Power grid node carbon emission intensity control method based on power flow constraint

Abstract

The invention provides a power grid node carbon emission intensity control method based on power flow constraint, and belongs to the technical field of power grid carbon emission optimization, and the method comprises the steps: 1, obtaining a carbon emission target, and determining a plurality of power flow constraint conditions based on the carbon emission target; 2, obtaining a plurality of key positions of the power grid, deploying preset monitoring equipment at each key position, and monitoring and collecting parameters of a preset type of the power grid; 3, determining a plurality of nodes of the power grid, and further determining state data of each node in the power grid; 4, determining the carbon emission of each node in the power grid based on the parameters of the preset type of the power grid; and 5, constructing a carbon emission optimization model based on the state data of each node under the preset operation condition and the carbon emission of each node, and determining a carbon emission optimization scheme based on the carbon emission optimization model. And fine control and optimization of the power grid node carbon emission intensity are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid carbon emission optimization, and in particular to a power grid node carbon emission intensity control method based on power flow constraints. Background Art

[0002] As the global carbon reduction target continues to advance, the carbon emission problem of the power grid during its operation, as an important part of the energy system, has gradually attracted attention. Existing carbon emission control methods usually focus on the calculation and macro optimization of overall carbon emissions, but lack detailed analysis of the carbon emission intensity of nodes within the power grid.

[0003] In the existing technology, node parameter monitoring and power flow optimization methods can improve the efficiency of power grid operation to a certain extent, but they fail to effectively combine carbon emission targets with node status, resulting in limitations in carbon emission intensity control. Specifically, these methods cannot accurately evaluate and optimize the carbon emissions of each node, making it difficult to meet the needs of precise carbon emission reduction.

[0004] Therefore, the present invention provides a method for controlling carbon emission intensity of power grid nodes based on power flow constraints. Summary of the invention

[0005] The present invention provides a method for controlling carbon emission intensity of power grid nodes based on flow constraints, which is used to construct a carbon emission optimization model by introducing flow constraints and accurate calculation of carbon emissions at the node level, combining power grid parameters and status data collected by monitoring equipment, thereby achieving refined control and optimization of carbon emission intensity of power grid nodes, and improving the low-carbon level and precise emission reduction capabilities of power grid operation.

[0006] The present invention provides a method for controlling carbon emission intensity of power grid nodes based on power flow constraints, comprising:

[0007] Step 1: Obtain the carbon emission target and determine several power flow constraints based on the carbon emission target;

[0008] Step 2: Obtain several key locations of the power grid and deploy preset monitoring equipment at each key location to monitor and collect parameters of preset types of the power grid;

[0009] Step 3: Determine several nodes of the power grid, and determine the status data of each node in the power grid based on the parameters of the preset type of the power grid, all power flow constraints and the preset calculation method;

[0010] Step 4: determining the carbon emission of each node in the power grid based on parameters of a preset type of the power grid;

[0011] Step 5: Build a carbon emission optimization model based on the status data of each node under preset operating conditions and the carbon emissions of each node, and then determine the carbon emission optimization plan based on the carbon emission optimization model.

[0012] The present invention provides a method for controlling carbon emission intensity of power grid nodes based on power flow constraints, obtains a carbon emission target, and determines a number of power flow constraints based on the carbon emission target, including:

[0013] Determine the carbon emission intensity target of the power grid based on the preset regional carbon emission targets;

[0014] Determine the carbon emission target coefficient of the power grid based on the carbon emission intensity target of the power grid;

[0015] Determine a number of power flow constraint types based on the carbon emission target coefficient of the power grid and a preset coefficient-type database;

[0016] determining initial conditions for each type of power flow constraint based on a preset type-condition database;

[0017] The initial conditions of each preset type of power flow constraint are adjusted based on the carbon emission intensity target of the power grid, and then several preset types of power flow constraint conditions are determined.

[0018] The present invention provides a method for controlling carbon emission intensity of power grid nodes based on power flow constraints, and preset types of parameters include: node parameters, line parameters and system parameters.

[0019] The present invention provides a method for controlling carbon emission intensity of power grid nodes based on power flow constraints, with preset monitoring equipment, including: PMU phase measurement unit, AMI smart meter, meteorological monitoring equipment, greenhouse gas sensor, transmission line monitoring equipment, DER monitoring equipment, generator real-time monitoring system and EMS monitoring module.

[0020] The present invention provides a method for controlling carbon emission intensity of power grid nodes based on power flow constraints, determining a number of nodes of the power grid, and determining state data of each node in the power grid based on parameters of a preset type of the power grid, all power flow constraints and a preset calculation method, including:

[0021] Performing a first analysis on parameters of a preset type of the power grid, thereby obtaining basic parameters of each node in the power grid;

[0022] Perform a second structural analysis on the power grid, and then construct a node admittance matrix of the power grid based on the basic parameters of each node in the power grid;

[0023] Construct a mathematical model of the power grid based on the node admittance matrix of the power grid and the preset power flow equation;

[0024] Performing a second analysis on parameters of a preset type of the power grid to determine a first parameter of each node;

[0025] Performing a third analysis on basic parameters of each node in the power grid, thereby determining the node type of each node in the power grid;

[0026] Determine the corresponding power flow equation type based on the node type of each node in the power grid and a preset type-equation type database;

[0027] constructing a power flow equation based on a first parameter of each node, a power flow equation type, and a mathematical model of the power grid;

[0028] Based on the preset calculation method, all power flow constraints and iterative solution of the power flow equation, the second parameter of each node under the preset operating conditions is determined;

[0029] determining a third parameter for each node in the electrical grid based on the second parameter for each node in the electrical grid;

[0030] A first parameter, a second parameter, and a third parameter of each node in the power grid are determined as status data of each node.

[0031] The present invention provides a method for controlling carbon emission intensity of power grid nodes based on power flow constraints, which is based on a preset calculation method, all power flow constraints and iterative solution of power flow equations, and then determines a second parameter of each node under preset operating conditions, including:

[0032] Determining a plurality of first initial parameters of each node in the power grid based on preset operating conditions;

[0033] Iteratively solving the power flow equation based on a plurality of first initial parameters of each node in the power grid and a preset calculation method, thereby determining a second initial parameter of each node in the power grid;

[0034] The second initial parameter of each node in the power grid is verified based on all power flow constraints. If the second initial parameter verification fails, the first initial parameter of each node in the power grid is adjusted based on a preset method, and the power flow equation is repeatedly solved iteratively until the second initial parameter verification is successful.

[0035] The present invention provides a method for controlling carbon emission intensity of power grid nodes based on power flow constraints, which determines the carbon emission of each node in the power grid based on parameters of a preset type of the power grid, including:

[0036] Performing characteristic analysis on parameters of preset types of power grids, and then obtaining carbon emission related parameters based on the characteristic analysis results;

[0037] Determine the carbon emissions of each node in the power grid based on carbon emission related parameters:

[0038] The carbon emissions of each node in the grid are determined based on the parameters of the preset type of grid, including:

[0039]

[0040] Among them, E i is the carbon emission of the ith node in the power grid, P1 i is the active power generated by the i-th node in the power grid, P2 is the power loss of the i-th node in the power grid, γ1 i is the power generation efficiency factor of the ith node in the power grid, γ2 i is the dynamic load distribution factor of the ith node in the power grid, EF i is the carbon emission factor of the ith node in the power grid, ∈ i is the fixed carbon emission compensation factor of the ith node in the power grid, ∝P0 is the preset power conversion coefficient, V i is the voltage amplitude of the ith node in the power grid, V j is the voltage amplitude of the jth node in the power grid, G ij is the conductance of the i-th node and the j-th node in the power grid, B ij is the susceptance of the i-th node and the j-th node in the power grid, θ ij is the voltage phase difference between the i-th node and the j-th node in the power grid, n is the total number of nodes in the power grid, P ij is the power flow between the i-th node and the j-th node in the power grid.

[0041] The present invention provides a method for controlling carbon emission intensity of power grid nodes based on power flow constraints, and a carbon emission optimization scheme, including: a power supply side optimization scheme, a load side optimization scheme, a power grid operation optimization scheme, an energy storage optimization scheme, and a carbon emission monitoring and feedback optimization scheme.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] By introducing power flow constraints and precise calculation of carbon emissions at the node level, and combining the grid parameters and status data collected by monitoring equipment, a carbon emission optimization model is constructed, which enables refined control and optimization of carbon emission intensity at grid nodes, and improves the low-carbon level of grid operation and precise emission reduction capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 It is a flow chart of a method for controlling carbon emission intensity of power grid nodes based on power flow constraints provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Embodiment 1:

[0048] The embodiment of the present invention provides a method for controlling carbon emission intensity of power grid nodes based on power flow constraints, such as Figure 1 As shown, including:

[0049] Step 1: Obtain the carbon emission target and determine several power flow constraints based on the carbon emission target;

[0050] Step 2: Obtain several key locations of the power grid and deploy preset monitoring equipment at each key location to monitor and collect parameters of preset types of the power grid;

[0051] Step 3: Determine several nodes of the power grid, and determine the status data of each node in the power grid based on the parameters of the preset type of the power grid, all power flow constraints and the preset calculation method;

[0052] Step 4: determining the carbon emission of each node in the power grid based on parameters of a preset type of the power grid;

[0053] Step 5: Build a carbon emission optimization model based on the status data of each node under preset operating conditions and the carbon emissions of each node, and then determine the carbon emission optimization plan based on the carbon emission optimization model.

[0054] In this embodiment, the carbon emission target refers to the total carbon emission limit or specific target for reducing carbon emissions set in the operation of the power grid in response to environmental protection requirements. This target is usually set by policy or by the operator according to actual needs. Assume that the carbon emission target of a regional power grid is to reduce total carbon emissions by 20% per year, which is specifically quantified as no more than 5 million tons of carbon dioxide emissions per year. This target will be used to guide the optimization operation plan of the power grid;

[0055] In this embodiment, the power flow constraint condition refers to the operating boundary and restriction condition of the power grid power flow (i.e., power flow), which are usually related to the safety, stability and physical capacity of the power grid, such as the capacity limitation of the transformer, the current capacity limitation of the line and the voltage amplitude limitation, etc. A typical power flow constraint condition is "the power flow of a certain line shall not exceed its rated capacity, for example, not more than 200MW", and another power flow constraint condition may be "the voltage amplitude of all nodes must be within the range of 0.95 to 1.05 times the rated voltage".

[0056] In this embodiment, a key location refers to a location or device in the power grid that has a significant impact on the overall operation. These locations are usually key areas for monitoring the operation status of the power grid, such as main transformers, power plant access points, major load centers, etc. For example, a key location may be the low-voltage side of a main substation, which is used to monitor real-time changes in load.

[0057] In this embodiment, the node status data refers to a set of parameters that describe the operating conditions of the power grid node, typically including the node's voltage, current, power (active power and reactive power), and load status. For a certain node, its status data may include: voltage value is 1.02pu, active power output is 50MW, reactive power is 10MVAr, and load demand is 30MW.

[0058] In this embodiment, the carbon emissions of each node refer to the carbon emissions caused by power generation, electricity consumption or other activities at a node in the power grid. It usually depends on the type of energy used at the node and its carbon emission factor. For example, coal-fired power plants will produce higher carbon emissions, while wind power nodes will have zero carbon emissions. Assuming that a node is connected to a coal-fired unit with an output power of 100MW, its carbon emission factor is 0.9kg CO 2 / kWh, the carbon emissions of this node are 2160 tons / day. Another node is connected to photovoltaic power generation. Since the carbon emissions of photovoltaic power generation are zero, the carbon emissions of this node are 0.

[0059] In this embodiment, the carbon emission optimization model is a mathematical model for achieving the carbon emission target by optimizing the power allocation of each node while satisfying the power flow constraint and other operation requirements. The model usually includes an objective function (such as minimizing carbon emissions) and constraints (such as power flow constraints, node balance constraints, etc.).

[0060] The beneficial effects of the above technical solution are: by introducing power flow constraints and accurate calculation of carbon emissions at the node level, combining the grid parameters and status data collected by monitoring equipment, a carbon emission optimization model is constructed, which realizes the refined control and optimization of carbon emission intensity of grid nodes, and improves the low-carbon level of grid operation and precise emission reduction capabilities.

[0061] Embodiment 2:

[0062] The embodiment of the present invention provides a method for controlling carbon emission intensity of power grid nodes based on power flow constraints, obtains a carbon emission target, and determines a number of power flow constraint conditions based on the carbon emission target, including:

[0063] Determine the carbon emission intensity target of the power grid based on the preset regional carbon emission targets;

[0064] Determine the carbon emission target coefficient of the power grid based on the carbon emission intensity target of the power grid;

[0065] Determine a number of power flow constraint types based on the carbon emission target coefficient of the power grid and a preset coefficient-type database;

[0066] determining initial conditions for each type of power flow constraint based on a preset type-condition database;

[0067] The initial conditions of each preset type of power flow constraint are adjusted based on the carbon emission intensity target of the power grid, and then several preset types of power flow constraint conditions are determined.

[0068] In this embodiment, the preset regional carbon emission target refers to a carbon emission limit or target set for a specific region (such as a province, city or power grid coverage area), which is usually formulated according to national or local policy requirements and aims to promote low-carbon energy development and reduce greenhouse gas emissions. For example, a regional government sets a goal of controlling the region's carbon emissions within 100 million tons by 2030, and a city's power sector plans to limit annual carbon emissions related to power generation to 2 million tons;

[0069] In this embodiment, the carbon emission intensity target of the power grid refers to the upper limit of carbon emissions corresponding to a unit of electricity (such as per kilowatt-hour), which is an important indicator for measuring the low-carbonization level of the power grid. The carbon emission intensity target is usually derived from the regional carbon emission target. Assuming that the carbon emission target of a region is 5 million tons, and the annual electricity demand is expected to be 5 billion kilowatt-hours, the carbon emission intensity target of the power grid is: Carbon emission intensity target = carbon emission target electricity demand = 0.1kgCO2 / kWh. Carbon emission intensity target = electricity demand carbon emission target = 0.1kgCO2 / kWh.

[0070] In this embodiment, the carbon emission target coefficient is a coefficient used to quantify the grid's achievement of the carbon emission intensity target, and is used to associate the grid's carbon emission intensity target with the type and condition of the flow constraint in the calculation. Typically, the coefficient is a parameter that represents the maximum carbon emission allowed per unit flow (such as power or current) of the grid.

[0071] In this embodiment, the preset coefficient-type database is a mapping table containing multiple coefficients and corresponding flow constraint types, which is used to quickly match the correspondence between different carbon emission target coefficients and flow types. Through this database, the applicable flow constraint type can be found according to the carbon emission target coefficient.

[0072] In this embodiment, the flow constraint type is a classification of the power grid flow operation mode, and the specific strategy of the power grid operation is determined according to the power grid operation status (such as new energy penetration rate, carbon emission coefficient, etc.). For example, different flow constraint types can give priority to supporting new energy access, restrict thermal power operation, or optimize power flow to reduce carbon emissions. For example: Type 1: Wind power priority access, description: For areas with a high proportion of wind power, priority is given to accessing wind power resources and limiting the output of thermal power units. Flow requirement: The power of wind power nodes is not less than 70%. Type 2: Peak-shaving operation of coal-fired units, description: During low-load periods, reduce the power output of coal-fired units, and give priority to the operation of photovoltaics and energy storage. Flow requirement: The power of coal-fired units must be reduced to less than 30% of the rated power.

[0073] In this embodiment, the initial condition of the power flow constraint refers to the default operating parameters satisfied by the power flow constraint when the carbon emission intensity target adjustment is not considered. It is usually derived from the basic parameters or design values ​​of the actual power grid operation. For example: Initial condition 1: Line power limit default condition: The maximum power flow power allowed by a certain line is 200MW. Initial condition 2: Node voltage range, default condition: The node voltage amplitude range is 0.95~1.05pu. These initial conditions may be modified through adjustments based on carbon emission intensity targets. For example, reduce the power limit of a line from 200MW to 150MW to reduce high-carbon emission coal-fired power generation.

[0074] The beneficial effects of the above technical solution are: by introducing carbon emission intensity targets and carbon emission target coefficients, combining the coefficient-type database and the type-condition database, the power grid operation and carbon emission control are deeply integrated, and by dynamically adjusting the initial conditions of the power flow constraints, the low-carbon and high-efficiency operation of the power grid is achieved, the carbon emission distribution is optimized, and the ability to accurately control the carbon emission targets is significantly improved.

[0075] Embodiment 3:

[0076] The embodiment of the present invention provides a method for controlling carbon emission intensity of power grid nodes based on power flow constraints, and the preset types of parameters include: node parameters, line parameters and system parameters.

[0077] In this embodiment, the node parameters are: node voltage amplitude: the voltage of a certain node is between 0.95 and 1.05 pu, node power injection: node A is connected to 100MW wind power, and the load of node B is 80MW;

[0078] In this embodiment, line parameters are: maximum transmission power of the line: the transmission power limit of a certain line is 150MW, line resistance and reactance: the resistance of a certain line is 0.01Ω / km, and the reactance is 0.05Ω / km;

[0079] In this embodiment, system parameters are: total system load: the current load of the regional power grid is 1000MW, system frequency: the system frequency is maintained at a rated value of 50Hz.

[0080] The beneficial effects of the above technical solution are: by introducing a comprehensive analysis of node parameters, line parameters and system parameters, precise control of the carbon emission intensity of power grid nodes is achieved, and the use of power flow constraints and mathematical modeling ensures the coexistence of low-carbon and safe power grid operation. The power grid status is linked to carbon emissions, providing an intelligent and precise solution for achieving the goal.

[0081] Embodiment 4:

[0082] The embodiment of the present invention provides a method for controlling carbon emission intensity of power grid nodes based on power flow constraints, with preset monitoring equipment, including: PMU phasor measurement unit, AMI smart meter, meteorological monitoring equipment, greenhouse gas sensor, transmission line monitoring equipment, DER monitoring equipment, generator real-time monitoring system and EMS monitoring module.

[0083] In this embodiment, the PMU phase measurement unit: Function: Real-time monitoring of the voltage amplitude, phase angle and frequency changes of the power grid node. Example: The voltage of the PMU monitoring node A is 1.03pu and the frequency deviation is 0.02Hz;

[0084] In this embodiment, AMI (smart meter): Function: record the power consumption data of the user side and support two-way communication. For example: the AMI device of a certain community monitors that the power of the user load peak is 2MW.

[0085] In this embodiment, the meteorological monitoring equipment: Function: monitors meteorological conditions such as wind speed and solar radiation intensity to provide support for the prediction of renewable energy power generation. Example: The meteorological equipment at the wind farm records that the current wind speed is 8m / s, which is suitable for the wind turbine to operate at full load;

[0086] In this embodiment, the greenhouse gas sensor: Function: Detects the concentration of greenhouse gases such as carbon dioxide and methane in the power station or power grid operation area. Example: A sensor at a power plant detects that the carbon dioxide emission concentration is 400ppm;

[0087] In this embodiment, the power transmission line monitoring device: Function: monitors the temperature, vibration, power flow and other information of the power transmission line. For example: a line monitoring device records that the transmission power is 120MW and the temperature is 70°C.

[0088] In this embodiment, DER (distributed energy resource) monitoring equipment: Function: monitor the operating status of distributed photovoltaic, energy storage, electric vehicles, etc. connected to the power grid. For example: DER equipment in a certain area monitors that the total power of 100 distributed photovoltaic power generation units is 10MW;

[0089] In this embodiment, the real-time monitoring system of the generator: Function: monitor the real-time operating parameters of the generator set, such as output power, fuel usage, etc. For example: the monitoring system of a thermal power unit records that the current output is 500MW and the coal consumption rate is 300g / kWh;

[0090] In this embodiment, the EMS (Energy Management System) monitoring module: Function: Comprehensively monitor and dispatch the load distribution, power generation and power flow direction of the entire power grid. For example: The EMS module shows that the current power grid load is 850MW, and the power flow mainly flows to the urban load center.

[0091] The beneficial effects of the above technical solution are: by integrating various monitoring equipment (such as PMU, AMI, meteorological equipment, etc.), all-round real-time monitoring and correlation analysis of power grid operation status, environmental conditions and carbon emission intensity are realized. Through accurate data collection and comprehensive processing, the accuracy and response speed of carbon emission control are improved.

[0092] Embodiment 5:

[0093] The embodiment of the present invention provides a method for controlling carbon emission intensity of power grid nodes based on power flow constraints, determining a plurality of nodes of the power grid, and determining state data of each node in the power grid based on parameters of a preset type of the power grid, all power flow constraints and a preset calculation method, including:

[0094] Performing a first analysis on parameters of a preset type of the power grid, thereby obtaining basic parameters of each node in the power grid;

[0095] Perform a second structural analysis on the power grid, and then construct a node admittance matrix of the power grid based on the basic parameters of each node in the power grid;

[0096] Construct a mathematical model of the power grid based on the node admittance matrix of the power grid and the preset power flow equation;

[0097] Performing a second analysis on parameters of a preset type of the power grid to determine a first parameter of each node;

[0098] Performing a third analysis on basic parameters of each node in the power grid, thereby determining the node type of each node in the power grid;

[0099] Determine the corresponding power flow equation type based on the node type of each node in the power grid and a preset type-equation type database;

[0100] constructing a power flow equation based on a first parameter of each node, a power flow equation type, and a mathematical model of the power grid;

[0101] Based on the preset calculation method, all power flow constraints and iterative solution of the power flow equation, the second parameter of each node under the preset operating conditions is determined;

[0102] determining a third parameter for each node in the electrical grid based on the second parameter for each node in the electrical grid;

[0103] A first parameter, a second parameter, and a third parameter of each node in the power grid are determined as status data of each node.

[0104] In this embodiment, the first analysis is to collect and process data of preset type parameters of the power grid (such as node voltage, power, etc.), extract basic information of each node operation, and provide support for subsequent analysis and modeling;

[0105] In this embodiment, the basic parameters are key parameters that describe the initial state of the grid node, including the voltage amplitude, phase angle, active power and reactive power of the node. For example: The first analysis determines the basic parameters of node A as follows: the voltage amplitude of node A is 1.02pu, the voltage phase angle of node A is 10°, the active power injected by node A is 50MW, and the reactive power is 20MVar.

[0106] In this embodiment, the second structural analysis is to generate a mathematical description form of the power grid by analyzing the topological structure of the power grid (such as the line connection relationship between nodes, reactance, resistance, etc.) and combining basic parameters;

[0107] In this embodiment, the node admittance matrix is ​​a matrix generated based on the topological structure and line parameters (admittance, resistance, reactance) of the power grid, and is used to describe the electrical connection relationship between nodes.

[0108] In this embodiment, the preset power flow equation is a mathematical expression used to describe the power flow relationship between power grid nodes, including the active and reactive power balance equations of the nodes;

[0109] In this embodiment, the mathematical model of the power grid is a mathematical model of the power grid constructed based on the node admittance matrix and the power flow equation, and is used to calculate the electrical state of each node.

[0110] In this embodiment, the second analysis is to further process the preset type parameters of the power grid (such as power generation power, load power, etc.), determine the operation parameters of the node (such as power injection, demand, etc.), and provide input data for the power flow equation;

[0111] In this embodiment, different first parameters are determined based on different node types and power flow equation types, and different second parameters and third parameters are determined by comprehensively considering the node types. For example, in actual power flow calculations, the power demand and power generation of the node can be adjusted according to the node type: PQ node: the load is known, and the voltage amplitude and phase angle need to be solved. PV node: the active power and voltage amplitude are known, and the reactive power and phase angle need to be solved. Slack node: the voltage amplitude and phase angle are known, and the active power and reactive power need to be solved.

[0112] In this embodiment, the preset calculation method is: Newton-Raphson Method: Applicable to large-scale complex power grids. Based on iterative nonlinear equation solving, fast convergence. Gauss-Seidel Method: Simple and easy to implement, applicable to small-scale power grids. Iterative update of node voltage. Fast Decoupled Power Flow: Simplifies calculations by approximation, improves efficiency, applicable to medium and large power grids.

[0113] In this embodiment, the third analysis is to determine the functional classification of the node in the power grid by analyzing the basic parameters and operating conditions of the power grid node. Common node types include balancing nodes (Slack), PQ nodes and PV nodes;

[0114] In this embodiment, the node type refers to the role of the node in the power flow calculation: Slack Node: Balances the system power and provides reference voltage and phase angle. PQ Node: The power injection is known, and the voltage amplitude and phase angle are solved. PV Node: The active power and voltage amplitude are known, and the reactive power and phase angle are solved. Example: The third analysis determines that: Node 1 is a slack node (Slack Node), and its voltage amplitude is 1.05pu and the phase angle is 0°. Node 2 is a PQ node, and the injected power is 40MW, 10MVar. Node 3 is a PV node, and the generated power is 60MW, and the voltage amplitude is 1.01pu.

[0115] In this embodiment, the preset type-equation type database stores power flow equations corresponding to different node types, for example, PQ nodes use active and reactive power balance equations, and PV nodes use active power and balanced reactive power equations;

[0116] In this embodiment, the power flow equation type is a power flow equation selected according to the node type and is used to solve the node state.

[0117] In this embodiment, the third parameter is the final operating state of the grid node after the power flow calculation, including the actual operating voltage, phase angle, active power output and reactive power output, etc. For example: the third parameter of node C is: the actual operating voltage is 1.03pu, the phase angle is 15°, the active power output is 55MW, and the reactive power output is 5MVar.

[0118] The beneficial effects of the above technical solution are: through multi-level analysis and mathematical modeling, the grid node admittance matrix and power flow equation are accurately constructed, and the type-equation type database is combined to realize the accurate calculation of node status data. The node type and power flow constraints are considered, and the accuracy and efficiency of node parameter calculation are improved through iterative solution, which provides a new technical means for realizing the refined control and optimized operation of the grid carbon emission intensity.

[0119] Embodiment 6:

[0120] The embodiment of the present invention provides a method for controlling carbon emission intensity of power grid nodes based on power flow constraints, which is based on a preset calculation method, all power flow constraints and iterative solution of power flow equations, and then determines a second parameter of each node under preset operating conditions, including:

[0121] Determining a plurality of first initial parameters of each node in the power grid based on preset operating conditions;

[0122] Iteratively solving the power flow equation based on a plurality of first initial parameters of each node in the power grid and a preset calculation method, thereby determining a second initial parameter of each node in the power grid;

[0123] The second initial parameter of each node in the power grid is verified based on all power flow constraints. If the second initial parameter verification fails, the first initial parameter of each node in the power grid is adjusted based on a preset method, and the power flow equation is repeatedly solved iteratively until the second initial parameter verification is successful.

[0124] In this embodiment, the preset operating conditions refer to the initial grid operating state and constraints used to solve the power flow equation, including external and internal environmental factors of the grid operation. These conditions are used to determine the boundaries and initial parameter ranges of the power flow calculation, for example: the power demand and power output of each node in the power grid (such as the load demand is 100MW and the generator power output is 200MW). The impedance value and transmission capacity limit of the transmission line (such as the transmission capacity of a certain line is 500MW). Environmental factors (such as the impact of wind speed, temperature and solar radiation intensity on distributed energy);

[0125] In this embodiment, the first initial parameter is the initial state variable value assigned to each node and line in the power grid according to the preset operating conditions, which is used as the starting point for solving the power flow equation. These parameters are usually estimated values ​​and may need to be adjusted to meet the power flow constraints. For example: the initial voltage amplitude and phase angle of the node (such as voltage amplitude 1.05pu, phase angle 10°), the initial active power and reactive power injection of the node (such as active power of 50MW and reactive power of 30MVar), the initial power flow value of the transmission line (such as a power flow of 150MW on a certain line).

[0126] In this embodiment, the second initial parameter is the operating state parameter of each node calculated by iteratively solving the power flow equation. These parameters reflect the actual operating state of the power grid under the current operating conditions and are used to verify whether all power flow constraints are met, for example: the calculated voltage amplitude and phase angle of the node (such as voltage amplitude 1.01pu, phase angle 7°), the active and reactive power distribution of the node (such as active power distribution to multiple load points, reactive power balance between generators and lines), verification results: if a node voltage exceeds the safety range (such as voltage below 0.95pu), it is necessary to adjust the first initial parameter and recalculate.

[0127] The beneficial effects of the above technical solution are: by introducing power flow constraints and iterative solution mechanism, dynamic adjustment and verification of node parameters are realized, the convergence and accuracy of the calculation results are guaranteed, and the combination of initial parameter adjustment and constraint verification effectively improves the accuracy and robustness of power flow calculation, providing reliable technical guarantee for the precise control of carbon emission intensity under complex power grid operation.

[0128] Embodiment 7:

[0129] The embodiment of the present invention provides a method for controlling carbon emission intensity of power grid nodes based on power flow constraints, which determines the carbon emission of each node in the power grid based on parameters of a preset type of the power grid, including:

[0130] Performing characteristic analysis on parameters of preset types of power grids, and then obtaining carbon emission related parameters based on the characteristic analysis results;

[0131] Determine the carbon emissions of each node in the power grid based on carbon emission related parameters:

[0132] The carbon emissions of each node in the grid are determined based on the parameters of the preset type of grid, including:

[0133]

[0134] Among them, E i is the carbon emission of the ith node in the power grid, P1 iis the active power generated by the i-th node in the power grid, P2 is the power loss of the i-th node in the power grid, γ1 i is the power generation efficiency factor of the ith node in the power grid, γ2 i is the dynamic load distribution factor of the ith node in the power grid, EF i is the carbon emission factor of the ith node in the power grid, ∈ i is the fixed carbon emission compensation factor of the ith node in the power grid, ∝P0 is the preset power conversion coefficient, V i is the voltage amplitude of the ith node in the power grid, V j is the voltage amplitude of the jth node in the power grid, G ij is the conductance of the i-th node and the j-th node in the power grid, B ij is the susceptance of the i-th node and the j-th node in the power grid, θ ij is the voltage phase angle difference between the i-th node and the j-th node in the power grid, n is the total number of nodes in the power grid, P ij is the power flow between the i-th node and the j-th node in the power grid.

[0135] In this embodiment, the power loss of a node refers to the power loss caused by factors such as the impedance of the transmission line (such as resistance and susceptance) during the transmission of electricity from the power generation side to the node or load. Power loss is a key parameter for evaluating power grid efficiency and carbon emissions. For example, if the resistance of the transmission line at a node in the power grid is 0.05Ω and the transmission current is 100A, the power loss of the line is 500W;

[0136] In this embodiment, power flow refers to the transmission of power between nodes in the power grid, including active power and reactive power transmitted from the power generation side to the load side. Power flow is usually affected by voltage amplitude, phase angle difference and line parameters (such as conductance and susceptance), and is calculated by the power flow equation. For example, in the line between two nodes A and B, the voltage of node A is 1.05pu, the voltage of node B is 1.03pu, the phase angle difference is 3°, the line conductance is 0.01S, and the susceptance is 0.001S. The power flow can be calculated by the formula, and the result indicates that the active power transmitted from A to B is 10MW and the reactive power is 2MVar.

[0137] The beneficial effects of the above technical solution are: by establishing a refined node carbon emission model based on the analysis of grid parameter characteristics and the calculation of carbon emission related parameters, combining the power generation power, power loss, dynamic load distribution factors, etc. with flow constraints, the accurate assessment and control of grid carbon emissions can be achieved, thereby improving the dynamic adaptability of carbon emission calculations and the accuracy of grid optimization control.

[0138] Embodiment 8:

[0139] The embodiments of the present invention provide a method for controlling carbon emission intensity of power grid nodes based on power flow constraints, and a carbon emission optimization scheme, including: a power supply side optimization scheme, a load side optimization scheme, a power grid operation optimization scheme, an energy storage optimization scheme, and a carbon emission monitoring and feedback optimization scheme.

[0140] In this embodiment, the power supply side optimization scheme is to reduce the carbon emission intensity per unit of power generation by adjusting the output and energy efficiency of the generator set, giving priority to the use of low-carbon energy (such as wind energy, solar energy, etc.) or high-efficiency fossil fuel generator sets. For example: in power generation scheduling, wind farms and photovoltaic power generation are prioritized to reduce the output of coal-fired generator sets. Flexible scheduling is implemented for thermal power units, and more efficient units are used. For example, units with a power generation efficiency of 40% are prioritized instead of units with a power generation efficiency of 30%;

[0141] In this embodiment, the load-side optimization scheme adjusts the power load distribution and adopts demand response technology to guide users to use electricity during low-carbon emission periods, thereby reducing overall carbon emissions. For example, time-of-use electricity prices are implemented for industrial users to encourage them to produce during the night when wind power output is high and carbon emissions are low, and smart home appliances are promoted in residential communities, such as washing machines and air conditioners that automatically run during low-carbon emission periods;

[0142] In this embodiment, the grid operation optimization reduces transmission losses and improves grid efficiency by adjusting power flow distribution and operation mode, thereby reducing carbon emissions caused by transmission losses. For example, by optimizing power flow distribution, the path with the lowest power loss in the grid undertakes the main power transmission task, and the overloaded lines are diverted to reduce the power loss caused by line overload;

[0143] In this embodiment, the energy storage optimization scheme stores electricity during low-carbon emission periods (such as when renewable energy output is high) and releases electricity during high-carbon emission periods by reasonably configuring the energy storage system, reducing dependence on high-emission power sources, and storing excess electricity in batteries during peak wind or photovoltaic power generation; releasing stored electricity during peak loads in the evening to reduce thermal power output, configuring regional energy storage stations, shaving peak loads and filling valleys, and reducing power generation demand during carbon emission periods;

[0144] In this embodiment, the carbon emission monitoring and feedback optimization solution is to monitor the carbon emission intensity of each node in real time, and dynamically adjust the operation strategy in combination with the feedback mechanism to ensure that the power grid operates in the optimal state. For example: after using the monitoring system to find that the carbon emission intensity of thermal power in a certain area is too high, the carbon emissions in the area are reduced by adjusting the load distribution or increasing the proportion of renewable energy. The node carbon emission data is fed back to the dispatching center in real time. The dispatching center optimizes the power dispatching plan and realizes dynamic carbon emission control.

[0145] The beneficial effects of the above technical solution are: by synergistically integrating the five optimization solutions of power supply side, load side, grid operation, energy storage, and carbon emission monitoring and feedback, and combining with flow constraints to achieve all-round and precise control of carbon emission intensity, the low-carbon level of grid operation is improved, and the system flexibility and dynamic adaptability are enhanced.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling carbon emission intensity of power grid nodes based on power flow constraints, characterized in that: include: Step 1: Obtain the carbon emission target and determine several power flow constraints based on the carbon emission target; Step 2: Obtain several key locations of the power grid and deploy preset monitoring equipment at each key location to monitor and collect parameters of preset types of the power grid; Step 3: Determine several nodes of the power grid, and determine the status data of each node in the power grid based on the parameters of the preset type of the power grid, all power flow constraints and the preset calculation method; Step 4: determining the carbon emission of each node in the power grid based on parameters of a preset type of the power grid; Step 5: Build a carbon emission optimization model based on the status data of each node under preset operating conditions and the carbon emissions of each node, and then determine the carbon emission optimization plan based on the carbon emission optimization model.

2. The method for controlling carbon emission intensity of power grid nodes based on power flow constraints according to claim 1 is characterized in that: Obtain the carbon emission target and determine several power flow constraints based on the carbon emission target, including: Determine the carbon emission intensity target of the power grid based on the preset regional carbon emission targets; Determine the carbon emission target coefficient of the power grid based on the carbon emission intensity target of the power grid; Determine a number of power flow constraint types based on the carbon emission target coefficient of the power grid and a preset coefficient-type database; determining initial conditions for each type of power flow constraint based on a preset type-condition database; The initial conditions of each preset type of power flow constraint are adjusted based on the carbon emission intensity target of the power grid, and then several preset types of power flow constraint conditions are determined.

3. The method for controlling carbon emission intensity of power grid nodes based on power flow constraints according to claim 1 is characterized in that: Preset types of parameters include: node parameters, line parameters, and system parameters.

4. The method for controlling carbon emission intensity of power grid nodes based on power flow constraints according to claim 1 is characterized in that: Preset monitoring equipment, including: PMU phase measurement unit, AMI smart meter, meteorological monitoring equipment, greenhouse gas sensor, transmission line monitoring equipment, DER monitoring equipment, generator real-time monitoring system and EMS monitoring module.

5. The method for controlling carbon emission intensity of power grid nodes based on power flow constraints according to claim 1 is characterized in that: Determine a number of nodes of the power grid, and determine the status data of each node in the power grid based on parameters of a preset type of the power grid, all power flow constraints, and a preset calculation method, including; Performing a first analysis on parameters of a preset type of the power grid, thereby obtaining basic parameters of each node in the power grid; Perform a second structural analysis on the power grid, and then construct a node admittance matrix of the power grid based on the basic parameters of each node in the power grid; Construct a mathematical model of the power grid based on the node admittance matrix of the power grid and the preset power flow equation; Performing a second analysis on parameters of a preset type of the power grid to determine a first parameter of each node; Performing a third analysis on basic parameters of each node in the power grid, thereby determining the node type of each node in the power grid; Determine the corresponding power flow equation type based on the node type of each node in the power grid and a preset type-equation type database; constructing a power flow equation based on a first parameter of each node, a power flow equation type, and a mathematical model of the power grid; Based on the preset calculation method, all power flow constraints and iterative solution of the power flow equation, the second parameter of each node under the preset operating conditions is determined; determining a third parameter for each node in the electrical grid based on the second parameter for each node in the electrical grid; A first parameter, a second parameter, and a third parameter of each node in the power grid are determined as status data of each node.

6. The method for controlling carbon emission intensity of power grid nodes based on power flow constraints according to claim 5 is characterized in that: Based on the preset calculation method, all power flow constraints and iterative solution of the power flow equation, the second parameter of each node under the preset operating conditions is determined, including: Determining a plurality of first initial parameters of each node in the power grid based on preset operating conditions; Iteratively solving the power flow equation based on a plurality of first initial parameters of each node in the power grid and a preset calculation method, thereby determining a second initial parameter of each node in the power grid; The second initial parameter of each node in the power grid is verified based on all power flow constraints. If the second initial parameter verification fails, the first initial parameter of each node in the power grid is adjusted based on a preset method, and the power flow equation is repeatedly solved iteratively until the second initial parameter verification is successful.

7. The method for controlling carbon emission intensity of power grid nodes based on power flow constraints according to claim 1, characterized in that: The carbon emissions of each node in the grid are determined based on the parameters of the preset type of grid, including: Performing characteristic analysis on parameters of preset types of power grids, and then obtaining carbon emission related parameters based on the characteristic analysis results; Determine the carbon emissions of each node in the power grid based on carbon emission related parameters: The carbon emissions of each node in the grid are determined based on the parameters of the preset type of grid, including: Among them, E i is the carbon emission of the ith node in the power grid, P1 i is the active power generated by the i-th node in the power grid, P2 is the power loss of the i-th node in the power grid, γ1 i is the power generation efficiency factor of the ith node in the power grid, γ2 i is the dynamic load distribution factor of the ith node in the power grid, EF i is the carbon emission factor of the ith node in the power grid, ∈ i is the fixed carbon emission compensation factor of the ith node in the power grid, ∝P0 is the preset power conversion coefficient, V i is the voltage amplitude of the ith node in the power grid, V j is the voltage amplitude of the jth node in the power grid, G ij is the conductance of the i-th node and the j-th node in the power grid, B ij is the susceptance of the i-th node and the j-th node in the power grid, θ ij is the voltage phase difference between the i-th node and the j-th node in the power grid, n is the total number of nodes in the power grid, P ij is the power flow between the i-th node and the j-th node in the power grid.

8. The method for controlling carbon emission intensity of power grid nodes based on power flow constraints according to claim 1 is characterized in that: Carbon emission optimization solutions include: power supply side optimization solution, load side optimization solution, grid operation optimization solution, energy storage optimization solution and carbon emission monitoring and feedback optimization solution.

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