Power grid low-carbon scheduling method and device, terminal equipment and storage medium

Abstract

The invention discloses a power grid low-carbon scheduling method and device, terminal equipment and a storage medium, and belongs to the technical field of low-carbon scheduling, and the method comprises the steps: calculating the marginal carbon intensity of a node according to the output of a thermal power generating unit, the carbon emission intensity of the thermal power generating unit, the load power of the node and the sensitivity of the node to the thermal power generating unit; nodes with the marginal carbon intensity exceeding a preset marginal carbon intensity threshold value are screened out to serve as standard exceeding nodes; the method comprises the following steps: performing output reduction analysis on a related thermal power generating unit of a standard exceeding node, generating an output reduction constraint to solve a low-carbon scheduling model, performing low-carbon scheduling on a power grid according to model output, realizing reduction of the output of the related thermal power generating unit, so as to pertinently reduce the carbon emission of the corresponding standard exceeding node, and realizing accurate control of the carbon emission. The problem of space misalignment in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of low-carbon dispatching technology, and in particular to a low-carbon dispatching method, apparatus, terminal equipment, and storage medium for power grids. Background Technology

[0002] Against the backdrop of the ongoing and in-depth advancement of the "dual carbon" goals, the power system, as a core area of ​​carbon emissions, is facing the severe challenge of controlling both total carbon emissions and intensity. Achieving a low-carbon transformation of the power system has become a key task for global efforts to address climate change and promote sustainable development. Most existing low-carbon dispatch technologies operate based on fixed quota mechanisms. This mechanism optimizes unit allocation and further calculates unit output plans by pre-setting annual carbon quotas, with the core objective of minimizing generation costs. This dispatch technology has helped control carbon emissions from the power system to some extent and has played a role in past practices.

[0003] However, with the development of power systems and increasingly stringent requirements for carbon emission control, existing low-carbon dispatch technologies based on fixed quotas suffer from spatial inaccuracies, lacking the ability to effectively identify localized high-carbon-emission areas. This results in carbon emissions at some key nodes consistently exceeding standards during actual dispatch, hindering precise carbon emission control and impeding the reduction of overall carbon emission intensity. Therefore, there is an urgent need to develop a novel low-carbon dispatch technology to address the spatial inaccuracies of current technologies. Summary of the Invention

[0004] This invention provides a low-carbon power grid dispatching method, device, terminal equipment, and storage medium, which can solve the problem of spatial inaccuracy in the prior art.

[0005] An embodiment of the present invention provides a low-carbon power grid dispatching method, comprising:

[0006] The output of each thermal power unit in the power grid, the carbon emission intensity of each thermal power unit, the load power of each node, and the first sensitivity of each node to each thermal power unit are obtained; wherein, the first sensitivity is used to quantify the degree of influence of the change in the output of the thermal power unit on the change in the node power.

[0007] For each of the aforementioned nodes, the marginal carbon intensity of the node is calculated based on the output of all the aforementioned thermal power units, the carbon emission intensity of all the aforementioned thermal power units, the load power of the node, and the first sensitivity of the node to all the aforementioned thermal power units.

[0008] Nodes whose marginal carbon intensity exceeds a preset marginal carbon intensity threshold are selected as out-of-limit nodes;

[0009] For each of the aforementioned non-compliant nodes, a second sensitivity of the non-compliant node to each thermal power unit is calculated based on the marginal carbon intensity of the non-compliant node and the output of each of the aforementioned thermal power units; thermal power units with a second sensitivity greater than a preset second sensitivity threshold are selected as associated thermal power units, and the non-compliant node is marked as an associated non-compliant node of each of the associated thermal power units; wherein, the second sensitivity is used to quantify the degree of influence of the thermal power unit output change on the marginal carbon intensity change of the non-compliant node;

[0010] For each associated thermal power unit, the reduced output power value of the associated thermal power unit is generated based on the marginal carbon intensity of all associated out-of-standard nodes of the associated thermal power unit; and the reduced output power constraint of the associated thermal power unit is constructed based on the reduced output power value.

[0011] Based on the reduced output constraint, the low-carbon scheduling model is solved to obtain the target values ​​of the decision variables;

[0012] The power grid is dispatched based on the target values ​​of the decision variables.

[0013] Furthermore, the first sensitivity of each node to each thermal power unit is obtained, including:

[0014] For each node, the shortest path between the node and each thermal power unit is determined, and the active power flow of each shortest path is obtained; based on the shortest path, the active power flow of the shortest path, and the output of the thermal power unit, the first sensitivity of the node to each thermal power unit is calculated.

[0015] The formula for calculating the first sensitivity is as follows:

[0016]

[0017] In the formula, H n,i Indicates the first sensitivity of node n to thermal power unit i; l n,i This represents the shortest path between node n and thermal power unit i. Indicates line l n,i Active power flow; G i This represents the output of thermal power unit i. Indicates line l n,i And the element corresponding to node n in the inverse of the Jacobian matrix.

[0018] Furthermore, the formula for calculating the marginal carbon intensity is:

[0019]

[0020] In the formula, CI n Γ represents the marginal carbon intensity of node n;thermal G represents a set of thermal power units; i This represents the output of thermal power unit i; CF i Indicates the carbon emission intensity of thermal power units; H n,i D represents the first sensitivity of node n to thermal power unit i; n This represents the load power of node n.

[0021] Furthermore, the formula for calculating the second sensitivity is:

[0022]

[0023] In the formula, S m,i CI represents the second sensitivity of node m (exceeding the standard) to thermal power unit i; m G represents the marginal carbon intensity of node m that exceeds the standard; i This represents the output of thermal power unit i.

[0024] Furthermore, the formula for calculating the reduced output value of the thermal power unit is as follows:

[0025]

[0026] Δg j,max =δ×G j,max ;

[0027] In the formula, ΔG j Δg represents the reduced output of the associated thermal power unit j; j,max This represents the upper limit of the reduced output of the associated thermal power unit j; Υ j Δg represents the set of associated out-of-specification nodes of associated thermal power unit j; j,y This indicates that the associated excessive node y requires the associated thermal power unit j to reduce its output by a certain value; CI y CI represents the marginal carbon intensity of the associated out-of-range node y; y,max G represents the maximum marginal carbon intensity of the associated node y that exceeds the standard; γ represents the adjustment coefficient; G j,max δ represents the upper limit of the output of the associated thermal power unit j; δ represents the output reduction protection coefficient.

[0028] Furthermore, the low-carbon scheduling model is a two-layer optimization model;

[0029] The upper-level optimization model of the two-level optimization model uses carbon quotas as the decision variable and minimizes the sum of fuel costs and carbon trading costs as the objective function.

[0030] The constraints of the upper-level optimization model include: total carbon emission constraints and carbon quota flexibility constraints;

[0031] The lower-level optimization model of the two-level optimization model takes the output of thermal power units and the amount of energy curtailed by new energy units as decision variables, and takes minimizing the sum of thermal power cost and energy curtailment cost as the objective function.

[0032] The constraints of the lower-level optimization model include: power output reduction constraints, carbon quota constraints, power balance constraints, and unit ramp rate constraints.

[0033] The construction of the carbon quota constraint includes:

[0034] Solve the aforementioned upper-level optimization model to obtain the target carbon quota;

[0035] The carbon quota constraint is constructed based on the target carbon quota.

[0036] Furthermore, the objective function of the upper-level optimization model is:

[0037]

[0038] In the formula, Q represents the carbon quota; T represents the time period set; Γ thermal Represents a set of thermal power units; C fuel (·) represents a function for calculating combustion costs based on the output of thermal power units; p represents the predicted output of thermal power unit i during time period t; carbon Indicates the price in the carbon trading market; E total This indicates the predicted total carbon emissions from the power grid;

[0039] The total carbon emission constraint is:

[0040] E total ≤Q;

[0041] The carbon quota flexibility constraint is:

[0042] Q min ≤Q≤Q max ;

[0043] Q min =θ1×Q baseline ;

[0044] Q max =θ2×Q baseline ;

[0045] In the formula, Q min Indicates the lower limit of carbon quota flexibility; Q max Indicates the upper limit of carbon quota flexibility; Q baseline θ1 represents the historical carbon emission baseline; θ2 represents the carbon quota lower limit coefficient; θ2 represents the carbon quota upper limit coefficient.

[0046] The objective function of the lower-level optimization model is:

[0047]

[0048] In the formula, G i C represents the output of thermal power unit i; gen (·) represents a function for calculating power generation costs based on the output of thermal power units; Γ wink Represents the set of new energy generating units; ρ represents the curtailment penalty coefficient; ΔW k This represents the amount of electricity wasted by the new energy unit k;

[0049] The reduced output force constraint is:

[0050] G i ≤G i,max -ΔG i ;

[0051] In the formula, G i,max ΔG represents the upper limit of the output of thermal power unit i; i This represents the reduced output value of thermal power unit i.

[0052] The carbon quota constraint is as follows:

[0053]

[0054] In the formula, CF i Indicates the carbon emission intensity of thermal power units; Q * Indicates the target carbon quota;

[0055] The power balance constraint is:

[0056]

[0057] In the formula, W k L represents the output of renewable energy unit k; L represents the set of nodes.

[0058] The unit's ramp rate constraint is:

[0059]

[0060] In the formula, R represents the predicted output of thermal power unit i during time period t; i This represents the maximum ramp rate of thermal power unit i.

[0061] Another embodiment of the present invention provides a low-carbon power grid dispatching device, comprising: a data acquisition module, a data analysis module, and a dispatching module;

[0062] The data acquisition module is used to acquire the thermal power unit output of each thermal power unit in the power grid, the thermal power unit carbon emission intensity of each thermal power unit, the load power of each node, and the first sensitivity of each node to each thermal power unit; wherein, the first sensitivity is used to quantify the degree of influence of the thermal power unit output change on the node power change.

[0063] The data analysis module is used to calculate the marginal carbon intensity of each node based on the output of all thermal power units, the carbon emission intensity of all thermal power units, the load power of the node, and the node's first sensitivity to all thermal power units; to filter out nodes whose marginal carbon intensity exceeds a preset marginal carbon intensity threshold as non-compliant nodes; and for each non-compliant node, to calculate the second sensitivity of the non-compliant node to each thermal power unit based on the marginal carbon intensity of the non-compliant node and the output of each thermal power unit; and to filter out nodes whose second sensitivity is greater than a preset second sensitivity threshold. The thermal power units with the specified values ​​are designated as associated thermal power units, and the nodes exceeding the standard are marked as associated excessive nodes for each associated thermal power unit; wherein, the second sensitivity is used to quantify the degree of influence of the output change of the thermal power unit on the marginal carbon intensity change of the excessive node; for each associated thermal power unit, the reduced output value of the associated thermal power unit is generated based on the marginal carbon intensity of all associated excessive nodes of the associated thermal power unit; the reduced output constraint of the associated thermal power unit is constructed based on the reduced output value; based on the reduced output constraint, the low-carbon scheduling model is solved to obtain the target value of the decision variable;

[0064] The scheduling module is used to schedule the power grid according to the target value of the decision variable.

[0065] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the steps of the power grid low-carbon dispatching method of the present invention.

[0066] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the power grid low-carbon dispatching method of the present invention.

[0067] The following benefits can be obtained by implementing the present invention:

[0068] This invention introduces a node marginal carbon intensity quantification method to determine regional carbon emissions, thereby identifying nodes exceeding emission standards. It then performs output reduction analysis on associated thermal power units of these nodes, generating a low-carbon scheduling model with output reduction constraints. Based on the model output, the power grid is scheduled for low-carbon operation, reducing the output of associated thermal power units to specifically reduce carbon emissions from the corresponding nodes exceeding emission standards. This achieves precise control of carbon emissions and solves the problem of spatial inaccuracy in existing technologies. Attached Figure Description

[0069] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments 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 from these drawings without creative effort.

[0070] Figure 1 This is a flowchart illustrating a low-carbon power grid dispatching method according to an embodiment of the present invention;

[0071] Figure 2 This is a schematic diagram of the structure of a low-carbon power grid dispatching device provided in an embodiment of the present invention. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0073] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the term "comprising" and any variations thereof in the specification, claims and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0074] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly indicating the number, specific order, or primary and secondary relationship of the indicated technical features.

[0075] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0076] See Figure 1 To address the spatial inaccuracy problem in existing technologies, an embodiment of the present invention provides a low-carbon power grid dispatching method, comprising:

[0077] S1. Obtain the thermal power unit output of each thermal power unit in the power grid, the thermal power unit carbon emission intensity of each thermal power unit, the load power of each node, and the first sensitivity of each node to each thermal power unit; wherein, the first sensitivity is used to quantify the degree of influence of thermal power unit output change on node power change.

[0078] It should be noted that a node refers to an electrical connection point in a power transmission network. The carbon emission intensity of a thermal power unit refers to the ratio of carbon emissions to the unit's output.

[0079] In step S1, the output and carbon emission intensity of each thermal power unit in the power grid system, the load power of each node, and the first sensitivity of each node to the thermal power unit are obtained in real time.

[0080] In a preferred embodiment, obtaining the first sensitivity of each node to each thermal power unit includes:

[0081] For each node, the shortest path between the node and each thermal power unit is determined, and the active power flow of each shortest path is obtained; based on the shortest path, the active power flow of the shortest path, and the output of the thermal power unit, the first sensitivity of the node to each thermal power unit is calculated.

[0082] The formula for calculating the first sensitivity is as follows:

[0083]

[0084] In the formula, H n,i Indicates the first sensitivity of node n to thermal power unit i; l n,i This represents the shortest path between node n and thermal power unit i. Indicates line l n,i Active power flow; G i This represents the output of thermal power unit i. Indicates line l n,i And the element corresponding to node n in the inverse of the Jacobian matrix.

[0085] In this embodiment, for each node, the shortest path between the node and each thermal power unit is first determined, and the active power flow of the shortest path is obtained. Then, based on the shortest path, the active power flow of the shortest path, and the output of the thermal power units, the first sensitivity between the node and each thermal power unit is calculated. The information of the shortest path and the node is used to confirm the matrix. The elements in.

[0086] It should be noted that the formula for calculating the power transfer distribution factor is:

[0087]

[0088] In the formula, PTDF represents the power transmission distribution factor matrix; S represents the line-stage correlation matrix, where (l,i) is 1 if the power flows from line l to node i, and -1 otherwise, and 0 otherwise; is the inverse of the node admittance matrix, with dimensions N×N, representing the relationship between the node voltage phase angle and the injected power; K represents the total number of lines in the power grid; N represents the total number of nodes in the power grid; and represents the inverse of the node admittance matrix, with dimensions N×N, representing the relationship between the node voltage phase angle and the injected power.

[0089] J -1 for The sub-block mapping; can be obtained from the power transfer distribution factor matrix. Then J is obtained -1 .

[0090] The power transmission distribution factor is a key indicator for quantifying the impact of power changes in generators or loads on the power flow of transmission lines. In existing technologies, it is mainly used for power distribution. This invention extends it to the field of carbon flow, combining the real-time output of generator sets and carbon intensity to characterize the first sensitivity of nodes to changes in generator power.

[0091] S2. For each node, calculate the marginal carbon intensity of the node based on the output of all thermal power units, the carbon emission intensity of all thermal power units, the load power of the node, and the first sensitivity of the node to all thermal power units.

[0092] In this embodiment, for each node, the marginal carbon intensity of the node is calculated based on the output of all thermal power units, the carbon emission intensity of all thermal power units, the load power of the node, and the first sensitivity of the node to all thermal power units.

[0093] In a preferred embodiment, the formula for calculating the marginal carbon intensity is:

[0094]

[0095] In the formula, CI n Γ represents the marginal carbon intensity of node n; thermal G represents a set of thermal power units; i This represents the output of thermal power unit i; CF i Indicates the carbon emission intensity of thermal power units; H n,i D represents the first sensitivity of node n to thermal power unit i; n This represents the load power of node n.

[0096] It should be noted that this invention quantifies the carbon intensity per unit of electricity consumption based on the first sensitivity definition node carbon intensity index, reflecting the principle of load responsibility sharing.

[0097] S3. Select nodes whose marginal carbon intensity exceeds the preset marginal carbon intensity threshold as out-of-limit nodes.

[0098] In step S3, nodes whose marginal carbon intensity exceeds the marginal carbon intensity threshold are designated as out-of-limit nodes.

[0099] It should be noted that this invention introduces a node marginal carbon intensity quantification regional carbon emission to identify nodes exceeding the standard and pinpoint areas exceeding the standard, so that subsequent steps can be taken to reduce the output of high-carbon units in a targeted manner.

[0100] S4. For each of the aforementioned non-compliant nodes, calculate the second sensitivity of the non-compliant node to each thermal power unit based on the marginal carbon intensity of the non-compliant node and the output of each of the aforementioned thermal power units; select thermal power units whose second sensitivity is greater than a preset second sensitivity threshold as associated thermal power units, and mark the non-compliant node as an associated non-compliant node of each of the aforementioned associated thermal power units; wherein, the second sensitivity is used to quantify the degree of influence of the change in the output of the thermal power unit on the change in the marginal carbon intensity of the non-compliant node.

[0101] In step S4, for each out-of-standard node, the second sensitivity of the out-of-standard node to each thermal power unit is calculated based on the marginal carbon intensity of the out-of-standard node and the output of all thermal power units; thermal power units whose second sensitivity exceeds the second sensitivity threshold are regarded as associated thermal power units of the out-of-standard node, and the out-of-standard node is marked as an associated out-of-standard node of the associated thermal power unit.

[0102] In one embodiment, the second sensitivity threshold ranges from 0.1 to 0.3, with an empirical value of 0.2.

[0103] It should be noted that the present invention determines the associated thermal power units based on the second sensitivity, and then performs low-carbon scheduling on the associated thermal power units to ensure maximum carbon reduction efficiency.

[0104] In a preferred embodiment, the formula for calculating the second sensitivity is:

[0105]

[0106] In the formula, S m,i CI represents the second sensitivity of node m (exceeding the standard) to thermal power unit i; m G represents the marginal carbon intensity of node m that exceeds the standard; i This represents the output of thermal power unit i.

[0107] It should be noted that,

[0108]

[0109] In the formula, H n,i D represents the first sensitivity of the out-of-specification node m to thermal power unit i; n This represents the load power of node m that exceeds the standard.

[0110] The physical meaning of the second sensitivity is the rate of change of node carbon intensity caused by the change in unit output of the thermal power unit. Essentially, it depends on the carbon intensity of the thermal power unit, the first sensitivity, and the load power of the node.

[0111] S5. For each associated thermal power unit, generate the reduced power output value of the associated thermal power unit based on the marginal carbon intensity of all associated excessive nodes of the associated thermal power unit; construct the reduced power output constraint of the associated thermal power unit based on the reduced power output value.

[0112] It should be noted that the output reduction value of a thermal power unit refers to the amount of output that the thermal power unit needs to reduce. The output reduction constraint is used to constrain the processing behavior of the thermal power unit, thereby reducing its output.

[0113] In step S5, for each associated thermal power unit, the power reduction value of the associated thermal power unit is generated based on the marginal carbon intensity of all associated excessive nodes associated with the associated thermal power unit; then, the power reduction constraint of the associated thermal power unit is constructed based on the power reduction value.

[0114] In a preferred embodiment, the formula for calculating the reduced output power of the thermal power unit is as follows:

[0115]

[0116] Δg j,max =δ×G j,max ;

[0117] In the formula, ΔG j Δg represents the reduced output of the associated thermal power unit j; j,max This represents the upper limit of the reduced output of the associated thermal power unit j; Υj Δg represents the set of associated out-of-specification nodes of associated thermal power unit j; j,y This indicates that the associated excessive node y requires the associated thermal power unit j to reduce its output by a certain value; CI y CI represents the marginal carbon intensity of the associated out-of-range node y; y,max G represents the maximum marginal carbon intensity of the associated node y that exceeds the standard; γ represents the adjustment coefficient; G j,max δ represents the upper limit of the output of the associated thermal power unit j; δ represents the output reduction protection coefficient.

[0118] In this embodiment, for each associated thermal power unit, first according to Δg j,y The formula is used to calculate the required power reduction for each associated excessive node of the associated thermal power unit. Then, the maximum value among the required power reductions of all associated excessive nodes is taken as a candidate power reduction value. Next, the upper limit of the power reduction of the thermal power unit is calculated based on the upper limit of the thermal power output of the associated thermal power unit, which is taken as another candidate power reduction value. Finally, the smaller of the two candidate power reduction values ​​is selected as the final power reduction value of the thermal power unit.

[0119] It should be noted that the upper limit of the reduced output of the associated thermal power unit is generally set as a fixed percentage of the output of the thermal power unit. This fixed percentage is the reduced output protection coefficient, so as to prevent excessive reduction of output from affecting the normal operation of the thermal power unit.

[0120] The adjustment coefficient ranges from (0,1), with an empirical value of 0.8. The reduced output protection coefficient has an empirical value of 0.3. CI y,max It is generally set to the marginal carbon intensity threshold used in the previous section for judging nodes that exceed the standard.

[0121] It should also be noted that the power output reduction adjustment of the associated thermal power units is calculated based on the marginal carbon intensity exceeding the standard ratio of the associated excessive nodes, which can achieve precise carbon control.

[0122] S6. Based on the reduced output constraint, solve the low-carbon scheduling model to obtain the target value of the decision variable.

[0123] It should be noted that the low-carbon dispatch model refers to the model used to dispatch the power grid to achieve low-carbon goals, and the decision variables refer to the variables that the low-carbon dispatch model needs to solve, which generally refer to the quantities in the power grid that need to be regulated and dispatched.

[0124] In step S6, the reduction force constraint is used as a constraint of the low-carbon scheduling model. Solving the low-carbon scheduling model, the target value of the decision variable obtained satisfies the reduction force constraint requirement, and low-carbon emission reduction can be achieved.

[0125] In a preferred embodiment, the low-carbon scheduling model is a two-layer optimization model;

[0126] The upper-level optimization model of the two-level optimization model uses carbon quotas as the decision variable and minimizes the sum of fuel costs and carbon trading costs as the objective function.

[0127] The constraints of the upper-level optimization model include: total carbon emission constraints and carbon quota flexibility constraints;

[0128] The lower-level optimization model of the two-level optimization model takes the output of thermal power units and the amount of energy curtailed by new energy units as decision variables, and takes minimizing the sum of thermal power cost and energy curtailment cost as the objective function.

[0129] The constraints of the lower-level optimization model include: power output reduction constraints, carbon quota constraints, power balance constraints, and unit ramp rate constraints.

[0130] The construction of the carbon quota constraint includes:

[0131] Solve the aforementioned upper-level optimization model to obtain the target carbon quota;

[0132] The carbon quota constraint is constructed based on the target carbon quota.

[0133] In this embodiment, a two-layer low-carbon scheduling model is provided, where the lower layer optimization is performed after the upper layer optimization is completed. The output of the upper layer optimization is the target carbon quota, which is used as the carbon quota constraint for the lower layer optimization. The output of the lower layer optimization is the target output of thermal power units and the target curtailment of renewable energy units, which are used for low-carbon scheduling of thermal power units and renewable energy units. The output reduction constraint of the lower layer optimization is the output reduction constraint constructed in step S5 above.

[0134] It should be noted that by constructing a two-layer optimization model, the upper layer dynamically adjusts carbon quotas to adapt to fluctuations in new energy sources, while the lower layer minimizes wind curtailment, thereby expanding the flexibility of carbon quotas and reducing wind curtailment.

[0135] In a preferred embodiment, the objective function of the upper-level optimization model is:

[0136]

[0137] In the formula, Q represents the carbon quota; T represents the time period set; Γ thermal Represents a set of thermal power units; C fuel (·) represents a function for calculating combustion costs based on the output of thermal power units; p represents the predicted output of thermal power unit i during time period t; carbon Indicates the price in the carbon trading market; E total This indicates the predicted total carbon emissions from the power grid;

[0138] The total carbon emission constraint is:

[0139] E total ≤Q;

[0140] The carbon quota flexibility constraint is:

[0141] Q min ≤Q≤Q max ;

[0142] Q min =θ1×Q baseline ;

[0143] Q max =θ2×Q baseline ;

[0144] In the formula, Q min Indicates the lower limit of carbon quota flexibility; Q max Indicates the upper limit of carbon quota flexibility; Q baseline θ1 represents the historical carbon emission baseline; θ2 represents the carbon quota lower limit coefficient; θ2 represents the carbon quota upper limit coefficient.

[0145] The objective function of the lower-level optimization model is:

[0146]

[0147] In the formula, G i C represents the output of thermal power unit i; gen (·) represents a function for calculating power generation costs based on the output of thermal power units; Γ wink Represents the set of new energy generating units; ρ represents the curtailment penalty coefficient; ΔW k This represents the amount of electricity wasted by the new energy unit k;

[0148] The reduced output force constraint is:

[0149] G i ≤G i,max -ΔG i ;

[0150] In the formula, G i,max ΔG represents the upper limit of the output of thermal power unit i; i This represents the reduced output value of thermal power unit i.

[0151] The carbon quota constraint is as follows:

[0152]

[0153] In the formula, CF i Indicates the carbon emission intensity of thermal power units; Q * Indicates the target carbon quota;

[0154] The power balance constraint is:

[0155]

[0156] In the formula, W k L represents the output of renewable energy unit k; L represents the set of nodes.

[0157] The unit's ramp rate constraint is:

[0158]

[0159] In the formula, R represents the predicted output of thermal power unit i during time period t; i This represents the maximum ramp rate of thermal power unit i.

[0160] It should be noted that Q is the dynamic carbon quota decision variable, which is an adaptive variable that changes over time. The length of the time period t is 4 hours, and the day is divided into 6 time periods.

[0161] Combustion cost calculation function C fuel (·) can be achieved using the traditional quadratic correlation function:

[0162]

[0163] In the formula, a i b i and c i This represents the cost coefficient, which is calibrated by thermal power unit thermal tests.

[0164] S7. Dispatch the power grid according to the target value of the decision variable.

[0165] In one embodiment, scheduling the power grid based on the target value of the decision variable includes:

[0166] The carbon quota of the power grid is set according to the target carbon quota output by the upper optimization model in the two-level optimization model.

[0167] Based on the target output of each thermal power unit outputted by the lower-level optimization model in the dual-layer optimization model, adjust the output of each thermal power unit.

[0168] Based on the target renewable energy unit curtailment of each thermal power unit output by the lower-level optimization model in the dual-level optimization model, the curtailment of each renewable energy unit is adjusted.

[0169] In one embodiment, the low-carbon power grid dispatching device further includes:

[0170] For each node, the marginal electricity price of the node is calculated based on its marginal carbon intensity; and the electricity price of the region where the node is located is adjusted based on the marginal electricity price.

[0171] The formula for calculating the marginal electricity price is as follows:

[0172] LMP n =λ energy,n +λ carbon,n ×CI n ;

[0173] In the formula, LMP n λ represents the marginal electricity price at node n; energy,n The dual variable representing the power balance constraint at node n is λ, which represents the change in total system cost resulting from adding 1MW of load to node n; carbon,n Let represent the dual variable of the carbon emission constraint at node n, representing the total cost change when node n increases by 1 ton of carbon emission permit.

[0174] It should be noted that this invention adds a component to the nodal marginal electricity price, so that the electricity price can explicitly reflect the carbon cost and drive load migration.

[0175] like Figure 2 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided;

[0176] One embodiment of the present invention provides a low-carbon power grid dispatching device, comprising: a data acquisition module, a data analysis module, and a dispatching module;

[0177] The data acquisition module is used to acquire the thermal power unit output of each thermal power unit in the power grid, the thermal power unit carbon emission intensity of each thermal power unit, the load power of each node, and the first sensitivity of each node to each thermal power unit; wherein, the first sensitivity is used to quantify the degree of influence of the thermal power unit output change on the node power change.

[0178] The data analysis module is used to calculate the marginal carbon intensity of each node based on the output of all thermal power units, the carbon emission intensity of all thermal power units, the load power of the node, and the node's first sensitivity to all thermal power units; to filter out nodes whose marginal carbon intensity exceeds a preset marginal carbon intensity threshold as non-compliant nodes; and for each non-compliant node, to calculate the second sensitivity of the non-compliant node to each thermal power unit based on the marginal carbon intensity of the non-compliant node and the output of each thermal power unit; and to filter out nodes whose second sensitivity is greater than a preset second sensitivity threshold. The thermal power units with the specified values ​​are designated as associated thermal power units, and the nodes exceeding the standard are marked as associated excessive nodes for each associated thermal power unit; wherein, the second sensitivity is used to quantify the degree of influence of the output change of the thermal power unit on the marginal carbon intensity change of the excessive node; for each associated thermal power unit, the reduced output value of the associated thermal power unit is generated based on the marginal carbon intensity of all associated excessive nodes of the associated thermal power unit; the reduced output constraint of the associated thermal power unit is constructed based on the reduced output value; based on the reduced output constraint, the low-carbon scheduling model is solved to obtain the target value of the decision variable;

[0179] The scheduling module is used to schedule the power grid according to the target value of the decision variable.

[0180] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the low-carbon power grid dispatching method provided by any of the above-described method embodiments of the present invention.

[0181] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0182] Based on the above-described method embodiments, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the power grid low-carbon dispatching method of any embodiment of the present invention.

[0183] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0184] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0185] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0186] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the power grid low-carbon dispatching method described in any of the above-described method embodiments of the present invention.

[0187] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0188] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A low-carbon power grid dispatching method, characterized in that, include: The output of each thermal power unit in the power grid, the carbon emission intensity of each thermal power unit, the load power of each node, and the first sensitivity of each node to each thermal power unit are obtained; wherein, the first sensitivity is used to quantify the degree of influence of the change in the output of the thermal power unit on the change in the node power. For each of the aforementioned nodes, the marginal carbon intensity of the node is calculated based on the output of all the aforementioned thermal power units, the carbon emission intensity of all the aforementioned thermal power units, the load power of the node, and the first sensitivity of the node to all the aforementioned thermal power units. Nodes whose marginal carbon intensity exceeds a preset marginal carbon intensity threshold are selected as out-of-limit nodes; For each of the aforementioned non-compliant nodes, a second sensitivity of the non-compliant node to each thermal power unit is calculated based on the marginal carbon intensity of the non-compliant node and the output of each of the aforementioned thermal power units; thermal power units with a second sensitivity greater than a preset second sensitivity threshold are selected as associated thermal power units, and the non-compliant node is marked as an associated non-compliant node of each of the associated thermal power units; wherein, the second sensitivity is used to quantify the degree of influence of the thermal power unit output change on the marginal carbon intensity change of the non-compliant node; For each associated thermal power unit, the reduced output power value of the associated thermal power unit is generated based on the marginal carbon intensity of all associated out-of-standard nodes of the associated thermal power unit; and the reduced output power constraint of the associated thermal power unit is constructed based on the reduced output power value. Based on the reduced output constraint, the low-carbon scheduling model is solved to obtain the target values ​​of the decision variables; The power grid is dispatched based on the target values ​​of the decision variables.

2. The low-carbon power grid dispatching method as described in claim 1, characterized in that, Obtain the first sensitivity of each node to each thermal power unit, including: For each node, the shortest path between the node and each thermal power unit is determined, and the active power flow of each shortest path is obtained; based on the shortest path, the active power flow of the shortest path, and the output of the thermal power unit, the first sensitivity of the node to each thermal power unit is calculated. The formula for calculating the first sensitivity is as follows: In the formula, H n,i Indicates the first sensitivity of node n to thermal power unit i; l n,i This represents the shortest path between node n and thermal power unit i. Indicates line l n,i Active power flow; G i This represents the output of thermal power unit i. Indicates line l n,i And the element corresponding to node n in the inverse of the Jacobian matrix.

3. The low-carbon power grid dispatching method as described in claim 1, characterized in that, The formula for calculating the marginal carbon intensity is: In the formula, CI n Γ represents the marginal carbon intensity of node n; thermal G represents a set of thermal power units; i This represents the output of thermal power unit i; CF i Indicates the carbon emission intensity of thermal power units; H n,i D represents the first sensitivity of node n to thermal power unit i; n This represents the load power of node n.

4. The low-carbon power grid dispatching method as described in claim 1, characterized in that, The formula for calculating the second sensitivity is: In the formula, S m,i CI represents the second sensitivity of node m (exceeding the standard) to thermal power unit i; m G represents the marginal carbon intensity of node m that exceeds the standard; i This represents the output of thermal power unit i.

5. The low-carbon power grid dispatching method as described in claim 1, characterized in that, The formula for calculating the reduced output value of the thermal power unit is as follows: Δg j,max =δ×G j,max ; In the formula, ΔG j Δg represents the reduced output of the associated thermal power unit j; j,max This represents the upper limit of the reduced output of the associated thermal power unit j; Υ j Δg represents the set of associated out-of-specification nodes of associated thermal power unit j; j,y This indicates that the associated excessive node y requires the associated thermal power unit j to reduce its output by a certain value; CI y CI represents the marginal carbon intensity of the associated out-of-range node y; y,max G represents the maximum marginal carbon intensity of the associated node y that exceeds the standard; γ represents the adjustment coefficient; G j,max This indicates the upper limit of the output of the associated thermal power unit j; δ represents the reduced output force protection coefficient.

6. The low-carbon power grid dispatching method as described in claim 1, characterized in that, The low-carbon scheduling model is a two-layer optimization model; The upper-level optimization model of the two-level optimization model uses carbon quotas as the decision variable and minimizes the sum of fuel costs and carbon trading costs as the objective function. The constraints of the upper-level optimization model include: total carbon emission constraints and carbon quota flexibility constraints; The lower-level optimization model of the two-level optimization model takes the output of thermal power units and the amount of energy curtailed by new energy units as decision variables, and takes minimizing the sum of thermal power cost and energy curtailment cost as the objective function. The constraints of the lower-level optimization model include: power output reduction constraints, carbon quota constraints, power balance constraints, and unit ramp rate constraints. The construction of the carbon quota constraint includes: Solve the aforementioned upper-level optimization model to obtain the target carbon quota; The carbon quota constraint is constructed based on the target carbon quota.

7. The low-carbon power grid dispatching method as described in claim 6, characterized in that, The objective function of the upper-level optimization model is: In the formula, Q represents the carbon quota; T represents the time period set; Γ thermal Represents a set of thermal power units; C fuel (·) represents a function for calculating combustion costs based on the output of thermal power units; p represents the predicted output of thermal power unit i during time period t; carbon Indicates the price in the carbon trading market; E total This indicates the predicted total carbon emissions from the power grid; The total carbon emission constraint is: AND total ≤Q; The carbon quota flexibility constraint is: Q min ≤Q≤Q max ; Q min =θ1×Q baseline ; Q max =θ2×Q baseline ; In the formula, Q min Indicates the lower limit of carbon quota flexibility; Q max This indicates the upper limit of flexibility in carbon quotas; Q baseline θ1 represents the historical carbon emission baseline; θ2 represents the carbon quota lower limit coefficient; θ2 represents the carbon quota upper limit coefficient. The objective function of the lower-level optimization model is: In the formula, G i C represents the output of thermal power unit i; gen (·) represents a function for calculating power generation costs based on the output of thermal power units; Γ wink Represents the set of new energy generating units; ρ represents the curtailment penalty coefficient; ΔW k This represents the amount of electricity wasted by the new energy unit k; The reduced output force constraint is: G i ≤G i,max -ΔG i ; In the formula, G i,max This indicates the upper limit of the output of thermal power unit i. ΔG i This represents the reduced output value of thermal power unit i. The carbon quota constraint is as follows: In the formula, CF i Indicates the carbon emission intensity of thermal power units; Q * Indicates the target carbon quota; The power balance constraint is: In the formula, W k L represents the output of renewable energy unit k; L represents the set of nodes. The unit's ramp rate constraint is: In the formula, R represents the predicted output of thermal power unit i during time period t; i This represents the maximum ramp rate of thermal power unit i.

8. A low-carbon power grid dispatching device, characterized in that, include: Data acquisition module, data analysis module, and scheduling module; The data acquisition module is used to acquire the thermal power unit output of each thermal power unit in the power grid, the thermal power unit carbon emission intensity of each thermal power unit, the load power of each node, and the first sensitivity of each node to each thermal power unit; wherein, the first sensitivity is used to quantify the degree of influence of the thermal power unit output change on the node power change. The data analysis module is used to calculate the marginal carbon intensity of each node based on the output of all thermal power units, the carbon emission intensity of all thermal power units, the load power of the node, and the node's first sensitivity to all thermal power units; to filter out nodes whose marginal carbon intensity exceeds a preset marginal carbon intensity threshold as non-compliant nodes; and for each non-compliant node, to calculate the second sensitivity of the non-compliant node to each thermal power unit based on the marginal carbon intensity of the non-compliant node and the output of each thermal power unit; and to filter out nodes whose second sensitivity is greater than a preset second sensitivity threshold. The thermal power units with the specified values ​​are designated as associated thermal power units, and the nodes exceeding the standard are marked as associated excessive nodes for each associated thermal power unit; wherein, the second sensitivity is used to quantify the degree of influence of the output change of the thermal power unit on the marginal carbon intensity change of the excessive node; for each associated thermal power unit, the reduced output value of the associated thermal power unit is generated based on the marginal carbon intensity of all associated excessive nodes of the associated thermal power unit; the reduced output constraint of the associated thermal power unit is constructed based on the reduced output value; based on the reduced output constraint, the low-carbon scheduling model is solved to obtain the target value of the decision variable; The scheduling module is used to schedule the power grid according to the target value of the decision variable.

9. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the low-carbon power grid dispatching method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the low-carbon power grid dispatching method as described in any one of claims 1-7.

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