A method for determining a flexible load access node for hydrogen production by electrolysis of water based on a node electricity price and power flow coordination mechanism

Abstract

The application discloses a kind of based on node electricity price and power flow coordination mechanism's electrolytic water hydrogen production flexible load access node determination method, belong to new energy power generation prediction, power system power flow analysis and power marketization transaction coordination optimization technical field, the specific steps of this determination method are as follows: S1: basic data acquisition and parameter initialization;S2: establish new energy output scene model;The application can comprehensively consider node access condition, access capacity and power grid operating state difference, realize the quantitative evaluation and optimization of different access nodes, improve the scientificity and rationality of access decision, be conducive to improving local consumption level of new energy, reduce the phenomenon of wind curtailment and light curtailment, can analyze the influence of different access nodes on local node electricity price, network power flow and system operating state, improve power grid economic operation and resource allocation capability, reduce the power flow pressure of key transmission section, to relieve local network congestion and improve transmission channel utilization efficiency.

Description

Technical Field

[0001] This invention relates to the fields of new energy power generation forecasting, power system flow analysis and power market transaction collaborative optimization, and in particular to a method for determining the access node of flexible loads for water electrolysis hydrogen production based on nodal pricing and flow coordination mechanism. Background Technology

[0002] With the continuous expansion of installed capacity of renewable energy sources such as wind power and photovoltaics, the uncertainty, volatility, and uneven spatial distribution of new energy output in the power system are becoming increasingly prominent. In some areas rich in new energy, especially when load levels are low or transmission channels are limited, phenomena such as low marginal electricity prices at nodes, local reverse power flow, increased congestion at power lines, and obstacles to the absorption of new energy often occur. This not only affects the profitability of new energy projects but also increases the pressure on system operation and regulation.

[0003] Meanwhile, to meet the demands of modern energy transition, hydrogen energy has been given a crucial role in connecting new energy power generation, end-use energy utilization, and cross-industry coupling. Water electrolysis for hydrogen production, as one of the main pathways for green hydrogen production, offers advantages such as continuously adjustable load, rapid start-up and shutdown, and the ability to locally absorb surplus wind and solar power. If water electrolysis for hydrogen production can be connected to new energy enrichment nodes, and its operating power can be dynamically adjusted based on node electricity prices and network power flow conditions, it is expected to improve local load characteristics, suppress excessive fluctuations in node electricity prices, alleviate branch network congestion, and increase the local absorption rate of green electricity. Therefore, it is particularly important to develop a method for determining the flexible load access node for water electrolysis for hydrogen production based on a node electricity price and power flow coordination mechanism.

[0004] Existing nodal pricing analysis methods typically treat loads as rigid or generally interruptible loads, failing to reflect the operational characteristics of water electrolysis hydrogen production units, such as continuous adjustability, rapid response, minimum stable output limits, rated power constraints, ramp rate limitations, start-stop switching characteristics, and the variation of hydrogen production efficiency with load changes. Consequently, existing models cannot accurately depict the true impact of hydrogen production load participation on nodal pricing formation and power flow distribution.

[0005] Furthermore, wind and solar power output exhibits significant time-varying characteristics and scenario uncertainties. When renewable energy output increases, local node electricity prices decrease and power transmission increases; conversely, when renewable energy output decreases, the system may switch to conventional unit compensation, leading to an increase in node electricity prices. If a water electrolysis hydrogen production unit can dynamically adjust its power output based on node electricity prices and network conditions, it could potentially establish a "higher electricity consumption at lower prices, lower electricity consumption at higher prices" response mechanism, thereby participating in renewable energy consumption and network regulation.

[0006] Furthermore, in areas rich in renewable energy, the network location, power supply scale, load level, line transmission capacity, and congestion sensitivity of different nodes vary significantly. Even when connecting water electrolysis hydrogen production units of the same scale, the effects brought by different nodes are significantly different: some nodes are more conducive to absorbing surplus renewable energy, some nodes are more conducive to reducing local cross-sectional load rates, and some nodes have a more significant effect on stabilizing nodal electricity prices. Most existing methods can only perform simple absorption or economic calculations, and it is difficult to comprehensively compare candidate nodes from multiple dimensions such as nodal electricity price improvement, cross-sectional congestion relief, power flow optimization, and improvement of local green energy utilization. To this end, this invention proposes a method for determining flexible load access nodes for water electrolysis hydrogen production based on a nodal electricity price and power flow coordination mechanism. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method for determining the access node of flexible loads for water electrolysis hydrogen production based on a nodal electricity price and power flow coordination mechanism.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: A method for determining the access node for flexible loads in water electrolysis hydrogen production based on nodal pricing and power flow coordination mechanism. The specific steps of this method are as follows: S1: Basic data acquisition and parameter initialization: Collect various basic data on power grid topology, generating units, new energy sources, loads, water electrolysis hydrogen production units and candidate nodes, and perform data standardization processing according to a unified time scale; S2: Establish new energy power output scenario models: Generate high, medium and low typical power output scenarios through clustering and sampling. At the same time, calculate the actual available power output of new energy under each node, time period and scenario based on the prediction error, and establish corresponding new energy power output scenario models. S3: Establish a flexible load access node model and dynamic response model for water electrolysis hydrogen production: Set node access decision variables, construct a flexible load access node model and dynamic response model for water electrolysis hydrogen production, and set various operational constraints; S4: Establish a multi-period optimal power flow model that takes into account the flexible load of hydrogen production: calculate the actual power flow of each branch in the corresponding time period and scenario, establish complete power flow constraints in each time period and operating scenario, and establish a complete multi-period optimal power flow model based on the actual power flow and power flow constraints. S5: Construct a multi-objective optimization function: With the goal of minimizing the overall cost, integrate unit scheduling, power curtailment penalty, branch congestion, hydrogen production regulation and start-up / shutdown costs, and perform multi-objective collaborative optimization through weighted summation; S6: Solving nodal electricity prices: Based on the solution results of the multi-period optimal power flow model, the dual variable of the nodal power balance constraint is used as the nodal marginal electricity price to obtain the real nodal electricity price including hydrogen production load; S7: Construct collaborative analysis and evaluation indicators and form an access node determination scheme: Set up access scenarios for each candidate node, solve the model to obtain data on electricity price, power flow, power curtailment, and response characteristics, and obtain a comprehensive score by weighted summation, and select the node with the highest comprehensive score as the optimal access node; S8: Output Analysis Results: Based on the final node determination scheme, output the optimal access node, recommended operating power range, electricity price and power flow improvement effect, renewable energy consumption increase and candidate node suitability ranking.

[0009] As a further aspect of the present invention, the specific steps for basic data acquisition and parameter initialization in step S1 are as follows: Q1.1: Collect basic data of each power grid to be analyzed, including power grid topology parameters, node and branch parameters, conventional unit parameters, new energy power station parameters, basic load parameters, water electrolysis hydrogen production device parameters, and candidate access node set. Q1.2: After completing the basic data collection, the basic data of each group are discretized according to a unified time scale to form a multi-time period analysis dataset. The time scale can be 5 minutes, 15 minutes, 30 minutes or 1 hour.

[0010] As a further aspect of the present invention, the power grid topology parameters mentioned in Q1.1 include node set, branch set, line connection relationship, reference slack node and network adjacency relationship; Node and branch parameters, including node type, line reactance, line resistance, thermal stability transmission limit, cross-sectional composition and key cross-sectional constraint parameters; Standard unit parameters include the node where the unit is located, minimum / maximum output, ramping constraints, regulation cost coefficient, and standby capacity parameters; New energy power station parameters include the node where the wind farm and photovoltaic power station are located, the installed capacity, historical output samples, predicted output sequence and prediction error distribution parameters; Basic load parameters, including conventional load curves for each node, load fluctuation coefficients, and load forecast deviation data when necessary; Parameters of the water electrolysis hydrogen production unit include rated power, minimum stable operating power, maximum allowable operating power, ramp rate, start-up and shutdown time, minimum start-up time, minimum shutdown time, unit hydrogen production power consumption, efficiency curve, dynamic response time constant, and optional hydrogen storage capacity parameters. The candidate access node set includes screening parameters such as node voltage level, remaining access capacity, degree of aggregation of nearby renewable energy sources, transmission margin at key sections, and feasibility of hydrogen production station construction. Let the candidate access node set be: (1) in, For the set of candidate access nodes, This represents the number of candidate nodes; this set of candidate nodes is used to compare the node electricity price improvement and power flow improvement effects after different nodes are connected to hydrogen production load. In addition, the predicted output sequence and load curve data of the new energy sources can be resampled or interpolated according to the time scale selected in Q1.2 to ensure that the data from different sources are consistent in time series.

[0011] As a further aspect of the present invention, the specific steps for establishing the new energy power output scenario model in step S2 are as follows: P1.1: To address the fluctuations in wind and solar power output, various methods such as historical sample clustering, probability distribution sampling, and scenario reduction are employed to form multiple typical new energy power output scenarios, thereby constructing a new energy power output scenario set; P1.2: The power output scenarios of new energy scenarios are uniformly divided into three categories: high-output scenarios, medium-output scenarios and low-output scenarios. Then, the actual available power output of new energy access nodes under any time period and any scenario is calculated, and the actual available power output of non-new energy access nodes is set to 0.

[0012] As a further aspect of the present invention, the specific calculation formula for the actual available output force described in P1.2 is as follows: (2) in, Indicates wind power or solar power at the corresponding node Time period and scene The available output is below; To predict the output value; This refers to the output deviation under different scenarios.

[0013] For non-new energy access nodes, the following applies: (3) in, This is a set of nodes for new energy access.

[0014] As a further aspect of the present invention, the specific steps for establishing the flexible load access node model and dynamic response model for water electrolysis hydrogen production in step S3 are as follows: P2.1: Define the node access decision variables for the water electrolysis hydrogen production unit. ,when A value of 1 indicates that the water electrolysis hydrogen production device is connected to the node. If its value is 0, it indicates that the node is not connected to the water electrolysis hydrogen production unit. Furthermore, for a single water electrolysis hydrogen production unit connection scenario, the sum of the decision variables for all candidate connection nodes is constrained to be 1, and node connection permission parameters are set. When node When the basic access conditions are met, ;otherwise ; P2.2: Establish a hydrogen production efficiency model for water electrolysis and obtain node efficiency data in real time. During the period Scene The hydrogen production capacity was determined, and then, based on the dynamic hysteresis characteristics of the water electrolysis hydrogen production unit from receiving the scheduling command to realizing the actual power adjustment, a first-order inertial discrete model was used to simulate the change process of the actual hydrogen production power of the water electrolysis hydrogen production unit. P2.3: The water electrolysis hydrogen production unit is abstracted as an adjustable electrical load. Combining the node access decision variables and the unit start-up and shutdown state variables, the power operation boundary constraints of the water electrolysis hydrogen production unit are limited. At the same time, power ramp-up constraints are set. Then, to avoid frequent start-up and shutdown of the water electrolysis hydrogen production unit, the minimum continuous start-up time constraint of the water electrolysis hydrogen production unit is set. Finally, combined with the actual carrying capacity of the grid nodes, the node access capacity constraints of the water electrolysis hydrogen production unit are set as follows.

[0015] As a further aspect of the present invention, the specific constraints of the scenario in which the single-set water electrolysis hydrogen production device described in P2.1 is connected are as follows: (4) The specific calculation formula for the hydrogen production capacity mentioned in P2.2 is as follows: (5) in: For time period Hydrogen production; The hydrogen production efficiency can be a constant or a piecewise function; The duration of the time period; Energy consumption parameters converted per unit of hydrogen. Preferably, It can be a constant, or it can be represented by a piecewise linear function or a fitting function based on the load rate of the water electrolysis hydrogen production unit to improve the accuracy of the model; The actual hydrogen production power described in P2.2 is expressed in the following specific form: (6) (7) in, The actual power consumption of the water electrolysis hydrogen production unit at node 𝑛 during time period 𝑡; Adjust the target power for time period A; The dynamic response time constant of the water electrolysis hydrogen production device; The power operating boundary constraints described in P2.3 are as follows: (8) The power ramping constraint conditions described in P2.3 are as follows: (9) in: Minimum stable operating power; Maximum permissible operating power; and These are the climbing limits for ascending and descending slopes, respectively. This is a start / stop status variable, in binary form. When its value is 1, it indicates that the water electrolysis hydrogen production unit is in operation at that moment; when its value is 0, it indicates that the water electrolysis hydrogen production unit is in shutdown state. The minimum continuous power-on time constraint mentioned in P2.3 is as follows: (10) in: Minimum power-on duration; The node access capacity constraints described in P2.3 are as follows: (11) in, For nodes The maximum capacity that can be accessed.

[0016] As a further aspect of the present invention, the specific steps for establishing the multi-period optimal power flow model considering the flexible hydrogen production load in step S4 are as follows: P3.1: Establish power balance constraints for each node in the power grid under any time period and any operating scenario, then calculate the actual power flow of each branch under the corresponding time period and scenario, and establish power flow transmission security constraints for each branch in the power grid. P3.2: For key transmission sections in the power grid, establish section power constraints, and then limit the amount of renewable energy abandoned by each node in the corresponding time period and scenario to not less than 0, while the amount of abandoned energy shall not exceed the actual available renewable energy output of the node at present, and establish corresponding renewable energy abandoned energy constraints. P3.3: Limit the actual output of conventional generator sets to be no less than the minimum stable operating power of the generator set itself, and no more than the maximum allowable operating power of the generator set itself, in order to establish upper and lower limit constraints on the output of conventional generator sets, and set ramping constraints that conform to the dynamic adjustment capabilities of the generator sets.

[0017] As a further aspect of the present invention, the power balance constraint described in P3.1 is specifically as follows: (12) in: It provides power to conventional power sources; Contribute to new energy; Basic load; For flexible loads in hydrogen production; For the abandoned electricity of new energy sources; This represents the power exchange amount of the branches connected to this node. It's important to note that the node... The number is greater than or equal to the number of new energy nodes The quantity, that is; The power flow transmission security constraints described in P3.1 are as follows: (13) in: branch road During the period Scene The current trend; This represents the maximum allowable transmission power for the branch.

[0018] The specific cross-sectional power constraint mentioned in P3.2 is as follows: (14) in, cross-section The set of branches included; This refers to the branch direction coefficient; This represents the upper limit of the cross-sectional transmission capacity.

[0019] The specific constraints on the amount of renewable energy curtailment mentioned in P3.2 are as follows: (15) The specific upper and lower output limits of the conventional units described in P3.3 are as follows: (16) Furthermore, the specific climbing constraints are as follows: (17) in: This represents the minimum stable operating power for conventional generating units. This represents the maximum permissible operating power of a conventional unit. and These are the ramp-up and ramp-down limits for conventional generating units.

[0020] As a further aspect of the present invention, the multi-objective optimization function in step S5 is specifically used to achieve coordinated optimization of conventional unit scheduling costs, renewable energy curtailment, power flow congestion, and hydrogen production load adjustment costs. Set the scene The probability is And satisfy The multi-objective optimization function can be expressed as: (18) (19) (20) (twenty one) (twenty two) in: For the scene Lowering the conventional unit scheduling cost; For the scene Reduce the cost of penalties for abandoning renewable energy sources; For the scene The cost of penalties for congestion on secondary roads; For the scene Adjustment deviation of hydrogen production load power and start-up / shutdown costs; The cost coefficient for conventional unit scheduling is Yuan / kWh; The penalty coefficient for curtailment of renewable energy is Yuan / kWh; This is the branch road congestion penalty coefficient; The cost factor for adjusting hydrogen production load power is Yuan / kWh; Cost of starting and stopping a water electrolysis hydrogen production unit, Yuan / time; The total number of start-ups and shutdowns of the water electrolysis hydrogen production unit during the operating cycle; In addition, the above costs can be weighted and summed to achieve a balance between grid security, renewable energy consumption, and the economics of hydrogen production.

[0021] Furthermore, in step S6, the solution for nodal electricity prices is specifically achieved after the optimal power flow model is solved, using the dual variable of the nodal power balance constraint as the nodal marginal electricity price, i.e.: (twenty three) in, For nodes During the period Scene The nodal electricity price; These are the Lagrange multipliers for the corresponding node balance constraints.

[0022] As a further aspect of the present invention, the specific steps for constructing collaborative analysis and evaluation indicators and forming an access node determination scheme in step S7 are as follows: P4.1: When the access decision variable of the candidate node to be evaluated in the current candidate access node set is assigned a value of 1, it means that the node is connected to the water electrolysis hydrogen production device. The access decision variables of all other candidate access nodes are assigned a value of 0, which means that they are not connected. P4.2: Under the same new energy scenario, call the models in steps S4 to S6 to obtain the node electricity price, power flow distribution, cross-sectional load rate, curtailment level, and dynamic tracking results after each candidate node is connected to the hydrogen production load. Calculate the comprehensive score for each candidate node and select the candidate node with the highest comprehensive score as the optimal connection node. Alternatively, priority access can be prioritized.

[0023] As a further aspect of the present invention, the specific formula for calculating the comprehensive score described on page 4.2 is as follows: (twenty four) (25) (26) (27) (28) in: , , and These are weighting coefficients, ranging from 0 to 1, used to reflect the importance of different evaluation objectives. The sum of the four is 1. The nodal price smoothing index is used to characterize the degree of improvement in the fluctuation range of nodal prices after the integration of flexible hydrogen production loads. and These represent the standard deviations of node electricity prices before and after the connection to the flexible hydrogen production load; The section congestion mitigation index is used to characterize the percentage decrease in the degree of exceeding the limit of a critical branch or critical section. and These are the over-limit indicators of key sections before and after connecting to the flexible hydrogen production load. Contribution to local consumption of green electricity, used to characterize the proportion of surplus local wind and solar power directly absorbed by hydrogen production units; The surplus renewable energy power absorbed by the hydrogen production unit; This refers to renewable energy that might otherwise have been abandoned. This is a dynamic response adaptability index, reflecting the ability of water electrolysis hydrogen production units to track fluctuations in new energy sources and changes in nodal electricity prices.

[0024] As a further aspect of the present invention, the analysis results in step S8 include, but are not limited to, the optimal access node for the water electrolysis hydrogen production device. Recommended operating power range for the corresponding node; electricity price change curves for nodes during typical periods; power flow improvement results for key branches; reduction in renewable energy curtailment and increase in green energy consumption; suitability ranking results for each candidate node.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows: This method for determining the access nodes for flexible loads in water electrolysis hydrogen production, based on a nodal pricing and power flow coordination mechanism, transforms the manual allocation of access locations into an optimizable solution by introducing a set of candidate access nodes and node access decision variables. It comprehensively considers differences in node access conditions, access capacity, and grid operating status, enabling quantitative evaluation and selection of different access nodes, thus improving the scientific rigor and rationality of access decisions. Simultaneously, by incorporating the flexible load for water electrolysis hydrogen production into the nodal power balance model, it achieves a coordinated match between renewable energy output fluctuations and hydrogen production load regulation capabilities, which is beneficial for improving the local consumption level of renewable energy and reducing wind and solar power curtailment. By simultaneously considering the nodal pricing mechanism and branch power flow distribution within a unified optimization framework, it can analyze the impact of different access nodes on local nodal pricing, network power flow, and system operating status, improving the grid's economic operation and resource allocation capabilities. Furthermore, by analyzing the regulatory effect of hydrogen production load access on power flow in key branches and key sections, it can optimize local power flow distribution, reduce power flow pressure on key transmission sections, thereby alleviating local network congestion and improving transmission channel utilization efficiency. Attached Figure Description

[0026] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0027] Figure 1 This is a flowchart of a method for determining the access node of flexible loads for hydrogen production by water electrolysis based on nodal pricing and power flow coordination mechanism proposed in this invention. Figure 2 This is an algorithm flowchart of a method for determining the access node of flexible loads for hydrogen production by water electrolysis based on nodal electricity price and power flow coordination mechanism proposed in this invention. Detailed Implementation

[0028] Example, refer to Figure 1-2 A method for determining the access node for flexible loads in water electrolysis hydrogen production based on nodal pricing and power flow coordination mechanism. The specific steps of this method are as follows: Basic data acquisition and parameter initialization: Collect various basic data on power grid topology, generating units, new energy sources, loads, water electrolysis hydrogen production units and candidate nodes, and perform data standardization processing according to a unified time scale.

[0029] Specifically, the basic data of each power grid to be analyzed is collected, including power grid topology parameters, node and branch parameters, conventional unit parameters, new energy power station parameters, basic load parameters, water electrolysis hydrogen production device parameters, and candidate access node set. After the basic data collection is completed, the basic data of each group is discretized according to a unified time scale to form a multi-time period analysis dataset. The time scale can be 5 minutes, 15 minutes, 30 minutes or 1 hour.

[0030] In addition, it should be noted that the power grid topology parameters include node sets, branch sets, line connection relationships, reference slack nodes, and network adjacency relationships; node and branch parameters include node type, line reactance, line resistance, thermal stability transmission upper limit, cross-sectional composition, and key cross-sectional constraint parameters; conventional unit parameters include the node where the unit is located, minimum / maximum output, ramping constraints, regulation cost coefficient, and reserve capacity parameters; new energy power station parameters include the node where wind farms and photovoltaic power stations are located, installed capacity, historical output samples, predicted output sequences, and prediction error distribution parameters; and base load parameters include the conventional load curves of each node, Load fluctuation coefficient and, if necessary, load forecast deviation data; parameters of the water electrolysis hydrogen production unit, including rated power, minimum stable operating power, maximum allowable operating power, ramp rate, start-up and shutdown time, minimum start-up time, minimum shutdown time, unit hydrogen production power consumption, efficiency curve, dynamic response time constant, and optional hydrogen storage capacity parameters; a set of candidate access nodes, including node voltage level, remaining access capacity, degree of convergence of nearby renewable energy sources, transmission margin of key sections, and feasibility of hydrogen production station construction, etc., wherein the set of candidate access nodes is used to compare the node electricity price improvement effect and power flow improvement effect after different nodes are connected to hydrogen production load.

[0031] Establish new energy power output scenario models: Generate high, medium and low typical power output scenarios through clustering and sampling. At the same time, calculate the actual available power output of new energy under each node, time period and scenario based on the prediction error, and establish corresponding new energy power output scenario models.

[0032] Specifically, in response to fluctuations in wind and solar power output, various methods such as historical sample clustering, probability distribution sampling, and scenario reduction are used to form multiple typical new energy output scenarios to construct a new energy output scenario set. The new energy output scenarios are uniformly divided into three categories: high-output scenarios, medium-output scenarios, and low-output scenarios. Then, the actual available output of new energy access nodes under any time period and any scenario is calculated, and the actual available output of non-new energy access nodes is set to 0.

[0033] It should be noted that the specific formula for calculating the actual available output is as follows: (1) in, This indicates the available output of wind or solar power at the corresponding node, time period, and scenario; To predict the output value; This refers to the output deviation under different scenarios.

[0034] For non-new energy access nodes, the following applies: (2) in, This is a set of nodes for new energy access.

[0035] Establish a flexible load access node model and dynamic response model for hydrogen production via water electrolysis: Set node access decision variables, construct a flexible load access node model and dynamic response model for hydrogen production via water electrolysis, and set various operational constraints.

[0036] Specifically, define the node access decision variables for the water electrolysis hydrogen production device. ,when A value of 1 indicates that the water electrolysis hydrogen production device is connected to the node. If its value is 0, it indicates that the node is not connected to the water electrolysis hydrogen production unit. Furthermore, for a single water electrolysis hydrogen production unit connection scenario, the sum of the decision variables for all candidate connection nodes is constrained to be 1, and node connection permission parameters are set. When node When the basic access conditions are met, ;otherwise Establish a hydrogen production efficiency model for water electrolysis and obtain node efficiency data in real time. During the period Scene The hydrogen production capacity is determined based on the dynamic hysteresis characteristics of the water electrolysis hydrogen production unit from receiving dispatch instructions to realizing actual power adjustment. A first-order inertial discrete model is used to simulate the change process of the actual hydrogen production power of the water electrolysis hydrogen production unit. The water electrolysis hydrogen production unit is abstracted as an adjustable electrical load. Combining node access decision variables and device start-up and shutdown state variables, the power operation boundary constraints of the water electrolysis hydrogen production unit are limited. At the same time, power ramp-up constraints are set. Then, to avoid frequent start-up and shutdown of the water electrolysis hydrogen production unit, the minimum continuous start-up time constraint of the water electrolysis hydrogen production unit is set. Finally, combined with the actual carrying capacity of the grid nodes, the node access capacity constraints of the water electrolysis hydrogen production unit are set as follows.

[0037] It should be noted that the specific constraints for connecting a single water electrolysis hydrogen production unit to the scenario are as follows: (3) The specific formula for calculating hydrogen production capacity is as follows: (4) in: For time period Hydrogen production; The hydrogen production efficiency can be a constant or a piecewise function; The duration of the time period; Energy consumption parameters converted per unit of hydrogen. Preferably, It can be a constant, or it can be represented by a piecewise linear function or a fitting function based on the load rate of the water electrolysis hydrogen production unit to improve the accuracy of the model; The actual hydrogen production capacity is manifested in the following ways: (5) (6) in, The actual power consumption of the water electrolysis hydrogen production unit at node 𝑛 during time period 𝑡; Adjust the target power for time period A; The dynamic response time constant of the water electrolysis hydrogen production device; The power operating boundary constraints are as follows: (7) The power ramp-up constraints are as follows: (8) in: Minimum stable operating power; Maximum permissible operating power; and These are the climbing limits for ascending and descending slopes, respectively. This is a start / stop status variable, in binary form. When its value is 1, it indicates that the water electrolysis hydrogen production unit is in operation at that moment; when its value is 0, it indicates that the water electrolysis hydrogen production unit is in shutdown state. The minimum continuous power-on time constraint is as follows: (9) in: Minimum power-on duration; The specific node access capacity constraints are as follows: (10) in, For nodes The maximum capacity that can be accessed.

[0038] Establish a multi-period optimal power flow model that takes into account the flexible load of hydrogen production: calculate the actual power flow of each branch in the corresponding time period and scenario, establish complete power flow constraints in each time period and operating scenario, and establish a complete multi-period optimal power flow model based on the actual power flow and power flow constraints.

[0039] Specifically, power balance constraints are established for each node in the power grid under any time period and any operating scenario. Then, the actual power flow of each branch under the corresponding time period and scenario is calculated. For each branch in the power grid, power flow transmission security constraints are established. For key transmission sections in the power grid, section power constraints are established. Then, the amount of renewable energy curtailment at each node under the corresponding time period and scenario is limited to not less than 0, and the curtailment amount does not exceed the actual available renewable energy output of the node. Corresponding renewable energy curtailment constraints are established. The actual output of conventional generating units under the corresponding time period and scenario is limited to not less than the unit's own minimum stable operating power, and not greater than the unit's own maximum allowable operating power, so as to establish upper and lower limit constraints for the output of conventional generating units. At the same time, ramping constraints that conform to the dynamic adjustment capabilities of the units are set for conventional generating units.

[0040] In this embodiment, the power balance constraint is specifically as follows: (11) in: It provides power to conventional power sources; Contribute to new energy; Basic load; For flexible loads in hydrogen production; For the abandoned electricity of new energy sources; This represents the power exchange amount of the branches connected to this node. It's important to note that the node... The number is greater than or equal to the number of new energy nodes Quantity; The specific security constraints for power flow transmission are as follows: (12) in: branch road During the period Scene The current trend; This represents the maximum allowable transmission power for the branch.

[0041] The specific cross-sectional power constraints are as follows: (13) in, cross-section The set of branches included; This refers to the branch direction coefficient; This represents the upper limit of the cross-sectional transmission capacity.

[0042] The specific constraints on the curtailment of renewable energy power are as follows: (14) The specific upper and lower output limits of the conventional units described in P3.3 are as follows: (15) In addition, the specific climbing constraints are as follows: (16) in: This represents the minimum stable operating power for conventional generating units. This represents the maximum permissible operating power of a conventional unit. and These are the ramp-up and ramp-down limits for conventional generating units.

[0043] Construct a multi-objective optimization function: with the goal of minimizing the overall cost, integrate unit scheduling, power curtailment penalty, branch congestion, hydrogen production regulation and start-up / shutdown costs, and perform multi-objective collaborative optimization through weighted summation.

[0044] It should be noted that the multi-objective optimization function is specifically used to achieve coordinated optimization of conventional unit scheduling costs, renewable energy curtailment, power flow congestion, and hydrogen production load adjustment costs; Set the scene The probability is And satisfy The multi-objective optimization function can be expressed as: (17) (18) (19) (20) (twenty one) in: For the scene Lowering the conventional unit scheduling cost; For the scene Reduce the cost of penalties for abandoning renewable energy sources; For the scene The cost of penalties for congestion on secondary roads; For the scene Adjustment deviation of hydrogen production load power and start-up / shutdown costs; The cost coefficient for conventional unit scheduling is Yuan / kWh; The penalty coefficient for curtailment of renewable energy is Yuan / kWh; This is the branch road congestion penalty coefficient; The cost factor for adjusting hydrogen production load power is Yuan / kWh; Cost of starting and stopping a water electrolysis hydrogen production unit, Yuan / time; This represents the total number of start-ups and shutdowns of the water electrolysis hydrogen production unit during its operating cycle; and all costs can be weighted and summed according to their respective weights, thereby achieving a balance between grid security, renewable energy consumption, and the economics of hydrogen production.

[0045] Solving for nodal electricity prices: Based on the solution results of the multi-period optimal power flow model, the dual variable of the nodal power balance constraint is used as the nodal marginal electricity price to obtain the true nodal electricity price that includes hydrogen production load.

[0046] It should be noted that, specifically, after the optimal power flow model is solved, the dual variable of the nodal power balance constraint is used as the nodal marginal price, i.e.: (twenty two) in, For nodes During the period Scene The nodal electricity price; These are the Lagrange multipliers for the corresponding node balance constraints.

[0047] Construct collaborative analysis and evaluation indicators and form an access node determination scheme: Set up access scenarios for each candidate node, solve the model to obtain data on electricity price, power flow, power curtailment, and response characteristics, and obtain a comprehensive score by weighted summation, and select the node with the highest comprehensive score as the optimal access node.

[0048] Specifically, when the access decision variable of the candidate node to be evaluated in the current candidate access node set is assigned a value of 1, it means that the node is connected to the water electrolysis hydrogen production unit. The access decision variable of all other candidate access nodes is assigned a value of 0, which means that they are not connected. Under the same new energy scenario, the models in steps S4 to S6 are called to obtain the node electricity price, power flow distribution, cross-sectional load rate, curtailment level and dynamic tracking results after each candidate node is connected to the hydrogen production load. The comprehensive score of each candidate node is calculated, and the candidate node with the highest comprehensive score is selected as the optimal access node. Alternatively, priority access can be prioritized.

[0049] It should be noted that the specific formula for calculating the overall score is as follows: (twenty three) (twenty four) (25) (26) (27) in: , , and , which is a weighting coefficient, with a value between 0 and 1, used to reflect the importance of different evaluation objectives; The nodal price smoothing index is used to characterize the degree of improvement in the fluctuation range of nodal prices after the integration of flexible hydrogen production loads. and These represent the standard deviations of node electricity prices before and after the connection to the flexible hydrogen production load; The section congestion mitigation index is used to characterize the percentage decrease in the degree of exceeding the limit of a critical branch or critical section. and These are the over-limit indicators of key sections before and after connecting to the flexible hydrogen production load. Contribution to local consumption of green electricity, used to characterize the proportion of surplus local wind and solar power directly absorbed by hydrogen production units; The surplus renewable energy power absorbed by the hydrogen production unit; This refers to renewable energy that might otherwise have been abandoned. This is a dynamic response adaptability index, reflecting the ability of water electrolysis hydrogen production units to track fluctuations in new energy sources and changes in nodal electricity prices.

[0050] Output analysis results: Based on the final node determination scheme, output the optimal access node, recommended operating power range, electricity price and power flow improvement effect, renewable energy consumption increase and candidate node suitability ranking.

[0051] The analysis results include, but are not limited to, the optimal connection node for the water electrolysis hydrogen production unit. Recommended operating power range for the corresponding node; electricity price change curves for nodes during typical periods; power flow improvement results for key branches; reduction in renewable energy curtailment and increase in green energy consumption; suitability ranking results for each candidate node.

[0052] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for determining the access node of flexible loads for hydrogen production via water electrolysis based on nodal pricing and power flow coordination mechanism, characterized in that, The specific steps for this determination method are as follows: S1: Basic data acquisition and parameter initialization: Collect various basic data on power grid topology, generating units, new energy sources, loads, water electrolysis hydrogen production units and candidate nodes, and perform data standardization processing according to a unified time scale; S2: Establish new energy power output scenario models: Generate high, medium and low typical power output scenarios through clustering and sampling. At the same time, calculate the actual available power output of new energy under each node, time period and scenario based on the prediction error, and establish corresponding new energy power output scenario models. S3: Establish a flexible load access node model and dynamic response model for water electrolysis hydrogen production: Set node access decision variables, construct a flexible load access node model and dynamic response model for water electrolysis hydrogen production, and set various operational constraints; S4: Establish a multi-period optimal power flow model that takes into account the flexible load of hydrogen production: calculate the actual power flow of each branch in the corresponding time period and scenario, establish complete power flow constraints in each time period and operating scenario, and establish a complete multi-period optimal power flow model based on the actual power flow and power flow constraints. S5: Construct a multi-objective optimization function: With the goal of minimizing the overall cost, integrate unit scheduling, power curtailment penalty, branch congestion, hydrogen production regulation and start-up / shutdown costs, and perform multi-objective collaborative optimization through weighted summation; S6: Solving nodal electricity prices: Based on the solution results of the multi-period optimal power flow model, the dual variable of the nodal power balance constraint is used as the nodal marginal electricity price to obtain the real nodal electricity price including hydrogen production load; S7: Construct collaborative analysis and evaluation indicators and form an access node determination scheme: Set up access scenarios for each candidate node, solve the model to obtain data on electricity price, power flow, power curtailment, and response characteristics, and obtain a comprehensive score by weighted summation, and select the node with the highest comprehensive score as the optimal access node; S8: Output Analysis Results: Based on the final node determination scheme, output the optimal access node, recommended operating power range, electricity price and power flow improvement effect, renewable energy consumption increase and candidate node suitability ranking.

2. The method for determining the access node of flexible loads for water electrolysis hydrogen production based on nodal pricing and power flow coordination mechanism according to claim 1, characterized in that, The specific steps for establishing the new energy power output scenario model in step S2 are as follows: P1.1: To address the fluctuations in wind and solar power output, various methods such as historical sample clustering, probability distribution sampling, and scenario reduction are employed to form multiple typical new energy power output scenarios, thereby constructing a new energy power output scenario set; P1.2: The power output scenarios of new energy scenarios are uniformly divided into three categories: high-output scenarios, medium-output scenarios and low-output scenarios. Then, the actual available power output of new energy access nodes under any time period and any scenario is calculated, and the actual available power output of non-new energy access nodes is set to 0.

3. The method for determining the access node of flexible loads for water electrolysis hydrogen production based on nodal pricing and power flow coordination mechanism according to claim 1, characterized in that, The specific steps for establishing the flexible load access node model and dynamic response model for water electrolysis hydrogen production in step S3 are as follows: P2.1: Define the node access decision variables for the water electrolysis hydrogen production unit. ,when A value of 1 indicates that the water electrolysis hydrogen production device is connected to the node. ; If its value is 0, it indicates that the node is not connected to the water electrolysis hydrogen production unit. Furthermore, for a single water electrolysis hydrogen production unit connection scenario, the sum of the decision variables for all candidate connection nodes is constrained to be 1, and node connection permission parameters are set. When node When the basic access conditions are met, ;otherwise ; P2.2: Establish a hydrogen production efficiency model for water electrolysis and obtain node efficiency data in real time. During the period Scene The hydrogen production capacity was determined, and then, based on the dynamic hysteresis characteristics of the water electrolysis hydrogen production unit from receiving the scheduling command to realizing the actual power adjustment, a first-order inertial discrete model was used to simulate the change process of the actual hydrogen production power of the water electrolysis hydrogen production unit. P2.3: The water electrolysis hydrogen production unit is abstracted as an adjustable electrical load. Combining the node access decision variables and the unit start-up and shutdown state variables, the power operation boundary constraints of the water electrolysis hydrogen production unit are limited. At the same time, power ramp-up constraints are set. Then, to avoid frequent start-up and shutdown of the water electrolysis hydrogen production unit, the minimum continuous start-up time constraint of the water electrolysis hydrogen production unit is set. Finally, combined with the actual carrying capacity of the grid nodes, the node access capacity constraints of the water electrolysis hydrogen production unit are set as follows.

4. The method for determining the access node of flexible loads for water electrolysis hydrogen production based on nodal pricing and power flow coordination mechanism according to claim 1, characterized in that, The specific steps for establishing the multi-period optimal power flow model considering the flexible hydrogen production load in step S4 are as follows: P3.1: Establish power balance constraints for each node in the power grid under any time period and any operating scenario, then calculate the actual power flow of each branch under the corresponding time period and scenario, and establish power flow transmission security constraints for each branch in the power grid. P3.2: For key transmission sections in the power grid, establish section power constraints, and then limit the amount of renewable energy abandoned by each node in the corresponding time period and scenario to not less than 0, while the amount of abandoned energy shall not exceed the actual available renewable energy output of the node at present, and establish corresponding renewable energy abandoned energy constraints. P3.3: Limit the actual output of conventional generator sets to be no less than the minimum stable operating power of the generator set itself, and no more than the maximum allowable operating power of the generator set itself, in order to establish upper and lower limit constraints on the output of conventional generator sets, and set ramping constraints that conform to the dynamic adjustment capabilities of the generator sets.

5. The method for determining the access node of flexible loads for water electrolysis hydrogen production based on nodal pricing and power flow coordination mechanism according to claim 1, characterized in that, The multi-objective optimization function described in step S5 is specifically used to achieve coordinated optimization of conventional unit scheduling costs, renewable energy curtailment, power flow congestion, and hydrogen production load adjustment costs.

6. The method for determining the access node of flexible loads for water electrolysis hydrogen production based on nodal pricing and power flow coordination mechanism according to claim 1, characterized in that, The specific steps for constructing collaborative analysis and evaluation indicators and forming an access node determination scheme as described in step S7 are as follows: P4.1: When the access decision variable of the candidate node to be evaluated in the current candidate access node set is assigned a value of 1, it means that the node is connected to the water electrolysis hydrogen production device. The access decision variables of all other candidate access nodes are assigned a value of 0, which means that they are not connected. P4.2: Under the same new energy scenario, call the models in steps S4 to S6 to obtain the node electricity price, power flow distribution, cross-sectional load rate, curtailment level, and dynamic tracking results after each candidate node is connected to the hydrogen production load. Calculate the comprehensive score for each candidate node and select the candidate node with the highest comprehensive score as the optimal connection node. Alternatively, priority access can be prioritized.

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