Comprehensive energy low-carbon operation optimization method, system and device

Abstract

The application discloses a kind of integrated energy low carbon operation optimization method, system and device, it is related to energy optimization technical field, it solves the technical problems that the existing technology often uses fixed global carbon emission factor calculation and constraint carbon emission, so that the quantitative result of carbon emission is seriously decoupled with the actual power flow of power grid and new energy space-time distribution, cannot reflect the carbon emission intensity difference of different nodes, different time, lead to the accuracy of low carbon optimization result is lower;By constructing space-time dynamic carbon flow distribution matrix based on multi-source heterogeneous data, and based on this, a double-objective optimization model is constructed combining multi-source heterogeneous data;Generation scheduling scheme based on double-objective optimization model, realize the strong coupling of carbon emission and power grid physical flow, acutely capture the carbon potential space-time difference caused by new energy fluctuation, to accurately identify high carbon period and low carbon node at the scheduling level, avoid invalid scheduling caused by carbon emission responsibility definition ambiguity, improve the accuracy of low carbon operation optimization.

Description

Technical Field

[0001] This application belongs to the field of energy optimization technology, specifically a comprehensive energy low-carbon operation optimization method, system and device. Background Technology

[0002] Driven by both energy structure transformation and dual carbon objectives, the integrated optimization of low-carbon energy operation through the synergy of green electricity consumption and carbon flow tracking has become a key path for building a new type of power system. Green electricity consumption aims to maximize the absorption of renewable energy sources such as wind and solar power through optimized dispatching, market mechanisms, and technological means, addressing the grid connection challenges caused by their volatility and intermittency. It is a core component in achieving clean energy substitution. Carbon flow tracking, relying on digital technology, accurately measures and traces the generation, transmission, and consumption of carbon emissions throughout the entire power system chain, providing a data foundation for clarifying carbon responsibilities and establishing a fair and effective carbon market. The synergistic operation of these two technologies not only guides the power system to prioritize the consumption of green electricity in a more economical and efficient manner, reducing carbon emissions at the source, but also incentivizes users to actively participate in low-carbon energy use through transparent and traceable carbon footprints, forcing green transformation in industries. It is a crucial engine driving the evolution of the energy system towards a clean, low-carbon, safe, and efficient future.

[0003] Existing technologies often use fixed global carbon emission factors or simple static emission coefficients to calculate and constrain carbon emissions, which makes the quantitative results of carbon emissions severely decoupled from the actual power flow of the power grid and the spatiotemporal distribution of new energy sources. This fails to reflect the differences in carbon emission intensity at different nodes and times, resulting in low accuracy of low-carbon optimization results. Therefore, further improvements are still needed for the optimization methods of integrated energy low-carbon operation. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a comprehensive energy low-carbon operation optimization method, system, and device to solve the technical problem that the prior art often uses a fixed global carbon emission factor to calculate and constrain carbon emissions, which makes the quantitative results of carbon emissions seriously decoupled from the actual power flow of the power grid and the spatiotemporal distribution of new energy sources. This fails to reflect the differences in carbon emission intensity at different nodes and at different times, resulting in low accuracy of low-carbon optimization results.

[0005] To achieve the above objectives, the first aspect of this application provides a comprehensive energy low-carbon operation optimization method, comprising: Acquire multi-source heterogeneous data; the multi-source heterogeneous data includes meteorological data, power grid topology data, equipment operating parameters, and load data; A spatiotemporal dynamic carbon flow distribution matrix was constructed based on multi-source heterogeneous data; A dual-objective optimization model is constructed based on the spatiotemporal dynamic carbon flow distribution matrix and multi-source heterogeneous data; A scheduling scheme is generated based on a dual-objective optimization model.

[0006] Through the above steps, this application achieves strong coupling between carbon emissions and power grid physical flow, enabling the optimization model to keenly capture the spatiotemporal differences in carbon potential caused by new energy fluctuations. This allows for accurate identification of high-carbon periods and low-carbon nodes at the scheduling level, effectively avoiding ineffective scheduling caused by ambiguous carbon emission responsibility definitions, and improving the accuracy and actual carbon reduction benefits of optimizing the low-carbon operation of the integrated energy system.

[0007] Furthermore, the construction of the spatiotemporal dynamic carbon flow distribution matrix based on multi-source heterogeneous data includes: Extract power grid topology data, meteorological data, and load data from multi-source heterogeneous data; A power grid topology map is constructed based on power grid topology data; the power grid topology map includes a set of nodes and a set of lines; the set of nodes consists of several nodes, including power generation nodes, load nodes, and energy storage nodes; the set of lines consists of the connection relationships between several nodes. After normalizing the meteorological data, it is input into the wind and solar power generation prediction model to obtain the zero-carbon predicted output corresponding to the meteorological data; the wind and solar power generation prediction model is a model trained by a neural network model using historical meteorological data and historical zero-carbon output. Extract the predicted load corresponding to several nodes in the load data; The net load corresponding to the node is obtained by performing a difference calculation between the predicted load corresponding to the node and the zero-carbon predicted output. The net load vector is determined based on the net load of several nodes; Branch power flow distribution matrices are obtained by using the forward-backward substitution method on the power grid topology and net load vector; the elements in the branch power flow distribution matrix... It represents the active power flowing from node j to node i at time t; Calculate the nodal carbon potential based on the branch power flow distribution matrix; When an element in the branch power flow distribution matrix is ​​0, the carbon flow corresponding to the corresponding position in the spatiotemporal dynamic carbon flow distribution matrix is ​​set to 0. Otherwise, the carbon flow rate corresponding to the corresponding position in the spatiotemporal dynamic carbon flow distribution matrix is ​​calculated by multiplying the element in the branch power flow distribution matrix with the carbon potential of the node; the carbon flow rate is expressed as the number of kilograms of carbon emissions corresponding to the electricity transmitted between adjacent nodes at time point t.

[0008] Furthermore, the calculation of nodal carbon potential based on the branch power flow distribution matrix includes: Obtain the carbon emission factor and branch power flow distribution matrix of the upstream power grid; When the node type corresponding to a node in the branch power flow distribution matrix is ​​a source node, the node carbon potential corresponding to the node is set as the carbon emission factor of the upstream power grid. When the node type corresponding to a node in the branch power flow distribution matrix is ​​a non-source node, the node carbon potential corresponding to that node is calculated using the formula; the formula satisfies: Where i and j both represent the node numbers; Represented as elements in the branch power flow distribution matrix; It is represented as the set of all upstream nodes j that point to node i; This is represented as the node carbon potential corresponding to the j-th node at time point t. It is represented as the node carbon potential corresponding to the i-th node at time t.

[0009] This application first constructs a directed topology graph containing power sources, loads, and energy storage nodes based on power grid topology data. Normalized meteorological data is then input into a wind and solar power prediction model trained by a neural network to obtain zero-carbon power output. This is followed by calculation of the net load vector reflecting the real-time supply-demand imbalance using node-predicted loads. Subsequently, a forward-backward substitution method is used to calculate the power flow of the topology graph, generating a branch power flow distribution matrix. Node carbon potential is then recursively calculated based on the proportional sharing principle. Finally, the spatiotemporal dynamic carbon flow distribution matrix is ​​filled based on the product relationship between branch power flow and node carbon potential. This approach abandons the extensive model of fixed emission coefficients and utilizes the power distribution characteristics of the physical power grid to achieve dynamic decoupling of carbon flow in the time and space dimensions. It accurately reflects the nonlinear impact of renewable energy fluctuations and network congestion on local carbon emission intensity, solving the problem of ambiguous carbon emission responsibility definition and providing a high-fidelity carbon flow data foundation for subsequent optimization.

[0010] Furthermore, the dual-objective optimization model includes an economic cost optimization function and a carbon emission optimization function; The economic cost optimization function is expressed as the sum of electricity purchase cost, gas cost, operation and maintenance cost, and wind and solar curtailment penalty cost at all time points within the time period; The cost of electricity purchase satisfy: ; This is expressed as the power exchanged by the power grid at time point t. This is represented as the electricity price at time point t; Gas cost satisfy: ; This is expressed as the gas turbine output power at time point t; Expressed as gas turbine power generation efficiency; This is expressed as the lower heating value of natural gas; Expressed as natural gas price; The operation and maintenance costs satisfy: ; This is expressed as the output power of the m-th device at time t. This represents the unit maintenance cost corresponding to the m-th device; The carbon emission optimization function satisfy: ; This is represented as the power of wind and solar power curtailed at time point t. Expressed as the unit price for power waste penalty, it refers to the penalty coefficient set for each unit of electricity wasted in order to encourage the consumption of green electricity; The carbon emission optimization function satisfy: Where i and j represent node numbers, and N represents the total number of nodes; It represents the carbon flow rate of the branch between node i and node j at time t in the spatiotemporal dynamic carbon flow distribution matrix; T represents the total number of time points within the time period.

[0011] This application constructs a dual-objective optimization model that deeply integrates economic efficiency and low carbon emissions. It quantifies total expenditure into a composite economic cost function that includes electricity purchase, gas consumption, equipment operation and maintenance, and wind and solar curtailment penalties. The spatiotemporal dynamic carbon flow distribution matrix is ​​used as the core input, and the total carbon emissions are defined as the spatiotemporal sum of carbon flows in all branches of the entire network. This not only achieves refined measurement of the costs of different energy types, but more importantly, it breaks the limitation of carbon emissions being used as an external coefficient in traditional optimization. By using the spatiotemporal dynamic carbon flow distribution matrix to strongly correlate carbon emissions with the physical power flow of the power grid, the optimization process can accurately identify high-carbon periods and low-carbon nodes. Thus, while ensuring economic operation, it achieves the most accurate quantification and control of real physical carbon emissions, effectively avoiding the risk of false low-carbon scheduling caused by carbon emission model distortion.

[0012] Furthermore, the constraints of the dual-objective optimization model consist of physical constraints and carbon flow constraints; The physical constraints include energy balance constraints, equipment operation constraints, and green electricity consumption rate constraints. The energy balance constraints include electrical balance constraints and thermal balance constraints; The equipment operation constraints include upper and lower limit constraints and energy storage constraints; The green energy absorption rate constraint satisfies: ;in, This represents the total amount of new energy actually consumed at time t. It is expressed as the total amount of new energy power generation at time t; This is represented as the minimum green energy consumption rate threshold; The carbon flow constraint condition refers to the condition that the total carbon flow out of a node is equal to the total carbon emissions out of the node.

[0013] Furthermore, the generation of the scheduling scheme based on the dual-objective optimization model includes: Extract the dual-objective optimization model and constraints; The dual-objective model is transformed using the weight coefficient transformation method to construct a single-objective optimization function; An initial solution list is obtained by iteratively solving the single-objective optimization function and constraints; the initial solution list includes several initial solutions. The overall satisfaction level of several initial schemes in the initial scheme list is evaluated by using fuzzy membership functions. The initial scheme corresponding to the maximum overall satisfaction value is selected as the scheduling scheme.

[0014] Furthermore, the single-objective optimization function satisfies: Where F represents a single-objective optimization function, and These are respectively expressed as economic cost optimization functions. and carbon emission optimization function The corresponding preference weight coefficients, and ∈[0,1], and min represents the minimization operation; Represented as the economic cost benchmark value, It is represented as a carbon emission baseline value, used to convert the economic cost optimization function and the carbon emission optimization function into a dimensionless value between 0 and 1.

[0015] Furthermore, the iterative solution process includes: Extract the single-objective optimization function and constraints; Extract the preference weight coefficients corresponding to the economic cost optimization function and the carbon emission optimization function in the single-objective optimization function. and ; make and The initial values ​​are 0 and 1 respectively; Using step size BC sequentially to and The values ​​are summed and differencing respectively until... and The values ​​are 1 and 0 respectively, and after each adjustment and Several initial schemes are obtained by solving the constructed single-objective optimization function and constraints; where BC∈(0,1).

[0016] A second aspect of this application provides a comprehensive energy low-carbon operation optimization system, comprising: a data acquisition module and a data analysis module; the data acquisition module and the data analysis module are connected together. The data acquisition module acquires multi-source heterogeneous data through data acquisition equipment; the multi-source heterogeneous data includes meteorological data, power grid topology data, equipment operating parameters, and load data. The data analysis module includes a carbon flow construction unit and a result generation unit; The carbon flow construction unit: constructs a spatiotemporal dynamic carbon flow distribution matrix based on multi-source heterogeneous data; The result generation unit: constructs a dual-objective optimization model based on the spatiotemporal dynamic carbon flow distribution matrix and multi-source heterogeneous data; and generates a scheduling scheme based on the dual-objective optimization model.

[0017] Another aspect of this application provides an integrated energy low-carbon operation optimization device, comprising: a processor and a storage medium; the storage medium includes instructions, and the processor is used to execute the instructions to implement the method described in the first aspect and any possible implementation thereof; the integrated energy low-carbon operation optimization device may be an electronic device or a chip in an electronic device.

[0018] Compared with the prior art, the beneficial effects of this application are: 1. This application constructs a spatiotemporal dynamic carbon flow distribution matrix based on multi-source heterogeneous data; constructs a dual-objective optimization model based on the spatiotemporal dynamic carbon flow distribution matrix and multi-source heterogeneous data; and generates a scheduling scheme based on the dual-objective optimization model. This achieves strong coupling between carbon emissions and power grid physical flow, enabling the optimization model to keenly capture the spatiotemporal differences in carbon potential caused by new energy fluctuations. This allows for accurate identification of high-carbon periods and low-carbon nodes at the scheduling level, effectively avoiding ineffective scheduling caused by ambiguous carbon emission responsibility definitions, and improving the accuracy and actual carbon reduction benefits of low-carbon operation optimization of the integrated energy system.

[0019] 2. This application first constructs a directed topology graph containing power sources, loads, and energy storage nodes based on power grid topology data. Normalized meteorological data is then input into a wind and solar forecasting model trained by a neural network to obtain zero-carbon power output. Subsequently, the net load vector reflecting the real-time supply-demand difference is calculated by combining the node predicted load. Then, the forward-backward substitution method is used to perform power flow calculation on the topology graph to generate a branch power flow distribution matrix, and the node carbon potential is recursively calculated based on the proportional sharing principle. Finally, the spatiotemporal dynamic carbon flow distribution matrix is ​​filled according to the product relationship between branch power flow and node carbon potential. This approach abandons the extensive mode of fixed emission coefficients and utilizes the power distribution characteristics of the physical power grid to achieve dynamic decoupling of carbon flow in the time and space dimensions. It can accurately reflect the nonlinear impact of new energy fluctuations and network congestion on local carbon emission intensity, solve the problem of ambiguous carbon emission responsibility definition, and provide a high-fidelity carbon flow data foundation for subsequent optimization.

[0020] 3. This application constructs a dual-objective optimization model that deeply integrates economic efficiency and low carbon emissions. It quantifies the total cost into a composite economic cost function that includes electricity purchase, gas consumption, equipment operation and maintenance, and wind and solar curtailment penalties. The spatiotemporal dynamic carbon flow distribution matrix is ​​used as the core input, and the total carbon emissions are defined as the spatiotemporal sum of carbon flows in all branches of the entire network. This not only achieves refined measurement of the costs of different energy types, but more importantly, it breaks the limitation of carbon emissions being used as an external coefficient in traditional optimization. By using the spatiotemporal dynamic carbon flow distribution matrix to strongly correlate carbon emissions with the physical power flow of the power grid, the optimization process can accurately identify high-carbon periods and low-carbon nodes. Thus, while ensuring economic operation, it achieves the most accurate quantification and control of real physical carbon emissions, effectively avoiding the risk of false low-carbon scheduling caused by carbon emission model distortion. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application 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 only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a comprehensive energy low-carbon operation optimization method according to this application; Figure 2 This is a schematic diagram illustrating the principle of a comprehensive energy low-carbon operation optimization system according to this application. Detailed Implementation

[0023] The technical solutions of this application will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0024] Please see Figure 1 The first aspect of this application provides a method for optimizing integrated energy low-carbon operation, including: Acquire multi-source heterogeneous data; the multi-source heterogeneous data includes meteorological data, power grid topology data, equipment operating parameters, and load data; in this embodiment, meteorological data includes wind speed and light intensity; power grid topology data includes the connection relationships between power sources, loads, and energy storage devices; equipment operating parameters include the operating parameters of power sources, loads, and energy storage devices; load data includes the predicted loads corresponding to several devices; A spatiotemporal dynamic carbon flow distribution matrix was constructed based on multi-source heterogeneous data; A dual-objective optimization model is constructed based on the spatiotemporal dynamic carbon flow distribution matrix and multi-source heterogeneous data; A scheduling scheme is generated based on a dual-objective optimization model.

[0025] The construction of a spatiotemporal dynamic carbon flow distribution matrix based on multi-source heterogeneous data in this embodiment includes: Extract power grid topology data, meteorological data, and load data from multi-source heterogeneous data; A power grid topology map is constructed based on power grid topology data. The power grid topology map includes a set of nodes and a set of lines. The set of nodes consists of several nodes, including power generation nodes, load nodes, and energy storage nodes. The set of lines consists of the connection relationships between several nodes. After normalizing the meteorological data, it is input into the wind and solar power generation prediction model to obtain the zero-carbon predicted output corresponding to the meteorological data. The wind and solar power generation prediction model is a model trained by a neural network model using historical meteorological data and historical zero-carbon output. Extract the predicted load corresponding to several nodes in the load data; The net load corresponding to a node is obtained by calculating the difference between the predicted load and the zero-carbon predicted output of the node. The net load vector is determined based on the net load of several nodes; Branch power flow distribution matrices are obtained by using the forward-backward substitution method on the power grid topology and net load vector; the elements in the branch power flow distribution matrix are... It represents the active power flowing from node j to node i at time t; Calculate the nodal carbon potential based on the branch power flow distribution matrix; When an element in the branch power flow distribution matrix is ​​0, the carbon flow corresponding to the corresponding position in the spatiotemporal dynamic carbon flow distribution matrix is ​​set to 0. Otherwise, the carbon flow rate corresponding to the corresponding position in the spatiotemporal dynamic carbon flow distribution matrix is ​​calculated by multiplying the element in the branch power flow distribution matrix with the node carbon potential; the carbon flow rate is expressed as the number of kilograms of carbon emissions corresponding to the electricity transmitted between adjacent nodes at time t.

[0026] The calculation of nodal carbon potential based on the branch power flow distribution matrix in this embodiment includes: Obtain the carbon emission factor and branch power flow distribution matrix of the upper-level power grid; in this embodiment, the carbon emission factor of the upper-level power grid is issued in real time by the dispatch center; When the node type corresponding to a node in the branch power flow distribution matrix is ​​a source node, the node carbon potential corresponding to the node is set as the carbon emission factor of the upstream power grid; in this embodiment, the source node represents the main grid access point. When the node type corresponding to a node in the branch power flow distribution matrix is ​​a non-source node, the node carbon potential corresponding to the node is calculated using the formula; the formula satisfies: Where i and j both represent the node numbers; Represented as elements in the branch power flow distribution matrix; It is represented as the set of all upstream nodes j that point to node i; This is represented as the node carbon potential corresponding to the j-th node at time point t. It is represented as the node carbon potential corresponding to the i-th node at time t.

[0027] The dual-objective optimization model in this embodiment includes an economic cost optimization function and a carbon emission optimization function; The economic cost optimization function is expressed as the sum of electricity purchase cost, gas cost, operation and maintenance cost, and wind and solar curtailment penalty cost at all time points within the time period; Electricity purchase cost satisfy: ; This is expressed as the power exchanged by the power grid at time point t. This is represented as the electricity price at time point t; Gas cost satisfy: ; This is expressed as the gas turbine output power at time point t; Expressed as gas turbine power generation efficiency; This is expressed as the lower heating value of natural gas; Expressed as natural gas price; Operation and maintenance costs satisfy: ; This is expressed as the output power of the m-th device at time t. This represents the unit maintenance cost corresponding to the m-th device; Carbon emission optimization function satisfy: ; This is represented as the curtailed wind and solar power at time point t. In this embodiment, curtailed wind and solar power refers to the wind and solar power output that is forcibly cut off in order to maintain grid power balance or due to limitations in energy storage capacity and line transmission capacity. Expressed as the unit price for power waste penalty, it refers to the penalty coefficient set for each unit of electricity wasted in order to encourage the consumption of green electricity; Carbon emission optimization function satisfy: Where i and j represent node numbers, and N represents the total number of nodes; It represents the carbon flow rate of the branch between node i and node j at time t in the spatiotemporal dynamic carbon flow distribution matrix; T represents the total number of time points within the time period.

[0028] The constraints of the dual-objective optimization model in this embodiment consist of physical constraints and carbon flow constraints. Physical constraints include energy balance constraints, equipment operation constraints, and green electricity consumption rate constraints; Energy balance constraints include electrical balance constraints and thermal balance constraints. In this embodiment, electrical balance constraint means that the total active power output of power generation equipment is equal to the total active power output of power consumption equipment; thermal balance constraint means that the thermal power is the same as the thermal load demand. The equipment operation constraints include upper and lower limit constraints and energy storage constraints. In this embodiment, the upper and lower limit constraints refer to the limitation of the equipment's output power within the corresponding physical range. The energy storage constraint refers to the fact that the remaining power of the energy storage device at time t is equal to the sum of the power at time t-1 and the difference between the charge and discharge power. The charge and discharge power is expressed as the difference between the charging power and the discharging power. The green electricity absorption rate constraint is satisfied: ;in, This represents the total amount of new energy actually consumed at time t. It is expressed as the total amount of new energy power generation at time t; This represents the minimum green energy absorption rate threshold, and the specific value is set based on experience. In this embodiment, it is set to 0.95. In this embodiment, new energy power generation refers to power generation through wind power and solar power. The carbon flow constraint condition means that the total carbon flow out of a node is equal to the total carbon emissions out of the node.

[0029] This embodiment introduces a dual constraint system of physical and carbon flow constraints into the dual-objective optimization model to ensure that the scheduling scheme has both engineering feasibility and carbon emission authenticity: At the physical level, it strictly limits the balance of electric / thermal energy, the upper and lower limits of equipment and ramp rate, the dynamic SOC of energy storage, and the green electricity consumption rate to ensure safe and stable operation and enforce priority use of renewable energy; At the carbon flow level, it applies the equation constraint that the total carbon outflow from the node is equal to the product of the node's carbon potential and the outflow power, solidifying the physical characteristics of the carbon flow distribution matrix into mathematical boundaries, effectively preventing the optimization process from violating the physical laws of the power grid or falsely reporting carbon reduction results in pursuit of the lowest economic cost, and achieving a high degree of synchronization between physical power grid operation and virtual carbon flow tracking, ensuring that the generated scheduling scheme can be implemented in the real physical network and achieve real low-carbon benefits.

[0030] The scheduling scheme generated based on the dual-objective optimization model in this embodiment includes: Extract the dual-objective optimization model and constraints; The dual-objective model is transformed using the weight coefficient transformation method to construct a single-objective optimization function; An initial solution list is obtained by iteratively solving the single-objective optimization function and constraints; the initial solution list includes several initial solutions. The overall satisfaction level of several initial schemes in the initial scheme list is evaluated by using fuzzy membership functions. The initial scheme corresponding to the maximum overall satisfaction level is selected as the scheduling scheme.

[0031] The single-objective optimization function in this embodiment satisfies: Where F represents a single-objective optimization function, and These are respectively expressed as economic cost optimization functions. and carbon emission optimization function The corresponding preference weight coefficients, and ∈[0,1], and min represents the minimization operation; Represented as the economic cost benchmark value, It is represented as a carbon emission baseline value, used to convert the economic cost optimization function and the carbon emission optimization function into a dimensionless value between 0 and 1.

[0032] The iterative solution process in this embodiment includes: Extract the single-objective optimization function and constraints; Extract the preference weight coefficients corresponding to the economic cost optimization function and the carbon emission optimization function in the single-objective optimization function. and ; make and The initial values ​​are 0 and 1 respectively; Using step size BC sequentially to and The values ​​are summed and differencing respectively until... and The values ​​are 1 and 0 respectively, and after each adjustment and Several initial schemes are obtained by solving the constructed single-objective optimization function and constraints; where BC∈(0,1), the specific value is set according to experience, and in this embodiment, BC is set to 0.05; in this embodiment, and The initial values ​​are 0 and 1 respectively, with a step size BC of 0.05. and The value is adjusted, after the first adjustment and The values ​​were 0.05 and 0.95 respectively, after the second adjustment. and The values ​​were 0.1 and 0.9 respectively; after several adjustments, and The values ​​are 1 and 0 respectively, and are adjusted each time. and The value will ensure .

[0033] This embodiment first normalizes the dual objectives of economic cost and carbon emission using benchmark values ​​and then weights them together to construct a single-objective optimization function. Subsequently, it systematically traverses the weight space with a set step size, sequentially increasing the economic weight from 0 to 1 and decreasing the carbon emission weight from 1 to 0, iteratively solving to obtain a Pareto optimal initial solution set covering extreme low-carbon, high-cost to extreme high-carbon, low-cost scenarios. Finally, a fuzzy membership function is introduced to quantitatively evaluate the overall satisfaction of each solution, automatically selecting the optimal balance point that considers both economics and environmental protection. This avoids the limitations of decision-makers' subjective preferences under a single weight, comprehensively revealing the game relationship between economic efficiency and low carbon emissions. Furthermore, fuzzy evaluation enables automated decision-making for selecting the optimal solution, improving the scientific rigor and adaptability of the scheduling scheme in practical applications.

[0034] Please see Figure 2 A second aspect of this application provides an integrated energy low-carbon operation optimization system, comprising: a data acquisition module and a data analysis module; the data acquisition module and the data analysis module are connected. Data acquisition module: Acquires multi-source heterogeneous data through data acquisition equipment; multi-source heterogeneous data includes meteorological data, power grid topology data, equipment operating parameters, and load data; The data analysis module includes a carbon flow construction unit and a result generation unit; Carbon flow building blocks: constructing a spatiotemporal dynamic carbon flow distribution matrix based on multi-source heterogeneous data; Result generation unit: Construct a dual-objective optimization model based on the spatiotemporal dynamic carbon flow distribution matrix and multi-source heterogeneous data; generate a scheduling scheme based on the dual-objective optimization model.

[0035] Another embodiment of this application provides a comprehensive energy low-carbon operation optimization device, including: a processor and a storage medium; the storage medium includes instructions, and the processor is used to execute the instructions to implement the method described in the first aspect embodiment and any possible implementation of the first aspect embodiment; a comprehensive energy low-carbon operation optimization device may be an electronic device or a chip in an electronic device.

[0036] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0037] The working principle of this application is as follows: Multi-source heterogeneous data is acquired; a spatiotemporal dynamic carbon flow distribution matrix is ​​constructed based on the multi-source heterogeneous data; a dual-objective optimization model is constructed based on the spatiotemporal dynamic carbon flow distribution matrix and the multi-source heterogeneous data; a scheduling scheme is generated based on the dual-objective optimization model. This achieves strong coupling between carbon emissions and the physical power flow of the power grid, enabling the optimization model to keenly capture the spatiotemporal differences in carbon potential caused by fluctuations in new energy sources. This allows for accurate identification of high-carbon periods and low-carbon nodes at the scheduling level, effectively avoiding ineffective scheduling caused by ambiguous carbon emission responsibility definitions. It improves the accuracy and actual carbon reduction benefits of low-carbon operation optimization of the integrated energy system. This avoids the problem that existing technologies often use fixed global carbon emission factors to calculate and constrain carbon emissions, resulting in a severe decoupling of the quantified carbon emission results from the actual power flow of the power grid and the spatiotemporal distribution of new energy sources. This fails to reflect the differences in carbon emission intensity at different nodes and times, leading to low accuracy in low-carbon optimization results.

[0038] The above embodiments are only used to illustrate the technical methods of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of this application without departing from the spirit and scope of the technical methods of this application.

Claims

1. A comprehensive energy low-carbon operation optimization method, characterized in that, include: Acquire multi-source heterogeneous data; the multi-source heterogeneous data includes meteorological data, power grid topology data, equipment operating parameters, and load data; A spatiotemporal dynamic carbon flow distribution matrix was constructed based on multi-source heterogeneous data; A dual-objective optimization model is constructed based on the spatiotemporal dynamic carbon flow distribution matrix and multi-source heterogeneous data; A scheduling scheme is generated based on a dual-objective optimization model.

2. The integrated energy low-carbon operation optimization method according to claim 1, characterized in that, The construction of the spatiotemporal dynamic carbon flow distribution matrix based on multi-source heterogeneous data includes: Extract power grid topology data, meteorological data, and load data from multi-source heterogeneous data; A power grid topology map is constructed based on power grid topology data; the power grid topology map includes a set of nodes and a set of lines; the set of nodes consists of several nodes, including power generation nodes, load nodes, and energy storage nodes; the set of lines consists of the connection relationships between several nodes. After normalizing the meteorological data, it is input into the wind and solar power generation prediction model to obtain the zero-carbon predicted output corresponding to the meteorological data; the wind and solar power generation prediction model is a model trained by a neural network model using historical meteorological data and historical zero-carbon output. Extract the predicted load corresponding to several nodes in the load data; The net load corresponding to the node is obtained by performing a difference calculation between the predicted load corresponding to the node and the zero-carbon predicted output. The net load vector is determined based on the net load of several nodes; Branch power flow distribution matrices are obtained by using the forward-backward substitution method on the power grid topology and net load vector; the elements in the branch power flow distribution matrix... It represents the active power flowing from node j to node i at time t; Calculate the nodal carbon potential based on the branch power flow distribution matrix; When an element in the branch power flow distribution matrix is ​​0, the carbon flow corresponding to the corresponding position in the spatiotemporal dynamic carbon flow distribution matrix is ​​set to 0. Otherwise, the carbon flow rate corresponding to the corresponding position in the spatiotemporal dynamic carbon flow distribution matrix is ​​calculated by multiplying the elements in the branch power flow distribution matrix with the carbon potential of the node.

3. The integrated energy low-carbon operation optimization method according to claim 2, characterized in that, The calculation of nodal carbon potential based on the branch power flow distribution matrix includes: Obtain the carbon emission factor and branch power flow distribution matrix of the upstream power grid; When the node type corresponding to a node in the branch power flow distribution matrix is ​​a source node, the node carbon potential corresponding to the node is set as the carbon emission factor of the upstream power grid. When the node type corresponding to a node in the branch power flow distribution matrix is ​​a non-source node, the node carbon potential corresponding to that node is calculated using the formula; the formula satisfies: Where i and j both represent the node numbers; Represented as elements in the branch power flow distribution matrix; It is represented as the set of all upstream nodes j that point to node i; This is represented as the node carbon potential corresponding to the j-th node at time point t. It is represented as the node carbon potential corresponding to the i-th node at time t.

4. The integrated energy low-carbon operation optimization method according to claim 1, characterized in that, The dual-objective optimization model includes an economic cost optimization function and a carbon emission optimization function; The economic cost optimization function is expressed as the sum of electricity purchase cost, gas cost, operation and maintenance cost, and wind and solar curtailment penalty cost at all time points within the time period; The cost of electricity purchase satisfy: ; This is expressed as the power exchanged by the power grid at time point t. This is represented as the electricity price at time point t; Gas cost satisfy: ; This is expressed as the gas turbine output power at time point t; Expressed as gas turbine power generation efficiency; This is expressed as the lower heating value of natural gas; Expressed as natural gas price; The operation and maintenance costs satisfy: ; This is expressed as the output power of the m-th device at time t. This represents the unit maintenance cost corresponding to the m-th device; The carbon emission optimization function satisfy: ; This is represented as the power of wind and solar power curtailed at time point t. This is expressed as the unit price for the penalty for power curtailment; The carbon emission optimization function satisfy: Where i and j represent node numbers, and N represents the total number of nodes; It represents the carbon flow rate of the branch between node i and node j at time t in the spatiotemporal dynamic carbon flow distribution matrix; T represents the total number of time points within the time period.

5. The integrated energy low-carbon operation optimization method according to claim 1, characterized in that, The constraints of the dual-objective optimization model consist of physical constraints and carbon flow constraints. The physical constraints include energy balance constraints, equipment operation constraints, and green electricity consumption rate constraints. The energy balance constraints include electrical balance constraints and thermal balance constraints; The equipment operation constraints include upper and lower limit constraints and energy storage constraints; The green energy absorption rate constraint satisfies: ;in, This represents the total amount of new energy actually consumed at time t. It is expressed as the total amount of new energy power generation at time t; This is represented as the minimum green energy consumption rate threshold; The carbon flow constraint condition refers to the condition that the total carbon flow out of a node is equal to the total carbon emissions out of the node.

6. The integrated energy low-carbon operation optimization method according to claim 1, characterized in that, The scheduling scheme generated based on the dual-objective optimization model includes: Extract the dual-objective optimization model and constraints; The dual-objective model is transformed using the weight coefficient transformation method to construct a single-objective optimization function; An initial solution list is obtained by iteratively solving the single-objective optimization function and constraints; the initial solution list includes several initial solutions. The overall satisfaction level of several initial schemes in the initial scheme list is evaluated by using fuzzy membership functions. The initial scheme corresponding to the maximum overall satisfaction value is selected as the scheduling scheme.

7. The integrated energy low-carbon operation optimization method according to claim 6, characterized in that, The single-objective optimization function satisfies: Where F represents a single-objective optimization function, and These are respectively expressed as economic cost optimization functions. and carbon emission optimization function The corresponding preference weight coefficients, and ∈[0,1], and min represents the minimization operation; Represented as the economic cost benchmark value, This is expressed as a carbon emission baseline value.

8. The integrated energy low-carbon operation optimization method according to claim 6, characterized in that, The iterative solution process includes: Extract the single-objective optimization function and constraints; Extract the preference weight coefficients corresponding to the economic cost optimization function and the carbon emission optimization function in the single-objective optimization function. and ; make and The initial values ​​are 0 and 1 respectively; Using step size BC sequentially to and The values ​​are summed and differencing respectively until... and The values ​​are 1 and 0 respectively, and after each adjustment and Several initial schemes are obtained by solving the constructed single-objective optimization function and constraints; where BC∈(0,1).

9. A comprehensive energy low-carbon operation optimization system, applied to the comprehensive energy low-carbon operation optimization method according to any one of claims 1-8, characterized in that, include: A data acquisition module and a data analysis module; the data acquisition module and the data analysis module are connected to each other; The data acquisition module acquires multi-source heterogeneous data through data acquisition equipment; the multi-source heterogeneous data includes meteorological data, power grid topology data, equipment operating parameters, and load data. The data analysis module includes a carbon flow construction unit and a result generation unit; The carbon flow construction unit: constructs a spatiotemporal dynamic carbon flow distribution matrix based on multi-source heterogeneous data; The result generation unit constructs a dual-objective optimization model based on the spatiotemporal dynamic carbon flow distribution matrix and multi-source heterogeneous data. A scheduling scheme is generated based on a dual-objective optimization model.

10. A comprehensive energy low-carbon operation optimization device, characterized in that, include: A processor and a storage medium; the storage medium includes instructions, and the processor is used to execute the instructions to implement the integrated energy low-carbon operation optimization method as described in any one of claims 1-8.

Images