Power market area joint clearing method based on sand table deduction

Abstract

The invention provides an electricity market area joint clearing method based on sand table deduction, and relates to the technical field of electrical engineering. A multi-regional power grid joint simulation model with an alternating current power flow equation as a core is constructed by obtaining physical parameters of each participating regional power grid, and node voltage, branch power flow and section power are solved through a Newton-Raphson method; based on a market clearing rule, constructing an optimization objective function containing an energy abandoning penalty term and a weight coefficient, and setting a power grid physical safety operation constraint; an improved multi-target particle swarm algorithm is adopted for iterative solution, security constraints are verified through whole-network load flow calculation in each iteration, and if the security constraints are not met, a comprehensive penalty function is constructed to adjust the search direction; and outputting a final safe clearing scheme after the convergence condition is met, and dynamically displaying the power grid state and the clearing result through a three-dimensional graphical interface. The method accurately simulates multi-regional power grid operation, gives consideration to economical efficiency and new energy consumption, guarantees the safety of the power grid, and is suitable for a power market regional joint clearing scene.

Description

Technical Field

[0001] This invention relates to a regional joint clearing method for the electricity market based on sand table simulation, belonging to the field of electrical engineering technology. Background Technology

[0002] As the scale of power grid operations continues to expand, the demand for cross-regional allocation of power resources is growing. Market clearing needs to simultaneously consider computational efficiency, operational economics, and the physical security of the power grid. Regional joint clearing, as a key link in achieving cross-regional resource optimization, requires precise matching of the complex physical characteristics of multi-regional power grids, while also addressing the challenges brought about by fluctuations in renewable energy output. Traditional single-region clearing methods are no longer sufficient to meet the technical requirements of multi-regional collaborative operation, making the development of clearing technologies adapted to regional joint scenarios a key focus for the industry.

[0003] For example, Chinese invention patent application CN120298041A discloses a method for identifying and eliminating redundant constraints in large-scale electricity market clearing simulations. This method constructs a safety-constrained unit combination and economic dispatch model through preprocessing, combines power flow calculations to determine and eliminate redundant line power flow constraints, and finally solves for nodal prices, thereby simplifying the computational scale of large-scale market clearing. However, this technical solution focuses on optimizing the computational efficiency of a single large-scale market. The original scenario does not require simulating cross-regional cross-sectional power interaction and multi-regional node voltage coupling, making it difficult to adapt to the physical simulation requirements of multi-regional grid collaborative operation in regional joint clearing when there is a need for cross-regional allocation of power resources. Furthermore, the optimization objective of this technical solution focuses on minimizing unit operating costs, failing to balance the economic viability and energy structure transformation needs under high-proportion renewable energy participation. In addition, the redundant constraint determination in this technical solution is based on a static comparison of the maximum possible transmission power and a threshold, ignoring the dynamic changes in power flow state during the clearing iteration process. This technical solution aims to simplify constraints in the preprocessing stage but lacks the ability to respond to real-time limit-crossing information during iteration, leading to safety hazards in subsequent clearing schemes due to delayed constraint verification.

[0004] In summary, there is an urgent need for a regional joint clearing method for the power market that can accurately construct multi-regional power grid simulation models, take into account both economic efficiency and renewable energy consumption, and can dynamically adjust and optimize its direction to ensure power grid security. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention proposes a regional joint clearing method for the electricity market based on sand table simulation.

[0006] The technical solution of the present invention is as follows: A regional joint clearing method for the electricity market based on sand table simulation, the method comprising: Obtain the power grid physical parameters of each participating region, and construct a multi-regional power grid joint simulation model based on the power grid physical parameters; The optimization objective function in the multi-regional power grid joint simulation model is constructed based on market clearing rules, and the power grid physical security operation constraints of the optimization objective function are set. An optimization algorithm is used to iteratively solve the optimization objective function to obtain candidate clearing schemes. In each iteration, the current candidate solution is substituted into the multi-regional power grid joint simulation model to perform power flow calculation of the entire network. The power flow calculation result of the entire network is checked to see if it meets the physical safety operation constraints of the power grid. If the check result is not met, the search direction of the optimization algorithm is adjusted according to the limit information to generate a new candidate clearing scheme and continue iterative deduction. When the optimization algorithm meets the convergence condition, the deduction is terminated and the final safe clearing scheme is output.

[0007] Preferably, the multi-regional power grid joint simulation model is based on the AC power flow equation. By solving the AC power flow equation, the voltage amplitude and phase angle of each node in the entire network are obtained. Based on the voltage amplitude and phase angle, the active power flow of branches and the cross-sectional power are calculated, wherein: The active power flow of the branch is expressed by the formula: ; In the formula, For the node Flow to Node The branch road has contributed to the trend; For nodes The voltage amplitude; For nodes The voltage amplitude; For nodes and nodes The conductance of the branch circuits between them; For nodes and nodes The susceptance to ground of the branch circuit; For nodes and nodes The phase angle difference between them; The cross-sectional power is expressed by the formula: ; In the formula, For the cross-section of the flow Net transmission power; cross-section The set of transmission lines included.

[0008] Preferably, the optimization objective function is expressed by the formula: ; in: ; In the formula, This represents the total number of simulation periods; For traditional dispatchable units During the period Those who have made meritorious contributions; For traditional dispatchable units During the period The power generation cost function; , and These are the coefficients of the quadratic, linear, and constant terms of the power generation cost, respectively. This is the energy curtailment penalty coefficient; Forecast renewable energy units During the period The maximum force that can be exerted; For renewable energy units During the period Actual planned output; and These are the resource allocation weighting coefficient and the green energy target weighting coefficient, respectively. and These represent the number of conventional dispatchable generating units and renewable energy generating units, respectively.

[0009] Preferably, the power grid physical safety operation constraints of the optimization objective function include generator output constraints, line transmission capacity constraints, node voltage safety constraints, and critical section stability constraints, wherein: The generator output constraint is expressed by the following formula: ; ; ; The line transmission capacity constraint is expressed by the following formula: ; The node voltage safety constraint is expressed by the following formula: ; The key section stability constraint is expressed by the following formula: ; In the formula, To provide the minimum technical output of the generator; To maximize the technical output of the generator; For traditional dispatchable units During the period Those who have made meritorious contributions; For traditional dispatchable units The rate of climb; For the time period From node Flow to Node The branch road has contributed to the trend; For nodes Flow to Node Thermal stability limit capacity; For nodes The minimum voltage amplitude; For nodes The maximum voltage amplitude; For the time period node The voltage amplitude; cross-section During the period Net transmission power; cross-section The stable limit power.

[0010] Preferably, if the verification result is not satisfied, the search direction of the optimization algorithm is adjusted according to the limit violation information, specifically as follows: A comprehensive penalty function is constructed based on the over-limit information corresponding to the unmet physical safety operation constraints of the power grid, expressed as the formula: ; In the formula, , , , and These are the weighting coefficients for line power flow exceeding limits penalty, node voltage exceeding limits penalty, cross-sectional power penalty, traditional unit ramp rate exceeding limits penalty, and power balance exceeding limits penalty, respectively. This represents the total number of load nodes. This represents the total number of transmission lines. The total number of nodes; For the first The next iteration of the transmission line The branch road has contributed to the trend; For power transmission lines Thermal stability limit capacity; For the first The node of the next iteration The voltage amplitude; Rated voltage; For the first The next iteration flows through the cross section Net transmission power; For the first Sub-iteration traditional schedulable units During the period Those who have made meritorious contributions; For the first Sub-iteration traditional schedulable units During the period Those who have made meritorious contributions; For the first Sub-iteration traditional schedulable units Those who have made meritorious contributions; For the first Second-generation renewable energy units Actual planned output; Forecast active power load; The comprehensive penalty function is incorporated into the optimization objective function and expressed as a formula: ; In the formula, For the first The optimization objective function after introducing a comprehensive penalty function in the next iteration; For the first The optimization objective function for this iteration does not introduce a comprehensive penalty function; For the first Weight of the over-limit penalty in the next iteration; The next search is performed based on the optimized objective function after introducing the comprehensive penalty function.

[0011] Preferably, the optimization algorithm is an improved multi-objective particle swarm optimization algorithm, specifically, it introduces a file maintenance mechanism based on crowding distance and a dynamic inertia weight strategy to solve the optimization objective function in the particle swarm algorithm. The dynamic inertia weight strategy dynamically adjusts the magnitude of the inertia weight according to the number of iterations, and the update formula is: ; In the formula, For the first The inertia weight of the next iteration; and These are the maximum and minimum values ​​of the inertia weight, respectively. This represents the maximum number of iterations.

[0012] Preferably, the final clearing scheme includes the output plans of conventional dispatchable units and renewable energy units that optimize the objective function output, as well as the nodal marginal electricity price, branch power flow, and cross-sectional power margin calculated based on the output plans, wherein: The marginal electricity price at the node is expressed by the formula: ; In the formula, For nodes During the period The marginal electricity price; For time period Shadow price of power balance constraints; For nodes With nodes The shadow price of transmission capacity constraints on power transmission lines between them; For nodes During the period Active load; cross-section Shadow prices under stability constraints; The cross-sectional power margin is expressed by the formula: ; In the formula, For the time period Section The cross-sectional power margin.

[0013] Preferably, the method further includes dynamically displaying the power grid status and clearing results through a three-dimensional graphical interface during the iterative deduction process and after the final clearing scheme is output, wherein: A three-dimensional power grid sand table model is constructed based on a geographic information system, mapping substations, power plants, and load centers onto a three-dimensional map; Using columns or streamlines of different heights, colors, and dynamic flow effects, the marginal electricity price of each node, the power magnitude and direction of key transmission sections, and the over-limit alarm information of line power flow are represented in three-dimensional space. The system uses 3D curves or surfaces to display the unit output plan, cross-sectional power trend, and regional electricity price changes over different time periods.

[0014] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a regional joint clearing method for the electricity market based on a sand table simulation as described in the present invention.

[0015] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a regional joint clearing method for the electricity market based on sand table simulation as described in the present invention.

[0016] The present invention has the following beneficial effects: 1. This invention is a joint regional clearing method for the power market based on sand table simulation. By obtaining the physical parameters of the power grid in each participating region, a joint simulation model of the multi-regional power grid with AC power flow equation as the core is constructed to fully restore the physical operation characteristics of the multi-regional power grid. By accurately capturing the cross-regional cross-sectional power interaction and the voltage coupling relationship of multi-regional nodes, the problem of distortion of physical characteristics caused by model simplification is avoided. 2. This invention is a regional joint clearing method for the power market based on sand table simulation. It introduces a curtailment penalty coefficient, a resource allocation weight coefficient, and a green energy target weight coefficient into the optimization objective function. While minimizing the power generation cost of traditional dispatchable units, it constrains the scale of renewable energy curtailment, achieving synergistic optimization between economic priority and new energy consumption. It is adapted to the demand for high proportion of new energy to participate in regional joint clearing and helps the transformation of the energy structure. 3. This invention is a joint regional clearing method for the power market based on sand table simulation. In the iterative solution process, each round substitutes the candidate solution into the joint simulation model of the multi-regional power grid for the whole network power flow calculation. If the verification does not meet the safety constraints, a comprehensive penalty function is constructed based on the limit information to adjust the search direction of the optimization algorithm, so that the iterative process always moves towards the safe and feasible region, avoids the safety risks caused by static constraint judgment, and ensures that the final output clearing scheme has both economic efficiency and power grid physical security, and guarantees the stable operation of the multi-regional power grid. 4. This invention is a regional joint clearing method for the power market based on sand table simulation. By introducing a three-dimensional graphical sand table simulation visualization step, the abstract power grid operation status and market clearing results are transformed into an intuitive and dynamic three-dimensional visual scene, which greatly improves the transparency of the regional joint market clearing process and the interpretability of the results. It assists dispatching and trading personnel in quickly identifying potential risks and making scientific decisions, thereby comprehensively improving the safety and efficiency of market operation under large-scale interconnected power grids. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0020] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0021] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0022] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0023] Example 1: like Figure 1 As shown in the figure, this embodiment provides a regional joint clearing method for the electricity market based on sand table simulation. The method includes: S1. Obtain the power grid physical parameters of each participating region, and construct a multi-region power grid joint simulation model based on the power grid physical parameters, wherein: S11. The power grid physical parameters include network topology data, branch parameters, generator parameters, and load data. Furthermore, the network topology data includes a set of nodes describing the power grid structure, a set of branches (transmission lines and transformers), and the connection relationships between nodes and branches. In this embodiment, the nodes are the connection points of different electrical devices in the power grid. The branch parameters include the resistance, reactance, susceptance to ground, and thermal stability limit capacity of the transmission lines connecting two nodes, as well as the transformer's turns ratio and impedance. The generator parameters include the maximum technical output, minimum technical output, ramp rate, and generation cost coefficient of conventional dispatchable units, as well as the predicted maximum output of renewable energy units. The load data includes predicted active and reactive loads. Preferably, in this embodiment, the prediction data is obtained through a pre-constructed bidirectional long short-term memory network Attention-BiLSTM based on an attention mechanism. The historical information corresponding features of the data to be predicted are used as input features. For example, when predicting the maximum power output of renewable energy units, the input features include power sequences, wind speed, light, temperature data from numerical weather forecasts, and date type features. The importance of the above historical information is dynamically weighted through the attention mechanism, and the predicted value for the future projection period is output. S12. The multi-regional power grid joint simulation model takes the AC power flow equations describing the power balance of the power grid as its core. By solving the AC power flow equations and further calculating node voltages, branch active power flow, and cross-sectional power, it simulates the physical operating state of the power grid. Simultaneously, to support the visualization of the sand table simulation, a three-dimensional power grid sand table model corresponding to the real geographical information is constructed based on the geographical coordinate information corresponding to the acquired power grid physical parameters. Substations, power plants, load centers, and other entities are accurately mapped onto a three-dimensional map as the basis for visualization. Wherein: S121. The AC power flow equations include active power balance equations and reactive power balance equations. The active power balance equations are expressed as follows: ; The reactive power balance equation is expressed by the following formula: ; in: ; ; ; In the formula, and Injection nodes Net active power and net reactive power, and They are nodes Traditional dispatchable units The effective and ineffective contributions; and They are nodes Up to renewable energy units The effective and ineffective contributions; and They are nodes The predicted active and reactive loads; and These represent the number of conventional dispatchable generating units and renewable energy generating units, respectively. For nodes The voltage amplitude; For nodes and nodes The conductance of the branch circuits between them; For nodes and nodes The susceptance to ground of the branch circuit; The total number of nodes; and They are nodes and nodes The phase angle; For nodes and nodes The phase angle difference between them; Furthermore, and The node admittance matrix is ​​obtained from network topology data and branch parameters. For transmission lines, the node... and nodes Admittance of the branch Expressed as a formula: ; In the formula, For nodes and nodes The resistance of the transmission lines between them; For nodes and nodes Reactance of the transmission lines between them; The imaginary unit; For transformers, the electrical behavior of transformers is simulated using a π-type equivalent circuit. Based on the transformer turns ratio and impedance parameters combined with the principle of the π-type equivalent circuit, the element values ​​of the transformer in the node admittance matrix are calculated. Since this calculation is a conventional technique in this field, the detailed derivation and calculation process will not be elaborated here. S122. In this embodiment, the AC power flow equations are solved by numerical algorithms such as the Newton-Raphson method to obtain the voltage amplitude and phase angle of each node in the entire power grid. Since numerical algorithms such as the Newton-Raphson method are conventional technical means in this field, the detailed calculation process will not be described here. S123. The active power flow of the branch is expressed by the following formula: ; In the formula, For the node Flow to Node The branch road has contributed to the trend; For nodes The voltage amplitude; S124. In this embodiment, the cross-section is an inter-regional connection cross-section of the power market, that is, a set of all transmission lines connecting two or more different power market regions (e.g., all lines connecting region A and region B). The cross-sectional power is expressed by the formula: ; In the formula, For the cross-section of the flow Net transmission power; cross-section The set of transmission lines included; S2. Construct the optimization objective function in the multi-regional power grid joint simulation model based on market clearing rules, and set the power grid physical security operation constraints of the optimization objective function; S21. In this embodiment, to ensure that the simulation accurately reflects the behavior of the regional joint market, the economic objective pursued by the market clearing rules is embedded into the multi-regional power grid joint simulation model. Specifically, the objective function is set to minimize the total power generation cost of the power system. To improve the grid's ability to absorb renewable energy, a penalty term for wind and solar curtailment is added to the objective function for optimization. The resulting optimized objective function is expressed by the formula: ; in: ; In the formula, This represents the total number of simulation periods; For traditional dispatchable units During the period Those who have made meritorious contributions; For traditional dispatchable units During the period The power generation cost function; , and These are the coefficients of the quadratic, linear, and constant terms of the power generation cost, respectively. Furthermore, It reflects the degree of non-linearity in unit cost and is related to the non-linear change in unit fuel efficiency (e.g., the higher the output, the faster the fuel combustion efficiency decreases, requiring more fuel input, leading to a secondary increase in cost). It is usually a positive number (ensuring increasing marginal cost). The linear cost component, reflecting the unit's linear cost, corresponds to the fuel consumption cost per unit of active power output (e.g., the fuel cost required to generate 1 MW·h of electricity), and is the main linear component of the cost. This reflects the fixed costs of the unit, which are costs unrelated to the unit's output, such as fixed maintenance costs and equipment depreciation costs during unit operation (even if the output is 0, if the unit is in the start-up state, it still needs to bear some fixed costs. The specific value needs to be combined with the start-up and shutdown status of the unit, but in this embodiment, it is assumed that the unit is already in the start-up state). The curtailment penalty coefficient represents the penalty cost that is willing to be borne for a unit of curtailed wind / solar power. In this embodiment, its value is set to be higher than the marginal cost of the traditional unit with the highest marginal power generation cost, so as to ensure that in economic comparison, the consumption of renewable energy has priority over calling up expensive traditional units. Forecast renewable energy units During the period The maximum force that can be exerted; For renewable energy units During the period Actual planned output; and These are the resource allocation weighting coefficient and the green energy target weighting coefficient, respectively. S22. The physical safety operation constraints of the power grid include power balance constraints, generator output constraints, line transmission capacity constraints, node voltage safety constraints, and critical section stability constraints, specifically: The power balance constraint is that the total power generation output (including renewable energy) of the power system must be equal to the total load; The generator output constraint is expressed by the following formula: ; ; ; The line transmission capacity constraint is expressed by the following formula: ; The node voltage safety constraint is expressed by the following formula: ; The key section stability constraint is expressed by the following formula: ; In the formula, To provide the minimum technical output of the generator; To maximize the technical output of the generator; For traditional dispatchable units During the period Those who have made meritorious contributions; For traditional dispatchable units The rate of climb; For the time period From node Flow to Node The branch road has contributed to the trend; For nodes Flow to Node Thermal stability limit capacity; For nodes The minimum voltage amplitude; For nodes The maximum voltage amplitude; For the time period node The voltage amplitude; cross-section During the period Net transmission power; cross-section The stable limit power; S3. The optimization objective function is iteratively solved using an optimization algorithm to obtain candidate clearing schemes. In each iteration, the current candidate solution is substituted into the multi-regional power grid joint simulation model to perform power flow calculations across the entire network. The specific calculations are as described in step S1 and will not be repeated here. The power flow calculation results across the entire network are checked to see if they meet the physical safety operation constraints of the power grid. If the check results are not met, the search direction of the optimization algorithm is adjusted according to the limit violation information to generate new candidate clearing schemes and continue iterative deduction. S31. The optimization algorithm is a genetic algorithm or a particle swarm optimization algorithm. Furthermore, in this embodiment, the optimization algorithm is an improved multi-objective particle swarm optimization algorithm. By introducing a crowding distance-based archive maintenance mechanism into the standard particle swarm optimization algorithm, the diversity of the Pareto solution set is maintained. This crowding distance-based archive maintenance mechanism calculates the crowding degree of each individual on the Pareto front, prioritizing the retention of individuals located in sparse regions, allowing the Pareto solution set to cover a wider solution space and providing decision-makers with more choices. Simultaneously, this embodiment employs a dynamic inertia weight strategy to balance global and local search capabilities. Since the complexity and scale of the search space vary in different application scenarios, the algorithm needs to adaptively adjust the search strategy. In this embodiment, the dynamic inertia weight strategy dynamically adjusts the magnitude of the inertia weight according to the number of iterations. Its update formula is: ; In the formula, For the first The inertia weight of the next iteration; and These are the maximum and minimum values ​​of the inertia weight, respectively. This represents the maximum number of iterations. During the iterative simulation, the key simulation data corresponding to the current candidate solution is dynamically updated and displayed through a three-dimensional graphical interface. For example, the output changes of units in each region are displayed in real time on a three-dimensional geographic map using columns of different heights and colors. Dynamic streamlines are used to display the direction and magnitude of power in key sections in real time. Lines or nodes that exceed the limits are highlighted and flashed as alarms, making the simulation process intuitive and visible. S32. Solve the objective function according to the optimization algorithm in step S31. The algorithm solves for the objective function in each iteration. This will generate a set of candidate solutions, which are the output plans of all units in all time periods; S33. Substitute the candidate solution into the multi-regional power grid joint simulation model, calculate the branch active power flow and cross-sectional power, and verify whether the candidate solution, branch active power flow and cross-sectional power meet the physical safety operation constraints of the power grid. If the verification result is satisfactory, the current candidate solution is used as the unit output plan in the final clearing scheme. S34. If the verification result is not satisfied, the search direction is adjusted according to the limit violation information. Specifically, penalty items are set according to the limit violation information corresponding to the unsatisfied constraint. For ease of understanding, this embodiment uses the failure to meet the line transmission capacity constraint as an example. When node With nodes Transmission lines between When overloaded, an over-limit penalty is set, which is expressed by the formula: ; In the formula, For the first The over-limit penalty term in the next iteration; This represents the total number of transmission lines. For the first The next iteration of the transmission line The branch road has contributed to the trend; For power transmission lines Thermal stability limit capacity; Since the physical safety operation constraints of the power grid in this embodiment include multiple constraints, a comprehensive penalty function is constructed, expressed by the formula: ; In the formula, , , , and These are the weighting coefficients for line power flow exceeding limits penalty, node voltage exceeding limits penalty, cross-sectional power penalty, traditional unit ramp rate exceeding limits penalty, and power balance exceeding limits penalty, respectively. This represents the total number of load nodes. This represents the total number of transmission lines. The total number of nodes; For the first The next iteration of the transmission line The branch road has contributed to the trend; For power transmission lines Thermal stability limit capacity; For the first The node of the next iteration The voltage amplitude; Rated voltage; For the first The next iteration flows through the cross section Net transmission power; For the first Sub-iteration traditional schedulable units During the period Those who have made meritorious contributions; For the first Sub-iteration traditional schedulable units During the period Those who have made meritorious contributions; For the first Sub-iteration traditional schedulable units Those who have made meritorious contributions; For the first Second-generation renewable energy units Actual planned output; Forecast active power load; It is worth noting that in this embodiment, the generator output constraint is a hard constraint, that is, the generator output must strictly meet its constraint conditions throughout the entire optimization algorithm iteration process and the final optimization result, and there is no possibility of violating the constraint. In this embodiment, adjusting the search direction based on the limit information is only for complex constraints (such as power flow and voltage) that may be temporarily violated during the optimization algorithm iteration process but must be met in the end. The comprehensive penalty function is incorporated into the optimization objective function and expressed as a formula: ; In the formula, For the first The optimization objective function after introducing a comprehensive penalty function in the next iteration; For the first The optimization objective function for this iteration does not introduce a comprehensive penalty function; For the first The penalty weight for exceeding the limit in each iteration can be adjusted according to the number of iterations. Increase and adaptively increase; The next search is conducted based on the optimized objective function after introducing the comprehensive penalty function, thereby guiding it toward a safe and feasible region. S4. When the optimization algorithm meets the convergence condition (in this embodiment, reaching the maximum number of iterations), the deduction is terminated and the final solution is output as the unit output plan in the final clearing scheme. The final clearing scheme also includes the final solution. The calculated nodal marginal electricity price, branch power flow, and cross-sectional power margin are as follows: The marginal electricity price at the node is expressed by the formula: ; In the formula, For nodes During the period The marginal electricity price; For time period Shadow price of power balance constraints; For nodes With nodes The shadow price of the transmission capacity constraint of the transmission lines between them is 0 if the transmission lines are not overloaded; For nodes During the period Active load; cross-section The shadow price under stability constraints, if cross-section If the limit is not exceeded, the value is 0; The cross-sectional power margin is expressed by the formula: ; In the formula, For the time period Section The cross-sectional power margin.

[0024] After outputting the final clearing scheme, the clearing results are comprehensively displayed through the three-dimensional graphical interface. This includes using three-dimensional surface plots to show the spatial distribution and temporal trend of nodal marginal electricity prices in different regions and time periods; displaying the optimal power flow distribution in the form of power flow animation on the three-dimensional power grid sand table model, clearly showing the transmission path of electricity from power plants through the transmission network to the load center; visually encoding the power margin of key sections (e.g., using color depth or a scale), and generating a safety analysis report.

[0025] This visualization step improves the interpretability of the clearing results, helping decision-makers quickly understand the market's operational status and the level of grid security.

[0026] Example 2: This embodiment provides a regional joint clearing method for the electricity market based on sand table simulation, which is based on the method described in Embodiment 1, with the following differences: The power grid physical parameters also include renewable energy output uncertainty parameters. Specifically, using historical output data and numerical weather prediction deviations, the kernel density estimation method is used to fit the output prediction error probability distribution of wind power and photovoltaic power in each simulation period. Based on the output prediction error probability distribution, multiple sets of renewable energy output scenarios with errors are generated. Each set of renewable energy output scenarios is substituted into a multi-regional power grid joint simulation model. The Newton-Raphson method is used to solve for the node voltage, branch power flow, and cross-sectional power under each scenario. The node voltage, branch power flow, and cross-sectional power of each scenario are verified to meet the physical safety operation constraints of the power grid. Based on the verification results, the scenario stability index is calculated and introduced into the optimization objective function, expressed by the formula: ; ; In the formula, The objective function for optimization after introducing the scenario stability index; These are the weighting coefficients corresponding to the scene stability indicators; As a metric for scene stability; The scenario loss cost (determined based on historical grid adjustment costs due to renewable energy fluctuations); Total number of scenarios contributing to renewable energy; Scenarios that contribute to renewable energy The weights; Scenarios that contribute to renewable energy The physical safety operation constraints of the power grid are met; The physical safety operation constraints of the power grid also include output deviation constraints between scenarios, that is, the output deviation of the same renewable energy unit in any two scenarios does not exceed 15% of its predicted maximum output, so as to avoid grid shock caused by sudden changes in unit output in extreme scenarios. When solving the optimization objective function after introducing the scenario stability index, a two-layer iterative solution is adopted. The outer layer selects renewable energy output scenarios in descending order of scenario weight (prioritizing high-probability scenarios), and uses the output data of the current scenario as the input parameter for the inner layer optimization. The inner layer adopts the improved multi-objective particle swarm optimization algorithm of Example 1. When verifying the physical safety operation constraints of the power grid, the scenario uncertainty penalty term of the current candidate solution under all iterated scenarios is calculated simultaneously. If a scenario exceeds the physical safety operation constraints of the power grid (such as line power flow overload), a scenario weighted penalty term is added to the comprehensive penalty function of Example 1, expressed by the formula: ; In the formula, Scenarios that contribute to renewable energy Scenario-weighted penalty term; This represents the scene penalty coefficient. Scenarios that contribute to renewable energy Downstream transmission lines During the period The branch road has contributed to the trend; When 5 consecutive iterations When (i.e., more than 95% of scenarios meet the safety constraints), the two-level iteration stops and the final clearing scheme generation stage begins. The final clearing scheme also includes scenario feasibility rate, which is to count the grid physical safety operation constraint satisfaction rate of renewable energy output scenarios in each region and time period, mark high-risk scenarios (scenario feasibility rate <80%) and provide corresponding adjustment suggestions (such as adding backup units).

[0027] Example 3: This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a regional joint clearing method for the electricity market based on sand table simulation as described in any embodiment of the present invention.

[0028] Example 4: This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a regional joint clearing method for the electricity market based on sand table simulation as described in any embodiment of the present invention.

[0029] It is worth noting that the system, electronic device, and computer-readable storage medium described in this invention are all based on the same inventive concept as the method described in this invention, and will not be described again here.

[0030] In this embodiment of the invention, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects have an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0031] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0032] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0033] In the embodiments provided by this invention, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0034] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A regional joint clearing method for the electricity market based on sand table simulation, characterized in that, The method includes: Obtain the power grid physical parameters of each participating region, and construct a multi-regional power grid joint simulation model based on the power grid physical parameters; The optimization objective function in the multi-regional power grid joint simulation model is constructed based on market clearing rules, and the power grid physical security operation constraints of the optimization objective function are set. An optimization algorithm is used to iteratively solve the objective function to obtain candidate clearing schemes. In each iteration, the current candidate solution is substituted into the multi-regional power grid joint simulation model to perform power flow calculation of the entire network. The power flow calculation result of the entire network is checked to see if it meets the physical safety operation constraints of the power grid. If the check result is not met, the search direction of the optimization algorithm is adjusted according to the limit information to generate a new candidate clearing scheme and continue iterative deduction. When the optimization algorithm meets the convergence condition, the deduction is terminated and the final safe clearing scheme is output.

2. The regional joint clearing method for the electricity market based on sand table simulation as described in claim 1, characterized in that, The multi-regional power grid joint simulation model uses AC power flow equations as its core. By solving these equations, the voltage amplitude and phase angle of each node in the entire network are obtained. Based on these voltage amplitudes and phase angles, the active power flow of branches and the cross-sectional power are calculated, wherein: The active power flow of the branch is expressed by the formula: ； In the formula, For the node Flow to Node The branch road has contributed to the trend; For nodes The voltage amplitude; For nodes The voltage amplitude; For nodes and nodes The electrical conductance of the branch circuits between them; For nodes and nodes The susceptance to ground of the branch circuit; For nodes and nodes The phase angle difference between them; The cross-sectional power is expressed by the formula: ； In the formula, For the cross-section of the flow Net transmission power; cross-section The set of transmission lines included.

3. The regional joint clearing method for the electricity market based on sand table simulation as described in claim 2, characterized in that, The optimization objective function is expressed by the following formula: ； in: ； In the formula, This represents the total number of simulation periods; For traditional dispatchable units During the period Those who have made meritorious contributions; For traditional dispatchable units During the period The power generation cost function; , and These are the coefficients of the quadratic, linear, and constant terms of the power generation cost, respectively. This is the energy curtailment penalty coefficient; Forecast renewable energy units During the period The maximum force that can be exerted; For renewable energy units During the period Actual planned output; and These are the resource allocation weighting coefficient and the green energy target weighting coefficient, respectively. and These represent the number of conventional dispatchable generating units and renewable energy generating units, respectively.

4. The regional joint clearing method for the electricity market based on sand table simulation as described in claim 3, characterized in that, The power grid physical safety operation constraints of the optimization objective function include generator output constraints, line transmission capacity constraints, node voltage safety constraints, and critical section stability constraints, among which: The generator output constraint is expressed by the following formula: ； ； ； The line transmission capacity constraint is expressed by the following formula: ； The node voltage safety constraint is expressed by the following formula: ； The key section stability constraint is expressed by the following formula: ； In the formula, To provide the minimum technical output of the generator; To maximize the technical output of the generator; For traditional dispatchable units During the period Those who have made meritorious contributions; For traditional dispatchable units The rate of climb; For the time period From node Flow to Node The branch road has contributed to the trend; For nodes Flow to Node Thermal stability limit capacity; For nodes The minimum voltage amplitude; For nodes The maximum voltage amplitude; For the time period node The voltage amplitude; cross-section During the period Net transmission power; cross-section The stable limit power.

5. A regional joint clearing method for the electricity market based on sand table simulation as described in claim 4, characterized in that, If the verification result is not met, the search direction of the optimization algorithm is adjusted according to the limit violation information, specifically as follows: A comprehensive penalty function is constructed based on the over-limit information corresponding to the unmet physical safety operation constraints of the power grid, expressed as the formula: ； In the formula, , , , and These are the weighting coefficients for line power flow exceeding limits penalty, node voltage exceeding limits penalty, cross-sectional power penalty, traditional unit ramp rate exceeding limits penalty, and power balance exceeding limits penalty, respectively. This represents the total number of load nodes. This represents the total number of transmission lines. The total number of nodes; For the first The next iteration of the transmission line The branch road has contributed to the trend; For power transmission lines Thermal stability limit capacity; For the first The node of the next iteration The voltage amplitude; Rated voltage; For the first The next iteration flows through the cross section Net transmission power; For the first Sub-iteration traditional schedulable units During the period Those who have made meritorious contributions; For the first Sub-iteration traditional schedulable units During the period Those who have made meritorious contributions; For the first Sub-iteration traditional schedulable units Those who have made meritorious contributions; For the first Second-generation renewable energy units Actual planned output; Forecast active power load; The comprehensive penalty function is incorporated into the optimization objective function and expressed as a formula: ； In the formula, For the first The optimization objective function after introducing a comprehensive penalty function in the next iteration; For the first The optimization objective function for this iteration does not introduce a comprehensive penalty function; For the first Weight of the over-limit penalty in the next iteration; The next search is performed based on the optimized objective function after introducing the comprehensive penalty function.

6. The regional joint clearing method for the electricity market based on sand table simulation according to claim 1, characterized in that, The optimization algorithm is an improved multi-objective particle swarm optimization algorithm. Specifically, it introduces a file maintenance mechanism based on crowding distance and a dynamic inertia weight strategy to solve the optimization objective function. The dynamic inertia weight strategy dynamically adjusts the magnitude of the inertia weight according to the number of iterations, and the update formula is as follows: ； In the formula, For the first The inertia weight of the next iteration; and These are the maximum and minimum values ​​of the inertia weight, respectively. This represents the maximum number of iterations.

7. A regional joint clearing method for the electricity market based on sand table simulation as described in claim 5, characterized in that, The final clearing scheme includes the output plans of conventional dispatchable units and renewable energy units that optimize the objective function output, as well as the nodal marginal electricity price, branch power flow, and cross-sectional power margin calculated based on the output plans, wherein: The marginal electricity price at the node is expressed by the formula: ； In the formula, For nodes During the period The marginal electricity price; For time period Shadow price of power balance constraints; For nodes With nodes The shadow price of transmission capacity constraints on power transmission lines between them; For nodes During the period Active load; cross-section Shadow prices under stability constraints; The cross-sectional power margin is expressed by the formula: ； In the formula, For the time period Section The cross-sectional power margin.

8. A regional joint clearing method for the electricity market based on sand table simulation as described in claim 7, characterized in that, The method also includes dynamically displaying the power grid status and clearing results through a three-dimensional graphical interface during the iterative deduction process and after the final clearing scheme is output, wherein: A three-dimensional power grid sand table model is constructed based on a geographic information system, mapping substations, power plants, and load centers onto a three-dimensional map; Using columns or streamlines of different heights, colors, and dynamic flow effects, the marginal electricity price of each node, the power magnitude and direction of key transmission sections, and the over-limit alarm information of line power flow are represented in three-dimensional space. The system uses 3D curves or surfaces to display the unit output plan, cross-sectional power trend, and regional electricity price changes over different time periods.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a regional joint clearing method for the electricity market based on sand table simulation as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements a regional joint clearing method for the electricity market based on sand table simulation as described in any one of claims 1 to 8.

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