading mechanism and incorporates the demand response of flexible loads, further reducing coal consumption and park operating costs, promoting the use of natural gas with high calorific value and low carbon content, reducing purchased electricity, promoting the consumption of renewable energy, and promoting the low-carbon, environmentally friendly, and economical operation of the park. In summary, the reliable dispatch method for low-carbon economy in industrial parks that considers electricity carbon trading proposed in this invention has significant advantages in reducing carbon emissions and promoting the consumption of renewable energy. The above results fully demonstrate the effectiveness and advancement of this method.

[0119] Table 5 Comparison of Optimization Results of Three Schemes

[0120]

[0121] The present invention provides a reliable scheduling method for low-carbon economy in industrial parks, considering carbon trading. Utilizing the decentralized, transparent, traceable, immutable, and autonomous characteristics of blockchain, it enables the storage and verification of information on industrial production enterprises, loads, wind, solar, and energy storage operations, as well as carbon trading information within the park. By introducing a tiered carbon trading mechanism to participate in the joint and coordinated scheduling of energy sources, loads, and storage, and considering the different types of fossil fuels used by high-energy-consuming and high-carbon-emission enterprises, it promotes the selection of high-calorific-value, low-carbon-content fossil fuels within the park, effectively limiting carbon emissions and thereby promoting the absorption of new energy sources within the park, achieving low-carbon economic operation of the park system. Therefore, the present invention addresses the technical problems of existing technologies neglecting carbon trading mechanisms and the difficulty in effectively measuring, storing, and tracing enterprise operation and market transaction data, which have a comprehensive impact on park scheduling.

[0122] For ease of understanding, such as Figure 2 As shown, the present invention provides an embodiment of a reliable dispatching device for a low-carbon economy in industrial parks that considers electricity carbon trading, comprising:

[0123] The sensing unit 201 is used to acquire relevant data information on load power, electricity purchases by enterprises, fossil energy purchases, carbon emissions, enterprise product output, and enterprise electricity consumption within the park.

[0124] The first model building unit 202, based on the blockchain architecture, sorts out the on-chain data of load, enterprise production and market transactions, and builds a data coupling model of load, enterprise and electricity carbon market;

[0125] The second model construction unit 203 constructs a flexible load demand response model and an enterprise operation mathematical model based on load data, relevant on-chain data of enterprise production and market transactions. The enterprise operation mathematical model includes a cement plant operation model and a new energy power generation enterprise operation model.

[0126] The third model construction unit 204 is used to construct a tiered carbon trading cost model for the target park based on the carbon trading mechanism. This model includes a carbon emission quota sub-model, a carbon emission sub-model, and a tiered carbon emission trading sub-model.

[0127] The park scheduling unit 205 is used to preset power balance constraints and preset cost constraints, construct a low-carbon economic scheduling model for the target park based on the data coupling model, the enterprise operation model, the movable flexible load demand response model and the tiered carbon trading cost model, and solve for the scheduling scheme.

[0128] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0129] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0130] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0131] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods described in the various embodiments of the present invention through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0132] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A low-carbon economy credible scheduling method for an industrial park considering electricity-carbon trading, characterized in that, Includes the following steps: S1. Obtain relevant data information within the park through smart meters and carbon emission meters, including load power, carbon emissions, enterprise electricity consumption and enterprise electricity purchase, as well as the amount of fossil energy purchased and the output of enterprise products uploaded by enterprises themselves. S2. Based on the blockchain architecture, organize the on-chain data of load, enterprise production and market transactions, and build datasets of load, enterprise and electricity carbon market; S3. Based on load data, relevant on-chain data of enterprise production and market transactions, construct a flexible load demand response model that can be shifted and an enterprise operation mathematical model. The enterprise operation mathematical model includes a cement plant operation model and a new energy power generation enterprise operation model. S4. Construct a tiered carbon trading cost model for the target park based on the carbon trading mechanism. The tiered carbon trading cost model includes a carbon emission quota sub-model, a carbon emission sub-model, and a tiered carbon emission trading sub-model. S5. Based on the preset power balance constraints and preset cost constraints, construct the low-carbon economic scheduling model of the target park according to the dataset, the movable flexible load demand response model, the enterprise operation mathematical model and the tiered carbon trading cost model, solve the scheduling scheme, and perform reliable low-carbon economic scheduling of the industrial park according to the scheduling scheme. 2.The method of claim 1, wherein, The dataset is expressed as follows: in, They are respectively t The first time in the park i The cement plant, the first k Data sets uploaded to the blockchain by various new energy power generation companies, flexible loads, and electricity and carbon markets; for t Time of the first i Cement output of a cement plant; for t Time of the first i Electricity consumption for cement production at a cement plant. for t Time of the first i The amount of electricity purchased by a cement plant from the upper-level power grid; They are respectively t Time of the first i The carbon emissions generated by the high-temperature decomposition of carbonates during the calcination of cement raw materials in a cement plant and the carbon emissions released by fuel combustion; for t Time of the first i The amount of carbon emissions transferred from a cement plant purchasing electricity from the power grid. for t Time of the first i The amount of coal consumed in the cement production process of a cement plant; for t Time of the first i The amount of natural gas consumed during the cement production process at a cement plant; For the first i The initial carbon emission allowance for each cement plant; They are respectively t Time of the first k Wind turbine output and wind curtailment power of individual new energy power generation enterprises; They are respectively t Time of the first k Photovoltaic output and curtailment power of individual new energy power generation enterprises; The first k Energy storage equipment for new energy power generation companies t Constant charging and discharging power; For the first k Energy storage equipment for new energy power generation companies t Energy storage at all times; for t Forecasted electrical load a day prior to the time point; for t Always participate in the electrical load response after demand response; for t The value indicates that the load can be transferred at any time. A positive value indicates that the load can be transferred out, and a negative value indicates that the load can be transferred in. They are respectively t The first time that happens in the electricity market a Buyer information, seller information, transaction volume, and transaction time in this transaction. a =1 , 2 ,…,n , n The total volume of transactions that occur in the electricity market within a trading cycle; They are respectively t The first thing that happens in the carbon market every moment b Buyer information, seller information, transaction volume, and transaction time in this transaction. b =1 , 2 ,…,m , m This represents the total trading volume that occurs in the carbon market within a trading cycle.

3. The reliable scheduling method for low-carbon economy in industrial parks considering electricity carbon trading as described in claim 1, characterized in that, Based on the load data, a movable flexible load demand response model for the target park is constructed as follows: in, T For the production cycle; for t Forecasted electrical load a day prior to the time point; They are respectively t Electrical loads can be shifted at any time and participate in demand response after the load is released. A positive value indicates that a transferable load is being transferred out, while a negative value indicates that an electrical load is being transferred in. This represents the demand response correlation coefficient.

4. The reliable scheduling method for low-carbon economy in industrial parks considering electricity carbon trading as described in claim 1, characterized in that, Based on the enterprise's production and market transaction information, a mathematical model for enterprise operation is constructed, which includes a cement plant operation model and a new energy power generation enterprise operation model. The cement plant operation model is expressed as follows: in, T For the production cycle; For the first i The maximum cement output of a cement plant per unit time. For the duration of continuous production; for t Time of the first i Cement output of a cement plant; For the first i The target output of a cement plant in one production cycle; For the first i A cement plant t Clinker production at any given moment; For the first i The supply ratio of each cement plant in the cement grinding stage; For the first i A cement plant t Electricity purchased from the upper-level power grid at all times; Parameters for calculating carbon emissions from coal-fired power units; For the first i The total carbon emissions of a cement plant during a production cycle; They are respectively t Time of the first i The carbon emissions generated by the high-temperature decomposition of carbonates during the calcination of cement raw materials in a cement plant and the carbon emissions released by fuel combustion; for t Time of the first i The amount of carbon emissions transferred from a cement plant purchasing electricity from the power grid. The operational model for the new energy power generation enterprise is expressed as follows: in, For the first k A new energy power generation company t Binary decision variables for the charging and discharging of energy storage devices at different times; For the first k The upper limit of charging and discharging power of energy storage equipment of a new energy power generation enterprise; For the first k Loss coefficient of energy storage equipment in a new energy power generation enterprise; The charge / discharge coefficient of the energy storage device; For the first k The upper and lower limits of energy storage capacity of energy storage equipment of new energy power generation enterprises; for t Time of the first k Energy storage capacity of energy storage equipment in a new energy power generation enterprise; For the duration of continuous production; T For the production cycle; For the first k The rated capacity of photovoltaic and wind power generation equipment of each new energy power generation enterprise; They are respectively t Time of the first k Photovoltaic output and curtailment power of individual new energy power generation enterprises; They are respectively t Time of the first k Wind turbine output and wind curtailment power of individual new energy power generation enterprises; They are respectively t Time of the first k The reactive power output of photovoltaic and wind power from a new energy power generation company; The first k Energy storage equipment for new energy power generation companies t Constant charging and discharging power; For the first k A new energy power generation company t The constant output of new energy sources.

5. The reliable scheduling method for low-carbon economy in industrial parks considering electricity carbon trading as described in claim 1, characterized in that, The carbon emission quota sub-model is expressed as follows: in, for t The amount of electricity purchased by the industrial park from the higher-level power grid at any given time; These are a collection and an index of cement plants, respectively. T For the production cycle; These are the carbon emission allowances for electricity purchased from the upper-level power grid and for cement plants, respectively. The total carbon emission allowance within the park; For the first i The initial carbon emission allowance for each cement plant; for t Time of the first i Cement output of a cement plant; for t Time of the first i clinker production of a cement plant; This serves as a benchmark for carbon emissions from coal-fired power units. These are the carbon emission benchmarks for cement clinker production and cement grinding, respectively.

6. The reliable scheduling method for low-carbon economy in industrial parks considering electricity carbon trading as described in claim 1, characterized in that, The carbon emission sub-model is expressed as follows: in, for t The amount of electricity purchased by the industrial park from the higher-level power grid at any given time; These are a collection and an index of cement plants, respectively. T For the production cycle; These are the electricity purchased from the upper-level power grid and the actual carbon emissions from the cement plant, respectively. They are respectively t Time of the first i The carbon emissions generated by the high-temperature decomposition of carbonates during the calcination of cement raw materials in a cement plant and the carbon emissions released by fuel combustion; This represents the total actual carbon emissions within the park. These are the parameters for calculating carbon emissions from coal-fired power units.

7. The reliable scheduling method for low-carbon economy in industrial parks considering electricity carbon trading as described in claim 1, characterized in that, The tiered carbon emissions trading sub-model is expressed as follows: in, The actual amount of carbon emission rights traded in the carbon trading market by the park, and the actual amount of carbon emissions. carbon emission allowances The difference in carbon emissions necessitates the adoption of a tiered pricing mechanism to further limit carbon emissions. For tiered carbon trading costs, if A value less than 0 indicates that the actual carbon emissions are less than the carbon emission allowance, and carbon trading brings revenue to the park. d Let be the interval length (t); The base price for carbon trading. This represents the price growth rate.

8. The reliable scheduling method for low-carbon economy in industrial parks considering electricity carbon trading as described in claim 1, characterized in that, The overall objective function of the low-carbon economic scheduling model C Carbon trading costs Electricity purchase cost Cement plant operating costs Operating costs of new energy power generation enterprises Composition, expressed as: in, For tiered carbon trading costs; The cost of purchasing electricity, of which for t The corresponding electricity purchase cost coefficient at any given time; for t The amount of electricity purchased by the industrial park from the higher-level power grid at all times; The operating costs of a cement plant include the material costs of cement production and the cost of purchasing fossil fuels. These are the cost coefficients for purchasing coal and gas, respectively. for t Time of the first i Coal consumption (t) in the cement production process of a cement plant; for t Time of the first i The amount of natural gas consumed during the cement production process at a cement plant; This is the material cost coefficient per unit output of cement. for t Time of the first i Cement output of a cement plant; The operating costs of new energy power generation companies include the costs of wind and solar curtailment and the operating costs of energy storage equipment; These are a collection and an index of cement plants, respectively. T For the production cycle; These are a collection and an index of new energy power generation companies, respectively. These are the penalty coefficients for wind curtailment and solar curtailment, respectively. This is the operating cost coefficient for energy storage equipment; They are respectively t Time of the first k Wind curtailment power of individual new energy power generation enterprises; for t Time of the first k The curtailed power of solar power generation enterprises; The first k Energy storage equipment for new energy power generation companies t Constant charging and discharging power; The preset power balance constraint is an electric power balance constraint, specifically expressed as follows: in, These are a collection and an index of cement plants, respectively. These are a collection and an index of new energy power generation companies, respectively. for t Always participate in the electrical load response after demand response; They are respectively t Time of the first k Predicted maximum photovoltaic output power and curtailment power of individual new energy power generation enterprises; They are respectively t Time of the first k Predicted maximum output power and wind curtailment power of wind turbine units of new energy power generation enterprises; for t The amount of electricity purchased by the industrial park from the higher-level power grid at any given time; The first k Energy storage equipment for new energy power generation companies t Constant charging and discharging power; for t Time of the first i The electricity consumption of a cement plant.

9. A reliable dispatching device for low-carbon economy in industrial parks considering electricity carbon trading, characterized in that, include: The sensing unit is used to acquire relevant data and information on load power, electricity purchases by enterprises, fossil energy purchases, carbon emissions, enterprise product output, and enterprise electricity consumption within the park. The first model building unit, based on the blockchain architecture, sorts out the on-chain data of load, enterprise production and market transactions, and builds datasets of load, enterprise and electricity carbon market; The second model construction unit constructs a flexible load demand response model and an enterprise operation mathematical model based on load data, relevant on-chain data of enterprise production and market transactions. The enterprise operation mathematical model includes a cement plant operation model and a new energy power generation enterprise operation model. The third model building unit is used to build a tiered carbon trading cost model for the target park based on the carbon trading mechanism. This model includes a carbon emission quota sub-model, a carbon emission sub-model, and a tiered carbon emission trading sub-model. The park scheduling unit is used to preset power balance constraints and preset cost constraints, construct a low-carbon economic scheduling model for the target park based on the dataset, the enterprise operation model, the movable flexible load demand response model and the tiered carbon trading cost model, solve for the scheduling scheme, and perform reliable low-carbon economic scheduling of the industrial park according to the scheduling scheme.

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