Controllable series compensator capacity planning method and evaluation method considering carbon flow blockage dredging
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-13
- Publication Date
- 2026-06-16
AI Technical Summary
Existing TCSC planning methods suffer from unclear equipment selection, low algorithm efficiency, and neglect of the uncertainties of new energy sources, leading to wasted investment or safety hazards, and making it difficult to effectively alleviate carbon flow blockage.
By constructing the relationship between the equivalent reactance of the TCSC and the carbon potential of the grid nodes, the random volatility of new energy sources is quantified. Using convex optimization theory and multi-scenario analysis, a carbon-energy consumption optimal scheduling model is constructed, Lagrange multipliers are extracted for capacity correction, and marginal gain analysis is combined to optimize the TCSC capacity.
It improves the efficiency of TCSC in channeling carbon flow, reduces planning complexity, enhances low-carbon benefits and risk resistance, and adapts to the low-carbon dispatching needs of high-proportion renewable energy access.
Smart Images
Figure CN122225459A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of low-carbon planning and operation technology for power systems; specifically, it relates to an optimization method for capacity planning of controllable series compensators (TCSCs) that considers the uncertainty of new energy sources and utilizes carbon-energy consumption sensitivity analysis. Background Technology
[0002] For a long time, the energy generation model dominated by fossil fuels has resulted in persistently high carbon emissions in the power industry. With the advancement of the "dual carbon" goals, the penetration rate of clean and low-carbon new energy sources in the power grid is continuously increasing. However, due to the thermal stability limits of transmission lines, "low-carbon" new energy power often cannot be transmitted (wind and solar curtailment), forcing load centers to use "high-carbon" thermal power, creating a unique "carbon congestion" phenomenon in the power grid. Since the Controllable Series Compensator (TCSC), as a series-type FACTS device, can directly control the power flow distribution on the lines, thereby reconstructing carbon flow paths and alleviating carbon congestion, it has become an important means of building a new low-carbon power system.
[0003] However, current TCSC planning technology has several shortcomings: First, the selection and mechanisms of equipment are unclear, often relying on expensive unified power flow controllers while neglecting the direct guiding role of equipment in carbon flow. Second, the algorithms are inefficient, often employing heuristic algorithms like genetic algorithms for random searches, resulting in high computational costs and a lack of clear economic explanation (shadow prices). Third, the uncertainties of renewable energy sources are ignored; capacity planned based on deterministic scenarios often fails to cope with severe congestion caused by sudden changes in wind and solar power output, leading to wasted investment or safety hazards. Therefore, there is an urgent need for a TCSC planning method that can comprehensively consider operational energy efficiency, low carbon emissions, and risk resistance. Summary of the Invention
[0004] The purpose of this invention is to improve existing power grid equipment planning methods by proposing a controllable series compensator (TCSC) capacity planning and evaluation method that considers carbon flow congestion mitigation. Based on TCSC site selection and capacity determination, this method considers the impact of sensitivity on the expected operating energy consumption and potential risks of the system. Through marginal gain analysis in multiple scenarios, it achieves accurate capacity correction, thereby reducing the complexity of planning and configuration and improving the efficiency of TCSC in mitigating carbon congestion.
[0005] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: The present invention provides a controllable series compensator capacity planning method that considers carbon flow blockage and guidance, characterized by the following steps: Step 1: Construct the equivalent reactance of the controllable series compensator TCSC Carbon potential of grid nodes Relationships; Step 2: Quantify the random fluctuations in wind and solar power output. A representative typical scenario and its probability of occurrence; Step 3, based on the relation, the first In the first typical scenario Active power output of the generator set As a decision variable, with the goal of minimizing the expected comprehensive energy consumption of the power grid under multiple typical scenarios, a carbon-energy consumption optimal scheduling model under different constraints is constructed. Step 4, based on TCSC on the line Rated capacity The Lagrange multipliers of the TCSC capacity constraint in the carbon-energy consumption optimal scheduling model are extracted using convex optimization theory, and combined with scenario probabilities, the TCSC performance on the line under multiple typical scenarios is obtained. Net sensitivity ; Step 5, Determine Whether it is true or not, if true, then... As the optimal configuration solution Otherwise, based on net sensitivity ,right Make corrections to obtain the corrected rated capacity. and will Assign to Then, return to step 4 and execute sequentially; among which, This is the convergence threshold.
[0006] The characteristic of the controllable series compensator capacity planning method considering carbon flow blockage and conduction described in this invention is that, in step 1, the equivalent reactance of the controllable series compensator TCSC is established using equation (1). Carbon potential of grid nodes Relationship: (1) In equation (1), This is the active power flux matrix of the power grid nodes; This is the power flow distribution matrix of the power grid branches; The injection distribution matrix of the power grid generator units; The vector represents the carbon emission intensity of the power grid's generating units; T denotes transpose. This represents the inverse operation of a matrix.
[0007] Furthermore, step 3 includes: Step 3.1: Construct the objective function of the carbon-energy consumption optimal scheduling model under multiple typical scenarios using equation (2). : (2) In equation (2), For the first The objective function for a typical scenario For the first The probability of occurrence of a typical scenario; Indicates the first Fuel consumption of one generator set Indicates the first Carbon emission penalties per generator set; For the first In the first typical scenario The active power output of the generator sets; This refers to the number of generator sets in the power grid. , , For the first Three consumption characteristic coefficients of the generator set; As a carbon emission penalty factor; For the first Carbon emission intensity per unit of electricity generated by a generator set; Step 3.2: Construct the constraints of the carbon-energy consumption optimal scheduling model under multiple typical scenarios, including AC power flow constraints, safe operation constraints, and operation and capacity constraints. Step 3.2.1: Construct AC power flow constraints with TCSC equivalent reactance in multiple typical scenarios using equation (3): (3) In equation (3), For grid connection nodes A collection of generator sets; , For the first In the first typical scenario The active and reactive power output of the generator sets; , For the first A typical scenario for connecting to the power grid node The active and reactive power output of wind or solar power; , For the first A typical scenario of power grid nodes Active load and reactive load; , For the first A typical scenario of power grid nodes Grid nodes The voltage amplitude; For the first A typical scenario of power grid nodes and power grid nodes The voltage phase angle difference between them; To connect with power grid nodes A set of connected nodes; For the first The equivalent reactance of TCSC in a typical scenario; , For power grid nodes and power grid nodes The lines between Corrected conductance and corrected susceptance controlled by TCSC; Step 3.2.2, construct the first using equation (4). Constraints on the safe operation of power grids under typical scenarios: (4) In equation (4), , For the first The lower and upper limits of the active power output of the generator set; , For the first Lower and upper limits of reactive power output of the generator set; , For power grid nodes The lower and upper limits of the permissible voltage amplitude; For the line The thermal stability limit; Step 3.2.3: Construct the operation and capacity constraints of TCSC under multiple typical scenarios: Step 3.2.3.1, use equation (5) to construct the equivalent reactance adjustment range constraint of TCSC: (5) In equation (5), This represents the maximum compensation allowed by TCSC. For the installation of TCSC on the line The original reactance; For the first Line in a typical scenario The actual operating reactance of the TCSC; Step 3.2.3.2, construct the apparent power constraint for TCSC operation using equation (6): (6) In equation (6), For the first The line flowing through in a typical scenario The current amplitude; For the first In a typical scenario, on the line Apparent power operating value of TCSC; For the line The rated capacity of the TCSC.
[0008] Furthermore, step 4 includes: Step 4.1, according to the route Rated capacity of TCSC Calculate the first using equation (7) Line in a typical scenario Lagrange multipliers with TCSC capacity constraints : (7) Step 4.2, use equation (8) to calculate the line under multiple typical scenarios. Desired sensitivity of TCSC : (8) Step 4.3: Use equation (9) to obtain the circuits under multiple typical scenarios. Net sensitivity of TCSC : (9) In equation (9), This represents the resource consumption coefficient for TCSC's construction.
[0009] Furthermore, in step 5, equation (10) is used to... Make corrections to obtain the circuit. TCSC revised rated capacity : (10) In equation (10), This is the step size factor for capacity correction.
[0010] The characteristic of the present invention, a method for evaluating the capacity of a controllable series compensator considering carbon flow blockage and guidance, is that it is based on the optimal configuration scheme obtained by the controllable series compensator capacity planning method. The assessment should be conducted, including the following steps: Step 6: Construct the comprehensive total energy consumption index of the power grid using equation (11). : (11) In equation (11), This indicates the selected set of lines with TCSC installed; Step 7: Use equation (12) to obtain the carbon emission reduction rate index. : (12) In equation (12), This represents the expected total carbon emissions from the power grid without TCSC installed. The expected total carbon emissions of the power grid after configuring TCSC with the optimal configuration scheme; For the first Carbon emission intensity of generator sets, For the first time when TCSC is not installed In the first typical scenario The active power output of the generator set, To configure TCSC, the solution obtained by solving the carbon-energy consumption optimal scheduling model is the first... In the first typical scenario The active power output of the generator set.
[0011] The present invention provides an electronic device, including a memory and a processor, characterized in that the memory is used to store a program supporting the processor in performing the method described therein, and the processor is configured to execute the program stored in the memory.
[0012] The present invention discloses a computer-readable storage medium storing a computer program, characterized in that the computer program is executed by a processor to perform the steps of the method described thereon.
[0013] Compared with the prior art, the beneficial effects of the present invention are reflected in: 1. This invention constructs a carbon flow tracking model for power systems incorporating TCSC (Total Power Flow Suppression System) and derives the analytical relationship between the TCSC control variable (equivalent reactance) and the carbon potential distribution of all network nodes. Unlike existing technologies that only focus on active power flow, this invention reveals the mechanism by which TCSC alters carbon flow distribution by reconstructing power flow, enabling the planning model to directly reduce the overall network carbon potential, effectively mitigating "carbon congestion" and improving the low-carbon benefits of the planning scheme.
[0014] 2. In solving the planning model, this invention utilizes Lagrange multipliers to construct a capacity sensitivity correction index. Compared to existing capacity-fixing methods based on heuristic search, this invention uses convex optimization theory to extract the shadow price of key constraints, quantifying the reduction in system operating energy consumption (including fuel consumption and carbon emission penalties) resulting from a unit increase in TCSC capacity. Gradient iteration based on the principle that "marginal gain equals marginal consumption" not only avoids repetitive blind searches and significantly improves computational efficiency, but also ensures the optimality of the final planning scheme.
[0015] 3. This invention employs a multi-scenario analysis method based on nonparametric probabilistic modeling and K-means clustering, and constructs a comprehensive evaluation system that includes annualized construction resource consumption, operating fuel consumption, and carbon emission penalties. Compared with existing technologies, this invention fully considers the impact of the randomness of new energy output on the long-term operating value of TCSCs, and internalizes environmental externalities into corporate costs by introducing a carbon emission penalty factor parameter. This ensures that the planned TCSC capacity not only meets thermal stability and safety constraints but also adapts to the low-carbon dispatching needs under future high-proportion new energy access. Attached Figure Description
[0016] Figure 1 This is a flowchart illustrating the overall implementation of the method of the present invention; Figure 2 Logic diagram for TCSC capacity iteration planning; Figure 3 This is the equivalent circuit of TCSC and the schematic diagram of carbon flow control. Detailed Implementation
[0017] In this embodiment, a controllable series compensator capacity planning method and evaluation method considering carbon flow blockage and guidance are described, such as... Figure 1 As shown, it includes the following steps: Step 1: Based on the power system carbon emission flow calculation model, construct a power system carbon flow tracking model including a Controllable Series Compensator (TCSC). This step aims to establish the mathematical mapping relationship between the control variables of the TCSC and the carbon potential distribution of all nodes in the network. For example... Figure 3 As shown, the specific process is as follows: Step 1.1: The TCSC, as a series-type FACTS device, consists of a series capacitor bank and a thyristor-controlled reactor connected in parallel. In steady-state power flow control, the equivalent fundamental reactance of the TCSC is smoothly adjusted by changing the current in the inductor branch through adjusting the firing angle of the thyristors. Assume the TCSC is installed at the node... With nodes Transmission lines between Above. The original impedance of the line is... The controllable equivalent reactance of TCSC is denoted as... .
[0018] Corrected line equivalent impedance Represented as Equation (1): (1) Corresponding corrected line admittance Represented as equation (2): (2) In equations (1) and (2), j represents the imaginary unit. It is a line The resistance on; It is a line The original resistance on; It is the controllable equivalent reactance of TCSC (when At times, it manifests as capacitive compensation, improving transmission capacity; when This manifests as inductive regulation, limiting short-circuit current or adjusting power flow. This is the corrected line conductance; It is the corrected line susceptance.
[0019] Step 1.2: Based on the corrected admittance parameters from Step 1.1, derive the active power flow expression for the line. Outflowing active power about function As shown in equation (3): (3) In equation (3), , They are nodes and nodes The voltage amplitude; For nodes With nodes The voltage phase angle difference.
[0020] Step 1.3: According to the principle of proportional sharing, the carbon flow out of a node is proportional to the active power flowing into it. The carbon flow matrix is then constructed. First, the entire network node set is defined as... .
[0021] Step 1.3.1, Define dimensional branch power flow distribution matrix Its elements Indicates from node Flow to Node The active power is shown in equation (4): (4) In equation (4), For the line The active power flow (absolute value) is calculated by equation (3); Power is injected into the node. The set of all upstream nodes.
[0022] Step 1.3.2, Definition Dimensional unit injection matrix As shown in equation (5): (5) In equation (5), For access nodes The generator at a node has active power output; if there is no generator at a node, the output is 0.
[0023] Step 1.3.3: Construct the node flux diagonal matrix ,node Total flux It equals the sum of all incoming power at that node, as shown in equation (6): (6) Thus, a diagonal matrix is constructed. As shown in equation (7): (7) Step 1.4, based on the law of conservation of carbon mass at nodes: the total carbon emissions flowing into a node per unit time are equal to the total carbon emissions flowing out of the node. The carbon balance equation is established as shown in equation (8): (8) In equation (8), The carbon potential vector of all nodes in the network, its elements Represents a node carbon emission intensity; Let the carbon emission intensity vector of the generator be represented by its elements. Indicates access node Carbon emissions per unit of electricity generated by the generating unit.
[0024] By rearranging equation (8), the carbon potential vector of all nodes in the network is obtained. The analytical expression is shown in equation (9): (9) Due to the matrix and The elements are composed of active currents Composition, therefore they are essentially The matrix function. Therefore, the carbon potential vector of the grid node. Equivalent reactance with TCSC The analytical relation is shown in equation (10): (10) Equation (10) shows that: TCSC adjusts the reactance Changing line parameters triggers a network-wide power flow. Redistribution, thereby altering the carbon flow tracking matrix and Ultimately, this will enable the control of nodal carbon potential. Regulation.
[0025] Step 2 aims to quantify the stochastic fluctuations in wind and solar power output into a finite number of representative typical scenarios and their probabilities of occurrence, providing discretized input data for subsequent carbon-economic optimal scheduling. This specifically includes the following sub-steps: Step 2.1: First, model the probability distribution of new energy output, specifically including wind power output and photovoltaic power output.
[0026] Step 2.1.1: The uncertainty in wind power output mainly stems from the random variation in wind speed. Wind speed It typically follows a two-parameter Weibull distribution, with its probability density function... As shown in equation (11): wind speed It follows a two-parameter Weibull distribution, and its probability density function is... for: (11) In equation (11), This is a shape parameter that reflects the degree of skewness in the wind speed distribution; It is a scale parameter that reflects the average wind speed level.
[0027] wind turbine output With wind speed There is a nonlinear mapping relationship between them, which is usually determined by the wind turbine's cut-in wind speed, rated wind speed, and cut-out wind speed, as shown in equation (12): (12) In equation (12), , , These are the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively. This is the rated power.
[0028] Step 2.1.2: The uncertainty in photovoltaic output mainly depends on sunlight intensity. Sunlight intensity It usually follows a Beta distribution, with its probability density function As shown in equation (13): (13) In equation (13), This represents the actual light intensity. , The shape parameters are obtained by fitting historical data; This represents the maximum light intensity.
[0029] Photovoltaic power output It is approximately assumed to be proportional to the light intensity, as shown in equation (14): (14) In equation (14), Rated power, The light intensity under standard test conditions (typically 1000) ).
[0030] Step 2.2: Based on the probability model established in Step 2.1, the Monte Carlo simulation method is used to randomly sample the power output of new energy sources across the entire network.
[0031] Assume the system contains A wind farm and One photovoltaic power station. The sampling number is set to [number]. Generate a large-scale initial scene set As shown in equation (15): (15) Among them (15), the first Scene vector obtained from the second sampling Defined as Equation (16): (16) In equation (16), For the first In the second sampling, the first The active power output of each wind farm; For the first In the second sampling, the first The active power output of each photovoltaic power station.
[0032] Step 2.3, due to the initial scene set Large-scale scenarios are computationally inefficient if directly used for subsequent optimization. This invention uses the K-means clustering algorithm to reduce large-scale scenarios to smaller ones. A typical scenario is used to achieve dimensionality reduction processing of uncertainty.
[0033] Step 2.3.1, will The initial scene is divided into For each cluster, the optimization objective is to minimize the sum of squared Euclidean distances from each scene to the center of its respective cluster, as shown in Equation (17): (17) In equation (17), This is the sum of squared clustering errors; The number of preset typical scenarios; For the first The cluster centers of the nth cluster (i.e., the final extracted nth cluster) A typical scenario vector.
[0034] Step 2.3.2: After clustering is completed, a set of typical scenarios is obtained. Each typical scenario probability of occurrence The proportion of samples contained in the cluster is determined, as shown in equation (18): (18) In equation (18), It belongs to the first The initial number of scene samples for each cluster; It is the first The probability of a typical scenario occurring.
[0035] Step 3, this step is based on the result generated in step 2. Based on typical scenarios and their probabilities, and with the goal of minimizing the overall expected energy consumption of the system, a carbon-energy optimal scheduling model is constructed under different constraints. In this model, the planned capacity of the TCSC is treated as a fixed parameter, while its operating reactance is treated as a control variable. The main purpose of this model is to simulate system operation and provide a mathematical basis for extracting the Lagrange multipliers (dual variable values) reflecting capacity scarcity in the subsequent step 4.
[0036] Step 3.1: Calculate the expected comprehensive operating energy consumption of the system under all typical scenarios. The optimization objective is to minimize the cost as shown in equation (19), where the cost includes the fuel consumption of the generator set. and carbon emission penalties .
[0037] (19) In equation (19), For the first The objective function for a typical scenario For the first The probability of occurrence of a typical scenario; Indicates the first Fuel consumption of one generator set Indicates the first Carbon emission penalties per generator set; For the first In the first typical scenario The active power output of the generator sets; This refers to the number of generator sets in the power grid. , , For the first Three consumption characteristic coefficients of the generator set; As a carbon emission penalty factor; For the first Carbon emission intensity per unit of electricity generated by a generator set.
[0038] Step 3.2: Construct multi-scenario communication power flow constraints containing TCSC variables. For each scenario... The system must satisfy power balance constraints. Since the TCSC alters the line parameters, the conductance and susceptance in the nodal power balance equations are TCSC control variables. The function of the node power balance equation is shown in equation (20): (20) In equation (20), For grid connection nodes A collection of generator sets; , For the first In the first typical scenario The active and reactive power output of the generator sets; , For the first A typical scenario for connecting to the power grid node The active and reactive power output of wind or solar power; , For the first A typical scenario of power grid nodes Active load and reactive load; , For the first A typical scenario of power grid nodes Grid nodes The voltage amplitude; For the first A typical scenario of power grid nodes and power grid nodes The voltage phase angle difference between them; To connect with power grid nodes A set of connected nodes; For the first The equivalent reactance of TCSC in a typical scenario; , For power grid nodes and power grid nodes The lines between Corrected conductance and corrected susceptance controlled by TCSC.
[0039] Step 3.3: Construct system safety operation constraints. To ensure power grid safety, constraints are set for each scenario. The following inequality constraints must be satisfied: (twenty one) In equation (21), , For the first The lower and upper limits of the active power output of the generator set; , For the first Lower and upper limits of reactive power output of the generator set; , For power grid nodes The lower and upper limits of the permissible voltage amplitude; For the line The thermal stability limit.
[0040] Step 3.4: Construct the TCSC operation and capacity constraints. The regulation capability of the TCSC is limited by its physical capacity and insulation level. First, its equivalent reactance regulation range constraint is as shown in equation (22): (twenty two) In equation (22), This represents the maximum compensation allowed by TCSC. For the installation of TCSC on the line The original reactance; For the first In a typical scenario, the line The actual operating reactance of the TCSC.
[0041] Secondly, the apparent power of the TCSC must not exceed the rated capacity given in the current planning phase. This is the key coupling constraint connecting the operation layer and the planning layer, as shown in equation (23): (twenty three) In equation (23), For the first The flow path in this scenario The current amplitude; For the first Apparent power operating values of TCSC in each scenario; For routes to be planned The rated capacity of the TCSC is a constant in this step (the planned capacity determined by the previous iteration).
[0042] Step 4: By solving the multi-scenario carbon-energy consumption optimal scheduling model constructed in Step 3, extract the dual variables (Lagrange multipliers) of key constraints, and combine them with scenario probabilities to construct the expected sensitivity index under all operating conditions.
[0043] Step 4.1: Solve the optimization model in Step 3 using the interior point method. While obtaining the optimal power flow distribution and unit output, extract the Lagrange multiplier corresponding to the TCSC planning capacity constraint shown in Equation (23) in Step 3.4. According to convex optimization theory, this multiplier reflects the sensitivity (i.e., the dual variable value) of the objective function (operating energy consumption) to the right-hand side of the constraint (planning capacity).
[0044] definition For the first In the scenario, the first The Lagrange multiplier for the TCSC capacity constraint of a line is expressed mathematically as shown in equation (24): (twenty four) When equation (23) holds the equality sign, that is This indicates that the actual operating capacity of TCSC in this scenario has reached its limit, resulting in "capacity blocking." At this time, ,and The value represents the planned capacity of this location. Increasing the MVA by 1 MVA will reduce the total energy consumption of the system in this scenario by a certain amount. When equation (23) takes the less than sign, that is... This indicates that there is sufficient capacity in this scenario, and system optimization is not limited. This indicates that there is no optimization gain at this time when expanding the capacity.
[0045] Step 4.2: Considering the randomness of new energy output, the sensitivity of a single scenario is insufficient to reflect the long-term value of the equipment. The probability of occurrence of each typical scenario obtained in Step 2 is used... The sensitivity of all scenarios is weighted and summed to calculate the first... Expected sensitivity of TCSC candidate points As shown in equation (25): (25) In equation (25), It represents the average marginal reduction in operating energy consumption (including fuel consumption reduction and carbon emission reduction benefits) that can be achieved by increasing the capacity of a unit TCSC at this position from the perspective of the whole life cycle or long-term operation.
[0046] Step 5: Based on the principle of "marginal gain equals marginal consumption" in systems engineering, this step constructs a capacity correction model. The expected sensitivity (marginal reduction in operating energy consumption) obtained in Step 4 is compared with the marginal investment cost of the equipment, and the optimal TCSC configuration scheme is found iteratively using the gradient method.
[0047] Step 5.1: First, define the unit construction resource consumption of TCSC. In order to keep pace with the time scale of operational energy consumption, the resource input during the construction period needs to be converted into annualized resource consumption indicators.
[0048] Definition of the first Net sensitivity of TCSC candidate points As shown in equation (26): (26) In equation (26), This is the construction resource consumption coefficient for TCSC (unit: energy equivalent / MVA year), representing the annual equipment investment and operation and maintenance resource consumption that needs to be allocated for each additional 1MVA of TCSC capacity. This indicates that the marginal energy improvement brought about by expanding the capacity at this location exceeds the construction resource consumption, and the system is in a state of "under-configuration," so the planned capacity should be increased; when This indicates that the energy efficiency gains from capacity expansion are insufficient to cover resource consumption, and the system is in a state of "configuration redundancy" or "optimal balance," so the planned capacity should be maintained or reduced.
[0049] Step 5.2, as follows Figure 2 As shown, capacity iteration correction is performed to update the upper limit of the planned capacity of TCSC. The correction formula is shown in equation (27): (27) In equation (27), For the line The rated capacity after TCSC correction; This is the capacity correction step size coefficient (a positive real number), used to control the convergence speed and stability of the algorithm.
[0050] To determine the final iterative capacity, the following iterative strategy is adopted: Set the initial planned capacity. and convergence threshold ; Set current capacity Substituting into step 3, we solve the multi-scenario operation model to obtain the first... In the first typical scenario The optimal active power output of the generator unit is denoted as . Extract according to step 4 And calculate the desired sensitivity Calculate the net sensitivity according to step 5. And update capacity to obtain Determine whether the convergence condition is met. (i.e., marginal gain approaches marginal consumption) or the change in capacity is less than a threshold. If these conditions are met, the iteration stops, and the current value is output. As the final planned capacity; otherwise, let Return to step 2 and continue the loop until convergence.
[0051] Step 5.3: In order to verify the effectiveness of the TCSC site selection and capacity determination method proposed in this invention, and to quantitatively evaluate the improvement effect of the planning scheme in terms of efficiency, low carbon emissions and new energy absorption capacity, the following performance evaluation index system is constructed.
[0052] (1) Total Energy Consumption Index of Power Grid This is used to evaluate the overall performance of the planning scheme, including the construction resource consumption of the TCSC and the expected operating energy consumption of the system. The calculation formula is shown in equation (28): (28) In equation (28), This indicates the selected set of lines with TCSC installed. The first one obtained after iterative convergence The final planned capacity of the TCSC for each line; To achieve the final planned capacity, the first Energy consumption of system operation in a typical scenario.
[0053] (2) Carbon emission reduction rate indicator This is used to quantify the effect of TCSC access on the grid's "carbon congestion" and its contribution to overall grid carbon emission reduction. The calculation formula is shown in equation (29): (29) Mode middle, This represents the expected total carbon emissions from the power grid without TCSC installed. The expected total carbon emissions of the power grid after configuring TCSC with the optimal configuration scheme; For the first Carbon emission intensity of generator sets, , The two scenarios are: without TCSC installed and after TCSC is configured. In the first typical scenario The active power output of the generator set.
[0054] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.
[0055] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.
Claims
1. A controllable series compensator capacity planning method considering carbon flow blockage and guidance, characterized in that, Includes the following steps: Step 1: Construct the equivalent reactance of the controllable series compensator TCSC Carbon potential of grid nodes Relationships; Step 2: Quantify the random fluctuations in wind and solar power output. A representative typical scenario and its probability of occurrence; Step 3, based on the relation, the first In the first typical scenario Active power output of the generator set As a decision variable, with the goal of minimizing the expected comprehensive energy consumption of the power grid under multiple typical scenarios, a carbon-energy consumption optimal scheduling model under different constraints is constructed. Step 4, based on TCSC on the line Rated capacity The Lagrange multipliers of the TCSC capacity constraint in the carbon-energy consumption optimal scheduling model are extracted using convex optimization theory, and combined with scenario probabilities, the TCSC performance on the line under multiple typical scenarios is obtained. Net sensitivity ; Step 5, Determine Whether it is true or not, if true, then... As the optimal configuration solution Otherwise, based on net sensitivity ,right Make corrections to obtain the corrected rated capacity. and will Assign to Then, return to step 4 and execute sequentially; among which, This is the convergence threshold.
2. The controllable series compensator capacity planning method considering carbon flow blockage and guidance as described in claim 1, characterized in that, In step 1, the equivalent reactance of the controllable series compensator TCSC is established using equation (1). Carbon potential of grid nodes Relationship: (1) In equation (1), This is the active power flux matrix of the power grid nodes; This is the power flow distribution matrix of the power grid branches; The injection distribution matrix of the power grid generator units; The vector represents the carbon emission intensity of the power grid's generating units; T denotes transpose. This represents the inverse operation of a matrix.
3. The controllable series compensator capacity planning method considering carbon flow blockage and guidance as described in claim 1, characterized in that, Step 3 includes: Step 3.1: Construct the objective function of the carbon-energy consumption optimal scheduling model under multiple typical scenarios using equation (2). : (2) In equation (2), For the first The objective function for a typical scenario For the first The probability of occurrence of a typical scenario; Indicates the first Fuel consumption of one generator set Indicates the first Carbon emission penalties per generator set; For the first In the first typical scenario The active power output of the generator sets; This refers to the number of generator sets in the power grid. , , For the first Three consumption characteristic coefficients of the generator set; As a carbon emission penalty factor; For the first Carbon emission intensity per unit of electricity generated by a generator set; Step 3.2: Construct the constraints of the carbon-energy consumption optimal scheduling model under multiple typical scenarios, including AC power flow constraints, safe operation constraints, and operation and capacity constraints. Step 3.2.1: Construct AC power flow constraints with TCSC equivalent reactance in multiple typical scenarios using equation (3): (3) In equation (3), For grid connection nodes A collection of generator sets; , For the first In the first typical scenario The active and reactive power output of the generator sets; , For the first A typical scenario for connecting to the power grid node The active and reactive power output of wind or solar power; , For the first A typical scenario of power grid nodes Active load and reactive load; , For the first A typical scenario of power grid nodes Grid nodes The voltage amplitude; For the first A typical scenario of power grid nodes and power grid nodes The voltage phase angle difference between them; To connect with power grid nodes A set of connected nodes; For the first The equivalent reactance of TCSC in a typical scenario; , For power grid nodes and power grid nodes The lines between Corrected conductance and corrected susceptance controlled by TCSC; Step 3.2.2, construct the first using equation (4). Constraints on the safe operation of power grids under typical scenarios: (4) In equation (4), , For the first The lower and upper limits of the active power output of the generator set; , For the first Lower and upper limits of reactive power output of the generator set; , For power grid nodes The lower and upper limits of the permissible voltage amplitude; For the line The thermal stability limit; Step 3.2.3: Construct the operation and capacity constraints of TCSC under multiple typical scenarios: Step 3.2.3.1, use equation (5) to construct the equivalent reactance adjustment range constraint of TCSC: (5) In equation (5), This represents the maximum compensation allowed by TCSC. For the installation of TCSC on the line The original reactance; For the first Line in a typical scenario The actual operating reactance of the TCSC; Step 3.2.3.2, construct the apparent power constraint for TCSC operation using equation (6): (6) In equation (6), For the first The line flowing through in a typical scenario The current amplitude; For the first In a typical scenario, on the line Apparent power operating value of TCSC; For the line The rated capacity of the TCSC.
4. The controllable series compensator capacity planning method considering carbon flow blockage and guidance as described in claim 3, characterized in that, Step 4 includes: Step 4.1, according to the route Rated capacity of TCSC Calculate the first using equation (7) Line in a typical scenario Lagrange multipliers with TCSC capacity constraints : (7) Step 4.2, use equation (8) to calculate the line under multiple typical scenarios. Desired sensitivity of TCSC : (8) Step 4.3: Use equation (9) to obtain the circuits under multiple typical scenarios. Net sensitivity of TCSC : (9) In equation (9), This represents the resource consumption coefficient for TCSC's construction.
5. The controllable series compensator capacity planning method considering carbon flow blockage and guidance as described in claim 4, characterized in that, In step 5, equation (10) is used to... Make corrections to obtain the circuit. TCSC revised rated capacity : (10) In equation (10), This is the step size factor for capacity correction.
6. A method for evaluating the capacity of a controllable series compensator considering carbon flow blockage and guidance, characterized in that, The optimal configuration scheme obtained by the capacity planning method for the controllable series compensator as described in claim 1. The assessment should be conducted, including the following steps: Step 6: Construct the comprehensive total energy consumption index of the power grid using equation (11). : (11) In equation (11), This indicates the selected set of lines with TCSC installed; Step 7: Use equation (12) to obtain the carbon emission reduction rate index. : (12) In equation (12), This represents the expected total carbon emissions from the power grid without TCSC installed. The expected total carbon emissions of the power grid after configuring TCSC with the optimal configuration scheme; For the first Carbon emission intensity of generator sets, For the first time when TCSC is not installed In the first typical scenario The active power output of the generator set, To configure TCSC, the solution obtained by solving the carbon-energy consumption optimal scheduling model is the first... In the first typical scenario The active power output of the generator set.
7. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports a processor in executing the method of any one of claims 1-6, the processor being configured to execute the program stored in the memory.
8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor to perform the steps of the method according to any one of claims 1-6.