Cross-market joint clearing and safety checking method, device, equipment and program product

Abstract

The invention relates to the technical field of power markets and distributed energy regulation and control, in particular to a cross-market joint clearing and safety checking method, device, equipment and program product. The method comprises the steps of obtaining cost information and income information of power generation of each target aggregator; taking the cost information of each node as the input of a power purchase cost planning layer in the game model, taking the income information of an operator as the input of an income planning layer in the game model, and solving the game model to obtain a scheduling plan; checking the scheduling plan by using the security constraint condition, and determining the scheduling plan as a target plan in the clearing process under the condition that the scheduling plan meets the security constraint condition; wherein the safety constraint condition comprises constraint on power transmission section out-of-limit and constraint on deviation cost of power supply quantity. By adopting the method, the safety in the combined clearing process can be improved.

Description

Technical Field

[0001] This application relates to the technical field of electricity market and distributed energy regulation, and in particular to a cross-market joint clearing and safety verification method, apparatus, equipment and program product. Background Technology

[0002] The rapid development of low-voltage distributed renewable energy (such as photovoltaic and wind power) is contributing to the energy transition. However, due to their intermittency and volatility, they pose challenges to the safe and stable operation of the power grid. For example, local power backflow can cause voltage spikes and power drops that affect power supply stability. The electricity generated by different types of energy power plants needs to be connected to the grid through nodes. Typically, the electricity generated by the same type of energy power plants is aggregated and dispatched through energy aggregators.

[0003] The clearing of the electricity spot market refers to the process in which, through market mechanisms, electricity is matched and priced based on the quotations of various power plants and the electricity demand of the grid, thereby determining the trading volume and price in the market and bringing the market to a state of supply and demand balance.

[0004] Traditional electricity market mechanisms are centered on centralized power sources. When facing joint clearing of the electricity market and ancillary services market, they do not fully consider the uncertainty and spatiotemporal characteristics of distributed energy resources. Therefore, they cannot guarantee the security of the clearing results and may lead to problems such as main network congestion or transmission section over-limit.

[0005] Therefore, how to improve the security of the joint clearing process is an urgent problem to be solved. Summary of the Invention

[0006] Therefore, it is necessary to provide a cross-market joint clearing and security verification method, apparatus, equipment and procedure product that can improve the security of the joint clearing process in order to address the above-mentioned technical problems.

[0007] Firstly, this application provides a cross-market joint clearing and security verification method, including:

[0008] In one embodiment, cost information of electricity purchased from the grid side and revenue information of each target aggregator are obtained;

[0009] The cost information is used as input to the electricity purchase cost planning layer of the game model, and the revenue information of each operator is used as input to the revenue planning layer of the game model. The game model is then solved to obtain the scheduling plan. The electricity purchase cost planning layer is constructed with the objective of minimizing the electricity purchase cost on the grid side, and the revenue planning layer is constructed with the objective of maximizing the operator's revenue. The game model aims to simultaneously satisfy the objectives of both the electricity purchase cost planning layer and the revenue planning layer. The scheduling plan includes the purchase price and the power supply volume of each target aggregator during the clearing period.

[0010] The scheduling plan is verified using safety constraints. If the scheduling plan meets the safety constraints, it is determined as the target plan in the clearing process. The safety constraints include constraints on transmission section overruns and constraints on deviation costs of power supply.

[0011] In one embodiment, the cost information includes operating costs, expected operating costs, and expected transition costs;

[0012] The operating costs include start-up costs, no-load costs, downtime costs, planned energy costs, adjustment, rotation, and ten-minute non-rotation reserve costs.

[0013] In one embodiment, the revenue information includes direct electricity trading revenue, power generation costs, and the charging and discharging costs of the energy storage system;

[0014] Where at least one of the target aggregators is a photovoltaic aggregator, the revenue information also includes power regulation costs.

[0015] In one embodiment, solving the game model to obtain the scheduling plan includes:

[0016] A first constraint condition for the line and network and a second constraint condition for the generator set are added to the game model, and the game model with added constraints is solved using a preset algorithm to obtain the scheduling plan.

[0017] The first constraint includes power balance constraint, transmission line flow amplitude constraint, power supply upward adjustment constraint, power supply downward adjustment constraint, and reserve constraint.

[0018] The second set of constraints includes output constraints, constraints between generator start-up time and start-up / shutdown costs, capacity reserve constraints, adjustment range constraints, and available capacity constraints.

[0019] In one embodiment, the preset algorithm is a dual gap minimization algorithm.

[0020] In one embodiment, the method further includes:

[0021] Based on the screening constraints, each aggregator is screened to obtain the target aggregators;

[0022] The screening constraints include constraints on effective capacity, adjustment range, response speed, and response duration.

[0023] Secondly, this application also provides a cross-market joint clearing and security verification device, the device comprising an information acquisition module, a model solving module, and a verification module, wherein:

[0024] The information acquisition module is used to acquire cost information of electricity purchased from the grid side and revenue information of each target aggregator;

[0025] The model solving module is used to take the cost information as input to the electricity purchase cost planning layer of the game model, and the revenue information of each operator as input to the revenue planning layer of the game model, to solve the game model and obtain a scheduling plan. The electricity purchase cost planning layer is constructed with the goal of minimizing the electricity purchase cost on the grid side, and the revenue planning layer is constructed with the goal of maximizing the operator's revenue. The game model aims to simultaneously satisfy the objectives of both the electricity purchase cost planning layer and the revenue planning layer. The scheduling plan includes the purchase price and the power supply of each target aggregator during the clearing period.

[0026] The verification module is used to verify the scheduling plan using safety constraints. If the scheduling plan meets the safety constraints, the scheduling plan is determined as the target plan in the clearing process. The safety constraints include constraints on transmission section overruns and constraints on deviation costs of power supply.

[0027] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the cross-market joint clearing and security verification method as described in any one of the first aspects above.

[0028] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the cross-market joint clearing and security verification method as described in any one of the first aspects above.

[0029] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the cross-market joint clearing and security verification method as described in any one of the first aspects above.

[0030] The aforementioned cross-market joint clearing and safety verification methods, devices, equipment, and procedures address the challenge of unified dispatch and clearing when a large number of renewable energy sources participate in the electricity market. They construct a two-layer aggregation model with both cost minimization and profit maximization objectives. Through game theory model optimization, the model aims to formulate a dispatch plan during the clearing process with the goals of maximizing profits and minimizing costs. Furthermore, during the verification process, constraints on transmission line over-limits reduce the probability of grid congestion, equipment overload, and even large-scale power outages caused by power flow overload, thus placing economic pursuits within safety constraints and achieving optimal profits under safety premises. The constraint on deviation costs provides generator units with the capability to cope with power prediction errors, thereby enhancing the robustness of supply and demand matching during the clearing process. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a diagram illustrating the application environment of a cross-market joint clearing and security verification method in one embodiment.

[0033] Figure 2 This is a flowchart illustrating a cross-market joint clearing and security verification method in one embodiment;

[0034] Figure 3 This is a schematic diagram of the overall logic of the cross-market joint clearing and security verification method in another embodiment;

[0035] Figure 4 This is a structural block diagram of a cross-market joint clearing and safety verification device in one embodiment;

[0036] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0038] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0039] The cross-market joint clearing and security verification method provided in this application can be applied to, for example... Figure 1 In the application environment shown, the power generated by different types of energy power plants needs to be connected to the grid through nodes. Typically, the power generated by the same type of energy power plant is aggregated and dispatched through energy aggregators. For example, aggregators can be categorized as photovoltaic aggregators, wind power aggregators, and tidal power aggregators. Each energy power plant is equipped with at least one generator unit, and each generator unit consists of multiple generators. Energy power plants connect to the electricity market (grid) through pre-defined nodes. Due to geographical location, different types of energy power plants may also connect to the electricity market through the same node; and multiple transmission lines are pre-defined between the electricity market and the energy power plants.

[0040] Aggregators have two-way communication capabilities, enabling them to exchange information with trading centers and dispatch centers via the network; the markets participating in the clearing process include the electricity market (grid) and the ancillary services market.

[0041] In one exemplary embodiment, a cross-market joint clearing and security verification method is provided, which is executed by a computer device. The computer device can be, but is not limited to, various personal computers, laptops, smartphones, tablets, servers, drones, low-altitude aircraft, IoT devices, and portable wearable devices. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc.; they can be independent physical servers, server clusters or distributed systems composed of multiple physical servers, or cloud servers providing cloud computing services.

[0042] like Figure 2As shown, a cross-market joint clearing and security verification method specifically includes steps 110-130, wherein:

[0043] Step 110: Obtain cost information for purchasing electricity from the grid side and revenue information for each target aggregator.

[0044] In this embodiment of the application, the purpose of clearing is to balance the power generation of each target aggregator with the power demand on the grid side. Since each target aggregator corresponds to at least one generator unit, in order to maximize profits, it is necessary to coordinate the power generation allocation among the generator units. That is, to encourage low-cost units to generate more power because they can obtain higher profits, while penalizing high-cost units because they may just break even or even incur losses. At the same time, when there is a surplus in power generation, factors such as the storage cost of the surplus power also need to be considered.

[0045] Since the target aggregators in this application all target renewable energy sources, such as photovoltaic power generation using solar energy, wind power generation using wind energy, and hydropower generation using tidal energy, the revenue of the generator sets corresponding to each target aggregator is greatly affected by seasonal and environmental factors. Therefore, in each clearing cycle, users need to determine the cost information of purchasing electricity from the grid and the revenue information of each target aggregator.

[0046] Step 120: Use cost information as input to the electricity purchase cost planning layer of the game model, and use the operator's revenue information as input to the revenue planning layer of the game model. Solve the game model to obtain the dispatch plan. The electricity purchase cost planning layer is constructed with the goal of minimizing the electricity purchase cost on the grid side, and the revenue planning layer is constructed with the goal of maximizing the operator's revenue. The game model aims to simultaneously satisfy the goals of the electricity purchase cost planning layer and the revenue planning layer. The dispatch plan includes the purchase price and the power supply of each target aggregator during the clearing period.

[0047] In this embodiment, the game model structure is pre-defined, including a cost planning layer and a revenue planning layer. The cost planning layer constructs a cost objective function based on the relationships between cost items, aiming to minimize costs. After obtaining cost information, this information is input into the cost planning layer for cost calculation. The revenue planning layer constructs a revenue objective function based on the relationships between cost items and necessary expenditures, aiming to maximize revenue. After obtaining revenue information, this information is input into the cost planning layer for revenue calculation. The game model is then solved to obtain a scheduling plan. The scheduling plan includes the purchase cost of electricity from the power grid and the power supply of each target aggregator during the clearing period. The purchase cost is a consistent price for all target aggregators. The power supply can include the total power supply of the target aggregators during the clearing period. Since the clearing period can be divided into multiple unit periods, the power supply can also include the unit power supply of each target aggregator in each unit period of the clearing period. The specific structure of the power supply is not specifically limited in this embodiment.

[0048] Step 130: Verify the dispatch plan using safety constraints. If the dispatch plan meets the safety constraints, determine the dispatch plan as the target plan in the clearing process. The safety constraints include constraints on transmission section overruns and constraints on deviation costs of power supply.

[0049] In the embodiments of this application, the procurement price in the scheduling plan output by the game model is fixed, while the power supply of each generator set in each target aggregator can be adjusted and optimized. This is also the dimension to be constrained by the security constraints.

[0050] Since generator sets corresponding to different target aggregators may use the same transmission section to transmit power to the grid, it is necessary to consider whether the flow (transmission capacity) in each transmission section exceeds the limit. The connection nodes between the generator sets corresponding to each target aggregator and the grid are fixed; therefore, the transmission sections between the generator sets corresponding to each target aggregator and the grid can also be pre-allocated and set.

[0051] Safety constraints include pre-defined limits for each transmission section between each target aggregator and the power grid, specifically including constraints on forward power flow limits and reverse power flow limits. Forward power flow limits are defined as the amount by which the operating parameters of certain electrical equipment (such as transmission lines and transformers) exceed their safe operating limits when power (active and reactive power) flows in a specified forward direction. Reverse power flow limits refer to the amount by which the operating parameters of electrical equipment exceed their safe operating limits when power flows in the opposite direction to the specified forward direction. In this embodiment, the direction from the generator set to its connection point to the power grid is defined as forward. Power supply deviation costs are used to constrain and correct over-limit sections, thereby better reducing the power supply deviation of each generator set.

[0052] In the aforementioned cross-market joint clearing and safety verification method, to address the difficulty of unified dispatch and clearing when a large number of renewable energy sources participate in the electricity market, a two-layer aggregation model is constructed, with the objectives of minimizing costs and maximizing benefits. Through optimization of the game theory model, on the one hand, a dispatch plan can be formulated during the clearing process with the objectives of maximizing benefits and minimizing costs, and a verification process for the dispatch plan is adopted. During the verification process, by constraining the over-limit of transmission sections, the probability of grid congestion, equipment overload, or even large-scale power outages caused by power flow overload can be reduced, thus placing the pursuit of economic efficiency within safety constraints and achieving optimal benefits under the premise of safety. On the other hand, the constraint on deviation costs reserves the ability of generating units to cope with power prediction errors, thereby enhancing the robustness of the supply and demand matching relationship during the clearing process.

[0053] In an exemplary embodiment, in order to further improve the redundancy of aggregators, the solution of this application imposes certain constraints on the qualifications of aggregators; that is, before step 110, it further includes: screening each aggregator based on screening constraints to obtain each target aggregator.

[0054] Specifically, the dimensions of the screening constraints include constraints on effective capacity, constraints on adjustment range, constraints on response speed, and constraints on response duration.

[0055] In one possible implementation, among the constraints across the various dimensions, the effective capacity constraint could be an effective capacity of no less than 5 MW; the adjustment range constraint could be a flexible adjustment capability (adjustment range) of no less than 1 MW; the response speed constraint could be a response speed of no less than 15 minutes; and the response duration constraint could be a response duration of no less than 1 hour. Furthermore, the screening constraints could also include requiring aggregators to have bidirectional communication capabilities, enabling them to submit application information to the trading platform and receive market transaction information and scheduling instructions issued by the trading platform or scheduling platform.

[0056] Of course, the above-mentioned screening constraints are just a possible example. The parameters of the specific conditions of each dimension of constraints can be set according to user needs. This application embodiment does not impose specific limitations on this.

[0057] In one embodiment, the cost information in step 10 includes operating costs, expected operating costs, and expected transition costs; wherein, operating costs include start-up costs, no-load costs, downtime costs, planned energy costs, adjustment, rotation, and ten-minute non-rotation reserve costs.

[0058] Specifically, operating costs include start-up costs, downtime costs, no-load costs, planned energy costs, regulation costs, and spinning and ten-minute non-spinning storage costs; expected operating costs refer to the expected operating costs of deploying ancillary services; and expected transition costs refer to the expected transition costs of quickly starting the generator.

[0059] Specifically, the cost objective function constructed by the cost planning layer is represented by formula (1):

[0060]

[0061] Where I represents the set of generator sets, and i represents the generator number in the generator set. and Let z represent the starting, no-load, and stopping costs of generator set i at time t, respectively. it C represents the binary variable representing unit i in state j (e.g., whether it is online); i P it , and Let $\mathbf$ represent the marginal cost of generating electricity, active power output, marginal cost of providing ancillary services, and quantity of ancillary services provided by generator unit $i$ at time $t, respectively; $x$ represents the type of ancillary service provided, $x∈{d,u,s,n}$, where $d$ represents downward reserve, $u$ represents upward reserve, $s$ represents spinning reserve, and $n$ represents non-spinning reserve. and These represent the magnitudes of power provided by generator unit i for downward and upward backup at time t, respectively, in scenario ω; v itω This is a ternary variable, representing whether unit i changes from offline to online during time period t in scenario ω (rapid start-up unit status change). Let ω represent the startup rate of unit i; ω∈Ω represent the set of safety verification scenarios, Ω={NR,UP,DN}, where NR represents the normal scheduling scenario, UP represents the upward standby activation scenario, and DN represents the downward standby activation scenario. Note that Δt is the duration of each unit cycle.

[0062] Specifically, the revenue information includes direct electricity trading revenue, power generation costs, and the charging and discharging costs of energy storage systems; where at least one photovoltaic aggregator is included among the target aggregators, the revenue information also includes power regulation costs.

[0063] Specifically, the revenue objective function constructed by the revenue planning layer aims to maximize the revenue of each target aggregator, and is specifically represented by formula (2):

[0064] max(F1-F2-F3-F4), Formula (2);

[0065] Among them, F1 represents the revenue that the distributed energy target aggregator obtains from the energy and backup market, F2 is the active and reactive costs of controllable distributed generation, F3 represents the penalty cost and reactive cost that photovoltaic power generation should reduce active power, and F4 represents the charging and discharging cost of the energy storage system.

[0066] The four parameters F1-F4 are represented by formulas (3)-(6) respectively:

[0067]

[0068] In formula (3), λ en,agg,t P represents the market-clearing electricity price at the target aggregator's access point. flex,agg,t P represents the net flexible load power of the target aggregator in time period t (usually a positive value, representing the overall demand of the target aggregator as a load to purchase electricity from the grid). infl,agg,t P represents the injected power of uncontrollable distributed generation by the target aggregator in time period t (a negative value, representing its supply to the grid). cv,agg,t λ represents the total power reduction required by the target aggregator in time period t; up,t and λ dn,t These represent the upward and downward adjustments to the reserve capacity price during time period t, respectively. up,agg,t and r dn,agg,t These represent the reserve capacity that the target aggregator needs to adjust upwards and downwards during time period t, respectively.

[0069]

[0070] In formula (4), N CDG A collection of controllable distributed generation units; b cdgp,i P represents the active power generation cost coefficient of the i-th controllable distributed generation unit. cdg,i,t Let represent the active power generated by the i-th controllable distributed generation unit. Let represent the reactive power generated by the i-th controllable distributed generation unit in scenario s. This indicates its reactive power generation cost coefficient.

[0071]

[0072] In formula (5), This represents the reactive power output provided by the photovoltaic power generation plan; and These represent the active and reactive power cost coefficients of photovoltaic power generation, respectively; NpV represents the set of photovoltaic power generation nodes.

[0073]

[0074] In formula (6), b cdgq,i and These represent the active and reactive power cost coefficients of controllable distributed generation, respectively; b ch,i and b dis,i These represent the charging and discharging cost coefficients of the energy storage system, respectively; NESS represents the set of nodes in the energy storage system.

[0075] In formulas (2)-(6), T1 represents the set of units of period, T1 = [1,2,···,24]; Represents the reactive power output of controllable distributed generation; NCDG represents the set of controllable distributed generation nodes; Ω represents the set of safety verification scenarios, Ω={NR,UP,DN}, where NR represents the normal scheduling scenario, UP represents the upward backup activation scenario, and DN represents the downward backup activation scenario; the subscripts i and t represent the node number and the unit period number, respectively; the superscript s represents the safety verification scenario.

[0076] In one embodiment, step 120 involves solving the game model to obtain a scheduling plan. Specifically, this may include adding a first constraint condition for the line and network and a second constraint condition for the generator set to the game model, and using a preset algorithm to solve the game model with added constraints to obtain a scheduling plan.

[0077] The first constraint and the second constraint are explained below:

[0078] Since it is necessary to determine the price of ancillary services, constraints are required for different types of ancillary services. Specifically, the first set of constraints includes power balance constraints, transmission line flow amplitude constraints, upward and downward adjustment constraints on power supply, and reserve constraints.

[0079] The first constraint can be specifically represented by formulas (7)-(15); the first constraint condition includes day-ahead constraints independent of the scenario and real-time constraints related to the scenario. Power balance constraints include power balance constraints in the day-ahead and real-time stages, given by formulas (7) and (8), respectively. The flow amplitude constraint of the transmission line limits the flow of the transmission line within the maximum transmission capacity, which is represented by formulas (9) and (10). Formulas (11) and (12) designate the first bus as the relaxation bus. Demand downsizing, upsizing, spinning reserves, and non-spinning reserves are given by formulas (13), (14), and (15). It should be noted that the priority substitution of ancillary services is considered to be downward, where lower-quality ancillary services can be substituted by higher-quality ancillary services, for example, non-spinning reserves can be provided by regulation and spinning reserve sources.

[0080]

[0081] The related dual variable appears after the first semicolon in formula (7). W qt This represents the predicted output of renewable energy q in time period t. D represents the amount of renewable energy q that must be discarded due to system constraints in scenario ω. lt B represents the load demand of node l within a unit period t; lm θ represents the line admittance between node l and node m. lt θ mt λ represents the voltage phase angle between node l and node m within a unit period t. lt This represents the marginal price of electricity at node l within a unit period t. This represents the real-time balanced dual variable of node l in scenario ω at time period t. This indicates the maximum transmission capacity of the line (1m). and These represent the lower and upper limits of the line transmission capacity, respectively. and Let θ represent the lower and upper bound multipliers of the line transmission capacity in scenario ω, respectively. 1t The phase angle is the reference node; and Let x represent the quantity of ancillary service provided by generator unit i to ancillary service x at time t, and s represent the quantity of ancillary service required by generator unit i at time t, respectively; x∈{d,u,s,n}, where d represents the reduction of reserves, u represents the increase of reserves, s represents the spinning reserve, and n represents the non-spinning reserve.

[0082] Specifically, the second constraint includes output constraints, constraints between generator start-up time and start-up / shutdown costs, capacity reserve constraints, adjustment range constraints, and available capacity constraints.

[0083] The second constraint can be expressed by formulas (16)-(34):

[0084]

[0085] The output constraints are used to force the output of generator sets and renewable energy sources to be within relevant limits, and are represented by formulas (16) and (17), respectively. The constraint between the start-up time and start-up / shutdown costs of generator sets is used to constrain the time-related start-up and shutdown costs of generator sets, and can be represented by formulas (18)–(21). The provision of ancillary services is limited by their relative boosting capabilities in formulas (20)–(23); therefore, when generator sets are offline, the capacity reserve constraint formula (24) is used to force generator sets to maintain sufficient capacity to meet regulation reserves unless all ancillary services other than spinning reserve are zero.

[0086]

[0087] The regulation range constraint is used to enforce that the total regulation rise, rotation and non-rotation reserves cannot exceed the generator set's ramp-up capability for 10 minutes, and it can be expressed by formula (25). The available capacity constraint is used to limit the sum of energy and ancillary services from exceeding the generator set's maximum available capacity, and it can be expressed by formula (26).

[0088]

[0089]

[0090] Furthermore, the second constraint also includes a slope compensation constraint, c, which can be represented by formulas (27)–(29).

[0091]

[0092] Formula (30) indicates that the binary commitment state of the generator set can be 0 or 1 (representing the off and on states of the generator set). Formulas (31) and (32) ensure that the deployed ancillary services do not exceed the predetermined number. Formula (33) is used to constrain the generator set to perform a ten-minute non-spinning standby ramp within its ten-minute capacity. Finally, renewable energy output overflow is enforced within the limits of Formula (34).

[0093] In the above formulas (16)-(34), P it z represents the planned active power output of generator unit i within a unit period t. it This is a binary start / stop status variable (1 indicates power on, 0 indicates power off). and These represent the minimum and maximum output of generator set i, respectively. and These represent the marginal value of relaxing the maximum and minimum output constraints, respectively. This represents the startup cost of generator set i in time period t. The word representing the start-up cost of generator set i. and These are the corresponding dual slack variables; This represents the shutdown cost of generator set i during time period t, and the relevant variables are defined in the same way as the startup cost. This represents the amount of ancillary service x provided by generator set i during time period t. This represents the rate at which generator set i provides auxiliary service x; This indicates that generator set i provides non-spinning reserve capacity during time period t. This indicates the maximum non-spinning reserve capacity that generator set i can provide. This indicates the power that can be adjusted downwards; This represents the continuous ramp rate of generator set i. SUR represents the normal ramp rate of generator set i. i and SDR i These represent the start-up and shutdown ramp rates, respectively.

[0094] Furthermore, the constraints on the game model also include a third constraint, which is a constraint on the feeder capacity and can be expressed by formulas (35)-(38):

[0095]

[0096] P i,min u i,t ≤P i,t ≤P i,max u i,t , formula (38).

[0097] Wherein, formula (35) represents the system power balance constraint, formula (36) represents the power plant power constraint, formula (37) represents the system reserve constraint, and formula (38) represents the generator output constraint. I is the total number of generator units in the system, P i,t For the verification calculation of the active power output of unit i in time period t, L t Let A be the system load demand for time period t; f Let E be the set of generator units of power plant f. f Let T be the power generation plan for power plant f, T be the system scheduling cycle in hours, and R be the power generation plan for power plant f. t For the backup demand in time period t, u i,t For unit i, calculate the start-up and shutdown status during time period t; P i,max P i,min These represent the upper and lower limits of the output power of unit i, respectively.

[0098] Furthermore, after applying the first, second, and third constraints to the game model, a preset algorithm is used to solve the game model, thereby obtaining the scheduling plan. In one possible implementation, the preset algorithm can be a dual gap minimization algorithm. Of course, other algorithms can also be used to solve the game model, and this application embodiment does not impose specific limitations on them.

[0099] In one embodiment, the safety constraints in step 130 may specifically include constraints on exceeding the limits of the transmission section and constraints on the cost of deviations in power supply.

[0100] Specifically, the constraint on transmission section over-limit is to check the feasibility of power flow with the goal of minimizing the over-limit amount of transmission section power flow. The reason for minimizing the over-limit amount of transmission section power flow is to ensure the physical safety of the power grid and to ensure that the market clearing result is physically feasible.

[0101] The objective function corresponding to the constraint on the over-limit of the transmission section is specifically expressed by formula (39):

[0102]

[0103] Where T2 is the number of time periods included in the system scheduling cycle, L is the number of transmission sections considered in the calculation, and V 1,t W 1,t Let c1 be the forward and reverse power flow over-limit of transmission section l in time period t, respectively, and c1 be the unit over-limit penalty cost of transmission section l.

[0104] Specifically, forward / reverse power flow exceedance refers to the amount by which the actual power flow value on transmission section l exceeds the forward / reverse transmission power limit of that section within a unit period t. Unit exceedance penalty cost represents the virtual penalty cost corresponding to each unit (MW) of power flow exceedance.

[0105] Specifically, the constraint on the deviation cost of power supply is to correct the over-limit section with the goal of minimizing the deviation of the power supply plan; the objective function of the constraint on the deviation cost of power supply is expressed by formula (40):

[0106]

[0107] in, For generator set i, the positive adjustment amount for the planned power output is... For the planned reverse adjustment of the power output of generator set i, The cost of positive adjustment to the power generation plan of generator set i. The cost of reverse adjustment of the power plan for generator set i.

[0108] In one embodiment, the overall logic of the cross-market joint clearing and security verification method provided in this application is as follows: Figure 3 As shown, in the initial stage, an admission mechanism for distributed renewable energy aggregators is established to screen aggregators and obtain target suppliers that meet the admission criteria (screening constraints). Then, a two-layer game model for joint clearing of power and ancillary services is constructed, and an improved dual gap minimization algorithm is used to solve the game model, thus obtaining the scheduling plan output by the game model. A two-stage security check is implemented on the scheduling plan. The first stage constrains transmission section overruns, checking the feasibility of power output with the goal of minimizing the overrun amount. The second stage constrains the deviation cost of power supply, correcting overrun sections with the goal of minimizing the planned power output deviation. After the scheduling plan passes the two-stage security check, it is determined whether the clearing cycle needs to be updated. If the clearing cycle has not been updated, the scheduling plan is published as the joint clearing result; if the clearing cycle has been updated, the aggregator screening step is repeated.

[0109] If the scheduling plan fails the two-stage safety check, the scheduling plan will undergo another two-stage safety check after the second-stage check is completed, based on the adjustments made in the second stage.

[0110] To address the challenge of solving the parameters and computational schemes of the Security Constrained Unit Combination Model (SCUC) under the premise of increased power system nonconvexity caused by renewable energy integration, the proposed joint clearing method for electrical energy and ancillary services is based on an improved duality gap minimization algorithm. In this method, the duality gap between the primal and dual problems is minimized. Since the problem described by the primal model (i.e., the SCUC problem) is a mixed-integer linear programming (MILP) problem, it is relaxed to a linear programming (LP) problem to obtain the dual problem, thus deriving the primal-dual formula. Finally, to ensure market participants comply with the dispatch strategy, an additional profit insurance constraint is added, guaranteeing non-negative net operating revenue for all generating units, including fast-start generators.

[0111] Since the dual gap is the difference between the primal and dual objective functions, this problem aims to minimize the social welfare gap. Minimizing the dual gap guarantees that the solution is as close as possible to the primal SCUC solution. When the gap is zero, the obtained solution determines the optimal scheduling and commitment variables.

[0112] The dual variables representing the supply and demand constraints on price and the required ancillary services cannot be directly obtained from the MILP problem. Relaxing the dual variables is the first step in obtaining the dual formula and subsequently determining the prices of electrical energy and ancillary services. By relaxing the original problem (the proposed SCUC problem), the dual variables will be relaxed to be represented by formulas (41) and (42);

[0113]

[0114] and These are the dual variables of the four constraints.

[0115] The cross-market joint clearing and security verification method provided in this application embodiment covers distributed resource pools including photovoltaic, energy storage, and flexible loads. It also constructs a virtual power plant adjustable resource pool by aggregating low-voltage distributed resources within a region that meet the access rules. This resource pool is continuously and dynamically updated based on distributed power output forecasts, load changes, and market signals to ensure the real-time accuracy of resource information included in the aggregation pool.

[0116] Before participating in the cross-market joint clearing of the electricity market and ancillary services market, based on the electricity market trading demand and the objective of the two-layer game model (minimizing the upper-layer electricity purchase cost and maximizing the lower-layer aggregator's profit), various types of resources in the resource pool are aggregated on demand to form a virtual power plant resource aggregator that participates in market bidding.

[0117] In the process of virtual power plants participating in the joint market clearing, based on a two-level game model, with the goal of optimizing the overall market clearing result, an improved dual gap minimization algorithm is used to generate an optimized scheme that includes the clearing results of power energy plans and ancillary services. This scheme needs to undergo two-stage security verification (first minimizing transmission section overruns, then correcting power energy plan deviations) and feeder capacity constraint verification to ensure the safe and executable nature of the clearing results at the transmission and distribution network level.

[0118] After each market clearing cycle concludes, the aggregate completes its operations based on the clearing results and dispatch instructions, and the relevant resource status is recorded and fed back to the resource pool. Simultaneously, the adjustable resource information in the resource pool is continuously updated over time. When entering the next market clearing cycle or facing real-time dispatch demands, the system, based on updated market conditions and grid status, re-executes the steps from the initial stage, dynamically re-aggregating and jointly clearing the resources in the resource pool to respond to the ever-changing electricity market and grid operation requirements.

[0119] The above methods address the challenge of unified dispatch and clearing of massive renewable energy participation in the electricity market by establishing an aggregation model for renewable energy market participation. To address the issue of insufficient frequency regulation capacity in typical power ancillary service markets, a clearing game model is constructed, incorporating a massive distributed renewable energy spot market and various active power ancillary service markets. This model analyzes the clearing process and settlement methods for corresponding ancillary service scenarios, achieving the goal of incentivizing user-side adjustable resources to participate in unified grid regulation.

[0120] Meanwhile, addressing the difficulty in solving the Safety Constraint Unit Combination Model (SCUC) calculation scheme under the premise of increased power system nonconvexity caused by the access of new energy sources, a two-stage verification and optimization technical framework under the medium- and long-term market was constructed, and an efficient solution algorithm was proposed. Regarding the impact of safety verification methods on the revenue of large-scale access of distributed new energy sources to the electricity spot market, the study investigated the degree of impact of distribution network safety verification and feeder congestion factors on market revenue when distributed new energy sources participate in the day-ahead and reserve markets. This research plays an important role in effectively guiding and positively incentivizing the participation of distributed energy sources in the spot market.

[0121] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0122] Based on the same inventive concept, this application also provides a cross-market joint clearing and security verification device for implementing the cross-market joint clearing and security verification method described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more cross-market joint clearing and security verification device embodiments provided below can be found in the limitations of the cross-market joint clearing and security verification method described above, and will not be repeated here.

[0123] In one exemplary embodiment, such as Figure 4 As shown, a cross-market joint clearing and security verification device 400 is provided. The device includes an information acquisition module 401, a model solving module 402, and a verification module 403, wherein:

[0124] The information acquisition module 401 is used to acquire cost information of electricity purchased from the power grid side and revenue information of each target aggregator;

[0125] The model solving module 402 is used to take cost information as input to the power purchase cost planning layer of the game model and the revenue information of each operator as input to the revenue planning layer of the game model, and solve the game model to obtain the scheduling plan. Among them, the power purchase cost planning layer is constructed with the goal of minimizing the power purchase cost on the grid side, and the revenue planning layer is constructed with the goal of maximizing the operator's revenue. The game model aims to simultaneously satisfy the goals of the power purchase cost planning layer and the revenue planning layer. The scheduling plan includes the purchase price and the power supply of each target aggregator during the clearing period.

[0126] The verification module 403 is used to verify the scheduling plan using safety constraints. If the scheduling plan meets the safety constraints, it is determined as the target plan in the clearing process. The safety constraints include constraints on the over-limit of transmission sections and constraints on the deviation cost of power supply.

[0127] In one embodiment, the cost information includes operating costs, expected operating costs, and expected transition costs;

[0128] Operating costs include start-up costs, no-load costs, downtime costs, planned energy costs, adjustment, rotation, and ten-minute non-rotation reserve costs.

[0129] In one embodiment, revenue information includes direct electricity trading revenue, power generation costs, and the charging and discharging costs of the energy storage system;

[0130] When at least one photovoltaic aggregator is included among the target aggregators, the revenue information also includes power regulation costs.

[0131] In one embodiment, the model solving module 402 is specifically used for:

[0132] Add a first constraint on the line and network and a second constraint on the generator set to the game model, and use a preset algorithm to solve the game model with added constraints to obtain the scheduling plan.

[0133] The first constraint includes power balance constraint, transmission line flow amplitude constraint, power supply upward adjustment constraint, power supply downward adjustment constraint, and reserve constraint.

[0134] The second set of constraints includes output constraints for each target aggregator, constraints between generator start-up time and start-up / shutdown costs, capacity reserve constraints, adjustment range constraints, and available capacity constraints.

[0135] In one embodiment, the preset algorithm is a dual gap minimization algorithm.

[0136] In one embodiment, the method further includes a filtering module, which is specifically used for:

[0137] Based on the screening constraints, each aggregator is screened to obtain the target aggregators;

[0138] The screening constraints include constraints on effective capacity, adjustment range, response speed, and response duration.

[0139] Each module in the aforementioned cross-market joint clearing and security verification device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0140] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a cross-market joint clearing and security verification method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0141] Those skilled in the art will understand that Figure 5The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0142] In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement any of the steps in the embodiments of the cross-market joint clearing and security verification method described above.

[0143] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements any of the steps in the embodiments of the cross-market joint clearing and security verification method described above.

[0144] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements any of the steps in the embodiments of the cross-market joint clearing and security verification method described above.

[0145] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0146] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0147] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0148] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for cross-market joint liquidation and security check, characterized in that, The method comprises: obtaining cost information of power grid side power purchase and income information of each target aggregator; inputting the cost information as an input of a power purchase cost planning layer in a game model, inputting the income information of each operator as an input of an income planning layer in the game model, solving the game model to obtain a scheduling plan; wherein the power purchase cost planning layer is constructed with the minimum power purchase cost of the power grid side as a target, the income planning layer is constructed with the maximum income of the operator as a target, and the game model aims to simultaneously satisfy the targets of the power purchase cost planning layer and the income planning layer; the scheduling plan comprises a procurement price and power supply of each target aggregator in a clearing cycle; checking the scheduling plan by using a safety constraint condition, and determining the scheduling plan as a target plan in a clearing process when the scheduling plan satisfies the safety constraint condition; wherein the safety constraint condition comprises a constraint on transmission section over-limit and a constraint on deviation cost of power supply.

2. The method of claim 1, wherein, The cost information comprises operating cost, expected operating cost and expected transition cost; wherein the operating cost comprises start-up cost, no-load cost, shutdown cost, planned energy cost, regulation, rotation and ten-minute non-rotation reserve cost.

3. The method according to claim 1 or 2, characterized in that, The income information comprises direct power transaction income, power generation cost and charging and discharging cost of energy storage system; wherein when at least one of the target aggregators is a photovoltaic type aggregator, the income information further comprises power regulation cost.

4. The method of claim 3, wherein, The solving of the game model to obtain a scheduling plan comprises: adding first constraint conditions for lines and networks and second constraint conditions for generator units in the game model, and solving the game model with added constraint conditions by using a preset algorithm to obtain the scheduling plan; wherein the first constraint conditions comprise power balance constraint, flow amplitude constraint of transmission lines, up-regulation constraint, down-regulation constraint and reserve constraint of power supply; the second constraint conditions comprise output constraint, constraint between start-up time of generator units and start-stop cost, capacity reserve constraint, regulation amplitude constraint and available capacity constraint.

5. The method of claim 4, wherein, The preset algorithm is a dual gap minimization algorithm.

6. The method of claim 1, wherein, The method further comprises: screening each aggregator based on a screening constraint condition to obtain each target aggregator; the screening constraint condition comprises a constraint on effective capacity, a constraint on regulation amplitude, a constraint on response speed and a constraint on response duration.

7. A cross-market joint clearing and security verification device, characterized in that, The device comprises an information acquisition module, a model solving module and a checking module, wherein: the information acquisition module is configured to obtain cost information of power grid side power purchase and income information of each target aggregator; the information acquisition module is configured to obtain cost information of power grid side power purchase and income information of each target aggregator; The model solving module is configured to take the cost information of each node as input of a purchase cost planning layer in a game model, take the income information of the operators as input of an income planning layer in the game model, solve the game model, and obtain a dispatch plan; the purchase cost planning layer is constructed with the minimum purchase cost of the power grid side as a target, the income planning layer is constructed with the maximum income of the operators as a target, and the game model aims to simultaneously satisfy the targets of the purchase cost planning layer and the income planning layer; the dispatch plan includes a purchase price and power supply of each target aggregator in a clearing cycle; The checking module is configured to check the dispatch plan by using a safety constraint condition, and determine the dispatch plan as a target plan in the clearing process when the dispatch plan satisfies the safety constraint condition; the safety constraint condition includes a constraint on a transmission section overrun and a constraint on a deviation cost of power supply.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor, when executing the computer program, implements the steps of the method of any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 6.

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